update samples from Release-117 as a part of SDK release

Update samples from Release as a part of SDK release 1.36.0

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

README.md

@@ -1,77 +1,43 @@
-# Azure Machine Learning service example notebooks
+# Azure Machine Learning Python SDK notebooks
 
 > a community-driven repository of examples using mlflow for tracking can be found at https://github.com/Azure/azureml-examples
 
-This repository contains example notebooks demonstrating the [Azure Machine Learning](https://azure.microsoft.com/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.
+Welcome to the Azure Machine Learning Python SDK notebooks repository!
 
-![Azure ML Workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/media/concept-azure-machine-learning-architecture/workflow.png)
+## Getting started
 
+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.
 
-## 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.
+However, the notebooks can be run in any development environment with the correct `azureml` packages installed.
 
-## 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. 
-
-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: [track and monitor experiments](./how-to-use-azureml/track-and-monitor-experiments).
- * ...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/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) and [distributed training](./how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb).
- * ...deploy models as a realtime scoring service, first learn the basics by [deploying to Azure Container Instance](./how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.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: [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 ML and DL frameworks](./how-to-use-azureml/ml-frameworks) - Examples demonstrating how to build and train machine learning models at scale on Azure ML and perform hyperparameter tuning.
-- [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
-- [Reinforcement Learning](./how-to-use-azureml/reinforcement-learning) - Examples showing how to train reinforcement learning agents
-
----
-## 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:
- - [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 Microsoft 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 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.37.0 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/fairness/fairlearn-azureml-mitigation.ipynb

@@ -36,9 +36,9 @@
         "\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.github.io) 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.github.io/).\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.github.io/master/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",
+        "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",
@@ -46,9 +46,10 @@
         "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==0.4.6` (v0.5.0 will work with minor modifications)\n",
+        "* `fairlearn>=0.6.2` (pre-v0.5.0 will work with minor modifications)\n",
         "* `joblib`\n",
-        "* `shap`\n",
+        "* `liac-arff`\n",
+        "* `raiwidgets~=0.7.0`\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:"
       ]
@@ -85,10 +86,9 @@
       "outputs": [],
       "source": [
         "from fairlearn.reductions import GridSearch, DemographicParity, ErrorRate\n",
-        "from fairlearn.widget import FairlearnDashboard\n",
+        "from raiwidgets import FairnessDashboard\n",
         "\n",
         "from sklearn.compose import ColumnTransformer\n",
-        "from sklearn.datasets import fetch_openml\n",
         "from sklearn.impute import SimpleImputer\n",
         "from sklearn.linear_model import LogisticRegression\n",
         "from sklearn.model_selection import train_test_split\n",
@@ -112,9 +112,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from fairness_nb_utils import fetch_openml_with_retries\n",
+        "from fairness_nb_utils import fetch_census_dataset\n",
         "\n",
-        "data = fetch_openml_with_retries(data_id=1590)\n",
+        "data = fetch_census_dataset()\n",
         "    \n",
         "# Extract the items we want\n",
         "X_raw = data.data\n",
@@ -137,7 +137,7 @@
       "outputs": [],
       "source": [
         "A = X_raw[['sex','race']]\n",
-        "X_raw = X_raw.drop(labels=['sex', 'race'],axis = 1)"
+        "X_raw = X_raw.drop(labels=['sex', 'race'], axis = 1)"
       ]
     },
     {
@@ -257,9 +257,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "FairlearnDashboard(sensitive_features=A_test, sensitive_feature_names=['Sex', 'Race'],\n",
-        "                   y_true=y_test,\n",
-        "                   y_pred={\"unmitigated\": unmitigated_predictor.predict(X_test)})"
+        "FairnessDashboard(sensitive_features=A_test,\n",
+        "                  y_true=y_test,\n",
+        "                  y_pred={\"unmitigated\": unmitigated_predictor.predict(X_test)})"
       ]
     },
     {
@@ -312,8 +312,8 @@
         "sweep.fit(X_train, y_train,\n",
         "          sensitive_features=A_train.sex)\n",
         "\n",
-        "# For Fairlearn v0.5.0, need sweep.predictors_\n",
-        "predictors = sweep._predictors"
+        "# For Fairlearn pre-v0.5.0, need sweep._predictors\n",
+        "predictors = sweep.predictors_"
       ]
     },
     {
@@ -330,16 +330,14 @@
       "outputs": [],
       "source": [
         "errors, disparities = [], []\n",
-        "for m in predictors:\n",
-        "    classifier = lambda X: m.predict(X)\n",
-        "    \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(classifier)[0])\n",
-        "    disparities.append(disparity.gamma(classifier).max())\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",
@@ -388,10 +386,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "FairlearnDashboard(sensitive_features=A_test, \n",
-        "                   sensitive_feature_names=['Sex', 'Race'],\n",
-        "                   y_true=y_test.tolist(),\n",
-        "                   y_pred=predictions_dominant)"
+        "FairnessDashboard(sensitive_features=A_test, \n",
+        "                  y_true=y_test.tolist(),\n",
+        "                  y_pred=predictions_dominant)"
       ]
     },
     {
@@ -410,7 +407,7 @@
         "<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 `FairlearnDashboard` 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",
+        "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",
@@ -584,7 +581,7 @@
         "<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.github.io/) provides that discussion"
+        "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"
       ]
     },
     {

contrib/fairness/fairlearn-azureml-mitigation.yml

@@ -3,5 +3,7 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-contrib-fairness
-  - fairlearn==0.4.6
+  - fairlearn>=0.6.2
   - joblib
+  - liac-arff
+  - raiwidgets~=0.15.0

contrib/fairness/fairness_nb_utils.py

@@ -4,7 +4,13 @@
 
 """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
 
 
@@ -15,7 +21,7 @@ def fetch_openml_with_retries(data_id, max_retries=4, retry_delay=60):
             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:
+        except Exception as e:  # noqa: B902
             print("Download attempt failed with exception:")
             print(e)
             if i + 1 != max_retries:
@@ -26,3 +32,80 @@ def fetch_openml_with_retries(data_id, max_retries=4, retry_delay=60):
         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

@@ -30,7 +30,7 @@
         "1. [Training Models](#TrainingModels)\n",
         "1. [Logging in to AzureML](#LoginAzureML)\n",
         "1. [Registering the Models](#RegisterModels)\n",
-        "1. [Using the Fairlearn Dashboard](#LocalDashboard)\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",
@@ -48,9 +48,10 @@
         "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==0.4.6` (should also work with v0.5.0)\n",
+        "* `fairlearn>=0.6.2` (also works for pre-v0.5.0 with slight modifications)\n",
         "* `joblib`\n",
-        "* `shap`\n",
+        "* `liac-arff`\n",
+        "* `raiwidgets~=0.7.0`\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:"
       ]
@@ -88,7 +89,6 @@
       "source": [
         "from sklearn import svm\n",
         "from sklearn.compose import ColumnTransformer\n",
-        "from sklearn.datasets import fetch_openml\n",
         "from sklearn.impute import SimpleImputer\n",
         "from sklearn.linear_model import LogisticRegression\n",
         "from sklearn.model_selection import train_test_split\n",
@@ -110,9 +110,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from fairness_nb_utils import fetch_openml_with_retries\n",
+        "from fairness_nb_utils import fetch_census_dataset\n",
         "\n",
-        "data = fetch_openml_with_retries(data_id=1590)\n",
+        "data = fetch_census_dataset()\n",
         "    \n",
         "# Extract the items we want\n",
         "X_raw = data.data\n",
@@ -389,12 +389,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from fairlearn.widget import FairlearnDashboard\n",
+        "from raiwidgets import FairnessDashboard\n",
         "\n",
-        "FairlearnDashboard(sensitive_features=A_test, \n",
-        "                   sensitive_feature_names=['Sex', 'Race'],\n",
-        "                   y_true=y_test.tolist(),\n",
-        "                   y_pred=ys_pred)"
+        "FairnessDashboard(sensitive_features=A_test, \n",
+        "                  y_true=y_test.tolist(),\n",
+        "                  y_pred=ys_pred)"
       ]
     },
     {
@@ -404,7 +403,7 @@
         "<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 `FairlearnDashboard` 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",
+        "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",

contrib/fairness/upload-fairness-dashboard.yml

@@ -3,5 +3,7 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-contrib-fairness
-  - fairlearn==0.4.6
+  - fairlearn>=0.6.2
   - joblib
+  - liac-arff
+  - raiwidgets~=0.15.0

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

@@ -2,9 +2,8 @@ name: azure_automl
 dependencies:
   # The python interpreter version.
   # Currently Azure ML only supports 3.5.2 and later.
-- pip==20.2.4
+- pip==21.1.2
 - python>=3.5.2,<3.8
-- nb_conda
 - boto3==1.15.18
 - matplotlib==2.1.0
 - numpy==1.18.5
@@ -18,11 +17,13 @@ dependencies:
 - holidays==0.9.11
 - pytorch::pytorch=1.4.0
 - cudatoolkit=10.1.243
+- tornado==6.1.0
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.25.0
+  - azureml-widgets~=1.37.0
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.25.0/validated_win32_requirements.txt [--no-deps]
+  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.37.0/validated_win32_requirements.txt [--no-deps]
+  - arch==4.14

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

@@ -2,7 +2,7 @@ name: azure_automl
 dependencies:
   # The python interpreter version.
   # Currently Azure ML only supports 3.5.2 and later.
-- pip==20.2.4
+- pip==21.1.2
 - python>=3.5.2,<3.8
 - nb_conda
 - boto3==1.15.18
@@ -18,11 +18,13 @@ dependencies:
 - holidays==0.9.11
 - pytorch::pytorch=1.4.0
 - cudatoolkit=10.1.243
+- tornado==6.1.0
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.25.0
+  - azureml-widgets~=1.37.0
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.25.0/validated_linux_requirements.txt [--no-deps]
+  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.37.0/validated_linux_requirements.txt [--no-deps]
+  - arch==4.14

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

@@ -2,7 +2,7 @@ name: azure_automl
 dependencies:
   # The python interpreter version.
   # Currently Azure ML only supports 3.5.2 and later.
-- pip==20.2.4
+- pip==21.1.2
 - nomkl
 - python>=3.5.2,<3.8
 - nb_conda
@@ -19,11 +19,13 @@ dependencies:
 - holidays==0.9.11
 - pytorch::pytorch=1.4.0
 - cudatoolkit=9.0
+- tornado==6.1.0
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.25.0
+  - azureml-widgets~=1.37.0
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.25.0/validated_darwin_requirements.txt [--no-deps]
+  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.37.0/validated_darwin_requirements.txt [--no-deps]
+  - arch==4.14

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

@@ -3,7 +3,7 @@ import platform
 
 try:
     import conda
-except:
+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"')

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

@@ -77,6 +77,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "import json\n",
         "import logging\n",
         "\n",
         "from matplotlib import pyplot as plt\n",
@@ -86,7 +87,6 @@
         "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.interpret import ExplanationClient"
@@ -105,7 +105,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -165,6 +165,9 @@
       "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](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
@@ -187,7 +190,7 @@
         "    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_D2_V2',\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
         "                                                           max_nodes=6)\n",
         "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
         "\n",
@@ -408,7 +411,8 @@
       "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()"
       ]
     },
     {
@@ -417,7 +421,7 @@
       "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."
       ]
     },
     {
@@ -426,36 +430,14 @@
       "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 featuurization summary JSON file locally\n",
+        "best_run.download_file(\"outputs/featurization_summary.json\", \"featurization_summary.json\")\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)"
       ]
     },
     {
@@ -497,7 +479,7 @@
         "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()"
       ]
     },
     {
@@ -596,27 +578,21 @@
         "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",
+        "        result = json.load(f)\n",
+        "    return result\n",
         "\n",
-        "if python_version_compatible:\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",
@@ -633,7 +609,16 @@
         "\n",
         "### Retrieve the Best Model\n",
         "\n",
-        "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*."
+        "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??"
       ]
     },
     {
@@ -653,7 +638,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "best_run, fitted_model = remote_run.get_output()"
+        "best_run = remote_run.get_best_child()"
       ]
     },
     {
@@ -705,14 +690,12 @@
       "source": [
         "from azureml.core.model import InferenceConfig\n",
         "from azureml.core.webservice import AciWebservice\n",
-        "from azureml.core.webservice import Webservice\n",
         "from azureml.core.model import Model\n",
-        "from azureml.core.environment import Environment\n",
         "\n",
-        "inference_config = InferenceConfig(entry_script=script_file_name)\n",
+        "inference_config = InferenceConfig(environment = best_run.get_environment(), entry_script=script_file_name)\n",
         "\n",
-        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
-        "                                               memory_gb = 1, \n",
+        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 2, \n",
+        "                                               memory_gb = 2, \n",
         "                                               tags = {'area': \"bmData\", 'type': \"automl_classification\"}, \n",
         "                                               description = 'sample service for Automl Classification')\n",
         "\n",
@@ -789,7 +772,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "import json\n",
         "import requests\n",
         "\n",
         "X_test_json = X_test.to_json(orient='records')\n",
@@ -829,7 +811,6 @@
       "source": [
         "%matplotlib notebook\n",
         "from sklearn.metrics import confusion_matrix\n",
-        "import numpy as np\n",
         "import itertools\n",
         "\n",
         "cf =confusion_matrix(actual,y_pred)\n",

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

@@ -93,7 +93,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -127,6 +127,9 @@
       "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."
@@ -212,7 +215,7 @@
       "source": [
         "automl_settings = {\n",
         "    \"n_cross_validations\": 3,\n",
-        "    \"primary_metric\": 'average_precision_score_weighted',\n",
+        "    \"primary_metric\": 'AUC_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",

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

@@ -63,6 +63,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "import json\n",
         "import logging\n",
         "import os\n",
         "import shutil\n",
@@ -96,7 +97,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -138,6 +139,8 @@
         "## 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."
       ]
     },
@@ -160,7 +163,7 @@
         "    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(vm_size = \"STANDARD_NC6\", # CPU for BiLSTM, such as \"STANDARD_D2_V2\" \n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_NC6\", # CPU for BiLSTM, such as \"STANDARD_DS12_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",
@@ -281,8 +284,8 @@
       "outputs": [],
       "source": [
         "automl_settings = {\n",
-        "    \"experiment_timeout_minutes\": 20,\n",
-        "    \"primary_metric\": 'accuracy',\n",
+        "    \"experiment_timeout_minutes\": 30,\n",
+        "    \"primary_metric\": 'AUC_weighted',\n",
         "    \"max_concurrent_iterations\": num_nodes, \n",
         "    \"max_cores_per_iteration\": -1,\n",
         "    \"enable_dnn\": True,\n",
@@ -338,8 +341,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "You can test the model locally to get a feel of the input/output. 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 MachineLearningNotebooks folder here:\n",
-        "MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/automl_env.yml"
+        "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 MachineLearningNotebooks folder here:\n",
+        "MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/automl_env.yml\n"
       ]
     },
     {
@@ -348,7 +352,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "best_run, fitted_model = automl_run.get_output()"
+        "# Retrieve the best Run object\n",
+        "best_run = automl_run.get_best_child()"
       ]
     },
     {
@@ -364,10 +369,15 @@
       "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 featuurization summary JSON file locally\n",
+        "best_run.download_file(\"outputs/featurization_summary.json\", \"featurization_summary.json\")\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()"
       ]
     },
     {
@@ -485,7 +495,7 @@
       "outputs": [],
       "source": [
         "test_run = run_inference(test_experiment, compute_target, script_folder, best_dnn_run,\n",
-        "                         train_dataset, test_dataset, target_column_name, model_name)"
+        "                         test_dataset, target_column_name, model_name)"
       ]
     },
     {

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

@@ -5,7 +5,7 @@ from azureml.core.run import Run
 
 
 def run_inference(test_experiment, compute_target, script_folder, train_run,
-                  train_dataset, test_dataset, target_column_name, model_name):
+                  test_dataset, target_column_name, model_name):
 
     inference_env = train_run.get_environment()
 
@@ -16,7 +16,6 @@ def run_inference(test_experiment, compute_target, script_folder, train_run,
                         '--model_name': model_name
                     },
                     inputs=[
-                        train_dataset.as_named_input('train_data'),
                         test_dataset.as_named_input('test_data')
                     ],
                     compute_target=compute_target,

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

@@ -1,5 +1,6 @@
 import argparse
 
+import pandas as pd
 import numpy as np
 
 from sklearn.externals import joblib
@@ -32,22 +33,21 @@ model = joblib.load(model_path)
 run = Run.get_context()
 # get input dataset by name
 test_dataset = run.input_datasets['test_data']
-train_dataset = run.input_datasets['train_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]) \
                         .to_pandas_dataframe()
-y_train_df = test_dataset.with_timestamp_columns(None) \
-                         .keep_columns(columns=[target_column_name]) \
-                         .to_pandas_dataframe()
 
 predicted = model.predict_proba(X_test_df)
 
+if isinstance(predicted, pd.DataFrame):
+    predicted = predicted.values
+
 # Use the AutoML scoring module
-class_labels = np.unique(np.concatenate((y_train_df.values, y_test_df.values)))
 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,

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

@@ -81,7 +81,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -141,6 +141,9 @@
         "#### 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](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
@@ -163,7 +166,7 @@
         "    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(vm_size='STANDARD_D2_V2',\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
         "                                                           max_nodes=4)\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",
@@ -345,7 +348,7 @@
         "    \"iteration_timeout_minutes\": 10,\n",
         "    \"experiment_timeout_hours\": 0.25,\n",
         "    \"n_cross_validations\": 3,\n",
-        "    \"primary_metric\": 'r2_score',\n",
+        "    \"primary_metric\": 'normalized_root_mean_squared_error',\n",
         "    \"max_concurrent_iterations\": 3,\n",
         "    \"max_cores_per_iteration\": -1,\n",
         "    \"verbosity\": logging.INFO,\n",

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

@@ -31,7 +31,7 @@ try:
     model = Model(ws, args.model_name)
     last_train_time = model.created_time
     print("Model was last trained on {0}.".format(last_train_time))
-except Exception as e:
+except Exception:
     print("Could not get last model train time.")
     last_train_time = datetime.min.replace(tzinfo=pytz.UTC)
 

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

@@ -49,22 +49,24 @@ 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:
+except Exception:
     print(traceback.format_exc())
     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(
+    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,

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 1.37.0 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', -1)\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

@@ -91,7 +91,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -143,7 +143,7 @@
         "    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_D2_V2',\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",

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

@@ -0,0 +1,167 @@
+from typing import Any, Dict, Optional, List
+
+import argparse
+import json
+import os
+import re
+
+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)
+    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 +1,725 @@
+{
+  "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",
+        "pd.set_option(\"display.max_colwidth\", -1)\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",
+        "| **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. |\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",
+        "    \"max_horizon\": 6,\n",
+        "    \"grain_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 +1,167 @@
+from typing import Any, Dict, Optional, List
+
+import argparse
+import json
+import os
+import re
+
+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)
+    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 +1,719 @@
+{
+  "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",
+        "pd.set_option(\"display.max_colwidth\", -1)\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 +1,166 @@
+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/auto-ml-forecasting-beer-remote.ipynb

@@ -113,7 +113,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -139,18 +139,18 @@
         "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 = \"beer-remote-cpu\"\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",
-        "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",
+        "pd.set_option(\"display.max_colwidth\", -1)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"
       ]
     },
@@ -162,7 +162,9 @@
       },
       "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."
       ]
     },
     {
@@ -183,10 +185,11 @@
         "# 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)"
@@ -243,17 +246,21 @@
         "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",
+        "plt.title(\"Beer Production By Year\")\n",
+        "df = pd.read_csv(\n",
+        "    \"Beer_no_valid_split_train.csv\", parse_dates=True, index_col=\"DATE\"\n",
+        ").drop(columns=\"grain\")\n",
+        "test_df = pd.read_csv(\n",
+        "    \"Beer_no_valid_split_test.csv\", parse_dates=True, index_col=\"DATE\"\n",
+        ").drop(columns=\"grain\")\n",
         "plt.plot(df)\n",
         "\n",
         "plt.subplot(2, 1, 2)\n",
-        "plt.title('Beer Production By Month')\n",
+        "plt.title(\"Beer Production 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, 13)\n",
         "months.boxplot()\n",
         "\n",
         "plt.show()"
@@ -268,10 +275,10 @@
       },
       "outputs": [],
       "source": [
-        "target_column_name = 'BeerProduction'\n",
-        "time_column_name = 'DATE'\n",
+        "target_column_name = \"BeerProduction\"\n",
+        "time_column_name = \"DATE\"\n",
         "time_series_id_column_names = []\n",
-        "freq = 'M' #Monthly data"
+        "freq = \"M\"  # Monthly data"
       ]
     },
     {
@@ -299,14 +306,36 @@
         "test_df.to_csv(\"test.csv\")\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",
+        "datastore.upload_files(\n",
+        "    files=[\"./train.csv\"],\n",
+        "    target_path=\"beer-dataset/tabular/\",\n",
+        "    overwrite=True,\n",
+        "    show_progress=True,\n",
+        ")\n",
+        "datastore.upload_files(\n",
+        "    files=[\"./valid.csv\"],\n",
+        "    target_path=\"beer-dataset/tabular/\",\n",
+        "    overwrite=True,\n",
+        "    show_progress=True,\n",
+        ")\n",
+        "datastore.upload_files(\n",
+        "    files=[\"./test.csv\"],\n",
+        "    target_path=\"beer-dataset/tabular/\",\n",
+        "    overwrite=True,\n",
+        "    show_progress=True,\n",
+        ")\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')])"
+        "\n",
+        "train_dataset = Dataset.Tabular.from_delimited_files(\n",
+        "    path=[(datastore, \"beer-dataset/tabular/train.csv\")]\n",
+        ")\n",
+        "valid_dataset = Dataset.Tabular.from_delimited_files(\n",
+        "    path=[(datastore, \"beer-dataset/tabular/valid.csv\")]\n",
+        ")\n",
+        "test_dataset = Dataset.Tabular.from_delimited_files(\n",
+        "    path=[(datastore, \"beer-dataset/tabular/test.csv\")]\n",
+        ")"
       ]
     },
     {
@@ -364,24 +393,29 @@
       "outputs": [],
       "source": [
         "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
+        "\n",
         "forecasting_parameters = ForecastingParameters(\n",
         "    time_column_name=time_column_name,\n",
         "    forecast_horizon=forecast_horizon,\n",
-        "    freq='MS' # Set the forecast frequency to be monthly (start of the month)\n",
+        "    freq=\"MS\",  # Set the forecast frequency to be monthly (start of the month)\n",
         ")\n",
         "\n",
-        "automl_config = AutoMLConfig(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",
-        "                             verbosity=logging.INFO,\n",
-        "                             compute_target=compute_target,\n",
-        "                             max_concurrent_iterations=4,\n",
-        "                             max_cores_per_iteration=-1,\n",
-        "                             enable_dnn=True,\n",
-        "                             forecasting_parameters=forecasting_parameters)"
+        "# We will disable the enable_early_stopping flag to ensure the DNN model is recommended for demonstration purpose.\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",
+        "    verbosity=logging.INFO,\n",
+        "    compute_target=compute_target,\n",
+        "    max_concurrent_iterations=4,\n",
+        "    max_cores_per_iteration=-1,\n",
+        "    enable_dnn=True,\n",
+        "    enable_early_stopping=False,\n",
+        "    forecasting_parameters=forecasting_parameters,\n",
+        ")"
       ]
     },
     {
@@ -403,7 +437,7 @@
       },
       "outputs": [],
       "source": [
-        "remote_run = experiment.submit(automl_config, show_output= True)"
+        "remote_run = experiment.submit(automl_config, show_output=True)"
       ]
     },
     {
@@ -451,6 +485,7 @@
       "outputs": [],
       "source": [
         "from helper import get_result_df\n",
+        "\n",
         "summary_df = get_result_df(remote_run)\n",
         "summary_df"
       ]
@@ -466,11 +501,12 @@
       "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[\"run_id\"][forecast_model]\n",
         "best_dnn_run = Run(experiment, best_dnn_run_id)"
       ]
     },
@@ -484,7 +520,7 @@
       "outputs": [],
       "source": [
         "best_dnn_run.parent\n",
-        "RunDetails(best_dnn_run.parent).show() "
+        "RunDetails(best_dnn_run.parent).show()"
       ]
     },
     {
@@ -497,7 +533,7 @@
       "outputs": [],
       "source": [
         "best_dnn_run\n",
-        "RunDetails(best_dnn_run).show() "
+        "RunDetails(best_dnn_run).show()"
       ]
     },
     {
@@ -532,7 +568,10 @@
       "outputs": [],
       "source": [
         "from azureml.core import Dataset\n",
-        "test_dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, 'beer-dataset/tabular/test.csv')])\n",
+        "\n",
+        "test_dataset = Dataset.Tabular.from_delimited_files(\n",
+        "    path=[(datastore, \"beer-dataset/tabular/test.csv\")]\n",
+        ")\n",
         "# preview the first 3 rows of the dataset\n",
         "test_dataset.take(5).to_pandas_dataframe()"
       ]
@@ -543,7 +582,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "compute_target = ws.compute_targets['beer-cluster']\n",
+        "compute_target = ws.compute_targets[\"beer-cluster\"]\n",
         "test_experiment = Experiment(ws, experiment_name + \"_test\")"
       ]
     },
@@ -559,9 +598,9 @@
         "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.copy('infer.py', script_folder)"
+        "shutil.copy(\"infer.py\", script_folder)"
       ]
     },
     {
@@ -572,8 +611,18 @@
       "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, forecast_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",
+        ")"
       ]
     },
     {
@@ -593,8 +642,19 @@
       "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, forecast_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",
+        ")"
       ]
     },
     {
@@ -614,7 +674,7 @@
         "    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)"
       ]
     },
@@ -660,7 +720,7 @@
       "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-beer-remote/helper.py

@@ -6,120 +6,158 @@ 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):
+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']
+        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_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_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])
+    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:
+    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)
+            df2.drop("tmp_grain_column", axis=1, inplace=True)
 
-        grain_column_names.remove('tmp_grain_column')
+        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):
+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
+    df["tmpindex"] = df.index
 
     train_df, test_df = split_fraction_by_grain(
-        df, test_split, time_column_name, grain_column_names)
+        df, test_split, time_column_name, grain_column_names
+    )
 
-    train_df = train_df.set_index('tmpindex')
+    train_df = train_df.set_index("tmpindex")
     train_df.index.name = index_name
 
-    test_df = test_df.set_index('tmpindex')
+    test_df = test_df.set_index("tmpindex")
     test_df.index.name = index_name
 
-    df.drop('tmpindex', axis=1, inplace=True)
+    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'])
+    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:
+        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[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
 
 
-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')
+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')
+        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)
+    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']
-        })
+        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 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):
+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)
@@ -127,12 +165,19 @@ def run_multiple_inferences(summary_df, train_experiment, test_experiment,
         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)
+            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
+        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-beer-remote/infer.py

@@ -19,9 +19,14 @@ 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
@@ -33,9 +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})
 
@@ -48,20 +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.
 
@@ -72,7 +82,7 @@ def do_rolling_forecast_with_lookback(fitted_model, X_test, y_test,
     origin time for constructing lag features.
 
     This function returns a concatenated DataFrame of rolling forecasts.
-     """
+    """
     print("Using lookback of size: ", y_lookback.size)
     df_list = []
     origin_time = X_test[time_column_name].min()
@@ -83,22 +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)
@@ -124,9 +140,14 @@ def do_rolling_forecast_with_lookback(fitted_model, X_test, y_test,
         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[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
@@ -134,7 +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.
 
@@ -145,7 +166,7 @@ def do_rolling_forecast(fitted_model, X_test, y_test, max_horizon, freq='D'):
     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():
@@ -153,23 +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)
@@ -193,10 +219,14 @@ def do_rolling_forecast(fitted_model, X_test, y_test, max_horizon, freq='D'):
         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]))
+        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],
+            )
+        )
 
         # Advance the origin time
         origin_time = horizon_time
@@ -230,20 +260,31 @@ def map_location_cuda(storage, loc):
 
 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
@@ -252,7 +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)
@@ -261,39 +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]
+)
 
 _, ext = os.path.splitext(model_path)
-if ext == '.pt':
+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:
+        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,
@@ -302,26 +345,28 @@ 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 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'])
+y_pred = np.array(df_all["predicted"])
 scores = scoring.score_regression(y_test, y_pred, regression_metrics)
 
 print("scores:")
@@ -331,12 +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-bike-share/auto-ml-forecasting-bike-share.ipynb

@@ -71,7 +71,8 @@
         "\n",
         "from azureml.core import Workspace, Experiment, Dataset\n",
         "from azureml.train.automl import AutoMLConfig\n",
-        "from datetime import datetime"
+        "from datetime import datetime\n",
+        "from azureml.automl.core.featurization import FeaturizationConfig"
       ]
     },
     {
@@ -87,7 +88,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -107,19 +108,19 @@
         "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['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",
+        "pd.set_option(\"display.max_colwidth\", -1)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"
       ]
     },
@@ -129,6 +130,9 @@
       "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](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
@@ -149,10 +153,11 @@
         "# 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",
+        "    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, amlcompute_cluster_name, compute_config)\n",
         "\n",
         "compute_target.wait_for_completion(show_output=True)"
@@ -174,7 +179,9 @@
       "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",
+        ")"
       ]
     },
     {
@@ -194,8 +201,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "target_column_name = 'cnt'\n",
-        "time_column_name = 'date'"
+        "target_column_name = \"cnt\"\n",
+        "time_column_name = \"date\""
       ]
     },
     {
@@ -204,10 +211,12 @@
       "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",
+        "dataset = dataset.drop_columns(columns=[\"casual\", \"registered\"])\n",
         "\n",
         "dataset.take(5).to_pandas_dataframe().reset_index(drop=True)"
       ]
@@ -300,6 +309,25 @@
         "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\")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -314,27 +342,31 @@
       "outputs": [],
       "source": [
         "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
+        "\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",
+        "    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(task='forecasting',                             \n",
-        "                             primary_metric='normalized_root_mean_squared_error',\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",
-        "                             max_concurrent_iterations=4,\n",
-        "                             max_cores_per_iteration=-1,\n",
-        "                             verbosity=logging.INFO,\n",
-        "                             forecasting_parameters=forecasting_parameters)"
+        "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",
+        "    max_concurrent_iterations=4,\n",
+        "    max_cores_per_iteration=-1,\n",
+        "    verbosity=logging.INFO,\n",
+        "    forecasting_parameters=forecasting_parameters,\n",
+        ")"
       ]
     },
     {
@@ -395,7 +427,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "fitted_model.named_steps['timeseriestransformer'].get_engineered_feature_names()"
+        "fitted_model.named_steps[\"timeseriestransformer\"].get_engineered_feature_names()"
       ]
     },
     {
@@ -420,7 +452,9 @@
       "outputs": [],
       "source": [
         "# Get the featurization summary as a list of JSON\n",
-        "featurization_summary = fitted_model.named_steps['timeseriestransformer'].get_featurization_summary()\n",
+        "featurization_summary = fitted_model.named_steps[\n",
+        "    \"timeseriestransformer\"\n",
+        "].get_featurization_summary()\n",
         "# View the featurization summary as a pandas dataframe\n",
         "pd.DataFrame.from_records(featurization_summary)"
       ]
@@ -467,9 +501,9 @@
         "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.copy('forecasting_script.py', script_folder)"
+        "shutil.copy(\"forecasting_script.py\", script_folder)"
       ]
     },
     {
@@ -487,7 +521,9 @@
       "source": [
         "from run_forecast import run_rolling_forecast\n",
         "\n",
-        "remote_run = run_rolling_forecast(test_experiment, compute_target, best_run, test, target_column_name)\n",
+        "remote_run = run_rolling_forecast(\n",
+        "    test_experiment, compute_target, best_run, test, target_column_name\n",
+        ")\n",
         "remote_run"
       ]
     },
@@ -504,7 +540,7 @@
       "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."
       ]
     },
@@ -514,8 +550,8 @@
       "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\")"
       ]
     },
     {
@@ -532,18 +568,23 @@
         "# use automl metrics module\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",
+        "    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()"
       ]
     },
@@ -564,10 +605,18 @@
       "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",
+        ")"
       ]
     },
     {
@@ -583,15 +632,18 @@
       "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, forecast_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()"
       ]

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

@@ -4,11 +4,14 @@ from sklearn.externals import joblib
 
 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(
-    '--test_dataset', type=str, dest='test_dataset',
-    help='Test Dataset')
+    "--test_dataset", type=str, dest="test_dataset", help="Test Dataset"
+)
 
 args = parser.parse_args()
 target_column_name = args.target_column_name
@@ -20,19 +23,30 @@ ws = run.experiment.workspace
 # get the input dataset by id
 test_dataset = Dataset.get_by_id(ws, id=test_dataset_id)
 
-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)
 
 # 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}
+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

@@ -1,32 +1,40 @@
 from azureml.core import ScriptRunConfig
 
 
-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')
+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 = 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)
+    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 = 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'])
+    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

@@ -24,10 +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. [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",
@@ -38,7 +39,7 @@
       "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",
@@ -49,15 +50,16 @@
         "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>"
       ]
     },
     {
@@ -97,7 +99,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -117,7 +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",
@@ -125,13 +127,13 @@
         "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",
-        "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",
+        "pd.set_option(\"display.max_colwidth\", -1)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"
       ]
     },
@@ -164,10 +166,11 @@
         "# 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",
+        "    print(\"Found existing cluster, use it.\")\n",
         "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
-        "                                                           max_nodes=6)\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)"
@@ -177,7 +180,7 @@
       "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",
@@ -202,8 +205,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "target_column_name = 'demand'\n",
-        "time_column_name = 'timeStamp'"
+        "target_column_name = \"demand\"\n",
+        "time_column_name = \"timeStamp\""
       ]
     },
     {
@@ -212,7 +215,9 @@
       "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)"
       ]
     },
@@ -309,7 +314,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Train\n",
+        "# 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",
@@ -341,23 +346,26 @@
       "outputs": [],
       "source": [
         "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
+        "\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",
+        "    freq=\"H\",  # Set the forecast frequency to be hourly\n",
         ")\n",
         "\n",
-        "automl_config = AutoMLConfig(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",
-        "                             verbosity=logging.INFO,\n",
-        "                             forecasting_parameters=forecasting_parameters)"
+        "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",
+        "    verbosity=logging.INFO,\n",
+        "    forecasting_parameters=forecasting_parameters,\n",
+        ")"
       ]
     },
     {
@@ -418,7 +426,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "fitted_model.named_steps['timeseriestransformer'].get_engineered_feature_names()"
+        "fitted_model.named_steps[\"timeseriestransformer\"].get_engineered_feature_names()"
       ]
     },
     {
@@ -442,7 +450,9 @@
       "outputs": [],
       "source": [
         "# Get the featurization summary as a list of JSON\n",
-        "featurization_summary = fitted_model.named_steps['timeseriestransformer'].get_featurization_summary()\n",
+        "featurization_summary = fitted_model.named_steps[\n",
+        "    \"timeseriestransformer\"\n",
+        "].get_featurization_summary()\n",
         "# View the featurization summary as a pandas dataframe\n",
         "pd.DataFrame.from_records(featurization_summary)"
       ]
@@ -451,9 +461,11 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Forecasting\n",
+        "# 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. First, we remove the target values from the test set:"
+        "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."
       ]
     },
     {
@@ -462,16 +474,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "X_test = test.to_pandas_dataframe().reset_index(drop=True)\n",
-        "y_test = X_test.pop(target_column_name).values"
+        "test_experiment = Experiment(ws, experiment_name + \"_inference\")"
       ]
     },
     {
       "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 the [forecast function notebook](../forecasting-forecast-function/auto-ml-forecasting-function.ipynb)."
+        "### Retreiving 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."
       ]
     },
     {
@@ -480,10 +491,19 @@
       "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)"
+        "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\")"
       ]
     },
     {
@@ -491,9 +511,7 @@
       "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). 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."
+        "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)."
       ]
     },
     {
@@ -502,9 +520,9 @@
       "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()"
       ]
     },
     {
@@ -519,19 +537,24 @@
         "\n",
         "# use automl metrics module\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",
+        "    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()"
       ]
     },
@@ -539,23 +562,7 @@
       "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",
+        "# 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."
       ]
     },
@@ -576,21 +583,33 @@
       "outputs": [],
       "source": [
         "advanced_forecasting_parameters = ForecastingParameters(\n",
-        "    time_column_name=time_column_name, forecast_horizon=forecast_horizon,\n",
-        "    target_lags=12, target_rolling_window_size=4\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",
-        "                             blocked_models = ['ElasticNet','ExtremeRandomTrees','GradientBoosting','XGBoostRegressor','ExtremeRandomTrees', 'AutoArima', 'Prophet'], #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",
-        "                             verbosity=logging.INFO,\n",
-        "                             forecasting_parameters=advanced_forecasting_parameters)"
+        "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",
+        "    verbosity=logging.INFO,\n",
+        "    forecasting_parameters=advanced_forecasting_parameters,\n",
+        ")"
       ]
     },
     {
@@ -638,7 +657,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Advanced Results<a id=\"advanced_results\"></a>\n",
+        "# 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."
       ]
     },
@@ -648,10 +667,21 @@
       "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)"
+        "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",
+        "# download the inference output file to the local machine\n",
+        "advanced_remote_run_infer.download_file(\n",
+        "    \"outputs/predictions.csv\", \"predictions_advanced.csv\"\n",
+        ")"
       ]
     },
     {
@@ -660,9 +690,8 @@
       "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)"
+        "fcst_adv_df = pd.read_csv(\"predictions_advanced.csv\", parse_dates=[time_column_name])\n",
+        "fcst_adv_df.head()"
       ]
     },
     {
@@ -677,19 +706,26 @@
         "\n",
         "# use automl metrics module\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",
+        "    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()"
       ]
     }
@@ -719,7 +755,7 @@
       "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/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

@@ -94,7 +94,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -111,19 +111,19 @@
         "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['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",
+        "pd.set_option(\"display.max_colwidth\", -1)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"
       ]
     },
@@ -141,17 +141,20 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "TIME_COLUMN_NAME = 'date'\n",
-        "TIME_SERIES_ID_COLUMN_NAME = 'time_series_id'\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",
-        "                   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",
+        "def get_timeseries(\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",
@@ -174,14 +177,18 @@
         "    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",
-        "            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",
-        "            time_series_id_column_name: np.repeat('ts{}'.format(i), data_length)\n",
-        "        })\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",
@@ -190,14 +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",
-        "                                                  test_len=n_test_periods,\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=2)"
+        "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",
+        "    time_series_id_column_name=TIME_SERIES_ID_COLUMN_NAME,\n",
+        "    time_series_number=2,\n",
+        ")"
       ]
     },
     {
@@ -224,11 +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('time_series_id'):    \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()"
       ]
@@ -250,12 +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))"
       ]
     },
@@ -263,7 +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."
       ]
     },
     {
@@ -281,10 +294,11 @@
         "# 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",
+        "    print(\"Found existing cluster, use it.\")\n",
         "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
-        "                                                           max_nodes=6)\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)"
@@ -313,14 +327,15 @@
       "outputs": [],
       "source": [
         "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
-        "lags = [1,2,3]\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",
+        "    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",
+        "    freq=\"H\",  # Set the forecast frequency to be hourly\n",
         ")"
       ]
     },
@@ -342,19 +357,21 @@
         "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",
-        "                             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",
-        "                             max_concurrent_iterations=4,\n",
-        "                             max_cores_per_iteration=-1,\n",
-        "                             label_column_name=target_label,\n",
-        "                             forecasting_parameters=forecasting_parameters)\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",
+        "    max_concurrent_iterations=4,\n",
+        "    max_cores_per_iteration=-1,\n",
+        "    label_column_name=target_label,\n",
+        "    forecasting_parameters=forecasting_parameters,\n",
+        ")\n",
         "\n",
         "remote_run = experiment.submit(automl_config, show_output=False)"
       ]
@@ -431,7 +448,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "y_pred_no_gap, xy_nogap =  fitted_model.forecast(X_test)\n",
+        "y_pred_no_gap, xy_nogap = fitted_model.forecast(X_test)\n",
         "\n",
         "# xy_nogap contains the predictions in the _automl_target_col column.\n",
         "# Those same numbers are output in y_pred_no_gap\n",
@@ -459,7 +476,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "quantiles =  fitted_model.forecast_quantiles(X_test)\n",
+        "quantiles = fitted_model.forecast_quantiles(X_test)\n",
         "quantiles"
       ]
     },
@@ -479,12 +496,12 @@
       "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",
+        "y_pred_quantiles = fitted_model.forecast_quantiles(X_test)\n",
         "\n",
-        "# quantile forecasts returned in a Dataframe along with the time and time series id columns \n",
+        "# quantile forecasts returned in a Dataframe along with the time and time series id columns\n",
         "y_pred_quantiles"
       ]
     },
@@ -532,14 +549,16 @@
       "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",
-        "                                      test_len=4,\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=2)\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",
+        "    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(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].max())\n",
@@ -560,7 +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",
@@ -582,7 +601,9 @@
       "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",
@@ -590,24 +611,24 @@
         "    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",
@@ -616,28 +637,30 @@
         "    ```\n",
         "    \"\"\"\n",
         "\n",
-        "    X_past = fulldata[ (fulldata[ time_column_name ] > forecast_origin - lookback) &\n",
-        "                       (fulldata[ time_column_name ] <= forecast_origin)\n",
-        "                     ]\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",
-        "                       ]\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"
@@ -656,8 +679,16 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(X_context.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].agg(['min','max','count']))\n",
-        "print(X_away.groupby(TIME_SERIES_ID_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)"
       ]
     },
@@ -667,11 +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",
@@ -682,7 +713,7 @@
         "# 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",
@@ -690,8 +721,9 @@
         "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",
@@ -718,7 +750,7 @@
         "# show the forecast aligned\n",
         "X_show = xy_away.reset_index()\n",
         "# without the generated features\n",
-        "X_show[['date', 'time_series_id', 'ext_predictor', '_automl_target_col']]\n",
+        "X_show[[\"date\", \"time_series_id\", \"ext_predictor\", \"_automl_target_col\"]]\n",
         "# prediction is in _automl_target_col"
       ]
     },
@@ -749,12 +781,14 @@
       "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(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",
+        "_, _, 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())"
@@ -777,9 +811,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# What forecast() function does in this case is equivalent to iterating it twice over the test set as the following. \n",
+        "# 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(X_test_long, np.concatenate((y_pred1, np.full(forecast_horizon, np.nan))))\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)"
       ]
     },

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

@@ -0,0 +1,639 @@
+{
+  "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",
+        "pd.set_option(\"display.max_colwidth\", -1)\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 11m using a Standard_D12_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 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 the \"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-many-models/auto-ml-forecasting-many-models.ipynb

@@ -0,0 +1,746 @@
+{
+  "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 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",
+        "pd.set_option(\"display.max_colwidth\", -1)\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": [
+        "## 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": [
+        "### 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.\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_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",
+        "| **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",
+        "    \"max_horizon\": 6,\n",
+        "    \"grain_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=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>"
       ]
     },
     {
@@ -60,7 +60,6 @@
       "source": [
         "import azureml.core\n",
         "import pandas as pd\n",
-        "import numpy as np\n",
         "import logging\n",
         "\n",
         "from azureml.core.workspace import Workspace\n",
@@ -82,7 +81,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -102,19 +101,19 @@
         "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['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",
+        "pd.set_option(\"display.max_colwidth\", -1)\n",
+        "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"
       ]
     },
@@ -122,8 +121,11 @@
       "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](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
@@ -144,10 +146,11 @@
         "# 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",
+        "    print(\"Found existing cluster, use it.\")\n",
         "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
-        "                                                           max_nodes=6)\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",
         "compute_target.wait_for_completion(show_output=True)"
@@ -157,7 +160,7 @@
       "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."
       ]
     },
@@ -167,11 +170,11 @@
       "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",
+        "data.drop(\"logQuantity\", axis=1, inplace=True)\n",
         "\n",
         "data.head()"
       ]
@@ -191,9 +194,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "time_series_id_column_names = ['Store', 'Brand']\n",
+        "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))"
+        "print(\"Data contains {0} individual time-series.\".format(nseries))"
       ]
     },
     {
@@ -212,7 +215,7 @@
         "use_stores = [2, 5, 8]\n",
         "data_subset = data[data.Store.isin(use_stores)]\n",
         "nseries = data_subset.groupby(time_series_id_column_names).ngroups\n",
-        "print('Data subset contains {0} individual time-series.'.format(nseries))"
+        "print(\"Data subset contains {0} individual time-series.\".format(nseries))"
       ]
     },
     {
@@ -231,14 +234,17 @@
       "source": [
         "n_test_periods = 20\n",
         "\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) # Sort by ascending time\n",
-        "                  .groupby(time_series_id_column_names, group_keys=False))\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",
+        "\n",
         "train, test = split_last_n_by_series_id(data_subset, n_test_periods)"
       ]
     },
@@ -256,18 +262,15 @@
       "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",
+        ")"
       ]
     },
     {
@@ -277,16 +280,6 @@
         "### 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,
@@ -320,7 +313,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "target_column_name = 'Quantity'"
+        "target_column_name = \"Quantity\""
       ]
     },
     {
@@ -348,13 +341,17 @@
       "source": [
         "featurization_config = FeaturizationConfig()\n",
         "# Force the CPWVOL5 feature to be numeric type.\n",
-        "featurization_config.add_column_purpose('CPWVOL5', 'Numeric')\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('Imputer', ['Quantity'], {\"strategy\": \"constant\", \"fill_value\": 0})\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('Imputer', ['INCOME'], {\"strategy\": \"median\"})\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\"})"
+        "featurization_config.add_transformer_params(\"Imputer\", [\"Price\"], {\"strategy\": \"ffill\"})"
       ]
     },
     {
@@ -377,7 +374,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Train\n",
+        "## 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",
@@ -420,26 +417,29 @@
       "outputs": [],
       "source": [
         "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
+        "\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",
+        "    freq=\"W-THU\",  # Set the forecast frequency to be weekly (start on each Thursday)\n",
         ")\n",
         "\n",
-        "automl_config = AutoMLConfig(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",
-        "                             max_cores_per_iteration=-1,\n",
-        "                             forecasting_parameters=forecasting_parameters)"
+        "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",
+        "    max_cores_per_iteration=-1,\n",
+        "    forecasting_parameters=forecasting_parameters,\n",
+        ")"
       ]
     },
     {
@@ -484,7 +484,7 @@
       "source": [
         "best_run, fitted_model = remote_run.get_output()\n",
         "print(fitted_model.steps)\n",
-        "model_name = best_run.properties['model_name']"
+        "model_name = best_run.properties[\"model_name\"]"
       ]
     },
     {
@@ -502,7 +502,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "custom_featurizer = fitted_model.named_steps['timeseriestransformer']"
+        "custom_featurizer = fitted_model.named_steps[\"timeseriestransformer\"]"
       ]
     },
     {
@@ -518,9 +518,11 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Forecasting\n",
+        "# 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. First, we remove the target values from the test set:"
+        "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."
       ]
     },
     {
@@ -529,17 +531,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "X_test = test\n",
-        "y_test = X_test.pop(target_column_name).values"
+        "test_experiment = Experiment(ws, experiment_name + \"_inference\")"
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
+      "cell_type": "markdown",
       "metadata": {},
-      "outputs": [],
       "source": [
-        "X_test.head()"
+        "### Retreiving 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."
       ]
     },
     {
@@ -555,18 +555,19 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# forecast returns the predictions and the featurized data, aligned to X_test.\n",
-        "# This contains the assumptions that were made in the forecast\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 [forecast function notebook](../forecasting-forecast-function/auto-ml-forecasting-function.ipynb)."
+        "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\")"
       ]
     },
     {
@@ -586,8 +587,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "assign_dict = {'predicted': y_predictions, target_column_name: y_test}\n",
-        "df_all = X_test.assign(**assign_dict)"
+        "# load forecast data frame\n",
+        "fcst_df = pd.read_csv(\"predictions.csv\", parse_dates=[time_column_name])\n",
+        "fcst_df.head()"
       ]
     },
     {
@@ -602,19 +604,24 @@
         "\n",
         "# use automl scoring module\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",
+        "    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()"
       ]
     },
@@ -622,7 +629,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Operationalize"
+        "# Operationalize<a id=\"operationalize\"></a>"
       ]
     },
     {
@@ -638,9 +645,11 @@
       "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)"
       ]
@@ -660,8 +669,8 @@
       "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)"
       ]
     },
     {
@@ -682,15 +691,18 @@
         "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",
@@ -720,19 +732,27 @@
       "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)"
       ]
@@ -759,8 +779,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "serv = Webservice(ws, 'automl-oj-forecast-01')\n",
-        "serv.delete()     # don't do it accidentally"
+        "serv = Webservice(ws, \"automl-oj-forecast-01\")\n",
+        "serv.delete()  # don't do it accidentally"
       ]
     }
   ],
@@ -802,7 +822,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.8"
+      "version": "3.6.9"
     },
     "tags": [
       "None"

how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/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-orange-juice-sales/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-recipes-univariate/auto-ml-forecasting-univariate-recipe-experiment-settings.ipynb

@@ -0,0 +1,494 @@
+{
+  "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-recipes-univariate/1_determine_experiment_settings.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this notebook we will explore the univaraite time-series data to determine the settings for an automated ML experiment. We will follow the thought process depicted in the following diagram:<br/>\n",
+        "![Forecasting after training](figures/univariate_settings_map_20210408.jpg)\n",
+        "\n",
+        "The objective is to answer the following questions:\n",
+        "\n",
+        "<ol>\n",
+        "    <li>Is there a seasonal pattern in the data? </li>\n",
+        "        <ul style=\"margin-top:-1px; list-style-type:none\"> \n",
+        "            <li> Importance: If we are able to detect regular seasonal patterns, the forecast accuracy may be improved by extracting these patterns and including them as features into the model.  </li>\n",
+        "        </ul>\n",
+        "    <li>Is the data stationary? </li>\n",
+        "        <ul style=\"margin-top:-1px; list-style-type:none\"> \n",
+        "            <li> Importance: In the absense of features that capture trend behavior, ML models (regression and tree based) are not well equiped to predict stochastic trends. Working with stationary data solves this problem.  </li>\n",
+        "        </ul>\n",
+        "    <li>Is there a detectable auto-regressive pattern in the stationary data? </li>\n",
+        "        <ul style=\"margin-top:-1px; list-style-type:none\"> \n",
+        "            <li> Importance: The accuracy of ML models can be improved if serial correlation is modeled by including lags of the dependent/target varaible as features. Including target lags in every experiment by default will result in a regression in accuracy scores if such setting is not warranted.  </li>\n",
+        "        </ul>\n",
+        "</ol>\n",
+        "\n",
+        "The answers to these questions will help determine the appropriate settings for the automated ML experiment.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import warnings\n",
+        "import pandas as pd\n",
+        "\n",
+        "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
+        "import matplotlib.pyplot as plt\n",
+        "from pandas.plotting import register_matplotlib_converters\n",
+        "\n",
+        "register_matplotlib_converters()  # fixes the future warning issue\n",
+        "\n",
+        "from helper_functions import unit_root_test_wrapper\n",
+        "from statsmodels.tools.sm_exceptions import InterpolationWarning\n",
+        "\n",
+        "warnings.simplefilter(\"ignore\", InterpolationWarning)\n",
+        "\n",
+        "\n",
+        "# set printing options\n",
+        "pd.set_option(\"display.max_columns\", 500)\n",
+        "pd.set_option(\"display.width\", 1000)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# load data\n",
+        "main_data_loc = \"data\"\n",
+        "train_file_name = \"S4248SM144SCEN.csv\"\n",
+        "\n",
+        "TARGET_COLNAME = \"S4248SM144SCEN\"\n",
+        "TIME_COLNAME = \"observation_date\"\n",
+        "COVID_PERIOD_START = \"2020-03-01\"\n",
+        "\n",
+        "df = pd.read_csv(os.path.join(main_data_loc, train_file_name))\n",
+        "df[TIME_COLNAME] = pd.to_datetime(df[TIME_COLNAME], format=\"%Y-%m-%d\")\n",
+        "df.sort_values(by=TIME_COLNAME, inplace=True)\n",
+        "df.set_index(TIME_COLNAME, inplace=True)\n",
+        "df.head(2)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# plot the entire dataset\n",
+        "fig, ax = plt.subplots(figsize=(6, 2), dpi=180)\n",
+        "ax.plot(df)\n",
+        "ax.title.set_text(\"Original Data Series\")\n",
+        "locs, labels = plt.xticks()\n",
+        "plt.xticks(rotation=45)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The graph plots the alcohol sales in the United States. Because the data is trending, it can be difficult to see cycles, seasonality or other interestng behaviors due to the scaling issues. For example, if there is a seasonal pattern, which we will discuss later, we cannot see them on the trending data. In such case, it is worth plotting the same data in first differences."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# plot the entire dataset in first differences\n",
+        "fig, ax = plt.subplots(figsize=(6, 2), dpi=180)\n",
+        "ax.plot(df.diff().dropna())\n",
+        "ax.title.set_text(\"Data in first differences\")\n",
+        "locs, labels = plt.xticks()\n",
+        "plt.xticks(rotation=45)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In the previous plot we observe that the data is more volatile towards the end of the series. This period coincides with the Covid-19 period, so we will exclude it from our experiment. Since in this example there are no user-provided features it is hard to make an argument that a model trained on the less volatile pre-covid data will be able to accurately predict the covid period."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 1. Seasonality\n",
+        "\n",
+        "#### Questions that need to be answered in this section:\n",
+        "1. Is there a seasonality?\n",
+        "2. If it's seasonal, does the data exhibit a trend (up or down)?\n",
+        "\n",
+        "It is hard to visually detect seasonality when the data is trending. The reason being is scale of seasonal fluctuations is dwarfed by the range of the trend in the data. One way to deal with this is to de-trend the data by taking the first differences. We will discuss this in more detail in the next section."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# plot the entire dataset in first differences\n",
+        "fig, ax = plt.subplots(figsize=(6, 2), dpi=180)\n",
+        "ax.plot(df.diff().dropna())\n",
+        "ax.title.set_text(\"Data in first differences\")\n",
+        "locs, labels = plt.xticks()\n",
+        "plt.xticks(rotation=45)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "For the  next plot, we will exclude the Covid period again. We will also shorten the length of data because plotting a very long time series may prevent us from seeing seasonal patterns, if there are any, because the plot may look like a random walk."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# remove COVID period\n",
+        "df = df[:COVID_PERIOD_START]\n",
+        "\n",
+        "# plot the entire dataset in first differences\n",
+        "fig, ax = plt.subplots(figsize=(6, 2), dpi=180)\n",
+        "ax.plot(df[\"2015-01-01\":].diff().dropna())\n",
+        "ax.title.set_text(\"Data in first differences\")\n",
+        "locs, labels = plt.xticks()\n",
+        "plt.xticks(rotation=45)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<p style=\"font-size:150%; color:blue\"> Conclusion </p>\n",
+        "\n",
+        "Visual examination does not suggest clear seasonal patterns. We will set the STL_TYPE = None, and we will move to the next section that examines stationarity. \n",
+        "\n",
+        "\n",
+        "Say, we are working with a different data set that shows clear patterns of seasonality, we have several options for setting the settings:is hard to say which option will work best in your case, hence you will need to run both options to see which one results in more accurate forecasts. </li>\n",
+        "<ol>\n",
+        "     <li> If the data does not appear to be trending, set DIFFERENCE_SERIES=False, TARGET_LAGS=None and STL_TYPE = \"season\" </li>\n",
+        "     <li> If the data appears to be trending, consider one of the following two settings:\n",
+        "          <ul>\n",
+        "               <ol type=\"a\">\n",
+        "                    <li> DIFFERENCE_SERIES=True, TARGET_LAGS=None and STL_TYPE = \"season\", or </li>\n",
+        "                    <li> DIFFERENCE_SERIES=False, TARGET_LAGS=None and STL_TYPE = \"trend_season\" </li>\n",
+        "               </ol>\n",
+        "               <li> In the first case, by taking first differences we are removing stochastic trend, but we do not remove seasonal patterns. In the second case, we do not remove the stochastic trend and it can be captured by the trend component of the STL decomposition. It is hard to say which option will work best in your case, hence you will need to run both options to see which one results in more accurate forecasts. </li>\n",
+        "          </ul>\n",
+        "</ol>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 2. Stationarity\n",
+        "If the data does not exhibit seasonal patterns, we would like to see if the data is non-stationary. Particularly, we want to see if there is a clear trending behavior. If such behavior is observed, we would like to first difference the data and examine the plot of an auto-correlation function (ACF) known as correlogram. If the data is seasonal, differencing it will not get rid off the seasonality and this will be shown on the correlogram as well.\n",
+        "\n",
+        "<ul>\n",
+        "    <li> Question: What is stationarity and how to we detect it? </li>\n",
+        "        <ul>\n",
+        "            <li> This is a fairly complex topic. Please read the following <a href=\"https://otexts.com/fpp2/stationarity.html\"> link </a> for a high level discussion on this subject. </li>\n",
+        "            <li> Simply put, we are looking for scenario when examining the time series plots the mean of the series is roughly the same, regardless which time interval you pick to compute it. Thus, trending and seasonal data are examples of non-stationary series. </li>\n",
+        "    </ul>\n",
+        "</ul>\n",
+        "\n",
+        "\n",
+        "<ul>\n",
+        "    <li> Question: Why do want to work with stationary data?</li>\n",
+        "    <ul> \n",
+        "        <li> In the absence of features that capture stochastic trends, the ML models that use (deterministic) time based features (hour of the day, day of the week, month of the year, etc) cannot capture such trends, and will over or under predict depending on the behavior of the time series. By working with stationary data, we eliminate the need to predict such trends, which improves the forecast accuracy. Classical time series models such as Arima and Exponential Smoothing handle non-stationary series by design and do not need such transformations. By differencing the data we are still able to run the same family of models. </li>\n",
+        "    </ul>\n",
+        "</ul>\n",
+        "\n",
+        "#### Questions that need to be answered in this section:\n",
+        "<ol> \n",
+        "    <li> Is the data stationary? </li>\n",
+        "    <li> Does the stationarized data (either the original or the differenced series) exhibit a clear auto-regressive pattern?</li>\n",
+        "</ol>\n",
+        "\n",
+        "To answer the first question, we run a series of tests (we call them unit root tests)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# unit root tests\n",
+        "test = unit_root_test_wrapper(df[TARGET_COLNAME])\n",
+        "print(\"---------------\", \"\\n\")\n",
+        "print(\"Summary table\", \"\\n\", test[\"summary\"], \"\\n\")\n",
+        "print(\"Is the {} series stationary?: {}\".format(TARGET_COLNAME, test[\"stationary\"]))\n",
+        "print(\"---------------\", \"\\n\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In the previous cell, we ran a series of unit root tests. The summary table contains the following columns:\n",
+        "<ul> \n",
+        "    <li> test_name is the name of the test.\n",
+        "        <ul> \n",
+        "            <li> ADF: Augmented Dickey-Fuller test </li>\n",
+        "            <li> KPSS: Kwiatkowski-Phillips\u00e2\u20ac\u201cSchmidt\u00e2\u20ac\u201cShin test </li>\n",
+        "            <li> PP: Phillips-Perron test\n",
+        "            <li> ADF GLS: Augmented Dickey-Fuller using generalized least squares method </li>\n",
+        "            <li> AZ: Andrews-Zivot test </li>\n",
+        "        </ul>\n",
+        "    <li> statistic: test statistic </li>\n",
+        "    <li> crit_val: critical value of the test statistic </li>\n",
+        "    <li> p_val: p-value of the test statistic. If the p-val is less than 0.05, the null hypothesis is rejected. </li>\n",
+        "    <li> stationary: is the series stationary based on the test result? </li>\n",
+        "    <li> Null hypothesis: what is being tested. Notice, some test such as ADF and PP assume the process has a unit root and looks for evidence to reject this hypothesis. Other tests, ex.g: KPSS, assumes the process is stationary and looks for evidence to reject such claim.\n",
+        "</ul>\n",
+        "\n",
+        "Each of the tests shows that the original time series is non-stationary. The final decision is based on the majority rule. If, there is a split decision, the algorithm will claim it is stationary. We run a series of tests because each test by itself may not be accurate. In many cases when there are conflicting test results, the user needs to make determination if the series is stationary or not.\n",
+        "\n",
+        "Since we found the series to be non-stationary, we will difference it and then test if the differenced series is stationary."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# unit root tests\n",
+        "test = unit_root_test_wrapper(df[TARGET_COLNAME].diff().dropna())\n",
+        "print(\"---------------\", \"\\n\")\n",
+        "print(\"Summary table\", \"\\n\", test[\"summary\"], \"\\n\")\n",
+        "print(\"Is the {} series stationary?: {}\".format(TARGET_COLNAME, test[\"stationary\"]))\n",
+        "print(\"---------------\", \"\\n\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Four out of five tests show that the series in first differences is stationary. Notice that this decision is not unanimous. Next, let's plot the original series in first-differences to illustrate the difference between non-stationary (unit root) process vs the stationary one."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# plot original and stationary data\n",
+        "fig = plt.figure(figsize=(10, 10))\n",
+        "ax1 = fig.add_subplot(211)\n",
+        "ax1.plot(df[TARGET_COLNAME], \"-b\")\n",
+        "ax2 = fig.add_subplot(212)\n",
+        "ax2.plot(df[TARGET_COLNAME].diff().dropna(), \"-b\")\n",
+        "ax1.title.set_text(\"Original data\")\n",
+        "ax2.title.set_text(\"Data in first differences\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If you were asked a question \"What is the mean of the series before and after 2008?\", for the series titled \"Original data\" the mean values will be significantly different. This implies that the first moment of the series (in this case, it is the mean) is time dependent, i.e., mean changes depending on the interval one is looking at. Thus, the series is deemed to be non-stationary. On the other hand, for the series titled \"Data in first differences\" the means for both periods are roughly the same. Hence, the first moment is time invariant; meaning it does not depend on the interval of time one is looking at. In this example it is easy to visually distinguish between stationary and non-stationary data. Often this distinction is not easy to make, therefore we rely on the statistical tests described above to help us make an informed decision. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<p style=\"font-size:150%; color:blue\"> Conclusion </p>\n",
+        "Since we found the original process to be non-stationary (contains unit root), we will have to model the data in first differences. As a result, we will set the DIFFERENCE_SERIES parameter to True."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 3 Check if there is a clear autoregressive pattern\n",
+        "We need to determine if we should include lags of the target variable as features in order to improve forecast accuracy. To do this, we will examine the ACF and partial ACF (PACF) plots of the stationary series. In our case, it is a series in first diffrences.\n",
+        "\n",
+        "<ul>\n",
+        "    <li> Question: What is an Auto-regressive pattern? What are we looking for? </li>\n",
+        "    <ul style=\"list-style-type:none;\">\n",
+        "        <li> We are looking for a classical profiles for an AR(p) process such as an exponential decay of an ACF and a the first $p$ significant lags of the PACF. For a more detailed explanation of ACF and PACF please refer to the appendix at the end of this notebook. For illustration purposes, let's examine the ACF/PACF profiles of the simulated data that follows a second order auto-regressive process, abbreviated as an AR(2). <li/>\n",
+        "        <li><img src=\"figures/ACF_PACF_for_AR2.png\" class=\"img_class\">\n",
+        "            <br/>\n",
+        "            The lag order is on the x-axis while the auto- and partial-correlation coefficients are on the y-axis. Vertical lines that are outside the shaded area represent statistically significant lags. Notice, the ACF function decays to zero and the PACF shows 2 significant spikes (we ignore the first spike for lag 0 in both plots since the linear relationship of any series with itself is always 1). <li/>\n",
+        "    </ul>\n",
+        "<ul/>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<ul>\n",
+        "    <li> Question: What do I do if I observe an auto-regressive behavior? </li>\n",
+        "    <ul style=\"list-style-type:none;\">\n",
+        "        <li> If such behavior is observed, we might improve the forecast accuracy by enabling the target lags feature in AutoML. There are a few options of doing this </li>\n",
+        "            <ol>\n",
+        "                <li> Set the target lags parameter to 'auto', or </li>\n",
+        "                <li> Specify the list of lags you want to include. Ex.g: target_lags = [1,2,5] </li>\n",
+        "            </ol>\n",
+        "    </ul>\n",
+        "    <br/>\n",
+        "    <li> Next, let's examine the ACF and PACF plots of the stationary target variable (depicted below). Here, we do not see a decay in the ACF, instead we see a decay in PACF. It is hard to make an argument the the target variable exhibits auto-regressive behavior. </li>\n",
+        "    </ul>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Plot the ACF/PACF for the series in differences\n",
+        "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
+        "plot_acf(df[TARGET_COLNAME].diff().dropna().values.squeeze(), ax=ax[0])\n",
+        "plot_pacf(df[TARGET_COLNAME].diff().dropna().values.squeeze(), ax=ax[1])\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<p style=\"font-size:150%; color:blue\"> Conclusion </p>\n",
+        "Since we do not see a clear indication of an AR(p) process, we will not be using target lags and will set the TARGET_LAGS parameter to None."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<p style=\"font-size:150%; color:blue; font-weight: bold\"> AutoML Experiment Settings </p>\n",
+        "Based on the analysis performed, we should try the following settings for the AutoML experiment and use them in the \"2_run_experiment\" notebook.\n",
+        "<ul>\n",
+        "    <li> STL_TYPE=None </li>\n",
+        "    <li> DIFFERENCE_SERIES=True </li>\n",
+        "    <li> TARGET_LAGS=None </li>\n",
+        "</ul>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Appendix: ACF, PACF and Lag Selection\n",
+        "To do this, we will examine the ACF and partial ACF (PACF) plots of the differenced series. \n",
+        "\n",
+        "<ul>\n",
+        "    <li> Question: What is the ACF? </li>\n",
+        "    <ul style=\"list-style-type:none;\">\n",
+        "        <li> To understand the ACF, first let's look at the correlation coefficient $\\rho_{xz}$\n",
+        "            \\begin{equation}\n",
+        "            \\rho_{xz} = \\frac{\\sigma_{xz}}{\\sigma_{x} \\sigma_{zy}}\n",
+        "            \\end{equation}\n",
+        "        </li>\n",
+        "        where $\\sigma_{xzy}$ is the covariance between two random variables $X$ and $Z$; $\\sigma_x$ and $\\sigma_z$ is the variance for $X$ and $Z$, respectively. The correlation coefficient measures the strength of linear relationship between two random variables. This metric can take any value from -1 to 1. <li/>\n",
+        "        <br/>\n",
+        "        <li> The auto-correlation coefficient $\\rho_{Y_{t} Y_{t-k}}$ is the time series equivalent of the correlation coefficient, except instead of measuring linear association between two random variables $X$ and $Z$, it measures the strength of a linear relationship between a random variable $Y_t$ and its lag $Y_{t-k}$ for any positive interger value of $k$. </li> \n",
+        "    <br />\n",
+        "        <li> To visualize the ACF for a particular lag, say lag 2, plot the second lag of a series $y_{t-2}$ on the x-axis, and plot the series itself $y_t$ on the y-axis. The autocorrelation coefficient is the slope of the best fitted regression line and can be interpreted as follows. A one unit increase in the lag of a variable one period ago leads to a $\\rho_{Y_{t} Y_{t-2}}$ units change in the variable in the current period. This interpreation can be applied to any lag. </li> \n",
+        "    <br />\n",
+        "    <li> In the interpretation posted above we need to be careful not to confuse the word \"leads\" with \"causes\" since these are not the same thing. We do not know the lagged value of the varaible causes it to change. Afterall, there are probably many other features that may explain the movement in $Y_t$. All we are trying to do in this section is to identify situations when the variable contains the strong auto-regressive components that needs to be included in the model to improve forecast accuracy. </li>\n",
+        "    </ul>\n",
+        "</ul>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<ul>\n",
+        "    <li> Question: What is the PACF? </li>\n",
+        "    <ul style=\"list-style-type:none;\">\n",
+        "        <li> When describing the ACF we essentially running a regression between a partigular lag of a series, say, lag 4, and the series itself. What this implies is the regression coefficient for lag 4 captures the impact of everything that happens in lags 1, 2 and 3. In other words, if lag 1 is the most important lag and we exclude it from the regression, naturally, the regression model will assign the importance of the 1st lag to the 4th one. Partial auto-correlation function fixes this problem since it measures the contribution of each lag accounting for the information added by the intermediary lags. If we were to illustrate ACF and PACF for the fourth lag using the regression analogy, the difference is a follows: \n",
+        "        \\begin{align}\n",
+        "            Y_{t} &= a_{0} + a_{4} Y_{t-4} + e_{t} \\\\\n",
+        "            Y_{t} &= b_{0} + b_{1} Y_{t-1} + b_{2} Y_{t-2} + b_{3} Y_{t-3} + b_{4} Y_{t-4} + \\varepsilon_{t} \\\\\n",
+        "        \\end{align}\n",
+        "         </li>\n",
+        "        <br/>\n",
+        "        <li>\n",
+        "            Here, you can think of $a_4$ and $b_{4}$ as the auto- and partial auto-correlation coefficients for lag 4. Notice, in the second equation we explicitely accounting for the intermediate lags by adding them as regrerssors.\n",
+        "        </li>\n",
+        "    </ul>\n",
+        "</ul>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<ul>\n",
+        "    <li> Question: Auto-regressive pattern? What are we looking for? </li>\n",
+        "    <ul style=\"list-style-type:none;\">\n",
+        "        <li> We are looking for a classical profiles for an AR(p) process such as an exponential decay of an ACF and a the first $p$ significant lags of the PACF. Let's examine the ACF/PACF profiles of the same simulated AR(2) shown in Section 3, and check if the ACF/PACF explanation are refelcted in these plots. <li/>\n",
+        "        <li><img src=\"figures/ACF_PACF_for_AR2.png\" class=\"img_class\">\n",
+        "        <li> The autocorrelation coefficient for the 3rd lag is 0.6, which can be interpreted that a one unit increase in the value of the target varaible three periods ago leads to 0.6 units increase in the current period.  However, the PACF plot shows that the partial autocorrealtion coefficient is zero (from a statistical point of view since it lies within the shaded region). This is happening because the 1st and 2nd lags are good predictors of the target variable. Ommiting these two lags from the regression results in the misleading conclusion that the third  lag is a good prediciton. <li/>\n",
+        "        <br/>\n",
+        "        <li> This is why it is important to examine both the ACF and the PACF plots when tring to determine the auto regressive order for the variable in question. <li/>\n",
+        "    </ul>\n",
+        "</ul> "
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "vlbejan"
+      }
+    ],
+    "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-recipes-univariate/auto-ml-forecasting-univariate-recipe-experiment-settings.yml

@@ -0,0 +1,4 @@
+name: auto-ml-forecasting-univariate-recipe-experiment-settings
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-run-experiment.ipynb

@@ -0,0 +1,593 @@
+{
+  "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-recipes-univariate/2_run_experiment.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Running AutoML experiments\n",
+        "\n",
+        "See the `auto-ml-forecasting-univariate-recipe-experiment-settings` notebook on how to determine settings for seasonal features, target lags and whether the series needs to be differenced or not. To make experimentation user-friendly, the user has to specify several parameters: DIFFERENCE_SERIES, TARGET_LAGS and STL_TYPE. Once these parameters are set, the notebook will generate correct transformations and settings to run experiments, generate forecasts, compute inference set metrics and plot forecast vs actuals. It will also convert the forecast from first differences to levels (original units of measurement) if the DIFFERENCE_SERIES parameter is set to True before calculating inference set metrics.\n",
+        "\n",
+        "<br/>\n",
+        "\n",
+        "The output generated by this notebook is saved in the `experiment_output`folder."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Setup"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import logging\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "import azureml.automl.runtime\n",
+        "from azureml.core.compute import AmlCompute\n",
+        "from azureml.core.compute import ComputeTarget\n",
+        "import matplotlib.pyplot as plt\n",
+        "from helper_functions import ts_train_test_split, compute_metrics\n",
+        "\n",
+        "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",
+        "\n",
+        "\n",
+        "# set printing options\n",
+        "np.set_printoptions(precision=4, suppress=True, linewidth=100)\n",
+        "pd.set_option(\"display.max_columns\", 500)\n",
+        "pd.set_option(\"display.width\", 1000)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "As part of the setup you have already created a **Workspace**. You will also 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",
+        "> 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": [
+        "ws = Workspace.from_config()\n",
+        "amlcompute_cluster_name = \"recipe-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(\n",
+        "        vm_size=\"STANDARD_D2_V2\", max_nodes=6\n",
+        "    )\n",
+        "\n",
+        "    # Create the cluster.\\n\",\n",
+        "    compute_target = ComputeTarget.create(\n",
+        "        ws, amlcompute_cluster_name, provisioning_config\n",
+        "    )\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(\n",
+        "    show_output=True, min_node_count=None, timeout_in_minutes=20\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Data\n",
+        "\n",
+        "Here, we will load the data from the csv file and drop the Covid period."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "main_data_loc = \"data\"\n",
+        "train_file_name = \"S4248SM144SCEN.csv\"\n",
+        "\n",
+        "TARGET_COLNAME = \"S4248SM144SCEN\"\n",
+        "TIME_COLNAME = \"observation_date\"\n",
+        "COVID_PERIOD_START = (\n",
+        "    \"2020-03-01\"  # start of the covid period. To be excluded from evaluation.\n",
+        ")\n",
+        "\n",
+        "# load data\n",
+        "df = pd.read_csv(os.path.join(main_data_loc, train_file_name))\n",
+        "df[TIME_COLNAME] = pd.to_datetime(df[TIME_COLNAME], format=\"%Y-%m-%d\")\n",
+        "df.sort_values(by=TIME_COLNAME, inplace=True)\n",
+        "\n",
+        "# remove the Covid period\n",
+        "df = df.query('{} <= \"{}\"'.format(TIME_COLNAME, COVID_PERIOD_START))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Set parameters\n",
+        "\n",
+        "The first set of parameters is based on the analysis performed in the `auto-ml-forecasting-univariate-recipe-experiment-settings` notebook. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# set parameters based on the settings notebook analysis\n",
+        "DIFFERENCE_SERIES = True\n",
+        "TARGET_LAGS = None\n",
+        "STL_TYPE = None"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Next, define additional parameters to be used in the <a href=\"https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig?view=azure-ml-py\"> AutoML config </a> class.\n",
+        "\n",
+        "<ul> \n",
+        "    <li> FORECAST_HORIZON:  The forecast horizon is the number of periods into the future that the model should predict. Here, we set the horizon to 12 periods (i.e. 12 quarters). For more discussion of forecast horizons and guiding principles for setting them, please see the <a href=\"https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand\"> energy demand notebook </a>. \n",
+        "    </li>\n",
+        "    <li> TIME_SERIES_ID_COLNAMES: The names of columns used to group a timeseries. It can be used to create multiple series. If time series identifier is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting. Since we are working with a single series, this list is empty.\n",
+        "    </li>\n",
+        "    <li> BLOCKED_MODELS: Optional list of models to be blocked from consideration during model selection stage. At this point we want to consider all ML and Time Series models.\n",
+        "        <ul>\n",
+        "            <li> See the following <a href=\"https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting?view=azure-ml-py\"> link </a> for a list of supported Forecasting models</li>\n",
+        "        </ul>\n",
+        "    </li>\n",
+        "</ul>\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# set other parameters\n",
+        "FORECAST_HORIZON = 12\n",
+        "TIME_SERIES_ID_COLNAMES = []\n",
+        "BLOCKED_MODELS = []"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To run AutoML, you also need to create an **Experiment**. An Experiment corresponds to a prediction problem you are trying to solve, while a Run corresponds to a specific approach to the problem."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# choose a name for the run history container in the workspace\n",
+        "if isinstance(TARGET_LAGS, list):\n",
+        "    TARGET_LAGS_STR = (\n",
+        "        \"-\".join(map(str, TARGET_LAGS)) if (len(TARGET_LAGS) > 0) else None\n",
+        "    )\n",
+        "else:\n",
+        "    TARGET_LAGS_STR = TARGET_LAGS\n",
+        "\n",
+        "experiment_desc = \"diff-{}_lags-{}_STL-{}\".format(\n",
+        "    DIFFERENCE_SERIES, TARGET_LAGS_STR, STL_TYPE\n",
+        ")\n",
+        "experiment_name = \"alcohol_{}\".format(experiment_desc)\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",
+        "print(outputDf.T)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# create output directory\n",
+        "output_dir = \"experiment_output/{}\".format(experiment_desc)\n",
+        "if not os.path.exists(output_dir):\n",
+        "    os.makedirs(output_dir)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# difference data and test for unit root\n",
+        "if DIFFERENCE_SERIES:\n",
+        "    df_delta = df.copy()\n",
+        "    df_delta[TARGET_COLNAME] = df[TARGET_COLNAME].diff()\n",
+        "    df_delta.dropna(axis=0, inplace=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# split the data into train and test set\n",
+        "if DIFFERENCE_SERIES:\n",
+        "    # generate train/inference sets using data in first differences\n",
+        "    df_train, df_test = ts_train_test_split(\n",
+        "        df_input=df_delta,\n",
+        "        n=FORECAST_HORIZON,\n",
+        "        time_colname=TIME_COLNAME,\n",
+        "        ts_id_colnames=TIME_SERIES_ID_COLNAMES,\n",
+        "    )\n",
+        "else:\n",
+        "    df_train, df_test = ts_train_test_split(\n",
+        "        df_input=df,\n",
+        "        n=FORECAST_HORIZON,\n",
+        "        time_colname=TIME_COLNAME,\n",
+        "        ts_id_colnames=TIME_SERIES_ID_COLNAMES,\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Upload files to the Datastore\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 bike share 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": [
+        "df_train.to_csv(\"train.csv\", index=False)\n",
+        "df_test.to_csv(\"test.csv\", index=False)\n",
+        "\n",
+        "datastore = ws.get_default_datastore()\n",
+        "datastore.upload_files(\n",
+        "    files=[\"./train.csv\"],\n",
+        "    target_path=\"uni-recipe-dataset/tabular/\",\n",
+        "    overwrite=True,\n",
+        "    show_progress=True,\n",
+        ")\n",
+        "datastore.upload_files(\n",
+        "    files=[\"./test.csv\"],\n",
+        "    target_path=\"uni-recipe-dataset/tabular/\",\n",
+        "    overwrite=True,\n",
+        "    show_progress=True,\n",
+        ")\n",
+        "\n",
+        "from azureml.core import Dataset\n",
+        "\n",
+        "train_dataset = Dataset.Tabular.from_delimited_files(\n",
+        "    path=[(datastore, \"uni-recipe-dataset/tabular/train.csv\")]\n",
+        ")\n",
+        "test_dataset = Dataset.Tabular.from_delimited_files(\n",
+        "    path=[(datastore, \"uni-recipe-dataset/tabular/test.csv\")]\n",
+        ")\n",
+        "\n",
+        "# print the first 5 rows of the Dataset\n",
+        "train_dataset.to_pandas_dataframe().reset_index(drop=True).head(5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Config AutoML"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "time_series_settings = {\n",
+        "    \"time_column_name\": TIME_COLNAME,\n",
+        "    \"forecast_horizon\": FORECAST_HORIZON,\n",
+        "    \"target_lags\": TARGET_LAGS,\n",
+        "    \"use_stl\": STL_TYPE,\n",
+        "    \"blocked_models\": BLOCKED_MODELS,\n",
+        "    \"time_series_id_column_names\": TIME_SERIES_ID_COLNAMES,\n",
+        "}\n",
+        "\n",
+        "automl_config = AutoMLConfig(\n",
+        "    task=\"forecasting\",\n",
+        "    debug_log=\"sample_experiment.log\",\n",
+        "    primary_metric=\"normalized_root_mean_squared_error\",\n",
+        "    experiment_timeout_minutes=20,\n",
+        "    iteration_timeout_minutes=5,\n",
+        "    enable_early_stopping=True,\n",
+        "    training_data=train_dataset,\n",
+        "    label_column_name=TARGET_COLNAME,\n",
+        "    n_cross_validations=5,\n",
+        "    verbosity=logging.INFO,\n",
+        "    max_cores_per_iteration=-1,\n",
+        "    compute_target=compute_target,\n",
+        "    **time_series_settings,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We will now run the experiment, you can go to Azure ML portal to view the run details."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run = experiment.submit(automl_config, show_output=False)\n",
+        "remote_run.wait_for_completion()"
+      ]
+    },
+    {
+      "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."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "best_run, fitted_model = remote_run.get_output()\n",
+        "fitted_model.steps"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Inference\n",
+        "\n",
+        "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 inference will run on a remote compute. In this example, it will re-use the training compute."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "test_experiment = Experiment(ws, experiment_name + \"_inference\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Retreiving 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 = 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_COLNAME,\n",
+        ")\n",
+        "remote_run.wait_for_completion(show_output=False)\n",
+        "\n",
+        "remote_run.download_file(\"outputs/predictions.csv\", f\"{output_dir}/predictions.csv\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Download the prediction result for metrics calcuation\n",
+        "The test data with predictions are saved in artifact `outputs/predictions.csv`. We will use it to calculate accuracy metrics and vizualize predictions versus actuals."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_trans = pd.read_csv(f\"{output_dir}/predictions.csv\", parse_dates=[TIME_COLNAME])\n",
+        "X_trans.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# convert forecast in differences to levels\n",
+        "def convert_fcst_diff_to_levels(fcst, yt, df_orig):\n",
+        "    \"\"\"Convert forecast from first differences to levels.\"\"\"\n",
+        "    fcst = fcst.reset_index(drop=False, inplace=False)\n",
+        "    fcst[\"predicted_level\"] = fcst[\"predicted\"].cumsum()\n",
+        "    fcst[\"predicted_level\"] = fcst[\"predicted_level\"].astype(float) + float(yt)\n",
+        "    # merge actuals\n",
+        "    out = pd.merge(\n",
+        "        fcst, df_orig[[TIME_COLNAME, TARGET_COLNAME]], on=[TIME_COLNAME], how=\"inner\"\n",
+        "    )\n",
+        "    out.rename(columns={TARGET_COLNAME: \"actual_level\"}, inplace=True)\n",
+        "    return out"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "if DIFFERENCE_SERIES:\n",
+        "    # convert forecast in differences to the levels\n",
+        "    INFORMATION_SET_DATE = max(df_train[TIME_COLNAME])\n",
+        "    YT = df.query(\"{} == @INFORMATION_SET_DATE\".format(TIME_COLNAME))[TARGET_COLNAME]\n",
+        "\n",
+        "    fcst_df = convert_fcst_diff_to_levels(fcst=X_trans, yt=YT, df_orig=df)\n",
+        "else:\n",
+        "    fcst_df = X_trans.copy()\n",
+        "    fcst_df[\"actual_level\"] = y_test\n",
+        "    fcst_df[\"predicted_level\"] = y_predictions\n",
+        "\n",
+        "del X_trans"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Calculate metrics and save output"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# compute metrics\n",
+        "metrics_df = compute_metrics(fcst_df=fcst_df, metric_name=None, ts_id_colnames=None)\n",
+        "# save output\n",
+        "metrics_file_name = \"{}_metrics.csv\".format(experiment_name)\n",
+        "fcst_file_name = \"{}_forecst.csv\".format(experiment_name)\n",
+        "plot_file_name = \"{}_plot.pdf\".format(experiment_name)\n",
+        "\n",
+        "metrics_df.to_csv(os.path.join(output_dir, metrics_file_name), index=True)\n",
+        "fcst_df.to_csv(os.path.join(output_dir, fcst_file_name), index=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Generate and save visuals"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "plot_df = df.query('{} > \"2010-01-01\"'.format(TIME_COLNAME))\n",
+        "plot_df.set_index(TIME_COLNAME, inplace=True)\n",
+        "fcst_df.set_index(TIME_COLNAME, inplace=True)\n",
+        "\n",
+        "# generate and save plots\n",
+        "fig, ax = plt.subplots(dpi=180)\n",
+        "ax.plot(plot_df[TARGET_COLNAME], \"-g\", label=\"Historical\")\n",
+        "ax.plot(fcst_df[\"actual_level\"], \"-b\", label=\"Actual\")\n",
+        "ax.plot(fcst_df[\"predicted_level\"], \"-r\", label=\"Forecast\")\n",
+        "ax.legend()\n",
+        "ax.set_title(\"Forecast vs Actuals\")\n",
+        "ax.set_xlabel(TIME_COLNAME)\n",
+        "ax.set_ylabel(TARGET_COLNAME)\n",
+        "locs, labels = plt.xticks()\n",
+        "\n",
+        "plt.setp(labels, rotation=45)\n",
+        "plt.savefig(os.path.join(output_dir, plot_file_name))"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "vlbejan"
+      }
+    ],
+    "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-recipes-univariate/auto-ml-forecasting-univariate-recipe-run-experiment.yml

@@ -0,0 +1,4 @@
+name: auto-ml-forecasting-univariate-recipe-run-experiment
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/data/S4248SM144SCEN.csv

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how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/forecasting_script.py

@@ -0,0 +1,70 @@
+"""
+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 azureml.automl.core.shared.constants import TimeSeriesInternal
+from sklearn.externals import joblib
+
+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.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()
+)
+
+# 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_df[target_column_name]
+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)
+]
+clean.rename(columns={target_column_name: "actual"}, inplace=True)
+
+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-recipes-univariate/helper_functions.py

@@ -0,0 +1,263 @@
+"""
+Helper functions to determine AutoML experiment settings for forecasting.
+"""
+import pandas as pd
+import statsmodels.tsa.stattools as stattools
+from arch import unitroot
+from azureml.automl.core.shared import constants
+from azureml.automl.runtime.shared.score import scoring
+
+
+def adf_test(series, **kw):
+    """
+    Wrapper for the augmented Dickey-Fuller test. Allows users to set the lag order.
+
+    :param series: series to test
+    :return: dictionary of results
+    """
+    if "lags" in kw.keys():
+        msg = "Lag order of {} detected. Running the ADF test...".format(
+            str(kw["lags"])
+        )
+        print(msg)
+        statistic, pval, critval, resstore = stattools.adfuller(
+            series, maxlag=kw["lags"], autolag=kw["autolag"], store=kw["store"]
+        )
+    else:
+        statistic, pval, critval, resstore = stattools.adfuller(
+            series, autolag=kw["IC"], store=kw["store"]
+        )
+
+    output = {
+        "statistic": statistic,
+        "pval": pval,
+        "critical": critval,
+        "resstore": resstore,
+    }
+    return output
+
+
+def kpss_test(series, **kw):
+    """
+    Wrapper for the KPSS test. Allows users to set the lag order.
+
+    :param series: series to test
+    :return: dictionary of results
+    """
+    if kw["store"]:
+        statistic, p_value, critical_values, rstore = stattools.kpss(
+            series, regression=kw["reg_type"], lags=kw["lags"], store=kw["store"]
+        )
+    else:
+        statistic, p_value, lags, critical_values = stattools.kpss(
+            series, regression=kw["reg_type"], lags=kw["lags"]
+        )
+    output = {
+        "statistic": statistic,
+        "pval": p_value,
+        "critical": critical_values,
+        "lags": rstore.lags if kw["store"] else lags,
+    }
+
+    if kw["store"]:
+        output.update({"resstore": rstore})
+    return output
+
+
+def format_test_output(test_name, test_res, H0_unit_root=True):
+    """
+    Helper function to format output. Return a dictionary with specific keys. Will be used to
+    construct the summary data frame for all unit root tests.
+
+    TODO: Add functionality of choosing based on the max lag order specified by user.
+
+    :param test_name: name of the test
+    :param test_res: object that contains corresponding test information. Can be None if test failed.
+    :param H0_unit_root: does the null hypothesis of the test assume a unit root process? Some tests do (ADF),
+                         some don't (KPSS).
+    :return: dictionary of summary table for all tests and final decision on stationary vs non-stationary.
+             If test failed (test_res is None), return empty dictionary.
+    """
+    # Check if the test failed by trying to extract the test statistic
+    if test_name in ("ADF", "KPSS"):
+        try:
+            test_res["statistic"]
+        except BaseException:
+            test_res = None
+    else:
+        try:
+            test_res.stat
+        except BaseException:
+            test_res = None
+
+    if test_res is None:
+        return {}
+
+    # extract necessary information
+    if test_name in ("ADF", "KPSS"):
+        statistic = test_res["statistic"]
+        crit_val = test_res["critical"]["5%"]
+        p_val = test_res["pval"]
+        lags = test_res["resstore"].usedlag if test_name == "ADF" else test_res["lags"]
+    else:
+        statistic = test_res.stat
+        crit_val = test_res.critical_values["5%"]
+        p_val = test_res.pvalue
+        lags = test_res.lags
+
+    if H0_unit_root:
+        H0 = "The process is non-stationary"
+        stationary = "yes" if p_val < 0.05 else "not"
+    else:
+        H0 = "The process is stationary"
+        stationary = "yes" if p_val > 0.05 else "not"
+
+    out = {
+        "test_name": test_name,
+        "statistic": statistic,
+        "crit_val": crit_val,
+        "p_val": p_val,
+        "lags": int(lags),
+        "stationary": stationary,
+        "Null Hypothesis": H0,
+    }
+    return out
+
+
+def unit_root_test_wrapper(series, lags=None):
+    """
+    Main function to run multiple stationarity tests. Runs five tests and returns a summary table + decision
+    based on the majority rule. If the number of tests that determine a series is stationary equals to the
+    number of tests that deem it non-stationary, we assume the series is non-stationary.
+        * Augmented Dickey-Fuller (ADF),
+        * KPSS,
+        * ADF using GLS,
+        * Phillips-Perron (PP),
+        * Zivot-Andrews (ZA)
+
+    :param lags: (optional) parameter that allows user to run a series of tests for a specific lag value.
+    :param series: series to test
+    :return: dictionary of summary table for all tests and final decision on stationary vs nonstaionary
+    """
+    # setting for ADF and KPSS tests
+    adf_settings = {"IC": "AIC", "store": True}
+
+    kpss_settings = {"reg_type": "c", "lags": "auto", "store": True}
+
+    arch_test_settings = {}  # settings for PP, ADF GLS and ZA tests
+    if lags is not None:
+        adf_settings.update({"lags": lags, "autolag": None})
+        kpss_settings.update({"lags:": lags})
+        arch_test_settings = {"lags": lags}
+    # Run individual tests
+    adf = adf_test(series, **adf_settings)  # ADF test
+    kpss = kpss_test(series, **kpss_settings)  # KPSS test
+    pp = unitroot.PhillipsPerron(series, **arch_test_settings)  # Phillips-Perron test
+    adfgls = unitroot.DFGLS(series, **arch_test_settings)  # ADF using GLS test
+    za = unitroot.ZivotAndrews(series, **arch_test_settings)  # Zivot-Andrews test
+
+    # generate output table
+    adf_dict = format_test_output(test_name="ADF", test_res=adf, H0_unit_root=True)
+    kpss_dict = format_test_output(test_name="KPSS", test_res=kpss, H0_unit_root=False)
+    pp_dict = format_test_output(
+        test_name="Philips Perron", test_res=pp, H0_unit_root=True
+    )
+    adfgls_dict = format_test_output(
+        test_name="ADF GLS", test_res=adfgls, H0_unit_root=True
+    )
+    za_dict = format_test_output(
+        test_name="Zivot-Andrews", test_res=za, H0_unit_root=True
+    )
+
+    test_dict = {
+        "ADF": adf_dict,
+        "KPSS": kpss_dict,
+        "PP": pp_dict,
+        "ADF GLS": adfgls_dict,
+        "ZA": za_dict,
+    }
+    test_sum = pd.DataFrame.from_dict(test_dict, orient="index").reset_index(drop=True)
+
+    # decision based on the majority rule
+    if test_sum.shape[0] > 0:
+        ratio = test_sum[test_sum["stationary"] == "yes"].shape[0] / test_sum.shape[0]
+    else:
+        ratio = 1  # all tests fail, assume the series is stationary
+
+    # Majority rule. If the ratio is exactly 0.5, assume the series in non-stationary.
+    stationary = "YES" if (ratio > 0.5) else "NO"
+
+    out = {"summary": test_sum, "stationary": stationary}
+    return out
+
+
+def ts_train_test_split(df_input, n, time_colname, ts_id_colnames=None):
+    """
+    Group data frame by time series ID and split on last n rows for each group.
+
+    :param df_input: input data frame
+    :param n: number of observations in the test set
+    :param time_colname: time column
+    :param ts_id_colnames: (optional) list of grain column names
+    :return train and test data frames
+    """
+    if ts_id_colnames is None:
+        ts_id_colnames = []
+    ts_id_colnames_original = ts_id_colnames.copy()
+    if len(ts_id_colnames) == 0:
+        ts_id_colnames = ["Grain"]
+        df_input[ts_id_colnames[0]] = "dummy"
+    # Sort by ascending time
+    df_grouped = df_input.sort_values(time_colname).groupby(
+        ts_id_colnames, group_keys=False
+    )
+    df_head = df_grouped.apply(lambda dfg: dfg.iloc[:-n])
+    df_tail = df_grouped.apply(lambda dfg: dfg.iloc[-n:])
+    # drop group column name if it was not originally provided
+    if len(ts_id_colnames_original) == 0:
+        df_head.drop(ts_id_colnames, axis=1, inplace=True)
+        df_tail.drop(ts_id_colnames, axis=1, inplace=True)
+    return df_head, df_tail
+
+
+def compute_metrics(fcst_df, metric_name=None, ts_id_colnames=None):
+    """
+    Calculate metrics per grain.
+
+    :param fcst_df: forecast data frame. Must contain 2 columns: 'actual_level' and 'predicted_level'
+    :param metric_name: (optional) name of the metric 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 ts_id_colnames is None:
+        ts_id_colnames = []
+    if len(ts_id_colnames) == 0:
+        ts_id_colnames = ["TS_ID"]
+        fcst_df[ts_id_colnames[0]] = "dummy"
+    metrics_list = []
+    for grain, df in fcst_df.groupby(ts_id_colnames):
+        try:
+            scores = scoring.score_regression(
+                y_test=df["actual_level"],
+                y_pred=df["predicted_level"],
+                metrics=list(constants.Metric.SCALAR_REGRESSION_SET),
+            )
+        except BaseException:
+            msg = "{}: metrics calculation failed.".format(grain)
+            print(msg)
+            scores = {}
+        one_grain_metrics_df = pd.DataFrame(
+            list(scores.items()), columns=["metric_name", "metric"]
+        ).sort_values(["metric_name"])
+        one_grain_metrics_df.reset_index(inplace=True, drop=True)
+        if len(ts_id_colnames) < 2:
+            one_grain_metrics_df["grain"] = ts_id_colnames[0]
+        else:
+            one_grain_metrics_df["grain"] = "|".join(list(grain))
+
+        metrics_list.append(one_grain_metrics_df)
+    # collect into a data frame
+    grain_metrics = pd.concat(metrics_list)
+    if metric_name is not None:
+        grain_metrics = grain_metrics.query("metric_name == @metric_name")
+    return grain_metrics

how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/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/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb

@@ -96,7 +96,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -173,7 +173,7 @@
       "source": [
         "automl_settings = {\n",
         "    \"n_cross_validations\": 3,\n",
-        "    \"primary_metric\": 'average_precision_score_weighted',\n",
+        "    \"primary_metric\": 'AUC_weighted',\n",
         "    \"experiment_timeout_hours\": 0.25, # This is a time limit for testing purposes, remove it for real use cases, this will drastically limit ability to find the best model possible\n",
         "    \"verbosity\": logging.INFO,\n",
         "    \"enable_stack_ensemble\": False\n",
@@ -436,7 +436,8 @@
         "\n",
         "automl_explainer_setup_obj = automl_setup_model_explanations(fitted_model, X=X_train, \n",
         "                                                             X_test=X_test, y=y_train, \n",
-        "                                                             task='classification')"
+        "                                                             task='classification',\n",
+        "                                                             automl_run=automl_run)"
       ]
     },
     {
@@ -453,11 +454,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from interpret.ext.glassbox import LGBMExplainableModel\n",
         "from azureml.interpret.mimic_wrapper import MimicWrapper\n",
         "explainer = MimicWrapper(ws, automl_explainer_setup_obj.automl_estimator,\n",
         "                         explainable_model=automl_explainer_setup_obj.surrogate_model, \n",
-        "                         init_dataset=automl_explainer_setup_obj.X_transform, run=automl_run,\n",
+        "                         init_dataset=automl_explainer_setup_obj.X_transform, run=automl_explainer_setup_obj.automl_run,\n",
         "                         features=automl_explainer_setup_obj.engineered_feature_names, \n",
         "                         feature_maps=[automl_explainer_setup_obj.feature_map],\n",
         "                         classes=automl_explainer_setup_obj.classes,\n",

how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb

@@ -68,6 +68,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "import json\n",
         "import logging\n",
         "\n",
         "from matplotlib import pyplot as plt\n",
@@ -77,7 +78,6 @@
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-        "import azureml.dataprep as dprep\n",
         "from azureml.automl.core.featurization import FeaturizationConfig\n",
         "from azureml.train.automl import AutoMLConfig\n",
         "from azureml.core.dataset import Dataset"
@@ -96,7 +96,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -130,6 +130,8 @@
         "### Create or Attach existing 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 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.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
         "\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."
@@ -152,7 +154,7 @@
         "    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(vm_size='STANDARD_D2_V2',\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
         "                                                           max_nodes=4)\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",
@@ -338,16 +340,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "best_run, fitted_model = remote_run.get_output()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "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()"
       ]
     },
     {
@@ -356,7 +350,7 @@
       "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."
       ]
     },
     {
@@ -365,41 +359,14 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "custom_featurizer = fitted_model_customized.named_steps['datatransformer']"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "custom_featurizer.get_featurization_summary()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "is_user_friendly=False allows for more detailed summary for transforms being applied"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "custom_featurizer.get_featurization_summary(is_user_friendly=False)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "custom_featurizer.get_stats_feature_type_summary()"
+        "# Download the featuurization summary JSON file locally\n",
+        "best_run.download_file(\"outputs/featurization_summary.json\", \"featurization_summary.json\")\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)"
       ]
     },
     {
@@ -439,7 +406,7 @@
         "\n",
         "### Retrieve any AutoML Model for explanations\n",
         "\n",
-        "Below we select the some AutoML pipeline from our iterations. The `get_output` method returns the a AutoML 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 an AutoML pipeline from our iterations. The `get_output` method returns the a AutoML 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`."
       ]
     },
     {
@@ -448,7 +415,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "automl_run, fitted_model = remote_run.get_output(metric='r2_score')"
+        "#automl_run, fitted_model = remote_run.get_output(metric='r2_score')\n",
+        "automl_run, fitted_model = remote_run.get_output(iteration=2)"
       ]
     },
     {
@@ -538,8 +506,6 @@
       "outputs": [],
       "source": [
         "from azureml.core.runconfig import RunConfiguration\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "import pkg_resources\n",
         "\n",
         "# create a new RunConfig object\n",
         "conda_run_config = RunConfiguration(framework=\"python\")\n",
@@ -717,14 +683,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.webservice import Webservice\n",
         "from azureml.core.model import InferenceConfig\n",
         "from azureml.core.webservice import AciWebservice\n",
         "from azureml.core.model import Model\n",
         "from azureml.core.environment import Environment\n",
         "\n",
-        "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
-        "                                               memory_gb=1, \n",
+        "aciconfig = AciWebservice.deploy_configuration(cpu_cores=2, \n",
+        "                                               memory_gb=2, \n",
         "                                               tags={\"data\": \"Machine Data\",  \n",
         "                                                     \"method\" : \"local_explanation\"}, \n",
         "                                               description='Get local explanations for Machine test data')\n",

how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/train_explainer.py

@@ -50,11 +50,13 @@ X_test = test_dataset.drop_columns(columns=['<<target_column_name>>'])
 # Setup the class for explaining the AutoML models
 automl_explainer_setup_obj = automl_setup_model_explanations(fitted_model, '<<task>>',
                                                              X=X_train, X_test=X_test,
-                                                             y=y_train)
+                                                             y=y_train,
+                                                             automl_run=automl_run)
 
 # Initialize the Mimic Explainer
 explainer = MimicWrapper(ws, automl_explainer_setup_obj.automl_estimator, LGBMExplainableModel,
-                         init_dataset=automl_explainer_setup_obj.X_transform, run=automl_run,
+                         init_dataset=automl_explainer_setup_obj.X_transform,
+                         run=automl_explainer_setup_obj.automl_run,
                          features=automl_explainer_setup_obj.engineered_feature_names,
                          feature_maps=[automl_explainer_setup_obj.feature_map],
                          classes=automl_explainer_setup_obj.classes)

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

@@ -92,7 +92,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.25.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.37.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -145,7 +145,7 @@
         "    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_D2_V2',\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",
@@ -213,7 +213,7 @@
       "source": [
         "automl_settings = {\n",
         "    \"n_cross_validations\": 3,\n",
-        "    \"primary_metric\": 'r2_score',\n",
+        "    \"primary_metric\": 'normalized_root_mean_squared_error',\n",
         "    \"enable_early_stopping\": True, \n",
         "    \"experiment_timeout_hours\": 0.3, #for real scenarios we reccommend a timeout of at least one hour \n",
         "    \"max_concurrent_iterations\": 4,\n",

how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb

@@ -350,32 +350,6 @@
         "displayHTML(\"<a href={} target='_blank'>Azure Portal: {}</a>\".format(local_run.get_portal_url(), local_run.id))"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Retrieve All Child Runs after the experiment is completed (in portal)\n",
-        "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "children = list(local_run.get_children())\n",
-        "metricslist = {}\n",
-        "for run in children:\n",
-        "    properties = run.get_properties()\n",
-        "    #print(properties)\n",
-        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
-        "    metricslist[int(properties['iteration'])] = metrics\n",
-        "\n",
-        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
-        "rundata"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},

how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb

@@ -352,32 +352,6 @@
         "displayHTML(\"<a href={} target='_blank'>Azure Portal: {}</a>\".format(local_run.get_portal_url(), local_run.id))"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Retrieve All Child Runs after the experiment is completed (in portal)\n",
-        "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "children = list(local_run.get_children())\n",
-        "metricslist = {}\n",
-        "for run in children:\n",
-        "    properties = run.get_properties()\n",
-        "    #print(properties)\n",
-        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
-        "    metricslist[int(properties['iteration'])] = metrics\n",
-        "\n",
-        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
-        "rundata"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},

how-to-use-azureml/azure-synapse/Synapse_Job_Scala_Support.ipynb

@@ -0,0 +1,186 @@
+{
+  "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/azure-arcadia/Synapse_Job_Scala_Support.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Get AML workspace which has synapse spark pool attached"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace, Experiment, Dataset, Environment\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Leverage ScriptRunConfig to submit scala job to an attached synapse spark cluster"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Prepare data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.datastore import Datastore\n",
+        "# Use the default blob storage\n",
+        "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n",
+        "\n",
+        "# We are uploading a sample file in the local directory to be used as a datasource\n",
+        "file_name = \"shakespeare.txt\"\n",
+        "def_blob_store.upload_files(files=[\"./{}\".format(file_name)], overwrite=False)\n",
+        "\n",
+        "# Create file dataset\n",
+        "file_dataset = Dataset.File.from_files(path=[(def_blob_store, file_name)])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.runconfig import RunConfiguration\n",
+        "from azureml.data import HDFSOutputDatasetConfig\n",
+        "import uuid\n",
+        "\n",
+        "run_config = RunConfiguration(framework=\"pyspark\")\n",
+        "run_config.target = \"link-pool\"\n",
+        "run_config.spark.configuration[\"spark.driver.memory\"] = \"2g\"\n",
+        "run_config.spark.configuration[\"spark.driver.cores\"] = 2\n",
+        "run_config.spark.configuration[\"spark.executor.memory\"] = \"2g\"\n",
+        "run_config.spark.configuration[\"spark.executor.cores\"] = 1\n",
+        "run_config.spark.configuration[\"spark.executor.instances\"] = 1\n",
+        "# This can be removed if you are using local jars in source folder\n",
+        "run_config.spark.configuration[\"spark.yarn.dist.jars\"]=\"wasbs://synapse@azuremlexamples.blob.core.windows.net/shared/wordcount.jar\"\n",
+        "\n",
+        "dir_name = \"wordcount-{}\".format(str(uuid.uuid4()))\n",
+        "input = file_dataset.as_named_input(\"input\").as_hdfs()\n",
+        "output = HDFSOutputDatasetConfig(destination=(ws.get_default_datastore(), \"{}/result\".format(dir_name)))\n",
+        "\n",
+        "from azureml.core import ScriptRunConfig\n",
+        "args = ['--input', input, '--output', output]\n",
+        "script_run_config = ScriptRunConfig(source_directory = '.',\n",
+        "                                    script= 'start_script.py',\n",
+        "                                    arguments= args,\n",
+        "                                    run_config = run_config)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "exp = Experiment(workspace=ws, name='synapse-spark')\n",
+        "run = exp.submit(config=script_run_config)\n",
+        "run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Leverage SynapseSparkStep in an AML pipeline to add dataprep step on synapse spark cluster"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import Pipeline\n",
+        "from azureml.pipeline.steps import SynapseSparkStep\n",
+        "\n",
+        "configs = {}\n",
+        "#configs[\"spark.yarn.dist.jars\"] = \"wasbs://synapse@azuremlexamples.blob.core.windows.net/shared/wordcount.jar\"\n",
+        "step_1 = SynapseSparkStep(name = 'synapse-spark',\n",
+        "                          file = 'start_script.py',\n",
+        "                          jars = \"wasbs://synapse@azuremlexamples.blob.core.windows.net/shared/wordcount.jar\",\n",
+        "                          source_directory=\".\",\n",
+        "                          arguments = args,\n",
+        "                          compute_target = 'link-pool',\n",
+        "                          driver_memory = \"2g\",\n",
+        "                          driver_cores = 2,\n",
+        "                          executor_memory = \"2g\",\n",
+        "                          executor_cores = 1,\n",
+        "                          num_executors = 1,\n",
+        "                          conf = configs)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline = Pipeline(workspace=ws, steps=[step_1])\n",
+        "pipeline_run = pipeline.submit('synapse-pipeline', regenerate_outputs=True)"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "feli1"
+      }
+    ],
+    "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": "3.6.5"
+    },
+    "nteract": {
+      "version": "0.28.0"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

how-to-use-azureml/azure-synapse/Synapse_Session_Scala_Support.ipynb

@@ -0,0 +1,240 @@
+{
+  "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/azure-arcadia/Synapse_Session_Scala_Support.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Interactive Spark Session on Synapse Spark Pool"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!pip install -U \"azureml-synapse\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "For JupyterLab, please additionally run:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!jupyter lab build --minimize=False"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## PLEASE restart kernel and then refresh web page before starting spark session."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 0. Magic Usage"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2020-06-05T03:22:14.965395Z",
+          "iopub.status.busy": "2020-06-05T03:22:14.965395Z",
+          "iopub.status.idle": "2020-06-05T03:22:14.970398Z",
+          "shell.execute_reply": "2020-06-05T03:22:14.969397Z",
+          "shell.execute_reply.started": "2020-06-05T03:22:14.965395Z"
+        },
+        "gather": {
+          "logged": 1615594584642
+        }
+      },
+      "outputs": [],
+      "source": [
+        "# show help\n",
+        "%synapse ?"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 1. Start Synapse Session"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "gather": {
+          "logged": 1615577715289
+        }
+      },
+      "outputs": [],
+      "source": [
+        "%synapse start -c linktestpool --start-timeout 1000"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "source": [
+        "# 2. Use Scala"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "source": [
+        "## (1) Read Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [],
+      "source": [
+        "%%synapse scala\n",
+        "\n",
+        "var df = spark.read.option(\"header\", \"true\").csv(\"wasbs://demo@dprepdata.blob.core.windows.net/Titanic.csv\")\n",
+        "df.show(5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## (2) Use Scala Sql"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [],
+      "source": [
+        "%%synapse scala\n",
+        "\n",
+        "df.createOrReplaceTempView(\"titanic\")\n",
+        "var sqlDF = spark.sql(\"SELECT Name, Fare from titanic\")\n",
+        "sqlDF.show(5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Stop Session"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [],
+      "source": [
+        "%synapse stop"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "feli1"
+      }
+    ],
+    "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": "3.6.5"
+    },
+    "nteract": {
+      "version": "0.28.0"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}

how-to-use-azureml/azure-synapse/shakespeare.txt

@@ -0,0 +1,270 @@
+This is the 100th Etext file presented by Project Gutenberg, and
+is presented in cooperation with World Library, Inc., from their
+Library of the Future and Shakespeare CDROMS.  Project Gutenberg
+often releases Etexts that are NOT placed in the Public Domain!!
+
+Shakespeare
+
+*This Etext has certain copyright implications you should read!*
+
+<<THIS ELECTRONIC VERSION OF THE COMPLETE WORKS OF WILLIAM
+SHAKESPEARE IS COPYRIGHT 1990-1993 BY WORLD LIBRARY, INC., AND IS
+PROVIDED BY PROJECT GUTENBERG ETEXT OF ILLINOIS BENEDICTINE COLLEGE
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+
+*Project Gutenberg is proud to cooperate with The World Library*
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+The Complete Works of William Shakespeare 
+
+January, 1994  [Etext #100]
+
+
+The Library of the Future Complete Works of William Shakespeare 
+Library of the Future is a TradeMark (TM) of World Library Inc.
+******This file should be named shaks12.txt or shaks12.zip*****
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+***** SMALL PRINT! for COMPLETE SHAKESPEARE *****
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+

how-to-use-azureml/azure-synapse/spark_job_on_synapse_spark_pool.ipynb

@@ -46,7 +46,7 @@
         "import azureml.core\n",
         "from azureml.core import Workspace, Experiment\n",
         "from azureml.core import LinkedService, SynapseWorkspaceLinkedServiceConfiguration\n",
-        "from azureml.core.compute import ComputeTarget, SynapseCompute\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute, SynapseCompute\n",
         "from azureml.exceptions import ComputeTargetException\n",
         "from azureml.data import HDFSOutputDatasetConfig\n",
         "from azureml.core.datastore import Datastore\n",

how-to-use-azureml/azure-synapse/start_script.py

@@ -0,0 +1,18 @@
+from pyspark.sql import SparkSession
+
+import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument("--input", default="")
+parser.add_argument("--output", default="")
+
+args, unparsed = parser.parse_known_args()
+
+spark = SparkSession.builder.getOrCreate()
+sc = spark.sparkContext
+
+arr = sc._gateway.new_array(sc._jvm.java.lang.String, 2)
+arr[0] = args.input
+arr[1] = args.output
+
+obj = sc._jvm.WordCount
+obj.main(arr)

how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.ipynb

@@ -157,7 +157,9 @@
       "metadata": {},
       "source": [
         "## Provision the AKS Cluster\n",
-        "If you already have an AKS cluster attached to this workspace, skip the step below and provide the name of the cluster."
+        "If you already have an AKS cluster attached to this workspace, skip the step below and provide the name of the cluster.\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."
       ]
     },
     {

how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb

@@ -267,7 +267,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Create AKS compute if you haven't done so."
+        "### Create AKS compute if you haven't done so.\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."
       ]
     },
     {

how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks-ssl.ipynb

@@ -211,6 +211,8 @@
         "# Provision the AKS Cluster with SSL\n",
         "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it.\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",
         "See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-secure-web-service) for more details"
       ]
     },

how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb

@@ -325,7 +325,9 @@
       "metadata": {},
       "source": [
         "# Provision the AKS Cluster\n",
-        "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it."
+        "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it.\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."
       ]
     },
     {

how-to-use-azureml/deployment/spark/model-register-and-deploy-spark.ipynb

@@ -121,8 +121,6 @@
       "source": [
         "You can now create and/or use an Environment object when deploying a Webservice. The Environment can have been previously registered with your Workspace, or it will be registered with it as a part of the Webservice deployment.\n",
         "\n",
-        "In this notebook, we will be using 'AzureML-PySpark-MmlSpark-0.15', a curated environment.\n",
-        "\n",
         "More information can be found in our [using environments notebook](../training/using-environments/using-environments.ipynb)."
       ]
     },
@@ -132,9 +130,17 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core import Environment\n",
-        "\n",
-        "env = Environment.get(ws, name='AzureML-PySpark-MmlSpark-0.15')\n"
+        "from azureml.core import Environment\r\n",
+        "from azureml.core.environment import SparkPackage\r\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\r\n",
+        "\r\n",
+        "myenv = Environment('my-pyspark-environment')\r\n",
+        "myenv.docker.base_image = \"mcr.microsoft.com/mmlspark/release:0.15\"\r\n",
+        "myenv.inferencing_stack_version = \"latest\"\r\n",
+        "myenv.python.conda_dependencies = CondaDependencies.create(pip_packages=[\"azureml-core\",\"azureml-defaults\",\"azureml-telemetry\",\"azureml-train-restclients-hyperdrive\",\"azureml-train-core\"], python_version=\"3.6.2\")\r\n",
+        "myenv.python.conda_dependencies.add_channel(\"conda-forge\")\r\n",
+        "myenv.spark.packages = [SparkPackage(\"com.microsoft.ml.spark\", \"mmlspark_2.11\", \"0.15\"), SparkPackage(\"com.microsoft.azure\", \"azure-storage\", \"2.0.0\"), SparkPackage(\"org.apache.hadoop\", \"hadoop-azure\", \"2.7.0\")]\r\n",
+        "myenv.spark.repositories = [\"https://mmlspark.azureedge.net/maven\"]\r\n"
       ]
     },
     {
@@ -171,7 +177,7 @@
       "source": [
         "from azureml.core.model import InferenceConfig\n",
         "\n",
-        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)"
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
       ]
     },
     {

how-to-use-azureml/explain-model/azure-integration/gpu-explanation/gpu_tree_explainer.py

@@ -0,0 +1,44 @@
+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+from azureml.core.run import Run
+import joblib
+import os
+import shap
+import xgboost
+
+OUTPUT_DIR = './outputs/'
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+run = Run.get_context()
+
+# get a dataset on income prediction
+X, y = shap.datasets.adult()
+
+# train an XGBoost model (but any other tree model type should work)
+model = xgboost.XGBClassifier()
+model.fit(X, y)
+
+explainer = shap.explainers.GPUTree(model, X)
+X_shap = X[:100]
+shap_values = explainer(X_shap)
+
+print("computed shap values:")
+print(shap_values)
+
+# write X_shap out as a pickle file for later visualization
+x_shap_pkl = 'x_shap.pkl'
+with open(x_shap_pkl, 'wb') as file:
+    joblib.dump(value=X_shap, filename=os.path.join(OUTPUT_DIR, x_shap_pkl))
+run.upload_file('x_shap_adult_census.pkl', os.path.join(OUTPUT_DIR, x_shap_pkl))
+
+model_file_name = 'xgboost_.pkl'
+# save model in the outputs folder so it automatically gets uploaded
+with open(model_file_name, 'wb') as file:
+    joblib.dump(value=model, filename=os.path.join(OUTPUT_DIR,
+                                                   model_file_name))
+
+# register the model
+run.upload_file('xgboost_model.pkl', os.path.join('./outputs/', model_file_name))
+original_model = run.register_model(model_name='xgboost_with_gpu_tree_explainer',
+                                    model_path='xgboost_model.pkl')

how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.ipynb

@@ -0,0 +1,297 @@
+{
+  "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": {},
+      "source": [
+        "# Explain tree-based models on GPU using GPUTreeExplainer\n",
+        "\n",
+        "\n",
+        "_**This notebook illustrates how to use shap's GPUTreeExplainer on an Azure GPU machine.**_\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "Problem: Train a tree-based model and explain the model on an Azure GPU machine using the GPUTreeExplainer.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "## Table of Contents\n",
+        "\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Run model explainer locally at training time](#Explain)\n",
+        "    1. Apply feature transformations\n",
+        "    1. Train a binary classification model\n",
+        "    1. Explain the model on raw features\n",
+        "        1. Generate global explanations\n",
+        "        1. Generate local explanations\n",
+        "1. [Visualize explanations](#Visualize)\n",
+        "1. [Deploy model and scoring explainer](#Deploy)\n",
+        "1. [Next steps](#Next)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "This notebook demonstrates how to use the GPUTreeExplainer on some simple datasets.  Like the TreeExplainer, the GPUTreeExplainer is specifically designed for tree-based machine learning models, but it is designed to accelerate the computations using NVIDIA GPUs.\n",
+        "\n",
+        "\n",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
+        "\n",
+        "Notebook synopsis:\n",
+        "\n",
+        "1. Creating an Experiment in an existing Workspace\n",
+        "2. Configuration and remote run with a GPU machine"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import logging\n",
+        "import os\n",
+        "import shutil\n",
+        "\n",
+        "import pandas as pd\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.core.compute import AmlCompute\n",
+        "from azureml.core.compute import ComputeTarget\n",
+        "from azureml.core.run import Run\n",
+        "from azureml.core.model import Model"
+      ]
+    },
+    {
+      "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 1.37.0 of the Azure ML SDK\")\n",
+        "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>. To run the script, you also need to create an <b>Experiment</b>. An Experiment corresponds to a prediction problem you are trying to solve, while a Run corresponds to a specific approach to the problem."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "\n",
+        "# Choose an experiment name.\n",
+        "experiment_name = 'gpu-tree-explainer'\n",
+        "\n",
+        "experiment = Experiment(ws, experiment_name)\n",
+        "\n",
+        "output = {}\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",
+        "outputDf.T"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create project directory\n",
+        "\n",
+        "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import shutil\n",
+        "\n",
+        "project_folder = './azureml-shap-gpu-tree-explainer'\n",
+        "os.makedirs(project_folder, exist_ok=True)\n",
+        "shutil.copy('gpu_tree_explainer.py', project_folder)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Set up a compute cluster\n",
+        "This section uses a user-provided compute cluster (named \"gpu-shap-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."
+      ]
+    },
+    {
+      "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",
+        "num_nodes = 1\n",
+        "\n",
+        "# Choose a name for your cluster.\n",
+        "amlcompute_cluster_name = \"gpu-shap-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(vm_size = \"STANDARD_NC6\",\n",
+        "                                                           # To use GPUTreeExplainer, select a GPU such as \"STANDARD_NC6\" \n",
+        "                                                           # or similar GPU option\n",
+        "                                                           # available in your workspace\n",
+        "                                                           max_nodes = num_nodes)\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": [
+        "### Configure & Run"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.runconfig import RunConfiguration\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "\n",
+        "# Create a new RunConfig object\n",
+        "run_config = RunConfiguration(framework=\"python\")\n",
+        "\n",
+        "# Set compute target to AmlCompute target created in previous step\n",
+        "run_config.target = amlcompute_cluster_name\n",
+        "\n",
+        "from azureml.core import Environment\n",
+        "\n",
+        "environment_name = \"shap-gpu-tree\"\n",
+        "\n",
+        "env = Environment(environment_name)\n",
+        "\n",
+        "env.docker.enabled = True\n",
+        "env.docker.base_image = None\n",
+        "env.docker.base_dockerfile = \"\"\"\n",
+        "FROM rapidsai/rapidsai:cuda10.0-devel-ubuntu18.04\n",
+        "RUN apt-get update && \\\n",
+        "apt-get install -y fuse && \\\n",
+        "apt-get install -y build-essential && \\\n",
+        "apt-get install -y python3-dev && \\\n",
+        "source activate rapids && \\\n",
+        "apt-get install -y g++ && \\\n",
+        "printenv && \\\n",
+        "echo \"which nvcc: \" && \\\n",
+        "which nvcc && \\\n",
+        "pip install azureml-defaults && \\\n",
+        "pip install azureml-telemetry && \\\n",
+        "cd /usr/local/src && \\\n",
+        "git clone https://github.com/slundberg/shap && \\\n",
+        "cd shap && \\\n",
+        "mkdir build && \\\n",
+        "python setup.py install --user && \\\n",
+        "pip uninstall -y xgboost && \\\n",
+        "rm /conda/envs/rapids/lib/libxgboost.so && \\\n",
+        "pip install xgboost==1.4.2\n",
+        "\"\"\"\n",
+        "\n",
+        "env.python.user_managed_dependencies = True\n",
+        "\n",
+        "from azureml.core import Run\n",
+        "from azureml.core import ScriptRunConfig\n",
+        "\n",
+        "src = ScriptRunConfig(source_directory=project_folder, \n",
+        "                      script='gpu_tree_explainer.py', \n",
+        "                      compute_target=amlcompute_cluster_name,\n",
+        "                      environment=env) \n",
+        "run = experiment.submit(config=src)\n",
+        "run"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "ilmat"
+      }
+    ],
+    "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": 2
+}

how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.yml

@@ -0,0 +1,5 @@
+name: train-explain-model-gpu-tree-explainer
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-interpret

how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb

@@ -203,6 +203,8 @@
       "source": [
         "### Provision a compute target\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",
         "You can provision an AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n",
         "\n",
         "* `vm_size`: VM family of the nodes provisioned by AmlCompute. Simply choose from the supported_vmsizes() above\n",
@@ -215,7 +217,6 @@
       "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",
@@ -255,9 +256,6 @@
         "# Set compute target to AmlCompute target created in previous step\n",
         "run_config.target = cpu_cluster.name\n",
         "\n",
-        "# Enable Docker \n",
-        "run_config.environment.docker.enabled = True\n",
-        "\n",
         "azureml_pip_packages = [\n",
         "    'azureml-defaults', 'azureml-telemetry', 'azureml-interpret'\n",
         "]\n",
@@ -268,7 +266,7 @@
         "available_packages = pkg_resources.working_set\n",
         "sklearn_ver = None\n",
         "pandas_ver = None\n",
-        "for dist in available_packages:\n",
+        "for dist in list(available_packages):\n",
         "    if dist.key == 'scikit-learn':\n",
         "        sklearn_ver = dist.version\n",
         "    elif dist.key == 'pandas':\n",
@@ -287,7 +285,6 @@
         "azureml_pip_packages.extend([sklearn_dep, pandas_dep])\n",
         "run_config.environment.python.conda_dependencies = CondaDependencies.create(pip_packages=azureml_pip_packages)\n",
         "\n",
-        "from azureml.core import Run\n",
         "from azureml.core import ScriptRunConfig\n",
         "\n",
         "src = ScriptRunConfig(source_directory=project_folder, \n",
@@ -417,7 +414,6 @@
       "outputs": [],
       "source": [
         "# Retrieve x_test for visualization\n",
-        "import joblib\n",
         "x_test_path = './x_test_boston_housing.pkl'\n",
         "run.download_file('x_test_boston_housing.pkl', output_file_path=x_test_path)"
       ]
@@ -445,7 +441,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from interpret_community.widget import ExplanationDashboard"
+        "from raiwidgets import ExplanationDashboard"
       ]
     },
     {
@@ -454,7 +450,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ExplanationDashboard(global_explanation, original_model, datasetX=x_test)"
+        "ExplanationDashboard(global_explanation, original_model, dataset=x_test)"
       ]
     },
     {

how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.yml

@@ -11,3 +11,4 @@ dependencies:
   - matplotlib
   - azureml-dataset-runtime
   - ipywidgets
+  - raiwidgets~=0.15.0

how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.ipynb

@@ -87,7 +87,6 @@
         "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
         "from sklearn.svm import SVC\n",
         "import pandas as pd\n",
-        "import numpy as np\n",
         "\n",
         "# Explainers:\n",
         "# 1. SHAP Tabular Explainer\n",
@@ -533,7 +532,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from interpret_community.widget import ExplanationDashboard"
+        "from raiwidgets import ExplanationDashboard"
       ]
     },
     {
@@ -542,7 +541,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ExplanationDashboard(downloaded_global_explanation, model, datasetX=x_test)"
+        "ExplanationDashboard(downloaded_global_explanation, model, dataset=x_test)"
       ]
     },
     {

how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.yml

@@ -10,3 +10,4 @@ dependencies:
   - ipython
   - matplotlib
   - ipywidgets
+  - raiwidgets~=0.15.0

how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb

@@ -170,7 +170,6 @@
         "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
         "from sklearn.impute import SimpleImputer\n",
         "from sklearn.pipeline import Pipeline\n",
-        "from sklearn.linear_model import LogisticRegression\n",
         "from sklearn.ensemble import RandomForestClassifier\n",
         "\n",
         "from interpret.ext.blackbox import TabularExplainer\n",
@@ -221,7 +220,6 @@
         "                      ('classifier', RandomForestClassifier())])\n",
         "\n",
         "# Split data into train and test\n",
-        "from sklearn.model_selection import train_test_split\n",
         "x_train, x_test, y_train, y_test = train_test_split(attritionXData,\n",
         "                                                    target,\n",
         "                                                    test_size=0.2,\n",
@@ -296,7 +294,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from interpret_community.widget import ExplanationDashboard"
+        "from raiwidgets import ExplanationDashboard"
       ]
     },
     {
@@ -305,7 +303,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ExplanationDashboard(global_explanation, clf, datasetX=x_test)"
+        "ExplanationDashboard(global_explanation, clf, dataset=x_test)"
       ]
     },
     {
@@ -326,13 +324,15 @@
       "outputs": [],
       "source": [
         "from azureml.core.conda_dependencies import CondaDependencies \n",
+        "import sys\n",
         "\n",
         "# azureml-defaults is required to host the model as a web service.\n",
         "azureml_pip_packages = [\n",
         "    'azureml-defaults', 'azureml-core', 'azureml-telemetry',\n",
         "    'azureml-interpret'\n",
         "]\n",
-        " \n",
+        "\n",
+        "python_version = '{0}.{1}'.format(sys.version_info[0], sys.version_info[1])\n",
         "\n",
         "# Note: this is to pin the scikit-learn and pandas versions to be same as notebook.\n",
         "# In production scenario user would choose their dependencies\n",
@@ -356,8 +356,9 @@
         "# the submitted job is run in.  Note the remote environment(s) needs to be similar to the local\n",
         "# environment, otherwise if a model is trained or deployed in a different environment this can\n",
         "# cause errors.  Please take extra care when specifying your dependencies in a production environment.\n",
-        "myenv = CondaDependencies.create(pip_packages=['pyyaml', sklearn_dep, pandas_dep] + azureml_pip_packages,\n",
-        "                                 pin_sdk_version=False)\n",
+        "myenv = CondaDependencies.create(\n",
+        "    python_version=python_version,\n",
+        "    pip_packages=['pyyaml', sklearn_dep, pandas_dep] + azureml_pip_packages)\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
         "    f.write(myenv.serialize_to_string())\n",
@@ -383,11 +384,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.webservice import Webservice\n",
         "from azureml.core.model import InferenceConfig\n",
         "from azureml.core.webservice import AciWebservice\n",
-        "from azureml.core.model import Model\n",
         "from azureml.core.environment import Environment\n",
+        "from azureml.exceptions import WebserviceException\n",
         "\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
@@ -401,7 +401,12 @@
         "\n",
         "# Use configs and models generated above\n",
         "service = Model.deploy(ws, 'model-scoring-deploy-local', [scoring_explainer_model, original_model], inference_config, aciconfig)\n",
-        "service.wait_for_deployment(show_output=True)"
+        "try:\n",
+        "    service.wait_for_deployment(show_output=True)\n",
+        "except WebserviceException as e:\n",
+        "    print(e.message)\n",
+        "    print(service.get_logs())\n",
+        "    raise"
       ]
     },
     {

how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.yml

@@ -10,3 +10,4 @@ dependencies:
   - ipython
   - matplotlib
   - ipywidgets
+  - raiwidgets~=0.15.0

how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb

@@ -204,6 +204,8 @@
       "source": [
         "### Provision a compute target\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",
         "You can provision an AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n",
         "\n",
         "* `vm_size`: VM family of the nodes provisioned by AmlCompute. Simply choose from the supported_vmsizes() above\n",
@@ -216,7 +218,6 @@
       "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",
@@ -250,6 +251,7 @@
         "from azureml.core.runconfig import RunConfiguration\n",
         "from azureml.core.conda_dependencies import CondaDependencies\n",
         "from azureml.core.runconfig import DEFAULT_CPU_IMAGE\n",
+        "import sys\n",
         "\n",
         "# Create a new runconfig object\n",
         "run_config = RunConfiguration()\n",
@@ -257,9 +259,6 @@
         "# Set compute target to AmlCompute target created in previous step\n",
         "run_config.target = cpu_cluster.name\n",
         "\n",
-        "# Enable Docker \n",
-        "run_config.environment.docker.enabled = True\n",
-        "\n",
         "# Set Docker base image to the default CPU-based image\n",
         "run_config.environment.docker.base_image = DEFAULT_CPU_IMAGE\n",
         "\n",
@@ -270,7 +269,7 @@
         "    'azureml-defaults', 'azureml-telemetry', 'azureml-interpret'\n",
         "]\n",
         " \n",
-        "\n",
+        "python_version = '{0}.{1}'.format(sys.version_info[0], sys.version_info[1])\n",
         "\n",
         "# Note: this is to pin the scikit-learn version to be same as notebook.\n",
         "# In production scenario user would choose their dependencies\n",
@@ -295,7 +294,10 @@
         "# environment, otherwise if a model is trained or deployed in a different environment this can\n",
         "# cause errors.  Please take extra care when specifying your dependencies in a production environment.\n",
         "azureml_pip_packages.extend(['pyyaml', sklearn_dep, pandas_dep])\n",
-        "run_config.environment.python.conda_dependencies = CondaDependencies.create(pip_packages=azureml_pip_packages)\n",
+        "run_config.environment.python.conda_dependencies = CondaDependencies.create(\n",
+        "    python_version=python_version,\n",
+        "    pip_packages=azureml_pip_packages)\n",
+        "\n",
         "# Now submit a run on AmlCompute\n",
         "from azureml.core.script_run_config import ScriptRunConfig\n",
         "\n",
@@ -381,7 +383,6 @@
       "outputs": [],
       "source": [
         "# Retrieve x_test for visualization\n",
-        "import joblib\n",
         "x_test_path = './x_test.pkl'\n",
         "run.download_file('x_test_ibm.pkl', output_file_path=x_test_path)\n",
         "x_test = joblib.load(x_test_path)"
@@ -401,7 +402,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from interpret_community.widget import ExplanationDashboard"
+        "from raiwidgets import ExplanationDashboard"
       ]
     },
     {
@@ -410,7 +411,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ExplanationDashboard(global_explanation, original_svm_model, datasetX=x_test)"
+        "ExplanationDashboard(global_explanation, original_svm_model, dataset=x_test)"
       ]
     },
     {
@@ -427,8 +428,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies \n",
-        "\n",
         "# WARNING: to install this, g++ needs to be available on the Docker image and is not by default (look at the next cell)\n",
         "azureml_pip_packages = [\n",
         "    'azureml-defaults', 'azureml-core', 'azureml-telemetry',\n",
@@ -438,7 +437,6 @@
         "\n",
         "# Note: this is to pin the scikit-learn and pandas versions to be same as notebook.\n",
         "# In production scenario user would choose their dependencies\n",
-        "import pkg_resources\n",
         "available_packages = pkg_resources.working_set\n",
         "sklearn_ver = None\n",
         "pandas_ver = None\n",
@@ -459,7 +457,7 @@
         "# environment, otherwise if a model is trained or deployed in a different environment this can\n",
         "# cause errors.  Please take extra care when specifying your dependencies in a production environment.\n",
         "azureml_pip_packages.extend(['pyyaml', sklearn_dep, pandas_dep])\n",
-        "myenv = CondaDependencies.create(pip_packages=azureml_pip_packages)\n",
+        "myenv = CondaDependencies.create(python_version=python_version, pip_packages=azureml_pip_packages)\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
         "    f.write(myenv.serialize_to_string())\n",
@@ -484,11 +482,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.webservice import Webservice\n",
         "from azureml.core.model import InferenceConfig\n",
         "from azureml.core.webservice import AciWebservice\n",
-        "from azureml.core.model import Model\n",
         "from azureml.core.environment import Environment\n",
+        "from azureml.exceptions import WebserviceException\n",
         "\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
@@ -502,7 +499,12 @@
         "\n",
         "# Use configs and models generated above\n",
         "service = Model.deploy(ws, 'model-scoring-service', [scoring_explainer_model, original_model], inference_config, aciconfig)\n",
-        "service.wait_for_deployment(show_output=True)"
+        "try:\n",
+        "    service.wait_for_deployment(show_output=True)\n",
+        "except WebserviceException as e:\n",
+        "    print(e.message)\n",
+        "    print(service.get_logs())\n",
+        "    raise"
       ]
     },
     {

how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.yml

@@ -12,3 +12,4 @@ dependencies:
   - azureml-dataset-runtime
   - azureml-core
   - ipywidgets
+  - raiwidgets~=0.15.0

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb

@@ -126,7 +126,7 @@
       },
       "outputs": [],
       "source": [
-        "from msrest.exceptions import HttpOperationError\n",
+        "from azureml.exceptions import UserErrorException\n",
         "\n",
         "blob_datastore_name='MyBlobDatastore'\n",
         "account_name=os.getenv(\"BLOB_ACCOUNTNAME_62\", \"<my-account-name>\") # Storage account name\n",
@@ -136,7 +136,7 @@
         "try:\n",
         "    blob_datastore = Datastore.get(ws, blob_datastore_name)\n",
         "    print(\"Found Blob Datastore with name: %s\" % blob_datastore_name)\n",
-        "except HttpOperationError:\n",
+        "except UserErrorException:\n",
         "    blob_datastore = Datastore.register_azure_blob_container(\n",
         "        workspace=ws,\n",
         "        datastore_name=blob_datastore_name,\n",
@@ -180,7 +180,7 @@
         "try:\n",
         "    adls_datastore = Datastore.get(ws, datastore_name)\n",
         "    print(\"Found datastore with name: %s\" % datastore_name)\n",
-        "except HttpOperationError:\n",
+        "except UserErrorException:\n",
         "    adls_datastore = Datastore.register_azure_data_lake(\n",
         "        workspace=ws,\n",
         "        datastore_name=datastore_name,\n",
@@ -270,7 +270,7 @@
         "try:\n",
         "    sql_datastore = Datastore.get(ws, sql_datastore_name)\n",
         "    print(\"Found sql database datastore with name: %s\" % sql_datastore_name)\n",
-        "except HttpOperationError:\n",
+        "except UserErrorException:\n",
         "    sql_datastore = Datastore.register_azure_sql_database(\n",
         "        workspace=ws,\n",
         "        datastore_name=sql_datastore_name,\n",
@@ -312,7 +312,7 @@
         "try:\n",
         "    psql_datastore = Datastore.get(ws, psql_datastore_name)\n",
         "    print(\"Found PostgreSQL database datastore with name: %s\" % psql_datastore_name)\n",
-        "except HttpOperationError:\n",
+        "except UserErrorException:\n",
         "    psql_datastore = Datastore.register_azure_postgre_sql(\n",
         "        workspace=ws,\n",
         "        datastore_name=psql_datastore_name,\n",
@@ -353,7 +353,7 @@
         "try:\n",
         "    mysql_datastore = Datastore.get(ws, mysql_datastore_name)\n",
         "    print(\"Found MySQL database datastore with name: %s\" % mysql_datastore_name)\n",
-        "except HttpOperationError:\n",
+        "except UserErrorException:\n",
         "    mysql_datastore = Datastore.register_azure_my_sql(\n",
         "        workspace=ws,\n",
         "        datastore_name=mysql_datastore_name,\n",

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb

@@ -63,6 +63,8 @@
       "outputs": [],
       "source": [
         "import os\n",
+        "import requests\n",
+        "import tempfile\n",
         "import azureml.core\n",
         "from azureml.core import Workspace, Experiment, Datastore\n",
         "from azureml.widgets import RunDetails\n",
@@ -158,9 +160,14 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "# download data file from remote\n",
+        "response = requests.get(\"https://dprepdata.blob.core.windows.net/demo/Titanic.csv\")\n",
+        "titanic_file = os.path.join(tempfile.mkdtemp(), \"Titanic.csv\")\n",
+        "with open(titanic_file, \"w\") as f:\n",
+        "    f.write(response.content.decode(\"utf-8\"))\n",
         "# get_default_datastore() gets the default Azure Blob Store associated with your workspace.\n",
         "# Here we are reusing the def_blob_store object we obtained earlier\n",
-        "def_blob_store.upload_files([\"./20news.pkl\"], target_path=\"20newsgroups\", overwrite=True)\n",
+        "def_blob_store.upload_files([titanic_file], target_path=\"titanic\", overwrite=True)\n",
         "print(\"Upload call completed\")"
       ]
     },
@@ -209,6 +216,8 @@
         "#### Retrieve or create a Azure Machine Learning compute\n",
         "Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's create a new Azure Machine Learning Compute in the current workspace, if it doesn't already exist. We will then run the training script on this compute target.\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",
         "If we could not find the compute with the given name in the previous cell, then we will create a new compute here. We will create an Azure Machine Learning Compute containing **STANDARD_D2_V2 CPU VMs**. This process is broken down into the following steps:\n",
         "\n",
         "1. Create the configuration\n",
@@ -284,7 +293,7 @@
         "- [**AzureBatchStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.azurebatch_step.azurebatchstep?view=azure-ml-py): Creates a step for submitting jobs to Azure Batch\n",
         "- [**EstimatorStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.estimator_step.estimatorstep?view=azure-ml-py): Adds a step to run Estimator in a Pipeline.\n",
         "- [**MpiStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.mpi_step.mpistep?view=azure-ml-py): Adds a step to run a MPI job in a Pipeline.\n",
-        "- [**AutoMLStep**](https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.automlstep?view=azure-ml-py): Creates a AutoML step in a Pipeline.\n",
+        "- [**AutoMLStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.automlstep?view=azure-ml-py): Creates a AutoML step in a Pipeline.\n",
         "\n",
         "The following code will create a PythonScriptStep to be executed in the Azure Machine Learning Compute we created above using train.py, one of the files already made available in the `source_directory`.\n",
         "\n",

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-pipeline-drafts.ipynb

@@ -55,7 +55,9 @@
       "metadata": {},
       "source": [
         "### Compute Target\n",
-        "Retrieve an already attached Azure Machine Learning Compute to use in the Pipeline."
+        "Retrieve an already attached Azure Machine Learning Compute to use in the Pipeline.\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."
       ]
     },
     {