update samples from Release-62 as a part of SDK release

Include additional details on user authentication

README.md

@@ -40,6 +40,7 @@ The [How to use Azure ML](./how-to-use-azureml) folder contains specific example
 - [Deployment](./how-to-use-azureml/deployment) - Examples showing how to deploy and manage machine learning models and solutions
 - [Azure Databricks](./how-to-use-azureml/azure-databricks) - Examples showing how to use Azure ML with Azure Databricks
 - [Monitor Models](./how-to-use-azureml/monitor-models) - Examples showing how to enable model monitoring services such as DataDrift
+- [Reinforcement Learning](./how-to-use-azureml/reinforcement-learning) - Examples showing how to train reinforcement learning agents
 
 ---
 ## Documentation

configuration.ipynb

@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.4.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

contrib/fairness/fairlearn-azureml-mitigation.ipynb

@@ -0,0 +1,564 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/contrib/fairness/fairlearn-azureml-mitigation.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Unfairness Mitigation with Fairlearn and Azure Machine Learning\n",
+        "**This notebook shows how to upload results from Fairlearn's GridSearch mitigation algorithm into a dashboard in Azure Machine Learning Studio**\n",
+        "\n",
+        "## Table of Contents\n",
+        "\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Loading the Data](#LoadingData)\n",
+        "1. [Training an Unmitigated Model](#UnmitigatedModel)\n",
+        "1. [Mitigation with GridSearch](#Mitigation)\n",
+        "1. [Uploading a Fairness Dashboard to Azure](#AzureUpload)\n",
+        "    1. Registering models\n",
+        "    1. Computing Fairness Metrics\n",
+        "    1. Uploading to Azure\n",
+        "1. [Conclusion](#Conclusion)\n",
+        "\n",
+        "<a id=\"Introduction\"></a>\n",
+        "## Introduction\n",
+        "This notebook shows how to use [Fairlearn (an open source fairness assessment and unfairness mitigation package)](http://fairlearn.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",
+        "\n",
+        "We will apply the [grid search algorithm](https://fairlearn.github.io/api_reference/fairlearn.reductions.html#fairlearn.reductions.GridSearch) from the Fairlearn package using a specific notion of fairness called Demographic Parity. This produces a set of models, and we will view these in a dashboard both locally and in the Azure Machine Learning Studio.\n",
+        "\n",
+        "### Setup\n",
+        "\n",
+        "To use this notebook, an Azure Machine Learning workspace is required.\n",
+        "Please see the [configuration notebook](../../configuration.ipynb) for information about creating one, if required.\n",
+        "This notebook also requires the following packages:\n",
+        "* `azureml-contrib-fairness`\n",
+        "* `fairlearn==0.4.6`\n",
+        "* `joblib`\n",
+        "* `shap`\n",
+        "\n",
+        "Fairlearn relies on features introduced in v0.22.1 of `scikit-learn`. If you have an older version already installed, please uncomment and run the following cell:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# !pip install --upgrade scikit-learn>=0.22.1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"LoadingData\"></a>\n",
+        "## Loading the Data\n",
+        "We use the well-known `adult` census dataset, which we load using `shap` (for convenience). We start with a fairly unremarkable set of imports:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from fairlearn.reductions import GridSearch, DemographicParity, ErrorRate\n",
+        "from fairlearn.widget import FairlearnDashboard\n",
+        "from sklearn import svm\n",
+        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "import pandas as pd\n",
+        "import shap"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can now load and inspect the data from the `shap` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_raw, Y = shap.datasets.adult()\n",
+        "X_raw[\"Race\"].value_counts().to_dict()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We are going to treat the sex of each individual as a protected attribute (where 0 indicates female and 1 indicates male), and in this particular case we are going separate this attribute out and drop it from the main data (this is not always the best option - see the [Fairlearn website](http://fairlearn.github.io/) for further discussion). We also separate out the Race column, but we will not perform any mitigation based on it. Finally, we perform some standard data preprocessing steps to convert the data into a format suitable for the ML algorithms"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "A = X_raw[['Sex','Race']]\n",
+        "X = X_raw.drop(labels=['Sex', 'Race'],axis = 1)\n",
+        "X = pd.get_dummies(X)\n",
+        "\n",
+        "\n",
+        "le = LabelEncoder()\n",
+        "Y = le.fit_transform(Y)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "With our data prepared, we can make the conventional split in to 'test' and 'train' subsets:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "X_train, X_test, Y_train, Y_test, A_train, A_test = train_test_split(X_raw, \n",
+        "                                                    Y, \n",
+        "                                                    A,\n",
+        "                                                    test_size = 0.2,\n",
+        "                                                    random_state=0,\n",
+        "                                                    stratify=Y)\n",
+        "\n",
+        "# Work around indexing issue\n",
+        "X_train = X_train.reset_index(drop=True)\n",
+        "A_train = A_train.reset_index(drop=True)\n",
+        "X_test = X_test.reset_index(drop=True)\n",
+        "A_test = A_test.reset_index(drop=True)\n",
+        "\n",
+        "# Improve labels\n",
+        "A_test.Sex.loc[(A_test['Sex'] == 0)] = 'female'\n",
+        "A_test.Sex.loc[(A_test['Sex'] == 1)] = 'male'\n",
+        "\n",
+        "\n",
+        "A_test.Race.loc[(A_test['Race'] == 0)] = 'Amer-Indian-Eskimo'\n",
+        "A_test.Race.loc[(A_test['Race'] == 1)] = 'Asian-Pac-Islander'\n",
+        "A_test.Race.loc[(A_test['Race'] == 2)] = 'Black'\n",
+        "A_test.Race.loc[(A_test['Race'] == 3)] = 'Other'\n",
+        "A_test.Race.loc[(A_test['Race'] == 4)] = 'White'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"UnmitigatedModel\"></a>\n",
+        "## Training an Unmitigated Model\n",
+        "\n",
+        "So we have a point of comparison, we first train a model (specifically, logistic regression from scikit-learn) on the raw data, without applying any mitigation algorithm:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "unmitigated_predictor = LogisticRegression(solver='liblinear', fit_intercept=True)\n",
+        "\n",
+        "unmitigated_predictor.fit(X_train, Y_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can view this model in the fairness dashboard, and see the disparities which appear:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "FairlearnDashboard(sensitive_features=A_test, sensitive_feature_names=['Sex', 'Race'],\n",
+        "                   y_true=Y_test,\n",
+        "                   y_pred={\"unmitigated\": unmitigated_predictor.predict(X_test)})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Looking at the disparity in accuracy when we select 'Sex' as the sensitive feature, we see that males have an error rate about three times greater than the females. More interesting is the disparity in opportunitiy - males are offered loans at three times the rate of females.\n",
+        "\n",
+        "Despite the fact that we removed the feature from the training data, our predictor still discriminates based on sex. This demonstrates that simply ignoring a protected attribute when fitting a predictor rarely eliminates unfairness. There will generally be enough other features correlated with the removed attribute to lead to disparate impact."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"Mitigation\"></a>\n",
+        "## Mitigation with GridSearch\n",
+        "\n",
+        "The `GridSearch` class in `Fairlearn` implements a simplified version of the exponentiated gradient reduction of [Agarwal et al. 2018](https://arxiv.org/abs/1803.02453). The user supplies a standard ML estimator, which is treated as a blackbox - for this simple example, we shall use the logistic regression estimator from scikit-learn. `GridSearch` works by generating a sequence of relabellings and reweightings, and trains a predictor for each.\n",
+        "\n",
+        "For this example, we specify demographic parity (on the protected attribute of sex) as the fairness metric. Demographic parity requires that individuals are offered the opportunity (a loan in this example) independent of membership in the protected class (i.e., females and males should be offered loans at the same rate). *We are using this metric for the sake of simplicity* in this example; the appropriate fairness metric can only be selected after *careful examination of the broader context* in which the model is to be used."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "sweep = GridSearch(LogisticRegression(solver='liblinear', fit_intercept=True),\n",
+        "                   constraints=DemographicParity(),\n",
+        "                   grid_size=71)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "With our estimator created, we can fit it to the data. After `fit()` completes, we extract the full set of predictors from the `GridSearch` object.\n",
+        "\n",
+        "The following cell trains a many copies of the underlying estimator, and may take a minute or two to run:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "sweep.fit(X_train, Y_train,\n",
+        "          sensitive_features=A_train.Sex)\n",
+        "\n",
+        "predictors = sweep._predictors"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We could load these predictors into the Fairness dashboard now. However, the plot would be somewhat confusing due to their number. In this case, we are going to remove the predictors which are dominated in the error-disparity space by others from the sweep (note that the disparity will only be calculated for the protected attribute; other potentially protected attributes will *not* be mitigated). In general, one might not want to do this, since there may be other considerations beyond the strict optimisation of error and disparity (of the given protected attribute)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "errors, disparities = [], []\n",
+        "for m in predictors:\n",
+        "    classifier = lambda X: m.predict(X)\n",
+        "    \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",
+        "    \n",
+        "all_results = pd.DataFrame( {\"predictor\": predictors, \"error\": errors, \"disparity\": disparities})\n",
+        "\n",
+        "dominant_models_dict = dict()\n",
+        "base_name_format = \"census_gs_model_{0}\"\n",
+        "row_id = 0\n",
+        "for row in all_results.itertuples():\n",
+        "    model_name = base_name_format.format(row_id)\n",
+        "    errors_for_lower_or_eq_disparity = all_results[\"error\"][all_results[\"disparity\"]<=row.disparity]\n",
+        "    if row.error <= errors_for_lower_or_eq_disparity.min():\n",
+        "        dominant_models_dict[model_name] = row.predictor\n",
+        "    row_id = row_id + 1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can construct predictions for the dominant models (we include the unmitigated predictor as well, for comparison):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "predictions_dominant = {\"census_unmitigated\": unmitigated_predictor.predict(X_test)}\n",
+        "models_dominant = {\"census_unmitigated\": unmitigated_predictor}\n",
+        "for name, predictor in dominant_models_dict.items():\n",
+        "    value = predictor.predict(X_test)\n",
+        "    predictions_dominant[name] = value\n",
+        "    models_dominant[name] = predictor"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "These predictions may then be viewed in the fairness dashboard. We include the race column from the dataset, as an alternative basis for assessing the models. However, since we have not based our mitigation on it, the variation in the models with respect to race can be large."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "FairlearnDashboard(sensitive_features=A_test, \n",
+        "                   sensitive_feature_names=['Sex', 'Race'],\n",
+        "                   y_true=Y_test.tolist(),\n",
+        "                   y_pred=predictions_dominant)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "When using sex as the sensitive feature, we see a Pareto front forming - the set of predictors which represent optimal tradeoffs between accuracy and disparity in predictions. In the ideal case, we would have a predictor at (1,0) - perfectly accurate and without any unfairness under demographic parity (with respect to the protected attribute \"sex\"). The Pareto front represents the closest we can come to this ideal based on our data and choice of estimator. Note the range of the axes - the disparity axis covers more values than the accuracy, so we can reduce disparity substantially for a small loss in accuracy. Finally, we also see that the unmitigated model is towards the top right of the plot, with high accuracy, but worst disparity.\n",
+        "\n",
+        "By clicking on individual models on the plot, we can inspect their metrics for disparity and accuracy in greater detail. In a real example, we would then pick the model which represented the best trade-off between accuracy and disparity given the relevant business constraints."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"AzureUpload\"></a>\n",
+        "## Uploading a Fairness Dashboard to Azure\n",
+        "\n",
+        "Uploading a fairness dashboard to Azure is a two stage process. The `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",
+        "1. Register the dominant models\n",
+        "1. Precompute all the required metrics\n",
+        "1. Upload to Azure\n",
+        "\n",
+        "Before that, we need to connect to Azure Machine Learning Studio:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace, Experiment, Model\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "ws.get_details()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"RegisterModels\"></a>\n",
+        "### Registering Models\n",
+        "\n",
+        "The fairness dashboard is designed to integrate with registered models, so we need to do this for the models we want in the Studio portal. The assumption is that the names of the models specified in the dashboard dictionary correspond to the `id`s (i.e. `<name>:<version>` pairs) of registered models in the workspace. We register each of the models in the `models_dominant` dictionary into the workspace. For this, we have to save each model to a file, and then register that file:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import joblib\n",
+        "import os\n",
+        "\n",
+        "os.makedirs('models', exist_ok=True)\n",
+        "def register_model(name, model):\n",
+        "    print(\"Registering \", name)\n",
+        "    model_path = \"models/{0}.pkl\".format(name)\n",
+        "    joblib.dump(value=model, filename=model_path)\n",
+        "    registered_model = Model.register(model_path=model_path,\n",
+        "                                      model_name=name,\n",
+        "                                      workspace=ws)\n",
+        "    print(\"Registered \", registered_model.id)\n",
+        "    return registered_model.id\n",
+        "\n",
+        "model_name_id_mapping = dict()\n",
+        "for name, model in models_dominant.items():\n",
+        "    m_id = register_model(name, model)\n",
+        "    model_name_id_mapping[name] = m_id"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now, produce new predictions dictionaries, with the updated names:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "predictions_dominant_ids = dict()\n",
+        "for name, y_pred in predictions_dominant.items():\n",
+        "    predictions_dominant_ids[model_name_id_mapping[name]] = y_pred"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"PrecomputeMetrics\"></a>\n",
+        "### Precomputing Metrics\n",
+        "\n",
+        "We create a _dashboard dictionary_ using Fairlearn's `metrics` package. The `_create_group_metric_set` method has arguments similar to the Dashboard constructor, except that the sensitive features are passed as a dictionary (to ensure that names are available), and we must specify the type of prediction. Note that we use the `predictions_dominant_ids` dictionary we just created:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "sf = { 'sex': A_test.Sex, 'race': A_test.Race }\n",
+        "\n",
+        "from fairlearn.metrics._group_metric_set import _create_group_metric_set\n",
+        "\n",
+        "\n",
+        "dash_dict = _create_group_metric_set(y_true=Y_test,\n",
+        "                                     predictions=predictions_dominant_ids,\n",
+        "                                     sensitive_features=sf,\n",
+        "                                     prediction_type='binary_classification')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"DashboardUpload\"></a>\n",
+        "### Uploading the Dashboard\n",
+        "\n",
+        "Now, we import our `contrib` package which contains the routine to perform the upload:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.fairness import upload_dashboard_dictionary, download_dashboard_by_upload_id"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we can create an Experiment, then a Run, and upload our dashboard to it:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "exp = Experiment(ws, \"Test_Fairlearn_GridSearch_Census_Demo\")\n",
+        "print(exp)\n",
+        "\n",
+        "run = exp.start_logging()\n",
+        "try:\n",
+        "    dashboard_title = \"Dominant Models from GridSearch\"\n",
+        "    upload_id = upload_dashboard_dictionary(run,\n",
+        "                                            dash_dict,\n",
+        "                                            dashboard_name=dashboard_title)\n",
+        "    print(\"\\nUploaded to id: {0}\\n\".format(upload_id))\n",
+        "\n",
+        "    downloaded_dict = download_dashboard_by_upload_id(run, upload_id)\n",
+        "finally:\n",
+        "    run.complete()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The dashboard can be viewed in the Run Details page.\n",
+        "\n",
+        "Finally, we can verify that the dashboard dictionary which we downloaded matches our upload:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(dash_dict == downloaded_dict)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"Conclusion\"></a>\n",
+        "## Conclusion\n",
+        "\n",
+        "In this notebook we have demonstrated how to use the `GridSearch` algorithm from Fairlearn to generate a collection of models, and then present them in the fairness dashboard in Azure Machine Learning Studio. Please remember that this notebook has not attempted to discuss the many considerations which should be part of any approach to unfairness mitigation. The [Fairlearn website](http://fairlearn.github.io/) provides that discussion"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "riedgar"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.10"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

contrib/fairness/upload-fairness-dashboard.ipynb

@@ -0,0 +1,507 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/contrib/fairness/upload-fairness-dashboard.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Upload a Fairness Dashboard to Azure Machine Learning Studio\n",
+        "**This notebook shows how to generate and upload a fairness assessment dashboard from Fairlearn to AzureML Studio**\n",
+        "\n",
+        "## Table of Contents\n",
+        "\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Loading the Data](#LoadingData)\n",
+        "1. [Processing the Data](#ProcessingData)\n",
+        "1. [Training Models](#TrainingModels)\n",
+        "1. [Logging in to AzureML](#LoginAzureML)\n",
+        "1. [Registering the Models](#RegisterModels)\n",
+        "1. [Using the Fairlearn Dashboard](#LocalDashboard)\n",
+        "1. [Uploading a Fairness Dashboard to Azure](#AzureUpload)\n",
+        "    1. Computing Fairness Metrics\n",
+        "    1. Uploading to Azure\n",
+        "1. [Conclusion](#Conclusion)\n",
+        "    \n",
+        "\n",
+        "<a id=\"Introduction\"></a>\n",
+        "## Introduction\n",
+        "\n",
+        "In this notebook, we walk through a simple example of using the `azureml-contrib-fairness` package to upload a collection of fairness statistics for a fairness dashboard. It is an example of integrating the [open source Fairlearn package](https://www.github.com/fairlearn/fairlearn) with Azure Machine Learning. This is not an example of fairness analysis or mitigation - this notebook simply shows how to get a fairness dashboard into the Azure Machine Learning portal. We will load the data and train a couple of simple models. We will then use Fairlearn to generate data for a Fairness dashboard, which we can upload to Azure Machine Learning portal and view there.\n",
+        "\n",
+        "### Setup\n",
+        "\n",
+        "To use this notebook, an Azure Machine Learning workspace is required.\n",
+        "Please see the [configuration notebook](../../configuration.ipynb) for information about creating one, if required.\n",
+        "This notebook also requires the following packages:\n",
+        "* `azureml-contrib-fairness`\n",
+        "* `fairlearn==0.4.6`\n",
+        "* `joblib`\n",
+        "* `shap`\n",
+        "\n",
+        "Fairlearn relies on features introduced in v0.22.1 of `scikit-learn`. If you have an older version already installed, please uncomment and run the following cell:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# !pip install --upgrade scikit-learn>=0.22.1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"LoadingData\"></a>\n",
+        "## Loading the Data\n",
+        "We use the well-known `adult` census dataset, which we load using `shap` (for convenience). We start with a fairly unremarkable set of imports:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn import svm\n",
+        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "import pandas as pd\n",
+        "import shap"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we can load the data:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_raw, Y = shap.datasets.adult()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can take a look at some of the data. For example, the next cells shows the counts of the different races identified in the dataset:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(X_raw[\"Race\"].value_counts().to_dict())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"ProcessingData\"></a>\n",
+        "## Processing the Data\n",
+        "\n",
+        "With the data loaded, we process it for our needs. First, we extract the sensitive features of interest into `A` (conventionally used in the literature) and put the rest of the feature data into `X`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "A = X_raw[['Sex','Race']]\n",
+        "X = X_raw.drop(labels=['Sex', 'Race'],axis = 1)\n",
+        "X = pd.get_dummies(X)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Next, we apply a standard set of scalings:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "sc = StandardScaler()\n",
+        "X_scaled = sc.fit_transform(X)\n",
+        "X_scaled = pd.DataFrame(X_scaled, columns=X.columns)\n",
+        "\n",
+        "le = LabelEncoder()\n",
+        "Y = le.fit_transform(Y)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we can then split our data into training and test sets, and also make the labels on our test portion of `A` human-readable:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "X_train, X_test, Y_train, Y_test, A_train, A_test = train_test_split(X_scaled, \n",
+        "                                                    Y, \n",
+        "                                                    A,\n",
+        "                                                    test_size = 0.2,\n",
+        "                                                    random_state=0,\n",
+        "                                                    stratify=Y)\n",
+        "\n",
+        "# Work around indexing issue\n",
+        "X_train = X_train.reset_index(drop=True)\n",
+        "A_train = A_train.reset_index(drop=True)\n",
+        "X_test = X_test.reset_index(drop=True)\n",
+        "A_test = A_test.reset_index(drop=True)\n",
+        "\n",
+        "# Improve labels\n",
+        "A_test.Sex.loc[(A_test['Sex'] == 0)] = 'female'\n",
+        "A_test.Sex.loc[(A_test['Sex'] == 1)] = 'male'\n",
+        "\n",
+        "\n",
+        "A_test.Race.loc[(A_test['Race'] == 0)] = 'Amer-Indian-Eskimo'\n",
+        "A_test.Race.loc[(A_test['Race'] == 1)] = 'Asian-Pac-Islander'\n",
+        "A_test.Race.loc[(A_test['Race'] == 2)] = 'Black'\n",
+        "A_test.Race.loc[(A_test['Race'] == 3)] = 'Other'\n",
+        "A_test.Race.loc[(A_test['Race'] == 4)] = 'White'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"TrainingModels\"></a>\n",
+        "## Training Models\n",
+        "\n",
+        "We now train a couple of different models on our data. The `adult` census dataset is a classification problem - the goal is to predict whether a particular individual exceeds an income threshold. For the purpose of generating a dashboard to upload, it is sufficient to train two basic classifiers. First, a logistic regression classifier:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "lr_predictor = LogisticRegression(solver='liblinear', fit_intercept=True)\n",
+        "\n",
+        "lr_predictor.fit(X_train, Y_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "And for comparison, a support vector classifier:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "svm_predictor = svm.SVC()\n",
+        "\n",
+        "svm_predictor.fit(X_train, Y_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"LoginAzureML\"></a>\n",
+        "## Logging in to AzureML\n",
+        "\n",
+        "With our two classifiers trained, we can log into our AzureML workspace:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace, Experiment, Model\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "ws.get_details()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"RegisterModels\"></a>\n",
+        "## Registering the Models\n",
+        "\n",
+        "Next, we register our models. By default, the subroutine which uploads the models checks that the names provided correspond to registered models in the workspace. We define a utility routine to do the registering:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import joblib\n",
+        "import os\n",
+        "\n",
+        "os.makedirs('models', exist_ok=True)\n",
+        "def register_model(name, model):\n",
+        "    print(\"Registering \", name)\n",
+        "    model_path = \"models/{0}.pkl\".format(name)\n",
+        "    joblib.dump(value=model, filename=model_path)\n",
+        "    registered_model = Model.register(model_path=model_path,\n",
+        "                                      model_name=name,\n",
+        "                                      workspace=ws)\n",
+        "    print(\"Registered \", registered_model.id)\n",
+        "    return registered_model.id"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now, we register the models. For convenience in subsequent method calls, we store the results in a dictionary, which maps the `id` of the registered model (a string in `name:version` format) to the predictor itself:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model_dict = {}\n",
+        "\n",
+        "lr_reg_id = register_model(\"fairness_linear_regression\", lr_predictor)\n",
+        "model_dict[lr_reg_id] = lr_predictor\n",
+        "svm_reg_id = register_model(\"fairness_svm\", svm_predictor)\n",
+        "model_dict[svm_reg_id] = svm_predictor"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"LocalDashboard\"></a>\n",
+        "## Using the Fairlearn Dashboard\n",
+        "\n",
+        "We can now examine the fairness of the two models we have training, both as a function of race and (binary) sex. Before uploading the dashboard to the AzureML portal, we will first instantiate a local instance of the Fairlearn dashboard.\n",
+        "\n",
+        "Regardless of the viewing location, the dashboard is based on three things - the true values, the model predictions and the sensitive feature values. The dashboard can use predictions from multiple models and multiple sensitive features if desired (as we are doing here).\n",
+        "\n",
+        "Our first step is to generate a dictionary mapping the `id` of the registered model to the corresponding array of predictions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ys_pred = {}\n",
+        "for n, p in model_dict.items():\n",
+        "    ys_pred[n] = p.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can examine these predictions in a locally invoked Fairlearn dashboard. This can be compared to the dashboard uploaded to the portal (in the next section):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from fairlearn.widget import FairlearnDashboard\n",
+        "\n",
+        "FairlearnDashboard(sensitive_features=A_test, \n",
+        "                   sensitive_feature_names=['Sex', 'Race'],\n",
+        "                   y_true=Y_test.tolist(),\n",
+        "                   y_pred=ys_pred)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"AzureUpload\"></a>\n",
+        "## Uploading a Fairness Dashboard to Azure\n",
+        "\n",
+        "Uploading a fairness dashboard to Azure is a two stage process. The `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",
+        "1. Precompute all the required metrics\n",
+        "1. Upload to Azure\n",
+        "\n",
+        "\n",
+        "### Computing Fairness Metrics\n",
+        "We use Fairlearn to create a dictionary which contains all the data required to display a dashboard. This includes both the raw data (true values, predicted values and sensitive features), and also the fairness metrics. The API is similar to that used to invoke the Dashboard locally. However, there are a few minor changes to the API, and the type of problem being examined (binary classification, regression etc.) needs to be specified explicitly:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "sf = { 'Race': A_test.Race, 'Sex': A_test.Sex }\n",
+        "\n",
+        "from fairlearn.metrics._group_metric_set import _create_group_metric_set\n",
+        "\n",
+        "dash_dict = _create_group_metric_set(y_true=Y_test,\n",
+        "                                     predictions=ys_pred,\n",
+        "                                     sensitive_features=sf,\n",
+        "                                     prediction_type='binary_classification')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The `_create_group_metric_set()` method is currently underscored since its exact design is not yet final in Fairlearn."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Uploading to Azure\n",
+        "\n",
+        "We can now import the `azureml.contrib.fairness` package itself. We will round-trip the data, so there are two required subroutines:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.fairness import upload_dashboard_dictionary, download_dashboard_by_upload_id"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we can upload the generated dictionary to AzureML. The upload method requires a run, so we first create an experiment and a run. The uploaded dashboard can be seen on the corresponding Run Details page in AzureML Studio. For completeness, we also download the dashboard dictionary which we uploaded."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "exp = Experiment(ws, \"notebook-01\")\n",
+        "print(exp)\n",
+        "\n",
+        "run = exp.start_logging()\n",
+        "try:\n",
+        "    dashboard_title = \"Sample notebook upload\"\n",
+        "    upload_id = upload_dashboard_dictionary(run,\n",
+        "                                            dash_dict,\n",
+        "                                            dashboard_name=dashboard_title)\n",
+        "    print(\"\\nUploaded to id: {0}\\n\".format(upload_id))\n",
+        "\n",
+        "    downloaded_dict = download_dashboard_by_upload_id(run, upload_id)\n",
+        "finally:\n",
+        "    run.complete()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we can verify that the dashboard dictionary which we downloaded matches our upload:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(dash_dict == downloaded_dict)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id=\"Conclusion\"></a>\n",
+        "## Conclusion\n",
+        "\n",
+        "In this notebook we have demonstrated how to generate and upload a fairness dashboard to AzureML Studio. We have not discussed how to analyse the results and apply mitigations. Those topics will be covered elsewhere."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "riedgar"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}

contrib/gbdt/lightgbm/binary0.test

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

contrib/gbdt/lightgbm/binary1.test

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

contrib/gbdt/lightgbm/lightgbm-example.ipynb

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

contrib/gbdt/lightgbm/lightgbm-example.yml

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

contrib/gbdt/lightgbm/train.conf

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

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

@@ -1,8 +1,8 @@
 # Table of Contents
 1. [Automated ML Introduction](#introduction)
-1. [Setup using Azure Notebooks](#jupyter)
+1. [Setup using Compute Instances](#jupyter)
-1. [Setup using Azure Databricks](#databricks)
 1. [Setup using a Local Conda environment](#localconda)
+1. [Setup using Azure Databricks](#databricks)
 1. [Automated ML SDK Sample Notebooks](#samples)
 1. [Documentation](#documentation)
 1. [Running using python command](#pythoncommand)
@@ -21,13 +21,13 @@ Below are the three execution environments supported by automated ML.
 
 
  <a name="jupyter"></a>
-## Setup using Notebook VMs - Jupyter based notebooks from a Azure VM
+## Setup using Compute Instances - Jupyter based notebooks from a Azure Virtual Machine
 
 1. Open the [ML Azure portal](https://ml.azure.com)
 1. Select Compute
-1. Select Notebook VMs
+1. Select Compute Instances
 1. Click New
-1. Type a name for the Vm and select a VM type
+1. Type a Compute Name, select a Virtual Machine type and select a Virtual Machine size
 1. Click Create
 
 <a name="localconda"></a>
@@ -144,7 +144,7 @@ jupyter notebook
     - Dataset: forecasting for a bike-sharing
     - Example of training an automated ML forecasting model on multiple time-series
 
-- [auto-ml-forecasting-function.ipynb](forecasting-high-frequency/auto-ml-forecasting-function.ipynb)
+- [auto-ml-forecasting-function.ipynb](forecasting-forecast-function/auto-ml-forecasting-function.ipynb)
     - Example of training an automated ML forecasting model on multiple time-series
 
 - [auto-ml-forecasting-beer-remote.ipynb](forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb)
@@ -230,6 +230,15 @@ You may check the version of tensorflow and uninstall as follows
 2) enter `pip freeze` and look for `tensorflow` , if found, the version listed should be < 1.13
 3) If the listed version is a not a supported version,  `pip uninstall tensorflow` in the command shell and enter y for confirmation.
 
+## KeyError: 'brand' when running AutoML on local compute or Azure Databricks cluster**
+If a new environment was created after 10 June 2020 using SDK 1.7.0 or lower, training may fail with the above error due to an update in the py-cpuinfo package. (Environments created on or before 10 June 2020 are unaffected, as well as experiments run on remote compute as cached training images are used.) To work around this issue, either of the two following steps can be taken:
+
+1) Update the SDK version to 1.8.0 or higher (this will also downgrade py-cpuinfo to 5.0.0):
+`pip install --upgrade azureml-sdk[automl]`
+
+2) Downgrade the installed version of py-cpuinfo to 5.0.0:
+`pip install py-cpuinfo==5.0.0`
+
 ## Remote run: DsvmCompute.create fails
 There are several reasons why the DsvmCompute.create can fail.  The reason is usually in the error message but you have to look at the end of the error message for the detailed reason.  Some common reasons are:
 1) `Compute name is invalid, it should start with a letter, be between 2 and 16 character, and only include letters (a-zA-Z), numbers (0-9) and \'-\'.`  Note that underscore is not allowed in the name.

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

@@ -4,24 +4,23 @@ dependencies:
   # Currently Azure ML only supports 3.5.2 and later.
 - pip<=19.3.1
 - python>=3.5.2,<3.6.8
-- wheel==0.30.0
 - nb_conda
 - matplotlib==2.1.0
-- numpy>=1.16.0,<=1.16.2
+- numpy~=1.16.0
 - cython
 - urllib3<1.24
-- scipy>=1.0.0,<=1.1.0
+- scipy==1.4.1
 - scikit-learn>=0.19.0,<=0.20.3
 - pandas>=0.22.0,<=0.23.4
 - py-xgboost<=0.90
-- fbprophet==0.5
+- conda-forge::fbprophet==0.5
-- pytorch=1.1.0
+- holidays==0.9.11
-- cudatoolkit=9.0
+- pytorch::pytorch=1.4.0
+- cudatoolkit=10.1.243
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
   - azureml-defaults
-  - azureml-dataprep[pandas]
   - azureml-train-automl
   - azureml-train
   - azureml-widgets
@@ -30,7 +29,3 @@ dependencies:
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
 
-channels:
-- anaconda
-- conda-forge
-- pytorch

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

@@ -5,24 +5,23 @@ dependencies:
 - pip<=19.3.1
 - nomkl
 - python>=3.5.2,<3.6.8
-- wheel==0.30.0
 - nb_conda
 - matplotlib==2.1.0
-- numpy>=1.16.0,<=1.16.2
+- numpy~=1.16.0
 - cython
 - urllib3<1.24
-- scipy>=1.0.0,<=1.1.0
+- scipy==1.4.1
 - scikit-learn>=0.19.0,<=0.20.3
-- pandas>=0.22.0,<0.23.0
+- pandas>=0.22.0,<=0.23.4
-- py-xgboost<=0.80
+- py-xgboost<=0.90
-- fbprophet==0.5
+- conda-forge::fbprophet==0.5
-- pytorch=1.1.0
+- holidays==0.9.11
+- pytorch::pytorch=1.4.0
 - cudatoolkit=9.0
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
   - azureml-defaults
-  - azureml-dataprep[pandas]
   - azureml-train-automl
   - azureml-train
   - azureml-widgets
@@ -30,8 +29,3 @@ dependencies:
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz  
-
-channels:
-- anaconda
-- conda-forge
-- pytorch

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

@@ -41,7 +41,7 @@
         "\n",
         "In this example we use the UCI Bank Marketing dataset to showcase how you can use AutoML for a  classification problem and deploy it to an Azure Container Instance (ACI). The classification goal is to predict if the client will subscribe to a term deposit with the bank.\n",
         "\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set.  Otherwise, go through the [configuration](../../../configuration.ipynb)  notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
+        "If you are using an Azure Machine Learning Compute Instance, you are all set.  Otherwise, go through the [configuration](../../../configuration.ipynb)  notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
         "\n",
         "Please find the ONNX related documentations [here](https://github.com/onnx/onnx).\n",
         "\n",
@@ -57,7 +57,7 @@
         "9. Test the ACI service.\n",
         "\n",
         "In addition this notebook showcases the following features\n",
-        "- **Blacklisting** certain pipelines\n",
+        "- **Blocking** certain pipelines\n",
         "- Specifying **target metrics** to indicate stopping criteria\n",
         "- Handling **missing data** in the input"
       ]
@@ -89,7 +89,7 @@
         "from azureml.automl.core.featurization import FeaturizationConfig\n",
         "from azureml.core.dataset import Dataset\n",
         "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.explain.model._internal.explanation_client import ExplanationClient"
+        "from azureml.interpret._internal.explanation_client import ExplanationClient"
       ]
     },
     {
@@ -105,7 +105,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.4.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -314,8 +314,8 @@
         "|**task**|classification or regression or forecasting|\n",
         "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
         "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
-        "|**blacklist_models** | *List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run. <br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGD</i><br><i>MultinomialNaiveBayes</i><br><i>BernoulliNaiveBayes</i><br><i>SVM</i><br><i>LinearSVM</i><br><i>KNN</i><br><i>DecisionTree</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>GradientBoosting</i><br><i>TensorFlowDNN</i><br><i>TensorFlowLinearClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i><br><br>Allowed values for **Forecasting**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i><br><i>Arima</i><br><i>Prophet</i>|\n",
+        "|**blocked_models** | *List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run. <br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGD</i><br><i>MultinomialNaiveBayes</i><br><i>BernoulliNaiveBayes</i><br><i>SVM</i><br><i>LinearSVM</i><br><i>KNN</i><br><i>DecisionTree</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>GradientBoosting</i><br><i>TensorFlowDNN</i><br><i>TensorFlowLinearClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i><br><br>Allowed values for **Forecasting**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i><br><i>Arima</i><br><i>Prophet</i>|\n",
-        "| **whitelist_models** |  *List* of *strings* indicating machine learning algorithms for AutoML to use in this run. Same values listed above for **blacklist_models** allowed for **whitelist_models**.|\n",
+        "|**allowed_models** |  *List* of *strings* indicating machine learning algorithms for AutoML to use in this run. Same values listed above for **blocked_models** allowed for **allowed_models**.|\n",
         "|**experiment_exit_score**| Value indicating the target for *primary_metric*. <br>Once the target is surpassed the run terminates.|\n",
         "|**experiment_timeout_hours**| Maximum amount of time in hours that all iterations combined can take before the experiment terminates.|\n",
         "|**enable_early_stopping**| Flag to enble early termination if the score is not improving in the short term.|\n",
@@ -349,7 +349,7 @@
         "                             debug_log = 'automl_errors.log',\n",
         "                             compute_target=compute_target,\n",
         "                             experiment_exit_score = 0.9984,\n",
-        "                             blacklist_models = ['KNN','LinearSVM'],\n",
+        "                             blocked_models = ['KNN','LinearSVM'],\n",
         "                             enable_onnx_compatible_models=True,\n",
         "                             training_data = train_data,\n",
         "                             label_column_name = label,\n",
@@ -362,7 +362,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while."
+        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
       ]
     },
     {
@@ -643,7 +643,7 @@
         "\n",
         "### Retrieve the Best Model\n",
         "\n",
-        "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+        "Below we select the best pipeline from our iterations.  The `get_output` method returns the best run and the fitted model. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
       ]
     },
     {
@@ -675,10 +675,8 @@
         "model_name = best_run.properties['model_name']\n",
         "\n",
         "script_file_name = 'inference/score.py'\n",
-        "conda_env_file_name = 'inference/env.yml'\n",
         "\n",
-        "best_run.download_file('outputs/scoring_file_v_1_0_0.py', 'inference/score.py')\n",
+        "best_run.download_file('outputs/scoring_file_v_1_0_0.py', 'inference/score.py')"
-        "best_run.download_file('outputs/conda_env_v_1_0_0.yml', 'inference/env.yml')"
       ]
     },
     {
@@ -721,8 +719,7 @@
         "from azureml.core.model import Model\n",
         "from azureml.core.environment import Environment\n",
         "\n",
-        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=conda_env_file_name)\n",
+        "inference_config = InferenceConfig(entry_script=script_file_name)\n",
-        "inference_config = InferenceConfig(entry_script=script_file_name, environment=myenv)\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
         "                                               memory_gb = 1, \n",
@@ -736,24 +733,6 @@
         "print(aci_service.state)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Delete a Web Service\n",
-        "\n",
-        "Deletes the specified web service."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#aci_service.delete()"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -778,7 +757,9 @@
       "source": [
         "## Test\n",
         "\n",
-        "Now that the model is trained, run the test data through the trained model to get the predicted values."
+        "Now that the model is trained, run the test data through the trained model to get the predicted values.  This calls the ACI web service to do the prediction.\n",
+        "\n",
+        "Note that the JSON passed to the ACI web service is an array of rows of data.  Each row should either be an array of values in the same order that was used for training or a dictionary where the keys are the same as the column names used for training.  The example below uses dictionary rows."
       ]
     },
     {
@@ -818,10 +799,27 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "y_pred  = fitted_model.predict(X_test)\n",
+        "import json\n",
+        "import requests\n",
+        "\n",
+        "X_test_json = X_test.to_json(orient='records')\n",
+        "data = \"{\\\"data\\\": \" + X_test_json +\"}\"\n",
+        "headers = {'Content-Type': 'application/json'}\n",
+        "\n",
+        "resp = requests.post(aci_service.scoring_uri, data, headers=headers)\n",
+        "\n",
+        "y_pred = json.loads(json.loads(resp.text))['result']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
         "actual = array(y_test)\n",
         "actual = actual[:,0]\n",
-        "print(y_pred.shape, \" \", actual.shape)"
+        "print(len(y_pred), \" \", len(actual))"
       ]
     },
     {
@@ -830,8 +828,7 @@
       "source": [
         "### Calculate metrics for the prediction\n",
         "\n",
-        "Now visualize the data on a scatter plot to show what our truth (actual) values are compared to the predicted values \n",
+        "Now visualize the data as a confusion matrix that compared the predicted values against the actual values.\n"
-        "from the trained model that was returned."
       ]
     },
     {
@@ -841,12 +838,45 @@
       "outputs": [],
       "source": [
         "%matplotlib notebook\n",
-        "test_pred = plt.scatter(actual, y_pred, color='b')\n",
+        "from sklearn.metrics import confusion_matrix\n",
-        "test_test = plt.scatter(actual, actual, color='g')\n",
+        "import numpy as np\n",
-        "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
+        "import itertools\n",
+        "\n",
+        "cf =confusion_matrix(actual,y_pred)\n",
+        "plt.imshow(cf,cmap=plt.cm.Blues,interpolation='nearest')\n",
+        "plt.colorbar()\n",
+        "plt.title('Confusion Matrix')\n",
+        "plt.xlabel('Predicted')\n",
+        "plt.ylabel('Actual')\n",
+        "class_labels = ['no','yes']\n",
+        "tick_marks = np.arange(len(class_labels))\n",
+        "plt.xticks(tick_marks,class_labels)\n",
+        "plt.yticks([-0.5,0,1,1.5],['','no','yes',''])\n",
+        "# plotting text value inside cells\n",
+        "thresh = cf.max() / 2.\n",
+        "for i,j in itertools.product(range(cf.shape[0]),range(cf.shape[1])):\n",
+        "    plt.text(j,i,format(cf[i,j],'d'),horizontalalignment='center',color='white' if cf[i,j] >thresh else 'black')\n",
         "plt.show()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Delete a Web Service\n",
+        "\n",
+        "Deletes the specified web service."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "aci_service.delete()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

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

@@ -2,7 +2,3 @@ name: auto-ml-classification-bank-marketing-all-features
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - onnxruntime==1.0.0

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

@@ -42,7 +42,7 @@
         "\n",
         "This notebook is using remote compute to train the model.\n",
         "\n",
-        "If you are using an Azure Machine Learning [Notebook VM](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-1st-experiment-sdk-setup), you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
+        "If you are using an Azure Machine Learning Compute Instance, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
         "\n",
         "In this notebook you will learn how to:\n",
         "1. Create an experiment using an existing workspace.\n",
@@ -93,7 +93,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.4.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -232,7 +232,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Call the `submit` method on the experiment object and pass the run configuration. Depending on the data and the number of iterations this can run for a while."
+        "Call the `submit` method on the experiment object and pass the run configuration. Depending on the data and the number of iterations this can run for a while. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
       ]
     },
     {
@@ -322,7 +322,7 @@
         "\n",
         "### Retrieve the Best Model\n",
         "\n",
-        "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
       ]
     },
     {

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

@@ -2,6 +2,3 @@ name: auto-ml-classification-credit-card-fraud
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib

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

@@ -97,7 +97,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.4.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -491,8 +491,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "test_run = run_inference(test_experiment, compute_target, script_folder, best_dnn_run, test_dataset,\n",
+        "test_run = run_inference(test_experiment, compute_target, script_folder, best_dnn_run,\n",
-        "                 target_column_name, model_name)"
+        "                         train_dataset, test_dataset, target_column_name, model_name)"
       ]
     },
     {

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

@@ -2,11 +2,3 @@ name: auto-ml-classification-text-dnn
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - https://download.pytorch.org/whl/cpu/torch-1.1.0-cp35-cp35m-win_amd64.whl
-  - sentencepiece==0.1.82
-  - pytorch-transformers==1.0
-  - spacy==2.1.8
-  - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz

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

@@ -6,7 +6,7 @@ from azureml.core.run import Run
 
 
 def run_inference(test_experiment, compute_target, script_folder, train_run,
-                  test_dataset, target_column_name, model_name):
+                  train_dataset, test_dataset, target_column_name, model_name):
 
     train_run.download_file('outputs/conda_env_v_1_0_0.yml',
                             'inference/condafile.yml')
@@ -22,7 +22,10 @@ def run_inference(test_experiment, compute_target, script_folder, train_run,
                         '--target_column_name': target_column_name,
                         '--model_name': model_name
                     },
-                    inputs=[test_dataset.as_named_input('test_data')],
+                    inputs=[
+                        train_dataset.as_named_input('train_data'),
+                        test_dataset.as_named_input('test_data')
+                    ],
                     compute_target=compute_target,
                     environment_definition=inference_env)
 

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

@@ -1,9 +1,11 @@
-import numpy as np
 import argparse
-from azureml.core import Run
+
+import numpy as np
+
 from sklearn.externals import joblib
-from azureml.automl.core._vendor.automl.client.core.common import metrics
+
-from automl.client.core.common import constants
+from azureml.automl.runtime.shared.score import scoring, constants
+from azureml.core import Run
 from azureml.core.model import Model
 
 
@@ -30,22 +32,26 @@ 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)
 
-# use automl metrics module
+# Use the AutoML scoring module
-scores = metrics.compute_metrics_classification(
+class_labels = np.unique(np.concatenate((y_train_df.values, y_test_df.values)))
-    np.array(predicted),
+train_labels = model.classes_
-    np.array(y_test_df),
+classification_metrics = list(constants.CLASSIFICATION_SCALAR_SET)
-    class_labels=model.classes_,
+scores = scoring.score_classification(y_test_df.values, predicted,
-    metrics=list(constants.Metric.SCALAR_CLASSIFICATION_SET)
+                                      classification_metrics,
-)
+                                      class_labels, train_labels)
 
 print("scores:")
 print(scores)

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

@@ -88,7 +88,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.4.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -201,10 +201,9 @@
         "conda_run_config.environment.docker.enabled = True\n",
         "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
         "\n",
-        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', 'applicationinsights', 'azureml-opendatasets'], \n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', 'applicationinsights', 'azureml-opendatasets', 'azureml-defaults'], \n",
         "                              conda_packages=['numpy==1.16.2'], \n",
         "                              pin_sdk_version=False)\n",
-        "#cd.add_pip_package('azureml-explain-model')\n",
         "conda_run_config.environment.python.conda_dependencies = cd\n",
         "\n",
         "print('run config is ready')"

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

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

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

@@ -114,7 +114,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.4.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -217,7 +217,7 @@
         "\n",
         "**Time column** is the time axis along which to predict.\n",
         "\n",
-        "**Grain** is another word for an individual time series in your dataset. Grains are identified by values of the columns listed `grain_column_names`, for example \"store\" and \"item\" if your data has multiple time series of sales, one series for each combination of store and item sold.\n",
+        "**Time series identifier columns** are identified by values of the columns listed `time_series_id_column_names`, for example \"store\" and \"item\" if your data has multiple time series of sales, one series for each combination of store and item sold.\n",
         "\n",
         "This dataset has only one time series. Please see the [orange juice notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales) for an example of a multi-time series dataset."
       ]
@@ -269,7 +269,7 @@
       "source": [
         "target_column_name = 'BeerProduction'\n",
         "time_column_name = 'DATE'\n",
-        "grain_column_names = []\n",
+        "time_series_id_column_names = []\n",
         "freq = 'M' #Monthly data"
       ]
     },
@@ -329,7 +329,7 @@
       },
       "outputs": [],
       "source": [
-        "max_horizon = 12"
+        "forecast_horizon = 12"
       ]
     },
     {
@@ -352,8 +352,6 @@
         "|**label_column_name**|The name of the label column.|\n",
         "|**enable_dnn**|Enable Forecasting DNNs|\n",
         "\n",
-        "This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blacklist_models list but you may need to increase the iteration_timeout_minutes parameter value to get results.\n",
-        "\n",
         "This step requires an Enterprise workspace to gain access to this feature. To learn more about creating an Enterprise workspace or upgrading to an Enterprise workspace from the Azure portal, please visit our [Workspace page.](https://docs.microsoft.com/azure/machine-learning/service/concept-workspace#upgrade)."
       ]
     },
@@ -366,11 +364,10 @@
       },
       "outputs": [],
       "source": [
-        "automl_settings = {\n",
+        "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
-        "    'time_column_name': time_column_name,\n",
+        "forecasting_parameters = ForecastingParameters(\n",
-        "    'max_horizon': max_horizon,\n",
+        "    time_column_name=time_column_name, forecast_horizon=forecast_horizon\n",
-        "    'enable_dnn' : True,\n",
+        ")\n",
-        "}\n",
         "\n",
         "automl_config = AutoMLConfig(task='forecasting',                             \n",
         "                             primary_metric='normalized_root_mean_squared_error',\n",
@@ -382,7 +379,8 @@
         "                             compute_target=compute_target,\n",
         "                             max_concurrent_iterations=4,\n",
         "                             max_cores_per_iteration=-1,\n",
-        "                            **automl_settings)"
+        "                             enable_dnn=True,\n",
+        "                             forecasting_parameters=forecasting_parameters)"
       ]
     },
     {
@@ -392,7 +390,7 @@
         "hidePrompt": false
       },
       "source": [
-        "We will now run the experiment, starting with 10 iterations of model search. The experiment can be continued for more iterations if more accurate results are required."
+        "We will now run the experiment, starting with 10 iterations of model search. The experiment can be continued for more iterations if more accurate results are required. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
       ]
     },
     {
@@ -583,7 +581,7 @@
       "source": [
         "from helper import run_inference\n",
         "\n",
-        "test_run = run_inference(test_experiment, compute_target, script_folder, best_dnn_run, test_dataset, valid_dataset, max_horizon,\n",
+        "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)"
       ]
     },
@@ -605,7 +603,7 @@
         "from helper import run_multiple_inferences\n",
         "\n",
         "summary_df = run_multiple_inferences(summary_df, experiment, test_experiment, compute_target, script_folder, test_dataset, \n",
-        "                  valid_dataset, max_horizon, target_column_name, time_column_name, freq)"
+        "                  valid_dataset, forecast_horizon, target_column_name, time_column_name, freq)"
       ]
     },
     {

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

@@ -1,11 +1,4 @@
 name: auto-ml-forecasting-beer-remote
 dependencies:
-- py-xgboost<=0.90
 - pip:
   - azureml-sdk
-  - numpy==1.16.2
-  - pandas==0.23.4
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - azureml-train

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

@@ -1,12 +1,14 @@
-import pandas as pd
-import numpy as np
 import argparse
-from azureml.core import Run
+
+import numpy as np
+import pandas as pd
+
+from pandas.tseries.frequencies import to_offset
 from sklearn.externals import joblib
 from sklearn.metrics import mean_absolute_error, mean_squared_error
-from azureml.automl.core._vendor.automl.client.core.common import metrics
+
-from automl.client.core.common import constants
+from azureml.automl.runtime.shared.score import scoring, constants
-from pandas.tseries.frequencies import to_offset
+from azureml.core import Run
 
 
 def align_outputs(y_predicted, X_trans, X_test, y_test,
@@ -300,12 +302,11 @@ print(df_all[target_column_name])
 print("predicted values:::")
 print(df_all['predicted'])
 
-# use automl metrics module
+# Use the AutoML scoring module
-scores = metrics.compute_metrics_regression(
+regression_metrics = list(constants.REGRESSION_SCALAR_SET)
-    df_all['predicted'],
+y_test = np.array(df_all[target_column_name])
-    df_all[target_column_name],
+y_pred = np.array(df_all['predicted'])
-    list(constants.Metric.SCALAR_REGRESSION_SET),
+scores = scoring.score_regression(y_test, y_pred, regression_metrics)
-    None, None, None)
 
 print("scores:")
 print(scores)

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

@@ -87,7 +87,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.4.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -238,6 +238,22 @@
         "test.to_pandas_dataframe().head(5).reset_index(drop=True)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Forecasting Parameters\n",
+        "To define forecasting parameters for your experiment training, you can leverage the ForecastingParameters class. The table below details the forecasting parameter we will be passing into our experiment.\n",
+        "\n",
+        "|Property|Description|\n",
+        "|-|-|\n",
+        "|**time_column_name**|The name of your time column.|\n",
+        "|**forecast_horizon**|The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly).|\n",
+        "|**country_or_region_for_holidays**|The country/region used to generate holiday features. These should be ISO 3166 two-letter country/region codes (i.e. 'US', 'GB').|\n",
+        "|**target_lags**|The target_lags specifies how far back we will construct the lags of the target variable.|\n",
+        "|**drop_column_names**|Name(s) of columns to drop prior to modeling|"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -250,20 +266,16 @@
         "|-|-|\n",
         "|**task**|forecasting|\n",
         "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>\n",
-        "|**blacklist_models**|Models in blacklist won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting?view=azure-ml-py).|\n",
+        "|**blocked_models**|Models in blocked_models won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting?view=azure-ml-py).|\n",
         "|**experiment_timeout_hours**|Experimentation timeout in hours.|\n",
         "|**training_data**|Input dataset, containing both features and label column.|\n",
         "|**label_column_name**|The name of the label column.|\n",
         "|**compute_target**|The remote compute for training.|\n",
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**enable_early_stopping**|If early stopping is on, training will stop when the primary metric is no longer improving.|\n",
-        "|**time_column_name**|Name of the datetime column in the input data|\n",
+        "|**forecasting_parameters**|A class that holds all the forecasting related parameters.|\n",
-        "|**max_horizon**|Maximum desired forecast horizon in units of time-series frequency|\n",
-        "|**country_or_region**|The country/region used to generate holiday features. These should be ISO 3166 two-letter country/region codes (i.e. 'US', 'GB').|\n",
-        "|**target_lags**|The target_lags specifies how far back we will construct the lags of the target variable.|\n",
-        "|**drop_column_names**|Name(s) of columns to drop prior to modeling|\n",
         "\n",
-        "This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blacklist_models list but you may need to increase the experiment_timeout_hours parameter value to get results."
+        "This notebook uses the blocked_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blocked_models list but you may need to increase the experiment_timeout_hours parameter value to get results."
       ]
     },
     {
@@ -281,7 +293,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "max_horizon = 14"
+        "forecast_horizon = 14"
       ]
     },
     {
@@ -297,17 +309,18 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "time_series_settings = {\n",
+        "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
-        "    'time_column_name': time_column_name,\n",
+        "forecasting_parameters = ForecastingParameters(\n",
-        "    'max_horizon': max_horizon,    \n",
+        "    time_column_name=time_column_name,\n",
-        "    'country_or_region': 'US', # set country_or_region will trigger holiday featurizer\n",
+        "    forecast_horizon=forecast_horizon,\n",
-        "    'target_lags': 'auto', # use heuristic based lag setting    \n",
+        "    country_or_region_for_holidays='US', # set country_or_region will trigger holiday featurizer\n",
-        "    'drop_column_names': ['casual', 'registered'] # these columns are a breakdown of the total and therefore a leak\n",
+        "    target_lags='auto', # use heuristic based lag setting    \n",
-        "}\n",
+        "    drop_column_names=['casual', 'registered'] # these columns are a breakdown of the total and therefore a leak\n",
+        ")\n",
         "\n",
         "automl_config = AutoMLConfig(task='forecasting',                             \n",
         "                             primary_metric='normalized_root_mean_squared_error',\n",
-        "                             blacklist_models = ['ExtremeRandomTrees'],                             \n",
+        "                             blocked_models = ['ExtremeRandomTrees'],                             \n",
         "                             experiment_timeout_hours=0.3,\n",
         "                             training_data=train,\n",
         "                             label_column_name=target_column_name,\n",
@@ -317,7 +330,7 @@
         "                             max_concurrent_iterations=4,\n",
         "                             max_cores_per_iteration=-1,\n",
         "                             verbosity=logging.INFO,\n",
-        "                            **time_series_settings)"
+        "                             forecasting_parameters=forecasting_parameters)"
       ]
     },
     {
@@ -422,7 +435,7 @@
       "source": [
         "We now use the best fitted model from the AutoML Run to make forecasts for the test set. We will do batch scoring on the test dataset which should have the same schema as training dataset.\n",
         "\n",
-        "The scoring will run on a remote compute. In this example, it will reuse the training compute.|"
+        "The scoring will run on a remote compute. In this example, it will reuse the training compute."
       ]
     },
     {
@@ -439,7 +452,7 @@
       "metadata": {},
       "source": [
         "### Retrieving forecasts from the model\n",
-        "To run the forecast on the remote compute we will use two helper scripts: forecasting_script and forecasting_helper. These scripts contain the utility methods which will be used by the remote estimator. We copy these scripts to the project folder to upload them to remote compute."
+        "To run the forecast on the remote compute we will use a helper script: forecasting_script. This script contains the utility methods which will be used by the remote estimator. We copy the script to the project folder to upload it to remote compute."
       ]
     },
     {
@@ -453,15 +466,14 @@
         "\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)\n",
+        "shutil.copy('forecasting_script.py', script_folder)"
-        "shutil.copy('forecasting_helper.py', script_folder)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "For brevity we have created the function called run_forecast. It submits the test data to the best model and run the estimation on the selected compute target."
+        "For brevity, we have created a function called run_forecast that submits the test data to the best model determined during the training run and retrieves forecasts. The test set is longer than the forecast horizon specified at train time, so the forecasting script uses a so-called rolling evaluation to generate predictions over the whole test set. A rolling evaluation iterates the forecaster over the test set, using the actuals in the test set to make lag features as needed. "
       ]
     },
     {
@@ -472,8 +484,7 @@
       "source": [
         "from run_forecast import run_rolling_forecast\n",
         "\n",
-        "remote_run = run_rolling_forecast(test_experiment, compute_target, best_run, test, max_horizon,\n",
+        "remote_run = run_rolling_forecast(test_experiment, compute_target, best_run, test, target_column_name)\n",
-        "                 target_column_name, time_column_name)\n",
         "remote_run"
       ]
     },
@@ -510,17 +521,16 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.automl.core._vendor.automl.client.core.common import metrics\n",
+        "from azureml.automl.core.shared import constants\n",
+        "from azureml.automl.runtime.shared.score import scoring\n",
         "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
         "from matplotlib import pyplot as plt\n",
-        "from automl.client.core.common import constants\n",
         "\n",
         "# use automl metrics module\n",
-        "scores = metrics.compute_metrics_regression(\n",
+        "scores = scoring.score_regression(\n",
-        "    df_all['predicted'],\n",
+        "    y_test=df_all[target_column_name],\n",
-        "    df_all[target_column_name],\n",
+        "    y_pred=df_all['predicted'],\n",
-        "    list(constants.Metric.SCALAR_REGRESSION_SET),\n",
+        "    metrics=list(constants.Metric.SCALAR_REGRESSION_SET))\n",
-        "    None, None, None)\n",
         "\n",
         "print(\"[Test data scores]\\n\")\n",
         "for key, value in scores.items():    \n",
@@ -538,7 +548,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "The MAPE seems high; it is being skewed by an actual with a small absolute value. For a more informative evaluation, we can calculate the metrics by forecast horizon:"
+        "Since we did a rolling evaluation on the test set, we can analyze the predictions by their forecast horizon relative to the rolling origin. The model was initially trained at a forecast horizon of 14, so each prediction from the model is associated with a horizon value from 1 to 14. The horizon values are in a column named, \"horizon_origin,\" in the prediction set. For example, we can calculate some of the error metrics grouped by the horizon:"
       ]
     },
     {
@@ -558,7 +568,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "It's also interesting to see the distributions of APE (absolute percentage error) by horizon. On a log scale, the outlying APE in the horizon-3 group is clear."
+        "To drill down more, we can look at the distributions of APE (absolute percentage error) by horizon. From the chart, it is clear that the overall MAPE is being skewed by one particular point where the actual value is of small absolute value."
       ]
     },
     {
@@ -568,7 +578,7 @@
       "outputs": [],
       "source": [
         "df_all_APE = df_all.assign(APE=APE(df_all[target_column_name], df_all['predicted']))\n",
-        "APEs = [df_all_APE[df_all['horizon_origin'] == h].APE.values for h in range(1, max_horizon + 1)]\n",
+        "APEs = [df_all_APE[df_all['horizon_origin'] == h].APE.values for h in range(1, forecast_horizon + 1)]\n",
         "\n",
         "%matplotlib inline\n",
         "plt.boxplot(APEs)\n",
@@ -632,5 +642,5 @@
     "version": 3
   },
   "nbformat": 4,
-  "nbformat_minor": 2
+  "nbformat_minor": 4
 }

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

@@ -1,10 +1,4 @@
 name: auto-ml-forecasting-bike-share
 dependencies:
-- py-xgboost<=0.90
 - pip:
   - azureml-sdk
-  - numpy==1.16.2
-  - pandas==0.23.4
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib

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

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

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

@@ -1,37 +1,21 @@
 import argparse
 import azureml.train.automl
-from azureml.automl.runtime._vendor.automl.client.core.runtime import forecasting_models
 from azureml.core import Run
 from sklearn.externals import joblib
-import forecasting_helper
 
 
 parser = argparse.ArgumentParser()
-parser.add_argument(
-    '--max_horizon', type=int, dest='max_horizon',
-    default=10, help='Max Horizon for forecasting')
 parser.add_argument(
     '--target_column_name', type=str, dest='target_column_name',
     help='Target Column Name')
-parser.add_argument(
-    '--time_column_name', type=str, dest='time_column_name',
-    help='Time Column Name')
-parser.add_argument(
-    '--frequency', type=str, dest='freq',
-    help='Frequency of prediction')
 
 args = parser.parse_args()
-max_horizon = args.max_horizon
 target_column_name = args.target_column_name
-time_column_name = args.time_column_name
-freq = args.freq
 
 run = Run.get_context()
 # get input dataset by name
 test_dataset = run.input_datasets['test_data']
 
-grain_column_names = []
-
 df = test_dataset.to_pandas_dataframe().reset_index(drop=True)
 
 X_test_df = test_dataset.drop_columns(columns=[target_column_name]).to_pandas_dataframe().reset_index(drop=True)
@@ -39,14 +23,12 @@ y_test_df = test_dataset.with_timestamp_columns(None).keep_columns(columns=[targ
 
 fitted_model = joblib.load('model.pkl')
 
-df_all = forecasting_helper.do_rolling_forecast(
+y_pred, X_trans = fitted_model.rolling_evaluation(X_test_df, y_test_df.values)
-    fitted_model,
+
-    X_test_df,
+# Add predictions, actuals, and horizon relative to rolling origin to the test feature data
-    y_test_df.values.T[0],
+assign_dict = {'horizon_origin': X_trans['horizon_origin'].values, 'predicted': y_pred,
-    target_column_name,
+               target_column_name: y_test_df[target_column_name].values}
-    time_column_name,
+df_all = X_test_df.assign(**assign_dict)
-    max_horizon,
-    freq)
 
 file_name = 'outputs/predictions.csv'
 export_csv = df_all.to_csv(file_name, header=True)

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

@@ -5,8 +5,7 @@ from azureml.core.run import Run
 
 
 def run_rolling_forecast(test_experiment, compute_target, train_run, test_dataset,
-                         max_horizon, target_column_name, time_column_name,
+                         target_column_name, inference_folder='./forecast'):
-                         freq='D', inference_folder='./forecast'):
     condafile = inference_folder + '/condafile.yml'
     train_run.download_file('outputs/model.pkl',
                             inference_folder + '/model.pkl')
@@ -20,10 +19,7 @@ def run_rolling_forecast(test_experiment, compute_target, train_run, test_datase
     est = Estimator(source_directory=inference_folder,
                     entry_script='forecasting_script.py',
                     script_params={
-                        '--max_horizon': max_horizon,
+                        '--target_column_name': target_column_name
-                        '--target_column_name': target_column_name,
-                        '--time_column_name': time_column_name,
-                        '--frequency': freq
                     },
                     inputs=[test_dataset.as_named_input('test_data')],
                     compute_target=compute_target,

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

@@ -42,7 +42,7 @@
         "\n",
         "In this example we use the associated New York City energy demand dataset to showcase how you can use AutoML for a simple forecasting problem and explore the results. The goal is predict the energy demand for the next 48 hours based on historic time-series data.\n",
         "\n",
-        "If you are using an Azure Machine Learning [Notebook VM](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-1st-experiment-sdk-setup), you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) first, if you haven't already, to establish your connection to the AzureML Workspace.\n",
+        "If you are using an Azure Machine Learning Compute Instance, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) first, if you haven't already, to establish your connection to the AzureML Workspace.\n",
         "\n",
         "In this notebook you will learn how to:\n",
         "1. Creating an Experiment using an existing Workspace\n",
@@ -97,7 +97,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.4.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -288,7 +288,20 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "max_horizon = 48"
+        "forecast_horizon = 48"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Forecasting Parameters\n",
+        "To define forecasting parameters for your experiment training, you can leverage the ForecastingParameters class. The table below details the forecasting parameter we will be passing into our experiment.\n",
+        "\n",
+        "|Property|Description|\n",
+        "|-|-|\n",
+        "|**time_column_name**|The name of your time column.|\n",
+        "|**forecast_horizon**|The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly).|"
       ]
     },
     {
@@ -297,28 +310,27 @@
       "source": [
         "## Train\n",
         "\n",
-        "Instantiate an AutoMLConfig object. This config defines the settings and data used to run the experiment. We can provide extra configurations within 'automl_settings', for this forecasting task we add the name of the time column and the maximum forecast horizon.\n",
+        "Instantiate an AutoMLConfig object. This config defines the settings and data used to run the experiment. We can provide extra configurations within 'automl_settings', for this forecasting task we add the forecasting parameters to hold all the additional forecasting parameters.\n",
         "\n",
         "|Property|Description|\n",
         "|-|-|\n",
         "|**task**|forecasting|\n",
         "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
-        "|**blacklist_models**|Models in blacklist won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting?view=azure-ml-py).|\n",
+        "|**blocked_models**|Models in blocked_models won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting?view=azure-ml-py).|\n",
         "|**experiment_timeout_hours**|Maximum amount of time in hours that the experiment take before it terminates.|\n",
         "|**training_data**|The training data to be used within the experiment.|\n",
         "|**label_column_name**|The name of the label column.|\n",
         "|**compute_target**|The remote compute for training.|\n",
         "|**n_cross_validations**|Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way.|\n",
         "|**enable_early_stopping**|Flag to enble early termination if the score is not improving in the short term.|\n",
-        "|**time_column_name**|The name of your time column.|\n",
+        "|**forecasting_parameters**|A class holds all the forecasting related parameters.|\n"
-        "|**max_horizon**|The number of periods out you would like to predict past your training data. Periods are inferred from your data.|\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blacklist_models list but you may need to increase the experiment_timeout_hours parameter value to get results."
+        "This notebook uses the blocked_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blocked_models list but you may need to increase the experiment_timeout_hours parameter value to get results."
       ]
     },
     {
@@ -327,14 +339,14 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "automl_settings = {\n",
+        "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
-        "    'time_column_name': time_column_name,\n",
+        "forecasting_parameters = ForecastingParameters(\n",
-        "    'max_horizon': max_horizon,\n",
+        "    time_column_name=time_column_name, forecast_horizon=forecast_horizon\n",
-        "}\n",
+        ")\n",
         "\n",
         "automl_config = AutoMLConfig(task='forecasting',                             \n",
         "                             primary_metric='normalized_root_mean_squared_error',\n",
-        "                             blacklist_models = ['ExtremeRandomTrees', 'AutoArima', 'Prophet'],                             \n",
+        "                             blocked_models = ['ExtremeRandomTrees', 'AutoArima', 'Prophet'],                             \n",
         "                             experiment_timeout_hours=0.3,\n",
         "                             training_data=train,\n",
         "                             label_column_name=target_column_name,\n",
@@ -342,7 +354,7 @@
         "                             enable_early_stopping=True,\n",
         "                             n_cross_validations=3,                             \n",
         "                             verbosity=logging.INFO,\n",
-        "                            **automl_settings)"
+        "                             forecasting_parameters=forecasting_parameters)"
       ]
     },
     {
@@ -465,7 +477,7 @@
       "metadata": {},
       "source": [
         "### Forecast Function\n",
-        "For forecasting, we will use the forecast function instead of the predict function. Using the predict method would result in getting predictions for EVERY horizon the forecaster can predict at. This is useful when training and evaluating the performance of the forecaster at various horizons, but the level of detail is excessive for normal use. Forecast function also can handle more complicated scenarios, see notebook on [high frequency forecasting](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/auto-ml-forecasting-function.ipynb)."
+        "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)."
       ]
     },
     {
@@ -507,16 +519,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.automl.core._vendor.automl.client.core.common import metrics\n",
+        "from azureml.automl.core.shared import constants\n",
+        "from azureml.automl.runtime.shared.score import scoring\n",
         "from matplotlib import pyplot as plt\n",
-        "from automl.client.core.common import constants\n",
         "\n",
         "# use automl metrics module\n",
-        "scores = metrics.compute_metrics_regression(\n",
+        "scores = scoring.score_regression(\n",
-        "    df_all['predicted'],\n",
+        "    y_test=df_all[target_column_name],\n",
-        "    df_all[target_column_name],\n",
+        "    y_pred=df_all['predicted'],\n",
-        "    list(constants.Metric.SCALAR_REGRESSION_SET),\n",
+        "    metrics=list(constants.Metric.SCALAR_REGRESSION_SET))\n",
-        "    None, None, None)\n",
         "\n",
         "print(\"[Test data scores]\\n\")\n",
         "for key, value in scores.items():    \n",
@@ -551,7 +562,7 @@
       "metadata": {},
       "source": [
         "## Advanced Training <a id=\"advanced_training\"></a>\n",
-        "We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, grain and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation."
+        "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."
       ]
     },
     {
@@ -559,9 +570,9 @@
       "metadata": {},
       "source": [
         "### Using lags and rolling window features\n",
-        "Now we will configure the target lags, that is the previous values of the target variables, meaning the prediction is no longer horizon-less. We therefore must still specify the `max_horizon` that the model will learn to forecast. The `target_lags` keyword specifies how far back we will construct the lags of the target variable, and the `target_rolling_window_size` specifies the size of the rolling window over which we will generate the `max`, `min` and `sum` features.\n",
+        "Now we will configure the target lags, that is the previous values of the target variables, meaning the prediction is no longer horizon-less. We therefore must still specify the `forecast_horizon` that the model will learn to forecast. The `target_lags` keyword specifies how far back we will construct the lags of the target variable, and the `target_rolling_window_size` specifies the size of the rolling window over which we will generate the `max`, `min` and `sum` features.\n",
         "\n",
-        "This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset.  You can choose to remove models from the blacklist_models list but you may need to increase the iteration_timeout_minutes parameter value to get results."
+        "This notebook uses the blocked_models parameter to exclude some models that take a longer time to train on this dataset.  You can choose to remove models from the blocked_models list but you may need to increase the iteration_timeout_minutes parameter value to get results."
       ]
     },
     {
@@ -570,16 +581,14 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "automl_advanced_settings = {\n",
+        "advanced_forecasting_parameters = ForecastingParameters(\n",
-        "    'time_column_name': time_column_name,\n",
+        "    time_column_name=time_column_name, forecast_horizon=forecast_horizon,\n",
-        "    'max_horizon': max_horizon,\n",
+        "    target_lags=12, target_rolling_window_size=4\n",
-        "    'target_lags': 12,\n",
+        ")\n",
-        "    'target_rolling_window_size': 4,\n",
-        "}\n",
         "\n",
         "automl_config = AutoMLConfig(task='forecasting',                             \n",
         "                             primary_metric='normalized_root_mean_squared_error',\n",
-        "                             blacklist_models = ['ElasticNet','ExtremeRandomTrees','GradientBoosting','XGBoostRegressor','ExtremeRandomTrees', 'AutoArima', 'Prophet'], #These models are blacklisted for tutorial purposes, remove this for real use cases.                            \n",
+        "                             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",
@@ -587,7 +596,7 @@
         "                             enable_early_stopping = True,\n",
         "                             n_cross_validations=3,                             \n",
         "                             verbosity=logging.INFO,\n",
-        "                            **automl_advanced_settings)"
+        "                             forecasting_parameters=advanced_forecasting_parameters)"
       ]
     },
     {
@@ -636,7 +645,7 @@
       "metadata": {},
       "source": [
         "## Advanced Results<a id=\"advanced_results\"></a>\n",
-        "We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, grain and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation."
+        "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."
       ]
     },
     {
@@ -668,16 +677,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.automl.core._vendor.automl.client.core.common import metrics\n",
+        "from azureml.automl.core.shared import constants\n",
+        "from azureml.automl.runtime.shared.score import scoring\n",
         "from matplotlib import pyplot as plt\n",
-        "from automl.client.core.common import constants\n",
         "\n",
         "# use automl metrics module\n",
-        "scores = metrics.compute_metrics_regression(\n",
+        "scores = scoring.score_regression(\n",
-        "    df_all['predicted'],\n",
+        "    y_test=df_all[target_column_name],\n",
-        "    df_all[target_column_name],\n",
+        "    y_pred=df_all['predicted'],\n",
-        "    list(constants.Metric.SCALAR_REGRESSION_SET),\n",
+        "    metrics=list(constants.Metric.SCALAR_REGRESSION_SET))\n",
-        "    None, None, None)\n",
         "\n",
         "print(\"[Test data scores]\\n\")\n",
         "for key, value in scores.items():    \n",

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

@@ -2,8 +2,3 @@ name: auto-ml-forecasting-energy-demand
 dependencies:
 - pip:
   - azureml-sdk
-  - numpy==1.16.2
-  - pandas==0.23.4
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib

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

@@ -35,7 +35,6 @@
         "Terminology:\n",
         "* forecast origin: the last period when the target value is known\n",
         "* forecast periods(s): the period(s) for which the value of the target is desired.\n",
-        "* forecast horizon: the number of forecast periods\n",
         "* lookback: how many past periods (before forecast origin) the model function depends on. The larger of number of lags and length of rolling window.\n",
         "* prediction context: `lookback` periods immediately preceding the forecast origin\n",
         "\n",
@@ -95,7 +94,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.4.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -143,15 +142,15 @@
       "outputs": [],
       "source": [
         "TIME_COLUMN_NAME = 'date'\n",
-        "GRAIN_COLUMN_NAME = 'grain'\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",
-        "                   grain_column_name: str,\n",
+        "                   time_series_id_column_name: str,\n",
-        "                   grains: int = 1,\n",
+        "                   time_series_number: int = 1,\n",
         "                   freq: str = 'H'):\n",
         "    \"\"\"\n",
         "    Return the time series of designed length.\n",
@@ -162,9 +161,8 @@
         "    :type test_len: int\n",
         "    :param time_column_name: The desired name of a time column.\n",
         "    :type time_column_name: str\n",
-        "    :param\n",
+        "    :param time_series_number: The number of time series in the data set.\n",
-        "    :param grains: The number of grains.\n",
+        "    :type time_series_number: int\n",
-        "    :type grains: 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",
@@ -175,14 +173,14 @@
         "    data_train = []  # type: List[pd.DataFrame]\n",
         "    data_test = []  # type: List[pd.DataFrame]\n",
         "    data_length = train_len + test_len\n",
-        "    for i in range(grains):\n",
+        "    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",
-        "            grain_column_name: np.repeat('g{}'.format(i), data_length)\n",
+        "            time_series_id_column_name: np.repeat('ts{}'.format(i), data_length)\n",
         "        })\n",
         "        data_train.append(X[:train_len])\n",
         "        data_test.append(X[train_len:])\n",
@@ -198,8 +196,8 @@
         "                                                  test_len=n_test_periods,\n",
         "                                                  time_column_name=TIME_COLUMN_NAME,\n",
         "                                                  target_column_name=TARGET_COLUMN_NAME,\n",
-        "                                                  grain_column_name=GRAIN_COLUMN_NAME,\n",
+        "                                                  time_series_id_column_name=TIME_SERIES_ID_COLUMN_NAME,\n",
-        "                                                  grains=2)"
+        "                                                  time_series_number=2)"
       ]
     },
     {
@@ -229,7 +227,7 @@
         "whole_data = X_train.copy()\n",
         "target_label = 'y'\n",
         "whole_data[target_label] = y_train\n",
-        "for g in whole_data.groupby('grain'):    \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()"
@@ -298,7 +296,7 @@
       "source": [
         "## Create the configuration and train a forecaster <a id=\"train\"></a>\n",
         "First generate the configuration, in which we:\n",
-        "* Set metadata columns: target, time column and grain column names.\n",
+        "* Set metadata columns: target, time column and time-series id column names.\n",
         "* Validate our data using cross validation with rolling window method.\n",
         "* Set normalized root mean squared error as a metric to select the best model.\n",
         "* Set early termination to True, so the iterations through the models will stop when no improvements in accuracy score will be made.\n",
@@ -313,21 +311,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
         "lags = [1,2,3]\n",
-        "max_horizon = n_test_periods\n",
+        "forecast_horizon = n_test_periods\n",
-        "time_series_settings = {    \n",
+        "forecasting_parameters = ForecastingParameters(\n",
-        "    'time_column_name': TIME_COLUMN_NAME,\n",
+        "    time_column_name=TIME_COLUMN_NAME,\n",
-        "    'grain_column_names': [ GRAIN_COLUMN_NAME ],\n",
+        "    forecast_horizon=forecast_horizon,\n",
-        "    'max_horizon': max_horizon,\n",
+        "    time_series_id_column_names=[ TIME_SERIES_ID_COLUMN_NAME ],\n",
-        "    'target_lags': lags\n",
+        "    target_lags=lags\n",
-        "}"
+        ")"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Run the model selection and training process."
+        "Run the model selection and training process.  Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
       ]
     },
     {
@@ -353,7 +352,7 @@
         "                             max_concurrent_iterations=4,\n",
         "                             max_cores_per_iteration=-1,\n",
         "                             label_column_name=target_label,\n",
-        "                             **time_series_settings)\n",
+        "                             forecasting_parameters=forecasting_parameters)\n",
         "\n",
         "remote_run = experiment.submit(automl_config, show_output=False)"
       ]
@@ -483,7 +482,7 @@
         "# use forecast_quantiles function, not the forecast() one\n",
         "y_pred_quantiles =  fitted_model.forecast_quantiles(X_test)\n",
         "\n",
-        "# quantile forecasts returned in a Dataframe along with the time and grain columns \n",
+        "# quantile forecasts returned in a Dataframe along with the time and time series id columns \n",
         "y_pred_quantiles"
       ]
     },
@@ -493,7 +492,7 @@
       "source": [
         "#### Destination-date forecast: \"just do something\"\n",
         "\n",
-        "In some scenarios, the X_test is not known. The forecast is likely to be weak, because it is missing contemporaneous predictors, which we will need to impute. If you still wish to predict forward under the assumption that the last known values will be carried forward, you can forecast out to \"destination date\". The destination date still needs to fit within the maximum horizon from training."
+        "In some scenarios, the X_test is not known. The forecast is likely to be weak, because it is missing contemporaneous predictors, which we will need to impute. If you still wish to predict forward under the assumption that the last known values will be carried forward, you can forecast out to \"destination date\". The destination date still needs to fit within the forecast horizon from training."
       ]
     },
     {
@@ -520,7 +519,7 @@
         "\n",
         "![Forecasting after training](forecast_function_away_from_train.png)\n",
         "\n",
-        "The notion of forecast origin comes into play: the forecast origin is **the last period for which we have seen the target value**. This applies per grain, so each grain can have a different forecast origin. \n",
+        "The notion of forecast origin comes into play: the forecast origin is **the last period for which we have seen the target value**. This applies per time-series, so each time-series can have a different forecast origin. \n",
         "\n",
         "The part of data before the forecast origin is the **prediction context**. To provide the context values the model needs when it looks back, we pass definite values in `y_test` (aligned with corresponding times in `X_test`)."
       ]
@@ -537,13 +536,13 @@
         "                                      test_len=4,\n",
         "                                      time_column_name=TIME_COLUMN_NAME,\n",
         "                                      target_column_name=TARGET_COLUMN_NAME,\n",
-        "                                      grain_column_name=GRAIN_COLUMN_NAME,\n",
+        "                                      time_series_id_column_name=TIME_SERIES_ID_COLUMN_NAME,\n",
-        "                                      grains=2)\n",
+        "                                      time_series_number=2)\n",
         "\n",
         "# end of the data we trained on\n",
-        "print(X_train.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].max())\n",
+        "print(X_train.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].max())\n",
         "# start of the data we want to predict on\n",
-        "print(X_away.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].min())"
+        "print(X_away.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].min())"
       ]
     },
     {
@@ -570,7 +569,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "How should we read that eror message? The forecast origin is at the last time the model saw an actual value of `y` (the target). That was at the end of the training data! The model is attempting to forecast from the end of training data. But the requested forecast periods are past the maximum horizon. We need to provide a define `y` value to establish the forecast origin.\n",
+        "How should we read that eror message? The forecast origin is at the last time the model saw an actual value of `y` (the target). That was at the end of the training data! The model is attempting to forecast from the end of training data. But the requested forecast periods are past the forecast horizon. We need to provide a define `y` value to establish the forecast origin.\n",
         "\n",
         "We will use this helper function to take the required amount of context from the data preceding the testing data. It's definition is intentionally simplified to keep the idea in the clear."
       ]
@@ -585,7 +584,7 @@
         "\n",
         "    \"\"\"\n",
         "    This function will take the full dataset, and create the query\n",
-        "    to predict all values of the grain from the `forecast_origin`\n",
+        "    to predict all values of the time series from the `forecast_origin`\n",
         "    forward for the next `horizon` horizons. Context from previous\n",
         "    `lookback` periods will be included.\n",
         "\n",
@@ -655,8 +654,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(X_context.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].agg(['min','max','count']))\n",
+        "print(X_context.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].agg(['min','max','count']))\n",
-        "print(X_away.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].agg(['min','max','count']))\n",
+        "print(X_away.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].agg(['min','max','count']))\n",
         "X_context.tail(5)"
       ]
     },
@@ -686,7 +685,7 @@
         "n_lookback_periods = max(lags)\n",
         "lookback = pd.DateOffset(hours=n_lookback_periods)\n",
         "\n",
-        "horizon = pd.DateOffset(hours=max_horizon)\n",
+        "horizon = pd.DateOffset(hours=forecast_horizon)\n",
         "\n",
         "# now make the forecast query from context (refer to figure)\n",
         "X_pred, y_pred = make_forecasting_query(fulldata, TIME_COLUMN_NAME, TARGET_COLUMN_NAME,\n",
@@ -702,7 +701,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Note that the forecast origin is at 17:00 for both grains, and periods from 18:00 are to be forecast."
+        "Note that the forecast origin is at 17:00 for both time-series, and periods from 18:00 are to be forecast."
       ]
     },
     {
@@ -717,9 +716,94 @@
         "# show the forecast aligned\n",
         "X_show = xy_away.reset_index()\n",
         "# without the generated features\n",
-        "X_show[['date', 'grain', 'ext_predictor', '_automl_target_col']]\n",
+        "X_show[['date', 'time_series_id', 'ext_predictor', '_automl_target_col']]\n",
         "# prediction is in _automl_target_col"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Forecasting farther than the forecast horizon <a id=\"recursive forecasting\"></a>\n",
+        "When the forecast destination, or the latest date in the prediction data frame, is farther into the future than the specified forecast horizon, the `forecast()` function will still make point predictions out to the later date using a recursive operation mode. Internally, the method recursively applies the regular forecaster to generate context so that we can forecast further into the future. \n",
+        "\n",
+        "To illustrate the use-case and operation of recursive forecasting, we'll consider an example with a single time-series where the forecasting period directly follows the training period and is twice as long as the forecasting horizon given at training time.\n",
+        "\n",
+        "![Recursive_forecast_overview](recursive_forecast_overview_small.png)\n",
+        "\n",
+        "Internally, we apply the forecaster in an iterative manner and finish the forecast task in two interations. In the first iteration, we apply the forecaster and get the prediction for the first forecast-horizon periods (y_pred1). In the second iteraction, y_pred1 is used as the context to produce the prediction for the next forecast-horizon periods (y_pred2). The combination of (y_pred1 and y_pred2) gives the results for the total forecast periods. \n",
+        "\n",
+        "A caveat: forecast accuracy will likely be worse the farther we predict into the future since errors are compounded with recursive application of the forecaster.\n",
+        "\n",
+        "![Recursive_forecast_iter1](recursive_forecast_iter1.png)\n",
+        "![Recursive_forecast_iter2](recursive_forecast_iter2.png)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# generate the same kind of test data we trained on, but with a single time-series and test period twice as long\n",
+        "# as the forecast_horizon.\n",
+        "_, _, X_test_long, y_test_long = get_timeseries(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",
+        "print(X_test_long.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].min())\n",
+        "print(X_test_long.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].max())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# forecast() function will invoke the recursive forecast method internally.\n",
+        "y_pred_long, X_trans_long = fitted_model.forecast(X_test_long)\n",
+        "y_pred_long"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# What forecast() function does in this case is equivalent to iterating it twice over the test set as the following. \n",
+        "y_pred1, _ = fitted_model.forecast(X_test_long[:forecast_horizon])\n",
+        "y_pred_all, _ = fitted_model.forecast(X_test_long, np.concatenate((y_pred1, np.full(forecast_horizon, np.nan))))\n",
+        "np.array_equal(y_pred_all, y_pred_long)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Confidence interval and distributional forecasts\n",
+        "AutoML cannot currently estimate forecast errors beyond the forecast horizon set during training, so the `forecast_quantiles()` function will return missing values for quantiles not equal to 0.5 beyond the forecast horizon. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "fitted_model.forecast_quantiles(X_test_long)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Similarly with the simple senarios illustrated above, forecasting farther than the forecast horizon in other senarios like 'multiple time-series', 'Destination-date forecast', and 'forecast away from the training data' are also automatically handled by the `forecast()` function. "
+      ]
     }
   ],
   "metadata": {

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

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

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

@@ -1,10 +0,0 @@
-name: auto-ml-forecasting-function
-dependencies:
-- py-xgboost<=0.90
-- pip:
-  - azureml-sdk
-  - numpy==1.16.2
-  - pandas==0.23.4
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib

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

@@ -82,7 +82,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.4.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -178,7 +178,7 @@
       "source": [
         "Each row in the DataFrame holds a quantity of weekly sales for an OJ brand at a single store. The data also includes the sales price, a flag indicating if the OJ brand was advertised in the store that week, and some customer demographic information based on the store location. For historical reasons, the data also include the logarithm of the sales quantity. The Dominick's grocery data is commonly used to illustrate econometric modeling techniques where logarithms of quantities are generally preferred.    \n",
         "\n",
-        "The task is now to build a time-series model for the _Quantity_ column. It is important to note that this dataset is comprised of many individual time-series - one for each unique combination of _Store_ and _Brand_. To distinguish the individual time-series, we thus define the **grain** - the columns whose values determine the boundaries between time-series: "
+        "The task is now to build a time-series model for the _Quantity_ column. It is important to note that this dataset is comprised of many individual time-series - one for each unique combination of _Store_ and _Brand_. To distinguish the individual time-series, we define the **time_series_id_column_names** - the columns whose values determine the boundaries between time-series: "
       ]
     },
     {
@@ -187,8 +187,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "grain_column_names = ['Store', 'Brand']\n",
+        "time_series_id_column_names = ['Store', 'Brand']\n",
-        "nseries = data.groupby(grain_column_names).ngroups\n",
+        "nseries = data.groupby(time_series_id_column_names).ngroups\n",
         "print('Data contains {0} individual time-series.'.format(nseries))"
       ]
     },
@@ -207,7 +207,7 @@
       "source": [
         "use_stores = [2, 5, 8]\n",
         "data_subset = data[data.Store.isin(use_stores)]\n",
-        "nseries = data_subset.groupby(grain_column_names).ngroups\n",
+        "nseries = data_subset.groupby(time_series_id_column_names).ngroups\n",
         "print('Data subset contains {0} individual time-series.'.format(nseries))"
       ]
     },
@@ -216,7 +216,7 @@
       "metadata": {},
       "source": [
         "### Data Splitting\n",
-        "We now split the data into a training and a testing set for later forecast evaluation. The test set will contain the final 20 weeks of observed sales for each time-series. The splits should be stratified by series, so we use a group-by statement on the grain columns."
+        "We now split the data into a training and a testing set for later forecast evaluation. The test set will contain the final 20 weeks of observed sales for each time-series. The splits should be stratified by series, so we use a group-by statement on the time series identifier columns."
       ]
     },
     {
@@ -227,15 +227,15 @@
       "source": [
         "n_test_periods = 20\n",
         "\n",
-        "def split_last_n_by_grain(df, n):\n",
+        "def split_last_n_by_series_id(df, n):\n",
-        "    \"\"\"Group df by grain and split on last n rows for each group.\"\"\"\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(grain_column_names, group_keys=False))\n",
+        "                  .groupby(time_series_id_column_names, group_keys=False))\n",
         "    df_head = df_grouped.apply(lambda dfg: dfg.iloc[:-n])\n",
         "    df_tail = df_grouped.apply(lambda dfg: dfg.iloc[-n:])\n",
         "    return df_head, df_tail\n",
         "\n",
-        "train, test = split_last_n_by_grain(data_subset, n_test_periods)"
+        "train, test = split_last_n_by_series_id(data_subset, n_test_periods)"
       ]
     },
     {
@@ -301,11 +301,11 @@
         "For forecasting tasks, AutoML uses pre-processing and estimation steps that are specific to time-series. AutoML will undertake the following pre-processing steps:\n",
         "* Detect time-series sample frequency (e.g. hourly, daily, weekly) and create new records for absent time points to make the series regular. A regular time series has a well-defined frequency and has a value at every sample point in a contiguous time span \n",
         "* Impute missing values in the target (via forward-fill) and feature columns (using median column values) \n",
-        "* Create grain-based features to enable fixed effects across different series\n",
+        "* Create features based on time series identifiers to enable fixed effects across different series\n",
         "* Create time-based features to assist in learning seasonal patterns\n",
         "* Encode categorical variables to numeric quantities\n",
         "\n",
-        "In this notebook, AutoML will train a single, regression-type model across **all** time-series in a given training set. This allows the model to generalize across related series. If you're looking for training multiple models for different time-series, please check out the forecasting grouping notebook. \n",
+        "In this notebook, AutoML will train a single, regression-type model across **all** time-series in a given training set. This allows the model to generalize across related series. If you're looking for training multiple models for different time-series, please see the many-models notebook.\n",
         "\n",
         "You are almost ready to start an AutoML training job. First, we need to separate the target column from the rest of the DataFrame: "
       ]
@@ -336,7 +336,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "sample-featurizationconfig-remarks"
+        ]
+      },
       "outputs": [],
       "source": [
         "featurization_config = FeaturizationConfig()\n",
@@ -349,17 +353,39 @@
         "featurization_config.add_transformer_params('Imputer', ['INCOME'], {\"strategy\": \"median\"})"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Forecasting Parameters\n",
+        "To define forecasting parameters for your experiment training, you can leverage the ForecastingParameters class. The table below details the forecasting parameter we will be passing into our experiment.\n",
+        "\n",
+        "\n",
+        "|Property|Description|\n",
+        "|-|-|\n",
+        "|**time_column_name**|The name of your time column.|\n",
+        "|**forecast_horizon**|The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly).|\n",
+        "|**time_series_id_column_names**|The column names used to uniquely identify the time series in data that has multiple rows with the same timestamp. If the time series identifiers are not defined, the data set is assumed to be one time series.|"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
         "## Train\n",
         "\n",
-        "The AutoMLConfig object defines the settings and data for an AutoML training job. Here, we set necessary inputs like the task type, the number of AutoML iterations to try, the training data, and cross-validation parameters. \n",
+        "The [AutoMLConfig](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig?view=azure-ml-py) object defines the settings and data for an AutoML training job. Here, we set necessary inputs like the task type, the number of AutoML iterations to try, the training data, and cross-validation parameters.\n",
         "\n",
-        "For forecasting tasks, there are some additional parameters that can be set: the name of the column holding the date/time, the grain column names, and the maximum forecast horizon. A time column is required for forecasting, while the grain is optional. If a grain is not given, AutoML assumes that the whole dataset is a single time-series. We also pass a list of columns to drop prior to modeling. The _logQuantity_ column is completely correlated with the target quantity, so it must be removed to prevent a target leak.\n",
+        "For forecasting tasks, there are some additional parameters that can be set in the `ForecastingParameters` class: the name of the column holding the date/time, the timeseries id column names, and the maximum forecast horizon. A time column is required for forecasting, while the time_series_id is optional. If time_series_id columns are not given, AutoML assumes that the whole dataset is a single time-series. We also pass a list of columns to drop prior to modeling. The _logQuantity_ column is completely correlated with the target quantity, so it must be removed to prevent a target leak.\n",
+        "\n",
+        "The forecast horizon is given in units of the time-series frequency; for instance, the OJ series frequency is weekly, so a horizon of 20 means that a trained model will estimate sales up to 20 weeks beyond the latest date in the training data for each series. In this example, we set the forecast horizon to the number of samples per series in the test set (n_test_periods). Generally, the value of this parameter will be dictated by business needs. For example, a demand planning application that estimates the next month of sales should set the horizon according to suitable planning time-scales. Please see the [energy_demand notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand) for more discussion of forecast horizon.\n",
+        "\n",
+        "We note here that AutoML can sweep over two types of time-series models:\n",
+        "* Models that are trained for each series such as ARIMA and Facebook's Prophet. Note that these models are only available for [Enterprise Edition Workspaces](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-workspace#upgrade).\n",
+        "* Models trained across multiple time-series using a regression approach.\n",
+        "\n",
+        "In the first case, AutoML loops over all time-series in your dataset and trains one model (e.g. AutoArima or Prophet, as the case may be) for each series. This can result in long runtimes to train these models if there are a lot of series in the data. One way to mitigate this problem is to fit models for different series in parallel if you have multiple compute cores available. To enable this behavior, set the `max_cores_per_iteration` parameter in your AutoMLConfig as shown in the example in the next cell. \n",
         "\n",
-        "The forecast horizon is given in units of the time-series frequency; for instance, the OJ series frequency is weekly, so a horizon of 20 means that a trained model will estimate sales up to 20 weeks beyond the latest date in the training data for each series. In this example, we set the maximum horizon to the number of samples per series in the test set (n_test_periods). Generally, the value of this parameter will be dictated by business needs. For example, a demand planning organizaion that needs to estimate the next month of sales would set the horizon accordingly. Please see the [energy_demand notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand) for more discussion of forecast horizon.\n",
         "\n",
         "Finally, a note about the cross-validation (CV) procedure for time-series data. AutoML uses out-of-sample error estimates to select a best pipeline/model, so it is important that the CV fold splitting is done correctly. Time-series can violate the basic statistical assumptions of the canonical K-Fold CV strategy, so AutoML implements a [rolling origin validation](https://robjhyndman.com/hyndsight/tscv/) procedure to create CV folds for time-series data. To use this procedure, you just need to specify the desired number of CV folds in the AutoMLConfig object. It is also possible to bypass CV and use your own validation set by setting the *validation_data* parameter of AutoMLConfig.\n",
         "\n",
@@ -378,10 +404,8 @@
         "|**enable_voting_ensemble**|Allow AutoML to create a Voting ensemble of the best performing models|\n",
         "|**enable_stack_ensemble**|Allow AutoML to create a Stack ensemble of the best performing models|\n",
         "|**debug_log**|Log file path for writing debugging information|\n",
-        "|**time_column_name**|Name of the datetime column in the input data|\n",
+        "|**featurization**| 'auto' / 'off' / FeaturizationConfig Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used. Setting this enables AutoML to perform featurization on the input to handle *missing data*, and to perform some common *feature extraction*.|\n",
-        "|**grain_column_names**|Name(s) of the columns defining individual series in the input data|\n",
+        "|**max_cores_per_iteration**|Maximum number of cores to utilize per iteration. A value of -1 indicates all available cores should be used"
-        "|**max_horizon**|Maximum desired forecast horizon in units of time-series frequency|\n",
-        "|**featurization**| 'auto' / 'off' / FeaturizationConfig Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used. Setting this enables AutoML to perform featurization on the input to handle *missing data*, and to perform some common *feature extraction*.|"
       ]
     },
     {
@@ -390,11 +414,12 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "time_series_settings = {\n",
+        "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
-        "    'time_column_name': time_column_name,\n",
+        "forecasting_parameters = ForecastingParameters(\n",
-        "    'grain_column_names': grain_column_names,\n",
+        "    time_column_name=time_column_name,\n",
-        "    'max_horizon': n_test_periods\n",
+        "    forecast_horizon=n_test_periods,\n",
-        "}\n",
+        "    time_series_id_column_names=time_series_id_column_names\n",
+        ")\n",
         "\n",
         "automl_config = AutoMLConfig(task='forecasting',\n",
         "                             debug_log='automl_oj_sales_errors.log',\n",
@@ -407,7 +432,8 @@
         "                             featurization=featurization_config,\n",
         "                             n_cross_validations=3,\n",
         "                             verbosity=logging.INFO,\n",
-        "                             **time_series_settings)"
+        "                             max_cores_per_iteration=-1,\n",
+        "                             forecasting_parameters=forecasting_parameters)"
       ]
     },
     {
@@ -415,7 +441,7 @@
       "metadata": {},
       "source": [
         "You can now submit a new training run. Depending on the data and number of iterations this operation may take several minutes.\n",
-        "Information from each iteration will be printed to the console."
+        "Information from each iteration will be printed to the console.  Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
       ]
     },
     {
@@ -524,9 +550,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# The featurized data, aligned to y, will also be returned.\n",
+        "# forecast returns the predictions and the featurized data, aligned to X_test.\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)"
       ]
     },
@@ -536,7 +561,7 @@
       "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",
         "\n",
-        "The [energy demand forecasting notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand) demonstrates the use of the forecast function in more detail in the context of using lags and rolling window features. "
+        "The [forecast function notebook](../forecasting-forecast-function/auto-ml-forecasting-function.ipynb)."
       ]
     },
     {
@@ -547,7 +572,7 @@
         "\n",
         "To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE). \n",
         "\n",
-        "It is a good practice to always align the output explicitly to the input, as the count and order of the rows may have changed during transformations that span multiple rows."
+        "We'll add predictions and actuals into a single dataframe for convenience in calculating the metrics."
       ]
     },
     {
@@ -556,9 +581,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from forecasting_helper import align_outputs\n",
+        "assign_dict = {'predicted': y_predictions, target_column_name: y_test}\n",
-        "\n",
+        "df_all = X_test.assign(**assign_dict)"
-        "df_all = align_outputs(y_predictions, X_trans, X_test, y_test, target_column_name)"
       ]
     },
     {
@@ -567,16 +591,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.automl.core._vendor.automl.client.core.common import metrics\n",
+        "from azureml.automl.core.shared import constants\n",
+        "from azureml.automl.runtime.shared.score import scoring\n",
         "from matplotlib import pyplot as plt\n",
-        "from automl.client.core.common import constants\n",
         "\n",
-        "# use automl metrics module\n",
+        "# use automl scoring module\n",
-        "scores = metrics.compute_metrics_regression(\n",
+        "scores = scoring.score_regression(\n",
-        "    df_all['predicted'],\n",
+        "    y_test=df_all[target_column_name],\n",
-        "    df_all[target_column_name],\n",
+        "    y_pred=df_all['predicted'],\n",
-        "    list(constants.Metric.SCALAR_REGRESSION_SET),\n",
+        "    metrics=list(constants.Metric.SCALAR_REGRESSION_SET))\n",
-        "    None, None, None)\n",
         "\n",
         "print(\"[Test data scores]\\n\")\n",
         "for key, value in scores.items():    \n",
@@ -782,5 +805,5 @@
     "task": "Forecasting"
   },
   "nbformat": 4,
-  "nbformat_minor": 2
+  "nbformat_minor": 4
 }

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

@@ -1,10 +1,4 @@
 name: auto-ml-forecasting-orange-juice-sales
 dependencies:
-- py-xgboost<=0.90
 - pip:
   - azureml-sdk
-  - numpy==1.16.2
-  - pandas==0.23.4
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib

how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/forecasting_helper.py

@@ -1,98 +0,0 @@
-import pandas as pd
-import numpy as np
-from pandas.tseries.frequencies import to_offset
-
-
-def align_outputs(y_predicted, X_trans, X_test, y_test, target_column_name,
-                  predicted_column_name='predicted',
-                  horizon_colname='horizon_origin'):
-    """
-    Demonstrates how to get the output aligned to the inputs
-    using pandas indexes. Helps understand what happened if
-    the output's shape differs from the input shape, or if
-    the data got re-sorted by time and grain during forecasting.
-
-    Typical causes of misalignment are:
-    * we predicted some periods that were missing in actuals -> drop from eval
-    * model was asked to predict past max_horizon -> increase max horizon
-    * data at start of X_test was needed for lags -> provide previous periods
-    """
-
-    if (horizon_colname in X_trans):
-        df_fcst = pd.DataFrame({predicted_column_name: y_predicted,
-                                horizon_colname: X_trans[horizon_colname]})
-    else:
-        df_fcst = pd.DataFrame({predicted_column_name: y_predicted})
-
-    # y and X outputs are aligned by forecast() function contract
-    df_fcst.index = X_trans.index
-
-    # align original X_test to y_test
-    X_test_full = X_test.copy()
-    X_test_full[target_column_name] = y_test
-
-    # X_test_full's index does not include origin, so reset for merge
-    df_fcst.reset_index(inplace=True)
-    X_test_full = X_test_full.reset_index().drop(columns='index')
-    together = df_fcst.merge(X_test_full, how='right')
-
-    # drop rows where prediction or actuals are nan
-    # happens because of missing actuals
-    # or at edges of time due to lags/rolling windows
-    clean = together[together[[target_column_name,
-                               predicted_column_name]].notnull().all(axis=1)]
-    return(clean)
-
-
-def do_rolling_forecast(fitted_model, X_test, y_test, target_column_name, time_column_name, max_horizon, freq='D'):
-    """
-    Produce forecasts on a rolling origin over the given test set.
-
-    Each iteration makes a forecast for the next 'max_horizon' periods
-    with respect to the current origin, then advances the origin by the
-    horizon time duration. The prediction context for each forecast is set so
-    that the forecaster uses the actual target values prior to the current
-    origin time for constructing lag features.
-
-    This function returns a concatenated DataFrame of rolling forecasts.
-     """
-    df_list = []
-    origin_time = X_test[time_column_name].min()
-    while origin_time <= X_test[time_column_name].max():
-        # Set the horizon time - end date of the forecast
-        horizon_time = origin_time + max_horizon * to_offset(freq)
-
-        # Extract test data from an expanding window up-to the horizon
-        expand_wind = (X_test[time_column_name] < horizon_time)
-        X_test_expand = X_test[expand_wind]
-        y_query_expand = np.zeros(len(X_test_expand)).astype(np.float)
-        y_query_expand.fill(np.NaN)
-
-        if origin_time != X_test[time_column_name].min():
-            # Set the context by including actuals up-to the origin time
-            test_context_expand_wind = (X_test[time_column_name] < origin_time)
-            context_expand_wind = (
-                X_test_expand[time_column_name] < origin_time)
-            y_query_expand[context_expand_wind] = y_test[
-                test_context_expand_wind]
-
-        # Make a forecast out to the maximum horizon
-        y_fcst, X_trans = fitted_model.forecast(X_test_expand, y_query_expand)
-
-        # Align forecast with test set for dates within the
-        # current rolling window
-        trans_tindex = X_trans.index.get_level_values(time_column_name)
-        trans_roll_wind = (trans_tindex >= origin_time) & (
-            trans_tindex < horizon_time)
-        test_roll_wind = expand_wind & (
-            X_test[time_column_name] >= origin_time)
-        df_list.append(align_outputs(y_fcst[trans_roll_wind],
-                                     X_trans[trans_roll_wind],
-                                     X_test[test_roll_wind],
-                                     y_test[test_roll_wind],
-                                     target_column_name))
-
-        # Advance the origin time
-        origin_time = horizon_time
-
-    return pd.concat(df_list, ignore_index=True)

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

@@ -28,7 +28,8 @@
         "1. [Setup](#Setup)\n",
         "1. [Train](#Train)\n",
         "1. [Results](#Results)\n",
-        "1. [Test](#Test)\n",
+        "1. [Test](#Tests)\n",
+        "1. [Explanation](#Explanation)\n",
         "1. [Acknowledgements](#Acknowledgements)"
       ]
     },
@@ -42,16 +43,16 @@
         "\n",
         "This notebook is using the local machine compute to train the model.\n",
         "\n",
-        "If you are using an Azure Machine Learning [Notebook VM](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-1st-experiment-sdk-setup), you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
+        "If you are using an Azure Machine Learning Compute Instance, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
         "\n",
         "In this notebook you will learn how to:\n",
         "1. Create an experiment using an existing workspace.\n",
         "2. Configure AutoML using `AutoMLConfig`.\n",
         "3. Train the model.\n",
         "4. Explore the results.\n",
-        "5. Visualization model's feature importance in azure portal\n",
+        "5. Test the fitted model.\n",
-        "6. Explore any model's explanation and explore feature importance in azure portal\n",
+        "6. Explore any model's explanation and explore feature importance in azure portal.\n",
-        "7. Test the fitted model."
+        "7. Create an AKS cluster, deploy the webservice of AutoML scoring model and the explainer model to the AKS and consume the web service."
       ]
     },
     {
@@ -79,7 +80,7 @@
         "from azureml.core.workspace import Workspace\n",
         "from azureml.core.dataset import Dataset\n",
         "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.explain.model._internal.explanation_client import ExplanationClient"
+        "from azureml.interpret._internal.explanation_client import ExplanationClient"
       ]
     },
     {
@@ -95,7 +96,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.4.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -255,9 +256,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Analyze results\n",
+        "### Analyze results\n",
         "\n",
-        "### Retrieve the Best Model\n",
+        "#### Retrieve the Best Model\n",
         "\n",
         "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
       ]
@@ -284,135 +285,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Best Model 's explanation\n",
+        "## Tests\n",
-        "Retrieve the explanation from the best_run which includes explanations for engineered features and raw features.\n",
-        "\n",
-        "#### Download engineered feature importance from artifact store\n",
-        "You can use ExplanationClient to download the engineered feature explanations from the artifact store of the best_run. You can also use azure portal url to view the dash board visualization of the feature importance values of the engineered features."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "client = ExplanationClient.from_run(best_run)\n",
-        "engineered_explanations = client.download_model_explanation(raw=False)\n",
-        "print(engineered_explanations.get_feature_importance_dict())\n",
-        "print(\"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + best_run.get_portal_url())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explanations\n",
-        "In this section, we will show how to compute model explanations and visualize the explanations using azureml-explain-model package. Besides retrieving an existing model explanation for an AutoML model, you can also explain your AutoML model with different test data. The following steps will allow you to compute and visualize engineered feature importance based on your test data."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Retrieve any other AutoML model from training"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "automl_run, fitted_model = local_run.get_output(metric='accuracy')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Setup the model explanations for AutoML models\n",
-        "The fitted_model can generate the following which will be used for getting the engineered explanations using automl_setup_model_explanations:-\n",
-        "\n",
-        "1. Featurized data from train samples/test samples\n",
-        "2. Gather engineered name lists\n",
-        "3. Find the classes in your labeled column in classification scenarios\n",
-        "\n",
-        "The automl_explainer_setup_obj contains all the structures from above list."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "X_train = training_data.drop_columns(columns=[label_column_name])\n",
-        "y_train = training_data.keep_columns(columns=[label_column_name], validate=True)\n",
-        "X_test = validation_data.drop_columns(columns=[label_column_name])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.automl.runtime.automl_explain_utilities import automl_setup_model_explanations\n",
-        "\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')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Initialize the Mimic Explainer for feature importance\n",
-        "For explaining the AutoML models, use the MimicWrapper from azureml.explain.model package. The MimicWrapper can be initialized with fields in automl_explainer_setup_obj, your workspace and a surrogate model to explain the AutoML model (fitted_model here). The MimicWrapper also takes the automl_run object where engineered explanations will be uploaded."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.explain.model.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",
-        "                         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",
-        "                         explainer_kwargs=automl_explainer_setup_obj.surrogate_model_params)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Use Mimic Explainer for computing and visualizing engineered feature importance\n",
-        "The explain() method in MimicWrapper can be called with the transformed test samples to get the feature importance for the generated engineered features. You can also use azure portal url to view the dash board visualization of the feature importance values of the engineered features."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "engineered_explanations = explainer.explain(['local', 'global'], eval_dataset=automl_explainer_setup_obj.X_test_transform)\n",
-        "print(engineered_explanations.get_feature_importance_dict())\n",
-        "print(\"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + automl_run.get_portal_url())"
-      ]
-    },
-    {
-      "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."
       ]
@@ -476,6 +349,408 @@
         "plt.show()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Explanation\n",
+        "In this section, we will show how to compute model explanations and visualize the explanations using azureml-interpret package. We will also show how to run the automl model and the explainer model through deploying an AKS web service.\n",
+        "\n",
+        "Besides retrieving an existing model explanation for an AutoML model, you can also explain your AutoML model with different test data. The following steps will allow you to compute and visualize engineered feature importance based on your test data.\n",
+        "\n",
+        "### Run the explanation\n",
+        "#### Download engineered feature importance from artifact store\n",
+        "You can use ExplanationClient to download the engineered feature explanations from the artifact store of the best_run. You can also use azure portal url to view the dash board visualization of the feature importance values of the engineered features."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "client = ExplanationClient.from_run(best_run)\n",
+        "engineered_explanations = client.download_model_explanation(raw=False)\n",
+        "print(engineered_explanations.get_feature_importance_dict())\n",
+        "print(\"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + best_run.get_portal_url())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Retrieve any other AutoML model from training"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "automl_run, fitted_model = local_run.get_output(metric='accuracy')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Setup the model explanations for AutoML models\n",
+        "The fitted_model can generate the following which will be used for getting the engineered explanations using automl_setup_model_explanations:-\n",
+        "\n",
+        "1. Featurized data from train samples/test samples\n",
+        "2. Gather engineered name lists\n",
+        "3. Find the classes in your labeled column in classification scenarios\n",
+        "\n",
+        "The automl_explainer_setup_obj contains all the structures from above list."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_train = training_data.drop_columns(columns=[label_column_name])\n",
+        "y_train = training_data.keep_columns(columns=[label_column_name], validate=True)\n",
+        "X_test = validation_data.drop_columns(columns=[label_column_name])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.automl.runtime.automl_explain_utilities import automl_setup_model_explanations\n",
+        "\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')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Initialize the Mimic Explainer for feature importance\n",
+        "For explaining the AutoML models, use the MimicWrapper from azureml-interpret package. The MimicWrapper can be initialized with fields in automl_explainer_setup_obj, your workspace and a surrogate model to explain the AutoML model (fitted_model here). The MimicWrapper also takes the automl_run object where engineered explanations will be uploaded."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "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",
+        "                         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",
+        "                         explainer_kwargs=automl_explainer_setup_obj.surrogate_model_params)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Use Mimic Explainer for computing and visualizing engineered feature importance\n",
+        "The explain() method in MimicWrapper can be called with the transformed test samples to get the feature importance for the generated engineered features. You can also use azure portal url to view the dash board visualization of the feature importance values of the engineered features."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Compute the engineered explanations\n",
+        "engineered_explanations = explainer.explain(['local', 'global'], eval_dataset=automl_explainer_setup_obj.X_test_transform)\n",
+        "print(engineered_explanations.get_feature_importance_dict())\n",
+        "print(\"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + automl_run.get_portal_url())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Initialize the scoring Explainer, save and upload it for later use in scoring explanation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.interpret.scoring.scoring_explainer import TreeScoringExplainer\n",
+        "import joblib\n",
+        "\n",
+        "# Initialize the ScoringExplainer\n",
+        "scoring_explainer = TreeScoringExplainer(explainer.explainer, feature_maps=[automl_explainer_setup_obj.feature_map])\n",
+        "\n",
+        "# Pickle scoring explainer locally to './scoring_explainer.pkl'\n",
+        "scoring_explainer_file_name = 'scoring_explainer.pkl'\n",
+        "with open(scoring_explainer_file_name, 'wb') as stream:\n",
+        "    joblib.dump(scoring_explainer, stream)\n",
+        "\n",
+        "# Upload the scoring explainer to the automl run\n",
+        "automl_run.upload_file('outputs/scoring_explainer.pkl', scoring_explainer_file_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Deploying the scoring and explainer models to a web service to Azure Kubernetes Service (AKS)\n",
+        "\n",
+        "We use the TreeScoringExplainer from azureml.interpret package to create the scoring explainer which will be used to compute the raw and engineered feature importances at the inference time. In the cell below, we register the AutoML model and the scoring explainer with the Model Management Service."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Register trained automl model present in the 'outputs' folder in the artifacts\n",
+        "original_model = automl_run.register_model(model_name='automl_model', \n",
+        "                                           model_path='outputs/model.pkl')\n",
+        "scoring_explainer_model = automl_run.register_model(model_name='scoring_explainer',\n",
+        "                                                    model_path='outputs/scoring_explainer.pkl')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Create the conda dependencies for setting up the service\n",
+        "\n",
+        "We need to download the conda dependencies using the automl_run object."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.automl.core.shared import constants\n",
+        "from azureml.core.environment import Environment\n",
+        "\n",
+        "automl_run.download_file(constants.CONDA_ENV_FILE_PATH, 'myenv.yml')\n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
+        "myenv"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Write the Entry Script\n",
+        "Write the script that will be used to predict on your model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%writefile score.py\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import os\n",
+        "import pickle\n",
+        "import azureml.train.automl\n",
+        "import azureml.interpret\n",
+        "from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplainerSetupClass, \\\n",
+        "    automl_setup_model_explanations\n",
+        "import joblib\n",
+        "from azureml.core.model import Model\n",
+        "\n",
+        "\n",
+        "def init():\n",
+        "    global automl_model\n",
+        "    global scoring_explainer\n",
+        "\n",
+        "    # Retrieve the path to the model file using the model name\n",
+        "    # Assume original model is named original_prediction_model\n",
+        "    automl_model_path = Model.get_model_path('automl_model')\n",
+        "    scoring_explainer_path = Model.get_model_path('scoring_explainer')\n",
+        "\n",
+        "    automl_model = joblib.load(automl_model_path)\n",
+        "    scoring_explainer = joblib.load(scoring_explainer_path)\n",
+        "\n",
+        "\n",
+        "def run(raw_data):\n",
+        "    data = pd.read_json(raw_data, orient='records')    \n",
+        "    # Make prediction\n",
+        "    predictions = automl_model.predict(data)\n",
+        "    # Setup for inferencing explanations\n",
+        "    automl_explainer_setup_obj = automl_setup_model_explanations(automl_model,\n",
+        "                                                                 X_test=data, task='classification')\n",
+        "    # Retrieve model explanations for engineered explanations\n",
+        "    engineered_local_importance_values = scoring_explainer.explain(automl_explainer_setup_obj.X_test_transform)    \n",
+        "    # You can return any data type as long as it is JSON-serializable\n",
+        "    return {'predictions': predictions.tolist(),\n",
+        "            'engineered_local_importance_values': engineered_local_importance_values}\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Create the InferenceConfig \n",
+        "Create the inference config that will be used when deploying the model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.model import InferenceConfig\n",
+        "\n",
+        "inf_config = InferenceConfig(entry_script='score.py', environment=myenv)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "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."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AksCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# Choose a name for your cluster.\n",
+        "aks_name = 'scoring-explain'\n",
+        "\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    aks_target = ComputeTarget(workspace=ws, name=aks_name)\n",
+        "    print('Found existing cluster, use it.')\n",
+        "except ComputeTargetException:\n",
+        "    prov_config = AksCompute.provisioning_configuration(vm_size='STANDARD_D3_V2')\n",
+        "    aks_target = ComputeTarget.create(workspace=ws, \n",
+        "                                      name=aks_name,\n",
+        "                                      provisioning_configuration=prov_config)\n",
+        "aks_target.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Deploy web service to AKS"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Set the web service configuration (using default here)\n",
+        "from azureml.core.webservice import AksWebservice\n",
+        "from azureml.core.model import Model\n",
+        "\n",
+        "aks_config = AksWebservice.deploy_configuration()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "aks_service_name ='model-scoring-local-aks'\n",
+        "\n",
+        "aks_service = Model.deploy(workspace=ws,\n",
+        "                           name=aks_service_name,\n",
+        "                           models=[scoring_explainer_model, original_model],\n",
+        "                           inference_config=inf_config,\n",
+        "                           deployment_config=aks_config,\n",
+        "                           deployment_target=aks_target)\n",
+        "\n",
+        "aks_service.wait_for_deployment(show_output = True)\n",
+        "print(aks_service.state)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### View the service logs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "aks_service.get_logs()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Consume the web service using run method to do the scoring and explanation of scoring.\n",
+        "We test the web sevice by passing data. Run() method retrieves API keys behind the scenes to make sure that call is authenticated."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Serialize the first row of the test data into json\n",
+        "X_test_json = X_test_df[:1].to_json(orient='records')\n",
+        "print(X_test_json)\n",
+        "\n",
+        "# Call the service to get the predictions and the engineered and raw explanations\n",
+        "output = aks_service.run(X_test_json)\n",
+        "\n",
+        "# Print the predicted value\n",
+        "print('predictions:\\n{}\\n'.format(output['predictions']))\n",
+        "# Print the engineered feature importances for the predicted value\n",
+        "print('engineered_local_importance_values:\\n{}\\n'.format(output['engineered_local_importance_values']))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Clean up\n",
+        "Delete the service."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "aks_service.delete()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

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

@@ -2,6 +2,3 @@ name: auto-ml-classification-credit-card-fraud-local
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib

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

@@ -40,7 +40,7 @@
         "In this example we use the Hardware Performance Dataset to showcase how you can use AutoML for a simple regression problem. The Regression goal is to predict the performance of certain combinations of hardware parts.\n",
         "After training AutoML models for this regression data set, we show how you can compute model explanations on your remote compute using a sample explainer script.\n",
         "\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set.  Otherwise, go through the [configuration](../../../configuration.ipynb)  notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
+        "If you are using an Azure Machine Learning Compute Instance, you are all set.  Otherwise, go through the [configuration](../../../configuration.ipynb)  notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
         "\n",
         "An Enterprise workspace is required for this notebook. To learn more about creating an Enterprise workspace or upgrading to an Enterprise workspace from the Azure portal, please visit our [Workspace page.](https://docs.microsoft.com/azure/machine-learning/service/concept-workspace#upgrade) \n",
         "\n",
@@ -98,7 +98,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.4.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -242,7 +242,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "sample-featurizationconfig-remarks2"
+        ]
+      },
       "outputs": [],
       "source": [
         "featurization_config = FeaturizationConfig()\n",
@@ -260,7 +264,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "sample-featurizationconfig-remarks3"
+        ]
+      },
       "outputs": [],
       "source": [
         "automl_settings = {\n",
@@ -617,7 +625,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.explain.model._internal.explanation_client import ExplanationClient\n",
+        "from azureml.interpret._internal.explanation_client import ExplanationClient\n",
         "client = ExplanationClient.from_run(automl_run)\n",
         "engineered_explanations = client.download_model_explanation(raw=False, comment='engineered explanations')\n",
         "print(engineered_explanations.get_feature_importance_dict())\n",
@@ -651,7 +659,7 @@
         "In this section we will show how you can operationalize an AutoML model and the explainer which was used to compute the explanations in the previous section.\n",
         "\n",
         "### Register the AutoML model and the scoring explainer\n",
-        "We use the *TreeScoringExplainer* from *azureml.explain.model* package to create the scoring explainer which will be used to compute the raw and engineered feature importances at the inference time. \n",
+        "We use the *TreeScoringExplainer* from *azureml-interpret* package to create the scoring explainer which will be used to compute the raw and engineered feature importances at the inference time. \n",
         "In the cell below, we register the AutoML model and the scoring explainer with the Model Management Service."
       ]
     },
@@ -673,7 +681,7 @@
       "metadata": {},
       "source": [
         "### Create the conda dependencies for setting up the service\n",
-        "We need to create the conda dependencies comprising of the *azureml-explain-model*, *azureml-train-automl* and *azureml-defaults* packages. "
+        "We need to create the conda dependencies comprising of the *azureml* packages using the training environment from the *automl_run*."
       ]
     },
     {

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

@@ -2,6 +2,3 @@ name: auto-ml-regression-explanation-featurization
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib

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

@@ -4,10 +4,10 @@ import pandas as pd
 import os
 import pickle
 import azureml.train.automl
-import azureml.explain.model
+import azureml.interpret
 from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplainerSetupClass, \
     automl_setup_model_explanations
-from sklearn.externals import joblib
+import joblib
 from azureml.core.model import Model
 
 

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

@@ -4,14 +4,14 @@ import os
 
 from azureml.core.run import Run
 from azureml.core.experiment import Experiment
-from sklearn.externals import joblib
 from azureml.core.dataset import Dataset
 from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplainerSetupClass, \
     automl_setup_model_explanations, automl_check_model_if_explainable
-from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel
+from interpret.ext.glassbox import LGBMExplainableModel
-from azureml.explain.model.mimic_wrapper import MimicWrapper
+from azureml.interpret.mimic_wrapper import MimicWrapper
 from automl.client.core.common.constants import MODEL_PATH
-from azureml.explain.model.scoring.scoring_explainer import TreeScoringExplainer, save
+from azureml.interpret.scoring.scoring_explainer import TreeScoringExplainer
+import joblib
 
 
 OUTPUT_DIR = './outputs/'
@@ -74,7 +74,8 @@ print("Engineered and raw explanations computed successfully")
 scoring_explainer = TreeScoringExplainer(explainer.explainer, feature_maps=[automl_explainer_setup_obj.feature_map])
 
 # Pickle scoring explainer locally
-save(scoring_explainer, exist_ok=True)
+with open('scoring_explainer.pkl', 'wb') as stream:
+    joblib.dump(scoring_explainer, stream)
 
 # Upload the scoring explainer to the automl run
 automl_run.upload_file('outputs/scoring_explainer.pkl', 'scoring_explainer.pkl')

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

@@ -40,7 +40,7 @@
         "## Introduction\n",
         "In this example we use the Hardware Performance Dataset to showcase how you can use AutoML for a simple regression problem. The Regression goal is to predict the performance of certain combinations of hardware parts.\n",
         "\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set.  Otherwise, go through the [configuration](../../../configuration.ipynb)  notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
+        "If you are using an Azure Machine Learning Compute Instance, you are all set.  Otherwise, go through the [configuration](../../../configuration.ipynb)  notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
         "\n",
         "In this notebook you will learn how to:\n",
         "1. Create an `Experiment` in an existing `Workspace`.\n",
@@ -92,7 +92,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.4.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.12.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -233,7 +233,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Call the `submit` method on the experiment object and pass the run configuration. Execution of remote runs is asynchronous. Depending on the data and the number of iterations this can run for a while."
+        "Call the `submit` method on the experiment object and pass the run configuration. Execution of remote runs is asynchronous. Depending on the data and the number of iterations this can run for a while.  Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
       ]
     },
     {

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

@@ -2,7 +2,3 @@ name: auto-ml-regression
 dependencies:
 - pip:
   - azureml-sdk
-  - pandas==0.23.4
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib

how-to-use-azureml/automated-machine-learning/sql-server/energy-demand/ForecastEnergyDemand.sql

@@ -1,23 +0,0 @@
--- This shows using the AutoMLForecast stored procedure to predict using a forecasting model for the nyc_energy dataset.
-
-DECLARE @Model NVARCHAR(MAX) = (SELECT TOP 1 Model FROM dbo.aml_model
-                                WHERE ExperimentName = 'automl-sql-forecast'
-								ORDER BY CreatedDate DESC)
-
-DECLARE @max_horizon INT = 48
-DECLARE @split_time NVARCHAR(22) = (SELECT DATEADD(hour, -@max_horizon, MAX(timeStamp)) FROM nyc_energy WHERE demand IS NOT NULL)
-
-DECLARE @TestDataQuery NVARCHAR(MAX) = '
-SELECT CAST(timeStamp AS NVARCHAR(30)) AS timeStamp,
-       demand,
-	   precip,
-	   temp
-FROM nyc_energy
-WHERE demand IS NOT NULL AND precip IS NOT NULL AND temp IS NOT NULL
-AND timeStamp > ''' + @split_time + ''''
-
-EXEC dbo.AutoMLForecast @input_query=@TestDataQuery,
-@label_column='demand',
-@time_column_name='timeStamp',
-@model=@model
-WITH RESULT SETS ((timeStamp DATETIME, grain NVARCHAR(255), predicted_demand FLOAT, precip FLOAT, temp FLOAT, actual_demand FLOAT))

how-to-use-azureml/automated-machine-learning/sql-server/energy-demand/GetMetrics.sql

@@ -1,10 +0,0 @@
--- This lists all the metrics for all iterations for the most recent run.
-
-DECLARE @RunId NVARCHAR(43)
-DECLARE @ExperimentName NVARCHAR(255)
-
-SELECT TOP 1 @ExperimentName=ExperimentName, @RunId=SUBSTRING(RunId, 1, 43)
-FROM aml_model
-ORDER BY CreatedDate DESC
-
-EXEC dbo.AutoMLGetMetrics @RunId, @ExperimentName

how-to-use-azureml/automated-machine-learning/sql-server/energy-demand/TrainEnergyDemand.sql

@@ -1,25 +0,0 @@
--- This shows using the AutoMLTrain stored procedure to create a forecasting model for the nyc_energy dataset.
-
-DECLARE @max_horizon INT = 48
-DECLARE @split_time NVARCHAR(22) = (SELECT DATEADD(hour, -@max_horizon, MAX(timeStamp)) FROM nyc_energy WHERE demand IS NOT NULL)
-
-DECLARE @TrainDataQuery NVARCHAR(MAX) = '
-SELECT CAST(timeStamp as NVARCHAR(30)) as timeStamp,
-       demand,
-	   precip,
-	   temp
-FROM nyc_energy
-WHERE demand IS NOT NULL AND precip IS NOT NULL AND temp IS NOT NULL
-and timeStamp < ''' + @split_time + ''''
-
-INSERT INTO dbo.aml_model(RunId, ExperimentName, Model, LogFileText, WorkspaceName)
-EXEC dbo.AutoMLTrain @input_query= @TrainDataQuery,
-@label_column='demand',
-@task='forecasting',
-@iterations=10,
-@iteration_timeout_minutes=5,
-@time_column_name='timeStamp',
-@max_horizon=@max_horizon,
-@experiment_name='automl-sql-forecast',
-@primary_metric='normalized_root_mean_squared_error'
-

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

@@ -1,161 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Train a model and use it for prediction\r\n",
-        "\r\n",
-        "Before running this notebook, run the auto-ml-sql-setup.ipynb notebook."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/sql-server/energy-demand/auto-ml-sql-energy-demand.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Set the default database"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "USE [automl]\r\n",
-        "GO"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Use the AutoMLTrain stored procedure to create a forecasting model for the nyc_energy dataset."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "INSERT INTO dbo.aml_model(RunId, ExperimentName, Model, LogFileText, WorkspaceName)\r\n",
-        "EXEC dbo.AutoMLTrain @input_query='\r\n",
-        "SELECT CAST(timeStamp as NVARCHAR(30)) as timeStamp,\r\n",
-        "       demand,\r\n",
-        "\t   precip,\r\n",
-        "\t   temp,\r\n",
-        "\t   CASE WHEN timeStamp < ''2017-01-01'' THEN 0 ELSE 1 END AS is_validate_column\r\n",
-        "FROM nyc_energy\r\n",
-        "WHERE demand IS NOT NULL AND precip IS NOT NULL AND temp IS NOT NULL\r\n",
-        "and timeStamp < ''2017-02-01''',\r\n",
-        "@label_column='demand',\r\n",
-        "@task='forecasting',\r\n",
-        "@iterations=10,\r\n",
-        "@iteration_timeout_minutes=5,\r\n",
-        "@time_column_name='timeStamp',\r\n",
-        "@is_validate_column='is_validate_column',\r\n",
-        "@experiment_name='automl-sql-forecast',\r\n",
-        "@primary_metric='normalized_root_mean_squared_error'"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Use the AutoMLPredict stored procedure to predict using the forecasting model for the nyc_energy dataset."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "DECLARE @Model NVARCHAR(MAX) = (SELECT TOP 1 Model FROM dbo.aml_model\r\n",
-        "                                WHERE ExperimentName = 'automl-sql-forecast'\r\n",
-        "\t\t\t\t\t\t\t\tORDER BY CreatedDate DESC)\r\n",
-        "\r\n",
-        "EXEC dbo.AutoMLPredict @input_query='\r\n",
-        "SELECT CAST(timeStamp AS NVARCHAR(30)) AS timeStamp,\r\n",
-        "       demand,\r\n",
-        "\t   precip,\r\n",
-        "\t   temp\r\n",
-        "FROM nyc_energy\r\n",
-        "WHERE demand IS NOT NULL AND precip IS NOT NULL AND temp IS NOT NULL\r\n",
-        "AND timeStamp >= ''2017-02-01''',\r\n",
-        "@label_column='demand',\r\n",
-        "@model=@model\r\n",
-        "WITH RESULT SETS ((timeStamp NVARCHAR(30), actual_demand FLOAT, precip FLOAT, temp FLOAT, predicted_demand FLOAT))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## List all the metrics for all iterations for the most recent training run."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "DECLARE @RunId NVARCHAR(43)\r\n",
-        "DECLARE @ExperimentName NVARCHAR(255)\r\n",
-        "\r\n",
-        "SELECT TOP 1 @ExperimentName=ExperimentName, @RunId=SUBSTRING(RunId, 1, 43)\r\n",
-        "FROM aml_model\r\n",
-        "ORDER BY CreatedDate DESC\r\n",
-        "\r\n",
-        "EXEC dbo.AutoMLGetMetrics @RunId, @ExperimentName"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "jeffshep"
-      }
-    ],
-    "category": "tutorial",
-    "compute": [
-      "Local"
-    ],
-    "datasets": [
-      "NYC Energy"
-    ],
-    "deployment": [
-      "None"
-    ],
-    "exclude_from_index": false,
-    "framework": [
-      "Azure ML AutoML"
-    ],
-    "tags": [
-      ""
-    ],
-    "friendly_name": "Forecasting with automated ML SQL integration",
-    "index_order": 1,
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "sql",
-      "name": "python36"
-    },
-    "language_info": {
-      "name": "sql",
-      "version": ""
-    },
-    "task": "Forecasting"
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/automated-machine-learning/sql-server/setup/AutoMLForecast.sql

@@ -1,92 +0,0 @@
--- This procedure forecast values based on a forecasting model returned by AutoMLTrain.
--- It returns a dataset with the forecasted values.
-SET ANSI_NULLS ON
-GO
-SET QUOTED_IDENTIFIER ON
-GO
-CREATE OR ALTER PROCEDURE [dbo].[AutoMLForecast]
- (
-   @input_query NVARCHAR(MAX),          -- A SQL query returning data to predict on.
-   @model NVARCHAR(MAX),                -- A model returned from AutoMLTrain.
-   @time_column_name  NVARCHAR(255)='', -- The name of the timestamp column for forecasting.
-   @label_column  NVARCHAR(255)='',     -- Optional name of the column from input_query, which should be ignored when predicting
-   @y_query_column NVARCHAR(255)='',    -- Optional value column that can be used for predicting.
-                                        -- If specified, this can contain values for past times (after the model was trained)
-									    -- and contain Nan for future times.
-   @forecast_column_name NVARCHAR(255) = 'predicted'
-                                        -- The name of the output column containing the forecast value.
- ) AS 
-BEGIN 
-  
-    EXEC sp_execute_external_script @language = N'Python', @script = N'import pandas as pd 
-import azureml.core  
-import numpy as np 
-from azureml.train.automl import AutoMLConfig  
-import pickle 
-import codecs 
-  
-model_obj = pickle.loads(codecs.decode(model.encode(), "base64")) 
-  
-test_data = input_data.copy() 
-
-if label_column != "" and label_column is not None:
-    y_test = test_data.pop(label_column).values
-else:
-    y_test = None 
-
-if y_query_column != "" and y_query_column is not None:
-    y_query = test_data.pop(y_query_column).values
-else:
-    y_query = np.repeat(np.nan, len(test_data))
-
-X_test = test_data 
-
-if time_column_name != "" and time_column_name is not None:
-    X_test[time_column_name] = pd.to_datetime(X_test[time_column_name])
-
-y_fcst, X_trans = model_obj.forecast(X_test, y_query) 
-
-def align_outputs(y_forecast, X_trans, X_test, y_test, forecast_column_name):
-    # Demonstrates how to get the output aligned to the inputs
-    # using pandas indexes. Helps understand what happened if
-    # the output 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
-
-    df_fcst = pd.DataFrame({forecast_column_name : y_forecast})
-    # 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()
-    if y_test is not None:
-        X_test_full[label_column] = y_test
-
-    # X_test_full 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[[label_column, forecast_column_name]].notnull().all(axis=1)]
-    return(clean)
-
-combined_output = align_outputs(y_fcst, X_trans, X_test, y_test, forecast_column_name)
-  
-' 
-    , @input_data_1 = @input_query 
-    , @input_data_1_name = N'input_data' 
-    , @output_data_1_name = N'combined_output' 
-    , @params = N'@model NVARCHAR(MAX), @time_column_name  NVARCHAR(255), @label_column NVARCHAR(255), @y_query_column NVARCHAR(255), @forecast_column_name NVARCHAR(255)' 
-    , @model = @model 
-	, @time_column_name = @time_column_name
-	, @label_column = @label_column
-	, @y_query_column = @y_query_column
-	, @forecast_column_name = @forecast_column_name
-END

how-to-use-azureml/automated-machine-learning/sql-server/setup/AutoMLGetMetrics.sql

@@ -1,70 +0,0 @@
--- This procedure returns a list of metrics for each iteration of a run.
-SET ANSI_NULLS ON
-GO
-SET QUOTED_IDENTIFIER ON
-GO
-CREATE OR ALTER PROCEDURE [dbo].[AutoMLGetMetrics]
- (
-	@run_id NVARCHAR(250),                           -- The RunId
-    @experiment_name NVARCHAR(32)='automl-sql-test', -- This can be used to find the experiment in the Azure Portal.
-    @connection_name NVARCHAR(255)='default'         -- The AML connection to use.
- ) AS
-BEGIN
-    DECLARE @tenantid NVARCHAR(255)
-    DECLARE @appid NVARCHAR(255)
-    DECLARE @password NVARCHAR(255)
-    DECLARE @config_file NVARCHAR(255)
-
-	SELECT @tenantid=TenantId, @appid=AppId, @password=Password, @config_file=ConfigFile
-	FROM aml_connection
-	WHERE ConnectionName = @connection_name;
-
-    EXEC sp_execute_external_script @language = N'Python', @script = N'import pandas as pd
-import logging 
-import azureml.core 
-import numpy as np
-from azureml.core.experiment import Experiment 
-from azureml.train.automl.run import AutoMLRun
-from azureml.core.authentication import ServicePrincipalAuthentication 
-from azureml.core.workspace import Workspace 
-
-auth = ServicePrincipalAuthentication(tenantid, appid, password) 
- 
-ws = Workspace.from_config(path=config_file, auth=auth) 
- 
-experiment = Experiment(ws, experiment_name) 
-
-ml_run = AutoMLRun(experiment = experiment, run_id = run_id)
-
-children = list(ml_run.get_children())
-iterationlist = []
-metricnamelist = []
-metricvaluelist = []
-
-for run in children:
-    properties = run.get_properties()
-    if "iteration" in properties:
-        iteration = int(properties["iteration"])
-        for metric_name, metric_value in run.get_metrics().items():
-            if isinstance(metric_value, float):
-                iterationlist.append(iteration)
-                metricnamelist.append(metric_name)
-                metricvaluelist.append(metric_value)
-             
-metrics = pd.DataFrame({"iteration": iterationlist, "metric_name": metricnamelist, "metric_value": metricvaluelist})
-'
-    , @output_data_1_name = N'metrics'
-	, @params = N'@run_id NVARCHAR(250), 
-				  @experiment_name NVARCHAR(32),
-  				  @tenantid NVARCHAR(255),
-				  @appid NVARCHAR(255),
-				  @password NVARCHAR(255),
-				  @config_file NVARCHAR(255)'
-    , @run_id = @run_id
-	, @experiment_name = @experiment_name
-	, @tenantid = @tenantid
-	, @appid = @appid
-	, @password = @password
-	, @config_file = @config_file
-WITH RESULT SETS ((iteration INT, metric_name NVARCHAR(100), metric_value FLOAT))
-END

how-to-use-azureml/automated-machine-learning/sql-server/setup/AutoMLPredict.sql

@@ -1,41 +0,0 @@
--- This procedure predicts values based on a model returned by AutoMLTrain and a dataset.
--- It returns the dataset with a new column added, which is the predicted value.
-SET ANSI_NULLS ON
-GO
-SET QUOTED_IDENTIFIER ON
-GO
-CREATE OR ALTER PROCEDURE [dbo].[AutoMLPredict]
- (
-   @input_query NVARCHAR(MAX),      -- A SQL query returning data to predict on.
-   @model NVARCHAR(MAX),            -- A model returned from AutoMLTrain.
-   @label_column  NVARCHAR(255)=''  -- Optional name of the column from input_query, which should be ignored when predicting
- ) AS 
-BEGIN 
-  
-    EXEC sp_execute_external_script @language = N'Python', @script = N'import pandas as pd 
-import azureml.core  
-import numpy as np 
-from azureml.train.automl import AutoMLConfig  
-import pickle 
-import codecs 
-  
-model_obj = pickle.loads(codecs.decode(model.encode(), "base64")) 
-  
-test_data = input_data.copy() 
-
-if label_column != "" and label_column is not None:
-    y_test = test_data.pop(label_column).values 
-X_test = test_data 
-  
-predicted = model_obj.predict(X_test) 
-  
-combined_output = input_data.assign(predicted=predicted)
-  
-' 
-    , @input_data_1 = @input_query 
-    , @input_data_1_name = N'input_data' 
-    , @output_data_1_name = N'combined_output' 
-    , @params = N'@model NVARCHAR(MAX), @label_column  NVARCHAR(255)' 
-    , @model = @model 
-	, @label_column = @label_column
-END

how-to-use-azureml/automated-machine-learning/sql-server/setup/AutoMLTrain.sql

@@ -1,240 +0,0 @@
--- This stored procedure uses automated machine learning to train several models
--- and returns the best model.
---
--- The result set has several columns:
---   best_run - iteration ID for the best model
---   experiment_name - experiment name pass in with the @experiment_name parameter
---   fitted_model - best model found
---   log_file_text - AutoML debug_log contents
---   workspace - name of the Azure ML workspace where run history is stored
---
--- An example call for a classification problem is:
---    insert into dbo.aml_model(RunId, ExperimentName, Model, LogFileText, WorkspaceName)
---    exec dbo.AutoMLTrain @input_query='
---    SELECT top 100000 
---          CAST([pickup_datetime] AS NVARCHAR(30)) AS pickup_datetime
---          ,CAST([dropoff_datetime] AS NVARCHAR(30)) AS dropoff_datetime
---          ,[passenger_count]
---          ,[trip_time_in_secs]
---          ,[trip_distance]
---          ,[payment_type]
---          ,[tip_class]
---      FROM [dbo].[nyctaxi_sample] order by [hack_license] ',
---      @label_column = 'tip_class',
---      @iterations=10
--- 
--- An example call for forecasting is:
---      insert into dbo.aml_model(RunId, ExperimentName, Model, LogFileText, WorkspaceName)
---      exec dbo.AutoMLTrain @input_query='
---      select cast(timeStamp as nvarchar(30)) as timeStamp,
---             demand,
---      	   precip,
---      	   temp,
---             case when timeStamp < ''2017-01-01'' then 0 else 1 end as is_validate_column
---      from nyc_energy
---      where demand is not null and precip is not null and temp is not null
---      and timeStamp < ''2017-02-01''',
---      @label_column='demand',
---      @task='forecasting',
---      @iterations=10,
---      @iteration_timeout_minutes=5,
---      @time_column_name='timeStamp',
---      @is_validate_column='is_validate_column',
---      @experiment_name='automl-sql-forecast',
---      @primary_metric='normalized_root_mean_squared_error'
-
-SET ANSI_NULLS ON
-GO
-SET QUOTED_IDENTIFIER ON
-GO
-CREATE OR ALTER PROCEDURE [dbo].[AutoMLTrain]
- (
-    @input_query NVARCHAR(MAX),                      -- The SQL Query that will return the data to train and validate the model.
-    @label_column NVARCHAR(255)='Label',             -- The name of the column in the result of @input_query that is the label.
-    @primary_metric NVARCHAR(40)='AUC_weighted',     -- The metric to optimize.
-    @iterations INT=100,                             -- The maximum number of pipelines to train.
-    @task NVARCHAR(40)='classification',             -- The type of task.  Can be classification, regression or forecasting.
-    @experiment_name NVARCHAR(32)='automl-sql-test', -- This can be used to find the experiment in the Azure Portal.
-    @iteration_timeout_minutes INT = 15,             -- The maximum time in minutes for training a single pipeline. 
-    @experiment_timeout_hours FLOAT = 1,             -- The maximum time in hours for training all pipelines.
-    @n_cross_validations INT = 3,                    -- The number of cross validations.
-    @blacklist_models NVARCHAR(MAX) = '',            -- A comma separated list of algos that will not be used.
-                                                     -- The list of possible models can be found at:
-                                                     -- https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#configure-your-experiment-settings
-    @whitelist_models NVARCHAR(MAX) = '',            -- A comma separated list of algos that can be used.
-                                                     -- The list of possible models can be found at:
-                                                     -- https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#configure-your-experiment-settings
-    @experiment_exit_score FLOAT = 0,                -- Stop the experiment if this score is acheived.
-    @sample_weight_column NVARCHAR(255)='',          -- The name of the column in the result of  @input_query that gives a sample weight.
-    @is_validate_column NVARCHAR(255)='',            -- The name of the column in the result of  @input_query that indicates if the row is for training or validation.
-	                                                 -- In the values of the column, 0 means for training and 1 means for validation.
-    @time_column_name  NVARCHAR(255)='',             -- The name of the timestamp column for forecasting.
-    @connection_name NVARCHAR(255)='default',        -- The AML connection to use.
-    @max_horizon INT = 0                             -- A forecast horizon is a time span into the future (or just beyond the latest date in the training data)
-                                                     -- where forecasts of the target quantity are needed.
-                                                     -- For example, if data is recorded daily and max_horizon is 5, we will predict 5 days ahead.
- ) AS
-BEGIN
-
-    DECLARE @tenantid NVARCHAR(255)
-    DECLARE @appid NVARCHAR(255)
-    DECLARE @password NVARCHAR(255)
-    DECLARE @config_file NVARCHAR(255)
-
-	SELECT @tenantid=TenantId, @appid=AppId, @password=Password, @config_file=ConfigFile
-	FROM aml_connection
-	WHERE ConnectionName = @connection_name;
-
-	EXEC sp_execute_external_script @language = N'Python', @script = N'import pandas as pd
-import logging 
-import azureml.core 
-import pandas as pd
-import numpy as np
-from azureml.core.experiment import Experiment 
-from azureml.train.automl import AutoMLConfig 
-from sklearn import datasets 
-import pickle
-import codecs
-from azureml.core.authentication import ServicePrincipalAuthentication 
-from azureml.core.workspace import Workspace 
-
-if __name__.startswith("sqlindb"):
-    auth = ServicePrincipalAuthentication(tenantid, appid, password) 
- 
-    ws = Workspace.from_config(path=config_file, auth=auth) 
- 
-    project_folder = "./sample_projects/" + experiment_name
- 
-    experiment = Experiment(ws, experiment_name) 
-
-    data_train = input_data
-    X_valid = None
-    y_valid = None
-    sample_weight_valid = None
-
-    if is_validate_column != "" and is_validate_column is not None:
-        data_train = input_data[input_data[is_validate_column] <= 0]
-        data_valid = input_data[input_data[is_validate_column] > 0]
-        data_train.pop(is_validate_column)
-        data_valid.pop(is_validate_column)
-        y_valid = data_valid.pop(label_column).values
-        if sample_weight_column != "" and sample_weight_column is not None:
-            sample_weight_valid = data_valid.pop(sample_weight_column).values
-        X_valid = data_valid
-        n_cross_validations = None
-
-    y_train = data_train.pop(label_column).values
-
-    sample_weight = None
-    if sample_weight_column != "" and sample_weight_column is not None:
-        sample_weight = data_train.pop(sample_weight_column).values
-
-    X_train = data_train
-
-    if experiment_timeout_hours == 0:
-        experiment_timeout_hours = None
-
-    if experiment_exit_score == 0:
-        experiment_exit_score = None
-
-    if blacklist_models == "":
-        blacklist_models = None
-
-    if blacklist_models is not None:
-        blacklist_models = blacklist_models.replace(" ", "").split(",")
-
-    if whitelist_models == "":
-        whitelist_models = None
-
-    if whitelist_models is not None:
-        whitelist_models = whitelist_models.replace(" ", "").split(",")
-
-    automl_settings = {}
-    preprocess = True
-    if time_column_name != "" and time_column_name is not None:
-        automl_settings = { "time_column_name": time_column_name }
-        preprocess = False
-        if max_horizon > 0:
-            automl_settings["max_horizon"] = max_horizon
-
-    log_file_name = "automl_sqlindb_errors.log"
-	 
-    automl_config = AutoMLConfig(task = task, 
-                                 debug_log = log_file_name, 
-                                 primary_metric = primary_metric, 
-                                 iteration_timeout_minutes = iteration_timeout_minutes, 
-                                 experiment_timeout_hours = experiment_timeout_hours,
-                                 iterations = iterations, 
-                                 n_cross_validations = n_cross_validations, 
-                                 preprocess = preprocess,
-                                 verbosity = logging.INFO, 
-                                 X = X_train,  
-                                 y = y_train, 
-                                 path = project_folder,
-                                 blacklist_models = blacklist_models,
-                                 whitelist_models = whitelist_models,
-                                 experiment_exit_score = experiment_exit_score,
-                                 sample_weight = sample_weight,
-                                 X_valid = X_valid,
-                                 y_valid = y_valid,
-                                 sample_weight_valid = sample_weight_valid,
-                                 **automl_settings) 
- 
-    local_run = experiment.submit(automl_config, show_output = True) 
-
-    best_run, fitted_model = local_run.get_output()
-
-    pickled_model = codecs.encode(pickle.dumps(fitted_model), "base64").decode()
-
-    log_file_text = ""
-
-    try:
-        with open(log_file_name, "r") as log_file:
-            log_file_text = log_file.read()
-    except:
-        log_file_text = "Log file not found"
-
-    returned_model = pd.DataFrame({"best_run": [best_run.id], "experiment_name": [experiment_name], "fitted_model": [pickled_model], "log_file_text": [log_file_text], "workspace": [ws.name]}, dtype=np.dtype(np.str))
-'
-	, @input_data_1 = @input_query
-	, @input_data_1_name = N'input_data'
-	, @output_data_1_name = N'returned_model'
-	, @params = N'@label_column NVARCHAR(255), 
-	              @primary_metric NVARCHAR(40),
-				  @iterations INT, @task NVARCHAR(40),
-				  @experiment_name NVARCHAR(32),
-				  @iteration_timeout_minutes INT,
-				  @experiment_timeout_hours FLOAT,
-				  @n_cross_validations INT,
-				  @blacklist_models NVARCHAR(MAX),
-				  @whitelist_models NVARCHAR(MAX),
-				  @experiment_exit_score FLOAT,
-				  @sample_weight_column NVARCHAR(255),
-				  @is_validate_column NVARCHAR(255),
-				  @time_column_name  NVARCHAR(255),
-				  @tenantid NVARCHAR(255),
-				  @appid NVARCHAR(255),
-				  @password NVARCHAR(255),
-				  @config_file NVARCHAR(255),
-				  @max_horizon INT'
-	, @label_column = @label_column
-	, @primary_metric = @primary_metric
-	, @iterations = @iterations
-	, @task = @task
-	, @experiment_name = @experiment_name
-	, @iteration_timeout_minutes = @iteration_timeout_minutes
-	, @experiment_timeout_hours = @experiment_timeout_hours
-	, @n_cross_validations = @n_cross_validations
-	, @blacklist_models = @blacklist_models
-	, @whitelist_models = @whitelist_models
-	, @experiment_exit_score = @experiment_exit_score
-	, @sample_weight_column = @sample_weight_column
-	, @is_validate_column = @is_validate_column
-	, @time_column_name = @time_column_name
-	, @tenantid = @tenantid
-	, @appid = @appid
-	, @password = @password
-	, @config_file = @config_file
-	, @max_horizon = @max_horizon
-WITH RESULT SETS ((best_run NVARCHAR(250), experiment_name NVARCHAR(100), fitted_model VARCHAR(MAX), log_file_text NVARCHAR(MAX), workspace NVARCHAR(100)))
-END

how-to-use-azureml/automated-machine-learning/sql-server/setup/aml_connection.sql

@@ -1,18 +0,0 @@
--- This is a table to store the Azure ML connection information.
-SET ANSI_NULLS ON
-GO
-
-SET QUOTED_IDENTIFIER ON
-GO
-
-CREATE TABLE [dbo].[aml_connection](
-    [Id] [int] IDENTITY(1,1) NOT NULL PRIMARY KEY,
-	[ConnectionName] [nvarchar](255) NULL,
-	[TenantId] [nvarchar](255) NULL,
-	[AppId] [nvarchar](255) NULL,
-	[Password] [nvarchar](255) NULL,
-	[ConfigFile] [nvarchar](255) NULL
-) ON [PRIMARY]
-GO
-
-

how-to-use-azureml/automated-machine-learning/sql-server/setup/aml_model.sql

@@ -1,22 +0,0 @@
--- This is a table to hold the results from the AutoMLTrain procedure.
-SET ANSI_NULLS ON
-GO
-
-SET QUOTED_IDENTIFIER ON
-GO
-
-CREATE TABLE [dbo].[aml_model](
-    [Id] [int] IDENTITY(1,1) NOT NULL PRIMARY KEY,
-    [Model] [varchar](max) NOT NULL,        -- The model, which can be passed to AutoMLPredict for testing or prediction.
-    [RunId] [nvarchar](250) NULL,           -- The RunId, which can be used to view the model in the Azure Portal.
-    [CreatedDate] [datetime] NULL,
-    [ExperimentName] [nvarchar](100) NULL,  -- Azure ML Experiment Name
-    [WorkspaceName] [nvarchar](100) NULL,   -- Azure ML Workspace Name
-	[LogFileText] [nvarchar](max) NULL
-) 
-GO
-
-ALTER TABLE [dbo].[aml_model] ADD  DEFAULT (getutcdate()) FOR [CreatedDate]
-GO
-
-

how-to-use-azureml/automated-machine-learning/sql-server/setup/auto-ml-sql-setup.ipynb

@@ -1,581 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Set up Azure ML Automated Machine Learning on SQL Server 2019 CTP 2.4 big data cluster\r\n",
-        "\r\n",
-        "\\# Prerequisites:  \r\n",
-        "\\# - An Azure subscription and resource group  \r\n",
-        "\\# - An Azure Machine Learning workspace  \r\n",
-        "\\# - A SQL Server 2019 CTP 2.4 big data cluster with Internet access and a database named 'automl'  \r\n",
-        "\\# - Azure CLI  \r\n",
-        "\\# - kubectl command  \r\n",
-        "\\# - The https://github.com/Azure/MachineLearningNotebooks repository downloaded (cloned) to your local machine\r\n",
-        "\r\n",
-        "\\# In the 'automl' database, create a table named 'dbo.nyc_energy' as follows:  \r\n",
-        "\\# - In SQL Server Management Studio, right-click the 'automl' database, select Tasks, then Import Flat File.  \r\n",
-        "\\# - Select the file AzureMlCli\\notebooks\\how-to-use-azureml\\automated-machine-learning\\forecasting-energy-demand\\nyc_energy.csv.  \r\n",
-        "\\# - Using the \"Modify Columns\" page, allow nulls for all columns. \r\n",
-        "\r\n",
-        "\\# Create an Azure Machine Learning Workspace using the instructions at https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace \r\n",
-        "\r\n",
-        "\\# Create an Azure service principal.  You can do this with the following commands: \r\n",
-        "\r\n",
-        "az login  \r\n",
-        "az account set --subscription *subscriptionid*  \r\n",
-        "\r\n",
-        "\\# The following command prints out the **appId** and **tenant**,  \r\n",
-        "\\# which you insert into the indicated cell later in this notebook  \r\n",
-        "\\# to allow AutoML to authenticate with Azure:  \r\n",
-        "\r\n",
-        "az ad sp create-for-rbac --name *principlename* --password *password*\r\n",
-        "\r\n",
-        "\\# Log into the master instance of SQL Server 2019 CTP 2.4:  \r\n",
-        "kubectl exec -it mssql-master-pool-0 -n *clustername* -c mssql-server -- /bin/bash\r\n",
-        "\r\n",
-        "mkdir /tmp/aml\r\n",
-        "\r\n",
-        "cd /tmp/aml\r\n",
-        "\r\n",
-        "\\# **Modify** the following with your subscription_id, resource_group, and workspace_name:  \r\n",
-        "cat > config.json << EOF  \r\n",
-        "{  \r\n",
-        "    \"subscription_id\": \"123456ab-78cd-0123-45ef-abcd12345678\",  \r\n",
-        "    \"resource_group\": \"myrg1\",  \r\n",
-        "    \"workspace_name\": \"myws1\"  \r\n",
-        "}  \r\n",
-        "EOF\r\n",
-        "\r\n",
-        "\\# The directory referenced below is appropriate for the master instance of SQL Server 2019 CTP 2.4.\r\n",
-        "\r\n",
-        "cd /opt/mssql/mlservices/runtime/python/bin\r\n",
-        "\r\n",
-        "./python -m pip install azureml-sdk[automl]\r\n",
-        "\r\n",
-        "./python -m pip install --upgrade numpy \r\n",
-        "\r\n",
-        "./python -m pip install --upgrade sklearn\r\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/sql-server/setup/auto-ml-sql-setup.png)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "-- Enable external scripts to allow invoking Python\r\n",
-        "sp_configure 'external scripts enabled',1 \r\n",
-        "reconfigure with override \r\n",
-        "GO\r\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "-- Use database 'automl'\r\n",
-        "USE [automl]\r\n",
-        "GO"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "-- This is a table to hold the Azure ML connection information.\r\n",
-        "SET ANSI_NULLS ON\r\n",
-        "GO\r\n",
-        "\r\n",
-        "SET QUOTED_IDENTIFIER ON\r\n",
-        "GO\r\n",
-        "\r\n",
-        "CREATE TABLE [dbo].[aml_connection](\r\n",
-        "    [Id] [int] IDENTITY(1,1) NOT NULL PRIMARY KEY,\r\n",
-        "\t[ConnectionName] [nvarchar](255) NULL,\r\n",
-        "\t[TenantId] [nvarchar](255) NULL,\r\n",
-        "\t[AppId] [nvarchar](255) NULL,\r\n",
-        "\t[Password] [nvarchar](255) NULL,\r\n",
-        "\t[ConfigFile] [nvarchar](255) NULL\r\n",
-        ") ON [PRIMARY]\r\n",
-        "GO"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Copy the values from create-for-rbac above into the cell below"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "-- Use the following values:\r\n",
-        "-- Leave the name as 'Default'\r\n",
-        "-- Insert <tenant> returned by create-for-rbac above\r\n",
-        "-- Insert <AppId> returned by create-for-rbac above\r\n",
-        "-- Insert <password> used in create-for-rbac above\r\n",
-        "-- Leave <path> as '/tmp/aml/config.json'\r\n",
-        "INSERT INTO [dbo].[aml_connection]  \r\n",
-        "VALUES (\r\n",
-        "    N'Default', -- Name\r\n",
-        "    N'11111111-2222-3333-4444-555555555555', -- Tenant\r\n",
-        "    N'aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee', -- AppId\r\n",
-        "    N'insertpasswordhere', -- Password\r\n",
-        "    N'/tmp/aml/config.json' -- Path\r\n",
-        "    );\r\n",
-        "GO"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "-- This is a table to hold the results from the AutoMLTrain procedure.\r\n",
-        "SET ANSI_NULLS ON\r\n",
-        "GO\r\n",
-        "\r\n",
-        "SET QUOTED_IDENTIFIER ON\r\n",
-        "GO\r\n",
-        "\r\n",
-        "CREATE TABLE [dbo].[aml_model](\r\n",
-        "    [Id] [int] IDENTITY(1,1) NOT NULL PRIMARY KEY,\r\n",
-        "    [Model] [varchar](max) NOT NULL,        -- The model, which can be passed to AutoMLPredict for testing or prediction.\r\n",
-        "    [RunId] [nvarchar](250) NULL,           -- The RunId, which can be used to view the model in the Azure Portal.\r\n",
-        "    [CreatedDate] [datetime] NULL,\r\n",
-        "    [ExperimentName] [nvarchar](100) NULL,  -- Azure ML Experiment Name\r\n",
-        "    [WorkspaceName] [nvarchar](100) NULL,   -- Azure ML Workspace Name\r\n",
-        "\t[LogFileText] [nvarchar](max) NULL\r\n",
-        ") \r\n",
-        "GO\r\n",
-        "\r\n",
-        "ALTER TABLE [dbo].[aml_model] ADD  DEFAULT (getutcdate()) FOR [CreatedDate]\r\n",
-        "GO\r\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "-- This stored procedure uses automated machine learning to train several models\r\n",
-        "-- and return the best model.\r\n",
-        "--\r\n",
-        "-- The result set has several columns:\r\n",
-        "--   best_run - ID of the best model found\r\n",
-        "--   experiment_name - training run name\r\n",
-        "--   fitted_model - best model found\r\n",
-        "--   log_file_text - console output\r\n",
-        "--   workspace - name of the Azure ML workspace where run history is stored\r\n",
-        "--\r\n",
-        "-- An example call for a classification problem is:\r\n",
-        "--    insert into dbo.aml_model(RunId, ExperimentName, Model, LogFileText, WorkspaceName)\r\n",
-        "--    exec dbo.AutoMLTrain @input_query='\r\n",
-        "--    SELECT top 100000 \r\n",
-        "--          CAST([pickup_datetime] AS NVARCHAR(30)) AS pickup_datetime\r\n",
-        "--          ,CAST([dropoff_datetime] AS NVARCHAR(30)) AS dropoff_datetime\r\n",
-        "--          ,[passenger_count]\r\n",
-        "--          ,[trip_time_in_secs]\r\n",
-        "--          ,[trip_distance]\r\n",
-        "--          ,[payment_type]\r\n",
-        "--          ,[tip_class]\r\n",
-        "--      FROM [dbo].[nyctaxi_sample] order by [hack_license] ',\r\n",
-        "--      @label_column = 'tip_class',\r\n",
-        "--      @iterations=10\r\n",
-        "-- \r\n",
-        "-- An example call for forecasting is:\r\n",
-        "--      insert into dbo.aml_model(RunId, ExperimentName, Model, LogFileText, WorkspaceName)\r\n",
-        "--      exec dbo.AutoMLTrain @input_query='\r\n",
-        "--      select cast(timeStamp as nvarchar(30)) as timeStamp,\r\n",
-        "--             demand,\r\n",
-        "--      \t   precip,\r\n",
-        "--      \t   temp,\r\n",
-        "--             case when timeStamp < ''2017-01-01'' then 0 else 1 end as is_validate_column\r\n",
-        "--      from nyc_energy\r\n",
-        "--      where demand is not null and precip is not null and temp is not null\r\n",
-        "--      and timeStamp < ''2017-02-01''',\r\n",
-        "--      @label_column='demand',\r\n",
-        "--      @task='forecasting',\r\n",
-        "--      @iterations=10,\r\n",
-        "--      @iteration_timeout_minutes=5,\r\n",
-        "--      @time_column_name='timeStamp',\r\n",
-        "--      @is_validate_column='is_validate_column',\r\n",
-        "--      @experiment_name='automl-sql-forecast',\r\n",
-        "--      @primary_metric='normalized_root_mean_squared_error'\r\n",
-        "\r\n",
-        "SET ANSI_NULLS ON\r\n",
-        "GO\r\n",
-        "SET QUOTED_IDENTIFIER ON\r\n",
-        "GO\r\n",
-        "CREATE OR ALTER PROCEDURE [dbo].[AutoMLTrain]\r\n",
-        " (\r\n",
-        "    @input_query NVARCHAR(MAX),                      -- The SQL Query that will return the data to train and validate the model.\r\n",
-        "    @label_column NVARCHAR(255)='Label',             -- The name of the column in the result of @input_query that is the label.\r\n",
-        "    @primary_metric NVARCHAR(40)='AUC_weighted',     -- The metric to optimize.\r\n",
-        "    @iterations INT=100,                             -- The maximum number of pipelines to train.\r\n",
-        "    @task NVARCHAR(40)='classification',             -- The type of task.  Can be classification, regression or forecasting.\r\n",
-        "    @experiment_name NVARCHAR(32)='automl-sql-test', -- This can be used to find the experiment in the Azure Portal.\r\n",
-        "    @iteration_timeout_minutes INT = 15,             -- The maximum time in minutes for training a single pipeline. \r\n",
-        "    @experiment_timeout_hours FLOAT = 1,             -- The maximum time in hours for training all pipelines.\r\n",
-        "    @n_cross_validations INT = 3,                    -- The number of cross validations.\r\n",
-        "    @blacklist_models NVARCHAR(MAX) = '',            -- A comma separated list of algos that will not be used.\r\n",
-        "                                                     -- The list of possible models can be found at:\r\n",
-        "                                                     -- https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#configure-your-experiment-settings\r\n",
-        "    @whitelist_models NVARCHAR(MAX) = '',            -- A comma separated list of algos that can be used.\r\n",
-        "                                                     -- The list of possible models can be found at:\r\n",
-        "                                                     -- https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#configure-your-experiment-settings\r\n",
-        "    @experiment_exit_score FLOAT = 0,                -- Stop the experiment if this score is acheived.\r\n",
-        "    @sample_weight_column NVARCHAR(255)='',          -- The name of the column in the result of  @input_query that gives a sample weight.\r\n",
-        "    @is_validate_column NVARCHAR(255)='',            -- The name of the column in the result of  @input_query that indicates if the row is for training or validation.\r\n",
-        "\t                                                 -- In the values of the column, 0 means for training and 1 means for validation.\r\n",
-        "    @time_column_name  NVARCHAR(255)='',             -- The name of the timestamp column for forecasting.\r\n",
-        "\t@connection_name NVARCHAR(255)='default'         -- The AML connection to use.\r\n",
-        " ) AS\r\n",
-        "BEGIN\r\n",
-        "\r\n",
-        "    DECLARE @tenantid NVARCHAR(255)\r\n",
-        "    DECLARE @appid NVARCHAR(255)\r\n",
-        "    DECLARE @password NVARCHAR(255)\r\n",
-        "    DECLARE @config_file NVARCHAR(255)\r\n",
-        "\r\n",
-        "\tSELECT @tenantid=TenantId, @appid=AppId, @password=Password, @config_file=ConfigFile\r\n",
-        "\tFROM aml_connection\r\n",
-        "\tWHERE ConnectionName = @connection_name;\r\n",
-        "\r\n",
-        "\tEXEC sp_execute_external_script @language = N'Python', @script = N'import pandas as pd\r\n",
-        "import logging \r\n",
-        "import azureml.core \r\n",
-        "import pandas as pd\r\n",
-        "import numpy as np\r\n",
-        "from azureml.core.experiment import Experiment \r\n",
-        "from azureml.train.automl import AutoMLConfig \r\n",
-        "from sklearn import datasets \r\n",
-        "import pickle\r\n",
-        "import codecs\r\n",
-        "from azureml.core.authentication import ServicePrincipalAuthentication \r\n",
-        "from azureml.core.workspace import Workspace \r\n",
-        "\r\n",
-        "if __name__.startswith(\"sqlindb\"):\r\n",
-        "    auth = ServicePrincipalAuthentication(tenantid, appid, password) \r\n",
-        " \r\n",
-        "    ws = Workspace.from_config(path=config_file, auth=auth) \r\n",
-        " \r\n",
-        "    project_folder = \"./sample_projects/\" + experiment_name\r\n",
-        " \r\n",
-        "    experiment = Experiment(ws, experiment_name) \r\n",
-        "\r\n",
-        "    data_train = input_data\r\n",
-        "    X_valid = None\r\n",
-        "    y_valid = None\r\n",
-        "    sample_weight_valid = None\r\n",
-        "\r\n",
-        "    if is_validate_column != \"\" and is_validate_column is not None:\r\n",
-        "        data_train = input_data[input_data[is_validate_column] <= 0]\r\n",
-        "        data_valid = input_data[input_data[is_validate_column] > 0]\r\n",
-        "        data_train.pop(is_validate_column)\r\n",
-        "        data_valid.pop(is_validate_column)\r\n",
-        "        y_valid = data_valid.pop(label_column).values\r\n",
-        "        if sample_weight_column != \"\" and sample_weight_column is not None:\r\n",
-        "            sample_weight_valid = data_valid.pop(sample_weight_column).values\r\n",
-        "        X_valid = data_valid\r\n",
-        "        n_cross_validations = None\r\n",
-        "\r\n",
-        "    y_train = data_train.pop(label_column).values\r\n",
-        "\r\n",
-        "    sample_weight = None\r\n",
-        "    if sample_weight_column != \"\" and sample_weight_column is not None:\r\n",
-        "        sample_weight = data_train.pop(sample_weight_column).values\r\n",
-        "\r\n",
-        "    X_train = data_train\r\n",
-        "\r\n",
-        "    if experiment_timeout_hours == 0:\r\n",
-        "        experiment_timeout_hours = None\r\n",
-        "\r\n",
-        "    if experiment_exit_score == 0:\r\n",
-        "        experiment_exit_score = None\r\n",
-        "\r\n",
-        "    if blacklist_models == \"\":\r\n",
-        "        blacklist_models = None\r\n",
-        "\r\n",
-        "    if blacklist_models is not None:\r\n",
-        "        blacklist_models = blacklist_models.replace(\" \", \"\").split(\",\")\r\n",
-        "\r\n",
-        "    if whitelist_models == \"\":\r\n",
-        "        whitelist_models = None\r\n",
-        "\r\n",
-        "    if whitelist_models is not None:\r\n",
-        "        whitelist_models = whitelist_models.replace(\" \", \"\").split(\",\")\r\n",
-        "\r\n",
-        "    automl_settings = {}\r\n",
-        "    preprocess = True\r\n",
-        "    if time_column_name != \"\" and time_column_name is not None:\r\n",
-        "        automl_settings = { \"time_column_name\": time_column_name }\r\n",
-        "        preprocess = False\r\n",
-        "\r\n",
-        "    log_file_name = \"automl_errors.log\"\r\n",
-        "\t \r\n",
-        "    automl_config = AutoMLConfig(task = task, \r\n",
-        "                                 debug_log = log_file_name, \r\n",
-        "                                 primary_metric = primary_metric, \r\n",
-        "                                 iteration_timeout_minutes = iteration_timeout_minutes, \r\n",
-        "                                 experiment_timeout_hours = experiment_timeout_hours,\r\n",
-        "                                 iterations = iterations, \r\n",
-        "                                 n_cross_validations = n_cross_validations, \r\n",
-        "                                 preprocess = preprocess,\r\n",
-        "                                 verbosity = logging.INFO, \r\n",
-        "                                 X = X_train,  \r\n",
-        "                                 y = y_train, \r\n",
-        "                                 path = project_folder,\r\n",
-        "                                 blacklist_models = blacklist_models,\r\n",
-        "                                 whitelist_models = whitelist_models,\r\n",
-        "                                 experiment_exit_score = experiment_exit_score,\r\n",
-        "                                 sample_weight = sample_weight,\r\n",
-        "                                 X_valid = X_valid,\r\n",
-        "                                 y_valid = y_valid,\r\n",
-        "                                 sample_weight_valid = sample_weight_valid,\r\n",
-        "                                 **automl_settings) \r\n",
-        " \r\n",
-        "    local_run = experiment.submit(automl_config, show_output = True) \r\n",
-        "\r\n",
-        "    best_run, fitted_model = local_run.get_output()\r\n",
-        "\r\n",
-        "    pickled_model = codecs.encode(pickle.dumps(fitted_model), \"base64\").decode()\r\n",
-        "\r\n",
-        "    log_file_text = \"\"\r\n",
-        "\r\n",
-        "    try:\r\n",
-        "        with open(log_file_name, \"r\") as log_file:\r\n",
-        "            log_file_text = log_file.read()\r\n",
-        "    except:\r\n",
-        "        log_file_text = \"Log file not found\"\r\n",
-        "\r\n",
-        "    returned_model = pd.DataFrame({\"best_run\": [best_run.id], \"experiment_name\": [experiment_name], \"fitted_model\": [pickled_model], \"log_file_text\": [log_file_text], \"workspace\": [ws.name]}, dtype=np.dtype(np.str))\r\n",
-        "'\r\n",
-        "\t, @input_data_1 = @input_query\r\n",
-        "\t, @input_data_1_name = N'input_data'\r\n",
-        "\t, @output_data_1_name = N'returned_model'\r\n",
-        "\t, @params = N'@label_column NVARCHAR(255), \r\n",
-        "\t              @primary_metric NVARCHAR(40),\r\n",
-        "\t\t\t\t  @iterations INT, @task NVARCHAR(40),\r\n",
-        "\t\t\t\t  @experiment_name NVARCHAR(32),\r\n",
-        "\t\t\t\t  @iteration_timeout_minutes INT,\r\n",
-        "\t\t\t\t  @experiment_timeout_hours FLOAT,\r\n",
-        "\t\t\t\t  @n_cross_validations INT,\r\n",
-        "\t\t\t\t  @blacklist_models NVARCHAR(MAX),\r\n",
-        "\t\t\t\t  @whitelist_models NVARCHAR(MAX),\r\n",
-        "\t\t\t\t  @experiment_exit_score FLOAT,\r\n",
-        "\t\t\t\t  @sample_weight_column NVARCHAR(255),\r\n",
-        "\t\t\t\t  @is_validate_column NVARCHAR(255),\r\n",
-        "\t\t\t\t  @time_column_name  NVARCHAR(255),\r\n",
-        "\t\t\t\t  @tenantid NVARCHAR(255),\r\n",
-        "\t\t\t\t  @appid NVARCHAR(255),\r\n",
-        "\t\t\t\t  @password NVARCHAR(255),\r\n",
-        "\t\t\t\t  @config_file NVARCHAR(255)'\r\n",
-        "\t, @label_column = @label_column\r\n",
-        "\t, @primary_metric = @primary_metric\r\n",
-        "\t, @iterations = @iterations\r\n",
-        "\t, @task = @task\r\n",
-        "\t, @experiment_name = @experiment_name\r\n",
-        "\t, @iteration_timeout_minutes = @iteration_timeout_minutes\r\n",
-        "\t, @experiment_timeout_hours = @experiment_timeout_hours\r\n",
-        "\t, @n_cross_validations = @n_cross_validations\r\n",
-        "\t, @blacklist_models = @blacklist_models\r\n",
-        "\t, @whitelist_models = @whitelist_models\r\n",
-        "\t, @experiment_exit_score = @experiment_exit_score\r\n",
-        "\t, @sample_weight_column = @sample_weight_column\r\n",
-        "\t, @is_validate_column = @is_validate_column\r\n",
-        "\t, @time_column_name = @time_column_name\r\n",
-        "\t, @tenantid = @tenantid\r\n",
-        "\t, @appid = @appid\r\n",
-        "\t, @password = @password\r\n",
-        "\t, @config_file = @config_file\r\n",
-        "WITH RESULT SETS ((best_run NVARCHAR(250), experiment_name NVARCHAR(100), fitted_model VARCHAR(MAX), log_file_text NVARCHAR(MAX), workspace NVARCHAR(100)))\r\n",
-        "END"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "-- This procedure returns a list of metrics for each iteration of a training run.\r\n",
-        "SET ANSI_NULLS ON\r\n",
-        "GO\r\n",
-        "SET QUOTED_IDENTIFIER ON\r\n",
-        "GO\r\n",
-        "CREATE OR ALTER PROCEDURE [dbo].[AutoMLGetMetrics]\r\n",
-        " (\r\n",
-        "\t@run_id NVARCHAR(250),                           -- The RunId\r\n",
-        "    @experiment_name NVARCHAR(32)='automl-sql-test', -- This can be used to find the experiment in the Azure Portal.\r\n",
-        "    @connection_name NVARCHAR(255)='default'         -- The AML connection to use.\r\n",
-        " ) AS\r\n",
-        "BEGIN\r\n",
-        "    DECLARE @tenantid NVARCHAR(255)\r\n",
-        "    DECLARE @appid NVARCHAR(255)\r\n",
-        "    DECLARE @password NVARCHAR(255)\r\n",
-        "    DECLARE @config_file NVARCHAR(255)\r\n",
-        "\r\n",
-        "\tSELECT @tenantid=TenantId, @appid=AppId, @password=Password, @config_file=ConfigFile\r\n",
-        "\tFROM aml_connection\r\n",
-        "\tWHERE ConnectionName = @connection_name;\r\n",
-        "\r\n",
-        "    EXEC sp_execute_external_script @language = N'Python', @script = N'import pandas as pd\r\n",
-        "import logging \r\n",
-        "import azureml.core \r\n",
-        "import numpy as np\r\n",
-        "from azureml.core.experiment import Experiment \r\n",
-        "from azureml.train.automl.run import AutoMLRun\r\n",
-        "from azureml.core.authentication import ServicePrincipalAuthentication \r\n",
-        "from azureml.core.workspace import Workspace \r\n",
-        "\r\n",
-        "auth = ServicePrincipalAuthentication(tenantid, appid, password) \r\n",
-        " \r\n",
-        "ws = Workspace.from_config(path=config_file, auth=auth) \r\n",
-        " \r\n",
-        "experiment = Experiment(ws, experiment_name) \r\n",
-        "\r\n",
-        "ml_run = AutoMLRun(experiment = experiment, run_id = run_id)\r\n",
-        "\r\n",
-        "children = list(ml_run.get_children())\r\n",
-        "iterationlist = []\r\n",
-        "metricnamelist = []\r\n",
-        "metricvaluelist = []\r\n",
-        "\r\n",
-        "for run in children:\r\n",
-        "    properties = run.get_properties()\r\n",
-        "    if \"iteration\" in properties:\r\n",
-        "        iteration = int(properties[\"iteration\"])\r\n",
-        "        for metric_name, metric_value in run.get_metrics().items():\r\n",
-        "            if isinstance(metric_value, float):\r\n",
-        "                iterationlist.append(iteration)\r\n",
-        "                metricnamelist.append(metric_name)\r\n",
-        "                metricvaluelist.append(metric_value)\r\n",
-        "             \r\n",
-        "metrics = pd.DataFrame({\"iteration\": iterationlist, \"metric_name\": metricnamelist, \"metric_value\": metricvaluelist})\r\n",
-        "'\r\n",
-        "    , @output_data_1_name = N'metrics'\r\n",
-        "\t, @params = N'@run_id NVARCHAR(250), \r\n",
-        "\t\t\t\t  @experiment_name NVARCHAR(32),\r\n",
-        "  \t\t\t\t  @tenantid NVARCHAR(255),\r\n",
-        "\t\t\t\t  @appid NVARCHAR(255),\r\n",
-        "\t\t\t\t  @password NVARCHAR(255),\r\n",
-        "\t\t\t\t  @config_file NVARCHAR(255)'\r\n",
-        "    , @run_id = @run_id\r\n",
-        "\t, @experiment_name = @experiment_name\r\n",
-        "\t, @tenantid = @tenantid\r\n",
-        "\t, @appid = @appid\r\n",
-        "\t, @password = @password\r\n",
-        "\t, @config_file = @config_file\r\n",
-        "WITH RESULT SETS ((iteration INT, metric_name NVARCHAR(100), metric_value FLOAT))\r\n",
-        "END"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "-- This procedure predicts values based on a model returned by AutoMLTrain and a dataset.\r\n",
-        "-- It returns the dataset with a new column added, which is the predicted value.\r\n",
-        "SET ANSI_NULLS ON\r\n",
-        "GO\r\n",
-        "SET QUOTED_IDENTIFIER ON\r\n",
-        "GO\r\n",
-        "CREATE OR ALTER PROCEDURE [dbo].[AutoMLPredict]\r\n",
-        " (\r\n",
-        "   @input_query NVARCHAR(MAX),      -- A SQL query returning data to predict on.\r\n",
-        "   @model NVARCHAR(MAX),            -- A model returned from AutoMLTrain.\r\n",
-        "   @label_column  NVARCHAR(255)=''  -- Optional name of the column from input_query, which should be ignored when predicting\r\n",
-        " ) AS \r\n",
-        "BEGIN \r\n",
-        "  \r\n",
-        "    EXEC sp_execute_external_script @language = N'Python', @script = N'import pandas as pd \r\n",
-        "import azureml.core  \r\n",
-        "import numpy as np \r\n",
-        "from azureml.train.automl import AutoMLConfig  \r\n",
-        "import pickle \r\n",
-        "import codecs \r\n",
-        "  \r\n",
-        "model_obj = pickle.loads(codecs.decode(model.encode(), \"base64\")) \r\n",
-        "  \r\n",
-        "test_data = input_data.copy() \r\n",
-        "\r\n",
-        "if label_column != \"\" and label_column is not None:\r\n",
-        "    y_test = test_data.pop(label_column).values \r\n",
-        "X_test = test_data \r\n",
-        "  \r\n",
-        "predicted = model_obj.predict(X_test) \r\n",
-        "  \r\n",
-        "combined_output = input_data.assign(predicted=predicted)\r\n",
-        "  \r\n",
-        "' \r\n",
-        "    , @input_data_1 = @input_query \r\n",
-        "    , @input_data_1_name = N'input_data' \r\n",
-        "    , @output_data_1_name = N'combined_output' \r\n",
-        "    , @params = N'@model NVARCHAR(MAX), @label_column  NVARCHAR(255)' \r\n",
-        "    , @model = @model \r\n",
-        "\t, @label_column = @label_column\r\n",
-        "END"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "jeffshep"
-      }
-    ],
-    "category": "tutorial",
-    "compute": [
-      "None"
-    ],
-    "datasets": [
-      "None"
-    ],
-    "deployment": [
-      "None"
-    ],
-    "exclude_from_index": false,
-    "framework": [
-      "Azure ML AutoML"
-    ],
-    "friendly_name": "Setup automated ML SQL integration",
-    "index_order": 1,
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "sql",
-      "name": "python36"
-    },
-    "language_info": {
-      "name": "sql",
-      "version": ""
-    },
-    "tags": [
-      ""
-    ],
-    "task": "None"
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

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

@@ -13,32 +13,45 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Automated ML on Azure Databricks\n",
+        "We support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as your PyPi package. You can select the option to attach the library to all clusters or just one cluster.\n",
         "\n",
-        "In this example we use the scikit-learn's <a href=\"http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset\" target=\"_blank\">digit dataset</a> to showcase how you can use AutoML for a simple classification problem.\n",
+        "**install azureml-sdk with Automated ML**\n",
-        "\n",
+        "* Source: Upload Python Egg or PyPi\n",
-        "In this notebook you will learn how to:\n",
+        "* PyPi Name: `azureml-sdk[automl]`\n",
-        "1. Create Azure Machine Learning Workspace object and initialize your notebook directory to easily reload this object from a configuration file.\n",
+        "* Select Install Library"
-        "2. Create an `Experiment` in an existing `Workspace`.\n",
-        "3. Configure Automated ML using `AutoMLConfig`.\n",
-        "4. Train the model using Azure Databricks.\n",
-        "5. Explore the results.\n",
-        "6. Viewing the engineered names for featurized data and featurization summary for all raw features.\n",
-        "7. Test the best fitted model.\n",
-        "\n",
-        "Before running this notebook, please follow the <a href=\"https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks\" target=\"_blank\">readme for using Automated ML on Azure Databricks</a> for installing necessary libraries to your cluster."
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "We support installing AML SDK with Automated ML as library from GUI. When attaching a library follow <a href=\"https://docs.databricks.com/user-guide/libraries.html\" target=\"_blank\">this link</a> and add the below string as your PyPi package. You can select the option to attach the library to all clusters or just one cluster.\n",
+        "# AutoML : Classification with Local Compute on Azure DataBricks\n",
         "\n",
-        "**azureml-sdk with automated ml**\n",
+        "In this example we use the scikit-learn's to showcase how you can use AutoML for a simple classification problem.\n",
-        "* Source: Upload Python Egg or PyPi\n",
+        "\n",
-        "* PyPi Name: `azureml-sdk[automl]`\n",
+        "In this notebook you will learn how to:\n",
-        "* Select Install Library"
+        "1. Create Azure Machine Learning Workspace object and initialize your notebook directory to easily reload this object from a configuration file.\n",
+        "2. Create an `Experiment` in an existing `Workspace`.\n",
+        "3. Configure AutoML using `AutoMLConfig`.\n",
+        "4. Train the model using AzureDataBricks.\n",
+        "5. Explore the results.\n",
+        "6. Test the best fitted model.\n",
+        "\n",
+        "Prerequisites:\n",
+        "Before running this notebook, please follow the readme for installing necessary libraries to your cluster."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Register Machine Learning Services Resource Provider\n",
+        "Microsoft.MachineLearningServices only needs to be registed once in the subscription. To register it:\n",
+        "Start the Azure portal.\n",
+        "Select your All services and then Subscription.\n",
+        "Select the subscription that you want to use.\n",
+        "Click on Resource providers\n",
+        "Click the Register link next to Microsoft.MachineLearningServices"
       ]
     },
     {
@@ -145,31 +158,8 @@
         "               resource_group = resource_group)\n",
         "\n",
         "# Persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
-        "ws.write_config()"
+        "ws.write_config()\n",
-      ]
+        "write_config(path=\"/databricks/driver/aml_config/\",file_name=<alias_conf.cfg>)"
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create a Folder to Host Sample Projects\n",
-        "Finally, create a folder where all the sample projects will be hosted."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "sample_projects_folder = './sample_projects'\n",
-        "\n",
-        "if not os.path.isdir(sample_projects_folder):\n",
-        "    os.mkdir(sample_projects_folder)\n",
-        "    \n",
-        "print('Sample projects will be created in {}.'.format(sample_projects_folder))"
       ]
     },
     {
@@ -178,7 +168,7 @@
       "source": [
         "## Create an Experiment\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."
+        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
       ]
     },
     {
@@ -191,6 +181,7 @@
         "import os\n",
         "import random\n",
         "import time\n",
+        "import json\n",
         "\n",
         "from matplotlib import pyplot as plt\n",
         "from matplotlib.pyplot import imshow\n",
@@ -212,7 +203,6 @@
       "source": [
         "# Choose a name for the experiment and specify the project folder.\n",
         "experiment_name = 'automl-local-classification'\n",
-        "project_folder = './sample_projects/automl-local-classification'\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
@@ -222,94 +212,11 @@
         "output['Workspace Name'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "pd.DataFrame(data = output, index = ['']).T"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Diagnostics\n",
-        "\n",
-        "Opt-in diagnostics for better experience, quality, and security of future releases."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.telemetry import set_diagnostics_collection\n",
-        "set_diagnostics_collection(send_diagnostics = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Registering Datastore"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Datastore is the way to save connection information to a storage service (e.g. Azure Blob, Azure Data Lake, Azure SQL) information to your workspace so you can access them without exposing credentials in your code. The first thing you will need to do is register a datastore, you can refer to our [python SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore.datastore?view=azure-ml-py) on how to register datastores. __Note: for best security practices, please do not check in code that contains registering datastores with secrets into your source control__\n",
-        "\n",
-        "The code below registers a datastore pointing to a publicly readable blob container."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Datastore\n",
-        "\n",
-        "datastore_name = 'demo_training'\n",
-        "container_name = 'digits' \n",
-        "account_name = 'automlpublicdatasets'\n",
-        "Datastore.register_azure_blob_container(\n",
-        "    workspace = ws, \n",
-        "    datastore_name = datastore_name, \n",
-        "    container_name = container_name, \n",
-        "    account_name = account_name,\n",
-        "    overwrite = True\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Below is an example on how to register a private blob container\n",
-        "```python\n",
-        "datastore = Datastore.register_azure_blob_container(\n",
-        "    workspace = ws, \n",
-        "    datastore_name = 'example_datastore', \n",
-        "    container_name = 'example-container', \n",
-        "    account_name = 'storageaccount',\n",
-        "    account_key = 'accountkey'\n",
-        ")\n",
-        "```\n",
-        "The example below shows how  to register an Azure Data Lake store. Please make sure you have granted the necessary permissions for the service principal to access the data lake.\n",
-        "```python\n",
-        "datastore = Datastore.register_azure_data_lake(\n",
-        "    workspace = ws,\n",
-        "    datastore_name = 'example_datastore',\n",
-        "    store_name = 'adlsstore',\n",
-        "    tenant_id = 'tenant-id-of-service-principal',\n",
-        "    client_id = 'client-id-of-service-principal',\n",
-        "    client_secret = 'client-secret-of-service-principal'\n",
-        ")\n",
-        "```"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -323,9 +230,7 @@
       "source": [
         "Automated ML takes a `TabularDataset` as input.\n",
         "\n",
-        "You are free to use the data preparation libraries/tools of your choice to do the require preparation and once you are done, you can write it to a datastore and create a TabularDataset from it.\n",
+        "You are free to use the data preparation libraries/tools of your choice to do the require preparation and once you are done, you can write it to a datastore and create a TabularDataset from it."
-        "\n",
-        "You will get the datastore you registered previously and pass it to Dataset for reading. The data comes from the digits dataset: `sklearn.datasets.load_digits()`. `DataPath` points to a specific location within a datastore. "
       ]
     },
     {
@@ -334,13 +239,12 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "# The data referenced here was a 1MB simple random sample of the Chicago Crime data into a local temporary directory.\n",
         "from azureml.core.dataset import Dataset\n",
-        "from azureml.data.datapath import DataPath\n",
         "\n",
-        "datastore = Datastore.get(workspace = ws, datastore_name = datastore_name)\n",
+        "example_data = 'https://dprepdata.blob.core.windows.net/demo/crime0-random.csv'\n",
-        "\n",
+        "dataset = Dataset.Tabular.from_delimited_files(example_data)\n",
-        "X_train = Dataset.Tabular.from_delimited_files(datastore.path('X.csv'))\n",
+        "dataset.take(5).to_pandas_dataframe()"
-        "y_train = Dataset.Tabular.from_delimited_files(datastore.path('y.csv'))"
       ]
     },
     {
@@ -357,16 +261,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "X_train.take(5).to_pandas_dataframe()"
+        "training_data = dataset.drop_columns(columns=['FBI Code'])\n",
-      ]
+        "label = 'Primary Type'"
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "y_train.take(5).to_pandas_dataframe()"
       ]
     },
     {
@@ -384,14 +280,11 @@
         "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
         "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
         "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
-        "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**spark_context**|Spark Context object. for Databricks, use spark_context=sc|\n",
         "|**max_concurrent_iterations**|Maximum number of iterations to execute in parallel. This should be <= number of worker nodes in your Azure Databricks cluster.|\n",
-        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+        "|**n_cross_validations**|Number of cross validation splits.|\n",
-        "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
+        "|**training_data**|Input dataset, containing both features and label column.|\n",
-        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
+        "|**label_column_name**|The name of the label column.|"
-        "|**preprocess**|set this to True to enable pre-processing of data eg. string to numeric using one-hot encoding|\n",
-        "|**exit_score**|Target score for experiment. It is associated with the metric. eg. exit_score=0.995 will exit experiment after that|"
       ]
     },
     {
@@ -404,15 +297,13 @@
         "                             debug_log = 'automl_errors.log',\n",
         "                             primary_metric = 'AUC_weighted',\n",
         "                             iteration_timeout_minutes = 10,\n",
-        "                             iterations = 3,\n",
+        "                             iterations = 5,\n",
-        "                             preprocess = True,\n",
         "                             n_cross_validations = 10,\n",
         "                             max_concurrent_iterations = 2, #change it based on number of worker nodes\n",
         "                             verbosity = logging.INFO,\n",
         "                             spark_context=sc, #databricks/spark related\n",
-        "                             X = X_train, \n",
+        "                             training_data=training_data,\n",
-        "                             y = y_train,\n",
+        "                             label_column_name=label)"
-        "                             path = project_folder)"
       ]
     },
     {
@@ -433,26 +324,6 @@
         "local_run = experiment.submit(automl_config, show_output = True)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Continue experiment"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_run.continue_experiment(iterations=2,\n",
-        "                              X=X_train, \n",
-        "                              y=y_train,\n",
-        "                              spark_context=sc,\n",
-        "                              show_output=True)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -475,14 +346,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "displayHTML(\"<a href={} target='_blank'>Your experiment in Azure Portal: {}</a>\".format(local_run.get_portal_url(), local_run.id))"
+        "displayHTML(\"<a href={} target='_blank'>Azure Portal: {}</a>\".format(local_run.get_portal_url(), local_run.id))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The following will show the child runs and waits for the parent run to complete."
       ]
     },
     {
@@ -503,6 +367,7 @@
         "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",
@@ -514,9 +379,11 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Retrieve the Best Model after the above run is complete \n",
+        "## Deploy\n",
         "\n",
-        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+        "### Retrieve the Best Model\n",
+        "\n",
+        "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
       ]
     },
     {
@@ -525,71 +392,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "best_run, fitted_model = local_run.get_output()\n",
+        "best_run, fitted_model = local_run.get_output()"
-        "print(best_run)\n",
-        "print(fitted_model)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Best Model Based on Any Other Metric after the above run is complete based on the child run\n",
-        "Show the run and the model that has the smallest `log_loss` value:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "lookup_metric = \"log_loss\"\n",
-        "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
-        "print(best_run)\n",
-        "print(fitted_model)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### View the engineered names for featurized data\n",
-        "Below we display the engineered feature names generated for the featurized data using the preprocessing featurization."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "fitted_model.named_steps['datatransformer'].get_engineered_feature_names()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### View the featurization summary\n",
-        "Below we display the featurization that was performed on different raw features in the user data. For each raw feature in the user data, the following information is displayed:-\n",
-        "- Raw feature name\n",
-        "- Number of engineered features formed out of this raw feature\n",
-        "- Type detected\n",
-        "- If feature was dropped\n",
-        "- List of feature transformations for the raw feature"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Get the featurization summary as a list of JSON\n",
-        "featurization_summary = fitted_model.named_steps['datatransformer'].get_featurization_summary()\n",
-        "# View the featurization summary as a pandas dataframe\n",
-        "pd.DataFrame.from_records(featurization_summary)"
       ]
     },
     {
@@ -607,11 +410,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "blob_location = \"https://{}.blob.core.windows.net/{}\".format(account_name, container_name)\n",
+        "dataset_test = Dataset.Tabular.from_delimited_files(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv')\n",
-        "X_test = pd.read_csv(\"{}./X_valid.csv\".format(blob_location), header=0)\n",
+        "\n",
-        "y_test = pd.read_csv(\"{}/y_valid.csv\".format(blob_location), header=0)\n",
+        "df_test = dataset_test.to_pandas_dataframe()\n",
-        "images  = pd.read_csv(\"{}/images.csv\".format(blob_location), header=None)\n",
+        "df_test = df_test[pd.notnull(df_test['Primary Type'])]\n",
-        "images = np.reshape(images.values, (100,8,8))"
+        "\n",
+        "y_test = df_test[['Primary Type']]\n",
+        "X_test = df_test.drop(['Primary Type', 'FBI Code'], axis=1)"
       ]
     },
     {
@@ -628,35 +433,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# Randomly select digits and test.\n",
+        "fitted_model.predict(X_test)"
-        "for index in np.random.choice(len(y_test), 2, replace = False):\n",
-        "    print(index)\n",
-        "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
-        "    label = y_test.values[index]\n",
-        "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
-        "    fig = plt.figure(3, figsize = (5,5))\n",
-        "    ax1 = fig.add_axes((0,0,.8,.8))\n",
-        "    ax1.set_title(title)\n",
-        "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
-        "    display(fig)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "When deploying an automated ML trained model, please specify _pippackages=['azureml-sdk[automl]']_ in your CondaDependencies.\n",
-        "\n",
-        "Please refer to only the **Deploy** section in this notebook - <a href=\"https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification-with-deployment\" target=\"_blank\">Deployment of Automated ML trained model</a>"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -689,10 +468,10 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.5"
+      "version": "3.6.8"
     },
     "name": "auto-ml-classification-local-adb",
-    "notebookId": 587284549713154
+    "notebookId": 1275190406842063
   },
   "nbformat": 4,
   "nbformat_minor": 1

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

@@ -27,7 +27,7 @@
       "source": [
         "# AutoML : Classification with Local Compute on Azure DataBricks with deployment to ACI\n",
         "\n",
-        "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
+        "In this example we use the scikit-learn's to showcase how you can use AutoML for a simple classification problem.\n",
         "\n",
         "In this notebook you will learn how to:\n",
         "1. Create Azure Machine Learning Workspace object and initialize your notebook directory to easily reload this object from a configuration file.\n",
@@ -164,30 +164,6 @@
         "write_config(path=\"/databricks/driver/aml_config/\",file_name=<alias_conf.cfg>)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create a Folder to Host Sample Projects\n",
-        "Finally, create a folder where all the sample projects will be hosted."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "sample_projects_folder = './sample_projects'\n",
-        "\n",
-        "if not os.path.isdir(sample_projects_folder):\n",
-        "    os.mkdir(sample_projects_folder)\n",
-        "    \n",
-        "print('Sample projects will be created in {}.'.format(sample_projects_folder))"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -229,7 +205,6 @@
       "source": [
         "# Choose a name for the experiment and specify the project folder.\n",
         "experiment_name = 'automl-local-classification'\n",
-        "project_folder = './sample_projects/automl-local-classification'\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
@@ -239,94 +214,11 @@
         "output['Workspace Name'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "pd.DataFrame(data = output, index = ['']).T"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Diagnostics\n",
-        "\n",
-        "Opt-in diagnostics for better experience, quality, and security of future releases."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.telemetry import set_diagnostics_collection\n",
-        "set_diagnostics_collection(send_diagnostics = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Registering Datastore"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Datastore is the way to save connection information to a storage service (e.g. Azure Blob, Azure Data Lake, Azure SQL) information to your workspace so you can access them without exposing credentials in your code. The first thing you will need to do is register a datastore, you can refer to our [python SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore.datastore?view=azure-ml-py) on how to register datastores. __Note: for best security practices, please do not check in code that contains registering datastores with secrets into your source control__\n",
-        "\n",
-        "The code below registers a datastore pointing to a publicly readable blob container."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Datastore\n",
-        "\n",
-        "datastore_name = 'demo_training'\n",
-        "container_name = 'digits' \n",
-        "account_name = 'automlpublicdatasets'\n",
-        "Datastore.register_azure_blob_container(\n",
-        "    workspace = ws, \n",
-        "    datastore_name = datastore_name, \n",
-        "    container_name = container_name, \n",
-        "    account_name = account_name,\n",
-        "     overwrite = True\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Below is an example on how to register a private blob container\n",
-        "```python\n",
-        "datastore = Datastore.register_azure_blob_container(\n",
-        "    workspace = ws, \n",
-        "    datastore_name = 'example_datastore', \n",
-        "    container_name = 'example-container', \n",
-        "    account_name = 'storageaccount',\n",
-        "    account_key = 'accountkey'\n",
-        ")\n",
-        "```\n",
-        "The example below shows how  to register an Azure Data Lake store. Please make sure you have granted the necessary permissions for the service principal to access the data lake.\n",
-        "```python\n",
-        "datastore = Datastore.register_azure_data_lake(\n",
-        "    workspace = ws,\n",
-        "    datastore_name = 'example_datastore',\n",
-        "    store_name = 'adlsstore',\n",
-        "    tenant_id = 'tenant-id-of-service-principal',\n",
-        "    client_id = 'client-id-of-service-principal',\n",
-        "    client_secret = 'client-secret-of-service-principal'\n",
-        ")\n",
-        "```"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -340,9 +232,7 @@
       "source": [
         "Automated ML takes a `TabularDataset` as input.\n",
         "\n",
-        "You are free to use the data preparation libraries/tools of your choice to do the require preparation and once you are done, you can write it to a datastore and create a TabularDataset from it.\n",
+        "You are free to use the data preparation libraries/tools of your choice to do the require preparation and once you are done, you can write it to a datastore and create a TabularDataset from it."
-        "\n",
-        "You will get the datastore you registered previously and pass it to Dataset for reading. The data comes from the digits dataset: `sklearn.datasets.load_digits()`. `DataPath` points to a specific location within a datastore. "
       ]
     },
     {
@@ -351,13 +241,12 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "# The data referenced here was a 1MB simple random sample of the Chicago Crime data into a local temporary directory.\n",
         "from azureml.core.dataset import Dataset\n",
-        "from azureml.data.datapath import DataPath\n",
         "\n",
-        "datastore = Datastore.get(workspace = ws, datastore_name = datastore_name)\n",
+        "example_data = 'https://dprepdata.blob.core.windows.net/demo/crime0-random.csv'\n",
-        "\n",
+        "dataset = Dataset.Tabular.from_delimited_files(example_data)\n",
-        "X_train = Dataset.Tabular.from_delimited_files(datastore.path('X.csv'))\n",
+        "dataset.take(5).to_pandas_dataframe()"
-        "y_train = Dataset.Tabular.from_delimited_files(datastore.path('y.csv'))"
       ]
     },
     {
@@ -374,16 +263,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "X_train.take(5).to_pandas_dataframe()"
+        "training_data = dataset.drop_columns(columns=['FBI Code'])\n",
-      ]
+        "label = 'Primary Type'"
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "y_train.take(5).to_pandas_dataframe()"
       ]
     },
     {
@@ -401,14 +282,11 @@
         "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
         "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
         "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
-        "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**spark_context**|Spark Context object. for Databricks, use spark_context=sc|\n",
         "|**max_concurrent_iterations**|Maximum number of iterations to execute in parallel. This should be <= number of worker nodes in your Azure Databricks cluster.|\n",
-        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+        "|**n_cross_validations**|Number of cross validation splits.|\n",
-        "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
+        "|**training_data**|Input dataset, containing both features and label column.|\n",
-        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
+        "|**label_column_name**|The name of the label column.|"
-        "|**preprocess**|set this to True to enable pre-processing of data eg. string to numeric using one-hot encoding|\n",
-        "|**exit_score**|Target score for experiment. It is associated with the metric. eg. exit_score=0.995 will exit experiment after that|"
       ]
     },
     {
@@ -422,14 +300,12 @@
         "                             primary_metric = 'AUC_weighted',\n",
         "                             iteration_timeout_minutes = 10,\n",
         "                             iterations = 5,\n",
-        "                             preprocess = True,\n",
         "                             n_cross_validations = 10,\n",
         "                             max_concurrent_iterations = 2, #change it based on number of worker nodes\n",
         "                             verbosity = logging.INFO,\n",
         "                             spark_context=sc, #databricks/spark related\n",
-        "                             X = X_train, \n",
+        "                             training_data=training_data,\n",
-        "                             y = y_train,\n",
+        "                             label_column_name=label)"
-        "                             path = project_folder)"
       ]
     },
     {
@@ -475,13 +351,6 @@
         "displayHTML(\"<a href={} target='_blank'>Azure Portal: {}</a>\".format(local_run.get_portal_url(), local_run.id))"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The following will show the child runs and waits for the parent run to complete."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -542,7 +411,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from automl.client.core.common import constants\n",
+        "from azureml.automl.core.shared import constants\n",
         "conda_env_file_name = 'conda_env.yml'\n",
         "best_run.download_file(name=\"outputs/conda_env_v_1_0_0.yml\", output_file_path=conda_env_file_name)\n",
         "with open(conda_env_file_name, \"r\") as conda_file:\n",
@@ -564,7 +433,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from automl.client.core.common import constants\n",
+        "from azureml.automl.core.shared import constants\n",
         "script_file_name = 'scoring_file.py'\n",
         "best_run.download_file(name=\"outputs/scoring_file_v_1_0_0.py\", output_file_path=script_file_name)\n",
         "with open(script_file_name, \"r\") as scoring_file:\n",
@@ -651,11 +520,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "blob_location = \"https://{}.blob.core.windows.net/{}\".format(account_name, container_name)\n",
+        "dataset_test = Dataset.Tabular.from_delimited_files(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv')\n",
-        "X_test = pd.read_csv(\"{}./X_valid.csv\".format(blob_location), header=0)\n",
+        "\n",
-        "y_test = pd.read_csv(\"{}/y_valid.csv\".format(blob_location), header=0)\n",
+        "df_test = dataset_test.to_pandas_dataframe()\n",
-        "images  = pd.read_csv(\"{}/images.csv\".format(blob_location), header=None)\n",
+        "df_test = df_test[pd.notnull(df_test['Primary Type'])]\n",
-        "images = np.reshape(images.values, (100,8,8))"
+        "\n",
+        "y_test = df_test[['Primary Type']]\n",
+        "X_test = df_test.drop(['Primary Type', 'FBI Code'], axis=1)"
       ]
     },
     {
@@ -672,20 +543,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "import json\n",
+        "fitted_model.predict(X_test)"
-        "# Randomly select digits and test.\n",
-        "for index in np.random.choice(len(y_test), 2, replace = False):\n",
-        "    print(index)\n",
-        "    test_sample = json.dumps({'data':X_test[index:index + 1].values.tolist()})\n",
-        "    predicted = aci_service.run(input_data = test_sample)\n",
-        "    label = y_test.values[index]\n",
-        "    predictedDict = json.loads(predicted)\n",
-        "    title = \"Label value = %d  Predicted value = %s \" % ( label,predictedDict['result'][0])    \n",
-        "    fig = plt.figure(3, figsize = (5,5))\n",
-        "    ax1 = fig.add_axes((0,0,.8,.8))\n",
-        "    ax1.set_title(title)\n",
-        "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
-        "    display(fig)"
       ]
     },
     {
@@ -703,7 +561,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "myservice.delete()"
+        "aci_service.delete()"
       ]
     },
     {
@@ -741,7 +599,7 @@
       "version": "3.6.8"
     },
     "name": "auto-ml-classification-local-adb",
-    "notebookId": 2733885892129020
+    "notebookId": 3772036807853791
   },
   "nbformat": 4,
   "nbformat_minor": 1

how-to-use-azureml/deployment/accelerated-models/README.md

@@ -50,10 +50,12 @@ pip install azureml-accel-models[gpu]
 
 ### Step 4: Follow our notebooks
 
-The notebooks in this repo walk through the following scenarios: 
+We provide notebooks to walk through the following scenarios, linked below: 
-* [Quickstart](accelerated-models-quickstart.ipynb), deploy and inference a ResNet50 model trained on ImageNet
+* [Quickstart](https://github.com/Azure/MachineLearningNotebooks/blob/33d6def8c30d3dd3a5bfbea50b9c727788185faf/how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.ipynb), deploy and inference a ResNet50 model trained on ImageNet
-* [Object Detection](accelerated-models-object-detection.ipynb), deploy and inference an SSD-VGG model that can do object detection
+* [Object Detection](https://github.com/Azure/MachineLearningNotebooks/blob/33d6def8c30d3dd3a5bfbea50b9c727788185faf/how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.ipynb), deploy and inference an SSD-VGG model that can do object detection
-* [Training models](accelerated-models-training.ipynb), train one of our accelerated models on the Kaggle Cats and Dogs dataset to see how to improve accuracy on custom datasets
+* [Training models](https://github.com/Azure/MachineLearningNotebooks/blob/33d6def8c30d3dd3a5bfbea50b9c727788185faf/how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.ipynb), train one of our accelerated models on the Kaggle Cats and Dogs dataset to see how to improve accuracy on custom datasets
+
+**Note**: the above notebooks work only for tensorflow >= 1.6,<2.0.
 
 <a name="model-classes"></a>
 ## Model Classes

how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.ipynb

@@ -1,497 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Azure ML Hardware Accelerated Object Detection"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This tutorial will show you how to deploy an object detection service based on the SSD-VGG model in just a few minutes using the Azure Machine Learning Accelerated AI service.\n",
-        "\n",
-        "We will use the SSD-VGG model accelerated on an FPGA. Our Accelerated Models Service handles translating deep neural networks (DNN) into an FPGA program.\n",
-        "\n",
-        "The steps in this notebook are: \n",
-        "1. [Setup Environment](#set-up-environment)\n",
-        "* [Construct Model](#construct-model)\n",
-        "    * Image Preprocessing\n",
-        "    * Featurizer\n",
-        "    * Save Model\n",
-        "    * Save input and output tensor names\n",
-        "* [Create Image](#create-image)\n",
-        "* [Deploy Image](#deploy-image)\n",
-        "* [Test the Service](#test-service)\n",
-        "    * Create Client\n",
-        "    * Serve the model\n",
-        "* [Cleanup](#cleanup)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"set-up-environment\"></a>\n",
-        "## 1. Set up Environment\n",
-        "### 1.a. Imports"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "import tensorflow as tf"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 1.b. Retrieve Workspace\n",
-        "If you haven't created a Workspace, please follow [this notebook](\"../../../configuration.ipynb\") to do so. If you have, run the codeblock below to retrieve it. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\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": [
-        "<a id=\"construct-model\"></a>\n",
-        "## 2. Construct model\n",
-        "### 2.a. Image preprocessing\n",
-        "We'd like our service to accept JPEG images as input. However the input to SSD-VGG is a float tensor of shape \\[1, 300, 300, 3\\]. The first dimension is batch, then height, width, and channels (i.e. NHWC). To bridge this gap, we need code that decodes JPEG images and resizes them appropriately for input to SSD-VGG. The Accelerated AI service can execute TensorFlow graphs as part of the service and we'll use that ability to do the image preprocessing. This code defines a TensorFlow graph that preprocesses an array of JPEG images (as TensorFlow strings) and produces a tensor that is ready to be featurized by SSD-VGG.\n",
-        "\n",
-        "**Note:** Expect to see TF deprecation warnings until we port our SDK over to use Tensorflow 2.0."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Input images as a two-dimensional tensor containing an arbitrary number of images represented a strings\n",
-        "import azureml.accel.models.utils as utils\n",
-        "tf.reset_default_graph()\n",
-        "\n",
-        "in_images = tf.placeholder(tf.string)\n",
-        "image_tensors = utils.preprocess_array(in_images, output_width=300, output_height=300, preserve_aspect_ratio=False)\n",
-        "print(image_tensors.shape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 2.b. Featurizer\n",
-        "The SSD-VGG model is different from our other models in that it generates 12 tensor outputs. These corresponds to x,y displacements of the anchor boxes and the detection confidence (for 21 classes). Because these outputs are not convenient to work with, we will later use a pre-defined post-processing utility to transform the outputs into a simplified list of bounding boxes with their respective class and confidence.\n",
-        "\n",
-        "For more information about the output tensors, take this example: the output tensor 'ssd_300_vgg/block4_box/Reshape_1:0' has a shape of [None, 37, 37, 4, 21]. This gives the pre-softmax confidence for 4 anchor boxes situated at each site of a 37 x 37 grid imposed on the image, one confidence score for each of the 21 classes. The first dimension is the batch dimension. Likewise, 'ssd_300_vgg/block4_box/Reshape:0' has shape [None, 37, 37, 4, 4] and encodes the (cx, cy) center shift and rescaling (sw, sh) relative to each anchor box. Refer to the [SSD-VGG paper](https://arxiv.org/abs/1512.02325) to understand how these are computed. The other 10 tensors are defined similarly."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.accel.models import SsdVgg\n",
-        "\n",
-        "saved_model_dir = os.path.join(os.path.expanduser('~'), 'models')\n",
-        "model_graph = SsdVgg(saved_model_dir, is_frozen = True)\n",
-        "\n",
-        "print('SSD-VGG Input Tensors:')\n",
-        "for idx, input_name in enumerate(model_graph.input_tensor_list):\n",
-        "    print('{}, {}'.format(input_name, model_graph.get_input_dims(idx)))\n",
-        "    \n",
-        "print('SSD-VGG Output Tensors:')\n",
-        "for idx, output_name in enumerate(model_graph.output_tensor_list):\n",
-        "    print('{}, {}'.format(output_name, model_graph.get_output_dims(idx)))\n",
-        "\n",
-        "ssd_outputs = model_graph.import_graph_def(image_tensors, is_training=False)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 2.c. Save Model\n",
-        "Now that we loaded both parts of the tensorflow graph (preprocessor and SSD-VGG featurizer), we can save the graph and associated variables to a directory which we can register as an Azure ML Model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model_name = \"ssdvgg\"\n",
-        "model_save_path = os.path.join(saved_model_dir, model_name, \"saved_model\")\n",
-        "print(\"Saving model in {}\".format(model_save_path))\n",
-        "\n",
-        "output_map = {}\n",
-        "for i, output in enumerate(ssd_outputs):\n",
-        "    output_map['out_{}'.format(i)] = output\n",
-        "\n",
-        "with tf.Session() as sess:\n",
-        "    model_graph.restore_weights(sess)\n",
-        "    tf.saved_model.simple_save(sess, \n",
-        "                               model_save_path, \n",
-        "                               inputs={'images': in_images}, \n",
-        "                               outputs=output_map)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 2.d. Important! Save names of input and output tensors\n",
-        "\n",
-        "These input and output tensors that were created during the preprocessing and classifier steps are also going to be used when **converting the model** to an Accelerated Model that can run on FPGA's and for **making an inferencing request**. It is very important to save this information!"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "register model from file"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "input_tensors = in_images.name\n",
-        "# We will use the list of output tensors during inferencing\n",
-        "output_tensors = [output.name for output in ssd_outputs]\n",
-        "# However, for multiple output tensors, our AccelOnnxConverter will \n",
-        "#    accept comma-delimited strings (lists will cause error)\n",
-        "output_tensors_str = \",\".join(output_tensors)\n",
-        "\n",
-        "print(input_tensors)\n",
-        "print(output_tensors)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"create-image\"></a>\n",
-        "## 3. Create AccelContainerImage\n",
-        "Below we will execute all the same steps as in the [Quickstart](./accelerated-models-quickstart.ipynb#create-image) to package the model we have saved locally into an accelerated Docker image saved in our workspace. To complete all the steps, it may take a few minutes. For more details on each step, check out the [Quickstart section on model registration](./accelerated-models-quickstart.ipynb#register-model)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "from azureml.core.model import Model\n",
-        "from azureml.core.image import Image\n",
-        "from azureml.accel import AccelOnnxConverter\n",
-        "from azureml.accel import AccelContainerImage\n",
-        "\n",
-        "# Retrieve workspace\n",
-        "ws = Workspace.from_config()\n",
-        "print(\"Successfully retrieved workspace:\", ws.name, ws.resource_group, ws.location, ws.subscription_id, '\\n')\n",
-        "\n",
-        "# Register model\n",
-        "registered_model = Model.register(workspace = ws,\n",
-        "                                  model_path = model_save_path,\n",
-        "                                  model_name = model_name)\n",
-        "print(\"Successfully registered: \", registered_model.name, registered_model.description, registered_model.version, '\\n', sep = '\\t')\n",
-        "\n",
-        "# Convert model\n",
-        "convert_request = AccelOnnxConverter.convert_tf_model(ws, registered_model, input_tensors, output_tensors_str)\n",
-        "if convert_request.wait_for_completion(show_output = False):\n",
-        "    # If the above call succeeded, get the converted model\n",
-        "    converted_model = convert_request.result\n",
-        "    print(\"\\nSuccessfully converted: \", converted_model.name, converted_model.url, converted_model.version, \n",
-        "          converted_model.id, converted_model.created_time, '\\n')\n",
-        "else:\n",
-        "    print(\"Model conversion failed. Showing output.\")\n",
-        "    convert_request.wait_for_completion(show_output = True)\n",
-        "\n",
-        "# Package into AccelContainerImage\n",
-        "image_config = AccelContainerImage.image_configuration()\n",
-        "# Image name must be lowercase\n",
-        "image_name = \"{}-image\".format(model_name)\n",
-        "image = Image.create(name = image_name,\n",
-        "                     models = [converted_model],\n",
-        "                     image_config = image_config, \n",
-        "                     workspace = ws)\n",
-        "image.wait_for_creation()\n",
-        "print(\"Created AccelContainerImage: {} {} {}\\n\".format(image.name, image.creation_state, image.image_location))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"deploy-image\"></a>\n",
-        "## 4. Deploy image\n",
-        "Once you have an Azure ML Accelerated Image in your Workspace, you can deploy it to two destinations, to a Databox Edge machine or to an AKS cluster. \n",
-        "\n",
-        "### 4.a. Deploy to Databox Edge Machine using IoT Hub\n",
-        "See the sample [here](https://github.com/Azure-Samples/aml-real-time-ai/) for using the Azure IoT CLI extension for deploying your Docker image to your Databox Edge Machine.\n",
-        "\n",
-        "### 4.b. Deploy to AKS Cluster\n",
-        "Same as in the [Quickstart section on image deployment](./accelerated-models-quickstart.ipynb#deploy-image), we are going to create an AKS cluster with FPGA-enabled machines, then deploy our service to it.\n",
-        "#### Create AKS ComputeTarget"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import AksCompute, ComputeTarget\n",
-        "\n",
-        "# Uses the specific FPGA enabled VM (sku: Standard_PB6s)\n",
-        "# Standard_PB6s are available in: eastus, westus2, westeurope, southeastasia\n",
-        "prov_config = AksCompute.provisioning_configuration(vm_size = \"Standard_PB6s\",\n",
-        "                                                    agent_count = 1, \n",
-        "                                                    location = \"eastus\")\n",
-        "\n",
-        "aks_name = 'aks-pb6-obj'\n",
-        "# Create the cluster\n",
-        "aks_target = ComputeTarget.create(workspace = ws, \n",
-        "                                  name = aks_name, \n",
-        "                                  provisioning_configuration = prov_config)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Provisioning an AKS cluster might take awhile (15 or so minutes), and we want to wait until it's successfully provisioned before we can deploy a service to it. If you interrupt this cell, provisioning of the cluster will continue. You can re-run it or check the status in your Workspace under Compute."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "aks_target.wait_for_completion(show_output = True)\n",
-        "print(aks_target.provisioning_state)\n",
-        "print(aks_target.provisioning_errors)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Deploy AccelContainerImage to AKS ComputeTarget"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "from azureml.core.webservice import Webservice, AksWebservice\n",
-        "\n",
-        "# Set the web service configuration (for creating a test service, we don't want autoscale enabled)\n",
-        "# Authentication is enabled by default, but for testing we specify False\n",
-        "aks_config = AksWebservice.deploy_configuration(autoscale_enabled=False,\n",
-        "                                                num_replicas=1,\n",
-        "                                                auth_enabled = False)\n",
-        "\n",
-        "aks_service_name ='my-aks-service-3'\n",
-        "\n",
-        "aks_service = Webservice.deploy_from_image(workspace = ws,\n",
-        "                                           name = aks_service_name,\n",
-        "                                           image = image,\n",
-        "                                           deployment_config = aks_config,\n",
-        "                                           deployment_target = aks_target)\n",
-        "aks_service.wait_for_deployment(show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"test-service\"></a>\n",
-        "## 5. Test the service\n",
-        "<a id=\"create-client\"></a>\n",
-        "### 5.a. Create Client\n",
-        "The image supports gRPC and the TensorFlow Serving \"predict\" API. We will create a PredictionClient from the Webservice object that can call into the docker image to get predictions. If you do not have the Webservice object, you can also create [PredictionClient](https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel.predictionclient?view=azure-ml-py) directly.\n",
-        "\n",
-        "**Note:** If you chose to use auth_enabled=True when creating your AksWebservice.deploy_configuration(), see documentation [here](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice(class)?view=azure-ml-py#get-keys--) on how to retrieve your keys and use either key as an argument to PredictionClient(...,access_token=key).\n",
-        "**WARNING:** If you are running on Azure Notebooks free compute, you will not be able to make outgoing calls to your service. Try locating your client on a different machine to consume it."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Using the grpc client in AzureML Accelerated Models SDK\n",
-        "from azureml.accel import client_from_service\n",
-        "\n",
-        "# Initialize AzureML Accelerated Models client\n",
-        "client = client_from_service(aks_service)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can adapt the client [code](https://github.com/Azure/aml-real-time-ai/blob/master/pythonlib/amlrealtimeai/client.py) to meet your needs. There is also an example C# [client](https://github.com/Azure/aml-real-time-ai/blob/master/sample-clients/csharp).\n",
-        "\n",
-        "The service provides an API that is compatible with TensorFlow Serving. There are instructions to download a sample client [here](https://www.tensorflow.org/serving/setup)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"serve-model\"></a>\n",
-        "### 5.b. Serve the model\n",
-        "The SSD-VGG model returns the confidence and bounding boxes for all possible anchor boxes. As mentioned earlier, we will use a post-processing routine to transform this into a list of bounding boxes (y1, x1, y2, x2) where x, y are fractional coordinates measured from left and top respectively. A respective list of classes and scores is also returned to tag each bounding box. Below we make use of this information to draw the bounding boxes on top the original image. Note that in the post-processing routine we select a confidence threshold of 0.5."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import cv2\n",
-        "from matplotlib import pyplot as plt\n",
-        "\n",
-        "colors_tableau = [(255, 255, 255), (31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),\n",
-        "                  (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),\n",
-        "                  (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),\n",
-        "                  (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),\n",
-        "                  (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]\n",
-        "\n",
-        "\n",
-        "def draw_boxes_on_img(img, classes, scores, bboxes, thickness=2):\n",
-        "    shape = img.shape\n",
-        "    for i in range(bboxes.shape[0]):\n",
-        "        bbox = bboxes[i]\n",
-        "        color = colors_tableau[classes[i]]\n",
-        "        # Draw bounding box...\n",
-        "        p1 = (int(bbox[0] * shape[0]), int(bbox[1] * shape[1]))\n",
-        "        p2 = (int(bbox[2] * shape[0]), int(bbox[3] * shape[1]))\n",
-        "        cv2.rectangle(img, p1[::-1], p2[::-1], color, thickness)\n",
-        "        # Draw text...\n",
-        "        s = '%s/%.3f' % (classes[i], scores[i])\n",
-        "        p1 = (p1[0]-5, p1[1])\n",
-        "        cv2.putText(img, s, p1[::-1], cv2.FONT_HERSHEY_DUPLEX, 0.4, color, 1)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.accel._external.ssdvgg_utils as ssdvgg_utils\n",
-        "\n",
-        "result = client.score_file(path=\"meeting.jpg\", input_name=input_tensors, outputs=output_tensors)\n",
-        "classes, scores, bboxes = ssdvgg_utils.postprocess(result, select_threshold=0.5)\n",
-        "\n",
-        "img = cv2.imread('meeting.jpg', 1)\n",
-        "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
-        "draw_boxes_on_img(img, classes, scores, bboxes)\n",
-        "plt.imshow(img)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"cleanup\"></a>\n",
-        "## 6. Cleanup\n",
-        "It's important to clean up your resources, so that you won't incur unnecessary costs. In the [next notebook](./accelerated-models-training.ipynb) you will learn how to train a classfier on a new dataset using transfer learning."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "aks_service.delete()\n",
-        "aks_target.delete()\n",
-        "image.delete()\n",
-        "registered_model.delete()\n",
-        "converted_model.delete()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "coverste"
-      },
-      {
-        "name": "paledger"
-      },
-      {
-        "name": "sukha"
-      }
-    ],
-    "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": "3.5.6"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.yml

@@ -1,7 +0,0 @@
-name: accelerated-models-object-detection
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-accel-models[cpu]
-  - opencv-python
-  - matplotlib

how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.ipynb

@@ -1,555 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Azure ML Hardware Accelerated Models Quickstart"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This tutorial will show you how to deploy an image recognition service based on the ResNet 50 classifier using the Azure Machine Learning Accelerated Models service.  Get more information about our service from our [documentation](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-accelerate-with-fpgas), [API reference](https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel?view=azure-ml-py), or [forum](https://aka.ms/aml-forum).\n",
-        "\n",
-        "We will use an accelerated ResNet50 featurizer running on an FPGA. Our Accelerated Models Service handles translating deep neural networks (DNN) into an FPGA program.\n",
-        "\n",
-        "For more information about using other models besides Resnet50, see the [README](./README.md).\n",
-        "\n",
-        "The steps covered in this notebook are: \n",
-        "1. [Set up environment](#set-up-environment)\n",
-        "* [Construct model](#construct-model)\n",
-        "    * Image Preprocessing\n",
-        "    * Featurizer (Resnet50)\n",
-        "    * Classifier\n",
-        "    * Save Model\n",
-        "* [Register Model](#register-model)\n",
-        "* [Convert into Accelerated Model](#convert-model)\n",
-        "* [Create Image](#create-image)\n",
-        "* [Deploy](#deploy-image)\n",
-        "* [Test service](#test-service)\n",
-        "* [Clean-up](#clean-up)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"set-up-environment\"></a>\n",
-        "## 1. Set up environment"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "import tensorflow as tf"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Retrieve Workspace\n",
-        "If you haven't created a Workspace, please follow [this notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/configuration.ipynb) to do so. If you have, run the codeblock below to retrieve it. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\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": [
-        "<a id=\"construct-model\"></a>\n",
-        "## 2. Construct model\n",
-        "\n",
-        "There are three parts to the model we are deploying: pre-processing, featurizer with ResNet50, and classifier with ImageNet dataset. Then we will save this complete Tensorflow model graph locally before registering it to your Azure ML Workspace.\n",
-        "\n",
-        "### 2.a. Image preprocessing\n",
-        "We'd like our service to accept JPEG images as input. However the input to ResNet50 is a tensor. So we need code that decodes JPEG images and does the preprocessing required by ResNet50. The Accelerated AI service can execute TensorFlow graphs as part of the service and we'll use that ability to do the image preprocessing. This code defines a TensorFlow graph that preprocesses an array of JPEG images (as strings) and produces a tensor that is ready to be featurized by ResNet50.\n",
-        "\n",
-        "**Note:** Expect to see TF deprecation warnings until we port our SDK over to use Tensorflow 2.0."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Input images as a two-dimensional tensor containing an arbitrary number of images represented a strings\n",
-        "import azureml.accel.models.utils as utils\n",
-        "tf.reset_default_graph()\n",
-        "\n",
-        "in_images = tf.placeholder(tf.string)\n",
-        "image_tensors = utils.preprocess_array(in_images)\n",
-        "print(image_tensors.shape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 2.b. Featurizer\n",
-        "We use ResNet50 as a featurizer. In this step we initialize the model. This downloads a TensorFlow checkpoint of the quantized ResNet50."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.accel.models import QuantizedResnet50\n",
-        "save_path = os.path.expanduser('~/models')\n",
-        "model_graph = QuantizedResnet50(save_path, is_frozen = True)\n",
-        "feature_tensor = model_graph.import_graph_def(image_tensors)\n",
-        "print(model_graph.version)\n",
-        "print(feature_tensor.name)\n",
-        "print(feature_tensor.shape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 2.c. Classifier\n",
-        "The model we downloaded includes a classifier which takes the output of the ResNet50 and identifies an image. This classifier is trained on the ImageNet dataset. We are going to use this classifier for our service. The next [notebook](./accelerated-models-training.ipynb) shows how to train a classifier for a different data set. The input to the classifier is a tensor matching the output of our ResNet50 featurizer."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "classifier_output = model_graph.get_default_classifier(feature_tensor)\n",
-        "print(classifier_output)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 2.d. Save Model\n",
-        "Now that we loaded all three parts of the tensorflow graph (preprocessor, resnet50 featurizer, and the classifier), we can save the graph and associated variables to a directory which we can register as an Azure ML Model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# model_name must be lowercase\n",
-        "model_name = \"resnet50\"\n",
-        "model_save_path = os.path.join(save_path, model_name)\n",
-        "print(\"Saving model in {}\".format(model_save_path))\n",
-        "\n",
-        "with tf.Session() as sess:\n",
-        "    model_graph.restore_weights(sess)\n",
-        "    tf.saved_model.simple_save(sess, model_save_path,\n",
-        "                                   inputs={'images': in_images},\n",
-        "                                   outputs={'output_alias': classifier_output})"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 2.e. Important! Save names of input and output tensors\n",
-        "\n",
-        "These input and output tensors that were created during the preprocessing and classifier steps are also going to be used when **converting the model** to an Accelerated Model that can run on FPGA's and for **making an inferencing request**. It is very important to save this information! You can see our defaults for all the models in the [README](./README.md).\n",
-        "\n",
-        "By default for Resnet50, these are the values you should see when running the cell below: \n",
-        "* input_tensors = \"Placeholder:0\"\n",
-        "* output_tensors = \"classifier/resnet_v1_50/predictions/Softmax:0\""
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "register model from file"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "input_tensors = in_images.name\n",
-        "output_tensors = classifier_output.name\n",
-        "\n",
-        "print(input_tensors)\n",
-        "print(output_tensors)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"register-model\"></a>\n",
-        "## 3. Register Model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can add tags and descriptions to your models. Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "register model from file"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core.model import Model\n",
-        "\n",
-        "registered_model = Model.register(workspace = ws,\n",
-        "                                  model_path = model_save_path,\n",
-        "                                  model_name = model_name)\n",
-        "\n",
-        "print(\"Successfully registered: \", registered_model.name, registered_model.description, registered_model.version, sep = '\\t')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"convert-model\"></a>\n",
-        "## 4. Convert Model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "For conversion you need to provide names of input and output tensors. This information can be found from the model_graph you saved in step 2.e. above.\n",
-        "\n",
-        "**Note**: Conversion may take a while and on average for FPGA model it is about 1-3 minutes and it depends on model type."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "register model from file"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.accel import AccelOnnxConverter\n",
-        "\n",
-        "convert_request = AccelOnnxConverter.convert_tf_model(ws, registered_model, input_tensors, output_tensors)\n",
-        "\n",
-        "if convert_request.wait_for_completion(show_output = False):\n",
-        "    # If the above call succeeded, get the converted model\n",
-        "    converted_model = convert_request.result\n",
-        "    print(\"\\nSuccessfully converted: \", converted_model.name, converted_model.url, converted_model.version, \n",
-        "          converted_model.id, converted_model.created_time, '\\n')\n",
-        "else:\n",
-        "    print(\"Model conversion failed. Showing output.\")\n",
-        "    convert_request.wait_for_completion(show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"create-image\"></a>\n",
-        "## 5. Package the model into an Image"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can add tags and descriptions to image. Also, for FPGA model an image can only contain **single** model.\n",
-        "\n",
-        "**Note**: The following command can take few minutes. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.image import Image\n",
-        "from azureml.accel import AccelContainerImage\n",
-        "\n",
-        "image_config = AccelContainerImage.image_configuration()\n",
-        "# Image name must be lowercase\n",
-        "image_name = \"{}-image\".format(model_name)\n",
-        "\n",
-        "image = Image.create(name = image_name,\n",
-        "                     models = [converted_model],\n",
-        "                     image_config = image_config, \n",
-        "                     workspace = ws)\n",
-        "image.wait_for_creation(show_output = False)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"deploy-image\"></a>\n",
-        "## 6. Deploy\n",
-        "Once you have an Azure ML Accelerated Image in your Workspace, you can deploy it to two destinations, to a Databox Edge machine or to an AKS cluster. \n",
-        "\n",
-        "### 6.a. Databox Edge Machine using IoT Hub\n",
-        "See the sample [here](https://github.com/Azure-Samples/aml-real-time-ai/) for using the Azure IoT CLI extension for deploying your Docker image to your Databox Edge Machine.\n",
-        "\n",
-        "### 6.b. Azure Kubernetes Service (AKS) using Azure ML Service\n",
-        "We are going to create an AKS cluster with FPGA-enabled machines, then deploy our service to it. For more information, see [AKS official docs](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where#aks).\n",
-        "\n",
-        "#### Create AKS ComputeTarget"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "sample-akscompute-provision"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import AksCompute, ComputeTarget\n",
-        "\n",
-        "# Uses the specific FPGA enabled VM (sku: Standard_PB6s)\n",
-        "# Standard_PB6s are available in: eastus, westus2, westeurope, southeastasia\n",
-        "prov_config = AksCompute.provisioning_configuration(vm_size = \"Standard_PB6s\",\n",
-        "                                                    agent_count = 1, \n",
-        "                                                    location = \"eastus\")\n",
-        "\n",
-        "aks_name = 'my-aks-pb6'\n",
-        "# Create the cluster\n",
-        "aks_target = ComputeTarget.create(workspace = ws, \n",
-        "                                  name = aks_name, \n",
-        "                                  provisioning_configuration = prov_config)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Provisioning an AKS cluster might take awhile (15 or so minutes), and we want to wait until it's successfully provisioned before we can deploy a service to it. If you interrupt this cell, provisioning of the cluster will continue. You can also check the status in your Workspace under Compute."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "aks_target.wait_for_completion(show_output = True)\n",
-        "print(aks_target.provisioning_state)\n",
-        "print(aks_target.provisioning_errors)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Deploy AccelContainerImage to AKS ComputeTarget"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "from azureml.core.webservice import Webservice, AksWebservice\n",
-        "\n",
-        "# Set the web service configuration (for creating a test service, we don't want autoscale enabled)\n",
-        "# Authentication is enabled by default, but for testing we specify False\n",
-        "aks_config = AksWebservice.deploy_configuration(autoscale_enabled=False,\n",
-        "                                                num_replicas=1,\n",
-        "                                                auth_enabled = False)\n",
-        "\n",
-        "aks_service_name ='my-aks-service-1'\n",
-        "\n",
-        "aks_service = Webservice.deploy_from_image(workspace = ws,\n",
-        "                                           name = aks_service_name,\n",
-        "                                           image = image,\n",
-        "                                           deployment_config = aks_config,\n",
-        "                                           deployment_target = aks_target)\n",
-        "aks_service.wait_for_deployment(show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"test-service\"></a>\n",
-        "## 7. Test the service"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 7.a. Create Client\n",
-        "The image supports gRPC and the TensorFlow Serving \"predict\" API. We will create a PredictionClient from the Webservice object that can call into the docker image to get predictions. If you do not have the Webservice object, you can also create [PredictionClient](https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel.predictionclient?view=azure-ml-py) directly.\n",
-        "\n",
-        "**Note:** If you chose to use auth_enabled=True when creating your AksWebservice, see documentation [here](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice(class)?view=azure-ml-py#get-keys--) on how to retrieve your keys and use either key as an argument to PredictionClient(...,access_token=key).\n",
-        "**WARNING:** If you are running on Azure Notebooks free compute, you will not be able to make outgoing calls to your service. Try locating your client on a different machine to consume it."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Using the grpc client in AzureML Accelerated Models SDK\n",
-        "from azureml.accel import client_from_service\n",
-        "\n",
-        "# Initialize AzureML Accelerated Models client\n",
-        "client = client_from_service(aks_service)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can adapt the client [code](https://github.com/Azure/aml-real-time-ai/blob/master/pythonlib/amlrealtimeai/client.py) to meet your needs. There is also an example C# [client](https://github.com/Azure/aml-real-time-ai/blob/master/sample-clients/csharp).\n",
-        "\n",
-        "The service provides an API that is compatible with TensorFlow Serving. There are instructions to download a sample client [here](https://www.tensorflow.org/serving/setup)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 7.b. Serve the model\n",
-        "To understand the results we need a mapping to the human readable imagenet classes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import requests\n",
-        "classes_entries = requests.get(\"https://raw.githubusercontent.com/Lasagne/Recipes/master/examples/resnet50/imagenet_classes.txt\").text.splitlines()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Score image with input and output tensor names\n",
-        "results = client.score_file(path=\"./snowleopardgaze.jpg\", \n",
-        "                             input_name=input_tensors, \n",
-        "                             outputs=output_tensors)\n",
-        "\n",
-        "# map results [class_id] => [confidence]\n",
-        "results = enumerate(results)\n",
-        "# sort results by confidence\n",
-        "sorted_results = sorted(results, key=lambda x: x[1], reverse=True)\n",
-        "# print top 5 results\n",
-        "for top in sorted_results[:5]:\n",
-        "    print(classes_entries[top[0]], 'confidence:', top[1])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"clean-up\"></a>\n",
-        "## 8. Clean-up\n",
-        "Run the cell below to delete your webservice, image, and model (must be done in that order). In the [next notebook](./accelerated-models-training.ipynb) you will learn how to train a classfier on a new dataset using transfer learning and finetune the weights."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "aks_service.delete()\n",
-        "aks_target.delete()\n",
-        "image.delete()\n",
-        "registered_model.delete()\n",
-        "converted_model.delete()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "coverste"
-      },
-      {
-        "name": "paledger"
-      },
-      {
-        "name": "aibhalla"
-      }
-    ],
-    "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": "3.7.3"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.yml

@@ -1,5 +0,0 @@
-name: accelerated-models-quickstart
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-accel-models[cpu]

how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.ipynb

@@ -1,870 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Training with the Azure Machine Learning Accelerated Models Service"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This notebook will introduce how to apply common machine learning techniques, like transfer learning, custom weights, and unquantized vs. quantized models, when working with our Azure Machine Learning Accelerated Models Service (Azure ML Accel Models).\n",
-        "\n",
-        "We will use Tensorflow for the preprocessing steps, ResNet50 for the featurizer, and the Keras API (built on Tensorflow backend) to build the classifier layers instead of the default ImageNet classifier used in Quickstart. Then we will train the model, evaluate it, and deploy it to run on an FPGA.\n",
-        "\n",
-        "#### Transfer Learning and Custom weights\n",
-        "We will walk you through two ways to build and train a ResNet50 model on the Kaggle Cats and Dogs dataset: transfer learning only and then transfer learning with custom weights.\n",
-        "\n",
-        "In using transfer learning, our goal is to re-purpose the ResNet50 model already trained on the [ImageNet image dataset](http://www.image-net.org/) as a basis for our training of the Kaggle Cats and Dogs dataset. The ResNet50 featurizer will be imported as frozen, so only the Keras classifier will be trained.\n",
-        "\n",
-        "With the addition of custom weights, we will build the model so that the ResNet50 featurizer weights as not frozen. This will let us retrain starting with custom weights trained with ImageNet on ResNet50 and then use the Kaggle Cats and Dogs dataset to retrain and fine-tune the quantized version of the model.\n",
-        "\n",
-        "#### Unquantized vs. Quantized models\n",
-        "The unquantized version of our models (ie. Resnet50, Resnet152, Densenet121, Vgg16, SsdVgg) uses native float precision (32-bit floats), which will be faster at training. We will use this for our first run through, then fine-tune the weights with the quantized version. The quantized version of our models (i.e. QuantizedResnet50, QuantizedResnet152, QuantizedDensenet121, QuantizedVgg16, QuantizedSsdVgg) will have the same node names as the unquantized version, but use quantized operations and will match the performance of the model when running on an FPGA.\n",
-        "\n",
-        "#### Contents\n",
-        "1. [Setup Environment](#setup)\n",
-        "* [Prepare Data](#prepare-data)\n",
-        "* [Construct Model](#construct-model)\n",
-        "    * Preprocessor\n",
-        "    * Classifier\n",
-        "    * Model construction\n",
-        "* [Train Model](#train-model)\n",
-        "* [Test Model](#test-model)\n",
-        "* [Execution](#execution)\n",
-        "    * [Transfer Learning](#transfer-learning)\n",
-        "    * [Transfer Learning with Custom Weights](#custom-weights)\n",
-        "* [Create Image](#create-image)\n",
-        "* [Deploy Image](#deploy-image)\n",
-        "* [Test the service](#test-service)\n",
-        "* [Clean-up](#cleanup)\n",
-        "* [Appendix](#appendix)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"setup\"></a>\n",
-        "## 1. Setup Environment\n",
-        "#### 1.a. Please set up your environment as described in the [Quickstart](./accelerated-models-quickstart.ipynb), meaning:\n",
-        "* Make sure your Workspace config.json exists and has the correct info\n",
-        "* Install Tensorflow\n",
-        "\n",
-        "#### 1.b. Download dataset into ~/catsanddogs \n",
-        "The dataset we will be using for training can be downloaded [here](https://www.microsoft.com/en-us/download/details.aspx?id=54765). Download the zip and extract to a directory named 'catsanddogs' under your user directory (\"~/catsanddogs\"). \n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### 1.c. Import packages"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "import sys\n",
-        "import tensorflow as tf\n",
-        "import numpy as np\n",
-        "from keras import backend as K\n",
-        "import sklearn\n",
-        "import tqdm"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### 1.d. Create directories for later use\n",
-        "After you train your model in float32, you'll write the weights to a place on disk. We also need a location to store the models that get downloaded."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "custom_weights_dir = os.path.expanduser(\"~/custom-weights\")\n",
-        "saved_model_dir = os.path.expanduser(\"~/models\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"prepare-data\"></a>\n",
-        "## 2. Prepare Data\n",
-        "Load the files we are going to use for training and testing. By default this notebook uses only a very small subset of the Cats and Dogs dataset. That makes it run relatively quickly."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import glob\n",
-        "import imghdr\n",
-        "datadir = os.path.expanduser(\"~/catsanddogs\")\n",
-        "\n",
-        "cat_files = glob.glob(os.path.join(datadir, 'PetImages', 'Cat', '*.jpg'))\n",
-        "dog_files = glob.glob(os.path.join(datadir, 'PetImages', 'Dog', '*.jpg'))\n",
-        "\n",
-        "# Limit the data set to make the notebook execute quickly.\n",
-        "cat_files = cat_files[:64]\n",
-        "dog_files = dog_files[:64]\n",
-        "\n",
-        "# The data set has a few images that are not jpeg. Remove them.\n",
-        "cat_files = [f for f in cat_files if imghdr.what(f) == 'jpeg']\n",
-        "dog_files = [f for f in dog_files if imghdr.what(f) == 'jpeg']\n",
-        "\n",
-        "if(not len(cat_files) or not len(dog_files)):\n",
-        "    print(\"Please download the Kaggle Cats and Dogs dataset form https://www.microsoft.com/en-us/download/details.aspx?id=54765 and extract the zip to \" + datadir)    \n",
-        "    raise ValueError(\"Data not found\")\n",
-        "else:\n",
-        "    print(cat_files[0])\n",
-        "    print(dog_files[0])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Construct a numpy array as labels\n",
-        "image_paths = cat_files + dog_files\n",
-        "total_files = len(cat_files) + len(dog_files)\n",
-        "labels = np.zeros(total_files)\n",
-        "labels[len(cat_files):] = 1"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Split images data as training data and test data\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "onehot_labels = np.array([[0,1] if i else [1,0] for i in labels])\n",
-        "img_train, img_test, label_train, label_test = train_test_split(image_paths, onehot_labels, random_state=42, shuffle=True)\n",
-        "\n",
-        "print(len(img_train), len(img_test), label_train.shape, label_test.shape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"construct-model\"></a>\n",
-        "## 3. Construct Model\n",
-        "We will define the functions to handle creating the preprocessor and the classifier first, and then run them together to actually construct the model with the Resnet50 featurizer in a single Tensorflow session in a separate cell.\n",
-        "\n",
-        "We use ResNet50 for the featurizer and build our own classifier using Keras layers. We train the featurizer and the classifier as one model. We will provide parameters to determine whether we are using the quantized version and whether we are using custom weights in training or not."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 3.a. Define image preprocessing step\n",
-        "Same as in the Quickstart, before passing image dataset to the ResNet50 featurizer, we need to preprocess the input file to get it into the form expected by ResNet50. ResNet50 expects float tensors representing the images in BGR, channel last order. We've provided a default implementation of the preprocessing that you can use.\n",
-        "\n",
-        "**Note:** Expect to see TF deprecation warnings until we port our SDK over to use Tensorflow 2.0."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.accel.models.utils as utils\n",
-        "\n",
-        "def preprocess_images(scaling_factor=1.0):\n",
-        "    # Convert images to 3D tensors [width,height,channel] - channels are in BGR order.\n",
-        "    in_images = tf.placeholder(tf.string)\n",
-        "    image_tensors = utils.preprocess_array(in_images, 'RGB', scaling_factor)\n",
-        "    return in_images, image_tensors"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 3.b. Define classifier\n",
-        "We use Keras layer APIs to construct the classifier. Because we're using the tensorflow backend, we can train this classifier in one session with our Resnet50 model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "def construct_classifier(in_tensor, seed=None):\n",
-        "    from keras.layers import Dropout, Dense, Flatten\n",
-        "    from keras.initializers import glorot_uniform\n",
-        "    K.set_session(tf.get_default_session())\n",
-        "\n",
-        "    FC_SIZE = 1024\n",
-        "    NUM_CLASSES = 2\n",
-        "\n",
-        "    x = Dropout(0.2, input_shape=(1, 1, int(in_tensor.shape[3]),), seed=seed)(in_tensor)\n",
-        "    x = Dense(FC_SIZE, activation='relu', input_dim=(1, 1, int(in_tensor.shape[3]),),\n",
-        "              kernel_initializer=glorot_uniform(seed=seed), bias_initializer='zeros')(x)\n",
-        "    x = Flatten()(x)\n",
-        "    preds = Dense(NUM_CLASSES, activation='softmax', input_dim=FC_SIZE, name='classifier_output',\n",
-        "                  kernel_initializer=glorot_uniform(seed=seed), bias_initializer='zeros')(x)\n",
-        "    return preds"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 3.c. Define model construction\n",
-        "Now that the preprocessor and classifier for the model are defined, we can define how we want to construct the model. \n",
-        "\n",
-        "Constructing the model has these steps: \n",
-        "1. Get preprocessing steps\n",
-        "* Get featurizer using the Azure ML Accel Models SDK:\n",
-        "    * import the graph definition\n",
-        "    * restore the weights of the model into a Tensorflow session\n",
-        "* Get classifier\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "def construct_model(quantized, starting_weights_directory = None):\n",
-        "    from azureml.accel.models import Resnet50, QuantizedResnet50\n",
-        "    \n",
-        "    # Convert images to 3D tensors [width,height,channel]\n",
-        "    in_images, image_tensors = preprocess_images(1.0)\n",
-        "\n",
-        "    # Construct featurizer using quantized or unquantized ResNet50 model\n",
-        "    if not quantized:\n",
-        "        featurizer = Resnet50(saved_model_dir)\n",
-        "    else:\n",
-        "        featurizer = QuantizedResnet50(saved_model_dir, custom_weights_directory = starting_weights_directory)\n",
-        "\n",
-        "    features = featurizer.import_graph_def(input_tensor=image_tensors)\n",
-        "    \n",
-        "    # Construct classifier\n",
-        "    preds = construct_classifier(features)\n",
-        "    \n",
-        "    # Initialize weights\n",
-        "    sess = tf.get_default_session()\n",
-        "    tf.global_variables_initializer().run()\n",
-        "\n",
-        "    featurizer.restore_weights(sess)\n",
-        "\n",
-        "    return in_images, image_tensors, features, preds, featurizer"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"train-model\"></a>\n",
-        "## 4. Train Model"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "def read_files(files):\n",
-        "    \"\"\" Read files to array\"\"\"\n",
-        "    contents = []\n",
-        "    for path in files:\n",
-        "        with open(path, 'rb') as f:\n",
-        "            contents.append(f.read())\n",
-        "    return contents"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "def train_model(preds, in_images, img_train, label_train, is_retrain = False, train_epoch = 10, learning_rate=None):\n",
-        "    \"\"\" training model \"\"\"\n",
-        "    from keras.objectives import binary_crossentropy\n",
-        "    from tqdm import tqdm\n",
-        "    \n",
-        "    learning_rate = learning_rate if learning_rate else 0.001 if is_retrain else 0.01\n",
-        "        \n",
-        "    # Specify the loss function\n",
-        "    in_labels = tf.placeholder(tf.float32, shape=(None, 2))   \n",
-        "    cross_entropy = tf.reduce_mean(binary_crossentropy(in_labels, preds))\n",
-        "    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)\n",
-        "\n",
-        "    def chunks(a, b, n):\n",
-        "        \"\"\"Yield successive n-sized chunks from a and b.\"\"\"\n",
-        "        if (len(a) != len(b)):\n",
-        "            print(\"a and b are not equal in chunks(a,b,n)\")\n",
-        "            raise ValueError(\"Parameter error\")\n",
-        "\n",
-        "        for i in range(0, len(a), n):\n",
-        "            yield a[i:i + n], b[i:i + n]\n",
-        "\n",
-        "    chunk_size = 16\n",
-        "    chunk_num = len(label_train) / chunk_size\n",
-        "\n",
-        "    sess = tf.get_default_session()\n",
-        "    for epoch in range(train_epoch):\n",
-        "        avg_loss = 0\n",
-        "        for img_chunk, label_chunk in tqdm(chunks(img_train, label_train, chunk_size)):\n",
-        "            contents = read_files(img_chunk)\n",
-        "            _, loss = sess.run([optimizer, cross_entropy],\n",
-        "                                feed_dict={in_images: contents,\n",
-        "                                           in_labels: label_chunk,\n",
-        "                                           K.learning_phase(): 1})\n",
-        "            avg_loss += loss / chunk_num\n",
-        "        print(\"Epoch:\", (epoch + 1), \"loss = \", \"{:.3f}\".format(avg_loss))\n",
-        "            \n",
-        "        # Reach desired performance\n",
-        "        if (avg_loss < 0.001):\n",
-        "            break"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"test-model\"></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"test-model\"></a>\n",
-        "## 5. Test Model"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "def test_model(preds, in_images, img_test, label_test):\n",
-        "    \"\"\"Test the model\"\"\"\n",
-        "    from keras.metrics import categorical_accuracy\n",
-        "\n",
-        "    in_labels = tf.placeholder(tf.float32, shape=(None, 2))\n",
-        "    accuracy = tf.reduce_mean(categorical_accuracy(in_labels, preds))\n",
-        "    contents = read_files(img_test)\n",
-        "\n",
-        "    accuracy = accuracy.eval(feed_dict={in_images: contents,\n",
-        "                                        in_labels: label_test,\n",
-        "                                        K.learning_phase(): 0})\n",
-        "    return accuracy"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"execution\"></a>\n",
-        "## 6. Execute steps\n",
-        "You can run through the Transfer Learning section, then skip to Create AccelContainerImage. By default, because the custom weights section takes much longer for training twice, it is not saved as executable cells. You can copy the code or change cell type to 'Code'.\n",
-        "\n",
-        "<a id=\"transfer-learning\"></a>\n",
-        "### 6.a. Training using Transfer Learning"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "# Launch the training\n",
-        "tf.reset_default_graph()\n",
-        "sess = tf.Session(graph=tf.get_default_graph())\n",
-        "\n",
-        "with sess.as_default():\n",
-        "    in_images, image_tensors, features, preds, featurizer = construct_model(quantized=True)\n",
-        "    train_model(preds, in_images, img_train, label_train, is_retrain=False, train_epoch=10, learning_rate=0.01)    \n",
-        "    accuracy = test_model(preds, in_images, img_test, label_test)  \n",
-        "    print(\"Accuracy:\", accuracy)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Save Model"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model_name = 'resnet50-catsanddogs-tl'\n",
-        "model_save_path = os.path.join(saved_model_dir, model_name)\n",
-        "\n",
-        "tf.saved_model.simple_save(sess, model_save_path,\n",
-        "                               inputs={'images': in_images},\n",
-        "                               outputs={'output_alias': preds})\n",
-        "\n",
-        "input_tensors = in_images.name\n",
-        "output_tensors = preds.name\n",
-        "\n",
-        "print(input_tensors)\n",
-        "print(output_tensors)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"custom-weights\"></a>\n",
-        "### 6.b. Traning using Custom Weights\n",
-        "\n",
-        "Because the quantized graph defintion and the float32 graph defintion share the same node names in the graph definitions, we can initally train the weights in float32, and then reload them with the quantized operations (which take longer) to fine-tune the model.\n",
-        "\n",
-        "First we train the model with custom weights but without quantization. Training is done with native float precision (32-bit floats). We load the training data set and batch the training with 10 epochs. When the performance reaches desired level or starts decredation, we stop the training iteration and save the weights as tensorflow checkpoint files. "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Launch the training\n",
-        "```\n",
-        "tf.reset_default_graph()\n",
-        "sess = tf.Session(graph=tf.get_default_graph())\n",
-        "\n",
-        "with sess.as_default():\n",
-        "    in_images, image_tensors, features, preds, featurizer = construct_model(quantized=False)\n",
-        "    train_model(preds, in_images, img_train, label_train, is_retrain=False, train_epoch=10)    \n",
-        "    accuracy = test_model(preds, in_images, img_test, label_test)  \n",
-        "    print(\"Accuracy:\", accuracy)\n",
-        "    featurizer.save_weights(custom_weights_dir + \"/rn50\", tf.get_default_session())\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Test Model\n",
-        "After training, we evaluate the trained model's accuracy on test dataset with quantization. So that we know the model's performance if it is deployed on the FPGA."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "```\n",
-        "tf.reset_default_graph()\n",
-        "sess = tf.Session(graph=tf.get_default_graph())\n",
-        "\n",
-        "with sess.as_default():\n",
-        "    print(\"Testing trained model with quantization\")\n",
-        "    in_images, image_tensors, features, preds, quantized_featurizer = construct_model(quantized=True, starting_weights_directory=custom_weights_dir)\n",
-        "    accuracy = test_model(preds, in_images, img_test, label_test)      \n",
-        "    print(\"Accuracy:\", accuracy)\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Fine-Tune Model\n",
-        "Sometimes, the model's accuracy can drop significantly after quantization. In those cases, we need to retrain the model enabled with quantization to get better model accuracy."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "```\n",
-        "if (accuracy < 0.93):\n",
-        "    with sess.as_default():\n",
-        "        print(\"Fine-tuning model with quantization\")\n",
-        "        train_model(preds, in_images, img_train, label_train, is_retrain=True, train_epoch=10)\n",
-        "        accuracy = test_model(preds, in_images, img_test, label_test)        \n",
-        "        print(\"Accuracy:\", accuracy)\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Save Model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "```\n",
-        "model_name = 'resnet50-catsanddogs-cw'\n",
-        "model_save_path = os.path.join(saved_model_dir, model_name)\n",
-        "\n",
-        "tf.saved_model.simple_save(sess, model_save_path,\n",
-        "                               inputs={'images': in_images},\n",
-        "                               outputs={'output_alias': preds})\n",
-        "\n",
-        "input_tensors = in_images.name\n",
-        "output_tensors = preds.name\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"create-image\"></a>\n",
-        "## 7. Create AccelContainerImage\n",
-        "\n",
-        "Below we will execute all the same steps as in the [Quickstart](./accelerated-models-quickstart.ipynb#create-image) to package the model we have saved locally into an accelerated Docker image saved in our workspace. To complete all the steps, it may take a few minutes. For more details on each step, check out the [Quickstart section on model registration](./accelerated-models-quickstart.ipynb#register-model)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "from azureml.core.model import Model\n",
-        "from azureml.core.image import Image\n",
-        "from azureml.accel import AccelOnnxConverter\n",
-        "from azureml.accel import AccelContainerImage\n",
-        "\n",
-        "# Retrieve workspace\n",
-        "ws = Workspace.from_config()\n",
-        "print(\"Successfully retrieved workspace:\", ws.name, ws.resource_group, ws.location, ws.subscription_id, '\\n')\n",
-        "\n",
-        "# Register model\n",
-        "registered_model = Model.register(workspace = ws,\n",
-        "                                  model_path = model_save_path,\n",
-        "                                  model_name = model_name)\n",
-        "print(\"Successfully registered: \", registered_model.name, registered_model.description, registered_model.version, '\\n', sep = '\\t')\n",
-        "\n",
-        "# Convert model\n",
-        "convert_request = AccelOnnxConverter.convert_tf_model(ws, registered_model, input_tensors, output_tensors)\n",
-        "if convert_request.wait_for_completion(show_output = False):\n",
-        "    # If the above call succeeded, get the converted model\n",
-        "    converted_model = convert_request.result\n",
-        "    print(\"\\nSuccessfully converted: \", converted_model.name, converted_model.url, converted_model.version, \n",
-        "          converted_model.id, converted_model.created_time, '\\n')\n",
-        "else:\n",
-        "    print(\"Model conversion failed. Showing output.\")\n",
-        "    convert_request.wait_for_completion(show_output = True)\n",
-        "\n",
-        "# Package into AccelContainerImage\n",
-        "image_config = AccelContainerImage.image_configuration()\n",
-        "# Image name must be lowercase\n",
-        "image_name = \"{}-image\".format(model_name)\n",
-        "image = Image.create(name = image_name,\n",
-        "                     models = [converted_model],\n",
-        "                     image_config = image_config, \n",
-        "                     workspace = ws)\n",
-        "image.wait_for_creation()\n",
-        "print(\"Created AccelContainerImage: {} {} {}\\n\".format(image.name, image.creation_state, image.image_location))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"deploy-image\"></a>\n",
-        "## 8. Deploy image\n",
-        "Once you have an Azure ML Accelerated Image in your Workspace, you can deploy it to two destinations, to a Databox Edge machine or to an AKS cluster. \n",
-        "\n",
-        "### 8.a. Deploy to Databox Edge Machine using IoT Hub\n",
-        "See the sample [here](https://github.com/Azure-Samples/aml-real-time-ai/) for using the Azure IoT CLI extension for deploying your Docker image to your Databox Edge Machine.\n",
-        "\n",
-        "### 8.b. Deploy to AKS Cluster"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Create AKS ComputeTarget"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import AksCompute, ComputeTarget\n",
-        "\n",
-        "# Uses the specific FPGA enabled VM (sku: Standard_PB6s)\n",
-        "# Standard_PB6s are available in: eastus, westus2, westeurope, southeastasia\n",
-        "prov_config = AksCompute.provisioning_configuration(vm_size = \"Standard_PB6s\",\n",
-        "                                                    agent_count = 1,\n",
-        "                                                    location = \"eastus\")\n",
-        "\n",
-        "aks_name = 'aks-pb6-tl'\n",
-        "# Create the cluster\n",
-        "aks_target = ComputeTarget.create(workspace = ws, \n",
-        "                                  name = aks_name, \n",
-        "                                  provisioning_configuration = prov_config)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Provisioning an AKS cluster might take awhile (15 or so minutes), and we want to wait until it's successfully provisioned before we can deploy a service to it. If you interrupt this cell, provisioning of the cluster will continue. You can re-run it or check the status in your Workspace under Compute."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "aks_target.wait_for_completion(show_output = True)\n",
-        "print(aks_target.provisioning_state)\n",
-        "print(aks_target.provisioning_errors)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Deploy AccelContainerImage to AKS ComputeTarget"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "sample-akswebservice-deploy-from-image"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "from azureml.core.webservice import Webservice, AksWebservice\n",
-        "\n",
-        "# Set the web service configuration (for creating a test service, we don't want autoscale enabled)\n",
-        "# Authentication is enabled by default, but for testing we specify False\n",
-        "aks_config = AksWebservice.deploy_configuration(autoscale_enabled=False,\n",
-        "                                                num_replicas=1,\n",
-        "                                                auth_enabled = False)\n",
-        "\n",
-        "aks_service_name ='my-aks-service-2'\n",
-        "\n",
-        "aks_service = Webservice.deploy_from_image(workspace = ws,\n",
-        "                                           name = aks_service_name,\n",
-        "                                           image = image,\n",
-        "                                           deployment_config = aks_config,\n",
-        "                                           deployment_target = aks_target)\n",
-        "aks_service.wait_for_deployment(show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"test-service\"></a>\n",
-        "## 9. Test the service\n",
-        "\n",
-        "<a id=\"create-client\"></a>\n",
-        "### 9.a. Create Client\n",
-        "The image supports gRPC and the TensorFlow Serving \"predict\" API. We will create a PredictionClient from the Webservice object that can call into the docker image to get predictions. If you do not have the Webservice object, you can also create [PredictionClient](https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel.predictionclient?view=azure-ml-py) directly.\n",
-        "\n",
-        "**Note:** If you chose to use auth_enabled=True when creating your AksWebservice.deploy_configuration(), see documentation [here](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice(class)?view=azure-ml-py#get-keys--) on how to retrieve your keys and use either key as an argument to PredictionClient(...,access_token=key).\n",
-        "**WARNING:** If you are running on Azure Notebooks free compute, you will not be able to make outgoing calls to your service. Try locating your client on a different machine to consume it."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Using the grpc client in AzureML Accelerated Models SDK\n",
-        "from azureml.accel import client_from_service\n",
-        "\n",
-        "# Initialize AzureML Accelerated Models client\n",
-        "client = client_from_service(aks_service)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"serve-model\"></a>\n",
-        "### 9.b. Serve the model\n",
-        "Let's see how our service does on a few images. It may get a few wrong."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Specify an image to classify\n",
-        "print('CATS')\n",
-        "for image_file in cat_files[:8]:\n",
-        "    results = client.score_file(path=image_file, \n",
-        "                                 input_name=input_tensors, \n",
-        "                                 outputs=output_tensors)\n",
-        "    result = 'CORRECT ' if results[0] > results[1] else 'WRONG '\n",
-        "    print(result + str(results))\n",
-        "print('DOGS')\n",
-        "for image_file in dog_files[:8]:\n",
-        "    results = client.score_file(path=image_file, \n",
-        "                                 input_name=input_tensors, \n",
-        "                                 outputs=output_tensors)\n",
-        "    result = 'CORRECT ' if results[1] > results[0] else 'WRONG '\n",
-        "    print(result + str(results))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"cleanup\"></a>\n",
-        "## 10. Cleanup\n",
-        "It's important to clean up your resources, so that you won't incur unnecessary costs."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "aks_service.delete()\n",
-        "aks_target.delete()\n",
-        "image.delete()\n",
-        "registered_model.delete()\n",
-        "converted_model.delete()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id=\"appendix\"></a>\n",
-        "## 11. Appendix"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "License for plot_confusion_matrix:\n",
-        "\n",
-        "New BSD License\n",
-        "\n",
-        "Copyright (c) 2007-2018 The scikit-learn developers.\n",
-        "All rights reserved.\n",
-        "\n",
-        "\n",
-        "Redistribution and use in source and binary forms, with or without\n",
-        "modification, are permitted provided that the following conditions are met:\n",
-        "\n",
-        "  a. Redistributions of source code must retain the above copyright notice,\n",
-        "     this list of conditions and the following disclaimer.\n",
-        "  b. Redistributions in binary form must reproduce the above copyright\n",
-        "     notice, this list of conditions and the following disclaimer in the\n",
-        "     documentation and/or other materials provided with the distribution.\n",
-        "  c. Neither the name of the Scikit-learn Developers  nor the names of\n",
-        "     its contributors may be used to endorse or promote products\n",
-        "     derived from this software without specific prior written\n",
-        "     permission. \n",
-        "\n",
-        "\n",
-        "THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n",
-        "AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n",
-        "IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n",
-        "ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR\n",
-        "ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n",
-        "DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n",
-        "SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n",
-        "CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT\n",
-        "LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY\n",
-        "OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH\n",
-        "DAMAGE.\n"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "coverste"
-      },
-      {
-        "name": "paledger"
-      }
-    ],
-    "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": "3.5.6"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.yml

@@ -1,8 +0,0 @@
-name: accelerated-models-training
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-accel-models[cpu]
-  - keras
-  - tqdm 
-  - sklearn

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

@@ -86,7 +86,37 @@
       "source": [
         "In this example, we will be using and registering two models. \n",
         "\n",
-        "You wil need to have a `first_model.pkl` file and `second_model.pkl` file in the current directory. The below call registers the files as Models with the names `my_first_model` and `my_second_model` in the workspace."
+        "First we will train two simple models on the [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset) included with scikit-learn, serializing them to files in the current directory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import joblib\n",
+        "import sklearn\n",
+        "\n",
+        "from sklearn.datasets import load_diabetes\n",
+        "from sklearn.linear_model import BayesianRidge, Ridge\n",
+        "\n",
+        "x, y = load_diabetes(return_X_y=True)\n",
+        "\n",
+        "first_model = Ridge().fit(x, y)\n",
+        "second_model = BayesianRidge().fit(x, y)\n",
+        "\n",
+        "joblib.dump(first_model, \"first_model.pkl\")\n",
+        "joblib.dump(second_model, \"second_model.pkl\")\n",
+        "\n",
+        "print(\"Trained models using scikit-learn {}.\".format(sklearn.__version__))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now that we have our trained models locally, we will register them as Models with the names `my_first_model` and `my_second_model` in the workspace."
       ]
     },
     {
@@ -102,12 +132,12 @@
         "from azureml.core.model import Model\n",
         "\n",
         "my_model_1 = Model.register(model_path=\"first_model.pkl\",\n",
-        "                       model_name=\"my_first_model\",\n",
+        "                            model_name=\"my_first_model\",\n",
-        "                       workspace=ws)\n",
+        "                            workspace=ws)\n",
         "\n",
         "my_model_2 = Model.register(model_path=\"second_model.pkl\",\n",
-        "                       model_name=\"my_second_model\",\n",
+        "                            model_name=\"my_second_model\",\n",
-        "                       workspace=ws)"
+        "                            workspace=ws)"
       ]
     },
     {
@@ -149,25 +179,24 @@
       "outputs": [],
       "source": [
         "%%writefile score.py\n",
-        "import pickle\n",
+        "import joblib\n",
         "import json\n",
         "import numpy as np\n",
-        "from sklearn.externals import joblib\n",
+        "\n",
-        "from sklearn.linear_model import Ridge\n",
         "from azureml.core.model import Model\n",
         "\n",
         "def init():\n",
         "    global model_1, model_2\n",
-        "    # note here \"my_first_model\" is the name of the model registered under the workspace\n",
+        "    # Here \"my_first_model\" is the name of the model registered under the workspace.\n",
-        "    # this call should return the path to the model.pkl file on the local disk.\n",
+        "    # This call will return the path to the .pkl file on the local disk.\n",
         "    model_1_path = Model.get_model_path(model_name='my_first_model')\n",
         "    model_2_path = Model.get_model_path(model_name='my_second_model')\n",
         "    \n",
-        "    # deserialize the model files back into a sklearn model\n",
+        "    # Deserialize the model files back into scikit-learn models.\n",
         "    model_1 = joblib.load(model_1_path)\n",
         "    model_2 = joblib.load(model_2_path)\n",
         "\n",
-        "# note you can pass in multiple rows for scoring\n",
+        "# Note you can pass in multiple rows for scoring.\n",
         "def run(raw_data):\n",
         "    try:\n",
         "        data = json.loads(raw_data)['data']\n",
@@ -177,7 +206,7 @@
         "        result_1 = model_1.predict(data)\n",
         "        result_2 = model_2.predict(data)\n",
         "\n",
-        "        # you can return any data type as long as it is JSON-serializable\n",
+        "        # You can return any JSON-serializable value.\n",
         "        return {\"prediction1\": result_1.tolist(), \"prediction2\": result_2.tolist()}\n",
         "    except Exception as e:\n",
         "        result = str(e)\n",
@@ -208,10 +237,10 @@
       "source": [
         "from azureml.core import Environment\n",
         "\n",
-        "env = Environment.from_conda_specification(name='deploytocloudenv', file_path='myenv.yml')\n",
+        "env = Environment(\"deploytocloudenv\")\n",
-        "\n",
+        "env.python.conda_dependencies.add_pip_package(\"joblib\")\n",
-        "# This is optional at this point\n",
+        "env.python.conda_dependencies.add_pip_package(\"numpy\")\n",
-        "# env.register(workspace=ws)"
+        "env.python.conda_dependencies.add_pip_package(\"scikit-learn=={}\".format(sklearn.__version__))"
       ]
     },
     {
@@ -281,25 +310,15 @@
       },
       "outputs": [],
       "source": [
-        "from azureml.core.webservice import AciWebservice, Webservice\n",
+        "from azureml.core.webservice import AciWebservice\n",
-        "from azureml.exceptions import WebserviceException\n",
+        "\n",
+        "aci_service_name = \"aciservice-multimodel\"\n",
         "\n",
         "deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n",
-        "aci_service_name = 'aciservice-multimodel'\n",
-        "\n",
-        "try:\n",
-        "    # if you want to get existing service below is the command\n",
-        "    # since aci name needs to be unique in subscription deleting existing aci if any\n",
-        "    # we use aci_service_name to create azure aci\n",
-        "    service = Webservice(ws, name=aci_service_name)\n",
-        "    if service:\n",
-        "        service.delete()\n",
-        "except WebserviceException as e:\n",
-        "    print()\n",
-        "\n",
-        "service = Model.deploy(ws, aci_service_name, [my_model_1, my_model_2], inference_config, deployment_config)\n",
         "\n",
+        "service = Model.deploy(ws, aci_service_name, [my_model_1, my_model_2], inference_config, deployment_config, overwrite=True)\n",
         "service.wait_for_deployment(True)\n",
+        "\n",
         "print(service.state)"
       ]
     },
@@ -317,13 +336,11 @@
       "outputs": [],
       "source": [
         "import json\n",
-        "test_sample = json.dumps({'data': [\n",
-        "    [1,2,3,4,5,6,7,8,9,10], \n",
-        "    [10,9,8,7,6,5,4,3,2,1]\n",
-        "]})\n",
         "\n",
-        "test_sample_encoded = bytes(test_sample, encoding='utf8')\n",
+        "test_sample = json.dumps({'data': x[0:2].tolist()})\n",
-        "prediction = service.run(input_data=test_sample_encoded)\n",
+        "\n",
+        "prediction = service.run(test_sample)\n",
+        "\n",
         "print(prediction)"
       ]
     },

how-to-use-azureml/deployment/deploy-multi-model/multi-model-register-and-deploy.yml

@@ -2,3 +2,5 @@ name: multi-model-register-and-deploy
 dependencies:
 - pip:
   - azureml-sdk
+  - numpy
+  - scikit-learn

how-to-use-azureml/deployment/deploy-multi-model/myenv.yml

@@ -1,8 +0,0 @@
-name: project_environment
-dependencies:
-  - python=3.6.2
-  - pip:
-    - azureml-defaults
-    - scikit-learn
-    - numpy
-    - inference-schema[numpy-support]

how-to-use-azureml/deployment/deploy-to-cloud/features.csv

@@ -1,442 +0,0 @@
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--1.882016527791040067e-03,-4.464163650698899782e-02,-5.147406123880610140e-02,-2.632783471735180084e-02,-8.448724111216979540e-03,-1.916333974822199970e-02,7.441156407875940126e-02,-3.949338287409189657e-02,-6.832974362442149896e-02,-9.220404962683000083e-02
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--8.906293935226029801e-02,-4.464163650698899782e-02,-1.159501450521270051e-02,-3.665644679856060184e-02,1.219056876180000040e-02,2.499059336410210108e-02,-3.603757004385269719e-02,3.430885887772629900e-02,2.269202256674450122e-02,-9.361911330135799444e-03
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--9.632801625429950054e-02,-4.464163650698899782e-02,-8.380842345523309422e-02,8.100872220010799790e-03,-1.033894713270950005e-01,-9.056118903623530669e-02,-1.394774321933030074e-02,-7.639450375000099436e-02,-6.291294991625119570e-02,-3.421455281914410201e-02
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--5.273755484206479882e-02,5.068011873981870252e-02,-1.806188694849819934e-02,8.040115678847230274e-02,8.924392882106320368e-02,1.076617872765389949e-01,-3.971920784793980114e-02,1.081111006295440019e-01,3.605579008983190309e-02,-4.249876664881350324e-02
--5.514554978810590376e-03,-4.464163650698899782e-02,4.229558918883229851e-02,4.941532054484590319e-02,2.457414448561009990e-02,-2.386056667506489953e-02,7.441156407875940126e-02,-3.949338287409189657e-02,5.227999979678119719e-02,2.791705090337660150e-02
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--3.820740103798660192e-02,-4.464163650698899782e-02,-1.051720243133190055e-02,-3.665644679856060184e-02,-3.734373413344069942e-02,-1.947648821001150138e-02,-2.867429443567860031e-02,-2.592261998182820038e-03,-1.811826730789670159e-02,-1.764612515980519894e-02
--2.730978568492789874e-02,-4.464163650698899782e-02,-1.806188694849819934e-02,-4.009931749229690007e-02,-2.944912678412469915e-03,-1.133462820348369975e-02,3.759518603788870178e-02,-3.949338287409189657e-02,-8.944018957797799166e-03,-5.492508739331759815e-02
--4.910501639104519755e-02,-4.464163650698899782e-02,-5.686312160821060252e-02,-4.354218818603310115e-02,-4.559945128264750180e-02,-4.327577130601600180e-02,7.788079970179680352e-04,-3.949338287409189657e-02,-1.190068480150809939e-02,1.549073015887240078e-02
--8.543040090124079389e-02,5.068011873981870252e-02,-2.237313524402180162e-02,1.215130832538269907e-03,-3.734373413344069942e-02,-2.636575436938120090e-02,1.550535921336619952e-02,-3.949338287409189657e-02,-7.212845460195599356e-02,-1.764612515980519894e-02
--8.543040090124079389e-02,-4.464163650698899782e-02,-4.050329988046450294e-03,-9.113481248670509197e-03,-2.944912678412469915e-03,7.767427965677820186e-03,2.286863482154040048e-02,-3.949338287409189657e-02,-6.117659509433449883e-02,-1.350401824497050006e-02
-4.534098333546320025e-02,5.068011873981870252e-02,6.061839444480759953e-02,3.105334362634819961e-02,2.870200306021350109e-02,-4.734670130927989828e-02,-5.444575906428809897e-02,7.120997975363539678e-02,1.335989800130079896e-01,1.356118306890790048e-01
--6.363517019512339445e-02,-4.464163650698899782e-02,3.582871674554689856e-02,-2.288496402361559975e-02,-3.046396984243510131e-02,-1.885019128643240088e-02,-6.584467611156170040e-03,-2.592261998182820038e-03,-2.595242443518940012e-02,-5.492508739331759815e-02
--6.726770864614299572e-02,5.068011873981870252e-02,-1.267282657909369996e-02,-4.009931749229690007e-02,-1.532848840222260020e-02,4.635943347782499856e-03,-5.812739686837520292e-02,3.430885887772629900e-02,1.919903307856710151e-02,-3.421455281914410201e-02
--1.072256316073579990e-01,-4.464163650698899782e-02,-7.734155101194770121e-02,-2.632783471735180084e-02,-8.962994274508359616e-02,-9.619786134844690584e-02,2.655027262562750096e-02,-7.639450375000099436e-02,-4.257210492279420166e-02,-5.219804415301099697e-03
--2.367724723390840155e-02,-4.464163650698899782e-02,5.954058237092670069e-02,-4.009931749229690007e-02,-4.284754556624519733e-02,-4.358891976780549654e-02,1.182372140927919965e-02,-3.949338287409189657e-02,-1.599826775813870117e-02,4.034337164788070335e-02
-5.260606023750229870e-02,-4.464163650698899782e-02,-2.129532317014089932e-02,-7.452802442965950069e-02,-4.009563984984299695e-02,-3.763909899380440266e-02,-6.584467611156170040e-03,-3.949338287409189657e-02,-6.092541861022970299e-04,-5.492508739331759815e-02
-6.713621404158050254e-02,5.068011873981870252e-02,-6.205954135808240159e-03,6.318680331979099896e-02,-4.284754556624519733e-02,-9.588471288665739722e-02,5.232173725423699961e-02,-7.639450375000099436e-02,5.942380044479410317e-02,5.276969239238479825e-02
--6.000263174410389727e-02,-4.464163650698899782e-02,4.445121333659410312e-02,-1.944209332987930153e-02,-9.824676969418109224e-03,-7.576846662009279788e-03,2.286863482154040048e-02,-3.949338287409189657e-02,-2.712864555432650121e-02,-9.361911330135799444e-03
--2.367724723390840155e-02,-4.464163650698899782e-02,-6.548561819925780014e-02,-8.141376581713200000e-02,-3.871968699164179961e-02,-5.360967054507050078e-02,5.968501286241110343e-02,-7.639450375000099436e-02,-3.712834601047360072e-02,-4.249876664881350324e-02
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-1.264813727628719998e-02,-4.464163650698899782e-02,2.289497185897609866e-02,5.285819123858220142e-02,8.062710187196569719e-03,-2.855779360190789998e-02,3.759518603788870178e-02,-3.949338287409189657e-02,5.472400334817909689e-02,-2.593033898947460017e-02
--9.147093429830140468e-03,-4.464163650698899782e-02,1.103903904628619932e-02,-5.731367096097819691e-02,-2.496015840963049931e-02,-4.296262284422640298e-02,3.023191042971450082e-02,-3.949338287409189657e-02,1.703713241477999851e-02,-5.219804415301099697e-03
--1.882016527791040067e-03,5.068011873981870252e-02,7.139651518361660176e-02,9.761551025715360652e-02,8.786797596286209655e-02,7.540749571221680436e-02,-2.131101882750449997e-02,7.120997975363539678e-02,7.142403278057639360e-02,2.377494398854190089e-02
--1.882016527791040067e-03,5.068011873981870252e-02,1.427247526792889930e-02,-7.452802442965950069e-02,2.558898754392050119e-03,6.201685656730160021e-03,-1.394774321933030074e-02,-2.592261998182820038e-03,1.919903307856710151e-02,3.064409414368320182e-03
-5.383060374248070309e-03,5.068011873981870252e-02,-8.361578283570040432e-03,2.187235499495579841e-02,5.484510736603499803e-02,7.321545647968999426e-02,-2.499265663159149983e-02,3.430885887772629900e-02,1.255315281338930007e-02,9.419076154073199869e-02
--9.996055470531900466e-02,-4.464163650698899782e-02,-6.764124234701959781e-02,-1.089567313670219972e-01,-7.449446130487119566e-02,-7.271172671423199729e-02,1.550535921336619952e-02,-3.949338287409189657e-02,-4.986846773523059828e-02,-9.361911330135799444e-03
--6.000263174410389727e-02,5.068011873981870252e-02,-1.051720243133190055e-02,-1.485159908304049987e-02,-4.972730985725089953e-02,-2.354741821327540133e-02,-5.812739686837520292e-02,1.585829843977170153e-02,-9.918957363154769225e-03,-3.421455281914410201e-02
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-4.534098333546320025e-02,5.068011873981870252e-02,6.816307896197400240e-02,8.100872220010799790e-03,-1.670444126042380101e-02,4.635943347782499856e-03,-7.653558588881050062e-02,7.120997975363539678e-02,3.243322577960189995e-02,-1.764612515980519894e-02
-2.717829108036539862e-02,5.068011873981870252e-02,-3.530688013059259805e-02,3.220096707616459941e-02,-1.120062982761920074e-02,1.504458729887179960e-03,-1.026610541524320026e-02,-2.592261998182820038e-03,-1.495647502491130078e-02,-5.078298047848289754e-02
--5.637009329308430294e-02,-4.464163650698899782e-02,-1.159501450521270051e-02,-3.321357610482440076e-02,-4.697540414084860200e-02,-4.765984977106939996e-02,4.460445801105040325e-03,-3.949338287409189657e-02,-7.979397554541639223e-03,-8.806194271199530021e-02
--7.816532399920170238e-02,-4.464163650698899782e-02,-7.303030271642410587e-02,-5.731367096097819691e-02,-8.412613131227909824e-02,-7.427746902317970690e-02,-2.499265663159149983e-02,-3.949338287409189657e-02,-1.811826730789670159e-02,-8.391983579716059960e-02
-6.713621404158050254e-02,5.068011873981870252e-02,-4.177375257387799801e-02,1.154374291374709975e-02,2.558898754392050119e-03,5.888537194940629722e-03,4.127682384197570165e-02,-3.949338287409189657e-02,-5.947269741072230137e-02,-2.178823207463989955e-02
--4.183993948900609910e-02,5.068011873981870252e-02,1.427247526792889930e-02,-5.670610554934250001e-03,-1.257658268582039982e-02,6.201685656730160021e-03,-7.285394808472339667e-02,7.120997975363539678e-02,3.546193866076970125e-02,-1.350401824497050006e-02
-3.444336798240450054e-02,-4.464163650698899782e-02,-7.283766209689159811e-03,1.498661360748330083e-02,-4.422349842444640161e-02,-3.732595053201490098e-02,-2.902829807069099918e-03,-3.949338287409189657e-02,-2.139368094035999993e-02,7.206516329203029904e-03
-5.987113713954139715e-02,5.068011873981870252e-02,1.642809941569069870e-02,2.875809638242839833e-02,-4.147159270804409714e-02,-2.918409052548700047e-02,-2.867429443567860031e-02,-2.592261998182820038e-03,-2.396681493414269844e-03,-2.178823207463989955e-02
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how-to-use-azureml/deployment/deploy-to-cloud/labels.csv

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

@@ -80,9 +80,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Register input and output datasets\n",
+        "## Create trained model\n",
         "\n",
-        "For this example, we have provided a small model (`sklearn_regression_model.pkl` in the notebook's directory) that was trained on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). Here, you will register the data used to create this model in your workspace."
+        "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). "
       ]
     },
     {
@@ -91,9 +91,42 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "import joblib\n",
+        "\n",
+        "from sklearn.datasets import load_diabetes\n",
+        "from sklearn.linear_model import Ridge\n",
+        "\n",
+        "\n",
+        "dataset_x, dataset_y = load_diabetes(return_X_y=True)\n",
+        "\n",
+        "model = Ridge().fit(dataset_x, dataset_y)\n",
+        "\n",
+        "joblib.dump(model, 'sklearn_regression_model.pkl')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Register input and output datasets\n",
+        "\n",
+        "Here, you will register the data used to create the model in your workspace."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "\n",
         "from azureml.core import Dataset\n",
         "\n",
         "\n",
+        "np.savetxt('features.csv', dataset_x, delimiter=',')\n",
+        "np.savetxt('labels.csv', dataset_y, delimiter=',')\n",
+        "\n",
         "datastore = ws.get_default_datastore()\n",
         "datastore.upload_files(files=['./features.csv', './labels.csv'],\n",
         "                       target_path='sklearn_regression/',\n",
@@ -125,6 +158,8 @@
       },
       "outputs": [],
       "source": [
+        "import sklearn\n",
+        "\n",
         "from azureml.core import Model\n",
         "from azureml.core.resource_configuration import ResourceConfiguration\n",
         "\n",
@@ -133,7 +168,7 @@
         "                       model_name='my-sklearn-model',                # Name of the registered model in your workspace.\n",
         "                       model_path='./sklearn_regression_model.pkl',  # Local file to upload and register as a model.\n",
         "                       model_framework=Model.Framework.SCIKITLEARN,  # Framework used to create the model.\n",
-        "                       model_framework_version='0.19.1',             # Version of scikit-learn used to create the model.\n",
+        "                       model_framework_version=sklearn.__version__,  # Version of scikit-learn used to create the model.\n",
         "                       sample_input_dataset=input_dataset,\n",
         "                       sample_output_dataset=output_dataset,\n",
         "                       resource_configuration=ResourceConfiguration(cpu=1, memory_in_gb=0.5),\n",
@@ -174,19 +209,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core import Webservice\n",
-        "from azureml.exceptions import WebserviceException\n",
-        "\n",
-        "\n",
         "service_name = 'my-sklearn-service'\n",
         "\n",
-        "# Remove any existing service under the same name.\n",
+        "service = Model.deploy(ws, service_name, [model], overwrite=True)\n",
-        "try:\n",
-        "    Webservice(ws, service_name).delete()\n",
-        "except WebserviceException:\n",
-        "    pass\n",
-        "\n",
-        "service = Model.deploy(ws, service_name, [model])\n",
         "service.wait_for_deployment(show_output=True)"
       ]
     },
@@ -207,10 +232,7 @@
         "\n",
         "\n",
         "input_payload = json.dumps({\n",
-        "    'data': [\n",
+        "    'data': dataset_x[0:2].tolist(),\n",
-        "        [ 0.03807591,  0.05068012,  0.06169621, 0.02187235, -0.0442235,\n",
-        "         -0.03482076, -0.04340085, -0.00259226, 0.01990842, -0.01764613]\n",
-        "    ],\n",
         "    'method': 'predict'  # If you have a classification model, you can get probabilities by changing this to 'predict_proba'.\n",
         "})\n",
         "\n",
@@ -262,7 +284,7 @@
         "    'inference-schema[numpy-support]',\n",
         "    'joblib',\n",
         "    'numpy',\n",
-        "    'scikit-learn'\n",
+        "    'scikit-learn=={}'.format(sklearn.__version__)\n",
         "])"
       ]
     },
@@ -303,20 +325,12 @@
       },
       "outputs": [],
       "source": [
-        "from azureml.core import Webservice\n",
         "from azureml.core.model import InferenceConfig\n",
         "from azureml.core.webservice import AciWebservice\n",
-        "from azureml.exceptions import WebserviceException\n",
         "\n",
         "\n",
         "service_name = 'my-custom-env-service'\n",
         "\n",
-        "# Remove any existing service under the same name.\n",
-        "try:\n",
-        "    Webservice(ws, service_name).delete()\n",
-        "except WebserviceException:\n",
-        "    pass\n",
-        "\n",
         "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n",
         "aci_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n",
         "\n",
@@ -324,7 +338,8 @@
         "                       name=service_name,\n",
         "                       models=[model],\n",
         "                       inference_config=inference_config,\n",
-        "                       deployment_config=aci_config)\n",
+        "                       deployment_config=aci_config,\n",
+        "                       overwrite=True)\n",
         "service.wait_for_deployment(show_output=True)"
       ]
     },
@@ -342,10 +357,7 @@
       "outputs": [],
       "source": [
         "input_payload = json.dumps({\n",
-        "    'data': [\n",
+        "    'data': dataset_x[0:2].tolist()\n",
-        "        [ 0.03807591,  0.05068012,  0.06169621, 0.02187235, -0.0442235,\n",
-        "         -0.03482076, -0.04340085, -0.00259226, 0.01990842, -0.01764613]\n",
-        "    ]\n",
         "})\n",
         "\n",
         "output = service.run(input_payload)\n",
@@ -383,6 +395,8 @@
         "- an inference configuration\n",
         "- a single column tabular dataset, where each row contains a string representing sample request data sent to the service.\n",
         "\n",
+        "Please, note that profiling is a long running operation and can take up to 25 minutes depending on the size of the dataset.\n",
+        "\n",
         "At this point we only support profiling of services that expect their request data to be a string, for example: string serialized json, text, string serialized image, etc. The content of each row of the dataset (string) will be put into the body of the HTTP request and sent to the service encapsulating the model for scoring.\n",
         "\n",
         "Below is an example of how you can construct an input dataset to profile a service which expects its incoming requests to contain serialized json. In this case we created a dataset based one hundred instances of the same request data. In real world scenarios however, we suggest that you use larger datasets with various inputs, especially if your model resource usage/behavior is input dependent."
@@ -469,7 +483,7 @@
         "    'inference-schema[numpy-support]',\n",
         "    'joblib',\n",
         "    'numpy',\n",
-        "    'scikit-learn'\n",
+        "    'scikit-learn=={}'.format(sklearn.__version__)\n",
         "])\n",
         "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n",
         "# if cpu and memory_in_gb parameters are not provided\n",
@@ -483,6 +497,7 @@
         "            cpu=1.0,\n",
         "            memory_in_gb=0.5)\n",
         "\n",
+        "# profiling is a long running operation and may take up to 25 min\n",
         "profile.wait_for_completion(True)\n",
         "details = profile.get_details()"
       ]

how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.yml

@@ -2,3 +2,5 @@ name: model-register-and-deploy
 dependencies:
 - pip:
   - azureml-sdk
+  - numpy
+  - scikit-learn

how-to-use-azureml/deployment/deploy-to-local/myenv.yml

@@ -1,8 +0,0 @@
-name: project_environment
-dependencies:
-  - python=3.6.2
-  - pip:
-    - azureml-defaults
-    - scikit-learn
-    - numpy
-    - inference-schema[numpy-support]

how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb

@@ -75,6 +75,33 @@
         "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create trained model\n",
+        "\n",
+        "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import joblib\n",
+        "\n",
+        "from sklearn.datasets import load_diabetes\n",
+        "from sklearn.linear_model import Ridge\n",
+        "\n",
+        "dataset_x, dataset_y = load_diabetes(return_X_y=True)\n",
+        "\n",
+        "sk_model = Ridge().fit(dataset_x, dataset_y)\n",
+        "\n",
+        "joblib.dump(sk_model, \"sklearn_regression_model.pkl\")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -86,7 +113,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "You can add tags and descriptions to your models. we are using `sklearn_regression_model.pkl` file in the current directory as a model with the name `sklearn_regression_model_local_adv` in the workspace.\n",
+        "You can add tags and descriptions to your models. we are using `sklearn_regression_model.pkl` file in the current directory as a model with the name `sklearn_regression_model` in the workspace.\n",
         "\n",
         "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model, framework, category, target customer etc. Note that tags must be alphanumeric."
       ]
@@ -105,7 +132,7 @@
         "from azureml.core.model import Model\n",
         "\n",
         "model = Model.register(model_path=\"sklearn_regression_model.pkl\",\n",
-        "                       model_name=\"sklearn_regression_model_local_adv\",\n",
+        "                       model_name=\"sklearn_regression_model\",\n",
         "                       tags={'area': \"diabetes\", 'type': \"regression\"},\n",
         "                       description=\"Ridge regression model to predict diabetes\",\n",
         "                       workspace=ws)"
@@ -126,12 +153,12 @@
       "source": [
         "import os\n",
         "\n",
-        "source_directory = \"C:/abc\"\n",
+        "source_directory = \"source_directory\"\n",
         "\n",
         "os.makedirs(source_directory, exist_ok=True)\n",
-        "os.makedirs(\"C:/abc/x/y\", exist_ok=True)\n",
+        "os.makedirs(os.path.join(source_directory, \"x/y\"), exist_ok=True)\n",
-        "os.makedirs(\"C:/abc/env\", exist_ok=True)\n",
+        "os.makedirs(os.path.join(source_directory, \"env\"), exist_ok=True)\n",
-        "os.makedirs(\"C:/abc/dockerstep\", exist_ok=True)"
+        "os.makedirs(os.path.join(source_directory, \"dockerstep\"), exist_ok=True)"
       ]
     },
     {
@@ -147,14 +174,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "%%writefile C:/abc/x/y/score.py\n",
+        "%%writefile source_directory/x/y/score.py\n",
-        "import os\n",
+        "import joblib\n",
-        "import pickle\n",
         "import json\n",
         "import numpy as np\n",
-        "from sklearn.externals import joblib\n",
+        "import os\n",
-        "from sklearn.linear_model import Ridge\n",
-        "from azureml.core.model import Model\n",
         "\n",
         "from inference_schema.schema_decorators import input_schema, output_schema\n",
         "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n",
@@ -165,16 +189,17 @@
         "    # It holds the path to the directory that contains the deployed model (./azureml-models/$MODEL_NAME/$VERSION)\n",
         "    # If there are multiple models, this value is the path to the directory containing all deployed models (./azureml-models)\n",
         "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
-        "    # deserialize the model file back into a sklearn model\n",
+        "    # Deserialize the model file back into a sklearn model.\n",
         "    model = joblib.load(model_path)\n",
+        "\n",
         "    global name\n",
-        "    # note here, entire source directory on inference config gets added into image\n",
+        "    # Note here, the entire source directory from inference config gets added into image.\n",
-        "    # bellow is the example how you can use any extra files in image\n",
+        "    # Below is an example of how you can use any extra files in image.\n",
-        "    with open('./abc/extradata.json') as json_file:  \n",
+        "    with open('./source_directory/extradata.json') as json_file:\n",
         "        data = json.load(json_file)\n",
         "        name = data[\"people\"][0][\"name\"]\n",
         "\n",
-        "input_sample = np.array([[10,9,8,7,6,5,4,3,2,1]])\n",
+        "input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n",
         "output_sample = np.array([3726.995])\n",
         "\n",
         "@input_schema('data', NumpyParameterType(input_sample))\n",
@@ -182,46 +207,20 @@
         "def run(data):\n",
         "    try:\n",
         "        result = model.predict(data)\n",
-        "        # you can return any datatype as long as it is JSON-serializable\n",
+        "        # You can return any JSON-serializable object.\n",
         "        return \"Hello \" + name + \" here is your result = \" + str(result)\n",
         "    except Exception as e:\n",
         "        error = str(e)\n",
         "        return error"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency for your environemnt. This package contains the functionality needed to host the model as a web service."
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {},
       "outputs": [],
       "source": [
-        "%%writefile C:/abc/env/myenv.yml\n",
+        "%%writefile source_directory/extradata.json\n",
-        "name: project_environment\n",
-        "dependencies:\n",
-        "  - python=3.6.2\n",
-        "  - pip:\n",
-        "    - azureml-defaults\n",
-        "    - scikit-learn\n",
-        "    - numpy\n",
-        "    - inference-schema[numpy-support]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile C:/abc/extradata.json\n",
         "{\n",
         "    \"people\": [\n",
         "        {\n",
@@ -251,19 +250,25 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "import sklearn\n",
+        "\n",
         "from azureml.core.environment import Environment\n",
         "from azureml.core.model import InferenceConfig\n",
         "\n",
         "\n",
-        "myenv = Environment.from_conda_specification(name='myenv', file_path='env/myenv.yml')\n",
+        "myenv = Environment('myenv')\n",
+        "myenv.python.conda_dependencies.add_pip_package(\"inference-schema[numpy-support]\")\n",
+        "myenv.python.conda_dependencies.add_pip_package(\"joblib\")\n",
+        "myenv.python.conda_dependencies.add_pip_package(\"scikit-learn=={}\".format(sklearn.__version__))\n",
         "\n",
         "# explicitly set base_image to None when setting base_dockerfile\n",
         "myenv.docker.base_image = None\n",
-        "myenv.docker.base_dockerfile = \"RUN echo \\\"this is test\\\"\"\n",
+        "myenv.docker.base_dockerfile = \"FROM mcr.microsoft.com/azureml/base:intelmpi2018.3-ubuntu16.04\\nRUN echo \\\"this is test\\\"\"\n",
+        "myenv.inferencing_stack_version = \"latest\"\n",
         "\n",
-        "inference_config = InferenceConfig(source_directory=\"C:/abc\",\n",
+        "inference_config = InferenceConfig(source_directory=source_directory,\n",
         "                                   entry_script=\"x/y/score.py\",\n",
-        "                                   environment=myenv)\n"
+        "                                   environment=myenv)"
       ]
     },
     {
@@ -353,15 +358,10 @@
         "import json\n",
         "\n",
         "sample_input = json.dumps({\n",
-        "    'data': [\n",
+        "    'data': dataset_x[0:2].tolist()\n",
-        "        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
-        "        [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]\n",
-        "    ]\n",
         "})\n",
         "\n",
-        "sample_input = bytes(sample_input, encoding='utf-8')\n",
+        "print(local_service.run(sample_input))"
-        "\n",
-        "print(local_service.run(input_data=sample_input))"
       ]
     },
     {
@@ -379,13 +379,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "%%writefile C:/abc/x/y/score.py\n",
+        "%%writefile source_directory/x/y/score.py\n",
-        "import os\n",
+        "import joblib\n",
-        "import pickle\n",
         "import json\n",
         "import numpy as np\n",
-        "from sklearn.externals import joblib\n",
+        "import os\n",
-        "from sklearn.linear_model import Ridge\n",
         "\n",
         "from inference_schema.schema_decorators import input_schema, output_schema\n",
         "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n",
@@ -396,17 +394,18 @@
         "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
         "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
         "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
-        "    # deserialize the model file back into a sklearn model\n",
+        "    # Deserialize the model file back into a sklearn model.\n",
         "    model = joblib.load(model_path)\n",
+        "\n",
         "    global name, from_location\n",
-        "    # note here, entire source directory on inference config gets added into image\n",
+        "    # Note here, the entire source directory from inference config gets added into image.\n",
-        "    # bellow is the example how you can use any extra files in image\n",
+        "    # Below is an example of how you can use any extra files in image.\n",
-        "    with open('./abc/extradata.json') as json_file:  \n",
+        "    with open('source_directory/extradata.json') as json_file:  \n",
         "        data = json.load(json_file)\n",
         "        name = data[\"people\"][0][\"name\"]\n",
         "        from_location = data[\"people\"][0][\"from\"]\n",
         "\n",
-        "input_sample = np.array([[10,9,8,7,6,5,4,3,2,1]])\n",
+        "input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n",
         "output_sample = np.array([3726.995])\n",
         "\n",
         "@input_schema('data', NumpyParameterType(input_sample))\n",
@@ -414,8 +413,8 @@
         "def run(data):\n",
         "    try:\n",
         "        result = model.predict(data)\n",
-        "        # you can return any datatype as long as it is JSON-serializable\n",
+        "        # You can return any JSON-serializable object.\n",
-        "        return \"Hello  \" + name + \" from \" + from_location + \" here is your result = \" + str(result)\n",
+        "        return \"Hello \" + name + \" from \" + from_location + \" here is your result = \" + str(result)\n",
         "    except Exception as e:\n",
         "        error = str(e)\n",
         "        return error"
@@ -431,7 +430,7 @@
         "print(\"--------------------------------------------------------------\")\n",
         "\n",
         "# After calling reload(), run() will return the updated message.\n",
-        "local_service.run(input_data=sample_input)"
+        "local_service.run(sample_input)"
       ]
     },
     {

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

@@ -71,6 +71,33 @@
         "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create trained model\n",
+        "\n",
+        "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import joblib\n",
+        "\n",
+        "from sklearn.datasets import load_diabetes\n",
+        "from sklearn.linear_model import Ridge\n",
+        "\n",
+        "dataset_x, dataset_y = load_diabetes(return_X_y=True)\n",
+        "\n",
+        "sk_model = Ridge().fit(dataset_x, dataset_y)\n",
+        "\n",
+        "joblib.dump(sk_model, \"sklearn_regression_model.pkl\")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -82,9 +109,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "You can add tags and descriptions to your models. we are using `sklearn_regression_model.pkl` file in the current directory as a model with the name `sklearn_regression_model_local` in the workspace.\n",
+        "Here we are registering the serialized file `sklearn_regression_model.pkl` in the current directory as a model with the name `sklearn_regression_model` in the workspace.\n",
         "\n",
-        "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model, framework, category, target customer etc. Note that tags must be alphanumeric."
+        "You can add tags and descriptions to your models. Using tags, you can track useful information such as the name and version of the machine learning library used to train the model, framework, category, target customer etc. Note that tags must be alphanumeric."
       ]
     },
     {
@@ -100,7 +127,7 @@
         "from azureml.core.model import Model\n",
         "\n",
         "model = Model.register(model_path=\"sklearn_regression_model.pkl\",\n",
-        "                       model_name=\"sklearn_regression_model_local\",\n",
+        "                       model_name=\"sklearn_regression_model\",\n",
         "                       tags={'area': \"diabetes\", 'type': \"regression\"},\n",
         "                       description=\"Ridge regression model to predict diabetes\",\n",
         "                       workspace=ws)"
@@ -119,11 +146,62 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "import sklearn\n",
+        "\n",
         "from azureml.core.environment import Environment\n",
         "\n",
         "environment = Environment(\"LocalDeploy\")\n",
-        "environment.python.conda_dependencies = CondaDependencies(\"myenv.yml\")"
+        "environment.python.conda_dependencies.add_pip_package(\"inference-schema[numpy-support]\")\n",
+        "environment.python.conda_dependencies.add_pip_package(\"joblib\")\n",
+        "environment.python.conda_dependencies.add_pip_package(\"scikit-learn=={}\".format(sklearn.__version__))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Provide the Scoring Script\n",
+        "\n",
+        "This Python script handles the model execution inside the service container. The `init()` method loads the model file, and `run(data)` is called for every input to the service."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%writefile score.py\n",
+        "import joblib\n",
+        "import json\n",
+        "import numpy as np\n",
+        "import os\n",
+        "\n",
+        "from inference_schema.schema_decorators import input_schema, output_schema\n",
+        "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n",
+        "\n",
+        "def init():\n",
+        "    global model\n",
+        "    # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
+        "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
+        "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
+        "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
+        "    # Deserialize the model file back into a sklearn model.\n",
+        "    model = joblib.load(model_path)\n",
+        "\n",
+        "input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n",
+        "output_sample = np.array([3726.995])\n",
+        "\n",
+        "@input_schema('data', NumpyParameterType(input_sample))\n",
+        "@output_schema(NumpyParameterType(output_sample))\n",
+        "def run(data):\n",
+        "    try:\n",
+        "        result = model.predict(data)\n",
+        "        # You can return any JSON-serializable object.\n",
+        "        return result.tolist()\n",
+        "    except Exception as e:\n",
+        "        error = str(e)\n",
+        "        return error"
       ]
     },
     {
@@ -145,110 +223,6 @@
         "                                   environment=environment)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Model Profiling\n",
-        "\n",
-        "Profile your model to understand how much CPU and memory the service, created as a result of its deployment, will need. Profiling returns information such as CPU usage, memory usage, and response latency. It also provides a CPU and memory recommendation based on the resource usage. You can profile your model (or more precisely the service built based on your model) on any CPU and/or memory combination where 0.1 <= CPU <= 3.5 and 0.1GB <= memory <= 15GB. If you do not provide a CPU and/or memory requirement, we will test it on the default configuration of 3.5 CPU and 15GB memory.\n",
-        "\n",
-        "In order to profile your model you will need:\n",
-        "- a registered model\n",
-        "- an entry script\n",
-        "- an inference configuration\n",
-        "- a single column tabular dataset, where each row contains a string representing sample request data sent to the service.\n",
-        "\n",
-        "At this point we only support profiling of services that expect their request data to be a string, for example: string serialized json, text, string serialized image, etc. The content of each row of the dataset (string) will be put into the body of the HTTP request and sent to the service encapsulating the model for scoring.\n",
-        "\n",
-        "Below is an example of how you can construct an input dataset to profile a service which expects its incoming requests to contain serialized json. In this case we created a dataset based one hundred instances of the same request data. In real world scenarios however, we suggest that you use larger datasets with various inputs, especially if your model resource usage/behavior is input dependent."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import json\n",
-        "from azureml.core import Datastore\n",
-        "from azureml.core.dataset import Dataset\n",
-        "from azureml.data import dataset_type_definitions\n",
-        "\n",
-        "\n",
-        "# create a string that can be put in the body of the request\n",
-        "serialized_input_json = json.dumps({\n",
-        "    'data': [\n",
-        "        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
-        "        [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]\n",
-        "    ]\n",
-        "})\n",
-        "dataset_content = []\n",
-        "for i in range(100):\n",
-        "    dataset_content.append(serialized_input_json)\n",
-        "dataset_content = '\\n'.join(dataset_content)\n",
-        "file_name = 'sample_request_data_diabetes.txt'\n",
-        "f = open(file_name, 'w')\n",
-        "f.write(dataset_content)\n",
-        "f.close()\n",
-        "\n",
-        "# upload the txt file created above to the Datastore and create a dataset from it\n",
-        "data_store = Datastore.get_default(ws)\n",
-        "data_store.upload_files(['./' + file_name], target_path='sample_request_data_diabetes')\n",
-        "datastore_path = [(data_store, 'sample_request_data_diabetes' +'/' + file_name)]\n",
-        "sample_request_data_diabetes = Dataset.Tabular.from_delimited_files(\n",
-        "    datastore_path,\n",
-        "    separator='\\n',\n",
-        "    infer_column_types=True,\n",
-        "    header=dataset_type_definitions.PromoteHeadersBehavior.NO_HEADERS)\n",
-        "sample_request_data_diabetes = sample_request_data_diabetes.register(workspace=ws,\n",
-        "                                                   name='sample_request_data_diabetes',\n",
-        "                                                   create_new_version=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now that we have an input dataset we are ready to go ahead with profiling. In this case we are testing the previously introduced sklearn regression model on 1 CPU and 0.5 GB memory. The memory usage and recommendation presented in the result is measured in Gigabytes. The CPU usage and recommendation is measured in CPU cores."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from datetime import datetime\n",
-        "from azureml.core import Environment\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "from azureml.core.model import Model, InferenceConfig\n",
-        "\n",
-        "\n",
-        "environment = Environment('my-sklearn-environment')\n",
-        "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
-        "    'azureml-defaults',\n",
-        "    'inference-schema[numpy-support]',\n",
-        "    'joblib',\n",
-        "    'numpy',\n",
-        "    'scikit-learn'\n",
-        "])\n",
-        "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n",
-        "# if cpu and memory_in_gb parameters are not provided\n",
-        "# the model will be profiled on default configuration of\n",
-        "# 3.5CPU and 15GB memory\n",
-        "profile = Model.profile(ws,\n",
-        "            'profile-%s' % datetime.now().strftime('%m%d%Y-%H%M%S'),\n",
-        "            [model],\n",
-        "            inference_config,\n",
-        "            input_dataset=sample_request_data_diabetes,\n",
-        "            cpu=1.0,\n",
-        "            memory_in_gb=0.5)\n",
-        "\n",
-        "profile.wait_for_completion(True)\n",
-        "details = profile.get_details()"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -335,15 +309,10 @@
         "import json\n",
         "\n",
         "sample_input = json.dumps({\n",
-        "    'data': [\n",
+        "    'data': dataset_x[0:2].tolist()\n",
-        "        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
-        "        [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]\n",
-        "    ]\n",
         "})\n",
         "\n",
-        "sample_input = bytes(sample_input, encoding='utf-8')\n",
+        "local_service.run(sample_input)"
-        "\n",
-        "local_service.run(input_data=sample_input)"
       ]
     },
     {
@@ -362,12 +331,10 @@
       "outputs": [],
       "source": [
         "%%writefile score.py\n",
-        "import os\n",
+        "import joblib\n",
-        "import pickle\n",
         "import json\n",
         "import numpy as np\n",
-        "from sklearn.externals import joblib\n",
+        "import os\n",
-        "from sklearn.linear_model import Ridge\n",
         "\n",
         "from inference_schema.schema_decorators import input_schema, output_schema\n",
         "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n",
@@ -378,10 +345,10 @@
         "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
         "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
         "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
-        "    # deserialize the model file back into a sklearn model\n",
+        "    # Deserialize the model file back into a sklearn model.\n",
         "    model = joblib.load(model_path)\n",
         "\n",
-        "input_sample = np.array([[10,9,8,7,6,5,4,3,2,1]])\n",
+        "input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n",
         "output_sample = np.array([3726.995])\n",
         "\n",
         "@input_schema('data', NumpyParameterType(input_sample))\n",
@@ -389,8 +356,8 @@
         "def run(data):\n",
         "    try:\n",
         "        result = model.predict(data)\n",
-        "        # you can return any datatype as long as it is JSON-serializable\n",
+        "        # You can return any JSON-serializable object.\n",
-        "        return 'hello from updated score.py'\n",
+        "        return 'Hello from the updated score.py: ' + str(result.tolist())\n",
         "    except Exception as e:\n",
         "        error = str(e)\n",
         "        return error"
@@ -406,7 +373,7 @@
         "print(\"--------------------------------------------------------------\")\n",
         "\n",
         "# After calling reload(), run() will return the updated message.\n",
-        "local_service.run(input_data=sample_input)"
+        "local_service.run(sample_input)"
       ]
     },
     {

how-to-use-azureml/deployment/deploy-to-local/score.py

@@ -1,35 +0,0 @@
-import os
-import pickle
-import json
-import numpy as np
-from sklearn.externals import joblib
-from sklearn.linear_model import Ridge
-
-from inference_schema.schema_decorators import input_schema, output_schema
-from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType
-
-
-def init():
-    global model
-    # AZUREML_MODEL_DIR is an environment variable created during deployment.
-    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)
-    # For multiple models, it points to the folder containing all deployed models (./azureml-models)
-    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')
-    # deserialize the model file back into a sklearn model
-    model = joblib.load(model_path)
-
-
-input_sample = np.array([[10, 9, 8, 7, 6, 5, 4, 3, 2, 1]])
-output_sample = np.array([3726.995])
-
-
-@input_schema('data', NumpyParameterType(input_sample))
-@output_schema(NumpyParameterType(output_sample))
-def run(data):
-    try:
-        result = model.predict(data)
-        # you can return any datatype as long as it is JSON-serializable
-        return result.tolist()
-    except Exception as e:
-        error = str(e)
-        return error

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

@@ -108,9 +108,9 @@
         "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
         "    'azureml-defaults',\n",
         "    'inference-schema[numpy-support]',\n",
-        "    'joblib',\n",
         "    'numpy',\n",
-        "    'scikit-learn'\n",
+        "    'scikit-learn==0.19.1',\n",
+        "    'scipy'\n",
         "])"
       ]
     },

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

@@ -5,7 +5,7 @@
       "metadata": {},
       "source": [
         "# Enabling App Insights for Services in Production\n",
-        "With this notebook, you can learn how to enable App Insights for standard service monitoring, plus, we provide examples for doing custom logging within a scoring files in a model. \n",
+        "With this notebook, you can learn how to enable App Insights for standard service monitoring, plus, we provide examples for doing custom logging within a scoring files in a model.\n",
         "\n",
         "\n",
         "## What does Application Insights monitor?\n",
@@ -45,11 +45,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "import azureml.core\n",
+        "import json\n",
+        "\n",
         "from azureml.core import Workspace\n",
         "from azureml.core.compute import AksCompute, ComputeTarget\n",
         "from azureml.core.webservice import AksWebservice\n",
-        "import azureml.core\n",
+        "\n",
-        "import json\n",
         "print(azureml.core.VERSION)"
       ]
     },
@@ -67,7 +69,7 @@
       "outputs": [],
       "source": [
         "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
       ]
     },
     {
@@ -84,13 +86,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "#Register the model\n",
+        "from azureml.core import Model\n",
-        "from azureml.core.model import Model\n",
+        "\n",
-        "model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n",
+        "model = Model.register(model_path=\"sklearn_regression_model.pkl\", # This points to a local file.\n",
-        "                       model_name = \"sklearn_regression_model.pkl\", # this is the name the model is registered as\n",
+        "                       model_name=\"sklearn_regression_model.pkl\", # This is the name the model is registered as.\n",
-        "                       tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
+        "                       tags={'area': \"diabetes\", 'type': \"regression\"},\n",
-        "                       description = \"Ridge regression model to predict diabetes\",\n",
+        "                       description=\"Ridge regression model to predict diabetes\",\n",
-        "                       workspace = ws)\n",
+        "                       workspace=ws)\n",
         "\n",
         "print(model.name, model.description, model.version)"
       ]
@@ -120,7 +122,7 @@
         "import os\n",
         "import pickle\n",
         "import json\n",
-        "import numpy \n",
+        "import numpy\n",
         "from sklearn.externals import joblib\n",
         "from sklearn.linear_model import Ridge\n",
         "import time\n",
@@ -129,15 +131,15 @@
         "    global model\n",
         "    #Print statement for appinsights custom traces:\n",
         "    print (\"model initialized\" + time.strftime(\"%H:%M:%S\"))\n",
-        "    \n",
+        "\n",
         "    # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
         "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
         "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
         "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
-        "    \n",
+        "\n",
         "    # deserialize the model file back into a sklearn model\n",
         "    model = joblib.load(model_path)\n",
-        "  \n",
+        "\n",
         "\n",
         "# note you can pass in multiple rows for scoring\n",
         "def run(raw_data):\n",
@@ -168,9 +170,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies \n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
         "\n",
-        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'],\n",
+        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn==0.20.3'],\n",
         "                                 pip_packages=['azureml-defaults'])\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
@@ -190,9 +192,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.model import InferenceConfig\n",
         "from azureml.core.environment import Environment\n",
-        "\n",
+        "from azureml.core.model import InferenceConfig\n",
         "\n",
         "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
         "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
@@ -213,11 +214,11 @@
       "source": [
         "from azureml.core.webservice import AciWebservice\n",
         "\n",
-        "aci_deployment_config = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
+        "aci_deployment_config = AciWebservice.deploy_configuration(cpu_cores=1,\n",
-        "                                               memory_gb = 1, \n",
+        "                                                           memory_gb=1,\n",
-        "                                               tags = {'area': \"diabetes\", 'type': \"regression\"}, \n",
+        "                                                           tags={'area': \"diabetes\", 'type': \"regression\"},\n",
-        "                                               description = 'Predict diabetes using regression model',\n",
+        "                                                           description=\"Predict diabetes using regression model\",\n",
-        "                                               enable_app_insights = True)"
+        "                                                           enable_app_insights=True)"
       ]
     },
     {
@@ -226,29 +227,14 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.webservice import Webservice\n",
+        "aci_service_name = \"aci-service-appinsights\"\n",
+        "\n",
+        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aci_deployment_config, overwrite=True)\n",
+        "aci_service.wait_for_deployment(show_output=True)\n",
         "\n",
-        "aci_service_name = 'my-aci-service-4'\n",
-        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aci_deployment_config)\n",
-        "aci_service.wait_for_deployment(True)\n",
         "print(aci_service.state)"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "\n",
-        "test_sample = json.dumps({'data': [\n",
-        "    [1,28,13,45,54,6,57,8,8,10], \n",
-        "    [101,9,8,37,6,45,4,3,2,41]\n",
-        "]})\n",
-        "test_sample = bytes(test_sample,encoding='utf8')"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -256,7 +242,15 @@
       "outputs": [],
       "source": [
         "if aci_service.state == \"Healthy\":\n",
-        "    prediction = aci_service.run(input_data=test_sample)\n",
+        "    test_sample = json.dumps({\n",
+        "        \"data\": [\n",
+        "            [1,28,13,45,54,6,57,8,8,10],\n",
+        "            [101,9,8,37,6,45,4,3,2,41]\n",
+        "        ]\n",
+        "    })\n",
+        "\n",
+        "    prediction = aci_service.run(test_sample)\n",
+        "\n",
         "    print(prediction)\n",
         "else:\n",
         "    raise ValueError(\"Service deployment isn't healthy, can't call the service. Error: \", aci_service.error)"
@@ -282,14 +276,21 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# Use the default configuration (can also provide parameters to customize)\n",
+        "from azureml.exceptions import ComputeTargetException\n",
-        "prov_config = AksCompute.provisioning_configuration()\n",
         "\n",
-        "aks_name = 'my-aks-test3' \n",
+        "aks_name = \"my-aks\"\n",
-        "# Create the cluster\n",
+        "\n",
-        "aks_target = ComputeTarget.create(workspace = ws, \n",
+        "try:\n",
-        "                                  name = aks_name, \n",
+        "    aks_target = ComputeTarget(ws, aks_name)\n",
-        "                                  provisioning_configuration = prov_config)"
+        "    print(\"Using existing AKS cluster {}.\".format(aks_name))\n",
+        "except ComputeTargetException:\n",
+        "    print(\"Creating a new AKS cluster {}.\".format(aks_name))\n",
+        "\n",
+        "    # Use the default configuration (can also provide parameters to customize).\n",
+        "    prov_config = AksCompute.provisioning_configuration()\n",
+        "    aks_target = ComputeTarget.create(workspace=ws,\n",
+        "                                      name=aks_name,\n",
+        "                                      provisioning_configuration=prov_config)"
       ]
     },
     {
@@ -299,7 +300,8 @@
       "outputs": [],
       "source": [
         "%%time\n",
-        "aks_target.wait_for_completion(show_output = True)"
+        "if aks_target.provisioning_state != \"Succeeded\":\n",
+        "    aks_target.wait_for_completion(show_output=True)"
       ]
     },
     {
@@ -323,13 +325,13 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "```python \n",
+        "```python\n",
         "%%time\n",
         "resource_id = '/subscriptions/<subscriptionid>/resourcegroups/<resourcegroupname>/providers/Microsoft.ContainerService/managedClusters/<aksservername>'\n",
         "create_name= 'myaks4'\n",
         "attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n",
-        "aks_target = ComputeTarget.attach(workspace = ws, \n",
+        "aks_target = ComputeTarget.attach(workspace=ws,\n",
-        "                                  name = create_name, \n",
+        "                                  name=create_name,\n",
         "                                  attach_configuration=attach_config)\n",
         "## Wait for the operation to complete\n",
         "aks_target.wait_for_provisioning(True)```"
@@ -349,7 +351,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "#Set the web service configuration\n",
+        "# Set the web service configuration.\n",
         "aks_deployment_config = AksWebservice.deploy_configuration(enable_app_insights=True)"
       ]
     },
@@ -366,15 +368,16 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "if aks_target.provisioning_state== \"Succeeded\": \n",
+        "if aks_target.provisioning_state == \"Succeeded\":\n",
-        "    aks_service_name ='aks-w-dc5'\n",
+        "    aks_service_name = \"aks-service-appinsights\"\n",
         "    aks_service = Model.deploy(ws,\n",
-        "                               aks_service_name, \n",
+        "                               aks_service_name,\n",
-        "                               [model], \n",
+        "                               [model],\n",
-        "                               inference_config, \n",
+        "                               inference_config,\n",
-        "                               aks_deployment_config, \n",
+        "                               aks_deployment_config,\n",
-        "                               deployment_target = aks_target)    \n",
+        "                               deployment_target=aks_target,\n",
-        "    aks_service.wait_for_deployment(show_output = True)\n",
+        "                               overwrite=True)\n",
+        "    aks_service.wait_for_deployment(show_output=True)\n",
         "    print(aks_service.state)\n",
         "else:\n",
         "    raise ValueError(\"AKS provisioning failed. Error: \", aks_service.error)"
@@ -395,13 +398,14 @@
       "source": [
         "%%time\n",
         "\n",
-        "test_sample = json.dumps({'data': [\n",
-        "    [1,28,13,45,54,6,57,8,8,10], \n",
-        "    [101,9,8,37,6,45,4,3,2,41]\n",
-        "]})\n",
-        "test_sample = bytes(test_sample,encoding='utf8')\n",
-        "\n",
         "if aks_service.state == \"Healthy\":\n",
+        "    test_sample = json.dumps({\n",
+        "        \"data\": [\n",
+        "            [1,28,13,45,54,6,57,8,8,10],\n",
+        "            [101,9,8,37,6,45,4,3,2,41]\n",
+        "        ]\n",
+        "    })\n",
+        "\n",
         "    prediction = aks_service.run(input_data=test_sample)\n",
         "    print(prediction)\n",
         "else:\n",
@@ -435,7 +439,7 @@
       "outputs": [],
       "source": [
         "aks_service.update(enable_app_insights=False)\n",
-        "aks_service.wait_for_deployment(show_output = True)"
+        "aks_service.wait_for_deployment(show_output=True)"
       ]
     },
     {
@@ -461,7 +465,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "shipatel"
+        "name": "gopalv"
       }
     ],
     "kernelspec": {

how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb

@@ -115,6 +115,11 @@
         "# Convert from CoreML into ONNX\n",
         "onnx_model = onnxmltools.convert_coreml(coreml_model, 'TinyYOLOv2')\n",
         "\n",
+        "# Fix the preprocessor bias in the ImageScaler\n",
+        "for init in onnx_model.graph.initializer:\n",
+        "    if init.name == 'scalerPreprocessor_bias':\n",
+        "        init.dims[1] = 1\n",
+        "\n",
         "# Save ONNX model\n",
         "onnxmltools.utils.save_model(onnx_model, 'tinyyolov2.onnx')\n",
         "\n",
@@ -255,7 +260,7 @@
       "source": [
         "from azureml.core.conda_dependencies import CondaDependencies \n",
         "\n",
-        "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime==0.4.0\", \"azureml-core\", \"azureml-defaults\"])\n",
+        "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
         "    f.write(myenv.serialize_to_string())"
@@ -316,7 +321,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "aci_service_name = 'my-aci-service-15ad'\n",
+        "aci_service_name = 'my-aci-service-tiny-yolo'\n",
         "print(\"Service\", aci_service_name)\n",
         "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
         "aci_service.wait_for_deployment(True)\n",

how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.yml

@@ -4,4 +4,5 @@ dependencies:
   - azureml-sdk
   - numpy
   - git+https://github.com/apple/coremltools@v2.1
-  - onnxmltools==1.3.1
+  - onnx<1.7.0
+  - onnxmltools

how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.yml

@@ -5,5 +5,5 @@ dependencies:
   - azureml-widgets
   - matplotlib
   - numpy
-  - onnx
+  - onnx<1.7.0
-  - opencv-python
+  - opencv-python-headless

how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.yml

@@ -5,5 +5,5 @@ dependencies:
   - azureml-widgets
   - matplotlib
   - numpy
-  - onnx
+  - onnx<1.7.0
-  - opencv-python
+  - opencv-python-headless

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

@@ -59,8 +59,44 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Register the model\n",
+        "# Download the model\n",
-        "Register an existing trained model, add descirption and tags. Prior to registering the model, you should have a TensorFlow [Saved Model](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md) in the `resnet50` directory. You can download a [pretrained resnet50](http://download.tensorflow.org/models/official/20181001_resnet/savedmodels/resnet_v1_fp32_savedmodel_NCHW_jpg.tar.gz) and unpack it to that directory."
+        "\n",
+        "Prior to registering the model, you should have a TensorFlow [Saved Model](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md) in the `resnet50` directory. This cell will download a [pretrained resnet50](http://download.tensorflow.org/models/official/20181001_resnet/savedmodels/resnet_v1_fp32_savedmodel_NCHW_jpg.tar.gz) and unpack it to that directory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import requests\n",
+        "import shutil\n",
+        "import tarfile\n",
+        "import tempfile\n",
+        "\n",
+        "from io import BytesIO\n",
+        "\n",
+        "model_url = \"http://download.tensorflow.org/models/official/20181001_resnet/savedmodels/resnet_v1_fp32_savedmodel_NCHW_jpg.tar.gz\"\n",
+        "\n",
+        "archive_prefix = \"./resnet_v1_fp32_savedmodel_NCHW_jpg/1538686758/\"\n",
+        "target_folder = \"resnet50\"\n",
+        "\n",
+        "if not os.path.exists(target_folder):\n",
+        "    response = requests.get(model_url)\n",
+        "    archive = tarfile.open(fileobj=BytesIO(response.content))\n",
+        "    with tempfile.TemporaryDirectory() as temp_folder:\n",
+        "        archive.extractall(temp_folder)\n",
+        "        shutil.copytree(os.path.join(temp_folder, archive_prefix), target_folder)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Register the model\n",
+        "Register an existing trained model, add description and tags."
       ]
     },
     {
@@ -69,13 +105,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "#Register the model\n",
         "from azureml.core.model import Model\n",
-        "model = Model.register(model_path = \"resnet50\", # this points to a local file\n",
+        "\n",
-        "                       model_name = \"resnet50\", # this is the name the model is registered as\n",
+        "model = Model.register(model_path=\"resnet50\", # This points to the local directory to upload.\n",
-        "                       tags = {'area': \"Image classification\", 'type': \"classification\"},\n",
+        "                       model_name=\"resnet50\", # This is the name the model is registered as.\n",
-        "                       description = \"Image classification trained on Imagenet Dataset\",\n",
+        "                       tags={'area': \"Image classification\", 'type': \"classification\"},\n",
-        "                       workspace = ws)\n",
+        "                       description=\"Image classification trained on Imagenet Dataset\",\n",
+        "                       workspace=ws)\n",
         "\n",
         "print(model.name, model.description, model.version)"
       ]