update samples from Release-149 as a part of SDK release

update samples from Release-148 as a part of  SDK release

CODE_OF_CONDUCT.md

@@ -0,0 +1,9 @@
+# Microsoft Open Source Code of Conduct
+
+This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
+
+Resources:
+
+- [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/)
+- [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
+- Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns

NBSETUP.md

@@ -28,7 +28,7 @@ git clone https://github.com/Azure/MachineLearningNotebooks.git
 pip install azureml-sdk[notebooks,tensorboard]
 
 # install model explainability component
-pip install azureml-sdk[explain]
+pip install azureml-sdk[interpret]
 
 # install automated ml components
 pip install azureml-sdk[automl]
@@ -86,7 +86,7 @@ If you need additional Azure ML SDK components, you can either modify the Docker
 pip install azureml-sdk[automl]
 
 # install the core SDK and model explainability component
-pip install azureml-sdk[explain]
+pip install azureml-sdk[interpret]
 
 # install the core SDK and experimental components
 pip install azureml-sdk[contrib]

README.md

@@ -1,76 +1,43 @@
-# Azure Machine Learning service example notebooks
+# Azure Machine Learning Python SDK notebooks
 
-This repository contains example notebooks demonstrating the [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning-service/) Python SDK which allows you to build, train, deploy and manage machine learning solutions using Azure.  The AML SDK allows you the choice of using local or cloud compute resources, while managing and maintaining the complete data science workflow from the cloud.
+> a community-driven repository of examples using mlflow for tracking can be found at https://github.com/Azure/azureml-examples
 
-![Azure ML Workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/media/concept-azure-machine-learning-architecture/workflow.png)
+Welcome to the Azure Machine Learning Python SDK notebooks repository!
 
+## Getting started
 
-## Quick installation
-```sh
-pip install azureml-sdk
-```
-Read more detailed instructions on [how to set up your environment](./NBSETUP.md) using Azure Notebook service, your own Jupyter notebook server, or Docker.
+These notebooks are recommended for use in an Azure Machine Learning [Compute Instance](https://docs.microsoft.com/azure/machine-learning/concept-compute-instance), where you can run them without any additional set up.
 
-## How to navigate and use the example notebooks?
-If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, you should always run the [Configuration](./configuration.ipynb) notebook first when setting up a notebook library on a new machine or in a new environment. It configures your notebook library to connect to an Azure Machine Learning workspace, and sets up your workspace and compute to be used by many of the other examples. 
-This [index](.index.md) should assist in navigating the Azure Machine Learning notebook samples and encourage efficient retrieval of topics and content. 
+However, the notebooks can be run in any development environment with the correct `azureml` packages installed.
 
-If you want to...
-
- * ...try out and explore Azure ML, start with image classification tutorials: [Part 1 (Training)](./tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb) and [Part 2 (Deployment)](./tutorials/image-classification-mnist-data/img-classification-part2-deploy.ipynb).
- * ...learn about experimentation and tracking run history, first [train within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then try [training on remote VM](./how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb) and [using logging APIs](./how-to-use-azureml/training/logging-api/logging-api.ipynb).
- * ...train deep learning models at scale, first learn about [Machine Learning Compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and then try [distributed hyperparameter tuning](./how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) and [distributed training](./how-to-use-azureml/training-with-deep-learning/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb).
- * ...deploy models as a realtime scoring service, first learn the basics by [training within Notebook and deploying to Azure Container Instance](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then learn how to [production deploy models on Azure Kubernetes Cluster](./how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb).
- * ...deploy models as a batch scoring service, first [train a model within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then [create Machine Learning Compute for scoring compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and [use Machine Learning Pipelines to deploy your model](https://aka.ms/pl-batch-scoring).
- * ...monitor your deployed models, learn about using [App Insights](./how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb).
-
-## Tutorials
-
-The [Tutorials](./tutorials) folder contains notebooks for the tutorials described in the [Azure Machine Learning documentation](https://aka.ms/aml-docs).
-  
-## How to use Azure ML
-
-The [How to use Azure ML](./how-to-use-azureml) folder contains specific examples demonstrating the features of the Azure Machine Learning SDK
-
-- [Training](./how-to-use-azureml/training) - Examples of how to build models using Azure ML's logging and execution capabilities on local and remote compute targets
-- [Training with Deep Learning](./how-to-use-azureml/training-with-deep-learning) - Examples demonstrating how to build deep learning models using estimators and parameter sweeps
-- [Manage Azure ML Service](./how-to-use-azureml/manage-azureml-service) - Examples how to perform tasks, such as authenticate against Azure ML service in different ways.
-- [Automated Machine Learning](./how-to-use-azureml/automated-machine-learning) - Examples using Automated Machine Learning to automatically generate optimal machine learning pipelines and models
-- [Machine Learning Pipelines](./how-to-use-azureml/machine-learning-pipelines) - Examples showing how to create and use reusable pipelines for training and batch scoring
-- [Deployment](./how-to-use-azureml/deployment) - Examples showing how to deploy and manage machine learning models and solutions
-- [Azure Databricks](./how-to-use-azureml/azure-databricks) - Examples showing how to use Azure ML with Azure Databricks
-- [Monitor Models](./how-to-use-azureml/monitor-models) - Examples showing how to enable model monitoring services such as DataDrift
-
----
-## Documentation
-
- * Quickstarts, end-to-end tutorials, and how-tos on the [official documentation site for Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/).
- * [Python SDK reference](https://docs.microsoft.com/en-us/python/api/overview/azure/ml/intro?view=azure-ml-py)
- * Azure ML Data Prep SDK [overview](https://aka.ms/data-prep-sdk), [Python SDK reference](https://aka.ms/aml-data-prep-apiref), and [tutorials and how-tos](https://aka.ms/aml-data-prep-notebooks).
-
----
-
-
-## Community Repository 
-Visit this [community repository](https://github.com/microsoft/MLOps/tree/master/examples) to find useful end-to-end sample notebooks. Also, please follow these [contribution guidelines](https://github.com/microsoft/MLOps/blob/master/contributing.md) when contributing to this repository.   
-
-## Projects using Azure Machine Learning
-
-Visit following repos to see projects contributed by Azure ML users:
- - [AMLSamples](https://github.com/Azure/AMLSamples) Number of end-to-end examples, including face recognition, predictive maintenance, customer churn and sentiment analysis.
- - [Learn about Natural Language Processing best practices using Azure Machine Learning service](https://github.com/microsoft/nlp)
- - [Pre-Train BERT models using Azure Machine Learning service](https://github.com/Microsoft/AzureML-BERT)
- - [Fashion MNIST with Azure ML SDK](https://github.com/amynic/azureml-sdk-fashion)
- - [UMass Amherst Student Samples](https://github.com/katiehouse3/microsoft-azure-ml-notebooks) - A number of end-to-end machine learning notebooks, including machine translation, image classification, and customer churn, created by students in the 696DS course at UMass Amherst.
- 
-## Data/Telemetry 
-This repository collects usage data and sends it to Mircosoft to help improve our products and services. Read Microsoft's [privacy statement to learn more](https://privacy.microsoft.com/en-US/privacystatement)
-
-To opt out of tracking, please go to the raw markdown or .ipynb files and remove the following line of code:
+Install the `azureml.core` Python package:
 
 ```sh
-    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/README.png)"
+pip install azureml-core
 ```
-This URL will be slightly different depending on the file. 
 
- ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/README.png)
+Install additional packages as needed:
+
+```sh
+pip install azureml-mlflow
+pip install azureml-dataset-runtime
+pip install azureml-automl-runtime
+pip install azureml-pipeline
+pip install azureml-pipeline-steps
+...
+```
+
+We recommend starting with one of the [quickstarts](tutorials/compute-instance-quickstarts).
+
+## Contributing
+
+This repository is a push-only mirror. Pull requests are ignored.
+
+## Code of Conduct
+
+This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). Please see the [code of conduct](CODE_OF_CONDUCT.md) for details.
+
+## Reference
+
+- [Documentation](https://docs.microsoft.com/azure/machine-learning)
+

configuration.ipynb

@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.1.1rc0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.43.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -254,6 +254,8 @@
         "\n",
         "Many of the sample notebooks use Azure ML managed compute (AmlCompute) to train models using a dynamically scalable pool of compute. In this section you will create default compute clusters for use by the other notebooks and any other operations you choose.\n",
         "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
+        "\n",
         "To create a cluster, you need to specify a compute configuration that specifies the type of machine to be used and the scalability behaviors.  Then you choose a name for the cluster that is unique within the workspace that can be used to address the cluster later.\n",
         "\n",
         "The cluster parameters are:\n",

contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb

@@ -188,13 +188,6 @@
         "### Script to process data and train model"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The _process_data.py_ script used in the step below is a slightly modified implementation of [RAPIDS Mortgage E2E example](https://github.com/rapidsai/notebooks-contrib/blob/master/intermediate_notebooks/E2E/mortgage/mortgage_e2e.ipynb)."
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -373,7 +366,7 @@
         "run_config.target = gpu_cluster_name\n",
         "run_config.environment.docker.enabled = True\n",
         "run_config.environment.docker.gpu_support = True\n",
-        "run_config.environment.docker.base_image = \"mcr.microsoft.com/azureml/base-gpu:intelmpi2018.3-cuda10.0-cudnn7-ubuntu16.04\"\n",
+        "run_config.environment.docker.base_image = \"mcr.microsoft.com/azureml/openmpi4.1.0-cuda11.1-cudnn8-ubuntu20.04\"\n",
         "run_config.environment.spark.precache_packages = False\n",
         "run_config.data_references={'data':data_ref.to_config()}"
       ]

contrib/fairness/fairlearn-azureml-mitigation.ipynb

@@ -0,0 +1,621 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/contrib/fairness/fairlearn-azureml-mitigation.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Unfairness Mitigation with Fairlearn and Azure Machine Learning\n",
+        "**This notebook shows how to upload results from Fairlearn's GridSearch mitigation algorithm into a dashboard in Azure Machine Learning Studio**\n",
+        "\n",
+        "## Table of Contents\n",
+        "\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Loading the Data](#LoadingData)\n",
+        "1. [Training an Unmitigated Model](#UnmitigatedModel)\n",
+        "1. [Mitigation with GridSearch](#Mitigation)\n",
+        "1. [Uploading a Fairness Dashboard to Azure](#AzureUpload)\n",
+        "    1. Registering models\n",
+        "    1. Computing Fairness Metrics\n",
+        "    1. Uploading to Azure\n",
+        "1. [Conclusion](#Conclusion)\n",
+        "\n",
+        "<a id=\"Introduction\"></a>\n",
+        "## Introduction\n",
+        "This notebook shows how to use [Fairlearn (an open source fairness assessment and unfairness mitigation package)](http://fairlearn.org) and Azure Machine Learning Studio for a binary classification problem. This example uses the well-known adult census dataset. For the purposes of this notebook, we shall treat this as a loan decision problem. We will pretend that the label indicates whether or not each individual repaid a loan in the past. We will use the data to train a predictor to predict whether previously unseen individuals will repay a loan or not. The assumption is that the model predictions are used to decide whether an individual should be offered a loan. Its purpose is purely illustrative of a workflow including a fairness dashboard - in particular, we do **not** include a full discussion of the detailed issues which arise when considering fairness in machine learning. For such discussions, please [refer to the Fairlearn website](http://fairlearn.org/).\n",
+        "\n",
+        "We will apply the [grid search algorithm](https://fairlearn.org/v0.4.6/api_reference/fairlearn.reductions.html#fairlearn.reductions.GridSearch) from the Fairlearn package using a specific notion of fairness called Demographic Parity. This produces a set of models, and we will view these in a dashboard both locally and in the Azure Machine Learning Studio.\n",
+        "\n",
+        "### Setup\n",
+        "\n",
+        "To use this notebook, an Azure Machine Learning workspace is required.\n",
+        "Please see the [configuration notebook](../../configuration.ipynb) for information about creating one, if required.\n",
+        "This notebook also requires the following packages:\n",
+        "* `azureml-contrib-fairness`\n",
+        "* `fairlearn>=0.6.2` (pre-v0.5.0 will work with minor modifications)\n",
+        "* `joblib`\n",
+        "* `liac-arff`\n",
+        "* `raiwidgets`\n",
+        "\n",
+        "Fairlearn relies on features introduced in v0.22.1 of `scikit-learn`. If you have an older version already installed, please uncomment and run the following cell:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# !pip install --upgrade scikit-learn>=0.22.1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, please ensure that when you downloaded this notebook, you also downloaded the `fairness_nb_utils.py` file from the same location, and placed it in the same directory as this notebook."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"LoadingData\"></a>\n",
+        "## Loading the Data\n",
+        "We use the well-known `adult` census dataset, which we will fetch from the OpenML website. We start with a fairly unremarkable set of imports:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from fairlearn.reductions import GridSearch, DemographicParity, ErrorRate\n",
+        "from raiwidgets import FairnessDashboard\n",
+        "\n",
+        "from sklearn.compose import ColumnTransformer\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+        "from sklearn.compose import make_column_selector as selector\n",
+        "from sklearn.pipeline import Pipeline\n",
+        "\n",
+        "import pandas as pd"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can now load and inspect the data:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from fairness_nb_utils import fetch_census_dataset\n",
+        "\n",
+        "data = fetch_census_dataset()\n",
+        "    \n",
+        "# Extract the items we want\n",
+        "X_raw = data.data\n",
+        "y = (data.target == '>50K') * 1\n",
+        "\n",
+        "X_raw[\"race\"].value_counts().to_dict()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We are going to treat the sex and race of each individual as protected attributes, and in this particular case we are going to remove these attributes from the main data (this is not always the best option - see the [Fairlearn website](http://fairlearn.github.io/) for further discussion). Protected attributes are often denoted by 'A' in the literature, and we follow that convention here:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "A = X_raw[['sex','race']]\n",
+        "X_raw = X_raw.drop(labels=['sex', 'race'], axis = 1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We now preprocess our data. To avoid the problem of data leakage, we split our data into training and test sets before performing any other transformations. Subsequent transformations (such as scalings) will be fit to the training data set, and then applied to the test dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "(X_train, X_test, y_train, y_test, A_train, A_test) = train_test_split(\n",
+        "    X_raw, y, A, test_size=0.3, random_state=12345, stratify=y\n",
+        ")\n",
+        "\n",
+        "# Ensure indices are aligned between X, y and A,\n",
+        "# after all the slicing and splitting of DataFrames\n",
+        "# and Series\n",
+        "\n",
+        "X_train = X_train.reset_index(drop=True)\n",
+        "X_test = X_test.reset_index(drop=True)\n",
+        "y_train = y_train.reset_index(drop=True)\n",
+        "y_test = y_test.reset_index(drop=True)\n",
+        "A_train = A_train.reset_index(drop=True)\n",
+        "A_test = A_test.reset_index(drop=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We have two types of column in the dataset - categorical columns which will need to be one-hot encoded, and numeric ones which will need to be rescaled. We also need to take care of missing values. We use a simple approach here, but please bear in mind that this is another way that bias could be introduced (especially if one subgroup tends to have more missing values).\n",
+        "\n",
+        "For this preprocessing, we make use of `Pipeline` objects from `sklearn`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "numeric_transformer = Pipeline(\n",
+        "    steps=[\n",
+        "        (\"impute\", SimpleImputer()),\n",
+        "        (\"scaler\", StandardScaler()),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "categorical_transformer = Pipeline(\n",
+        "    [\n",
+        "        (\"impute\", SimpleImputer(strategy=\"most_frequent\")),\n",
+        "        (\"ohe\", OneHotEncoder(handle_unknown=\"ignore\", sparse=False)),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "preprocessor = ColumnTransformer(\n",
+        "    transformers=[\n",
+        "        (\"num\", numeric_transformer, selector(dtype_exclude=\"category\")),\n",
+        "        (\"cat\", categorical_transformer, selector(dtype_include=\"category\")),\n",
+        "    ]\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now, the preprocessing pipeline is defined, we can run it on our training data, and apply the generated transform to our test data:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_train = preprocessor.fit_transform(X_train)\n",
+        "X_test = preprocessor.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"UnmitigatedModel\"></a>\n",
+        "## Training an Unmitigated Model\n",
+        "\n",
+        "So we have a point of comparison, we first train a model (specifically, logistic regression from scikit-learn) on the raw data, without applying any mitigation algorithm:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "unmitigated_predictor = LogisticRegression(solver='liblinear', fit_intercept=True)\n",
+        "\n",
+        "unmitigated_predictor.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can view this model in the fairness dashboard, and see the disparities which appear:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "FairnessDashboard(sensitive_features=A_test,\n",
+        "                  y_true=y_test,\n",
+        "                  y_pred={\"unmitigated\": unmitigated_predictor.predict(X_test)})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Looking at the disparity in accuracy when we select 'Sex' as the sensitive feature, we see that males have an error rate about three times greater than the females. More interesting is the disparity in opportunitiy - males are offered loans at three times the rate of females.\n",
+        "\n",
+        "Despite the fact that we removed the feature from the training data, our predictor still discriminates based on sex. This demonstrates that simply ignoring a protected attribute when fitting a predictor rarely eliminates unfairness. There will generally be enough other features correlated with the removed attribute to lead to disparate impact."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"Mitigation\"></a>\n",
+        "## Mitigation with GridSearch\n",
+        "\n",
+        "The `GridSearch` class in `Fairlearn` implements a simplified version of the exponentiated gradient reduction of [Agarwal et al. 2018](https://arxiv.org/abs/1803.02453). The user supplies a standard ML estimator, which is treated as a blackbox - for this simple example, we shall use the logistic regression estimator from scikit-learn. `GridSearch` works by generating a sequence of relabellings and reweightings, and trains a predictor for each.\n",
+        "\n",
+        "For this example, we specify demographic parity (on the protected attribute of sex) as the fairness metric. Demographic parity requires that individuals are offered the opportunity (a loan in this example) independent of membership in the protected class (i.e., females and males should be offered loans at the same rate). *We are using this metric for the sake of simplicity* in this example; the appropriate fairness metric can only be selected after *careful examination of the broader context* in which the model is to be used."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "sweep = GridSearch(LogisticRegression(solver='liblinear', fit_intercept=True),\n",
+        "                   constraints=DemographicParity(),\n",
+        "                   grid_size=71)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "With our estimator created, we can fit it to the data. After `fit()` completes, we extract the full set of predictors from the `GridSearch` object.\n",
+        "\n",
+        "The following cell trains a many copies of the underlying estimator, and may take a minute or two to run:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "sweep.fit(X_train, y_train,\n",
+        "          sensitive_features=A_train.sex)\n",
+        "\n",
+        "# For Fairlearn pre-v0.5.0, need sweep._predictors\n",
+        "predictors = sweep.predictors_"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We could load these predictors into the Fairness dashboard now. However, the plot would be somewhat confusing due to their number. In this case, we are going to remove the predictors which are dominated in the error-disparity space by others from the sweep (note that the disparity will only be calculated for the protected attribute; other potentially protected attributes will *not* be mitigated). In general, one might not want to do this, since there may be other considerations beyond the strict optimisation of error and disparity (of the given protected attribute)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "errors, disparities = [], []\n",
+        "for predictor in predictors:\n",
+        "    error = ErrorRate()\n",
+        "    error.load_data(X_train, pd.Series(y_train), sensitive_features=A_train.sex)\n",
+        "    disparity = DemographicParity()\n",
+        "    disparity.load_data(X_train, pd.Series(y_train), sensitive_features=A_train.sex)\n",
+        "    \n",
+        "    errors.append(error.gamma(predictor.predict)[0])\n",
+        "    disparities.append(disparity.gamma(predictor.predict).max())\n",
+        "    \n",
+        "all_results = pd.DataFrame( {\"predictor\": predictors, \"error\": errors, \"disparity\": disparities})\n",
+        "\n",
+        "dominant_models_dict = dict()\n",
+        "base_name_format = \"census_gs_model_{0}\"\n",
+        "row_id = 0\n",
+        "for row in all_results.itertuples():\n",
+        "    model_name = base_name_format.format(row_id)\n",
+        "    errors_for_lower_or_eq_disparity = all_results[\"error\"][all_results[\"disparity\"]<=row.disparity]\n",
+        "    if row.error <= errors_for_lower_or_eq_disparity.min():\n",
+        "        dominant_models_dict[model_name] = row.predictor\n",
+        "    row_id = row_id + 1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can construct predictions for the dominant models (we include the unmitigated predictor as well, for comparison):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "predictions_dominant = {\"census_unmitigated\": unmitigated_predictor.predict(X_test)}\n",
+        "models_dominant = {\"census_unmitigated\": unmitigated_predictor}\n",
+        "for name, predictor in dominant_models_dict.items():\n",
+        "    value = predictor.predict(X_test)\n",
+        "    predictions_dominant[name] = value\n",
+        "    models_dominant[name] = predictor"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "These predictions may then be viewed in the fairness dashboard. We include the race column from the dataset, as an alternative basis for assessing the models. However, since we have not based our mitigation on it, the variation in the models with respect to race can be large."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "FairnessDashboard(sensitive_features=A_test, \n",
+        "                  y_true=y_test.tolist(),\n",
+        "                  y_pred=predictions_dominant)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "When using sex as the sensitive feature and accuracy as the metric, we see a Pareto front forming - the set of predictors which represent optimal tradeoffs between accuracy and disparity in predictions. In the ideal case, we would have a predictor at (1,0) - perfectly accurate and without any unfairness under demographic parity (with respect to the protected attribute \"sex\"). The Pareto front represents the closest we can come to this ideal based on our data and choice of estimator. Note the range of the axes - the disparity axis covers more values than the accuracy, so we can reduce disparity substantially for a small loss in accuracy. Finally, we also see that the unmitigated model is towards the top right of the plot, with high accuracy, but worst disparity.\n",
+        "\n",
+        "By clicking on individual models on the plot, we can inspect their metrics for disparity and accuracy in greater detail. In a real example, we would then pick the model which represented the best trade-off between accuracy and disparity given the relevant business constraints."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"AzureUpload\"></a>\n",
+        "## Uploading a Fairness Dashboard to Azure\n",
+        "\n",
+        "Uploading a fairness dashboard to Azure is a two stage process. The `FairnessDashboard` invoked in the previous section relies on the underlying Python kernel to compute metrics on demand. This is obviously not available when the fairness dashboard is rendered in AzureML Studio. By default, the dashboard in Azure Machine Learning Studio also requires the models to be registered. The required stages are therefore:\n",
+        "1. Register the dominant models\n",
+        "1. Precompute all the required metrics\n",
+        "1. Upload to Azure\n",
+        "\n",
+        "Before that, we need to connect to Azure Machine Learning Studio:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace, Experiment, Model\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "ws.get_details()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"RegisterModels\"></a>\n",
+        "### Registering Models\n",
+        "\n",
+        "The fairness dashboard is designed to integrate with registered models, so we need to do this for the models we want in the Studio portal. The assumption is that the names of the models specified in the dashboard dictionary correspond to the `id`s (i.e. `<name>:<version>` pairs) of registered models in the workspace. We register each of the models in the `models_dominant` dictionary into the workspace. For this, we have to save each model to a file, and then register that file:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import joblib\n",
+        "import os\n",
+        "\n",
+        "os.makedirs('models', exist_ok=True)\n",
+        "def register_model(name, model):\n",
+        "    print(\"Registering \", name)\n",
+        "    model_path = \"models/{0}.pkl\".format(name)\n",
+        "    joblib.dump(value=model, filename=model_path)\n",
+        "    registered_model = Model.register(model_path=model_path,\n",
+        "                                      model_name=name,\n",
+        "                                      workspace=ws)\n",
+        "    print(\"Registered \", registered_model.id)\n",
+        "    return registered_model.id\n",
+        "\n",
+        "model_name_id_mapping = dict()\n",
+        "for name, model in models_dominant.items():\n",
+        "    m_id = register_model(name, model)\n",
+        "    model_name_id_mapping[name] = m_id"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now, produce new predictions dictionaries, with the updated names:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "predictions_dominant_ids = dict()\n",
+        "for name, y_pred in predictions_dominant.items():\n",
+        "    predictions_dominant_ids[model_name_id_mapping[name]] = y_pred"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"PrecomputeMetrics\"></a>\n",
+        "### Precomputing Metrics\n",
+        "\n",
+        "We create a _dashboard dictionary_ using Fairlearn's `metrics` package. The `_create_group_metric_set` method has arguments similar to the Dashboard constructor, except that the sensitive features are passed as a dictionary (to ensure that names are available), and we must specify the type of prediction. Note that we use the `predictions_dominant_ids` dictionary we just created:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "sf = { 'sex': A_test.sex, 'race': A_test.race }\n",
+        "\n",
+        "from fairlearn.metrics._group_metric_set import _create_group_metric_set\n",
+        "\n",
+        "\n",
+        "dash_dict = _create_group_metric_set(y_true=y_test,\n",
+        "                                     predictions=predictions_dominant_ids,\n",
+        "                                     sensitive_features=sf,\n",
+        "                                     prediction_type='binary_classification')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"DashboardUpload\"></a>\n",
+        "### Uploading the Dashboard\n",
+        "\n",
+        "Now, we import our `contrib` package which contains the routine to perform the upload:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.fairness import upload_dashboard_dictionary, download_dashboard_by_upload_id"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we can create an Experiment, then a Run, and upload our dashboard to it:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "exp = Experiment(ws, \"Test_Fairlearn_GridSearch_Census_Demo\")\n",
+        "print(exp)\n",
+        "\n",
+        "run = exp.start_logging()\n",
+        "try:\n",
+        "    dashboard_title = \"Dominant Models from GridSearch\"\n",
+        "    upload_id = upload_dashboard_dictionary(run,\n",
+        "                                            dash_dict,\n",
+        "                                            dashboard_name=dashboard_title)\n",
+        "    print(\"\\nUploaded to id: {0}\\n\".format(upload_id))\n",
+        "\n",
+        "    downloaded_dict = download_dashboard_by_upload_id(run, upload_id)\n",
+        "finally:\n",
+        "    run.complete()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The dashboard can be viewed in the Run Details page.\n",
+        "\n",
+        "Finally, we can verify that the dashboard dictionary which we downloaded matches our upload:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(dash_dict == downloaded_dict)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"Conclusion\"></a>\n",
+        "## Conclusion\n",
+        "\n",
+        "In this notebook we have demonstrated how to use the `GridSearch` algorithm from Fairlearn to generate a collection of models, and then present them in the fairness dashboard in Azure Machine Learning Studio. Please remember that this notebook has not attempted to discuss the many considerations which should be part of any approach to unfairness mitigation. The [Fairlearn website](http://fairlearn.org/) provides that discussion"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "riedgar"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.10"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

contrib/fairness/fairlearn-azureml-mitigation.yml

@@ -0,0 +1,12 @@
+name: fairlearn-azureml-mitigation
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-contrib-fairness
+  - fairlearn>=0.6.2
+  - joblib
+  - liac-arff
+  - raiwidgets~=0.19.0
+  - itsdangerous==2.0.1
+  - markupsafe<2.1.0
+  - protobuf==3.20.0

contrib/fairness/fairness_nb_utils.py

@@ -0,0 +1,111 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+"""Utilities for azureml-contrib-fairness notebooks."""
+
+import arff
+from collections import OrderedDict
+from contextlib import closing
+import gzip
+import pandas as pd
+from sklearn.datasets import fetch_openml
+from sklearn.utils import Bunch
+import time
+
+
+def fetch_openml_with_retries(data_id, max_retries=4, retry_delay=60):
+    """Fetch a given dataset from OpenML with retries as specified."""
+    for i in range(max_retries):
+        try:
+            print("Download attempt {0} of {1}".format(i + 1, max_retries))
+            data = fetch_openml(data_id=data_id, as_frame=True)
+            break
+        except Exception as e:  # noqa: B902
+            print("Download attempt failed with exception:")
+            print(e)
+            if i + 1 != max_retries:
+                print("Will retry after {0} seconds".format(retry_delay))
+                time.sleep(retry_delay)
+                retry_delay = retry_delay * 2
+    else:
+        raise RuntimeError("Unable to download dataset from OpenML")
+
+    return data
+
+
+_categorical_columns = [
+    'workclass',
+    'education',
+    'marital-status',
+    'occupation',
+    'relationship',
+    'race',
+    'sex',
+    'native-country'
+]
+
+
+def fetch_census_dataset():
+    """Fetch the Adult Census Dataset.
+
+    This uses a particular URL for the Adult Census dataset. The code
+    is a simplified version of fetch_openml() in sklearn.
+
+    The data are copied from:
+    https://openml.org/data/v1/download/1595261.gz
+    (as of 2021-03-31)
+    """
+    try:
+        from urllib import urlretrieve
+    except ImportError:
+        from urllib.request import urlretrieve
+
+    filename = "1595261.gz"
+    data_url = "https://rainotebookscdn.blob.core.windows.net/datasets/"
+
+    remaining_attempts = 5
+    sleep_duration = 10
+    while remaining_attempts > 0:
+        try:
+            urlretrieve(data_url + filename, filename)
+
+            http_stream = gzip.GzipFile(filename=filename, mode='rb')
+
+            with closing(http_stream):
+                def _stream_generator(response):
+                    for line in response:
+                        yield line.decode('utf-8')
+
+                stream = _stream_generator(http_stream)
+                data = arff.load(stream)
+        except Exception as exc:  # noqa: B902
+            remaining_attempts -= 1
+            print("Error downloading dataset from {} ({} attempt(s) remaining)"
+                  .format(data_url, remaining_attempts))
+            print(exc)
+            time.sleep(sleep_duration)
+            sleep_duration *= 2
+            continue
+        else:
+            # dataset successfully downloaded
+            break
+    else:
+        raise Exception("Could not retrieve dataset from {}.".format(data_url))
+
+    attributes = OrderedDict(data['attributes'])
+    arff_columns = list(attributes)
+
+    raw_df = pd.DataFrame(data=data['data'], columns=arff_columns)
+
+    target_column_name = 'class'
+    target = raw_df.pop(target_column_name)
+    for col_name in _categorical_columns:
+        dtype = pd.api.types.CategoricalDtype(attributes[col_name])
+        raw_df[col_name] = raw_df[col_name].astype(dtype, copy=False)
+
+    result = Bunch()
+    result.data = raw_df
+    result.target = target
+
+    return result

contrib/fairness/upload-fairness-dashboard.ipynb

@@ -0,0 +1,545 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/contrib/fairness/upload-fairness-dashboard.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Upload a Fairness Dashboard to Azure Machine Learning Studio\n",
+        "**This notebook shows how to generate and upload a fairness assessment dashboard from Fairlearn to AzureML Studio**\n",
+        "\n",
+        "## Table of Contents\n",
+        "\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Loading the Data](#LoadingData)\n",
+        "1. [Processing the Data](#ProcessingData)\n",
+        "1. [Training Models](#TrainingModels)\n",
+        "1. [Logging in to AzureML](#LoginAzureML)\n",
+        "1. [Registering the Models](#RegisterModels)\n",
+        "1. [Using the Fairness Dashboard](#LocalDashboard)\n",
+        "1. [Uploading a Fairness Dashboard to Azure](#AzureUpload)\n",
+        "    1. Computing Fairness Metrics\n",
+        "    1. Uploading to Azure\n",
+        "1. [Conclusion](#Conclusion)\n",
+        "    \n",
+        "\n",
+        "<a id=\"Introduction\"></a>\n",
+        "## Introduction\n",
+        "\n",
+        "In this notebook, we walk through a simple example of using the `azureml-contrib-fairness` package to upload a collection of fairness statistics for a fairness dashboard. It is an example of integrating the [open source Fairlearn package](https://www.github.com/fairlearn/fairlearn) with Azure Machine Learning. This is not an example of fairness analysis or mitigation - this notebook simply shows how to get a fairness dashboard into the Azure Machine Learning portal. We will load the data and train a couple of simple models. We will then use Fairlearn to generate data for a Fairness dashboard, which we can upload to Azure Machine Learning portal and view there.\n",
+        "\n",
+        "### Setup\n",
+        "\n",
+        "To use this notebook, an Azure Machine Learning workspace is required.\n",
+        "Please see the [configuration notebook](../../configuration.ipynb) for information about creating one, if required.\n",
+        "This notebook also requires the following packages:\n",
+        "* `azureml-contrib-fairness`\n",
+        "* `fairlearn>=0.6.2` (also works for pre-v0.5.0 with slight modifications)\n",
+        "* `joblib`\n",
+        "* `liac-arff`\n",
+        "* `raiwidgets`\n",
+        "\n",
+        "Fairlearn relies on features introduced in v0.22.1 of `scikit-learn`. If you have an older version already installed, please uncomment and run the following cell:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# !pip install --upgrade scikit-learn>=0.22.1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, please ensure that when you downloaded this notebook, you also downloaded the `fairness_nb_utils.py` file from the same location, and placed it in the same directory as this notebook."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"LoadingData\"></a>\n",
+        "## Loading the Data\n",
+        "We use the well-known `adult` census dataset, which we fetch from the OpenML website. We start with a fairly unremarkable set of imports:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn import svm\n",
+        "from sklearn.compose import ColumnTransformer\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+        "from sklearn.compose import make_column_selector as selector\n",
+        "from sklearn.pipeline import Pipeline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we can load the data:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from fairness_nb_utils import fetch_census_dataset\n",
+        "\n",
+        "data = fetch_census_dataset()\n",
+        "    \n",
+        "# Extract the items we want\n",
+        "X_raw = data.data\n",
+        "y = (data.target == '>50K') * 1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can take a look at some of the data. For example, the next cells shows the counts of the different races identified in the dataset:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(X_raw[\"race\"].value_counts().to_dict())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"ProcessingData\"></a>\n",
+        "## Processing the Data\n",
+        "\n",
+        "With the data loaded, we process it for our needs. First, we extract the sensitive features of interest into `A` (conventionally used in the literature) and leave the rest of the feature data in `X_raw`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "A = X_raw[['sex','race']]\n",
+        "X_raw = X_raw.drop(labels=['sex', 'race'],axis = 1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We now preprocess our data. To avoid the problem of data leakage, we split our data into training and test sets before performing any other transformations. Subsequent transformations (such as scalings) will be fit to the training data set, and then applied to the test dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "(X_train, X_test, y_train, y_test, A_train, A_test) = train_test_split(\n",
+        "    X_raw, y, A, test_size=0.3, random_state=12345, stratify=y\n",
+        ")\n",
+        "\n",
+        "# Ensure indices are aligned between X, y and A,\n",
+        "# after all the slicing and splitting of DataFrames\n",
+        "# and Series\n",
+        "\n",
+        "X_train = X_train.reset_index(drop=True)\n",
+        "X_test = X_test.reset_index(drop=True)\n",
+        "y_train = y_train.reset_index(drop=True)\n",
+        "y_test = y_test.reset_index(drop=True)\n",
+        "A_train = A_train.reset_index(drop=True)\n",
+        "A_test = A_test.reset_index(drop=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We have two types of column in the dataset - categorical columns which will need to be one-hot encoded, and numeric ones which will need to be rescaled. We also need to take care of missing values. We use a simple approach here, but please bear in mind that this is another way that bias could be introduced (especially if one subgroup tends to have more missing values).\n",
+        "\n",
+        "For this preprocessing, we make use of `Pipeline` objects from `sklearn`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "numeric_transformer = Pipeline(\n",
+        "    steps=[\n",
+        "        (\"impute\", SimpleImputer()),\n",
+        "        (\"scaler\", StandardScaler()),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "categorical_transformer = Pipeline(\n",
+        "    [\n",
+        "        (\"impute\", SimpleImputer(strategy=\"most_frequent\")),\n",
+        "        (\"ohe\", OneHotEncoder(handle_unknown=\"ignore\", sparse=False)),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "preprocessor = ColumnTransformer(\n",
+        "    transformers=[\n",
+        "        (\"num\", numeric_transformer, selector(dtype_exclude=\"category\")),\n",
+        "        (\"cat\", categorical_transformer, selector(dtype_include=\"category\")),\n",
+        "    ]\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now, the preprocessing pipeline is defined, we can run it on our training data, and apply the generated transform to our test data:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_train = preprocessor.fit_transform(X_train)\n",
+        "X_test = preprocessor.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"TrainingModels\"></a>\n",
+        "## Training Models\n",
+        "\n",
+        "We now train a couple of different models on our data. The `adult` census dataset is a classification problem - the goal is to predict whether a particular individual exceeds an income threshold. For the purpose of generating a dashboard to upload, it is sufficient to train two basic classifiers. First, a logistic regression classifier:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "lr_predictor = LogisticRegression(solver='liblinear', fit_intercept=True)\n",
+        "\n",
+        "lr_predictor.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "And for comparison, a support vector classifier:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "svm_predictor = svm.SVC()\n",
+        "\n",
+        "svm_predictor.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"LoginAzureML\"></a>\n",
+        "## Logging in to AzureML\n",
+        "\n",
+        "With our two classifiers trained, we can log into our AzureML workspace:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace, Experiment, Model\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "ws.get_details()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"RegisterModels\"></a>\n",
+        "## Registering the Models\n",
+        "\n",
+        "Next, we register our models. By default, the subroutine which uploads the models checks that the names provided correspond to registered models in the workspace. We define a utility routine to do the registering:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import joblib\n",
+        "import os\n",
+        "\n",
+        "os.makedirs('models', exist_ok=True)\n",
+        "def register_model(name, model):\n",
+        "    print(\"Registering \", name)\n",
+        "    model_path = \"models/{0}.pkl\".format(name)\n",
+        "    joblib.dump(value=model, filename=model_path)\n",
+        "    registered_model = Model.register(model_path=model_path,\n",
+        "                                      model_name=name,\n",
+        "                                      workspace=ws)\n",
+        "    print(\"Registered \", registered_model.id)\n",
+        "    return registered_model.id"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now, we register the models. For convenience in subsequent method calls, we store the results in a dictionary, which maps the `id` of the registered model (a string in `name:version` format) to the predictor itself:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model_dict = {}\n",
+        "\n",
+        "lr_reg_id = register_model(\"fairness_linear_regression\", lr_predictor)\n",
+        "model_dict[lr_reg_id] = lr_predictor\n",
+        "svm_reg_id = register_model(\"fairness_svm\", svm_predictor)\n",
+        "model_dict[svm_reg_id] = svm_predictor"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"LocalDashboard\"></a>\n",
+        "## Using the Fairlearn Dashboard\n",
+        "\n",
+        "We can now examine the fairness of the two models we have training, both as a function of race and (binary) sex. Before uploading the dashboard to the AzureML portal, we will first instantiate a local instance of the Fairlearn dashboard.\n",
+        "\n",
+        "Regardless of the viewing location, the dashboard is based on three things - the true values, the model predictions and the sensitive feature values. The dashboard can use predictions from multiple models and multiple sensitive features if desired (as we are doing here).\n",
+        "\n",
+        "Our first step is to generate a dictionary mapping the `id` of the registered model to the corresponding array of predictions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ys_pred = {}\n",
+        "for n, p in model_dict.items():\n",
+        "    ys_pred[n] = p.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can examine these predictions in a locally invoked Fairlearn dashboard. This can be compared to the dashboard uploaded to the portal (in the next section):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from raiwidgets import FairnessDashboard\n",
+        "\n",
+        "FairnessDashboard(sensitive_features=A_test, \n",
+        "                  y_true=y_test.tolist(),\n",
+        "                  y_pred=ys_pred)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"AzureUpload\"></a>\n",
+        "## Uploading a Fairness Dashboard to Azure\n",
+        "\n",
+        "Uploading a fairness dashboard to Azure is a two stage process. The `FairnessDashboard` invoked in the previous section relies on the underlying Python kernel to compute metrics on demand. This is obviously not available when the fairness dashboard is rendered in AzureML Studio. The required stages are therefore:\n",
+        "1. Precompute all the required metrics\n",
+        "1. Upload to Azure\n",
+        "\n",
+        "\n",
+        "### Computing Fairness Metrics\n",
+        "We use Fairlearn to create a dictionary which contains all the data required to display a dashboard. This includes both the raw data (true values, predicted values and sensitive features), and also the fairness metrics. The API is similar to that used to invoke the Dashboard locally. However, there are a few minor changes to the API, and the type of problem being examined (binary classification, regression etc.) needs to be specified explicitly:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "sf = { 'Race': A_test.race, 'Sex': A_test.sex }\n",
+        "\n",
+        "from fairlearn.metrics._group_metric_set import _create_group_metric_set\n",
+        "\n",
+        "dash_dict = _create_group_metric_set(y_true=y_test,\n",
+        "                                     predictions=ys_pred,\n",
+        "                                     sensitive_features=sf,\n",
+        "                                     prediction_type='binary_classification')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The `_create_group_metric_set()` method is currently underscored since its exact design is not yet final in Fairlearn."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Uploading to Azure\n",
+        "\n",
+        "We can now import the `azureml.contrib.fairness` package itself. We will round-trip the data, so there are two required subroutines:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.fairness import upload_dashboard_dictionary, download_dashboard_by_upload_id"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we can upload the generated dictionary to AzureML. The upload method requires a run, so we first create an experiment and a run. The uploaded dashboard can be seen on the corresponding Run Details page in AzureML Studio. For completeness, we also download the dashboard dictionary which we uploaded."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "exp = Experiment(ws, \"notebook-01\")\n",
+        "print(exp)\n",
+        "\n",
+        "run = exp.start_logging()\n",
+        "try:\n",
+        "    dashboard_title = \"Sample notebook upload\"\n",
+        "    upload_id = upload_dashboard_dictionary(run,\n",
+        "                                            dash_dict,\n",
+        "                                            dashboard_name=dashboard_title)\n",
+        "    print(\"\\nUploaded to id: {0}\\n\".format(upload_id))\n",
+        "\n",
+        "    downloaded_dict = download_dashboard_by_upload_id(run, upload_id)\n",
+        "finally:\n",
+        "    run.complete()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we can verify that the dashboard dictionary which we downloaded matches our upload:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(dash_dict == downloaded_dict)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"Conclusion\"></a>\n",
+        "## Conclusion\n",
+        "\n",
+        "In this notebook we have demonstrated how to generate and upload a fairness dashboard to AzureML Studio. We have not discussed how to analyse the results and apply mitigations. Those topics will be covered elsewhere."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "riedgar"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.10"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}

contrib/fairness/upload-fairness-dashboard.yml

@@ -0,0 +1,12 @@
+name: upload-fairness-dashboard
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-contrib-fairness
+  - fairlearn>=0.6.2
+  - joblib
+  - liac-arff
+  - raiwidgets~=0.19.0
+  - itsdangerous==2.0.1
+  - markupsafe<2.1.0
+  - protobuf==3.20.0

contrib/gbdt/lightgbm/binary0.test

@@ -1,500 +0,0 @@
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-1	0.546	-0.954	0.715	1.335	-1.689	0.783	-0.443	-1.735	2.173	1.081	0.185	-0.435	0.000	1.433	-0.662	-0.389	0.000	0.969	0.924	1.099	0.000	0.910	0.879	0.988	0.683	0.753	0.878	0.865
-1	0.596	0.276	-1.054	1.358	1.355	1.444	1.813	-0.208	0.000	1.175	-0.949	-1.573	0.000	0.855	-1.228	-0.925	2.548	1.837	-0.400	0.913	0.000	0.637	0.901	1.028	0.553	0.790	0.679	0.677
-0	0.458	2.292	1.530	0.291	1.283	0.749	-0.930	-0.198	0.000	0.300	-1.560	0.990	0.000	0.811	-0.176	0.995	2.548	1.085	-0.178	-1.213	3.102	0.891	0.648	0.999	0.732	0.655	0.619	0.620
-0	0.638	-0.575	-1.048	0.125	0.178	0.846	-0.753	-0.339	1.087	0.799	-0.727	1.182	0.000	0.888	0.283	0.717	0.000	1.051	-1.046	-1.557	3.102	0.889	0.871	0.989	0.884	0.923	0.836	0.779
-1	0.434	-1.119	-0.313	2.427	0.461	0.497	0.261	-1.177	2.173	0.618	-0.737	-0.688	0.000	1.150	-1.237	-1.652	2.548	0.757	-0.054	1.700	0.000	0.809	0.741	0.982	1.450	0.936	1.086	0.910
-1	0.431	-1.144	-1.030	0.778	-0.655	0.490	0.047	-1.546	0.000	1.583	-0.014	0.891	2.215	0.516	0.956	0.567	2.548	0.935	-1.123	-0.082	0.000	0.707	0.995	0.995	0.700	0.602	0.770	0.685
-1	1.894	0.222	1.224	1.578	1.715	0.966	2.890	-0.013	0.000	0.922	-0.703	-0.844	0.000	0.691	2.056	1.039	0.000	0.900	-0.733	-1.240	3.102	1.292	1.992	1.026	0.881	0.684	1.759	1.755
-0	0.985	-0.316	0.141	1.067	-0.946	0.819	-1.177	1.307	2.173	1.080	-0.429	0.557	1.107	1.726	1.435	-1.075	0.000	1.100	1.547	-0.647	0.000	0.873	1.696	1.179	1.146	1.015	1.538	1.270
-0	0.998	-0.187	-0.236	0.882	0.755	0.468	0.950	-0.439	2.173	0.579	-0.550	-0.624	0.000	1.847	1.196	1.384	1.274	0.846	1.273	-1.072	0.000	1.194	0.797	1.013	1.319	1.174	0.963	0.898
-0	0.515	0.246	-0.593	1.082	1.591	0.912	-0.623	-0.957	2.173	0.858	0.418	0.844	0.000	0.948	2.519	1.599	0.000	1.158	1.385	-0.095	3.102	0.973	1.033	0.988	0.998	1.716	1.054	0.901
-0	0.919	-1.001	1.506	1.389	0.653	0.507	-0.616	-0.689	2.173	0.808	0.536	-0.467	2.215	0.496	2.187	-0.859	0.000	0.822	0.807	1.163	0.000	0.876	0.861	1.088	0.947	0.614	0.911	1.087
-0	0.794	0.051	1.477	1.504	-1.695	0.716	0.315	0.264	1.087	0.879	-0.135	-1.094	2.215	1.433	-0.741	0.201	0.000	1.566	0.534	-0.989	0.000	0.627	0.882	0.974	0.807	1.130	0.929	0.925
-1	0.455	-0.946	-1.175	1.453	-0.580	0.763	-0.856	0.840	0.000	0.829	1.223	1.174	2.215	0.714	0.638	-0.466	0.000	1.182	0.223	-1.333	0.000	0.977	0.938	0.986	0.713	0.714	0.796	0.843
-1	0.662	-0.296	-1.287	1.212	-0.707	0.641	1.457	0.222	0.000	0.600	0.525	-1.700	2.215	0.784	-0.835	-0.961	2.548	0.865	1.131	1.162	0.000	0.854	0.877	0.978	0.740	0.734	0.888	0.811
-0	0.390	0.698	-1.629	1.888	0.298	0.990	1.614	-1.572	0.000	1.666	0.170	0.719	2.215	1.590	1.064	-0.886	1.274	0.952	0.305	-1.216	0.000	1.048	0.897	1.173	0.891	1.936	1.273	1.102
-0	1.014	0.117	1.384	0.686	-1.047	0.609	-1.245	-0.850	0.000	1.076	-1.158	0.814	1.107	1.598	-0.389	-0.111	0.000	0.907	1.688	-1.673	0.000	1.333	0.866	0.989	0.975	0.442	0.797	0.788
-0	1.530	-1.408	-0.207	0.440	-1.357	0.902	-0.647	1.325	1.087	1.320	-0.819	0.246	1.107	0.503	1.407	-1.683	0.000	1.189	-0.972	-0.925	0.000	0.386	1.273	0.988	0.829	1.335	1.173	1.149
-1	1.689	-0.590	0.915	2.076	1.202	0.644	-0.478	-0.238	0.000	0.809	-1.660	-1.184	0.000	1.227	-0.224	-0.808	2.548	1.655	1.047	-0.623	0.000	0.621	1.192	0.988	1.309	0.866	0.924	1.012
-0	1.102	0.402	-1.622	1.262	1.022	0.576	0.271	-0.269	0.000	0.591	0.495	-1.278	0.000	1.271	0.209	0.575	2.548	0.941	0.964	-0.685	3.102	0.989	0.963	1.124	0.857	0.858	0.716	0.718
-0	2.491	0.825	0.581	1.593	0.205	0.782	-0.815	1.499	0.000	1.179	-0.999	-1.509	0.000	0.926	0.920	-0.522	2.548	2.068	-1.021	-1.050	3.102	0.874	0.943	0.980	0.945	1.525	1.570	1.652
-0	0.666	0.254	1.601	1.303	-0.250	1.236	-1.929	0.793	0.000	1.074	0.447	-0.871	0.000	0.991	1.059	-0.342	0.000	1.703	-0.393	-1.419	3.102	0.921	0.945	1.285	0.931	0.462	0.770	0.729
-0	0.937	-1.126	1.424	1.395	1.743	0.760	0.428	-0.238	2.173	0.846	0.494	1.320	2.215	0.872	-1.826	-0.507	0.000	0.612	1.860	1.403	0.000	3.402	2.109	0.985	1.298	1.165	1.404	1.240
-1	0.881	-1.086	-0.870	0.513	0.266	2.049	-1.870	1.160	0.000	2.259	-0.428	-0.935	2.215	1.321	-0.655	-0.449	2.548	1.350	-1.766	-0.108	0.000	0.911	1.852	0.987	1.167	0.820	1.903	1.443
-0	0.410	0.835	-0.819	1.257	1.112	0.871	-1.737	-0.401	0.000	0.927	0.158	1.253	0.000	1.183	0.405	-1.570	0.000	0.807	-0.704	-0.438	3.102	0.932	0.962	0.987	0.653	0.315	0.616	0.648
-1	0.634	0.196	-1.679	1.379	-0.967	2.260	-0.273	1.114	0.000	1.458	1.070	-0.278	1.107	1.195	0.110	-0.688	2.548	0.907	0.298	-1.359	0.000	0.949	1.129	0.984	0.675	0.877	0.938	0.824
-1	0.632	-1.254	1.201	0.496	-0.106	0.235	2.731	-0.955	0.000	0.615	-0.805	0.600	0.000	0.633	-0.934	1.641	0.000	1.407	-0.483	-0.962	1.551	0.778	0.797	0.989	0.578	0.722	0.576	0.539
-0	0.714	1.122	1.566	2.399	-1.431	1.665	0.299	0.323	0.000	1.489	1.087	-0.861	2.215	1.174	0.140	1.083	2.548	0.404	-0.968	1.105	0.000	0.867	0.969	0.981	1.039	1.552	1.157	1.173
-1	0.477	-0.321	-0.471	1.966	1.034	2.282	1.359	-0.874	0.000	1.672	-0.258	1.109	0.000	1.537	0.604	0.231	2.548	1.534	-0.640	0.827	0.000	0.746	1.337	1.311	0.653	0.721	0.795	0.742
-1	1.351	0.460	0.031	1.194	-1.185	0.670	-1.157	-1.637	2.173	0.599	-0.823	0.680	0.000	0.478	0.373	1.716	0.000	0.809	-0.919	0.010	1.551	0.859	0.839	1.564	0.994	0.777	0.971	0.826
-1	0.520	-1.442	-0.348	0.840	1.654	1.273	-0.760	1.317	0.000	0.861	2.579	-0.791	0.000	1.779	0.257	-0.703	0.000	2.154	1.928	0.457	0.000	1.629	3.194	0.992	0.730	1.107	2.447	2.747
-0	0.700	-0.308	0.920	0.438	-0.879	0.516	1.409	1.101	0.000	0.960	0.701	-0.049	2.215	1.442	-0.416	-1.439	2.548	0.628	1.009	-0.364	0.000	0.848	0.817	0.987	0.759	1.421	0.937	0.920
-1	0.720	1.061	-0.546	0.798	-1.521	1.066	0.173	0.271	1.087	1.453	0.114	1.336	1.107	0.702	0.616	-0.367	0.000	0.543	-0.386	-1.301	0.000	0.653	0.948	0.989	1.031	1.500	0.965	0.790
-1	0.735	-0.416	0.588	1.308	-0.382	1.042	0.344	1.609	0.000	0.926	0.163	-0.520	1.107	1.050	-0.427	1.159	2.548	0.834	0.613	0.948	0.000	0.848	1.189	1.042	0.844	1.099	0.829	0.843
-1	0.777	-0.396	1.540	1.608	0.638	0.955	0.040	0.918	2.173	1.315	1.116	-0.823	0.000	0.781	-0.762	0.564	2.548	0.945	-0.573	1.379	0.000	0.679	0.706	1.124	0.608	0.593	0.515	0.493
-1	0.934	0.319	-0.257	0.970	-0.980	0.726	0.774	0.731	0.000	0.896	0.038	-1.465	1.107	0.773	-0.055	-0.831	2.548	1.439	-0.229	0.698	0.000	0.964	1.031	0.995	0.845	0.480	0.810	0.762
-0	0.461	0.771	0.019	2.055	-1.288	1.043	0.147	0.261	2.173	0.833	-0.156	1.425	0.000	0.832	0.805	-0.491	2.548	0.589	1.252	1.414	0.000	0.850	0.906	1.245	1.364	0.850	0.908	0.863
-1	0.858	-0.116	-0.937	0.966	1.167	0.825	-0.108	1.111	1.087	0.733	1.163	-0.634	0.000	0.894	0.771	0.020	0.000	0.846	-1.124	-1.195	3.102	0.724	1.194	1.195	0.813	0.969	0.985	0.856
-0	0.720	-0.335	-0.307	1.445	0.540	1.108	-0.034	-1.691	1.087	0.883	-1.356	-0.678	2.215	0.440	1.093	0.253	0.000	0.389	-1.582	-1.097	0.000	1.113	1.034	0.988	1.256	1.572	1.062	0.904
-1	0.750	-0.811	-0.542	0.985	0.408	0.471	0.477	0.355	0.000	1.347	-0.875	-1.556	2.215	0.564	1.082	-0.724	0.000	0.793	-0.958	-0.020	3.102	0.836	0.825	0.986	1.066	0.924	0.927	0.883
-0	0.392	-0.468	-0.216	0.680	1.565	1.086	-0.765	-0.581	1.087	1.264	-1.035	1.189	2.215	0.986	-0.338	0.747	0.000	0.884	-1.328	-0.965	0.000	1.228	0.988	0.982	1.135	1.741	1.108	0.956
-1	0.434	-1.269	0.643	0.713	0.608	0.597	0.832	1.627	0.000	0.708	-0.422	0.079	2.215	1.533	-0.823	-1.127	2.548	0.408	-1.357	-0.828	0.000	1.331	1.087	0.999	1.075	1.015	0.875	0.809
-0	0.828	-1.803	0.342	0.847	-0.162	1.585	-1.128	-0.272	2.173	1.974	0.039	-1.717	0.000	0.900	0.764	-1.741	0.000	1.349	-0.079	1.035	3.102	0.984	0.815	0.985	0.780	1.661	1.403	1.184
-1	1.089	-0.350	-0.747	1.472	0.792	1.087	-0.069	-1.192	0.000	0.512	-0.841	-1.284	0.000	2.162	-0.821	0.545	2.548	1.360	2.243	-0.183	0.000	0.977	0.628	1.725	1.168	0.635	0.823	0.822
-1	0.444	0.451	-1.332	1.176	-0.247	0.898	0.194	0.007	0.000	1.958	0.576	-1.618	2.215	0.584	1.203	0.268	0.000	0.939	1.033	1.264	3.102	0.829	0.886	0.985	1.265	0.751	1.032	0.948
-0	0.629	0.114	1.177	0.917	-1.204	0.845	0.828	-0.088	0.000	0.962	-1.302	0.823	2.215	0.732	0.358	-1.334	2.548	0.538	0.582	1.561	0.000	1.028	0.834	0.988	0.904	1.205	1.039	0.885
-1	1.754	-1.259	-0.573	0.959	-1.483	0.358	0.448	-1.452	0.000	0.711	0.313	0.499	2.215	1.482	-0.390	1.474	2.548	1.879	-1.540	0.668	0.000	0.843	0.825	1.313	1.315	0.939	1.048	0.871
-1	0.549	0.706	-1.437	0.894	0.891	0.680	-0.762	-1.568	0.000	0.981	0.499	-0.425	2.215	1.332	0.678	0.485	1.274	0.803	0.022	-0.893	0.000	0.793	1.043	0.987	0.761	0.899	0.915	0.794
-0	0.475	0.542	-0.987	1.569	0.069	0.551	1.543	-1.488	0.000	0.608	0.301	1.734	2.215	0.277	0.499	-0.522	0.000	1.375	1.212	0.696	3.102	0.652	0.756	0.987	0.828	0.830	0.715	0.679
-1	0.723	0.049	-1.153	1.300	0.083	0.723	-0.749	0.630	0.000	1.126	0.412	-0.384	0.000	1.272	1.256	1.358	2.548	3.108	0.777	-1.486	3.102	0.733	1.096	1.206	1.269	0.899	1.015	0.903
-1	1.062	0.296	0.725	0.285	-0.531	0.819	1.277	-0.667	0.000	0.687	0.829	-0.092	0.000	1.158	0.447	1.047	2.548	1.444	-0.186	-1.491	3.102	0.863	1.171	0.986	0.769	0.828	0.919	0.840
-0	0.572	-0.349	1.396	2.023	0.795	0.577	0.457	-0.533	0.000	1.351	0.701	-1.091	0.000	0.724	-1.012	-0.182	2.548	0.923	-0.012	0.789	3.102	0.936	1.025	0.985	1.002	0.600	0.828	0.909
-1	0.563	0.387	0.412	0.553	1.050	0.723	-0.992	-0.447	0.000	0.748	0.948	0.546	2.215	1.761	-0.559	-1.183	0.000	1.114	-0.251	1.192	3.102	0.936	0.912	0.976	0.578	0.722	0.829	0.892
-1	1.632	1.577	-0.697	0.708	-1.263	0.863	0.012	1.197	2.173	0.498	0.990	-0.806	0.000	0.627	2.387	-1.283	0.000	0.607	1.290	-0.174	3.102	0.916	1.328	0.986	0.557	0.971	0.935	0.836
-1	0.562	-0.360	0.399	0.803	-1.334	1.443	-0.116	1.628	2.173	0.750	0.987	0.135	1.107	0.795	0.298	-0.556	0.000	1.150	-0.113	-0.093	0.000	0.493	1.332	0.985	1.001	1.750	1.013	0.886
-1	0.987	0.706	-0.492	0.861	0.607	0.593	0.088	-0.184	0.000	0.802	0.894	1.608	2.215	0.782	-0.471	1.500	2.548	0.521	0.772	-0.960	0.000	0.658	0.893	1.068	0.877	0.664	0.709	0.661
-1	1.052	0.883	-0.581	1.566	0.860	0.931	1.515	-0.873	0.000	0.493	0.145	-0.672	0.000	1.133	0.935	1.581	2.548	1.630	0.695	0.923	3.102	1.105	1.087	1.713	0.948	0.590	0.872	0.883
-1	2.130	-0.516	-0.291	0.776	-1.230	0.689	-0.257	0.800	2.173	0.730	-0.274	-1.437	0.000	0.615	0.241	1.083	0.000	0.834	0.757	1.613	3.102	0.836	0.806	1.333	1.061	0.730	0.889	0.783
-1	0.742	0.797	1.628	0.311	-0.418	0.620	0.685	-1.457	0.000	0.683	1.774	-1.082	0.000	1.700	1.104	0.225	2.548	0.382	-2.184	-1.307	0.000	0.945	1.228	0.984	0.864	0.931	0.988	0.838
-0	0.311	-1.249	-0.927	1.272	-1.262	0.642	-1.228	-0.136	0.000	1.220	-0.804	-1.558	2.215	0.950	-0.828	0.495	1.274	2.149	-1.672	0.634	0.000	1.346	0.887	0.981	0.856	1.101	1.001	1.106
-0	0.660	-1.834	-0.667	0.601	1.236	0.932	-0.933	-0.135	2.173	1.373	-0.122	1.429	0.000	0.654	-0.034	-0.847	2.548	0.711	0.911	0.703	0.000	1.144	0.942	0.984	0.822	0.739	0.992	0.895
-0	3.609	-0.590	0.851	0.615	0.455	1.280	0.003	-0.866	1.087	1.334	0.708	-1.131	0.000	0.669	0.480	0.092	0.000	0.975	0.983	-1.429	3.102	1.301	1.089	0.987	1.476	0.934	1.469	1.352
-1	0.905	-0.403	1.567	2.651	0.953	1.194	-0.241	-0.567	1.087	0.308	-0.384	-0.007	0.000	0.608	-0.175	-1.163	2.548	0.379	0.941	1.662	0.000	0.580	0.721	1.126	0.895	0.544	1.097	0.836
-1	0.983	0.255	1.093	0.905	-0.874	0.863	0.060	-0.368	0.000	0.824	-0.747	-0.633	0.000	0.614	0.961	1.052	0.000	0.792	-0.260	1.632	3.102	0.874	0.883	1.280	0.663	0.406	0.592	0.645
-1	1.160	-1.027	0.274	0.460	0.322	2.085	-1.623	-0.840	0.000	1.634	-1.046	1.182	2.215	0.492	-0.367	1.174	0.000	0.824	-0.998	1.617	0.000	0.943	0.884	1.001	1.209	1.313	1.034	0.866
-0	0.299	0.028	-1.372	1.930	-0.661	0.840	-0.979	0.664	1.087	0.535	-2.041	1.434	0.000	1.087	-1.797	0.344	0.000	0.485	-0.560	-1.105	3.102	0.951	0.890	0.980	0.483	0.684	0.730	0.706
-0	0.293	1.737	-1.418	2.074	0.794	0.679	1.024	-1.457	0.000	1.034	1.094	-0.168	1.107	0.506	1.680	-0.661	0.000	0.523	-0.042	-1.274	3.102	0.820	0.944	0.987	0.842	0.694	0.761	0.750
-0	0.457	-0.393	1.560	0.738	-0.007	0.475	-0.230	0.246	0.000	0.776	-1.264	-0.606	2.215	0.865	-0.731	-1.576	2.548	1.153	0.343	1.436	0.000	1.060	0.883	0.988	0.972	0.703	0.758	0.720
-0	0.935	-0.582	0.240	2.401	0.818	1.231	-0.618	-1.289	0.000	0.799	0.544	-0.228	2.215	0.525	-1.494	-0.969	0.000	0.609	-1.123	1.168	3.102	0.871	0.767	1.035	1.154	0.919	0.868	1.006
-1	0.902	-0.745	-1.215	1.174	-0.501	1.215	0.167	1.162	0.000	0.896	1.217	-0.976	0.000	0.585	-0.429	1.036	0.000	1.431	-0.416	0.151	3.102	0.524	0.952	0.990	0.707	0.271	0.592	0.826
-1	0.653	0.337	-0.320	1.118	-0.934	1.050	0.745	0.529	1.087	1.075	1.742	-1.538	0.000	0.585	1.090	0.973	0.000	1.091	-0.187	1.160	1.551	1.006	1.108	0.978	1.121	0.838	0.947	0.908
-0	1.157	1.401	0.340	0.395	-1.218	0.945	1.928	-0.876	0.000	1.384	0.320	1.002	1.107	1.900	1.177	-0.462	2.548	1.122	1.316	1.720	0.000	1.167	1.096	0.989	0.937	1.879	1.307	1.041
-0	0.960	0.355	-0.152	0.872	-0.338	0.391	0.348	0.956	1.087	0.469	2.664	1.409	0.000	0.756	-1.561	1.500	0.000	0.525	1.436	1.728	3.102	1.032	0.946	0.996	0.929	0.470	0.698	0.898
-1	1.038	0.274	0.825	1.198	0.963	1.078	-0.496	-1.014	2.173	0.739	-0.727	-0.151	2.215	1.035	-0.799	0.398	0.000	1.333	-0.872	-1.498	0.000	0.849	1.033	0.985	0.886	0.936	0.975	0.823
-0	0.490	0.277	0.318	1.303	0.694	1.333	-1.620	-0.563	0.000	1.459	-1.326	1.140	0.000	0.779	-0.673	-1.324	2.548	0.860	-1.247	0.043	0.000	0.857	0.932	0.992	0.792	0.278	0.841	1.498
-0	1.648	-0.688	-1.386	2.790	0.995	1.087	1.359	-0.687	0.000	1.050	-0.223	-0.261	2.215	0.613	-0.889	1.335	0.000	1.204	0.827	0.309	3.102	0.464	0.973	2.493	1.737	0.827	1.319	1.062
-0	1.510	-0.662	1.668	0.860	0.280	0.705	0.974	-1.647	1.087	0.662	-0.393	-0.225	0.000	0.610	-0.996	0.532	2.548	0.464	1.305	0.102	0.000	0.859	1.057	1.498	0.799	1.260	0.946	0.863
-1	0.850	-1.185	-0.117	0.943	-0.449	1.142	0.875	-0.030	0.000	2.223	-0.461	1.627	2.215	0.767	-1.761	-1.692	0.000	1.012	-0.727	0.639	3.102	3.649	2.062	0.985	1.478	1.087	1.659	1.358
-0	0.933	1.259	0.130	0.326	-0.890	0.306	1.136	1.142	0.000	0.964	0.705	-1.373	2.215	0.546	-0.196	-0.001	0.000	0.578	-1.169	1.004	3.102	0.830	0.836	0.988	0.837	1.031	0.749	0.655
-0	0.471	0.697	1.570	1.109	0.201	1.248	0.348	-1.448	0.000	2.103	0.773	0.686	2.215	1.451	-0.087	-0.453	2.548	1.197	-0.045	-1.026	0.000	0.793	1.094	0.987	0.851	1.804	1.378	1.089
-1	2.446	-0.701	-1.568	0.059	0.822	1.401	-0.600	-0.044	2.173	0.324	-0.001	1.344	2.215	0.913	-0.818	1.049	0.000	0.442	-1.088	-0.005	0.000	0.611	1.062	0.979	0.562	0.988	0.998	0.806
-0	0.619	2.029	0.933	0.528	-0.903	0.974	0.760	-0.311	2.173	0.825	0.658	-1.466	1.107	0.894	1.594	0.370	0.000	0.882	-0.258	1.661	0.000	1.498	1.088	0.987	0.867	1.139	0.900	0.779
-1	0.674	-0.131	-0.362	0.518	-1.574	0.876	0.442	0.145	1.087	0.497	-1.526	-1.704	0.000	0.680	2.514	-1.374	0.000	0.792	-0.479	0.773	1.551	0.573	1.198	0.984	0.800	0.667	0.987	0.832
-1	1.447	1.145	-0.937	0.307	-1.458	0.478	1.264	0.816	1.087	0.558	1.015	-0.101	2.215	0.937	-0.190	1.177	0.000	0.699	0.954	-1.512	0.000	0.877	0.838	0.990	0.873	0.566	0.646	0.713
-1	0.976	0.308	-0.844	0.436	0.610	1.253	0.149	-1.585	2.173	1.415	0.568	0.096	2.215	0.953	-0.855	0.441	0.000	0.867	-0.650	1.643	0.000	0.890	1.234	0.988	0.796	2.002	1.179	0.977
-0	0.697	0.401	-0.718	0.920	0.735	0.958	-0.172	0.168	2.173	0.872	-0.097	-1.335	0.000	0.513	-1.192	-1.710	1.274	0.426	-1.637	1.368	0.000	0.997	1.227	1.072	0.800	1.013	0.786	0.749
-1	1.305	-2.157	1.740	0.661	-0.912	0.705	-0.516	0.759	2.173	0.989	-0.716	-0.300	2.215	0.627	-1.052	-1.736	0.000	0.467	-2.467	0.568	0.000	0.807	0.964	0.988	1.427	1.012	1.165	0.926
-0	1.847	1.663	-0.618	0.280	1.258	1.462	-0.054	1.371	0.000	0.900	0.309	-0.544	0.000	0.331	-2.149	-0.341	0.000	1.091	-0.833	0.710	3.102	1.496	0.931	0.989	1.549	0.115	1.140	1.150
-0	0.410	-0.323	1.069	2.160	0.010	0.892	0.942	-1.640	2.173	0.946	0.938	1.314	0.000	1.213	-1.099	-0.794	2.548	0.650	0.053	0.056	0.000	1.041	0.916	1.063	0.985	1.910	1.246	1.107
-1	0.576	1.092	-0.088	0.777	-1.579	0.757	0.271	0.109	0.000	0.819	0.827	-1.554	2.215	1.313	2.341	-1.568	0.000	2.827	0.239	-0.338	0.000	0.876	0.759	0.986	0.692	0.457	0.796	0.791
-1	0.537	0.925	-1.406	0.306	-0.050	0.906	1.051	0.037	0.000	1.469	-0.177	-1.320	2.215	1.872	0.723	1.158	0.000	1.313	0.227	-0.501	3.102	0.953	0.727	0.978	0.755	0.892	0.932	0.781
-0	0.716	-0.065	-0.484	1.313	-1.563	0.596	-0.242	0.678	2.173	0.426	-1.909	0.616	0.000	0.885	-0.406	-1.343	2.548	0.501	-1.327	-0.340	0.000	0.470	0.728	1.109	0.919	0.881	0.665	0.692
-1	0.624	-0.389	0.128	1.636	-1.110	1.025	0.573	-0.843	2.173	0.646	-0.697	1.064	0.000	0.632	-1.442	0.961	0.000	0.863	-0.106	1.717	0.000	0.825	0.917	1.257	0.983	0.713	0.890	0.824
-0	0.484	2.101	1.714	1.131	-0.823	0.750	0.583	-1.304	1.087	0.894	0.421	0.559	2.215	0.921	-0.063	0.282	0.000	0.463	-0.474	-1.387	0.000	0.742	0.886	0.995	0.993	1.201	0.806	0.754
-0	0.570	0.339	-1.478	0.528	0.439	0.978	1.479	-1.411	2.173	0.763	1.541	-0.734	0.000	1.375	0.840	0.903	0.000	0.965	1.599	0.364	0.000	0.887	1.061	0.992	1.322	1.453	1.013	0.969
-0	0.940	1.303	1.636	0.851	-1.732	0.803	-0.030	-0.177	0.000	0.480	-0.125	-0.954	0.000	0.944	0.709	0.296	2.548	1.342	-0.418	1.197	3.102	0.853	0.989	0.979	0.873	0.858	0.719	0.786
-1	0.599	0.544	-0.238	0.816	1.043	0.857	0.660	1.128	2.173	0.864	-0.624	-0.843	0.000	1.159	0.367	0.174	0.000	1.520	-0.543	-1.508	0.000	0.842	0.828	0.984	0.759	0.895	0.918	0.791
-1	1.651	1.897	-0.914	0.423	0.315	0.453	0.619	-1.607	2.173	0.532	-0.424	0.209	1.107	0.369	2.479	0.034	0.000	0.701	0.217	0.984	0.000	0.976	0.951	1.035	0.879	0.825	0.915	0.798
-1	0.926	-0.574	-0.763	0.285	1.094	0.672	2.314	1.545	0.000	1.124	0.415	0.809	0.000	1.387	0.270	-0.949	2.548	1.547	-0.631	-0.200	3.102	0.719	0.920	0.986	0.889	0.933	0.797	0.777
-0	0.677	1.698	-0.890	0.641	-0.449	0.607	1.754	1.720	0.000	0.776	0.372	0.782	2.215	0.511	1.491	-0.480	0.000	0.547	-0.341	0.853	3.102	0.919	1.026	0.997	0.696	0.242	0.694	0.687
-0	1.266	0.602	0.958	0.487	1.256	0.709	0.843	-1.196	0.000	0.893	1.303	-0.594	1.107	1.090	1.320	0.354	0.000	0.797	1.846	1.139	0.000	0.780	0.896	0.986	0.661	0.709	0.790	0.806
-1	0.628	-0.616	-0.329	0.764	-1.150	0.477	-0.715	1.187	2.173	1.250	0.607	1.026	2.215	0.983	-0.023	-0.583	0.000	0.377	1.344	-1.015	0.000	0.744	0.954	0.987	0.837	0.841	0.795	0.694
-1	1.035	-0.828	-1.358	1.870	-1.060	1.075	0.130	0.448	2.173	0.660	0.697	0.641	0.000	0.425	1.006	-1.035	0.000	0.751	1.055	1.364	3.102	0.826	0.822	0.988	0.967	0.901	1.077	0.906
-1	0.830	0.265	-0.150	0.660	1.105	0.592	-0.557	0.908	2.173	0.670	-1.419	-0.671	0.000	1.323	-0.409	1.644	2.548	0.850	-0.033	-0.615	0.000	0.760	0.967	0.984	0.895	0.681	0.747	0.770
-1	1.395	1.100	1.167	1.088	0.218	0.400	-0.132	0.024	2.173	0.743	0.530	-1.361	2.215	0.341	-0.691	-0.238	0.000	0.396	-1.426	-0.933	0.000	0.363	0.472	1.287	0.922	0.810	0.792	0.656
-1	1.070	1.875	-1.298	1.215	-0.106	0.767	0.795	0.514	1.087	0.401	2.780	1.276	0.000	0.686	1.127	1.721	2.548	0.391	-0.259	-1.167	0.000	1.278	1.113	1.389	0.852	0.824	0.838	0.785
-0	1.114	-0.071	1.719	0.399	-1.383	0.849	0.254	0.481	0.000	0.958	-0.579	0.742	0.000	1.190	-0.140	-0.862	2.548	0.479	1.390	0.856	0.000	0.952	0.988	0.985	0.764	0.419	0.835	0.827
-0	0.714	0.376	-0.568	1.578	-1.165	0.648	0.141	0.639	2.173	0.472	0.569	1.449	1.107	0.783	1.483	0.361	0.000	0.540	-0.790	0.032	0.000	0.883	0.811	0.982	0.775	0.572	0.760	0.745
-0	0.401	-1.731	0.765	0.974	1.648	0.652	-1.024	0.191	0.000	0.544	-0.366	-1.246	2.215	0.627	0.140	1.008	2.548	0.810	0.409	0.429	0.000	0.950	0.934	0.977	0.621	0.580	0.677	0.650
-1	0.391	1.679	-1.298	0.605	-0.832	0.549	1.338	0.522	2.173	1.244	0.884	1.070	0.000	1.002	0.846	-1.345	2.548	0.783	-2.464	-0.237	0.000	4.515	2.854	0.981	0.877	0.939	1.942	1.489
-1	0.513	-0.220	-0.444	1.699	0.479	1.109	0.181	-0.999	2.173	0.883	-0.335	-1.716	2.215	1.075	-0.380	1.352	0.000	0.857	0.048	0.147	0.000	0.937	0.758	0.986	1.206	0.958	0.949	0.876
-0	1.367	-0.388	0.798	1.158	1.078	0.811	-1.024	-1.628	0.000	1.504	0.097	-0.999	2.215	1.652	-0.860	0.054	2.548	0.573	-0.142	-1.401	0.000	0.869	0.833	1.006	1.412	1.641	1.214	1.041
-1	1.545	-0.533	-1.517	1.177	1.289	2.331	-0.370	-0.073	0.000	1.295	-0.358	-0.891	2.215	0.476	0.756	0.985	0.000	1.945	-0.016	-1.651	3.102	1.962	1.692	1.073	0.656	0.941	1.312	1.242
-0	0.858	0.978	-1.258	0.286	0.161	0.729	1.230	1.087	2.173	0.561	2.670	-0.109	0.000	0.407	2.346	0.938	0.000	1.078	0.729	-0.658	3.102	0.597	0.921	0.982	0.579	0.954	0.733	0.769
-1	1.454	-1.384	0.870	0.067	0.394	1.033	-0.673	0.318	0.000	1.166	-0.763	-1.533	2.215	2.848	-0.045	-0.856	2.548	0.697	-0.140	1.134	0.000	0.931	1.293	0.977	1.541	1.326	1.201	1.078
-1	0.559	-0.913	0.486	1.104	-0.321	1.073	-0.348	1.345	0.000	0.901	-0.827	-0.842	0.000	0.739	0.047	-0.415	2.548	0.433	-1.132	1.268	0.000	0.797	0.695	0.985	0.868	0.346	0.674	0.623
-1	1.333	0.780	-0.964	0.916	1.202	1.822	-0.071	0.742	2.173	1.486	-0.399	-0.824	0.000	0.740	0.568	-0.134	0.000	0.971	-0.070	-1.589	3.102	1.278	0.929	1.421	1.608	1.214	1.215	1.137
-1	2.417	0.631	-0.317	0.323	0.581	0.841	1.524	-1.738	0.000	0.543	1.176	-0.325	0.000	0.827	0.700	0.866	0.000	0.834	-0.262	-1.702	3.102	0.932	0.820	0.988	0.646	0.287	0.595	0.589
-0	0.955	-1.242	0.938	1.104	0.474	0.798	-0.743	1.535	0.000	1.356	-1.357	-1.080	2.215	1.320	-1.396	-0.132	2.548	0.728	-0.529	-0.633	0.000	0.832	0.841	0.988	0.923	1.077	0.988	0.816
-1	1.305	-1.918	0.391	1.161	0.063	0.724	2.593	1.481	0.000	0.592	-1.207	-0.329	0.000	0.886	-0.836	-1.168	2.548	1.067	-1.481	-1.440	0.000	0.916	0.688	0.991	0.969	0.550	0.665	0.638
-0	1.201	0.071	-1.123	2.242	-1.533	0.702	-0.256	0.688	0.000	0.967	0.491	1.040	2.215	1.271	-0.558	0.095	0.000	1.504	0.676	-0.383	3.102	0.917	1.006	0.985	1.017	1.057	0.928	1.057
-0	0.994	-1.607	1.596	0.774	-1.391	0.625	-0.134	-0.862	2.173	0.746	-0.765	-0.316	2.215	1.131	-0.320	0.869	0.000	0.607	0.826	0.301	0.000	0.798	0.967	0.999	0.880	0.581	0.712	0.774
-1	0.482	-0.467	0.729	1.419	1.458	0.824	0.376	-0.242	0.000	1.368	0.023	1.459	2.215	0.826	0.669	-1.079	2.548	0.936	2.215	-0.309	0.000	1.883	1.216	0.997	1.065	0.946	1.224	1.526
-1	0.383	1.588	1.611	0.748	1.194	0.866	-0.279	-0.636	0.000	0.707	0.536	0.801	2.215	1.647	-1.155	0.367	0.000	1.292	0.303	-1.681	3.102	2.016	1.581	0.986	0.584	0.684	1.107	0.958
-0	0.629	0.203	0.736	0.671	-0.271	1.350	-0.486	0.761	2.173	0.496	-0.805	-1.718	0.000	2.393	0.044	-1.046	1.274	0.651	-0.116	-0.541	0.000	0.697	1.006	0.987	1.069	2.317	1.152	0.902
-0	0.905	-0.564	-0.570	0.263	1.096	1.219	-1.397	-1.414	1.087	1.164	-0.533	-0.208	0.000	1.459	1.965	0.784	0.000	2.220	-1.421	0.452	0.000	0.918	1.360	0.993	0.904	0.389	2.118	1.707
-1	1.676	1.804	1.171	0.529	1.175	1.664	0.354	-0.530	0.000	1.004	0.691	-1.280	2.215	0.838	0.373	0.626	2.548	1.094	1.774	0.501	0.000	0.806	1.100	0.991	0.769	0.976	0.807	0.740
-1	1.364	-1.936	0.020	1.327	0.428	1.021	-1.665	-0.907	2.173	0.818	-2.701	1.303	0.000	0.716	-0.590	-1.629	2.548	0.895	-2.280	-1.602	0.000	1.211	0.849	0.989	1.320	0.864	1.065	0.949
-0	0.629	-0.626	0.609	1.828	1.280	0.644	-0.856	-0.873	2.173	0.555	1.066	-0.640	0.000	0.477	-1.364	-1.021	2.548	1.017	0.036	0.380	0.000	0.947	0.941	0.994	1.128	0.241	0.793	0.815
-1	1.152	-0.843	0.926	1.802	0.800	2.493	-1.449	-1.127	0.000	1.737	0.833	0.488	0.000	1.026	0.929	-0.990	2.548	1.408	0.689	1.142	3.102	1.171	0.956	0.993	2.009	0.867	1.499	1.474
-0	2.204	0.081	0.008	1.021	-0.679	2.676	0.090	1.163	0.000	2.210	-1.686	-1.195	0.000	1.805	0.891	-0.148	2.548	0.450	-0.502	-1.295	3.102	6.959	3.492	1.205	0.908	0.845	2.690	2.183
-1	0.957	0.954	1.702	0.043	-0.503	1.113	0.033	-0.308	0.000	0.757	-0.363	-1.129	2.215	1.635	0.068	1.048	1.274	0.415	-2.098	0.061	0.000	1.010	0.979	0.992	0.704	1.125	0.761	0.715
-0	1.222	0.418	1.059	1.303	1.442	0.282	-1.499	-1.286	0.000	1.567	0.016	-0.164	2.215	0.451	2.229	-1.229	0.000	0.660	-0.513	-0.296	3.102	2.284	1.340	0.985	1.531	0.314	1.032	1.094
-1	0.603	1.675	-0.973	0.703	-1.709	1.023	0.652	1.296	2.173	1.078	0.363	-0.263	0.000	0.734	-0.457	-0.745	1.274	0.561	1.434	-0.042	0.000	0.888	0.771	0.984	0.847	1.234	0.874	0.777
-0	0.897	0.949	-0.848	1.115	-0.085	0.522	-1.267	-1.418	0.000	0.684	-0.599	1.474	0.000	1.176	0.922	0.641	2.548	0.470	0.103	0.148	3.102	0.775	0.697	0.984	0.839	0.358	0.847	1.008
-1	0.987	1.013	-1.504	0.468	-0.259	1.160	0.476	-0.971	2.173	1.266	0.919	0.780	0.000	0.634	1.695	0.233	0.000	0.487	-0.082	0.719	3.102	0.921	0.641	0.991	0.730	0.828	0.952	0.807
-1	0.847	1.581	-1.397	1.629	1.529	1.053	0.816	-0.344	2.173	0.895	0.779	0.332	0.000	0.750	1.311	0.419	2.548	1.604	0.844	1.367	0.000	1.265	0.798	0.989	1.328	0.783	0.930	0.879
-1	0.805	1.416	-1.327	0.397	0.589	0.488	0.982	0.843	0.000	0.664	-0.999	0.129	0.000	0.624	0.613	-0.558	0.000	1.431	-0.667	-1.561	3.102	0.959	1.103	0.989	0.590	0.632	0.926	0.798
-0	1.220	-0.313	-0.489	1.759	0.201	1.698	-0.220	0.241	2.173	1.294	1.390	-1.682	0.000	1.447	-1.623	-1.296	0.000	1.710	0.872	-1.356	3.102	1.198	0.981	1.184	0.859	2.165	1.807	1.661
-0	0.772	-0.611	-0.549	0.465	-1.528	1.103	-0.140	0.001	2.173	0.854	-0.406	1.655	0.000	0.733	-1.250	1.072	0.000	0.883	0.627	-1.132	3.102	0.856	0.927	0.987	1.094	1.013	0.938	0.870
-1	1.910	0.771	0.828	0.231	1.267	1.398	1.455	-0.295	2.173	0.837	-2.564	0.770	0.000	0.540	2.189	1.287	0.000	1.345	1.311	-1.151	0.000	0.861	0.869	0.984	1.359	1.562	1.105	0.963
-1	0.295	0.832	1.399	1.222	-0.517	2.480	0.013	1.591	0.000	2.289	0.436	0.287	2.215	1.995	-0.367	-0.409	1.274	0.375	1.367	-1.716	0.000	1.356	2.171	0.990	1.467	1.664	1.855	1.705
-1	1.228	0.339	-0.575	0.417	1.474	0.480	-1.416	-1.498	2.173	0.614	-0.933	-0.961	0.000	1.189	1.690	1.003	0.000	1.690	-1.065	0.106	3.102	0.963	1.147	0.987	1.086	0.948	0.930	0.866
-0	2.877	-1.014	1.440	0.782	0.483	1.134	-0.735	-0.196	2.173	1.123	0.084	-0.596	0.000	1.796	-0.356	1.044	2.548	1.406	1.582	-0.991	0.000	0.939	1.178	1.576	0.996	1.629	1.216	1.280
-1	2.178	0.259	1.107	0.256	1.222	0.979	-0.440	-0.538	1.087	0.496	-0.760	-0.049	0.000	1.471	1.683	-1.486	0.000	0.646	0.695	-1.577	3.102	1.093	1.070	0.984	0.608	0.889	0.962	0.866
-1	0.604	0.592	1.295	0.964	0.348	1.178	-0.016	0.832	2.173	1.626	-0.420	-0.760	0.000	0.748	0.461	-0.906	0.000	0.728	0.309	-1.269	1.551	0.852	0.604	0.989	0.678	0.949	1.021	0.878
-0	0.428	-1.352	-0.912	1.713	0.797	1.894	-1.452	0.191	2.173	2.378	2.113	-1.190	0.000	0.860	2.174	0.949	0.000	1.693	0.759	1.426	3.102	0.885	1.527	1.186	1.090	3.294	4.492	3.676
-0	0.473	0.485	0.154	1.433	-1.504	0.766	1.257	-1.302	2.173	0.414	0.119	0.238	0.000	0.805	0.242	-0.691	2.548	0.734	0.749	0.753	0.000	0.430	0.893	1.137	0.686	0.724	0.618	0.608
-1	0.763	-0.601	0.876	0.182	-1.678	0.818	0.599	0.481	2.173	0.658	-0.737	-0.553	0.000	0.857	-1.138	-1.435	0.000	1.540	-1.466	-0.447	0.000	0.870	0.566	0.989	0.728	0.658	0.821	0.726
-0	0.619	-0.273	-0.143	0.992	-1.267	0.566	0.876	-1.396	2.173	0.515	0.892	0.618	0.000	0.434	-0.902	0.862	2.548	0.490	-0.539	0.549	0.000	0.568	0.794	0.984	0.667	0.867	0.597	0.578
-0	0.793	0.970	0.324	0.570	0.816	0.761	-0.550	1.519	2.173	1.150	0.496	-0.447	0.000	0.925	0.724	1.008	1.274	1.135	-0.275	-0.843	0.000	0.829	1.068	0.978	1.603	0.892	1.041	1.059
-1	0.480	0.364	-0.067	1.906	-1.582	1.397	1.159	0.140	0.000	0.639	0.398	-1.102	0.000	1.597	-0.668	1.607	2.548	1.306	-0.797	0.288	3.102	0.856	1.259	1.297	1.022	1.032	1.049	0.939
-0	0.514	1.304	1.490	1.741	-0.220	0.648	0.155	0.535	0.000	0.562	-1.016	0.837	0.000	0.863	-0.780	-0.815	2.548	1.688	-0.130	-1.545	3.102	0.887	0.980	1.309	1.269	0.654	1.044	1.035
-0	1.225	0.333	0.656	0.893	0.859	1.037	-0.876	1.603	1.087	1.769	0.272	-0.227	2.215	1.000	0.579	-1.690	0.000	1.385	0.471	-0.860	0.000	0.884	1.207	0.995	1.097	2.336	1.282	1.145
-0	2.044	-1.472	-0.294	0.392	0.369	0.927	0.718	1.492	1.087	1.619	-0.736	0.047	2.215	1.884	-0.101	-1.540	0.000	0.548	-0.441	1.117	0.000	0.798	0.877	0.981	0.750	2.272	1.469	1.276
-0	1.037	-0.276	0.735	3.526	1.156	2.498	0.401	-0.590	1.087	0.714	-1.203	1.393	2.215	0.681	0.629	1.534	0.000	0.719	-0.355	-0.706	0.000	0.831	0.857	0.988	2.864	2.633	1.988	1.466
-1	0.651	-1.218	-0.791	0.770	-1.449	0.610	-0.535	0.960	2.173	0.380	-1.072	-0.031	2.215	0.415	2.123	-1.100	0.000	0.776	0.217	0.420	0.000	0.986	1.008	1.001	0.853	0.588	0.799	0.776
-0	1.586	-0.409	0.085	3.258	0.405	1.647	-0.674	-1.519	0.000	0.640	-1.027	-1.681	0.000	1.452	-0.444	-0.957	2.548	0.927	-0.017	1.215	3.102	0.519	0.866	0.992	0.881	0.847	1.018	1.278
-0	0.712	0.092	-0.466	0.688	1.236	0.921	-1.217	-1.022	2.173	2.236	-1.167	0.868	2.215	0.851	-1.892	-0.753	0.000	0.475	-1.216	-0.383	0.000	0.668	0.758	0.988	1.180	2.093	1.157	0.934
-0	0.419	0.471	0.974	2.805	0.235	1.473	-0.198	1.255	1.087	0.931	1.083	-0.712	0.000	1.569	1.358	-1.179	2.548	2.506	0.199	-0.842	0.000	0.929	0.991	0.992	1.732	2.367	1.549	1.430
-1	0.667	1.003	1.504	0.368	1.061	0.885	-0.318	-0.353	0.000	1.438	-1.939	0.710	0.000	1.851	0.277	-1.460	2.548	1.403	0.517	-0.157	0.000	0.883	1.019	1.000	0.790	0.859	0.938	0.841
-1	1.877	-0.492	0.372	0.441	0.955	1.034	-1.220	-0.846	1.087	0.952	-0.320	1.125	0.000	0.542	0.308	-1.261	2.548	1.018	-1.415	-1.547	0.000	1.280	0.932	0.991	1.273	0.878	0.921	0.906
-0	1.052	0.901	1.176	1.280	1.517	0.562	-1.150	-0.079	2.173	1.228	-0.308	-0.354	0.000	0.790	-1.492	-0.963	0.000	0.942	-0.672	-1.588	3.102	1.116	0.902	0.988	1.993	0.765	1.375	1.325
-1	0.518	-0.254	1.642	0.865	0.725	0.980	0.734	0.023	0.000	1.448	0.780	-1.736	2.215	0.955	0.513	-0.519	0.000	0.365	-0.444	-0.243	3.102	0.833	0.555	0.984	0.827	0.795	0.890	0.786
-0	0.870	0.815	-0.506	0.663	-0.518	0.935	0.289	-1.675	2.173	1.188	0.005	0.635	0.000	0.580	0.066	-1.455	2.548	0.580	-0.634	-0.199	0.000	0.852	0.788	0.979	1.283	0.208	0.856	0.950
-0	0.628	1.382	0.135	0.683	0.571	1.097	0.564	-0.950	2.173	0.617	-0.326	0.371	0.000	1.093	0.918	1.667	2.548	0.460	1.221	0.708	0.000	0.743	0.861	0.975	1.067	1.007	0.843	0.762
-0	4.357	0.816	-1.609	1.845	-1.288	3.292	0.726	0.324	2.173	1.528	0.583	-0.801	2.215	0.605	0.572	1.406	0.000	0.794	-0.791	0.122	0.000	0.967	1.132	1.124	3.602	2.811	2.460	1.861
-0	0.677	-1.265	1.559	0.866	-0.618	0.823	0.260	0.185	0.000	1.133	0.337	1.589	2.215	0.563	-0.830	0.510	0.000	0.777	0.117	-0.941	3.102	0.839	0.763	0.986	1.182	0.649	0.796	0.851
-0	2.466	-1.838	-1.648	1.717	1.533	1.676	-1.553	-0.109	2.173	0.670	-0.666	0.284	0.000	0.334	-2.480	0.316	0.000	0.366	-0.804	-1.298	3.102	0.875	0.894	0.997	0.548	0.770	1.302	1.079
-1	1.403	0.129	-1.307	0.688	0.306	0.579	0.753	0.814	1.087	0.474	0.694	-1.400	0.000	0.520	1.995	0.185	0.000	0.929	-0.504	1.270	3.102	0.972	0.998	1.353	0.948	0.650	0.688	0.724
-1	0.351	1.188	-0.360	0.254	-0.346	1.129	0.545	1.691	0.000	0.652	-0.039	-0.258	2.215	1.089	0.655	0.472	2.548	0.554	-0.493	1.366	0.000	0.808	1.045	0.992	0.570	0.649	0.809	0.744
-0	1.875	-0.013	-0.128	0.236	1.163	0.902	0.426	0.590	2.173	1.251	-1.210	-0.616	0.000	1.035	1.534	0.912	0.000	1.944	1.789	-1.691	0.000	0.974	1.113	0.990	0.925	1.120	0.956	0.912
-0	0.298	0.750	-0.507	1.555	1.463	0.804	1.200	-0.665	0.000	0.439	-0.829	-0.252	1.107	0.770	-1.090	0.947	2.548	1.165	-0.166	-0.763	0.000	1.140	0.997	0.988	1.330	0.555	1.005	1.012
-0	0.647	0.342	0.245	4.340	-0.157	2.229	0.068	1.170	2.173	2.133	-0.201	-1.441	0.000	1.467	0.697	-0.532	1.274	1.457	0.583	-1.640	0.000	0.875	1.417	0.976	2.512	2.390	1.794	1.665
-1	1.731	-0.803	-1.013	1.492	-0.020	1.646	-0.541	1.121	2.173	0.459	-1.251	-1.495	2.215	0.605	-1.711	-0.232	0.000	0.658	0.634	-0.068	0.000	1.214	0.886	1.738	1.833	1.024	1.192	1.034
-0	0.515	1.416	-1.089	1.697	1.426	1.414	0.941	0.027	0.000	1.480	0.133	-1.595	2.215	1.110	0.752	0.760	2.548	1.062	0.697	-0.492	0.000	0.851	0.955	0.994	1.105	1.255	1.175	1.095
-0	1.261	0.858	1.465	0.757	0.305	2.310	0.679	1.080	2.173	1.544	2.518	-0.464	0.000	2.326	0.270	-0.841	0.000	2.163	0.839	-0.500	3.102	0.715	0.825	1.170	0.980	2.371	1.527	1.221
-1	1.445	1.509	1.471	0.414	-1.285	0.767	0.864	-0.677	2.173	0.524	1.388	0.171	0.000	0.826	0.190	0.121	2.548	0.572	1.691	-1.603	0.000	0.870	0.935	0.994	0.968	0.735	0.783	0.777
-1	0.919	-0.264	-1.245	0.681	-1.722	1.022	1.010	0.097	2.173	0.685	0.403	-1.351	0.000	1.357	-0.429	1.262	1.274	0.687	1.021	-0.563	0.000	0.953	0.796	0.991	0.873	1.749	1.056	0.917
-1	0.293	-2.258	-1.427	1.191	1.202	0.394	-2.030	1.438	0.000	0.723	0.596	-0.024	2.215	0.525	-1.678	-0.290	0.000	0.788	-0.824	-1.029	3.102	0.821	0.626	0.976	1.080	0.810	0.842	0.771
-0	3.286	0.386	1.688	1.619	-1.620	1.392	-0.009	0.280	0.000	1.179	-0.776	-0.110	2.215	1.256	0.248	-1.114	2.548	0.777	0.825	-0.156	0.000	1.026	1.065	0.964	0.909	1.249	1.384	1.395
-1	1.075	0.603	0.561	0.656	-0.685	0.985	0.175	0.979	2.173	1.154	0.584	-0.886	0.000	1.084	-0.354	-1.004	2.548	0.865	1.224	1.269	0.000	1.346	1.073	1.048	0.873	1.310	1.003	0.865
-1	1.098	-0.091	1.466	1.558	0.915	0.649	1.314	-1.182	2.173	0.791	0.073	0.351	0.000	0.517	0.940	1.195	0.000	1.150	1.187	-0.692	3.102	0.866	0.822	0.980	1.311	0.394	1.119	0.890
-1	0.481	-1.042	0.148	1.135	-1.249	1.202	-0.344	0.308	1.087	0.779	-1.431	1.581	0.000	0.860	-0.860	-1.125	0.000	0.785	0.303	1.199	3.102	0.878	0.853	0.988	1.072	0.827	0.936	0.815
-0	1.348	0.497	0.318	0.806	0.976	1.393	-0.152	0.632	2.173	2.130	0.515	-1.054	0.000	0.908	0.062	-0.780	0.000	1.185	0.687	1.668	1.551	0.720	0.898	0.985	0.683	1.292	1.320	1.131
-0	2.677	-0.420	-1.685	1.828	1.433	2.040	-0.718	-0.039	0.000	0.400	-0.873	0.472	0.000	0.444	0.340	-0.830	2.548	0.431	0.768	-1.417	3.102	0.869	0.917	0.996	0.707	0.193	0.728	1.154
-1	1.300	0.586	-0.122	1.306	0.609	0.727	-0.556	-1.652	2.173	0.636	0.720	1.393	2.215	0.328	1.280	-0.390	0.000	0.386	0.752	-0.905	0.000	0.202	0.751	1.106	0.864	0.799	0.928	0.717
-0	0.637	-0.176	1.737	1.322	-0.414	0.702	-0.964	-0.680	0.000	1.054	-0.461	0.889	2.215	0.861	-0.267	0.225	0.000	1.910	-1.888	1.027	0.000	0.919	0.899	1.186	0.993	1.109	0.862	0.775
-1	0.723	-0.104	1.572	0.428	-0.840	0.655	0.544	1.401	2.173	1.522	-0.154	-0.452	2.215	0.996	0.190	0.273	0.000	1.906	-0.176	0.966	0.000	0.945	0.894	0.990	0.981	1.555	0.988	0.893
-0	2.016	-0.570	1.612	0.798	0.441	0.334	0.191	-0.909	0.000	0.939	0.146	0.021	2.215	0.553	-0.444	1.156	2.548	0.781	-1.545	-0.520	0.000	0.922	0.956	1.528	0.722	0.699	0.778	0.901
-0	1.352	-0.707	1.284	0.665	0.580	0.694	-1.040	-0.899	2.173	0.692	-2.048	0.029	0.000	0.545	-2.042	1.259	0.000	0.661	-0.808	-1.251	3.102	0.845	0.991	0.979	0.662	0.225	0.685	0.769
-1	1.057	-1.561	-0.411	0.952	-0.681	1.236	-1.107	1.045	2.173	1.288	-2.521	-0.521	0.000	1.361	-1.239	1.546	0.000	0.373	-1.540	0.028	0.000	0.794	0.782	0.987	0.889	0.832	0.972	0.828
-0	1.118	-0.017	-1.227	1.077	1.256	0.714	0.624	-0.811	0.000	0.800	0.704	0.387	1.107	0.604	0.234	0.986	0.000	1.306	-0.456	0.094	3.102	0.828	0.984	1.195	0.987	0.672	0.774	0.748
-1	0.602	2.201	0.212	0.119	0.182	0.474	2.130	1.270	0.000	0.370	2.088	-0.573	0.000	0.780	-0.725	-1.033	0.000	1.642	0.598	0.303	3.102	0.886	0.988	0.985	0.644	0.756	0.651	0.599
-0	1.677	-0.844	1.581	0.585	0.887	1.012	-2.315	0.752	0.000	1.077	0.748	-0.195	0.000	0.718	0.832	-1.337	1.274	1.181	-0.557	-1.006	3.102	1.018	1.247	0.988	0.908	0.651	1.311	1.120
-1	1.695	0.259	1.224	1.344	1.067	0.718	-1.752	-0.215	0.000	0.473	0.991	-0.993	0.000	0.891	1.285	-1.500	2.548	0.908	-0.131	0.288	0.000	0.945	0.824	0.979	1.009	0.951	0.934	0.833
-0	0.793	0.628	0.432	1.707	0.302	0.919	1.045	-0.784	0.000	1.472	0.175	-1.284	2.215	1.569	0.155	0.971	2.548	0.435	0.735	1.625	0.000	0.801	0.907	0.992	0.831	1.446	1.082	1.051
-1	0.537	-0.664	-0.244	1.104	1.272	1.154	0.394	1.633	0.000	1.527	0.963	0.559	2.215	1.744	0.650	-0.912	0.000	1.097	0.730	-0.368	3.102	1.953	1.319	1.045	1.309	0.869	1.196	1.126
-1	0.585	-1.469	1.005	0.749	-1.060	1.224	-0.717	-0.323	2.173	1.012	-0.201	1.268	0.000	0.359	-0.567	0.476	0.000	1.117	-1.124	1.557	3.102	0.636	1.281	0.986	0.616	1.289	0.890	0.881
-1	0.354	-1.517	0.667	2.534	-1.298	1.020	-0.375	1.254	0.000	1.119	-0.060	-1.538	2.215	1.059	-0.395	-0.140	0.000	2.609	0.199	-0.778	1.551	0.957	0.975	1.286	1.666	1.003	1.224	1.135
-1	0.691	-1.619	-1.380	0.361	1.727	1.493	-1.093	-0.289	0.000	1.447	-0.640	1.341	0.000	1.453	-0.617	-1.456	1.274	1.061	-1.481	-0.091	0.000	0.744	0.649	0.987	0.596	0.727	0.856	0.797
-0	1.336	1.293	-1.359	0.357	0.067	1.110	-0.058	-0.515	0.000	0.976	1.498	1.207	0.000	1.133	0.437	1.053	2.548	0.543	1.374	0.171	0.000	0.764	0.761	0.984	0.827	0.553	0.607	0.612
-0	0.417	-1.111	1.661	2.209	-0.683	1.931	-0.642	0.959	1.087	1.514	-2.032	-0.686	0.000	1.521	-0.539	1.344	0.000	0.978	-0.866	0.363	1.551	2.813	1.850	1.140	1.854	0.799	1.600	1.556
-0	1.058	0.390	-0.591	0.134	1.149	0.346	-1.550	0.186	0.000	1.108	-0.999	0.843	1.107	1.124	0.415	-1.514	0.000	1.067	-0.426	-1.000	3.102	1.744	1.050	0.985	1.006	1.010	0.883	0.789
-1	1.655	0.253	1.216	0.270	1.703	0.500	-0.006	-1.418	2.173	0.690	-0.350	0.170	2.215	1.045	-0.924	-0.774	0.000	0.996	-0.745	-0.123	0.000	0.839	0.820	0.993	0.921	0.869	0.725	0.708
-0	1.603	-0.850	0.564	0.829	0.093	1.270	-1.113	-1.155	2.173	0.853	-1.021	1.248	2.215	0.617	-1.270	1.733	0.000	0.935	-0.092	0.136	0.000	1.011	1.074	0.977	0.823	1.269	1.054	0.878
-0	1.568	-0.792	1.005	0.545	0.896	0.895	-1.698	-0.988	0.000	0.608	-1.634	1.705	0.000	0.826	0.208	0.618	1.274	2.063	-1.743	-0.520	0.000	0.939	0.986	0.990	0.600	0.435	1.033	1.087
-0	0.489	-1.335	-1.102	1.738	1.028	0.628	-0.992	-0.627	0.000	0.652	-0.064	-0.215	0.000	1.072	0.173	-1.251	2.548	1.042	0.057	0.841	3.102	0.823	0.895	1.200	1.164	0.770	0.837	0.846
-1	1.876	0.870	1.234	0.556	-1.262	1.764	0.855	-0.467	2.173	1.079	1.351	0.852	0.000	0.773	0.383	0.874	0.000	1.292	0.829	-1.228	3.102	0.707	0.969	1.102	1.601	1.017	1.112	1.028
-0	1.033	0.407	-0.374	0.705	-1.254	0.690	-0.231	1.502	2.173	0.433	-2.009	-0.057	0.000	0.861	1.151	0.334	0.000	0.960	-0.839	1.299	3.102	2.411	1.480	0.982	0.995	0.377	1.012	0.994
-0	1.092	0.653	-0.801	0.463	0.426	0.529	-1.055	0.040	0.000	0.663	0.999	1.255	1.107	0.749	-1.106	1.185	2.548	0.841	-0.745	-1.029	0.000	0.841	0.743	0.988	0.750	1.028	0.831	0.868
-1	0.799	-0.285	-0.011	0.531	1.392	1.063	0.854	0.494	2.173	1.187	-1.065	-0.851	0.000	0.429	-0.296	1.072	0.000	0.942	-1.985	1.172	0.000	0.873	0.693	0.992	0.819	0.689	1.131	0.913
-0	0.503	1.973	-0.377	1.515	-1.514	0.708	1.081	-0.313	2.173	1.110	-0.417	0.839	0.000	0.712	-1.153	1.165	0.000	0.675	-0.303	-0.930	1.551	0.709	0.761	1.032	0.986	0.698	0.963	1.291
-0	0.690	-0.574	-1.608	1.182	1.118	0.557	-2.243	0.144	0.000	0.969	0.216	-1.383	1.107	1.054	0.888	-0.709	2.548	0.566	1.663	-0.550	0.000	0.752	1.528	0.987	1.408	0.740	1.290	1.123
-1	0.890	1.501	0.786	0.779	-0.615	1.126	0.716	1.541	2.173	0.887	0.728	-0.673	2.215	1.216	0.332	-0.020	0.000	0.965	1.828	0.101	0.000	0.827	0.715	1.099	1.088	1.339	0.924	0.878
-0	0.566	0.883	0.655	1.600	0.034	1.155	2.028	-1.499	0.000	0.723	-0.871	0.763	0.000	1.286	-0.696	-0.676	2.548	1.134	-0.113	1.207	3.102	4.366	2.493	0.984	0.960	0.962	1.843	1.511
-0	1.146	1.086	-0.911	0.838	1.298	0.821	0.127	-0.145	0.000	1.352	0.474	-1.580	2.215	1.619	-0.081	0.675	2.548	1.382	-0.748	0.127	0.000	0.958	0.976	1.239	0.876	1.481	1.116	1.076
-0	1.739	-0.326	-1.661	0.420	-1.705	1.193	-0.031	-1.212	2.173	1.783	-0.442	0.522	0.000	1.064	-0.692	0.027	0.000	1.314	0.359	-0.037	3.102	0.968	0.897	0.986	0.907	1.196	1.175	1.112
-1	0.669	0.194	-0.703	0.657	-0.260	0.899	-2.511	0.311	0.000	1.482	0.773	0.974	2.215	3.459	0.037	-1.299	1.274	2.113	0.067	1.516	0.000	0.740	0.871	0.979	1.361	2.330	1.322	1.046
-1	1.355	-1.033	-1.173	0.552	-0.048	0.899	-0.482	-1.287	2.173	1.422	-1.227	0.390	1.107	1.937	-0.028	0.914	0.000	0.849	-0.230	-1.734	0.000	0.986	1.224	1.017	1.051	1.788	1.150	1.009
-1	0.511	-0.202	1.029	0.780	1.154	0.816	0.532	-0.731	0.000	0.757	0.517	0.749	2.215	1.302	0.289	-1.188	0.000	0.584	1.211	-0.350	0.000	0.876	0.943	0.995	0.963	0.256	0.808	0.891
-1	1.109	0.572	1.484	0.753	1.543	1.711	-0.145	-0.746	1.087	1.759	0.631	0.845	2.215	0.945	0.542	0.003	0.000	0.378	-1.150	-0.044	0.000	0.764	1.042	0.992	1.045	2.736	1.441	1.140
-0	0.712	-0.025	0.553	0.928	-0.711	1.304	0.045	-0.300	0.000	0.477	0.720	0.969	0.000	1.727	-0.474	1.328	1.274	1.282	2.222	1.684	0.000	0.819	0.765	1.023	0.961	0.657	0.799	0.744
-1	1.131	-0.302	1.079	0.901	0.236	0.904	-0.249	1.694	2.173	1.507	-0.702	-1.128	0.000	0.774	0.565	0.284	2.548	1.802	1.446	-0.192	0.000	3.720	2.108	0.986	0.930	1.101	1.484	1.238
-0	1.392	1.253	0.118	0.864	-1.358	0.922	-0.447	-1.243	1.087	1.969	1.031	0.774	2.215	1.333	-0.359	-0.681	0.000	1.099	-0.257	1.473	0.000	1.246	0.909	1.475	1.234	2.531	1.449	1.306
-0	1.374	2.291	-0.479	1.339	-0.243	0.687	2.345	1.310	0.000	0.467	1.081	0.772	0.000	0.656	1.155	-1.636	2.548	0.592	0.536	-1.269	3.102	0.981	0.821	1.010	0.877	0.217	0.638	0.758
-1	0.401	-1.516	0.909	2.738	0.519	0.887	0.566	-1.202	0.000	0.909	-0.176	1.682	0.000	2.149	-0.878	-0.514	2.548	0.929	-0.563	-1.555	3.102	1.228	0.803	0.980	1.382	0.884	1.025	1.172
-1	0.430	-1.589	1.417	2.158	1.226	1.180	-0.829	-0.781	2.173	0.798	1.400	-0.111	0.000	0.939	-0.878	1.076	2.548	0.576	1.335	-0.826	0.000	0.861	0.970	0.982	1.489	1.308	1.015	0.992
-1	1.943	-0.391	-0.840	0.621	-1.613	2.026	1.734	1.025	0.000	0.930	0.573	-0.912	0.000	1.326	0.847	-0.220	1.274	1.181	0.079	0.709	3.102	1.164	1.007	0.987	1.094	0.821	0.857	0.786
-1	0.499	0.436	0.887	0.859	1.509	0.733	-0.559	1.111	1.087	1.011	-0.796	0.279	2.215	1.472	-0.510	-0.982	0.000	1.952	0.379	-0.733	0.000	1.076	1.358	0.991	0.589	0.879	1.068	0.922
-0	0.998	-0.407	-1.711	0.139	0.652	0.810	-0.331	-0.721	0.000	0.471	-0.533	0.442	0.000	0.531	-1.405	0.120	2.548	0.707	0.098	-1.176	1.551	1.145	0.809	0.988	0.529	0.612	0.562	0.609
-1	1.482	0.872	0.638	1.288	0.362	0.856	0.900	-0.511	1.087	1.072	1.061	-1.432	2.215	1.770	-2.292	-1.547	0.000	1.131	1.374	0.783	0.000	6.316	4.381	1.002	1.317	1.048	2.903	2.351
-1	2.084	-0.422	1.289	1.125	0.735	1.104	-0.518	-0.326	2.173	0.413	-0.719	-0.699	0.000	0.857	0.108	-1.631	0.000	0.527	0.641	-1.362	3.102	0.791	0.952	1.016	0.776	0.856	0.987	0.836
-0	0.464	0.674	0.025	0.430	-1.703	0.982	-1.311	-0.808	2.173	1.875	1.060	0.821	2.215	0.954	-0.480	-1.677	0.000	0.567	0.702	-0.939	0.000	0.781	1.076	0.989	1.256	3.632	1.652	1.252
-1	0.457	-1.944	-1.010	1.409	0.931	1.098	-0.742	-0.415	0.000	1.537	-0.834	0.945	2.215	1.752	-0.287	-1.269	2.548	0.692	-1.537	-0.223	0.000	0.801	1.192	1.094	1.006	1.659	1.175	1.122
-0	3.260	-0.943	1.737	0.920	1.309	0.946	-0.139	-0.271	2.173	0.994	-0.952	-0.311	0.000	0.563	-0.136	-0.881	0.000	1.236	-0.507	0.906	1.551	0.747	0.869	0.985	1.769	1.034	1.179	1.042
-0	0.615	-0.778	0.246	1.861	1.619	0.560	-0.943	-0.204	2.173	0.550	-0.759	-1.342	2.215	0.578	0.076	-0.973	0.000	0.939	0.035	0.680	0.000	0.810	0.747	1.401	0.772	0.702	0.719	0.662
-1	2.370	-0.064	-0.237	1.737	0.154	2.319	-1.838	-1.673	0.000	1.053	-1.305	-0.075	0.000	0.925	0.149	0.318	1.274	0.851	-0.922	0.981	3.102	0.919	0.940	0.989	0.612	0.598	1.219	1.626
-1	1.486	0.311	-1.262	1.354	-0.847	0.886	-0.158	1.213	2.173	1.160	-0.218	0.239	0.000	1.166	0.494	0.278	2.548	0.575	1.454	-1.701	0.000	0.429	1.129	0.983	1.111	1.049	1.006	0.920
-1	1.294	1.587	-0.864	0.487	-0.312	0.828	1.051	-0.031	1.087	2.443	1.216	1.609	2.215	1.167	0.813	0.921	0.000	1.751	-0.415	0.119	0.000	1.015	1.091	0.974	1.357	2.093	1.178	1.059
-1	0.984	0.465	-1.661	0.379	-0.554	0.977	0.237	0.365	0.000	0.510	0.143	1.101	0.000	1.099	-0.662	-1.593	2.548	1.104	-0.197	-0.648	3.102	0.925	0.922	0.986	0.642	0.667	0.806	0.722
-1	0.930	-0.009	0.047	0.667	1.367	1.065	-0.231	0.815	0.000	1.199	-1.114	-0.877	2.215	0.940	0.824	-1.583	0.000	1.052	-0.407	-0.076	1.551	1.843	1.257	1.013	1.047	0.751	1.158	0.941
-0	0.767	-0.011	-0.637	0.341	-1.437	1.438	-0.425	-0.450	2.173	1.073	-0.718	1.341	2.215	0.633	-1.394	0.486	0.000	0.603	-1.945	-1.626	0.000	0.703	0.790	0.984	1.111	1.848	1.129	1.072
-1	1.779	0.017	0.432	0.402	1.022	0.959	1.480	1.595	2.173	1.252	1.365	0.006	0.000	1.188	-0.174	-1.107	0.000	1.181	0.518	-0.258	0.000	1.057	0.910	0.991	1.616	0.779	1.158	1.053
-0	0.881	0.630	1.029	1.990	0.508	1.102	0.742	-1.298	2.173	1.565	1.085	0.686	2.215	2.691	1.391	-0.904	0.000	0.499	1.388	-1.199	0.000	0.347	0.861	0.997	0.881	1.920	1.233	1.310
-0	1.754	-0.266	0.389	0.347	-0.030	0.462	-1.408	-0.957	2.173	0.515	-2.341	-1.700	0.000	0.588	-0.797	1.355	2.548	0.608	0.329	-1.389	0.000	1.406	0.909	0.988	0.760	0.593	0.768	0.847
-0	1.087	0.311	-1.447	0.173	0.567	0.854	0.362	0.584	0.000	1.416	-0.716	-1.211	2.215	0.648	-0.358	-0.692	1.274	0.867	-0.513	0.206	0.000	0.803	0.813	0.984	1.110	0.491	0.921	0.873
-0	0.279	1.114	-1.190	3.004	-0.738	1.233	0.896	1.092	2.173	0.454	-0.374	0.117	2.215	0.357	0.119	1.270	0.000	0.458	1.343	0.316	0.000	0.495	0.540	0.988	1.715	1.139	1.618	1.183
-1	1.773	-0.694	-1.518	2.306	-1.200	3.104	0.749	0.362	0.000	1.871	0.230	-1.686	2.215	0.805	-0.179	-0.871	1.274	0.910	0.607	-0.246	0.000	1.338	1.598	0.984	1.050	0.919	1.678	1.807
-0	0.553	0.683	0.827	0.973	-0.706	1.488	0.149	1.140	2.173	1.788	0.447	-0.478	0.000	0.596	1.043	1.607	0.000	0.373	-0.868	-1.308	1.551	1.607	1.026	0.998	1.134	0.808	1.142	0.936
-1	0.397	1.101	-1.139	1.688	0.146	0.972	0.541	1.518	0.000	1.549	-0.873	-1.012	0.000	2.282	-0.151	0.314	2.548	1.174	0.033	-1.368	0.000	0.937	0.776	1.039	1.143	0.959	0.986	1.013
-1	0.840	1.906	-0.959	0.869	0.576	0.642	0.554	-1.351	0.000	0.756	0.923	-0.823	2.215	1.251	1.130	0.545	2.548	1.513	0.410	1.073	0.000	1.231	0.985	1.163	0.812	0.987	0.816	0.822
-1	0.477	1.665	0.814	0.763	-0.382	0.828	-0.008	0.280	2.173	1.213	-0.001	1.560	0.000	1.136	0.311	-1.289	0.000	0.797	1.091	-0.616	3.102	1.026	0.964	0.992	0.772	0.869	0.916	0.803
-0	2.655	0.020	0.273	1.464	0.482	1.709	-0.107	-1.456	2.173	0.825	0.141	-0.386	0.000	1.342	-0.592	1.635	1.274	0.859	-0.175	-0.874	0.000	0.829	0.946	1.003	2.179	0.836	1.505	1.176
-0	0.771	-1.992	-0.720	0.732	-1.464	0.869	-1.290	0.388	2.173	0.926	-1.072	-1.489	2.215	0.640	-1.232	0.840	0.000	0.528	-2.440	-0.446	0.000	0.811	0.868	0.993	0.995	1.317	0.809	0.714
-0	1.357	1.302	0.076	0.283	-1.060	0.783	1.559	-0.994	0.000	0.947	1.212	1.617	0.000	1.127	0.311	0.442	2.548	0.582	-0.052	1.186	1.551	1.330	0.995	0.985	0.846	0.404	0.858	0.815
-0	0.442	-0.381	-0.424	1.244	0.591	0.731	0.605	-0.713	2.173	0.629	2.762	1.040	0.000	0.476	2.693	-0.617	0.000	0.399	0.442	1.486	3.102	0.839	0.755	0.988	0.869	0.524	0.877	0.918
-0	0.884	0.422	0.055	0.818	0.624	0.950	-0.763	1.624	0.000	0.818	-0.609	-1.166	0.000	1.057	-0.528	1.070	2.548	1.691	-0.124	-0.335	3.102	1.104	0.933	0.985	0.913	1.000	0.863	1.056
-0	1.276	0.156	1.714	1.053	-1.189	0.672	-0.464	-0.030	2.173	0.469	-2.483	0.442	0.000	0.564	2.580	-0.253	0.000	0.444	-0.628	1.080	1.551	5.832	2.983	0.985	1.162	0.494	1.809	1.513
-0	1.106	-0.556	0.406	0.573	-1.400	0.769	-0.518	1.457	2.173	0.743	-0.352	-0.010	0.000	1.469	-0.550	-0.930	2.548	0.540	1.236	-0.571	0.000	0.962	0.970	1.101	0.805	1.107	0.873	0.773
-0	0.539	-0.964	-0.464	1.371	-1.606	0.667	-0.160	0.655	0.000	0.952	0.352	-0.740	2.215	0.952	0.007	1.123	0.000	1.061	-0.505	1.389	3.102	1.063	0.991	1.019	0.633	0.967	0.732	0.799
-1	0.533	-0.989	-1.608	0.462	-1.723	1.204	-0.598	-0.098	2.173	1.343	-0.460	1.632	2.215	0.577	0.221	-0.492	0.000	0.628	-0.073	0.472	0.000	0.518	0.880	0.988	1.179	1.874	1.041	0.813
-1	1.024	1.075	-0.795	0.286	-1.436	1.365	0.857	-0.309	2.173	0.804	1.532	1.435	0.000	1.511	0.722	1.494	0.000	1.778	0.903	0.753	1.551	0.686	0.810	0.999	0.900	1.360	1.133	0.978
-1	2.085	-0.269	-1.423	0.789	1.298	0.281	1.652	0.187	0.000	0.658	-0.760	-0.042	2.215	0.663	0.024	0.120	0.000	0.552	-0.299	-0.428	3.102	0.713	0.811	1.130	0.705	0.218	0.675	0.743
-1	0.980	-0.443	0.813	0.785	-1.253	0.719	0.448	-1.458	0.000	1.087	0.595	0.635	1.107	1.428	0.029	-0.995	0.000	1.083	1.562	-0.092	0.000	0.834	0.891	1.165	0.967	0.661	0.880	0.817
-1	0.903	-0.733	-0.980	0.634	-0.639	0.780	0.266	-0.287	2.173	1.264	-0.936	1.004	0.000	1.002	-0.056	-1.344	2.548	1.183	-0.098	1.169	0.000	0.733	1.002	0.985	0.711	0.916	0.966	0.875
-0	0.734	-0.304	-1.175	2.851	1.674	0.904	-0.634	0.412	2.173	1.363	-1.050	-0.282	0.000	1.476	-1.603	0.103	0.000	2.231	-0.718	1.708	3.102	0.813	0.896	1.088	0.686	1.392	1.033	1.078
-1	1.680	0.591	-0.243	0.111	-0.478	0.326	-0.079	-1.555	2.173	0.711	0.714	0.922	2.215	0.355	0.858	1.682	0.000	0.727	1.620	1.360	0.000	0.334	0.526	1.001	0.862	0.633	0.660	0.619
-1	1.163	0.225	-0.202	0.501	-0.979	1.609	-0.938	1.424	0.000	1.224	-0.118	-1.274	0.000	2.034	1.241	-0.254	0.000	1.765	0.536	0.237	3.102	0.894	0.838	0.988	0.693	0.579	0.762	0.726
-0	1.223	1.232	1.471	0.489	1.728	0.703	-0.111	0.411	0.000	1.367	1.014	-1.294	1.107	1.524	-0.414	-0.164	2.548	1.292	0.833	0.316	0.000	0.861	0.752	0.994	0.836	1.814	1.089	0.950
-0	0.816	1.637	-1.557	1.036	-0.342	0.913	1.333	0.949	2.173	0.812	0.756	-0.628	2.215	1.333	0.470	1.495	0.000	1.204	-2.222	-1.675	0.000	1.013	0.924	1.133	0.758	1.304	0.855	0.860
-0	0.851	-0.564	-0.691	0.692	1.345	1.219	1.014	0.318	0.000	1.422	-0.262	-1.635	2.215	0.531	1.802	0.008	0.000	0.508	0.515	-1.267	3.102	0.821	0.787	1.026	0.783	0.432	1.149	1.034
-0	0.800	-0.599	0.204	0.552	-0.484	0.974	0.413	0.961	2.173	1.269	-0.984	-1.039	2.215	0.380	-1.213	1.371	0.000	0.551	0.332	-0.659	0.000	0.694	0.852	0.984	1.057	2.037	1.096	0.846
-0	0.744	-0.071	-0.255	0.638	0.512	1.125	0.407	0.844	2.173	0.860	-0.481	-0.677	0.000	1.102	0.181	-1.194	0.000	1.011	-1.081	-1.713	3.102	0.854	0.862	0.982	1.111	1.372	1.042	0.920
-1	0.400	1.049	-0.625	0.880	-0.407	1.040	2.150	-1.359	0.000	0.747	-0.144	0.847	2.215	0.560	-1.829	0.698	0.000	1.663	-0.668	0.267	0.000	0.845	0.964	0.996	0.820	0.789	0.668	0.668
-0	1.659	-0.705	-1.057	1.803	-1.436	1.008	0.693	0.005	0.000	0.895	-0.007	0.681	1.107	1.085	0.125	1.476	2.548	1.214	1.068	0.486	0.000	0.867	0.919	0.986	1.069	0.692	1.026	1.313
-0	0.829	-0.153	0.861	0.615	-0.548	0.589	1.077	-0.041	2.173	1.056	0.763	-1.737	0.000	0.639	0.970	0.725	0.000	0.955	1.227	-0.799	3.102	1.020	1.024	0.985	0.750	0.525	0.685	0.671
-1	0.920	-0.806	-0.840	1.048	0.278	0.973	-0.077	-1.364	2.173	1.029	0.309	0.133	0.000	1.444	1.484	1.618	1.274	1.419	-0.482	0.417	0.000	0.831	1.430	1.151	1.829	1.560	1.343	1.224
-1	0.686	0.249	-0.905	0.343	-1.731	0.724	-2.823	-0.901	0.000	0.982	0.303	1.312	1.107	1.016	0.245	0.610	0.000	1.303	-0.557	-0.360	3.102	1.384	1.030	0.984	0.862	1.144	0.866	0.779
-0	1.603	0.444	0.508	0.586	0.401	0.610	0.467	-1.735	2.173	0.914	0.626	-1.019	0.000	0.812	0.422	-0.408	2.548	0.902	1.679	1.490	0.000	1.265	0.929	0.990	1.004	0.816	0.753	0.851
-1	0.623	0.780	-0.203	0.056	0.015	0.899	0.793	1.326	1.087	0.803	1.478	-1.499	2.215	1.561	1.492	-0.120	0.000	0.904	0.795	0.137	0.000	0.548	1.009	0.850	0.924	0.838	0.914	0.860
-0	1.654	-2.032	-1.160	0.859	-1.583	0.689	-1.965	0.891	0.000	0.646	-1.014	-0.288	2.215	0.630	-0.815	0.402	0.000	0.638	0.316	0.655	3.102	0.845	0.879	0.993	1.067	0.625	1.041	0.958
-1	0.828	-1.269	-1.203	0.744	-0.213	0.626	-1.017	-0.404	0.000	1.281	-0.931	1.733	2.215	0.699	-0.351	1.287	0.000	1.251	-1.171	0.197	0.000	0.976	1.186	0.987	0.646	0.655	0.733	0.671
-1	0.677	0.111	1.090	1.580	1.591	1.560	0.654	-0.341	2.173	0.794	-0.266	0.702	0.000	0.823	0.651	-1.239	2.548	0.730	1.467	-1.530	0.000	1.492	1.023	0.983	1.909	1.022	1.265	1.127
-1	0.736	0.882	-1.060	0.589	0.168	1.663	0.781	1.022	2.173	2.025	1.648	-1.292	0.000	1.240	0.924	-0.421	1.274	1.354	0.065	0.501	0.000	0.316	0.925	0.988	0.664	1.736	0.992	0.807
-1	1.040	-0.822	1.638	0.974	-0.674	0.393	0.830	0.011	2.173	0.770	-0.140	-0.402	0.000	0.294	-0.133	0.030	0.000	1.220	0.807	0.638	0.000	0.826	1.063	1.216	1.026	0.705	0.934	0.823
-1	0.711	0.602	0.048	1.145	0.966	0.934	0.263	-1.589	2.173	0.971	-0.496	-0.421	1.107	0.628	-0.865	0.845	0.000	0.661	-0.008	-0.565	0.000	0.893	0.705	0.988	0.998	1.339	0.908	0.872
-1	0.953	-1.651	-0.167	0.885	1.053	1.013	-1.239	0.133	0.000	1.884	-1.122	1.222	2.215	1.906	-0.860	-1.184	1.274	1.413	-0.668	-1.647	0.000	1.873	1.510	1.133	1.050	1.678	1.246	1.061
-1	0.986	-0.892	-1.380	0.917	1.134	0.950	-1.162	-0.469	0.000	0.569	-1.393	0.215	0.000	0.320	2.667	1.712	0.000	1.570	-0.375	1.457	3.102	0.925	1.128	1.011	0.598	0.824	0.913	0.833
-1	1.067	0.099	1.154	0.527	-0.789	1.085	0.623	-1.602	2.173	1.511	-0.230	0.022	2.215	0.269	-0.377	0.883	0.000	0.571	-0.540	-0.512	0.000	0.414	0.803	1.022	0.959	2.053	1.041	0.780
-0	0.825	-2.118	0.217	1.453	-0.493	0.819	0.313	-0.942	0.000	2.098	-0.725	1.096	2.215	0.484	1.336	1.458	0.000	0.482	0.100	1.163	0.000	0.913	0.536	0.990	1.679	0.957	1.095	1.143
-1	1.507	0.054	1.120	0.698	-1.340	0.912	0.384	0.015	1.087	0.720	0.247	-0.820	0.000	0.286	0.154	1.578	2.548	0.629	1.582	-0.576	0.000	0.828	0.893	1.136	0.514	0.632	0.699	0.709
-1	0.610	1.180	-0.993	0.816	0.301	0.932	0.758	1.539	0.000	0.726	-0.830	0.248	2.215	0.883	0.857	-1.305	0.000	1.338	1.009	-0.252	3.102	0.901	1.074	0.987	0.875	1.159	1.035	0.858
-1	1.247	-1.360	1.502	1.525	-1.332	0.618	1.063	0.755	0.000	0.582	-0.155	0.473	2.215	1.214	-0.422	-0.551	2.548	0.838	-1.171	-1.166	0.000	2.051	1.215	1.062	1.091	0.725	0.896	1.091
-0	0.373	-0.600	1.291	2.573	0.207	0.765	-0.209	1.667	0.000	0.668	0.724	-1.499	0.000	1.045	-0.338	-0.754	2.548	0.558	-0.469	0.029	3.102	0.868	0.939	1.124	0.519	0.383	0.636	0.838
-0	0.791	0.336	-0.307	0.494	1.213	1.158	0.336	1.081	2.173	0.918	1.289	-0.449	0.000	0.735	-0.521	-0.969	0.000	1.052	0.499	-1.188	3.102	0.699	1.013	0.987	0.622	1.050	0.712	0.661
-0	1.321	0.856	0.464	0.202	0.901	1.144	0.120	-1.651	0.000	0.803	0.577	-0.509	2.215	0.695	-0.114	0.423	2.548	0.621	1.852	-0.420	0.000	0.697	0.964	0.983	0.527	0.659	0.719	0.729
-0	0.563	2.081	0.913	0.982	-0.533	0.549	-0.481	-1.730	0.000	0.962	0.921	0.569	2.215	0.731	1.184	-0.679	1.274	0.918	0.931	-1.432	0.000	1.008	0.919	0.993	0.895	0.819	0.810	0.878
-1	1.148	0.345	0.953	0.921	0.617	0.991	1.103	-0.484	0.000	0.970	1.978	1.525	0.000	1.150	0.689	-0.757	2.548	0.517	0.995	1.245	0.000	1.093	1.140	0.998	1.006	0.756	0.864	0.838
-1	1.400	0.128	-1.695	1.169	1.070	1.094	-0.345	-0.249	0.000	1.224	0.364	-0.036	2.215	1.178	0.530	-1.544	0.000	1.334	0.933	1.604	0.000	0.560	1.267	1.073	0.716	0.780	0.832	0.792
-0	0.330	-2.133	1.403	0.628	0.379	1.686	-0.995	0.030	1.087	2.071	0.127	-0.457	0.000	4.662	-0.855	1.477	0.000	2.072	-0.917	-1.416	3.102	5.403	3.074	0.977	0.936	1.910	2.325	1.702
-0	0.989	0.473	0.968	1.970	1.368	0.844	0.574	-0.290	2.173	0.866	-0.345	-1.019	0.000	1.130	0.605	-0.752	0.000	0.956	-0.888	0.870	3.102	0.885	0.886	0.982	1.157	1.201	1.100	1.068
-1	0.773	0.418	0.753	1.388	1.070	1.104	-0.378	-0.758	0.000	1.027	0.397	-0.496	2.215	1.234	0.027	1.084	2.548	0.936	0.209	1.677	0.000	1.355	1.020	0.983	0.550	1.206	0.916	0.931
-0	0.319	2.015	1.534	0.570	-1.134	0.632	0.124	0.757	0.000	0.477	0.598	-1.109	1.107	0.449	0.438	-0.755	2.548	0.574	-0.659	0.691	0.000	0.440	0.749	0.985	0.517	0.158	0.505	0.522
-0	1.215	1.453	-1.386	1.276	1.298	0.643	0.570	-0.196	2.173	0.588	2.104	0.498	0.000	0.617	-0.296	-0.801	2.548	0.452	0.110	0.313	0.000	0.815	0.953	1.141	1.166	0.547	0.892	0.807
-1	1.257	-1.869	-0.060	0.265	0.653	1.527	-0.346	1.163	2.173	0.758	-2.119	-0.604	0.000	1.473	-1.133	-1.290	2.548	0.477	-0.428	-0.066	0.000	0.818	0.841	0.984	1.446	1.729	1.211	1.054
-1	1.449	0.464	1.585	1.418	-1.488	1.540	0.942	0.087	0.000	0.898	0.402	-0.631	2.215	0.753	0.039	-1.729	0.000	0.859	0.849	-1.054	0.000	0.791	0.677	0.995	0.687	0.527	0.703	0.606
-1	1.084	-1.997	0.900	1.333	1.024	0.872	-0.864	-1.500	2.173	1.072	-0.813	-0.421	2.215	0.924	0.478	0.304	0.000	0.992	-0.398	-1.022	0.000	0.741	1.085	0.980	1.221	1.176	1.032	0.961
-0	1.712	1.129	0.125	1.120	-1.402	1.749	0.951	-1.575	2.173	1.711	0.445	0.578	0.000	1.114	0.234	-1.011	0.000	1.577	-0.088	0.086	3.102	2.108	1.312	1.882	1.597	2.009	1.441	1.308
-0	0.530	0.248	1.622	1.450	-1.012	1.221	-1.154	-0.763	2.173	1.698	-0.586	0.733	0.000	0.889	1.042	1.038	1.274	0.657	0.008	0.701	0.000	0.430	1.005	0.983	0.930	2.264	1.357	1.146
-1	0.921	1.735	0.883	0.699	-1.614	0.821	1.463	0.319	1.087	1.099	0.814	-1.600	2.215	1.375	0.702	-0.691	0.000	0.869	1.326	-0.790	0.000	0.980	0.900	0.988	0.832	1.452	0.816	0.709
-0	2.485	-0.823	-0.297	0.886	-1.404	0.989	0.835	1.615	2.173	0.382	0.588	-0.224	0.000	1.029	-0.456	1.546	2.548	0.613	-0.359	-0.789	0.000	0.768	0.977	1.726	2.007	0.913	1.338	1.180
-1	0.657	-0.069	-0.078	1.107	1.549	0.804	1.335	-1.630	2.173	1.271	0.481	0.153	1.107	1.028	0.144	-0.762	0.000	1.098	0.132	1.570	0.000	0.830	0.979	1.175	1.069	1.624	1.000	0.868
-1	2.032	0.329	-1.003	0.493	-0.136	1.159	-0.224	0.750	1.087	0.396	0.546	0.587	0.000	0.620	1.805	0.982	0.000	1.236	0.744	-1.621	0.000	0.930	1.200	0.988	0.482	0.771	0.887	0.779
-0	0.524	-1.319	0.634	0.471	1.221	0.599	-0.588	-0.461	0.000	1.230	-1.504	-1.517	1.107	1.436	-0.035	0.104	2.548	0.629	1.997	-1.282	0.000	2.084	1.450	0.984	1.084	1.827	1.547	1.213
-1	0.871	0.618	-1.544	0.718	0.186	1.041	-1.180	0.434	2.173	1.133	1.558	-1.301	0.000	0.452	-0.595	0.522	0.000	0.665	0.567	0.130	3.102	1.872	1.114	1.095	1.398	0.979	1.472	1.168
-1	3.308	1.037	-0.634	0.690	-0.619	1.975	0.949	1.280	0.000	0.826	0.546	-0.139	2.215	0.635	-0.045	0.427	0.000	1.224	0.112	1.339	3.102	1.756	1.050	0.992	0.738	0.903	0.968	1.238
-0	0.588	2.104	-0.872	1.136	1.743	0.842	0.638	0.015	0.000	0.481	0.928	1.000	2.215	0.595	0.125	1.429	0.000	0.951	-1.140	-0.511	3.102	1.031	1.057	0.979	0.673	1.064	1.001	0.891
-0	0.289	0.823	0.013	0.615	-1.601	0.177	2.403	-0.015	0.000	0.258	1.151	1.036	2.215	0.694	0.553	-1.326	2.548	0.411	0.366	0.106	0.000	0.482	0.562	0.989	0.670	0.404	0.516	0.561
-1	0.294	-0.660	-1.162	1.752	0.384	0.860	0.513	1.119	0.000	2.416	0.107	-1.342	0.000	1.398	0.361	-0.350	2.548	1.126	-0.902	0.040	1.551	0.650	1.125	0.988	0.531	0.843	0.912	0.911
-0	0.599	-0.616	1.526	1.381	0.507	0.955	-0.646	-0.085	2.173	0.775	-0.533	1.116	2.215	0.789	-0.136	-1.176	0.000	2.449	1.435	-1.433	0.000	1.692	1.699	1.000	0.869	1.119	1.508	1.303
-1	1.100	-1.174	-1.114	1.601	-1.576	1.056	-1.343	0.547	2.173	0.555	0.367	0.592	2.215	0.580	-1.862	-0.914	0.000	0.904	0.508	-0.444	0.000	1.439	1.105	0.986	1.408	1.104	1.190	1.094
-1	2.237	-0.701	1.470	0.719	-0.199	0.745	-0.132	-0.737	1.087	0.976	-0.227	0.093	2.215	0.699	0.057	1.133	0.000	0.661	0.573	-0.679	0.000	0.785	0.772	1.752	1.235	0.856	0.990	0.825
-1	0.455	-0.880	-1.482	1.260	-0.178	1.499	0.158	1.022	0.000	1.867	-0.435	-0.675	2.215	1.234	0.783	1.586	0.000	0.641	-0.454	-0.409	3.102	1.002	0.964	0.986	0.761	0.240	1.190	0.995
-1	1.158	-0.778	-0.159	0.823	1.641	1.341	-0.830	-1.169	2.173	0.840	-1.554	0.934	0.000	0.693	0.488	-1.218	2.548	1.042	1.395	0.276	0.000	0.946	0.785	1.350	1.079	0.893	1.267	1.151
-1	0.902	-0.078	-0.055	0.872	-0.012	0.843	1.276	1.739	2.173	0.838	1.492	0.918	0.000	0.626	0.904	-0.648	2.548	0.412	-2.027	-0.883	0.000	2.838	1.664	0.988	1.803	0.768	1.244	1.280
-1	0.649	-1.028	-1.521	1.097	0.774	1.216	-0.383	-0.318	2.173	1.643	-0.285	-1.705	0.000	0.911	-0.091	0.341	0.000	0.592	0.537	0.732	3.102	0.911	0.856	1.027	1.160	0.874	0.986	0.893
-1	1.192	1.846	-0.781	1.326	-0.747	1.550	1.177	1.366	0.000	1.196	0.151	0.387	2.215	0.527	2.261	-0.190	0.000	0.390	1.474	0.381	0.000	0.986	1.025	1.004	1.392	0.761	0.965	1.043
-0	0.438	-0.358	-1.549	0.836	0.436	0.818	0.276	-0.708	2.173	0.707	0.826	0.392	0.000	1.050	1.741	-1.066	0.000	1.276	-1.583	0.842	0.000	1.475	1.273	0.986	0.853	1.593	1.255	1.226
-1	1.083	0.142	1.701	0.605	-0.253	1.237	0.791	1.183	2.173	0.842	2.850	-0.082	0.000	0.724	-0.464	-0.694	0.000	1.499	0.456	-0.226	3.102	0.601	0.799	1.102	0.995	1.389	1.013	0.851
-0	0.828	1.897	-0.615	0.572	-0.545	0.572	0.461	0.464	2.173	0.393	0.356	1.069	2.215	1.840	0.088	1.500	0.000	0.407	-0.663	-0.787	0.000	0.950	0.965	0.979	0.733	0.363	0.618	0.733
-0	0.735	1.438	1.197	1.123	-0.214	0.641	0.949	0.858	0.000	1.162	0.524	-0.896	2.215	0.992	0.454	-1.475	2.548	0.902	1.079	0.019	0.000	0.822	0.917	1.203	1.032	0.569	0.780	0.764
-0	0.437	-2.102	0.044	1.779	-1.042	1.231	-0.181	-0.515	1.087	2.666	0.863	1.466	2.215	1.370	0.345	-1.371	0.000	0.906	0.363	1.611	0.000	1.140	1.362	1.013	3.931	3.004	2.724	2.028
-1	0.881	1.814	-0.987	0.384	0.800	2.384	1.422	0.640	0.000	1.528	0.292	-0.962	1.107	2.126	-0.371	-1.401	2.548	0.700	0.109	0.203	0.000	0.450	0.813	0.985	0.956	1.013	0.993	0.774
-1	0.630	0.408	0.152	0.194	0.316	0.710	-0.824	-0.358	2.173	0.741	0.535	-0.851	2.215	0.933	0.406	1.148	0.000	0.523	-0.479	-0.625	0.000	0.873	0.960	0.988	0.830	0.921	0.711	0.661
-1	0.870	-0.448	-1.134	0.616	0.135	0.600	0.649	-0.622	2.173	0.768	0.709	-0.123	0.000	1.308	0.500	1.468	0.000	1.973	-0.286	1.462	3.102	0.909	0.944	0.990	0.835	1.250	0.798	0.776
-0	1.290	0.552	1.330	0.615	-1.353	0.661	0.240	-0.393	0.000	0.531	0.053	-1.588	0.000	0.675	0.839	-0.345	1.274	1.597	0.020	0.536	3.102	1.114	0.964	0.987	0.783	0.675	0.662	0.675
-1	0.943	0.936	1.068	1.373	0.671	2.170	-2.011	-1.032	0.000	0.640	0.361	-0.806	0.000	2.239	-0.083	0.590	2.548	1.224	0.646	-1.723	0.000	0.879	0.834	0.981	1.436	0.568	0.916	0.931
-1	0.431	1.686	-1.053	0.388	1.739	0.457	-0.471	-0.743	2.173	0.786	1.432	-0.547	2.215	0.537	-0.413	1.256	0.000	0.413	2.311	-0.408	0.000	1.355	1.017	0.982	0.689	1.014	0.821	0.715
-0	1.620	-0.055	-0.862	1.341	-1.571	0.634	-0.906	0.935	2.173	0.501	-2.198	-0.525	0.000	0.778	-0.708	-0.060	0.000	0.988	-0.621	0.489	3.102	0.870	0.956	1.216	0.992	0.336	0.871	0.889
-1	0.549	0.304	-1.443	1.309	-0.312	1.116	0.644	1.519	2.173	1.078	-0.303	-0.736	0.000	1.261	0.387	0.628	2.548	0.945	-0.190	0.090	0.000	0.893	1.043	1.000	1.124	1.077	1.026	0.886
-0	0.412	-0.618	-1.486	1.133	-0.665	0.646	0.436	1.520	0.000	0.993	0.976	0.106	2.215	0.832	0.091	0.164	2.548	0.672	-0.650	1.256	0.000	0.695	1.131	0.991	1.017	0.455	1.226	1.087
-0	1.183	-0.084	1.644	1.389	0.967	0.843	0.938	-0.670	0.000	0.480	0.256	0.123	2.215	0.437	1.644	0.491	0.000	0.501	-0.416	0.101	3.102	1.060	0.804	1.017	0.775	0.173	0.535	0.760
-0	1.629	-1.486	-0.683	2.786	-0.492	1.347	-2.638	1.453	0.000	1.857	0.208	0.873	0.000	0.519	-1.265	-1.602	1.274	0.903	-1.102	-0.329	1.551	6.892	3.522	0.998	0.570	0.477	2.039	2.006
-1	2.045	-0.671	-1.235	0.490	-0.952	0.525	-1.252	1.289	0.000	1.088	-0.993	0.648	2.215	0.975	-0.109	-0.254	2.548	0.556	-1.095	-0.194	0.000	0.803	0.861	0.980	1.282	0.945	0.925	0.811
-0	0.448	-0.058	-0.974	0.945	-1.633	1.181	-1.139	0.266	2.173	1.118	-0.761	1.502	1.107	1.706	0.585	-0.680	0.000	0.487	-1.951	0.945	0.000	2.347	1.754	0.993	1.161	1.549	1.414	1.176
-0	0.551	0.519	0.448	2.183	1.293	1.220	0.628	-0.627	2.173	1.019	-0.002	-0.652	0.000	1.843	-0.386	1.042	2.548	0.400	-1.102	-1.014	0.000	0.648	0.792	1.049	0.888	2.132	1.262	1.096
-0	1.624	0.488	1.403	0.760	0.559	0.812	0.777	-1.244	2.173	0.613	0.589	-0.030	2.215	0.692	1.058	0.683	0.000	1.054	1.165	-0.765	0.000	0.915	0.875	1.059	0.821	0.927	0.792	0.721
-1	0.774	0.444	1.257	0.515	-0.689	0.515	1.448	-1.271	0.000	0.793	0.118	0.811	1.107	0.679	0.326	-0.426	0.000	1.066	-0.865	-0.049	3.102	0.960	1.046	0.986	0.716	0.772	0.855	0.732
-1	2.093	-1.240	1.615	0.918	-1.202	1.412	-0.541	0.640	1.087	2.019	0.872	-0.639	0.000	0.672	-0.936	0.972	0.000	0.896	0.235	0.212	0.000	0.810	0.700	1.090	0.797	0.862	1.049	0.874
-1	0.908	1.069	0.283	0.400	1.293	0.609	1.452	-1.136	0.000	0.623	0.417	-0.098	2.215	1.023	0.775	1.054	1.274	0.706	2.346	-1.305	0.000	0.744	1.006	0.991	0.606	0.753	0.796	0.753
-0	0.403	-1.328	-0.065	0.901	1.052	0.708	-0.354	-0.718	2.173	0.892	0.633	1.684	2.215	0.999	-1.205	0.941	0.000	0.930	1.072	-0.809	0.000	2.105	1.430	0.989	0.838	1.147	1.042	0.883
-0	1.447	0.453	0.118	1.731	0.650	0.771	0.446	-1.564	0.000	0.973	-2.014	0.354	0.000	1.949	-0.643	-1.531	1.274	1.106	-0.334	-1.163	0.000	0.795	0.821	1.013	1.699	0.918	1.118	1.018
-1	1.794	0.123	-0.454	0.057	1.489	0.966	-1.190	1.090	1.087	0.539	-0.535	1.035	0.000	1.096	-1.069	-1.236	2.548	0.659	-1.196	-0.283	0.000	0.803	0.756	0.985	1.343	1.109	0.993	0.806
-0	1.484	-2.047	0.813	0.591	-0.295	0.923	0.312	-1.164	2.173	0.654	-0.316	0.752	2.215	0.599	1.966	-1.128	0.000	0.626	-0.304	-1.431	0.000	1.112	0.910	1.090	0.986	1.189	1.350	1.472
-0	0.417	-2.016	0.849	1.817	0.040	1.201	-1.676	-1.394	0.000	0.792	0.537	0.641	2.215	0.794	-1.222	0.187	0.000	0.825	-0.217	1.334	3.102	1.470	0.931	0.987	1.203	0.525	0.833	0.827
-1	0.603	1.009	0.033	0.486	1.225	0.884	-0.617	-1.058	0.000	0.500	-1.407	-0.567	0.000	1.476	-0.876	0.605	2.548	0.970	0.560	1.092	3.102	0.853	1.153	0.988	0.846	0.920	0.944	0.835
-1	1.381	-0.326	0.552	0.417	-0.027	1.030	-0.835	-1.287	2.173	0.941	-0.421	1.519	2.215	0.615	-1.650	0.377	0.000	0.606	0.644	0.650	0.000	1.146	0.970	0.990	1.191	0.884	0.897	0.826
-1	0.632	1.200	-0.703	0.438	-1.700	0.779	-0.731	0.958	1.087	0.605	0.393	-1.376	0.000	0.670	-0.827	-1.315	2.548	0.626	-0.501	0.417	0.000	0.904	0.903	0.998	0.673	0.803	0.722	0.640
-1	1.561	-0.569	1.580	0.329	0.237	1.059	0.731	0.415	2.173	0.454	0.016	-0.828	0.000	0.587	0.008	-0.291	1.274	0.597	1.119	1.191	0.000	0.815	0.908	0.988	0.733	0.690	0.892	0.764
-1	2.102	0.087	0.449	1.164	-0.390	1.085	-0.408	-1.116	2.173	0.578	0.197	-0.137	0.000	1.202	0.917	1.523	0.000	0.959	-0.832	1.404	3.102	1.380	1.109	1.486	1.496	0.886	1.066	1.025
-1	1.698	-0.489	-0.552	0.976	-1.009	1.620	-0.721	0.648	1.087	1.481	-1.860	-1.354	0.000	1.142	-1.140	1.401	2.548	1.000	-1.274	-0.158	0.000	1.430	1.130	0.987	1.629	1.154	1.303	1.223
-1	1.111	-0.249	-1.457	0.421	0.939	0.646	-2.076	0.362	0.000	1.315	0.796	-1.436	2.215	0.780	0.130	0.055	0.000	1.662	-0.834	0.461	0.000	0.920	0.948	0.990	1.046	0.905	1.493	1.169
-1	0.945	0.390	-1.159	1.675	0.437	0.356	0.261	0.543	1.087	0.574	0.838	1.599	2.215	0.496	-1.220	-0.022	0.000	0.558	-2.454	1.440	0.000	0.763	0.983	1.728	1.000	0.578	0.922	1.003
-1	2.076	0.014	-1.314	0.854	-0.306	3.446	1.341	0.598	0.000	2.086	0.227	-0.747	2.215	1.564	-0.216	1.649	2.548	0.965	-0.857	-1.062	0.000	0.477	0.734	1.456	1.003	1.660	1.001	0.908
-1	1.992	0.192	-0.103	0.108	-1.599	0.938	0.595	-1.360	2.173	0.869	-1.012	1.432	0.000	1.302	0.850	0.436	2.548	0.487	1.051	-1.027	0.000	0.502	0.829	0.983	1.110	1.394	0.904	0.836
-0	0.460	1.625	1.485	1.331	1.242	0.675	-0.329	-1.039	1.087	0.671	-1.028	-0.514	0.000	1.265	-0.788	0.415	1.274	0.570	-0.683	-1.738	0.000	0.725	0.758	1.004	1.024	1.156	0.944	0.833
-0	0.871	0.839	-1.536	0.428	1.198	0.875	-1.256	-0.466	1.087	0.684	-0.768	0.150	0.000	0.556	-1.793	0.389	0.000	0.942	-1.126	1.339	1.551	0.624	0.734	0.986	1.357	0.960	1.474	1.294
-1	0.951	1.651	0.576	1.273	1.495	0.834	0.048	-0.578	2.173	0.386	-0.056	-1.448	0.000	0.597	-0.196	0.162	2.548	0.524	1.649	1.625	0.000	0.737	0.901	1.124	1.014	0.556	1.039	0.845
-1	1.049	-0.223	0.685	0.256	-1.191	2.506	0.238	-0.359	0.000	1.510	-0.904	1.158	1.107	2.733	-0.902	1.679	2.548	0.407	-0.474	-1.572	0.000	1.513	2.472	0.982	1.238	0.978	1.985	1.510
-0	0.455	-0.028	0.265	1.286	1.373	0.459	0.331	-0.922	0.000	0.343	0.634	0.430	0.000	0.279	-0.084	-0.272	0.000	0.475	0.926	-0.123	3.102	0.803	0.495	0.987	0.587	0.211	0.417	0.445
-1	2.074	0.388	0.878	1.110	1.557	1.077	-0.226	-0.295	2.173	0.865	-0.319	-1.116	2.215	0.707	-0.835	0.722	0.000	0.632	-0.608	-0.728	0.000	0.715	0.802	1.207	1.190	0.960	1.143	0.926
-1	1.390	0.265	1.196	0.919	-1.371	1.858	0.506	0.786	0.000	1.280	-1.367	-0.720	2.215	1.483	-0.441	-0.675	2.548	1.076	0.294	-0.539	0.000	1.126	0.830	1.155	1.551	0.702	1.103	0.933
-1	1.014	-0.079	1.597	1.038	-0.281	1.135	-0.722	-0.177	2.173	0.544	-1.475	-1.501	0.000	1.257	-1.315	1.212	0.000	0.496	-0.060	1.180	1.551	0.815	0.611	1.411	1.110	0.792	0.846	0.853
-0	0.335	1.267	-1.154	2.011	-0.574	0.753	0.618	1.411	0.000	0.474	0.748	0.681	2.215	0.608	-0.446	-0.354	2.548	0.399	1.295	-0.581	0.000	0.911	0.882	0.975	0.832	0.598	0.580	0.678
-1	0.729	-0.189	1.182	0.293	1.310	0.412	0.459	-0.632	0.000	0.869	-1.128	-0.625	2.215	1.173	-0.893	0.478	2.548	0.584	-2.394	-1.727	0.000	2.016	1.272	0.995	1.034	0.905	0.966	1.038
-1	1.225	-1.215	-0.088	0.881	-0.237	0.600	-0.976	1.462	2.173	0.876	0.506	1.583	2.215	0.718	1.228	-0.031	0.000	0.653	-1.292	1.216	0.000	0.838	1.108	0.981	1.805	0.890	1.251	1.197
-1	2.685	-0.444	0.847	0.253	0.183	0.641	-1.541	-0.873	2.173	0.417	2.874	-0.551	0.000	0.706	-1.431	0.764	0.000	1.390	-0.596	-1.397	0.000	0.894	0.829	0.993	0.789	0.654	0.883	0.746
-0	0.638	-0.481	0.683	1.457	-1.024	0.707	-1.338	1.498	0.000	0.980	0.518	0.289	2.215	0.964	-0.531	-0.423	0.000	0.694	-0.654	-1.314	3.102	0.807	1.283	1.335	0.658	0.907	0.797	0.772
-1	1.789	-0.765	-0.732	0.421	-0.020	1.142	-1.353	1.439	2.173	0.725	-1.518	-1.261	0.000	0.812	-2.597	-0.463	0.000	1.203	-0.120	1.001	0.000	0.978	0.673	0.985	1.303	1.400	1.078	0.983
-1	0.784	-1.431	1.724	0.848	0.559	0.615	-1.643	-1.456	0.000	1.339	-0.513	0.040	2.215	0.394	-2.483	1.304	0.000	0.987	0.889	-0.339	0.000	0.732	0.713	0.987	0.973	0.705	0.875	0.759
-1	0.911	1.098	-1.289	0.421	0.823	1.218	-0.503	0.431	0.000	0.775	0.432	-1.680	0.000	0.855	-0.226	-0.460	2.548	0.646	-0.947	-1.243	1.551	2.201	1.349	0.985	0.730	0.451	0.877	0.825
-1	0.959	0.372	-0.269	1.255	0.702	1.151	0.097	0.805	2.173	0.993	1.011	0.767	2.215	1.096	0.185	0.381	0.000	1.001	-0.205	0.059	0.000	0.979	0.997	1.168	0.796	0.771	0.839	0.776
-0	0.283	-1.864	-1.663	0.219	1.624	0.955	-1.213	0.932	2.173	0.889	0.395	-0.268	0.000	0.597	-1.083	-0.921	2.548	0.584	1.325	-1.072	0.000	0.856	0.927	0.996	0.937	0.936	1.095	0.892
-0	2.017	-0.488	-0.466	1.029	-0.870	3.157	0.059	-0.343	2.173	3.881	0.872	1.502	1.107	3.631	1.720	0.963	0.000	0.633	-1.264	-1.734	0.000	4.572	3.339	1.005	1.407	5.590	3.614	3.110
-1	1.088	0.414	-0.841	0.485	0.605	0.860	1.110	-0.568	0.000	1.152	-0.325	1.203	2.215	0.324	1.652	-0.104	0.000	0.510	1.095	-1.728	0.000	0.880	0.722	0.989	0.977	0.711	0.888	0.762
-0	0.409	-1.717	0.712	0.809	-1.301	0.701	-1.529	-1.411	0.000	1.191	-0.582	0.438	2.215	1.147	0.813	-0.571	2.548	1.039	0.543	0.892	0.000	0.636	0.810	0.986	0.861	1.411	0.907	0.756
-1	1.094	1.577	-0.988	0.497	-0.149	0.891	-2.459	1.034	0.000	0.646	0.792	-1.022	0.000	1.573	0.254	-0.053	2.548	1.428	0.190	-1.641	3.102	4.322	2.687	0.985	0.881	1.135	1.907	1.831
-1	0.613	1.993	-0.280	0.544	0.931	0.909	1.526	1.559	0.000	0.840	1.473	-0.483	2.215	0.856	0.352	0.408	2.548	1.058	1.733	-1.396	0.000	0.801	1.066	0.984	0.639	0.841	0.871	0.748
-0	0.958	-1.202	0.600	0.434	0.170	0.783	-0.214	1.319	0.000	0.835	-0.454	-0.615	2.215	0.658	-1.858	-0.891	0.000	0.640	0.172	-1.204	3.102	1.790	1.086	0.997	0.804	0.403	0.793	0.756
-1	1.998	-0.238	0.972	0.058	0.266	0.759	1.576	-0.357	2.173	1.004	-0.349	-0.747	2.215	0.962	0.490	-0.453	0.000	1.592	0.661	-1.405	0.000	0.874	1.086	0.990	1.436	1.527	1.177	0.993
-1	0.796	-0.171	-0.818	0.574	-1.625	1.201	-0.737	1.451	2.173	0.651	0.404	-0.452	0.000	1.150	-0.652	-0.120	0.000	1.008	-0.093	0.531	3.102	0.884	0.706	0.979	1.193	0.937	0.943	0.881
-1	0.773	1.023	0.527	1.537	-0.201	2.967	-0.574	-1.534	2.173	2.346	-0.307	0.394	2.215	1.393	0.135	-0.027	0.000	3.015	0.187	0.516	0.000	0.819	1.260	0.982	2.552	3.862	2.179	1.786
-0	1.823	1.008	-1.489	0.234	-0.962	0.591	0.461	0.996	2.173	0.568	-1.297	-0.410	0.000	0.887	2.157	1.194	0.000	2.079	0.369	-0.085	3.102	0.770	0.945	0.995	1.179	0.971	0.925	0.983
-0	0.780	0.640	0.490	0.680	-1.301	0.715	-0.137	0.152	2.173	0.616	-0.831	1.668	0.000	1.958	0.528	-0.982	2.548	0.966	-1.551	0.462	0.000	1.034	1.079	1.008	0.827	1.369	1.152	0.983
-1	0.543	0.801	1.543	1.134	-0.772	0.954	-0.849	0.410	1.087	0.851	-1.988	1.686	0.000	0.799	-0.912	-1.156	0.000	0.479	0.097	1.334	0.000	0.923	0.597	0.989	1.231	0.759	0.975	0.867
-0	1.241	-0.014	0.129	1.158	0.670	0.445	-0.732	1.739	2.173	0.918	0.659	-1.340	2.215	0.557	2.410	-1.404	0.000	0.966	-1.545	-1.120	0.000	0.874	0.918	0.987	1.001	0.798	0.904	0.937
-0	1.751	-0.266	-1.575	0.489	1.292	1.112	1.533	0.137	2.173	1.204	-0.414	-0.928	0.000	0.879	1.237	-0.415	2.548	1.479	1.469	0.913	0.000	2.884	1.747	0.989	1.742	0.600	1.363	1.293
-1	1.505	1.208	-1.476	0.995	-0.836	2.800	-1.600	0.111	0.000	2.157	1.241	1.110	2.215	1.076	2.619	-0.913	0.000	1.678	2.204	-1.575	0.000	0.849	1.224	0.990	1.412	0.976	1.271	1.105
-0	0.816	0.611	0.779	1.694	0.278	0.575	-0.787	1.592	2.173	1.148	1.076	-0.831	2.215	0.421	1.316	0.632	0.000	0.589	0.452	-1.466	0.000	0.779	0.909	0.990	1.146	1.639	1.236	0.949
-1	0.551	-0.808	0.330	1.188	-0.294	0.447	-0.035	-0.993	0.000	0.432	-0.276	-0.481	2.215	1.959	-0.288	1.195	2.548	0.638	0.583	1.107	0.000	0.832	0.924	0.993	0.723	0.976	0.968	0.895
-0	1.316	-0.093	0.995	0.860	-0.621	0.593	-0.560	-1.599	2.173	0.524	-0.318	-0.240	2.215	0.566	0.759	-0.368	0.000	0.483	-2.030	-1.104	0.000	1.468	1.041	1.464	0.811	0.778	0.690	0.722
-1	1.528	0.067	-0.855	0.959	-1.464	1.143	-0.082	1.023	0.000	0.702	-0.763	-0.244	0.000	0.935	-0.881	0.206	2.548	0.614	-0.831	1.657	3.102	1.680	1.105	0.983	1.078	0.559	0.801	0.809
-0	0.558	-0.833	-0.598	1.436	-1.724	1.316	-0.661	1.593	2.173	1.148	-0.503	-0.132	1.107	1.584	-0.125	0.380	0.000	1.110	-1.216	-0.181	0.000	1.258	0.860	1.053	0.790	1.814	1.159	1.007
-1	0.819	0.879	1.221	0.598	-1.450	0.754	0.417	-0.369	2.173	0.477	1.199	0.274	0.000	1.073	0.368	0.273	2.548	1.599	2.047	1.690	0.000	0.933	0.984	0.983	0.788	0.613	0.728	0.717
-0	0.981	-1.007	0.489	0.923	1.261	0.436	-0.698	-0.506	2.173	0.764	-1.105	-1.241	2.215	0.577	-2.573	-0.036	0.000	0.565	-1.628	1.610	0.000	0.688	0.801	0.991	0.871	0.554	0.691	0.656
-0	2.888	0.568	-1.416	1.461	-1.157	1.756	-0.900	0.522	0.000	0.657	0.409	1.076	2.215	1.419	0.672	-0.019	0.000	1.436	-0.184	-0.980	3.102	0.946	0.919	0.995	1.069	0.890	0.834	0.856
-1	0.522	1.805	-0.963	1.136	0.418	0.727	-0.195	-1.695	2.173	0.309	2.559	-0.178	0.000	0.521	1.794	0.919	0.000	0.788	0.174	-0.406	3.102	0.555	0.729	1.011	1.385	0.753	0.927	0.832
-1	0.793	-0.162	-1.643	0.634	0.337	0.898	-0.633	1.689	0.000	0.806	-0.826	-0.356	2.215	0.890	-0.142	-1.268	0.000	1.293	0.574	0.725	0.000	0.833	1.077	0.988	0.721	0.679	0.867	0.753
-0	1.298	1.098	0.280	0.371	-0.373	0.855	-0.306	-1.186	0.000	0.977	-0.421	1.003	0.000	0.978	0.956	-1.249	2.548	0.735	0.577	-0.037	3.102	0.974	1.002	0.992	0.549	0.587	0.725	0.954
-1	0.751	-0.520	-1.653	0.168	-0.419	0.878	-1.023	-1.364	2.173	1.310	-0.667	0.863	0.000	1.196	-0.827	0.358	0.000	1.154	-0.165	-0.360	1.551	0.871	0.950	0.983	0.907	0.955	0.959	0.874
-0	1.730	0.666	-1.432	0.446	1.302	0.921	-0.203	0.621	0.000	1.171	-0.365	-0.611	1.107	0.585	0.807	1.150	0.000	0.415	-0.843	1.311	0.000	0.968	0.786	0.986	1.059	0.371	0.790	0.848
-1	0.596	-1.486	0.690	1.045	-1.344	0.928	0.867	0.820	2.173	0.610	0.999	-1.329	2.215	0.883	-0.001	-0.106	0.000	1.145	2.184	-0.808	0.000	2.019	1.256	1.056	1.751	1.037	1.298	1.518
-1	0.656	-1.993	-0.519	1.643	-0.143	0.815	0.256	1.220	1.087	0.399	-1.184	-1.458	0.000	0.738	1.361	-1.443	0.000	0.842	0.033	0.293	0.000	0.910	0.891	0.993	0.668	0.562	0.958	0.787
-1	1.127	-0.542	0.645	0.318	-1.496	0.661	-0.640	0.369	2.173	0.992	0.358	1.702	0.000	1.004	0.316	-1.109	0.000	1.616	-0.936	-0.707	1.551	0.875	1.191	0.985	0.651	0.940	0.969	0.834
-0	0.916	-1.423	-1.490	1.248	-0.538	0.625	-0.535	-0.174	0.000	0.769	-0.389	1.608	2.215	0.667	-1.138	-1.738	1.274	0.877	-0.019	0.482	0.000	0.696	0.917	1.121	0.678	0.347	0.647	0.722
-1	2.756	-0.637	-1.715	1.331	1.124	0.913	-0.296	-0.491	0.000	0.983	-0.831	0.000	2.215	1.180	-0.428	0.742	0.000	1.113	0.005	-1.157	1.551	1.681	1.096	1.462	0.976	0.917	1.009	1.040
-0	0.755	1.754	0.701	2.111	0.256	1.243	0.057	-1.502	2.173	0.565	-0.034	-1.078	1.107	0.529	1.696	-1.090	0.000	0.665	0.292	0.107	0.000	0.870	0.780	0.990	2.775	0.465	1.876	1.758
-1	0.593	-0.762	1.743	0.908	0.442	0.773	-1.357	-0.768	2.173	0.432	1.421	1.236	0.000	0.579	0.291	-0.403	0.000	0.966	-0.309	1.016	3.102	0.893	0.743	0.989	0.857	1.030	0.943	0.854
-1	0.891	-1.151	-1.269	0.504	-0.622	0.893	-0.549	0.700	0.000	0.828	-0.825	0.154	2.215	1.083	0.632	-1.141	0.000	1.059	-0.557	1.526	3.102	2.117	1.281	0.987	0.819	0.802	0.917	0.828
-1	2.358	-0.248	0.080	0.747	-0.975	1.019	1.374	1.363	0.000	0.935	0.127	-1.707	2.215	0.312	-0.827	0.017	0.000	0.737	1.059	-0.327	0.000	0.716	0.828	1.495	0.953	0.704	0.880	0.745
-0	0.660	-0.017	-1.138	0.453	1.002	0.645	0.518	0.703	2.173	0.751	0.705	-0.592	2.215	0.744	-0.909	-1.596	0.000	0.410	-1.135	0.481	0.000	0.592	0.922	0.989	0.897	0.948	0.777	0.701
-1	0.718	0.518	0.225	1.710	-0.022	1.888	-0.424	1.092	0.000	4.134	0.185	-1.366	0.000	1.415	1.293	0.242	2.548	2.351	0.264	-0.057	3.102	0.830	1.630	0.976	1.215	0.890	1.422	1.215
-1	1.160	0.203	0.941	0.594	0.212	0.636	-0.556	0.679	2.173	1.089	-0.481	-1.008	1.107	1.245	-0.056	-1.357	0.000	0.587	1.007	0.056	0.000	1.106	0.901	0.987	0.786	1.224	0.914	0.837
-1	0.697	0.542	0.619	0.985	1.481	0.745	0.415	1.644	2.173	0.903	0.495	-0.958	2.215	1.165	1.195	0.346	0.000	1.067	-0.881	-0.264	0.000	0.830	1.025	0.987	0.690	0.863	0.894	0.867
-0	1.430	0.190	-0.700	0.246	0.518	1.302	0.660	-0.247	2.173	1.185	-0.539	1.504	0.000	1.976	-0.401	1.079	0.000	0.855	-0.958	-1.110	3.102	0.886	0.953	0.993	0.889	1.400	1.376	1.119
-1	1.122	-0.795	0.202	0.397	-1.553	0.597	-1.459	-0.734	2.173	0.522	1.044	1.027	2.215	0.783	-1.243	1.701	0.000	0.371	1.737	0.199	0.000	1.719	1.176	0.988	0.723	1.583	1.063	0.914
-0	1.153	0.526	1.236	0.266	0.001	1.139	-1.236	-0.585	2.173	1.337	-0.215	-1.356	2.215	1.780	1.129	0.902	0.000	1.608	-0.391	-0.161	0.000	1.441	1.633	0.990	1.838	1.516	1.635	1.373
-1	0.760	1.012	0.758	0.937	0.051	0.941	0.687	-1.247	2.173	1.288	-0.743	0.822	0.000	1.552	1.782	-1.533	0.000	0.767	1.349	0.168	0.000	0.716	0.862	0.988	0.595	0.359	0.697	0.623
-1	1.756	-1.469	1.395	1.345	-1.595	0.817	0.017	-0.741	2.173	0.483	-0.008	0.293	0.000	1.768	-0.663	0.438	1.274	1.202	-1.387	-0.222	0.000	1.022	1.058	0.992	1.407	1.427	1.356	1.133
-0	0.397	0.582	-0.758	1.260	-1.735	0.889	-0.515	1.139	2.173	0.973	1.616	0.460	0.000	1.308	1.001	-0.709	2.548	0.858	0.995	-0.231	0.000	0.749	0.888	0.979	1.487	1.804	1.208	1.079
-0	0.515	-0.984	0.425	1.114	-0.439	1.999	0.818	1.561	0.000	1.407	0.009	-0.380	0.000	1.332	0.230	0.397	0.000	1.356	-0.616	-1.057	3.102	0.978	1.017	0.990	1.118	0.862	0.835	0.919
-1	1.368	-0.921	-0.866	0.842	-0.598	0.456	-1.176	1.219	1.087	0.419	-1.974	-0.819	0.000	0.791	-1.640	0.881	0.000	1.295	-0.782	0.442	3.102	0.945	0.761	0.974	0.915	0.535	0.733	0.651
-0	2.276	0.134	0.399	2.525	0.376	1.111	-1.078	-1.571	0.000	0.657	2.215	-0.900	0.000	1.183	-0.662	-0.508	2.548	1.436	-0.517	0.960	3.102	0.569	0.931	0.993	1.170	0.967	0.879	1.207
-0	0.849	0.907	0.124	0.652	1.585	0.715	0.355	-1.200	0.000	0.599	-0.892	1.301	0.000	1.106	1.151	0.582	0.000	1.895	-0.279	-0.568	3.102	0.881	0.945	0.998	0.559	0.649	0.638	0.660
-1	2.105	0.248	-0.797	0.530	0.206	1.957	-2.175	0.797	0.000	1.193	0.637	-1.646	2.215	0.881	1.111	-1.046	0.000	0.872	-0.185	1.085	1.551	0.986	1.343	1.151	1.069	0.714	2.063	1.951
-1	1.838	1.060	1.637	1.017	1.370	0.913	0.461	-0.609	1.087	0.766	-0.461	0.303	2.215	0.724	-0.061	0.886	0.000	0.941	1.123	-0.745	0.000	0.858	0.847	0.979	1.313	1.083	1.094	0.910
-0	0.364	1.274	1.066	1.570	-0.394	0.485	0.012	-1.716	0.000	0.317	-1.233	0.534	2.215	0.548	-2.165	0.762	0.000	0.729	0.169	-0.318	3.102	0.892	0.944	1.013	0.594	0.461	0.688	0.715
-1	0.503	1.343	-0.031	1.134	-1.204	0.590	-0.309	0.174	2.173	0.408	2.372	-0.628	0.000	1.850	0.400	1.147	2.548	0.664	-0.458	-0.885	0.000	1.445	1.283	0.989	1.280	1.118	1.127	1.026
-0	1.873	0.258	0.103	2.491	0.530	1.678	0.644	-1.738	2.173	1.432	0.848	-1.340	0.000	0.621	1.323	-1.316	0.000	0.628	0.789	-0.206	1.551	0.426	0.802	1.125	0.688	1.079	1.338	1.239
-1	0.826	-0.732	1.587	0.582	-1.236	0.495	0.757	-0.741	2.173	0.940	1.474	0.354	2.215	0.474	1.055	-1.657	0.000	0.415	1.758	0.841	0.000	0.451	0.578	0.984	0.757	0.922	0.860	0.696
-0	0.935	-1.614	-0.597	0.299	1.223	0.707	-0.853	-1.026	0.000	0.751	0.007	-1.691	0.000	1.062	-0.125	0.976	2.548	0.877	1.275	0.646	0.000	0.962	1.074	0.980	0.608	0.726	0.741	0.662
-1	0.643	0.542	-1.285	0.474	-0.366	0.667	-0.446	1.195	2.173	1.076	0.145	-0.126	0.000	0.970	-0.661	0.394	1.274	1.218	-0.184	-1.722	0.000	1.331	1.019	0.985	1.192	0.677	0.973	0.910
-0	0.713	0.164	1.080	1.427	-0.460	0.960	-0.152	-0.940	2.173	1.427	-0.901	1.036	1.107	0.440	-1.269	-0.194	0.000	0.452	1.932	-0.532	0.000	1.542	1.210	1.374	1.319	1.818	1.220	1.050
-0	0.876	-0.463	-1.224	2.458	-1.689	1.007	-0.752	0.398	0.000	2.456	-1.285	-0.152	1.107	1.641	1.838	1.717	0.000	0.458	0.194	0.488	3.102	4.848	2.463	0.986	1.981	0.974	2.642	2.258
-1	0.384	-0.275	0.387	1.403	-0.994	0.620	-1.529	1.685	0.000	1.091	-1.644	1.078	0.000	0.781	-1.311	0.326	2.548	1.228	-0.728	-0.633	1.551	0.920	0.854	0.987	0.646	0.609	0.740	0.884
-0	0.318	-1.818	-1.008	0.977	1.268	0.457	2.451	-1.522	0.000	0.881	1.351	0.461	2.215	0.929	0.239	-0.380	2.548	0.382	-0.613	1.330	0.000	1.563	1.193	0.994	0.829	0.874	0.901	1.026
-1	0.612	-1.120	1.098	0.402	-0.480	0.818	0.188	1.511	0.000	0.800	-0.253	0.977	0.000	1.175	0.271	-1.289	1.274	2.531	0.226	-0.409	3.102	0.889	0.947	0.979	1.486	0.940	1.152	1.119
-1	0.587	-0.737	-0.228	0.970	1.119	0.823	0.184	1.594	0.000	1.104	0.301	-0.818	2.215	0.819	0.712	-0.560	0.000	2.240	-0.419	0.340	3.102	1.445	1.103	0.988	0.715	1.363	1.019	0.926
-0	1.030	-0.694	-1.638	0.893	-1.074	1.160	-0.766	0.485	0.000	1.632	-0.698	-1.142	2.215	1.050	-1.092	0.952	0.000	1.475	0.286	0.125	3.102	0.914	1.075	0.982	0.732	1.493	1.219	1.079
-1	2.142	0.617	1.517	0.387	-0.862	0.345	1.203	-1.014	2.173	0.609	1.092	0.275	0.000	1.331	0.582	-0.183	2.548	0.557	1.540	-1.642	0.000	0.801	0.737	1.060	0.715	0.626	0.749	0.674
-0	1.076	0.240	-0.246	0.871	-1.241	0.496	0.282	0.746	2.173	1.095	-0.648	1.100	2.215	0.446	-1.756	0.764	0.000	0.434	0.788	-0.991	0.000	1.079	0.868	1.047	0.818	0.634	0.795	0.733
-0	1.400	0.901	-1.617	0.625	-0.163	0.661	-0.411	-1.616	2.173	0.685	0.524	0.425	0.000	0.881	-0.766	0.312	0.000	0.979	0.255	-0.667	3.102	0.898	1.105	1.253	0.730	0.716	0.738	0.795
-0	3.302	1.132	1.051	0.658	0.768	1.308	0.251	-0.374	1.087	1.673	0.015	-0.898	0.000	0.688	-0.535	1.363	1.274	0.871	1.325	-1.583	0.000	1.646	1.249	0.995	1.919	1.288	1.330	1.329
-0	1.757	0.202	0.750	0.767	-0.362	0.932	-1.033	-1.366	0.000	1.529	-1.012	-0.771	0.000	1.161	-0.287	0.059	0.000	2.185	1.147	1.099	3.102	0.795	0.529	1.354	1.144	1.491	1.319	1.161
-0	1.290	0.905	-1.711	1.017	-0.695	1.008	-1.038	0.693	2.173	1.202	-0.595	0.187	0.000	1.011	0.139	-1.607	0.000	0.789	-0.613	-1.041	3.102	1.304	0.895	1.259	1.866	0.955	1.211	1.200
-1	1.125	-0.004	1.694	0.373	0.329	0.978	0.640	-0.391	0.000	1.122	-0.376	1.521	2.215	0.432	2.413	-1.259	0.000	0.969	0.730	0.512	3.102	0.716	0.773	0.991	0.624	0.977	0.981	0.875
-0	1.081	0.861	1.252	1.621	1.474	1.293	0.600	0.630	0.000	1.991	-0.090	-0.675	2.215	0.861	1.105	-0.201	0.000	1.135	2.489	-1.659	0.000	1.089	0.657	0.991	2.179	0.412	1.334	1.071
-1	0.652	-0.294	1.241	1.034	0.490	1.033	0.551	-0.963	2.173	0.661	1.031	-1.654	2.215	1.376	-0.018	0.843	0.000	0.943	-0.329	-0.269	0.000	1.085	1.067	0.991	1.504	0.773	1.135	0.993
-1	1.408	-1.028	-1.018	0.252	-0.242	0.465	-0.364	-0.200	0.000	1.466	0.669	0.739	1.107	1.031	0.415	-1.468	2.548	0.457	-1.091	-1.722	0.000	0.771	0.811	0.979	1.459	1.204	1.041	0.866
-1	0.781	-1.143	-0.659	0.961	1.266	1.183	-0.686	0.119	2.173	1.126	-0.064	1.447	0.000	0.730	1.430	-1.535	0.000	1.601	0.513	1.658	0.000	0.871	1.345	1.184	1.058	0.620	1.107	0.978
-1	1.300	-0.616	1.032	0.751	-0.731	0.961	-0.716	1.592	0.000	2.079	-1.063	-0.271	2.215	0.475	0.518	1.695	1.274	0.395	-2.204	0.349	0.000	1.350	0.983	1.369	1.265	1.428	1.135	0.982
-1	0.833	0.809	1.657	1.637	1.019	0.705	1.077	-0.968	2.173	1.261	0.114	-0.298	1.107	1.032	0.017	0.236	0.000	0.640	-0.026	-1.598	0.000	0.894	0.982	0.981	1.250	1.054	1.018	0.853
-1	1.686	-1.090	-0.301	0.890	0.557	1.304	-0.284	-1.393	2.173	0.388	2.118	0.513	0.000	0.514	-0.015	0.891	0.000	0.460	0.547	0.627	3.102	0.942	0.524	1.186	1.528	0.889	1.015	1.122
-1	0.551	0.911	0.879	0.379	-0.796	1.154	-0.808	-0.966	0.000	1.168	-0.513	0.355	2.215	0.646	-1.309	0.773	0.000	0.544	-0.283	1.301	3.102	0.847	0.705	0.990	0.772	0.546	0.790	0.719
-1	1.597	0.793	-1.119	0.691	-1.455	0.370	0.337	1.354	0.000	0.646	-1.005	0.732	2.215	1.019	0.040	0.209	0.000	0.545	0.958	0.239	3.102	0.962	0.793	0.994	0.719	0.745	0.812	0.739
-0	1.033	-1.193	-0.452	0.247	0.970	0.503	-1.424	1.362	0.000	1.062	-0.416	-1.156	2.215	0.935	-0.023	0.555	2.548	0.410	-1.766	0.379	0.000	0.590	0.953	0.991	0.717	1.081	0.763	0.690
-1	0.859	-1.004	1.521	0.781	-0.993	0.677	0.643	-0.338	2.173	0.486	0.409	1.283	0.000	0.679	0.110	0.285	0.000	0.715	-0.735	-0.157	1.551	0.702	0.773	0.984	0.627	0.633	0.694	0.643
-0	0.612	-1.127	1.074	1.225	-0.426	0.927	-2.141	-0.473	0.000	1.290	-0.927	-1.085	2.215	1.183	1.981	-1.687	0.000	2.176	0.406	-1.581	0.000	0.945	0.651	1.170	0.895	1.604	1.179	1.142
-1	0.535	0.321	-1.095	0.281	-0.960	0.876	-0.709	-0.076	0.000	1.563	-0.666	1.536	2.215	0.773	-0.321	0.435	0.000	0.682	-0.801	-0.952	3.102	0.711	0.667	0.985	0.888	0.741	0.872	0.758
-1	0.745	1.586	1.578	0.863	-1.423	0.530	1.714	1.085	0.000	1.174	0.679	1.015	0.000	1.158	0.609	-1.186	2.548	1.851	0.832	-0.248	3.102	0.910	1.164	0.983	0.947	0.858	0.928	0.823
-0	0.677	-1.014	-1.648	1.455	1.461	0.596	-2.358	0.517	0.000	0.800	0.849	-0.743	2.215	1.024	-0.282	-1.004	0.000	1.846	-0.977	0.378	3.102	2.210	1.423	0.982	1.074	1.623	1.417	1.258
-1	0.815	-1.263	0.057	1.018	-0.208	0.339	-0.347	-1.646	2.173	1.223	0.600	-1.658	2.215	1.435	0.042	0.926	0.000	0.777	1.698	-0.698	0.000	1.022	1.058	1.000	0.784	0.477	0.886	0.836
-0	3.512	-1.094	-0.220	0.338	-0.328	1.962	-1.099	1.544	1.087	1.461	-1.305	-0.922	2.215	1.219	-1.289	0.400	0.000	0.731	0.155	1.249	0.000	1.173	1.366	0.993	2.259	2.000	1.626	1.349
-0	0.904	1.248	0.325	0.317	-1.624	0.685	-0.538	1.665	2.173	0.685	-2.145	-1.106	0.000	0.632	-1.460	1.017	0.000	1.085	-0.182	0.162	3.102	0.885	0.801	0.989	0.930	0.904	1.012	0.961

contrib/gbdt/lightgbm/binary1.test

@@ -1,500 +0,0 @@
-1	0.644	0.247	-0.447	0.862	0.374	0.854	-1.126	-0.790	2.173	1.015	-0.201	1.400	0.000	1.575	1.807	1.607	0.000	1.585	-0.190	-0.744	3.102	0.958	1.061	0.980	0.875	0.581	0.905	0.796
-0	0.385	1.800	1.037	1.044	0.349	1.502	-0.966	1.734	0.000	0.966	-1.960	-0.249	0.000	1.501	0.465	-0.354	2.548	0.834	-0.440	0.638	3.102	0.695	0.909	0.981	0.803	0.813	1.149	1.116
-0	1.214	-0.166	0.004	0.505	1.434	0.628	-1.174	-1.230	1.087	0.579	-1.047	-0.118	0.000	0.835	0.340	1.234	2.548	0.711	-1.383	1.355	0.000	0.848	0.911	1.043	0.931	1.058	0.744	0.696
-1	0.420	1.111	0.137	1.516	-1.657	0.854	0.623	1.605	1.087	1.511	-1.297	0.251	0.000	0.872	-0.368	-0.721	0.000	0.543	0.731	1.424	3.102	1.597	1.282	1.105	0.730	0.148	1.231	1.234
-0	0.897	-1.703	-1.306	1.022	-0.729	0.836	0.859	-0.333	2.173	1.336	-0.965	0.972	2.215	0.671	1.021	-1.439	0.000	0.493	-2.019	-0.289	0.000	0.805	0.930	0.984	1.430	2.198	1.934	1.684
-0	0.756	1.126	-0.945	2.355	-0.555	0.889	0.800	1.440	0.000	0.585	0.271	0.631	2.215	0.722	1.744	1.051	0.000	0.618	0.924	0.698	1.551	0.976	0.864	0.988	0.803	0.234	0.822	0.911
-0	1.141	-0.741	0.953	1.478	-0.524	1.197	-0.871	1.689	2.173	0.875	1.321	-0.518	1.107	0.540	0.037	-0.987	0.000	0.879	1.187	0.245	0.000	0.888	0.701	1.747	1.358	2.479	1.491	1.223
-1	0.606	-0.936	-0.384	1.257	-1.162	2.719	-0.600	0.100	2.173	3.303	-0.284	1.561	1.107	0.689	1.786	-0.326	0.000	0.780	-0.532	1.216	0.000	0.936	2.022	0.985	1.574	4.323	2.263	1.742
-1	0.603	0.429	-0.279	1.448	1.301	1.008	2.423	-1.295	0.000	0.452	1.305	0.533	0.000	1.076	1.011	1.256	2.548	2.021	1.260	-0.343	0.000	0.890	0.969	1.281	0.763	0.652	0.827	0.785
-0	1.171	-0.962	0.521	0.841	-0.315	1.196	-0.744	-0.882	2.173	0.726	-1.305	1.377	1.107	0.643	-1.790	-1.264	0.000	1.257	0.222	0.817	0.000	0.862	0.911	0.987	0.846	1.293	0.899	0.756
-1	1.392	-0.358	0.235	1.494	-0.461	0.895	-0.848	1.549	2.173	0.841	-0.384	0.666	1.107	1.199	2.509	-0.891	0.000	1.109	-0.364	-0.945	0.000	0.693	2.135	1.170	1.362	0.959	2.056	1.842
-1	1.024	1.076	-0.886	0.851	1.530	0.673	-0.449	0.187	1.087	0.628	-0.895	1.176	2.215	0.696	-0.232	-0.875	0.000	0.411	1.501	0.048	0.000	0.842	0.919	1.063	1.193	0.777	0.964	0.807
-1	0.890	-0.760	1.182	1.369	0.751	0.696	-0.959	-0.710	1.087	0.775	-0.130	-1.409	2.215	0.701	-0.110	-0.739	0.000	0.508	-0.451	0.390	0.000	0.762	0.738	0.998	1.126	0.788	0.940	0.790
-1	0.460	0.537	0.636	1.442	-0.269	0.585	0.323	-1.731	2.173	0.503	1.034	-0.927	0.000	0.928	-1.024	1.006	2.548	0.513	-0.618	-1.336	0.000	0.802	0.831	0.992	1.019	0.925	1.056	0.833
-1	0.364	1.648	0.560	1.720	0.829	1.110	0.811	-0.588	0.000	0.408	1.045	1.054	2.215	0.319	-1.138	1.545	0.000	0.423	1.025	-1.265	3.102	1.656	0.928	1.003	0.544	0.327	0.670	0.746
-1	0.525	-0.096	1.206	0.948	-1.103	1.519	-0.582	0.606	2.173	1.274	-0.572	-0.934	0.000	0.855	-1.028	-1.222	0.000	0.578	-1.000	-1.725	3.102	0.896	0.878	0.981	0.498	0.909	0.772	0.668
-0	0.536	-0.821	-1.029	0.703	1.113	0.363	-0.711	0.022	1.087	0.325	1.503	1.249	2.215	0.673	1.041	-0.401	0.000	0.480	2.127	1.681	0.000	0.767	1.034	0.990	0.671	0.836	0.669	0.663
-1	1.789	-0.583	1.641	0.897	0.799	0.515	-0.100	-1.483	0.000	1.101	0.031	-0.326	2.215	1.195	0.001	0.126	2.548	0.768	-0.148	0.601	0.000	0.916	0.921	1.207	1.069	0.483	0.934	0.795
-1	1.332	-0.571	0.986	0.580	1.508	0.582	0.634	-0.746	1.087	1.084	-0.964	-0.489	0.000	0.785	0.274	0.343	2.548	0.779	0.721	1.489	0.000	1.733	1.145	0.990	1.270	0.715	0.897	0.915
-0	1.123	0.629	-1.708	0.597	-0.882	0.752	0.195	1.522	2.173	1.671	1.515	-0.003	0.000	0.778	0.514	0.139	1.274	0.801	1.260	1.600	0.000	1.495	0.976	0.988	0.676	0.921	1.010	0.943
-0	1.816	-0.515	0.171	0.980	-0.454	0.870	0.202	-1.399	2.173	1.130	1.066	-1.593	0.000	0.844	0.735	1.275	2.548	1.125	-1.133	0.348	0.000	0.837	0.693	0.988	1.112	0.784	1.009	0.974
-1	0.364	0.694	0.445	1.862	0.159	0.963	-1.356	1.260	1.087	0.887	-0.540	-1.533	2.215	0.658	-2.544	-1.236	0.000	0.516	-0.807	0.039	0.000	0.891	1.004	0.991	1.092	0.976	1.000	0.953
-1	0.790	-1.175	0.475	1.846	0.094	0.999	-1.090	0.257	0.000	1.422	0.854	1.112	2.215	1.302	1.004	-1.702	1.274	2.557	-0.787	-1.048	0.000	0.890	1.429	0.993	2.807	0.840	2.248	1.821
-1	0.765	-0.500	-0.603	1.843	-0.560	1.068	0.007	0.746	2.173	1.154	-0.017	1.329	0.000	1.165	1.791	-1.585	0.000	1.116	0.441	-0.886	0.000	0.774	0.982	0.989	1.102	0.633	1.178	1.021
-1	1.407	1.293	-1.418	0.502	-1.527	2.005	-2.122	0.622	0.000	1.699	1.508	-0.649	2.215	1.665	0.748	-0.755	0.000	2.555	0.811	1.423	1.551	7.531	5.520	0.985	1.115	1.881	4.487	3.379
-1	0.772	-0.186	-1.372	0.823	-0.140	0.781	0.763	0.046	2.173	1.128	0.516	1.380	0.000	0.797	-0.640	-0.134	2.548	2.019	-0.972	-1.670	0.000	2.022	1.466	0.989	0.856	0.808	1.230	0.991
-1	0.546	-0.954	0.715	1.335	-1.689	0.783	-0.443	-1.735	2.173	1.081	0.185	-0.435	0.000	1.433	-0.662	-0.389	0.000	0.969	0.924	1.099	0.000	0.910	0.879	0.988	0.683	0.753	0.878	0.865
-1	0.596	0.276	-1.054	1.358	1.355	1.444	1.813	-0.208	0.000	1.175	-0.949	-1.573	0.000	0.855	-1.228	-0.925	2.548	1.837	-0.400	0.913	0.000	0.637	0.901	1.028	0.553	0.790	0.679	0.677
-0	0.458	2.292	1.530	0.291	1.283	0.749	-0.930	-0.198	0.000	0.300	-1.560	0.990	0.000	0.811	-0.176	0.995	2.548	1.085	-0.178	-1.213	3.102	0.891	0.648	0.999	0.732	0.655	0.619	0.620
-0	0.638	-0.575	-1.048	0.125	0.178	0.846	-0.753	-0.339	1.087	0.799	-0.727	1.182	0.000	0.888	0.283	0.717	0.000	1.051	-1.046	-1.557	3.102	0.889	0.871	0.989	0.884	0.923	0.836	0.779
-1	0.434	-1.119	-0.313	2.427	0.461	0.497	0.261	-1.177	2.173	0.618	-0.737	-0.688	0.000	1.150	-1.237	-1.652	2.548	0.757	-0.054	1.700	0.000	0.809	0.741	0.982	1.450	0.936	1.086	0.910
-1	0.431	-1.144	-1.030	0.778	-0.655	0.490	0.047	-1.546	0.000	1.583	-0.014	0.891	2.215	0.516	0.956	0.567	2.548	0.935	-1.123	-0.082	0.000	0.707	0.995	0.995	0.700	0.602	0.770	0.685
-1	1.894	0.222	1.224	1.578	1.715	0.966	2.890	-0.013	0.000	0.922	-0.703	-0.844	0.000	0.691	2.056	1.039	0.000	0.900	-0.733	-1.240	3.102	1.292	1.992	1.026	0.881	0.684	1.759	1.755
-0	0.985	-0.316	0.141	1.067	-0.946	0.819	-1.177	1.307	2.173	1.080	-0.429	0.557	1.107	1.726	1.435	-1.075	0.000	1.100	1.547	-0.647	0.000	0.873	1.696	1.179	1.146	1.015	1.538	1.270
-0	0.998	-0.187	-0.236	0.882	0.755	0.468	0.950	-0.439	2.173	0.579	-0.550	-0.624	0.000	1.847	1.196	1.384	1.274	0.846	1.273	-1.072	0.000	1.194	0.797	1.013	1.319	1.174	0.963	0.898
-0	0.515	0.246	-0.593	1.082	1.591	0.912	-0.623	-0.957	2.173	0.858	0.418	0.844	0.000	0.948	2.519	1.599	0.000	1.158	1.385	-0.095	3.102	0.973	1.033	0.988	0.998	1.716	1.054	0.901
-0	0.919	-1.001	1.506	1.389	0.653	0.507	-0.616	-0.689	2.173	0.808	0.536	-0.467	2.215	0.496	2.187	-0.859	0.000	0.822	0.807	1.163	0.000	0.876	0.861	1.088	0.947	0.614	0.911	1.087
-0	0.794	0.051	1.477	1.504	-1.695	0.716	0.315	0.264	1.087	0.879	-0.135	-1.094	2.215	1.433	-0.741	0.201	0.000	1.566	0.534	-0.989	0.000	0.627	0.882	0.974	0.807	1.130	0.929	0.925
-1	0.455	-0.946	-1.175	1.453	-0.580	0.763	-0.856	0.840	0.000	0.829	1.223	1.174	2.215	0.714	0.638	-0.466	0.000	1.182	0.223	-1.333	0.000	0.977	0.938	0.986	0.713	0.714	0.796	0.843
-1	0.662	-0.296	-1.287	1.212	-0.707	0.641	1.457	0.222	0.000	0.600	0.525	-1.700	2.215	0.784	-0.835	-0.961	2.548	0.865	1.131	1.162	0.000	0.854	0.877	0.978	0.740	0.734	0.888	0.811
-0	0.390	0.698	-1.629	1.888	0.298	0.990	1.614	-1.572	0.000	1.666	0.170	0.719	2.215	1.590	1.064	-0.886	1.274	0.952	0.305	-1.216	0.000	1.048	0.897	1.173	0.891	1.936	1.273	1.102
-0	1.014	0.117	1.384	0.686	-1.047	0.609	-1.245	-0.850	0.000	1.076	-1.158	0.814	1.107	1.598	-0.389	-0.111	0.000	0.907	1.688	-1.673	0.000	1.333	0.866	0.989	0.975	0.442	0.797	0.788
-0	1.530	-1.408	-0.207	0.440	-1.357	0.902	-0.647	1.325	1.087	1.320	-0.819	0.246	1.107	0.503	1.407	-1.683	0.000	1.189	-0.972	-0.925	0.000	0.386	1.273	0.988	0.829	1.335	1.173	1.149
-1	1.689	-0.590	0.915	2.076	1.202	0.644	-0.478	-0.238	0.000	0.809	-1.660	-1.184	0.000	1.227	-0.224	-0.808	2.548	1.655	1.047	-0.623	0.000	0.621	1.192	0.988	1.309	0.866	0.924	1.012
-0	1.102	0.402	-1.622	1.262	1.022	0.576	0.271	-0.269	0.000	0.591	0.495	-1.278	0.000	1.271	0.209	0.575	2.548	0.941	0.964	-0.685	3.102	0.989	0.963	1.124	0.857	0.858	0.716	0.718
-0	2.491	0.825	0.581	1.593	0.205	0.782	-0.815	1.499	0.000	1.179	-0.999	-1.509	0.000	0.926	0.920	-0.522	2.548	2.068	-1.021	-1.050	3.102	0.874	0.943	0.980	0.945	1.525	1.570	1.652
-0	0.666	0.254	1.601	1.303	-0.250	1.236	-1.929	0.793	0.000	1.074	0.447	-0.871	0.000	0.991	1.059	-0.342	0.000	1.703	-0.393	-1.419	3.102	0.921	0.945	1.285	0.931	0.462	0.770	0.729
-0	0.937	-1.126	1.424	1.395	1.743	0.760	0.428	-0.238	2.173	0.846	0.494	1.320	2.215	0.872	-1.826	-0.507	0.000	0.612	1.860	1.403	0.000	3.402	2.109	0.985	1.298	1.165	1.404	1.240
-1	0.881	-1.086	-0.870	0.513	0.266	2.049	-1.870	1.160	0.000	2.259	-0.428	-0.935	2.215	1.321	-0.655	-0.449	2.548	1.350	-1.766	-0.108	0.000	0.911	1.852	0.987	1.167	0.820	1.903	1.443
-0	0.410	0.835	-0.819	1.257	1.112	0.871	-1.737	-0.401	0.000	0.927	0.158	1.253	0.000	1.183	0.405	-1.570	0.000	0.807	-0.704	-0.438	3.102	0.932	0.962	0.987	0.653	0.315	0.616	0.648
-1	0.634	0.196	-1.679	1.379	-0.967	2.260	-0.273	1.114	0.000	1.458	1.070	-0.278	1.107	1.195	0.110	-0.688	2.548	0.907	0.298	-1.359	0.000	0.949	1.129	0.984	0.675	0.877	0.938	0.824
-1	0.632	-1.254	1.201	0.496	-0.106	0.235	2.731	-0.955	0.000	0.615	-0.805	0.600	0.000	0.633	-0.934	1.641	0.000	1.407	-0.483	-0.962	1.551	0.778	0.797	0.989	0.578	0.722	0.576	0.539
-0	0.714	1.122	1.566	2.399	-1.431	1.665	0.299	0.323	0.000	1.489	1.087	-0.861	2.215	1.174	0.140	1.083	2.548	0.404	-0.968	1.105	0.000	0.867	0.969	0.981	1.039	1.552	1.157	1.173
-1	0.477	-0.321	-0.471	1.966	1.034	2.282	1.359	-0.874	0.000	1.672	-0.258	1.109	0.000	1.537	0.604	0.231	2.548	1.534	-0.640	0.827	0.000	0.746	1.337	1.311	0.653	0.721	0.795	0.742
-1	1.351	0.460	0.031	1.194	-1.185	0.670	-1.157	-1.637	2.173	0.599	-0.823	0.680	0.000	0.478	0.373	1.716	0.000	0.809	-0.919	0.010	1.551	0.859	0.839	1.564	0.994	0.777	0.971	0.826
-1	0.520	-1.442	-0.348	0.840	1.654	1.273	-0.760	1.317	0.000	0.861	2.579	-0.791	0.000	1.779	0.257	-0.703	0.000	2.154	1.928	0.457	0.000	1.629	3.194	0.992	0.730	1.107	2.447	2.747
-0	0.700	-0.308	0.920	0.438	-0.879	0.516	1.409	1.101	0.000	0.960	0.701	-0.049	2.215	1.442	-0.416	-1.439	2.548	0.628	1.009	-0.364	0.000	0.848	0.817	0.987	0.759	1.421	0.937	0.920
-1	0.720	1.061	-0.546	0.798	-1.521	1.066	0.173	0.271	1.087	1.453	0.114	1.336	1.107	0.702	0.616	-0.367	0.000	0.543	-0.386	-1.301	0.000	0.653	0.948	0.989	1.031	1.500	0.965	0.790
-1	0.735	-0.416	0.588	1.308	-0.382	1.042	0.344	1.609	0.000	0.926	0.163	-0.520	1.107	1.050	-0.427	1.159	2.548	0.834	0.613	0.948	0.000	0.848	1.189	1.042	0.844	1.099	0.829	0.843
-1	0.777	-0.396	1.540	1.608	0.638	0.955	0.040	0.918	2.173	1.315	1.116	-0.823	0.000	0.781	-0.762	0.564	2.548	0.945	-0.573	1.379	0.000	0.679	0.706	1.124	0.608	0.593	0.515	0.493
-1	0.934	0.319	-0.257	0.970	-0.980	0.726	0.774	0.731	0.000	0.896	0.038	-1.465	1.107	0.773	-0.055	-0.831	2.548	1.439	-0.229	0.698	0.000	0.964	1.031	0.995	0.845	0.480	0.810	0.762
-0	0.461	0.771	0.019	2.055	-1.288	1.043	0.147	0.261	2.173	0.833	-0.156	1.425	0.000	0.832	0.805	-0.491	2.548	0.589	1.252	1.414	0.000	0.850	0.906	1.245	1.364	0.850	0.908	0.863
-1	0.858	-0.116	-0.937	0.966	1.167	0.825	-0.108	1.111	1.087	0.733	1.163	-0.634	0.000	0.894	0.771	0.020	0.000	0.846	-1.124	-1.195	3.102	0.724	1.194	1.195	0.813	0.969	0.985	0.856
-0	0.720	-0.335	-0.307	1.445	0.540	1.108	-0.034	-1.691	1.087	0.883	-1.356	-0.678	2.215	0.440	1.093	0.253	0.000	0.389	-1.582	-1.097	0.000	1.113	1.034	0.988	1.256	1.572	1.062	0.904
-1	0.750	-0.811	-0.542	0.985	0.408	0.471	0.477	0.355	0.000	1.347	-0.875	-1.556	2.215	0.564	1.082	-0.724	0.000	0.793	-0.958	-0.020	3.102	0.836	0.825	0.986	1.066	0.924	0.927	0.883
-0	0.392	-0.468	-0.216	0.680	1.565	1.086	-0.765	-0.581	1.087	1.264	-1.035	1.189	2.215	0.986	-0.338	0.747	0.000	0.884	-1.328	-0.965	0.000	1.228	0.988	0.982	1.135	1.741	1.108	0.956
-1	0.434	-1.269	0.643	0.713	0.608	0.597	0.832	1.627	0.000	0.708	-0.422	0.079	2.215	1.533	-0.823	-1.127	2.548	0.408	-1.357	-0.828	0.000	1.331	1.087	0.999	1.075	1.015	0.875	0.809
-0	0.828	-1.803	0.342	0.847	-0.162	1.585	-1.128	-0.272	2.173	1.974	0.039	-1.717	0.000	0.900	0.764	-1.741	0.000	1.349	-0.079	1.035	3.102	0.984	0.815	0.985	0.780	1.661	1.403	1.184
-1	1.089	-0.350	-0.747	1.472	0.792	1.087	-0.069	-1.192	0.000	0.512	-0.841	-1.284	0.000	2.162	-0.821	0.545	2.548	1.360	2.243	-0.183	0.000	0.977	0.628	1.725	1.168	0.635	0.823	0.822
-1	0.444	0.451	-1.332	1.176	-0.247	0.898	0.194	0.007	0.000	1.958	0.576	-1.618	2.215	0.584	1.203	0.268	0.000	0.939	1.033	1.264	3.102	0.829	0.886	0.985	1.265	0.751	1.032	0.948
-0	0.629	0.114	1.177	0.917	-1.204	0.845	0.828	-0.088	0.000	0.962	-1.302	0.823	2.215	0.732	0.358	-1.334	2.548	0.538	0.582	1.561	0.000	1.028	0.834	0.988	0.904	1.205	1.039	0.885
-1	1.754	-1.259	-0.573	0.959	-1.483	0.358	0.448	-1.452	0.000	0.711	0.313	0.499	2.215	1.482	-0.390	1.474	2.548	1.879	-1.540	0.668	0.000	0.843	0.825	1.313	1.315	0.939	1.048	0.871
-1	0.549	0.706	-1.437	0.894	0.891	0.680	-0.762	-1.568	0.000	0.981	0.499	-0.425	2.215	1.332	0.678	0.485	1.274	0.803	0.022	-0.893	0.000	0.793	1.043	0.987	0.761	0.899	0.915	0.794
-0	0.475	0.542	-0.987	1.569	0.069	0.551	1.543	-1.488	0.000	0.608	0.301	1.734	2.215	0.277	0.499	-0.522	0.000	1.375	1.212	0.696	3.102	0.652	0.756	0.987	0.828	0.830	0.715	0.679
-1	0.723	0.049	-1.153	1.300	0.083	0.723	-0.749	0.630	0.000	1.126	0.412	-0.384	0.000	1.272	1.256	1.358	2.548	3.108	0.777	-1.486	3.102	0.733	1.096	1.206	1.269	0.899	1.015	0.903
-1	1.062	0.296	0.725	0.285	-0.531	0.819	1.277	-0.667	0.000	0.687	0.829	-0.092	0.000	1.158	0.447	1.047	2.548	1.444	-0.186	-1.491	3.102	0.863	1.171	0.986	0.769	0.828	0.919	0.840
-0	0.572	-0.349	1.396	2.023	0.795	0.577	0.457	-0.533	0.000	1.351	0.701	-1.091	0.000	0.724	-1.012	-0.182	2.548	0.923	-0.012	0.789	3.102	0.936	1.025	0.985	1.002	0.600	0.828	0.909
-1	0.563	0.387	0.412	0.553	1.050	0.723	-0.992	-0.447	0.000	0.748	0.948	0.546	2.215	1.761	-0.559	-1.183	0.000	1.114	-0.251	1.192	3.102	0.936	0.912	0.976	0.578	0.722	0.829	0.892
-1	1.632	1.577	-0.697	0.708	-1.263	0.863	0.012	1.197	2.173	0.498	0.990	-0.806	0.000	0.627	2.387	-1.283	0.000	0.607	1.290	-0.174	3.102	0.916	1.328	0.986	0.557	0.971	0.935	0.836
-1	0.562	-0.360	0.399	0.803	-1.334	1.443	-0.116	1.628	2.173	0.750	0.987	0.135	1.107	0.795	0.298	-0.556	0.000	1.150	-0.113	-0.093	0.000	0.493	1.332	0.985	1.001	1.750	1.013	0.886
-1	0.987	0.706	-0.492	0.861	0.607	0.593	0.088	-0.184	0.000	0.802	0.894	1.608	2.215	0.782	-0.471	1.500	2.548	0.521	0.772	-0.960	0.000	0.658	0.893	1.068	0.877	0.664	0.709	0.661
-1	1.052	0.883	-0.581	1.566	0.860	0.931	1.515	-0.873	0.000	0.493	0.145	-0.672	0.000	1.133	0.935	1.581	2.548	1.630	0.695	0.923	3.102	1.105	1.087	1.713	0.948	0.590	0.872	0.883
-1	2.130	-0.516	-0.291	0.776	-1.230	0.689	-0.257	0.800	2.173	0.730	-0.274	-1.437	0.000	0.615	0.241	1.083	0.000	0.834	0.757	1.613	3.102	0.836	0.806	1.333	1.061	0.730	0.889	0.783
-1	0.742	0.797	1.628	0.311	-0.418	0.620	0.685	-1.457	0.000	0.683	1.774	-1.082	0.000	1.700	1.104	0.225	2.548	0.382	-2.184	-1.307	0.000	0.945	1.228	0.984	0.864	0.931	0.988	0.838
-0	0.311	-1.249	-0.927	1.272	-1.262	0.642	-1.228	-0.136	0.000	1.220	-0.804	-1.558	2.215	0.950	-0.828	0.495	1.274	2.149	-1.672	0.634	0.000	1.346	0.887	0.981	0.856	1.101	1.001	1.106
-0	0.660	-1.834	-0.667	0.601	1.236	0.932	-0.933	-0.135	2.173	1.373	-0.122	1.429	0.000	0.654	-0.034	-0.847	2.548	0.711	0.911	0.703	0.000	1.144	0.942	0.984	0.822	0.739	0.992	0.895
-0	3.609	-0.590	0.851	0.615	0.455	1.280	0.003	-0.866	1.087	1.334	0.708	-1.131	0.000	0.669	0.480	0.092	0.000	0.975	0.983	-1.429	3.102	1.301	1.089	0.987	1.476	0.934	1.469	1.352
-1	0.905	-0.403	1.567	2.651	0.953	1.194	-0.241	-0.567	1.087	0.308	-0.384	-0.007	0.000	0.608	-0.175	-1.163	2.548	0.379	0.941	1.662	0.000	0.580	0.721	1.126	0.895	0.544	1.097	0.836
-1	0.983	0.255	1.093	0.905	-0.874	0.863	0.060	-0.368	0.000	0.824	-0.747	-0.633	0.000	0.614	0.961	1.052	0.000	0.792	-0.260	1.632	3.102	0.874	0.883	1.280	0.663	0.406	0.592	0.645
-1	1.160	-1.027	0.274	0.460	0.322	2.085	-1.623	-0.840	0.000	1.634	-1.046	1.182	2.215	0.492	-0.367	1.174	0.000	0.824	-0.998	1.617	0.000	0.943	0.884	1.001	1.209	1.313	1.034	0.866
-0	0.299	0.028	-1.372	1.930	-0.661	0.840	-0.979	0.664	1.087	0.535	-2.041	1.434	0.000	1.087	-1.797	0.344	0.000	0.485	-0.560	-1.105	3.102	0.951	0.890	0.980	0.483	0.684	0.730	0.706
-0	0.293	1.737	-1.418	2.074	0.794	0.679	1.024	-1.457	0.000	1.034	1.094	-0.168	1.107	0.506	1.680	-0.661	0.000	0.523	-0.042	-1.274	3.102	0.820	0.944	0.987	0.842	0.694	0.761	0.750
-0	0.457	-0.393	1.560	0.738	-0.007	0.475	-0.230	0.246	0.000	0.776	-1.264	-0.606	2.215	0.865	-0.731	-1.576	2.548	1.153	0.343	1.436	0.000	1.060	0.883	0.988	0.972	0.703	0.758	0.720
-0	0.935	-0.582	0.240	2.401	0.818	1.231	-0.618	-1.289	0.000	0.799	0.544	-0.228	2.215	0.525	-1.494	-0.969	0.000	0.609	-1.123	1.168	3.102	0.871	0.767	1.035	1.154	0.919	0.868	1.006
-1	0.902	-0.745	-1.215	1.174	-0.501	1.215	0.167	1.162	0.000	0.896	1.217	-0.976	0.000	0.585	-0.429	1.036	0.000	1.431	-0.416	0.151	3.102	0.524	0.952	0.990	0.707	0.271	0.592	0.826
-1	0.653	0.337	-0.320	1.118	-0.934	1.050	0.745	0.529	1.087	1.075	1.742	-1.538	0.000	0.585	1.090	0.973	0.000	1.091	-0.187	1.160	1.551	1.006	1.108	0.978	1.121	0.838	0.947	0.908
-0	1.157	1.401	0.340	0.395	-1.218	0.945	1.928	-0.876	0.000	1.384	0.320	1.002	1.107	1.900	1.177	-0.462	2.548	1.122	1.316	1.720	0.000	1.167	1.096	0.989	0.937	1.879	1.307	1.041
-0	0.960	0.355	-0.152	0.872	-0.338	0.391	0.348	0.956	1.087	0.469	2.664	1.409	0.000	0.756	-1.561	1.500	0.000	0.525	1.436	1.728	3.102	1.032	0.946	0.996	0.929	0.470	0.698	0.898
-1	1.038	0.274	0.825	1.198	0.963	1.078	-0.496	-1.014	2.173	0.739	-0.727	-0.151	2.215	1.035	-0.799	0.398	0.000	1.333	-0.872	-1.498	0.000	0.849	1.033	0.985	0.886	0.936	0.975	0.823
-0	0.490	0.277	0.318	1.303	0.694	1.333	-1.620	-0.563	0.000	1.459	-1.326	1.140	0.000	0.779	-0.673	-1.324	2.548	0.860	-1.247	0.043	0.000	0.857	0.932	0.992	0.792	0.278	0.841	1.498
-0	1.648	-0.688	-1.386	2.790	0.995	1.087	1.359	-0.687	0.000	1.050	-0.223	-0.261	2.215	0.613	-0.889	1.335	0.000	1.204	0.827	0.309	3.102	0.464	0.973	2.493	1.737	0.827	1.319	1.062
-0	1.510	-0.662	1.668	0.860	0.280	0.705	0.974	-1.647	1.087	0.662	-0.393	-0.225	0.000	0.610	-0.996	0.532	2.548	0.464	1.305	0.102	0.000	0.859	1.057	1.498	0.799	1.260	0.946	0.863
-1	0.850	-1.185	-0.117	0.943	-0.449	1.142	0.875	-0.030	0.000	2.223	-0.461	1.627	2.215	0.767	-1.761	-1.692	0.000	1.012	-0.727	0.639	3.102	3.649	2.062	0.985	1.478	1.087	1.659	1.358
-0	0.933	1.259	0.130	0.326	-0.890	0.306	1.136	1.142	0.000	0.964	0.705	-1.373	2.215	0.546	-0.196	-0.001	0.000	0.578	-1.169	1.004	3.102	0.830	0.836	0.988	0.837	1.031	0.749	0.655
-0	0.471	0.697	1.570	1.109	0.201	1.248	0.348	-1.448	0.000	2.103	0.773	0.686	2.215	1.451	-0.087	-0.453	2.548	1.197	-0.045	-1.026	0.000	0.793	1.094	0.987	0.851	1.804	1.378	1.089
-1	2.446	-0.701	-1.568	0.059	0.822	1.401	-0.600	-0.044	2.173	0.324	-0.001	1.344	2.215	0.913	-0.818	1.049	0.000	0.442	-1.088	-0.005	0.000	0.611	1.062	0.979	0.562	0.988	0.998	0.806
-0	0.619	2.029	0.933	0.528	-0.903	0.974	0.760	-0.311	2.173	0.825	0.658	-1.466	1.107	0.894	1.594	0.370	0.000	0.882	-0.258	1.661	0.000	1.498	1.088	0.987	0.867	1.139	0.900	0.779
-1	0.674	-0.131	-0.362	0.518	-1.574	0.876	0.442	0.145	1.087	0.497	-1.526	-1.704	0.000	0.680	2.514	-1.374	0.000	0.792	-0.479	0.773	1.551	0.573	1.198	0.984	0.800	0.667	0.987	0.832
-1	1.447	1.145	-0.937	0.307	-1.458	0.478	1.264	0.816	1.087	0.558	1.015	-0.101	2.215	0.937	-0.190	1.177	0.000	0.699	0.954	-1.512	0.000	0.877	0.838	0.990	0.873	0.566	0.646	0.713
-1	0.976	0.308	-0.844	0.436	0.610	1.253	0.149	-1.585	2.173	1.415	0.568	0.096	2.215	0.953	-0.855	0.441	0.000	0.867	-0.650	1.643	0.000	0.890	1.234	0.988	0.796	2.002	1.179	0.977
-0	0.697	0.401	-0.718	0.920	0.735	0.958	-0.172	0.168	2.173	0.872	-0.097	-1.335	0.000	0.513	-1.192	-1.710	1.274	0.426	-1.637	1.368	0.000	0.997	1.227	1.072	0.800	1.013	0.786	0.749
-1	1.305	-2.157	1.740	0.661	-0.912	0.705	-0.516	0.759	2.173	0.989	-0.716	-0.300	2.215	0.627	-1.052	-1.736	0.000	0.467	-2.467	0.568	0.000	0.807	0.964	0.988	1.427	1.012	1.165	0.926
-0	1.847	1.663	-0.618	0.280	1.258	1.462	-0.054	1.371	0.000	0.900	0.309	-0.544	0.000	0.331	-2.149	-0.341	0.000	1.091	-0.833	0.710	3.102	1.496	0.931	0.989	1.549	0.115	1.140	1.150
-0	0.410	-0.323	1.069	2.160	0.010	0.892	0.942	-1.640	2.173	0.946	0.938	1.314	0.000	1.213	-1.099	-0.794	2.548	0.650	0.053	0.056	0.000	1.041	0.916	1.063	0.985	1.910	1.246	1.107
-1	0.576	1.092	-0.088	0.777	-1.579	0.757	0.271	0.109	0.000	0.819	0.827	-1.554	2.215	1.313	2.341	-1.568	0.000	2.827	0.239	-0.338	0.000	0.876	0.759	0.986	0.692	0.457	0.796	0.791
-1	0.537	0.925	-1.406	0.306	-0.050	0.906	1.051	0.037	0.000	1.469	-0.177	-1.320	2.215	1.872	0.723	1.158	0.000	1.313	0.227	-0.501	3.102	0.953	0.727	0.978	0.755	0.892	0.932	0.781
-0	0.716	-0.065	-0.484	1.313	-1.563	0.596	-0.242	0.678	2.173	0.426	-1.909	0.616	0.000	0.885	-0.406	-1.343	2.548	0.501	-1.327	-0.340	0.000	0.470	0.728	1.109	0.919	0.881	0.665	0.692
-1	0.624	-0.389	0.128	1.636	-1.110	1.025	0.573	-0.843	2.173	0.646	-0.697	1.064	0.000	0.632	-1.442	0.961	0.000	0.863	-0.106	1.717	0.000	0.825	0.917	1.257	0.983	0.713	0.890	0.824
-0	0.484	2.101	1.714	1.131	-0.823	0.750	0.583	-1.304	1.087	0.894	0.421	0.559	2.215	0.921	-0.063	0.282	0.000	0.463	-0.474	-1.387	0.000	0.742	0.886	0.995	0.993	1.201	0.806	0.754
-0	0.570	0.339	-1.478	0.528	0.439	0.978	1.479	-1.411	2.173	0.763	1.541	-0.734	0.000	1.375	0.840	0.903	0.000	0.965	1.599	0.364	0.000	0.887	1.061	0.992	1.322	1.453	1.013	0.969
-0	0.940	1.303	1.636	0.851	-1.732	0.803	-0.030	-0.177	0.000	0.480	-0.125	-0.954	0.000	0.944	0.709	0.296	2.548	1.342	-0.418	1.197	3.102	0.853	0.989	0.979	0.873	0.858	0.719	0.786
-1	0.599	0.544	-0.238	0.816	1.043	0.857	0.660	1.128	2.173	0.864	-0.624	-0.843	0.000	1.159	0.367	0.174	0.000	1.520	-0.543	-1.508	0.000	0.842	0.828	0.984	0.759	0.895	0.918	0.791
-1	1.651	1.897	-0.914	0.423	0.315	0.453	0.619	-1.607	2.173	0.532	-0.424	0.209	1.107	0.369	2.479	0.034	0.000	0.701	0.217	0.984	0.000	0.976	0.951	1.035	0.879	0.825	0.915	0.798
-1	0.926	-0.574	-0.763	0.285	1.094	0.672	2.314	1.545	0.000	1.124	0.415	0.809	0.000	1.387	0.270	-0.949	2.548	1.547	-0.631	-0.200	3.102	0.719	0.920	0.986	0.889	0.933	0.797	0.777
-0	0.677	1.698	-0.890	0.641	-0.449	0.607	1.754	1.720	0.000	0.776	0.372	0.782	2.215	0.511	1.491	-0.480	0.000	0.547	-0.341	0.853	3.102	0.919	1.026	0.997	0.696	0.242	0.694	0.687
-0	1.266	0.602	0.958	0.487	1.256	0.709	0.843	-1.196	0.000	0.893	1.303	-0.594	1.107	1.090	1.320	0.354	0.000	0.797	1.846	1.139	0.000	0.780	0.896	0.986	0.661	0.709	0.790	0.806
-1	0.628	-0.616	-0.329	0.764	-1.150	0.477	-0.715	1.187	2.173	1.250	0.607	1.026	2.215	0.983	-0.023	-0.583	0.000	0.377	1.344	-1.015	0.000	0.744	0.954	0.987	0.837	0.841	0.795	0.694
-1	1.035	-0.828	-1.358	1.870	-1.060	1.075	0.130	0.448	2.173	0.660	0.697	0.641	0.000	0.425	1.006	-1.035	0.000	0.751	1.055	1.364	3.102	0.826	0.822	0.988	0.967	0.901	1.077	0.906
-1	0.830	0.265	-0.150	0.660	1.105	0.592	-0.557	0.908	2.173	0.670	-1.419	-0.671	0.000	1.323	-0.409	1.644	2.548	0.850	-0.033	-0.615	0.000	0.760	0.967	0.984	0.895	0.681	0.747	0.770
-1	1.395	1.100	1.167	1.088	0.218	0.400	-0.132	0.024	2.173	0.743	0.530	-1.361	2.215	0.341	-0.691	-0.238	0.000	0.396	-1.426	-0.933	0.000	0.363	0.472	1.287	0.922	0.810	0.792	0.656
-1	1.070	1.875	-1.298	1.215	-0.106	0.767	0.795	0.514	1.087	0.401	2.780	1.276	0.000	0.686	1.127	1.721	2.548	0.391	-0.259	-1.167	0.000	1.278	1.113	1.389	0.852	0.824	0.838	0.785
-0	1.114	-0.071	1.719	0.399	-1.383	0.849	0.254	0.481	0.000	0.958	-0.579	0.742	0.000	1.190	-0.140	-0.862	2.548	0.479	1.390	0.856	0.000	0.952	0.988	0.985	0.764	0.419	0.835	0.827
-0	0.714	0.376	-0.568	1.578	-1.165	0.648	0.141	0.639	2.173	0.472	0.569	1.449	1.107	0.783	1.483	0.361	0.000	0.540	-0.790	0.032	0.000	0.883	0.811	0.982	0.775	0.572	0.760	0.745
-0	0.401	-1.731	0.765	0.974	1.648	0.652	-1.024	0.191	0.000	0.544	-0.366	-1.246	2.215	0.627	0.140	1.008	2.548	0.810	0.409	0.429	0.000	0.950	0.934	0.977	0.621	0.580	0.677	0.650
-1	0.391	1.679	-1.298	0.605	-0.832	0.549	1.338	0.522	2.173	1.244	0.884	1.070	0.000	1.002	0.846	-1.345	2.548	0.783	-2.464	-0.237	0.000	4.515	2.854	0.981	0.877	0.939	1.942	1.489
-1	0.513	-0.220	-0.444	1.699	0.479	1.109	0.181	-0.999	2.173	0.883	-0.335	-1.716	2.215	1.075	-0.380	1.352	0.000	0.857	0.048	0.147	0.000	0.937	0.758	0.986	1.206	0.958	0.949	0.876
-0	1.367	-0.388	0.798	1.158	1.078	0.811	-1.024	-1.628	0.000	1.504	0.097	-0.999	2.215	1.652	-0.860	0.054	2.548	0.573	-0.142	-1.401	0.000	0.869	0.833	1.006	1.412	1.641	1.214	1.041
-1	1.545	-0.533	-1.517	1.177	1.289	2.331	-0.370	-0.073	0.000	1.295	-0.358	-0.891	2.215	0.476	0.756	0.985	0.000	1.945	-0.016	-1.651	3.102	1.962	1.692	1.073	0.656	0.941	1.312	1.242
-0	0.858	0.978	-1.258	0.286	0.161	0.729	1.230	1.087	2.173	0.561	2.670	-0.109	0.000	0.407	2.346	0.938	0.000	1.078	0.729	-0.658	3.102	0.597	0.921	0.982	0.579	0.954	0.733	0.769
-1	1.454	-1.384	0.870	0.067	0.394	1.033	-0.673	0.318	0.000	1.166	-0.763	-1.533	2.215	2.848	-0.045	-0.856	2.548	0.697	-0.140	1.134	0.000	0.931	1.293	0.977	1.541	1.326	1.201	1.078
-1	0.559	-0.913	0.486	1.104	-0.321	1.073	-0.348	1.345	0.000	0.901	-0.827	-0.842	0.000	0.739	0.047	-0.415	2.548	0.433	-1.132	1.268	0.000	0.797	0.695	0.985	0.868	0.346	0.674	0.623
-1	1.333	0.780	-0.964	0.916	1.202	1.822	-0.071	0.742	2.173	1.486	-0.399	-0.824	0.000	0.740	0.568	-0.134	0.000	0.971	-0.070	-1.589	3.102	1.278	0.929	1.421	1.608	1.214	1.215	1.137
-1	2.417	0.631	-0.317	0.323	0.581	0.841	1.524	-1.738	0.000	0.543	1.176	-0.325	0.000	0.827	0.700	0.866	0.000	0.834	-0.262	-1.702	3.102	0.932	0.820	0.988	0.646	0.287	0.595	0.589
-0	0.955	-1.242	0.938	1.104	0.474	0.798	-0.743	1.535	0.000	1.356	-1.357	-1.080	2.215	1.320	-1.396	-0.132	2.548	0.728	-0.529	-0.633	0.000	0.832	0.841	0.988	0.923	1.077	0.988	0.816
-1	1.305	-1.918	0.391	1.161	0.063	0.724	2.593	1.481	0.000	0.592	-1.207	-0.329	0.000	0.886	-0.836	-1.168	2.548	1.067	-1.481	-1.440	0.000	0.916	0.688	0.991	0.969	0.550	0.665	0.638
-0	1.201	0.071	-1.123	2.242	-1.533	0.702	-0.256	0.688	0.000	0.967	0.491	1.040	2.215	1.271	-0.558	0.095	0.000	1.504	0.676	-0.383	3.102	0.917	1.006	0.985	1.017	1.057	0.928	1.057
-0	0.994	-1.607	1.596	0.774	-1.391	0.625	-0.134	-0.862	2.173	0.746	-0.765	-0.316	2.215	1.131	-0.320	0.869	0.000	0.607	0.826	0.301	0.000	0.798	0.967	0.999	0.880	0.581	0.712	0.774
-1	0.482	-0.467	0.729	1.419	1.458	0.824	0.376	-0.242	0.000	1.368	0.023	1.459	2.215	0.826	0.669	-1.079	2.548	0.936	2.215	-0.309	0.000	1.883	1.216	0.997	1.065	0.946	1.224	1.526
-1	0.383	1.588	1.611	0.748	1.194	0.866	-0.279	-0.636	0.000	0.707	0.536	0.801	2.215	1.647	-1.155	0.367	0.000	1.292	0.303	-1.681	3.102	2.016	1.581	0.986	0.584	0.684	1.107	0.958
-0	0.629	0.203	0.736	0.671	-0.271	1.350	-0.486	0.761	2.173	0.496	-0.805	-1.718	0.000	2.393	0.044	-1.046	1.274	0.651	-0.116	-0.541	0.000	0.697	1.006	0.987	1.069	2.317	1.152	0.902
-0	0.905	-0.564	-0.570	0.263	1.096	1.219	-1.397	-1.414	1.087	1.164	-0.533	-0.208	0.000	1.459	1.965	0.784	0.000	2.220	-1.421	0.452	0.000	0.918	1.360	0.993	0.904	0.389	2.118	1.707
-1	1.676	1.804	1.171	0.529	1.175	1.664	0.354	-0.530	0.000	1.004	0.691	-1.280	2.215	0.838	0.373	0.626	2.548	1.094	1.774	0.501	0.000	0.806	1.100	0.991	0.769	0.976	0.807	0.740
-1	1.364	-1.936	0.020	1.327	0.428	1.021	-1.665	-0.907	2.173	0.818	-2.701	1.303	0.000	0.716	-0.590	-1.629	2.548	0.895	-2.280	-1.602	0.000	1.211	0.849	0.989	1.320	0.864	1.065	0.949
-0	0.629	-0.626	0.609	1.828	1.280	0.644	-0.856	-0.873	2.173	0.555	1.066	-0.640	0.000	0.477	-1.364	-1.021	2.548	1.017	0.036	0.380	0.000	0.947	0.941	0.994	1.128	0.241	0.793	0.815
-1	1.152	-0.843	0.926	1.802	0.800	2.493	-1.449	-1.127	0.000	1.737	0.833	0.488	0.000	1.026	0.929	-0.990	2.548	1.408	0.689	1.142	3.102	1.171	0.956	0.993	2.009	0.867	1.499	1.474
-0	2.204	0.081	0.008	1.021	-0.679	2.676	0.090	1.163	0.000	2.210	-1.686	-1.195	0.000	1.805	0.891	-0.148	2.548	0.450	-0.502	-1.295	3.102	6.959	3.492	1.205	0.908	0.845	2.690	2.183
-1	0.957	0.954	1.702	0.043	-0.503	1.113	0.033	-0.308	0.000	0.757	-0.363	-1.129	2.215	1.635	0.068	1.048	1.274	0.415	-2.098	0.061	0.000	1.010	0.979	0.992	0.704	1.125	0.761	0.715
-0	1.222	0.418	1.059	1.303	1.442	0.282	-1.499	-1.286	0.000	1.567	0.016	-0.164	2.215	0.451	2.229	-1.229	0.000	0.660	-0.513	-0.296	3.102	2.284	1.340	0.985	1.531	0.314	1.032	1.094
-1	0.603	1.675	-0.973	0.703	-1.709	1.023	0.652	1.296	2.173	1.078	0.363	-0.263	0.000	0.734	-0.457	-0.745	1.274	0.561	1.434	-0.042	0.000	0.888	0.771	0.984	0.847	1.234	0.874	0.777
-0	0.897	0.949	-0.848	1.115	-0.085	0.522	-1.267	-1.418	0.000	0.684	-0.599	1.474	0.000	1.176	0.922	0.641	2.548	0.470	0.103	0.148	3.102	0.775	0.697	0.984	0.839	0.358	0.847	1.008
-1	0.987	1.013	-1.504	0.468	-0.259	1.160	0.476	-0.971	2.173	1.266	0.919	0.780	0.000	0.634	1.695	0.233	0.000	0.487	-0.082	0.719	3.102	0.921	0.641	0.991	0.730	0.828	0.952	0.807
-1	0.847	1.581	-1.397	1.629	1.529	1.053	0.816	-0.344	2.173	0.895	0.779	0.332	0.000	0.750	1.311	0.419	2.548	1.604	0.844	1.367	0.000	1.265	0.798	0.989	1.328	0.783	0.930	0.879
-1	0.805	1.416	-1.327	0.397	0.589	0.488	0.982	0.843	0.000	0.664	-0.999	0.129	0.000	0.624	0.613	-0.558	0.000	1.431	-0.667	-1.561	3.102	0.959	1.103	0.989	0.590	0.632	0.926	0.798
-0	1.220	-0.313	-0.489	1.759	0.201	1.698	-0.220	0.241	2.173	1.294	1.390	-1.682	0.000	1.447	-1.623	-1.296	0.000	1.710	0.872	-1.356	3.102	1.198	0.981	1.184	0.859	2.165	1.807	1.661
-0	0.772	-0.611	-0.549	0.465	-1.528	1.103	-0.140	0.001	2.173	0.854	-0.406	1.655	0.000	0.733	-1.250	1.072	0.000	0.883	0.627	-1.132	3.102	0.856	0.927	0.987	1.094	1.013	0.938	0.870
-1	1.910	0.771	0.828	0.231	1.267	1.398	1.455	-0.295	2.173	0.837	-2.564	0.770	0.000	0.540	2.189	1.287	0.000	1.345	1.311	-1.151	0.000	0.861	0.869	0.984	1.359	1.562	1.105	0.963
-1	0.295	0.832	1.399	1.222	-0.517	2.480	0.013	1.591	0.000	2.289	0.436	0.287	2.215	1.995	-0.367	-0.409	1.274	0.375	1.367	-1.716	0.000	1.356	2.171	0.990	1.467	1.664	1.855	1.705
-1	1.228	0.339	-0.575	0.417	1.474	0.480	-1.416	-1.498	2.173	0.614	-0.933	-0.961	0.000	1.189	1.690	1.003	0.000	1.690	-1.065	0.106	3.102	0.963	1.147	0.987	1.086	0.948	0.930	0.866
-0	2.877	-1.014	1.440	0.782	0.483	1.134	-0.735	-0.196	2.173	1.123	0.084	-0.596	0.000	1.796	-0.356	1.044	2.548	1.406	1.582	-0.991	0.000	0.939	1.178	1.576	0.996	1.629	1.216	1.280
-1	2.178	0.259	1.107	0.256	1.222	0.979	-0.440	-0.538	1.087	0.496	-0.760	-0.049	0.000	1.471	1.683	-1.486	0.000	0.646	0.695	-1.577	3.102	1.093	1.070	0.984	0.608	0.889	0.962	0.866
-1	0.604	0.592	1.295	0.964	0.348	1.178	-0.016	0.832	2.173	1.626	-0.420	-0.760	0.000	0.748	0.461	-0.906	0.000	0.728	0.309	-1.269	1.551	0.852	0.604	0.989	0.678	0.949	1.021	0.878
-0	0.428	-1.352	-0.912	1.713	0.797	1.894	-1.452	0.191	2.173	2.378	2.113	-1.190	0.000	0.860	2.174	0.949	0.000	1.693	0.759	1.426	3.102	0.885	1.527	1.186	1.090	3.294	4.492	3.676
-0	0.473	0.485	0.154	1.433	-1.504	0.766	1.257	-1.302	2.173	0.414	0.119	0.238	0.000	0.805	0.242	-0.691	2.548	0.734	0.749	0.753	0.000	0.430	0.893	1.137	0.686	0.724	0.618	0.608
-1	0.763	-0.601	0.876	0.182	-1.678	0.818	0.599	0.481	2.173	0.658	-0.737	-0.553	0.000	0.857	-1.138	-1.435	0.000	1.540	-1.466	-0.447	0.000	0.870	0.566	0.989	0.728	0.658	0.821	0.726
-0	0.619	-0.273	-0.143	0.992	-1.267	0.566	0.876	-1.396	2.173	0.515	0.892	0.618	0.000	0.434	-0.902	0.862	2.548	0.490	-0.539	0.549	0.000	0.568	0.794	0.984	0.667	0.867	0.597	0.578
-0	0.793	0.970	0.324	0.570	0.816	0.761	-0.550	1.519	2.173	1.150	0.496	-0.447	0.000	0.925	0.724	1.008	1.274	1.135	-0.275	-0.843	0.000	0.829	1.068	0.978	1.603	0.892	1.041	1.059
-1	0.480	0.364	-0.067	1.906	-1.582	1.397	1.159	0.140	0.000	0.639	0.398	-1.102	0.000	1.597	-0.668	1.607	2.548	1.306	-0.797	0.288	3.102	0.856	1.259	1.297	1.022	1.032	1.049	0.939
-0	0.514	1.304	1.490	1.741	-0.220	0.648	0.155	0.535	0.000	0.562	-1.016	0.837	0.000	0.863	-0.780	-0.815	2.548	1.688	-0.130	-1.545	3.102	0.887	0.980	1.309	1.269	0.654	1.044	1.035
-0	1.225	0.333	0.656	0.893	0.859	1.037	-0.876	1.603	1.087	1.769	0.272	-0.227	2.215	1.000	0.579	-1.690	0.000	1.385	0.471	-0.860	0.000	0.884	1.207	0.995	1.097	2.336	1.282	1.145
-0	2.044	-1.472	-0.294	0.392	0.369	0.927	0.718	1.492	1.087	1.619	-0.736	0.047	2.215	1.884	-0.101	-1.540	0.000	0.548	-0.441	1.117	0.000	0.798	0.877	0.981	0.750	2.272	1.469	1.276
-0	1.037	-0.276	0.735	3.526	1.156	2.498	0.401	-0.590	1.087	0.714	-1.203	1.393	2.215	0.681	0.629	1.534	0.000	0.719	-0.355	-0.706	0.000	0.831	0.857	0.988	2.864	2.633	1.988	1.466
-1	0.651	-1.218	-0.791	0.770	-1.449	0.610	-0.535	0.960	2.173	0.380	-1.072	-0.031	2.215	0.415	2.123	-1.100	0.000	0.776	0.217	0.420	0.000	0.986	1.008	1.001	0.853	0.588	0.799	0.776
-0	1.586	-0.409	0.085	3.258	0.405	1.647	-0.674	-1.519	0.000	0.640	-1.027	-1.681	0.000	1.452	-0.444	-0.957	2.548	0.927	-0.017	1.215	3.102	0.519	0.866	0.992	0.881	0.847	1.018	1.278
-0	0.712	0.092	-0.466	0.688	1.236	0.921	-1.217	-1.022	2.173	2.236	-1.167	0.868	2.215	0.851	-1.892	-0.753	0.000	0.475	-1.216	-0.383	0.000	0.668	0.758	0.988	1.180	2.093	1.157	0.934
-0	0.419	0.471	0.974	2.805	0.235	1.473	-0.198	1.255	1.087	0.931	1.083	-0.712	0.000	1.569	1.358	-1.179	2.548	2.506	0.199	-0.842	0.000	0.929	0.991	0.992	1.732	2.367	1.549	1.430
-1	0.667	1.003	1.504	0.368	1.061	0.885	-0.318	-0.353	0.000	1.438	-1.939	0.710	0.000	1.851	0.277	-1.460	2.548	1.403	0.517	-0.157	0.000	0.883	1.019	1.000	0.790	0.859	0.938	0.841
-1	1.877	-0.492	0.372	0.441	0.955	1.034	-1.220	-0.846	1.087	0.952	-0.320	1.125	0.000	0.542	0.308	-1.261	2.548	1.018	-1.415	-1.547	0.000	1.280	0.932	0.991	1.273	0.878	0.921	0.906
-0	1.052	0.901	1.176	1.280	1.517	0.562	-1.150	-0.079	2.173	1.228	-0.308	-0.354	0.000	0.790	-1.492	-0.963	0.000	0.942	-0.672	-1.588	3.102	1.116	0.902	0.988	1.993	0.765	1.375	1.325
-1	0.518	-0.254	1.642	0.865	0.725	0.980	0.734	0.023	0.000	1.448	0.780	-1.736	2.215	0.955	0.513	-0.519	0.000	0.365	-0.444	-0.243	3.102	0.833	0.555	0.984	0.827	0.795	0.890	0.786
-0	0.870	0.815	-0.506	0.663	-0.518	0.935	0.289	-1.675	2.173	1.188	0.005	0.635	0.000	0.580	0.066	-1.455	2.548	0.580	-0.634	-0.199	0.000	0.852	0.788	0.979	1.283	0.208	0.856	0.950
-0	0.628	1.382	0.135	0.683	0.571	1.097	0.564	-0.950	2.173	0.617	-0.326	0.371	0.000	1.093	0.918	1.667	2.548	0.460	1.221	0.708	0.000	0.743	0.861	0.975	1.067	1.007	0.843	0.762
-0	4.357	0.816	-1.609	1.845	-1.288	3.292	0.726	0.324	2.173	1.528	0.583	-0.801	2.215	0.605	0.572	1.406	0.000	0.794	-0.791	0.122	0.000	0.967	1.132	1.124	3.602	2.811	2.460	1.861
-0	0.677	-1.265	1.559	0.866	-0.618	0.823	0.260	0.185	0.000	1.133	0.337	1.589	2.215	0.563	-0.830	0.510	0.000	0.777	0.117	-0.941	3.102	0.839	0.763	0.986	1.182	0.649	0.796	0.851
-0	2.466	-1.838	-1.648	1.717	1.533	1.676	-1.553	-0.109	2.173	0.670	-0.666	0.284	0.000	0.334	-2.480	0.316	0.000	0.366	-0.804	-1.298	3.102	0.875	0.894	0.997	0.548	0.770	1.302	1.079
-1	1.403	0.129	-1.307	0.688	0.306	0.579	0.753	0.814	1.087	0.474	0.694	-1.400	0.000	0.520	1.995	0.185	0.000	0.929	-0.504	1.270	3.102	0.972	0.998	1.353	0.948	0.650	0.688	0.724
-1	0.351	1.188	-0.360	0.254	-0.346	1.129	0.545	1.691	0.000	0.652	-0.039	-0.258	2.215	1.089	0.655	0.472	2.548	0.554	-0.493	1.366	0.000	0.808	1.045	0.992	0.570	0.649	0.809	0.744
-0	1.875	-0.013	-0.128	0.236	1.163	0.902	0.426	0.590	2.173	1.251	-1.210	-0.616	0.000	1.035	1.534	0.912	0.000	1.944	1.789	-1.691	0.000	0.974	1.113	0.990	0.925	1.120	0.956	0.912
-0	0.298	0.750	-0.507	1.555	1.463	0.804	1.200	-0.665	0.000	0.439	-0.829	-0.252	1.107	0.770	-1.090	0.947	2.548	1.165	-0.166	-0.763	0.000	1.140	0.997	0.988	1.330	0.555	1.005	1.012
-0	0.647	0.342	0.245	4.340	-0.157	2.229	0.068	1.170	2.173	2.133	-0.201	-1.441	0.000	1.467	0.697	-0.532	1.274	1.457	0.583	-1.640	0.000	0.875	1.417	0.976	2.512	2.390	1.794	1.665
-1	1.731	-0.803	-1.013	1.492	-0.020	1.646	-0.541	1.121	2.173	0.459	-1.251	-1.495	2.215	0.605	-1.711	-0.232	0.000	0.658	0.634	-0.068	0.000	1.214	0.886	1.738	1.833	1.024	1.192	1.034
-0	0.515	1.416	-1.089	1.697	1.426	1.414	0.941	0.027	0.000	1.480	0.133	-1.595	2.215	1.110	0.752	0.760	2.548	1.062	0.697	-0.492	0.000	0.851	0.955	0.994	1.105	1.255	1.175	1.095
-0	1.261	0.858	1.465	0.757	0.305	2.310	0.679	1.080	2.173	1.544	2.518	-0.464	0.000	2.326	0.270	-0.841	0.000	2.163	0.839	-0.500	3.102	0.715	0.825	1.170	0.980	2.371	1.527	1.221
-1	1.445	1.509	1.471	0.414	-1.285	0.767	0.864	-0.677	2.173	0.524	1.388	0.171	0.000	0.826	0.190	0.121	2.548	0.572	1.691	-1.603	0.000	0.870	0.935	0.994	0.968	0.735	0.783	0.777
-1	0.919	-0.264	-1.245	0.681	-1.722	1.022	1.010	0.097	2.173	0.685	0.403	-1.351	0.000	1.357	-0.429	1.262	1.274	0.687	1.021	-0.563	0.000	0.953	0.796	0.991	0.873	1.749	1.056	0.917
-1	0.293	-2.258	-1.427	1.191	1.202	0.394	-2.030	1.438	0.000	0.723	0.596	-0.024	2.215	0.525	-1.678	-0.290	0.000	0.788	-0.824	-1.029	3.102	0.821	0.626	0.976	1.080	0.810	0.842	0.771
-0	3.286	0.386	1.688	1.619	-1.620	1.392	-0.009	0.280	0.000	1.179	-0.776	-0.110	2.215	1.256	0.248	-1.114	2.548	0.777	0.825	-0.156	0.000	1.026	1.065	0.964	0.909	1.249	1.384	1.395
-1	1.075	0.603	0.561	0.656	-0.685	0.985	0.175	0.979	2.173	1.154	0.584	-0.886	0.000	1.084	-0.354	-1.004	2.548	0.865	1.224	1.269	0.000	1.346	1.073	1.048	0.873	1.310	1.003	0.865
-1	1.098	-0.091	1.466	1.558	0.915	0.649	1.314	-1.182	2.173	0.791	0.073	0.351	0.000	0.517	0.940	1.195	0.000	1.150	1.187	-0.692	3.102	0.866	0.822	0.980	1.311	0.394	1.119	0.890
-1	0.481	-1.042	0.148	1.135	-1.249	1.202	-0.344	0.308	1.087	0.779	-1.431	1.581	0.000	0.860	-0.860	-1.125	0.000	0.785	0.303	1.199	3.102	0.878	0.853	0.988	1.072	0.827	0.936	0.815
-0	1.348	0.497	0.318	0.806	0.976	1.393	-0.152	0.632	2.173	2.130	0.515	-1.054	0.000	0.908	0.062	-0.780	0.000	1.185	0.687	1.668	1.551	0.720	0.898	0.985	0.683	1.292	1.320	1.131
-0	2.677	-0.420	-1.685	1.828	1.433	2.040	-0.718	-0.039	0.000	0.400	-0.873	0.472	0.000	0.444	0.340	-0.830	2.548	0.431	0.768	-1.417	3.102	0.869	0.917	0.996	0.707	0.193	0.728	1.154
-1	1.300	0.586	-0.122	1.306	0.609	0.727	-0.556	-1.652	2.173	0.636	0.720	1.393	2.215	0.328	1.280	-0.390	0.000	0.386	0.752	-0.905	0.000	0.202	0.751	1.106	0.864	0.799	0.928	0.717
-0	0.637	-0.176	1.737	1.322	-0.414	0.702	-0.964	-0.680	0.000	1.054	-0.461	0.889	2.215	0.861	-0.267	0.225	0.000	1.910	-1.888	1.027	0.000	0.919	0.899	1.186	0.993	1.109	0.862	0.775
-1	0.723	-0.104	1.572	0.428	-0.840	0.655	0.544	1.401	2.173	1.522	-0.154	-0.452	2.215	0.996	0.190	0.273	0.000	1.906	-0.176	0.966	0.000	0.945	0.894	0.990	0.981	1.555	0.988	0.893
-0	2.016	-0.570	1.612	0.798	0.441	0.334	0.191	-0.909	0.000	0.939	0.146	0.021	2.215	0.553	-0.444	1.156	2.548	0.781	-1.545	-0.520	0.000	0.922	0.956	1.528	0.722	0.699	0.778	0.901
-0	1.352	-0.707	1.284	0.665	0.580	0.694	-1.040	-0.899	2.173	0.692	-2.048	0.029	0.000	0.545	-2.042	1.259	0.000	0.661	-0.808	-1.251	3.102	0.845	0.991	0.979	0.662	0.225	0.685	0.769
-1	1.057	-1.561	-0.411	0.952	-0.681	1.236	-1.107	1.045	2.173	1.288	-2.521	-0.521	0.000	1.361	-1.239	1.546	0.000	0.373	-1.540	0.028	0.000	0.794	0.782	0.987	0.889	0.832	0.972	0.828
-0	1.118	-0.017	-1.227	1.077	1.256	0.714	0.624	-0.811	0.000	0.800	0.704	0.387	1.107	0.604	0.234	0.986	0.000	1.306	-0.456	0.094	3.102	0.828	0.984	1.195	0.987	0.672	0.774	0.748
-1	0.602	2.201	0.212	0.119	0.182	0.474	2.130	1.270	0.000	0.370	2.088	-0.573	0.000	0.780	-0.725	-1.033	0.000	1.642	0.598	0.303	3.102	0.886	0.988	0.985	0.644	0.756	0.651	0.599
-0	1.677	-0.844	1.581	0.585	0.887	1.012	-2.315	0.752	0.000	1.077	0.748	-0.195	0.000	0.718	0.832	-1.337	1.274	1.181	-0.557	-1.006	3.102	1.018	1.247	0.988	0.908	0.651	1.311	1.120
-1	1.695	0.259	1.224	1.344	1.067	0.718	-1.752	-0.215	0.000	0.473	0.991	-0.993	0.000	0.891	1.285	-1.500	2.548	0.908	-0.131	0.288	0.000	0.945	0.824	0.979	1.009	0.951	0.934	0.833
-0	0.793	0.628	0.432	1.707	0.302	0.919	1.045	-0.784	0.000	1.472	0.175	-1.284	2.215	1.569	0.155	0.971	2.548	0.435	0.735	1.625	0.000	0.801	0.907	0.992	0.831	1.446	1.082	1.051
-1	0.537	-0.664	-0.244	1.104	1.272	1.154	0.394	1.633	0.000	1.527	0.963	0.559	2.215	1.744	0.650	-0.912	0.000	1.097	0.730	-0.368	3.102	1.953	1.319	1.045	1.309	0.869	1.196	1.126
-1	0.585	-1.469	1.005	0.749	-1.060	1.224	-0.717	-0.323	2.173	1.012	-0.201	1.268	0.000	0.359	-0.567	0.476	0.000	1.117	-1.124	1.557	3.102	0.636	1.281	0.986	0.616	1.289	0.890	0.881
-1	0.354	-1.517	0.667	2.534	-1.298	1.020	-0.375	1.254	0.000	1.119	-0.060	-1.538	2.215	1.059	-0.395	-0.140	0.000	2.609	0.199	-0.778	1.551	0.957	0.975	1.286	1.666	1.003	1.224	1.135
-1	0.691	-1.619	-1.380	0.361	1.727	1.493	-1.093	-0.289	0.000	1.447	-0.640	1.341	0.000	1.453	-0.617	-1.456	1.274	1.061	-1.481	-0.091	0.000	0.744	0.649	0.987	0.596	0.727	0.856	0.797
-0	1.336	1.293	-1.359	0.357	0.067	1.110	-0.058	-0.515	0.000	0.976	1.498	1.207	0.000	1.133	0.437	1.053	2.548	0.543	1.374	0.171	0.000	0.764	0.761	0.984	0.827	0.553	0.607	0.612
-0	0.417	-1.111	1.661	2.209	-0.683	1.931	-0.642	0.959	1.087	1.514	-2.032	-0.686	0.000	1.521	-0.539	1.344	0.000	0.978	-0.866	0.363	1.551	2.813	1.850	1.140	1.854	0.799	1.600	1.556
-0	1.058	0.390	-0.591	0.134	1.149	0.346	-1.550	0.186	0.000	1.108	-0.999	0.843	1.107	1.124	0.415	-1.514	0.000	1.067	-0.426	-1.000	3.102	1.744	1.050	0.985	1.006	1.010	0.883	0.789
-1	1.655	0.253	1.216	0.270	1.703	0.500	-0.006	-1.418	2.173	0.690	-0.350	0.170	2.215	1.045	-0.924	-0.774	0.000	0.996	-0.745	-0.123	0.000	0.839	0.820	0.993	0.921	0.869	0.725	0.708
-0	1.603	-0.850	0.564	0.829	0.093	1.270	-1.113	-1.155	2.173	0.853	-1.021	1.248	2.215	0.617	-1.270	1.733	0.000	0.935	-0.092	0.136	0.000	1.011	1.074	0.977	0.823	1.269	1.054	0.878
-0	1.568	-0.792	1.005	0.545	0.896	0.895	-1.698	-0.988	0.000	0.608	-1.634	1.705	0.000	0.826	0.208	0.618	1.274	2.063	-1.743	-0.520	0.000	0.939	0.986	0.990	0.600	0.435	1.033	1.087
-0	0.489	-1.335	-1.102	1.738	1.028	0.628	-0.992	-0.627	0.000	0.652	-0.064	-0.215	0.000	1.072	0.173	-1.251	2.548	1.042	0.057	0.841	3.102	0.823	0.895	1.200	1.164	0.770	0.837	0.846
-1	1.876	0.870	1.234	0.556	-1.262	1.764	0.855	-0.467	2.173	1.079	1.351	0.852	0.000	0.773	0.383	0.874	0.000	1.292	0.829	-1.228	3.102	0.707	0.969	1.102	1.601	1.017	1.112	1.028
-0	1.033	0.407	-0.374	0.705	-1.254	0.690	-0.231	1.502	2.173	0.433	-2.009	-0.057	0.000	0.861	1.151	0.334	0.000	0.960	-0.839	1.299	3.102	2.411	1.480	0.982	0.995	0.377	1.012	0.994
-0	1.092	0.653	-0.801	0.463	0.426	0.529	-1.055	0.040	0.000	0.663	0.999	1.255	1.107	0.749	-1.106	1.185	2.548	0.841	-0.745	-1.029	0.000	0.841	0.743	0.988	0.750	1.028	0.831	0.868
-1	0.799	-0.285	-0.011	0.531	1.392	1.063	0.854	0.494	2.173	1.187	-1.065	-0.851	0.000	0.429	-0.296	1.072	0.000	0.942	-1.985	1.172	0.000	0.873	0.693	0.992	0.819	0.689	1.131	0.913
-0	0.503	1.973	-0.377	1.515	-1.514	0.708	1.081	-0.313	2.173	1.110	-0.417	0.839	0.000	0.712	-1.153	1.165	0.000	0.675	-0.303	-0.930	1.551	0.709	0.761	1.032	0.986	0.698	0.963	1.291
-0	0.690	-0.574	-1.608	1.182	1.118	0.557	-2.243	0.144	0.000	0.969	0.216	-1.383	1.107	1.054	0.888	-0.709	2.548	0.566	1.663	-0.550	0.000	0.752	1.528	0.987	1.408	0.740	1.290	1.123
-1	0.890	1.501	0.786	0.779	-0.615	1.126	0.716	1.541	2.173	0.887	0.728	-0.673	2.215	1.216	0.332	-0.020	0.000	0.965	1.828	0.101	0.000	0.827	0.715	1.099	1.088	1.339	0.924	0.878
-0	0.566	0.883	0.655	1.600	0.034	1.155	2.028	-1.499	0.000	0.723	-0.871	0.763	0.000	1.286	-0.696	-0.676	2.548	1.134	-0.113	1.207	3.102	4.366	2.493	0.984	0.960	0.962	1.843	1.511
-0	1.146	1.086	-0.911	0.838	1.298	0.821	0.127	-0.145	0.000	1.352	0.474	-1.580	2.215	1.619	-0.081	0.675	2.548	1.382	-0.748	0.127	0.000	0.958	0.976	1.239	0.876	1.481	1.116	1.076
-0	1.739	-0.326	-1.661	0.420	-1.705	1.193	-0.031	-1.212	2.173	1.783	-0.442	0.522	0.000	1.064	-0.692	0.027	0.000	1.314	0.359	-0.037	3.102	0.968	0.897	0.986	0.907	1.196	1.175	1.112
-1	0.669	0.194	-0.703	0.657	-0.260	0.899	-2.511	0.311	0.000	1.482	0.773	0.974	2.215	3.459	0.037	-1.299	1.274	2.113	0.067	1.516	0.000	0.740	0.871	0.979	1.361	2.330	1.322	1.046
-1	1.355	-1.033	-1.173	0.552	-0.048	0.899	-0.482	-1.287	2.173	1.422	-1.227	0.390	1.107	1.937	-0.028	0.914	0.000	0.849	-0.230	-1.734	0.000	0.986	1.224	1.017	1.051	1.788	1.150	1.009
-1	0.511	-0.202	1.029	0.780	1.154	0.816	0.532	-0.731	0.000	0.757	0.517	0.749	2.215	1.302	0.289	-1.188	0.000	0.584	1.211	-0.350	0.000	0.876	0.943	0.995	0.963	0.256	0.808	0.891
-1	1.109	0.572	1.484	0.753	1.543	1.711	-0.145	-0.746	1.087	1.759	0.631	0.845	2.215	0.945	0.542	0.003	0.000	0.378	-1.150	-0.044	0.000	0.764	1.042	0.992	1.045	2.736	1.441	1.140
-0	0.712	-0.025	0.553	0.928	-0.711	1.304	0.045	-0.300	0.000	0.477	0.720	0.969	0.000	1.727	-0.474	1.328	1.274	1.282	2.222	1.684	0.000	0.819	0.765	1.023	0.961	0.657	0.799	0.744
-1	1.131	-0.302	1.079	0.901	0.236	0.904	-0.249	1.694	2.173	1.507	-0.702	-1.128	0.000	0.774	0.565	0.284	2.548	1.802	1.446	-0.192	0.000	3.720	2.108	0.986	0.930	1.101	1.484	1.238
-0	1.392	1.253	0.118	0.864	-1.358	0.922	-0.447	-1.243	1.087	1.969	1.031	0.774	2.215	1.333	-0.359	-0.681	0.000	1.099	-0.257	1.473	0.000	1.246	0.909	1.475	1.234	2.531	1.449	1.306
-0	1.374	2.291	-0.479	1.339	-0.243	0.687	2.345	1.310	0.000	0.467	1.081	0.772	0.000	0.656	1.155	-1.636	2.548	0.592	0.536	-1.269	3.102	0.981	0.821	1.010	0.877	0.217	0.638	0.758
-1	0.401	-1.516	0.909	2.738	0.519	0.887	0.566	-1.202	0.000	0.909	-0.176	1.682	0.000	2.149	-0.878	-0.514	2.548	0.929	-0.563	-1.555	3.102	1.228	0.803	0.980	1.382	0.884	1.025	1.172
-1	0.430	-1.589	1.417	2.158	1.226	1.180	-0.829	-0.781	2.173	0.798	1.400	-0.111	0.000	0.939	-0.878	1.076	2.548	0.576	1.335	-0.826	0.000	0.861	0.970	0.982	1.489	1.308	1.015	0.992
-1	1.943	-0.391	-0.840	0.621	-1.613	2.026	1.734	1.025	0.000	0.930	0.573	-0.912	0.000	1.326	0.847	-0.220	1.274	1.181	0.079	0.709	3.102	1.164	1.007	0.987	1.094	0.821	0.857	0.786
-1	0.499	0.436	0.887	0.859	1.509	0.733	-0.559	1.111	1.087	1.011	-0.796	0.279	2.215	1.472	-0.510	-0.982	0.000	1.952	0.379	-0.733	0.000	1.076	1.358	0.991	0.589	0.879	1.068	0.922
-0	0.998	-0.407	-1.711	0.139	0.652	0.810	-0.331	-0.721	0.000	0.471	-0.533	0.442	0.000	0.531	-1.405	0.120	2.548	0.707	0.098	-1.176	1.551	1.145	0.809	0.988	0.529	0.612	0.562	0.609
-1	1.482	0.872	0.638	1.288	0.362	0.856	0.900	-0.511	1.087	1.072	1.061	-1.432	2.215	1.770	-2.292	-1.547	0.000	1.131	1.374	0.783	0.000	6.316	4.381	1.002	1.317	1.048	2.903	2.351
-1	2.084	-0.422	1.289	1.125	0.735	1.104	-0.518	-0.326	2.173	0.413	-0.719	-0.699	0.000	0.857	0.108	-1.631	0.000	0.527	0.641	-1.362	3.102	0.791	0.952	1.016	0.776	0.856	0.987	0.836
-0	0.464	0.674	0.025	0.430	-1.703	0.982	-1.311	-0.808	2.173	1.875	1.060	0.821	2.215	0.954	-0.480	-1.677	0.000	0.567	0.702	-0.939	0.000	0.781	1.076	0.989	1.256	3.632	1.652	1.252
-1	0.457	-1.944	-1.010	1.409	0.931	1.098	-0.742	-0.415	0.000	1.537	-0.834	0.945	2.215	1.752	-0.287	-1.269	2.548	0.692	-1.537	-0.223	0.000	0.801	1.192	1.094	1.006	1.659	1.175	1.122
-0	3.260	-0.943	1.737	0.920	1.309	0.946	-0.139	-0.271	2.173	0.994	-0.952	-0.311	0.000	0.563	-0.136	-0.881	0.000	1.236	-0.507	0.906	1.551	0.747	0.869	0.985	1.769	1.034	1.179	1.042
-0	0.615	-0.778	0.246	1.861	1.619	0.560	-0.943	-0.204	2.173	0.550	-0.759	-1.342	2.215	0.578	0.076	-0.973	0.000	0.939	0.035	0.680	0.000	0.810	0.747	1.401	0.772	0.702	0.719	0.662
-1	2.370	-0.064	-0.237	1.737	0.154	2.319	-1.838	-1.673	0.000	1.053	-1.305	-0.075	0.000	0.925	0.149	0.318	1.274	0.851	-0.922	0.981	3.102	0.919	0.940	0.989	0.612	0.598	1.219	1.626
-1	1.486	0.311	-1.262	1.354	-0.847	0.886	-0.158	1.213	2.173	1.160	-0.218	0.239	0.000	1.166	0.494	0.278	2.548	0.575	1.454	-1.701	0.000	0.429	1.129	0.983	1.111	1.049	1.006	0.920
-1	1.294	1.587	-0.864	0.487	-0.312	0.828	1.051	-0.031	1.087	2.443	1.216	1.609	2.215	1.167	0.813	0.921	0.000	1.751	-0.415	0.119	0.000	1.015	1.091	0.974	1.357	2.093	1.178	1.059
-1	0.984	0.465	-1.661	0.379	-0.554	0.977	0.237	0.365	0.000	0.510	0.143	1.101	0.000	1.099	-0.662	-1.593	2.548	1.104	-0.197	-0.648	3.102	0.925	0.922	0.986	0.642	0.667	0.806	0.722
-1	0.930	-0.009	0.047	0.667	1.367	1.065	-0.231	0.815	0.000	1.199	-1.114	-0.877	2.215	0.940	0.824	-1.583	0.000	1.052	-0.407	-0.076	1.551	1.843	1.257	1.013	1.047	0.751	1.158	0.941
-0	0.767	-0.011	-0.637	0.341	-1.437	1.438	-0.425	-0.450	2.173	1.073	-0.718	1.341	2.215	0.633	-1.394	0.486	0.000	0.603	-1.945	-1.626	0.000	0.703	0.790	0.984	1.111	1.848	1.129	1.072
-1	1.779	0.017	0.432	0.402	1.022	0.959	1.480	1.595	2.173	1.252	1.365	0.006	0.000	1.188	-0.174	-1.107	0.000	1.181	0.518	-0.258	0.000	1.057	0.910	0.991	1.616	0.779	1.158	1.053
-0	0.881	0.630	1.029	1.990	0.508	1.102	0.742	-1.298	2.173	1.565	1.085	0.686	2.215	2.691	1.391	-0.904	0.000	0.499	1.388	-1.199	0.000	0.347	0.861	0.997	0.881	1.920	1.233	1.310
-0	1.754	-0.266	0.389	0.347	-0.030	0.462	-1.408	-0.957	2.173	0.515	-2.341	-1.700	0.000	0.588	-0.797	1.355	2.548	0.608	0.329	-1.389	0.000	1.406	0.909	0.988	0.760	0.593	0.768	0.847
-0	1.087	0.311	-1.447	0.173	0.567	0.854	0.362	0.584	0.000	1.416	-0.716	-1.211	2.215	0.648	-0.358	-0.692	1.274	0.867	-0.513	0.206	0.000	0.803	0.813	0.984	1.110	0.491	0.921	0.873
-0	0.279	1.114	-1.190	3.004	-0.738	1.233	0.896	1.092	2.173	0.454	-0.374	0.117	2.215	0.357	0.119	1.270	0.000	0.458	1.343	0.316	0.000	0.495	0.540	0.988	1.715	1.139	1.618	1.183
-1	1.773	-0.694	-1.518	2.306	-1.200	3.104	0.749	0.362	0.000	1.871	0.230	-1.686	2.215	0.805	-0.179	-0.871	1.274	0.910	0.607	-0.246	0.000	1.338	1.598	0.984	1.050	0.919	1.678	1.807
-0	0.553	0.683	0.827	0.973	-0.706	1.488	0.149	1.140	2.173	1.788	0.447	-0.478	0.000	0.596	1.043	1.607	0.000	0.373	-0.868	-1.308	1.551	1.607	1.026	0.998	1.134	0.808	1.142	0.936
-1	0.397	1.101	-1.139	1.688	0.146	0.972	0.541	1.518	0.000	1.549	-0.873	-1.012	0.000	2.282	-0.151	0.314	2.548	1.174	0.033	-1.368	0.000	0.937	0.776	1.039	1.143	0.959	0.986	1.013
-1	0.840	1.906	-0.959	0.869	0.576	0.642	0.554	-1.351	0.000	0.756	0.923	-0.823	2.215	1.251	1.130	0.545	2.548	1.513	0.410	1.073	0.000	1.231	0.985	1.163	0.812	0.987	0.816	0.822
-1	0.477	1.665	0.814	0.763	-0.382	0.828	-0.008	0.280	2.173	1.213	-0.001	1.560	0.000	1.136	0.311	-1.289	0.000	0.797	1.091	-0.616	3.102	1.026	0.964	0.992	0.772	0.869	0.916	0.803
-0	2.655	0.020	0.273	1.464	0.482	1.709	-0.107	-1.456	2.173	0.825	0.141	-0.386	0.000	1.342	-0.592	1.635	1.274	0.859	-0.175	-0.874	0.000	0.829	0.946	1.003	2.179	0.836	1.505	1.176
-0	0.771	-1.992	-0.720	0.732	-1.464	0.869	-1.290	0.388	2.173	0.926	-1.072	-1.489	2.215	0.640	-1.232	0.840	0.000	0.528	-2.440	-0.446	0.000	0.811	0.868	0.993	0.995	1.317	0.809	0.714
-0	1.357	1.302	0.076	0.283	-1.060	0.783	1.559	-0.994	0.000	0.947	1.212	1.617	0.000	1.127	0.311	0.442	2.548	0.582	-0.052	1.186	1.551	1.330	0.995	0.985	0.846	0.404	0.858	0.815
-0	0.442	-0.381	-0.424	1.244	0.591	0.731	0.605	-0.713	2.173	0.629	2.762	1.040	0.000	0.476	2.693	-0.617	0.000	0.399	0.442	1.486	3.102	0.839	0.755	0.988	0.869	0.524	0.877	0.918
-0	0.884	0.422	0.055	0.818	0.624	0.950	-0.763	1.624	0.000	0.818	-0.609	-1.166	0.000	1.057	-0.528	1.070	2.548	1.691	-0.124	-0.335	3.102	1.104	0.933	0.985	0.913	1.000	0.863	1.056
-0	1.276	0.156	1.714	1.053	-1.189	0.672	-0.464	-0.030	2.173	0.469	-2.483	0.442	0.000	0.564	2.580	-0.253	0.000	0.444	-0.628	1.080	1.551	5.832	2.983	0.985	1.162	0.494	1.809	1.513
-0	1.106	-0.556	0.406	0.573	-1.400	0.769	-0.518	1.457	2.173	0.743	-0.352	-0.010	0.000	1.469	-0.550	-0.930	2.548	0.540	1.236	-0.571	0.000	0.962	0.970	1.101	0.805	1.107	0.873	0.773
-0	0.539	-0.964	-0.464	1.371	-1.606	0.667	-0.160	0.655	0.000	0.952	0.352	-0.740	2.215	0.952	0.007	1.123	0.000	1.061	-0.505	1.389	3.102	1.063	0.991	1.019	0.633	0.967	0.732	0.799
-1	0.533	-0.989	-1.608	0.462	-1.723	1.204	-0.598	-0.098	2.173	1.343	-0.460	1.632	2.215	0.577	0.221	-0.492	0.000	0.628	-0.073	0.472	0.000	0.518	0.880	0.988	1.179	1.874	1.041	0.813
-1	1.024	1.075	-0.795	0.286	-1.436	1.365	0.857	-0.309	2.173	0.804	1.532	1.435	0.000	1.511	0.722	1.494	0.000	1.778	0.903	0.753	1.551	0.686	0.810	0.999	0.900	1.360	1.133	0.978
-1	2.085	-0.269	-1.423	0.789	1.298	0.281	1.652	0.187	0.000	0.658	-0.760	-0.042	2.215	0.663	0.024	0.120	0.000	0.552	-0.299	-0.428	3.102	0.713	0.811	1.130	0.705	0.218	0.675	0.743
-1	0.980	-0.443	0.813	0.785	-1.253	0.719	0.448	-1.458	0.000	1.087	0.595	0.635	1.107	1.428	0.029	-0.995	0.000	1.083	1.562	-0.092	0.000	0.834	0.891	1.165	0.967	0.661	0.880	0.817
-1	0.903	-0.733	-0.980	0.634	-0.639	0.780	0.266	-0.287	2.173	1.264	-0.936	1.004	0.000	1.002	-0.056	-1.344	2.548	1.183	-0.098	1.169	0.000	0.733	1.002	0.985	0.711	0.916	0.966	0.875
-0	0.734	-0.304	-1.175	2.851	1.674	0.904	-0.634	0.412	2.173	1.363	-1.050	-0.282	0.000	1.476	-1.603	0.103	0.000	2.231	-0.718	1.708	3.102	0.813	0.896	1.088	0.686	1.392	1.033	1.078
-1	1.680	0.591	-0.243	0.111	-0.478	0.326	-0.079	-1.555	2.173	0.711	0.714	0.922	2.215	0.355	0.858	1.682	0.000	0.727	1.620	1.360	0.000	0.334	0.526	1.001	0.862	0.633	0.660	0.619
-1	1.163	0.225	-0.202	0.501	-0.979	1.609	-0.938	1.424	0.000	1.224	-0.118	-1.274	0.000	2.034	1.241	-0.254	0.000	1.765	0.536	0.237	3.102	0.894	0.838	0.988	0.693	0.579	0.762	0.726
-0	1.223	1.232	1.471	0.489	1.728	0.703	-0.111	0.411	0.000	1.367	1.014	-1.294	1.107	1.524	-0.414	-0.164	2.548	1.292	0.833	0.316	0.000	0.861	0.752	0.994	0.836	1.814	1.089	0.950
-0	0.816	1.637	-1.557	1.036	-0.342	0.913	1.333	0.949	2.173	0.812	0.756	-0.628	2.215	1.333	0.470	1.495	0.000	1.204	-2.222	-1.675	0.000	1.013	0.924	1.133	0.758	1.304	0.855	0.860
-0	0.851	-0.564	-0.691	0.692	1.345	1.219	1.014	0.318	0.000	1.422	-0.262	-1.635	2.215	0.531	1.802	0.008	0.000	0.508	0.515	-1.267	3.102	0.821	0.787	1.026	0.783	0.432	1.149	1.034
-0	0.800	-0.599	0.204	0.552	-0.484	0.974	0.413	0.961	2.173	1.269	-0.984	-1.039	2.215	0.380	-1.213	1.371	0.000	0.551	0.332	-0.659	0.000	0.694	0.852	0.984	1.057	2.037	1.096	0.846
-0	0.744	-0.071	-0.255	0.638	0.512	1.125	0.407	0.844	2.173	0.860	-0.481	-0.677	0.000	1.102	0.181	-1.194	0.000	1.011	-1.081	-1.713	3.102	0.854	0.862	0.982	1.111	1.372	1.042	0.920
-1	0.400	1.049	-0.625	0.880	-0.407	1.040	2.150	-1.359	0.000	0.747	-0.144	0.847	2.215	0.560	-1.829	0.698	0.000	1.663	-0.668	0.267	0.000	0.845	0.964	0.996	0.820	0.789	0.668	0.668
-0	1.659	-0.705	-1.057	1.803	-1.436	1.008	0.693	0.005	0.000	0.895	-0.007	0.681	1.107	1.085	0.125	1.476	2.548	1.214	1.068	0.486	0.000	0.867	0.919	0.986	1.069	0.692	1.026	1.313
-0	0.829	-0.153	0.861	0.615	-0.548	0.589	1.077	-0.041	2.173	1.056	0.763	-1.737	0.000	0.639	0.970	0.725	0.000	0.955	1.227	-0.799	3.102	1.020	1.024	0.985	0.750	0.525	0.685	0.671
-1	0.920	-0.806	-0.840	1.048	0.278	0.973	-0.077	-1.364	2.173	1.029	0.309	0.133	0.000	1.444	1.484	1.618	1.274	1.419	-0.482	0.417	0.000	0.831	1.430	1.151	1.829	1.560	1.343	1.224
-1	0.686	0.249	-0.905	0.343	-1.731	0.724	-2.823	-0.901	0.000	0.982	0.303	1.312	1.107	1.016	0.245	0.610	0.000	1.303	-0.557	-0.360	3.102	1.384	1.030	0.984	0.862	1.144	0.866	0.779
-0	1.603	0.444	0.508	0.586	0.401	0.610	0.467	-1.735	2.173	0.914	0.626	-1.019	0.000	0.812	0.422	-0.408	2.548	0.902	1.679	1.490	0.000	1.265	0.929	0.990	1.004	0.816	0.753	0.851
-1	0.623	0.780	-0.203	0.056	0.015	0.899	0.793	1.326	1.087	0.803	1.478	-1.499	2.215	1.561	1.492	-0.120	0.000	0.904	0.795	0.137	0.000	0.548	1.009	0.850	0.924	0.838	0.914	0.860
-0	1.654	-2.032	-1.160	0.859	-1.583	0.689	-1.965	0.891	0.000	0.646	-1.014	-0.288	2.215	0.630	-0.815	0.402	0.000	0.638	0.316	0.655	3.102	0.845	0.879	0.993	1.067	0.625	1.041	0.958
-1	0.828	-1.269	-1.203	0.744	-0.213	0.626	-1.017	-0.404	0.000	1.281	-0.931	1.733	2.215	0.699	-0.351	1.287	0.000	1.251	-1.171	0.197	0.000	0.976	1.186	0.987	0.646	0.655	0.733	0.671
-1	0.677	0.111	1.090	1.580	1.591	1.560	0.654	-0.341	2.173	0.794	-0.266	0.702	0.000	0.823	0.651	-1.239	2.548	0.730	1.467	-1.530	0.000	1.492	1.023	0.983	1.909	1.022	1.265	1.127
-1	0.736	0.882	-1.060	0.589	0.168	1.663	0.781	1.022	2.173	2.025	1.648	-1.292	0.000	1.240	0.924	-0.421	1.274	1.354	0.065	0.501	0.000	0.316	0.925	0.988	0.664	1.736	0.992	0.807
-1	1.040	-0.822	1.638	0.974	-0.674	0.393	0.830	0.011	2.173	0.770	-0.140	-0.402	0.000	0.294	-0.133	0.030	0.000	1.220	0.807	0.638	0.000	0.826	1.063	1.216	1.026	0.705	0.934	0.823
-1	0.711	0.602	0.048	1.145	0.966	0.934	0.263	-1.589	2.173	0.971	-0.496	-0.421	1.107	0.628	-0.865	0.845	0.000	0.661	-0.008	-0.565	0.000	0.893	0.705	0.988	0.998	1.339	0.908	0.872
-1	0.953	-1.651	-0.167	0.885	1.053	1.013	-1.239	0.133	0.000	1.884	-1.122	1.222	2.215	1.906	-0.860	-1.184	1.274	1.413	-0.668	-1.647	0.000	1.873	1.510	1.133	1.050	1.678	1.246	1.061
-1	0.986	-0.892	-1.380	0.917	1.134	0.950	-1.162	-0.469	0.000	0.569	-1.393	0.215	0.000	0.320	2.667	1.712	0.000	1.570	-0.375	1.457	3.102	0.925	1.128	1.011	0.598	0.824	0.913	0.833
-1	1.067	0.099	1.154	0.527	-0.789	1.085	0.623	-1.602	2.173	1.511	-0.230	0.022	2.215	0.269	-0.377	0.883	0.000	0.571	-0.540	-0.512	0.000	0.414	0.803	1.022	0.959	2.053	1.041	0.780
-0	0.825	-2.118	0.217	1.453	-0.493	0.819	0.313	-0.942	0.000	2.098	-0.725	1.096	2.215	0.484	1.336	1.458	0.000	0.482	0.100	1.163	0.000	0.913	0.536	0.990	1.679	0.957	1.095	1.143
-1	1.507	0.054	1.120	0.698	-1.340	0.912	0.384	0.015	1.087	0.720	0.247	-0.820	0.000	0.286	0.154	1.578	2.548	0.629	1.582	-0.576	0.000	0.828	0.893	1.136	0.514	0.632	0.699	0.709
-1	0.610	1.180	-0.993	0.816	0.301	0.932	0.758	1.539	0.000	0.726	-0.830	0.248	2.215	0.883	0.857	-1.305	0.000	1.338	1.009	-0.252	3.102	0.901	1.074	0.987	0.875	1.159	1.035	0.858
-1	1.247	-1.360	1.502	1.525	-1.332	0.618	1.063	0.755	0.000	0.582	-0.155	0.473	2.215	1.214	-0.422	-0.551	2.548	0.838	-1.171	-1.166	0.000	2.051	1.215	1.062	1.091	0.725	0.896	1.091
-0	0.373	-0.600	1.291	2.573	0.207	0.765	-0.209	1.667	0.000	0.668	0.724	-1.499	0.000	1.045	-0.338	-0.754	2.548	0.558	-0.469	0.029	3.102	0.868	0.939	1.124	0.519	0.383	0.636	0.838
-0	0.791	0.336	-0.307	0.494	1.213	1.158	0.336	1.081	2.173	0.918	1.289	-0.449	0.000	0.735	-0.521	-0.969	0.000	1.052	0.499	-1.188	3.102	0.699	1.013	0.987	0.622	1.050	0.712	0.661
-0	1.321	0.856	0.464	0.202	0.901	1.144	0.120	-1.651	0.000	0.803	0.577	-0.509	2.215	0.695	-0.114	0.423	2.548	0.621	1.852	-0.420	0.000	0.697	0.964	0.983	0.527	0.659	0.719	0.729
-0	0.563	2.081	0.913	0.982	-0.533	0.549	-0.481	-1.730	0.000	0.962	0.921	0.569	2.215	0.731	1.184	-0.679	1.274	0.918	0.931	-1.432	0.000	1.008	0.919	0.993	0.895	0.819	0.810	0.878
-1	1.148	0.345	0.953	0.921	0.617	0.991	1.103	-0.484	0.000	0.970	1.978	1.525	0.000	1.150	0.689	-0.757	2.548	0.517	0.995	1.245	0.000	1.093	1.140	0.998	1.006	0.756	0.864	0.838
-1	1.400	0.128	-1.695	1.169	1.070	1.094	-0.345	-0.249	0.000	1.224	0.364	-0.036	2.215	1.178	0.530	-1.544	0.000	1.334	0.933	1.604	0.000	0.560	1.267	1.073	0.716	0.780	0.832	0.792
-0	0.330	-2.133	1.403	0.628	0.379	1.686	-0.995	0.030	1.087	2.071	0.127	-0.457	0.000	4.662	-0.855	1.477	0.000	2.072	-0.917	-1.416	3.102	5.403	3.074	0.977	0.936	1.910	2.325	1.702
-0	0.989	0.473	0.968	1.970	1.368	0.844	0.574	-0.290	2.173	0.866	-0.345	-1.019	0.000	1.130	0.605	-0.752	0.000	0.956	-0.888	0.870	3.102	0.885	0.886	0.982	1.157	1.201	1.100	1.068
-1	0.773	0.418	0.753	1.388	1.070	1.104	-0.378	-0.758	0.000	1.027	0.397	-0.496	2.215	1.234	0.027	1.084	2.548	0.936	0.209	1.677	0.000	1.355	1.020	0.983	0.550	1.206	0.916	0.931
-0	0.319	2.015	1.534	0.570	-1.134	0.632	0.124	0.757	0.000	0.477	0.598	-1.109	1.107	0.449	0.438	-0.755	2.548	0.574	-0.659	0.691	0.000	0.440	0.749	0.985	0.517	0.158	0.505	0.522
-0	1.215	1.453	-1.386	1.276	1.298	0.643	0.570	-0.196	2.173	0.588	2.104	0.498	0.000	0.617	-0.296	-0.801	2.548	0.452	0.110	0.313	0.000	0.815	0.953	1.141	1.166	0.547	0.892	0.807
-1	1.257	-1.869	-0.060	0.265	0.653	1.527	-0.346	1.163	2.173	0.758	-2.119	-0.604	0.000	1.473	-1.133	-1.290	2.548	0.477	-0.428	-0.066	0.000	0.818	0.841	0.984	1.446	1.729	1.211	1.054
-1	1.449	0.464	1.585	1.418	-1.488	1.540	0.942	0.087	0.000	0.898	0.402	-0.631	2.215	0.753	0.039	-1.729	0.000	0.859	0.849	-1.054	0.000	0.791	0.677	0.995	0.687	0.527	0.703	0.606
-1	1.084	-1.997	0.900	1.333	1.024	0.872	-0.864	-1.500	2.173	1.072	-0.813	-0.421	2.215	0.924	0.478	0.304	0.000	0.992	-0.398	-1.022	0.000	0.741	1.085	0.980	1.221	1.176	1.032	0.961
-0	1.712	1.129	0.125	1.120	-1.402	1.749	0.951	-1.575	2.173	1.711	0.445	0.578	0.000	1.114	0.234	-1.011	0.000	1.577	-0.088	0.086	3.102	2.108	1.312	1.882	1.597	2.009	1.441	1.308
-0	0.530	0.248	1.622	1.450	-1.012	1.221	-1.154	-0.763	2.173	1.698	-0.586	0.733	0.000	0.889	1.042	1.038	1.274	0.657	0.008	0.701	0.000	0.430	1.005	0.983	0.930	2.264	1.357	1.146
-1	0.921	1.735	0.883	0.699	-1.614	0.821	1.463	0.319	1.087	1.099	0.814	-1.600	2.215	1.375	0.702	-0.691	0.000	0.869	1.326	-0.790	0.000	0.980	0.900	0.988	0.832	1.452	0.816	0.709
-0	2.485	-0.823	-0.297	0.886	-1.404	0.989	0.835	1.615	2.173	0.382	0.588	-0.224	0.000	1.029	-0.456	1.546	2.548	0.613	-0.359	-0.789	0.000	0.768	0.977	1.726	2.007	0.913	1.338	1.180
-1	0.657	-0.069	-0.078	1.107	1.549	0.804	1.335	-1.630	2.173	1.271	0.481	0.153	1.107	1.028	0.144	-0.762	0.000	1.098	0.132	1.570	0.000	0.830	0.979	1.175	1.069	1.624	1.000	0.868
-1	2.032	0.329	-1.003	0.493	-0.136	1.159	-0.224	0.750	1.087	0.396	0.546	0.587	0.000	0.620	1.805	0.982	0.000	1.236	0.744	-1.621	0.000	0.930	1.200	0.988	0.482	0.771	0.887	0.779
-0	0.524	-1.319	0.634	0.471	1.221	0.599	-0.588	-0.461	0.000	1.230	-1.504	-1.517	1.107	1.436	-0.035	0.104	2.548	0.629	1.997	-1.282	0.000	2.084	1.450	0.984	1.084	1.827	1.547	1.213
-1	0.871	0.618	-1.544	0.718	0.186	1.041	-1.180	0.434	2.173	1.133	1.558	-1.301	0.000	0.452	-0.595	0.522	0.000	0.665	0.567	0.130	3.102	1.872	1.114	1.095	1.398	0.979	1.472	1.168
-1	3.308	1.037	-0.634	0.690	-0.619	1.975	0.949	1.280	0.000	0.826	0.546	-0.139	2.215	0.635	-0.045	0.427	0.000	1.224	0.112	1.339	3.102	1.756	1.050	0.992	0.738	0.903	0.968	1.238
-0	0.588	2.104	-0.872	1.136	1.743	0.842	0.638	0.015	0.000	0.481	0.928	1.000	2.215	0.595	0.125	1.429	0.000	0.951	-1.140	-0.511	3.102	1.031	1.057	0.979	0.673	1.064	1.001	0.891
-0	0.289	0.823	0.013	0.615	-1.601	0.177	2.403	-0.015	0.000	0.258	1.151	1.036	2.215	0.694	0.553	-1.326	2.548	0.411	0.366	0.106	0.000	0.482	0.562	0.989	0.670	0.404	0.516	0.561
-1	0.294	-0.660	-1.162	1.752	0.384	0.860	0.513	1.119	0.000	2.416	0.107	-1.342	0.000	1.398	0.361	-0.350	2.548	1.126	-0.902	0.040	1.551	0.650	1.125	0.988	0.531	0.843	0.912	0.911
-0	0.599	-0.616	1.526	1.381	0.507	0.955	-0.646	-0.085	2.173	0.775	-0.533	1.116	2.215	0.789	-0.136	-1.176	0.000	2.449	1.435	-1.433	0.000	1.692	1.699	1.000	0.869	1.119	1.508	1.303
-1	1.100	-1.174	-1.114	1.601	-1.576	1.056	-1.343	0.547	2.173	0.555	0.367	0.592	2.215	0.580	-1.862	-0.914	0.000	0.904	0.508	-0.444	0.000	1.439	1.105	0.986	1.408	1.104	1.190	1.094
-1	2.237	-0.701	1.470	0.719	-0.199	0.745	-0.132	-0.737	1.087	0.976	-0.227	0.093	2.215	0.699	0.057	1.133	0.000	0.661	0.573	-0.679	0.000	0.785	0.772	1.752	1.235	0.856	0.990	0.825
-1	0.455	-0.880	-1.482	1.260	-0.178	1.499	0.158	1.022	0.000	1.867	-0.435	-0.675	2.215	1.234	0.783	1.586	0.000	0.641	-0.454	-0.409	3.102	1.002	0.964	0.986	0.761	0.240	1.190	0.995
-1	1.158	-0.778	-0.159	0.823	1.641	1.341	-0.830	-1.169	2.173	0.840	-1.554	0.934	0.000	0.693	0.488	-1.218	2.548	1.042	1.395	0.276	0.000	0.946	0.785	1.350	1.079	0.893	1.267	1.151
-1	0.902	-0.078	-0.055	0.872	-0.012	0.843	1.276	1.739	2.173	0.838	1.492	0.918	0.000	0.626	0.904	-0.648	2.548	0.412	-2.027	-0.883	0.000	2.838	1.664	0.988	1.803	0.768	1.244	1.280
-1	0.649	-1.028	-1.521	1.097	0.774	1.216	-0.383	-0.318	2.173	1.643	-0.285	-1.705	0.000	0.911	-0.091	0.341	0.000	0.592	0.537	0.732	3.102	0.911	0.856	1.027	1.160	0.874	0.986	0.893
-1	1.192	1.846	-0.781	1.326	-0.747	1.550	1.177	1.366	0.000	1.196	0.151	0.387	2.215	0.527	2.261	-0.190	0.000	0.390	1.474	0.381	0.000	0.986	1.025	1.004	1.392	0.761	0.965	1.043
-0	0.438	-0.358	-1.549	0.836	0.436	0.818	0.276	-0.708	2.173	0.707	0.826	0.392	0.000	1.050	1.741	-1.066	0.000	1.276	-1.583	0.842	0.000	1.475	1.273	0.986	0.853	1.593	1.255	1.226
-1	1.083	0.142	1.701	0.605	-0.253	1.237	0.791	1.183	2.173	0.842	2.850	-0.082	0.000	0.724	-0.464	-0.694	0.000	1.499	0.456	-0.226	3.102	0.601	0.799	1.102	0.995	1.389	1.013	0.851
-0	0.828	1.897	-0.615	0.572	-0.545	0.572	0.461	0.464	2.173	0.393	0.356	1.069	2.215	1.840	0.088	1.500	0.000	0.407	-0.663	-0.787	0.000	0.950	0.965	0.979	0.733	0.363	0.618	0.733
-0	0.735	1.438	1.197	1.123	-0.214	0.641	0.949	0.858	0.000	1.162	0.524	-0.896	2.215	0.992	0.454	-1.475	2.548	0.902	1.079	0.019	0.000	0.822	0.917	1.203	1.032	0.569	0.780	0.764
-0	0.437	-2.102	0.044	1.779	-1.042	1.231	-0.181	-0.515	1.087	2.666	0.863	1.466	2.215	1.370	0.345	-1.371	0.000	0.906	0.363	1.611	0.000	1.140	1.362	1.013	3.931	3.004	2.724	2.028
-1	0.881	1.814	-0.987	0.384	0.800	2.384	1.422	0.640	0.000	1.528	0.292	-0.962	1.107	2.126	-0.371	-1.401	2.548	0.700	0.109	0.203	0.000	0.450	0.813	0.985	0.956	1.013	0.993	0.774
-1	0.630	0.408	0.152	0.194	0.316	0.710	-0.824	-0.358	2.173	0.741	0.535	-0.851	2.215	0.933	0.406	1.148	0.000	0.523	-0.479	-0.625	0.000	0.873	0.960	0.988	0.830	0.921	0.711	0.661
-1	0.870	-0.448	-1.134	0.616	0.135	0.600	0.649	-0.622	2.173	0.768	0.709	-0.123	0.000	1.308	0.500	1.468	0.000	1.973	-0.286	1.462	3.102	0.909	0.944	0.990	0.835	1.250	0.798	0.776
-0	1.290	0.552	1.330	0.615	-1.353	0.661	0.240	-0.393	0.000	0.531	0.053	-1.588	0.000	0.675	0.839	-0.345	1.274	1.597	0.020	0.536	3.102	1.114	0.964	0.987	0.783	0.675	0.662	0.675
-1	0.943	0.936	1.068	1.373	0.671	2.170	-2.011	-1.032	0.000	0.640	0.361	-0.806	0.000	2.239	-0.083	0.590	2.548	1.224	0.646	-1.723	0.000	0.879	0.834	0.981	1.436	0.568	0.916	0.931
-1	0.431	1.686	-1.053	0.388	1.739	0.457	-0.471	-0.743	2.173	0.786	1.432	-0.547	2.215	0.537	-0.413	1.256	0.000	0.413	2.311	-0.408	0.000	1.355	1.017	0.982	0.689	1.014	0.821	0.715
-0	1.620	-0.055	-0.862	1.341	-1.571	0.634	-0.906	0.935	2.173	0.501	-2.198	-0.525	0.000	0.778	-0.708	-0.060	0.000	0.988	-0.621	0.489	3.102	0.870	0.956	1.216	0.992	0.336	0.871	0.889
-1	0.549	0.304	-1.443	1.309	-0.312	1.116	0.644	1.519	2.173	1.078	-0.303	-0.736	0.000	1.261	0.387	0.628	2.548	0.945	-0.190	0.090	0.000	0.893	1.043	1.000	1.124	1.077	1.026	0.886
-0	0.412	-0.618	-1.486	1.133	-0.665	0.646	0.436	1.520	0.000	0.993	0.976	0.106	2.215	0.832	0.091	0.164	2.548	0.672	-0.650	1.256	0.000	0.695	1.131	0.991	1.017	0.455	1.226	1.087
-0	1.183	-0.084	1.644	1.389	0.967	0.843	0.938	-0.670	0.000	0.480	0.256	0.123	2.215	0.437	1.644	0.491	0.000	0.501	-0.416	0.101	3.102	1.060	0.804	1.017	0.775	0.173	0.535	0.760
-0	1.629	-1.486	-0.683	2.786	-0.492	1.347	-2.638	1.453	0.000	1.857	0.208	0.873	0.000	0.519	-1.265	-1.602	1.274	0.903	-1.102	-0.329	1.551	6.892	3.522	0.998	0.570	0.477	2.039	2.006
-1	2.045	-0.671	-1.235	0.490	-0.952	0.525	-1.252	1.289	0.000	1.088	-0.993	0.648	2.215	0.975	-0.109	-0.254	2.548	0.556	-1.095	-0.194	0.000	0.803	0.861	0.980	1.282	0.945	0.925	0.811
-0	0.448	-0.058	-0.974	0.945	-1.633	1.181	-1.139	0.266	2.173	1.118	-0.761	1.502	1.107	1.706	0.585	-0.680	0.000	0.487	-1.951	0.945	0.000	2.347	1.754	0.993	1.161	1.549	1.414	1.176
-0	0.551	0.519	0.448	2.183	1.293	1.220	0.628	-0.627	2.173	1.019	-0.002	-0.652	0.000	1.843	-0.386	1.042	2.548	0.400	-1.102	-1.014	0.000	0.648	0.792	1.049	0.888	2.132	1.262	1.096
-0	1.624	0.488	1.403	0.760	0.559	0.812	0.777	-1.244	2.173	0.613	0.589	-0.030	2.215	0.692	1.058	0.683	0.000	1.054	1.165	-0.765	0.000	0.915	0.875	1.059	0.821	0.927	0.792	0.721
-1	0.774	0.444	1.257	0.515	-0.689	0.515	1.448	-1.271	0.000	0.793	0.118	0.811	1.107	0.679	0.326	-0.426	0.000	1.066	-0.865	-0.049	3.102	0.960	1.046	0.986	0.716	0.772	0.855	0.732
-1	2.093	-1.240	1.615	0.918	-1.202	1.412	-0.541	0.640	1.087	2.019	0.872	-0.639	0.000	0.672	-0.936	0.972	0.000	0.896	0.235	0.212	0.000	0.810	0.700	1.090	0.797	0.862	1.049	0.874
-1	0.908	1.069	0.283	0.400	1.293	0.609	1.452	-1.136	0.000	0.623	0.417	-0.098	2.215	1.023	0.775	1.054	1.274	0.706	2.346	-1.305	0.000	0.744	1.006	0.991	0.606	0.753	0.796	0.753
-0	0.403	-1.328	-0.065	0.901	1.052	0.708	-0.354	-0.718	2.173	0.892	0.633	1.684	2.215	0.999	-1.205	0.941	0.000	0.930	1.072	-0.809	0.000	2.105	1.430	0.989	0.838	1.147	1.042	0.883
-0	1.447	0.453	0.118	1.731	0.650	0.771	0.446	-1.564	0.000	0.973	-2.014	0.354	0.000	1.949	-0.643	-1.531	1.274	1.106	-0.334	-1.163	0.000	0.795	0.821	1.013	1.699	0.918	1.118	1.018
-1	1.794	0.123	-0.454	0.057	1.489	0.966	-1.190	1.090	1.087	0.539	-0.535	1.035	0.000	1.096	-1.069	-1.236	2.548	0.659	-1.196	-0.283	0.000	0.803	0.756	0.985	1.343	1.109	0.993	0.806
-0	1.484	-2.047	0.813	0.591	-0.295	0.923	0.312	-1.164	2.173	0.654	-0.316	0.752	2.215	0.599	1.966	-1.128	0.000	0.626	-0.304	-1.431	0.000	1.112	0.910	1.090	0.986	1.189	1.350	1.472
-0	0.417	-2.016	0.849	1.817	0.040	1.201	-1.676	-1.394	0.000	0.792	0.537	0.641	2.215	0.794	-1.222	0.187	0.000	0.825	-0.217	1.334	3.102	1.470	0.931	0.987	1.203	0.525	0.833	0.827
-1	0.603	1.009	0.033	0.486	1.225	0.884	-0.617	-1.058	0.000	0.500	-1.407	-0.567	0.000	1.476	-0.876	0.605	2.548	0.970	0.560	1.092	3.102	0.853	1.153	0.988	0.846	0.920	0.944	0.835
-1	1.381	-0.326	0.552	0.417	-0.027	1.030	-0.835	-1.287	2.173	0.941	-0.421	1.519	2.215	0.615	-1.650	0.377	0.000	0.606	0.644	0.650	0.000	1.146	0.970	0.990	1.191	0.884	0.897	0.826
-1	0.632	1.200	-0.703	0.438	-1.700	0.779	-0.731	0.958	1.087	0.605	0.393	-1.376	0.000	0.670	-0.827	-1.315	2.548	0.626	-0.501	0.417	0.000	0.904	0.903	0.998	0.673	0.803	0.722	0.640
-1	1.561	-0.569	1.580	0.329	0.237	1.059	0.731	0.415	2.173	0.454	0.016	-0.828	0.000	0.587	0.008	-0.291	1.274	0.597	1.119	1.191	0.000	0.815	0.908	0.988	0.733	0.690	0.892	0.764
-1	2.102	0.087	0.449	1.164	-0.390	1.085	-0.408	-1.116	2.173	0.578	0.197	-0.137	0.000	1.202	0.917	1.523	0.000	0.959	-0.832	1.404	3.102	1.380	1.109	1.486	1.496	0.886	1.066	1.025
-1	1.698	-0.489	-0.552	0.976	-1.009	1.620	-0.721	0.648	1.087	1.481	-1.860	-1.354	0.000	1.142	-1.140	1.401	2.548	1.000	-1.274	-0.158	0.000	1.430	1.130	0.987	1.629	1.154	1.303	1.223
-1	1.111	-0.249	-1.457	0.421	0.939	0.646	-2.076	0.362	0.000	1.315	0.796	-1.436	2.215	0.780	0.130	0.055	0.000	1.662	-0.834	0.461	0.000	0.920	0.948	0.990	1.046	0.905	1.493	1.169
-1	0.945	0.390	-1.159	1.675	0.437	0.356	0.261	0.543	1.087	0.574	0.838	1.599	2.215	0.496	-1.220	-0.022	0.000	0.558	-2.454	1.440	0.000	0.763	0.983	1.728	1.000	0.578	0.922	1.003
-1	2.076	0.014	-1.314	0.854	-0.306	3.446	1.341	0.598	0.000	2.086	0.227	-0.747	2.215	1.564	-0.216	1.649	2.548	0.965	-0.857	-1.062	0.000	0.477	0.734	1.456	1.003	1.660	1.001	0.908
-1	1.992	0.192	-0.103	0.108	-1.599	0.938	0.595	-1.360	2.173	0.869	-1.012	1.432	0.000	1.302	0.850	0.436	2.548	0.487	1.051	-1.027	0.000	0.502	0.829	0.983	1.110	1.394	0.904	0.836
-0	0.460	1.625	1.485	1.331	1.242	0.675	-0.329	-1.039	1.087	0.671	-1.028	-0.514	0.000	1.265	-0.788	0.415	1.274	0.570	-0.683	-1.738	0.000	0.725	0.758	1.004	1.024	1.156	0.944	0.833
-0	0.871	0.839	-1.536	0.428	1.198	0.875	-1.256	-0.466	1.087	0.684	-0.768	0.150	0.000	0.556	-1.793	0.389	0.000	0.942	-1.126	1.339	1.551	0.624	0.734	0.986	1.357	0.960	1.474	1.294
-1	0.951	1.651	0.576	1.273	1.495	0.834	0.048	-0.578	2.173	0.386	-0.056	-1.448	0.000	0.597	-0.196	0.162	2.548	0.524	1.649	1.625	0.000	0.737	0.901	1.124	1.014	0.556	1.039	0.845
-1	1.049	-0.223	0.685	0.256	-1.191	2.506	0.238	-0.359	0.000	1.510	-0.904	1.158	1.107	2.733	-0.902	1.679	2.548	0.407	-0.474	-1.572	0.000	1.513	2.472	0.982	1.238	0.978	1.985	1.510
-0	0.455	-0.028	0.265	1.286	1.373	0.459	0.331	-0.922	0.000	0.343	0.634	0.430	0.000	0.279	-0.084	-0.272	0.000	0.475	0.926	-0.123	3.102	0.803	0.495	0.987	0.587	0.211	0.417	0.445
-1	2.074	0.388	0.878	1.110	1.557	1.077	-0.226	-0.295	2.173	0.865	-0.319	-1.116	2.215	0.707	-0.835	0.722	0.000	0.632	-0.608	-0.728	0.000	0.715	0.802	1.207	1.190	0.960	1.143	0.926
-1	1.390	0.265	1.196	0.919	-1.371	1.858	0.506	0.786	0.000	1.280	-1.367	-0.720	2.215	1.483	-0.441	-0.675	2.548	1.076	0.294	-0.539	0.000	1.126	0.830	1.155	1.551	0.702	1.103	0.933
-1	1.014	-0.079	1.597	1.038	-0.281	1.135	-0.722	-0.177	2.173	0.544	-1.475	-1.501	0.000	1.257	-1.315	1.212	0.000	0.496	-0.060	1.180	1.551	0.815	0.611	1.411	1.110	0.792	0.846	0.853
-0	0.335	1.267	-1.154	2.011	-0.574	0.753	0.618	1.411	0.000	0.474	0.748	0.681	2.215	0.608	-0.446	-0.354	2.548	0.399	1.295	-0.581	0.000	0.911	0.882	0.975	0.832	0.598	0.580	0.678
-1	0.729	-0.189	1.182	0.293	1.310	0.412	0.459	-0.632	0.000	0.869	-1.128	-0.625	2.215	1.173	-0.893	0.478	2.548	0.584	-2.394	-1.727	0.000	2.016	1.272	0.995	1.034	0.905	0.966	1.038
-1	1.225	-1.215	-0.088	0.881	-0.237	0.600	-0.976	1.462	2.173	0.876	0.506	1.583	2.215	0.718	1.228	-0.031	0.000	0.653	-1.292	1.216	0.000	0.838	1.108	0.981	1.805	0.890	1.251	1.197
-1	2.685	-0.444	0.847	0.253	0.183	0.641	-1.541	-0.873	2.173	0.417	2.874	-0.551	0.000	0.706	-1.431	0.764	0.000	1.390	-0.596	-1.397	0.000	0.894	0.829	0.993	0.789	0.654	0.883	0.746
-0	0.638	-0.481	0.683	1.457	-1.024	0.707	-1.338	1.498	0.000	0.980	0.518	0.289	2.215	0.964	-0.531	-0.423	0.000	0.694	-0.654	-1.314	3.102	0.807	1.283	1.335	0.658	0.907	0.797	0.772
-1	1.789	-0.765	-0.732	0.421	-0.020	1.142	-1.353	1.439	2.173	0.725	-1.518	-1.261	0.000	0.812	-2.597	-0.463	0.000	1.203	-0.120	1.001	0.000	0.978	0.673	0.985	1.303	1.400	1.078	0.983
-1	0.784	-1.431	1.724	0.848	0.559	0.615	-1.643	-1.456	0.000	1.339	-0.513	0.040	2.215	0.394	-2.483	1.304	0.000	0.987	0.889	-0.339	0.000	0.732	0.713	0.987	0.973	0.705	0.875	0.759
-1	0.911	1.098	-1.289	0.421	0.823	1.218	-0.503	0.431	0.000	0.775	0.432	-1.680	0.000	0.855	-0.226	-0.460	2.548	0.646	-0.947	-1.243	1.551	2.201	1.349	0.985	0.730	0.451	0.877	0.825
-1	0.959	0.372	-0.269	1.255	0.702	1.151	0.097	0.805	2.173	0.993	1.011	0.767	2.215	1.096	0.185	0.381	0.000	1.001	-0.205	0.059	0.000	0.979	0.997	1.168	0.796	0.771	0.839	0.776
-0	0.283	-1.864	-1.663	0.219	1.624	0.955	-1.213	0.932	2.173	0.889	0.395	-0.268	0.000	0.597	-1.083	-0.921	2.548	0.584	1.325	-1.072	0.000	0.856	0.927	0.996	0.937	0.936	1.095	0.892
-0	2.017	-0.488	-0.466	1.029	-0.870	3.157	0.059	-0.343	2.173	3.881	0.872	1.502	1.107	3.631	1.720	0.963	0.000	0.633	-1.264	-1.734	0.000	4.572	3.339	1.005	1.407	5.590	3.614	3.110
-1	1.088	0.414	-0.841	0.485	0.605	0.860	1.110	-0.568	0.000	1.152	-0.325	1.203	2.215	0.324	1.652	-0.104	0.000	0.510	1.095	-1.728	0.000	0.880	0.722	0.989	0.977	0.711	0.888	0.762
-0	0.409	-1.717	0.712	0.809	-1.301	0.701	-1.529	-1.411	0.000	1.191	-0.582	0.438	2.215	1.147	0.813	-0.571	2.548	1.039	0.543	0.892	0.000	0.636	0.810	0.986	0.861	1.411	0.907	0.756
-1	1.094	1.577	-0.988	0.497	-0.149	0.891	-2.459	1.034	0.000	0.646	0.792	-1.022	0.000	1.573	0.254	-0.053	2.548	1.428	0.190	-1.641	3.102	4.322	2.687	0.985	0.881	1.135	1.907	1.831
-1	0.613	1.993	-0.280	0.544	0.931	0.909	1.526	1.559	0.000	0.840	1.473	-0.483	2.215	0.856	0.352	0.408	2.548	1.058	1.733	-1.396	0.000	0.801	1.066	0.984	0.639	0.841	0.871	0.748
-0	0.958	-1.202	0.600	0.434	0.170	0.783	-0.214	1.319	0.000	0.835	-0.454	-0.615	2.215	0.658	-1.858	-0.891	0.000	0.640	0.172	-1.204	3.102	1.790	1.086	0.997	0.804	0.403	0.793	0.756
-1	1.998	-0.238	0.972	0.058	0.266	0.759	1.576	-0.357	2.173	1.004	-0.349	-0.747	2.215	0.962	0.490	-0.453	0.000	1.592	0.661	-1.405	0.000	0.874	1.086	0.990	1.436	1.527	1.177	0.993
-1	0.796	-0.171	-0.818	0.574	-1.625	1.201	-0.737	1.451	2.173	0.651	0.404	-0.452	0.000	1.150	-0.652	-0.120	0.000	1.008	-0.093	0.531	3.102	0.884	0.706	0.979	1.193	0.937	0.943	0.881
-1	0.773	1.023	0.527	1.537	-0.201	2.967	-0.574	-1.534	2.173	2.346	-0.307	0.394	2.215	1.393	0.135	-0.027	0.000	3.015	0.187	0.516	0.000	0.819	1.260	0.982	2.552	3.862	2.179	1.786
-0	1.823	1.008	-1.489	0.234	-0.962	0.591	0.461	0.996	2.173	0.568	-1.297	-0.410	0.000	0.887	2.157	1.194	0.000	2.079	0.369	-0.085	3.102	0.770	0.945	0.995	1.179	0.971	0.925	0.983
-0	0.780	0.640	0.490	0.680	-1.301	0.715	-0.137	0.152	2.173	0.616	-0.831	1.668	0.000	1.958	0.528	-0.982	2.548	0.966	-1.551	0.462	0.000	1.034	1.079	1.008	0.827	1.369	1.152	0.983
-1	0.543	0.801	1.543	1.134	-0.772	0.954	-0.849	0.410	1.087	0.851	-1.988	1.686	0.000	0.799	-0.912	-1.156	0.000	0.479	0.097	1.334	0.000	0.923	0.597	0.989	1.231	0.759	0.975	0.867
-0	1.241	-0.014	0.129	1.158	0.670	0.445	-0.732	1.739	2.173	0.918	0.659	-1.340	2.215	0.557	2.410	-1.404	0.000	0.966	-1.545	-1.120	0.000	0.874	0.918	0.987	1.001	0.798	0.904	0.937
-0	1.751	-0.266	-1.575	0.489	1.292	1.112	1.533	0.137	2.173	1.204	-0.414	-0.928	0.000	0.879	1.237	-0.415	2.548	1.479	1.469	0.913	0.000	2.884	1.747	0.989	1.742	0.600	1.363	1.293
-1	1.505	1.208	-1.476	0.995	-0.836	2.800	-1.600	0.111	0.000	2.157	1.241	1.110	2.215	1.076	2.619	-0.913	0.000	1.678	2.204	-1.575	0.000	0.849	1.224	0.990	1.412	0.976	1.271	1.105
-0	0.816	0.611	0.779	1.694	0.278	0.575	-0.787	1.592	2.173	1.148	1.076	-0.831	2.215	0.421	1.316	0.632	0.000	0.589	0.452	-1.466	0.000	0.779	0.909	0.990	1.146	1.639	1.236	0.949
-1	0.551	-0.808	0.330	1.188	-0.294	0.447	-0.035	-0.993	0.000	0.432	-0.276	-0.481	2.215	1.959	-0.288	1.195	2.548	0.638	0.583	1.107	0.000	0.832	0.924	0.993	0.723	0.976	0.968	0.895
-0	1.316	-0.093	0.995	0.860	-0.621	0.593	-0.560	-1.599	2.173	0.524	-0.318	-0.240	2.215	0.566	0.759	-0.368	0.000	0.483	-2.030	-1.104	0.000	1.468	1.041	1.464	0.811	0.778	0.690	0.722
-1	1.528	0.067	-0.855	0.959	-1.464	1.143	-0.082	1.023	0.000	0.702	-0.763	-0.244	0.000	0.935	-0.881	0.206	2.548	0.614	-0.831	1.657	3.102	1.680	1.105	0.983	1.078	0.559	0.801	0.809
-0	0.558	-0.833	-0.598	1.436	-1.724	1.316	-0.661	1.593	2.173	1.148	-0.503	-0.132	1.107	1.584	-0.125	0.380	0.000	1.110	-1.216	-0.181	0.000	1.258	0.860	1.053	0.790	1.814	1.159	1.007
-1	0.819	0.879	1.221	0.598	-1.450	0.754	0.417	-0.369	2.173	0.477	1.199	0.274	0.000	1.073	0.368	0.273	2.548	1.599	2.047	1.690	0.000	0.933	0.984	0.983	0.788	0.613	0.728	0.717
-0	0.981	-1.007	0.489	0.923	1.261	0.436	-0.698	-0.506	2.173	0.764	-1.105	-1.241	2.215	0.577	-2.573	-0.036	0.000	0.565	-1.628	1.610	0.000	0.688	0.801	0.991	0.871	0.554	0.691	0.656
-0	2.888	0.568	-1.416	1.461	-1.157	1.756	-0.900	0.522	0.000	0.657	0.409	1.076	2.215	1.419	0.672	-0.019	0.000	1.436	-0.184	-0.980	3.102	0.946	0.919	0.995	1.069	0.890	0.834	0.856
-1	0.522	1.805	-0.963	1.136	0.418	0.727	-0.195	-1.695	2.173	0.309	2.559	-0.178	0.000	0.521	1.794	0.919	0.000	0.788	0.174	-0.406	3.102	0.555	0.729	1.011	1.385	0.753	0.927	0.832
-1	0.793	-0.162	-1.643	0.634	0.337	0.898	-0.633	1.689	0.000	0.806	-0.826	-0.356	2.215	0.890	-0.142	-1.268	0.000	1.293	0.574	0.725	0.000	0.833	1.077	0.988	0.721	0.679	0.867	0.753
-0	1.298	1.098	0.280	0.371	-0.373	0.855	-0.306	-1.186	0.000	0.977	-0.421	1.003	0.000	0.978	0.956	-1.249	2.548	0.735	0.577	-0.037	3.102	0.974	1.002	0.992	0.549	0.587	0.725	0.954
-1	0.751	-0.520	-1.653	0.168	-0.419	0.878	-1.023	-1.364	2.173	1.310	-0.667	0.863	0.000	1.196	-0.827	0.358	0.000	1.154	-0.165	-0.360	1.551	0.871	0.950	0.983	0.907	0.955	0.959	0.874
-0	1.730	0.666	-1.432	0.446	1.302	0.921	-0.203	0.621	0.000	1.171	-0.365	-0.611	1.107	0.585	0.807	1.150	0.000	0.415	-0.843	1.311	0.000	0.968	0.786	0.986	1.059	0.371	0.790	0.848
-1	0.596	-1.486	0.690	1.045	-1.344	0.928	0.867	0.820	2.173	0.610	0.999	-1.329	2.215	0.883	-0.001	-0.106	0.000	1.145	2.184	-0.808	0.000	2.019	1.256	1.056	1.751	1.037	1.298	1.518
-1	0.656	-1.993	-0.519	1.643	-0.143	0.815	0.256	1.220	1.087	0.399	-1.184	-1.458	0.000	0.738	1.361	-1.443	0.000	0.842	0.033	0.293	0.000	0.910	0.891	0.993	0.668	0.562	0.958	0.787
-1	1.127	-0.542	0.645	0.318	-1.496	0.661	-0.640	0.369	2.173	0.992	0.358	1.702	0.000	1.004	0.316	-1.109	0.000	1.616	-0.936	-0.707	1.551	0.875	1.191	0.985	0.651	0.940	0.969	0.834
-0	0.916	-1.423	-1.490	1.248	-0.538	0.625	-0.535	-0.174	0.000	0.769	-0.389	1.608	2.215	0.667	-1.138	-1.738	1.274	0.877	-0.019	0.482	0.000	0.696	0.917	1.121	0.678	0.347	0.647	0.722
-1	2.756	-0.637	-1.715	1.331	1.124	0.913	-0.296	-0.491	0.000	0.983	-0.831	0.000	2.215	1.180	-0.428	0.742	0.000	1.113	0.005	-1.157	1.551	1.681	1.096	1.462	0.976	0.917	1.009	1.040
-0	0.755	1.754	0.701	2.111	0.256	1.243	0.057	-1.502	2.173	0.565	-0.034	-1.078	1.107	0.529	1.696	-1.090	0.000	0.665	0.292	0.107	0.000	0.870	0.780	0.990	2.775	0.465	1.876	1.758
-1	0.593	-0.762	1.743	0.908	0.442	0.773	-1.357	-0.768	2.173	0.432	1.421	1.236	0.000	0.579	0.291	-0.403	0.000	0.966	-0.309	1.016	3.102	0.893	0.743	0.989	0.857	1.030	0.943	0.854
-1	0.891	-1.151	-1.269	0.504	-0.622	0.893	-0.549	0.700	0.000	0.828	-0.825	0.154	2.215	1.083	0.632	-1.141	0.000	1.059	-0.557	1.526	3.102	2.117	1.281	0.987	0.819	0.802	0.917	0.828
-1	2.358	-0.248	0.080	0.747	-0.975	1.019	1.374	1.363	0.000	0.935	0.127	-1.707	2.215	0.312	-0.827	0.017	0.000	0.737	1.059	-0.327	0.000	0.716	0.828	1.495	0.953	0.704	0.880	0.745
-0	0.660	-0.017	-1.138	0.453	1.002	0.645	0.518	0.703	2.173	0.751	0.705	-0.592	2.215	0.744	-0.909	-1.596	0.000	0.410	-1.135	0.481	0.000	0.592	0.922	0.989	0.897	0.948	0.777	0.701
-1	0.718	0.518	0.225	1.710	-0.022	1.888	-0.424	1.092	0.000	4.134	0.185	-1.366	0.000	1.415	1.293	0.242	2.548	2.351	0.264	-0.057	3.102	0.830	1.630	0.976	1.215	0.890	1.422	1.215
-1	1.160	0.203	0.941	0.594	0.212	0.636	-0.556	0.679	2.173	1.089	-0.481	-1.008	1.107	1.245	-0.056	-1.357	0.000	0.587	1.007	0.056	0.000	1.106	0.901	0.987	0.786	1.224	0.914	0.837
-1	0.697	0.542	0.619	0.985	1.481	0.745	0.415	1.644	2.173	0.903	0.495	-0.958	2.215	1.165	1.195	0.346	0.000	1.067	-0.881	-0.264	0.000	0.830	1.025	0.987	0.690	0.863	0.894	0.867
-0	1.430	0.190	-0.700	0.246	0.518	1.302	0.660	-0.247	2.173	1.185	-0.539	1.504	0.000	1.976	-0.401	1.079	0.000	0.855	-0.958	-1.110	3.102	0.886	0.953	0.993	0.889	1.400	1.376	1.119
-1	1.122	-0.795	0.202	0.397	-1.553	0.597	-1.459	-0.734	2.173	0.522	1.044	1.027	2.215	0.783	-1.243	1.701	0.000	0.371	1.737	0.199	0.000	1.719	1.176	0.988	0.723	1.583	1.063	0.914
-0	1.153	0.526	1.236	0.266	0.001	1.139	-1.236	-0.585	2.173	1.337	-0.215	-1.356	2.215	1.780	1.129	0.902	0.000	1.608	-0.391	-0.161	0.000	1.441	1.633	0.990	1.838	1.516	1.635	1.373
-1	0.760	1.012	0.758	0.937	0.051	0.941	0.687	-1.247	2.173	1.288	-0.743	0.822	0.000	1.552	1.782	-1.533	0.000	0.767	1.349	0.168	0.000	0.716	0.862	0.988	0.595	0.359	0.697	0.623
-1	1.756	-1.469	1.395	1.345	-1.595	0.817	0.017	-0.741	2.173	0.483	-0.008	0.293	0.000	1.768	-0.663	0.438	1.274	1.202	-1.387	-0.222	0.000	1.022	1.058	0.992	1.407	1.427	1.356	1.133
-0	0.397	0.582	-0.758	1.260	-1.735	0.889	-0.515	1.139	2.173	0.973	1.616	0.460	0.000	1.308	1.001	-0.709	2.548	0.858	0.995	-0.231	0.000	0.749	0.888	0.979	1.487	1.804	1.208	1.079
-0	0.515	-0.984	0.425	1.114	-0.439	1.999	0.818	1.561	0.000	1.407	0.009	-0.380	0.000	1.332	0.230	0.397	0.000	1.356	-0.616	-1.057	3.102	0.978	1.017	0.990	1.118	0.862	0.835	0.919
-1	1.368	-0.921	-0.866	0.842	-0.598	0.456	-1.176	1.219	1.087	0.419	-1.974	-0.819	0.000	0.791	-1.640	0.881	0.000	1.295	-0.782	0.442	3.102	0.945	0.761	0.974	0.915	0.535	0.733	0.651
-0	2.276	0.134	0.399	2.525	0.376	1.111	-1.078	-1.571	0.000	0.657	2.215	-0.900	0.000	1.183	-0.662	-0.508	2.548	1.436	-0.517	0.960	3.102	0.569	0.931	0.993	1.170	0.967	0.879	1.207
-0	0.849	0.907	0.124	0.652	1.585	0.715	0.355	-1.200	0.000	0.599	-0.892	1.301	0.000	1.106	1.151	0.582	0.000	1.895	-0.279	-0.568	3.102	0.881	0.945	0.998	0.559	0.649	0.638	0.660
-1	2.105	0.248	-0.797	0.530	0.206	1.957	-2.175	0.797	0.000	1.193	0.637	-1.646	2.215	0.881	1.111	-1.046	0.000	0.872	-0.185	1.085	1.551	0.986	1.343	1.151	1.069	0.714	2.063	1.951
-1	1.838	1.060	1.637	1.017	1.370	0.913	0.461	-0.609	1.087	0.766	-0.461	0.303	2.215	0.724	-0.061	0.886	0.000	0.941	1.123	-0.745	0.000	0.858	0.847	0.979	1.313	1.083	1.094	0.910
-0	0.364	1.274	1.066	1.570	-0.394	0.485	0.012	-1.716	0.000	0.317	-1.233	0.534	2.215	0.548	-2.165	0.762	0.000	0.729	0.169	-0.318	3.102	0.892	0.944	1.013	0.594	0.461	0.688	0.715
-1	0.503	1.343	-0.031	1.134	-1.204	0.590	-0.309	0.174	2.173	0.408	2.372	-0.628	0.000	1.850	0.400	1.147	2.548	0.664	-0.458	-0.885	0.000	1.445	1.283	0.989	1.280	1.118	1.127	1.026
-0	1.873	0.258	0.103	2.491	0.530	1.678	0.644	-1.738	2.173	1.432	0.848	-1.340	0.000	0.621	1.323	-1.316	0.000	0.628	0.789	-0.206	1.551	0.426	0.802	1.125	0.688	1.079	1.338	1.239
-1	0.826	-0.732	1.587	0.582	-1.236	0.495	0.757	-0.741	2.173	0.940	1.474	0.354	2.215	0.474	1.055	-1.657	0.000	0.415	1.758	0.841	0.000	0.451	0.578	0.984	0.757	0.922	0.860	0.696
-0	0.935	-1.614	-0.597	0.299	1.223	0.707	-0.853	-1.026	0.000	0.751	0.007	-1.691	0.000	1.062	-0.125	0.976	2.548	0.877	1.275	0.646	0.000	0.962	1.074	0.980	0.608	0.726	0.741	0.662
-1	0.643	0.542	-1.285	0.474	-0.366	0.667	-0.446	1.195	2.173	1.076	0.145	-0.126	0.000	0.970	-0.661	0.394	1.274	1.218	-0.184	-1.722	0.000	1.331	1.019	0.985	1.192	0.677	0.973	0.910
-0	0.713	0.164	1.080	1.427	-0.460	0.960	-0.152	-0.940	2.173	1.427	-0.901	1.036	1.107	0.440	-1.269	-0.194	0.000	0.452	1.932	-0.532	0.000	1.542	1.210	1.374	1.319	1.818	1.220	1.050
-0	0.876	-0.463	-1.224	2.458	-1.689	1.007	-0.752	0.398	0.000	2.456	-1.285	-0.152	1.107	1.641	1.838	1.717	0.000	0.458	0.194	0.488	3.102	4.848	2.463	0.986	1.981	0.974	2.642	2.258
-1	0.384	-0.275	0.387	1.403	-0.994	0.620	-1.529	1.685	0.000	1.091	-1.644	1.078	0.000	0.781	-1.311	0.326	2.548	1.228	-0.728	-0.633	1.551	0.920	0.854	0.987	0.646	0.609	0.740	0.884
-0	0.318	-1.818	-1.008	0.977	1.268	0.457	2.451	-1.522	0.000	0.881	1.351	0.461	2.215	0.929	0.239	-0.380	2.548	0.382	-0.613	1.330	0.000	1.563	1.193	0.994	0.829	0.874	0.901	1.026
-1	0.612	-1.120	1.098	0.402	-0.480	0.818	0.188	1.511	0.000	0.800	-0.253	0.977	0.000	1.175	0.271	-1.289	1.274	2.531	0.226	-0.409	3.102	0.889	0.947	0.979	1.486	0.940	1.152	1.119
-1	0.587	-0.737	-0.228	0.970	1.119	0.823	0.184	1.594	0.000	1.104	0.301	-0.818	2.215	0.819	0.712	-0.560	0.000	2.240	-0.419	0.340	3.102	1.445	1.103	0.988	0.715	1.363	1.019	0.926
-0	1.030	-0.694	-1.638	0.893	-1.074	1.160	-0.766	0.485	0.000	1.632	-0.698	-1.142	2.215	1.050	-1.092	0.952	0.000	1.475	0.286	0.125	3.102	0.914	1.075	0.982	0.732	1.493	1.219	1.079
-1	2.142	0.617	1.517	0.387	-0.862	0.345	1.203	-1.014	2.173	0.609	1.092	0.275	0.000	1.331	0.582	-0.183	2.548	0.557	1.540	-1.642	0.000	0.801	0.737	1.060	0.715	0.626	0.749	0.674
-0	1.076	0.240	-0.246	0.871	-1.241	0.496	0.282	0.746	2.173	1.095	-0.648	1.100	2.215	0.446	-1.756	0.764	0.000	0.434	0.788	-0.991	0.000	1.079	0.868	1.047	0.818	0.634	0.795	0.733
-0	1.400	0.901	-1.617	0.625	-0.163	0.661	-0.411	-1.616	2.173	0.685	0.524	0.425	0.000	0.881	-0.766	0.312	0.000	0.979	0.255	-0.667	3.102	0.898	1.105	1.253	0.730	0.716	0.738	0.795
-0	3.302	1.132	1.051	0.658	0.768	1.308	0.251	-0.374	1.087	1.673	0.015	-0.898	0.000	0.688	-0.535	1.363	1.274	0.871	1.325	-1.583	0.000	1.646	1.249	0.995	1.919	1.288	1.330	1.329
-0	1.757	0.202	0.750	0.767	-0.362	0.932	-1.033	-1.366	0.000	1.529	-1.012	-0.771	0.000	1.161	-0.287	0.059	0.000	2.185	1.147	1.099	3.102	0.795	0.529	1.354	1.144	1.491	1.319	1.161
-0	1.290	0.905	-1.711	1.017	-0.695	1.008	-1.038	0.693	2.173	1.202	-0.595	0.187	0.000	1.011	0.139	-1.607	0.000	0.789	-0.613	-1.041	3.102	1.304	0.895	1.259	1.866	0.955	1.211	1.200
-1	1.125	-0.004	1.694	0.373	0.329	0.978	0.640	-0.391	0.000	1.122	-0.376	1.521	2.215	0.432	2.413	-1.259	0.000	0.969	0.730	0.512	3.102	0.716	0.773	0.991	0.624	0.977	0.981	0.875
-0	1.081	0.861	1.252	1.621	1.474	1.293	0.600	0.630	0.000	1.991	-0.090	-0.675	2.215	0.861	1.105	-0.201	0.000	1.135	2.489	-1.659	0.000	1.089	0.657	0.991	2.179	0.412	1.334	1.071
-1	0.652	-0.294	1.241	1.034	0.490	1.033	0.551	-0.963	2.173	0.661	1.031	-1.654	2.215	1.376	-0.018	0.843	0.000	0.943	-0.329	-0.269	0.000	1.085	1.067	0.991	1.504	0.773	1.135	0.993
-1	1.408	-1.028	-1.018	0.252	-0.242	0.465	-0.364	-0.200	0.000	1.466	0.669	0.739	1.107	1.031	0.415	-1.468	2.548	0.457	-1.091	-1.722	0.000	0.771	0.811	0.979	1.459	1.204	1.041	0.866
-1	0.781	-1.143	-0.659	0.961	1.266	1.183	-0.686	0.119	2.173	1.126	-0.064	1.447	0.000	0.730	1.430	-1.535	0.000	1.601	0.513	1.658	0.000	0.871	1.345	1.184	1.058	0.620	1.107	0.978
-1	1.300	-0.616	1.032	0.751	-0.731	0.961	-0.716	1.592	0.000	2.079	-1.063	-0.271	2.215	0.475	0.518	1.695	1.274	0.395	-2.204	0.349	0.000	1.350	0.983	1.369	1.265	1.428	1.135	0.982
-1	0.833	0.809	1.657	1.637	1.019	0.705	1.077	-0.968	2.173	1.261	0.114	-0.298	1.107	1.032	0.017	0.236	0.000	0.640	-0.026	-1.598	0.000	0.894	0.982	0.981	1.250	1.054	1.018	0.853
-1	1.686	-1.090	-0.301	0.890	0.557	1.304	-0.284	-1.393	2.173	0.388	2.118	0.513	0.000	0.514	-0.015	0.891	0.000	0.460	0.547	0.627	3.102	0.942	0.524	1.186	1.528	0.889	1.015	1.122
-1	0.551	0.911	0.879	0.379	-0.796	1.154	-0.808	-0.966	0.000	1.168	-0.513	0.355	2.215	0.646	-1.309	0.773	0.000	0.544	-0.283	1.301	3.102	0.847	0.705	0.990	0.772	0.546	0.790	0.719
-1	1.597	0.793	-1.119	0.691	-1.455	0.370	0.337	1.354	0.000	0.646	-1.005	0.732	2.215	1.019	0.040	0.209	0.000	0.545	0.958	0.239	3.102	0.962	0.793	0.994	0.719	0.745	0.812	0.739
-0	1.033	-1.193	-0.452	0.247	0.970	0.503	-1.424	1.362	0.000	1.062	-0.416	-1.156	2.215	0.935	-0.023	0.555	2.548	0.410	-1.766	0.379	0.000	0.590	0.953	0.991	0.717	1.081	0.763	0.690
-1	0.859	-1.004	1.521	0.781	-0.993	0.677	0.643	-0.338	2.173	0.486	0.409	1.283	0.000	0.679	0.110	0.285	0.000	0.715	-0.735	-0.157	1.551	0.702	0.773	0.984	0.627	0.633	0.694	0.643
-0	0.612	-1.127	1.074	1.225	-0.426	0.927	-2.141	-0.473	0.000	1.290	-0.927	-1.085	2.215	1.183	1.981	-1.687	0.000	2.176	0.406	-1.581	0.000	0.945	0.651	1.170	0.895	1.604	1.179	1.142
-1	0.535	0.321	-1.095	0.281	-0.960	0.876	-0.709	-0.076	0.000	1.563	-0.666	1.536	2.215	0.773	-0.321	0.435	0.000	0.682	-0.801	-0.952	3.102	0.711	0.667	0.985	0.888	0.741	0.872	0.758
-1	0.745	1.586	1.578	0.863	-1.423	0.530	1.714	1.085	0.000	1.174	0.679	1.015	0.000	1.158	0.609	-1.186	2.548	1.851	0.832	-0.248	3.102	0.910	1.164	0.983	0.947	0.858	0.928	0.823
-0	0.677	-1.014	-1.648	1.455	1.461	0.596	-2.358	0.517	0.000	0.800	0.849	-0.743	2.215	1.024	-0.282	-1.004	0.000	1.846	-0.977	0.378	3.102	2.210	1.423	0.982	1.074	1.623	1.417	1.258
-1	0.815	-1.263	0.057	1.018	-0.208	0.339	-0.347	-1.646	2.173	1.223	0.600	-1.658	2.215	1.435	0.042	0.926	0.000	0.777	1.698	-0.698	0.000	1.022	1.058	1.000	0.784	0.477	0.886	0.836
-0	3.512	-1.094	-0.220	0.338	-0.328	1.962	-1.099	1.544	1.087	1.461	-1.305	-0.922	2.215	1.219	-1.289	0.400	0.000	0.731	0.155	1.249	0.000	1.173	1.366	0.993	2.259	2.000	1.626	1.349
-0	0.904	1.248	0.325	0.317	-1.624	0.685	-0.538	1.665	2.173	0.685	-2.145	-1.106	0.000	0.632	-1.460	1.017	0.000	1.085	-0.182	0.162	3.102	0.885	0.801	0.989	0.930	0.904	1.012	0.961

contrib/gbdt/lightgbm/lightgbm-example.ipynb

@@ -1,270 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/contrib/gbdt/lightgbm/lightgbm-example.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Use LightGBM Estimator in Azure Machine Learning\n",
-        "In this notebook we will demonstrate how to run a training job using LightGBM Estimator. [LightGBM](https://lightgbm.readthedocs.io/en/latest/) is a gradient boosting framework that uses tree based learning algorithms. "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "This notebook uses azureml-contrib-gbdt package, if you don't already have the package, please install by uncommenting below cell."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#!pip install azureml-contrib-gbdt"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace, Run, Experiment\n",
-        "import shutil, os\n",
-        "from azureml.widgets import RunDetails\n",
-        "from azureml.contrib.gbdt import LightGBM\n",
-        "from azureml.train.dnn import Mpi\n",
-        "from azureml.core.compute import AmlCompute, ComputeTarget\n",
-        "from azureml.core.compute_target import ComputeTargetException"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "If you are using an AzureML Compute Instance, you are all set. Otherwise, go through the [configuration.ipynb](../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML Workspace"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Set up machine learning resources"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "print('Workspace name: ' + ws.name, \n",
-        "      'Azure region: ' + ws.location, \n",
-        "      'Subscription id: ' + ws.subscription_id, \n",
-        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "cluster_vm_size = \"STANDARD_DS14_V2\"\n",
-        "cluster_min_nodes = 0\n",
-        "cluster_max_nodes = 20\n",
-        "cpu_cluster_name = 'TrainingCompute2' \n",
-        "\n",
-        "try:\n",
-        "    cpu_cluster = AmlCompute(ws, cpu_cluster_name)\n",
-        "    if cpu_cluster and type(cpu_cluster) is AmlCompute:\n",
-        "        print('found compute target: ' + cpu_cluster_name)\n",
-        "except ComputeTargetException:\n",
-        "    print('creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = cluster_vm_size, \n",
-        "                                                                vm_priority = 'lowpriority', \n",
-        "                                                                min_nodes = cluster_min_nodes, \n",
-        "                                                                max_nodes = cluster_max_nodes)\n",
-        "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, provisioning_config)\n",
-        "    \n",
-        "    # can poll for a minimum number of nodes and for a specific timeout. \n",
-        "    # if no min node count is provided it will use the scale settings for the cluster\n",
-        "    cpu_cluster.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
-        "    \n",
-        "     # For a more detailed view of current Azure Machine Learning Compute  status, use get_status()\n",
-        "    print(cpu_cluster.get_status().serialize())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "From this point, you can either upload training data file directly or use Datastore for training data storage\n",
-        "## Upload training file from local"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "scripts_folder = \"scripts_folder\"\n",
-        "if not os.path.isdir(scripts_folder):\n",
-        "    os.mkdir(scripts_folder)\n",
-        "shutil.copy('./train.conf', os.path.join(scripts_folder, 'train.conf'))\n",
-        "shutil.copy('./binary0.train', os.path.join(scripts_folder, 'binary0.train'))\n",
-        "shutil.copy('./binary1.train', os.path.join(scripts_folder, 'binary1.train'))\n",
-        "shutil.copy('./binary0.test', os.path.join(scripts_folder, 'binary0.test'))\n",
-        "shutil.copy('./binary1.test', os.path.join(scripts_folder, 'binary1.test'))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "training_data_list=[\"binary0.train\", \"binary1.train\"]\n",
-        "validation_data_list = [\"binary0.test\", \"binary1.test\"]\n",
-        "lgbm = LightGBM(source_directory=scripts_folder, \n",
-        "                compute_target=cpu_cluster, \n",
-        "                distributed_training=Mpi(),\n",
-        "                node_count=2,\n",
-        "                lightgbm_config='train.conf',\n",
-        "                data=training_data_list,\n",
-        "                valid=validation_data_list\n",
-        "               )\n",
-        "experiment_name = 'lightgbm-estimator-test'\n",
-        "experiment = Experiment(ws, name=experiment_name)\n",
-        "run = experiment.submit(lgbm, tags={\"test public docker image\": None})\n",
-        "RunDetails(run).show()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Use data reference"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.datastore import Datastore\n",
-        "from azureml.data.data_reference import DataReference\n",
-        "datastore = ws.get_default_datastore()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "datastore.upload(src_dir='.',\n",
-        "                 target_path='.',\n",
-        "                 show_progress=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "training_data_list=[\"binary0.train\", \"binary1.train\"]\n",
-        "validation_data_list = [\"binary0.test\", \"binary1.test\"]\n",
-        "lgbm = LightGBM(source_directory='.', \n",
-        "                compute_target=cpu_cluster, \n",
-        "                distributed_training=Mpi(),\n",
-        "                node_count=2,\n",
-        "                inputs=[datastore.as_mount()],\n",
-        "                lightgbm_config='train.conf',\n",
-        "                data=training_data_list,\n",
-        "                valid=validation_data_list\n",
-        "               )\n",
-        "experiment_name = 'lightgbm-estimator-test'\n",
-        "experiment = Experiment(ws, name=experiment_name)\n",
-        "run = experiment.submit(lgbm, tags={\"use datastore.as_mount()\": None})\n",
-        "RunDetails(run).show()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# uncomment below and run if compute resources are no longer needed\n",
-        "# cpu_cluster.delete() "
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "jingywa"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.9"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

contrib/gbdt/lightgbm/lightgbm-example.yml

@@ -1,7 +0,0 @@
-name: lightgbm-example
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-contrib-gbdt
-  - azureml-widgets
-  - azureml-core

contrib/gbdt/lightgbm/train.conf

@@ -1,111 +0,0 @@
-# task type, support train and predict
-task = train
-
-# boosting type, support gbdt for now, alias: boosting, boost
-boosting_type = gbdt
-
-# application type, support following application
-# regression , regression task
-# binary , binary classification task
-# lambdarank , lambdarank task
-# alias: application, app
-objective = binary
-
-# eval metrics, support multi metric, delimite by ',' , support following metrics
-# l1 
-# l2 , default metric for regression
-# ndcg , default metric for lambdarank
-# auc 
-# binary_logloss , default metric for binary
-# binary_error
-metric = binary_logloss,auc
-
-# frequence for metric output
-metric_freq = 1
-
-# true if need output metric for training data, alias: tranining_metric, train_metric
-is_training_metric = true
-
-# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. 
-max_bin = 255
-
-# training data
-# if exsting weight file, should name to "binary.train.weight"
-# alias: train_data, train
-data = binary.train
-
-# validation data, support multi validation data, separated by ','
-# if exsting weight file, should name to "binary.test.weight"
-# alias: valid, test, test_data, 
-valid_data = binary.test
-
-# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds
-num_trees = 100
-
-# shrinkage rate , alias: shrinkage_rate
-learning_rate = 0.1
-
-# number of leaves for one tree, alias: num_leaf
-num_leaves = 63
-
-# type of tree learner, support following types:
-# serial , single machine version
-# feature , use feature parallel to train
-# data , use data parallel to train
-# voting , use voting based parallel to train
-# alias: tree
-tree_learner = feature
-
-# number of threads for multi-threading. One thread will use one CPU, defalut is setted to #cpu. 
-# num_threads = 8
-
-# feature sub-sample, will random select 80% feature to train on each iteration 
-# alias: sub_feature
-feature_fraction = 0.8
-
-# Support bagging (data sub-sample), will perform bagging every 5 iterations
-bagging_freq = 5
-
-# Bagging farction, will random select 80% data on bagging
-# alias: sub_row
-bagging_fraction = 0.8
-
-# minimal number data for one leaf, use this to deal with over-fit
-# alias : min_data_per_leaf, min_data
-min_data_in_leaf = 50
-
-# minimal sum hessians for one leaf, use this to deal with over-fit
-min_sum_hessian_in_leaf = 5.0
-
-# save memory and faster speed for sparse feature, alias: is_sparse
-is_enable_sparse = true
-
-# when data is bigger than memory size, set this to true. otherwise set false will have faster speed
-# alias: two_round_loading, two_round
-use_two_round_loading = false
-
-# true if need to save data to binary file and application will auto load data from binary file next time
-# alias: is_save_binary, save_binary
-is_save_binary_file = false
-
-# output model file
-output_model = LightGBM_model.txt
-
-# support continuous train from trained gbdt model
-# input_model= trained_model.txt
-
-# output prediction file for predict task
-# output_result= prediction.txt
-
-# support continuous train from initial score file
-# input_init_score= init_score.txt
-
-
-# number of machines in parallel training, alias: num_machine
-num_machines = 2
-
-# local listening port in parallel training, alias: local_port
-local_listen_port = 12400
-
-# machines list file for parallel training, alias: mlist
-machine_list_file = mlist.txt

how-to-use-azureml/README.md

@@ -4,7 +4,7 @@ Learn how to use Azure Machine Learning services for experimentation and model m
 
 As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) notebook first to set up your Azure ML Workspace. Then, run the notebooks in following recommended order.
 
-* [train-within-notebook](./training/train-within-notebook): Train a model hile tracking run history, and learn how to deploy the model as web service to Azure Container Instance.
+* [train-within-notebook](./training/train-within-notebook): Train a model while tracking run history, and learn how to deploy the model as web service to Azure Container Instance.
 * [train-on-local](./training/train-on-local): Learn how to submit a run to local computer and use Azure ML managed run configuration.
 * [train-on-amlcompute](./training/train-on-amlcompute): Use a 1-n node Azure ML managed compute cluster for remote runs on Azure CPU or GPU infrastructure.
 * [train-on-remote-vm](./training/train-on-remote-vm): Use Data Science Virtual Machine as a target for remote runs.

how-to-use-azureml/automated-machine-learning/README.md

@@ -1,8 +1,8 @@
 # Table of Contents
 1. [Automated ML Introduction](#introduction)
-1. [Setup using Azure Notebooks](#jupyter)
-1. [Setup using Azure Databricks](#databricks)
+1. [Setup using Compute Instances](#jupyter)
 1. [Setup using a Local Conda environment](#localconda)
+1. [Setup using Azure Databricks](#databricks)
 1. [Automated ML SDK Sample Notebooks](#samples)
 1. [Documentation](#documentation)
 1. [Running using python command](#pythoncommand)
@@ -21,13 +21,13 @@ Below are the three execution environments supported by automated ML.
 
 
  <a name="jupyter"></a>
-## Setup using Notebook VMs - Jupyter based notebooks from a Azure VM
+## Setup using Compute Instances - Jupyter based notebooks from a Azure Virtual Machine
 
 1. Open the [ML Azure portal](https://ml.azure.com)
 1. Select Compute
-1. Select Notebook VMs
+1. Select Compute Instances
 1. Click New
-1. Type a name for the Vm and select a VM type
+1. Type a Compute Name, select a Virtual Machine type and select a Virtual Machine size
 1. Click Create
 
 <a name="localconda"></a>
@@ -97,68 +97,96 @@ jupyter notebook
  <a name="databricks"></a>
 ## Setup using Azure Databricks
 
-**NOTE**: Please create your Azure Databricks cluster as v6.0 (high concurrency preferred) with **Python 3** (dropdown).
+**NOTE**: Please create your Azure Databricks cluster as v7.1 (high concurrency preferred) with **Python 3** (dropdown).
 **NOTE**: You should at least have contributor access to your Azure subcription to run the notebook.
-- Please remove the previous SDK version if there is any and install the latest SDK by installing **azureml-sdk[automl]** as a PyPi library in Azure Databricks workspace.
-- You can find the detail Readme instructions at [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks).
-- Download the sample notebook automl-databricks-local-01.ipynb from [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks) and import into the Azure databricks workspace.
+- You can find the detail Readme instructions at [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks/automl).
+- Download the sample notebook automl-databricks-local-01.ipynb from [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks/automl) and import into the Azure databricks workspace.
 - Attach the notebook to the cluster.
 
 <a name="samples"></a>
 # Automated ML SDK Sample Notebooks
 
-- [auto-ml-classification-credit-card-fraud.ipynb](classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb)
-    - Dataset: Kaggle's [credit card fraud detection dataset](https://www.kaggle.com/mlg-ulb/creditcardfraud)
-    - Simple example of using automated ML for classification to fraudulent credit card transactions
-    - Uses azure compute for training
+## Classification
+- **Classify Credit Card Fraud**
+    - Dataset: [Kaggle's credit card fraud detection dataset](https://www.kaggle.com/mlg-ulb/creditcardfraud)
+      - **[Jupyter Notebook (remote run)](classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb)**
+          - run the experiment remotely on AML Compute cluster
+          - test the performance of the best model in the local environment
+      - **[Jupyter Notebook (local run)](local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb)**
+          - run experiment in the local environment
+          - use Mimic Explainer for computing feature importance
+          - deploy the best model along with the explainer to an Azure Kubernetes (AKS) cluster, which will compute the raw and engineered feature importances at inference time
+- **Predict Term Deposit Subscriptions in a Bank**
+    - Dataset: [UCI's bank marketing dataset](https://www.kaggle.com/janiobachmann/bank-marketing-dataset)
+        - **[Jupyter Notebook](classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb)**
+          - run experiment remotely on AML Compute cluster to generate ONNX compatible models
+          - view the featurization steps that were applied during training
+          - view feature importance for the best model
+          - download the best model in ONNX format and use it for inferencing using ONNXRuntime
+          - deploy the best model in PKL format to Azure Container Instance (ACI)
+- **Predict Newsgroup based on Text from News Article**
+    - Dataset: [20 newsgroups text dataset](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html)
+        - **[Jupyter Notebook](classification-text-dnn/auto-ml-classification-text-dnn.ipynb)**
+          - AutoML highlights here include using deep neural networks (DNNs) to create embedded features from text data
+          - AutoML will use Bidirectional Encoder Representations from Transformers (BERT) when a GPU compute is used
+          - Bidirectional Long-Short Term neural network (BiLSTM) will be utilized when a CPU compute is used, thereby optimizing the choice of DNN
 
-- [auto-ml-regression.ipynb](regression/auto-ml-regression.ipynb)
+## Regression
+- **Predict Performance of Hardware Parts**
     - Dataset: Hardware Performance Dataset
-    - Simple example of using automated ML for regression
-    - Uses azure compute for training
+        - **[Jupyter Notebook](regression/auto-ml-regression.ipynb)**
+            - run the experiment remotely on AML Compute cluster
+            - get best trained model for a different metric than the one the experiment was optimized for
+            - test the performance of the best model in the local environment
+        - **[Jupyter Notebook (advanced)](regression/auto-ml-regression.ipynb)**
+            - run the experiment remotely on AML Compute cluster
+            - customize featurization: override column purpose within the dataset, configure transformer parameters
+            - get best trained model for a different metric than the one the experiment was optimized for
+            - run a model explanation experiment on the remote cluster
+            - deploy the model along the explainer and run online inferencing
 
-- [auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb](regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb)
-    - Dataset: Hardware Performance Dataset
-    - Shows featurization and excplanation
-    - Uses azure compute for training
-
-- [auto-ml-forecasting-energy-demand.ipynb](forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb)
-    - Dataset: [NYC energy demand data](forecasting-a/nyc_energy.csv)
-    - Example of using automated ML for training a forecasting model
-
-- [auto-ml-classification-credit-card-fraud-local.ipynb](local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb)
-    - Dataset: Kaggle's [credit card fraud detection dataset](https://www.kaggle.com/mlg-ulb/creditcardfraud)
-    - Simple example of using automated ML for classification to fraudulent credit card transactions
-    - Uses local compute for training
-
-- [auto-ml-classification-bank-marketing-all-features.ipynb](classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb)
-    - Dataset: UCI's [bank marketing dataset](https://www.kaggle.com/janiobachmann/bank-marketing-dataset)
-    - Simple example of using automated ML for classification to predict term deposit subscriptions for a bank
-    - Uses azure compute for training
-
-- [auto-ml-forecasting-orange-juice-sales.ipynb](forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb)
-    - Dataset: [Dominick's grocery sales of orange juice](forecasting-b/dominicks_OJ.csv)
-    - Example of training an automated ML forecasting model on multiple time-series
-
-- [auto-ml-forecasting-bike-share.ipynb](forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb)
-    - Dataset: forecasting for a bike-sharing
-    - Example of training an automated ML forecasting model on multiple time-series
-
-- [automl-forecasting-function.ipynb](forecasting-high-frequency/automl-forecasting-function.ipynb)
-    - Example of training an automated ML forecasting model on multiple time-series
-
-- [auto-ml-forecasting-beer-remote.ipynb](forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb)
-    - Example of training an automated ML forecasting model on multiple time-series
-    - Beer Production Forecasting
-
-- [auto-ml-continuous-retraining.ipynb](continuous-retraining/auto-ml-continuous-retraining.ipynb)
-    - Continous retraining using Pipelines and Time-Series TabularDataset
-
-- [auto-ml-classification-text-dnn.ipynb](classification-text-dnn/auto-ml-classification-text-dnn.ipynb)
-    - Classification with text data using deep learning in AutoML
-    - AutoML highlights here include using deep neural networks (DNNs) to create embedded features from text data.
-    - Depending on the compute cluster the user provides, AutoML tried out Bidirectional Encoder Representations from Transformers (BERT) when a GPU compute is used.
-    - Bidirectional Long-Short Term neural network (BiLSTM) when a CPU compute is used, thereby optimizing the choice of DNN for the uesr's setup.
+## Time Series Forecasting
+- **Forecast Energy Demand**
+    - Dataset: [NYC energy demand data](http://mis.nyiso.com/public/P-58Blist.htm)
+        - **[Jupyter Notebook](forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb)**
+          - run experiment remotely on AML Compute cluster
+          - use lags and rolling window features
+          - view the featurization steps that were applied during training
+          - get the best model, use it to forecast on test data and compare the accuracy of predictions against real data
+- **Forecast Orange Juice Sales (Multi-Series)**
+    - Dataset: [Dominick's grocery sales of orange juice](forecasting-orange-juice-sales/dominicks_OJ.csv)
+        - **[Jupyter Notebook](forecasting-orange-juice-sales/dominicks_OJ.csv)**
+          - run experiment remotely on AML Compute cluster
+          - customize time-series featurization, change column purpose and override transformer hyper parameters
+          - evaluate locally the performance of the generated best model
+          - deploy the best model as a webservice on Azure Container Instance (ACI)
+          - get online predictions from the deployed model
+- **Forecast Demand of a Bike-Sharing Service**
+    - Dataset: [Bike demand data](forecasting-bike-share/bike-no.csv)
+        - **[Jupyter Notebook](forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb)**
+          - run experiment remotely on AML Compute cluster
+          - integrate holiday features
+          - run rolling forecast for test set that is longer than the forecast horizon
+          - compute metrics on the predictions from the remote forecast
+- **The Forecast Function Interface**
+    - Dataset: Generated for sample purposes
+        - **[Jupyter Notebook](forecasting-forecast-function/auto-ml-forecasting-function.ipynb)**
+          - train a forecaster using a remote AML Compute cluster
+          - capabilities of forecast function (e.g. forecast farther into the horizon)
+          - generate confidence intervals
+- **Forecast Beverage Production**
+    - Dataset: [Monthly beer production data](forecasting-beer-remote/Beer_no_valid_split_train.csv)
+        - **[Jupyter Notebook](forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb)**
+          - train using a remote AML Compute cluster
+          - enable the DNN learning model
+          - forecast on a remote compute cluster and compare different model performance
+- **Continuous Retraining with NOAA Weather Data**
+    - Dataset: [NOAA weather data from Azure Open Datasets](https://azure.microsoft.com/en-us/services/open-datasets/)
+        - **[Jupyter Notebook](continuous-retraining/auto-ml-continuous-retraining.ipynb)**
+          - continuously retrain a model using Pipelines and AutoML
+          - create a Pipeline to upload a time series dataset to an Azure blob
+          - create a Pipeline to run an AutoML experiment and register the best resulting model in the Workspace
+          - publish the training pipeline created and schedule it to run daily
 
 <a name="documentation"></a>
 See [Configure automated machine learning experiments](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-auto-train) to learn how more about the the settings and features available for automated machine learning experiments.
@@ -179,7 +207,7 @@ The main code of the file must be indented so that it is under this condition.
 ## automl_setup fails
 1. On Windows, make sure that you are running automl_setup from an Anconda Prompt window rather than a regular cmd window.  You can launch the "Anaconda Prompt" window by hitting the Start button and typing "Anaconda Prompt".  If you don't see the application "Anaconda Prompt", you might not have conda or mini conda installed.  In that case, you can install it [here](https://conda.io/miniconda.html)
 2. Check that you have conda 64-bit installed rather than 32-bit.  You can check this with the command `conda info`.  The `platform` should be `win-64` for Windows or `osx-64` for Mac.
-3. Check that you have conda 4.4.10 or later.  You can check the version with the command `conda -V`.  If you have a previous version installed, you can update it using the command: `conda update conda`.
+3. Check that you have conda 4.7.8 or later.  You can check the version with the command `conda -V`.  If you have a previous version installed, you can update it using the command: `conda update conda`.
 4. On Linux, if the error is `gcc: error trying to exec 'cc1plus': execvp: No such file or directory`, install build essentials using the command `sudo apt-get install build-essential`.
 5. Pass a new name as the first parameter to automl_setup so that it creates a new conda environment. You can view existing conda environments using `conda env list` and remove them with `conda env remove -n <environmentname>`.
 
@@ -230,6 +258,15 @@ You may check the version of tensorflow and uninstall as follows
 2) enter `pip freeze` and look for `tensorflow` , if found, the version listed should be < 1.13
 3) If the listed version is a not a supported version,  `pip uninstall tensorflow` in the command shell and enter y for confirmation.
 
+## KeyError: 'brand' when running AutoML on local compute or Azure Databricks cluster**
+If a new environment was created after 10 June 2020 using SDK 1.7.0 or lower, training may fail with the above error due to an update in the py-cpuinfo package. (Environments created on or before 10 June 2020 are unaffected, as well as experiments run on remote compute as cached training images are used.) To work around this issue, either of the two following steps can be taken:
+
+1) Update the SDK version to 1.8.0 or higher (this will also downgrade py-cpuinfo to 5.0.0):
+`pip install --upgrade azureml-sdk[automl]`
+
+2) Downgrade the installed version of py-cpuinfo to 5.0.0:
+`pip install py-cpuinfo==5.0.0`
+
 ## Remote run: DsvmCompute.create fails
 There are several reasons why the DsvmCompute.create can fail.  The reason is usually in the error message but you have to look at the end of the error message for the detailed reason.  Some common reasons are:
 1) `Compute name is invalid, it should start with a letter, be between 2 and 16 character, and only include letters (a-zA-Z), numbers (0-9) and \'-\'.`  Note that underscore is not allowed in the name.

how-to-use-azureml/automated-machine-learning/automl_env.yml

@@ -1,37 +1,31 @@
 name: azure_automl
+channels:
+  - conda-forge
+  - pytorch
+  - main
 dependencies:
   # The python interpreter version.
-  # Currently Azure ML only supports 3.5.2 and later.
-- pip<=19.3.1
-- python>=3.5.2,<3.6.8
-- wheel==0.30.0
-- nb_conda
-- matplotlib==2.1.0
-- numpy>=1.16.0,<=1.16.2
-- cython
-- urllib3<1.24
-- scipy>=1.0.0,<=1.1.0
-- scikit-learn>=0.19.0,<=0.20.3
-- pandas>=0.22.0,<=0.23.4
-- py-xgboost<=0.80
-- fbprophet==0.5
-- pytorch=1.1.0
-- cudatoolkit=9.0
+  # Currently Azure ML only supports 3.6.0 and later.
+- pip==20.2.4
+- python>=3.6,<3.9
+- matplotlib==3.2.1
+- py-xgboost==1.3.3
+- pytorch::pytorch=1.4.0
+- conda-forge::fbprophet==0.7.1
+- cudatoolkit=10.1.243
+- scipy==1.5.3
+- notebook
+- pywin32==227
+- PySocks==1.7.1
+- jsonschema==4.6.0
+- conda-forge::pyqt==5.12.3
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-defaults
-  - azureml-train-automl
-  - azureml-train
-  - azureml-widgets
-  - azureml-pipeline
-  - azureml-contrib-interpret
+  - azureml-widgets~=1.43.0
   - pytorch-transformers==1.0.0
-  - spacy==2.1.8
-  - joblib
-  - onnxruntime==1.0.0
+  - spacy==2.2.4
+  - pystan==2.19.1.1
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-
-channels:
-- conda-forge
-- pytorch
+  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.43.0/validated_win32_requirements.txt [--no-deps]
+  - arch==4.14

how-to-use-azureml/automated-machine-learning/automl_env_linux.yml

@@ -0,0 +1,33 @@
+name: azure_automl
+channels:
+  - conda-forge
+  - pytorch
+  - main
+dependencies:
+  # The python interpreter version.
+  # Currently Azure ML only supports 3.6.0 and later.
+- pip==20.2.4
+- python>=3.6,<3.9
+- boto3==1.20.19
+- botocore<=1.23.19
+- matplotlib==3.2.1
+- numpy==1.19.5
+- cython==0.29.14
+- urllib3==1.26.7
+- scipy>=1.4.1,<=1.5.3
+- scikit-learn==0.22.1
+- py-xgboost<=1.3.3
+- holidays==0.10.3
+- conda-forge::fbprophet==0.7.1
+- pytorch::pytorch=1.4.0
+- cudatoolkit=10.1.243
+
+- pip:
+  # Required packages for AzureML execution, history, and data preparation.
+  - azureml-widgets~=1.43.0
+  - pytorch-transformers==1.0.0
+  - spacy==2.2.4
+  - pystan==2.19.1.1
+  - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
+  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.43.0/validated_linux_requirements.txt [--no-deps]
+  - arch==4.14

how-to-use-azureml/automated-machine-learning/automl_env_mac.yml

@@ -1,38 +1,34 @@
 name: azure_automl
+channels:
+  - conda-forge
+  - pytorch
+  - main
 dependencies:
   # The python interpreter version.
-  # Currently Azure ML only supports 3.5.2 and later.
-- pip<=19.3.1
+  # Currently Azure ML only supports 3.6.0 and later.
+- pip==20.2.4
 - nomkl
-- python>=3.5.2,<3.6.8
-- wheel==0.30.0
-- nb_conda
-- matplotlib==2.1.0
-- numpy>=1.16.0,<=1.16.2
-- cython
-- urllib3<1.24
-- scipy>=1.0.0,<=1.1.0
-- scikit-learn>=0.19.0,<=0.20.3
-- pandas>=0.22.0,<0.23.0
-- py-xgboost<=0.80
-- fbprophet==0.5
-- pytorch=1.1.0
+- python>=3.6,<3.9
+- boto3==1.20.19
+- botocore<=1.23.19
+- matplotlib==3.2.1
+- numpy==1.19.5
+- cython==0.29.14
+- urllib3==1.26.7
+- scipy>=1.4.1,<=1.5.3
+- scikit-learn==0.22.1
+- py-xgboost<=1.3.3
+- holidays==0.10.3
+- conda-forge::fbprophet==0.7.1
+- pytorch::pytorch=1.4.0
 - cudatoolkit=9.0
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-defaults
-  - azureml-train-automl
-  - azureml-train
-  - azureml-widgets
-  - azureml-pipeline
-  - azureml-contrib-interpret
+  - azureml-widgets~=1.43.0
   - pytorch-transformers==1.0.0
-  - spacy==2.1.8
-  - joblib
-  - onnxruntime==1.0.0
+  - spacy==2.2.4
+  - pystan==2.19.1.1
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-
-channels:
-- conda-forge
-- pytorch
+  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.43.0/validated_darwin_requirements.txt [--no-deps]
+  - arch==4.14

how-to-use-azureml/automated-machine-learning/automl_setup.cmd

@@ -6,11 +6,22 @@ set PIP_NO_WARN_SCRIPT_LOCATION=0
 
 IF "%conda_env_name%"=="" SET conda_env_name="azure_automl"
 IF "%automl_env_file%"=="" SET automl_env_file="automl_env.yml"
+SET check_conda_version_script="check_conda_version.py"
 
 IF NOT EXIST %automl_env_file% GOTO YmlMissing
 
 IF "%CONDA_EXE%"=="" GOTO CondaMissing
 
+IF NOT EXIST %check_conda_version_script% GOTO VersionCheckMissing
+
+python "%check_conda_version_script%"
+IF errorlevel 1 GOTO ErrorExit:
+
+SET replace_version_script="replace_latest_version.ps1"
+IF EXIST %replace_version_script% (
+  powershell -file %replace_version_script% %automl_env_file%
+)
+
 call conda activate %conda_env_name% 2>nul:
 
 if not errorlevel 1 (
@@ -54,6 +65,10 @@ echo If you are running an older version of Miniconda or Anaconda,
 echo you can upgrade using the command: conda update conda
 goto End
 
+:VersionCheckMissing
+echo File %check_conda_version_script% not found.
+goto End
+
 :YmlMissing
 echo File %automl_env_file% not found.
 

how-to-use-azureml/automated-machine-learning/automl_setup_linux.sh

@@ -4,6 +4,7 @@ CONDA_ENV_NAME=$1
 AUTOML_ENV_FILE=$2
 OPTIONS=$3
 PIP_NO_WARN_SCRIPT_LOCATION=0
+CHECK_CONDA_VERSION_SCRIPT="check_conda_version.py"
 
 if [ "$CONDA_ENV_NAME" == "" ]
 then
@@ -12,7 +13,7 @@ fi
 
 if [ "$AUTOML_ENV_FILE" == "" ]
 then
-  AUTOML_ENV_FILE="automl_env.yml"
+  AUTOML_ENV_FILE="automl_env_linux.yml"
 fi
 
 if [ ! -f $AUTOML_ENV_FILE ]; then
@@ -20,6 +21,18 @@ if [ ! -f $AUTOML_ENV_FILE ]; then
     exit 1
 fi
 
+if [ ! -f $CHECK_CONDA_VERSION_SCRIPT ]; then
+    echo "File $CHECK_CONDA_VERSION_SCRIPT not found"
+    exit 1
+fi
+
+python "$CHECK_CONDA_VERSION_SCRIPT"
+if [ $? -ne 0 ]; then
+    exit 1
+fi
+
+sed -i 's/AZUREML-SDK-VERSION/latest/' $AUTOML_ENV_FILE
+
 if source activate $CONDA_ENV_NAME 2> /dev/null
 then
    echo "Upgrading existing conda environment" $CONDA_ENV_NAME

how-to-use-azureml/automated-machine-learning/automl_setup_mac.sh

@@ -4,6 +4,7 @@ CONDA_ENV_NAME=$1
 AUTOML_ENV_FILE=$2
 OPTIONS=$3
 PIP_NO_WARN_SCRIPT_LOCATION=0
+CHECK_CONDA_VERSION_SCRIPT="check_conda_version.py"
 
 if [ "$CONDA_ENV_NAME" == "" ]
 then
@@ -20,6 +21,19 @@ if [ ! -f $AUTOML_ENV_FILE ]; then
     exit 1
 fi
 
+if [ ! -f $CHECK_CONDA_VERSION_SCRIPT ]; then
+    echo "File $CHECK_CONDA_VERSION_SCRIPT not found"
+    exit 1
+fi
+
+python "$CHECK_CONDA_VERSION_SCRIPT"
+if [ $? -ne 0 ]; then
+    exit 1
+fi
+
+sed -i '' 's/AZUREML-SDK-VERSION/latest/' $AUTOML_ENV_FILE
+brew install libomp
+
 if source activate $CONDA_ENV_NAME 2> /dev/null
 then
    echo "Upgrading existing conda environment" $CONDA_ENV_NAME

how-to-use-azureml/automated-machine-learning/check_conda_version.py

@@ -0,0 +1,26 @@
+from distutils.version import LooseVersion
+import platform
+
+try:
+    import conda
+except Exception:
+    print('Failed to import conda.')
+    print('This setup is usually run from the base conda environment.')
+    print('You can activate the base environment using the command "conda activate base"')
+    exit(1)
+
+architecture = platform.architecture()[0]
+
+if architecture != "64bit":
+    print('This setup requires 64bit Anaconda or Miniconda.  Found: ' + architecture)
+    exit(1)
+
+minimumVersion = "4.7.8"
+
+versionInvalid = (LooseVersion(conda.__version__) < LooseVersion(minimumVersion))
+
+if versionInvalid:
+    print('Setup requires conda version ' + minimumVersion + ' or higher.')
+    print('You can use the command "conda update conda" to upgrade conda.')
+
+exit(versionInvalid)

how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb

@@ -1,21 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing.png)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -30,6 +14,7 @@
         "1. [Results](#Results)\n",
         "1. [Deploy](#Deploy)\n",
         "1. [Test](#Test)\n",
+        "1. [Use auto-generated code for retraining](#Using-the-auto-generated-model-training-code-for-retraining-on-new-data)\n",
         "1. [Acknowledgements](#Acknowledgements)"
       ]
     },
@@ -41,7 +26,7 @@
         "\n",
         "In this example we use the UCI Bank Marketing dataset to showcase how you can use AutoML for a  classification problem and deploy it to an Azure Container Instance (ACI). The classification goal is to predict if the client will subscribe to a term deposit with the bank.\n",
         "\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set.  Otherwise, go through the [configuration](../../../configuration.ipynb)  notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
+        "If you are using an Azure Machine Learning Compute Instance, you are all set.  Otherwise, go through the [configuration](../../../configuration.ipynb)  notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
         "\n",
         "Please find the ONNX related documentations [here](https://github.com/onnx/onnx).\n",
         "\n",
@@ -55,9 +40,10 @@
         "7. Create a container image.\n",
         "8. Create an Azure Container Instance (ACI) service.\n",
         "9. Test the ACI service.\n",
+        "10. Leverage the auto generated training code and use it for retraining on an updated dataset\n",
         "\n",
         "In addition this notebook showcases the following features\n",
-        "- **Blacklisting** certain pipelines\n",
+        "- **Blocking** certain pipelines\n",
         "- Specifying **target metrics** to indicate stopping criteria\n",
         "- Handling **missing data** in the input"
       ]
@@ -74,9 +60,12 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "name": "automl-import"
+      },
       "outputs": [],
       "source": [
+        "import json\n",
         "import logging\n",
         "\n",
         "from matplotlib import pyplot as plt\n",
@@ -86,10 +75,16 @@
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-        "from azureml.automl.core.featurization import FeaturizationConfig\n",
         "from azureml.core.dataset import Dataset\n",
         "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.explain.model._internal.explanation_client import ExplanationClient"
+        "from azureml.interpret import ExplanationClient"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
       ]
     },
     {
@@ -121,25 +116,27 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "name": "ws-setup"
+      },
       "outputs": [],
       "source": [
         "ws = Workspace.from_config()\n",
         "\n",
         "# choose a name for experiment\n",
-        "experiment_name = 'automl-classification-bmarketing-all'\n",
+        "experiment_name = \"automl-classification-bmarketing-all\"\n",
         "\n",
-        "experiment=Experiment(ws, experiment_name)\n",
+        "experiment = Experiment(ws, experiment_name)\n",
         "\n",
         "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "output[\"Subscription ID\"] = ws.subscription_id\n",
+        "output[\"Workspace\"] = ws.name\n",
+        "output[\"Resource Group\"] = ws.resource_group\n",
+        "output[\"Location\"] = ws.location\n",
+        "output[\"Experiment Name\"] = experiment.name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"
       ]
     },
@@ -149,46 +146,38 @@
       "source": [
         "## Create or Attach existing AmlCompute\n",
         "You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
+        "\n",
         "#### Creation of AmlCompute takes approximately 5 minutes. \n",
         "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
-        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this article on the default limits and how to request more quota."
+        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": []
+      },
       "outputs": [],
       "source": [
-        "from azureml.core.compute import AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
-        "# Choose a name for your cluster.\n",
-        "amlcompute_cluster_name = \"cpu-cluster-4\"\n",
+        "# Choose a name for your CPU cluster\n",
+        "cpu_cluster_name = \"cpu-cluster-4\"\n",
         "\n",
-        "found = False\n",
-        "# Check if this compute target already exists in the workspace.\n",
-        "cts = ws.compute_targets\n",
-        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
-        "    found = True\n",
-        "    print('Found existing compute target.')\n",
-        "    compute_target = cts[amlcompute_cluster_name]\n",
-        "    \n",
-        "if not found:\n",
-        "    print('Creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-        "                                                                #vm_priority = 'lowpriority', # optional\n",
-        "                                                                max_nodes = 6)\n",
-        "\n",
-        "    # Create the cluster.\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "    \n",
-        "print('Checking cluster status...')\n",
-        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "    \n",
-        "# For a more detailed view of current AmlCompute status, use get_status()."
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
+        "    print(\"Found existing cluster, use it.\")\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(\n",
+        "        vm_size=\"STANDARD_DS12_V2\", max_nodes=6\n",
+        "    )\n",
+        "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
+        "compute_target.wait_for_completion(show_output=True)"
       ]
     },
     {
@@ -220,7 +209,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "data = pd.read_csv(\"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\")\n",
+        "data = pd.read_csv(\n",
+        "    \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
+        ")\n",
         "data.head()"
       ]
     },
@@ -235,7 +226,12 @@
         "\n",
         "missing_rate = 0.75\n",
         "n_missing_samples = int(np.floor(data.shape[0] * missing_rate))\n",
-        "missing_samples = np.hstack((np.zeros(data.shape[0] - n_missing_samples, dtype=np.bool), np.ones(n_missing_samples, dtype=np.bool)))\n",
+        "missing_samples = np.hstack(\n",
+        "    (\n",
+        "        np.zeros(data.shape[0] - n_missing_samples, dtype=np.bool),\n",
+        "        np.ones(n_missing_samples, dtype=np.bool),\n",
+        "    )\n",
+        ")\n",
         "rng = np.random.RandomState(0)\n",
         "rng.shuffle(missing_samples)\n",
         "missing_features = rng.randint(0, data.shape[1], n_missing_samples)\n",
@@ -248,19 +244,21 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "if not os.path.isdir('data'):\n",
-        "    os.mkdir('data')\n",
-        "    \n",
+        "if not os.path.isdir(\"data\"):\n",
+        "    os.mkdir(\"data\")\n",
         "# Save the train data to a csv to be uploaded to the datastore\n",
         "pd.DataFrame(data).to_csv(\"data/train_data.csv\", index=False)\n",
         "\n",
         "ds = ws.get_default_datastore()\n",
-        "ds.upload(src_dir='./data', target_path='bankmarketing', overwrite=True, show_progress=True)\n",
+        "ds.upload(\n",
+        "    src_dir=\"./data\", target_path=\"bankmarketing\", overwrite=True, show_progress=True\n",
+        ")\n",
         "\n",
-        " \n",
         "\n",
         "# Upload the training data as a tabular dataset for access during training on remote compute\n",
-        "train_data = Dataset.Tabular.from_delimited_files(path=ds.path('bankmarketing/train_data.csv'))\n",
+        "train_data = Dataset.Tabular.from_delimited_files(\n",
+        "    path=ds.path(\"bankmarketing/train_data.csv\")\n",
+        ")\n",
         "label = \"y\""
       ]
     },
@@ -311,8 +309,8 @@
         "|**task**|classification or regression or forecasting|\n",
         "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
         "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
-        "|**blacklist_models** | *List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run. <br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGD</i><br><i>MultinomialNaiveBayes</i><br><i>BernoulliNaiveBayes</i><br><i>SVM</i><br><i>LinearSVM</i><br><i>KNN</i><br><i>DecisionTree</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>GradientBoosting</i><br><i>TensorFlowDNN</i><br><i>TensorFlowLinearClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i><br><br>Allowed values for **Forecasting**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i><br><i>Arima</i><br><i>Prophet</i>|\n",
-        "| **whitelist_models** |  *List* of *strings* indicating machine learning algorithms for AutoML to use in this run. Same values listed above for **blacklist_models** allowed for **whitelist_models**.|\n",
+        "|**blocked_models** | *List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run. <br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGD</i><br><i>MultinomialNaiveBayes</i><br><i>BernoulliNaiveBayes</i><br><i>SVM</i><br><i>LinearSVM</i><br><i>KNN</i><br><i>DecisionTree</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>GradientBoosting</i><br><i>TensorFlowDNN</i><br><i>TensorFlowLinearClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i><br><br>Allowed values for **Forecasting**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i><br><i>Arima</i><br><i>Prophet</i>|\n",
+        "|**allowed_models** |  *List* of *strings* indicating machine learning algorithms for AutoML to use in this run. Same values listed above for **blocked_models** allowed for **allowed_models**.|\n",
         "|**experiment_exit_score**| Value indicating the target for *primary_metric*. <br>Once the target is surpassed the run terminates.|\n",
         "|**experiment_timeout_hours**| Maximum amount of time in hours that all iterations combined can take before the experiment terminates.|\n",
         "|**enable_early_stopping**| Flag to enble early termination if the score is not improving in the short term.|\n",
@@ -320,7 +318,7 @@
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**training_data**|Input dataset, containing both features and label column.|\n",
         "|**label_column_name**|The name of the label column.|\n",
-        "|**model_explainability**|Indicate to explain each trained pipeline or not.|\n",
+        "|**enable_code_generation**|Flag to enable generation of training code for each of the models that AutoML is creating.\n",
         "\n",
         "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
       ]
@@ -332,59 +330,57 @@
       "outputs": [],
       "source": [
         "automl_settings = {\n",
-        "    \"experiment_timeout_hours\" : 0.3,\n",
-        "    \"enable_early_stopping\" : True,\n",
+        "    \"experiment_timeout_hours\": 0.3,\n",
+        "    \"enable_early_stopping\": True,\n",
         "    \"iteration_timeout_minutes\": 5,\n",
         "    \"max_concurrent_iterations\": 4,\n",
         "    \"max_cores_per_iteration\": -1,\n",
-        "    #\"n_cross_validations\": 2,\n",
-        "    \"primary_metric\": 'AUC_weighted',\n",
-        "    \"featurization\": 'auto',\n",
+        "    # \"n_cross_validations\": 2,\n",
+        "    \"primary_metric\": \"AUC_weighted\",\n",
+        "    \"featurization\": \"auto\",\n",
         "    \"verbosity\": logging.INFO,\n",
+        "    \"enable_code_generation\": True,\n",
         "}\n",
         "\n",
-        "automl_config = AutoMLConfig(task = 'classification',\n",
-        "                             debug_log = 'automl_errors.log',\n",
+        "automl_config = AutoMLConfig(\n",
+        "    task=\"classification\",\n",
+        "    debug_log=\"automl_errors.log\",\n",
         "    compute_target=compute_target,\n",
-        "                             experiment_exit_score = 0.9984,\n",
-        "                             blacklist_models = ['KNN','LinearSVM'],\n",
+        "    experiment_exit_score=0.9984,\n",
+        "    blocked_models=[\"KNN\", \"LinearSVM\"],\n",
         "    enable_onnx_compatible_models=True,\n",
-        "                             training_data = train_data,\n",
-        "                             label_column_name = label,\n",
-        "                             validation_data = validation_dataset,\n",
-        "                             model_explainability=True,\n",
-        "                             **automl_settings\n",
-        "                            )"
+        "    training_data=train_data,\n",
+        "    label_column_name=label,\n",
+        "    validation_data=validation_dataset,\n",
+        "    **automl_settings,\n",
+        ")"
       ]
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "tags": []
+      },
       "source": [
-        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while."
+        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "remote_run = experiment.submit(automl_config, show_output = False)"
-      ]
+      "metadata": {
+        "name": "experiment-submit"
       },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
       "outputs": [],
       "source": [
-        "remote_run"
+        "remote_run = experiment.submit(automl_config, show_output=False)"
       ]
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "tags": []
+      },
       "source": [
         "Run the following cell to access previous runs. Uncomment the cell below and update the run_id."
       ]
@@ -395,11 +391,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "#from azureml.train.automl.run import AutoMLRun\n",
-        "#experiment_name = 'automl-classification-bmarketing'\n",
-        "#experiment = Experiment(ws, experiment_name)\n",
-        "#remote_run = AutoMLRun(experiment=experiment, run_id='<run_ID_goes_here')\n",
-        "#remote_run"
+        "# from azureml.train.automl.run import AutoMLRun\n",
+        "# remote_run = AutoMLRun(experiment=experiment, run_id='<run_ID_goes_here')\n",
+        "# remote_run"
       ]
     },
     {
@@ -418,7 +412,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "best_run_customized, fitted_model_customized = remote_run.get_output()"
+        "# Retrieve the best Run object\n",
+        "best_run = remote_run.get_best_child()"
       ]
     },
     {
@@ -427,7 +422,7 @@
       "source": [
         "## Transparency\n",
         "\n",
-        "View updated featurization summary"
+        "View featurization summary for the best model - to study how different features were transformed. This is stored as a JSON file in the outputs directory for the run."
       ]
     },
     {
@@ -436,36 +431,16 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "custom_featurizer = fitted_model_customized.named_steps['datatransformer']\n",
-        "df = custom_featurizer.get_featurization_summary()\n",
-        "pd.DataFrame(data=df)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Set `is_user_friendly=False` to get a more detailed summary for the transforms being applied."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "df = custom_featurizer.get_featurization_summary(is_user_friendly=False)\n",
-        "pd.DataFrame(data=df)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "df = custom_featurizer.get_stats_feature_type_summary()\n",
-        "pd.DataFrame(data=df)"
+        "# Download the featurization summary JSON file locally\n",
+        "best_run.download_file(\n",
+        "    \"outputs/featurization_summary.json\", \"featurization_summary.json\"\n",
+        ")\n",
+        "\n",
+        "# Render the JSON as a pandas DataFrame\n",
+        "with open(\"featurization_summary.json\", \"r\") as f:\n",
+        "    records = json.load(f)\n",
+        "\n",
+        "pd.DataFrame.from_records(records)"
       ]
     },
     {
@@ -478,11 +453,14 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "name": "run-details"
+      },
       "outputs": [],
       "source": [
         "from azureml.widgets import RunDetails\n",
-        "RunDetails(remote_run).show() "
+        "\n",
+        "RunDetails(remote_run).show()"
       ]
     },
     {
@@ -500,15 +478,17 @@
       "outputs": [],
       "source": [
         "# Wait for the best model explanation run to complete\n",
-        "from azureml.train.automl.run import AutoMLRun\n",
-        "model_explainability_run_id = remote_run.get_properties().get('ModelExplainRunId')\n",
+        "from azureml.core.run import Run\n",
+        "\n",
+        "model_explainability_run_id = remote_run.id + \"_\" + \"ModelExplain\"\n",
         "print(model_explainability_run_id)\n",
-        "if model_explainability_run_id is not None:\n",
-        "    model_explainability_run = AutoMLRun(experiment=experiment, run_id=model_explainability_run_id)\n",
-        "    model_explainability_run.wait_for_completion()\n",
+        "model_explainability_run = Run(\n",
+        "    experiment=experiment, run_id=model_explainability_run_id\n",
+        ")\n",
+        "model_explainability_run.wait_for_completion()\n",
         "\n",
         "# Get the best run object\n",
-        "best_run, fitted_model = remote_run.get_output()"
+        "best_run = remote_run.get_best_child()"
       ]
     },
     {
@@ -585,6 +565,7 @@
       "outputs": [],
       "source": [
         "from azureml.automl.runtime.onnx_convert import OnnxConverter\n",
+        "\n",
         "onnx_fl_path = \"./best_model.onnx\"\n",
         "OnnxConverter.save_onnx_model(onnx_mdl, onnx_fl_path)"
       ]
@@ -607,33 +588,31 @@
         "from azureml.automl.core.onnx_convert import OnnxConvertConstants\n",
         "from azureml.train.automl import constants\n",
         "\n",
-        "if sys.version_info < OnnxConvertConstants.OnnxIncompatiblePythonVersion:\n",
-        "    python_version_compatible = True\n",
-        "else:\n",
-        "    python_version_compatible = False\n",
-        "\n",
-        "import onnxruntime\n",
         "from azureml.automl.runtime.onnx_convert import OnnxInferenceHelper\n",
         "\n",
-        "def get_onnx_res(run):\n",
-        "    res_path = 'onnx_resource.json'\n",
-        "    run.download_file(name=constants.MODEL_RESOURCE_PATH_ONNX, output_file_path=res_path)\n",
-        "    with open(res_path) as f:\n",
-        "        onnx_res = json.load(f)\n",
-        "    return onnx_res\n",
         "\n",
-        "if python_version_compatible:\n",
+        "def get_onnx_res(run):\n",
+        "    res_path = \"onnx_resource.json\"\n",
+        "    run.download_file(\n",
+        "        name=constants.MODEL_RESOURCE_PATH_ONNX, output_file_path=res_path\n",
+        "    )\n",
+        "    with open(res_path) as f:\n",
+        "        result = json.load(f)\n",
+        "    return result\n",
+        "\n",
+        "\n",
+        "if sys.version_info < OnnxConvertConstants.OnnxIncompatiblePythonVersion:\n",
         "    test_df = test_dataset.to_pandas_dataframe()\n",
         "    mdl_bytes = onnx_mdl.SerializeToString()\n",
-        "    onnx_res = get_onnx_res(best_run)\n",
+        "    onnx_result = get_onnx_res(best_run)\n",
         "\n",
-        "    onnxrt_helper = OnnxInferenceHelper(mdl_bytes, onnx_res)\n",
+        "    onnxrt_helper = OnnxInferenceHelper(mdl_bytes, onnx_result)\n",
         "    pred_onnx, pred_prob_onnx = onnxrt_helper.predict(test_df)\n",
         "\n",
         "    print(pred_onnx)\n",
         "    print(pred_prob_onnx)\n",
         "else:\n",
-        "    print('Please use Python version 3.6 or 3.7 to run the inference helper.')"
+        "    print(\"Please use Python version 3.6 or 3.7 to run the inference helper.\")"
       ]
     },
     {
@@ -644,7 +623,16 @@
         "\n",
         "### Retrieve the Best Model\n",
         "\n",
-        "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+        "Below we select the best pipeline from our iterations.  The `get_best_child` method returns the Run object for the best model based on the default primary metric. There are additional flags that can be passed to the method if we want to retrieve the best Run based on any of the other supported metrics, or if we are just interested in the best run among the ONNX compatible runs. As always, you can execute `??remote_run.get_best_child` in a new cell to view the source or docs for the function."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "??remote_run.get_best_child"
       ]
     },
     {
@@ -664,7 +652,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "best_run, fitted_model = remote_run.get_output()"
+        "best_run = remote_run.get_best_child()"
       ]
     },
     {
@@ -673,13 +661,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "model_name = best_run.properties['model_name']\n",
+        "model_name = best_run.properties[\"model_name\"]\n",
         "\n",
-        "script_file_name = 'inference/score.py'\n",
-        "conda_env_file_name = 'inference/env.yml'\n",
+        "script_file_name = \"inference/score.py\"\n",
         "\n",
-        "best_run.download_file('outputs/scoring_file_v_1_0_0.py', 'inference/score.py')\n",
-        "best_run.download_file('outputs/conda_env_v_1_0_0.yml', 'inference/env.yml')"
+        "best_run.download_file(\"outputs/scoring_file_v_1_0_0.py\", \"inference/score.py\")"
       ]
     },
     {
@@ -696,11 +682,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "description = 'AutoML Model trained on bank marketing data to predict if a client will subscribe to a term deposit'\n",
+        "description = \"AutoML Model trained on bank marketing data to predict if a client will subscribe to a term deposit\"\n",
         "tags = None\n",
-        "model = remote_run.register_model(model_name = model_name, description = description, tags = tags)\n",
+        "model = remote_run.register_model(\n",
+        "    model_name=model_name, description=description, tags=tags\n",
+        ")\n",
         "\n",
-        "print(remote_run.model_id) # This will be written to the script file later in the notebook."
+        "print(\n",
+        "    remote_run.model_id\n",
+        ")  # This will be written to the script file later in the notebook."
       ]
     },
     {
@@ -722,39 +712,22 @@
         "from azureml.core.model import Model\n",
         "from azureml.core.environment import Environment\n",
         "\n",
-        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=conda_env_file_name)\n",
-        "inference_config = InferenceConfig(entry_script=script_file_name, environment=myenv)\n",
+        "inference_config = InferenceConfig(entry_script=script_file_name)\n",
         "\n",
-        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
-        "                                               memory_gb = 1, \n",
-        "                                               tags = {'area': \"bmData\", 'type': \"automl_classification\"}, \n",
-        "                                               description = 'sample service for Automl Classification')\n",
+        "aciconfig = AciWebservice.deploy_configuration(\n",
+        "    cpu_cores=2,\n",
+        "    memory_gb=2,\n",
+        "    tags={\"area\": \"bmData\", \"type\": \"automl_classification\"},\n",
+        "    description=\"sample service for Automl Classification\",\n",
+        ")\n",
         "\n",
-        "aci_service_name = 'automl-sample-bankmarketing-all'\n",
+        "aci_service_name = model_name.lower()\n",
         "print(aci_service_name)\n",
         "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
         "aci_service.wait_for_deployment(True)\n",
         "print(aci_service.state)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Delete a Web Service\n",
-        "\n",
-        "Deletes the specified web service."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#aci_service.delete()"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -770,7 +743,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "#aci_service.get_logs()"
+        "# aci_service.get_logs()"
       ]
     },
     {
@@ -779,7 +752,9 @@
       "source": [
         "## Test\n",
         "\n",
-        "Now that the model is trained, run the test data through the trained model to get the predicted values."
+        "Now that the model is trained, run the test data through the trained model to get the predicted values.  This calls the ACI web service to do the prediction.\n",
+        "\n",
+        "Note that the JSON passed to the ACI web service is an array of rows of data.  Each row should either be an array of values in the same order that was used for training or a dictionary where the keys are the same as the column names used for training.  The example below uses dictionary rows."
       ]
     },
     {
@@ -798,8 +773,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "X_test = test_dataset.drop_columns(columns=['y'])\n",
-        "y_test = test_dataset.keep_columns(columns=['y'], validate=True)\n",
+        "X_test = test_dataset.drop_columns(columns=[\"y\"])\n",
+        "y_test = test_dataset.keep_columns(columns=[\"y\"], validate=True)\n",
         "test_dataset.take(5).to_pandas_dataframe()"
       ]
     },
@@ -819,20 +794,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "y_pred  = fitted_model.predict(X_test)\n",
-        "actual = array(y_test)\n",
-        "actual = actual[:,0]\n",
-        "print(y_pred.shape, \" \", actual.shape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Calculate metrics for the prediction\n",
+        "import requests\n",
         "\n",
-        "Now visualize the data on a scatter plot to show what our truth (actual) values are compared to the predicted values \n",
-        "from the trained model that was returned."
+        "X_test_json = X_test.to_json(orient=\"records\")\n",
+        "data = '{\"data\": ' + X_test_json + \"}\"\n",
+        "headers = {\"Content-Type\": \"application/json\"}\n",
+        "\n",
+        "resp = requests.post(aci_service.scoring_uri, data, headers=headers)\n",
+        "\n",
+        "y_pred = json.loads(json.loads(resp.text))[\"result\"]"
       ]
     },
     {
@@ -840,14 +810,210 @@
       "execution_count": null,
       "metadata": {},
       "outputs": [],
+      "source": [
+        "actual = array(y_test)\n",
+        "actual = actual[:, 0]\n",
+        "print(len(y_pred), \" \", len(actual))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Calculate metrics for the prediction\n",
+        "\n",
+        "Now visualize the data as a confusion matrix that compared the predicted values against the actual values.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "scrolled": true
+      },
+      "outputs": [],
       "source": [
         "%matplotlib notebook\n",
-        "test_pred = plt.scatter(actual, y_pred, color='b')\n",
-        "test_test = plt.scatter(actual, actual, color='g')\n",
-        "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
+        "from sklearn.metrics import confusion_matrix\n",
+        "import itertools\n",
+        "\n",
+        "cf = confusion_matrix(actual, y_pred)\n",
+        "plt.imshow(cf, cmap=plt.cm.Blues, interpolation=\"nearest\")\n",
+        "plt.colorbar()\n",
+        "plt.title(\"Confusion Matrix\")\n",
+        "plt.xlabel(\"Predicted\")\n",
+        "plt.ylabel(\"Actual\")\n",
+        "class_labels = [\"no\", \"yes\"]\n",
+        "tick_marks = np.arange(len(class_labels))\n",
+        "plt.xticks(tick_marks, class_labels)\n",
+        "plt.yticks([-0.5, 0, 1, 1.5], [\"\", \"no\", \"yes\", \"\"])\n",
+        "# plotting text value inside cells\n",
+        "thresh = cf.max() / 2.0\n",
+        "for i, j in itertools.product(range(cf.shape[0]), range(cf.shape[1])):\n",
+        "    plt.text(\n",
+        "        j,\n",
+        "        i,\n",
+        "        format(cf[i, j], \"d\"),\n",
+        "        horizontalalignment=\"center\",\n",
+        "        color=\"white\" if cf[i, j] > thresh else \"black\",\n",
+        "    )\n",
         "plt.show()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Delete a Web Service\n",
+        "\n",
+        "Deletes the specified web service."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "aci_service.delete()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Using the auto generated model training code for retraining on new data\n",
+        "\n",
+        "Because we enabled code generation when the original experiment was created, we now have access to the code that was used to generate any of the AutoML tried models. Below we'll be using the generated training script of the best model to retrain on a new dataset.\n",
+        "\n",
+        "For this demo, we'll begin by creating new retraining dataset by combining the Train & Validation datasets that were used in the original experiment."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "original_train_data = pd.read_csv(\n",
+        "    \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
+        ")\n",
+        "\n",
+        "valid_data = pd.read_csv(\n",
+        "    \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_validate.csv\"\n",
+        ")\n",
+        "\n",
+        "# we'll emulate an updated dataset for retraining by combining the Train & Validation datasets into a new one\n",
+        "retrain_pd = pd.concat([original_train_data, valid_data])\n",
+        "retrain_pd.to_csv(\"data/retrain_data.csv\", index=False)\n",
+        "ds.upload_files(\n",
+        "    files=[\"data/retrain_data.csv\"],\n",
+        "    target_path=\"bankmarketing/\",\n",
+        "    overwrite=True,\n",
+        "    show_progress=True,\n",
+        ")\n",
+        "retrain_dataset = Dataset.Tabular.from_delimited_files(\n",
+        "    path=ds.path(\"bankmarketing/retrain_data.csv\")\n",
+        ")\n",
+        "\n",
+        "# after creating and uploading the retraining dataset, let's register it with the workspace for reuse\n",
+        "retrain_dataset = retrain_dataset.register(\n",
+        "    workspace=ws,\n",
+        "    name=\"Bankmarketing_retrain\",\n",
+        "    description=\"Updated training dataset, includes validation data\",\n",
+        "    create_new_version=True,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Next, we'll download the generated script for the best run and use it for retraining. For more advanced scenarios, you can customize the training script as you need: change the featurization pipeline, change the learner algorithm or its hyperparameters, etc. \n",
+        "\n",
+        "For this exercise, we'll leave the script as it was generated."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# download the autogenerated training script into the generated_code folder\n",
+        "best_run.download_file(\n",
+        "    \"outputs/generated_code/script.py\", \"generated_code/training_script.py\"\n",
+        ")\n",
+        "\n",
+        "# view the contents of the autogenerated training script\n",
+        "! cat generated_code/training_script.py"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import uuid\n",
+        "from azureml.core import ScriptRunConfig\n",
+        "from azureml._restclient.models import RunTypeV2\n",
+        "from azureml._restclient.models.create_run_dto import CreateRunDto\n",
+        "from azureml._restclient.run_client import RunClient\n",
+        "\n",
+        "codegen_runid = str(uuid.uuid4())\n",
+        "client = RunClient(\n",
+        "    experiment.workspace.service_context,\n",
+        "    experiment.name,\n",
+        "    codegen_runid,\n",
+        "    experiment_id=experiment.id,\n",
+        ")\n",
+        "\n",
+        "# override the training_dataset_id to point to our new retraining dataset we just registered above\n",
+        "dataset_arguments = [\"--training_dataset_id\", retrain_dataset.id]\n",
+        "\n",
+        "# create the retraining run as a child of the AutoML generated training run\n",
+        "create_run_dto = CreateRunDto(\n",
+        "    run_id=codegen_runid,\n",
+        "    parent_run_id=best_run.id,\n",
+        "    description=\"AutoML Codegen Script Run using an updated training dataset\",\n",
+        "    target=cpu_cluster_name,\n",
+        "    run_type_v2=RunTypeV2(orchestrator=\"Execution\", traits=[\"automl-codegen\"]),\n",
+        ")\n",
+        "\n",
+        "# the script for retraining run is pointing to the AutoML generated script\n",
+        "src = ScriptRunConfig(\n",
+        "    source_directory=\"generated_code\",\n",
+        "    script=\"training_script.py\",\n",
+        "    arguments=dataset_arguments,\n",
+        "    compute_target=cpu_cluster_name,\n",
+        "    environment=best_run.get_environment(),\n",
+        ")\n",
+        "run_dto = client.create_run(run_id=codegen_runid, create_run_dto=create_run_dto)\n",
+        "\n",
+        "# submit the experiment\n",
+        "retraining_run = experiment.submit(config=src, run_id=codegen_runid)\n",
+        "retraining_run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "After the run completes, we can get download/test/deploy to the model it has built."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "retraining_run.wait_for_completion()\n",
+        "\n",
+        "retraining_run.download_file(\"outputs/model.pkl\", \"generated_code/model.pkl\")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -871,7 +1037,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "anumamah"
+        "name": "ratanase"
       }
     ],
     "category": "tutorial",
@@ -890,6 +1056,9 @@
     ],
     "friendly_name": "Automated ML run with basic edition features.",
     "index_order": 5,
+    "kernel_info": {
+      "name": "python3-azureml"
+    },
     "kernelspec": {
       "display_name": "Python 3.6",
       "language": "python",
@@ -905,7 +1074,10 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.6.9"
+    },
+    "nteract": {
+      "version": "nteract-front-end@1.0.0"
     },
     "tags": [
       "featurization",
@@ -916,5 +1088,5 @@
     "task": "Classification"
   },
   "nbformat": 4,
-  "nbformat_minor": 2
+  "nbformat_minor": 1
 }

how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.yml

@@ -2,10 +2,3 @@ name: auto-ml-classification-bank-marketing-all-features
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - interpret
-  - onnxruntime==1.0.0
-  - azureml-explain-model
-  - azureml-contrib-interpret

how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb

@@ -1,21 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.png)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -42,7 +26,7 @@
         "\n",
         "This notebook is using remote compute to train the model.\n",
         "\n",
-        "If you are using an Azure Machine Learning [Notebook VM](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-1st-experiment-sdk-setup), you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
+        "If you are using an Azure Machine Learning Compute Instance, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
         "\n",
         "In this notebook you will learn how to:\n",
         "1. Create an experiment using an existing workspace.\n",
@@ -80,6 +64,13 @@
         "from azureml.train.automl import AutoMLConfig"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -89,19 +80,19 @@
         "ws = Workspace.from_config()\n",
         "\n",
         "# choose a name for experiment\n",
-        "experiment_name = 'automl-classification-ccard-remote'\n",
+        "experiment_name = \"automl-classification-ccard-remote\"\n",
         "\n",
-        "experiment=Experiment(ws, experiment_name)\n",
+        "experiment = Experiment(ws, experiment_name)\n",
         "\n",
         "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "output[\"Subscription ID\"] = ws.subscription_id\n",
+        "output[\"Workspace\"] = ws.name\n",
+        "output[\"Resource Group\"] = ws.resource_group\n",
+        "output[\"Location\"] = ws.location\n",
+        "output[\"Experiment Name\"] = experiment.name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"
       ]
     },
@@ -111,6 +102,9 @@
       "source": [
         "## Create or Attach existing AmlCompute\n",
         "A compute target is required to execute the Automated ML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
+        "\n",
         "#### Creation of AmlCompute takes approximately 5 minutes. \n",
         "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
         "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
@@ -122,35 +116,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
-        "# Choose a name for your AmlCompute cluster.\n",
-        "amlcompute_cluster_name = \"cpu-cluster-1\"\n",
+        "# Choose a name for your CPU cluster\n",
+        "cpu_cluster_name = \"cpu-cluster-1\"\n",
         "\n",
-        "found = False\n",
-        "# Check if this compute target already exists in the workspace.\n",
-        "cts = ws.compute_targets\n",
-        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'cpu-cluster-1':\n",
-        "    found = True\n",
-        "    print('Found existing compute target.')\n",
-        "    compute_target = cts[amlcompute_cluster_name]\n",
-        "    \n",
-        "if not found:\n",
-        "    print('Creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_DS12_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-        "                                                                #vm_priority = 'lowpriority', # optional\n",
-        "                                                                max_nodes = 6)\n",
-        "\n",
-        "    # Create the cluster.\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "    \n",
-        "print('Checking cluster status...')\n",
-        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "\n",
-        "# For a more detailed view of current AmlCompute status, use get_status()."
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
+        "    print(\"Found existing cluster, use it.\")\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(\n",
+        "        vm_size=\"STANDARD_DS12_V2\", max_nodes=6\n",
+        "    )\n",
+        "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
+        "compute_target.wait_for_completion(show_output=True)"
       ]
     },
     {
@@ -172,13 +153,15 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "name": "load-data"
+      },
       "outputs": [],
       "source": [
         "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/creditcard.csv\"\n",
         "dataset = Dataset.Tabular.from_delimited_files(data)\n",
         "training_data, validation_data = dataset.random_split(percentage=0.8, seed=223)\n",
-        "label_column_name = 'Class'"
+        "label_column_name = \"Class\""
       ]
     },
     {
@@ -204,32 +187,35 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "name": "automl-config"
+      },
       "outputs": [],
       "source": [
         "automl_settings = {\n",
         "    \"n_cross_validations\": 3,\n",
-        "    \"primary_metric\": 'average_precision_score_weighted',\n",
+        "    \"primary_metric\": \"average_precision_score_weighted\",\n",
         "    \"enable_early_stopping\": True,\n",
         "    \"max_concurrent_iterations\": 2,  # This is a limit for testing purpose, please increase it as per cluster size\n",
         "    \"experiment_timeout_hours\": 0.25,  # This is a time limit for testing purposes, remove it for real use cases, this will drastically limit ablity to find the best model possible\n",
         "    \"verbosity\": logging.INFO,\n",
         "}\n",
         "\n",
-        "automl_config = AutoMLConfig(task = 'classification',\n",
-        "                             debug_log = 'automl_errors.log',\n",
-        "                             compute_target = compute_target,\n",
-        "                             training_data = training_data,\n",
-        "                             label_column_name = label_column_name,\n",
-        "                             **automl_settings\n",
-        "                            )"
+        "automl_config = AutoMLConfig(\n",
+        "    task=\"classification\",\n",
+        "    debug_log=\"automl_errors.log\",\n",
+        "    compute_target=compute_target,\n",
+        "    training_data=training_data,\n",
+        "    label_column_name=label_column_name,\n",
+        "    **automl_settings,\n",
+        ")"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Call the `submit` method on the experiment object and pass the run configuration. Depending on the data and the number of iterations this can run for a while."
+        "Call the `submit` method on the experiment object and pass the run configuration. Depending on the data and the number of iterations this can run for a while. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
       ]
     },
     {
@@ -238,7 +224,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "remote_run = experiment.submit(automl_config, show_output = False)"
+        "remote_run = experiment.submit(automl_config, show_output=False)"
       ]
     },
     {
@@ -248,17 +234,8 @@
       "outputs": [],
       "source": [
         "# If you need to retrieve a run that already started, use the following code\n",
-        "#from azureml.train.automl.run import AutoMLRun\n",
-        "#remote_run = AutoMLRun(experiment = experiment, run_id = '<replace with your run id>')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "remote_run"
+        "# from azureml.train.automl.run import AutoMLRun\n",
+        "# remote_run = AutoMLRun(experiment = experiment, run_id = '<replace with your run id>')"
       ]
     },
     {
@@ -290,6 +267,7 @@
       "outputs": [],
       "source": [
         "from azureml.widgets import RunDetails\n",
+        "\n",
         "RunDetails(remote_run).show()"
       ]
     },
@@ -319,7 +297,7 @@
         "\n",
         "### Retrieve the Best Model\n",
         "\n",
-        "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
       ]
     },
     {
@@ -356,8 +334,12 @@
       "outputs": [],
       "source": [
         "# convert the test data to dataframe\n",
-        "X_test_df = validation_data.drop_columns(columns=[label_column_name]).to_pandas_dataframe()\n",
-        "y_test_df = validation_data.keep_columns(columns=[label_column_name], validate=True).to_pandas_dataframe()"
+        "X_test_df = validation_data.drop_columns(\n",
+        "    columns=[label_column_name]\n",
+        ").to_pandas_dataframe()\n",
+        "y_test_df = validation_data.keep_columns(\n",
+        "    columns=[label_column_name], validate=True\n",
+        ").to_pandas_dataframe()"
       ]
     },
     {
@@ -391,20 +373,26 @@
         "import numpy as np\n",
         "import itertools\n",
         "\n",
-        "cf =confusion_matrix(y_test_df.values,y_pred)\n",
-        "plt.imshow(cf,cmap=plt.cm.Blues,interpolation='nearest')\n",
+        "cf = confusion_matrix(y_test_df.values, y_pred)\n",
+        "plt.imshow(cf, cmap=plt.cm.Blues, interpolation=\"nearest\")\n",
         "plt.colorbar()\n",
-        "plt.title('Confusion Matrix')\n",
-        "plt.xlabel('Predicted')\n",
-        "plt.ylabel('Actual')\n",
-        "class_labels = ['False','True']\n",
+        "plt.title(\"Confusion Matrix\")\n",
+        "plt.xlabel(\"Predicted\")\n",
+        "plt.ylabel(\"Actual\")\n",
+        "class_labels = [\"False\", \"True\"]\n",
         "tick_marks = np.arange(len(class_labels))\n",
-        "plt.xticks(tick_marks,class_labels)\n",
-        "plt.yticks([-0.5,0,1,1.5],['','False','True',''])\n",
+        "plt.xticks(tick_marks, class_labels)\n",
+        "plt.yticks([-0.5, 0, 1, 1.5], [\"\", \"False\", \"True\", \"\"])\n",
         "# plotting text value inside cells\n",
-        "thresh = cf.max() / 2.\n",
-        "for i,j in itertools.product(range(cf.shape[0]),range(cf.shape[1])):\n",
-        "    plt.text(j,i,format(cf[i,j],'d'),horizontalalignment='center',color='white' if cf[i,j] >thresh else 'black')\n",
+        "thresh = cf.max() / 2.0\n",
+        "for i, j in itertools.product(range(cf.shape[0]), range(cf.shape[1])):\n",
+        "    plt.text(\n",
+        "        j,\n",
+        "        i,\n",
+        "        format(cf[i, j], \"d\"),\n",
+        "        horizontalalignment=\"center\",\n",
+        "        color=\"white\" if cf[i, j] > thresh else \"black\",\n",
+        "    )\n",
         "plt.show()"
       ]
     },
@@ -421,22 +409,33 @@
       "source": [
         "This Credit Card fraud Detection dataset is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/ and is available at: https://www.kaggle.com/mlg-ulb/creditcardfraud\n",
         "\n",
+        "The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Universit\u00c3\u00a9 Libre de Bruxelles) on big data mining and fraud detection.\n",
+        "More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project\n",
         "\n",
-        "The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Universit\u00c3\u0192\u00c2\u00a9 Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project\n",
-        "Please cite the following works: \n",
-        "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tAndrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015\n",
-        "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tDal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon\n",
-        "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tDal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE\n",
-        "o\tDal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi)\n",
-        "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tCarcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-A\u00c3\u0192\u00c2\u00abl; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier\n",
-        "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tCarcillo, Fabrizio; Le Borgne, Yann-A\u00c3\u0192\u00c2\u00abl; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing"
+        "Please cite the following works:\n",
+        "\n",
+        "Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015\n",
+        "\n",
+        "Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon\n",
+        "\n",
+        "Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE\n",
+        "\n",
+        "Dal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi)\n",
+        "\n",
+        "Carcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-A\u00c3\u00abl; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier\n",
+        "\n",
+        "Carcillo, Fabrizio; Le Borgne, Yann-A\u00c3\u00abl; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing\n",
+        "\n",
+        "Bertrand Lebichot, Yann-A\u00c3\u00abl Le Borgne, Liyun He, Frederic Obl\u00c3\u00a9, Gianluca Bontempi Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection, INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019\n",
+        "\n",
+        "Fabrizio Carcillo, Yann-A\u00c3\u00abl Le Borgne, Olivier Caelen, Frederic Obl\u00c3\u00a9, Gianluca Bontempi Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection Information Sciences, 2019"
       ]
     }
   ],
   "metadata": {
     "authors": [
       {
-        "name": "tzvikei"
+        "name": "ratanase"
       }
     ],
     "category": "tutorial",

how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.yml

@@ -2,8 +2,3 @@ name: auto-ml-classification-credit-card-fraud
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - interpret
-  - azureml-explain-model

how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb

@@ -1,21 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.png)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -42,13 +26,12 @@
         "\n",
         "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
         "\n",
-        "An Enterprise workspace is required for this notebook. To learn more about creating an Enterprise workspace or upgrading to an Enterprise workspace from the Azure portal, please visit our [Workspace page](https://docs.microsoft.com/azure/machine-learning/service/concept-workspace#upgrade).\n",
-        "\n",
         "Notebook synopsis:\n",
+        "\n",
         "1. Creating an Experiment in an existing Workspace\n",
         "2. Configuration and remote run of AutoML for a text dataset (20 Newsgroups dataset from scikit-learn) for classification\n",
-        "3. Evaluating the final model on a test set\n",
-        "4. Deploying the model on ACI"
+        "3. Registering the best model for future use\n",
+        "4. Evaluating the final model on a test set"
       ]
     },
     {
@@ -64,6 +47,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "import json\n",
         "import logging\n",
         "import os\n",
         "import shutil\n",
@@ -78,12 +62,19 @@
         "from azureml.core.compute import ComputeTarget\n",
         "from azureml.core.run import Run\n",
         "from azureml.widgets import RunDetails\n",
-        "from azureml.core.model import Model \n",
+        "from azureml.core.model import Model\n",
         "from helper import run_inference, get_result_df\n",
         "from azureml.train.automl import AutoMLConfig\n",
         "from sklearn.datasets import fetch_20newsgroups"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -100,19 +91,19 @@
         "ws = Workspace.from_config()\n",
         "\n",
         "# Choose an experiment name.\n",
-        "experiment_name = 'automl-classification-text-dnn'\n",
+        "experiment_name = \"automl-classification-text-dnn\"\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
         "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace Name'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "output[\"Subscription ID\"] = ws.subscription_id\n",
+        "output[\"Workspace Name\"] = ws.name\n",
+        "output[\"Resource Group\"] = ws.resource_group\n",
+        "output[\"Location\"] = ws.location\n",
+        "output[\"Experiment Name\"] = experiment.name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"
       ]
     },
@@ -123,6 +114,8 @@
         "## Set up a compute cluster\n",
         "This section uses a user-provided compute cluster (named \"dnntext-cluster\" in this example). If a cluster with this name does not exist in the user's workspace, the below code will create a new cluster. You can choose the parameters of the cluster as mentioned in the comments.\n",
         "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
+        "\n",
         "Whether you provide/select a CPU or GPU cluster, AutoML will choose the appropriate DNN for that setup - BiLSTM or BERT text featurizer will be included in the candidate featurizers on CPU and GPU respectively.  If your goal is to obtain the most accurate model, we recommend you use GPU clusters since BERT featurizers usually outperform BiLSTM featurizers."
       ]
     },
@@ -132,34 +125,30 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "num_nodes = 2\n",
+        "\n",
         "# Choose a name for your cluster.\n",
         "amlcompute_cluster_name = \"dnntext-cluster\"\n",
         "\n",
-        "found = False\n",
-        "# Check if this compute target already exists in the workspace.\n",
-        "cts = ws.compute_targets\n",
-        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
-        "    found = True\n",
-        "    print('Found existing compute target.')\n",
-        "    compute_target = cts[amlcompute_cluster_name]\n",
-        "\n",
-        "if not found:\n",
-        "    print('Creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_NC6\", # CPU for BiLSTM, such as \"STANDARD_D2_V2\" \n",
-        "                                                                # To use BERT (this is recommended for best performance), select a GPU such as \"STANDARD_NC6\" \n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
+        "    print(\"Found existing cluster, use it.\")\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(\n",
+        "        vm_size=\"STANDARD_NC6\",  # CPU for BiLSTM, such as \"STANDARD_D2_V2\"\n",
+        "        # To use BERT (this is recommended for best performance), select a GPU such as \"STANDARD_NC6\"\n",
         "        # or similar GPU option\n",
         "        # available in your workspace\n",
-        "                                                                max_nodes = 1)\n",
+        "        idle_seconds_before_scaledown=60,\n",
+        "        max_nodes=num_nodes,\n",
+        "    )\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",
-        "    # Create the cluster\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "\n",
-        "print('Checking cluster status...')\n",
-        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "\n",
-        "# For a more detailed view of current AmlCompute status, use get_status()."
+        "compute_target.wait_for_completion(show_output=True)"
       ]
     },
     {
@@ -178,39 +167,53 @@
       "source": [
         "data_dir = \"text-dnn-data\"  # Local directory to store data\n",
         "blobstore_datadir = data_dir  # Blob store directory to store data in\n",
-        "target_column_name = 'y'\n",
-        "feature_column_name = 'X'\n",
+        "target_column_name = \"y\"\n",
+        "feature_column_name = \"X\"\n",
+        "\n",
         "\n",
         "def get_20newsgroups_data():\n",
-        "    '''Fetches 20 Newsgroups data from scikit-learn\n",
+        "    \"\"\"Fetches 20 Newsgroups data from scikit-learn\n",
         "    Returns them in form of pandas dataframes\n",
-        "    '''\n",
-        "    remove = ('headers', 'footers', 'quotes')\n",
+        "    \"\"\"\n",
+        "    remove = (\"headers\", \"footers\", \"quotes\")\n",
         "    categories = [\n",
-        "        'alt.atheism',\n",
-        "        'talk.religion.misc',\n",
-        "        'comp.graphics',\n",
-        "        'sci.space',\n",
+        "        \"rec.sport.baseball\",\n",
+        "        \"rec.sport.hockey\",\n",
+        "        \"comp.graphics\",\n",
+        "        \"sci.space\",\n",
         "    ]\n",
         "\n",
-        "    data = fetch_20newsgroups(subset = 'train', categories = categories,\n",
-        "                                    shuffle = True, random_state = 42,\n",
-        "                                    remove = remove)\n",
-        "    data = pd.DataFrame({feature_column_name: data.data, target_column_name: data.target})\n",
+        "    data = fetch_20newsgroups(\n",
+        "        subset=\"train\",\n",
+        "        categories=categories,\n",
+        "        shuffle=True,\n",
+        "        random_state=42,\n",
+        "        remove=remove,\n",
+        "    )\n",
+        "    data = pd.DataFrame(\n",
+        "        {feature_column_name: data.data, target_column_name: data.target}\n",
+        "    )\n",
         "\n",
         "    data_train = data[:200]\n",
-        "    data_test = data[200:300]    \n",
+        "    data_test = data[200:300]\n",
         "\n",
-        "    data_train = remove_blanks_20news(data_train, feature_column_name, target_column_name)\n",
+        "    data_train = remove_blanks_20news(\n",
+        "        data_train, feature_column_name, target_column_name\n",
+        "    )\n",
         "    data_test = remove_blanks_20news(data_test, feature_column_name, target_column_name)\n",
-        "    \n",
+        "\n",
         "    return data_train, data_test\n",
-        "    \n",
+        "\n",
+        "\n",
         "def remove_blanks_20news(data, feature_column_name, target_column_name):\n",
-        "    \n",
-        "    data[feature_column_name] = data[feature_column_name].replace(r'\\n', ' ', regex=True).apply(lambda x: x.strip())\n",
-        "    data = data[data[feature_column_name] != '']\n",
-        "    \n",
+        "\n",
+        "    data[feature_column_name] = (\n",
+        "        data[feature_column_name]\n",
+        "        .replace(r\"\\n\", \" \", regex=True)\n",
+        "        .apply(lambda x: x.strip())\n",
+        "    )\n",
+        "    data = data[data[feature_column_name] != \"\"]\n",
+        "\n",
         "    return data"
       ]
     },
@@ -231,16 +234,15 @@
         "\n",
         "if not os.path.isdir(data_dir):\n",
         "    os.mkdir(data_dir)\n",
-        "    \n",
-        "train_data_fname = data_dir + '/train_data.csv'\n",
-        "test_data_fname = data_dir + '/test_data.csv'\n",
+        "\n",
+        "train_data_fname = data_dir + \"/train_data.csv\"\n",
+        "test_data_fname = data_dir + \"/test_data.csv\"\n",
         "\n",
         "data_train.to_csv(train_data_fname, index=False)\n",
         "data_test.to_csv(test_data_fname, index=False)\n",
         "\n",
         "datastore = ws.get_default_datastore()\n",
-        "datastore.upload(src_dir=data_dir, target_path=blobstore_datadir,\n",
-        "                    overwrite=True)"
+        "datastore.upload(src_dir=data_dir, target_path=blobstore_datadir, overwrite=True)"
       ]
     },
     {
@@ -249,7 +251,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "train_dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, blobstore_datadir + '/train_data.csv')])"
+        "train_dataset = Dataset.Tabular.from_delimited_files(\n",
+        "    path=[(datastore, blobstore_datadir + \"/train_data.csv\")]\n",
+        ")"
       ]
     },
     {
@@ -263,7 +267,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "This step requires an Enterprise workspace to gain access to this feature. To learn more about creating an Enterprise workspace or upgrading to an Enterprise workspace from the Azure portal, please visit our [Workspace page](https://docs.microsoft.com/azure/machine-learning/service/concept-workspace#upgrade)."
+        "This notebook uses the blocked_models parameter to exclude some models that can take a longer time to train on some text datasets. You can choose to remove models from the blocked_models list but you may need to increase the experiment_timeout_hours parameter value to get results."
       ]
     },
     {
@@ -273,9 +277,9 @@
       "outputs": [],
       "source": [
         "automl_settings = {\n",
-        "    \"experiment_timeout_minutes\": 20,\n",
-        "    \"primary_metric\": 'accuracy',\n",
-        "    \"max_concurrent_iterations\": 4, \n",
+        "    \"experiment_timeout_minutes\": 30,\n",
+        "    \"primary_metric\": \"accuracy\",\n",
+        "    \"max_concurrent_iterations\": num_nodes,\n",
         "    \"max_cores_per_iteration\": -1,\n",
         "    \"enable_dnn\": True,\n",
         "    \"enable_early_stopping\": True,\n",
@@ -285,13 +289,15 @@
         "    \"enable_stack_ensemble\": False,\n",
         "}\n",
         "\n",
-        "automl_config = AutoMLConfig(task = 'classification',\n",
-        "                             debug_log = 'automl_errors.log',\n",
+        "automl_config = AutoMLConfig(\n",
+        "    task=\"classification\",\n",
+        "    debug_log=\"automl_errors.log\",\n",
         "    compute_target=compute_target,\n",
         "    training_data=train_dataset,\n",
         "    label_column_name=target_column_name,\n",
-        "                             **automl_settings\n",
-        "                            )"
+        "    blocked_models=[\"LightGBM\", \"XGBoostClassifier\"],\n",
+        "    **automl_settings,\n",
+        ")"
       ]
     },
     {
@@ -310,15 +316,6 @@
         "automl_run = experiment.submit(automl_config, show_output=True)"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "automl_run"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -338,7 +335,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "You can test the model locally to get a feel of the input/output. This step may require additional package installations such as pytorch."
+        "For local inferencing, you can load the model locally via. the method `remote_run.get_output()`. For more information on the arguments expected by this method, you can run `remote_run.get_output??`.\n",
+        "Note that when the model contains BERT, this step will require pytorch and pytorch-transformers installed in your local environment. The exact versions of these packages can be found in the **automl_env.yml** file located in the local copy of your azureml-examples folder here: \"azureml-examples/python-sdk/tutorials/automl-with-azureml\""
       ]
     },
     {
@@ -347,7 +345,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "best_run, fitted_model = automl_run.get_output()"
+        "# Retrieve the best Run object\n",
+        "best_run = automl_run.get_best_child()"
       ]
     },
     {
@@ -363,18 +362,25 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "text_transformations_used = []\n",
-        "for column_group in fitted_model.named_steps['datatransformer'].get_featurization_summary():\n",
-        "    text_transformations_used.extend(column_group['Transformations'])\n",
-        "text_transformations_used"
+        "# Download the featurization summary JSON file locally\n",
+        "best_run.download_file(\n",
+        "    \"outputs/featurization_summary.json\", \"featurization_summary.json\"\n",
+        ")\n",
+        "\n",
+        "# Render the JSON as a pandas DataFrame\n",
+        "with open(\"featurization_summary.json\", \"r\") as f:\n",
+        "    records = json.load(f)\n",
+        "\n",
+        "featurization_summary = pd.DataFrame.from_records(records)\n",
+        "featurization_summary[\"Transformations\"].tolist()"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Deploying the model\n",
-        "We now use the best fitted model from the AutoML Run to make predictions on the test set.  "
+        "### Registering the best model\n",
+        "We now register the best fitted model from the AutoML Run for use in future deployments.  "
       ]
     },
     {
@@ -391,7 +397,7 @@
       "outputs": [],
       "source": [
         "summary_df = get_result_df(automl_run)\n",
-        "best_dnn_run_id = summary_df['run_id'].iloc[0]\n",
+        "best_dnn_run_id = summary_df[\"run_id\"].iloc[0]\n",
         "best_dnn_run = Run(experiment, best_dnn_run_id)"
       ]
     },
@@ -401,11 +407,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "model_dir = 'Model' # Local folder where the model will be stored temporarily\n",
+        "model_dir = \"Model\"  # Local folder where the model will be stored temporarily\n",
         "if not os.path.isdir(model_dir):\n",
         "    os.mkdir(model_dir)\n",
-        "    \n",
-        "best_dnn_run.download_file('outputs/model.pkl', model_dir + '/model.pkl')"
+        "\n",
+        "best_dnn_run.download_file(\"outputs/model.pkl\", model_dir + \"/model.pkl\")"
       ]
     },
     {
@@ -422,11 +428,10 @@
       "outputs": [],
       "source": [
         "# Register the model\n",
-        "model_name = 'textDNN-20News'\n",
-        "model = Model.register(model_path = model_dir + '/model.pkl',\n",
-        "                       model_name = model_name,\n",
-        "                       tags=None,\n",
-        "                       workspace=ws)"
+        "model_name = \"textDNN-20News\"\n",
+        "model = Model.register(\n",
+        "    model_path=model_dir + \"/model.pkl\", model_name=model_name, tags=None, workspace=ws\n",
+        ")"
       ]
     },
     {
@@ -451,7 +456,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "test_dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, blobstore_datadir + '/test_data.csv')])\n",
+        "test_dataset = Dataset.Tabular.from_delimited_files(\n",
+        "    path=[(datastore, blobstore_datadir + \"/test_data.csv\")]\n",
+        ")\n",
         "\n",
         "# preview the first 3 rows of the dataset\n",
         "test_dataset.take(3).to_pandas_dataframe()"
@@ -472,9 +479,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "script_folder = os.path.join(os.getcwd(), 'inference')\n",
+        "script_folder = os.path.join(os.getcwd(), \"inference\")\n",
         "os.makedirs(script_folder, exist_ok=True)\n",
-        "shutil.copy2('infer.py', script_folder)"
+        "shutil.copy(\"infer.py\", script_folder)"
       ]
     },
     {
@@ -483,8 +490,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "test_run = run_inference(test_experiment, compute_target, script_folder, best_dnn_run, test_dataset,\n",
-        "                 target_column_name, model_name)"
+        "test_run = run_inference(\n",
+        "    test_experiment,\n",
+        "    compute_target,\n",
+        "    script_folder,\n",
+        "    best_dnn_run,\n",
+        "    test_dataset,\n",
+        "    target_column_name,\n",
+        "    model_name,\n",
+        ")"
       ]
     },
     {

how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.yml

@@ -2,12 +2,3 @@ name: auto-ml-classification-text-dnn
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - azurmel-train
-  - https://download.pytorch.org/whl/cpu/torch-1.1.0-cp35-cp35m-win_amd64.whl
-  - sentencepiece==0.1.82
-  - pytorch-transformers==1.0
-  - spacy==2.1.8
-  - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz

how-to-use-azureml/automated-machine-learning/classification-text-dnn/helper.py

@@ -1,60 +1,68 @@
 import pandas as pd
 from azureml.core import Environment
-from azureml.core.conda_dependencies import CondaDependencies
 from azureml.train.estimator import Estimator
 from azureml.core.run import Run
 
 
-def run_inference(test_experiment, compute_target, script_folder, train_run,
-                  test_dataset, target_column_name, model_name):
+def run_inference(
+    test_experiment,
+    compute_target,
+    script_folder,
+    train_run,
+    test_dataset,
+    target_column_name,
+    model_name,
+):
 
-    train_run.download_file('outputs/conda_env_v_1_0_0.yml',
-                            'inference/condafile.yml')
+    inference_env = train_run.get_environment()
 
-    inference_env = Environment("myenv")
-    inference_env.docker.enabled = True
-    inference_env.python.conda_dependencies = CondaDependencies(
-        conda_dependencies_file_path='inference/condafile.yml')
-
-    est = Estimator(source_directory=script_folder,
-                    entry_script='infer.py',
+    est = Estimator(
+        source_directory=script_folder,
+        entry_script="infer.py",
         script_params={
-                        '--target_column_name': target_column_name,
-                        '--model_name': model_name
+            "--target_column_name": target_column_name,
+            "--model_name": model_name,
         },
-                    inputs=[test_dataset.as_named_input('test_data')],
+        inputs=[test_dataset.as_named_input("test_data")],
         compute_target=compute_target,
-                    environment_definition=inference_env)
+        environment_definition=inference_env,
+    )
 
     run = test_experiment.submit(
-        est, tags={
-            'training_run_id': train_run.id,
-            'run_algorithm': train_run.properties['run_algorithm'],
-            'valid_score': train_run.properties['score'],
-            'primary_metric': train_run.properties['primary_metric']
-        })
+        est,
+        tags={
+            "training_run_id": train_run.id,
+            "run_algorithm": train_run.properties["run_algorithm"],
+            "valid_score": train_run.properties["score"],
+            "primary_metric": train_run.properties["primary_metric"],
+        },
+    )
 
-    run.log("run_algorithm", run.tags['run_algorithm'])
+    run.log("run_algorithm", run.tags["run_algorithm"])
     return run
 
 
 def get_result_df(remote_run):
 
     children = list(remote_run.get_children(recursive=True))
-    summary_df = pd.DataFrame(index=['run_id', 'run_algorithm',
-                                     'primary_metric', 'Score'])
+    summary_df = pd.DataFrame(
+        index=["run_id", "run_algorithm", "primary_metric", "Score"]
+    )
     goal_minimize = False
     for run in children:
-        if('run_algorithm' in run.properties and 'score' in run.properties):
-            summary_df[run.id] = [run.id, run.properties['run_algorithm'],
-                                  run.properties['primary_metric'],
-                                  float(run.properties['score'])]
-            if('goal' in run.properties):
-                goal_minimize = run.properties['goal'].split('_')[-1] == 'min'
+        if "run_algorithm" in run.properties and "score" in run.properties:
+            summary_df[run.id] = [
+                run.id,
+                run.properties["run_algorithm"],
+                run.properties["primary_metric"],
+                float(run.properties["score"]),
+            ]
+            if "goal" in run.properties:
+                goal_minimize = run.properties["goal"].split("_")[-1] == "min"
 
     summary_df = summary_df.T.sort_values(
-        'Score',
-        ascending=goal_minimize).drop_duplicates(['run_algorithm'])
-    summary_df = summary_df.set_index('run_algorithm')
+        "Score", ascending=goal_minimize
+    ).drop_duplicates(["run_algorithm"])
+    summary_df = summary_df.set_index("run_algorithm")
 
     return summary_df

how-to-use-azureml/automated-machine-learning/classification-text-dnn/infer.py

@@ -1,27 +1,33 @@
-import numpy as np
 import argparse
-from azureml.core import Run
+
+import pandas as pd
+import numpy as np
+
 from sklearn.externals import joblib
-from azureml.automl.core._vendor.automl.client.core.common import metrics
-from automl.client.core.common import constants
+
+from azureml.automl.runtime.shared.score import scoring, constants
+from azureml.core import Run
 from azureml.core.model import Model
 
 
 parser = argparse.ArgumentParser()
 parser.add_argument(
-    '--target_column_name', type=str, dest='target_column_name',
-    help='Target Column Name')
+    "--target_column_name",
+    type=str,
+    dest="target_column_name",
+    help="Target Column Name",
+)
 parser.add_argument(
-    '--model_name', type=str, dest='model_name',
-    help='Name of registered model')
+    "--model_name", type=str, dest="model_name", help="Name of registered model"
+)
 
 args = parser.parse_args()
 target_column_name = args.target_column_name
 model_name = args.model_name
 
-print('args passed are: ')
-print('Target column name: ', target_column_name)
-print('Name of registered model: ', model_name)
+print("args passed are: ")
+print("Target column name: ", target_column_name)
+print("Name of registered model: ", model_name)
 
 model_path = Model.get_model_path(model_name)
 # deserialize the model file back into a sklearn model
@@ -29,22 +35,30 @@ model = joblib.load(model_path)
 
 run = Run.get_context()
 # get input dataset by name
-test_dataset = run.input_datasets['test_data']
+test_dataset = run.input_datasets["test_data"]
 
-X_test_df = test_dataset.drop_columns(columns=[target_column_name]) \
-                        .to_pandas_dataframe()
-y_test_df = test_dataset.with_timestamp_columns(None) \
-                        .keep_columns(columns=[target_column_name]) \
+X_test_df = test_dataset.drop_columns(
+    columns=[target_column_name]
+).to_pandas_dataframe()
+y_test_df = (
+    test_dataset.with_timestamp_columns(None)
+    .keep_columns(columns=[target_column_name])
     .to_pandas_dataframe()
+)
 
 predicted = model.predict_proba(X_test_df)
 
-# use automl metrics module
-scores = metrics.compute_metrics_classification(
-    np.array(predicted),
-    np.array(y_test_df),
-    class_labels=model.classes_,
-    metrics=list(constants.Metric.SCALAR_CLASSIFICATION_SET)
+if isinstance(predicted, pd.DataFrame):
+    predicted = predicted.values
+
+# Use the AutoML scoring module
+train_labels = model.classes_
+class_labels = np.unique(
+    np.concatenate((y_test_df.values, np.reshape(train_labels, (-1, 1))))
+)
+classification_metrics = list(constants.CLASSIFICATION_SCALAR_SET)
+scores = scoring.score_classification(
+    y_test_df.values, predicted, classification_metrics, class_labels, train_labels
 )
 
 print("scores:")

how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb

@@ -1,26 +1,11 @@
 {
   "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/continous-retraining/auto-ml-continuous-retraining.png)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
         "# Automated Machine Learning \n",
-        "**Continous retraining using Pipelines and Time-Series TabularDataset**\n",
+        "**Continuous retraining using Pipelines and Time-Series TabularDataset**\n",
         "## Contents\n",
         "1. [Introduction](#Introduction)\n",
         "2. [Setup](#Setup)\n",
@@ -32,13 +17,6 @@
         "8. [Test Retraining](#Test-Retraining)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "An Enterprise workspace is required for this notebook. To learn more about creating an Enterprise workspace or upgrading to an Enterprise workspace from the Azure portal, please visit our [Workspace page.](https://docs.microsoft.com/azure/machine-learning/service/concept-workspace#upgrade)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -75,6 +53,13 @@
         "from azureml.train.automl import AutoMLConfig"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -108,18 +93,18 @@
         "dstor = ws.get_default_datastore()\n",
         "\n",
         "# Choose a name for the run history container in the workspace.\n",
-        "experiment_name = 'retrain-noaaweather'\n",
+        "experiment_name = \"retrain-noaaweather\"\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
         "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Run History Name'] = experiment_name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "output[\"Subscription ID\"] = ws.subscription_id\n",
+        "output[\"Workspace\"] = ws.name\n",
+        "output[\"Resource Group\"] = ws.resource_group\n",
+        "output[\"Location\"] = ws.location\n",
+        "output[\"Run History Name\"] = experiment_name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"
       ]
     },
@@ -132,9 +117,12 @@
         "#### Create or Attach existing AmlCompute\n",
         "\n",
         "You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
+        "\n",
         "#### Creation of AmlCompute takes approximately 5 minutes. \n",
         "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
-        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this article on the default limits and how to request more quota."
+        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
       ]
     },
     {
@@ -143,33 +131,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import AmlCompute, ComputeTarget\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
-        "# Choose a name for your cluster.\n",
-        "amlcompute_cluster_name = \"cpu-cluster-42\"\n",
+        "# Choose a name for your CPU cluster\n",
+        "amlcompute_cluster_name = \"cont-cluster\"\n",
         "\n",
-        "found = False\n",
-        "# Check if this compute target already exists in the workspace.\n",
-        "cts = ws.compute_targets\n",
-        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
-        "    found = True\n",
-        "    print('Found existing compute target.')\n",
-        "    compute_target = cts[amlcompute_cluster_name]\n",
-        "    \n",
-        "if not found:\n",
-        "    print('Creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-        "                                                                #vm_priority = 'lowpriority', # optional\n",
-        "                                                                max_nodes = 4)\n",
-        "\n",
-        "    # Create the cluster.\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "    \n",
-        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "    compute_target.wait_for_completion(show_output = True, min_node_count = 0, timeout_in_minutes = 10)\n",
-        "    \n",
-        "     # For a more detailed view of current AmlCompute status, use get_status()."
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
+        "    print(\"Found existing cluster, use it.\")\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(\n",
+        "        vm_size=\"STANDARD_DS12_V2\", max_nodes=4\n",
+        "    )\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
+        "compute_target.wait_for_completion(show_output=True)"
       ]
     },
     {
@@ -185,7 +162,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.runconfig import CondaDependencies, DEFAULT_CPU_IMAGE, RunConfiguration\n",
+        "from azureml.core.runconfig import CondaDependencies, RunConfiguration\n",
         "\n",
         "# create a new RunConfig object\n",
         "conda_run_config = RunConfiguration(framework=\"python\")\n",
@@ -194,15 +171,20 @@
         "conda_run_config.target = compute_target\n",
         "\n",
         "conda_run_config.environment.docker.enabled = True\n",
-        "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
         "\n",
-        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', 'applicationinsights', 'azureml-opendatasets'], \n",
-        "                              conda_packages=['numpy==1.16.2'], \n",
-        "                              pin_sdk_version=False)\n",
-        "#cd.add_pip_package('azureml-explain-model')\n",
+        "cd = CondaDependencies.create(\n",
+        "    pip_packages=[\n",
+        "        \"azureml-sdk[automl]\",\n",
+        "        \"applicationinsights\",\n",
+        "        \"azureml-opendatasets\",\n",
+        "        \"azureml-defaults\",\n",
+        "    ],\n",
+        "    conda_packages=[\"numpy==1.19.5\"],\n",
+        "    pin_sdk_version=False,\n",
+        ")\n",
         "conda_run_config.environment.python.conda_dependencies = cd\n",
         "\n",
-        "print('run config is ready')"
+        "print(\"run config is ready\")"
       ]
     },
     {
@@ -219,7 +201,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# The name and target column of the Dataset to create \n",
+        "# The name and target column of the Dataset to create\n",
         "dataset = \"NOAA-Weather-DS4\"\n",
         "target_column_name = \"temperature\""
       ]
@@ -243,12 +225,14 @@
         "from azureml.pipeline.steps import PythonScriptStep\n",
         "\n",
         "ds_name = PipelineParameter(name=\"ds_name\", default_value=dataset)\n",
-        "upload_data_step = PythonScriptStep(script_name=\"upload_weather_data.py\", \n",
+        "upload_data_step = PythonScriptStep(\n",
+        "    script_name=\"upload_weather_data.py\",\n",
         "    allow_reuse=False,\n",
         "    name=\"upload_weather_data\",\n",
         "    arguments=[\"--ds_name\", ds_name],\n",
-        "                                         compute_target=compute_target, \n",
-        "                                         runconfig=conda_run_config)"
+        "    compute_target=compute_target,\n",
+        "    runconfig=conda_run_config,\n",
+        ")"
       ]
     },
     {
@@ -265,10 +249,11 @@
       "outputs": [],
       "source": [
         "data_pipeline = Pipeline(\n",
-        "    description=\"pipeline_with_uploaddata\",\n",
-        "    workspace=ws,    \n",
-        "    steps=[upload_data_step])\n",
-        "data_pipeline_run = experiment.submit(data_pipeline, pipeline_parameters={\"ds_name\":dataset})"
+        "    description=\"pipeline_with_uploaddata\", workspace=ws, steps=[upload_data_step]\n",
+        ")\n",
+        "data_pipeline_run = experiment.submit(\n",
+        "    data_pipeline, pipeline_parameters={\"ds_name\": dataset}\n",
+        ")"
       ]
     },
     {
@@ -308,13 +293,14 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "data_prep_step = PythonScriptStep(script_name=\"check_data.py\", \n",
+        "data_prep_step = PythonScriptStep(\n",
+        "    script_name=\"check_data.py\",\n",
         "    allow_reuse=False,\n",
         "    name=\"check_data\",\n",
-        "                                         arguments=[\"--ds_name\", ds_name,\n",
-        "                                                    \"--model_name\", model_name],\n",
-        "                                         compute_target=compute_target, \n",
-        "                                         runconfig=conda_run_config)"
+        "    arguments=[\"--ds_name\", ds_name, \"--model_name\", model_name],\n",
+        "    compute_target=compute_target,\n",
+        "    runconfig=conda_run_config,\n",
+        ")"
       ]
     },
     {
@@ -324,6 +310,7 @@
       "outputs": [],
       "source": [
         "from azureml.core import Dataset\n",
+        "\n",
         "train_ds = Dataset.get_by_name(ws, dataset)\n",
         "train_ds = train_ds.drop_columns([\"partition_date\"])"
       ]
@@ -343,27 +330,28 @@
       "outputs": [],
       "source": [
         "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.train.automl import AutoMLStep\n",
+        "from azureml.pipeline.steps import AutoMLStep\n",
         "\n",
         "automl_settings = {\n",
         "    \"iteration_timeout_minutes\": 10,\n",
         "    \"experiment_timeout_hours\": 0.25,\n",
         "    \"n_cross_validations\": 3,\n",
-        "    \"primary_metric\": 'r2_score',\n",
+        "    \"primary_metric\": \"r2_score\",\n",
         "    \"max_concurrent_iterations\": 3,\n",
         "    \"max_cores_per_iteration\": -1,\n",
         "    \"verbosity\": logging.INFO,\n",
-        "    \"enable_early_stopping\": True\n",
+        "    \"enable_early_stopping\": True,\n",
         "}\n",
         "\n",
-        "automl_config = AutoMLConfig(task = 'regression',\n",
-        "                             debug_log = 'automl_errors.log',\n",
-        "                             path = \".\",\n",
+        "automl_config = AutoMLConfig(\n",
+        "    task=\"regression\",\n",
+        "    debug_log=\"automl_errors.log\",\n",
+        "    path=\".\",\n",
         "    compute_target=compute_target,\n",
-        "                             training_data = train_ds,\n",
-        "                             label_column_name = target_column_name,\n",
-        "                             **automl_settings\n",
-        "                            )"
+        "    training_data=train_ds,\n",
+        "    label_column_name=target_column_name,\n",
+        "    **automl_settings,\n",
+        ")"
       ]
     },
     {
@@ -374,17 +362,21 @@
       "source": [
         "from azureml.pipeline.core import PipelineData, TrainingOutput\n",
         "\n",
-        "metrics_output_name = 'metrics_output'\n",
-        "best_model_output_name = 'best_model_output'\n",
+        "metrics_output_name = \"metrics_output\"\n",
+        "best_model_output_name = \"best_model_output\"\n",
         "\n",
-        "metrics_data = PipelineData(name='metrics_data',\n",
+        "metrics_data = PipelineData(\n",
+        "    name=\"metrics_data\",\n",
         "    datastore=dstor,\n",
         "    pipeline_output_name=metrics_output_name,\n",
-        "                           training_output=TrainingOutput(type='Metrics'))\n",
-        "model_data = PipelineData(name='model_data',\n",
+        "    training_output=TrainingOutput(type=\"Metrics\"),\n",
+        ")\n",
+        "model_data = PipelineData(\n",
+        "    name=\"model_data\",\n",
         "    datastore=dstor,\n",
         "    pipeline_output_name=best_model_output_name,\n",
-        "                           training_output=TrainingOutput(type='Model'))"
+        "    training_output=TrainingOutput(type=\"Model\"),\n",
+        ")"
       ]
     },
     {
@@ -394,10 +386,11 @@
       "outputs": [],
       "source": [
         "automl_step = AutoMLStep(\n",
-        "    name='automl_module',\n",
+        "    name=\"automl_module\",\n",
         "    automl_config=automl_config,\n",
         "    outputs=[metrics_data, model_data],\n",
-        "    allow_reuse=False)"
+        "    allow_reuse=False,\n",
+        ")"
       ]
     },
     {
@@ -414,13 +407,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "register_model_step = PythonScriptStep(script_name=\"register_model.py\",\n",
+        "register_model_step = PythonScriptStep(\n",
+        "    script_name=\"register_model.py\",\n",
         "    name=\"register_model\",\n",
         "    allow_reuse=False,\n",
-        "                                       arguments=[\"--model_name\", model_name, \"--model_path\", model_data, \"--ds_name\", ds_name],\n",
+        "    arguments=[\n",
+        "        \"--model_name\",\n",
+        "        model_name,\n",
+        "        \"--model_path\",\n",
+        "        model_data,\n",
+        "        \"--ds_name\",\n",
+        "        ds_name,\n",
+        "    ],\n",
         "    inputs=[model_data],\n",
         "    compute_target=compute_target,\n",
-        "                                       runconfig=conda_run_config)"
+        "    runconfig=conda_run_config,\n",
+        ")"
       ]
     },
     {
@@ -438,8 +440,9 @@
       "source": [
         "training_pipeline = Pipeline(\n",
         "    description=\"training_pipeline\",\n",
-        "    workspace=ws,    \n",
-        "    steps=[data_prep_step, automl_step, register_model_step])"
+        "    workspace=ws,\n",
+        "    steps=[data_prep_step, automl_step, register_model_step],\n",
+        ")"
       ]
     },
     {
@@ -448,8 +451,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "training_pipeline_run = experiment.submit(training_pipeline, pipeline_parameters={\n",
-        "        \"ds_name\": dataset, \"model_name\": \"noaaweatherds\"})"
+        "training_pipeline_run = experiment.submit(\n",
+        "    training_pipeline,\n",
+        "    pipeline_parameters={\"ds_name\": dataset, \"model_name\": \"noaaweatherds\"},\n",
+        ")"
       ]
     },
     {
@@ -478,8 +483,8 @@
         "pipeline_name = \"Retraining-Pipeline-NOAAWeather\"\n",
         "\n",
         "published_pipeline = training_pipeline.publish(\n",
-        "    name=pipeline_name, \n",
-        "    description=\"Pipeline that retrains AutoML model\")\n",
+        "    name=pipeline_name, description=\"Pipeline that retrains AutoML model\"\n",
+        ")\n",
         "\n",
         "published_pipeline"
       ]
@@ -491,13 +496,17 @@
       "outputs": [],
       "source": [
         "from azureml.pipeline.core import Schedule\n",
-        "schedule = Schedule.create(workspace=ws, name=\"RetrainingSchedule\",\n",
+        "\n",
+        "schedule = Schedule.create(\n",
+        "    workspace=ws,\n",
+        "    name=\"RetrainingSchedule\",\n",
         "    pipeline_parameters={\"ds_name\": dataset, \"model_name\": \"noaaweatherds\"},\n",
-        "                           pipeline_id=published_pipeline.id, \n",
-        "                           experiment_name=experiment_name, \n",
+        "    pipeline_id=published_pipeline.id,\n",
+        "    experiment_name=experiment_name,\n",
         "    datastore=dstor,\n",
         "    wait_for_provisioning=True,\n",
-        "                           polling_interval=1440)"
+        "    polling_interval=1440,\n",
+        ")"
       ]
     },
     {
@@ -521,8 +530,8 @@
         "pipeline_name = \"DataIngestion-Pipeline-NOAAWeather\"\n",
         "\n",
         "published_pipeline = training_pipeline.publish(\n",
-        "    name=pipeline_name, \n",
-        "    description=\"Pipeline that updates NOAAWeather Dataset\")\n",
+        "    name=pipeline_name, description=\"Pipeline that updates NOAAWeather Dataset\"\n",
+        ")\n",
         "\n",
         "published_pipeline"
       ]
@@ -534,13 +543,17 @@
       "outputs": [],
       "source": [
         "from azureml.pipeline.core import Schedule\n",
-        "schedule = Schedule.create(workspace=ws, name=\"RetrainingSchedule-DataIngestion\",\n",
-        "                           pipeline_parameters={\"ds_name\":dataset},\n",
-        "                           pipeline_id=published_pipeline.id, \n",
-        "                           experiment_name=experiment_name, \n",
+        "\n",
+        "schedule = Schedule.create(\n",
+        "    workspace=ws,\n",
+        "    name=\"RetrainingSchedule-DataIngestion\",\n",
+        "    pipeline_parameters={\"ds_name\": dataset},\n",
+        "    pipeline_id=published_pipeline.id,\n",
+        "    experiment_name=experiment_name,\n",
         "    datastore=dstor,\n",
         "    wait_for_provisioning=True,\n",
-        "                           polling_interval=1440)"
+        "    polling_interval=1440,\n",
+        ")"
       ]
     }
   ],

how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.yml

@@ -2,7 +2,3 @@ name: auto-ml-continuous-retraining
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - azureml-pipeline

how-to-use-azureml/automated-machine-learning/continuous-retraining/register_model.py

@@ -25,9 +25,11 @@ datasets = [(Dataset.Scenario.TRAINING, train_ds)]
 
 # Register model with training dataset
 
-model = Model.register(workspace=ws,
+model = Model.register(
+    workspace=ws,
     model_path=args.model_path,
     model_name=args.model_name,
-                       datasets=datasets)
+    datasets=datasets,
+)
 
 print("Registered version {0} of model {1}".format(model.version, model.name))

how-to-use-azureml/automated-machine-learning/continuous-retraining/upload_weather_data.py

@@ -16,26 +16,82 @@ if type(run) == _OfflineRun:
 else:
     ws = run.experiment.workspace
 
-usaf_list = ['725724', '722149', '723090', '722159', '723910', '720279',
-             '725513', '725254', '726430', '720381', '723074', '726682',
-             '725486', '727883', '723177', '722075', '723086', '724053',
-             '725070', '722073', '726060', '725224', '725260', '724520',
-             '720305', '724020', '726510', '725126', '722523', '703333',
-             '722249', '722728', '725483', '722972', '724975', '742079',
-             '727468', '722193', '725624', '722030', '726380', '720309',
-             '722071', '720326', '725415', '724504', '725665', '725424',
-             '725066']
+usaf_list = [
+    "725724",
+    "722149",
+    "723090",
+    "722159",
+    "723910",
+    "720279",
+    "725513",
+    "725254",
+    "726430",
+    "720381",
+    "723074",
+    "726682",
+    "725486",
+    "727883",
+    "723177",
+    "722075",
+    "723086",
+    "724053",
+    "725070",
+    "722073",
+    "726060",
+    "725224",
+    "725260",
+    "724520",
+    "720305",
+    "724020",
+    "726510",
+    "725126",
+    "722523",
+    "703333",
+    "722249",
+    "722728",
+    "725483",
+    "722972",
+    "724975",
+    "742079",
+    "727468",
+    "722193",
+    "725624",
+    "722030",
+    "726380",
+    "720309",
+    "722071",
+    "720326",
+    "725415",
+    "724504",
+    "725665",
+    "725424",
+    "725066",
+]
 
 
 def get_noaa_data(start_time, end_time):
-    columns = ['usaf', 'wban', 'datetime', 'latitude', 'longitude', 'elevation',
-               'windAngle', 'windSpeed', 'temperature', 'stationName', 'p_k']
+    columns = [
+        "usaf",
+        "wban",
+        "datetime",
+        "latitude",
+        "longitude",
+        "elevation",
+        "windAngle",
+        "windSpeed",
+        "temperature",
+        "stationName",
+        "p_k",
+    ]
     isd = NoaaIsdWeather(start_time, end_time, cols=columns)
     noaa_df = isd.to_pandas_dataframe()
     df_filtered = noaa_df[noaa_df["usaf"].isin(usaf_list)]
     df_filtered.reset_index(drop=True)
-    print("Received {0} rows of training data between {1} and {2}".format(
-        df_filtered.shape[0], start_time, end_time))
+    print(
+        "Received {0} rows of training data between {1} and {2}".format(
+            df_filtered.shape[0], start_time, end_time
+        )
+    )
     return df_filtered
 
 
@@ -49,41 +105,53 @@ print("Argument 1(ds_name): %s" % args.ds_name)
 
 dstor = ws.get_default_datastore()
 register_dataset = False
+end_time = datetime.utcnow()
+
 try:
     ds = Dataset.get_by_name(ws, args.ds_name)
     end_time_last_slice = ds.data_changed_time.replace(tzinfo=None)
-    print("Dataset {0} last updated on {1}".format(args.ds_name,
-                                                   end_time_last_slice))
-except Exception as e:
+    print("Dataset {0} last updated on {1}".format(args.ds_name, end_time_last_slice))
+except Exception:
     print(traceback.format_exc())
-    print("Dataset with name {0} not found, registering new dataset.".format(args.ds_name))
+    print(
+        "Dataset with name {0} not found, registering new dataset.".format(args.ds_name)
+    )
     register_dataset = True
-    end_time_last_slice = datetime.today() - relativedelta(weeks=2)
+    end_time = datetime(2021, 5, 1, 0, 0)
+    end_time_last_slice = end_time - relativedelta(weeks=2)
 
-end_time = datetime.utcnow()
 train_df = get_noaa_data(end_time_last_slice, end_time)
 
 if train_df.size > 0:
-    print("Received {0} rows of new data after {0}.".format(
-        train_df.shape[0], end_time_last_slice))
-    folder_name = "{}/{:04d}/{:02d}/{:02d}/{:02d}/{:02d}/{:02d}".format(args.ds_name, end_time.year,
-                                                                        end_time.month, end_time.day,
-                                                                        end_time.hour, end_time.minute,
-                                                                        end_time.second)
+    print(
+        "Received {0} rows of new data after {1}.".format(
+            train_df.shape[0], end_time_last_slice
+        )
+    )
+    folder_name = "{}/{:04d}/{:02d}/{:02d}/{:02d}/{:02d}/{:02d}".format(
+        args.ds_name,
+        end_time.year,
+        end_time.month,
+        end_time.day,
+        end_time.hour,
+        end_time.minute,
+        end_time.second,
+    )
     file_path = "{0}/data.csv".format(folder_name)
 
     # Add a new partition to the registered dataset
     os.makedirs(folder_name, exist_ok=True)
     train_df.to_csv(file_path, index=False)
 
-    dstor.upload_files(files=[file_path],
-                       target_path=folder_name,
-                       overwrite=True,
-                       show_progress=True)
+    dstor.upload_files(
+        files=[file_path], target_path=folder_name, overwrite=True, show_progress=True
+    )
 else:
     print("No new data since {0}.".format(end_time_last_slice))
 
 if register_dataset:
-    ds = Dataset.Tabular.from_delimited_files(dstor.path("{}/**/*.csv".format(
-        args.ds_name)), partition_format='/{partition_date:yyyy/MM/dd/HH/mm/ss}/data.csv')
+    ds = Dataset.Tabular.from_delimited_files(
+        dstor.path("{}/**/*.csv".format(args.ds_name)),
+        partition_format="/{partition_date:yyyy/MM/dd/HH/mm/ss}/data.csv",
+    )
     ds.register(ws, name=args.ds_name)

how-to-use-azureml/automated-machine-learning/experimental/README.md

@@ -0,0 +1,92 @@
+# Experimental Notebooks for Automated ML
+Notebooks listed in this folder are leveraging experimental features. Namespaces or function signitures may change in future SDK releases. The notebooks published here will reflect the latest supported APIs. All of these notebooks can run on a client-only installation of the Automated ML SDK.
+The client only installation doesn't contain any of the machine learning libraries, such as scikit-learn, xgboost, or tensorflow, making it much faster to install and is less likely to conflict with any packages in an existing environment. However, since the ML libraries are not available locally, models cannot be downloaded and loaded directly in the client. To replace the functionality of having models locally, these notebooks also demonstrate the ModelProxy feature which will allow you to submit a predict/forecast to the training environment. 
+
+<a name="localconda"></a>
+## Setup using a Local Conda environment
+
+To run these notebook on your own notebook server, use these installation instructions.
+The instructions below will install everything you need and then start a Jupyter notebook.
+If you would like to use a lighter-weight version of the client that does not install all of the machine learning libraries locally, you can leverage the [experimental notebooks.](experimental/README.md)
+
+### 1. Install mini-conda from [here](https://conda.io/miniconda.html), choose 64-bit Python 3.7 or higher.
+- **Note**: if you already have conda installed, you can keep using it but it should be version 4.4.10 or later (as shown by: conda -V).  If you have a previous version installed, you can update it using the command: conda update conda.
+There's no need to install mini-conda specifically.
+
+### 2. Downloading the sample notebooks
+- Download the sample notebooks from [GitHub](https://github.com/Azure/MachineLearningNotebooks) as zip and extract the contents to a local directory.  The automated ML sample notebooks are in the "automated-machine-learning" folder.
+
+### 3. Setup a new conda environment
+The **automl_setup_thin_client** script creates a new conda environment, installs the necessary packages, configures the widget and starts a jupyter notebook. It takes the conda environment name as an optional parameter.  The default conda environment name is azure_automl_experimental.  The exact command depends on the operating system.  See the specific sections below for Windows, Mac and Linux.  It can take about 10 minutes to execute.
+
+Packages installed by the **automl_setup** script:
+    <ul><li>python</li><li>nb_conda</li><li>matplotlib</li><li>numpy</li><li>cython</li><li>urllib3</li><li>pandas</li><li>azureml-sdk</li><li>azureml-widgets</li><li>pandas-ml</li></ul>
+
+For more details refer to the [automl_env_thin_client.yml](./automl_env_thin_client.yml)
+## Windows
+Start an **Anaconda Prompt** window, cd to the **how-to-use-azureml/automated-machine-learning/experimental** folder where the sample notebooks were extracted and then run:
+```
+automl_setup_thin_client
+```
+## Mac
+Install "Command line developer tools" if it is not already installed (you can use the command: `xcode-select --install`).
+
+Start a Terminal windows, cd to the **how-to-use-azureml/automated-machine-learning/experimental** folder where the sample notebooks were extracted and then run:
+
+```
+bash automl_setup_thin_client_mac.sh
+```
+
+## Linux
+cd to the **how-to-use-azureml/automated-machine-learning/experimental** folder where the sample notebooks were extracted and then run:
+
+```
+bash automl_setup_thin_client_linux.sh
+```
+
+### 4. Running configuration.ipynb
+- Before running any samples you next need to run the configuration notebook. Click on [configuration](../../configuration.ipynb) notebook
+- Execute the cells in the notebook to Register Machine Learning Services Resource Provider and create a workspace. (*instructions in notebook*)
+
+### 5. Running Samples
+- Please make sure you use the Python [conda env:azure_automl_experimental] kernel when trying the sample Notebooks.
+- Follow the instructions in the individual notebooks to explore various features in automated ML.
+
+### 6. Starting jupyter notebook manually
+To start your Jupyter notebook manually, use:
+
+```
+conda activate azure_automl
+jupyter notebook
+```
+
+or on Mac or Linux:
+
+```
+source activate azure_automl
+jupyter notebook
+```
+
+
+<a name="samples"></a>
+# Automated ML SDK Sample Notebooks
+
+- [auto-ml-regression-model-proxy.ipynb](regression-model-proxy/auto-ml-regression-model-proxy.ipynb)
+    - Dataset: Hardware Performance Dataset
+    - Simple example of using automated ML for regression
+    - Uses azure compute for training
+    - Uses ModelProxy for submitting prediction to training environment on azure compute
+
+<a name="documentation"></a>
+See [Configure automated machine learning experiments](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-auto-train) to learn how more about the the settings and features available for automated machine learning experiments.
+
+<a name="pythoncommand"></a>
+# Running using python command
+Jupyter notebook provides a File / Download as / Python (.py) option for saving the notebook as a Python file.
+You can then run this file using the python command.
+However, on Windows the file needs to be modified before it can be run.
+The following condition must be added to the main code in the file:
+
+    if __name__ == "__main__":
+
+The main code of the file must be indented so that it is under this condition.

how-to-use-azureml/automated-machine-learning/experimental/automl_setup_thin_client.cmd

@@ -0,0 +1,63 @@
+@echo off
+set conda_env_name=%1
+set automl_env_file=%2
+set options=%3
+set PIP_NO_WARN_SCRIPT_LOCATION=0
+
+IF "%conda_env_name%"=="" SET conda_env_name="azure_automl_experimental"
+IF "%automl_env_file%"=="" SET automl_env_file="automl_thin_client_env.yml"
+
+IF NOT EXIST %automl_env_file% GOTO YmlMissing
+
+IF "%CONDA_EXE%"=="" GOTO CondaMissing
+
+call conda activate %conda_env_name% 2>nul:
+
+if not errorlevel 1 (
+  echo Upgrading existing conda environment %conda_env_name%
+  call pip uninstall azureml-train-automl -y -q
+  call conda env update --name %conda_env_name% --file %automl_env_file%
+  if errorlevel 1 goto ErrorExit
+) else (
+  call conda env create -f %automl_env_file% -n %conda_env_name%
+)
+
+call conda activate %conda_env_name% 2>nul:
+if errorlevel 1 goto ErrorExit
+
+call python -m ipykernel install --user --name %conda_env_name% --display-name "Python (%conda_env_name%)"
+
+REM azureml.widgets is now installed as part of the pip install under the conda env.
+REM Removing the old user install so that the notebooks will use the latest widget.
+call jupyter nbextension uninstall --user --py azureml.widgets
+
+echo.
+echo.
+echo ***************************************
+echo * AutoML setup completed successfully *
+echo ***************************************
+IF NOT "%options%"=="nolaunch" (
+  echo.
+  echo Starting jupyter notebook - please run the configuration notebook 
+  echo.
+  jupyter notebook --log-level=50 --notebook-dir='..\..'
+)
+
+goto End
+
+:CondaMissing
+echo Please run this script from an Anaconda Prompt window.
+echo You can start an Anaconda Prompt window by
+echo typing Anaconda Prompt on the Start menu.
+echo If you don't see the Anaconda Prompt app, install Miniconda.
+echo If you are running an older version of Miniconda or Anaconda,
+echo you can upgrade using the command: conda update conda
+goto End
+
+:YmlMissing
+echo File %automl_env_file% not found.
+
+:ErrorExit
+echo Install failed
+
+:End

how-to-use-azureml/automated-machine-learning/experimental/automl_setup_thin_client_linux.sh

@@ -0,0 +1,53 @@
+#!/bin/bash
+
+CONDA_ENV_NAME=$1
+AUTOML_ENV_FILE=$2
+OPTIONS=$3
+PIP_NO_WARN_SCRIPT_LOCATION=0
+
+if [ "$CONDA_ENV_NAME" == "" ]
+then
+  CONDA_ENV_NAME="azure_automl_experimental"
+fi
+
+if [ "$AUTOML_ENV_FILE" == "" ]
+then
+  AUTOML_ENV_FILE="automl_thin_client_env.yml"
+fi
+
+if [ ! -f $AUTOML_ENV_FILE ]; then
+    echo "File $AUTOML_ENV_FILE not found"
+    exit 1
+fi
+
+if source activate $CONDA_ENV_NAME 2> /dev/null
+then
+   echo "Upgrading existing conda environment" $CONDA_ENV_NAME
+   pip uninstall azureml-train-automl -y -q
+   conda env update --name $CONDA_ENV_NAME --file $AUTOML_ENV_FILE &&
+   jupyter nbextension uninstall --user --py azureml.widgets
+else
+   conda env create -f $AUTOML_ENV_FILE -n $CONDA_ENV_NAME &&
+   source activate $CONDA_ENV_NAME &&
+   python -m ipykernel install --user --name $CONDA_ENV_NAME --display-name "Python ($CONDA_ENV_NAME)" &&
+   jupyter nbextension uninstall --user --py azureml.widgets &&
+   echo "" &&
+   echo "" &&
+   echo "***************************************" &&
+   echo "* AutoML setup completed successfully *" &&
+   echo "***************************************" &&
+   if [ "$OPTIONS" != "nolaunch" ]
+   then
+      echo "" &&
+      echo "Starting jupyter notebook - please run the configuration notebook" &&
+      echo "" &&
+      jupyter notebook --log-level=50 --notebook-dir '../..'
+   fi
+fi
+
+if [ $? -gt 0 ]
+then
+   echo "Installation failed"
+fi
+
+

how-to-use-azureml/automated-machine-learning/experimental/automl_setup_thin_client_mac.sh

@@ -0,0 +1,55 @@
+#!/bin/bash
+
+CONDA_ENV_NAME=$1
+AUTOML_ENV_FILE=$2
+OPTIONS=$3
+PIP_NO_WARN_SCRIPT_LOCATION=0
+
+if [ "$CONDA_ENV_NAME" == "" ]
+then
+  CONDA_ENV_NAME="azure_automl_experimental"
+fi
+
+if [ "$AUTOML_ENV_FILE" == "" ]
+then
+  AUTOML_ENV_FILE="automl_thin_client_env_mac.yml"
+fi
+
+if [ ! -f $AUTOML_ENV_FILE ]; then
+    echo "File $AUTOML_ENV_FILE not found"
+    exit 1
+fi
+
+if source activate $CONDA_ENV_NAME 2> /dev/null
+then
+   echo "Upgrading existing conda environment" $CONDA_ENV_NAME
+   pip uninstall azureml-train-automl -y -q
+   conda env update --name $CONDA_ENV_NAME --file $AUTOML_ENV_FILE &&
+   jupyter nbextension uninstall --user --py azureml.widgets
+else
+   conda env create -f $AUTOML_ENV_FILE -n $CONDA_ENV_NAME &&
+   source activate $CONDA_ENV_NAME &&
+   conda install lightgbm -c conda-forge -y &&
+   python -m ipykernel install --user --name $CONDA_ENV_NAME --display-name "Python ($CONDA_ENV_NAME)" &&
+   jupyter nbextension uninstall --user --py azureml.widgets &&
+   echo "" &&
+   echo "" &&
+   echo "***************************************" &&
+   echo "* AutoML setup completed successfully *" &&
+   echo "***************************************" &&
+   if [ "$OPTIONS" != "nolaunch" ]
+   then
+      echo "" &&
+      echo "Starting jupyter notebook - please run the configuration notebook" &&
+      echo "" &&
+      jupyter notebook --log-level=50 --notebook-dir '../..'
+   fi
+fi
+
+if [ $? -gt 0 ]
+then
+   echo "Installation failed"
+fi
+
+
+

how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env.yml

@@ -0,0 +1,22 @@
+name: azure_automl_experimental
+dependencies:
+  # The python interpreter version.
+  # Currently Azure ML only supports 3.6.0 and later.
+- pip<=20.2.4
+- python>=3.6.0,<3.9
+- cython==0.29.14
+- urllib3==1.26.7
+- PyJWT < 2.0.0
+- numpy==1.18.5
+- pywin32==227
+- cryptography<37.0.0
+
+- pip:
+  # Required packages for AzureML execution, history, and data preparation.
+  - azure-mgmt-core==1.3.0
+  - azure-core==1.21.1
+  - azure-identity==1.7.0
+  - azureml-defaults
+  - azureml-sdk
+  - azureml-widgets
+  - pandas

how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env_mac.yml

@@ -0,0 +1,24 @@
+name: azure_automl_experimental
+channels:
+  - conda-forge
+  - main
+dependencies:
+  # The python interpreter version.
+  # Currently Azure ML only supports 3.6.0 and later.
+- pip<=20.2.4
+- nomkl
+- python>=3.6.0,<3.9
+- urllib3==1.26.7
+- PyJWT < 2.0.0
+- numpy==1.19.5
+- cryptography<37.0.0
+
+- pip:
+  # Required packages for AzureML execution, history, and data preparation.
+  - azure-mgmt-core==1.3.0
+  - azure-core==1.21.1
+  - azure-identity==1.7.0
+  - azureml-defaults
+  - azureml-sdk
+  - azureml-widgets
+  - pandas

how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.ipynb

@@ -0,0 +1,420 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Automated Machine Learning\n",
+        "_**Classification of credit card fraudulent transactions on local managed compute **_\n",
+        "\n",
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Train](#Train)\n",
+        "1. [Results](#Results)\n",
+        "1. [Test](#Test)\n",
+        "1. [Acknowledgements](#Acknowledgements)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "\n",
+        "In this example we use the associated credit card dataset to showcase how you can use AutoML for a simple classification problem. The goal is to predict if a credit card transaction is considered a fraudulent charge.\n",
+        "\n",
+        "This notebook is using local managed compute to train the model.\n",
+        "\n",
+        "If you are using an Azure Machine Learning Compute Instance, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
+        "\n",
+        "In this notebook you will learn how to:\n",
+        "1. Create an experiment using an existing workspace.\n",
+        "2. Configure AutoML using `AutoMLConfig`.\n",
+        "3. Train the model using local managed compute.\n",
+        "4. Explore the results.\n",
+        "5. Test the fitted model."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup\n",
+        "\n",
+        "As part of the setup you have already created an Azure ML `Workspace` object. For Automated ML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import logging\n",
+        "\n",
+        "import pandas as pd\n",
+        "\n",
+        "import azureml.core\n",
+        "from azureml.core.compute_target import LocalTarget\n",
+        "from azureml.core.experiment import Experiment\n",
+        "from azureml.core.workspace import Workspace\n",
+        "from azureml.core.dataset import Dataset\n",
+        "from azureml.train.automl import AutoMLConfig"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"This notebook was created using version 1.43.0 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "\n",
+        "# choose a name for experiment\n",
+        "experiment_name = 'automl-local-managed'\n",
+        "\n",
+        "experiment=Experiment(ws, experiment_name)\n",
+        "\n",
+        "output = {}\n",
+        "output['Subscription ID'] = ws.subscription_id\n",
+        "output['Workspace'] = ws.name\n",
+        "output['Resource Group'] = ws.resource_group\n",
+        "output['Location'] = ws.location\n",
+        "output['Experiment Name'] = experiment.name\n",
+        "pd.set_option('display.max_colwidth', None)\n",
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Determine if local docker is configured for Linux images\n",
+        "\n",
+        "Local managed runs will leverage a Linux docker container to submit the run to. Due to this, the docker needs to be configured to use Linux containers."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Check if Docker is installed and Linux containers are enabled\n",
+        "import subprocess\n",
+        "from subprocess import CalledProcessError\n",
+        "try:\n",
+        "    assert subprocess.run(\"docker -v\", shell=True).returncode == 0, 'Local Managed runs require docker to be installed.'\n",
+        "    out = subprocess.check_output(\"docker system info\", shell=True).decode('ascii')\n",
+        "    assert \"OSType: linux\" in out, 'Docker engine needs to be configured to use Linux containers.' \\\n",
+        "        'https://docs.docker.com/docker-for-windows/#switch-between-windows-and-linux-containers'\n",
+        "except CalledProcessError as ex:\n",
+        "    raise Exception('Local Managed runs require docker to be installed.') from ex"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Load Data\n",
+        "\n",
+        "Load the credit card dataset from a csv file containing both training features and labels. The features are inputs to the model, while the training labels represent the expected output of the model. Next, we'll split the data using random_split and extract the training data for the model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/creditcard.csv\"\n",
+        "dataset = Dataset.Tabular.from_delimited_files(data)\n",
+        "training_data, validation_data = dataset.random_split(percentage=0.8, seed=223)\n",
+        "label_column_name = 'Class'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train\n",
+        "\n",
+        "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
+        "\n",
+        "|Property|Description|\n",
+        "|-|-|\n",
+        "|**task**|classification or regression|\n",
+        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
+        "|**enable_early_stopping**|Stop the run if the metric score is not showing improvement.|\n",
+        "|**n_cross_validations**|Number of cross validation splits.|\n",
+        "|**training_data**|Input dataset, containing both features and label column.|\n",
+        "|**label_column_name**|The name of the label column.|\n",
+        "|**enable_local_managed**|Enable the experimental local-managed scenario.|\n",
+        "\n",
+        "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "automl_settings = {\n",
+        "    \"n_cross_validations\": 3,\n",
+        "    \"primary_metric\": 'average_precision_score_weighted',\n",
+        "    \"enable_early_stopping\": True,\n",
+        "    \"experiment_timeout_hours\": 0.3, #for real scenarios we recommend a timeout of at least one hour \n",
+        "    \"verbosity\": logging.INFO,\n",
+        "}\n",
+        "\n",
+        "automl_config = AutoMLConfig(task = 'classification',\n",
+        "                             debug_log = 'automl_errors.log',\n",
+        "                             compute_target = LocalTarget(),\n",
+        "                             enable_local_managed = True,\n",
+        "                             training_data = training_data,\n",
+        "                             label_column_name = label_column_name,\n",
+        "                             **automl_settings\n",
+        "                            )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Call the `submit` method on the experiment object and pass the run configuration. Depending on the data and the number of iterations this can run for a while. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "parent_run = experiment.submit(automl_config, show_output = True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# If you need to retrieve a run that already started, use the following code\n",
+        "#from azureml.train.automl.run import AutoMLRun\n",
+        "#parent_run = AutoMLRun(experiment = experiment, run_id = '<replace with your run id>')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "parent_run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Results"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Explain model\n",
+        "\n",
+        "Automated ML models can be explained and visualized using the SDK Explainability library. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Analyze results\n",
+        "\n",
+        "### Retrieve the Best Child Run\n",
+        "\n",
+        "Below we select the best pipeline from our iterations. The `get_best_child` method returns the best run. Overloads on `get_best_child` allow you to retrieve the best run for *any* logged metric."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "best_run = parent_run.get_best_child()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Test the fitted model\n",
+        "\n",
+        "Now that the model is trained, split the data in the same way the data was split for training (The difference here is the data is being split locally) and then run the test data through the trained model to get the predicted values."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_test_df = validation_data.drop_columns(columns=[label_column_name])\n",
+        "y_test_df = validation_data.keep_columns(columns=[label_column_name], validate=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Creating ModelProxy for submitting prediction runs to the training environment.\n",
+        "We will create a ModelProxy for the best child run, which will allow us to submit a run that does the prediction in the training environment. Unlike the local client, which can have different versions of some libraries, the training environment will have all the compatible libraries for the model already."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.automl.model_proxy import ModelProxy\n",
+        "best_model_proxy = ModelProxy(best_run)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# call the predict functions on the model proxy\n",
+        "y_pred = best_model_proxy.predict(X_test_df).to_pandas_dataframe()\n",
+        "y_pred"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Acknowledgements"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This Credit Card fraud Detection dataset is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/ and is available at: https://www.kaggle.com/mlg-ulb/creditcardfraud\n",
+        "\n",
+        "\n",
+        "The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Universit\u00c3\u0192\u00c2\u00a9 Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project\n",
+        "Please cite the following works: \n",
+        "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tAndrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015\n",
+        "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tDal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon\n",
+        "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tDal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE\n",
+        "o\tDal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi)\n",
+        "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tCarcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-A\u00c3\u0192\u00c2\u00abl; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier\n",
+        "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tCarcillo, Fabrizio; Le Borgne, Yann-A\u00c3\u0192\u00c2\u00abl; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "sekrupa"
+      }
+    ],
+    "category": "tutorial",
+    "compute": [
+      "AML Compute"
+    ],
+    "datasets": [
+      "Creditcard"
+    ],
+    "deployment": [
+      "None"
+    ],
+    "exclude_from_index": false,
+    "file_extension": ".py",
+    "framework": [
+      "None"
+    ],
+    "friendly_name": "Classification of credit card fraudulent transactions using Automated ML",
+    "index_order": 5,
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.7"
+    },
+    "mimetype": "text/x-python",
+    "name": "python",
+    "nbconvert_exporter": "python",
+    "pygments_lexer": "ipython3",
+    "tags": [
+      "AutomatedML"
+    ],
+    "task": "Classification",
+    "version": "3.6.7"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.yml

@@ -0,0 +1,4 @@
+name: auto-ml-classification-credit-card-fraud-local-managed
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb

@@ -0,0 +1,470 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Automated Machine Learning\n",
+        "_**Regression with Aml Compute**_\n",
+        "\n",
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Data](#Data)\n",
+        "1. [Train](#Train)\n",
+        "1. [Results](#Results)\n",
+        "1. [Test](#Test)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "In this example we use an experimental feature, Model Proxy, to do a predict on the best generated model without downloading the model locally. The prediction will happen on same compute and environment that was used to train the model. This feature is currently in the experimental state, which means that the API is prone to changing, please make sure to run on the latest version of this notebook if you face any issues.\n",
+        "This notebook will also leverage MLFlow for saving models, allowing for more portability of the resulting models. See https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow for more details around MLFlow is AzureML.\n",
+        "\n",
+        "If you are using an Azure Machine Learning Compute Instance, you are all set.  Otherwise, go through the [configuration](../../../../configuration.ipynb)  notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
+        "\n",
+        "In this notebook you will learn how to:\n",
+        "1. Create an `Experiment` in an existing `Workspace`.\n",
+        "2. Configure AutoML using `AutoMLConfig`.\n",
+        "3. Train the model using remote compute.\n",
+        "4. Explore the results.\n",
+        "5. Test the best fitted model."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup\n",
+        "\n",
+        "As part of the setup you have already created an Azure ML `Workspace` object. For Automated ML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import logging\n",
+        "\n",
+        "import json\n",
+        "\n",
+        "\n",
+        "import azureml.core\n",
+        "from azureml.core.experiment import Experiment\n",
+        "from azureml.core.workspace import Workspace\n",
+        "from azureml.core.dataset import Dataset\n",
+        "from azureml.train.automl import AutoMLConfig"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"This notebook was created using version 1.43.0 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "\n",
+        "# Choose a name for the experiment.\n",
+        "experiment_name = 'automl-regression-model-proxy'\n",
+        "\n",
+        "experiment = Experiment(ws, experiment_name)\n",
+        "\n",
+        "output = {}\n",
+        "output['Subscription ID'] = ws.subscription_id\n",
+        "output['Workspace'] = ws.name\n",
+        "output['Resource Group'] = ws.resource_group\n",
+        "output['Location'] = ws.location\n",
+        "output['Run History Name'] = experiment_name\n",
+        "output"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Using AmlCompute\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you use `AmlCompute` as your training compute resource."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# Choose a name for your CPU cluster\n",
+        "# Try to ensure that the cluster name is unique across the notebooks\n",
+        "cpu_cluster_name = \"reg-model-proxy\"\n",
+        "\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
+        "    print('Found existing cluster, use it.')\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
+        "                                                           max_nodes=4)\n",
+        "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
+        "\n",
+        "compute_target.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Data\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Load Data\n",
+        "Load the hardware dataset from a csv file containing both training features and labels. The features are inputs to the model, while the training labels represent the expected output of the model. Next, we'll split the data using random_split and extract the training data for the model. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/machineData.csv\"\n",
+        "dataset = Dataset.Tabular.from_delimited_files(data)\n",
+        "\n",
+        "# Split the dataset into train and test datasets\n",
+        "train_data, test_data = dataset.random_split(percentage=0.8, seed=223)\n",
+        "\n",
+        "label = \"ERP\"\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The split data will be used in the remote compute by ModelProxy and locally to compare results.\n",
+        "So, we need to persist the split data to avoid descrepencies from different package versions in the local and remote."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ds = ws.get_default_datastore()\n",
+        "\n",
+        "train_data = Dataset.Tabular.register_pandas_dataframe(\n",
+        "    train_data.to_pandas_dataframe(), target=(ds, \"machineTrainData\"), name=\"train_data\")\n",
+        "\n",
+        "test_data = Dataset.Tabular.register_pandas_dataframe(\n",
+        "    test_data.to_pandas_dataframe(), target=(ds, \"machineTestData\"), name=\"test_data\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train\n",
+        "\n",
+        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
+        "\n",
+        "|Property|Description|\n",
+        "|-|-|\n",
+        "|**task**|classification, regression or forecasting|\n",
+        "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
+        "|**n_cross_validations**|Number of cross validation splits.|\n",
+        "|**training_data**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+        "|**label_column_name**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
+        "\n",
+        "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "automlconfig-remarks-sample"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "automl_settings = {\n",
+        "    \"n_cross_validations\": 3,\n",
+        "    \"primary_metric\": 'r2_score',\n",
+        "    \"enable_early_stopping\": True, \n",
+        "    \"experiment_timeout_hours\": 0.3, #for real scenarios we recommend a timeout of at least one hour \n",
+        "    \"max_concurrent_iterations\": 4,\n",
+        "    \"max_cores_per_iteration\": -1,\n",
+        "    \"verbosity\": logging.INFO,\n",
+        "    \"save_mlflow\": True,\n",
+        "}\n",
+        "\n",
+        "automl_config = AutoMLConfig(task = 'regression',\n",
+        "                             compute_target = compute_target,\n",
+        "                             training_data = train_data,\n",
+        "                             label_column_name = label,\n",
+        "                             **automl_settings\n",
+        "                            )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Call the `submit` method on the experiment object and pass the run configuration. Execution of remote runs is asynchronous. Depending on the data and the number of iterations this can run for a while.  Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run = experiment.submit(automl_config, show_output = False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# If you need to retrieve a run that already started, use the following code\n",
+        "#from azureml.train.automl.run import AutoMLRun\n",
+        "#remote_run = AutoMLRun(experiment = experiment, run_id = '<replace with your run id>')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Results"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Retrieve the Best Child Run\n",
+        "\n",
+        "Below we select the best pipeline from our iterations. The `get_best_child` method returns the best run. Overloads on `get_best_child` allow you to retrieve the best run for *any* logged metric."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "best_run = remote_run.get_best_child()\n",
+        "print(best_run)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Show hyperparameters\n",
+        "Show the model pipeline used for the best run with its hyperparameters.\n",
+        "For ensemble pipelines it shows the iterations and algorithms that are ensembled."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run_properties = best_run.get_details()['properties']\n",
+        "pipeline_script = json.loads(run_properties['pipeline_script'])\n",
+        "print(json.dumps(pipeline_script, indent = 1)) \n",
+        "\n",
+        "if 'ensembled_iterations' in run_properties:\n",
+        "    print(\"\")\n",
+        "    print(\"Ensembled Iterations\")\n",
+        "    print(run_properties['ensembled_iterations'])\n",
+        "    \n",
+        "if 'ensembled_algorithms' in run_properties:\n",
+        "    print(\"\")\n",
+        "    print(\"Ensembled Algorithms\")\n",
+        "    print(run_properties['ensembled_algorithms'])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Best Child Run Based on Any Other Metric\n",
+        "Show the run and the model that has the smallest `root_mean_squared_error` value (which turned out to be the same as the one with largest `spearman_correlation` value):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "lookup_metric = \"root_mean_squared_error\"\n",
+        "best_run = remote_run.get_best_child(metric = lookup_metric)\n",
+        "print(best_run)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "y_test = test_data.keep_columns('ERP')\n",
+        "test_data = test_data.drop_columns('ERP')\n",
+        "\n",
+        "\n",
+        "y_train = train_data.keep_columns('ERP')\n",
+        "train_data = train_data.drop_columns('ERP')\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Creating ModelProxy for submitting prediction runs to the training environment.\n",
+        "We will create a ModelProxy for the best child run, which will allow us to submit a run that does the prediction in the training environment. Unlike the local client, which can have different versions of some libraries, the training environment will have all the compatible libraries for the model already."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.automl.model_proxy import ModelProxy\n",
+        "best_model_proxy = ModelProxy(best_run)\n",
+        "y_pred_train = best_model_proxy.predict(train_data)\n",
+        "y_pred_test = best_model_proxy.predict(test_data)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Exploring results"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "y_pred_train = y_pred_train.to_pandas_dataframe().values.flatten()\n",
+        "y_train = y_train.to_pandas_dataframe().values.flatten()\n",
+        "y_residual_train = y_train - y_pred_train\n",
+        "\n",
+        "y_pred_test = y_pred_test.to_pandas_dataframe().values.flatten()\n",
+        "y_test = y_test.to_pandas_dataframe().values.flatten()\n",
+        "y_residual_test = y_test - y_pred_test\n",
+        "print(y_residual_train)\n",
+        "print(y_residual_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "sekrupa"
+      }
+    ],
+    "categories": [
+      "how-to-use-azureml",
+      "automated-machine-learning"
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.2"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.yml

@@ -0,0 +1,4 @@
+name: auto-ml-regression-model-proxy
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/assets/score.py

@@ -0,0 +1,171 @@
+from typing import Any, Dict, Optional, List
+
+import argparse
+import json
+import os
+import re
+
+import numpy as np
+import pandas as pd
+
+from matplotlib import pyplot as plt
+from matplotlib.backends.backend_pdf import PdfPages
+
+from azureml.automl.core.shared import constants
+from azureml.automl.core.shared.types import GrainType
+from azureml.automl.runtime.shared.score import scoring
+
+GRAIN = "time_series_id"
+BACKTEST_ITER = "backtest_iteration"
+ACTUALS = "actual_level"
+PREDICTIONS = "predicted_level"
+ALL_GRAINS = "all_sets"
+
+FORECASTS_FILE = "forecast.csv"
+SCORES_FILE = "scores.csv"
+PLOTS_FILE = "plots_fcst_vs_actual.pdf"
+RE_INVALID_SYMBOLS = re.compile("[: ]")
+
+
+def _compute_metrics(df: pd.DataFrame, metrics: List[str]):
+    """
+    Compute metrics for one data frame.
+
+    :param df: The data frame which contains actual_level and predicted_level columns.
+    :return: The data frame with two columns - metric_name and metric.
+    """
+    scores = scoring.score_regression(
+        y_test=df[ACTUALS], y_pred=df[PREDICTIONS], metrics=metrics
+    )
+    metrics_df = pd.DataFrame(list(scores.items()), columns=["metric_name", "metric"])
+    metrics_df.sort_values(["metric_name"], inplace=True)
+    metrics_df.reset_index(drop=True, inplace=True)
+    return metrics_df
+
+
+def _format_grain_name(grain: GrainType) -> str:
+    """
+    Convert grain name to string.
+
+    :param grain: the grain name.
+    :return: the string representation of the given grain.
+    """
+    if not isinstance(grain, tuple) and not isinstance(grain, list):
+        return str(grain)
+    grain = list(map(str, grain))
+    return "|".join(grain)
+
+
+def compute_all_metrics(
+    fcst_df: pd.DataFrame,
+    ts_id_colnames: List[str],
+    metric_names: Optional[List[set]] = None,
+):
+    """
+    Calculate metrics per grain.
+
+    :param fcst_df: forecast data frame. Must contain 2 columns: 'actual_level' and 'predicted_level'
+    :param metric_names: (optional) the list of metric names to return
+    :param ts_id_colnames: (optional) list of grain column names
+    :return: dictionary of summary table for all tests and final decision on stationary vs nonstaionary
+    """
+    if not metric_names:
+        metric_names = list(constants.Metric.SCALAR_REGRESSION_SET)
+
+    if ts_id_colnames is None:
+        ts_id_colnames = []
+
+    metrics_list = []
+    if ts_id_colnames:
+        for grain, df in fcst_df.groupby(ts_id_colnames):
+            one_grain_metrics_df = _compute_metrics(df, metric_names)
+            one_grain_metrics_df[GRAIN] = _format_grain_name(grain)
+            metrics_list.append(one_grain_metrics_df)
+
+    # overall metrics
+    one_grain_metrics_df = _compute_metrics(fcst_df, metric_names)
+    one_grain_metrics_df[GRAIN] = ALL_GRAINS
+    metrics_list.append(one_grain_metrics_df)
+
+    # collect into a data frame
+    return pd.concat(metrics_list)
+
+
+def _draw_one_plot(
+    df: pd.DataFrame,
+    time_column_name: str,
+    grain_column_names: List[str],
+    pdf: PdfPages,
+) -> None:
+    """
+    Draw the single plot.
+
+    :param df: The data frame with the data to build plot.
+    :param time_column_name: The name of a time column.
+    :param grain_column_names: The name of grain columns.
+    :param pdf: The pdf backend used to render the plot.
+    """
+    fig, _ = plt.subplots(figsize=(20, 10))
+    df = df.set_index(time_column_name)
+    plt.plot(df[[ACTUALS, PREDICTIONS]])
+    plt.xticks(rotation=45)
+    iteration = df[BACKTEST_ITER].iloc[0]
+    if grain_column_names:
+        grain_name = [df[grain].iloc[0] for grain in grain_column_names]
+        plt.title(f"Time series ID: {_format_grain_name(grain_name)} {iteration}")
+    plt.legend(["actual", "forecast"])
+    plt.close(fig)
+    pdf.savefig(fig)
+
+
+def calculate_scores_and_build_plots(
+    input_dir: str, output_dir: str, automl_settings: Dict[str, Any]
+):
+    os.makedirs(output_dir, exist_ok=True)
+    grains = automl_settings.get(constants.TimeSeries.TIME_SERIES_ID_COLUMN_NAMES)
+    time_column_name = automl_settings.get(constants.TimeSeries.TIME_COLUMN_NAME)
+    if grains is None:
+        grains = []
+    if isinstance(grains, str):
+        grains = [grains]
+    while BACKTEST_ITER in grains:
+        grains.remove(BACKTEST_ITER)
+
+    dfs = []
+    for fle in os.listdir(input_dir):
+        file_path = os.path.join(input_dir, fle)
+        if os.path.isfile(file_path) and file_path.endswith(".csv"):
+            df_iter = pd.read_csv(file_path, parse_dates=[time_column_name])
+            for _, iteration in df_iter.groupby(BACKTEST_ITER):
+                dfs.append(iteration)
+    forecast_df = pd.concat(dfs, sort=False, ignore_index=True)
+    # To make sure plots are in order, sort the predictions by grain and iteration.
+    ts_index = grains + [BACKTEST_ITER]
+    forecast_df.sort_values(by=ts_index, inplace=True)
+    pdf = PdfPages(os.path.join(output_dir, PLOTS_FILE))
+    for _, one_forecast in forecast_df.groupby(ts_index):
+        _draw_one_plot(one_forecast, time_column_name, grains, pdf)
+    pdf.close()
+    forecast_df.to_csv(os.path.join(output_dir, FORECASTS_FILE), index=False)
+    # Remove np.NaN and np.inf from the prediction and actuals data.
+    forecast_df.replace([np.inf, -np.inf], np.nan, inplace=True)
+    forecast_df.dropna(subset=[ACTUALS, PREDICTIONS], inplace=True)
+    metrics = compute_all_metrics(forecast_df, grains + [BACKTEST_ITER])
+    metrics.to_csv(os.path.join(output_dir, SCORES_FILE), index=False)
+
+
+if __name__ == "__main__":
+    args = {"forecasts": "--forecasts", "scores_out": "--output-dir"}
+    parser = argparse.ArgumentParser("Parsing input arguments.")
+    for argname, arg in args.items():
+        parser.add_argument(arg, dest=argname, required=True)
+    parsed_args, _ = parser.parse_known_args()
+    input_dir = parsed_args.forecasts
+    output_dir = parsed_args.scores_out
+    with open(
+        os.path.join(
+            os.path.dirname(os.path.realpath(__file__)), "automl_settings.json"
+        )
+    ) as json_file:
+        automl_settings = json.load(json_file)
+    calculate_scores_and_build_plots(input_dir, output_dir, automl_settings)

how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/auto-ml-forecasting-backtest-many-models.ipynb

@@ -0,0 +1,726 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Many Models with Backtesting - Automated ML\n",
+        "**_Backtest many models time series forecasts with Automated Machine Learning_**\n",
+        "\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "For this notebook we are using a synthetic dataset to demonstrate the back testing in many model scenario. This allows us to check historical performance of AutoML on a historical data. To do that we step back on the backtesting period by the data set several times and split the data to train and test sets. Then these data sets are used for training and evaluation of model.<br>\n",
+        "\n",
+        "Thus, it is a quick way of evaluating AutoML as if it was in production. Here, we do not test historical performance of a particular model, for this see the [notebook](../forecasting-backtest-single-model/auto-ml-forecasting-backtest-single-model.ipynb). Instead, the best model for every backtest iteration can be different since AutoML chooses the best model for a given training set.\n",
+        "![Backtesting](Backtesting.png)\n",
+        "\n",
+        "**NOTE: There are limits on how many runs we can do in parallel per workspace, and we currently recommend to set the parallelism to maximum of 320 runs per experiment per workspace. If users want to have more parallelism and increase this limit they might encounter Too Many Requests errors (HTTP 429).**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Prerequisites\n",
+        "You'll need to create a compute Instance by following the instructions in the [EnvironmentSetup.md](../Setup_Resources/EnvironmentSetup.md)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 1.0 Set up workspace, datastore, experiment"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "gather": {
+          "logged": 1613003526897
+        }
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "import azureml.core\n",
+        "from azureml.core import Workspace, Datastore\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "from pandas.tseries.frequencies import to_offset\n",
+        "\n",
+        "# Set up your workspace\n",
+        "ws = Workspace.from_config()\n",
+        "ws.get_details()\n",
+        "\n",
+        "# Set up your datastores\n",
+        "dstore = ws.get_default_datastore()\n",
+        "\n",
+        "output = {}\n",
+        "output[\"SDK version\"] = azureml.core.VERSION\n",
+        "output[\"Subscription ID\"] = ws.subscription_id\n",
+        "output[\"Workspace\"] = ws.name\n",
+        "output[\"Resource Group\"] = ws.resource_group\n",
+        "output[\"Location\"] = ws.location\n",
+        "output[\"Default datastore name\"] = dstore.name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
+        "outputDf.T"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This notebook is compatible with Azure ML SDK version 1.35.1 or later."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Choose an experiment"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "gather": {
+          "logged": 1613003540729
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "\n",
+        "experiment = Experiment(ws, \"automl-many-models-backtest\")\n",
+        "\n",
+        "print(\"Experiment name: \" + experiment.name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 2.0 Data\n",
+        "\n",
+        "#### 2.1 Data generation\n",
+        "For this notebook we will generate the artificial data set with two [time series IDs](https://docs.microsoft.com/en-us/python/api/azureml-automl-core/azureml.automl.core.forecasting_parameters.forecastingparameters?view=azure-ml-py). Then we will generate backtest folds and will upload it to the default BLOB storage and create a [TabularDataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabular_dataset.tabulardataset?view=azure-ml-py)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# simulate data: 2 grains - 700\n",
+        "TIME_COLNAME = \"date\"\n",
+        "TARGET_COLNAME = \"value\"\n",
+        "TIME_SERIES_ID_COLNAME = \"ts_id\"\n",
+        "\n",
+        "sample_size = 700\n",
+        "# Set the random seed for reproducibility of results.\n",
+        "np.random.seed(20)\n",
+        "X1 = pd.DataFrame(\n",
+        "    {\n",
+        "        TIME_COLNAME: pd.date_range(start=\"2018-01-01\", periods=sample_size),\n",
+        "        TARGET_COLNAME: np.random.normal(loc=100, scale=20, size=sample_size),\n",
+        "        TIME_SERIES_ID_COLNAME: \"ts_A\",\n",
+        "    }\n",
+        ")\n",
+        "X2 = pd.DataFrame(\n",
+        "    {\n",
+        "        TIME_COLNAME: pd.date_range(start=\"2018-01-01\", periods=sample_size),\n",
+        "        TARGET_COLNAME: np.random.normal(loc=100, scale=20, size=sample_size),\n",
+        "        TIME_SERIES_ID_COLNAME: \"ts_B\",\n",
+        "    }\n",
+        ")\n",
+        "\n",
+        "X = pd.concat([X1, X2], ignore_index=True, sort=False)\n",
+        "print(\"Simulated dataset contains {} rows \\n\".format(X.shape[0]))\n",
+        "X.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we will generate 8 backtesting folds with backtesting period of 7 days and with the same forecasting horizon. We will add the column \"backtest_iteration\", which will identify the backtesting period by the last training date."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "offset_type = \"7D\"\n",
+        "NUMBER_OF_BACKTESTS = 8  # number of train/test sets to generate\n",
+        "\n",
+        "dfs_train = []\n",
+        "dfs_test = []\n",
+        "for ts_id, df_one in X.groupby(TIME_SERIES_ID_COLNAME):\n",
+        "\n",
+        "    data_end = df_one[TIME_COLNAME].max()\n",
+        "\n",
+        "    for i in range(NUMBER_OF_BACKTESTS):\n",
+        "        train_cutoff_date = data_end - to_offset(offset_type)\n",
+        "        df_one = df_one.copy()\n",
+        "        df_one[\"backtest_iteration\"] = \"iteration_\" + str(train_cutoff_date)\n",
+        "        train = df_one[df_one[TIME_COLNAME] <= train_cutoff_date]\n",
+        "        test = df_one[\n",
+        "            (df_one[TIME_COLNAME] > train_cutoff_date)\n",
+        "            & (df_one[TIME_COLNAME] <= data_end)\n",
+        "        ]\n",
+        "        data_end = train[TIME_COLNAME].max()\n",
+        "        dfs_train.append(train)\n",
+        "        dfs_test.append(test)\n",
+        "\n",
+        "X_train = pd.concat(dfs_train, sort=False, ignore_index=True)\n",
+        "X_test = pd.concat(dfs_test, sort=False, ignore_index=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### 2.2 Create the Tabular Data Set.\n",
+        "\n",
+        "A Datastore is a place where data can be stored that is then made accessible to a compute either by means of mounting or copying the data to the compute target.\n",
+        "\n",
+        "Please refer to [Datastore](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore(class)?view=azure-ml-py) documentation on how to access data from Datastore.\n",
+        "\n",
+        "In this next step, we will upload the data and create a TabularDataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.data.dataset_factory import TabularDatasetFactory\n",
+        "\n",
+        "ds = ws.get_default_datastore()\n",
+        "# Upload saved data to the default data store.\n",
+        "train_data = TabularDatasetFactory.register_pandas_dataframe(\n",
+        "    X_train, target=(ds, \"data_mm\"), name=\"data_train\"\n",
+        ")\n",
+        "test_data = TabularDatasetFactory.register_pandas_dataframe(\n",
+        "    X_test, target=(ds, \"data_mm\"), name=\"data_test\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 3.0 Build the training pipeline\n",
+        "Now that the dataset, WorkSpace, and datastore are set up, we can put together a pipeline for training.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Choose a compute target\n",
+        "\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
+        "\n",
+        "\\*\\*Creation of AmlCompute takes approximately 5 minutes.**\n",
+        "\n",
+        "If the AmlCompute with that name is already in your workspace this code will skip the creation process. As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this [article](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-quotas) on the default limits and how to request more quota."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "gather": {
+          "logged": 1613007037308
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "\n",
+        "# Name your cluster\n",
+        "compute_name = \"backtest-mm\"\n",
+        "\n",
+        "\n",
+        "if compute_name in ws.compute_targets:\n",
+        "    compute_target = ws.compute_targets[compute_name]\n",
+        "    if compute_target and type(compute_target) is AmlCompute:\n",
+        "        print(\"Found compute target: \" + compute_name)\n",
+        "else:\n",
+        "    print(\"Creating a new compute target...\")\n",
+        "    provisioning_config = AmlCompute.provisioning_configuration(\n",
+        "        vm_size=\"STANDARD_DS12_V2\", max_nodes=6\n",
+        "    )\n",
+        "    # Create the compute target\n",
+        "    compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n",
+        "\n",
+        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
+        "    # If no min node count is provided it will use the scale settings for the cluster\n",
+        "    compute_target.wait_for_completion(\n",
+        "        show_output=True, min_node_count=None, timeout_in_minutes=20\n",
+        "    )\n",
+        "\n",
+        "    # For a more detailed view of current cluster status, use the 'status' property\n",
+        "    print(compute_target.status.serialize())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Set up training parameters\n",
+        "\n",
+        "This dictionary defines the AutoML and many models settings. For this forecasting task we need to define several settings including the name of the time column, the maximum forecast horizon, and the partition column name definition. Please note, that in this case we are setting grain_column_names to be the time series ID column plus iteration, because we want to train a separate model for each time series and iteration.\n",
+        "\n",
+        "| Property                           | Description|\n",
+        "| :---------------                   | :------------------- |\n",
+        "| **task**                           | forecasting |\n",
+        "| **primary_metric**                 | This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>normalized_root_mean_squared_error</i><br><i>normalized_mean_absolute_error</i> |\n",
+        "| **iteration_timeout_minutes**      | Maximum amount of time in minutes that the model can train. This is optional but provides customers with greater control on exit criteria. |\n",
+        "| **iterations**                     | Number of models to train. This is optional but provides customers with greater control on exit criteria. |\n",
+        "| **experiment_timeout_hours**       | Maximum amount of time in hours that the experiment can take before it terminates. This is optional but provides customers with greater control on exit criteria. |\n",
+        "| **label_column_name**              | The name of the label column. |\n",
+        "| **forecast_horizon**               | The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly). Periods are inferred from your data. |\n",
+        "| **n_cross_validations**            | Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way. |\n",
+        "| **time_column_name**               | The name of your time column. |\n",
+        "| **time_series_id_column_names**     | The column names used to uniquely identify timeseries in data that has multiple rows with the same timestamp. |\n",
+        "| **track_child_runs**               | Flag to disable tracking of child runs. Only best run is tracked if the flag is set to False (this includes the model and metrics of the run). |\n",
+        "| **partition_column_names**         | The names of columns used to group your models. For timeseries, the groups must not split up individual time-series. That is, each group must contain one or more whole time-series. |"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "gather": {
+          "logged": 1613007061544
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.train.automl.runtime._many_models.many_models_parameters import (\n",
+        "    ManyModelsTrainParameters,\n",
+        ")\n",
+        "\n",
+        "partition_column_names = [TIME_SERIES_ID_COLNAME, \"backtest_iteration\"]\n",
+        "automl_settings = {\n",
+        "    \"task\": \"forecasting\",\n",
+        "    \"primary_metric\": \"normalized_root_mean_squared_error\",\n",
+        "    \"iteration_timeout_minutes\": 10,  # This needs to be changed based on the dataset. We ask customer to explore how long training is taking before settings this value\n",
+        "    \"iterations\": 15,\n",
+        "    \"experiment_timeout_hours\": 0.25,  # This also needs to be changed based on the dataset. For larger data set this number needs to be bigger.\n",
+        "    \"label_column_name\": TARGET_COLNAME,\n",
+        "    \"n_cross_validations\": 3,\n",
+        "    \"time_column_name\": TIME_COLNAME,\n",
+        "    \"forecast_horizon\": 6,\n",
+        "    \"time_series_id_column_names\": partition_column_names,\n",
+        "    \"track_child_runs\": False,\n",
+        "}\n",
+        "\n",
+        "mm_paramters = ManyModelsTrainParameters(\n",
+        "    automl_settings=automl_settings, partition_column_names=partition_column_names\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Set up many models pipeline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Parallel run step is leveraged to train multiple models at once. To configure the ParallelRunConfig you will need to determine the appropriate number of workers and nodes for your use case. The process_count_per_node is based off the number of cores of the compute VM. The node_count will determine the number of master nodes to use, increasing the node count will speed up the training process.\n",
+        "\n",
+        "| Property                           | Description|\n",
+        "| :---------------                   | :------------------- |\n",
+        "| **experiment**                     | The experiment used for training. |\n",
+        "| **train_data**                     | The file dataset to be used as input to the training run. |\n",
+        "| **node_count**                     | The number of compute nodes to be used for running the user script. We recommend to start with 3 and increase the node_count if the training time is taking too long. |\n",
+        "| **process_count_per_node**         | Process count per node, we recommend 2:1 ratio for number of cores: number of processes per node. eg. If node has 16 cores then configure 8 or less process count per node or optimal performance. |\n",
+        "| **train_pipeline_parameters**      | The set of configuration parameters defined in the previous section. |\n",
+        "\n",
+        "Calling this method will create a new aggregated dataset which is generated dynamically on pipeline execution."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.automl.pipeline.steps import AutoMLPipelineBuilder\n",
+        "\n",
+        "\n",
+        "training_pipeline_steps = AutoMLPipelineBuilder.get_many_models_train_steps(\n",
+        "    experiment=experiment,\n",
+        "    train_data=train_data,\n",
+        "    compute_target=compute_target,\n",
+        "    node_count=2,\n",
+        "    process_count_per_node=2,\n",
+        "    run_invocation_timeout=920,\n",
+        "    train_pipeline_parameters=mm_paramters,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import Pipeline\n",
+        "\n",
+        "training_pipeline = Pipeline(ws, steps=training_pipeline_steps)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Submit the pipeline to run\n",
+        "Next we submit our pipeline to run. The whole training pipeline takes about 20 minutes using a STANDARD_DS12_V2 VM with our current ParallelRunConfig setting."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "training_run = experiment.submit(training_pipeline)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "training_run.wait_for_completion(show_output=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Check the run status, if training_run is in completed state, continue to next section. Otherwise, check the portal for failures."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 4.0 Backtesting\n",
+        "Now that we selected the best AutoML model for each backtest fold, we will use these models to generate the forecasts and compare with the actuals."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Set up output dataset for inference data\n",
+        "Output of inference can be represented as [OutputFileDatasetConfig](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.output_dataset_config.outputdatasetconfig?view=azure-ml-py) object and OutputFileDatasetConfig can be registered as a dataset. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.data import OutputFileDatasetConfig\n",
+        "\n",
+        "output_inference_data_ds = OutputFileDatasetConfig(\n",
+        "    name=\"many_models_inference_output\",\n",
+        "    destination=(dstore, \"backtesting/inference_data/\"),\n",
+        ").register_on_complete(name=\"backtesting_data_ds\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "For many models we need to provide the ManyModelsInferenceParameters object.\n",
+        "\n",
+        "#### ManyModelsInferenceParameters arguments\n",
+        "| Property                           | Description|\n",
+        "| :---------------                   | :------------------- |\n",
+        "| **partition_column_names**         | List of column names that identifies groups.                                           |\n",
+        "| **target_column_name**             | \\[Optional\\] Column name only if the inference dataset has the target. |\n",
+        "| **time_column_name**               | Column name only if it is timeseries. |\n",
+        "| **many_models_run_id**             | \\[Optional\\] Many models pipeline run id where models were trained. |\n",
+        "\n",
+        "#### get_many_models_batch_inference_steps arguments\n",
+        "| Property                           | Description|\n",
+        "| :---------------                   | :------------------- |\n",
+        "| **experiment**                     | The experiment used for inference run. |\n",
+        "| **inference_data**                 | The data to use for inferencing. It should be the same schema as used for training.\n",
+        "| **compute_target**                 | The compute target that runs the inference pipeline.|\n",
+        "| **node_count**                     | The number of compute nodes to be used for running the user script. We recommend to start with the number of cores per node (varies by compute sku). |\n",
+        "| **process_count_per_node**         | The number of processes per node.\n",
+        "| **train_run_id**                   | \\[Optional\\] The run id of the hierarchy training, by default it is the latest successful training many model run in the experiment. |\n",
+        "| **train_experiment_name**          | \\[Optional\\] The train experiment that contains the train pipeline. This one is only needed when the train pipeline is not in the same experiement as the inference pipeline. |\n",
+        "| **process_count_per_node**         | \\[Optional\\] The number of processes per node, by default it's 4. |"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.automl.pipeline.steps import AutoMLPipelineBuilder\n",
+        "from azureml.train.automl.runtime._many_models.many_models_parameters import (\n",
+        "    ManyModelsInferenceParameters,\n",
+        ")\n",
+        "\n",
+        "mm_parameters = ManyModelsInferenceParameters(\n",
+        "    partition_column_names=partition_column_names,\n",
+        "    time_column_name=TIME_COLNAME,\n",
+        "    target_column_name=TARGET_COLNAME,\n",
+        ")\n",
+        "\n",
+        "inference_steps = AutoMLPipelineBuilder.get_many_models_batch_inference_steps(\n",
+        "    experiment=experiment,\n",
+        "    inference_data=test_data,\n",
+        "    node_count=2,\n",
+        "    process_count_per_node=2,\n",
+        "    compute_target=compute_target,\n",
+        "    run_invocation_timeout=300,\n",
+        "    output_datastore=output_inference_data_ds,\n",
+        "    train_run_id=training_run.id,\n",
+        "    train_experiment_name=training_run.experiment.name,\n",
+        "    inference_pipeline_parameters=mm_parameters,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import Pipeline\n",
+        "\n",
+        "inference_pipeline = Pipeline(ws, steps=inference_steps)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "inference_run = experiment.submit(inference_pipeline)\n",
+        "inference_run.wait_for_completion(show_output=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 5.0 Retrieve results and calculate metrics\n",
+        "\n",
+        "The pipeline returns one file with the predictions for each times series ID and outputs the result to the forecasting_output Blob container. The details of the blob container is listed in 'forecasting_output.txt' under Outputs+logs. \n",
+        "\n",
+        "The next code snippet does the following:\n",
+        "1. Downloads the contents of the output folder that is passed in the parallel run step \n",
+        "2. Reads the parallel_run_step.txt file that has the predictions as pandas dataframe \n",
+        "3. Saves the table in csv format and \n",
+        "4. Displays the top 10 rows of the predictions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.automl.pipeline.steps.utilities import get_output_from_mm_pipeline\n",
+        "\n",
+        "forecasting_results_name = \"forecasting_results\"\n",
+        "forecasting_output_name = \"many_models_inference_output\"\n",
+        "forecast_file = get_output_from_mm_pipeline(\n",
+        "    inference_run, forecasting_results_name, forecasting_output_name\n",
+        ")\n",
+        "df = pd.read_csv(forecast_file, delimiter=\" \", header=None, parse_dates=[0])\n",
+        "df.columns = list(X_train.columns) + [\"predicted_level\"]\n",
+        "print(\n",
+        "    \"Prediction has \", df.shape[0], \" rows. Here the first 10 rows are being displayed.\"\n",
+        ")\n",
+        "# Save the scv file with header to read it in the next step.\n",
+        "df.rename(columns={TARGET_COLNAME: \"actual_level\"}, inplace=True)\n",
+        "df.to_csv(os.path.join(forecasting_results_name, \"forecast.csv\"), index=False)\n",
+        "df.head(10)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## View metrics\n",
+        "We will read in the obtained results and run the helper script, which will generate metrics and create the plots of predicted versus actual values."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from assets.score import calculate_scores_and_build_plots\n",
+        "\n",
+        "backtesting_results = \"backtesting_mm_results\"\n",
+        "os.makedirs(backtesting_results, exist_ok=True)\n",
+        "calculate_scores_and_build_plots(\n",
+        "    forecasting_results_name, backtesting_results, automl_settings\n",
+        ")\n",
+        "pd.DataFrame({\"File\": os.listdir(backtesting_results)})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The directory contains a set of files with results:\n",
+        "- forecast.csv contains forecasts for all backtest iterations. The backtest_iteration column contains iteration identifier with the last training date as a suffix\n",
+        "- scores.csv contains all metrics. If data set contains several time series, the metrics are given for all combinations of time series id and iterations, as well as scores for all iterations and time series ids, which are marked as \"all_sets\"\n",
+        "- plots_fcst_vs_actual.pdf contains the predictions vs forecast plots for each iteration and, eash time series is saved as separate plot.\n",
+        "\n",
+        "For demonstration purposes we will display the table of metrics for one of the time series with ID \"ts0\". We will create the utility function, which will build the table with metrics."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def get_metrics_for_ts(all_metrics, ts):\n",
+        "    \"\"\"\n",
+        "    Get the metrics for the time series with ID ts and return it as pandas data frame.\n",
+        "\n",
+        "    :param all_metrics: The table with all the metrics.\n",
+        "    :param ts: The ID of a time series of interest.\n",
+        "    :return: The pandas DataFrame with metrics for one time series.\n",
+        "    \"\"\"\n",
+        "    results_df = None\n",
+        "    for ts_id, one_series in all_metrics.groupby(\"time_series_id\"):\n",
+        "        if not ts_id.startswith(ts):\n",
+        "            continue\n",
+        "        iteration = ts_id.split(\"|\")[-1]\n",
+        "        df = one_series[[\"metric_name\", \"metric\"]]\n",
+        "        df.rename({\"metric\": iteration}, axis=1, inplace=True)\n",
+        "        df.set_index(\"metric_name\", inplace=True)\n",
+        "        if results_df is None:\n",
+        "            results_df = df\n",
+        "        else:\n",
+        "            results_df = results_df.merge(\n",
+        "                df, how=\"inner\", left_index=True, right_index=True\n",
+        "            )\n",
+        "    results_df.sort_index(axis=1, inplace=True)\n",
+        "    return results_df\n",
+        "\n",
+        "\n",
+        "metrics_df = pd.read_csv(os.path.join(backtesting_results, \"scores.csv\"))\n",
+        "ts = \"ts_A\"\n",
+        "get_metrics_for_ts(metrics_df, ts)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Forecast vs actuals plots."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from IPython.display import IFrame\n",
+        "\n",
+        "IFrame(\"./backtesting_mm_results/plots_fcst_vs_actual.pdf\", width=800, height=300)"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "jialiu"
+      }
+    ],
+    "categories": [
+      "how-to-use-azureml",
+      "automated-machine-learning"
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.9"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}

how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/auto-ml-forecasting-backtest-many-models.yml

@@ -0,0 +1,4 @@
+name: auto-ml-forecasting-backtest-many-models
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/assets/data_split.py

@@ -0,0 +1,45 @@
+import argparse
+import os
+
+import pandas as pd
+
+import azureml.train.automl.runtime._hts.hts_runtime_utilities as hru
+
+from azureml.core import Run
+from azureml.core.dataset import Dataset
+
+# Parse the arguments.
+args = {
+    "step_size": "--step-size",
+    "step_number": "--step-number",
+    "time_column_name": "--time-column-name",
+    "time_series_id_column_names": "--time-series-id-column-names",
+    "out_dir": "--output-dir",
+}
+parser = argparse.ArgumentParser("Parsing input arguments.")
+for argname, arg in args.items():
+    parser.add_argument(arg, dest=argname, required=True)
+parsed_args, _ = parser.parse_known_args()
+step_number = int(parsed_args.step_number)
+step_size = int(parsed_args.step_size)
+# Create the working dirrectory to store the temporary csv files.
+working_dir = parsed_args.out_dir
+os.makedirs(working_dir, exist_ok=True)
+# Set input and output
+script_run = Run.get_context()
+input_dataset = script_run.input_datasets["training_data"]
+X_train = input_dataset.to_pandas_dataframe()
+# Split the data.
+for i in range(step_number):
+    file_name = os.path.join(working_dir, "backtest_{}.csv".format(i))
+    if parsed_args.time_series_id_column_names:
+        dfs = []
+        for _, one_series in X_train.groupby([parsed_args.time_series_id_column_names]):
+            one_series = one_series.sort_values(
+                by=[parsed_args.time_column_name], inplace=False
+            )
+            dfs.append(one_series.iloc[: len(one_series) - step_size * i])
+        pd.concat(dfs, sort=False, ignore_index=True).to_csv(file_name, index=False)
+    else:
+        X_train.sort_values(by=[parsed_args.time_column_name], inplace=True)
+        X_train.iloc[: len(X_train) - step_size * i].to_csv(file_name, index=False)

how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/assets/retrain_models.py

@@ -0,0 +1,173 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+"""The batch script needed for back testing of models using PRS."""
+import argparse
+import json
+import logging
+import os
+import pickle
+import re
+
+import pandas as pd
+
+from azureml.core.experiment import Experiment
+from azureml.core.model import Model
+from azureml.core.run import Run
+from azureml.automl.core.shared import constants
+from azureml.automl.runtime.shared.score import scoring
+from azureml.train.automl import AutoMLConfig
+
+RE_INVALID_SYMBOLS = re.compile(r"[:\s]")
+
+model_name = None
+target_column_name = None
+current_step_run = None
+output_dir = None
+
+logger = logging.getLogger(__name__)
+
+
+def _get_automl_settings():
+    with open(
+        os.path.join(
+            os.path.dirname(os.path.realpath(__file__)), "automl_settings.json"
+        )
+    ) as json_file:
+        return json.load(json_file)
+
+
+def init():
+    global model_name
+    global target_column_name
+    global output_dir
+    global automl_settings
+    global model_uid
+    logger.info("Initialization of the run.")
+    parser = argparse.ArgumentParser("Parsing input arguments.")
+    parser.add_argument("--output-dir", dest="out", required=True)
+    parser.add_argument("--model-name", dest="model", default=None)
+    parser.add_argument("--model-uid", dest="model_uid", default=None)
+
+    parsed_args, _ = parser.parse_known_args()
+    model_name = parsed_args.model
+    automl_settings = _get_automl_settings()
+    target_column_name = automl_settings.get("label_column_name")
+    output_dir = parsed_args.out
+    model_uid = parsed_args.model_uid
+    os.makedirs(output_dir, exist_ok=True)
+    os.environ["AUTOML_IGNORE_PACKAGE_VERSION_INCOMPATIBILITIES".lower()] = "True"
+
+
+def get_run():
+    global current_step_run
+    if current_step_run is None:
+        current_step_run = Run.get_context()
+    return current_step_run
+
+
+def run_backtest(data_input_name: str, file_name: str, experiment: Experiment):
+    """Re-train the model and return metrics."""
+    data_input = pd.read_csv(
+        data_input_name,
+        parse_dates=[automl_settings[constants.TimeSeries.TIME_COLUMN_NAME]],
+    )
+    print(data_input.head())
+    if not automl_settings.get(constants.TimeSeries.GRAIN_COLUMN_NAMES):
+        # There is no grains.
+        data_input.sort_values(
+            [automl_settings[constants.TimeSeries.TIME_COLUMN_NAME]], inplace=True
+        )
+        X_train = data_input.iloc[: -automl_settings["max_horizon"]]
+        y_train = X_train.pop(target_column_name).values
+        X_test = data_input.iloc[-automl_settings["max_horizon"] :]
+        y_test = X_test.pop(target_column_name).values
+    else:
+        # The data contain grains.
+        dfs_train = []
+        dfs_test = []
+        for _, one_series in data_input.groupby(
+            automl_settings.get(constants.TimeSeries.GRAIN_COLUMN_NAMES)
+        ):
+            one_series.sort_values(
+                [automl_settings[constants.TimeSeries.TIME_COLUMN_NAME]], inplace=True
+            )
+            dfs_train.append(one_series.iloc[: -automl_settings["max_horizon"]])
+            dfs_test.append(one_series.iloc[-automl_settings["max_horizon"] :])
+        X_train = pd.concat(dfs_train, sort=False, ignore_index=True)
+        y_train = X_train.pop(target_column_name).values
+        X_test = pd.concat(dfs_test, sort=False, ignore_index=True)
+        y_test = X_test.pop(target_column_name).values
+
+    last_training_date = str(
+        X_train[automl_settings[constants.TimeSeries.TIME_COLUMN_NAME]].max()
+    )
+
+    if file_name:
+        # If file name is provided, we will load model and retrain it on backtest data.
+        with open(file_name, "rb") as fp:
+            fitted_model = pickle.load(fp)
+        fitted_model.fit(X_train, y_train)
+    else:
+        # We will run the experiment and select the best model.
+        X_train[target_column_name] = y_train
+        automl_config = AutoMLConfig(training_data=X_train, **automl_settings)
+        automl_run = current_step_run.submit_child(automl_config, show_output=True)
+        best_run, fitted_model = automl_run.get_output()
+        # As we have generated models, we need to register them for the future use.
+        description = "Backtest model example"
+        tags = {"last_training_date": last_training_date, "experiment": experiment.name}
+        if model_uid:
+            tags["model_uid"] = model_uid
+        automl_run.register_model(
+            model_name=best_run.properties["model_name"],
+            description=description,
+            tags=tags,
+        )
+        print(f"The model {best_run.properties['model_name']} was registered.")
+
+    _, x_pred = fitted_model.forecast(X_test)
+    x_pred.reset_index(inplace=True, drop=False)
+    columns = [automl_settings[constants.TimeSeries.TIME_COLUMN_NAME]]
+    if automl_settings.get(constants.TimeSeries.GRAIN_COLUMN_NAMES):
+        # We know that fitted_model.grain_column_names is a list.
+        columns.extend(fitted_model.grain_column_names)
+    columns.append(constants.TimeSeriesInternal.DUMMY_TARGET_COLUMN)
+    # Remove featurized columns.
+    x_pred = x_pred[columns]
+    x_pred.rename(
+        {constants.TimeSeriesInternal.DUMMY_TARGET_COLUMN: "predicted_level"},
+        axis=1,
+        inplace=True,
+    )
+    x_pred["actual_level"] = y_test
+    x_pred["backtest_iteration"] = f"iteration_{last_training_date}"
+    date_safe = RE_INVALID_SYMBOLS.sub("_", last_training_date)
+    x_pred.to_csv(os.path.join(output_dir, f"iteration_{date_safe}.csv"), index=False)
+    return x_pred
+
+
+def run(input_files):
+    """Run the script"""
+    logger.info("Running mini batch.")
+    ws = get_run().experiment.workspace
+    file_name = None
+    if model_name:
+        models = Model.list(ws, name=model_name)
+        cloud_model = None
+        if models:
+            for one_mod in models:
+                if cloud_model is None or one_mod.version > cloud_model.version:
+                    logger.info(
+                        "Using existing model from the workspace. Model version: {}".format(
+                            one_mod.version
+                        )
+                    )
+                    cloud_model = one_mod
+        file_name = cloud_model.download(exist_ok=True)
+
+    forecasts = []
+    logger.info("Running backtest.")
+    for input_file in input_files:
+        forecasts.append(run_backtest(input_file, file_name, get_run().experiment))
+    return pd.concat(forecasts)

how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/assets/score.py

@@ -0,0 +1,171 @@
+from typing import Any, Dict, Optional, List
+
+import argparse
+import json
+import os
+import re
+
+import numpy as np
+import pandas as pd
+
+from matplotlib import pyplot as plt
+from matplotlib.backends.backend_pdf import PdfPages
+
+from azureml.automl.core.shared import constants
+from azureml.automl.core.shared.types import GrainType
+from azureml.automl.runtime.shared.score import scoring
+
+GRAIN = "time_series_id"
+BACKTEST_ITER = "backtest_iteration"
+ACTUALS = "actual_level"
+PREDICTIONS = "predicted_level"
+ALL_GRAINS = "all_sets"
+
+FORECASTS_FILE = "forecast.csv"
+SCORES_FILE = "scores.csv"
+PLOTS_FILE = "plots_fcst_vs_actual.pdf"
+RE_INVALID_SYMBOLS = re.compile("[: ]")
+
+
+def _compute_metrics(df: pd.DataFrame, metrics: List[str]):
+    """
+    Compute metrics for one data frame.
+
+    :param df: The data frame which contains actual_level and predicted_level columns.
+    :return: The data frame with two columns - metric_name and metric.
+    """
+    scores = scoring.score_regression(
+        y_test=df[ACTUALS], y_pred=df[PREDICTIONS], metrics=metrics
+    )
+    metrics_df = pd.DataFrame(list(scores.items()), columns=["metric_name", "metric"])
+    metrics_df.sort_values(["metric_name"], inplace=True)
+    metrics_df.reset_index(drop=True, inplace=True)
+    return metrics_df
+
+
+def _format_grain_name(grain: GrainType) -> str:
+    """
+    Convert grain name to string.
+
+    :param grain: the grain name.
+    :return: the string representation of the given grain.
+    """
+    if not isinstance(grain, tuple) and not isinstance(grain, list):
+        return str(grain)
+    grain = list(map(str, grain))
+    return "|".join(grain)
+
+
+def compute_all_metrics(
+    fcst_df: pd.DataFrame,
+    ts_id_colnames: List[str],
+    metric_names: Optional[List[set]] = None,
+):
+    """
+    Calculate metrics per grain.
+
+    :param fcst_df: forecast data frame. Must contain 2 columns: 'actual_level' and 'predicted_level'
+    :param metric_names: (optional) the list of metric names to return
+    :param ts_id_colnames: (optional) list of grain column names
+    :return: dictionary of summary table for all tests and final decision on stationary vs nonstaionary
+    """
+    if not metric_names:
+        metric_names = list(constants.Metric.SCALAR_REGRESSION_SET)
+
+    if ts_id_colnames is None:
+        ts_id_colnames = []
+
+    metrics_list = []
+    if ts_id_colnames:
+        for grain, df in fcst_df.groupby(ts_id_colnames):
+            one_grain_metrics_df = _compute_metrics(df, metric_names)
+            one_grain_metrics_df[GRAIN] = _format_grain_name(grain)
+            metrics_list.append(one_grain_metrics_df)
+
+    # overall metrics
+    one_grain_metrics_df = _compute_metrics(fcst_df, metric_names)
+    one_grain_metrics_df[GRAIN] = ALL_GRAINS
+    metrics_list.append(one_grain_metrics_df)
+
+    # collect into a data frame
+    return pd.concat(metrics_list)
+
+
+def _draw_one_plot(
+    df: pd.DataFrame,
+    time_column_name: str,
+    grain_column_names: List[str],
+    pdf: PdfPages,
+) -> None:
+    """
+    Draw the single plot.
+
+    :param df: The data frame with the data to build plot.
+    :param time_column_name: The name of a time column.
+    :param grain_column_names: The name of grain columns.
+    :param pdf: The pdf backend used to render the plot.
+    """
+    fig, _ = plt.subplots(figsize=(20, 10))
+    df = df.set_index(time_column_name)
+    plt.plot(df[[ACTUALS, PREDICTIONS]])
+    plt.xticks(rotation=45)
+    iteration = df[BACKTEST_ITER].iloc[0]
+    if grain_column_names:
+        grain_name = [df[grain].iloc[0] for grain in grain_column_names]
+        plt.title(f"Time series ID: {_format_grain_name(grain_name)} {iteration}")
+    plt.legend(["actual", "forecast"])
+    plt.close(fig)
+    pdf.savefig(fig)
+
+
+def calculate_scores_and_build_plots(
+    input_dir: str, output_dir: str, automl_settings: Dict[str, Any]
+):
+    os.makedirs(output_dir, exist_ok=True)
+    grains = automl_settings.get(constants.TimeSeries.GRAIN_COLUMN_NAMES)
+    time_column_name = automl_settings.get(constants.TimeSeries.TIME_COLUMN_NAME)
+    if grains is None:
+        grains = []
+    if isinstance(grains, str):
+        grains = [grains]
+    while BACKTEST_ITER in grains:
+        grains.remove(BACKTEST_ITER)
+
+    dfs = []
+    for fle in os.listdir(input_dir):
+        file_path = os.path.join(input_dir, fle)
+        if os.path.isfile(file_path) and file_path.endswith(".csv"):
+            df_iter = pd.read_csv(file_path, parse_dates=[time_column_name])
+            for _, iteration in df_iter.groupby(BACKTEST_ITER):
+                dfs.append(iteration)
+    forecast_df = pd.concat(dfs, sort=False, ignore_index=True)
+    # To make sure plots are in order, sort the predictions by grain and iteration.
+    ts_index = grains + [BACKTEST_ITER]
+    forecast_df.sort_values(by=ts_index, inplace=True)
+    pdf = PdfPages(os.path.join(output_dir, PLOTS_FILE))
+    for _, one_forecast in forecast_df.groupby(ts_index):
+        _draw_one_plot(one_forecast, time_column_name, grains, pdf)
+    pdf.close()
+    forecast_df.to_csv(os.path.join(output_dir, FORECASTS_FILE), index=False)
+    # Remove np.NaN and np.inf from the prediction and actuals data.
+    forecast_df.replace([np.inf, -np.inf], np.nan, inplace=True)
+    forecast_df.dropna(subset=[ACTUALS, PREDICTIONS], inplace=True)
+    metrics = compute_all_metrics(forecast_df, grains + [BACKTEST_ITER])
+    metrics.to_csv(os.path.join(output_dir, SCORES_FILE), index=False)
+
+
+if __name__ == "__main__":
+    args = {"forecasts": "--forecasts", "scores_out": "--output-dir"}
+    parser = argparse.ArgumentParser("Parsing input arguments.")
+    for argname, arg in args.items():
+        parser.add_argument(arg, dest=argname, required=True)
+    parsed_args, _ = parser.parse_known_args()
+    input_dir = parsed_args.forecasts
+    output_dir = parsed_args.scores_out
+    with open(
+        os.path.join(
+            os.path.dirname(os.path.realpath(__file__)), "automl_settings.json"
+        )
+    ) as json_file:
+        automl_settings = json.load(json_file)
+    calculate_scores_and_build_plots(input_dir, output_dir, automl_settings)

how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/auto-ml-forecasting-backtest-single-model.ipynb

@@ -0,0 +1,720 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License.\n",
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/automl-forecasting-function.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Automated MachineLearning\n",
+        "_**The model backtesting**_\n",
+        "\n",
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "2. [Setup](#Setup)\n",
+        "3. [Data](#Data)\n",
+        "4. [Prepare remote compute and data.](#prepare_remote)\n",
+        "5. [Create the configuration for AutoML backtesting](#train)\n",
+        "6. [Backtest AutoML](#backtest_automl)\n",
+        "7. [View metrics](#Metrics)\n",
+        "8. [Backtest the best model](#backtest_model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "Model backtesting is used to evaluate its performance on historical data. To do that we step back on the backtesting period by the data set several times and split the data to train and test sets. Then these data sets are used for training and evaluation of model.<br>\n",
+        "This notebook is intended to demonstrate backtesting on a single model, this is the best solution for small data sets with a few or one time series in it. For scenarios where we would like to choose the best AutoML model for every backtest iteration, please see [AutoML Forecasting Backtest Many Models Example](../forecasting-backtest-many-models/auto-ml-forecasting-backtest-many-models.ipynb) notebook.\n",
+        "![Backtesting](Backtesting.png)\n",
+        "This notebook demonstrates two ways of backtesting:\n",
+        "- AutoML backtesting: we will train separate AutoML models for historical data\n",
+        "- Model backtesting: from the first run we will select the best model trained on the most recent data, retrain it on the past data and evaluate."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import shutil\n",
+        "\n",
+        "import azureml.core\n",
+        "from azureml.core import Experiment, Model, Workspace"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This notebook is compatible with Azure ML SDK version 1.35.1 or later."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "As part of the setup you have already created a <b>Workspace</b>."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "\n",
+        "output = {}\n",
+        "output[\"Subscription ID\"] = ws.subscription_id\n",
+        "output[\"Workspace\"] = ws.name\n",
+        "output[\"SKU\"] = ws.sku\n",
+        "output[\"Resource Group\"] = ws.resource_group\n",
+        "output[\"Location\"] = ws.location\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
+        "outputDf.T"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Data\n",
+        "For the demonstration purposes we will simulate one year of daily data. To do this we need to specify the following parameters: time column name, time series ID column names and label column name. Our intention is to forecast for two weeks ahead."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "TIME_COLUMN_NAME = \"date\"\n",
+        "TIME_SERIES_ID_COLUMN_NAMES = \"time_series_id\"\n",
+        "LABEL_COLUMN_NAME = \"y\"\n",
+        "FORECAST_HORIZON = 14\n",
+        "FREQUENCY = \"D\"\n",
+        "\n",
+        "\n",
+        "def simulate_timeseries_data(\n",
+        "    train_len: int,\n",
+        "    test_len: int,\n",
+        "    time_column_name: str,\n",
+        "    target_column_name: str,\n",
+        "    time_series_id_column_name: str,\n",
+        "    time_series_number: int = 1,\n",
+        "    freq: str = \"H\",\n",
+        "):\n",
+        "    \"\"\"\n",
+        "    Return the time series of designed length.\n",
+        "\n",
+        "    :param train_len: The length of training data (one series).\n",
+        "    :type train_len: int\n",
+        "    :param test_len: The length of testing data (one series).\n",
+        "    :type test_len: int\n",
+        "    :param time_column_name: The desired name of a time column.\n",
+        "    :type time_column_name: str\n",
+        "    :param time_series_number: The number of time series in the data set.\n",
+        "    :type time_series_number: int\n",
+        "    :param freq: The frequency string representing pandas offset.\n",
+        "                 see https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html\n",
+        "    :type freq: str\n",
+        "    :returns: the tuple of train and test data sets.\n",
+        "    :rtype: tuple\n",
+        "\n",
+        "    \"\"\"\n",
+        "    data_train = []  # type: List[pd.DataFrame]\n",
+        "    data_test = []  # type: List[pd.DataFrame]\n",
+        "    data_length = train_len + test_len\n",
+        "    for i in range(time_series_number):\n",
+        "        X = pd.DataFrame(\n",
+        "            {\n",
+        "                time_column_name: pd.date_range(\n",
+        "                    start=\"2000-01-01\", periods=data_length, freq=freq\n",
+        "                ),\n",
+        "                target_column_name: np.arange(data_length).astype(float)\n",
+        "                + np.random.rand(data_length)\n",
+        "                + i * 5,\n",
+        "                \"ext_predictor\": np.asarray(range(42, 42 + data_length)),\n",
+        "                time_series_id_column_name: np.repeat(\"ts{}\".format(i), data_length),\n",
+        "            }\n",
+        "        )\n",
+        "        data_train.append(X[:train_len])\n",
+        "        data_test.append(X[train_len:])\n",
+        "    train = pd.concat(data_train)\n",
+        "    label_train = train.pop(target_column_name).values\n",
+        "    test = pd.concat(data_test)\n",
+        "    label_test = test.pop(target_column_name).values\n",
+        "    return train, label_train, test, label_test\n",
+        "\n",
+        "\n",
+        "n_test_periods = FORECAST_HORIZON\n",
+        "n_train_periods = 365\n",
+        "X_train, y_train, X_test, y_test = simulate_timeseries_data(\n",
+        "    train_len=n_train_periods,\n",
+        "    test_len=n_test_periods,\n",
+        "    time_column_name=TIME_COLUMN_NAME,\n",
+        "    target_column_name=LABEL_COLUMN_NAME,\n",
+        "    time_series_id_column_name=TIME_SERIES_ID_COLUMN_NAMES,\n",
+        "    time_series_number=2,\n",
+        "    freq=FREQUENCY,\n",
+        ")\n",
+        "X_train[LABEL_COLUMN_NAME] = y_train"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Let's see what the training data looks like."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_train.tail()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Prepare remote compute and data. <a id=\"prepare_remote\"></a>\n",
+        "The [Machine Learning service workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-workspace), is paired with the storage account, which contains the default data store. We will use it to upload the artificial data and create [tabular dataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) for training. A tabular dataset defines a series of lazily-evaluated, immutable operations to load data from the data source into tabular representation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.data.dataset_factory import TabularDatasetFactory\n",
+        "\n",
+        "ds = ws.get_default_datastore()\n",
+        "# Upload saved data to the default data store.\n",
+        "train_data = TabularDatasetFactory.register_pandas_dataframe(\n",
+        "    X_train, target=(ds, \"data\"), name=\"data_backtest\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You will need to create a compute target for backtesting. In this [tutorial](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute), you create AmlCompute as your training compute resource.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# Choose a name for your CPU cluster\n",
+        "amlcompute_cluster_name = \"backtest-cluster\"\n",
+        "\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
+        "    print(\"Found existing cluster, use it.\")\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(\n",
+        "        vm_size=\"STANDARD_DS12_V2\", max_nodes=6\n",
+        "    )\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
+        "\n",
+        "compute_target.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create the configuration for AutoML backtesting <a id=\"train\"></a>\n",
+        "\n",
+        "This dictionary defines the AutoML and many models settings. For this forecasting task we need to define several settings including the name of the time column, the maximum forecast horizon, and the partition column name definition.\n",
+        "\n",
+        "| Property                           | Description|\n",
+        "| :---------------                   | :------------------- |\n",
+        "| **task**                           | forecasting |\n",
+        "| **primary_metric**                 | This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>normalized_root_mean_squared_error</i><br><i>normalized_mean_absolute_error</i> |\n",
+        "| **iteration_timeout_minutes**      | Maximum amount of time in minutes that the model can train. This is optional but provides customers with greater control on exit criteria. |\n",
+        "| **iterations**                     | Number of models to train. This is optional but provides customers with greater control on exit criteria. |\n",
+        "| **experiment_timeout_hours**       | Maximum amount of time in hours that the experiment can take before it terminates. This is optional but provides customers with greater control on exit criteria. |\n",
+        "| **label_column_name**              | The name of the label column. |\n",
+        "| **max_horizon**               | The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly). Periods are inferred from your data. |\n",
+        "| **n_cross_validations**            | Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way. |\n",
+        "| **time_column_name**               | The name of your time column. |\n",
+        "| **grain_column_names**     | The column names used to uniquely identify timeseries in data that has multiple rows with the same timestamp. |"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "automl_settings = {\n",
+        "    \"task\": \"forecasting\",\n",
+        "    \"primary_metric\": \"normalized_root_mean_squared_error\",\n",
+        "    \"iteration_timeout_minutes\": 10,  # This needs to be changed based on the dataset. We ask customer to explore how long training is taking before settings this value\n",
+        "    \"iterations\": 15,\n",
+        "    \"experiment_timeout_hours\": 1,  # This also needs to be changed based on the dataset. For larger data set this number needs to be bigger.\n",
+        "    \"label_column_name\": LABEL_COLUMN_NAME,\n",
+        "    \"n_cross_validations\": 3,\n",
+        "    \"time_column_name\": TIME_COLUMN_NAME,\n",
+        "    \"max_horizon\": FORECAST_HORIZON,\n",
+        "    \"track_child_runs\": False,\n",
+        "    \"grain_column_names\": TIME_SERIES_ID_COLUMN_NAMES,\n",
+        "}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Backtest AutoML <a id=\"backtest_automl\"></a>\n",
+        "First we set backtesting parameters: we will step back by 30 days and will make 5 such steps; for each step we will forecast for next two weeks."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# The number of periods to step back on each backtest iteration.\n",
+        "BACKTESTING_PERIOD = 30\n",
+        "# The number of times we will back test the model.\n",
+        "NUMBER_OF_BACKTESTS = 5"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To train AutoML on backtesting folds we will use the [Azure Machine Learning pipeline](https://docs.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines). It will generate backtest folds, then train model for each of them and calculate the accuracy metrics. To run pipeline, you also need to create an <b>Experiment</b>. An Experiment corresponds to a prediction problem you are trying to solve (here, it is a forecasting), while a Run corresponds to a specific approach to the problem."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from uuid import uuid1\n",
+        "\n",
+        "from pipeline_helper import get_backtest_pipeline\n",
+        "\n",
+        "pipeline_exp = Experiment(ws, \"automl-backtesting\")\n",
+        "\n",
+        "# We will create the unique identifier to mark our models.\n",
+        "model_uid = str(uuid1())\n",
+        "\n",
+        "pipeline = get_backtest_pipeline(\n",
+        "    experiment=pipeline_exp,\n",
+        "    dataset=train_data,\n",
+        "    # The STANDARD_DS12_V2 has 4 vCPU per node, we will set 2 process per node to be safe.\n",
+        "    process_per_node=2,\n",
+        "    # The maximum number of nodes for our compute is 6.\n",
+        "    node_count=6,\n",
+        "    compute_target=compute_target,\n",
+        "    automl_settings=automl_settings,\n",
+        "    step_size=BACKTESTING_PERIOD,\n",
+        "    step_number=NUMBER_OF_BACKTESTS,\n",
+        "    model_uid=model_uid,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Run the pipeline and wait for results."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline_run = pipeline_exp.submit(pipeline)\n",
+        "pipeline_run.wait_for_completion(show_output=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "After the run is complete, we can download the results. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "metrics_output = pipeline_run.get_pipeline_output(\"results\")\n",
+        "metrics_output.download(\"backtest_metrics\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## View metrics<a id=\"Metrics\"></a>\n",
+        "To distinguish these metrics from the model backtest, which we will obtain in the next section, we will move the directory with metrics out of the backtest_metrics and will remove the parent folder. We will create the utility function for that."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def copy_scoring_directory(new_name):\n",
+        "    scores_path = os.path.join(\"backtest_metrics\", \"azureml\")\n",
+        "    directory_list = [os.path.join(scores_path, d) for d in os.listdir(scores_path)]\n",
+        "    latest_file = max(directory_list, key=os.path.getctime)\n",
+        "    print(\n",
+        "        f\"The output directory {latest_file} was created on {pd.Timestamp(os.path.getctime(latest_file), unit='s')} GMT.\"\n",
+        "    )\n",
+        "    shutil.move(os.path.join(latest_file, \"results\"), new_name)\n",
+        "    shutil.rmtree(\"backtest_metrics\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Move the directory and list its contents."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "copy_scoring_directory(\"automl_backtest\")\n",
+        "pd.DataFrame({\"File\": os.listdir(\"automl_backtest\")})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The directory contains a set of files with results:\n",
+        "- forecast.csv contains forecasts for all backtest iterations. The backtest_iteration column contains iteration identifier with the last training date as a suffix\n",
+        "- scores.csv contains all metrics. If data set contains several time series, the metrics are given for all combinations of time series id and iterations, as well as scores for all iterations and time series id are marked as \"all_sets\"\n",
+        "- plots_fcst_vs_actual.pdf contains the predictions vs forecast plots for each iteration and time series.\n",
+        "\n",
+        "For demonstration purposes we will display the table of metrics for one of the time series with ID \"ts0\". Again, we will create the utility function, which will be re used in model backtesting."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def get_metrics_for_ts(all_metrics, ts):\n",
+        "    \"\"\"\n",
+        "    Get the metrics for the time series with ID ts and return it as pandas data frame.\n",
+        "\n",
+        "    :param all_metrics: The table with all the metrics.\n",
+        "    :param ts: The ID of a time series of interest.\n",
+        "    :return: The pandas DataFrame with metrics for one time series.\n",
+        "    \"\"\"\n",
+        "    results_df = None\n",
+        "    for ts_id, one_series in all_metrics.groupby(\"time_series_id\"):\n",
+        "        if not ts_id.startswith(ts):\n",
+        "            continue\n",
+        "        iteration = ts_id.split(\"|\")[-1]\n",
+        "        df = one_series[[\"metric_name\", \"metric\"]]\n",
+        "        df.rename({\"metric\": iteration}, axis=1, inplace=True)\n",
+        "        df.set_index(\"metric_name\", inplace=True)\n",
+        "        if results_df is None:\n",
+        "            results_df = df\n",
+        "        else:\n",
+        "            results_df = results_df.merge(\n",
+        "                df, how=\"inner\", left_index=True, right_index=True\n",
+        "            )\n",
+        "    results_df.sort_index(axis=1, inplace=True)\n",
+        "    return results_df\n",
+        "\n",
+        "\n",
+        "metrics_df = pd.read_csv(os.path.join(\"automl_backtest\", \"scores.csv\"))\n",
+        "ts_id = \"ts0\"\n",
+        "get_metrics_for_ts(metrics_df, ts_id)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Forecast vs actuals plots."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from IPython.display import IFrame\n",
+        "\n",
+        "IFrame(\"./automl_backtest/plots_fcst_vs_actual.pdf\", width=800, height=300)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# <font color='blue'>Backtest the best model</font> <a id=\"backtest_model\"></a>\n",
+        "\n",
+        "For model backtesting we will use the same parameters we used to backtest AutoML. All the models, we have obtained in the previous run were registered in our workspace. To identify the model, each was assigned a tag with the last trainig date."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model_list = Model.list(ws, tags={\"experiment\": \"automl-backtesting\"})\n",
+        "model_data = {\"name\": [], \"last_training_date\": []}\n",
+        "for model in model_list:\n",
+        "    if (\n",
+        "        \"last_training_date\" not in model.tags\n",
+        "        or \"model_uid\" not in model.tags\n",
+        "        or model.tags[\"model_uid\"] != model_uid\n",
+        "    ):\n",
+        "        continue\n",
+        "    model_data[\"name\"].append(model.name)\n",
+        "    model_data[\"last_training_date\"].append(\n",
+        "        pd.Timestamp(model.tags[\"last_training_date\"])\n",
+        "    )\n",
+        "df_models = pd.DataFrame(model_data)\n",
+        "df_models.sort_values([\"last_training_date\"], inplace=True)\n",
+        "df_models.reset_index(inplace=True, drop=True)\n",
+        "df_models"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We will backtest the model trained on the most recet data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model_name = df_models[\"name\"].iloc[-1]\n",
+        "model_name"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Retrain the models.\n",
+        "Assemble the pipeline, which will retrain the best model from AutoML run on historical data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline_exp = Experiment(ws, \"model-backtesting\")\n",
+        "\n",
+        "pipeline = get_backtest_pipeline(\n",
+        "    experiment=pipeline_exp,\n",
+        "    dataset=train_data,\n",
+        "    # The STANDARD_DS12_V2 has 4 vCPU per node, we will set 2 process per node to be safe.\n",
+        "    process_per_node=2,\n",
+        "    # The maximum number of nodes for our compute is 6.\n",
+        "    node_count=6,\n",
+        "    compute_target=compute_target,\n",
+        "    automl_settings=automl_settings,\n",
+        "    step_size=BACKTESTING_PERIOD,\n",
+        "    step_number=NUMBER_OF_BACKTESTS,\n",
+        "    model_name=model_name,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Launch the backtesting pipeline."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline_run = pipeline_exp.submit(pipeline)\n",
+        "pipeline_run.wait_for_completion(show_output=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The metrics are stored in the pipeline output named \"score\". The next code will download the table with metrics."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "metrics_output = pipeline_run.get_pipeline_output(\"results\")\n",
+        "metrics_output.download(\"backtest_metrics\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Again, we will copy the data files from the downloaded directory, but in this case we will call the folder \"model_backtest\"; it will contain the same files as the one for AutoML backtesting."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "copy_scoring_directory(\"model_backtest\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we will display the metrics."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model_metrics_df = pd.read_csv(os.path.join(\"model_backtest\", \"scores.csv\"))\n",
+        "get_metrics_for_ts(model_metrics_df, ts_id)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Forecast vs actuals plots."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from IPython.display import IFrame\n",
+        "\n",
+        "IFrame(\"./model_backtest/plots_fcst_vs_actual.pdf\", width=800, height=300)"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "jialiu"
+      }
+    ],
+    "category": "tutorial",
+    "compute": [
+      "Remote"
+    ],
+    "datasets": [
+      "None"
+    ],
+    "deployment": [
+      "None"
+    ],
+    "exclude_from_index": false,
+    "framework": [
+      "Azure ML AutoML"
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.9"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}

how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/auto-ml-forecasting-backtest-single-model.yml

@@ -0,0 +1,4 @@
+name: auto-ml-forecasting-backtest-single-model
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/pipeline_helper.py

@@ -0,0 +1,161 @@
+from typing import Any, Dict, Optional
+
+import os
+
+import azureml.train.automl.runtime._hts.hts_runtime_utilities as hru
+
+from azureml._restclient.jasmine_client import JasmineClient
+from azureml.contrib.automl.pipeline.steps import utilities
+from azureml.core import RunConfiguration
+from azureml.core.compute import ComputeTarget
+from azureml.core.experiment import Experiment
+from azureml.data import LinkTabularOutputDatasetConfig, TabularDataset
+from azureml.pipeline.core import Pipeline, PipelineData, PipelineParameter
+from azureml.pipeline.steps import ParallelRunConfig, ParallelRunStep, PythonScriptStep
+from azureml.train.automl.constants import Scenarios
+from azureml.data.dataset_consumption_config import DatasetConsumptionConfig
+
+
+PROJECT_FOLDER = "assets"
+SETTINGS_FILE = "automl_settings.json"
+
+
+def get_backtest_pipeline(
+    experiment: Experiment,
+    dataset: TabularDataset,
+    process_per_node: int,
+    node_count: int,
+    compute_target: ComputeTarget,
+    automl_settings: Dict[str, Any],
+    step_size: int,
+    step_number: int,
+    model_name: Optional[str] = None,
+    model_uid: Optional[str] = None,
+) -> Pipeline:
+    """
+    :param experiment: The experiment used to run the pipeline.
+    :param dataset: Tabular data set to be used for model training.
+    :param process_per_node: The number of processes per node. Generally it should be the number of cores
+                             on the node divided by two.
+    :param node_count: The number of nodes to be used.
+    :param compute_target: The compute target to be used to run the pipeline.
+    :param model_name: The name of a model to be back tested.
+    :param automl_settings: The dictionary with automl settings.
+    :param step_size: The number of periods to step back in backtesting.
+    :param step_number: The number of backtesting iterations.
+    :param model_uid: The uid to mark models from this run of the experiment.
+    :return: The pipeline to be used for model retraining.
+             **Note:** The output will be uploaded in the pipeline output
+             called 'score'.
+    """
+    jasmine_client = JasmineClient(
+        service_context=experiment.workspace.service_context,
+        experiment_name=experiment.name,
+        experiment_id=experiment.id,
+    )
+    env = jasmine_client.get_curated_environment(
+        scenario=Scenarios.AUTOML,
+        enable_dnn=False,
+        enable_gpu=False,
+        compute=compute_target,
+        compute_sku=experiment.workspace.compute_targets.get(
+            compute_target.name
+        ).vm_size,
+    )
+    data_results = PipelineData(
+        name="results", datastore=None, pipeline_output_name="results"
+    )
+    ############################################################
+    # Split the data set using python script.
+    ############################################################
+    run_config = RunConfiguration()
+    run_config.docker.use_docker = True
+    run_config.environment = env
+
+    split_data = PipelineData(name="split_data_output", datastore=None).as_dataset()
+    split_step = PythonScriptStep(
+        name="split_data_for_backtest",
+        script_name="data_split.py",
+        inputs=[dataset.as_named_input("training_data")],
+        outputs=[split_data],
+        source_directory=PROJECT_FOLDER,
+        arguments=[
+            "--step-size",
+            step_size,
+            "--step-number",
+            step_number,
+            "--time-column-name",
+            automl_settings.get("time_column_name"),
+            "--time-series-id-column-names",
+            automl_settings.get("grain_column_names"),
+            "--output-dir",
+            split_data,
+        ],
+        runconfig=run_config,
+        compute_target=compute_target,
+        allow_reuse=False,
+    )
+    ############################################################
+    # We will do the backtest the parallel run step.
+    ############################################################
+    settings_path = os.path.join(PROJECT_FOLDER, SETTINGS_FILE)
+    hru.dump_object_to_json(automl_settings, settings_path)
+    mini_batch_size = PipelineParameter(name="batch_size_param", default_value=str(1))
+    back_test_config = ParallelRunConfig(
+        source_directory=PROJECT_FOLDER,
+        entry_script="retrain_models.py",
+        mini_batch_size=mini_batch_size,
+        error_threshold=-1,
+        output_action="append_row",
+        append_row_file_name="outputs.txt",
+        compute_target=compute_target,
+        environment=env,
+        process_count_per_node=process_per_node,
+        run_invocation_timeout=3600,
+        node_count=node_count,
+    )
+    forecasts = PipelineData(name="forecasts", datastore=None)
+    if model_name:
+        parallel_step_name = "{}-backtest".format(model_name.replace("_", "-"))
+    else:
+        parallel_step_name = "AutoML-backtest"
+
+    prs_args = [
+        "--target_column_name",
+        automl_settings.get("label_column_name"),
+        "--output-dir",
+        forecasts,
+    ]
+    if model_name is not None:
+        prs_args.append("--model-name")
+        prs_args.append(model_name)
+    if model_uid is not None:
+        prs_args.append("--model-uid")
+        prs_args.append(model_uid)
+    backtest_prs = ParallelRunStep(
+        name=parallel_step_name,
+        parallel_run_config=back_test_config,
+        arguments=prs_args,
+        inputs=[split_data],
+        output=forecasts,
+        allow_reuse=False,
+    )
+    ############################################################
+    # Then we collect the output and return it as scores output.
+    ############################################################
+    collection_step = PythonScriptStep(
+        name="score",
+        script_name="score.py",
+        inputs=[forecasts.as_mount()],
+        outputs=[data_results],
+        source_directory=PROJECT_FOLDER,
+        arguments=["--forecasts", forecasts, "--output-dir", data_results],
+        runconfig=run_config,
+        compute_target=compute_target,
+        allow_reuse=False,
+    )
+    # Build and return the pipeline.
+    return Pipeline(
+        workspace=experiment.workspace,
+        steps=[split_step, backtest_prs, collection_step],
+    )

how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/Beer_no_valid_split_test.csv

@@ -1,20 +0,0 @@
-DATE,grain,BeerProduction
-2017-01-01,grain,9049
-2017-02-01,grain,10458
-2017-03-01,grain,12489
-2017-04-01,grain,11499
-2017-05-01,grain,13553
-2017-06-01,grain,14740
-2017-07-01,grain,11424
-2017-08-01,grain,13412
-2017-09-01,grain,11917
-2017-10-01,grain,12721
-2017-11-01,grain,13272
-2017-12-01,grain,14278
-2018-01-01,grain,9572
-2018-02-01,grain,10423
-2018-03-01,grain,12667
-2018-04-01,grain,11904
-2018-05-01,grain,14120
-2018-06-01,grain,14565
-2018-07-01,grain,12622

how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/Beer_no_valid_split_train.csv

@@ -1,301 +0,0 @@
-DATE,grain,BeerProduction
-1992-01-01,grain,3459
-1992-02-01,grain,3458
-1992-03-01,grain,4002
-1992-04-01,grain,4564
-1992-05-01,grain,4221
-1992-06-01,grain,4529
-1992-07-01,grain,4466
-1992-08-01,grain,4137
-1992-09-01,grain,4126
-1992-10-01,grain,4259
-1992-11-01,grain,4240
-1992-12-01,grain,4936
-1993-01-01,grain,3031
-1993-02-01,grain,3261
-1993-03-01,grain,4160
-1993-04-01,grain,4377
-1993-05-01,grain,4307
-1993-06-01,grain,4696
-1993-07-01,grain,4458
-1993-08-01,grain,4457
-1993-09-01,grain,4364
-1993-10-01,grain,4236
-1993-11-01,grain,4500
-1993-12-01,grain,4974
-1994-01-01,grain,3075
-1994-02-01,grain,3377
-1994-03-01,grain,4443
-1994-04-01,grain,4261
-1994-05-01,grain,4460
-1994-06-01,grain,4985
-1994-07-01,grain,4324
-1994-08-01,grain,4719
-1994-09-01,grain,4374
-1994-10-01,grain,4248
-1994-11-01,grain,4784
-1994-12-01,grain,4971
-1995-01-01,grain,3370
-1995-02-01,grain,3484
-1995-03-01,grain,4269
-1995-04-01,grain,3994
-1995-05-01,grain,4715
-1995-06-01,grain,4974
-1995-07-01,grain,4223
-1995-08-01,grain,5000
-1995-09-01,grain,4235
-1995-10-01,grain,4554
-1995-11-01,grain,4851
-1995-12-01,grain,4826
-1996-01-01,grain,3699
-1996-02-01,grain,3983
-1996-03-01,grain,4262
-1996-04-01,grain,4619
-1996-05-01,grain,5219
-1996-06-01,grain,4836
-1996-07-01,grain,4941
-1996-08-01,grain,5062
-1996-09-01,grain,4365
-1996-10-01,grain,5012
-1996-11-01,grain,4850
-1996-12-01,grain,5097
-1997-01-01,grain,3758
-1997-02-01,grain,3825
-1997-03-01,grain,4454
-1997-04-01,grain,4635
-1997-05-01,grain,5210
-1997-06-01,grain,5057
-1997-07-01,grain,5231
-1997-08-01,grain,5034
-1997-09-01,grain,4970
-1997-10-01,grain,5342
-1997-11-01,grain,4831
-1997-12-01,grain,5965
-1998-01-01,grain,3796
-1998-02-01,grain,4019
-1998-03-01,grain,4898
-1998-04-01,grain,5090
-1998-05-01,grain,5237
-1998-06-01,grain,5447
-1998-07-01,grain,5435
-1998-08-01,grain,5107
-1998-09-01,grain,5515
-1998-10-01,grain,5583
-1998-11-01,grain,5346
-1998-12-01,grain,6286
-1999-01-01,grain,4032
-1999-02-01,grain,4435
-1999-03-01,grain,5479
-1999-04-01,grain,5483
-1999-05-01,grain,5587
-1999-06-01,grain,6176
-1999-07-01,grain,5621
-1999-08-01,grain,5889
-1999-09-01,grain,5828
-1999-10-01,grain,5849
-1999-11-01,grain,6180
-1999-12-01,grain,6771
-2000-01-01,grain,4243
-2000-02-01,grain,4952
-2000-03-01,grain,6008
-2000-04-01,grain,5353
-2000-05-01,grain,6435
-2000-06-01,grain,6673
-2000-07-01,grain,5636
-2000-08-01,grain,6630
-2000-09-01,grain,5887
-2000-10-01,grain,6322
-2000-11-01,grain,6520
-2000-12-01,grain,6678
-2001-01-01,grain,5082
-2001-02-01,grain,5216
-2001-03-01,grain,5893
-2001-04-01,grain,5894
-2001-05-01,grain,6799
-2001-06-01,grain,6667
-2001-07-01,grain,6374
-2001-08-01,grain,6840
-2001-09-01,grain,5575
-2001-10-01,grain,6545
-2001-11-01,grain,6789
-2001-12-01,grain,7180
-2002-01-01,grain,5117
-2002-02-01,grain,5442
-2002-03-01,grain,6337
-2002-04-01,grain,6525
-2002-05-01,grain,7216
-2002-06-01,grain,6761
-2002-07-01,grain,6958
-2002-08-01,grain,7070
-2002-09-01,grain,6148
-2002-10-01,grain,6924
-2002-11-01,grain,6716
-2002-12-01,grain,7975
-2003-01-01,grain,5326
-2003-02-01,grain,5609
-2003-03-01,grain,6414
-2003-04-01,grain,6741
-2003-05-01,grain,7144
-2003-06-01,grain,7133
-2003-07-01,grain,7568
-2003-08-01,grain,7266
-2003-09-01,grain,6634
-2003-10-01,grain,7626
-2003-11-01,grain,6843
-2003-12-01,grain,8540
-2004-01-01,grain,5629
-2004-02-01,grain,5898
-2004-03-01,grain,7045
-2004-04-01,grain,7094
-2004-05-01,grain,7333
-2004-06-01,grain,7918
-2004-07-01,grain,7289
-2004-08-01,grain,7396
-2004-09-01,grain,7259
-2004-10-01,grain,7268
-2004-11-01,grain,7731
-2004-12-01,grain,9058
-2005-01-01,grain,5557
-2005-02-01,grain,6237
-2005-03-01,grain,7723
-2005-04-01,grain,7262
-2005-05-01,grain,8241
-2005-06-01,grain,8757
-2005-07-01,grain,7352
-2005-08-01,grain,8496
-2005-09-01,grain,7741
-2005-10-01,grain,7710
-2005-11-01,grain,8247
-2005-12-01,grain,8902
-2006-01-01,grain,6066
-2006-02-01,grain,6590
-2006-03-01,grain,7923
-2006-04-01,grain,7335
-2006-05-01,grain,8843
-2006-06-01,grain,9327
-2006-07-01,grain,7792
-2006-08-01,grain,9156
-2006-09-01,grain,8037
-2006-10-01,grain,8640
-2006-11-01,grain,9128
-2006-12-01,grain,9545
-2007-01-01,grain,6627
-2007-02-01,grain,6743
-2007-03-01,grain,8195
-2007-04-01,grain,7828
-2007-05-01,grain,9570
-2007-06-01,grain,9484
-2007-07-01,grain,8608
-2007-08-01,grain,9543
-2007-09-01,grain,8123
-2007-10-01,grain,9649
-2007-11-01,grain,9390
-2007-12-01,grain,10065
-2008-01-01,grain,7093
-2008-02-01,grain,7483
-2008-03-01,grain,8365
-2008-04-01,grain,8895
-2008-05-01,grain,9794
-2008-06-01,grain,9977
-2008-07-01,grain,9553
-2008-08-01,grain,9375
-2008-09-01,grain,9225
-2008-10-01,grain,9948
-2008-11-01,grain,8758
-2008-12-01,grain,10839
-2009-01-01,grain,7266
-2009-02-01,grain,7578
-2009-03-01,grain,8688
-2009-04-01,grain,9162
-2009-05-01,grain,9369
-2009-06-01,grain,10167
-2009-07-01,grain,9507
-2009-08-01,grain,8923
-2009-09-01,grain,9272
-2009-10-01,grain,9075
-2009-11-01,grain,8949
-2009-12-01,grain,10843
-2010-01-01,grain,6558
-2010-02-01,grain,7481
-2010-03-01,grain,9475
-2010-04-01,grain,9424
-2010-05-01,grain,9351
-2010-06-01,grain,10552
-2010-07-01,grain,9077
-2010-08-01,grain,9273
-2010-09-01,grain,9420
-2010-10-01,grain,9413
-2010-11-01,grain,9866
-2010-12-01,grain,11455
-2011-01-01,grain,6901
-2011-02-01,grain,8014
-2011-03-01,grain,9832
-2011-04-01,grain,9281
-2011-05-01,grain,9967
-2011-06-01,grain,11344
-2011-07-01,grain,9106
-2011-08-01,grain,10469
-2011-09-01,grain,10085
-2011-10-01,grain,9612
-2011-11-01,grain,10328
-2011-12-01,grain,11483
-2012-01-01,grain,7486
-2012-02-01,grain,8641
-2012-03-01,grain,9709
-2012-04-01,grain,9423
-2012-05-01,grain,11342
-2012-06-01,grain,11274
-2012-07-01,grain,9845
-2012-08-01,grain,11163
-2012-09-01,grain,9532
-2012-10-01,grain,10754
-2012-11-01,grain,10953
-2012-12-01,grain,11922
-2013-01-01,grain,8395
-2013-02-01,grain,8888
-2013-03-01,grain,10110
-2013-04-01,grain,10493
-2013-05-01,grain,12218
-2013-06-01,grain,11385
-2013-07-01,grain,11186
-2013-08-01,grain,11462
-2013-09-01,grain,10494
-2013-10-01,grain,11540
-2013-11-01,grain,11138
-2013-12-01,grain,12709
-2014-01-01,grain,8557
-2014-02-01,grain,9059
-2014-03-01,grain,10055
-2014-04-01,grain,10977
-2014-05-01,grain,11792
-2014-06-01,grain,11904
-2014-07-01,grain,10965
-2014-08-01,grain,10981
-2014-09-01,grain,10828
-2014-10-01,grain,11817
-2014-11-01,grain,10470
-2014-12-01,grain,13310
-2015-01-01,grain,8400
-2015-02-01,grain,9062
-2015-03-01,grain,10722
-2015-04-01,grain,11107
-2015-05-01,grain,11508
-2015-06-01,grain,12904
-2015-07-01,grain,11869
-2015-08-01,grain,11224
-2015-09-01,grain,12022
-2015-10-01,grain,11983
-2015-11-01,grain,11506
-2015-12-01,grain,14183
-2016-01-01,grain,8650
-2016-02-01,grain,10323
-2016-03-01,grain,12110
-2016-04-01,grain,11424
-2016-05-01,grain,12243
-2016-06-01,grain,13686
-2016-07-01,grain,10956
-2016-08-01,grain,12706
-2016-09-01,grain,12279
-2016-10-01,grain,11914
-2016-11-01,grain,13025
-2016-12-01,grain,14431

how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.yml

@@ -1,10 +0,0 @@
-name: auto-ml-forecasting-beer-remote
-dependencies:
-- fbprophet==0.5
-- py-xgboost<=0.80
-- pip:
-  - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - azureml-train

how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/helper.py

@@ -1,137 +0,0 @@
-import pandas as pd
-from azureml.core import Environment
-from azureml.core.conda_dependencies import CondaDependencies
-from azureml.train.estimator import Estimator
-from azureml.core.run import Run
-
-
-def split_fraction_by_grain(df, fraction, time_column_name,
-                            grain_column_names=None):
-
-    if not grain_column_names:
-        df['tmp_grain_column'] = 'grain'
-        grain_column_names = ['tmp_grain_column']
-
-    """Group df by grain and split on last n rows for each group."""
-    df_grouped = (df.sort_values(time_column_name)
-                  .groupby(grain_column_names, group_keys=False))
-
-    df_head = df_grouped.apply(lambda dfg: dfg.iloc[:-int(len(dfg) *
-                               fraction)] if fraction > 0 else dfg)
-
-    df_tail = df_grouped.apply(lambda dfg: dfg.iloc[-int(len(dfg) *
-                               fraction):] if fraction > 0 else dfg[:0])
-
-    if 'tmp_grain_column' in grain_column_names:
-        for df2 in (df, df_head, df_tail):
-            df2.drop('tmp_grain_column', axis=1, inplace=True)
-
-        grain_column_names.remove('tmp_grain_column')
-
-    return df_head, df_tail
-
-
-def split_full_for_forecasting(df, time_column_name,
-                               grain_column_names=None, test_split=0.2):
-    index_name = df.index.name
-
-    # Assumes that there isn't already a column called tmpindex
-
-    df['tmpindex'] = df.index
-
-    train_df, test_df = split_fraction_by_grain(
-        df, test_split, time_column_name, grain_column_names)
-
-    train_df = train_df.set_index('tmpindex')
-    train_df.index.name = index_name
-
-    test_df = test_df.set_index('tmpindex')
-    test_df.index.name = index_name
-
-    df.drop('tmpindex', axis=1, inplace=True)
-
-    return train_df, test_df
-
-
-def get_result_df(remote_run):
-    children = list(remote_run.get_children(recursive=True))
-    summary_df = pd.DataFrame(index=['run_id', 'run_algorithm',
-                                     'primary_metric', 'Score'])
-    goal_minimize = False
-    for run in children:
-        if('run_algorithm' in run.properties and 'score' in run.properties):
-            summary_df[run.id] = [run.id, run.properties['run_algorithm'],
-                                  run.properties['primary_metric'],
-                                  float(run.properties['score'])]
-            if('goal' in run.properties):
-                goal_minimize = run.properties['goal'].split('_')[-1] == 'min'
-
-    summary_df = summary_df.T.sort_values(
-        'Score',
-        ascending=goal_minimize).drop_duplicates(['run_algorithm'])
-    summary_df = summary_df.set_index('run_algorithm')
-    return summary_df
-
-
-def run_inference(test_experiment, compute_target, script_folder, train_run,
-                  test_dataset, lookback_dataset, max_horizon,
-                  target_column_name, time_column_name, freq):
-    model_base_name = 'model.pkl'
-    if 'model_data_location' in train_run.properties:
-        model_location = train_run.properties['model_data_location']
-        _, model_base_name = model_location.rsplit('/', 1)
-    train_run.download_file('outputs/{}'.format(model_base_name), 'inference/{}'.format(model_base_name))
-    train_run.download_file('outputs/conda_env_v_1_0_0.yml', 'inference/condafile.yml')
-
-    inference_env = Environment("myenv")
-    inference_env.docker.enabled = True
-    inference_env.python.conda_dependencies = CondaDependencies(
-        conda_dependencies_file_path='inference/condafile.yml')
-
-    est = Estimator(source_directory=script_folder,
-                    entry_script='infer.py',
-                    script_params={
-                        '--max_horizon': max_horizon,
-                        '--target_column_name': target_column_name,
-                        '--time_column_name': time_column_name,
-                        '--frequency': freq,
-                        '--model_path': model_base_name
-                    },
-                    inputs=[test_dataset.as_named_input('test_data'),
-                            lookback_dataset.as_named_input('lookback_data')],
-                    compute_target=compute_target,
-                    environment_definition=inference_env)
-
-    run = test_experiment.submit(
-        est, tags={
-            'training_run_id': train_run.id,
-            'run_algorithm': train_run.properties['run_algorithm'],
-            'valid_score': train_run.properties['score'],
-            'primary_metric': train_run.properties['primary_metric']
-        })
-
-    run.log("run_algorithm", run.tags['run_algorithm'])
-    return run
-
-
-def run_multiple_inferences(summary_df, train_experiment, test_experiment,
-                            compute_target, script_folder, test_dataset,
-                            lookback_dataset, max_horizon, target_column_name,
-                            time_column_name, freq):
-
-    for run_name, run_summary in summary_df.iterrows():
-        print(run_name)
-        print(run_summary)
-        run_id = run_summary.run_id
-        train_run = Run(train_experiment, run_id)
-
-        test_run = run_inference(
-            test_experiment, compute_target, script_folder, train_run,
-            test_dataset, lookback_dataset, max_horizon, target_column_name,
-            time_column_name, freq)
-
-        print(test_run)
-        summary_df.loc[summary_df.run_id == run_id,
-                       'test_run_id'] = test_run.id
-
-    return summary_df

how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb

@@ -64,14 +64,32 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "import azureml.core\n",
-        "import pandas as pd\n",
-        "import numpy as np\n",
+        "import json\n",
         "import logging\n",
+        "from datetime import datetime\n",
         "\n",
-        "from azureml.core import Workspace, Experiment, Dataset\n",
-        "from azureml.train.automl import AutoMLConfig\n",
-        "from datetime import datetime"
+        "import azureml.core\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from azureml.automl.core.featurization import FeaturizationConfig\n",
+        "from azureml.core import Dataset, Experiment, Workspace\n",
+        "from azureml.train.automl import AutoMLConfig"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This notebook is compatible with Azure ML SDK version 1.35.0 or later."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
     {
@@ -90,20 +108,20 @@
         "ws = Workspace.from_config()\n",
         "\n",
         "# choose a name for the run history container in the workspace\n",
-        "experiment_name = 'automl-bikeshareforecasting'\n",
+        "experiment_name = \"automl-bikeshareforecasting\"\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
         "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['SKU'] = ws.sku\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Run History Name'] = experiment_name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "output[\"Subscription ID\"] = ws.subscription_id\n",
+        "output[\"Workspace\"] = ws.name\n",
+        "output[\"SKU\"] = ws.sku\n",
+        "output[\"Resource Group\"] = ws.resource_group\n",
+        "output[\"Location\"] = ws.location\n",
+        "output[\"Run History Name\"] = experiment_name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"
       ]
     },
@@ -113,9 +131,12 @@
       "source": [
         "## Compute\n",
         "You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
+        "\n",
         "#### Creation of AmlCompute takes approximately 5 minutes. \n",
         "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
-        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this article on the default limits and how to request more quota."
+        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
       ]
     },
     {
@@ -124,35 +145,23 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
         "# Choose a name for your cluster.\n",
-        "amlcompute_cluster_name = \"cpu-cluster-bike\"\n",
+        "amlcompute_cluster_name = \"bike-cluster\"\n",
         "\n",
-        "found = False\n",
-        "# Check if this compute target already exists in the workspace.\n",
-        "cts = ws.compute_targets\n",
-        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
-        "    found = True\n",
-        "    print('Found existing compute target.')\n",
-        "    compute_target = cts[amlcompute_cluster_name]\n",
-        "    \n",
-        "if not found:\n",
-        "    print('Creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-        "                                                                #vm_priority = 'lowpriority', # optional\n",
-        "                                                                max_nodes = 4)\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
+        "    print(\"Found existing cluster, use it.\")\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(\n",
+        "        vm_size=\"STANDARD_DS12_V2\", max_nodes=4\n",
+        "    )\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",
-        "    # Create the cluster.\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "    \n",
-        "print('Checking cluster status...')\n",
-        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "    \n",
-        "# For a more detailed view of current AmlCompute status, use get_status()."
+        "compute_target.wait_for_completion(show_output=True)"
       ]
     },
     {
@@ -171,7 +180,9 @@
       "outputs": [],
       "source": [
         "datastore = ws.get_default_datastore()\n",
-        "datastore.upload_files(files = ['./bike-no.csv'], target_path = 'dataset/', overwrite = True,show_progress = True)"
+        "datastore.upload_files(\n",
+        "    files=[\"./bike-no.csv\"], target_path=\"dataset/\", overwrite=True, show_progress=True\n",
+        ")"
       ]
     },
     {
@@ -191,8 +202,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "target_column_name = 'cnt'\n",
-        "time_column_name = 'date'"
+        "target_column_name = \"cnt\"\n",
+        "time_column_name = \"date\""
       ]
     },
     {
@@ -201,7 +212,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, 'dataset/bike-no.csv')]).with_timestamp_columns(fine_grain_timestamp=time_column_name) \n",
+        "dataset = Dataset.Tabular.from_delimited_files(\n",
+        "    path=[(datastore, \"dataset/bike-no.csv\")]\n",
+        ").with_timestamp_columns(fine_grain_timestamp=time_column_name)\n",
+        "\n",
+        "# Drop the columns 'casual' and 'registered' as these columns are a breakdown of the total and therefore a leak.\n",
+        "dataset = dataset.drop_columns(columns=[\"casual\", \"registered\"])\n",
+        "\n",
         "dataset.take(5).to_pandas_dataframe().reset_index(drop=True)"
       ]
     },
@@ -235,6 +252,22 @@
         "test.to_pandas_dataframe().head(5).reset_index(drop=True)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Forecasting Parameters\n",
+        "To define forecasting parameters for your experiment training, you can leverage the ForecastingParameters class. The table below details the forecasting parameter we will be passing into our experiment.\n",
+        "\n",
+        "|Property|Description|\n",
+        "|-|-|\n",
+        "|**time_column_name**|The name of your time column.|\n",
+        "|**forecast_horizon**|The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly).|\n",
+        "|**country_or_region_for_holidays**|The country/region used to generate holiday features. These should be ISO 3166 two-letter country/region codes (i.e. 'US', 'GB').|\n",
+        "|**target_lags**|The target_lags specifies how far back we will construct the lags of the target variable.|\n",
+        "|**freq**|Forecast frequency. This optional parameter represents the period with which the forecast is desired, for example, daily, weekly, yearly, etc. Use this parameter for the correction of time series containing irregular data points or for padding of short time series. The frequency needs to be a pandas offset alias. Please refer to [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) for more information."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -247,20 +280,16 @@
         "|-|-|\n",
         "|**task**|forecasting|\n",
         "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>\n",
-        "|**blacklist_models**|Models in blacklist won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting?view=azure-ml-py).|\n",
+        "|**blocked_models**|Models in blocked_models won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting?view=azure-ml-py).|\n",
         "|**experiment_timeout_hours**|Experimentation timeout in hours.|\n",
         "|**training_data**|Input dataset, containing both features and label column.|\n",
         "|**label_column_name**|The name of the label column.|\n",
         "|**compute_target**|The remote compute for training.|\n",
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**enable_early_stopping**|If early stopping is on, training will stop when the primary metric is no longer improving.|\n",
-        "|**time_column_name**|Name of the datetime column in the input data|\n",
-        "|**max_horizon**|Maximum desired forecast horizon in units of time-series frequency|\n",
-        "|**country_or_region**|The country/region used to generate holiday features. These should be ISO 3166 two-letter country/region codes (i.e. 'US', 'GB').|\n",
-        "|**target_lags**|The target_lags specifies how far back we will construct the lags of the target variable.|\n",
-        "|**drop_column_names**|Name(s) of columns to drop prior to modeling|\n",
+        "|**forecasting_parameters**|A class that holds all the forecasting related parameters.|\n",
         "\n",
-        "This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blacklist_models list but you may need to increase the experiment_timeout_hours parameter value to get results."
+        "This notebook uses the blocked_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blocked_models list but you may need to increase the experiment_timeout_hours parameter value to get results."
       ]
     },
     {
@@ -278,7 +307,26 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "max_horizon = 14"
+        "forecast_horizon = 14"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Convert prediction type to integer\n",
+        "The featurization configuration can be used to change the default prediction type from decimal numbers to integer. This customization can be used in the scenario when the target column is expected to contain whole values as the number of rented bikes per day."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "featurization_config = FeaturizationConfig()\n",
+        "# Force the target column, to be integer type.\n",
+        "featurization_config.add_prediction_transform_type(\"Integer\")"
       ]
     },
     {
@@ -294,27 +342,32 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "time_series_settings = {\n",
-        "    'time_column_name': time_column_name,\n",
-        "    'max_horizon': max_horizon,    \n",
-        "    'country_or_region': 'US', # set country_or_region will trigger holiday featurizer\n",
-        "    'target_lags': 'auto', # use heuristic based lag setting    \n",
-        "    'drop_column_names': ['casual', 'registered'] # these columns are a breakdown of the total and therefore a leak\n",
-        "}\n",
+        "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
         "\n",
-        "automl_config = AutoMLConfig(task='forecasting',                             \n",
-        "                             primary_metric='normalized_root_mean_squared_error',\n",
-        "                             blacklist_models = ['ExtremeRandomTrees'],                             \n",
+        "forecasting_parameters = ForecastingParameters(\n",
+        "    time_column_name=time_column_name,\n",
+        "    forecast_horizon=forecast_horizon,\n",
+        "    country_or_region_for_holidays=\"US\",  # set country_or_region will trigger holiday featurizer\n",
+        "    target_lags=\"auto\",  # use heuristic based lag setting\n",
+        "    freq=\"D\",  # Set the forecast frequency to be daily\n",
+        ")\n",
+        "\n",
+        "automl_config = AutoMLConfig(\n",
+        "    task=\"forecasting\",\n",
+        "    primary_metric=\"normalized_root_mean_squared_error\",\n",
+        "    featurization=featurization_config,\n",
+        "    blocked_models=[\"ExtremeRandomTrees\"],\n",
         "    experiment_timeout_hours=0.3,\n",
         "    training_data=train,\n",
         "    label_column_name=target_column_name,\n",
         "    compute_target=compute_target,\n",
         "    enable_early_stopping=True,\n",
-        "                             n_cross_validations=3, \n",
+        "    n_cross_validations=3,\n",
         "    max_concurrent_iterations=4,\n",
         "    max_cores_per_iteration=-1,\n",
         "    verbosity=logging.INFO,\n",
-        "                            **time_series_settings)"
+        "    forecasting_parameters=forecasting_parameters,\n",
+        ")"
       ]
     },
     {
@@ -330,8 +383,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "remote_run = experiment.submit(automl_config, show_output=False)\n",
-        "remote_run"
+        "remote_run = experiment.submit(automl_config, show_output=False)"
       ]
     },
     {
@@ -347,8 +399,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Retrieve the Best Model\n",
-        "Below we select the best model from all the training iterations using get_output method."
+        "### Retrieve the Best Run details\n",
+        "Below we retrieve the best Run object from among all the runs in the experiment."
       ]
     },
     {
@@ -357,8 +409,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "best_run, fitted_model = remote_run.get_output()\n",
-        "fitted_model.steps"
+        "best_run = remote_run.get_best_child()\n",
+        "best_run"
       ]
     },
     {
@@ -367,7 +419,7 @@
       "source": [
         "## Featurization\n",
         "\n",
-        "You can access the engineered feature names generated in time-series featurization. Note that a number of named holiday periods are represented. We recommend that you have at least one year of data when using this feature to ensure that all yearly holidays are captured in the training featurization."
+        "We can look at the engineered feature names generated in time-series featurization via. the JSON file named 'engineered_feature_names.json' under the run outputs. Note that a number of named holiday periods are represented. We recommend that you have at least one year of data when using this feature to ensure that all yearly holidays are captured in the training featurization."
       ]
     },
     {
@@ -376,7 +428,14 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "fitted_model.named_steps['timeseriestransformer'].get_engineered_feature_names()"
+        "# Download the JSON file locally\n",
+        "best_run.download_file(\n",
+        "    \"outputs/engineered_feature_names.json\", \"engineered_feature_names.json\"\n",
+        ")\n",
+        "with open(\"engineered_feature_names.json\", \"r\") as f:\n",
+        "    records = json.load(f)\n",
+        "\n",
+        "records"
       ]
     },
     {
@@ -400,10 +459,26 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# Get the featurization summary as a list of JSON\n",
-        "featurization_summary = fitted_model.named_steps['timeseriestransformer'].get_featurization_summary()\n",
-        "# View the featurization summary as a pandas dataframe\n",
-        "pd.DataFrame.from_records(featurization_summary)"
+        "# Download the featurization summary JSON file locally\n",
+        "best_run.download_file(\n",
+        "    \"outputs/featurization_summary.json\", \"featurization_summary.json\"\n",
+        ")\n",
+        "\n",
+        "# Render the JSON as a pandas DataFrame\n",
+        "with open(\"featurization_summary.json\", \"r\") as f:\n",
+        "    records = json.load(f)\n",
+        "fs = pd.DataFrame.from_records(records)\n",
+        "\n",
+        "# View a summary of the featurization\n",
+        "fs[\n",
+        "    [\n",
+        "        \"RawFeatureName\",\n",
+        "        \"TypeDetected\",\n",
+        "        \"Dropped\",\n",
+        "        \"EngineeredFeatureCount\",\n",
+        "        \"Transformations\",\n",
+        "    ]\n",
+        "]"
       ]
     },
     {
@@ -419,7 +494,7 @@
       "source": [
         "We now use the best fitted model from the AutoML Run to make forecasts for the test set. We will do batch scoring on the test dataset which should have the same schema as training dataset.\n",
         "\n",
-        "The scoring will run on a remote compute. In this example, it will reuse the training compute.|"
+        "The scoring will run on a remote compute. In this example, it will reuse the training compute."
       ]
     },
     {
@@ -436,7 +511,7 @@
       "metadata": {},
       "source": [
         "### Retrieving forecasts from the model\n",
-        "To run the forecast on the remote compute we will use two helper scripts: forecasting_script and forecasting_helper. These scripts contain the utility methods which will be used by the remote estimator. We copy these scripts to the project folder to upload them to remote compute."
+        "To run the forecast on the remote compute we will use a helper script: forecasting_script. This script contains the utility methods which will be used by the remote estimator. We copy the script to the project folder to upload it to remote compute."
       ]
     },
     {
@@ -448,17 +523,16 @@
         "import os\n",
         "import shutil\n",
         "\n",
-        "script_folder = os.path.join(os.getcwd(), 'forecast')\n",
+        "script_folder = os.path.join(os.getcwd(), \"forecast\")\n",
         "os.makedirs(script_folder, exist_ok=True)\n",
-        "shutil.copy2('forecasting_script.py', script_folder)\n",
-        "shutil.copy2('forecasting_helper.py', script_folder)"
+        "shutil.copy(\"forecasting_script.py\", script_folder)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "For brevity we have created the function called run_forecast. It submits the test data to the best model and run the estimation on the selected compute target."
+        "For brevity, we have created a function called run_forecast that submits the test data to the best model determined during the training run and retrieves forecasts. The test set is longer than the forecast horizon specified at train time, so the forecasting script uses a so-called rolling evaluation to generate predictions over the whole test set. A rolling evaluation iterates the forecaster over the test set, using the actuals in the test set to make lag features as needed. "
       ]
     },
     {
@@ -469,8 +543,9 @@
       "source": [
         "from run_forecast import run_rolling_forecast\n",
         "\n",
-        "remote_run = run_rolling_forecast(test_experiment, compute_target, best_run, test, max_horizon,\n",
-        "                 target_column_name, time_column_name)\n",
+        "remote_run = run_rolling_forecast(\n",
+        "    test_experiment, compute_target, best_run, test, target_column_name\n",
+        ")\n",
         "remote_run"
       ]
     },
@@ -487,7 +562,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Download the prediction result for metrics calcuation\n",
+        "### Download the prediction result for metrics calculation\n",
         "The test data with predictions are saved in artifact outputs/predictions.csv. You can download it and calculation some error metrics for the forecasts and vizualize the predictions vs. the actuals."
       ]
     },
@@ -497,8 +572,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "remote_run.download_file('outputs/predictions.csv', 'predictions.csv')\n",
-        "df_all = pd.read_csv('predictions.csv')"
+        "remote_run.download_file(\"outputs/predictions.csv\", \"predictions.csv\")\n",
+        "df_all = pd.read_csv(\"predictions.csv\")"
       ]
     },
     {
@@ -507,27 +582,31 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.automl.core._vendor.automl.client.core.common import metrics\n",
+        "from azureml.automl.core.shared import constants\n",
+        "from azureml.automl.runtime.shared.score import scoring\n",
         "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
         "from matplotlib import pyplot as plt\n",
-        "from automl.client.core.common import constants\n",
         "\n",
         "# use automl metrics module\n",
-        "scores = metrics.compute_metrics_regression(\n",
-        "    df_all['predicted'],\n",
-        "    df_all[target_column_name],\n",
-        "    list(constants.Metric.SCALAR_REGRESSION_SET),\n",
-        "    None, None, None)\n",
+        "scores = scoring.score_regression(\n",
+        "    y_test=df_all[target_column_name],\n",
+        "    y_pred=df_all[\"predicted\"],\n",
+        "    metrics=list(constants.Metric.SCALAR_REGRESSION_SET),\n",
+        ")\n",
         "\n",
         "print(\"[Test data scores]\\n\")\n",
-        "for key, value in scores.items():    \n",
-        "    print('{}:   {:.3f}'.format(key, value))\n",
-        "    \n",
+        "for key, value in scores.items():\n",
+        "    print(\"{}:   {:.3f}\".format(key, value))\n",
+        "\n",
         "# Plot outputs\n",
         "%matplotlib inline\n",
-        "test_pred = plt.scatter(df_all[target_column_name], df_all['predicted'], color='b')\n",
-        "test_test = plt.scatter(df_all[target_column_name], df_all[target_column_name], color='g')\n",
-        "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
+        "test_pred = plt.scatter(df_all[target_column_name], df_all[\"predicted\"], color=\"b\")\n",
+        "test_test = plt.scatter(\n",
+        "    df_all[target_column_name], df_all[target_column_name], color=\"g\"\n",
+        ")\n",
+        "plt.legend(\n",
+        "    (test_pred, test_test), (\"prediction\", \"truth\"), loc=\"upper left\", fontsize=8\n",
+        ")\n",
         "plt.show()"
       ]
     },
@@ -535,7 +614,10 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "The MAPE seems high; it is being skewed by an actual with a small absolute value. For a more informative evaluation, we can calculate the metrics by forecast horizon:"
+        "For more details on what metrics are included and how they are calculated, please refer to [supported metrics](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#regressionforecasting-metrics). You could also calculate residuals, like described [here](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#residuals).\n",
+        "\n",
+        "\n",
+        "Since we did a rolling evaluation on the test set, we can analyze the predictions by their forecast horizon relative to the rolling origin. The model was initially trained at a forecast horizon of 14, so each prediction from the model is associated with a horizon value from 1 to 14. The horizon values are in a column named, \"horizon_origin,\" in the prediction set. For example, we can calculate some of the error metrics grouped by the horizon:"
       ]
     },
     {
@@ -545,17 +627,25 @@
       "outputs": [],
       "source": [
         "from metrics_helper import MAPE, APE\n",
-        "df_all.groupby('horizon_origin').apply(\n",
-        "    lambda df: pd.Series({'MAPE': MAPE(df[target_column_name], df['predicted']),\n",
-        "                          'RMSE': np.sqrt(mean_squared_error(df[target_column_name], df['predicted'])),\n",
-        "                          'MAE': mean_absolute_error(df[target_column_name], df['predicted'])}))"
+        "\n",
+        "df_all.groupby(\"horizon_origin\").apply(\n",
+        "    lambda df: pd.Series(\n",
+        "        {\n",
+        "            \"MAPE\": MAPE(df[target_column_name], df[\"predicted\"]),\n",
+        "            \"RMSE\": np.sqrt(\n",
+        "                mean_squared_error(df[target_column_name], df[\"predicted\"])\n",
+        "            ),\n",
+        "            \"MAE\": mean_absolute_error(df[target_column_name], df[\"predicted\"]),\n",
+        "        }\n",
+        "    )\n",
+        ")"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "It's also interesting to see the distributions of APE (absolute percentage error) by horizon. On a log scale, the outlying APE in the horizon-3 group is clear."
+        "To drill down more, we can look at the distributions of APE (absolute percentage error) by horizon. From the chart, it is clear that the overall MAPE is being skewed by one particular point where the actual value is of small absolute value."
       ]
     },
     {
@@ -564,15 +654,18 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "df_all_APE = df_all.assign(APE=APE(df_all[target_column_name], df_all['predicted']))\n",
-        "APEs = [df_all_APE[df_all['horizon_origin'] == h].APE.values for h in range(1, max_horizon + 1)]\n",
+        "df_all_APE = df_all.assign(APE=APE(df_all[target_column_name], df_all[\"predicted\"]))\n",
+        "APEs = [\n",
+        "    df_all_APE[df_all[\"horizon_origin\"] == h].APE.values\n",
+        "    for h in range(1, forecast_horizon + 1)\n",
+        "]\n",
         "\n",
         "%matplotlib inline\n",
         "plt.boxplot(APEs)\n",
-        "plt.yscale('log')\n",
-        "plt.xlabel('horizon')\n",
-        "plt.ylabel('APE (%)')\n",
-        "plt.title('Absolute Percentage Errors by Forecast Horizon')\n",
+        "plt.yscale(\"log\")\n",
+        "plt.xlabel(\"horizon\")\n",
+        "plt.ylabel(\"APE (%)\")\n",
+        "plt.title(\"Absolute Percentage Errors by Forecast Horizon\")\n",
         "\n",
         "plt.show()"
       ]
@@ -581,7 +674,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "erwright"
+        "name": "jialiu"
       }
     ],
     "category": "tutorial",
@@ -629,5 +722,5 @@
     "version": 3
   },
   "nbformat": 4,
-  "nbformat_minor": 2
+  "nbformat_minor": 4
 }

how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.yml

@@ -1,9 +1,4 @@
 name: auto-ml-forecasting-bike-share
 dependencies:
-- fbprophet==0.5
-- py-xgboost<=0.80
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib

how-to-use-azureml/automated-machine-learning/forecasting-bike-share/forecasting_helper.py

@@ -1,99 +0,0 @@
-import pandas as pd
-import numpy as np
-from pandas.tseries.frequencies import to_offset
-
-
-def align_outputs(y_predicted, X_trans, X_test, y_test, target_column_name,
-                  predicted_column_name='predicted',
-                  horizon_colname='horizon_origin'):
-    """
-    Demonstrates how to get the output aligned to the inputs
-    using pandas indexes. Helps understand what happened if
-    the output's shape differs from the input shape, or if
-    the data got re-sorted by time and grain during forecasting.
-
-    Typical causes of misalignment are:
-    * we predicted some periods that were missing in actuals -> drop from eval
-    * model was asked to predict past max_horizon -> increase max horizon
-    * data at start of X_test was needed for lags -> provide previous periods
-    """
-
-    if (horizon_colname in X_trans):
-        df_fcst = pd.DataFrame({predicted_column_name: y_predicted,
-                                horizon_colname: X_trans[horizon_colname]})
-    else:
-        df_fcst = pd.DataFrame({predicted_column_name: y_predicted})
-
-    # y and X outputs are aligned by forecast() function contract
-    df_fcst.index = X_trans.index
-
-    # align original X_test to y_test
-    X_test_full = X_test.copy()
-    X_test_full[target_column_name] = y_test
-
-    # X_test_full's index does not include origin, so reset for merge
-    df_fcst.reset_index(inplace=True)
-    X_test_full = X_test_full.reset_index().drop(columns='index')
-    together = df_fcst.merge(X_test_full, how='right')
-
-    # drop rows where prediction or actuals are nan
-    # happens because of missing actuals
-    # or at edges of time due to lags/rolling windows
-    clean = together[together[[target_column_name,
-                               predicted_column_name]].notnull().all(axis=1)]
-    return(clean)
-
-
-def do_rolling_forecast(fitted_model, X_test, y_test, target_column_name,
-                        time_column_name, max_horizon, freq='D'):
-    """
-    Produce forecasts on a rolling origin over the given test set.
-
-    Each iteration makes a forecast for the next 'max_horizon' periods
-    with respect to the current origin, then advances the origin by the
-    horizon time duration. The prediction context for each forecast is set so
-    that the forecaster uses the actual target values prior to the current
-    origin time for constructing lag features.
-
-    This function returns a concatenated DataFrame of rolling forecasts.
-     """
-    df_list = []
-    origin_time = X_test[time_column_name].min()
-    while origin_time <= X_test[time_column_name].max():
-        # Set the horizon time - end date of the forecast
-        horizon_time = origin_time + max_horizon * to_offset(freq)
-
-        # Extract test data from an expanding window up-to the horizon
-        expand_wind = (X_test[time_column_name] < horizon_time)
-        X_test_expand = X_test[expand_wind]
-        y_query_expand = np.zeros(len(X_test_expand)).astype(np.float)
-        y_query_expand.fill(np.NaN)
-
-        if origin_time != X_test[time_column_name].min():
-            # Set the context by including actuals up-to the origin time
-            test_context_expand_wind = (X_test[time_column_name] < origin_time)
-            context_expand_wind = (
-                X_test_expand[time_column_name] < origin_time)
-            y_query_expand[context_expand_wind] = y_test[
-                test_context_expand_wind]
-
-        # Make a forecast out to the maximum horizon
-        y_fcst, X_trans = fitted_model.forecast(X_test_expand, y_query_expand)
-
-        # Align forecast with test set for dates within the
-        # current rolling window
-        trans_tindex = X_trans.index.get_level_values(time_column_name)
-        trans_roll_wind = (trans_tindex >= origin_time) & (
-            trans_tindex < horizon_time)
-        test_roll_wind = expand_wind & (
-            X_test[time_column_name] >= origin_time)
-        df_list.append(align_outputs(y_fcst[trans_roll_wind],
-                                     X_trans[trans_roll_wind],
-                                     X_test[test_roll_wind],
-                                     y_test[test_roll_wind],
-                                     target_column_name))
-
-        # Advance the origin time
-        origin_time = horizon_time
-
-    return pd.concat(df_list, ignore_index=True)

how-to-use-azureml/automated-machine-learning/forecasting-bike-share/forecasting_script.py

@@ -1,54 +1,52 @@
 import argparse
-import azureml.train.automl
-from azureml.automl.runtime._vendor.automl.client.core.runtime import forecasting_models
-from azureml.core import Run
+from azureml.core import Dataset, Run
 from sklearn.externals import joblib
-import forecasting_helper
-
 
 parser = argparse.ArgumentParser()
 parser.add_argument(
-    '--max_horizon', type=int, dest='max_horizon',
-    default=10, help='Max Horizon for forecasting')
+    "--target_column_name",
+    type=str,
+    dest="target_column_name",
+    help="Target Column Name",
+)
 parser.add_argument(
-    '--target_column_name', type=str, dest='target_column_name',
-    help='Target Column Name')
-parser.add_argument(
-    '--time_column_name', type=str, dest='time_column_name',
-    help='Time Column Name')
-parser.add_argument(
-    '--frequency', type=str, dest='freq',
-    help='Frequency of prediction')
+    "--test_dataset", type=str, dest="test_dataset", help="Test Dataset"
+)
 
 args = parser.parse_args()
-max_horizon = args.max_horizon
 target_column_name = args.target_column_name
-time_column_name = args.time_column_name
-freq = args.freq
+test_dataset_id = args.test_dataset
 
 run = Run.get_context()
-# get input dataset by name
-test_dataset = run.input_datasets['test_data']
+ws = run.experiment.workspace
 
-grain_column_names = []
+# get the input dataset by id
+test_dataset = Dataset.get_by_id(ws, id=test_dataset_id)
 
-df = test_dataset.to_pandas_dataframe().reset_index(drop=True)
+X_test_df = (
+    test_dataset.drop_columns(columns=[target_column_name])
+    .to_pandas_dataframe()
+    .reset_index(drop=True)
+)
+y_test_df = (
+    test_dataset.with_timestamp_columns(None)
+    .keep_columns(columns=[target_column_name])
+    .to_pandas_dataframe()
+)
 
-X_test_df = test_dataset.drop_columns(columns=[target_column_name]).to_pandas_dataframe().reset_index(drop=True)
-y_test_df = test_dataset.with_timestamp_columns(None).keep_columns(columns=[target_column_name]).to_pandas_dataframe()
+fitted_model = joblib.load("model.pkl")
 
-fitted_model = joblib.load('model.pkl')
+y_pred, X_trans = fitted_model.rolling_evaluation(X_test_df, y_test_df.values)
 
-df_all = forecasting_helper.do_rolling_forecast(
-    fitted_model,
-    X_test_df,
-    y_test_df.values.T[0],
-    target_column_name,
-    time_column_name,
-    max_horizon,
-    freq)
+# Add predictions, actuals, and horizon relative to rolling origin to the test feature data
+assign_dict = {
+    "horizon_origin": X_trans["horizon_origin"].values,
+    "predicted": y_pred,
+    target_column_name: y_test_df[target_column_name].values,
+}
+df_all = X_test_df.assign(**assign_dict)
 
-file_name = 'outputs/predictions.csv'
+file_name = "outputs/predictions.csv"
 export_csv = df_all.to_csv(file_name, header=True)
 
 # Upload the predictions into artifacts

how-to-use-azureml/automated-machine-learning/forecasting-bike-share/run_forecast.py

@@ -1,41 +1,40 @@
-from azureml.core import Environment
-from azureml.core.conda_dependencies import CondaDependencies
-from azureml.train.estimator import Estimator
-from azureml.core.run import Run
+from azureml.core import ScriptRunConfig
 
 
-def run_rolling_forecast(test_experiment, compute_target, train_run, test_dataset,
-                         max_horizon, target_column_name, time_column_name,
-                         freq='D', inference_folder='./forecast'):
-    condafile = inference_folder + '/condafile.yml'
-    train_run.download_file('outputs/model.pkl',
-                            inference_folder + '/model.pkl')
-    train_run.download_file('outputs/conda_env_v_1_0_0.yml', condafile)
+def run_rolling_forecast(
+    test_experiment,
+    compute_target,
+    train_run,
+    test_dataset,
+    target_column_name,
+    inference_folder="./forecast",
+):
+    train_run.download_file("outputs/model.pkl", inference_folder + "/model.pkl")
 
-    inference_env = Environment("myenv")
-    inference_env.docker.enabled = True
-    inference_env.python.conda_dependencies = CondaDependencies(
-        conda_dependencies_file_path=condafile)
+    inference_env = train_run.get_environment()
 
-    est = Estimator(source_directory=inference_folder,
-                    entry_script='forecasting_script.py',
-                    script_params={
-                        '--max_horizon': max_horizon,
-                        '--target_column_name': target_column_name,
-                        '--time_column_name': time_column_name,
-                        '--frequency': freq
-                    },
-                    inputs=[test_dataset.as_named_input('test_data')],
+    config = ScriptRunConfig(
+        source_directory=inference_folder,
+        script="forecasting_script.py",
+        arguments=[
+            "--target_column_name",
+            target_column_name,
+            "--test_dataset",
+            test_dataset.as_named_input(test_dataset.name),
+        ],
         compute_target=compute_target,
-                    environment_definition=inference_env)
+        environment=inference_env,
+    )
 
-    run = test_experiment.submit(est,
+    run = test_experiment.submit(
+        config,
         tags={
-                                     'training_run_id': train_run.id,
-                                     'run_algorithm': train_run.properties['run_algorithm'],
-                                     'valid_score': train_run.properties['score'],
-                                     'primary_metric': train_run.properties['primary_metric']
-                                 })
+            "training_run_id": train_run.id,
+            "run_algorithm": train_run.properties["run_algorithm"],
+            "valid_score": train_run.properties["score"],
+            "primary_metric": train_run.properties["primary_metric"],
+        },
+    )
 
-    run.log("run_algorithm", run.tags['run_algorithm'])
+    run.log("run_algorithm", run.tags["run_algorithm"])
     return run

how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb

@@ -24,11 +24,11 @@
         "_**Forecasting using the Energy Demand Dataset**_\n",
         "\n",
         "## Contents\n",
-        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#Setup)\n",
-        "1. [Data and Forecasting Configurations](#Data)\n",
-        "1. [Train](#Train)\n",
-        "1. [Results](#Results)\n",
+        "1. [Introduction](#introduction)\n",
+        "1. [Setup](#setup)\n",
+        "1. [Data and Forecasting Configurations](#data)\n",
+        "1. [Train](#train)\n",
+        "1. [Generate and Evaluate the Forecast](#forecast)\n",
         "\n",
         "Advanced Forecasting\n",
         "1. [Advanced Training](#advanced_training)\n",
@@ -39,26 +39,27 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Introduction\n",
+        "# Introduction<a id=\"introduction\"></a>\n",
         "\n",
         "In this example we use the associated New York City energy demand dataset to showcase how you can use AutoML for a simple forecasting problem and explore the results. The goal is predict the energy demand for the next 48 hours based on historic time-series data.\n",
         "\n",
-        "If you are using an Azure Machine Learning [Notebook VM](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-1st-experiment-sdk-setup), you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) first, if you haven't already, to establish your connection to the AzureML Workspace.\n",
+        "If you are using an Azure Machine Learning Compute Instance, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) first, if you haven't already, to establish your connection to the AzureML Workspace.\n",
         "\n",
         "In this notebook you will learn how to:\n",
         "1. Creating an Experiment using an existing Workspace\n",
         "1. Configure AutoML using 'AutoMLConfig'\n",
         "1. Train the model using AmlCompute\n",
         "1. Explore the engineered features and results\n",
+        "1. Generate the forecast and compute the out-of-sample accuracy metrics\n",
         "1. Configuration and remote run of AutoML for a time-series model with lag and rolling window features\n",
-        "1. Run and explore the forecast"
+        "1. Run and explore the forecast with lagging features"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Setup"
+        "# Setup<a id=\"setup\"></a>"
       ]
     },
     {
@@ -67,6 +68,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "import json\n",
         "import logging\n",
         "\n",
         "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
@@ -85,6 +87,22 @@
         "from datetime import datetime"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This notebook is compatible with Azure ML SDK version 1.35.0 or later."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -101,7 +119,7 @@
         "ws = Workspace.from_config()\n",
         "\n",
         "# choose a name for the run history container in the workspace\n",
-        "experiment_name = 'automl-forecasting-energydemand'\n",
+        "experiment_name = \"automl-forecasting-energydemand\"\n",
         "\n",
         "# # project folder\n",
         "# project_folder = './sample_projects/automl-forecasting-energy-demand'\n",
@@ -109,14 +127,14 @@
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
         "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Run History Name'] = experiment_name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "output[\"Subscription ID\"] = ws.subscription_id\n",
+        "output[\"Workspace\"] = ws.name\n",
+        "output[\"Resource Group\"] = ws.resource_group\n",
+        "output[\"Location\"] = ws.location\n",
+        "output[\"Run History Name\"] = experiment_name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"
       ]
     },
@@ -140,42 +158,30 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
         "# Choose a name for your cluster.\n",
-        "amlcompute_cluster_name = \"aml-compute\"\n",
+        "amlcompute_cluster_name = \"energy-cluster\"\n",
         "\n",
-        "found = False\n",
-        "# Check if this compute target already exists in the workspace.\n",
-        "cts = ws.compute_targets\n",
-        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
-        "    found = True\n",
-        "    print('Found existing compute target.')\n",
-        "    compute_target = cts[amlcompute_cluster_name]\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
+        "    print(\"Found existing cluster, use it.\")\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(\n",
+        "        vm_size=\"STANDARD_DS12_V2\", max_nodes=6\n",
+        "    )\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",
-        "if not found:\n",
-        "    print('Creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_DS12_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-        "                                                                #vm_priority = 'lowpriority', # optional\n",
-        "                                                                max_nodes = 6)\n",
-        "\n",
-        "    # Create the cluster.\\n\",\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "\n",
-        "print('Checking cluster status...')\n",
-        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "\n",
-        "# For a more detailed view of current AmlCompute status, use get_status()."
+        "compute_target.wait_for_completion(show_output=True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Data\n",
+        "# Data<a id=\"data\"></a>\n",
         "\n",
         "We will use energy consumption [data from New York City](http://mis.nyiso.com/public/P-58Blist.htm) for model training. The data is stored in a tabular format and includes energy demand and basic weather data at an hourly frequency. \n",
         "\n",
@@ -200,8 +206,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "target_column_name = 'demand'\n",
-        "time_column_name = 'timeStamp'"
+        "target_column_name = \"demand\"\n",
+        "time_column_name = \"timeStamp\""
       ]
     },
     {
@@ -210,7 +216,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "dataset = Dataset.Tabular.from_delimited_files(path = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/nyc_energy.csv\").with_timestamp_columns(fine_grain_timestamp=time_column_name) \n",
+        "dataset = Dataset.Tabular.from_delimited_files(\n",
+        "    path=\"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/nyc_energy.csv\"\n",
+        ").with_timestamp_columns(fine_grain_timestamp=time_column_name)\n",
         "dataset.take(5).to_pandas_dataframe().reset_index(drop=True)"
       ]
     },
@@ -286,37 +294,50 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "max_horizon = 48"
+        "forecast_horizon = 48"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Train\n",
+        "## Forecasting Parameters\n",
+        "To define forecasting parameters for your experiment training, you can leverage the ForecastingParameters class. The table below details the forecasting parameter we will be passing into our experiment.\n",
         "\n",
-        "Instantiate an AutoMLConfig object. This config defines the settings and data used to run the experiment. We can provide extra configurations within 'automl_settings', for this forecasting task we add the name of the time column and the maximum forecast horizon.\n",
+        "|Property|Description|\n",
+        "|-|-|\n",
+        "|**time_column_name**|The name of your time column.|\n",
+        "|**forecast_horizon**|The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly).|\n",
+        "|**freq**|Forecast frequency. This optional parameter represents the period with which the forecast is desired, for example, daily, weekly, yearly, etc. Use this parameter for the correction of time series containing irregular data points or for padding of short time series. The frequency needs to be a pandas offset alias. Please refer to [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) for more information."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Train<a id=\"train\"></a>\n",
+        "\n",
+        "Instantiate an AutoMLConfig object. This config defines the settings and data used to run the experiment. We can provide extra configurations within 'automl_settings', for this forecasting task we add the forecasting parameters to hold all the additional forecasting parameters.\n",
         "\n",
         "|Property|Description|\n",
         "|-|-|\n",
         "|**task**|forecasting|\n",
         "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
-        "|**blacklist_models**|Models in blacklist won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting?view=azure-ml-py).|\n",
+        "|**blocked_models**|Models in blocked_models won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting?view=azure-ml-py).|\n",
         "|**experiment_timeout_hours**|Maximum amount of time in hours that the experiment take before it terminates.|\n",
         "|**training_data**|The training data to be used within the experiment.|\n",
         "|**label_column_name**|The name of the label column.|\n",
         "|**compute_target**|The remote compute for training.|\n",
         "|**n_cross_validations**|Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way.|\n",
         "|**enable_early_stopping**|Flag to enble early termination if the score is not improving in the short term.|\n",
-        "|**time_column_name**|The name of your time column.|\n",
-        "|**max_horizon**|The number of periods out you would like to predict past your training data. Periods are inferred from your data.|\n"
+        "|**forecasting_parameters**|A class holds all the forecasting related parameters.|\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blacklist_models list but you may need to increase the experiment_timeout_hours parameter value to get results."
+        "This notebook uses the blocked_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blocked_models list but you may need to increase the experiment_timeout_hours parameter value to get results."
       ]
     },
     {
@@ -325,22 +346,27 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "automl_settings = {\n",
-        "    'time_column_name': time_column_name,\n",
-        "    'max_horizon': max_horizon,\n",
-        "}\n",
+        "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
         "\n",
-        "automl_config = AutoMLConfig(task='forecasting',                             \n",
-        "                             primary_metric='normalized_root_mean_squared_error',\n",
-        "                             blacklist_models = ['ExtremeRandomTrees', 'AutoArima', 'Prophet'],                             \n",
+        "forecasting_parameters = ForecastingParameters(\n",
+        "    time_column_name=time_column_name,\n",
+        "    forecast_horizon=forecast_horizon,\n",
+        "    freq=\"H\",  # Set the forecast frequency to be hourly\n",
+        ")\n",
+        "\n",
+        "automl_config = AutoMLConfig(\n",
+        "    task=\"forecasting\",\n",
+        "    primary_metric=\"normalized_root_mean_squared_error\",\n",
+        "    blocked_models=[\"ExtremeRandomTrees\", \"AutoArima\", \"Prophet\"],\n",
         "    experiment_timeout_hours=0.3,\n",
         "    training_data=train,\n",
         "    label_column_name=target_column_name,\n",
         "    compute_target=compute_target,\n",
         "    enable_early_stopping=True,\n",
-        "                             n_cross_validations=3,                             \n",
+        "    n_cross_validations=3,\n",
         "    verbosity=logging.INFO,\n",
-        "                            **automl_settings)"
+        "    forecasting_parameters=forecasting_parameters,\n",
+        ")"
       ]
     },
     {
@@ -360,15 +386,6 @@
         "remote_run = experiment.submit(automl_config, show_output=False)"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "remote_run"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -382,8 +399,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Retrieve the Best Model\n",
-        "Below we select the best model from all the training iterations using get_output method."
+        "## Retrieve the Best Run details\n",
+        "Below we retrieve the best Run object from among all the runs in the experiment."
       ]
     },
     {
@@ -392,8 +409,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "best_run, fitted_model = remote_run.get_output()\n",
-        "fitted_model.steps"
+        "best_run = remote_run.get_best_child()\n",
+        "best_run"
       ]
     },
     {
@@ -401,7 +418,7 @@
       "metadata": {},
       "source": [
         "## Featurization\n",
-        "You can access the engineered feature names generated in time-series featurization."
+        "We can look at the engineered feature names generated in time-series featurization via. the JSON file named 'engineered_feature_names.json' under the run outputs."
       ]
     },
     {
@@ -410,7 +427,14 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "fitted_model.named_steps['timeseriestransformer'].get_engineered_feature_names()"
+        "# Download the JSON file locally\n",
+        "best_run.download_file(\n",
+        "    \"outputs/engineered_feature_names.json\", \"engineered_feature_names.json\"\n",
+        ")\n",
+        "with open(\"engineered_feature_names.json\", \"r\") as f:\n",
+        "    records = json.load(f)\n",
+        "\n",
+        "records"
       ]
     },
     {
@@ -433,37 +457,37 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# Get the featurization summary as a list of JSON\n",
-        "featurization_summary = fitted_model.named_steps['timeseriestransformer'].get_featurization_summary()\n",
-        "# View the featurization summary as a pandas dataframe\n",
-        "pd.DataFrame.from_records(featurization_summary)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Forecasting\n",
+        "# Download the featurization summary JSON file locally\n",
+        "best_run.download_file(\n",
+        "    \"outputs/featurization_summary.json\", \"featurization_summary.json\"\n",
+        ")\n",
         "\n",
-        "Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. First, we remove the target values from the test set:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "X_test = test.to_pandas_dataframe().reset_index(drop=True)\n",
-        "y_test = X_test.pop(target_column_name).values"
+        "# Render the JSON as a pandas DataFrame\n",
+        "with open(\"featurization_summary.json\", \"r\") as f:\n",
+        "    records = json.load(f)\n",
+        "fs = pd.DataFrame.from_records(records)\n",
+        "\n",
+        "# View a summary of the featurization\n",
+        "fs[\n",
+        "    [\n",
+        "        \"RawFeatureName\",\n",
+        "        \"TypeDetected\",\n",
+        "        \"Dropped\",\n",
+        "        \"EngineeredFeatureCount\",\n",
+        "        \"Transformations\",\n",
+        "    ]\n",
+        "]"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Forecast Function\n",
-        "For forecasting, we will use the forecast function instead of the predict function. Using the predict method would result in getting predictions for EVERY horizon the forecaster can predict at. This is useful when training and evaluating the performance of the forecaster at various horizons, but the level of detail is excessive for normal use. Forecast function also can handle more complicated scenarios, see notebook on [high frequency forecasting](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.ipynb)."
+        "# Forecasting<a id=\"forecast\"></a>\n",
+        "\n",
+        "Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. We will do batch scoring on the test dataset which should have the same schema as training dataset.\n",
+        "\n",
+        "The inference will run on a remote compute. In this example, it will re-use the training compute."
       ]
     },
     {
@@ -472,10 +496,36 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# The featurized data, aligned to y, will also be returned.\n",
-        "# This contains the assumptions that were made in the forecast\n",
-        "# and helps align the forecast to the original data\n",
-        "y_predictions, X_trans = fitted_model.forecast(X_test)"
+        "test_experiment = Experiment(ws, experiment_name + \"_inference\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Retrieving forecasts from the model\n",
+        "We have created a function called `run_forecast` that submits the test data to the best model determined during the training run and retrieves forecasts. This function uses a helper script `forecasting_script` which is uploaded and expecuted on the remote compute."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from run_forecast import run_remote_inference\n",
+        "\n",
+        "remote_run_infer = run_remote_inference(\n",
+        "    test_experiment=test_experiment,\n",
+        "    compute_target=compute_target,\n",
+        "    train_run=best_run,\n",
+        "    test_dataset=test,\n",
+        "    target_column_name=target_column_name,\n",
+        ")\n",
+        "remote_run_infer.wait_for_completion(show_output=False)\n",
+        "\n",
+        "# download the inference output file to the local machine\n",
+        "remote_run_infer.download_file(\"outputs/predictions.csv\", \"predictions.csv\")"
       ]
     },
     {
@@ -483,9 +533,7 @@
       "metadata": {},
       "source": [
         "### Evaluate\n",
-        "To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE).\n",
-        "\n",
-        "It is a good practice to always align the output explicitly to the input, as the count and order of the rows may have changed during transformations that span multiple rows."
+        "To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE). For more metrics that can be used for evaluation after training, please see [supported metrics](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#regressionforecasting-metrics), and [how to calculate residuals](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#residuals)."
       ]
     },
     {
@@ -494,9 +542,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from forecasting_helper import align_outputs\n",
-        "\n",
-        "df_all = align_outputs(y_predictions, X_trans, X_test, y_test, target_column_name)"
+        "# load forecast data frame\n",
+        "fcst_df = pd.read_csv(\"predictions.csv\", parse_dates=[time_column_name])\n",
+        "fcst_df.head()"
       ]
     },
     {
@@ -505,26 +553,30 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.automl.core._vendor.automl.client.core.common import metrics\n",
+        "from azureml.automl.core.shared import constants\n",
+        "from azureml.automl.runtime.shared.score import scoring\n",
         "from matplotlib import pyplot as plt\n",
-        "from automl.client.core.common import constants\n",
         "\n",
         "# use automl metrics module\n",
-        "scores = metrics.compute_metrics_regression(\n",
-        "    df_all['predicted'],\n",
-        "    df_all[target_column_name],\n",
-        "    list(constants.Metric.SCALAR_REGRESSION_SET),\n",
-        "    None, None, None)\n",
+        "scores = scoring.score_regression(\n",
+        "    y_test=fcst_df[target_column_name],\n",
+        "    y_pred=fcst_df[\"predicted\"],\n",
+        "    metrics=list(constants.Metric.SCALAR_REGRESSION_SET),\n",
+        ")\n",
         "\n",
         "print(\"[Test data scores]\\n\")\n",
-        "for key, value in scores.items():    \n",
-        "    print('{}:   {:.3f}'.format(key, value))\n",
-        "    \n",
+        "for key, value in scores.items():\n",
+        "    print(\"{}:   {:.3f}\".format(key, value))\n",
+        "\n",
         "# Plot outputs\n",
         "%matplotlib inline\n",
-        "test_pred = plt.scatter(df_all[target_column_name], df_all['predicted'], color='b')\n",
-        "test_test = plt.scatter(df_all[target_column_name], df_all[target_column_name], color='g')\n",
-        "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
+        "test_pred = plt.scatter(fcst_df[target_column_name], fcst_df[\"predicted\"], color=\"b\")\n",
+        "test_test = plt.scatter(\n",
+        "    fcst_df[target_column_name], fcst_df[target_column_name], color=\"g\"\n",
+        ")\n",
+        "plt.legend(\n",
+        "    (test_pred, test_test), (\"prediction\", \"truth\"), loc=\"upper left\", fontsize=8\n",
+        ")\n",
         "plt.show()"
       ]
     },
@@ -532,24 +584,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Looking at `X_trans` is also useful to see what featurization happened to the data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "X_trans"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Advanced Training <a id=\"advanced_training\"></a>\n",
-        "We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, grain and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation."
+        "# Advanced Training <a id=\"advanced_training\"></a>\n",
+        "We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, time series identifier columns and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation."
       ]
     },
     {
@@ -557,9 +593,9 @@
       "metadata": {},
       "source": [
         "### Using lags and rolling window features\n",
-        "Now we will configure the target lags, that is the previous values of the target variables, meaning the prediction is no longer horizon-less. We therefore must still specify the `max_horizon` that the model will learn to forecast. The `target_lags` keyword specifies how far back we will construct the lags of the target variable, and the `target_rolling_window_size` specifies the size of the rolling window over which we will generate the `max`, `min` and `sum` features.\n",
+        "Now we will configure the target lags, that is the previous values of the target variables, meaning the prediction is no longer horizon-less. We therefore must still specify the `forecast_horizon` that the model will learn to forecast. The `target_lags` keyword specifies how far back we will construct the lags of the target variable, and the `target_rolling_window_size` specifies the size of the rolling window over which we will generate the `max`, `min` and `sum` features.\n",
         "\n",
-        "This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset.  You can choose to remove models from the blacklist_models list but you may need to increase the iteration_timeout_minutes parameter value to get results."
+        "This notebook uses the blocked_models parameter to exclude some models that take a longer time to train on this dataset.  You can choose to remove models from the blocked_models list but you may need to increase the iteration_timeout_minutes parameter value to get results."
       ]
     },
     {
@@ -568,24 +604,34 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "automl_advanced_settings = {\n",
-        "    'time_column_name': time_column_name,\n",
-        "    'max_horizon': max_horizon,\n",
-        "    'target_lags': 12,\n",
-        "    'target_rolling_window_size': 4,\n",
-        "}\n",
+        "advanced_forecasting_parameters = ForecastingParameters(\n",
+        "    time_column_name=time_column_name,\n",
+        "    forecast_horizon=forecast_horizon,\n",
+        "    target_lags=12,\n",
+        "    target_rolling_window_size=4,\n",
+        ")\n",
         "\n",
-        "automl_config = AutoMLConfig(task='forecasting',                             \n",
-        "                             primary_metric='normalized_root_mean_squared_error',\n",
-        "                             blacklist_models = ['ElasticNet','ExtremeRandomTrees','GradientBoosting','XGBoostRegressor','ExtremeRandomTrees', 'AutoArima', 'Prophet'], #These models are blacklisted for tutorial purposes, remove this for real use cases.                            \n",
+        "automl_config = AutoMLConfig(\n",
+        "    task=\"forecasting\",\n",
+        "    primary_metric=\"normalized_root_mean_squared_error\",\n",
+        "    blocked_models=[\n",
+        "        \"ElasticNet\",\n",
+        "        \"ExtremeRandomTrees\",\n",
+        "        \"GradientBoosting\",\n",
+        "        \"XGBoostRegressor\",\n",
+        "        \"ExtremeRandomTrees\",\n",
+        "        \"AutoArima\",\n",
+        "        \"Prophet\",\n",
+        "    ],  # These models are blocked for tutorial purposes, remove this for real use cases.\n",
         "    experiment_timeout_hours=0.3,\n",
         "    training_data=train,\n",
         "    label_column_name=target_column_name,\n",
         "    compute_target=compute_target,\n",
-        "                             enable_early_stopping = True,\n",
-        "                             n_cross_validations=3,                             \n",
+        "    enable_early_stopping=True,\n",
+        "    n_cross_validations=3,\n",
         "    verbosity=logging.INFO,\n",
-        "                            **automl_advanced_settings)"
+        "    forecasting_parameters=advanced_forecasting_parameters,\n",
+        ")"
       ]
     },
     {
@@ -617,7 +663,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Retrieve the Best Model"
+        "### Retrieve the Best Run details"
       ]
     },
     {
@@ -626,15 +672,16 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "best_run_lags, fitted_model_lags = advanced_remote_run.get_output()"
+        "best_run_lags = remote_run.get_best_child()\n",
+        "best_run_lags"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Advanced Results<a id=\"advanced_results\"></a>\n",
-        "We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, grain and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation."
+        "# Advanced Results<a id=\"advanced_results\"></a>\n",
+        "We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, time series identifier columns and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation."
       ]
     },
     {
@@ -643,21 +690,21 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# The featurized data, aligned to y, will also be returned.\n",
-        "# This contains the assumptions that were made in the forecast\n",
-        "# and helps align the forecast to the original data\n",
-        "y_predictions, X_trans = fitted_model_lags.forecast(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from forecasting_helper import align_outputs\n",
+        "test_experiment_advanced = Experiment(ws, experiment_name + \"_inference_advanced\")\n",
+        "advanced_remote_run_infer = run_remote_inference(\n",
+        "    test_experiment=test_experiment_advanced,\n",
+        "    compute_target=compute_target,\n",
+        "    train_run=best_run_lags,\n",
+        "    test_dataset=test,\n",
+        "    target_column_name=target_column_name,\n",
+        "    inference_folder=\"./forecast_advanced\",\n",
+        ")\n",
+        "advanced_remote_run_infer.wait_for_completion(show_output=False)\n",
         "\n",
-        "df_all = align_outputs(y_predictions, X_trans, X_test, y_test, target_column_name)"
+        "# download the inference output file to the local machine\n",
+        "advanced_remote_run_infer.download_file(\n",
+        "    \"outputs/predictions.csv\", \"predictions_advanced.csv\"\n",
+        ")"
       ]
     },
     {
@@ -666,26 +713,42 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.automl.core._vendor.automl.client.core.common import metrics\n",
+        "fcst_adv_df = pd.read_csv(\"predictions_advanced.csv\", parse_dates=[time_column_name])\n",
+        "fcst_adv_df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.automl.core.shared import constants\n",
+        "from azureml.automl.runtime.shared.score import scoring\n",
         "from matplotlib import pyplot as plt\n",
-        "from automl.client.core.common import constants\n",
         "\n",
         "# use automl metrics module\n",
-        "scores = metrics.compute_metrics_regression(\n",
-        "    df_all['predicted'],\n",
-        "    df_all[target_column_name],\n",
-        "    list(constants.Metric.SCALAR_REGRESSION_SET),\n",
-        "    None, None, None)\n",
+        "scores = scoring.score_regression(\n",
+        "    y_test=fcst_adv_df[target_column_name],\n",
+        "    y_pred=fcst_adv_df[\"predicted\"],\n",
+        "    metrics=list(constants.Metric.SCALAR_REGRESSION_SET),\n",
+        ")\n",
         "\n",
         "print(\"[Test data scores]\\n\")\n",
-        "for key, value in scores.items():    \n",
-        "    print('{}:   {:.3f}'.format(key, value))\n",
-        "    \n",
+        "for key, value in scores.items():\n",
+        "    print(\"{}:   {:.3f}\".format(key, value))\n",
+        "\n",
         "# Plot outputs\n",
         "%matplotlib inline\n",
-        "test_pred = plt.scatter(df_all[target_column_name], df_all['predicted'], color='b')\n",
-        "test_test = plt.scatter(df_all[target_column_name], df_all[target_column_name], color='g')\n",
-        "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
+        "test_pred = plt.scatter(\n",
+        "    fcst_adv_df[target_column_name], fcst_adv_df[\"predicted\"], color=\"b\"\n",
+        ")\n",
+        "test_test = plt.scatter(\n",
+        "    fcst_adv_df[target_column_name], fcst_adv_df[target_column_name], color=\"g\"\n",
+        ")\n",
+        "plt.legend(\n",
+        "    (test_pred, test_test), (\"prediction\", \"truth\"), loc=\"upper left\", fontsize=8\n",
+        ")\n",
         "plt.show()"
       ]
     }
@@ -693,7 +756,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "erwright"
+        "name": "jialiu"
       }
     ],
     "categories": [
@@ -715,7 +778,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.8"
+      "version": "3.6.9"
     }
   },
   "nbformat": 4,

how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.yml

@@ -2,9 +2,3 @@ name: auto-ml-forecasting-energy-demand
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - interpret
-  - azureml-explain-model
-  - azureml-contrib-interpret

how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/forecasting_helper.py

@@ -1,44 +0,0 @@
-import pandas as pd
-import numpy as np
-from pandas.tseries.frequencies import to_offset
-
-
-def align_outputs(y_predicted, X_trans, X_test, y_test, target_column_name,
-                  predicted_column_name='predicted',
-                  horizon_colname='horizon_origin'):
-    """
-    Demonstrates how to get the output aligned to the inputs
-    using pandas indexes. Helps understand what happened if
-    the output's shape differs from the input shape, or if
-    the data got re-sorted by time and grain during forecasting.
-
-    Typical causes of misalignment are:
-    * we predicted some periods that were missing in actuals -> drop from eval
-    * model was asked to predict past max_horizon -> increase max horizon
-    * data at start of X_test was needed for lags -> provide previous periods
-    """
-
-    if (horizon_colname in X_trans):
-        df_fcst = pd.DataFrame({predicted_column_name: y_predicted,
-                                horizon_colname: X_trans[horizon_colname]})
-    else:
-        df_fcst = pd.DataFrame({predicted_column_name: y_predicted})
-
-    # y and X outputs are aligned by forecast() function contract
-    df_fcst.index = X_trans.index
-
-    # align original X_test to y_test
-    X_test_full = X_test.copy()
-    X_test_full[target_column_name] = y_test
-
-    # X_test_full's index does not include origin, so reset for merge
-    df_fcst.reset_index(inplace=True)
-    X_test_full = X_test_full.reset_index().drop(columns='index')
-    together = df_fcst.merge(X_test_full, how='right')
-
-    # drop rows where prediction or actuals are nan
-    # happens because of missing actuals
-    # or at edges of time due to lags/rolling windows
-    clean = together[together[[target_column_name,
-                               predicted_column_name]].notnull().all(axis=1)]
-    return(clean)

how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/forecasting_script.py

@@ -0,0 +1,61 @@
+"""
+This is the script that is executed on the compute instance. It relies
+on the model.pkl file which is uploaded along with this script to the
+compute instance.
+"""
+
+import argparse
+from azureml.core import Dataset, Run
+from sklearn.externals import joblib
+from pandas.tseries.frequencies import to_offset
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--target_column_name",
+    type=str,
+    dest="target_column_name",
+    help="Target Column Name",
+)
+parser.add_argument(
+    "--test_dataset", type=str, dest="test_dataset", help="Test Dataset"
+)
+
+args = parser.parse_args()
+target_column_name = args.target_column_name
+test_dataset_id = args.test_dataset
+
+run = Run.get_context()
+ws = run.experiment.workspace
+
+# get the input dataset by id
+test_dataset = Dataset.get_by_id(ws, id=test_dataset_id)
+
+X_test = test_dataset.to_pandas_dataframe().reset_index(drop=True)
+y_test = X_test.pop(target_column_name).values
+
+# generate forecast
+fitted_model = joblib.load("model.pkl")
+# We have default quantiles values set as below(95th percentile)
+quantiles = [0.025, 0.5, 0.975]
+predicted_column_name = "predicted"
+PI = "prediction_interval"
+fitted_model.quantiles = quantiles
+pred_quantiles = fitted_model.forecast_quantiles(X_test)
+pred_quantiles[PI] = pred_quantiles[[min(quantiles), max(quantiles)]].apply(
+    lambda x: "[{}, {}]".format(x[0], x[1]), axis=1
+)
+X_test[target_column_name] = y_test
+X_test[PI] = pred_quantiles[PI]
+X_test[predicted_column_name] = pred_quantiles[0.5]
+# drop rows where prediction or actuals are nan
+# happens because of missing actuals
+# or at edges of time due to lags/rolling windows
+clean = X_test[
+    X_test[[target_column_name, predicted_column_name]].notnull().all(axis=1)
+]
+
+file_name = "outputs/predictions.csv"
+export_csv = clean.to_csv(file_name, header=True, index=False)  # added Index
+
+# Upload the predictions into artifacts
+run.upload_file(name=file_name, path_or_stream=file_name)

how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/metrics_helper.py

@@ -1,22 +0,0 @@
-import pandas as pd
-import numpy as np
-
-
-def APE(actual, pred):
-    """
-    Calculate absolute percentage error.
-    Returns a vector of APE values with same length as actual/pred.
-    """
-    return 100 * np.abs((actual - pred) / actual)
-
-
-def MAPE(actual, pred):
-    """
-    Calculate mean absolute percentage error.
-    Remove NA and values where actual is close to zero
-    """
-    not_na = ~(np.isnan(actual) | np.isnan(pred))
-    not_zero = ~np.isclose(actual, 0.0)
-    actual_safe = actual[not_na & not_zero]
-    pred_safe = pred[not_na & not_zero]
-    return np.mean(APE(actual_safe, pred_safe))

how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/run_forecast.py

@@ -0,0 +1,49 @@
+import os
+import shutil
+from azureml.core import ScriptRunConfig
+
+
+def run_remote_inference(
+    test_experiment,
+    compute_target,
+    train_run,
+    test_dataset,
+    target_column_name,
+    inference_folder="./forecast",
+):
+    # Create local directory to copy the model.pkl and forecsting_script.py files into.
+    # These files will be uploaded to and executed on the compute instance.
+    os.makedirs(inference_folder, exist_ok=True)
+    shutil.copy("forecasting_script.py", inference_folder)
+
+    train_run.download_file(
+        "outputs/model.pkl", os.path.join(inference_folder, "model.pkl")
+    )
+
+    inference_env = train_run.get_environment()
+
+    config = ScriptRunConfig(
+        source_directory=inference_folder,
+        script="forecasting_script.py",
+        arguments=[
+            "--target_column_name",
+            target_column_name,
+            "--test_dataset",
+            test_dataset.as_named_input(test_dataset.name),
+        ],
+        compute_target=compute_target,
+        environment=inference_env,
+    )
+
+    run = test_experiment.submit(
+        config,
+        tags={
+            "training_run_id": train_run.id,
+            "run_algorithm": train_run.properties["run_algorithm"],
+            "valid_score": train_run.properties["score"],
+            "primary_metric": train_run.properties["primary_metric"],
+        },
+    )
+
+    run.log("run_algorithm", run.tags["run_algorithm"])
+    return run

how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb

@@ -24,7 +24,7 @@
       "metadata": {},
       "source": [
         "## Introduction\n",
-        "This notebook demonstrates the full interface to the `forecast()` function. \n",
+        "This notebook demonstrates the full interface of the `forecast()` function. \n",
         "\n",
         "The best known and most frequent usage of `forecast` enables forecasting on test sets that immediately follows training data. \n",
         "\n",
@@ -35,7 +35,6 @@
         "Terminology:\n",
         "* forecast origin: the last period when the target value is known\n",
         "* forecast periods(s): the period(s) for which the value of the target is desired.\n",
-        "* forecast horizon: the number of forecast periods\n",
         "* lookback: how many past periods (before forecast origin) the model function depends on. The larger of number of lags and length of rolling window.\n",
         "* prediction context: `lookback` periods immediately preceding the forecast origin\n",
         "\n",
@@ -68,6 +67,7 @@
         "import logging\n",
         "import warnings\n",
         "\n",
+        "import azureml.core\n",
         "from azureml.core.dataset import Dataset\n",
         "from pandas.tseries.frequencies import to_offset\n",
         "from azureml.core.compute import AmlCompute\n",
@@ -81,13 +81,28 @@
         "np.set_printoptions(precision=4, suppress=True, linewidth=120)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This notebook is compatible with Azure ML SDK version 1.35.0 or later."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {},
       "outputs": [],
       "source": [
-        "import azureml.core\n",
         "from azureml.core.workspace import Workspace\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.train.automl import AutoMLConfig\n",
@@ -95,20 +110,20 @@
         "ws = Workspace.from_config()\n",
         "\n",
         "# choose a name for the run history container in the workspace\n",
-        "experiment_name = 'automl-forecast-function-demo'\n",
+        "experiment_name = \"automl-forecast-function-demo\"\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
         "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['SKU'] = ws.sku\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Run History Name'] = experiment_name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "output[\"Subscription ID\"] = ws.subscription_id\n",
+        "output[\"Workspace\"] = ws.name\n",
+        "output[\"SKU\"] = ws.sku\n",
+        "output[\"Resource Group\"] = ws.resource_group\n",
+        "output[\"Location\"] = ws.location\n",
+        "output[\"Run History Name\"] = experiment_name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"
       ]
     },
@@ -126,17 +141,20 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "TIME_COLUMN_NAME = 'date'\n",
-        "GRAIN_COLUMN_NAME = 'grain'\n",
-        "TARGET_COLUMN_NAME = 'y'\n",
+        "TIME_COLUMN_NAME = \"date\"\n",
+        "TIME_SERIES_ID_COLUMN_NAME = \"time_series_id\"\n",
+        "TARGET_COLUMN_NAME = \"y\"\n",
         "\n",
-        "def get_timeseries(train_len: int,\n",
+        "\n",
+        "def get_timeseries(\n",
+        "    train_len: int,\n",
         "    test_len: int,\n",
         "    time_column_name: str,\n",
         "    target_column_name: str,\n",
-        "                   grain_column_name: str,\n",
-        "                   grains: int = 1,\n",
-        "                   freq: str = 'H'):\n",
+        "    time_series_id_column_name: str,\n",
+        "    time_series_number: int = 1,\n",
+        "    freq: str = \"H\",\n",
+        "):\n",
         "    \"\"\"\n",
         "    Return the time series of designed length.\n",
         "\n",
@@ -146,9 +164,8 @@
         "    :type test_len: int\n",
         "    :param time_column_name: The desired name of a time column.\n",
         "    :type time_column_name: str\n",
-        "    :param\n",
-        "    :param grains: The number of grains.\n",
-        "    :type grains: int\n",
+        "    :param time_series_number: The number of time series in the data set.\n",
+        "    :type time_series_number: int\n",
         "    :param freq: The frequency string representing pandas offset.\n",
         "                 see https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html\n",
         "    :type freq: str\n",
@@ -159,15 +176,19 @@
         "    data_train = []  # type: List[pd.DataFrame]\n",
         "    data_test = []  # type: List[pd.DataFrame]\n",
         "    data_length = train_len + test_len\n",
-        "    for i in range(grains):\n",
-        "        X = pd.DataFrame({\n",
-        "            time_column_name: pd.date_range(start='2000-01-01',\n",
-        "                                            periods=data_length,\n",
-        "                                            freq=freq),\n",
-        "            target_column_name: np.arange(data_length).astype(float) + np.random.rand(data_length) + i*5,\n",
-        "            'ext_predictor': np.asarray(range(42, 42 + data_length)),\n",
-        "            grain_column_name: np.repeat('g{}'.format(i), data_length)\n",
-        "        })\n",
+        "    for i in range(time_series_number):\n",
+        "        X = pd.DataFrame(\n",
+        "            {\n",
+        "                time_column_name: pd.date_range(\n",
+        "                    start=\"2000-01-01\", periods=data_length, freq=freq\n",
+        "                ),\n",
+        "                target_column_name: np.arange(data_length).astype(float)\n",
+        "                + np.random.rand(data_length)\n",
+        "                + i * 5,\n",
+        "                \"ext_predictor\": np.asarray(range(42, 42 + data_length)),\n",
+        "                time_series_id_column_name: np.repeat(\"ts{}\".format(i), data_length),\n",
+        "            }\n",
+        "        )\n",
         "        data_train.append(X[:train_len])\n",
         "        data_test.append(X[train_len:])\n",
         "    X_train = pd.concat(data_train)\n",
@@ -176,14 +197,17 @@
         "    y_test = X_test.pop(target_column_name).values\n",
         "    return X_train, y_train, X_test, y_test\n",
         "\n",
+        "\n",
         "n_test_periods = 6\n",
         "n_train_periods = 30\n",
-        "X_train, y_train, X_test, y_test = get_timeseries(train_len=n_train_periods,\n",
+        "X_train, y_train, X_test, y_test = get_timeseries(\n",
+        "    train_len=n_train_periods,\n",
         "    test_len=n_test_periods,\n",
         "    time_column_name=TIME_COLUMN_NAME,\n",
         "    target_column_name=TARGET_COLUMN_NAME,\n",
-        "                                                  grain_column_name=GRAIN_COLUMN_NAME,\n",
-        "                                                  grains=2)"
+        "    time_series_id_column_name=TIME_SERIES_ID_COLUMN_NAME,\n",
+        "    time_series_number=2,\n",
+        ")"
       ]
     },
     {
@@ -210,11 +234,12 @@
       "source": [
         "# plot the example time series\n",
         "import matplotlib.pyplot as plt\n",
+        "\n",
         "whole_data = X_train.copy()\n",
-        "target_label = 'y'\n",
+        "target_label = \"y\"\n",
         "whole_data[target_label] = y_train\n",
-        "for g in whole_data.groupby('grain'):    \n",
-        "    plt.plot(g[1]['date'].values, g[1]['y'].values, label=g[0])\n",
+        "for g in whole_data.groupby(\"time_series_id\"):\n",
+        "    plt.plot(g[1][\"date\"].values, g[1][\"y\"].values, label=g[0])\n",
         "plt.legend()\n",
         "plt.show()"
       ]
@@ -236,12 +261,12 @@
         "# We need to save thw artificial data and then upload them to default workspace datastore.\n",
         "DATA_PATH = \"fc_fn_data\"\n",
         "DATA_PATH_X = \"{}/data_train.csv\".format(DATA_PATH)\n",
-        "if not os.path.isdir('data'):\n",
-        "    os.mkdir('data')\n",
+        "if not os.path.isdir(\"data\"):\n",
+        "    os.mkdir(\"data\")\n",
         "pd.DataFrame(whole_data).to_csv(\"data/data_train.csv\", index=False)\n",
         "# Upload saved data to the default data store.\n",
         "ds = ws.get_default_datastore()\n",
-        "ds.upload(src_dir='./data', target_path=DATA_PATH, overwrite=True, show_progress=True)\n",
+        "ds.upload(src_dir=\"./data\", target_path=DATA_PATH, overwrite=True, show_progress=True)\n",
         "train_data = Dataset.Tabular.from_delimited_files(path=ds.path(DATA_PATH_X))"
       ]
     },
@@ -249,7 +274,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource."
+        "You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
       ]
     },
     {
@@ -258,29 +285,23 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "amlcompute_cluster_name = \"cpu-cluster-fcfn\"\n",
-        "        \n",
-        "found = False\n",
-        "# Check if this compute target already exists in the workspace.\n",
-        "cts = ws.compute_targets\n",
-        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
-        "    found = True\n",
-        "    print('Found existing compute target.')\n",
-        "    compute_target = cts[amlcompute_cluster_name]\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
-        "if not found:\n",
-        "    print('Creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-        "                                                                #vm_priority = 'lowpriority', # optional\n",
-        "                                                                max_nodes = 6)\n",
+        "# Choose a name for your CPU cluster\n",
+        "amlcompute_cluster_name = \"fcfn-cluster\"\n",
         "\n",
-        "    # Create the cluster.\\n\",\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
+        "    print(\"Found existing cluster, use it.\")\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(\n",
+        "        vm_size=\"STANDARD_DS12_V2\", max_nodes=6\n",
+        "    )\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",
-        "print('Checking cluster status...')\n",
-        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)"
+        "compute_target.wait_for_completion(show_output=True)"
       ]
     },
     {
@@ -289,13 +310,14 @@
       "source": [
         "## Create the configuration and train a forecaster <a id=\"train\"></a>\n",
         "First generate the configuration, in which we:\n",
-        "* Set metadata columns: target, time column and grain column names.\n",
+        "* Set metadata columns: target, time column and time-series id column names.\n",
         "* Validate our data using cross validation with rolling window method.\n",
         "* Set normalized root mean squared error as a metric to select the best model.\n",
         "* Set early termination to True, so the iterations through the models will stop when no improvements in accuracy score will be made.\n",
         "* Set limitations on the length of experiment run to 15 minutes.\n",
         "* Finally, we set the task to be forecasting.\n",
-        "* We apply the lag lead operator to the target value i.e. we use the previous values as a predictor for the future ones."
+        "* We apply the lag lead operator to the target value i.e. we use the previous values as a predictor for the future ones.\n",
+        "* [Optional] Forecast frequency parameter (freq) represents the period with which the forecast is desired, for example, daily, weekly, yearly, etc. Use this parameter for the correction of time series containing irregular data points or for padding of short time series. The frequency needs to be a pandas offset alias. Please refer to [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) for more information."
       ]
     },
     {
@@ -304,21 +326,24 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "lags = [1,2,3]\n",
-        "max_horizon = n_test_periods\n",
-        "time_series_settings = {    \n",
-        "    'time_column_name': TIME_COLUMN_NAME,\n",
-        "    'grain_column_names': [ GRAIN_COLUMN_NAME ],\n",
-        "    'max_horizon': max_horizon,\n",
-        "    'target_lags': lags\n",
-        "}"
+        "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
+        "\n",
+        "lags = [1, 2, 3]\n",
+        "forecast_horizon = n_test_periods\n",
+        "forecasting_parameters = ForecastingParameters(\n",
+        "    time_column_name=TIME_COLUMN_NAME,\n",
+        "    forecast_horizon=forecast_horizon,\n",
+        "    time_series_id_column_names=[TIME_SERIES_ID_COLUMN_NAME],\n",
+        "    target_lags=lags,\n",
+        "    freq=\"H\",  # Set the forecast frequency to be hourly\n",
+        ")"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Run the model selection and training process."
+        "Run the model selection and training process.  Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
       ]
     },
     {
@@ -332,23 +357,40 @@
         "from azureml.train.automl import AutoMLConfig\n",
         "\n",
         "\n",
-        "automl_config = AutoMLConfig(task='forecasting',\n",
-        "                             debug_log='automl_forecasting_function.log',\n",
-        "                             primary_metric='normalized_root_mean_squared_error',\n",
+        "automl_config = AutoMLConfig(\n",
+        "    task=\"forecasting\",\n",
+        "    debug_log=\"automl_forecasting_function.log\",\n",
+        "    primary_metric=\"normalized_root_mean_squared_error\",\n",
         "    experiment_timeout_hours=0.25,\n",
         "    enable_early_stopping=True,\n",
         "    training_data=train_data,\n",
         "    compute_target=compute_target,\n",
         "    n_cross_validations=3,\n",
-        "                             verbosity = logging.INFO,\n",
+        "    verbosity=logging.INFO,\n",
         "    max_concurrent_iterations=4,\n",
         "    max_cores_per_iteration=-1,\n",
         "    label_column_name=target_label,\n",
-        "                             **time_series_settings)\n",
-        "\n",
-        "remote_run = experiment.submit(automl_config, show_output=False)\n",
-        "remote_run.wait_for_completion()\n",
+        "    forecasting_parameters=forecasting_parameters,\n",
+        ")\n",
         "\n",
+        "remote_run = experiment.submit(automl_config, show_output=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run.wait_for_completion()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
         "# Retrieve the best model to use it further.\n",
         "_, fitted_model = remote_run.get_output()"
       ]
@@ -454,13 +496,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# specify which quantiles you would like \n",
+        "# specify which quantiles you would like\n",
         "fitted_model.quantiles = [0.01, 0.5, 0.95]\n",
         "# use forecast_quantiles function, not the forecast() one\n",
         "y_pred_quantiles = fitted_model.forecast_quantiles(X_test)\n",
         "\n",
-        "# it all nicely aligns column-wise\n",
-        "pd.concat([X_test.reset_index(), y_pred_quantiles], axis=1)"
+        "# quantile forecasts returned in a Dataframe along with the time and time series id columns\n",
+        "y_pred_quantiles"
       ]
     },
     {
@@ -469,7 +511,7 @@
       "source": [
         "#### Destination-date forecast: \"just do something\"\n",
         "\n",
-        "In some scenarios, the X_test is not known. The forecast is likely to be weak, because it is missing contemporaneous predictors, which we will need to impute. If you still wish to predict forward under the assumption that the last known values will be carried forward, you can forecast out to \"destination date\". The destination date still needs to fit within the maximum horizon from training."
+        "In some scenarios, the X_test is not known. The forecast is likely to be weak, because it is missing contemporaneous predictors, which we will need to impute. If you still wish to predict forward under the assumption that the last known values will be carried forward, you can forecast out to \"destination date\". The destination date still needs to fit within the forecast horizon from training."
       ]
     },
     {
@@ -496,7 +538,7 @@
         "\n",
         "![Forecasting after training](forecast_function_away_from_train.png)\n",
         "\n",
-        "The notion of forecast origin comes into play: the forecast origin is **the last period for which we have seen the target value**. This applies per grain, so each grain can have a different forecast origin. \n",
+        "The notion of forecast origin comes into play: the forecast origin is **the last period for which we have seen the target value**. This applies per time-series, so each time-series can have a different forecast origin. \n",
         "\n",
         "The part of data before the forecast origin is the **prediction context**. To provide the context values the model needs when it looks back, we pass definite values in `y_test` (aligned with corresponding times in `X_test`)."
       ]
@@ -507,19 +549,21 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# generate the same kind of test data we trained on, \n",
+        "# generate the same kind of test data we trained on,\n",
         "# but now make the train set much longer, so that the test set will be in the future\n",
-        "X_context, y_context, X_away, y_away = get_timeseries(train_len=42, # train data was 30 steps long\n",
+        "X_context, y_context, X_away, y_away = get_timeseries(\n",
+        "    train_len=42,  # train data was 30 steps long\n",
         "    test_len=4,\n",
         "    time_column_name=TIME_COLUMN_NAME,\n",
         "    target_column_name=TARGET_COLUMN_NAME,\n",
-        "                                      grain_column_name=GRAIN_COLUMN_NAME,\n",
-        "                                      grains=2)\n",
+        "    time_series_id_column_name=TIME_SERIES_ID_COLUMN_NAME,\n",
+        "    time_series_number=2,\n",
+        ")\n",
         "\n",
         "# end of the data we trained on\n",
-        "print(X_train.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].max())\n",
+        "print(X_train.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].max())\n",
         "# start of the data we want to predict on\n",
-        "print(X_away.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].min())"
+        "print(X_away.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].min())"
       ]
     },
     {
@@ -535,7 +579,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "try: \n",
+        "try:\n",
         "    y_pred_away, xy_away = fitted_model.forecast(X_away)\n",
         "    xy_away\n",
         "except Exception as e:\n",
@@ -546,7 +590,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "How should we read that eror message? The forecast origin is at the last time the model saw an actual value of `y` (the target). That was at the end of the training data! The model is attempting to forecast from the end of training data. But the requested forecast periods are past the maximum horizon. We need to provide a define `y` value to establish the forecast origin.\n",
+        "How should we read that eror message? The forecast origin is at the last time the model saw an actual value of `y` (the target). That was at the end of the training data! The model is attempting to forecast from the end of training data. But the requested forecast periods are past the forecast horizon. We need to provide a define `y` value to establish the forecast origin.\n",
         "\n",
         "We will use this helper function to take the required amount of context from the data preceding the testing data. It's definition is intentionally simplified to keep the idea in the clear."
       ]
@@ -557,32 +601,34 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "def make_forecasting_query(fulldata, time_column_name, target_column_name, forecast_origin, horizon, lookback):\n",
+        "def make_forecasting_query(\n",
+        "    fulldata, time_column_name, target_column_name, forecast_origin, horizon, lookback\n",
+        "):\n",
         "\n",
         "    \"\"\"\n",
         "    This function will take the full dataset, and create the query\n",
-        "    to predict all values of the grain from the `forecast_origin`\n",
+        "    to predict all values of the time series from the `forecast_origin`\n",
         "    forward for the next `horizon` horizons. Context from previous\n",
         "    `lookback` periods will be included.\n",
         "\n",
-        "    \n",
+        "\n",
         "\n",
         "    fulldata: pandas.DataFrame           a time series dataset. Needs to contain X and y.\n",
         "    time_column_name: string             which column (must be in fulldata) is the time axis\n",
         "    target_column_name: string           which column (must be in fulldata) is to be forecast\n",
-        "    forecast_origin: datetime type       the last time we (pretend to) have target values \n",
+        "    forecast_origin: datetime type       the last time we (pretend to) have target values\n",
         "    horizon: timedelta                   how far forward, in time units (not periods)\n",
-        "    lookback: timedelta                  how far back does the model look?\n",
+        "    lookback: timedelta                  how far back does the model look\n",
         "\n",
         "    Example:\n",
         "\n",
         "\n",
         "    ```\n",
         "\n",
-        "    forecast_origin = pd.to_datetime('2012-09-01') + pd.DateOffset(days=5) # forecast 5 days after end of training\n",
+        "    forecast_origin = pd.to_datetime(\"2012-09-01\") + pd.DateOffset(days=5) # forecast 5 days after end of training\n",
         "    print(forecast_origin)\n",
         "\n",
-        "    X_query, y_query = make_forecasting_query(data, \n",
+        "    X_query, y_query = make_forecasting_query(data,\n",
         "                       forecast_origin = forecast_origin,\n",
         "                       horizon = pd.DateOffset(days=7), # 7 days into the future\n",
         "                       lookback = pd.DateOffset(days=1), # model has lag 1 period (day)\n",
@@ -591,28 +637,30 @@
         "    ```\n",
         "    \"\"\"\n",
         "\n",
-        "    X_past = fulldata[ (fulldata[ time_column_name ] > forecast_origin - lookback) &\n",
-        "                       (fulldata[ time_column_name ] <= forecast_origin)\n",
+        "    X_past = fulldata[\n",
+        "        (fulldata[time_column_name] > forecast_origin - lookback)\n",
+        "        & (fulldata[time_column_name] <= forecast_origin)\n",
         "    ]\n",
         "\n",
-        "    X_future = fulldata[ (fulldata[ time_column_name ] > forecast_origin) &\n",
-        "                         (fulldata[ time_column_name ] <= forecast_origin + horizon)\n",
+        "    X_future = fulldata[\n",
+        "        (fulldata[time_column_name] > forecast_origin)\n",
+        "        & (fulldata[time_column_name] <= forecast_origin + horizon)\n",
         "    ]\n",
         "\n",
         "    y_past = X_past.pop(target_column_name).values.astype(np.float)\n",
         "    y_future = X_future.pop(target_column_name).values.astype(np.float)\n",
         "\n",
         "    # Now take y_future and turn it into question marks\n",
-        "    y_query = y_future.copy().astype(np.float)  # because sometimes life hands you an int\n",
+        "    y_query = y_future.copy().astype(\n",
+        "        np.float\n",
+        "    )  # because sometimes life hands you an int\n",
         "    y_query.fill(np.NaN)\n",
         "\n",
-        "\n",
         "    print(\"X_past is \" + str(X_past.shape) + \" - shaped\")\n",
         "    print(\"X_future is \" + str(X_future.shape) + \" - shaped\")\n",
         "    print(\"y_past is \" + str(y_past.shape) + \" - shaped\")\n",
         "    print(\"y_query is \" + str(y_query.shape) + \" - shaped\")\n",
         "\n",
-        "\n",
         "    X_pred = pd.concat([X_past, X_future])\n",
         "    y_pred = np.concatenate([y_past, y_query])\n",
         "    return X_pred, y_pred"
@@ -631,8 +679,16 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(X_context.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].agg(['min','max','count']))\n",
-        "print(X_away.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].agg(['min','max','count']))\n",
+        "print(\n",
+        "    X_context.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].agg(\n",
+        "        [\"min\", \"max\", \"count\"]\n",
+        "    )\n",
+        ")\n",
+        "print(\n",
+        "    X_away.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].agg(\n",
+        "        [\"min\", \"max\", \"count\"]\n",
+        "    )\n",
+        ")\n",
         "X_context.tail(5)"
       ]
     },
@@ -642,11 +698,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# Since the length of the lookback is 3, \n",
+        "# Since the length of the lookback is 3,\n",
         "# we need to add 3 periods from the context to the request\n",
         "# so that the model has the data it needs\n",
         "\n",
-        "# Put the X and y back together for a while. \n",
+        "# Put the X and y back together for a while.\n",
         "# They like each other and it makes them happy.\n",
         "X_context[TARGET_COLUMN_NAME] = y_context\n",
         "X_away[TARGET_COLUMN_NAME] = y_away\n",
@@ -657,16 +713,17 @@
         "# it is indeed the last point of the context\n",
         "assert forecast_origin == X_context[TIME_COLUMN_NAME].max()\n",
         "print(\"Forecast origin: \" + str(forecast_origin))\n",
-        "      \n",
+        "\n",
         "# the model uses lags and rolling windows to look back in time\n",
         "n_lookback_periods = max(lags)\n",
         "lookback = pd.DateOffset(hours=n_lookback_periods)\n",
         "\n",
-        "horizon = pd.DateOffset(hours=max_horizon)\n",
+        "horizon = pd.DateOffset(hours=forecast_horizon)\n",
         "\n",
         "# now make the forecast query from context (refer to figure)\n",
-        "X_pred, y_pred = make_forecasting_query(fulldata, TIME_COLUMN_NAME, TARGET_COLUMN_NAME,\n",
-        "                                        forecast_origin, horizon, lookback)\n",
+        "X_pred, y_pred = make_forecasting_query(\n",
+        "    fulldata, TIME_COLUMN_NAME, TARGET_COLUMN_NAME, forecast_origin, horizon, lookback\n",
+        ")\n",
         "\n",
         "# show the forecast request aligned\n",
         "X_show = X_pred.copy()\n",
@@ -678,7 +735,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Note that the forecast origin is at 17:00 for both grains, and periods from 18:00 are to be forecast."
+        "Note that the forecast origin is at 17:00 for both time-series, and periods from 18:00 are to be forecast."
       ]
     },
     {
@@ -693,15 +750,104 @@
         "# show the forecast aligned\n",
         "X_show = xy_away.reset_index()\n",
         "# without the generated features\n",
-        "X_show[['date', 'grain', 'ext_predictor', '_automl_target_col']]\n",
+        "X_show[[\"date\", \"time_series_id\", \"ext_predictor\", \"_automl_target_col\"]]\n",
         "# prediction is in _automl_target_col"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Forecasting farther than the forecast horizon <a id=\"recursive forecasting\"></a>\n",
+        "When the forecast destination, or the latest date in the prediction data frame, is farther into the future than the specified forecast horizon, the `forecast()` function will still make point predictions out to the later date using a recursive operation mode. Internally, the method recursively applies the regular forecaster to generate context so that we can forecast further into the future. \n",
+        "\n",
+        "To illustrate the use-case and operation of recursive forecasting, we'll consider an example with a single time-series where the forecasting period directly follows the training period and is twice as long as the forecasting horizon given at training time.\n",
+        "\n",
+        "![Recursive_forecast_overview](recursive_forecast_overview_small.png)\n",
+        "\n",
+        "Internally, we apply the forecaster in an iterative manner and finish the forecast task in two interations. In the first iteration, we apply the forecaster and get the prediction for the first forecast-horizon periods (y_pred1). In the second iteraction, y_pred1 is used as the context to produce the prediction for the next forecast-horizon periods (y_pred2). The combination of (y_pred1 and y_pred2) gives the results for the total forecast periods. \n",
+        "\n",
+        "A caveat: forecast accuracy will likely be worse the farther we predict into the future since errors are compounded with recursive application of the forecaster.\n",
+        "\n",
+        "![Recursive_forecast_iter1](recursive_forecast_iter1.png)\n",
+        "![Recursive_forecast_iter2](recursive_forecast_iter2.png)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# generate the same kind of test data we trained on, but with a single time-series and test period twice as long\n",
+        "# as the forecast_horizon.\n",
+        "_, _, X_test_long, y_test_long = get_timeseries(\n",
+        "    train_len=n_train_periods,\n",
+        "    test_len=forecast_horizon * 2,\n",
+        "    time_column_name=TIME_COLUMN_NAME,\n",
+        "    target_column_name=TARGET_COLUMN_NAME,\n",
+        "    time_series_id_column_name=TIME_SERIES_ID_COLUMN_NAME,\n",
+        "    time_series_number=1,\n",
+        ")\n",
+        "\n",
+        "print(X_test_long.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].min())\n",
+        "print(X_test_long.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].max())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# forecast() function will invoke the recursive forecast method internally.\n",
+        "y_pred_long, X_trans_long = fitted_model.forecast(X_test_long)\n",
+        "y_pred_long"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# What forecast() function does in this case is equivalent to iterating it twice over the test set as the following.\n",
+        "y_pred1, _ = fitted_model.forecast(X_test_long[:forecast_horizon])\n",
+        "y_pred_all, _ = fitted_model.forecast(\n",
+        "    X_test_long, np.concatenate((y_pred1, np.full(forecast_horizon, np.nan)))\n",
+        ")\n",
+        "np.array_equal(y_pred_all, y_pred_long)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Confidence interval and distributional forecasts\n",
+        "AutoML cannot currently estimate forecast errors beyond the forecast horizon set during training, so the `forecast_quantiles()` function will return missing values for quantiles not equal to 0.5 beyond the forecast horizon. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "fitted_model.forecast_quantiles(X_test_long)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Similarly with the simple senarios illustrated above, forecasting farther than the forecast horizon in other senarios like 'multiple time-series', 'Destination-date forecast', and 'forecast away from the training data' are also automatically handled by the `forecast()` function. "
+      ]
     }
   ],
   "metadata": {
     "authors": [
       {
-        "name": "erwright, nirovins"
+        "name": "jialiu"
       }
     ],
     "category": "tutorial",

how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.yml

@@ -0,0 +1,4 @@
+name: auto-ml-forecasting-function
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/automated-machine-learning/forecasting-github-dau/auto-ml-forecasting-github-dau.ipynb

@@ -30,7 +30,7 @@
       },
       "source": [
         "# Automated Machine Learning\n",
-        "**Beer Production Forecasting**\n",
+        "**Github DAU Forecasting**\n",
         "\n",
         "## Contents\n",
         "1. [Introduction](#Introduction)\n",
@@ -48,17 +48,16 @@
       },
       "source": [
         "## Introduction\n",
-        "This notebook demonstrates demand forecasting for Beer Production Dataset using AutoML.\n",
+        "This notebook demonstrates demand forecasting for Github Daily Active Users Dataset using AutoML.\n",
         "\n",
         "AutoML highlights here include using Deep Learning forecasts, Arima, Prophet,  Remote Execution and Remote Inferencing, and working with the `forecast` function. Please also look at the additional forecasting notebooks, which document lagging, rolling windows, forecast quantiles, other ways to use the forecast function, and forecaster deployment.\n",
         "\n",
         "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
         "\n",
-        "An Enterprise workspace is required for this notebook. To learn more about creating an Enterprise workspace or upgrading to an Enterprise workspace from the Azure portal, please visit our [Workspace page.](https://docs.microsoft.com/azure/machine-learning/service/concept-workspace#upgrade)\n",
-        "\n",
         "Notebook synopsis:\n",
+        "\n",
         "1. Creating an Experiment in an existing Workspace\n",
-        "2. Configuration and remote run of AutoML for a time-series model exploring Regression learners, Arima, Prophet and DNNs\n",
+        "2. Configuration and remote run of AutoML for a time-series model exploring DNNs\n",
         "4. Evaluating the fitted model using a rolling test "
       ]
     },
@@ -93,14 +92,29 @@
         "# Squash warning messages for cleaner output in the notebook\n",
         "warnings.showwarning = lambda *args, **kwargs: None\n",
         "\n",
-        "from azureml.core.workspace import Workspace\n",
-        "from azureml.core.experiment import Experiment\n",
+        "from azureml.core import Workspace, Experiment, Dataset\n",
         "from azureml.train.automl import AutoMLConfig\n",
         "from matplotlib import pyplot as plt\n",
         "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
         "from azureml.train.estimator import Estimator"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This notebook is compatible with Azure ML SDK version 1.35.0 or later."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -123,19 +137,19 @@
         "ws = Workspace.from_config()\n",
         "\n",
         "# choose a name for the run history container in the workspace\n",
-        "experiment_name = 'beer-remote-cpu'\n",
+        "experiment_name = \"github-remote-cpu\"\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
         "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Run History Name'] = experiment_name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "output[\"Subscription ID\"] = ws.subscription_id\n",
+        "output[\"Workspace\"] = ws.name\n",
+        "output[\"Resource Group\"] = ws.resource_group\n",
+        "output[\"Location\"] = ws.location\n",
+        "output[\"Run History Name\"] = experiment_name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"
       ]
     },
@@ -147,7 +161,9 @@
       },
       "source": [
         "### Using AmlCompute\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you use `AmlCompute` as your training compute resource."
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you use `AmlCompute` as your training compute resource.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
       ]
     },
     {
@@ -163,15 +179,16 @@
         "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
         "# Choose a name for your CPU cluster\n",
-        "cpu_cluster_name = \"cpu-cluster\"\n",
+        "cpu_cluster_name = \"github-cluster\"\n",
         "\n",
         "# Verify that cluster does not exist already\n",
         "try:\n",
         "    compute_target = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
-        "    print('Found existing cluster, use it.')\n",
+        "    print(\"Found existing cluster, use it.\")\n",
         "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
-        "                                                           max_nodes=4)\n",
+        "    compute_config = AmlCompute.provisioning_configuration(\n",
+        "        vm_size=\"STANDARD_DS12_V2\", max_nodes=4\n",
+        "    )\n",
         "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
         "\n",
         "compute_target.wait_for_completion(show_output=True)"
@@ -185,7 +202,7 @@
       },
       "source": [
         "## Data\n",
-        "Read Beer demand data from file, and preview data."
+        "Read Github DAU data from file, and preview data."
       ]
     },
     {
@@ -201,7 +218,9 @@
         "\n",
         "**Time column** is the time axis along which to predict.\n",
         "\n",
-        "**Grain** is another word for an individual time series in your dataset. Grains are identified by values of the columns listed `grain_column_names`, for example \"store\" and \"item\" if your data has multiple time series of sales, one series for each combination of store and item sold.\n",
+        "**Time series identifier columns** are identified by values of the columns listed `time_series_id_column_names`, for example \"store\" and \"item\" if your data has multiple time series of sales, one series for each combination of store and item sold.\n",
+        "\n",
+        "**Forecast frequency (freq)** This optional parameter represents the period with which the forecast is desired, for example, daily, weekly, yearly, etc. Use this parameter for the correction of time series containing irregular data points or for padding of short time series. The frequency needs to be a pandas offset alias. Please refer to [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) for more information.\n",
         "\n",
         "This dataset has only one time series. Please see the [orange juice notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales) for an example of a multi-time series dataset."
       ]
@@ -218,28 +237,30 @@
         "import pandas as pd\n",
         "from pandas import DataFrame\n",
         "from pandas import Grouper\n",
-        "from matplotlib import pyplot\n",
         "from pandas import concat\n",
-        "from matplotlib import pyplot\n",
         "from pandas.plotting import register_matplotlib_converters\n",
+        "\n",
         "register_matplotlib_converters()\n",
-        "plt.tight_layout()\n",
         "plt.figure(figsize=(20, 10))\n",
+        "plt.tight_layout()\n",
         "\n",
         "plt.subplot(2, 1, 1)\n",
-        "plt.title('Beer Production By Year')\n",
-        "df = pd.read_csv(\"Beer_no_valid_split_train.csv\", parse_dates=True, index_col= 'DATE').drop(columns='grain')\n",
-        "test_df = pd.read_csv(\"Beer_no_valid_split_test.csv\", parse_dates=True, index_col= 'DATE').drop(columns='grain')\n",
-        "pyplot.plot(df)\n",
+        "plt.title(\"Github Daily Active User By Year\")\n",
+        "df = pd.read_csv(\"github_dau_2011-2018_train.csv\", parse_dates=True, index_col=\"date\")\n",
+        "test_df = pd.read_csv(\n",
+        "    \"github_dau_2011-2018_test.csv\", parse_dates=True, index_col=\"date\"\n",
+        ")\n",
+        "plt.plot(df)\n",
         "\n",
         "plt.subplot(2, 1, 2)\n",
-        "plt.title('Beer Production By Month')\n",
+        "plt.title(\"Github Daily Active User By Month\")\n",
         "groups = df.groupby(df.index.month)\n",
         "months = concat([DataFrame(x[1].values) for x in groups], axis=1)\n",
         "months = DataFrame(months)\n",
-        "months.columns = range(1,13)\n",
+        "months.columns = range(1, 49)\n",
         "months.boxplot()\n",
-        "pyplot.show()\n"
+        "\n",
+        "plt.show()"
       ]
     },
     {
@@ -251,10 +272,10 @@
       },
       "outputs": [],
       "source": [
-        "target_column_name = 'BeerProduction'\n",
-        "time_column_name = 'DATE'\n",
-        "grain_column_names = []\n",
-        "freq = 'M' #Monthly data"
+        "target_column_name = \"count\"\n",
+        "time_column_name = \"date\"\n",
+        "time_series_id_column_names = []\n",
+        "freq = \"D\"  # Daily data"
       ]
     },
     {
@@ -277,19 +298,22 @@
         "from helper import split_full_for_forecasting\n",
         "\n",
         "train, valid = split_full_for_forecasting(df, time_column_name)\n",
-        "train.to_csv(\"train.csv\")\n",
-        "valid.to_csv(\"valid.csv\")\n",
-        "test_df.to_csv(\"test.csv\")\n",
+        "\n",
+        "# Reset index to create a Tabualr Dataset.\n",
+        "train.reset_index(inplace=True)\n",
+        "valid.reset_index(inplace=True)\n",
+        "test_df.reset_index(inplace=True)\n",
         "\n",
         "datastore = ws.get_default_datastore()\n",
-        "datastore.upload_files(files = ['./train.csv'], target_path = 'beer-dataset/tabular/', overwrite = True,show_progress = True)\n",
-        "datastore.upload_files(files = ['./valid.csv'], target_path = 'beer-dataset/tabular/', overwrite = True,show_progress = True)\n",
-        "datastore.upload_files(files = ['./test.csv'], target_path = 'beer-dataset/tabular/', overwrite = True,show_progress = True)\n",
-        "\n",
-        "from azureml.core import Dataset\n",
-        "train_dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, 'beer-dataset/tabular/train.csv')])\n",
-        "valid_dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, 'beer-dataset/tabular/valid.csv')])\n",
-        "test_dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, 'beer-dataset/tabular/test.csv')])"
+        "train_dataset = Dataset.Tabular.register_pandas_dataframe(\n",
+        "    train, target=(datastore, \"dataset/\"), name=\"Github_DAU_train\"\n",
+        ")\n",
+        "valid_dataset = Dataset.Tabular.register_pandas_dataframe(\n",
+        "    valid, target=(datastore, \"dataset/\"), name=\"Github_DAU_valid\"\n",
+        ")\n",
+        "test_dataset = Dataset.Tabular.register_pandas_dataframe(\n",
+        "    test_df, target=(datastore, \"dataset/\"), name=\"Github_DAU_test\"\n",
+        ")"
       ]
     },
     {
@@ -313,7 +337,7 @@
       },
       "outputs": [],
       "source": [
-        "max_horizon = 12"
+        "forecast_horizon = 12"
       ]
     },
     {
@@ -334,11 +358,7 @@
         "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
         "|**training_data**|Input dataset, containing both features and label column.|\n",
         "|**label_column_name**|The name of the label column.|\n",
-        "|**enable_dnn**|Enable Forecasting DNNs|\n",
-        "\n",
-        "This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blacklist_models list but you may need to increase the iteration_timeout_minutes parameter value to get results.\n",
-        "\n",
-        "This step requires an Enterprise workspace to gain access to this feature. To learn more about creating an Enterprise workspace or upgrading to an Enterprise workspace from the Azure portal, please visit our [Workspace page.](https://docs.microsoft.com/azure/machine-learning/service/concept-workspace#upgrade)."
+        "|**enable_dnn**|Enable Forecasting DNNs|\n"
       ]
     },
     {
@@ -350,23 +370,30 @@
       },
       "outputs": [],
       "source": [
-        "automl_settings = {\n",
-        "    'time_column_name': time_column_name,\n",
-        "    'max_horizon': max_horizon,\n",
-        "    'enable_dnn' : True,\n",
-        "}\n",
+        "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
         "\n",
-        "automl_config = AutoMLConfig(task='forecasting',                             \n",
-        "                             primary_metric='normalized_root_mean_squared_error',\n",
-        "                             experiment_timeout_hours = 1,\n",
+        "forecasting_parameters = ForecastingParameters(\n",
+        "    time_column_name=time_column_name,\n",
+        "    forecast_horizon=forecast_horizon,\n",
+        "    freq=\"D\",  # Set the forecast frequency to be daily\n",
+        ")\n",
+        "\n",
+        "# To only allow the TCNForecaster we set the allowed_models parameter to reflect this.\n",
+        "automl_config = AutoMLConfig(\n",
+        "    task=\"forecasting\",\n",
+        "    primary_metric=\"normalized_root_mean_squared_error\",\n",
+        "    experiment_timeout_hours=1,\n",
         "    training_data=train_dataset,\n",
         "    label_column_name=target_column_name,\n",
-        "                             validation_data=valid_dataset, \n",
+        "    validation_data=valid_dataset,\n",
         "    verbosity=logging.INFO,\n",
         "    compute_target=compute_target,\n",
         "    max_concurrent_iterations=4,\n",
         "    max_cores_per_iteration=-1,\n",
-        "                            **automl_settings)"
+        "    enable_dnn=True,\n",
+        "    allowed_models=[\"TCNForecaster\"],\n",
+        "    forecasting_parameters=forecasting_parameters,\n",
+        ")"
       ]
     },
     {
@@ -376,7 +403,7 @@
         "hidePrompt": false
       },
       "source": [
-        "We will now run the experiment, starting with 10 iterations of model search. The experiment can be continued for more iterations if more accurate results are required."
+        "We will now run the experiment, starting with 10 iterations of model search. The experiment can be continued for more iterations if more accurate results are required. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
       ]
     },
     {
@@ -388,8 +415,7 @@
       },
       "outputs": [],
       "source": [
-        "remote_run = experiment.submit(automl_config, show_output= False)\n",
-        "remote_run"
+        "remote_run = experiment.submit(automl_config, show_output=True)"
       ]
     },
     {
@@ -406,15 +432,6 @@
         "# remote_run = AutoMLRun(experiment = experiment, run_id = '<replace with your run id>')"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "remote_run.wait_for_completion()"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -446,6 +463,7 @@
       "outputs": [],
       "source": [
         "from helper import get_result_df\n",
+        "\n",
         "summary_df = get_result_df(remote_run)\n",
         "summary_df"
       ]
@@ -461,11 +479,14 @@
       "source": [
         "from azureml.core.run import Run\n",
         "from azureml.widgets import RunDetails\n",
-        "forecast_model = 'TCNForecaster'\n",
-        "if not forecast_model in summary_df['run_id']:\n",
-        "    forecast_model = 'ForecastTCN'\n",
-        "    \n",
-        "best_dnn_run_id = summary_df['run_id'][forecast_model]\n",
+        "\n",
+        "forecast_model = \"TCNForecaster\"\n",
+        "if not forecast_model in summary_df[\"run_id\"]:\n",
+        "    forecast_model = \"ForecastTCN\"\n",
+        "\n",
+        "best_dnn_run_id = summary_df[summary_df[\"Score\"] == summary_df[\"Score\"].min()][\n",
+        "    \"run_id\"\n",
+        "][forecast_model]\n",
         "best_dnn_run = Run(experiment, best_dnn_run_id)"
       ]
     },
@@ -479,7 +500,7 @@
       "outputs": [],
       "source": [
         "best_dnn_run.parent\n",
-        "RunDetails(best_dnn_run.parent).show() "
+        "RunDetails(best_dnn_run.parent).show()"
       ]
     },
     {
@@ -492,7 +513,7 @@
       "outputs": [],
       "source": [
         "best_dnn_run\n",
-        "RunDetails(best_dnn_run).show() "
+        "RunDetails(best_dnn_run).show()"
       ]
     },
     {
@@ -526,8 +547,6 @@
       },
       "outputs": [],
       "source": [
-        "from azureml.core import Dataset\n",
-        "test_dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, 'beer-dataset/tabular/test.csv')])\n",
         "# preview the first 3 rows of the dataset\n",
         "test_dataset.take(5).to_pandas_dataframe()"
       ]
@@ -538,7 +557,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "compute_target = ws.compute_targets['cpu-cluster']\n",
+        "compute_target = ws.compute_targets[\"github-cluster\"]\n",
         "test_experiment = Experiment(ws, experiment_name + \"_test\")"
       ]
     },
@@ -554,9 +573,9 @@
         "import os\n",
         "import shutil\n",
         "\n",
-        "script_folder = os.path.join(os.getcwd(), 'inference')\n",
+        "script_folder = os.path.join(os.getcwd(), \"inference\")\n",
         "os.makedirs(script_folder, exist_ok=True)\n",
-        "shutil.copy2('infer.py', script_folder)"
+        "shutil.copy(\"infer.py\", script_folder)"
       ]
     },
     {
@@ -567,8 +586,18 @@
       "source": [
         "from helper import run_inference\n",
         "\n",
-        "test_run = run_inference(test_experiment, compute_target, script_folder, best_dnn_run, test_dataset, valid_dataset, max_horizon,\n",
-        "                 target_column_name, time_column_name, freq)"
+        "test_run = run_inference(\n",
+        "    test_experiment,\n",
+        "    compute_target,\n",
+        "    script_folder,\n",
+        "    best_dnn_run,\n",
+        "    test_dataset,\n",
+        "    valid_dataset,\n",
+        "    forecast_horizon,\n",
+        "    target_column_name,\n",
+        "    time_column_name,\n",
+        "    freq,\n",
+        ")"
       ]
     },
     {
@@ -588,8 +617,19 @@
       "source": [
         "from helper import run_multiple_inferences\n",
         "\n",
-        "summary_df = run_multiple_inferences(summary_df, experiment, test_experiment, compute_target, script_folder, test_dataset, \n",
-        "                  valid_dataset, max_horizon, target_column_name, time_column_name, freq)"
+        "summary_df = run_multiple_inferences(\n",
+        "    summary_df,\n",
+        "    experiment,\n",
+        "    test_experiment,\n",
+        "    compute_target,\n",
+        "    script_folder,\n",
+        "    test_dataset,\n",
+        "    valid_dataset,\n",
+        "    forecast_horizon,\n",
+        "    target_column_name,\n",
+        "    time_column_name,\n",
+        "    freq,\n",
+        ")"
       ]
     },
     {
@@ -609,7 +649,7 @@
         "    test_run = Run(test_experiment, test_run_id)\n",
         "    test_run.wait_for_completion()\n",
         "    test_score = test_run.get_metrics()[run_summary.primary_metric]\n",
-        "    summary_df.loc[summary_df.run_id == run_id, 'Test Score'] = test_score\n",
+        "    summary_df.loc[summary_df.run_id == run_id, \"Test Score\"] = test_score\n",
         "    print(\"Test Score: \", test_score)"
       ]
     },
@@ -636,7 +676,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "omkarm"
+        "name": "jialiu"
       }
     ],
     "hide_code_all_hidden": false,
@@ -655,7 +695,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.6.9"
     }
   },
   "nbformat": 4,

how-to-use-azureml/automated-machine-learning/forecasting-github-dau/auto-ml-forecasting-github-dau.yml

@@ -0,0 +1,4 @@
+name: auto-ml-forecasting-github-dau
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/automated-machine-learning/forecasting-github-dau/github_dau_2011-2018_test.csv

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how-to-use-azureml/automated-machine-learning/forecasting-github-dau/helper.py

@@ -0,0 +1,176 @@
+import pandas as pd
+from azureml.core import Environment
+from azureml.core.conda_dependencies import CondaDependencies
+from azureml.train.estimator import Estimator
+from azureml.core.run import Run
+from azureml.automl.core.shared import constants
+
+
+def split_fraction_by_grain(df, fraction, time_column_name, grain_column_names=None):
+    if not grain_column_names:
+        df["tmp_grain_column"] = "grain"
+        grain_column_names = ["tmp_grain_column"]
+
+    """Group df by grain and split on last n rows for each group."""
+    df_grouped = df.sort_values(time_column_name).groupby(
+        grain_column_names, group_keys=False
+    )
+
+    df_head = df_grouped.apply(
+        lambda dfg: dfg.iloc[: -int(len(dfg) * fraction)] if fraction > 0 else dfg
+    )
+
+    df_tail = df_grouped.apply(
+        lambda dfg: dfg.iloc[-int(len(dfg) * fraction) :] if fraction > 0 else dfg[:0]
+    )
+
+    if "tmp_grain_column" in grain_column_names:
+        for df2 in (df, df_head, df_tail):
+            df2.drop("tmp_grain_column", axis=1, inplace=True)
+
+        grain_column_names.remove("tmp_grain_column")
+
+    return df_head, df_tail
+
+
+def split_full_for_forecasting(
+    df, time_column_name, grain_column_names=None, test_split=0.2
+):
+    index_name = df.index.name
+
+    # Assumes that there isn't already a column called tmpindex
+
+    df["tmpindex"] = df.index
+
+    train_df, test_df = split_fraction_by_grain(
+        df, test_split, time_column_name, grain_column_names
+    )
+
+    train_df = train_df.set_index("tmpindex")
+    train_df.index.name = index_name
+
+    test_df = test_df.set_index("tmpindex")
+    test_df.index.name = index_name
+
+    df.drop("tmpindex", axis=1, inplace=True)
+
+    return train_df, test_df
+
+
+def get_result_df(remote_run):
+    children = list(remote_run.get_children(recursive=True))
+    summary_df = pd.DataFrame(
+        index=["run_id", "run_algorithm", "primary_metric", "Score"]
+    )
+    goal_minimize = False
+    for run in children:
+        if (
+            run.get_status().lower() == constants.RunState.COMPLETE_RUN
+            and "run_algorithm" in run.properties
+            and "score" in run.properties
+        ):
+            # We only count in the completed child runs.
+            summary_df[run.id] = [
+                run.id,
+                run.properties["run_algorithm"],
+                run.properties["primary_metric"],
+                float(run.properties["score"]),
+            ]
+            if "goal" in run.properties:
+                goal_minimize = run.properties["goal"].split("_")[-1] == "min"
+
+    summary_df = summary_df.T.sort_values("Score", ascending=goal_minimize)
+    summary_df = summary_df.set_index("run_algorithm")
+    return summary_df
+
+
+def run_inference(
+    test_experiment,
+    compute_target,
+    script_folder,
+    train_run,
+    test_dataset,
+    lookback_dataset,
+    max_horizon,
+    target_column_name,
+    time_column_name,
+    freq,
+):
+    model_base_name = "model.pkl"
+    if "model_data_location" in train_run.properties:
+        model_location = train_run.properties["model_data_location"]
+        _, model_base_name = model_location.rsplit("/", 1)
+    train_run.download_file(
+        "outputs/{}".format(model_base_name), "inference/{}".format(model_base_name)
+    )
+
+    inference_env = train_run.get_environment()
+
+    est = Estimator(
+        source_directory=script_folder,
+        entry_script="infer.py",
+        script_params={
+            "--max_horizon": max_horizon,
+            "--target_column_name": target_column_name,
+            "--time_column_name": time_column_name,
+            "--frequency": freq,
+            "--model_path": model_base_name,
+        },
+        inputs=[
+            test_dataset.as_named_input("test_data"),
+            lookback_dataset.as_named_input("lookback_data"),
+        ],
+        compute_target=compute_target,
+        environment_definition=inference_env,
+    )
+
+    run = test_experiment.submit(
+        est,
+        tags={
+            "training_run_id": train_run.id,
+            "run_algorithm": train_run.properties["run_algorithm"],
+            "valid_score": train_run.properties["score"],
+            "primary_metric": train_run.properties["primary_metric"],
+        },
+    )
+
+    run.log("run_algorithm", run.tags["run_algorithm"])
+    return run
+
+
+def run_multiple_inferences(
+    summary_df,
+    train_experiment,
+    test_experiment,
+    compute_target,
+    script_folder,
+    test_dataset,
+    lookback_dataset,
+    max_horizon,
+    target_column_name,
+    time_column_name,
+    freq,
+):
+    for run_name, run_summary in summary_df.iterrows():
+        print(run_name)
+        print(run_summary)
+        run_id = run_summary.run_id
+        train_run = Run(train_experiment, run_id)
+
+        test_run = run_inference(
+            test_experiment,
+            compute_target,
+            script_folder,
+            train_run,
+            test_dataset,
+            lookback_dataset,
+            max_horizon,
+            target_column_name,
+            time_column_name,
+            freq,
+        )
+
+        print(test_run)
+        summary_df.loc[summary_df.run_id == run_id, "test_run_id"] = test_run.id
+
+    return summary_df

how-to-use-azureml/automated-machine-learning/forecasting-github-dau/infer.py

@@ -1,17 +1,32 @@
-import pandas as pd
-import numpy as np
 import argparse
-from azureml.core import Run
+import os
+
+import numpy as np
+import pandas as pd
+
+from pandas.tseries.frequencies import to_offset
 from sklearn.externals import joblib
 from sklearn.metrics import mean_absolute_error, mean_squared_error
-from azureml.automl.core._vendor.automl.client.core.common import metrics
-from automl.client.core.common import constants
-from pandas.tseries.frequencies import to_offset
+
+from azureml.automl.runtime.shared.score import scoring, constants
+from azureml.core import Run
+
+try:
+    import torch
+
+    _torch_present = True
+except ImportError:
+    _torch_present = False
 
 
-def align_outputs(y_predicted, X_trans, X_test, y_test,
-                  predicted_column_name='predicted',
-                  horizon_colname='horizon_origin'):
+def align_outputs(
+    y_predicted,
+    X_trans,
+    X_test,
+    y_test,
+    predicted_column_name="predicted",
+    horizon_colname="horizon_origin",
+):
     """
     Demonstrates how to get the output aligned to the inputs
     using pandas indexes. Helps understand what happened if
@@ -23,9 +38,13 @@ def align_outputs(y_predicted, X_trans, X_test, y_test,
     * model was asked to predict past max_horizon -> increase max horizon
     * data at start of X_test was needed for lags -> provide previous periods
     """
-    if (horizon_colname in X_trans):
-        df_fcst = pd.DataFrame({predicted_column_name: y_predicted,
-                                horizon_colname: X_trans[horizon_colname]})
+    if horizon_colname in X_trans:
+        df_fcst = pd.DataFrame(
+            {
+                predicted_column_name: y_predicted,
+                horizon_colname: X_trans[horizon_colname],
+            }
+        )
     else:
         df_fcst = pd.DataFrame({predicted_column_name: y_predicted})
 
@@ -38,20 +57,21 @@ def align_outputs(y_predicted, X_trans, X_test, y_test,
 
     # X_test_full's index does not include origin, so reset for merge
     df_fcst.reset_index(inplace=True)
-    X_test_full = X_test_full.reset_index().drop(columns='index')
-    together = df_fcst.merge(X_test_full, how='right')
+    X_test_full = X_test_full.reset_index().drop(columns="index")
+    together = df_fcst.merge(X_test_full, how="right")
 
     # drop rows where prediction or actuals are nan
     # happens because of missing actuals
     # or at edges of time due to lags/rolling windows
-    clean = together[together[[target_column_name,
-                               predicted_column_name]].notnull().all(axis=1)]
-    return(clean)
+    clean = together[
+        together[[target_column_name, predicted_column_name]].notnull().all(axis=1)
+    ]
+    return clean
 
 
-def do_rolling_forecast_with_lookback(fitted_model, X_test, y_test,
-                                      max_horizon, X_lookback, y_lookback,
-                                      freq='D'):
+def do_rolling_forecast_with_lookback(
+    fitted_model, X_test, y_test, max_horizon, X_lookback, y_lookback, freq="D"
+):
     """
     Produce forecasts on a rolling origin over the given test set.
 
@@ -73,23 +93,28 @@ def do_rolling_forecast_with_lookback(fitted_model, X_test, y_test,
         horizon_time = origin_time + max_horizon * to_offset(freq)
 
         # Extract test data from an expanding window up-to the horizon
-        expand_wind = (X[time_column_name] < horizon_time)
+        expand_wind = X[time_column_name] < horizon_time
         X_test_expand = X[expand_wind]
         y_query_expand = np.zeros(len(X_test_expand)).astype(np.float)
         y_query_expand.fill(np.NaN)
 
         if origin_time != X[time_column_name].min():
             # Set the context by including actuals up-to the origin time
-            test_context_expand_wind = (X[time_column_name] < origin_time)
-            context_expand_wind = (
-                X_test_expand[time_column_name] < origin_time)
+            test_context_expand_wind = X[time_column_name] < origin_time
+            context_expand_wind = X_test_expand[time_column_name] < origin_time
             y_query_expand[context_expand_wind] = y[test_context_expand_wind]
 
         # Print some debug info
-        print("Horizon_time:", horizon_time,
-              " origin_time: ", origin_time,
-              " max_horizon: ", max_horizon,
-              " freq: ", freq)
+        print(
+            "Horizon_time:",
+            horizon_time,
+            " origin_time: ",
+            origin_time,
+            " max_horizon: ",
+            max_horizon,
+            " freq: ",
+            freq,
+        )
         print("expand_wind: ", expand_wind)
         print("y_query_expand")
         print(y_query_expand)
@@ -113,12 +138,16 @@ def do_rolling_forecast_with_lookback(fitted_model, X_test, y_test,
         # Align forecast with test set for dates within
         # the current rolling window
         trans_tindex = X_trans.index.get_level_values(time_column_name)
-        trans_roll_wind = (trans_tindex >= origin_time) & (
-            trans_tindex < horizon_time)
+        trans_roll_wind = (trans_tindex >= origin_time) & (trans_tindex < horizon_time)
         test_roll_wind = expand_wind & (X[time_column_name] >= origin_time)
-        df_list.append(align_outputs(
-            y_fcst[trans_roll_wind], X_trans[trans_roll_wind],
-            X[test_roll_wind], y[test_roll_wind]))
+        df_list.append(
+            align_outputs(
+                y_fcst[trans_roll_wind],
+                X_trans[trans_roll_wind],
+                X[test_roll_wind],
+                y[test_roll_wind],
+            )
+        )
 
         # Advance the origin time
         origin_time = horizon_time
@@ -126,7 +155,7 @@ def do_rolling_forecast_with_lookback(fitted_model, X_test, y_test,
     return pd.concat(df_list, ignore_index=True)
 
 
-def do_rolling_forecast(fitted_model, X_test, y_test, max_horizon, freq='D'):
+def do_rolling_forecast(fitted_model, X_test, y_test, max_horizon, freq="D"):
     """
     Produce forecasts on a rolling origin over the given test set.
 
@@ -145,24 +174,28 @@ def do_rolling_forecast(fitted_model, X_test, y_test, max_horizon, freq='D'):
         horizon_time = origin_time + max_horizon * to_offset(freq)
 
         # Extract test data from an expanding window up-to the horizon
-        expand_wind = (X_test[time_column_name] < horizon_time)
+        expand_wind = X_test[time_column_name] < horizon_time
         X_test_expand = X_test[expand_wind]
         y_query_expand = np.zeros(len(X_test_expand)).astype(np.float)
         y_query_expand.fill(np.NaN)
 
         if origin_time != X_test[time_column_name].min():
             # Set the context by including actuals up-to the origin time
-            test_context_expand_wind = (X_test[time_column_name] < origin_time)
-            context_expand_wind = (
-                X_test_expand[time_column_name] < origin_time)
-            y_query_expand[context_expand_wind] = y_test[
-                test_context_expand_wind]
+            test_context_expand_wind = X_test[time_column_name] < origin_time
+            context_expand_wind = X_test_expand[time_column_name] < origin_time
+            y_query_expand[context_expand_wind] = y_test[test_context_expand_wind]
 
         # Print some debug info
-        print("Horizon_time:", horizon_time,
-              " origin_time: ", origin_time,
-              " max_horizon: ", max_horizon,
-              " freq: ", freq)
+        print(
+            "Horizon_time:",
+            horizon_time,
+            " origin_time: ",
+            origin_time,
+            " max_horizon: ",
+            max_horizon,
+            " freq: ",
+            freq,
+        )
         print("expand_wind: ", expand_wind)
         print("y_query_expand")
         print(y_query_expand)
@@ -184,14 +217,16 @@ def do_rolling_forecast(fitted_model, X_test, y_test, max_horizon, freq='D'):
         # Align forecast with test set for dates within the
         # current rolling window
         trans_tindex = X_trans.index.get_level_values(time_column_name)
-        trans_roll_wind = (trans_tindex >= origin_time) & (
-            trans_tindex < horizon_time)
-        test_roll_wind = expand_wind & (
-            X_test[time_column_name] >= origin_time)
-        df_list.append(align_outputs(y_fcst[trans_roll_wind],
+        trans_roll_wind = (trans_tindex >= origin_time) & (trans_tindex < horizon_time)
+        test_roll_wind = expand_wind & (X_test[time_column_name] >= origin_time)
+        df_list.append(
+            align_outputs(
+                y_fcst[trans_roll_wind],
                 X_trans[trans_roll_wind],
                 X_test[test_roll_wind],
-                                     y_test[test_roll_wind]))
+                y_test[test_roll_wind],
+            )
+        )
 
         # Advance the origin time
         origin_time = horizon_time
@@ -219,23 +254,37 @@ def MAPE(actual, pred):
     return np.mean(APE(actual_safe, pred_safe))
 
 
+def map_location_cuda(storage, loc):
+    return storage.cuda()
+
+
 parser = argparse.ArgumentParser()
 parser.add_argument(
-    '--max_horizon', type=int, dest='max_horizon',
-    default=10, help='Max Horizon for forecasting')
+    "--max_horizon",
+    type=int,
+    dest="max_horizon",
+    default=10,
+    help="Max Horizon for forecasting",
+)
 parser.add_argument(
-    '--target_column_name', type=str, dest='target_column_name',
-    help='Target Column Name')
+    "--target_column_name",
+    type=str,
+    dest="target_column_name",
+    help="Target Column Name",
+)
 parser.add_argument(
-    '--time_column_name', type=str, dest='time_column_name',
-    help='Time Column Name')
+    "--time_column_name", type=str, dest="time_column_name", help="Time Column Name"
+)
 parser.add_argument(
-    '--frequency', type=str, dest='freq',
-    help='Frequency of prediction')
+    "--frequency", type=str, dest="freq", help="Frequency of prediction"
+)
 parser.add_argument(
-    '--model_path', type=str, dest='model_path',
-    default='model.pkl', help='Filename of model to be loaded')
-
+    "--model_path",
+    type=str,
+    dest="model_path",
+    default="model.pkl",
+    help="Filename of model to be loaded",
+)
 
 args = parser.parse_args()
 max_horizon = args.max_horizon
@@ -244,8 +293,7 @@ time_column_name = args.time_column_name
 freq = args.freq
 model_path = args.model_path
 
-
-print('args passed are: ')
+print("args passed are: ")
 print(max_horizon)
 print(target_column_name)
 print(time_column_name)
@@ -254,28 +302,41 @@ print(model_path)
 
 run = Run.get_context()
 # get input dataset by name
-test_dataset = run.input_datasets['test_data']
-lookback_dataset = run.input_datasets['lookback_data']
+test_dataset = run.input_datasets["test_data"]
+lookback_dataset = run.input_datasets["lookback_data"]
 
 grain_column_names = []
 
 df = test_dataset.to_pandas_dataframe()
 
-print('Read df')
+print("Read df")
 print(df)
 
 X_test_df = test_dataset.drop_columns(columns=[target_column_name])
-y_test_df = test_dataset.with_timestamp_columns(
-    None).keep_columns(columns=[target_column_name])
+y_test_df = test_dataset.with_timestamp_columns(None).keep_columns(
+    columns=[target_column_name]
+)
 
 X_lookback_df = lookback_dataset.drop_columns(columns=[target_column_name])
-y_lookback_df = lookback_dataset.with_timestamp_columns(
-    None).keep_columns(columns=[target_column_name])
+y_lookback_df = lookback_dataset.with_timestamp_columns(None).keep_columns(
+    columns=[target_column_name]
+)
 
-fitted_model = joblib.load(model_path)
+_, ext = os.path.splitext(model_path)
+if ext == ".pt":
+    # Load the fc-tcn torch model.
+    assert _torch_present
+    if torch.cuda.is_available():
+        map_location = map_location_cuda
+    else:
+        map_location = "cpu"
+    with open(model_path, "rb") as fh:
+        fitted_model = torch.load(fh, map_location=map_location)
+else:
+    # Load the sklearn pipeline.
+    fitted_model = joblib.load(model_path)
 
-
-if hasattr(fitted_model, 'get_lookback'):
+if hasattr(fitted_model, "get_lookback"):
     lookback = fitted_model.get_lookback()
     df_all = do_rolling_forecast_with_lookback(
         fitted_model,
@@ -284,28 +345,29 @@ if hasattr(fitted_model, 'get_lookback'):
         max_horizon,
         X_lookback_df.to_pandas_dataframe()[-lookback:],
         y_lookback_df.to_pandas_dataframe().values.T[0][-lookback:],
-        freq)
+        freq,
+    )
 else:
     df_all = do_rolling_forecast(
         fitted_model,
         X_test_df.to_pandas_dataframe(),
         y_test_df.to_pandas_dataframe().values.T[0],
         max_horizon,
-        freq)
+        freq,
+    )
 
 print(df_all)
 
 print("target values:::")
 print(df_all[target_column_name])
 print("predicted values:::")
-print(df_all['predicted'])
+print(df_all["predicted"])
 
-# use automl metrics module
-scores = metrics.compute_metrics_regression(
-    df_all['predicted'],
-    df_all[target_column_name],
-    list(constants.Metric.SCALAR_REGRESSION_SET),
-    None, None, None)
+# Use the AutoML scoring module
+regression_metrics = list(constants.REGRESSION_SCALAR_SET)
+y_test = np.array(df_all[target_column_name])
+y_pred = np.array(df_all["predicted"])
+scores = scoring.score_regression(y_test, y_pred, regression_metrics)
 
 print("scores:")
 print(scores)
@@ -314,12 +376,11 @@ for key, value in scores.items():
     run.log(key, value)
 
 print("Simple forecasting model")
-rmse = np.sqrt(mean_squared_error(
-    df_all[target_column_name], df_all['predicted']))
+rmse = np.sqrt(mean_squared_error(df_all[target_column_name], df_all["predicted"]))
 print("[Test Data] \nRoot Mean squared error: %.2f" % rmse)
-mae = mean_absolute_error(df_all[target_column_name], df_all['predicted'])
-print('mean_absolute_error score: %.2f' % mae)
-print('MAPE: %.2f' % MAPE(df_all[target_column_name], df_all['predicted']))
+mae = mean_absolute_error(df_all[target_column_name], df_all["predicted"])
+print("mean_absolute_error score: %.2f" % mae)
+print("MAPE: %.2f" % MAPE(df_all[target_column_name], df_all["predicted"]))
 
-run.log('rmse', rmse)
-run.log('mae', mae)
+run.log("rmse", rmse)
+run.log("mae", mae)

how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.ipynb

@@ -0,0 +1,640 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Hierarchical Time Series - Automated ML\n",
+        "**_Generate hierarchical time series forecasts with Automated Machine Learning_**\n",
+        "\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "For this notebook we are using a synthetic dataset portraying sales data to predict the the quantity of a vartiety of product skus across several states, stores, and product categories.\n",
+        "\n",
+        "**NOTE: There are limits on how many runs we can do in parallel per workspace, and we currently recommend to set the parallelism to maximum of 320 runs per experiment per workspace. If users want to have more parallelism and increase this limit they might encounter Too Many Requests errors (HTTP 429).**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Prerequisites\n",
+        "You'll need to create a compute Instance by following the instructions in the [EnvironmentSetup.md](../Setup_Resources/EnvironmentSetup.md)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 1.0 Set up workspace, datastore, experiment"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "gather": {
+          "logged": 1613003526897
+        }
+      },
+      "outputs": [],
+      "source": [
+        "import azureml.core\n",
+        "from azureml.core import Workspace, Datastore\n",
+        "import pandas as pd\n",
+        "\n",
+        "# Set up your workspace\n",
+        "ws = Workspace.from_config()\n",
+        "ws.get_details()\n",
+        "\n",
+        "# Set up your datastores\n",
+        "dstore = ws.get_default_datastore()\n",
+        "\n",
+        "output = {}\n",
+        "output[\"SDK version\"] = azureml.core.VERSION\n",
+        "output[\"Subscription ID\"] = ws.subscription_id\n",
+        "output[\"Workspace\"] = ws.name\n",
+        "output[\"Resource Group\"] = ws.resource_group\n",
+        "output[\"Location\"] = ws.location\n",
+        "output[\"Default datastore name\"] = dstore.name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
+        "outputDf.T"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Choose an experiment"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "gather": {
+          "logged": 1613003540729
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "\n",
+        "experiment = Experiment(ws, \"automl-hts\")\n",
+        "\n",
+        "print(\"Experiment name: \" + experiment.name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 2.0 Data\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "source": [
+        "### Upload local csv files to datastore\n",
+        "You can upload your train and inference csv files to the default datastore in your workspace. \n",
+        "\n",
+        "A Datastore is a place where data can be stored that is then made accessible to a compute either by means of mounting or copying the data to the compute target.\n",
+        "Please refer to [Datastore](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore.datastore?view=azure-ml-py) documentation on how to access data from Datastore."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "datastore_path = \"hts-sample\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "datastore = ws.get_default_datastore()\n",
+        "datastore"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create the TabularDatasets \n",
+        "\n",
+        "Datasets in Azure Machine Learning are references to specific data in a Datastore. The data can be retrieved as a [TabularDatasets](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py). We will read in the data as a pandas DataFrame, upload to the data store and register them to your Workspace using ```register_pandas_dataframe``` so they can be called as an input into the training pipeline. We will use the inference dataset as part of the forecasting pipeline. The step need only be completed once."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "gather": {
+          "logged": 1613007017296
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.data.dataset_factory import TabularDatasetFactory\n",
+        "\n",
+        "registered_train = TabularDatasetFactory.register_pandas_dataframe(\n",
+        "    pd.read_csv(\"Data/hts-sample-train.csv\"),\n",
+        "    target=(datastore, \"hts-sample\"),\n",
+        "    name=\"hts-sales-train\",\n",
+        ")\n",
+        "registered_inference = TabularDatasetFactory.register_pandas_dataframe(\n",
+        "    pd.read_csv(\"Data/hts-sample-test.csv\"),\n",
+        "    target=(datastore, \"hts-sample\"),\n",
+        "    name=\"hts-sales-test\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 3.0 Build the training pipeline\n",
+        "Now that the dataset, WorkSpace, and datastore are set up, we can put together a pipeline for training.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Choose a compute target\n",
+        "\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
+        "\n",
+        "\\*\\*Creation of AmlCompute takes approximately 5 minutes.**\n",
+        "\n",
+        "If the AmlCompute with that name is already in your workspace this code will skip the creation process. As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this [article](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-quotas) on the default limits and how to request more quota."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "gather": {
+          "logged": 1613007037308
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "\n",
+        "# Name your cluster\n",
+        "compute_name = \"hts-compute\"\n",
+        "\n",
+        "\n",
+        "if compute_name in ws.compute_targets:\n",
+        "    compute_target = ws.compute_targets[compute_name]\n",
+        "    if compute_target and type(compute_target) is AmlCompute:\n",
+        "        print(\"Found compute target: \" + compute_name)\n",
+        "else:\n",
+        "    print(\"Creating a new compute target...\")\n",
+        "    provisioning_config = AmlCompute.provisioning_configuration(\n",
+        "        vm_size=\"STANDARD_D16S_V3\", max_nodes=20\n",
+        "    )\n",
+        "    # Create the compute target\n",
+        "    compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n",
+        "\n",
+        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
+        "    # If no min node count is provided it will use the scale settings for the cluster\n",
+        "    compute_target.wait_for_completion(\n",
+        "        show_output=True, min_node_count=None, timeout_in_minutes=20\n",
+        "    )\n",
+        "\n",
+        "    # For a more detailed view of current cluster status, use the 'status' property\n",
+        "    print(compute_target.status.serialize())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Set up training parameters\n",
+        "\n",
+        "This dictionary defines the AutoML and hierarchy settings. For this forecasting task we need to define several settings inncluding the name of the time column, the maximum forecast horizon, the hierarchy definition, and the level of the hierarchy at which to train.\n",
+        "\n",
+        "| Property                           | Description|\n",
+        "| :---------------                   | :------------------- |\n",
+        "| **task**                           | forecasting |\n",
+        "| **primary_metric**                 | This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i> |\n",
+        "| **blocked_models**                 | Blocked models won't be used by AutoML. |\n",
+        "| **iteration_timeout_minutes**      | Maximum amount of time in minutes that the model can train. This is optional but provides customers with greater control on exit criteria. |\n",
+        "| **iterations**                     | Number of models to train. This is optional but provides customers with greater control on exit criteria. |\n",
+        "| **experiment_timeout_hours**       | Maximum amount of time in hours that the experiment can take before it terminates. This is optional but provides customers with greater control on exit criteria. |\n",
+        "| **label_column_name**              | The name of the label column. |\n",
+        "| **forecast_horizon**               | The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly). Periods are inferred from your data. |\n",
+        "| **n_cross_validations**            | Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way. |\n",
+        "| **enable_early_stopping**          | Flag to enable early termination if the score is not improving in the short term. |\n",
+        "| **time_column_name**               | The name of your time column. |\n",
+        "| **hierarchy_column_names**         | The names of columns that define the hierarchical structure of the data from highest level to most granular. |\n",
+        "| **training_level**                 | The level of the hierarchy to be used for training models. |\n",
+        "| **enable_engineered_explanations** | Engineered feature explanations will be downloaded if enable_engineered_explanations flag is set to True. By default it is set to False to save storage space. |\n",
+        "| **time_series_id_column_name**     | The column names used to uniquely identify timeseries in data that has multiple rows with the same timestamp. |\n",
+        "| **track_child_runs**               | Flag to disable tracking of child runs. Only best run is tracked if the flag is set to False (this includes the model and metrics of the run). |\n",
+        "| **pipeline_fetch_max_batch_size**  | Determines how many pipelines (training algorithms) to fetch at a time for training, this helps reduce throttling when training at large scale. |\n",
+        "| **model_explainability**           | Flag to disable explaining the best automated ML model at the end of all training iterations. The default is True and will block non-explainable models which may impact the forecast accuracy. For more information, see [Interpretability: model explanations in automated machine learning](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability-automl). |"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "gather": {
+          "logged": 1613007061544
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.train.automl.runtime._hts.hts_parameters import HTSTrainParameters\n",
+        "\n",
+        "model_explainability = True\n",
+        "\n",
+        "engineered_explanations = False\n",
+        "# Define your hierarchy. Adjust the settings below based on your dataset.\n",
+        "hierarchy = [\"state\", \"store_id\", \"product_category\", \"SKU\"]\n",
+        "training_level = \"SKU\"\n",
+        "\n",
+        "# Set your forecast parameters. Adjust the settings below based on your dataset.\n",
+        "time_column_name = \"date\"\n",
+        "label_column_name = \"quantity\"\n",
+        "forecast_horizon = 7\n",
+        "\n",
+        "\n",
+        "automl_settings = {\n",
+        "    \"task\": \"forecasting\",\n",
+        "    \"primary_metric\": \"normalized_root_mean_squared_error\",\n",
+        "    \"label_column_name\": label_column_name,\n",
+        "    \"time_column_name\": time_column_name,\n",
+        "    \"forecast_horizon\": forecast_horizon,\n",
+        "    \"hierarchy_column_names\": hierarchy,\n",
+        "    \"hierarchy_training_level\": training_level,\n",
+        "    \"track_child_runs\": False,\n",
+        "    \"pipeline_fetch_max_batch_size\": 15,\n",
+        "    \"model_explainability\": model_explainability,\n",
+        "    # The following settings are specific to this sample and should be adjusted according to your own needs.\n",
+        "    \"iteration_timeout_minutes\": 10,\n",
+        "    \"iterations\": 10,\n",
+        "    \"n_cross_validations\": 2,\n",
+        "}\n",
+        "\n",
+        "hts_parameters = HTSTrainParameters(\n",
+        "    automl_settings=automl_settings,\n",
+        "    hierarchy_column_names=hierarchy,\n",
+        "    training_level=training_level,\n",
+        "    enable_engineered_explanations=engineered_explanations,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Set up hierarchy training pipeline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Parallel run step is leveraged to train the hierarchy. To configure the ParallelRunConfig you will need to determine the appropriate number of workers and nodes for your use case. The `process_count_per_node` is based off the number of cores of the compute VM. The node_count will determine the number of master nodes to use, increasing the node count will speed up the training process.\n",
+        "\n",
+        "* **experiment:** The experiment used for training.\n",
+        "* **train_data:** The tabular dataset to be used as input to the training run.\n",
+        "* **node_count:** The number of compute nodes to be used for running the user script. We recommend to start with 3 and increase the node_count if the training time is taking too long.\n",
+        "* **process_count_per_node:** Process count per node, we recommend 2:1 ratio for number of cores: number of processes per node. eg. If node has 16 cores then configure 8 or less process count per node or optimal performance.\n",
+        "* **train_pipeline_parameters:** The set of configuration parameters defined in the previous section. \n",
+        "\n",
+        "Calling this method will create a new aggregated dataset which is generated dynamically on pipeline execution."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.automl.pipeline.steps import AutoMLPipelineBuilder\n",
+        "\n",
+        "\n",
+        "training_pipeline_steps = AutoMLPipelineBuilder.get_many_models_train_steps(\n",
+        "    experiment=experiment,\n",
+        "    train_data=registered_train,\n",
+        "    compute_target=compute_target,\n",
+        "    node_count=2,\n",
+        "    process_count_per_node=8,\n",
+        "    train_pipeline_parameters=hts_parameters,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import Pipeline\n",
+        "\n",
+        "training_pipeline = Pipeline(ws, steps=training_pipeline_steps)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Submit the pipeline to run\n",
+        "Next we submit our pipeline to run. The whole training pipeline takes about 1h using a Standard_D16_V3 VM with our current ParallelRunConfig setting."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "training_run = experiment.submit(training_pipeline)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "training_run.wait_for_completion(show_output=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Check the run status, if training_run is in completed state, continue to forecasting. If training_run is in another state, check the portal for failures."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### [Optional] Get the explanations\n",
+        "First we need to download the explanations to the local disk."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "if model_explainability:\n",
+        "    expl_output = training_run.get_pipeline_output(\"explanations\")\n",
+        "    expl_output.download(\"training_explanations\")\n",
+        "else:\n",
+        "    print(\n",
+        "        \"Model explanations are available only if model_explainability is set to True.\"\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The explanations are downloaded to the \"training_explanations/azureml\" directory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "if model_explainability:\n",
+        "    explanations_dirrectory = os.listdir(\n",
+        "        os.path.join(\"training_explanations\", \"azureml\")\n",
+        "    )\n",
+        "    if len(explanations_dirrectory) > 1:\n",
+        "        print(\n",
+        "            \"Warning! The directory contains multiple explanations, only the first one will be displayed.\"\n",
+        "        )\n",
+        "    print(\"The explanations are located at {}.\".format(explanations_dirrectory[0]))\n",
+        "    # Now we will list all the explanations.\n",
+        "    explanation_path = os.path.join(\n",
+        "        \"training_explanations\",\n",
+        "        \"azureml\",\n",
+        "        explanations_dirrectory[0],\n",
+        "        \"training_explanations\",\n",
+        "    )\n",
+        "    print(\"Available explanations\")\n",
+        "    print(\"==============================\")\n",
+        "    print(\"\\n\".join(os.listdir(explanation_path)))\n",
+        "else:\n",
+        "    print(\n",
+        "        \"Model explanations are available only if model_explainability is set to True.\"\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "View the explanations on \"state\" level."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from IPython.display import display\n",
+        "\n",
+        "explanation_type = \"raw\"\n",
+        "level = \"state\"\n",
+        "\n",
+        "if model_explainability:\n",
+        "    display(\n",
+        "        pd.read_csv(\n",
+        "            os.path.join(explanation_path, \"{}_explanations_{}.csv\").format(\n",
+        "                explanation_type, level\n",
+        "            )\n",
+        "        )\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 5.0 Forecasting\n",
+        "For hierarchical forecasting we need to provide the HTSInferenceParameters object.\n",
+        "#### HTSInferenceParameters arguments\n",
+        "* **hierarchy_forecast_level:** The default level of the hierarchy to produce prediction/forecast on.\n",
+        "* **allocation_method:** \\[Optional] The disaggregation method to use if the hierarchy forecast level specified is below the define hierarchy training level. <br><i>(average historical proportions) 'average_historical_proportions'</i><br><i>(proportions of the historical averages) 'proportions_of_historical_average'</i>\n",
+        "\n",
+        "#### get_many_models_batch_inference_steps arguments\n",
+        "* **experiment:** The experiment used for inference run.\n",
+        "* **inference_data:** The data to use for inferencing. It should be the same schema as used for training.\n",
+        "* **compute_target:** The compute target that runs the inference pipeline.\n",
+        "* **node_count:** The number of compute nodes to be used for running the user script. We recommend to start with the number of cores per node (varies by compute sku).\n",
+        "* **process_count_per_node:** The number of processes per node.\n",
+        "* **train_run_id:** \\[Optional] The run id of the hierarchy training, by default it is the latest successful training hts run in the experiment.\n",
+        "* **train_experiment_name:** \\[Optional] The train experiment that contains the train pipeline. This one is only needed when the train pipeline is not in the same experiement as the inference pipeline.\n",
+        "* **process_count_per_node:** \\[Optional] The number of processes per node, by default it's 4."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.automl.runtime._hts.hts_parameters import HTSInferenceParameters\n",
+        "\n",
+        "inference_parameters = HTSInferenceParameters(\n",
+        "    hierarchy_forecast_level=\"store_id\",  # The setting is specific to this dataset and should be changed based on your dataset.\n",
+        "    allocation_method=\"proportions_of_historical_average\",\n",
+        ")\n",
+        "\n",
+        "steps = AutoMLPipelineBuilder.get_many_models_batch_inference_steps(\n",
+        "    experiment=experiment,\n",
+        "    inference_data=registered_inference,\n",
+        "    compute_target=compute_target,\n",
+        "    inference_pipeline_parameters=inference_parameters,\n",
+        "    node_count=2,\n",
+        "    process_count_per_node=8,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import Pipeline\n",
+        "\n",
+        "inference_pipeline = Pipeline(ws, steps=steps)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "inference_run = experiment.submit(inference_pipeline)\n",
+        "inference_run.wait_for_completion(show_output=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Retrieve results\n",
+        "\n",
+        "Forecast results can be retrieved through the following code. The prediction results summary and the actual predictions are downloaded in forecast_results folder"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "forecasts = inference_run.get_pipeline_output(\"forecasts\")\n",
+        "forecasts.download(\"forecast_results\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Resbumit the Pipeline\n",
+        "\n",
+        "The inference pipeline can be submitted with different configurations."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "inference_run = experiment.submit(\n",
+        "    inference_pipeline, pipeline_parameters={\"hierarchy_forecast_level\": \"state\"}\n",
+        ")\n",
+        "inference_run.wait_for_completion(show_output=False)"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "jialiu"
+      }
+    ],
+    "categories": [
+      "how-to-use-azureml",
+      "automated-machine-learning"
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}

how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.yml

@@ -0,0 +1,4 @@
+name: auto-ml-forecasting-hierarchical-timeseries
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.yml

@@ -1,9 +0,0 @@
-name: automl-forecasting-function
-dependencies:
-- fbprophet==0.5
-- py-xgboost<=0.80
-- pip:
-  - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib

how-to-use-azureml/automated-machine-learning/forecasting-many-models/auto-ml-forecasting-many-models.ipynb

@@ -0,0 +1,857 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Many Models - Automated ML\n",
+        "**_Generate many models time series forecasts with Automated Machine Learning_**\n",
+        "\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "For this notebook we are using a synthetic dataset portraying sales data to predict the the quantity of a vartiety of product skus across several states, stores, and product categories.\n",
+        "\n",
+        "**NOTE: There are limits on how many runs we can do in parallel per workspace, and we currently recommend to set the parallelism to maximum of 320 runs per experiment per workspace. If users want to have more parallelism and increase this limit they might encounter Too Many Requests errors (HTTP 429).**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Prerequisites\n",
+        "You'll need to create a compute Instance by following the instructions in the [EnvironmentSetup.md](../Setup_Resources/EnvironmentSetup.md)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 1.0 Set up workspace, datastore, experiment"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "gather": {
+          "logged": 1613003526897
+        }
+      },
+      "outputs": [],
+      "source": [
+        "import azureml.core\n",
+        "from azureml.core import Workspace, Datastore\n",
+        "import pandas as pd\n",
+        "\n",
+        "# Set up your workspace\n",
+        "ws = Workspace.from_config()\n",
+        "ws.get_details()\n",
+        "\n",
+        "# Set up your datastores\n",
+        "dstore = ws.get_default_datastore()\n",
+        "\n",
+        "output = {}\n",
+        "output[\"SDK version\"] = azureml.core.VERSION\n",
+        "output[\"Subscription ID\"] = ws.subscription_id\n",
+        "output[\"Workspace\"] = ws.name\n",
+        "output[\"Resource Group\"] = ws.resource_group\n",
+        "output[\"Location\"] = ws.location\n",
+        "output[\"Default datastore name\"] = dstore.name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
+        "outputDf.T"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Choose an experiment"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "gather": {
+          "logged": 1613003540729
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "\n",
+        "experiment = Experiment(ws, \"automl-many-models\")\n",
+        "\n",
+        "print(\"Experiment name: \" + experiment.name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 2.0 Data\n",
+        "\n",
+        "This notebook uses simulated orange juice sales data to walk you through the process of training many models on Azure Machine Learning using Automated ML. \n",
+        "\n",
+        "The time series data used in this example was simulated based on the University of Chicago's Dominick's Finer Foods dataset which featured two years of sales of 3 different orange juice brands for individual stores. The full simulated dataset includes 3,991 stores with 3 orange juice brands each thus allowing 11,973 models to be trained to showcase the power of the many models pattern.\n",
+        "\n",
+        "  \n",
+        "In this notebook, two datasets will be created: one with all 11,973 files and one with only 10 files that can be used to quickly test and debug. For each dataset, you'll be walked through the process of:\n",
+        "\n",
+        "1. Registering the blob container as a Datastore to the Workspace\n",
+        "2. Registering a tabular dataset to the Workspace"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "source": [
+        "### 2.1 Data Preparation\n",
+        "The OJ data is available in the public blob container. The data is split to be used for training and for inferencing. For the current dataset, the data was split on time column ('WeekStarting') before and after '1992-5-28' .\n",
+        "\n",
+        "The container has\n",
+        "<ol>\n",
+        "    <li><b>'oj-data-tabular'</b> and <b>'oj-inference-tabular'</b> folders that contains training and inference data respectively for the 11,973 models. </li>\n",
+        "    <li>It also has <b>'oj-data-small-tabular'</b> and <b>'oj-inference-small-tabular'</b> folders that has training and inference data for 10 models.</li>\n",
+        "</ol>\n",
+        "\n",
+        "To create the [TabularDataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabular_dataset.tabulardataset?view=azure-ml-py) needed for the ParallelRunStep, you first need to register the blob container to the workspace."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "source": [
+        "<b> To use your own data, put your own data in a blobstore folder. As shown it can be one file or multiple files. We can then register datastore using that blob as shown below.\n",
+        "    \n",
+        "<h3> How sample data in blob store looks like</h3>\n",
+        "\n",
+        "['oj-data-tabular'](https://ms.portal.azure.com/#blade/Microsoft_Azure_Storage/ContainerMenuBlade/overview/storageAccountId/%2Fsubscriptions%2F102a16c3-37d3-48a8-9237-4c9b1e8e80e0%2FresourceGroups%2FAutoMLSampleNotebooksData%2Fproviders%2FMicrosoft.Storage%2FstorageAccounts%2Fautomlsamplenotebookdata/path/automl-sample-notebook-data/etag/%220x8D84EAA65DE50B7%22/defaultEncryptionScope/%24account-encryption-key/denyEncryptionScopeOverride//defaultId//publicAccessVal/Container)</b>\n",
+        "![image-4.png](mm-1.png)\n",
+        "\n",
+        "['oj-inference-tabular'](https://ms.portal.azure.com/#blade/Microsoft_Azure_Storage/ContainerMenuBlade/overview/storageAccountId/%2Fsubscriptions%2F102a16c3-37d3-48a8-9237-4c9b1e8e80e0%2FresourceGroups%2FAutoMLSampleNotebooksData%2Fproviders%2FMicrosoft.Storage%2FstorageAccounts%2Fautomlsamplenotebookdata/path/automl-sample-notebook-data/etag/%220x8D84EAA65DE50B7%22/defaultEncryptionScope/%24account-encryption-key/denyEncryptionScopeOverride//defaultId//publicAccessVal/Container)\n",
+        "![image-3.png](mm-2.png)\n",
+        "\n",
+        "['oj-data-small-tabular'](https://ms.portal.azure.com/#blade/Microsoft_Azure_Storage/ContainerMenuBlade/overview/storageAccountId/%2Fsubscriptions%2F102a16c3-37d3-48a8-9237-4c9b1e8e80e0%2FresourceGroups%2FAutoMLSampleNotebooksData%2Fproviders%2FMicrosoft.Storage%2FstorageAccounts%2Fautomlsamplenotebookdata/path/automl-sample-notebook-data/etag/%220x8D84EAA65DE50B7%22/defaultEncryptionScope/%24account-encryption-key/denyEncryptionScopeOverride//defaultId//publicAccessVal/Container)\n",
+        "\n",
+        "![image-5.png](mm-3.png)\n",
+        "\n",
+        "['oj-inference-small-tabular'](https://ms.portal.azure.com/#blade/Microsoft_Azure_Storage/ContainerMenuBlade/overview/storageAccountId/%2Fsubscriptions%2F102a16c3-37d3-48a8-9237-4c9b1e8e80e0%2FresourceGroups%2FAutoMLSampleNotebooksData%2Fproviders%2FMicrosoft.Storage%2FstorageAccounts%2Fautomlsamplenotebookdata/path/automl-sample-notebook-data/etag/%220x8D84EAA65DE50B7%22/defaultEncryptionScope/%24account-encryption-key/denyEncryptionScopeOverride//defaultId//publicAccessVal/Container)\n",
+        "![image-6.png](mm-4.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### 2.2 Register the blob container as DataStore\n",
+        "\n",
+        "A Datastore is a place where data can be stored that is then made accessible to a compute either by means of mounting or copying the data to the compute target.\n",
+        "\n",
+        "Please refer to [Datastore](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore(class)?view=azure-ml-py) documentation on how to access data from Datastore.\n",
+        "\n",
+        "In this next step, we will be registering blob storage as datastore to the Workspace."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Datastore\n",
+        "\n",
+        "# Please change the following to point to your own blob container and pass in account_key\n",
+        "blob_datastore_name = \"automl_many_models\"\n",
+        "container_name = \"automl-sample-notebook-data\"\n",
+        "account_name = \"automlsamplenotebookdata\"\n",
+        "\n",
+        "oj_datastore = Datastore.register_azure_blob_container(\n",
+        "    workspace=ws,\n",
+        "    datastore_name=blob_datastore_name,\n",
+        "    container_name=container_name,\n",
+        "    account_name=account_name,\n",
+        "    create_if_not_exists=True,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### 2.3 Using tabular datasets \n",
+        "\n",
+        "Now that the datastore is available from the Workspace, [TabularDataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabular_dataset.tabulardataset?view=azure-ml-py) can be created. Datasets in Azure Machine Learning are references to specific data in a Datastore.  We are using TabularDataset, so that users who have their data which can be in one or many files (*.parquet or *.csv) and have not split up data according to group columns needed for training, can do so using out of box support for 'partiion_by' feature of TabularDataset shown in section 5.0 below."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "gather": {
+          "logged": 1613007017296
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core import Dataset\n",
+        "\n",
+        "ds_name_small = \"oj-data-small-tabular\"\n",
+        "input_ds_small = Dataset.Tabular.from_delimited_files(\n",
+        "    path=oj_datastore.path(ds_name_small + \"/\"), validate=False\n",
+        ")\n",
+        "\n",
+        "inference_name_small = \"oj-inference-small-tabular\"\n",
+        "inference_ds_small = Dataset.Tabular.from_delimited_files(\n",
+        "    path=oj_datastore.path(inference_name_small + \"/\"), validate=False\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### 2.4 Configure data with ``OutputFileDatasetConfig`` objects\n",
+        "This step shows how to configure output data from a pipeline step. One of the use cases for this step is when you want to do some preprocessing before feeding the data to training step. Intermediate data (or output of a step) is represented by an ``OutputFileDatasetConfig`` object. ``output_data`` is produced as the output of a step. Optionally, this data can be registered as a dataset by calling the ``register_on_complete`` method. If you create an ``OutputFileDatasetConfig`` in one step and use it as an input to another step, that data dependency between steps creates an implicit execution order in the pipeline.\n",
+        "\n",
+        "``OutputFileDatasetConfig`` objects return a directory, and by default write output to the default datastore of the workspace.\n",
+        "\n",
+        "Since instance creation for class ``OutputTabularDatasetConfig`` is not allowed, we first create an instance of this class. Then we use the ``read_parquet_files`` method to read the parquet file into ``OutputTabularDatasetConfig``."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.data.output_dataset_config import OutputFileDatasetConfig\n",
+        "\n",
+        "output_data = OutputFileDatasetConfig(\n",
+        "    name=\"processed_data\", destination=(dstore, \"outputdataset/{run-id}/{output-name}\")\n",
+        ").as_upload()\n",
+        "# output_data_dataset = output_data.register_on_complete(\n",
+        "#     name='processed_data', description = 'files from prev step')\n",
+        "output_data = output_data.read_parquet_files()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 3.0 Build the training pipeline\n",
+        "Now that the dataset, WorkSpace, and datastore are set up, we can put together a pipeline for training.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Choose a compute target\n",
+        "\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
+        "\n",
+        "\\*\\*Creation of AmlCompute takes approximately 5 minutes.**\n",
+        "\n",
+        "If the AmlCompute with that name is already in your workspace this code will skip the creation process. As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this [article](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-quotas) on the default limits and how to request more quota."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "gather": {
+          "logged": 1613007037308
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "\n",
+        "# Name your cluster\n",
+        "compute_name = \"mm-compute\"\n",
+        "\n",
+        "\n",
+        "if compute_name in ws.compute_targets:\n",
+        "    compute_target = ws.compute_targets[compute_name]\n",
+        "    if compute_target and type(compute_target) is AmlCompute:\n",
+        "        print(\"Found compute target: \" + compute_name)\n",
+        "else:\n",
+        "    print(\"Creating a new compute target...\")\n",
+        "    provisioning_config = AmlCompute.provisioning_configuration(\n",
+        "        vm_size=\"STANDARD_D16S_V3\", max_nodes=20\n",
+        "    )\n",
+        "    # Create the compute target\n",
+        "    compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n",
+        "\n",
+        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
+        "    # If no min node count is provided it will use the scale settings for the cluster\n",
+        "    compute_target.wait_for_completion(\n",
+        "        show_output=True, min_node_count=None, timeout_in_minutes=20\n",
+        "    )\n",
+        "\n",
+        "    # For a more detailed view of current cluster status, use the 'status' property\n",
+        "    print(compute_target.status.serialize())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Configure the training run's environment\n",
+        "The next step is making sure that the remote training run has all the dependencies needed by the training steps. Dependencies and the runtime context are set by creating and configuring a RunConfiguration object.\n",
+        "\n",
+        "The code below shows two options for handling dependencies. As presented, with ``USE_CURATED_ENV = True``, the configuration is based on a [curated environment](https://docs.microsoft.com/en-us/azure/machine-learning/resource-curated-environments). Curated environments have prebuilt Docker images in the [Microsoft Container Registry](https://hub.docker.com/publishers/microsoftowner). For more information, see [Azure Machine Learning curated environments](https://docs.microsoft.com/en-us/azure/machine-learning/resource-curated-environments).\n",
+        "\n",
+        "The path taken if you change ``USE_CURATED_ENV`` to False shows the pattern for explicitly setting your dependencies. In that scenario, a new custom Docker image will be created and registered in an Azure Container Registry within your resource group (see [Introduction to private Docker container registries in Azure](https://docs.microsoft.com/en-us/azure/container-registry/container-registry-intro)). Building and registering this image can take quite a few minutes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.runconfig import RunConfiguration\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "from azureml.core import Environment\n",
+        "\n",
+        "aml_run_config = RunConfiguration()\n",
+        "aml_run_config.target = compute_target\n",
+        "\n",
+        "USE_CURATED_ENV = True\n",
+        "if USE_CURATED_ENV:\n",
+        "    curated_environment = Environment.get(\n",
+        "        workspace=ws, name=\"AzureML-sklearn-0.24-ubuntu18.04-py37-cpu\"\n",
+        "    )\n",
+        "    aml_run_config.environment = curated_environment\n",
+        "else:\n",
+        "    aml_run_config.environment.python.user_managed_dependencies = False\n",
+        "\n",
+        "    # Add some packages relied on by data prep step\n",
+        "    aml_run_config.environment.python.conda_dependencies = CondaDependencies.create(\n",
+        "        conda_packages=[\"pandas\", \"scikit-learn\"],\n",
+        "        pip_packages=[\"azureml-sdk\", \"azureml-dataset-runtime[fuse,pandas]\"],\n",
+        "        pin_sdk_version=False,\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Set up training parameters\n",
+        "\n",
+        "This dictionary defines the AutoML and many models settings. For this forecasting task we need to define several settings inncluding the name of the time column, the maximum forecast horizon, and the partition column name definition.\n",
+        "\n",
+        "| Property                           | Description|\n",
+        "| :---------------                   | :------------------- |\n",
+        "| **task**                           | forecasting |\n",
+        "| **primary_metric**                 | This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i> |\n",
+        "| **blocked_models**                 | Blocked models won't be used by AutoML. |\n",
+        "| **iteration_timeout_minutes**      | Maximum amount of time in minutes that the model can train. This is optional but provides customers with greater control on exit criteria. |\n",
+        "| **iterations**                     | Number of models to train. This is optional but provides customers with greater control on exit criteria. |\n",
+        "| **experiment_timeout_hours**       | Maximum amount of time in hours that the experiment can take before it terminates. This is optional but provides customers with greater control on exit criteria. |\n",
+        "| **label_column_name**              | The name of the label column. |\n",
+        "| **forecast_horizon**               | The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly). Periods are inferred from your data. |\n",
+        "| **n_cross_validations**            | Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way. |\n",
+        "| **enable_early_stopping**          | Flag to enable early termination if the score is not improving in the short term. |\n",
+        "| **time_column_name**               | The name of your time column. |\n",
+        "| **enable_engineered_explanations** | Engineered feature explanations will be downloaded if enable_engineered_explanations flag is set to True. By default it is set to False to save storage space. |\n",
+        "| **time_series_id_column_names**     | The column names used to uniquely identify timeseries in data that has multiple rows with the same timestamp. |\n",
+        "| **track_child_runs**               | Flag to disable tracking of child runs. Only best run is tracked if the flag is set to False (this includes the model and metrics of the run). |\n",
+        "| **pipeline_fetch_max_batch_size**  | Determines how many pipelines (training algorithms) to fetch at a time for training, this helps reduce throttling when training at large scale. |\n",
+        "| **partition_column_names**         | The names of columns used to group your models. For timeseries, the groups must not split up individual time-series. That is, each group must contain one or more whole time-series. |"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "gather": {
+          "logged": 1613007061544
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.train.automl.runtime._many_models.many_models_parameters import (\n",
+        "    ManyModelsTrainParameters,\n",
+        ")\n",
+        "\n",
+        "partition_column_names = [\"Store\", \"Brand\"]\n",
+        "automl_settings = {\n",
+        "    \"task\": \"forecasting\",\n",
+        "    \"primary_metric\": \"normalized_root_mean_squared_error\",\n",
+        "    \"iteration_timeout_minutes\": 10,  # This needs to be changed based on the dataset. We ask customer to explore how long training is taking before settings this value\n",
+        "    \"iterations\": 15,\n",
+        "    \"experiment_timeout_hours\": 0.25,\n",
+        "    \"label_column_name\": \"Quantity\",\n",
+        "    \"n_cross_validations\": 3,\n",
+        "    \"time_column_name\": \"WeekStarting\",\n",
+        "    \"drop_column_names\": \"Revenue\",\n",
+        "    \"forecast_horizon\": 6,\n",
+        "    \"time_series_id_column_names\": partition_column_names,\n",
+        "    \"track_child_runs\": False,\n",
+        "}\n",
+        "\n",
+        "mm_paramters = ManyModelsTrainParameters(\n",
+        "    automl_settings=automl_settings, partition_column_names=partition_column_names\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Construct your pipeline steps\n",
+        "Once you have the compute resource and environment created, you're ready to define your pipeline's steps. There are many built-in steps available via the Azure Machine Learning SDK, as you can see on the [reference documentation for the azureml.pipeline.steps package](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps?view=azure-ml-py). The most flexible class is [PythonScriptStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.python_script_step.pythonscriptstep?view=azure-ml-py), which runs a Python script.\n",
+        "\n",
+        "Your data preparation code is in a subdirectory (in this example, \"data_preprocessing_tabular.py\" in the directory \"./scripts\"). As part of the pipeline creation process, this directory is zipped and uploaded to the compute_target and the step runs the script specified as the value for ``script_name``.\n",
+        "\n",
+        "The ``arguments`` values specify the inputs and outputs of the step. In the example below, the baseline data is the ``input_ds_small`` dataset. The script data_preprocessing_tabular.py does whatever data-transformation tasks are appropriate to the task at hand and outputs the data to ``output_data``, of type ``OutputFileDatasetConfig``. For more information, see [Moving data into and between ML pipeline steps (Python)](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-move-data-in-out-of-pipelines). The step will run on the machine defined by ``compute_target``, using the configuration ``aml_run_config``.\n",
+        "\n",
+        "Reuse of previous results (``allow_reuse``) is key when using pipelines in a collaborative environment since eliminating unnecessary reruns offers agility. Reuse is the default behavior when the ``script_name``, ``inputs``, and the parameters of a step remain the same. When reuse is allowed, results from the previous run are immediately sent to the next step. If ``allow_reuse`` is set to False, a new run will always be generated for this step during pipeline execution.\n",
+        "\n",
+        "> Note that we only support partitioned FileDataset and TabularDataset without partition when using such output as input."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.steps import PythonScriptStep\n",
+        "\n",
+        "dataprep_source_dir = \"./scripts\"\n",
+        "entry_point = \"data_preprocessing_tabular.py\"\n",
+        "ds_input = input_ds_small.as_named_input(\"train_10_models\")\n",
+        "\n",
+        "data_prep_step = PythonScriptStep(\n",
+        "    script_name=entry_point,\n",
+        "    source_directory=dataprep_source_dir,\n",
+        "    arguments=[\"--input\", ds_input, \"--output\", output_data],\n",
+        "    compute_target=compute_target,\n",
+        "    runconfig=aml_run_config,\n",
+        "    allow_reuse=False,\n",
+        ")\n",
+        "\n",
+        "input_ds_small = output_data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Set up many models pipeline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Parallel run step is leveraged to train multiple models at once. To configure the ParallelRunConfig you will need to determine the appropriate number of workers and nodes for your use case. The process_count_per_node is based off the number of cores of the compute VM. The node_count will determine the number of master nodes to use, increasing the node count will speed up the training process.\n",
+        "\n",
+        "| Property                           | Description|\n",
+        "| :---------------                   | :------------------- |\n",
+        "| **experiment**                     | The experiment used for training. |\n",
+        "| **train_data**                     | The file dataset to be used as input to the training run. |\n",
+        "| **node_count**                     | The number of compute nodes to be used for running the user script. We recommend to start with 3 and increase the node_count if the training time is taking too long. |\n",
+        "| **process_count_per_node**         | Process count per node, we recommend 2:1 ratio for number of cores: number of processes per node. eg. If node has 16 cores then configure 8 or less process count per node or optimal performance. |\n",
+        "| **train_pipeline_parameters**      | The set of configuration parameters defined in the previous section. |\n",
+        "\n",
+        "Calling this method will create a new aggregated dataset which is generated dynamically on pipeline execution."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.automl.pipeline.steps import AutoMLPipelineBuilder\n",
+        "\n",
+        "\n",
+        "training_pipeline_steps = AutoMLPipelineBuilder.get_many_models_train_steps(\n",
+        "    experiment=experiment,\n",
+        "    train_data=input_ds_small,\n",
+        "    compute_target=compute_target,\n",
+        "    node_count=2,\n",
+        "    process_count_per_node=8,\n",
+        "    run_invocation_timeout=920,\n",
+        "    train_pipeline_parameters=mm_paramters,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import Pipeline\n",
+        "\n",
+        "training_pipeline = Pipeline(ws, steps=training_pipeline_steps)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Submit the pipeline to run\n",
+        "Next we submit our pipeline to run. The whole training pipeline takes about 40m using a STANDARD_D16S_V3 VM with our current ParallelRunConfig setting."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "training_run = experiment.submit(training_pipeline)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "training_run.wait_for_completion(show_output=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Check the run status, if training_run is in completed state, continue to forecasting. If training_run is in another state, check the portal for failures."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 5.0 Publish and schedule the train pipeline (Optional)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 5.1 Publish the pipeline\n",
+        "\n",
+        "Once you have a pipeline you're happy with, you can publish a pipeline so you can call it programmatically later on. See this [tutorial](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-your-first-pipeline#publish-a-pipeline) for additional information on publishing and calling pipelines."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# published_pipeline = training_pipeline.publish(name = 'automl_train_many_models',\n",
+        "#                                                description = 'train many models',\n",
+        "#                                                version = '1',\n",
+        "#                                                continue_on_step_failure = False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 7.2 Schedule the pipeline\n",
+        "You can also [schedule the pipeline](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-schedule-pipelines) to run on a time-based or change-based schedule. This could be used to automatically retrain models every month or based on another trigger such as data drift."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# from azureml.pipeline.core import Schedule, ScheduleRecurrence\n",
+        "\n",
+        "# training_pipeline_id = published_pipeline.id\n",
+        "\n",
+        "# recurrence = ScheduleRecurrence(frequency=\"Month\", interval=1, start_time=\"2020-01-01T09:00:00\")\n",
+        "# recurring_schedule = Schedule.create(ws, name=\"automl_training_recurring_schedule\",\n",
+        "#                                      description=\"Schedule Training Pipeline to run on the first day of every month\",\n",
+        "#                                      pipeline_id=training_pipeline_id,\n",
+        "#                                      experiment_name=experiment.name,\n",
+        "#                                      recurrence=recurrence)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 6.0 Forecasting"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Set up output dataset for inference data\n",
+        "Output of inference can be represented as [OutputFileDatasetConfig](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.output_dataset_config.outputdatasetconfig?view=azure-ml-py) object and OutputFileDatasetConfig can be registered as a dataset. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.data import OutputFileDatasetConfig\n",
+        "\n",
+        "output_inference_data_ds = OutputFileDatasetConfig(\n",
+        "    name=\"many_models_inference_output\", destination=(dstore, \"oj/inference_data/\")\n",
+        ").register_on_complete(name=\"oj_inference_data_ds\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "For many models we need to provide the ManyModelsInferenceParameters object.\n",
+        "\n",
+        "#### ManyModelsInferenceParameters arguments\n",
+        "| Property                           | Description|\n",
+        "| :---------------                   | :------------------- |\n",
+        "| **partition_column_names**         | List of column names that identifies groups.                                           |\n",
+        "| **target_column_name**             | \\[Optional] Column name only if the inference dataset has the target. |\n",
+        "| **time_column_name**               | \\[Optional] Column name only if it is timeseries. |\n",
+        "| **many_models_run_id**             | \\[Optional] Many models run id where models were trained. |\n",
+        "\n",
+        "#### get_many_models_batch_inference_steps arguments\n",
+        "| Property                           | Description|\n",
+        "| :---------------                   | :------------------- |\n",
+        "| **experiment**                     | The experiment used for inference run. |\n",
+        "| **inference_data**                 | The data to use for inferencing. It should be the same schema as used for training.\n",
+        "| **compute_target** The compute target that runs the inference pipeline.|\n",
+        "| **node_count**                     | The number of compute nodes to be used for running the user script. We recommend to start with the number of cores per node (varies by compute sku). |\n",
+        "| **process_count_per_node** The number of processes per node.\n",
+        "| **train_run_id**                   | \\[Optional] The run id of the hierarchy training, by default it is the latest successful training many model run in the experiment. |\n",
+        "| **train_experiment_name**          | \\[Optional] The train experiment that contains the train pipeline. This one is only needed when the train pipeline is not in the same experiement as the inference pipeline. |\n",
+        "| **process_count_per_node**         | \\[Optional] The number of processes per node, by default it's 4. |"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.automl.pipeline.steps import AutoMLPipelineBuilder\n",
+        "from azureml.train.automl.runtime._many_models.many_models_parameters import (\n",
+        "    ManyModelsInferenceParameters,\n",
+        ")\n",
+        "\n",
+        "mm_parameters = ManyModelsInferenceParameters(\n",
+        "    partition_column_names=[\"Store\", \"Brand\"],\n",
+        "    time_column_name=\"WeekStarting\",\n",
+        "    target_column_name=\"Quantity\",\n",
+        ")\n",
+        "\n",
+        "inference_steps = AutoMLPipelineBuilder.get_many_models_batch_inference_steps(\n",
+        "    experiment=experiment,\n",
+        "    inference_data=inference_ds_small,\n",
+        "    node_count=2,\n",
+        "    process_count_per_node=8,\n",
+        "    compute_target=compute_target,\n",
+        "    run_invocation_timeout=300,\n",
+        "    output_datastore=output_inference_data_ds,\n",
+        "    train_run_id=training_run.id,\n",
+        "    train_experiment_name=training_run.experiment.name,\n",
+        "    inference_pipeline_parameters=mm_parameters,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import Pipeline\n",
+        "\n",
+        "inference_pipeline = Pipeline(ws, steps=inference_steps)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "inference_run = experiment.submit(inference_pipeline)\n",
+        "inference_run.wait_for_completion(show_output=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Retrieve results\n",
+        "\n",
+        "The forecasting pipeline forecasts the orange juice quantity for a Store by Brand. The pipeline returns one file with the predictions for each store and outputs the result to the forecasting_output Blob container. The details of the blob container is listed in 'forecasting_output.txt' under Outputs+logs. \n",
+        "\n",
+        "The following code snippet:\n",
+        "1. Downloads the contents of the output folder that is passed in the parallel run step \n",
+        "2. Reads the parallel_run_step.txt file that has the predictions as pandas dataframe and \n",
+        "3. Displays the top 10 rows of the predictions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.automl.pipeline.steps.utilities import get_output_from_mm_pipeline\n",
+        "\n",
+        "forecasting_results_name = \"forecasting_results\"\n",
+        "forecasting_output_name = \"many_models_inference_output\"\n",
+        "forecast_file = get_output_from_mm_pipeline(\n",
+        "    inference_run, forecasting_results_name, forecasting_output_name\n",
+        ")\n",
+        "df = pd.read_csv(forecast_file, delimiter=\" \", header=None)\n",
+        "df.columns = [\n",
+        "    \"Week Starting\",\n",
+        "    \"Store\",\n",
+        "    \"Brand\",\n",
+        "    \"Quantity\",\n",
+        "    \"Advert\",\n",
+        "    \"Price\",\n",
+        "    \"Revenue\",\n",
+        "    \"Predicted\",\n",
+        "]\n",
+        "print(\n",
+        "    \"Prediction has \", df.shape[0], \" rows. Here the first 10 rows are being displayed.\"\n",
+        ")\n",
+        "df.head(10)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 7.0 Publish and schedule the inference pipeline (Optional)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 7.1 Publish the pipeline\n",
+        "\n",
+        "Once you have a pipeline you're happy with, you can publish a pipeline so you can call it programmatically later on. See this [tutorial](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-your-first-pipeline#publish-a-pipeline) for additional information on publishing and calling pipelines."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# published_pipeline_inf = inference_pipeline.publish(name = 'automl_forecast_many_models',\n",
+        "#                                                     description = 'forecast many models',\n",
+        "#                                                     version = '1',\n",
+        "#                                                     continue_on_step_failure = False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 7.2 Schedule the pipeline\n",
+        "You can also [schedule the pipeline](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-schedule-pipelines) to run on a time-based or change-based schedule. This could be used to automatically retrain or forecast models every month or based on another trigger such as data drift."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# from azureml.pipeline.core import Schedule, ScheduleRecurrence\n",
+        "\n",
+        "# forecasting_pipeline_id = published_pipeline.id\n",
+        "\n",
+        "# recurrence = ScheduleRecurrence(frequency=\"Month\", interval=1, start_time=\"2020-01-01T09:00:00\")\n",
+        "# recurring_schedule = Schedule.create(ws, name=\"automl_forecasting_recurring_schedule\",\n",
+        "#                             description=\"Schedule Forecasting Pipeline to run on the first day of every week\",\n",
+        "#                             pipeline_id=forecasting_pipeline_id,\n",
+        "#                             experiment_name=experiment.name,\n",
+        "#                             recurrence=recurrence)"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "jialiu"
+      }
+    ],
+    "categories": [
+      "how-to-use-azureml",
+      "automated-machine-learning"
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}

how-to-use-azureml/automated-machine-learning/forecasting-many-models/auto-ml-forecasting-many-models.yml

@@ -0,0 +1,4 @@
+name: auto-ml-forecasting-many-models
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb

@@ -24,20 +24,20 @@
         "_**Orange Juice Sales Forecasting**_\n",
         "\n",
         "## Contents\n",
-        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#Setup)\n",
-        "1. [Compute](#Compute)\n",
-        "1. [Data](#Data)\n",
-        "1. [Train](#Train)\n",
-        "1. [Predict](#Predict)\n",
-        "1. [Operationalize](#Operationalize)"
+        "1. [Introduction](#introduction)\n",
+        "1. [Setup](#setup)\n",
+        "1. [Compute](#compute)\n",
+        "1. [Data](#data)\n",
+        "1. [Train](#train)\n",
+        "1. [Forecast](#forecast)\n",
+        "1. [Operationalize](#operationalize)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Introduction\n",
+        "## Introduction<a id=\"introduction\"></a>\n",
         "In this example, we use AutoML to train, select, and operationalize a time-series forecasting model for multiple time-series.\n",
         "\n",
         "Make sure you have executed the [configuration notebook](../../../configuration.ipynb) before running this notebook.\n",
@@ -49,7 +49,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Setup"
+        "## Setup<a id=\"setup\"></a>"
       ]
     },
     {
@@ -58,16 +58,33 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "import azureml.core\n",
-        "import pandas as pd\n",
-        "import numpy as np\n",
+        "import json\n",
         "import logging\n",
         "\n",
-        "from azureml.core.workspace import Workspace\n",
+        "import azureml.core\n",
+        "import pandas as pd\n",
+        "from azureml.automl.core.featurization import FeaturizationConfig\n",
         "from azureml.core.experiment import Experiment\n",
+        "from azureml.core.workspace import Workspace\n",
         "from azureml.train.automl import AutoMLConfig"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This notebook is compatible with Azure ML SDK version 1.35.0 or later."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -84,20 +101,20 @@
         "ws = Workspace.from_config()\n",
         "\n",
         "# choose a name for the run history container in the workspace\n",
-        "experiment_name = 'automl-ojforecasting'\n",
+        "experiment_name = \"automl-ojforecasting\"\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
         "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['SKU'] = ws.sku\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Run History Name'] = experiment_name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "output[\"Subscription ID\"] = ws.subscription_id\n",
+        "output[\"Workspace\"] = ws.name\n",
+        "output[\"SKU\"] = ws.sku\n",
+        "output[\"Resource Group\"] = ws.resource_group\n",
+        "output[\"Location\"] = ws.location\n",
+        "output[\"Run History Name\"] = experiment_name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
+        "pd.set_option(\"display.max_colwidth\", None)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"
       ]
     },
@@ -105,11 +122,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Compute\n",
+        "## Compute<a id=\"compute\"></a>\n",
         "You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
+        "\n",
         "#### Creation of AmlCompute takes approximately 5 minutes. \n",
         "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
-        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this article on the default limits and how to request more quota."
+        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
       ]
     },
     {
@@ -118,42 +138,30 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
-        "# Choose a name for your cluster.\n",
-        "amlcompute_cluster_name = \"cpu-cluster-oj\"\n",
+        "# Choose a name for your CPU cluster\n",
+        "amlcompute_cluster_name = \"oj-cluster\"\n",
         "\n",
-        "found = False\n",
-        "# Check if this compute target already exists in the workspace.\n",
-        "cts = ws.compute_targets\n",
-        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
-        "    found = True\n",
-        "    print('Found existing compute target.')\n",
-        "    compute_target = cts[amlcompute_cluster_name]\n",
-        "    \n",
-        "if not found:\n",
-        "    print('Creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-        "                                                                #vm_priority = 'lowpriority', # optional\n",
-        "                                                                max_nodes = 6)\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
+        "    print(\"Found existing cluster, use it.\")\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(\n",
+        "        vm_size=\"STANDARD_D12_V2\", max_nodes=6\n",
+        "    )\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",
-        "    # Create the cluster.\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "    \n",
-        "print('Checking cluster status...')\n",
-        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "    \n",
-        "# For a more detailed view of current AmlCompute status, use get_status()."
+        "compute_target.wait_for_completion(show_output=True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Data\n",
+        "## Data<a id=\"data\"></a>\n",
         "You are now ready to load the historical orange juice sales data. We will load the CSV file into a plain pandas DataFrame; the time column in the CSV is called _WeekStarting_, so it will be specially parsed into the datetime type."
       ]
     },
@@ -163,8 +171,12 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "time_column_name = 'WeekStarting'\n",
+        "time_column_name = \"WeekStarting\"\n",
         "data = pd.read_csv(\"dominicks_OJ.csv\", parse_dates=[time_column_name])\n",
+        "\n",
+        "# Drop the columns 'logQuantity' as it is a leaky feature.\n",
+        "data.drop(\"logQuantity\", axis=1, inplace=True)\n",
+        "\n",
         "data.head()"
       ]
     },
@@ -174,7 +186,7 @@
       "source": [
         "Each row in the DataFrame holds a quantity of weekly sales for an OJ brand at a single store. The data also includes the sales price, a flag indicating if the OJ brand was advertised in the store that week, and some customer demographic information based on the store location. For historical reasons, the data also include the logarithm of the sales quantity. The Dominick's grocery data is commonly used to illustrate econometric modeling techniques where logarithms of quantities are generally preferred.    \n",
         "\n",
-        "The task is now to build a time-series model for the _Quantity_ column. It is important to note that this dataset is comprised of many individual time-series - one for each unique combination of _Store_ and _Brand_. To distinguish the individual time-series, we thus define the **grain** - the columns whose values determine the boundaries between time-series: "
+        "The task is now to build a time-series model for the _Quantity_ column. It is important to note that this dataset is comprised of many individual time-series - one for each unique combination of _Store_ and _Brand_. To distinguish the individual time-series, we define the **time_series_id_column_names** - the columns whose values determine the boundaries between time-series: "
       ]
     },
     {
@@ -183,9 +195,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "grain_column_names = ['Store', 'Brand']\n",
-        "nseries = data.groupby(grain_column_names).ngroups\n",
-        "print('Data contains {0} individual time-series.'.format(nseries))"
+        "time_series_id_column_names = [\"Store\", \"Brand\"]\n",
+        "nseries = data.groupby(time_series_id_column_names).ngroups\n",
+        "print(\"Data contains {0} individual time-series.\".format(nseries))"
       ]
     },
     {
@@ -203,8 +215,8 @@
       "source": [
         "use_stores = [2, 5, 8]\n",
         "data_subset = data[data.Store.isin(use_stores)]\n",
-        "nseries = data_subset.groupby(grain_column_names).ngroups\n",
-        "print('Data subset contains {0} individual time-series.'.format(nseries))"
+        "nseries = data_subset.groupby(time_series_id_column_names).ngroups\n",
+        "print(\"Data subset contains {0} individual time-series.\".format(nseries))"
       ]
     },
     {
@@ -212,7 +224,7 @@
       "metadata": {},
       "source": [
         "### Data Splitting\n",
-        "We now split the data into a training and a testing set for later forecast evaluation. The test set will contain the final 20 weeks of observed sales for each time-series. The splits should be stratified by series, so we use a group-by statement on the grain columns."
+        "We now split the data into a training and a testing set for later forecast evaluation. The test set will contain the final 20 weeks of observed sales for each time-series. The splits should be stratified by series, so we use a group-by statement on the time series identifier columns."
       ]
     },
     {
@@ -223,15 +235,18 @@
       "source": [
         "n_test_periods = 20\n",
         "\n",
-        "def split_last_n_by_grain(df, n):\n",
-        "    \"\"\"Group df by grain and split on last n rows for each group.\"\"\"\n",
-        "    df_grouped = (df.sort_values(time_column_name) # Sort by ascending time\n",
-        "                  .groupby(grain_column_names, group_keys=False))\n",
+        "\n",
+        "def split_last_n_by_series_id(df, n):\n",
+        "    \"\"\"Group df by series identifiers and split on last n rows for each group.\"\"\"\n",
+        "    df_grouped = df.sort_values(time_column_name).groupby(  # Sort by ascending time\n",
+        "        time_series_id_column_names, group_keys=False\n",
+        "    )\n",
         "    df_head = df_grouped.apply(lambda dfg: dfg.iloc[:-n])\n",
         "    df_tail = df_grouped.apply(lambda dfg: dfg.iloc[-n:])\n",
         "    return df_head, df_tail\n",
         "\n",
-        "train, test = split_last_n_by_grain(data_subset, n_test_periods)"
+        "\n",
+        "train, test = split_last_n_by_series_id(data_subset, n_test_periods)"
       ]
     },
     {
@@ -248,18 +263,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "train.to_csv (r'./dominicks_OJ_train.csv', index = None, header=True)\n",
-        "test.to_csv (r'./dominicks_OJ_test.csv', index = None, header=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
+        "from azureml.data.dataset_factory import TabularDatasetFactory\n",
+        "\n",
         "datastore = ws.get_default_datastore()\n",
-        "datastore.upload_files(files = ['./dominicks_OJ_train.csv', './dominicks_OJ_test.csv'], target_path = 'dataset/', overwrite = True,show_progress = True)"
+        "train_dataset = TabularDatasetFactory.register_pandas_dataframe(\n",
+        "    train, target=(datastore, \"dataset/\"), name=\"dominicks_OJ_train\"\n",
+        ")\n",
+        "test_dataset = TabularDatasetFactory.register_pandas_dataframe(\n",
+        "    test, target=(datastore, \"dataset/\"), name=\"dominicks_OJ_test\"\n",
+        ")"
       ]
     },
     {
@@ -269,16 +281,6 @@
         "### Create dataset for training"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.dataset import Dataset\n",
-        "train_dataset = Dataset.Tabular.from_delimited_files(path=datastore.path('dataset/dominicks_OJ_train.csv'))"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -297,11 +299,11 @@
         "For forecasting tasks, AutoML uses pre-processing and estimation steps that are specific to time-series. AutoML will undertake the following pre-processing steps:\n",
         "* Detect time-series sample frequency (e.g. hourly, daily, weekly) and create new records for absent time points to make the series regular. A regular time series has a well-defined frequency and has a value at every sample point in a contiguous time span \n",
         "* Impute missing values in the target (via forward-fill) and feature columns (using median column values) \n",
-        "* Create grain-based features to enable fixed effects across different series\n",
+        "* Create features based on time series identifiers to enable fixed effects across different series\n",
         "* Create time-based features to assist in learning seasonal patterns\n",
         "* Encode categorical variables to numeric quantities\n",
         "\n",
-        "In this notebook, AutoML will train a single, regression-type model across **all** time-series in a given training set. This allows the model to generalize across related series. If you're looking for training multiple models for different time-series, please check out the forecasting grouping notebook. \n",
+        "In this notebook, AutoML will train a single, regression-type model across **all** time-series in a given training set. This allows the model to generalize across related series. If you're looking for training multiple models for different time-series, please see the many-models notebook.\n",
         "\n",
         "You are almost ready to start an AutoML training job. First, we need to separate the target column from the rest of the DataFrame: "
       ]
@@ -312,20 +314,81 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "target_column_name = 'Quantity'"
+        "target_column_name = \"Quantity\""
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Train\n",
+        "## Customization\n",
         "\n",
-        "The AutoMLConfig object defines the settings and data for an AutoML training job. Here, we set necessary inputs like the task type, the number of AutoML iterations to try, the training data, and cross-validation parameters. \n",
+        "The featurization customization in forecasting is an advanced feature in AutoML which allows our customers to change the default forecasting featurization behaviors and column types through `FeaturizationConfig`. The supported scenarios include:\n",
         "\n",
-        "For forecasting tasks, there are some additional parameters that can be set: the name of the column holding the date/time, the grain column names, and the maximum forecast horizon. A time column is required for forecasting, while the grain is optional. If a grain is not given, AutoML assumes that the whole dataset is a single time-series. We also pass a list of columns to drop prior to modeling. The _logQuantity_ column is completely correlated with the target quantity, so it must be removed to prevent a target leak.\n",
+        "1. Column purposes update: Override feature type for the specified column. Currently supports DateTime, Categorical and Numeric. This customization can be used in the scenario that the type of the column cannot correctly reflect its purpose. Some numerical columns, for instance, can be treated as Categorical columns which need to be converted to categorical while some can be treated as epoch timestamp which need to be converted to datetime. To tell our SDK to correctly preprocess these columns, a configuration need to be add with the columns and their desired types.\n",
+        "2. Transformer parameters update: Currently supports parameter change for Imputer only. User can customize imputation methods. The supported imputing methods for target column are constant and ffill (forward fill). The supported imputing methods for feature columns are mean, median, most frequent, constant and ffill (forward fill). This customization can be used for the scenario that our customers know which imputation methods fit best to the input data. For instance, some datasets use NaN to represent 0 which the correct behavior should impute all the missing value with 0. To achieve this behavior, these columns need to be configured as constant imputation with `fill_value` 0.\n",
+        "3. Drop columns: Columns to drop from being featurized. These usually are the columns which are leaky or the columns contain no useful data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "sample-featurizationconfig-remarks"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "featurization_config = FeaturizationConfig()\n",
+        "# Force the CPWVOL5 feature to be numeric type.\n",
+        "featurization_config.add_column_purpose(\"CPWVOL5\", \"Numeric\")\n",
+        "# Fill missing values in the target column, Quantity, with zeros.\n",
+        "featurization_config.add_transformer_params(\n",
+        "    \"Imputer\", [\"Quantity\"], {\"strategy\": \"constant\", \"fill_value\": 0}\n",
+        ")\n",
+        "# Fill missing values in the INCOME column with median value.\n",
+        "featurization_config.add_transformer_params(\n",
+        "    \"Imputer\", [\"INCOME\"], {\"strategy\": \"median\"}\n",
+        ")\n",
+        "# Fill missing values in the Price column with forward fill (last value carried forward).\n",
+        "featurization_config.add_transformer_params(\"Imputer\", [\"Price\"], {\"strategy\": \"ffill\"})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Forecasting Parameters\n",
+        "To define forecasting parameters for your experiment training, you can leverage the ForecastingParameters class. The table below details the forecasting parameter we will be passing into our experiment.\n",
+        "\n",
+        "\n",
+        "|Property|Description|\n",
+        "|-|-|\n",
+        "|**time_column_name**|The name of your time column.|\n",
+        "|**forecast_horizon**|The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly).|\n",
+        "|**time_series_id_column_names**|The column names used to uniquely identify the time series in data that has multiple rows with the same timestamp. If the time series identifiers are not defined, the data set is assumed to be one time series.|\n",
+        "|**freq**|Forecast frequency. This optional parameter represents the period with which the forecast is desired, for example, daily, weekly, yearly, etc. Use this parameter for the correction of time series containing irregular data points or for padding of short time series. The frequency needs to be a pandas offset alias. Please refer to [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) for more information."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train<a id=\"train\"></a>\n",
+        "\n",
+        "The [AutoMLConfig](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig?view=azure-ml-py) object defines the settings and data for an AutoML training job. Here, we set necessary inputs like the task type, the number of AutoML iterations to try, the training data, and cross-validation parameters.\n",
+        "\n",
+        "For forecasting tasks, there are some additional parameters that can be set in the `ForecastingParameters` class: the name of the column holding the date/time, the timeseries id column names, and the maximum forecast horizon. A time column is required for forecasting, while the time_series_id is optional. If time_series_id columns are not given, AutoML assumes that the whole dataset is a single time-series. We also pass a list of columns to drop prior to modeling. The _logQuantity_ column is completely correlated with the target quantity, so it must be removed to prevent a target leak.\n",
+        "\n",
+        "The forecast horizon is given in units of the time-series frequency; for instance, the OJ series frequency is weekly, so a horizon of 20 means that a trained model will estimate sales up to 20 weeks beyond the latest date in the training data for each series. In this example, we set the forecast horizon to the number of samples per series in the test set (n_test_periods). Generally, the value of this parameter will be dictated by business needs. For example, a demand planning application that estimates the next month of sales should set the horizon according to suitable planning time-scales. Please see the [energy_demand notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand) for more discussion of forecast horizon.\n",
+        "\n",
+        "We note here that AutoML can sweep over two types of time-series models:\n",
+        "* Models that are trained for each series such as ARIMA and Facebook's Prophet.\n",
+        "* Models trained across multiple time-series using a regression approach.\n",
+        "\n",
+        "In the first case, AutoML loops over all time-series in your dataset and trains one model (e.g. AutoArima or Prophet, as the case may be) for each series. This can result in long runtimes to train these models if there are a lot of series in the data. One way to mitigate this problem is to fit models for different series in parallel if you have multiple compute cores available. To enable this behavior, set the `max_cores_per_iteration` parameter in your AutoMLConfig as shown in the example in the next cell. \n",
         "\n",
-        "The forecast horizon is given in units of the time-series frequency; for instance, the OJ series frequency is weekly, so a horizon of 20 means that a trained model will estimate sales up to 20 weeks beyond the latest date in the training data for each series. In this example, we set the maximum horizon to the number of samples per series in the test set (n_test_periods). Generally, the value of this parameter will be dictated by business needs. For example, a demand planning organizaion that needs to estimate the next month of sales would set the horizon accordingly. Please see the [energy_demand notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand) for more discussion of forecast horizon.\n",
         "\n",
         "Finally, a note about the cross-validation (CV) procedure for time-series data. AutoML uses out-of-sample error estimates to select a best pipeline/model, so it is important that the CV fold splitting is done correctly. Time-series can violate the basic statistical assumptions of the canonical K-Fold CV strategy, so AutoML implements a [rolling origin validation](https://robjhyndman.com/hyndsight/tscv/) procedure to create CV folds for time-series data. To use this procedure, you just need to specify the desired number of CV folds in the AutoMLConfig object. It is also possible to bypass CV and use your own validation set by setting the *validation_data* parameter of AutoMLConfig.\n",
         "\n",
@@ -344,10 +407,8 @@
         "|**enable_voting_ensemble**|Allow AutoML to create a Voting ensemble of the best performing models|\n",
         "|**enable_stack_ensemble**|Allow AutoML to create a Stack ensemble of the best performing models|\n",
         "|**debug_log**|Log file path for writing debugging information|\n",
-        "|**time_column_name**|Name of the datetime column in the input data|\n",
-        "|**grain_column_names**|Name(s) of the columns defining individual series in the input data|\n",
-        "|**drop_column_names**|Name(s) of columns to drop prior to modeling|\n",
-        "|**max_horizon**|Maximum desired forecast horizon in units of time-series frequency|"
+        "|**featurization**| 'auto' / 'off' / FeaturizationConfig Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used. Setting this enables AutoML to perform featurization on the input to handle *missing data*, and to perform some common *feature extraction*.|\n",
+        "|**max_cores_per_iteration**|Maximum number of cores to utilize per iteration. A value of -1 indicates all available cores should be used"
       ]
     },
     {
@@ -356,24 +417,30 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "time_series_settings = {\n",
-        "    'time_column_name': time_column_name,\n",
-        "    'grain_column_names': grain_column_names,\n",
-        "    'drop_column_names': ['logQuantity'],  # 'logQuantity' is a leaky feature, so we remove it.\n",
-        "    'max_horizon': n_test_periods\n",
-        "}\n",
+        "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
         "\n",
-        "automl_config = AutoMLConfig(task='forecasting',\n",
-        "                             debug_log='automl_oj_sales_errors.log',\n",
-        "                             primary_metric='normalized_mean_absolute_error',\n",
+        "forecasting_parameters = ForecastingParameters(\n",
+        "    time_column_name=time_column_name,\n",
+        "    forecast_horizon=n_test_periods,\n",
+        "    time_series_id_column_names=time_series_id_column_names,\n",
+        "    freq=\"W-THU\",  # Set the forecast frequency to be weekly (start on each Thursday)\n",
+        ")\n",
+        "\n",
+        "automl_config = AutoMLConfig(\n",
+        "    task=\"forecasting\",\n",
+        "    debug_log=\"automl_oj_sales_errors.log\",\n",
+        "    primary_metric=\"normalized_mean_absolute_error\",\n",
         "    experiment_timeout_hours=0.25,\n",
         "    training_data=train_dataset,\n",
         "    label_column_name=target_column_name,\n",
         "    compute_target=compute_target,\n",
         "    enable_early_stopping=True,\n",
+        "    featurization=featurization_config,\n",
         "    n_cross_validations=3,\n",
         "    verbosity=logging.INFO,\n",
-        "                             **time_series_settings)"
+        "    max_cores_per_iteration=-1,\n",
+        "    forecasting_parameters=forecasting_parameters,\n",
+        ")"
       ]
     },
     {
@@ -381,7 +448,7 @@
       "metadata": {},
       "source": [
         "You can now submit a new training run. Depending on the data and number of iterations this operation may take several minutes.\n",
-        "Information from each iteration will be printed to the console."
+        "Information from each iteration will be printed to the console.  Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
       ]
     },
     {
@@ -390,8 +457,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "remote_run = experiment.submit(automl_config, show_output=False)\n",
-        "remote_run"
+        "remote_run = experiment.submit(automl_config, show_output=False)"
       ]
     },
     {
@@ -407,8 +473,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Retrieve the Best Model\n",
-        "Each run within an Experiment stores serialized (i.e. pickled) pipelines from the AutoML iterations. We can now retrieve the pipeline with the best performance on the validation dataset:"
+        "### Retrieve the Best Run details\n",
+        "Below we retrieve the best Run object from among all the runs in the experiment."
       ]
     },
     {
@@ -417,18 +483,18 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "best_run, fitted_model = remote_run.get_output()\n",
-        "print(fitted_model.steps)\n",
-        "model_name = best_run.properties['model_name']"
+        "best_run = remote_run.get_best_child()\n",
+        "model_name = best_run.properties[\"model_name\"]\n",
+        "best_run"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Forecasting\n",
+        "## Transparency\n",
         "\n",
-        "Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. First, we remove the target values from the test set:"
+        "View updated featurization summary"
       ]
     },
     {
@@ -437,8 +503,37 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "X_test = test\n",
-        "y_test = X_test.pop(target_column_name).values"
+        "# Download the featurization summary JSON file locally\n",
+        "best_run.download_file(\n",
+        "    \"outputs/featurization_summary.json\", \"featurization_summary.json\"\n",
+        ")\n",
+        "\n",
+        "# Render the JSON as a pandas DataFrame\n",
+        "with open(\"featurization_summary.json\", \"r\") as f:\n",
+        "    records = json.load(f)\n",
+        "fs = pd.DataFrame.from_records(records)\n",
+        "\n",
+        "# View a summary of the featurization\n",
+        "fs[\n",
+        "    [\n",
+        "        \"RawFeatureName\",\n",
+        "        \"TypeDetected\",\n",
+        "        \"Dropped\",\n",
+        "        \"EngineeredFeatureCount\",\n",
+        "        \"Transformations\",\n",
+        "    ]\n",
+        "]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Forecast<a id=\"forecast\"></a>\n",
+        "\n",
+        "Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. We will do batch scoring on the test dataset which should have the same schema as training dataset.\n",
+        "\n",
+        "The inference will run on a remote compute. In this example, it will re-use the training compute."
       ]
     },
     {
@@ -447,7 +542,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "X_test.head()"
+        "test_experiment = Experiment(ws, experiment_name + \"_inference\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Retrieving forecasts from the model\n",
+        "We have created a function called `run_forecast` that submits the test data to the best model determined during the training run and retrieves forecasts. This function uses a helper script `forecasting_script` which is uploaded and expecuted on the remote compute."
       ]
     },
     {
@@ -463,19 +566,19 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# The featurized data, aligned to y, will also be returned.\n",
-        "# This contains the assumptions that were made in the forecast\n",
-        "# and helps align the forecast to the original data\n",
-        "y_predictions, X_trans = fitted_model.forecast(X_test)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "If you are used to scikit pipelines, perhaps you expected `predict(X_test)`. However, forecasting requires a more general interface that also supplies the past target `y` values. Please use `forecast(X,y)` as `predict(X)` is reserved for internal purposes on forecasting models.\n",
+        "from run_forecast import run_remote_inference\n",
         "\n",
-        "The [energy demand forecasting notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand) demonstrates the use of the forecast function in more detail in the context of using lags and rolling window features. "
+        "remote_run_infer = run_remote_inference(\n",
+        "    test_experiment=test_experiment,\n",
+        "    compute_target=compute_target,\n",
+        "    train_run=best_run,\n",
+        "    test_dataset=test_dataset,\n",
+        "    target_column_name=target_column_name,\n",
+        ")\n",
+        "remote_run_infer.wait_for_completion(show_output=False)\n",
+        "\n",
+        "# download the forecast file to the local machine\n",
+        "remote_run_infer.download_file(\"outputs/predictions.csv\", \"predictions.csv\")"
       ]
     },
     {
@@ -484,9 +587,9 @@
       "source": [
         "# Evaluate\n",
         "\n",
-        "To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE). \n",
+        "To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE). For more metrics that can be used for evaluation after training, please see [supported metrics](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#regressionforecasting-metrics), and [how to calculate residuals](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#residuals).\n",
         "\n",
-        "It is a good practice to always align the output explicitly to the input, as the count and order of the rows may have changed during transformations that span multiple rows."
+        "We'll add predictions and actuals into a single dataframe for convenience in calculating the metrics."
       ]
     },
     {
@@ -495,9 +598,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from forecasting_helper import align_outputs\n",
-        "\n",
-        "df_all = align_outputs(y_predictions, X_trans, X_test, y_test, target_column_name)"
+        "# load forecast data frame\n",
+        "fcst_df = pd.read_csv(\"predictions.csv\", parse_dates=[time_column_name])\n",
+        "fcst_df.head()"
       ]
     },
     {
@@ -506,26 +609,30 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.automl.core._vendor.automl.client.core.common import metrics\n",
+        "from azureml.automl.core.shared import constants\n",
+        "from azureml.automl.runtime.shared.score import scoring\n",
         "from matplotlib import pyplot as plt\n",
-        "from automl.client.core.common import constants\n",
         "\n",
-        "# use automl metrics module\n",
-        "scores = metrics.compute_metrics_regression(\n",
-        "    df_all['predicted'],\n",
-        "    df_all[target_column_name],\n",
-        "    list(constants.Metric.SCALAR_REGRESSION_SET),\n",
-        "    None, None, None)\n",
+        "# use automl scoring module\n",
+        "scores = scoring.score_regression(\n",
+        "    y_test=fcst_df[target_column_name],\n",
+        "    y_pred=fcst_df[\"predicted\"],\n",
+        "    metrics=list(constants.Metric.SCALAR_REGRESSION_SET),\n",
+        ")\n",
         "\n",
         "print(\"[Test data scores]\\n\")\n",
-        "for key, value in scores.items():    \n",
-        "    print('{}:   {:.3f}'.format(key, value))\n",
-        "    \n",
+        "for key, value in scores.items():\n",
+        "    print(\"{}:   {:.3f}\".format(key, value))\n",
+        "\n",
         "# Plot outputs\n",
         "%matplotlib inline\n",
-        "test_pred = plt.scatter(df_all[target_column_name], df_all['predicted'], color='b')\n",
-        "test_test = plt.scatter(df_all[target_column_name], df_all[target_column_name], color='g')\n",
-        "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
+        "test_pred = plt.scatter(fcst_df[target_column_name], fcst_df[\"predicted\"], color=\"b\")\n",
+        "test_test = plt.scatter(\n",
+        "    fcst_df[target_column_name], fcst_df[target_column_name], color=\"g\"\n",
+        ")\n",
+        "plt.legend(\n",
+        "    (test_pred, test_test), (\"prediction\", \"truth\"), loc=\"upper left\", fontsize=8\n",
+        ")\n",
         "plt.show()"
       ]
     },
@@ -533,7 +640,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Operationalize"
+        "# Operationalize<a id=\"operationalize\"></a>"
       ]
     },
     {
@@ -549,9 +656,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "description = 'AutoML OJ forecaster'\n",
+        "description = \"AutoML OJ forecaster\"\n",
         "tags = None\n",
-        "model = remote_run.register_model(model_name = model_name, description = description, tags = tags)\n",
+        "model = remote_run.register_model(\n",
+        "    model_name=model_name, description=description, tags=tags\n",
+        ")\n",
         "\n",
         "print(remote_run.model_id)"
       ]
@@ -571,8 +680,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "script_file_name = 'score_fcast.py'\n",
-        "best_run.download_file('outputs/scoring_file_v_1_0_0.py', script_file_name)"
+        "script_file_name = \"score_fcast.py\"\n",
+        "best_run.download_file(\"outputs/scoring_file_v_1_0_0.py\", script_file_name)"
       ]
     },
     {
@@ -593,15 +702,18 @@
         "from azureml.core.webservice import Webservice\n",
         "from azureml.core.model import Model\n",
         "\n",
-        "inference_config = InferenceConfig(environment = best_run.get_environment(), \n",
-        "                                   entry_script = script_file_name)\n",
+        "inference_config = InferenceConfig(\n",
+        "    environment=best_run.get_environment(), entry_script=script_file_name\n",
+        ")\n",
         "\n",
-        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
-        "                                               memory_gb = 2, \n",
-        "                                               tags = {'type': \"automl-forecasting\"},\n",
-        "                                               description = \"Automl forecasting sample service\")\n",
+        "aciconfig = AciWebservice.deploy_configuration(\n",
+        "    cpu_cores=2,\n",
+        "    memory_gb=4,\n",
+        "    tags={\"type\": \"automl-forecasting\"},\n",
+        "    description=\"Automl forecasting sample service\",\n",
+        ")\n",
         "\n",
-        "aci_service_name = 'automl-oj-forecast-01'\n",
+        "aci_service_name = \"automl-oj-forecast-01\"\n",
         "print(aci_service_name)\n",
         "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
         "aci_service.wait_for_deployment(True)\n",
@@ -631,19 +743,27 @@
       "outputs": [],
       "source": [
         "import json\n",
-        "X_query = X_test.copy()\n",
+        "\n",
+        "X_query = test.copy()\n",
+        "X_query.pop(target_column_name)\n",
         "# We have to convert datetime to string, because Timestamps cannot be serialized to JSON.\n",
         "X_query[time_column_name] = X_query[time_column_name].astype(str)\n",
         "# The Service object accept the complex dictionary, which is internally converted to JSON string.\n",
         "# The section 'data' contains the data frame in the form of dictionary.\n",
-        "test_sample = json.dumps({'data': X_query.to_dict(orient='records')})\n",
-        "response = aci_service.run(input_data = test_sample)\n",
+        "sample_quantiles = [0.025, 0.975]\n",
+        "test_sample = json.dumps(\n",
+        "    {\"data\": X_query.to_dict(orient=\"records\"), \"quantiles\": sample_quantiles}\n",
+        ")\n",
+        "response = aci_service.run(input_data=test_sample)\n",
         "# translate from networkese to datascientese\n",
-        "try: \n",
+        "try:\n",
         "    res_dict = json.loads(response)\n",
-        "    y_fcst_all = pd.DataFrame(res_dict['index'])\n",
-        "    y_fcst_all[time_column_name] = pd.to_datetime(y_fcst_all[time_column_name], unit = 'ms')\n",
-        "    y_fcst_all['forecast'] = res_dict['forecast']    \n",
+        "    y_fcst_all = pd.DataFrame(res_dict[\"index\"])\n",
+        "    y_fcst_all[time_column_name] = pd.to_datetime(\n",
+        "        y_fcst_all[time_column_name], unit=\"ms\"\n",
+        "    )\n",
+        "    y_fcst_all[\"forecast\"] = res_dict[\"forecast\"]\n",
+        "    y_fcst_all[\"prediction_interval\"] = res_dict[\"prediction_interval\"]\n",
         "except:\n",
         "    print(res_dict)"
       ]
@@ -670,7 +790,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "serv = Webservice(ws, 'automl-oj-forecast-01')\n",
+        "serv = Webservice(ws, \"automl-oj-forecast-01\")\n",
         "serv.delete()  # don't do it accidentally"
       ]
     }
@@ -678,7 +798,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "erwright"
+        "name": "jialiu"
       }
     ],
     "category": "tutorial",
@@ -713,7 +833,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.8"
+      "version": "3.6.9"
     },
     "tags": [
       "None"
@@ -721,5 +841,5 @@
     "task": "Forecasting"
   },
   "nbformat": 4,
-  "nbformat_minor": 2
+  "nbformat_minor": 4
 }

how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.yml

@@ -1,9 +1,4 @@
 name: auto-ml-forecasting-orange-juice-sales
 dependencies:
-- fbprophet==0.5
-- py-xgboost<=0.80
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib