Update auto-ml-classification-bank-marketing.ipynb

removing old explain model directories

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,30 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: "[Notebook issue]"
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+Provide the following if applicable:
++ Your Python & SDK version 
++ Python Scripts or the full notebook name
++ Pipeline definition 
++ Environment definition 
++ Example data 
++ Any log files. 
++ Run and Workspace Id 
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. 
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Additional context**
+Add any other context about the problem here.

.github/ISSUE_TEMPLATE/notebook-issue.md

@@ -0,0 +1,30 @@
+---
+name: Notebook issue
+about: Create a report to help us improve
+title: "[Notebook issue]"
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+Provide the following if applicable:
++ Your Python & SDK version 
++ Python Scripts or the full notebook name
++ Pipeline definition 
++ Environment definition 
++ Example data 
++ Any log files. 
++ Run and Workspace Id 
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. 
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Additional context**
+Add any other context about the problem here.

README.md

@@ -38,6 +38,7 @@ The [How to use Azure ML](./how-to-use-azureml) folder contains specific example
 - [Machine Learning Pipelines](./how-to-use-azureml/machine-learning-pipelines) - Examples showing how to create and use reusable pipelines for training and batch scoring
 - [Deployment](./how-to-use-azureml/deployment) - Examples showing how to deploy and manage machine learning models and solutions
 - [Azure Databricks](./how-to-use-azureml/azure-databricks) - Examples showing how to use Azure ML with Azure Databricks
+- [Monitor Models](./how-to-use-azureml/monitor-models) - Examples showing how to enable model monitoring services such as DataDrift
 
 ---
 ## Documentation
@@ -52,6 +53,7 @@ The [How to use Azure ML](./how-to-use-azureml) folder contains specific example
 
 Visit following repos to see projects contributed by Azure ML users:
 
+ - [AMLSamples](https://github.com/Azure/AMLSamples) Number of end-to-end examples, including face recognition, predictive maintenance, customer churn and sentiment analysis.
  - [Fine tune natural language processing models using Azure Machine Learning service](https://github.com/Microsoft/AzureML-BERT)
  - [Fashion MNIST with Azure ML SDK](https://github.com/amynic/azureml-sdk-fashion)
  

build_nb_index.py

@@ -0,0 +1,215 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+### USAGE
+#  
+# 1. Add following metadata elements to the notebook
+#
+# "friendly_name": "string", friendly name for notebook
+# "exclude_from_index": true/false, setting true excludes the notebook from index
+# "order_index": integer, smaller value moves notebook closer to beginning
+# "category": "starter", "tutorial", "training", "deployment" or "other"
+# "tags": [ "featured" ], optional, only supported tag to highlight notebook with :star: symbol
+# "task": "string", description of notebook task
+# "datasets": [ "dataset 1", "dataset 2"], list of datasets, can be ["None"]
+# "compute": [ "compute 1", "compute 2" ], list of computes, can be ["None"]
+# "deployment": ["deployment 1", "deployment 2"], list of deployment targets, can be ["None"]
+# "framework": ["fw 1", "fw2"], list of ml framework, can be ["None"]
+#
+# 2. Then run
+# 
+# build_nb_index.py <root folder of notebooks>
+#
+# 3. The script should produce index.md file with tables of notebook indices
+
+### Example metadata section
+
+'''
+  "metadata": {
+    "authors": [
+      {
+        "name": "cforbe"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.7"
+    },
+    "msauthor": "trbye",
+    "friendly_name": "Prepare data for regression modeling",
+    "exclude_from_index": false,
+    "order_index": 1,
+    "category": "tutorial",
+    "tags": [
+      "featured"
+    ],
+    "task": "Regression",
+    "datasets": [
+      "NYC Taxi"
+    ],
+    "compute": [
+      "local"
+    ],
+    "deployment": [
+      "None"
+    ],
+    "framework": [
+      "Azure ML AutoML"
+    ]
+  }
+'''
+
+import os, json, sys
+from shutil import copyfile, copytree, rmtree
+
+
+# Index building walk over notebook folder
+def post_process(notebooks_dir):
+    indexer = NotebookIndex()
+    n_dest = len(notebooks_dir)
+    for r, d, f in os.walk(notebooks_dir): 
+        for file in f:
+            # Handle only notebooks
+            if file.endswith(".ipynb") and not file.endswith('checkpoint.ipynb'):
+                try:
+                    file_path = os.path.join(r, file)
+                    with open(file_path, 'r') as fin:
+                        content = json.load(fin)
+                    print(file)
+                    indexer.add_to_index(os.path.join(r[n_dest:],file), content["metadata"])
+                except Exception as e:
+                    print("Problem: ",str(e))
+    indexer.write_index("./index.md")
+
+### Customize these make index look different
+
+index_template = '''
+# Index 
+Azure Machine Learning is a cloud service that you use to train, deploy, automate, and manage machine learning models. This index should assist in navigating the Azure Machine Learning notebook samples and encourage efficient retrieval of topics and content.
+
+
+## Getting Started 
+
+|Title| Task | Dataset | Training Compute | Deployment Target | ML Framework |
+|:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|
+GETTING_STARTED_NBS
+
+## Tutorials 
+
+|Title| Task | Dataset | Training Compute | Deployment Target | ML Framework |
+|:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|
+TUTORIAL_NBS
+
+## Training 
+
+|Title| Task | Dataset | Training Compute | Deployment Target | ML Framework |
+|:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|
+TRAINING_NBS
+
+## Deployment 
+
+|Title| Task | Dataset | Training Compute | Deployment Target | ML Framework |
+|:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|
+DEPLOYMENT_NBS
+
+## Other Notebooks
+|Title| Task | Dataset | Training Compute | Deployment Target | ML Framework |
+|:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|
+OTHER_NBS
+'''
+
+index_row = '''| NB_SYMBOL[NB_NAME](NB_PATH) | NB_TASK | NB_DATASET | NB_COMPUTE | NB_DEPLOYMENT | NB_FRAMEWORK |'''
+
+index_file = "index.md"
+
+nb_types = ["starter", "tutorial", "training", "deployment", "other"]
+replace_strings = ["GETTING_STARTED_NBS", "TUTORIAL_NBS", "TRAINING_NBS", "DEPLOYMENT_NBS", "OTHER_NBS"]
+
+class NotebookIndex:
+    def __init__(self):
+        self.index =  index_template
+        self.nb_rows = {}
+        for elem in nb_types:
+            self.nb_rows[elem] = []
+
+    def add_to_index(self, path_to_notebook, metadata):
+        repo_url = "https://github.com/Azure/MachineLearningNotebooks/blob/master/"
+
+        if "exclude_from_index" in metadata:
+            if metadata["exclude_from_index"]:
+                return
+
+        if "friendly_name" in metadata:
+            this_row = index_row.replace("NB_NAME",metadata["friendly_name"])
+        else:
+            this_name = os.path.basename(path_to_notebook)
+            this_row = index_row.replace("NB_NAME", this_name[:-6])
+        
+        path_to_notebook = path_to_notebook.replace("\\","/")
+        this_row = this_row.replace("NB_PATH", repo_url + path_to_notebook)
+
+        if "task" in metadata:
+            this_row = this_row.replace("NB_TASK", metadata["task"])
+        if "datasets" in metadata:
+            this_row = this_row.replace("NB_DATASET", ", ".join(metadata["datasets"]))         
+        if "compute" in metadata:
+            this_row = this_row.replace("NB_COMPUTE", ", ".join(metadata["compute"]))
+        if "deployment" in metadata:
+            this_row = this_row.replace("NB_DEPLOYMENT", ", ".join(metadata["deployment"]))        
+        if "framework" in metadata:
+            this_row = this_row.replace("NB_FRAMEWORK", ", ".join(metadata["framework"]))
+        ## Fall back
+        this_row = this_row.replace("NB_TASK","")
+        this_row = this_row.replace("NB_DATASET","")
+        this_row = this_row.replace("NB_COMPUTE","")
+        this_row = this_row.replace("NB_DEPLOYMENT","")        
+        this_row = this_row.replace("NB_FRAMEWORK","")
+
+        if "tags" in metadata:
+            if "featured" in metadata["tags"]:  
+                this_row = this_row.replace("NB_SYMBOL",":star:")
+        ## Fall back
+        this_row =this_row.replace("NB_SYMBOL","")
+
+        index_order = 9999999
+        if "index_order" in metadata:
+            index_order = metadata["index_order"]
+
+        if "category" in metadata:
+            self.nb_rows[metadata["category"]].append((index_order, this_row))
+        else:
+            self.nb_rows["other"].append((index_order, this_row))
+    
+    def sort_and_stringify(self,section):
+        sorted_index = sorted(self.nb_rows[section], key = lambda x: x[0])
+        sorted_index = [x[1] for x in sorted_index]
+        ## TODO: Make this portable
+        return "\n".join(sorted_index)
+
+    def write_index(self, index_file):
+        for nb_type, replace_string in zip(nb_types, replace_strings):
+            nb_string = self.sort_and_stringify(nb_type)
+            self.index = self.index.replace(replace_string, nb_string)
+        with open(index_file,"w") as fin:
+            fin.write(self.index)
+
+try:
+    dest_repo = sys.argv[1]
+except:
+    dest_repo = "./MachineLearningNotebooks"  
+
+post_process(dest_repo)

configuration.ipynb

@@ -58,7 +58,7 @@
         "\n",
         "### What is an Azure Machine Learning workspace\n",
         "\n",
-        "An Azure ML Workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an Azure ML Workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, deployment, inferencing, and the monitoring of deployed models."
+        "An Azure ML Workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an Azure ML Workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, deployment, inference, and the monitoring of deployed models."
       ]
     },
     {
@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.0.45 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.0.55 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -214,7 +214,8 @@
         "* You do not have permission to create a resource group if it's non-existing.\n",
         "* You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription\n",
         "\n",
-        "If workspace creation fails, please work with your IT admin to provide you with the appropriate permissions or to provision the required resources."
+        "If workspace creation fails, please work with your IT admin to provide you with the appropriate permissions or to provision the required resources.\n",
+        "To learn more about the Enterprise SKU, please visit the Pricing and SKU details page."
       ]
     },
     {
@@ -230,11 +231,14 @@
         "from azureml.core import Workspace\n",
         "\n",
         "# Create the workspace using the specified parameters\n",
+        "# To create an Enterprise workspace, please specify the sku = enterprise\n",
+        
         "ws = Workspace.create(name = workspace_name,\n",
         "                      subscription_id = subscription_id,\n",
         "                      resource_group = resource_group, \n",
         "                      location = workspace_region,\n",
         "                      create_resource_group = True,\n",    
+        "                      sku = basic,\n",
         "                      exist_ok = True)\n",
         "ws.get_details()\n",
         "\n",
@@ -258,7 +262,7 @@
         "```shell\n",
         "az vm list-skus -o tsv\n",
         "```\n",
-        "* min_nodes - this sets the minimum size of the cluster.  If you set the minimum to 0 the cluster will shut down all nodes while note in use.  Setting this number to a value higher than 0 will allow for faster start-up times, but you will also be billed when the cluster is not in use.\n",
+        "* min_nodes - this sets the minimum size of the cluster.  If you set the minimum to 0 the cluster will shut down all nodes while not in use.  Setting this number to a value higher than 0 will allow for faster start-up times, but you will also be billed when the cluster is not in use.\n",
         "* max_nodes - this sets the maximum size of the cluster.  Setting this to a larger number allows for more concurrency and a greater distributed processing of scale-out jobs.\n",
         "\n",
         "\n",

configuration.yml

@@ -0,0 +1,4 @@
+name: configuration
+dependencies:
+- pip:
+  - azureml-sdk

contrib/datadrift/azure-ml-datadrift.ipynb

@@ -33,10 +33,9 @@
       "source": [
         "## Install the DataDrift package\n",
         "\n",
-        "Install the azureml-contrib-datadrift, azureml-contrib-opendatasets and lightgbm packages before running this notebook.\n",
+        "Install the azureml-contrib-datadrift, azureml-opendatasets and lightgbm packages before running this notebook.\n",
         "```\n",
         "pip install azureml-contrib-datadrift\n",
-        "pip install azureml-contrib-datasets\n",
         "pip install lightgbm\n",
         "```"
       ]
@@ -63,7 +62,7 @@
         "import pandas as pd\n",
         "import requests\n",
         "from azureml.contrib.datadrift import DataDriftDetector, AlertConfiguration\n",
-        "from azureml.contrib.opendatasets import NoaaIsdWeather\n",
+        "from azureml.opendatasets import NoaaIsdWeather\n",
         "from azureml.core import Dataset, Workspace, Run\n",
         "from azureml.core.compute import AksCompute, ComputeTarget\n",
         "from azureml.core.conda_dependencies import CondaDependencies\n",
@@ -259,8 +258,7 @@
         "trainingDataset = Dataset.auto_read_files(dpath, include_path=True)\n",
         "trainingDataset = trainingDataset.register(workspace=ws, name=dataset_name, description=\"dset\", exist_ok=True)\n",
         "\n",
-        "trainingDataSnapshot = trainingDataset.create_snapshot(snapshot_name=snapshot_name, compute_target=None, create_data_snapshot=True)\n",
-        "datasets = [(Dataset.Scenario.TRAINING, trainingDataSnapshot)]\n",
+        "datasets = [(Dataset.Scenario.TRAINING, trainingDataset)]\n",
         "print(\"dataset registration done.\\n\")\n",
         "datasets"
       ]
@@ -574,6 +572,22 @@
         "        time.sleep(3)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We need to wait up to 10 minutes for the Model Data Collector to dump the model input and inference data to storage in the Workspace, where it's used by the DataDriftDetector job."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "time.sleep(600)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

contrib/datadrift/azure-ml-datadrift.yml

@@ -0,0 +1,8 @@
+name: azure-ml-datadrift
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-contrib-datadrift
+  - azureml-opendatasets
+  - lightgbm
+  - azureml-widgets

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

@@ -175,10 +175,19 @@ jupyter notebook
     - Example of training an automated ML forecasting model on multiple time-series
 
 - [auto-ml-classification-with-onnx.ipynb](classification-with-onnx/auto-ml-classification-with-onnx.ipynb)
-    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
+    - Dataset: scikit learn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html)
     - Simple example of using automated ML for classification with ONNX models
     - Uses local compute for training
 
+- [auto-ml-remote-amlcompute-with-onnx.ipynb](remote-amlcompute-with-onnx/auto-ml-remote-amlcompute-with-onnx.ipynb)
+    - Dataset: scikit learn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html)
+    - Example of using automated ML for classification using remote AmlCompute for training
+    - Train the models with ONNX compatible config on
+    - Parallel execution of iterations
+    - Async tracking of progress
+    - Cancelling individual iterations or entire run
+    - Retrieving the ONNX models and do the inference with them
+
 - [auto-ml-bank-marketing-subscribers-with-deployment.ipynb](bank-marketing-subscribers-with-deployment/auto-ml-bank-marketing-with-deployment.ipynb)
     - Dataset: UCI's [bank marketing dataset](https://www.kaggle.com/janiobachmann/bank-marketing-dataset)
     - Simple example of using automated ML for classification to predict term deposit subscriptions for a bank

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

@@ -2,6 +2,7 @@ name: azure_automl
 dependencies:
   # The python interpreter version.
   # Currently Azure ML only supports 3.5.2 and later.
+- pip
 - python>=3.5.2,<3.6.8
 - nb_conda
 - matplotlib==2.1.0

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

@@ -2,6 +2,7 @@ name: azure_automl
 dependencies:
   # The python interpreter version.
   # Currently Azure ML only supports 3.5.2 and later.
+- pip
 - nomkl
 - python>=3.5.2,<3.6.8
 - nb_conda

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

@@ -1,729 +1,224 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-    "\n",
-    "Licensed under the MIT License."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-bank-marketing/auto-ml-classification-bank-marketing.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Automated Machine Learning\n",
-    "_**Classification with Deployment using a Bank Marketing Dataset**_\n",
-    "\n",
-    "## Contents\n",
-    "1. [Introduction](#Introduction)\n",
-    "1. [Setup](#Setup)\n",
-    "1. [Train](#Train)\n",
-    "1. [Results](#Results)\n",
-    "1. [Deploy](#Deploy)\n",
-    "1. [Test](#Test)\n",
-    "1. [Acknowledgements](#Acknowledgements)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Introduction\n",
-    "\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",
-    "\n",
-    "In this notebook you will learn how to:\n",
-    "1. Create an experiment using an existing workspace.\n",
-    "2. Configure AutoML using `AutoMLConfig`.\n",
-    "3. Train the model using local compute.\n",
-    "4. Explore the results.\n",
-    "5. Register the model.\n",
-    "6. Create a container image.\n",
-    "7. Create an Azure Container Instance (ACI) service.\n",
-    "8. Test the ACI service."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup\n",
-    "\n",
-    "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."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "import logging\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import os\n",
-    "from sklearn import datasets\n",
-    "import azureml.dataprep as dprep\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "import azureml.core\n",
-    "from azureml.core.experiment import Experiment\n",
-    "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.train.automl.run import AutoMLRun"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ws = Workspace.from_config()\n",
-    "\n",
-    "# choose a name for experiment\n",
-    "experiment_name = 'automl-classification-bmarketing'\n",
-    "# project folder\n",
-    "project_folder = './sample_projects/automl-classification-bankmarketing'\n",
-    "\n",
-    "experiment=Experiment(ws, experiment_name)\n",
-    "\n",
-    "output = {}\n",
-    "output['SDK version'] = azureml.core.VERSION\n",
-    "output['Subscription ID'] = ws.subscription_id\n",
-    "output['Workspace'] = ws.name\n",
-    "output['Resource Group'] = ws.resource_group\n",
-    "output['Location'] = ws.location\n",
-    "output['Project Directory'] = project_folder\n",
-    "output['Experiment Name'] = experiment.name\n",
-    "pd.set_option('display.max_colwidth', -1)\n",
-    "outputDf = pd.DataFrame(data = output, index = [''])\n",
-    "outputDf.T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create or Attach existing AmlCompute\n",
-    "You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
-    "#### Creation of AmlCompute takes approximately 5 minutes. \n",
-    "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
-    "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this article on the default limits and how to request more quota."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.compute import AmlCompute\n",
-    "from azureml.core.compute import ComputeTarget\n",
-    "\n",
-    "# Choose a name for your cluster.\n",
-    "amlcompute_cluster_name = \"automlcl\"\n",
-    "\n",
-    "found = False\n",
-    "# Check if this compute target already exists in the workspace.\n",
-    "cts = ws.compute_targets\n",
-    "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
-    "    found = True\n",
-    "    print('Found existing compute target.')\n",
-    "    compute_target = cts[amlcompute_cluster_name]\n",
-    "    \n",
-    "if not found:\n",
-    "    print('Creating a new compute target...')\n",
-    "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-    "                                                                #vm_priority = 'lowpriority', # optional\n",
-    "                                                                max_nodes = 6)\n",
-    "\n",
-    "    # Create the cluster.\n",
-    "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-    "    \n",
-    "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
-    "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-    "    compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-    "    \n",
-    "     # For a more detailed view of current AmlCompute status, use get_status()."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Data\n",
-    "\n",
-    "Here load the data in the get_data() script to be utilized in azure compute. To do this  first load all the necessary libraries and dependencies to set up paths for the data and to create the conda_Run_config."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if not os.path.isdir('data'):\n",
-    "    os.mkdir('data')\n",
-    "    \n",
-    "if not os.path.exists(project_folder):\n",
-    "    os.makedirs(project_folder)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.runconfig import RunConfiguration\n",
-    "from azureml.core.conda_dependencies import CondaDependencies\n",
-    "\n",
-    "# create a new RunConfig object\n",
-    "conda_run_config = RunConfiguration(framework=\"python\")\n",
-    "\n",
-    "# Set compute target to AmlCompute\n",
-    "conda_run_config.target = compute_target\n",
-    "conda_run_config.environment.docker.enabled = True\n",
-    "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
-    "\n",
-    "\n",
-    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy','py-xgboost<=0.80'])\n",
-    "conda_run_config.environment.python.conda_dependencies = cd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load Data\n",
-    "\n",
-    "Here we create the script to be run in azure comput for loading the data, we load the bank marketing dataset into X_train and y_train. Next X_train and y_train is returned for training the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
-    "dflow = dprep.auto_read_file(data)\n",
-    "dflow.get_profile()\n",
-    "X_train = dflow.drop_columns(columns=['y'])\n",
-    "y_train = dflow.keep_columns(columns=['y'], validate_column_exists=True)\n",
-    "dflow.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Train\n",
-    "\n",
-    "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
-    "\n",
-    "|Property|Description|\n",
-    "|-|-|\n",
-    "|**task**|classification or regression|\n",
-    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
-    "|**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",
-    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
-    "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\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",
-    "\n",
-    "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "automl_settings = {\n",
-    "    \"iteration_timeout_minutes\": 5,\n",
-    "    \"iterations\": 10,\n",
-    "    \"n_cross_validations\": 2,\n",
-    "    \"primary_metric\": 'AUC_weighted',\n",
-    "    \"preprocess\": True,\n",
-    "    \"max_concurrent_iterations\": 5,\n",
-    "    \"verbosity\": logging.INFO,\n",
-    "}\n",
-    "\n",
-    "automl_config = AutoMLConfig(task = 'classification',\n",
-    "                             debug_log = 'automl_errors.log',\n",
-    "                             path = project_folder,\n",
-    "                             run_configuration=conda_run_config,\n",
-    "                             X = X_train,\n",
-    "                             y = y_train,\n",
-    "                             **automl_settings\n",
-    "                            )"
-   ]
-  },
-  {
-   "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.\n",
-    "In this example, we specify `show_output = True` to print currently running iterations to the console."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "remote_run = experiment.submit(automl_config, show_output = True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "remote_run"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Widget for Monitoring Runs\n",
-    "\n",
-    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
-    "\n",
-    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.widgets import RunDetails\n",
-    "RunDetails(remote_run).show() "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Deploy\n",
-    "\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*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_run, fitted_model = remote_run.get_output()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Register the Fitted Model for Deployment\n",
-    "If neither `metric` nor `iteration` are specified in the `register_model` call, the iteration with the best primary metric is registered."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "description = 'AutoML Model trained on bank marketing data to predict if a client will subscribe to a term deposit'\n",
-    "tags = None\n",
-    "model = remote_run.register_model(description = description, tags = tags)\n",
-    "\n",
-    "print(remote_run.model_id) # This will be written to the script file later in the notebook."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create Scoring Script\n",
-    "The scoring script is required to generate the image for deployment. It contains the code to do the predictions on input data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%writefile score.py\n",
-    "import pickle\n",
-    "import json\n",
-    "import numpy\n",
-    "import azureml.train.automl\n",
-    "from sklearn.externals import joblib\n",
-    "from azureml.core.model import Model\n",
-    "\n",
-    "\n",
-    "def init():\n",
-    "    global model\n",
-    "    model_path = Model.get_model_path(model_name = '<<modelid>>') # this name is model.id of model that we want to deploy\n",
-    "    # deserialize the model file back into a sklearn model\n",
-    "    model = joblib.load(model_path)\n",
-    "\n",
-    "def run(rawdata):\n",
-    "    try:\n",
-    "        data = json.loads(rawdata)['data']\n",
-    "        data = numpy.array(data)\n",
-    "        result = model.predict(data)\n",
-    "    except Exception as e:\n",
-    "        result = str(e)\n",
-    "        return json.dumps({\"error\": result})\n",
-    "    return json.dumps({\"result\":result.tolist()})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create a YAML File for the Environment"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To ensure the fit results are consistent with the training results, the SDK dependency versions need to be the same as the environment that trains the model. Details about retrieving the versions can be found in notebook [12.auto-ml-retrieve-the-training-sdk-versions](12.auto-ml-retrieve-the-training-sdk-versions.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dependencies = remote_run.get_run_sdk_dependencies(iteration = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
-    "    print('{}\\t{}'.format(p, dependencies[p]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.conda_dependencies import CondaDependencies\n",
-    "\n",
-    "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost<=0.80'],\n",
-    "                                 pip_packages=['azureml-sdk[automl]'])\n",
-    "\n",
-    "conda_env_file_name = 'myenv.yml'\n",
-    "myenv.save_to_file('.', conda_env_file_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Substitute the actual version number in the environment file.\n",
-    "# This is not strictly needed in this notebook because the model should have been generated using the current SDK version.\n",
-    "# However, we include this in case this code is used on an experiment from a previous SDK version.\n",
-    "\n",
-    "with open(conda_env_file_name, 'r') as cefr:\n",
-    "    content = cefr.read()\n",
-    "\n",
-    "with open(conda_env_file_name, 'w') as cefw:\n",
-    "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
-    "\n",
-    "# Substitute the actual model id in the script file.\n",
-    "\n",
-    "script_file_name = 'score.py'\n",
-    "\n",
-    "with open(script_file_name, 'r') as cefr:\n",
-    "    content = cefr.read()\n",
-    "\n",
-    "with open(script_file_name, 'w') as cefw:\n",
-    "    cefw.write(content.replace('<<modelid>>', remote_run.model_id))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create a Container Image\n",
-    "\n",
-    "Next use Azure Container Instances for deploying models as a web service for quickly deploying and validating your model\n",
-    "or when testing a model that is under development."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.image import Image, ContainerImage\n",
-    "\n",
-    "image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
-    "                                 execution_script = script_file_name,\n",
-    "                                 conda_file = conda_env_file_name,\n",
-    "                                 tags = {'area': \"bmData\", 'type': \"automl_classification\"},\n",
-    "                                 description = \"Image for automl classification sample\")\n",
-    "\n",
-    "image = Image.create(name = \"automlsampleimage\",\n",
-    "                     # this is the model object \n",
-    "                     models = [model],\n",
-    "                     image_config = image_config, \n",
-    "                     workspace = ws)\n",
-    "\n",
-    "image.wait_for_creation(show_output = True)\n",
-    "\n",
-    "if image.creation_state == 'Failed':\n",
-    "    print(\"Image build log at: \" + image.image_build_log_uri)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Deploy the Image as a Web Service on Azure Container Instance\n",
-    "\n",
-    "Deploy an image that contains the model and other assets needed by the service."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.webservice import AciWebservice\n",
-    "\n",
-    "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
-    "                                               memory_gb = 1, \n",
-    "                                               tags = {'area': \"bmData\", 'type': \"automl_classification\"}, \n",
-    "                                               description = 'sample service for Automl Classification')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.webservice import Webservice\n",
-    "\n",
-    "aci_service_name = 'automl-sample-bankmarketing'\n",
-    "print(aci_service_name)\n",
-    "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
-    "                                           image = image,\n",
-    "                                           name = aci_service_name,\n",
-    "                                           workspace = ws)\n",
-    "aci_service.wait_for_deployment(True)\n",
-    "print(aci_service.state)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Delete a Web Service\n",
-    "\n",
-    "Deletes the specified web service."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#aci_service.delete()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Get Logs from a Deployed Web Service\n",
-    "\n",
-    "Gets logs from a deployed web service."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#aci_service.get_logs()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Test\n",
-    "\n",
-    "Now that the model is trained split our data in the same way the data was split for training (The difference here is the data is being split locally) and then run the test data through the trained model to get the predicted values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load the bank marketing datasets.\n",
-    "from sklearn.datasets import load_diabetes\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from numpy import array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_validate.csv\"\n",
-    "dflow = dprep.auto_read_file(data)\n",
-    "dflow.get_profile()\n",
-    "X_test = dflow.drop_columns(columns=['y'])\n",
-    "y_test = dflow.keep_columns(columns=['y'], validate_column_exists=True)\n",
-    "dflow.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_test = X_test.to_pandas_dataframe()\n",
-    "y_test = y_test.to_pandas_dataframe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_pred  = fitted_model.predict(X_test)\n",
-    "actual = array(y_test)\n",
-    "actual = actual[:,0]\n",
-    "print(y_pred.shape, \" \", actual.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Calculate metrics for the prediction\n",
-    "\n",
-    "Now visualize the data on a scatter plot to show what our truth (actual) values are compared to the predicted values \n",
-    "from the trained model that was returned."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib notebook\n",
-    "test_pred = plt.scatter(actual, y_pred, color='b')\n",
-    "test_test = plt.scatter(actual, actual, color='g')\n",
-    "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Acknowledgements"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This Bank Marketing dataset is made available under the Creative Commons (CCO: Public Domain) License: https://creativecommons.org/publicdomain/zero/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: https://creativecommons.org/publicdomain/zero/1.0/ and is available at: https://www.kaggle.com/janiobachmann/bank-marketing-dataset .\n",
-    "\n",
-    "_**Acknowledgements**_\n",
-    "This data set is originally available within the UCI Machine Learning Database: https://archive.ics.uci.edu/ml/datasets/bank+marketing\n",
-    "\n",
-    "[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014"
-   ]
-  }
- ],
- "metadata": {
-  "authors": [
-   {
-    "name": "v-rasav"
-   }
+  "cells": [
+      {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-bank-marketing/auto-ml-classification-bank-marketing.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": [
+        "# Unique Descriptive Title\n",
+        "_**Unique Subtitle**_\n",
+        "\n",
+        "Introduction that describes in a customer friendly language, what they will do and accomplish.\n".
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Prerequisites](#Prerequisites)\n",
+        "1. [Configuration and Setup](#Setup)\n",
+        "1. [Working with Data](#Working with Data)\n",
+        "1. [Training](#Training)\n",
+        "1. [Productionizing](#Productionizing)\n",
+        "1. [Model Monitoring](#Model Monitoring)\n",
+        "1. [Clean up resources](#Clean up resources)\n",
+        "1. [Next Steps](#Next Steps)\n",
+        "1. [Acknowledgements](#Acknowledgements)"
+      ]
+    },
+      {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Configuration\n",
+        "\n",
+        "If you are using an Azure Machine Learning Compute Instance, you are all set.  Otherwise, go through the [configuration](../../../configuration.ipynb)  notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
+        "Please note that a Basic edition workspace is created by default in the configuration.ipynb file.\n",
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup\n",
+        "\n",
+        "As part of the setup you have already created an Azure ML `Workspace` object....\n",
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import azureml.core\n",
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "tenant_id = os.environ['TENANT_ID’]\n",
+        "client_id = os.environ['CLIENT_ID’]\n",
+        "run = Run.get_context()\n",
+        "secret_name = “{0}-secret”.format(client_id)\n",
+        "secret = run.get_secret(name=secret_name)\n",
+        "sp_auth = ServicePrincipalAuthentication(tenant_id, client_id, secret)\n",
+        "ws = Workspace.from_config(auth=sp_auth)\n",  
+        "\n",
+        "# choose a unique name for experiment\n",
+        "experiment_name = 'unique-name'\n",
+        "# project folder\n",
+        "project_folder = './sample_projects/test'\n",
+        "\n",
+        "experiment=Experiment(ws, experiment_name)\n",
+        "\n",
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create or Attach existing AmlCompute\n",
+        "You will need to create a compute target for your run. In this tutorial, you create AmlCompute as your training compute resource.\n",
+        "#### Creation of AmlCompute takes approximately 5 minutes. \n",
+        "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
+        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this article on the default limits and how to request more quota."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Working with Data\n",
+        "\n",
+        "Here you would learn how to perform Data labeling and use Open Datasets etc..\n",
+        "To do this first load....\n",
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Training\n",
+        "\n",
+        "Here you would learn how to train a DNN using...\n",
+      ]
+    },
+     {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Productionizing\n",
+        "\n",
+        "Here you would learn how to deploy your model to ACI to perform...\n",
+      ]
+    },
+     {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Model Monitoring\n",
+        "\n",
+        "Here you would learn how to detect datadrift etc...\n",
+      ]
+    },
+     {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Clean up resources\n",
+        "\n",
+        "Now, let's clean up the resources we created...\n",
+      ]
+    },
+     {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Next Steps\n",
+        "\n",
+        "In this notebook, you’ve done x, y, z. You can learn more with these resources:\n",
+        "+ [SDK reference documentation for `MyClass`]()\n",
+        "+ [About this feature](https://docs.microsoft.com/azure/machine-learning/service/thisfeature)\n", 
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This dataset is made available under the Creative Commons (CCO: Public Domain) License: https://creativecommons.org/publicdomain/zero/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: https://creativecommons.org/publicdomain/zero/1.0/ and is available at: https://www.kaggle.com/janiobachmann/bank-marketing-dataset .\n",
+        "\n",
+        "_**Acknowledgements**_\n",
+        "This dataset is originally available within the UCI Machine Learning Database: https://archive.ics.uci.edu/ml/datasets/bank+marketing\n",
+        "\n",
+        "[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014"
+      ]
+    }
   ],
-  "kernelspec": {
-   "display_name": "Python 3.6",
-   "language": "python",
-   "name": "python36"
+  "metadata": {
+    "authors": [
+      {
+        "name": "YOUR ALIAS"
+      }
+    ],    
+    "category": "tutorial",
+    "compute": [
+      "AML Compute"
+    ],
+    "datasets": [
+      "MNIST"
+    ],
+    "deployment": [
+      "AKS"
+    ],
+    "exclude_from_index": false,
+    "framework": [
+      "PyTorch"
+    ],
+    "friendly_name": "How to use ModuleStep with AML Pipelines",
+    },
+    "order_index": 14,
+    "star_tag": [],
+    "tags": [
+      "Pipeline Builder"
+    ],
+    "task": "Demonstrates the use of ModuleStep"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
 }
+    
+    
+    
+ 

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

@@ -0,0 +1,8 @@
+name: auto-ml-classification-bank-marketing
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml

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

@@ -0,0 +1,8 @@
+name: auto-ml-classification-credit-card-fraud
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml

how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb

@@ -41,6 +41,8 @@
         "\n",
         "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
         "\n",
+        "An Enterprise workspace is required for this notebook. To learn more about creating an Enterprise workspace or upgrading to an Enterprise workspace from the Azure portal, please visit our [Workspace page](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-workspace#upgrade).\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",
@@ -61,61 +63,13 @@
         "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."
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import json\n",
-        "import logging\n",
-        "\n",
-        "from matplotlib import pyplot as plt\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "from sklearn import datasets\n",
-        "\n",
-        "import azureml.core\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.core.workspace import Workspace\n",
-        "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.train.automl.run import AutoMLRun"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "# choose a name for experiment\n",
-        "experiment_name = 'automl-classification-deployment'\n",
-        "# project folder\n",
-        "project_folder = './sample_projects/automl-classification-deployment'\n",
-        "\n",
-        "experiment=Experiment(ws, experiment_name)\n",
-        "\n",
-        "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
-        "outputDf.T"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
         "## Train\n",
         "\n",
+        "The following steps require an Enterprise workspace to gain access to these features.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/en-us/azure/machine-learning/service/concept-workspace#upgrade).\n",
         "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
         "\n",
         "|Property|Description|\n",
@@ -484,7 +438,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "savitam"
+        "name": "shwinne"
       }
     ],
     "kernelspec": {

how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.yml

@@ -0,0 +1,8 @@
+name: auto-ml-classification-with-deployment
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml

how-to-use-azureml/automated-machine-learning/classification-with-onnx/auto-ml-classification-with-onnx.ipynb

@@ -29,7 +29,6 @@
         "1. [Data](#Data)\n",
         "1. [Train](#Train)\n",
         "1. [Results](#Results)\n",
-        "1. [Test](#Test)\n",
         "\n"
       ]
     },
@@ -39,7 +38,7 @@
       "source": [
         "## Introduction\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 [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) to showcase how you can use AutoML for a simple classification problem.\n",
         "\n",
         "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
         "\n",
@@ -49,7 +48,8 @@
         "1. Create an `Experiment` in an existing `Workspace`.\n",
         "2. Configure AutoML using `AutoMLConfig`.\n",
         "3. Train the model using local compute with ONNX compatible config on.\n",
-        "4. Explore the results and save the ONNX model."
+        "4. Explore the results and save the ONNX model.\n",
+        "5. Inference with the ONNX model."
       ]
     },
     {
@@ -156,11 +156,11 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Train with enable ONNX compatible models config on\n",
+        "## Train\n",
         "\n",
         "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
         "\n",
-        "Set the parameter enable_onnx_compatible_models=True, if you also want to generate the ONNX compatible models. Please note, the forecasting task and TensorFlow models are not ONNX compatible yet.\n",
+        "**Note:** Set the parameter enable_onnx_compatible_models=True, if you also want to generate the ONNX compatible models. Please note, the forecasting task and TensorFlow models are not ONNX compatible yet.\n",
         "\n",
         "|Property|Description|\n",
         "|-|-|\n",

how-to-use-azureml/automated-machine-learning/classification-with-onnx/auto-ml-classification-with-onnx.yml

@@ -0,0 +1,9 @@
+name: auto-ml-classification-with-onnx
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml
+  - onnxruntime

how-to-use-azureml/automated-machine-learning/classification-with-whitelisting/auto-ml-classification-with-whitelisting.ipynb

@@ -41,7 +41,7 @@
         "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",
         "\n",
         "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
-        "This notebooks shows how can automl can be trained on a a selected list of models,see the readme.md for the models.\n",
+        "This notebooks shows how can automl can be trained on a selected list of models, see the readme.md for the models.\n",
         "This trains the model exclusively on tensorflow based models.\n",
         "\n",
         "In this notebook you will learn how to:\n",

how-to-use-azureml/automated-machine-learning/classification-with-whitelisting/auto-ml-classification-with-whitelisting.yml

@@ -0,0 +1,8 @@
+name: auto-ml-classification-with-whitelisting
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml

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

@@ -258,7 +258,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "widget-rundetails-sample"
+        ]
+      },
       "outputs": [],
       "source": [
         "from azureml.widgets import RunDetails\n",
@@ -475,7 +479,27 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.6"
-    }
+    },
+    "friendly_name": "Testing index",
+    "exclude_from_index": false,
+    "order_index": 1,
+    "category": "tutorial",
+    "tags": [
+      "featured"
+    ],
+    "task": "Regression",
+    "datasets": [
+      "NYC Taxi"
+    ],
+    "compute": [
+      "local"
+    ],
+    "deployment": [
+      "None"
+    ],
+    "framework": [
+      "Azure ML AutoML"
+    ],   
   },
   "nbformat": 4,
   "nbformat_minor": 2

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

@@ -0,0 +1,8 @@
+name: auto-ml-classification
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml

how-to-use-azureml/automated-machine-learning/dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb

@@ -128,7 +128,7 @@
         "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
         "# and convert column types manually.\n",
         "example_data = 'https://dprepdata.blob.core.windows.net/demo/crime0-random.csv'\n",
-        "dflow = dprep.auto_read_file(example_data).skip(1)  # Remove the header row.\n",
+        "dflow = dprep.read_csv(example_data, infer_column_types=True)\n",
         "dflow.get_profile()"
       ]
     },
@@ -241,6 +241,7 @@
       "source": [
         "from azureml.core.runconfig import RunConfiguration\n",
         "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "import pkg_resources\n",
         "\n",
         "# create a new RunConfig object\n",
         "conda_run_config = RunConfiguration(framework=\"python\")\n",
@@ -250,7 +251,9 @@
         "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]'], conda_packages=['numpy','py-xgboost<=0.80'])\n",
+        "dprep_dependency = 'azureml-dataprep==' + pkg_resources.get_distribution(\"azureml-dataprep\").version\n",
+        "\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', dprep_dependency], conda_packages=['numpy','py-xgboost<=0.80'])\n",
         "conda_run_config.environment.python.conda_dependencies = cd"
       ]
     },

how-to-use-azureml/automated-machine-learning/dataprep-remote-execution/auto-ml-dataprep-remote-execution.yml

@@ -0,0 +1,8 @@
+name: auto-ml-dataprep-remote-execution
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml

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

@@ -1,5 +1,12 @@
 {
   "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/dataprep/auto-ml-dataprep.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -9,13 +16,6 @@
         "Licensed under the MIT License."
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/dataprep/auto-ml-dataprep.png)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},

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

@@ -0,0 +1,8 @@
+name: auto-ml-dataprep
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml

how-to-use-azureml/automated-machine-learning/exploring-previous-runs/auto-ml-exploring-previous-runs.ipynb

@@ -197,12 +197,12 @@
         "display(HTML('<h3>Iterations</h3>'))\n",
         "RunDetails(ml_run).show() \n",
         "\n",
-        "children = list(ml_run.get_children())\n",
+        "all_metrics = ml_run.get_metrics(recursive=True)\n",
         "metricslist = {}\n",
-        "for run in children:\n",
-        "    properties = run.get_properties()\n",
-        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
-        "    metricslist[int(properties['iteration'])] = metrics\n",
+        "for run_id, metrics in all_metrics.items():\n",
+        "    iteration = int(run_id.split('_')[-1])\n",
+        "    float_metrics = {k: v for k, v in metrics.items() if isinstance(v, float)}\n",
+        "    metricslist[iteration] = float_metrics\n",
         "\n",
         "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
         "display(HTML('<h3>Metrics</h3>'))\n",

how-to-use-azureml/automated-machine-learning/exploring-previous-runs/auto-ml-exploring-previous-runs.yml

@@ -0,0 +1,8 @@
+name: auto-ml-exploring-previous-runs
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml

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

@@ -36,19 +36,17 @@
       "metadata": {},
       "source": [
         "## Introduction\n",
-        "In this example, we show how AutoML can be used for bike share forecasting.\n",
+        "This notebook demonstrates demand forecasting for a bike-sharing service using AutoML.\n",
         "\n",
-        "The purpose is to demonstrate how to take advantage of the built-in holiday featurization, access the feature names, and further demonstrate how to work with the `forecast` function. Please also look at the additional forecasting notebooks, which document lagging, rolling windows, forecast quantiles, other ways to use the forecast function, and forecaster deployment.\n",
+        "AutoML highlights here include built-in holiday featurization, accessing engineered feature names, and working with the `forecast` function. Please also look at the additional forecasting notebooks, which document lagging, rolling windows, forecast quantiles, other ways to use the forecast function, and forecaster deployment.\n",
         "\n",
         "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
         "\n",
-        "In this notebook you would see\n",
+        "Notebook synopsis:\n",
         "1. Creating an Experiment in an existing Workspace\n",
-        "2. Instantiating AutoMLConfig with new task type \"forecasting\" for timeseries data training, and other timeseries related settings: for this dataset we use the basic one: \"time_column_name\" \n",
-        "3. Training the Model using local compute\n",
-        "4. Exploring the results\n",
-        "5. Viewing the engineered names for featurized data and featurization summary for all raw features\n",
-        "6. Testing the fitted model"
+        "2. Configuration and local run of AutoML for a time-series model with lag and holiday features \n",
+        "3. Viewing the engineered names for featurized data and featurization summary for all raw features\n",
+        "4. Evaluating the fitted model using a rolling test "
       ]
     },
     {
@@ -69,6 +67,9 @@
         "import numpy as np\n",
         "import logging\n",
         "import warnings\n",
+        "\n",
+        "from pandas.tseries.frequencies import to_offset\n",
+        "\n",
         "# Squash warning messages for cleaner output in the notebook\n",
         "warnings.showwarning = lambda *args, **kwargs: None\n",
         "\n",
@@ -83,7 +84,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
+        "As part of the setup you have already created a <b>Workspace</b>. To run AutoML, you also need to create an <b>Experiment</b>. An Experiment corresponds to a prediction problem you are trying to solve, while a Run corresponds to a specific approach to the problem."
       ]
     },
     {
@@ -128,14 +129,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "data = pd.read_csv('bike-no.csv', parse_dates=['date'])"
+        "data = pd.read_csv('bike-no.csv', parse_dates=['date'])\n",
+        "data.head()"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Let's set up what we know abou the dataset. \n",
+        "Let's set up what we know about the dataset. \n",
         "\n",
         "**Target column** is what we want to forecast.\n",
         "\n",
@@ -193,8 +195,7 @@
       "source": [
         "### Setting forecaster maximum horizon \n",
         "\n",
-        "Assuming your test data forms a full and regular time series(regular time intervals and no holes), \n",
-        "the maximum horizon you will need to forecast is the length of the longest grain in your test set."
+        "The forecast horizon is the number of periods into the future that the model should predict. Here, we set the horizon to 14 periods (i.e. 14 days). Notice that this is much shorter than the number of days in the test set; we will need to use a rolling test to evaluate the performance on the whole test set. For more discussion of forecast horizons and guiding principles for setting them, please see the [energy demand notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand).  "
       ]
     },
     {
@@ -203,10 +204,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "if len(grain_column_names) == 0:\n",
-        "    max_horizon = len(X_test)\n",
-        "else:\n",
-        "    max_horizon = X_test.groupby(grain_column_names)[time_column_name].count().max()"
+        "max_horizon = 14"
       ]
     },
     {
@@ -236,26 +234,25 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "time_column_name = 'date'\n",
         "automl_settings = {\n",
-        "    \"time_column_name\": time_column_name,\n",
-        "    # these columns are a breakdown of the total and therefore a leak\n",
-        "    \"drop_column_names\": ['casual', 'registered'],\n",
+        "    'time_column_name': time_column_name,\n",
+        "    'max_horizon': max_horizon,\n",
         "    # knowing the country/region allows Automated ML to bring in holidays\n",
-        "    \"country_or_region\" : 'US',\n",
-        "    \"max_horizon\" : max_horizon,\n",
-        "    \"target_lags\": 1    \n",
+        "    'country_or_region': 'US',\n",
+        "    'target_lags': 1,\n",
+        "    # these columns are a breakdown of the total and therefore a leak\n",
+        "    'drop_column_names': ['casual', 'registered']\n",
         "}\n",
         "\n",
-        "automl_config = AutoMLConfig(task = 'forecasting',                             \n",
+        "automl_config = AutoMLConfig(task='forecasting',                             \n",
         "                             primary_metric='normalized_root_mean_squared_error',\n",
-        "                             iterations = 10,\n",
-        "                             iteration_timeout_minutes = 5,\n",
-        "                             X = X_train,\n",
-        "                             y = y_train,\n",
-        "                             n_cross_validations = 3,                             \n",
+        "                             iterations=10,\n",
+        "                             iteration_timeout_minutes=5,\n",
+        "                             X=X_train,\n",
+        "                             y=y_train,\n",
+        "                             n_cross_validations=3,                             \n",
         "                             path=project_folder,\n",
-        "                             verbosity = logging.INFO,\n",
+        "                             verbosity=logging.INFO,\n",
         "                            **automl_settings)"
       ]
     },
@@ -263,7 +260,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "We will now run the experiment, starting with 10 iterations of model search. Experiment can be continued for more iterations if the results are not yet good. You will see the currently running iterations printing to the console."
+        "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. You will see the currently running iterations printing to the console."
       ]
     },
     {
@@ -348,18 +345,26 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "fitted_model.named_steps['timeseriestransformer'].get_featurization_summary()"
+        "# Get the featurization summary as a list of JSON\n",
+        "featurization_summary = fitted_model.named_steps['timeseriestransformer'].get_featurization_summary()\n",
+        "# View the featurization summary as a pandas dataframe\n",
+        "pd.DataFrame.from_records(featurization_summary)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Test the Best Fitted Model\n",
+        "## Evaluate"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We now use the best fitted model from the AutoML Run to make forecasts for the test set.  \n",
         "\n",
-        "Predict on training and test set, and calculate residual values.\n",
-        "\n",
-        "We always score on the original dataset whose schema matches the scheme of the training dataset."
+        "We always score on the original dataset whose schema matches the training set schema."
       ]
     },
     {
@@ -371,21 +376,12 @@
         "X_test.head()"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "y_query = y_test.copy().astype(np.float)\n",
-        "y_query.fill(np.NaN)\n",
-        "y_fcst, X_trans = fitted_model.forecast(X_test, y_query)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
+        "We now define some functions for aligning output to input and for producing rolling forecasts over the full test set. As previously stated, the forecast horizon of 14 days is shorter than the length of the test set - which is about 120 days. To get predictions over the full test set, we iterate over the test set, making forecasts 14 days at a time and combining the results. We also make sure that each 14-day forecast uses up-to-date actuals - the current context - to construct lag features. \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."
       ]
     },
@@ -395,7 +391,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "def align_outputs(y_predicted, X_trans, X_test, y_test, predicted_column_name = 'predicted'):\n",
+        "def align_outputs(y_predicted, X_trans, X_test, y_test, predicted_column_name='predicted',\n",
+        "                  horizon_colname='horizon_origin'):\n",
         "    \"\"\"\n",
         "    Demonstrates how to get the output aligned to the inputs\n",
         "    using pandas indexes. Helps understand what happened if\n",
@@ -407,7 +404,8 @@
         "    * model was asked to predict past max_horizon -> increase max horizon\n",
         "    * data at start of X_test was needed for lags -> provide previous periods\n",
         "    \"\"\"\n",
-        "    df_fcst = pd.DataFrame({predicted_column_name : y_predicted})\n",
+        "    df_fcst = pd.DataFrame({predicted_column_name : y_predicted,\n",
+        "                            horizon_colname: X_trans[horizon_colname]})\n",
         "    # y and X outputs are aligned by forecast() function contract\n",
         "    df_fcst.index = X_trans.index\n",
         "    \n",
@@ -426,7 +424,49 @@
         "    clean = together[together[[target_column_name, predicted_column_name]].notnull().all(axis=1)]\n",
         "    return(clean)\n",
         "\n",
-        "df_all = align_outputs(y_fcst, X_trans, X_test, y_test)\n"
+        "def do_rolling_forecast(fitted_model, X_test, y_test, max_horizon, freq='D'):\n",
+        "    \"\"\"\n",
+        "    Produce forecasts on a rolling origin over the given test set.\n",
+        "    \n",
+        "    Each iteration makes a forecast for the next 'max_horizon' periods \n",
+        "    with respect to the current origin, then advances the origin by the horizon time duration. \n",
+        "    The prediction context for each forecast is set so that the forecaster uses \n",
+        "    the actual target values prior to the current origin time for constructing lag features.\n",
+        "    \n",
+        "    This function returns a concatenated DataFrame of rolling forecasts.\n",
+        "     \"\"\"\n",
+        "    df_list = []\n",
+        "    origin_time = X_test[time_column_name].min()\n",
+        "    while origin_time <= X_test[time_column_name].max():\n",
+        "        # Set the horizon time - end date of the forecast\n",
+        "        horizon_time = origin_time + max_horizon * to_offset(freq)\n",
+        "        \n",
+        "        # Extract test data from an expanding window up-to the horizon \n",
+        "        expand_wind = (X_test[time_column_name] < horizon_time)\n",
+        "        X_test_expand = X_test[expand_wind]\n",
+        "        y_query_expand = np.zeros(len(X_test_expand)).astype(np.float)\n",
+        "        y_query_expand.fill(np.NaN)\n",
+        "        \n",
+        "        if origin_time != X_test[time_column_name].min():\n",
+        "            # Set the context by including actuals up-to the origin time\n",
+        "            test_context_expand_wind = (X_test[time_column_name] < origin_time)\n",
+        "            context_expand_wind = (X_test_expand[time_column_name] < origin_time)\n",
+        "            y_query_expand[context_expand_wind] = y_test[test_context_expand_wind]\n",
+        "        \n",
+        "        # Make a forecast out to the maximum horizon\n",
+        "        y_fcst, X_trans = fitted_model.forecast(X_test_expand, y_query_expand)\n",
+        "        \n",
+        "        # Align forecast with test set for dates within the current rolling window \n",
+        "        trans_tindex = X_trans.index.get_level_values(time_column_name)\n",
+        "        trans_roll_wind = (trans_tindex >= origin_time) & (trans_tindex < horizon_time)\n",
+        "        test_roll_wind = expand_wind & (X_test[time_column_name] >= origin_time)\n",
+        "        df_list.append(align_outputs(y_fcst[trans_roll_wind], X_trans[trans_roll_wind],\n",
+        "                                     X_test[test_roll_wind], y_test[test_roll_wind]))\n",
+        "        \n",
+        "        # Advance the origin time\n",
+        "        origin_time = horizon_time\n",
+        "    \n",
+        "    return pd.concat(df_list, ignore_index=True)"
       ]
     },
     {
@@ -435,6 +475,30 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "df_all = do_rolling_forecast(fitted_model, X_test, y_test, max_horizon)\n",
+        "df_all"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We now calculate some error metrics for the forecasts and vizualize the predictions vs. the actuals."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def APE(actual, pred):\n",
+        "    \"\"\"\n",
+        "    Calculate absolute percentage error.\n",
+        "    Returns a vector of APE values with same length as actual/pred.\n",
+        "    \"\"\"\n",
+        "    return 100*np.abs((actual - pred)/actual)\n",
+        "\n",
         "def MAPE(actual, pred):\n",
         "    \"\"\"\n",
         "    Calculate mean absolute percentage error.\n",
@@ -444,8 +508,7 @@
         "    not_zero = ~np.isclose(actual, 0.0)\n",
         "    actual_safe = actual[not_na & not_zero]\n",
         "    pred_safe = pred[not_na & not_zero]\n",
-        "    APE = 100*np.abs((actual_safe - pred_safe)/actual_safe)\n",
-        "    return np.mean(APE)"
+        "    return np.mean(APE(actual_safe, pred_safe))"
       ]
     },
     {
@@ -462,18 +525,63 @@
         "print('MAPE: %.2f' % MAPE(df_all[target_column_name], df_all['predicted']))\n",
         "\n",
         "# Plot outputs\n",
-        "%matplotlib notebook\n",
+        "%matplotlib inline\n",
         "test_pred = plt.scatter(df_all[target_column_name], df_all['predicted'], color='b')\n",
         "test_test = plt.scatter(y_test, y_test, color='g')\n",
         "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
         "plt.show()"
       ]
+    },
+    {
+      "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:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "df_all.groupby('horizon_origin').apply(\n",
+        "    lambda df: pd.Series({'MAPE': MAPE(df[target_column_name], df['predicted']),\n",
+        "                          'RMSE': np.sqrt(mean_squared_error(df[target_column_name], df['predicted'])),\n",
+        "                          'MAE': mean_absolute_error(df[target_column_name], df['predicted'])}))"
+      ]
+    },
+    {
+      "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."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "df_all_APE = df_all.assign(APE=APE(df_all[target_column_name], df_all['predicted']))\n",
+        "APEs = [df_all_APE[df_all['horizon_origin'] == h].APE.values for h in range(1, max_horizon + 1)]\n",
+        "\n",
+        "%matplotlib inline\n",
+        "plt.boxplot(APEs)\n",
+        "plt.yscale('log')\n",
+        "plt.xlabel('horizon')\n",
+        "plt.ylabel('APE (%)')\n",
+        "plt.title('Absolute Percentage Errors by Forecast Horizon')\n",
+        "\n",
+        "plt.show()"
+      ]
     }
   ],
   "metadata": {
     "authors": [
       {
-        "name": "xiaga@microsoft.com, tosingli@microsoft.com"
+        "name": "erwright"
       }
     ],
     "kernelspec": {
@@ -491,7 +599,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.6.8"
     }
   },
   "nbformat": 4,

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

@@ -0,0 +1,9 @@
+name: auto-ml-forecasting-bike-share
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml
+  - statsmodels

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

@@ -35,17 +35,16 @@
       "metadata": {},
       "source": [
         "## Introduction\n",
-        "In this example, we show how AutoML can be used for energy demand forecasting.\n",
+        "In this example, we show how AutoML can be used to forecast a single time-series in the energy demand application area. \n",
         "\n",
         "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
         "\n",
-        "In this notebook you would see\n",
+        "Notebook synopsis:\n",
         "1. Creating an Experiment in an existing Workspace\n",
-        "2. Instantiating AutoMLConfig with new task type \"forecasting\" for timeseries data training, and other timeseries related settings: for this dataset we use the basic one: \"time_column_name\" \n",
-        "3. Training the Model using local compute\n",
-        "4. Exploring the results\n",
-        "5. Viewing the engineered names for featurized data and featurization summary for all raw features\n",
-        "6. Testing the fitted model"
+        "2. Configuration and local run of AutoML for a simple time-series model\n",
+        "3. View engineered features and prediction results\n",
+        "4. Configuration and local run of AutoML for a time-series model with lag and rolling window features\n",
+        "5. Estimate feature importance"
       ]
     },
     {
@@ -65,6 +64,10 @@
         "import pandas as pd\n",
         "import numpy as np\n",
         "import logging\n",
+        "import warnings\n",
+        "\n",
+        "# Squash warning messages for cleaner output in the notebook\n",
+        "warnings.showwarning = lambda *args, **kwargs: None\n",
         "\n",
         "from azureml.core.workspace import Workspace\n",
         "from azureml.core.experiment import Experiment\n",
@@ -77,7 +80,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
+        "As part of the setup you have already created a <b>Workspace</b>. To run AutoML, you also need to create an <b>Experiment</b>. An Experiment corresponds to a prediction problem you are trying to solve, while a Run corresponds to a specific approach to the problem."
       ]
     },
     {
@@ -113,7 +116,7 @@
       "metadata": {},
       "source": [
         "## Data\n",
-        "Read energy demanding data from file, and preview data."
+        "We will use energy consumption data from New York City for model training. The data is stored in a tabular format and includes energy demand and basic weather data at an hourly frequency. Pandas CSV reader is used to read the file into memory. Special attention is given to the \"timeStamp\" column in the data since it contains text which should be parsed as datetime-type objects. "
       ]
     },
     {
@@ -126,13 +129,20 @@
         "data.head()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We must now define the schema of this dataset. Every time-series must have a time column and a target. The target quantity is what will be eventually forecasted by a trained model. In this case, the target is the \"demand\" column. The other columns, \"temp\" and \"precip,\" are implicitly designated as features."
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {},
       "outputs": [],
       "source": [
-        "# let's take note of what columns means what in the data\n",
+        "# Dataset schema\n",
         "time_column_name = 'timeStamp'\n",
         "target_column_name = 'demand'"
       ]
@@ -141,7 +151,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Split the data into train and test sets\n"
+        "### Forecast Horizon\n",
+        "\n",
+        "In addition to the data schema, we must also specify the forecast horizon. 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. Choosing a forecast horizon is application specific, but a rule-of-thumb is that **the horizon should be the time-frame where you need actionable decisions based on the forecast.** The horizon usually has a strong relationship with the frequency of the time-series data, that is, the sampling interval of the target quantity and the features. For instance, the NYC energy demand data has an hourly frequency. A decision that requires a demand forecast to the hour is unlikely to be made weeks or months in advance, particularly if we expect weather to be a strong determinant of demand. We may have fairly accurate meteorological forecasts of the hourly temperature and precipitation on a the time-scale of a day or two, however.\n",
+        "\n",
+        "Given the above discussion, we generally recommend that users set forecast horizons to less than 100 time periods (i.e. less than 100 hours in the NYC energy example). Furthermore, **AutoML's memory use and computation time increase in proportion to the length of the horizon**, so the user should consider carefully how they set this value. If a long horizon forecast really is necessary, it may be good practice to aggregate the series to a coarser time scale.  \n",
+        "\n",
+        "\n",
+        "Forecast horizons in AutoML are given as integer multiples of the time-series frequency. In this example, we set the horizon to 48 hours."
       ]
     },
     {
@@ -150,8 +167,32 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "X_train = data[data[time_column_name] < '2017-02-01']\n",
-        "X_test = data[data[time_column_name] >= '2017-02-01']\n",
+        "max_horizon = 48"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Split the data into train and test sets\n",
+        "We now split the data into a train and a test set so that we may evaluate model performance. We note that the tail of the dataset contains a large number of NA values in the target column, so we designate the test set as the 48 hour window ending on the latest date of known energy demand. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Find time point to split on\n",
+        "latest_known_time = data[~pd.isnull(data[target_column_name])][time_column_name].max()\n",
+        "split_time = latest_known_time - pd.Timedelta(hours=max_horizon)\n",
+        "\n",
+        "# Split into train/test sets\n",
+        "X_train = data[data[time_column_name] <= split_time]\n",
+        "X_test = data[(data[time_column_name] > split_time) & (data[time_column_name] <= latest_known_time)]\n",
+        "\n",
+        "# Move the target values into their own arrays \n",
         "y_train = X_train.pop(target_column_name).values\n",
         "y_test = X_test.pop(target_column_name).values"
       ]
@@ -162,7 +203,7 @@
       "source": [
         "## Train\n",
         "\n",
-        "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
+        "We now instantiate an AutoMLConfig object. This config defines the settings and data used to run the experiment. For forecasting tasks, we must provide extra configuration related to the time-series data schema and forecasting context. Here, only the name of the time column and the maximum forecast horizon are needed. Other settings are described below:\n",
         "\n",
         "|Property|Description|\n",
         "|-|-|\n",
@@ -172,7 +213,7 @@
         "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
         "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
         "|**y**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
-        "|**n_cross_validations**|Number of cross validation splits.|\n",
+        "|**n_cross_validations**|Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way.|\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. "
       ]
     },
@@ -182,22 +223,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "automl_settings = {\n",
-        "    \"time_column_name\": time_column_name    \n",
+        "time_series_settings = {\n",
+        "    'time_column_name': time_column_name,\n",
+        "    'max_horizon': max_horizon\n",
         "}\n",
         "\n",
-        "\n",
-        "automl_config = AutoMLConfig(task = 'forecasting',\n",
-        "                             debug_log = 'automl_nyc_energy_errors.log',\n",
+        "automl_config = AutoMLConfig(task='forecasting',\n",
+        "                             debug_log='automl_nyc_energy_errors.log',\n",
         "                             primary_metric='normalized_root_mean_squared_error',\n",
-        "                             iterations = 10,\n",
-        "                             iteration_timeout_minutes = 5,\n",
-        "                             X = X_train,\n",
-        "                             y = y_train,\n",
-        "                             n_cross_validations = 3,\n",
+        "                             iterations=10,\n",
+        "                             iteration_timeout_minutes=5,\n",
+        "                             X=X_train,\n",
+        "                             y=y_train,\n",
+        "                             n_cross_validations=3,\n",
         "                             path=project_folder,\n",
         "                             verbosity = logging.INFO,\n",
-        "                            **automl_settings)"
+        "                             **time_series_settings)"
       ]
     },
     {
@@ -354,7 +395,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Calculate accuracy metrics\n"
+        "### Calculate accuracy metrics\n",
+        "Finally, we calculate some accuracy metrics for the forecast and plot the predictions vs. the actuals over the time range in the test set."
       ]
     },
     {
@@ -390,10 +432,13 @@
         "print('MAPE: %.2f' % MAPE(df_all[target_column_name], df_all['predicted']))\n",
         "\n",
         "# Plot outputs\n",
-        "%matplotlib notebook\n",
-        "test_pred = plt.scatter(df_all[target_column_name], df_all['predicted'], color='b')\n",
-        "test_test = plt.scatter(y_test, y_test, color='g')\n",
-        "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
+        "%matplotlib inline\n",
+        "pred, = plt.plot(df_all[time_column_name], df_all['predicted'], color='b')\n",
+        "actual, = plt.plot(df_all[time_column_name], df_all[target_column_name], color='g')\n",
+        "plt.xticks(fontsize=8)\n",
+        "plt.legend((pred, actual), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
+        "plt.title('Prediction vs. Actual Time-Series')\n",
+        "\n",
         "plt.show()"
       ]
     },
@@ -408,16 +453,16 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Using lags and rolling window features to improve the forecast"
+        "### Using lags and rolling window features"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "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.\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.\n",
         "\n",
-        "Now that we configured target lags, that is the previous values of the target variables, and the prediction is no longer horizon-less. We therefore must 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."
+        "Now that we configured target lags, that is the previous values of the target variables, and 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."
       ]
     },
     {
@@ -426,27 +471,32 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "automl_settings_lags = {\n",
+        "time_series_settings_with_lags = {\n",
         "    'time_column_name': time_column_name,\n",
-        "    'target_lags': 1,\n",
-        "    'target_rolling_window_size': 5,\n",
-        "    # you MUST set the max_horizon when using lags and rolling windows\n",
-        "    # it is optional when looking-back features are not used \n",
-        "    'max_horizon': len(y_test), # only one grain\n",
+        "    'max_horizon': max_horizon,\n",
+        "    'target_lags': 12,\n",
+        "    'target_rolling_window_size': 4\n",
         "}\n",
         "\n",
-        "\n",
-        "automl_config_lags = AutoMLConfig(task = 'forecasting',\n",
-        "                             debug_log = 'automl_nyc_energy_errors.log',\n",
-        "                             primary_metric='normalized_root_mean_squared_error',\n",
-        "                             iterations = 10,\n",
-        "                             iteration_timeout_minutes = 5,\n",
-        "                             X = X_train,\n",
-        "                             y = y_train,\n",
-        "                             n_cross_validations = 3,\n",
-        "                             path=project_folder,\n",
-        "                             verbosity = logging.INFO,\n",
-        "                            **automl_settings_lags)"
+        "automl_config_lags = AutoMLConfig(task='forecasting',\n",
+        "                                  debug_log='automl_nyc_energy_errors.log',\n",
+        "                                  primary_metric='normalized_root_mean_squared_error',\n",
+        "                                  blacklist_models=['ElasticNet'],\n",
+        "                                  iterations=10,\n",
+        "                                  iteration_timeout_minutes=10,\n",
+        "                                  X=X_train,\n",
+        "                                  y=y_train,\n",
+        "                                  n_cross_validations=3,\n",
+        "                                  path=project_folder,\n",
+        "                                  verbosity=logging.INFO,\n",
+        "                                  **time_series_settings_with_lags)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We now start a new local run, this time with lag and rolling window featurization. AutoML applies featurizations in the setup stage, prior to iterating over ML models. The full training set is featurized first, followed by featurization of each of the CV splits. Lag and rolling window features introduce additional complexity, so the run will take longer than in the previous example that lacked these featurizations."
       ]
     },
     {
@@ -493,10 +543,11 @@
         "print('MAPE: %.2f' % MAPE(df_lags[target_column_name], df_lags['predicted']))\n",
         "\n",
         "# Plot outputs\n",
-        "%matplotlib notebook\n",
-        "test_pred = plt.scatter(df_lags[target_column_name], df_lags['predicted'], color='b')\n",
-        "test_test = plt.scatter(y_test, y_test, color='g')\n",
-        "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
+        "%matplotlib inline\n",
+        "pred, = plt.plot(df_lags[time_column_name], df_lags['predicted'], color='b')\n",
+        "actual, = plt.plot(df_lags[time_column_name], df_lags[target_column_name], color='g')\n",
+        "plt.xticks(fontsize=8)\n",
+        "plt.legend((pred, actual), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
         "plt.show()"
       ]
     },
@@ -516,8 +567,8 @@
         "from azureml.train.automl.automlexplainer import explain_model\n",
         "\n",
         "# feature names are everything in the transformed data except the target\n",
-        "features = X_trans.columns[:-1]\n",
-        "expl = explain_model(fitted_model, X_train, X_test, features = features, best_run=best_run_lags, y_train = y_train)\n",
+        "features = X_trans_lags.columns[:-1]\n",
+        "expl = explain_model(fitted_model_lags, X_train.copy(), X_test.copy(), features=features, best_run=best_run_lags, y_train=y_train)\n",
         "# unpack the tuple\n",
         "shap_values, expected_values, feat_overall_imp, feat_names, per_class_summary, per_class_imp = expl\n",
         "best_run_lags"
@@ -536,7 +587,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "xiaga, tosingli"
+        "name": "erwright"
       }
     ],
     "kernelspec": {
@@ -554,7 +605,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.6.8"
     }
   },
   "nbformat": 4,

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

@@ -0,0 +1,10 @@
+name: auto-ml-forecasting-energy-demand
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml
+  - statsmodels
+  - azureml-explain-model

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

@@ -37,16 +37,10 @@
       "metadata": {},
       "source": [
         "## Introduction\n",
-        "In this example, we use AutoML to find and tune a time-series forecasting model.\n",
+        "In this example, we use AutoML to train, select, and operationalize a time-series forecasting model for multiple time-series.\n",
         "\n",
         "Make sure you have executed the [configuration notebook](../../../configuration.ipynb) before running this notebook.\n",
         "\n",
-        "In this notebook, you will:\n",
-        "1. Create an Experiment in an existing Workspace\n",
-        "2. Instantiate an AutoMLConfig \n",
-        "3. Find and train a forecasting model using local compute\n",
-        "4. Evaluate the performance of the model\n",
-        "\n",
         "The examples in the follow code samples use the University of Chicago's Dominick's Finer Foods dataset to forecast orange juice sales. Dominick's was a grocery chain in the Chicago metropolitan area."
       ]
     },
@@ -67,6 +61,10 @@
         "import pandas as pd\n",
         "import numpy as np\n",
         "import logging\n",
+        "import warnings\n",
+        "\n",
+        "# Squash warning messages for cleaner output in the notebook\n",
+        "warnings.showwarning = lambda *args, **kwargs: None\n",
         "\n",
         "from azureml.core.workspace import Workspace\n",
         "from azureml.core.experiment import Experiment\n",
@@ -78,7 +76,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "As part of the setup you have already created a <b>Workspace</b>. To run AutoML, you also need to create an <b>Experiment</b>. An Experiment is a named object in a Workspace which represents a predictive task, the output of which is a trained model and a set of evaluation metrics for the model. "
+        "As part of the setup you have already created a <b>Workspace</b>. To run AutoML, you also need to create an <b>Experiment</b>. An Experiment corresponds to a prediction problem you are trying to solve, while a Run corresponds to a specific approach to the problem. "
       ]
     },
     {
@@ -232,7 +230,7 @@
         "\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",
         "\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.   \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 *X_valid* and *y_valid* parameters of AutoMLConfig.\n",
         "\n",
@@ -265,7 +263,7 @@
         "    'time_column_name': time_column_name,\n",
         "    'grain_column_names': grain_column_names,\n",
         "    'drop_column_names': ['logQuantity'],\n",
-        "    'max_horizon': n_test_periods # optional\n",
+        "    'max_horizon': n_test_periods\n",
         "}\n",
         "\n",
         "automl_config = AutoMLConfig(task='forecasting',\n",
@@ -274,7 +272,7 @@
         "                             iterations=10,\n",
         "                             X=X_train,\n",
         "                             y=y_train,\n",
-        "                             n_cross_validations=5,\n",
+        "                             n_cross_validations=3,\n",
         "                             enable_ensembling=False,\n",
         "                             path=project_folder,\n",
         "                             verbosity=logging.INFO,\n",
@@ -320,7 +318,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Predict\n",
+        "# Forecasting\n",
+        "\n",
         "Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. First, we remove the target values from the test set:"
       ]
     },
@@ -464,7 +463,7 @@
         "# Plot outputs\n",
         "import matplotlib.pyplot as plt\n",
         "\n",
-        "%matplotlib notebook\n",
+        "%matplotlib inline\n",
         "test_pred = plt.scatter(df_all[target_column_name], df_all['predicted'], color='b')\n",
         "test_test = plt.scatter(y_test, y_test, color='g')\n",
         "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
@@ -830,7 +829,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "erwright, tosingli"
+        "name": "erwright"
       }
     ],
     "kernelspec": {
@@ -848,7 +847,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.6.8"
     }
   },
   "nbformat": 4,

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

@@ -0,0 +1,9 @@
+name: auto-ml-forecasting-orange-juice-sales
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml
+  - statsmodels

how-to-use-azureml/automated-machine-learning/missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.ipynb

@@ -360,7 +360,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "fitted_model.named_steps['datatransformer'].get_featurization_summary()"
+        "# 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)"
       ]
     },
     {

how-to-use-azureml/automated-machine-learning/missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.yml

@@ -0,0 +1,8 @@
+name: auto-ml-missing-data-blacklist-early-termination
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml

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

@@ -0,0 +1,9 @@
+name: auto-ml-model-explanation
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml
+  - azureml-explain-model

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

@@ -0,0 +1,8 @@
+name: auto-ml-regression-concrete-strength
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml

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

@@ -0,0 +1,8 @@
+name: auto-ml-regression-hardware-performance
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml

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

@@ -0,0 +1,9 @@
+name: auto-ml-regression
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml
+  - paramiko<2.5.0

how-to-use-azureml/automated-machine-learning/remote-amlcompute-with-onnx/auto-ml-remote-amlcompute-with-onnx.ipynb

@@ -0,0 +1,554 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/remote-amlcompute/auto-ml-remote-amlcompute.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Automated Machine Learning\n",
+        "_**Remote Execution using AmlCompute**_\n",
+        "\n",
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Data](#Data)\n",
+        "1. [Train](#Train)\n",
+        "1. [Results](#Results)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "In this example we use the scikit-learn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) to showcase how you can use AutoML for a simple classification problem.\n",
+        "\n",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
+        "\n",
+        "In this notebook you would see\n",
+        "1. Create an `Experiment` in an existing `Workspace`.\n",
+        "2. Create or Attach existing AmlCompute to a workspace.\n",
+        "3. Configure AutoML using `AutoMLConfig`.\n",
+        "4. Train the model using AmlCompute with ONNX compatible config on.\n",
+        "5. Explore the results and save the ONNX model.\n",
+        "6. Inference with the ONNX model.\n",
+        "\n",
+        "In addition this notebook showcases the following features\n",
+        "- **Parallel** executions for iterations\n",
+        "- **Asynchronous** tracking of progress\n",
+        "- **Cancellation** of individual iterations or the entire run\n",
+        "- Retrieving models for any iteration or logged metric\n",
+        "- Specifying AutoML settings as `**kwargs`"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup\n",
+        "\n",
+        "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."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import logging\n",
+        "import os\n",
+        "import csv\n",
+        "\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import datasets\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "import azureml.core\n",
+        "from azureml.core.experiment import Experiment\n",
+        "from azureml.core.workspace import Workspace\n",
+        "from azureml.train.automl import AutoMLConfig\n",
+        "import azureml.dataprep as dprep"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "\n",
+        "# Choose a name for the run history container in the workspace.\n",
+        "experiment_name = 'automl-remote-amlcompute-with-onnx'\n",
+        "project_folder = './project'\n",
+        "\n",
+        "experiment = Experiment(ws, experiment_name)\n",
+        "\n",
+        "output = {}\n",
+        "output['SDK version'] = azureml.core.VERSION\n",
+        "output['Subscription ID'] = ws.subscription_id\n",
+        "output['Workspace Name'] = ws.name\n",
+        "output['Resource Group'] = ws.resource_group\n",
+        "output['Location'] = ws.location\n",
+        "output['Project Directory'] = project_folder\n",
+        "output['Experiment Name'] = experiment.name\n",
+        "pd.set_option('display.max_colwidth', -1)\n",
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create or Attach existing AmlCompute\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
+        "\n",
+        "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
+        "\n",
+        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import AmlCompute\n",
+        "from azureml.core.compute import ComputeTarget\n",
+        "\n",
+        "# Choose a name for your cluster.\n",
+        "amlcompute_cluster_name = \"cpu-cluster\"\n",
+        "\n",
+        "found = False\n",
+        "# Check if this compute target already exists in the workspace.\n",
+        "cts = ws.compute_targets\n",
+        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
+        "    found = True\n",
+        "    print('Found existing compute target.')\n",
+        "    compute_target = cts[amlcompute_cluster_name]\n",
+        "\n",
+        "if not found:\n",
+        "    print('Creating a new compute target...')\n",
+        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
+        "                                                                #vm_priority = 'lowpriority', # optional\n",
+        "                                                                max_nodes = 6)\n",
+        "\n",
+        "    # Create the cluster.\\n\",\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
+        "\n",
+        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
+        "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
+        "    compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
+        "\n",
+        "     # For a more detailed view of current AmlCompute status, use get_status()."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Data\n",
+        "For remote executions, you need to make the data accessible from the remote compute.\n",
+        "This can be done by uploading the data to DataStore.\n",
+        "In this example, we upload scikit-learn's [load_iris](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "iris = datasets.load_iris()\n",
+        "\n",
+        "if not os.path.isdir('data'):\n",
+        "    os.mkdir('data')\n",
+        "\n",
+        "if not os.path.exists(project_folder):\n",
+        "    os.makedirs(project_folder)\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(iris.data, \n",
+        "                                                    iris.target, \n",
+        "                                                    test_size=0.2, \n",
+        "                                                    random_state=0)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Ensure the x_train and x_test are pandas DataFrame."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Convert the X_train and X_test to pandas DataFrame and set column names,\n",
+        "# This is needed for initializing the input variable names of ONNX model, \n",
+        "# and the prediction with the ONNX model using the inference helper.\n",
+        "X_train = pd.DataFrame(X_train, columns=['c1', 'c2', 'c3', 'c4'])\n",
+        "X_test = pd.DataFrame(X_test, columns=['c1', 'c2', 'c3', 'c4'])\n",
+        "y_train = pd.DataFrame(y_train, columns=['label'])\n",
+        "\n",
+        "X_train.to_csv(\"data/X_train.csv\", index=False)\n",
+        "y_train.to_csv(\"data/y_train.csv\", index=False)\n",
+        "\n",
+        "ds = ws.get_default_datastore()\n",
+        "ds.upload(src_dir='./data', target_path='irisdata', overwrite=True, show_progress=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.runconfig import RunConfiguration\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "import pkg_resources\n",
+        "\n",
+        "# create a new RunConfig object\n",
+        "conda_run_config = RunConfiguration(framework=\"python\")\n",
+        "\n",
+        "# Set compute target to AmlCompute\n",
+        "conda_run_config.target = compute_target\n",
+        "conda_run_config.environment.docker.enabled = True\n",
+        "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
+        "\n",
+        "dprep_dependency = 'azureml-dataprep==' + pkg_resources.get_distribution(\"azureml-dataprep\").version\n",
+        "\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', dprep_dependency], conda_packages=['numpy','py-xgboost<=0.80'])\n",
+        "conda_run_config.environment.python.conda_dependencies = cd"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "###  Dprep reference\n",
+        "\n",
+        "Defined X and y as dprep references, which are passed to automated machine learning in the AutoMLConfig."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X = dprep.read_csv(path=ds.path('irisdata/X_train.csv'), infer_column_types=True)\n",
+        "y = dprep.read_csv(path=ds.path('irisdata/y_train.csv'), infer_column_types=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train\n",
+        "\n",
+        "You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
+        "\n",
+        "**Note:** Set the parameter enable_onnx_compatible_models=True, if you also want to generate the ONNX compatible models. Please note, the forecasting task and TensorFlow models are not ONNX compatible yet.\n",
+        "\n",
+        "**Note:** When using AmlCompute, you can't pass Numpy arrays directly to the fit method.\n",
+        "\n",
+        "|Property|Description|\n",
+        "|-|-|\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",
+        "|**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",
+        "|**max_concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of cores on the DSVM.|\n",
+        "|**enable_onnx_compatible_models**|Enable the ONNX compatible models in the experiment.|"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Set the preprocess=True,  currently the InferenceHelper only supports this mode."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "automl_settings = {\n",
+        "    \"iteration_timeout_minutes\": 10,\n",
+        "    \"iterations\": 10,\n",
+        "    \"n_cross_validations\": 5,\n",
+        "    \"primary_metric\": 'AUC_weighted',\n",
+        "    \"preprocess\": True,\n",
+        "    \"max_concurrent_iterations\": 5,\n",
+        "    \"verbosity\": logging.INFO\n",
+        "}\n",
+        "\n",
+        "automl_config = AutoMLConfig(task = 'classification',\n",
+        "                             debug_log = 'automl_errors.log',\n",
+        "                             path = project_folder,\n",
+        "                             run_configuration=conda_run_config,\n",
+        "                             X = X,\n",
+        "                             y = y,\n",
+        "                             enable_onnx_compatible_models=True, # This will generate ONNX compatible models.\n",
+        "                             **automl_settings\n",
+        "                            )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run.\n",
+        "In this example, we specify `show_output = False` to suppress console output while the run is in progress."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run = experiment.submit(automl_config, show_output = False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Results\n",
+        "\n",
+        "#### Loading executed runs\n",
+        "In case you need to load a previously executed run, enable the cell below and replace the `run_id` value."
+      ]
+    },
+    {
+      "cell_type": "raw",
+      "metadata": {},
+      "source": [
+        "remote_run = AutoMLRun(experiment = experiment, run_id = 'AutoML_5db13491-c92a-4f1d-b622-8ab8d973a058')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Widget for Monitoring Runs\n",
+        "\n",
+        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+        "\n",
+        "You can click on a pipeline to see run properties and output logs.  Logs are also available on the DSVM under `/tmp/azureml_run/{iterationid}/azureml-logs`\n",
+        "\n",
+        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.widgets import RunDetails\n",
+        "RunDetails(remote_run).show() "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Wait until the run finishes.\n",
+        "remote_run.wait_for_completion(show_output = True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Cancelling Runs\n",
+        "\n",
+        "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Cancel the ongoing experiment and stop scheduling new iterations.\n",
+        "# remote_run.cancel()\n",
+        "\n",
+        "# Cancel iteration 1 and move onto iteration 2.\n",
+        "# remote_run.cancel_iteration(1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Retrieve the Best ONNX Model\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*.\n",
+        "\n",
+        "Set the parameter return_onnx_model=True to retrieve the best ONNX model, instead of the Python model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "best_run, onnx_mdl = remote_run.get_output(return_onnx_model=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Save the best ONNX model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.automl.core.onnx_convert import OnnxConverter\n",
+        "onnx_fl_path = \"./best_model.onnx\"\n",
+        "OnnxConverter.save_onnx_model(onnx_mdl, onnx_fl_path)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Predict with the ONNX model, using onnxruntime package"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "import json\n",
+        "from azureml.automl.core.onnx_convert import OnnxConvertConstants\n",
+        "from azureml.train.automl import constants\n",
+        "\n",
+        "if sys.version_info < OnnxConvertConstants.OnnxIncompatiblePythonVersion:\n",
+        "    python_version_compatible = True\n",
+        "else:\n",
+        "    python_version_compatible = False\n",
+        "\n",
+        "try:\n",
+        "    import onnxruntime\n",
+        "    from azureml.automl.core.onnx_convert import OnnxInferenceHelper    \n",
+        "    onnxrt_present = True\n",
+        "except ImportError:\n",
+        "    onnxrt_present = False\n",
+        "\n",
+        "def get_onnx_res(run):\n",
+        "    res_path = 'onnx_resource.json'\n",
+        "    run.download_file(name=constants.MODEL_RESOURCE_PATH_ONNX, output_file_path=res_path)\n",
+        "    with open(res_path) as f:\n",
+        "        onnx_res = json.load(f)\n",
+        "    return onnx_res\n",
+        "\n",
+        "if onnxrt_present and python_version_compatible:    \n",
+        "    mdl_bytes = onnx_mdl.SerializeToString()\n",
+        "    onnx_res = get_onnx_res(best_run)\n",
+        "\n",
+        "    onnxrt_helper = OnnxInferenceHelper(mdl_bytes, onnx_res)\n",
+        "    pred_onnx, pred_prob_onnx = onnxrt_helper.predict(X_test)\n",
+        "\n",
+        "    print(pred_onnx)\n",
+        "    print(pred_prob_onnx)\n",
+        "else:\n",
+        "    if not python_version_compatible:\n",
+        "        print('Please use Python version 3.6 or 3.7 to run the inference helper.')    \n",
+        "    if not onnxrt_present:\n",
+        "        print('Please install the onnxruntime package to do the prediction with ONNX model.')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "savitam"
+      }
+    ],
+    "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": "3.6.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/remote-amlcompute-with-onnx/auto-ml-remote-amlcompute-with-onnx.yml

@@ -0,0 +1,9 @@
+name: auto-ml-remote-amlcompute-with-onnx
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml
+  - onnxruntime

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

@@ -84,7 +84,8 @@
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-        "from azureml.train.automl import AutoMLConfig"
+        "from azureml.train.automl import AutoMLConfig\n",
+        "import azureml.dataprep as dprep"
       ]
     },
     {
@@ -186,18 +187,11 @@
         "if not os.path.exists(project_folder):\n",
         "    os.makedirs(project_folder)\n",
         "    \n",
-        "pd.DataFrame(data_train.data).to_csv(\"data/X_train.tsv\", index=False, header=False, quoting=csv.QUOTE_ALL, sep=\"\\t\")\n",
-        "pd.DataFrame(data_train.target).to_csv(\"data/y_train.tsv\", index=False, header=False, sep=\"\\t\")\n",
+        "pd.DataFrame(data_train.data[100:,:]).to_csv(\"data/X_train.csv\", index=False)\n",
+        "pd.DataFrame(data_train.target[100:]).to_csv(\"data/y_train.csv\", index=False)\n",
         "\n",
         "ds = ws.get_default_datastore()\n",
-        "ds.upload(src_dir='./data', target_path='bai_data', overwrite=True, show_progress=True)\n",
-        "\n",
-        "from azureml.core.runconfig import DataReferenceConfiguration\n",
-        "dr = DataReferenceConfiguration(datastore_name=ds.name, \n",
-        "                   path_on_datastore='bai_data', \n",
-        "                   path_on_compute='/tmp/azureml_runs',\n",
-        "                   mode='download', # download files from datastore to compute target\n",
-        "                   overwrite=False)"
+        "ds.upload(src_dir='./data', target_path='digitsdata', overwrite=True, show_progress=True)"
       ]
     },
     {
@@ -208,6 +202,7 @@
       "source": [
         "from azureml.core.runconfig import RunConfiguration\n",
         "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "import pkg_resources\n",
         "\n",
         "# create a new RunConfig object\n",
         "conda_run_config = RunConfiguration(framework=\"python\")\n",
@@ -217,28 +212,29 @@
         "conda_run_config.environment.docker.enabled = True\n",
         "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
         "\n",
-        "# set the data reference of the run coonfiguration\n",
-        "conda_run_config.data_references = {ds.name: dr}\n",
+        "dprep_dependency = 'azureml-dataprep==' + pkg_resources.get_distribution(\"azureml-dataprep\").version\n",
         "\n",
-        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy','py-xgboost<=0.80'])\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', dprep_dependency], conda_packages=['numpy','py-xgboost<=0.80'])\n",
         "conda_run_config.environment.python.conda_dependencies = cd"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "###  Dprep reference\n",
+        "\n",
+        "Defined X and y as dprep references, which are passed to automated machine learning in the AutoMLConfig."
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {},
       "outputs": [],
       "source": [
-        "%%writefile $project_folder/get_data.py\n",
-        "\n",
-        "import pandas as pd\n",
-        "\n",
-        "def get_data():\n",
-        "    X_train = pd.read_csv(\"/tmp/azureml_runs/bai_data/X_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
-        "    y_train = pd.read_csv(\"/tmp/azureml_runs/bai_data/y_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
-        "\n",
-        "    return { \"X\" : X_train.values, \"y\" : y_train[0].values }\n"
+        "X = dprep.read_csv(path=ds.path('digitsdata/X_train.csv'), infer_column_types=True)\n",
+        "y = dprep.read_csv(path=ds.path('digitsdata/y_train.csv'), infer_column_types=True)"
       ]
     },
     {
@@ -280,7 +276,8 @@
         "                             debug_log = 'automl_errors.log',\n",
         "                             path = project_folder,\n",
         "                             run_configuration=conda_run_config,\n",
-        "                             data_script = project_folder + \"/get_data.py\",\n",
+        "                             X = X,\n",
+        "                             y = y,\n",
         "                             **automl_settings\n",
         "                            )\n"
       ]

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

@@ -0,0 +1,8 @@
+name: auto-ml-remote-amlcompute
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml

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

@@ -0,0 +1,8 @@
+name: auto-ml-sample-weight
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml

how-to-use-azureml/automated-machine-learning/sparse-data-train-test-split/auto-ml-sparse-data-train-test-split.yml

@@ -0,0 +1,8 @@
+name: auto-ml-sparse-data-train-test-split
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml

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

@@ -87,7 +87,7 @@ These instruction setup the integration for SQL Server 2017 on Windows.
    sudo /opt/mssql/mlservices/bin/python/python -m pip install --upgrade sklearn
 ```
 7. Start SQL Server. 
-8. Execute the files aml_model.sql, aml_connection.sql, AutoMLGetMetrics.sql, AutoMLPredict.sql and AutoMLTrain.sql in SQL Server Management Studio. 
+8. Execute the files aml_model.sql, aml_connection.sql, AutoMLGetMetrics.sql, AutoMLPredict.sql, AutoMLForecast.sql and AutoMLTrain.sql in SQL Server Management Studio. 
 9. Create an Azure Machine Learning Workspace.  You can use the instructions at: [https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace)
 10. Create a config.json file file using the subscription id, resource group name and workspace name that you use to create the workspace.  The file is described at: [https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#workspace)
 11. Create an Azure service principal.  You can do this with the commands: 
@@ -109,5 +109,5 @@ First you need to load the sample data in the database.
 
 You can then run the queries in the energy-demand folder:
 * TrainEnergyDemand.sql runs AutoML, trains multiple models on data and selects the best model.
-* PredictEnergyDemand.sql predicts based on the most recent training run.
+* ForecastEnergyDemand.sql forecasts based on the most recent training run.
 * GetMetrics.sql returns all the metrics for each model in the most recent training run.

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

@@ -0,0 +1,23 @@
+-- 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/TrainEnergyDemand.sql

@@ -1,21 +1,25 @@
 -- This shows using the AutoMLTrain stored procedure to create a forecasting model for the nyc_energy dataset.
 
-INSERT INTO dbo.aml_model(RunId, ExperimentName, Model, LogFileText, WorkspaceName)
-EXEC dbo.AutoMLTrain @input_query='
+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,
-	   CASE WHEN timeStamp < ''2017-01-01'' THEN 0 ELSE 1 END AS is_validate_column
+	   temp
 FROM nyc_energy
 WHERE demand IS NOT NULL AND precip IS NOT NULL AND temp IS NOT NULL
-and timeStamp < ''2017-02-01''',
+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',
-@is_validate_column='is_validate_column',
+@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

@@ -1,141 +1,141 @@
 {
- "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"
-   }
+  "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"
+      ]
+    }
   ],
-  "kernelspec": {
-   "display_name": "SQL",
-   "language": "sql",
-   "name": "SQL"
+  "metadata": {
+    "authors": [
+      {
+        "name": "jeffshep"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "sql",
+      "name": "python36"
+    },
+    "language_info": {
+      "name": "sql",
+      "version": ""
+    }
   },
-  "language_info": {
-   "name": "sql",
-   "version": ""
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+  "nbformat": 4,
+  "nbformat_minor": 2
 }

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

@@ -0,0 +1,92 @@
+-- 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/AutoMLTrain.sql

@@ -69,7 +69,10 @@ CREATE OR ALTER PROCEDURE [dbo].[AutoMLTrain]
     @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.
+    @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
 
@@ -151,8 +154,10 @@ if __name__.startswith("sqlindb"):
     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_errors.log"
+    log_file_name = "automl_sqlindb_errors.log"
 	 
     automl_config = AutoMLConfig(task = task, 
                                  debug_log = log_file_name, 
@@ -163,7 +168,6 @@ if __name__.startswith("sqlindb"):
                                  n_cross_validations = n_cross_validations, 
                                  preprocess = preprocess,
                                  verbosity = logging.INFO, 
-                                 enable_ensembling = False,
                                  X = X_train,  
                                  y = y_train, 
                                  path = project_folder,
@@ -211,7 +215,8 @@ if __name__.startswith("sqlindb"):
 				  @tenantid NVARCHAR(255),
 				  @appid NVARCHAR(255),
 				  @password NVARCHAR(255),
-				  @config_file NVARCHAR(255)'
+				  @config_file NVARCHAR(255),
+				  @max_horizon INT'
 	, @label_column = @label_column
 	, @primary_metric = @primary_metric
 	, @iterations = @iterations
@@ -230,5 +235,6 @@ if __name__.startswith("sqlindb"):
 	, @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/subsampling/auto-ml-subsampling-local.yml

@@ -0,0 +1,8 @@
+name: auto-ml-subsampling-local
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml

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

@@ -593,7 +593,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "fitted_model.named_steps['datatransformer'].get_featurization_summary()"
+        "# 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)"
       ]
     },
     {

how-to-use-azureml/azure-databricks/databricks-as-remote-compute-target/aml-pipelines-use-databricks-as-compute-target.ipynb

@@ -13,7 +13,7 @@
       "metadata": {},
       "source": [
         "# Using Databricks as a Compute Target from Azure Machine Learning Pipeline\n",
-        "To use Databricks as a compute target from [Azure Machine Learning Pipeline](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines), a [DatabricksStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step.databricksstep?view=azure-ml-py) is used. This notebook demonstrates the use of DatabricksStep in Azure Machine Learning Pipeline.\n",
+        "To use Databricks as a compute target from [Azure Machine Learning Pipeline](https://aka.ms/pl-concept), a [DatabricksStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step.databricksstep?view=azure-ml-py) is used. This notebook demonstrates the use of DatabricksStep in Azure Machine Learning Pipeline.\n",
         "\n",
         "The notebook will show:\n",
         "1. Running an arbitrary Databricks notebook that the customer has in Databricks workspace\n",
@@ -675,7 +675,7 @@
       "metadata": {},
       "source": [
         "# Next: ADLA as a Compute Target\n",
-        "To use ADLA as a compute target from Azure Machine Learning Pipeline, a AdlaStep is used. This [notebook](./aml-pipelines-use-adla-as-compute-target.ipynb) demonstrates the use of AdlaStep in Azure Machine Learning Pipeline."
+        "To use ADLA as a compute target from Azure Machine Learning Pipeline, a AdlaStep is used. This [notebook](https://aka.ms/pl-adla) demonstrates the use of AdlaStep in Azure Machine Learning Pipeline."
       ]
     },
     {

how-to-use-azureml/data-drift/azure-ml-datadrift.ipynb

@@ -1,709 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Track Data Drift between Training and Inference Data in Production \n",
-    "\n",
-    "With this notebook, you will learn how to enable the DataDrift service to automatically track and determine whether your inference data is drifting from the data your model was initially trained on. The DataDrift service provides metrics and visualizations to help stakeholders identify which specific features cause the concept drift to occur.\n",
-    "\n",
-    "Please email driftfeedback@microsoft.com with any issues. A member from the DataDrift team will respond shortly. \n",
-    "\n",
-    "The DataDrift Public Preview API can be found [here](https://docs.microsoft.com/en-us/python/api/azureml-contrib-datadrift/?view=azure-ml-py). "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/contrib/datadrift/azureml-datadrift.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Prerequisites and Setup"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Install the DataDrift package\n",
-    "\n",
-    "Install the azureml-contrib-datadrift, azureml-contrib-opendatasets and lightgbm packages before running this notebook.\n",
-    "```\n",
-    "pip install azureml-contrib-datadrift\n",
-    "pip install azureml-contrib-datasets\n",
-    "pip install lightgbm\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Import Dependencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "import os\n",
-    "import time\n",
-    "from datetime import datetime, timedelta\n",
-    "\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import requests\n",
-    "from azureml.contrib.datadrift import DataDriftDetector, AlertConfiguration\n",
-    "from azureml.contrib.opendatasets import NoaaIsdWeather\n",
-    "from azureml.core import Dataset, Workspace, Run\n",
-    "from azureml.core.compute import AksCompute, ComputeTarget\n",
-    "from azureml.core.conda_dependencies import CondaDependencies\n",
-    "from azureml.core.experiment import Experiment\n",
-    "from azureml.core.image import ContainerImage\n",
-    "from azureml.core.model import Model\n",
-    "from azureml.core.webservice import Webservice, AksWebservice\n",
-    "from azureml.widgets import RunDetails\n",
-    "from sklearn.externals import joblib\n",
-    "from sklearn.model_selection import train_test_split\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Set up Configuraton and Create Azure ML Workspace\n",
-    "\n",
-    "If you are using an Azure Machine Learning Notebook VM, 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."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Please type in your initials/alias. The prefix is prepended to the names of resources created by this notebook. \n",
-    "prefix = \"dd\"\n",
-    "\n",
-    "# NOTE: Please do not change the model_name, as it's required by the score.py file\n",
-    "model_name = \"driftmodel\"\n",
-    "image_name = \"{}driftimage\".format(prefix)\n",
-    "service_name = \"{}driftservice\".format(prefix)\n",
-    "\n",
-    "# optionally, set email address to receive an email alert for DataDrift\n",
-    "email_address = \"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ws = Workspace.from_config()\n",
-    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generate Train/Testing Data\n",
-    "\n",
-    "For this demo, we will use NOAA weather data from [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/). You may replace this step with your own dataset. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "usaf_list = ['725724', '722149', '723090', '722159', '723910', '720279',\n",
-    "             '725513', '725254', '726430', '720381', '723074', '726682',\n",
-    "             '725486', '727883', '723177', '722075', '723086', '724053',\n",
-    "             '725070', '722073', '726060', '725224', '725260', '724520',\n",
-    "             '720305', '724020', '726510', '725126', '722523', '703333',\n",
-    "             '722249', '722728', '725483', '722972', '724975', '742079',\n",
-    "             '727468', '722193', '725624', '722030', '726380', '720309',\n",
-    "             '722071', '720326', '725415', '724504', '725665', '725424',\n",
-    "             '725066']\n",
-    "\n",
-    "columns = ['usaf', 'wban', 'datetime', 'latitude', 'longitude', 'elevation', 'windAngle', 'windSpeed', 'temperature', 'stationName', 'p_k']\n",
-    "\n",
-    "def enrich_weather_noaa_data(noaa_df):\n",
-    "    hours_in_day = 23\n",
-    "    week_in_year = 52\n",
-    "    \n",
-    "\n",
-    "    noaa_df = noaa_df.assign(hour=noaa_df[\"datetime\"].dt.hour,\n",
-    "                          weekofyear=noaa_df[\"datetime\"].dt.week,\n",
-    "                          sine_weekofyear=noaa_df['datetime'].transform(lambda x: np.sin((2*np.pi*x.dt.week-1)/week_in_year)),\n",
-    "                          cosine_weekofyear=noaa_df['datetime'].transform(lambda x: np.cos((2*np.pi*x.dt.week-1)/week_in_year)),\n",
-    "                          sine_hourofday=noaa_df['datetime'].transform(lambda x: np.sin(2*np.pi*x.dt.hour/hours_in_day)),\n",
-    "                          cosine_hourofday=noaa_df['datetime'].transform(lambda x: np.cos(2*np.pi*x.dt.hour/hours_in_day))\n",
-    "                  )\n",
-    "    \n",
-    "    return noaa_df\n",
-    "\n",
-    "\n",
-    "def add_window_col(input_df):\n",
-    "    shift_interval = pd.Timedelta('-7 days') # your X days interval\n",
-    "    df_shifted = input_df.copy()\n",
-    "    df_shifted.loc[:,'datetime'] = df_shifted['datetime'] - shift_interval\n",
-    "    df_shifted.drop(list(input_df.columns.difference(['datetime', 'usaf', 'wban', 'sine_hourofday', 'temperature'])), axis=1, inplace=True)\n",
-    "\n",
-    "    # merge, keeping only observations where -1 lag is present\n",
-    "    df2 = pd.merge(input_df,\n",
-    "                   df_shifted,\n",
-    "                   on=['datetime', 'usaf', 'wban', 'sine_hourofday'],\n",
-    "                   how='inner',  # use 'left' to keep observations without lags\n",
-    "                   suffixes=['', '-7'])\n",
-    "    return df2\n",
-    "\n",
-    "def get_noaa_data(start_time, end_time, cols, station_list):\n",
-    "    isd = NoaaIsdWeather(start_time, end_time, cols=cols)\n",
-    "    # Read into Pandas data frame.\n",
-    "    noaa_df = isd.to_pandas_dataframe()\n",
-    "    noaa_df = noaa_df.rename(columns={\"stationName\": \"station_name\"})\n",
-    "    \n",
-    "    df_filtered = noaa_df[noaa_df[\"usaf\"].isin(station_list)]\n",
-    "    df_filtered.reset_index(drop=True)\n",
-    "    \n",
-    "    # Enrich with time features\n",
-    "    df_enriched = enrich_weather_noaa_data(df_filtered)\n",
-    "    \n",
-    "    return df_enriched\n",
-    "\n",
-    "def get_featurized_noaa_df(start_time, end_time, cols, station_list):\n",
-    "    df_1 = get_noaa_data(start_time - timedelta(days=7), start_time - timedelta(seconds=1), cols, station_list)\n",
-    "    df_2 = get_noaa_data(start_time, end_time, cols, station_list)\n",
-    "    noaa_df = pd.concat([df_1, df_2])\n",
-    "    \n",
-    "    print(\"Adding window feature\")\n",
-    "    df_window = add_window_col(noaa_df)\n",
-    "    \n",
-    "    cat_columns = df_window.dtypes == object\n",
-    "    cat_columns = cat_columns[cat_columns == True]\n",
-    "    \n",
-    "    print(\"Encoding categorical columns\")\n",
-    "    df_encoded = pd.get_dummies(df_window, columns=cat_columns.keys().tolist())\n",
-    "    \n",
-    "    print(\"Dropping unnecessary columns\")\n",
-    "    df_featurized = df_encoded.drop(['windAngle', 'windSpeed', 'datetime', 'elevation'], axis=1).dropna().drop_duplicates()\n",
-    "    \n",
-    "    return df_featurized"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Train model on Jan 1 - 14, 2009 data\n",
-    "df = get_featurized_noaa_df(datetime(2009, 1, 1), datetime(2009, 1, 14, 23, 59, 59), columns, usaf_list)\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "label = \"temperature\"\n",
-    "x_df = df.drop(label, axis=1)\n",
-    "y_df = df[[label]]\n",
-    "x_train, x_test, y_train, y_test = train_test_split(df, y_df, test_size=0.2, random_state=223)\n",
-    "print(x_train.shape, x_test.shape, y_train.shape, y_test.shape)\n",
-    "\n",
-    "training_dir = 'outputs/training'\n",
-    "training_file = \"training.csv\"\n",
-    "\n",
-    "# Generate training dataframe to register as Training Dataset\n",
-    "os.makedirs(training_dir, exist_ok=True)\n",
-    "training_df = pd.merge(x_train.drop(label, axis=1), y_train, left_index=True, right_index=True)\n",
-    "training_df.to_csv(training_dir + \"/\" + training_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create/Register Training Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset_name = \"dataset\"\n",
-    "name_suffix = datetime.utcnow().strftime(\"%Y-%m-%d-%H-%M-%S\")\n",
-    "snapshot_name = \"snapshot-{}\".format(name_suffix)\n",
-    "\n",
-    "dstore = ws.get_default_datastore()\n",
-    "dstore.upload(training_dir, \"data/training\", show_progress=True)\n",
-    "dpath = dstore.path(\"data/training/training.csv\")\n",
-    "trainingDataset = Dataset.auto_read_files(dpath, include_path=True)\n",
-    "trainingDataset = trainingDataset.register(workspace=ws, name=dataset_name, description=\"dset\", exist_ok=True)\n",
-    "\n",
-    "trainingDataSnapshot = trainingDataset.create_snapshot(snapshot_name=snapshot_name, compute_target=None, create_data_snapshot=True)\n",
-    "datasets = [(Dataset.Scenario.TRAINING, trainingDataSnapshot)]\n",
-    "print(\"dataset registration done.\\n\")\n",
-    "datasets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Train and Save Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import lightgbm as lgb\n",
-    "\n",
-    "train = lgb.Dataset(data=x_train, \n",
-    "                    label=y_train)\n",
-    "\n",
-    "test = lgb.Dataset(data=x_test, \n",
-    "                   label=y_test,\n",
-    "                   reference=train)\n",
-    "\n",
-    "params = {'learning_rate'    : 0.1,\n",
-    "          'boosting'         : 'gbdt',\n",
-    "          'metric'           : 'rmse',\n",
-    "          'feature_fraction' : 1,\n",
-    "          'bagging_fraction' : 1,\n",
-    "          'max_depth': 6,\n",
-    "          'num_leaves'       : 31,\n",
-    "          'objective'        : 'regression',\n",
-    "          'bagging_freq'     : 1,\n",
-    "          \"verbose\": -1,\n",
-    "          'min_data_per_leaf': 100}\n",
-    "\n",
-    "model = lgb.train(params, \n",
-    "                  num_boost_round=500,\n",
-    "                  train_set=train,\n",
-    "                  valid_sets=[train, test],\n",
-    "                  verbose_eval=50,\n",
-    "                  early_stopping_rounds=25)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_file = 'outputs/{}.pkl'.format(model_name)\n",
-    "\n",
-    "os.makedirs('outputs', exist_ok=True)\n",
-    "joblib.dump(model, model_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Register Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = Model.register(model_path=model_file,\n",
-    "                       model_name=model_name,\n",
-    "                       workspace=ws,\n",
-    "                       datasets=datasets)\n",
-    "\n",
-    "print(model_name, image_name, service_name, model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Deploy Model To AKS"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prepare Environment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn', 'joblib', 'lightgbm', 'pandas'],\n",
-    "                                 pip_packages=['azureml-monitoring', 'azureml-sdk[automl]'])\n",
-    "\n",
-    "with open(\"myenv.yml\",\"w\") as f:\n",
-    "    f.write(myenv.serialize_to_string())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Image"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Image creation may take up to 15 minutes.\n",
-    "\n",
-    "image_name = image_name + str(model.version)\n",
-    "\n",
-    "if not image_name in ws.images:\n",
-    "    # Use the score.py defined in this directory as the execution script\n",
-    "    # NOTE: The Model Data Collector must be enabled in the execution script for DataDrift to run correctly\n",
-    "    image_config = ContainerImage.image_configuration(execution_script=\"score.py\",\n",
-    "                                                      runtime=\"python\",\n",
-    "                                                      conda_file=\"myenv.yml\",\n",
-    "                                                      description=\"Image with weather dataset model\")\n",
-    "    image = ContainerImage.create(name=image_name,\n",
-    "                                  models=[model],\n",
-    "                                  image_config=image_config,\n",
-    "                                  workspace=ws)\n",
-    "\n",
-    "    image.wait_for_creation(show_output=True)\n",
-    "else:\n",
-    "    image = ws.images[image_name]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Compute Target"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "aks_name = 'dd-demo-e2e'\n",
-    "prov_config = AksCompute.provisioning_configuration()\n",
-    "\n",
-    "if not aks_name in ws.compute_targets:\n",
-    "    aks_target = ComputeTarget.create(workspace=ws,\n",
-    "                                      name=aks_name,\n",
-    "                                      provisioning_configuration=prov_config)\n",
-    "\n",
-    "    aks_target.wait_for_completion(show_output=True)\n",
-    "    print(aks_target.provisioning_state)\n",
-    "    print(aks_target.provisioning_errors)\n",
-    "else:\n",
-    "    aks_target=ws.compute_targets[aks_name]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Deploy Service"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "aks_service_name = service_name\n",
-    "\n",
-    "if not aks_service_name in ws.webservices:\n",
-    "    aks_config = AksWebservice.deploy_configuration(collect_model_data=True, enable_app_insights=True)\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)\n",
-    "    print(aks_service.state)\n",
-    "else:\n",
-    "    aks_service = ws.webservices[aks_service_name]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Run DataDrift Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Send Scoring Data to Service"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Download Scoring Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Score Model on March 15, 2016 data\n",
-    "scoring_df = get_noaa_data(datetime(2016, 3, 15) - timedelta(days=7), datetime(2016, 3, 16),  columns, usaf_list)\n",
-    "# Add the window feature column\n",
-    "scoring_df = add_window_col(scoring_df)\n",
-    "\n",
-    "# Drop features not used by the model\n",
-    "print(\"Dropping unnecessary columns\")\n",
-    "scoring_df = scoring_df.drop(['windAngle', 'windSpeed', 'datetime', 'elevation'], axis=1).dropna()\n",
-    "scoring_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# One Hot Encode the scoring dataset to match the training dataset schema\n",
-    "columns_dict = model.datasets[\"training\"][0].get_profile().columns\n",
-    "extra_cols = ('Path', 'Column1')\n",
-    "for k in extra_cols:\n",
-    "    columns_dict.pop(k, None)\n",
-    "training_columns = list(columns_dict.keys())\n",
-    "\n",
-    "categorical_columns = scoring_df.dtypes == object\n",
-    "categorical_columns = categorical_columns[categorical_columns == True]\n",
-    "\n",
-    "test_df = pd.get_dummies(scoring_df[categorical_columns.keys().tolist()])\n",
-    "encoded_df = scoring_df.join(test_df)\n",
-    "\n",
-    "# Populate missing OHE columns with 0 values to match traning dataset schema\n",
-    "difference = list(set(training_columns) - set(encoded_df.columns.tolist()))\n",
-    "for col in difference:\n",
-    "    encoded_df[col] = 0\n",
-    "encoded_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Serialize dataframe to list of row dictionaries\n",
-    "encoded_dict = encoded_df.to_dict('records')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Submit Scoring Data to Service"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "\n",
-    "# retreive the API keys. AML generates two keys.\n",
-    "key1, key2 = aks_service.get_keys()\n",
-    "\n",
-    "total_count = len(scoring_df)\n",
-    "i = 0\n",
-    "load = []\n",
-    "for row in encoded_dict:\n",
-    "    load.append(row)\n",
-    "    i = i + 1\n",
-    "    if i % 100 == 0:\n",
-    "        payload = json.dumps({\"data\": load})\n",
-    "        \n",
-    "        # construct raw HTTP request and send to the service\n",
-    "        payload_binary = bytes(payload,encoding = 'utf8')\n",
-    "        headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n",
-    "        resp = requests.post(aks_service.scoring_uri, payload_binary, headers=headers)\n",
-    "        \n",
-    "        print(\"prediction:\", resp.content, \"Progress: {}/{}\".format(i, total_count))   \n",
-    "\n",
-    "        load = []\n",
-    "        time.sleep(3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Configure DataDrift"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "services = [service_name]\n",
-    "start = datetime.now() - timedelta(days=2)\n",
-    "end = datetime(year=2020, month=1, day=22, hour=15, minute=16)\n",
-    "feature_list = ['usaf', 'wban', 'latitude', 'longitude', 'station_name', 'p_k',  'sine_hourofday', 'cosine_hourofday', 'temperature-7']\n",
-    "alert_config = AlertConfiguration([email_address]) if email_address else None\n",
-    "\n",
-    "# there will be an exception indicating using get() method if DataDrift object already exist\n",
-    "try:\n",
-    "    datadrift = DataDriftDetector.create(ws, model.name, model.version, services, frequency=\"Day\", alert_config=alert_config)\n",
-    "except KeyError:\n",
-    "    datadrift = DataDriftDetector.get(ws, model.name, model.version)\n",
-    "    \n",
-    "print(\"Details of DataDrift Object:\\n{}\".format(datadrift))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Run an Adhoc DataDriftDetector Run"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "target_date = datetime.today()\n",
-    "run = datadrift.run(target_date, services, feature_list=feature_list, create_compute_target=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "exp = Experiment(ws, datadrift._id)\n",
-    "dd_run = Run(experiment=exp, run_id=run)\n",
-    "RunDetails(dd_run).show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Get Drift Analysis Results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "children = list(dd_run.get_children())\n",
-    "for child in children:\n",
-    "    child.wait_for_completion()\n",
-    "\n",
-    "drift_metrics = datadrift.get_output(start_time=start, end_time=end)\n",
-    "drift_metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Show all drift figures, one per serivice.\n",
-    "# If setting with_details is False (by default), only drift will be shown; if it's True, all details will be shown.\n",
-    "\n",
-    "drift_figures = datadrift.show(with_details=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Enable DataDrift Schedule"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "datadrift.enable_schedule()"
-   ]
-  }
- ],
- "metadata": {
-  "authors": [
-   {
-    "name": "rafarmah"
-   }
-  ],
-  "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": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

how-to-use-azureml/data-drift/readme.md

@@ -1,3 +0,0 @@
-## Using data drift APIs
-
-1. [Detect data drift for a model](azure-ml-datadrift.ipynb): Detect data drift for a deployed model.

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

@@ -77,7 +77,7 @@
         "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')"
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
       ]
     },
     {
@@ -108,11 +108,11 @@
       "source": [
         "from azureml.core.model import Model\n",
         "\n",
-        "model = Model.register(model_path = \"sklearn_regression_model.pkl\",\n",
-        "                       model_name = \"sklearn_regression_model.pkl\",\n",
-        "                       tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
-        "                       description = \"Ridge regression model to predict diabetes\",\n",
-        "                       workspace = ws)"
+        "model = Model.register(model_path=\"sklearn_regression_model.pkl\",\n",
+        "                       model_name=\"sklearn_regression_model.pkl\",\n",
+        "                       tags={'area': \"diabetes\", 'type': \"regression\"},\n",
+        "                       description=\"Ridge regression model to predict diabetes\",\n",
+        "                       workspace=ws)"
       ]
     },
     {
@@ -177,7 +177,7 @@
         "from azureml.core.webservice import AciWebservice, Webservice\n",
         "from azureml.exceptions import WebserviceException\n",
         "\n",
-        "deployment_config = AciWebservice.deploy_configuration(cpu_cores = 1, memory_gb = 1)\n",
+        "deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n",
         "aci_service_name = 'aciservice1'\n",
         "\n",
         "try:\n",
@@ -215,7 +215,7 @@
         "    [10,9,8,7,6,5,4,3,2,1]\n",
         "]})\n",
         "\n",
-        "test_sample_encoded = bytes(test_sample,encoding = 'utf8')\n",
+        "test_sample_encoded = bytes(test_sample, encoding='utf8')\n",
         "prediction = service.run(input_data=test_sample_encoded)\n",
         "print(prediction)"
       ]
@@ -247,15 +247,38 @@
       "source": [
         "### Model Profiling\n",
         "\n",
-        "you can also take advantage of profiling feature for model\n",
+        "You can also take advantage of the profiling feature to estimate CPU and memory requirements for models.\n",
         "\n",
         "```python\n",
-        "\n",
-        "profile = model.profile(ws, \"profilename\", [model], inference_config, test_sample)\n",
+        "profile = Model.profile(ws, \"profilename\", [model], inference_config, test_sample)\n",
         "profile.wait_for_profiling(True)\n",
         "profiling_results = profile.get_results()\n",
         "print(profiling_results)\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Model Packaging\n",
         "\n",
+        "If you want to build a Docker image that encapsulates your model and its dependencies, you can use the model packaging option. The output image will be pushed to your workspace's ACR.\n",
+        "\n",
+        "You must include an Environment object in your inference configuration to use `Model.package()`.\n",
+        "\n",
+        "```python\n",
+        "package = Model.package(ws, [model], inference_config)\n",
+        "package.wait_for_creation(show_output=True)  # Or show_output=False to hide the Docker build logs.\n",
+        "package.pull()\n",
+        "```\n",
+        "\n",
+        "Instead of a fully-built image, you can also generate a Dockerfile and download all the assets needed to build an image on top of your Environment.\n",
+        "\n",
+        "```python\n",
+        "package = Model.package(ws, [model], inference_config, generate_dockerfile=True)\n",
+        "package.wait_for_creation(show_output=True)\n",
+        "package.save(\"./local_context_dir\")\n",
         "```"
       ]
     }

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

@@ -0,0 +1,4 @@
+name: model-register-and-deploy
+dependencies:
+- pip:
+  - azureml-sdk

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

@@ -72,7 +72,7 @@
         "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')"
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
       ]
     },
     {
@@ -103,11 +103,11 @@
       "source": [
         "from azureml.core.model import Model\n",
         "\n",
-        "model = Model.register(model_path = \"sklearn_regression_model.pkl\",\n",
-        "                       model_name = \"sklearn_regression_model.pkl\",\n",
-        "                       tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
-        "                       description = \"Ridge regression model to predict diabetes\",\n",
-        "                       workspace = ws)"
+        "model = Model.register(model_path=\"sklearn_regression_model.pkl\",\n",
+        "                       model_name=\"sklearn_regression_model.pkl\",\n",
+        "                       tags={'area': \"diabetes\", 'type': \"regression\"},\n",
+        "                       description=\"Ridge regression model to predict diabetes\",\n",
+        "                       workspace=ws)"
       ]
     },
     {
@@ -127,10 +127,10 @@
         "\n",
         "source_directory = \"C:/abc\"\n",
         "\n",
-        "os.makedirs(source_directory, exist_ok = True)\n",
-        "os.makedirs(\"C:/abc/x/y\", exist_ok = True)\n",
-        "os.makedirs(\"C:/abc/env\", exist_ok = True)\n",
-        "os.makedirs(\"C:/abc/dockerstep\", exist_ok = True)"
+        "os.makedirs(source_directory, exist_ok=True)\n",
+        "os.makedirs(\"C:/abc/x/y\", exist_ok=True)\n",
+        "os.makedirs(\"C:/abc/env\", exist_ok=True)\n",
+        "os.makedirs(\"C:/abc/dockerstep\", exist_ok=True)"
       ]
     },
     {
@@ -253,7 +253,7 @@
         "from azureml.core.model import InferenceConfig\n",
         "\n",
         "inference_config = InferenceConfig(source_directory=\"C:/abc\",\n",
-        "                                   runtime= \"python\", \n",
+        "                                   runtime=\"python\", \n",
         "                                   entry_script=\"x/y/score.py\",\n",
         "                                   conda_file=\"env/myenv.yml\", \n",
         "                                   extra_docker_file_steps=\"dockerstep/customDockerStep.txt\")"
@@ -271,15 +271,10 @@
         "\n",
         "NOTE:\n",
         "\n",
-        "we require docker running with linux container. If you are running Docker for Windows, you need to ensure the Linux Engine is running\n",
+        "The Docker image runs as a Linux container. If you are running Docker for Windows, you need to ensure the Linux Engine is running:\n",
         "\n",
-        "    powershell command to switch to linux engine\n",
-        "    & 'C:\\Program Files\\Docker\\Docker\\DockerCli.exe' -SwitchLinuxEngine\n",
-        "\n",
-        "and c drive is shared https://docs.docker.com/docker-for-windows/#shared-drives\n",
-        "sometimes you have to reshare c drive as docker \n",
-        "\n",
-        "<img src=\"./dockerSharedDrive.JPG\" align=\"left\"/>"
+        "    # PowerShell command to switch to Linux engine\n",
+        "    & 'C:\\Program Files\\Docker\\Docker\\DockerCli.exe' -SwitchLinuxEngine"
       ]
     },
     {
@@ -295,7 +290,7 @@
       "source": [
         "from azureml.core.webservice import LocalWebservice\n",
         "\n",
-        "#this is optional, if not provided we choose random port\n",
+        "# This is optional, if not provided Docker will choose a random unused port.\n",
         "deployment_config = LocalWebservice.deploy_configuration(port=6789)\n",
         "\n",
         "local_service = Model.deploy(ws, \"test\", [model], inference_config, deployment_config)\n",
@@ -427,9 +422,8 @@
         "local_service.reload()\n",
         "print(\"--------------------------------------------------------------\")\n",
         "\n",
-        "# after reload now if you call run this will return updated return message\n",
-        "\n",
-        "print(local_service.run(input_data=sample_input))"
+        "# After calling reload(), run() will return the updated message.\n",
+        "local_service.run(input_data=sample_input)"
       ]
     },
     {
@@ -442,9 +436,9 @@
         "\n",
         "```python\n",
         "\n",
-        "local_service.update(models = [SomeOtherModelObject],\n",
-        "             deployment_config = local_config,\n",
-        "             inference_config = inference_config)\n",
+        "local_service.update(models=[SomeOtherModelObject],\n",
+        "                     deployment_config=local_config,\n",
+        "                     inference_config=inference_config)\n",
         "```"
       ]
     },
@@ -468,7 +462,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "raymondl"
+        "name": "keriehm"
       }
     ],
     "kernelspec": {

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

@@ -68,7 +68,7 @@
         "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')"
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
       ]
     },
     {
@@ -99,11 +99,31 @@
       "source": [
         "from azureml.core.model import Model\n",
         "\n",
-        "model = Model.register(model_path = \"sklearn_regression_model.pkl\",\n",
-        "                       model_name = \"sklearn_regression_model.pkl\",\n",
-        "                       tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
-        "                       description = \"Ridge regression model to predict diabetes\",\n",
-        "                       workspace = ws)"
+        "model = Model.register(model_path=\"sklearn_regression_model.pkl\",\n",
+        "                       model_name=\"sklearn_regression_model.pkl\",\n",
+        "                       tags={'area': \"diabetes\", 'type': \"regression\"},\n",
+        "                       description=\"Ridge regression model to predict diabetes\",\n",
+        "                       workspace=ws)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create Environment"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "from azureml.core.environment import Environment\n",
+        "\n",
+        "environment = Environment(\"LocalDeploy\")\n",
+        "environment.python.conda_dependencies = CondaDependencies(\"myenv.yml\")"
       ]
     },
     {
@@ -121,9 +141,8 @@
       "source": [
         "from azureml.core.model import InferenceConfig\n",
         "\n",
-        "inference_config = InferenceConfig(runtime= \"python\", \n",
-        "                                   entry_script=\"score.py\",\n",
-        "                                   conda_file=\"myenv.yml\")"
+        "inference_config = InferenceConfig(entry_script=\"score.py\",\n",
+        "                                   environment=environment)"
       ]
     },
     {
@@ -138,15 +157,10 @@
         "\n",
         "NOTE:\n",
         "\n",
-        "we require docker running with linux container. If you are running Docker for Windows, you need to ensure the Linux Engine is running\n",
+        "The Docker image runs as a Linux container. If you are running Docker for Windows, you need to ensure the Linux Engine is running:\n",
         "\n",
-        "    powershell command to switch to linux engine\n",
-        "    & 'C:\\Program Files\\Docker\\Docker\\DockerCli.exe' -SwitchLinuxEngine\n",
-        "\n",
-        "and c drive is shared https://docs.docker.com/docker-for-windows/#shared-drives\n",
-        "sometimes you have to reshare c drive as docker \n",
-        "\n",
-        "<img src=\"./dockerSharedDrive.JPG\" align=\"left\"/>"
+        "    # PowerShell command to switch to Linux engine\n",
+        "    & 'C:\\Program Files\\Docker\\Docker\\DockerCli.exe' -SwitchLinuxEngine"
       ]
     },
     {
@@ -157,7 +171,7 @@
       "source": [
         "from azureml.core.webservice import LocalWebservice\n",
         "\n",
-        "#this is optional, if not provided we choose random port\n",
+        "# This is optional, if not provided Docker will choose a random unused port.\n",
         "deployment_config = LocalWebservice.deploy_configuration(port=6789)\n",
         "\n",
         "local_service = Model.deploy(ws, \"test\", [model], inference_config, deployment_config)\n",
@@ -221,7 +235,7 @@
         "\n",
         "sample_input = bytes(sample_input, encoding='utf-8')\n",
         "\n",
-        "print(local_service.run(input_data=sample_input))"
+        "local_service.run(input_data=sample_input)"
       ]
     },
     {
@@ -282,9 +296,8 @@
         "local_service.reload()\n",
         "print(\"--------------------------------------------------------------\")\n",
         "\n",
-        "# after reload now if you call run this will return updated return message\n",
-        "\n",
-        "print(local_service.run(input_data=sample_input))"
+        "# After calling reload(), run() will return the updated message.\n",
+        "local_service.run(input_data=sample_input)"
       ]
     },
     {
@@ -296,10 +309,9 @@
         "If you want to change your model(s), Conda dependencies, or deployment configuration, call `update()` to rebuild the Docker image.\n",
         "\n",
         "```python\n",
-        "\n",
-        "local_service.update(models = [SomeOtherModelObject],\n",
-        "             deployment_config = local_config,\n",
-        "             inference_config = inference_config)\n",
+        "local_service.update(models=[SomeOtherModelObject],\n",
+        "                     inference_config=inference_config,\n",
+        "                     deployment_config=local_config)\n",
         "```"
       ]
     },
@@ -323,7 +335,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "raymondl"
+        "name": "keriehm"
       }
     ],
     "kernelspec": {

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

@@ -12,7 +12,7 @@ Easily create and train a model using various deep neural networks (DNNs) as a f
 To learn more about the azureml-accel-model classes, see the section [Model Classes](#model-classes) below or the [Azure ML Accel Models SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel?view=azure-ml-py).
 
 ### Step 1: Create an Azure ML workspace
-Follow [these instructions](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python) to install the Azure ML SDK on your local machine, create an Azure ML workspace, and set up your notebook environment, which is required for the next step.
+Follow [these instructions](https://docs.microsoft.com/en-us/azure/machine-learning/service/setup-create-workspace) to install the Azure ML SDK on your local machine, create an Azure ML workspace, and set up your notebook environment, which is required for the next step.
 
 ### Step 2: Check your FPGA quota
 Use the Azure CLI to check whether you have quota.

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

@@ -1,5 +1,12 @@
 {
   "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": {},
@@ -230,11 +237,14 @@
         "\n",
         "# Convert model\n",
         "convert_request = AccelOnnxConverter.convert_tf_model(ws, registered_model, input_tensors, output_tensors_str)\n",
-        "# If it fails, you can run wait_for_completion again with show_output=True.\n",
-        "convert_request.wait_for_completion(show_output=False)\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",
+        "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",
@@ -298,6 +308,7 @@
       "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)"
@@ -316,6 +327,7 @@
       "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",
@@ -342,10 +354,9 @@
         "## 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 have a client that can call into the docker image to get predictions. \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).",
+        "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."
       ]
     },
@@ -356,18 +367,10 @@
       "outputs": [],
       "source": [
         "# Using the grpc client in AzureML Accelerated Models SDK\n",
-        "from azureml.accel.client import PredictionClient\n",
-        "\n",
-        "address = aks_service.scoring_uri\n",
-        "ssl_enabled = address.startswith(\"https\")\n",
-        "address = address[address.find('/')+2:].strip('/')\n",
-        "port = 443 if ssl_enabled else 80\n",
+        "from azureml.accel import client_from_service\n",
         "\n",
         "# Initialize AzureML Accelerated Models client\n",
-        "client = PredictionClient(address=address,\n",
-        "                          port=port,\n",
-        "                          use_ssl=ssl_enabled,\n",
-        "                          service_name=aks_service.name)"
+        "client = client_from_service(aks_service)"
       ]
     },
     {
@@ -486,7 +489,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.0"
+      "version": "3.5.6"
     }
   },
   "nbformat": 4,

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

@@ -0,0 +1,8 @@
+name: accelerated-models-object-detection
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-accel-models
+  - tensorflow
+  - opencv-python
+  - matplotlib

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

@@ -1,5 +1,12 @@
 {
   "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": {},
@@ -270,12 +277,15 @@
         "from azureml.accel import AccelOnnxConverter\n",
         "\n",
         "convert_request = AccelOnnxConverter.convert_tf_model(ws, registered_model, input_tensors, output_tensors)\n",
-        "# If it fails, you can run wait_for_completion again with show_output=True.\n",
-        "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",
+        "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)"
       ]
     },
     {
@@ -366,6 +376,7 @@
       "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)"
@@ -384,9 +395,10 @@
       "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",
+        "# 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",
@@ -415,10 +427,9 @@
       "metadata": {},
       "source": [
         "### 7.a. Create Client\n",
-        "The image supports gRPC and the TensorFlow Serving \"predict\" API. We have a client that can call into the docker image to get predictions.\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).",
+        "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."
       ]
     },
@@ -429,18 +440,10 @@
       "outputs": [],
       "source": [
         "# Using the grpc client in AzureML Accelerated Models SDK\n",
-        "from azureml.accel.client import PredictionClient\n",
-        "\n",
-        "address = aks_service.scoring_uri\n",
-        "ssl_enabled = address.startswith(\"https\")\n",
-        "address = address[address.find('/')+2:].strip('/')\n",
-        "port = 443 if ssl_enabled else 80\n",
+        "from azureml.accel import client_from_service\n",
         "\n",
         "# Initialize AzureML Accelerated Models client\n",
-        "client = PredictionClient(address=address,\n",
-        "                          port=port,\n",
-        "                          use_ssl=ssl_enabled,\n",
-        "                          service_name=aks_service.name)"
+        "client = client_from_service(aks_service)"
       ]
     },
     {
@@ -540,7 +543,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.0"
+      "version": "3.5.6"
     }
   },
   "nbformat": 4,

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

@@ -0,0 +1,6 @@
+name: accelerated-models-quickstart
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-accel-models
+  - tensorflow

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

@@ -1,5 +1,12 @@
 {
   "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": {},
@@ -410,6 +417,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "%%time\n",
         "# Launch the training\n",
         "tf.reset_default_graph()\n",
         "sess = tf.Session(graph=tf.get_default_graph())\n",
@@ -582,11 +590,14 @@
         "\n",
         "# Convert model\n",
         "convert_request = AccelOnnxConverter.convert_tf_model(ws, registered_model, input_tensors, output_tensors)\n",
-        "# If it fails, you can run wait_for_completion again with show_output=True.\n",
-        "convert_request.wait_for_completion(show_output=False)\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",
+        "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",
@@ -655,6 +666,7 @@
       "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)"
@@ -673,6 +685,7 @@
       "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",
@@ -700,10 +713,9 @@
         "\n",
         "<a id=\"create-client\"></a>\n",
         "### 9.a. Create Client\n",
-        "The image supports gRPC and the TensorFlow Serving \"predict\" API. We have a client that can call into the docker image to get predictions. \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).",
+        "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."
       ]
     },
@@ -714,18 +726,10 @@
       "outputs": [],
       "source": [
         "# Using the grpc client in AzureML Accelerated Models SDK\n",
-        "from azureml.accel.client import PredictionClient\n",
-        "\n",
-        "address = aks_service.scoring_uri\n",
-        "ssl_enabled = address.startswith(\"https\")\n",
-        "address = address[address.find('/')+2:].strip('/')\n",
-        "port = 443 if ssl_enabled else 80\n",
+        "from azureml.accel import client_from_service\n",
         "\n",
         "# Initialize AzureML Accelerated Models client\n",
-        "client = PredictionClient(address=address,\n",
-        "                          port=port,\n",
-        "                          use_ssl=ssl_enabled,\n",
-        "                          service_name=aks_service.name)"
+        "client = client_from_service(aks_service)"
       ]
     },
     {
@@ -854,7 +858,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.0"
+      "version": "3.5.6"
     }
   },
   "nbformat": 4,

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

@@ -0,0 +1,9 @@
+name: accelerated-models-training
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-accel-models
+  - tensorflow
+  - keras
+  - tqdm 
+  - sklearn

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

@@ -0,0 +1,4 @@
+name: enable-app-insights-in-production-service
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/deployment/enable-data-collection-for-models-in-aks/enable-data-collection-for-models-in-aks.yml

@@ -0,0 +1,4 @@
+name: enable-data-collection-for-models-in-aks
+dependencies:
+- pip:
+  - azureml-sdk

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

@@ -0,0 +1,6 @@
+name: onnx-convert-aml-deploy-tinyyolo
+dependencies:
+- pip:
+  - azureml-sdk
+  - git+https://github.com/apple/coremltools
+  - onnxmltools==1.3.1

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

@@ -0,0 +1,9 @@
+name: onnx-inference-facial-expression-recognition-deploy
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets
+  - matplotlib
+  - numpy
+  - onnx
+  - opencv-python

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

@@ -0,0 +1,9 @@
+name: onnx-inference-mnist-deploy
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets
+  - matplotlib
+  - numpy
+  - onnx
+  - opencv-python

how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.yml

@@ -0,0 +1,4 @@
+name: onnx-modelzoo-aml-deploy-resnet50
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.yml

@@ -0,0 +1,5 @@
+name: onnx-train-pytorch-aml-deploy-mnist
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets

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

@@ -470,7 +470,27 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.6"
-    }
+    },
+    "friendly_name": "Prepare data for regression modeling",
+    "exclude_from_index": false,
+    "order_index": 1,
+    "category": "deployment",
+    "tags": [
+      "featured"
+    ],
+    "task": "Regression",
+    "datasets": [
+      "test"
+    ],
+    "compute": [
+      "localtest"
+    ],
+    "deployment": [
+      "AKS"
+    ],
+    "framework": [
+      "test1"
+    ]
   },
   "nbformat": 4,
   "nbformat_minor": 2

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

@@ -0,0 +1,8 @@
+name: production-deploy-to-aks
+dependencies:
+- pip:
+  - azureml-sdk
+  - matplotlib
+  - tqdm
+  - scipy
+  - sklearn

how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.yml

@@ -0,0 +1,8 @@
+name: register-model-create-image-deploy-service
+dependencies:
+- pip:
+  - azureml-sdk
+  - matplotlib
+  - tqdm
+  - scipy
+  - sklearn

how-to-use-azureml/explain-model/README.md

@@ -1,8 +1,11 @@
 ## Using explain model APIs
 
+<a name="samples"></a>
+# Explain Model SDK Sample Notebooks
+
 Follow these sample notebooks to learn:
 
-1. [Explain tabular data locally](explain-tabular-data-local): Basic example of explaining model trained on tabular data.
-4. [Explain on remote AMLCompute](explain-on-amlcompute): Explain a model on a remote AMLCompute target.
-5. [Explain tabular data with Run History](explain-tabular-data-run-history): Explain a model with Run History.
-7. [Explain raw features](explain-tabular-data-raw-features): Explain the raw features of a trained model.
+1. [Explain tabular data locally](tabular-data): Basic examples of explaining model trained on tabular data.
+2. [Explain on remote AMLCompute](azure-integration/remote-explanation): Explain a model on a remote AMLCompute target.
+3. [Explain tabular data with Run History](azure-integration/run-history): Explain a model with Run History.
+4. [Operationalize model explanation](azure-integration/scoring-time): Operationalize model explanation as a web service.

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

@@ -0,0 +1,645 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Save and retrieve explanations via Azure Machine Learning Run History\n",
+        "\n",
+        "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to save and retrieve classification model explanations to/from Azure Machine Learning Run History.**_\n",
+        "\n",
+        "\n",
+        "## Table of Contents\n",
+        "\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Run model explainer locally at training time](#Explain)\n",
+        "    1. Apply feature transformations\n",
+        "    1. Train a binary classification model\n",
+        "    1. Explain the model on raw features\n",
+        "        1. Generate global explanations\n",
+        "        1. Generate local explanations\n",
+        "1. [Upload model explanations to Azure Machine Learning Run History](#Upload)\n",
+        "1. [Download model explanations from Azure Machine Learning Run History](#Download)\n",
+        "1. [Visualize explanations](#Visualize)\n",
+        "1. [Next steps](#Next)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "\n",
+        "This notebook showcases how to explain a classification model predictions locally at training time, upload explanations to the Azure Machine Learning's run history, and download previously-uploaded explanations from the Run History.\n",
+        "It demonstrates the API calls that you need to make to upload/download the global and local explanations and a visualization dashboard that provides an interactive way of discovering patterns in data and downloaded explanations.\n",
+        "\n",
+        "We will showcase three tabular data explainers: TabularExplainer (SHAP), MimicExplainer (global surrogate), and PFIExplainer.\n",
+        "\n",
+        "\n",
+        "\n",
+        "Problem: IBM employee attrition classification with scikit-learn (run model explainer locally and upload explanation to the Azure Machine Learning Run History)\n",
+        "\n",
+        "1. Train a SVM classification model using Scikit-learn\n",
+        "2. Run 'explain_model' with AML Run History, which leverages run history service to store and manage the explanation data\n",
+        "---\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "You will need to have extensions enabled prior to jupyter kernel starting to see the visualization dashboard.\n",
+        "```\n",
+        "(myenv) $ jupyter nbextension install --py --sys-prefix azureml.contrib.explain.model.visualize\n",
+        "(myenv) $ jupyter nbextension enable --py --sys-prefix azureml.contrib.explain.model.visualize\n",
+        "```\n",
+        "Or\n",
+        "\n",
+        "```\n",
+        "(myenv) $ jupyter nbextension install azureml.contrib.explain.model.visualize --user --py\n",
+        "(myenv) $ jupyter nbextension enable azureml.contrib.explain.model.visualize --user --py\n",
+        "```\n",
+        "\n",
+        "If you are using Jupyter Labs run the following commands instead:\n",
+        "```\n",
+        "(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
+        "(myenv) $ jupyter labextension install microsoft-mli-widget\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Explain\n",
+        "\n",
+        "### Run model explainer locally at training time"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+        "from sklearn.svm import SVC\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "# Explainers:\n",
+        "# 1. SHAP Tabular Explainer\n",
+        "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
+        "\n",
+        "# OR\n",
+        "\n",
+        "# 2. Mimic Explainer\n",
+        "from azureml.explain.model.mimic.mimic_explainer import MimicExplainer\n",
+        "# You can use one of the following four interpretable models as a global surrogate to the black box model\n",
+        "from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel\n",
+        "from azureml.explain.model.mimic.models.linear_model import LinearExplainableModel\n",
+        "from azureml.explain.model.mimic.models.linear_model import SGDExplainableModel\n",
+        "from azureml.explain.model.mimic.models.tree_model import DecisionTreeExplainableModel\n",
+        "\n",
+        "# OR\n",
+        "\n",
+        "# 3. PFI Explainer\n",
+        "from azureml.explain.model.permutation.permutation_importance import PFIExplainer "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Load the IBM employee attrition data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# get the IBM employee attrition dataset\n",
+        "outdirname = 'dataset.6.21.19'\n",
+        "try:\n",
+        "    from urllib import urlretrieve\n",
+        "except ImportError:\n",
+        "    from urllib.request import urlretrieve\n",
+        "import zipfile\n",
+        "zipfilename = outdirname + '.zip'\n",
+        "urlretrieve('https://publictestdatasets.blob.core.windows.net/data/' + zipfilename, zipfilename)\n",
+        "with zipfile.ZipFile(zipfilename, 'r') as unzip:\n",
+        "    unzip.extractall('.')\n",
+        "attritionData = pd.read_csv('./WA_Fn-UseC_-HR-Employee-Attrition.csv')\n",
+        "\n",
+        "# Dropping Employee count as all values are 1 and hence attrition is independent of this feature\n",
+        "attritionData = attritionData.drop(['EmployeeCount'], axis=1)\n",
+        "# Dropping Employee Number since it is merely an identifier\n",
+        "attritionData = attritionData.drop(['EmployeeNumber'], axis=1)\n",
+        "\n",
+        "attritionData = attritionData.drop(['Over18'], axis=1)\n",
+        "\n",
+        "# Since all values are 80\n",
+        "attritionData = attritionData.drop(['StandardHours'], axis=1)\n",
+        "\n",
+        "# Converting target variables from string to numerical values\n",
+        "target_map = {'Yes': 1, 'No': 0}\n",
+        "attritionData[\"Attrition_numerical\"] = attritionData[\"Attrition\"].apply(lambda x: target_map[x])\n",
+        "target = attritionData[\"Attrition_numerical\"]\n",
+        "\n",
+        "attritionXData = attritionData.drop(['Attrition_numerical', 'Attrition'], axis=1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Split data into train and test\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "x_train, x_test, y_train, y_test = train_test_split(attritionXData, \n",
+        "                                                    target, \n",
+        "                                                    test_size = 0.2,\n",
+        "                                                    random_state=0,\n",
+        "                                                    stratify=target)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Creating dummy columns for each categorical feature\n",
+        "categorical = []\n",
+        "for col, value in attritionXData.iteritems():\n",
+        "    if value.dtype == 'object':\n",
+        "        categorical.append(col)\n",
+        "        \n",
+        "# Store the numerical columns in a list numerical\n",
+        "numerical = attritionXData.columns.difference(categorical)        "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Transform raw features"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can explain raw features by either using a `sklearn.compose.ColumnTransformer` or a list of fitted transformer tuples. The cell below uses `sklearn.compose.ColumnTransformer`. In case you want to run the example with the list of fitted transformer tuples, comment the cell below and uncomment the cell that follows after. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.compose import ColumnTransformer\n",
+        "\n",
+        "# We create the preprocessing pipelines for both numeric and categorical data.\n",
+        "numeric_transformer = Pipeline(steps=[\n",
+        "    ('imputer', SimpleImputer(strategy='median')),\n",
+        "    ('scaler', StandardScaler())])\n",
+        "\n",
+        "categorical_transformer = Pipeline(steps=[\n",
+        "    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n",
+        "    ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n",
+        "\n",
+        "transformations = ColumnTransformer(\n",
+        "    transformers=[\n",
+        "        ('num', numeric_transformer, numerical),\n",
+        "        ('cat', categorical_transformer, categorical)])\n",
+        "\n",
+        "# Append classifier to preprocessing pipeline.\n",
+        "# Now we have a full prediction pipeline.\n",
+        "clf = Pipeline(steps=[('preprocessor', transformations),\n",
+        "                      ('classifier', SVC(kernel='linear', C = 1.0, probability=True))])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "'''\n",
+        "# Uncomment below if sklearn-pandas is not installed\n",
+        "#!pip install sklearn-pandas\n",
+        "from sklearn_pandas import DataFrameMapper\n",
+        "\n",
+        "# Impute, standardize the numeric features and one-hot encode the categorical features.    \n",
+        "\n",
+        "\n",
+        "numeric_transformations = [([f], Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())])) for f in numerical]\n",
+        "\n",
+        "categorical_transformations = [([f], OneHotEncoder(handle_unknown='ignore', sparse=False)) for f in categorical]\n",
+        "\n",
+        "transformations = numeric_transformations + categorical_transformations\n",
+        "\n",
+        "# Append classifier to preprocessing pipeline.\n",
+        "# Now we have a full prediction pipeline.\n",
+        "clf = Pipeline(steps=[('preprocessor', transformations),\n",
+        "                      ('classifier', SVC(kernel='linear', C = 1.0, probability=True))]) \n",
+        "\n",
+        "\n",
+        "\n",
+        "'''"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Train a SVM classification model, which you want to explain"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model = clf.fit(x_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Explain predictions on your local machine"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# 1. Using SHAP TabularExplainer\n",
+        "# clf.steps[-1][1] returns the trained classification model\n",
+        "explainer = TabularExplainer(clf.steps[-1][1], \n",
+        "                             initialization_examples=x_train, \n",
+        "                             features=attritionXData.columns, \n",
+        "                             classes=[\"Not leaving\", \"leaving\"], \n",
+        "                             transformations=transformations)\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "# 2. Using MimicExplainer\n",
+        "# augment_data is optional and if true, oversamples the initialization examples to improve surrogate model accuracy to fit original model.  Useful for high-dimensional data where the number of rows is less than the number of columns. \n",
+        "# max_num_of_augmentations is optional and defines max number of times we can increase the input data size.\n",
+        "# LGBMExplainableModel can be replaced with LinearExplainableModel, SGDExplainableModel, or DecisionTreeExplainableModel\n",
+        "# explainer = MimicExplainer(clf.steps[-1][1], \n",
+        "#                            x_train, \n",
+        "#                            LGBMExplainableModel, \n",
+        "#                            augment_data=True, \n",
+        "#                            max_num_of_augmentations=10, \n",
+        "#                            features=attritionXData.columns, \n",
+        "#                            classes=[\"Not leaving\", \"leaving\"], \n",
+        "#                            transformations=transformations)\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "# 3. Using PFIExplainer\n",
+        "\n",
+        "# Use the parameter \"metric\" to pass a metric name or function to evaluate the permutation. \n",
+        "# Note that if a metric function is provided a higher value must be better.\n",
+        "# Otherwise, take the negative of the function or set the parameter \"is_error_metric\" to True.\n",
+        "# Default metrics: \n",
+        "# F1 Score for binary classification, F1 Score with micro average for multiclass classification and\n",
+        "# Mean absolute error for regression\n",
+        "\n",
+        "# explainer = PFIExplainer(clf.steps[-1][1], \n",
+        "#                          features=x_train.columns, \n",
+        "#                          transformations=transformations,\n",
+        "#                          classes=[\"Not leaving\", \"leaving\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Generate global explanations\n",
+        "Explain overall model predictions (global explanation)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
+        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
+        "global_explanation = explainer.explain_global(x_test)\n",
+        "\n",
+        "# Note: if you used the PFIExplainer in the previous step, use the next line of code instead\n",
+        "# global_explanation = explainer.explain_global(x_test, true_labels=y_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Sorted SHAP values\n",
+        "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
+        "# Corresponding feature names\n",
+        "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
+        "# Feature ranks (based on original order of features)\n",
+        "print('global importance rank: {}'.format(global_explanation.global_importance_rank))\n",
+        "\n",
+        "# Note: PFIExplainer does not support per class explanations\n",
+        "# Per class feature names\n",
+        "print('ranked per class feature names: {}'.format(global_explanation.get_ranked_per_class_names()))\n",
+        "# Per class feature importance values\n",
+        "print('ranked per class feature values: {}'.format(global_explanation.get_ranked_per_class_values()))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Print out a dictionary that holds the sorted feature importance names and values\n",
+        "print('global importance rank: {}'.format(global_explanation.get_feature_importance_dict()))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Explain overall model predictions as a collection of local (instance-level) explanations"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# feature shap values for all features and all data points in the training data\n",
+        "print('local importance values: {}'.format(global_explanation.local_importance_values))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Generate local explanations\n",
+        "Explain local data points (individual instances)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Note: PFIExplainer does not support local explanations\n",
+        "# You can pass a specific data point or a group of data points to the explain_local function\n",
+        "\n",
+        "# E.g., Explain the first data point in the test set\n",
+        "instance_num = 1\n",
+        "local_explanation = explainer.explain_local(x_test[:instance_num])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Get the prediction for the first member of the test set and explain why model made that prediction\n",
+        "prediction_value = clf.predict(x_test)[instance_num]\n",
+        "\n",
+        "sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
+        "sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
+        "\n",
+        "print('local importance values: {}'.format(sorted_local_importance_values))\n",
+        "print('local importance names: {}'.format(sorted_local_importance_names))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Upload\n",
+        "Upload explanations to Azure Machine Learning Run History"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import azureml.core\n",
+        "from azureml.core import Workspace, Experiment, Run\n",
+        "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
+        "from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient\n",
+        "# Check core SDK version number\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\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": [
+        "experiment_name = 'explain_model'\n",
+        "experiment = Experiment(ws, experiment_name)\n",
+        "run = experiment.start_logging()\n",
+        "client = ExplanationClient.from_run(run)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Uploading model explanation data for storage or visualization in webUX\n",
+        "# The explanation can then be downloaded on any compute\n",
+        "# Multiple explanations can be uploaded\n",
+        "client.upload_model_explanation(global_explanation, comment='global explanation: all features')\n",
+        "# Or you can only upload the explanation object with the top k feature info\n",
+        "#client.upload_model_explanation(global_explanation, top_k=2, comment='global explanation: Only top 2 features')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Uploading model explanation data for storage or visualization in webUX\n",
+        "# The explanation can then be downloaded on any compute\n",
+        "# Multiple explanations can be uploaded\n",
+        "client.upload_model_explanation(local_explanation, comment='local explanation for test point 1: all features')\n",
+        "\n",
+        "# Alterntively, you can only upload the local explanation object with the top k feature info\n",
+        "#client.upload_model_explanation(local_explanation, top_k=2, comment='local explanation: top 2 features')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Download\n",
+        "Download explanations from Azure Machine Learning Run History"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# List uploaded explanations\n",
+        "client.list_model_explanations()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "for explanation in client.list_model_explanations():\n",
+        "    \n",
+        "    if explanation['comment'] == 'local explanation for test point 1: all features':\n",
+        "        downloaded_local_explanation = client.download_model_explanation(explanation_id=explanation['id'])\n",
+        "        # You can pass a k value to only download the top k feature importance values\n",
+        "        downloaded_local_explanation_top2 = client.download_model_explanation(top_k=2, explanation_id=explanation['id'])\n",
+        "    \n",
+        "    \n",
+        "    elif explanation['comment'] == 'global explanation: all features':\n",
+        "        downloaded_global_explanation = client.download_model_explanation(explanation_id=explanation['id'])\n",
+        "        # You can pass a k value to only download the top k feature importance values\n",
+        "        downloaded_global_explanation_top2 = client.download_model_explanation(top_k=2, explanation_id=explanation['id'])\n",
+        "    "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Visualize\n",
+        "Load the visualization dashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.explain.model.visualize import ExplanationDashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ExplanationDashboard(downloaded_global_explanation, model, x_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Next\n",
+        "Learn about other use cases of the explain package on a:\n",
+        "1. [Training time: regression problem](../../tabular-data/explain-binary-classification-local.ipynb)       \n",
+        "1. [Training time: binary classification problem](../../tabular-data/explain-binary-classification-local.ipynb)\n",
+        "1. [Training time: multiclass classification problem](../../tabular-data/explain-multiclass-classification-local.ipynb)\n",
+        "1. Explain models with engineered features:\n",
+        "    1. [Simple feature transformations](../../tabular-data/simple-feature-transformations-explain-local.ipynb)\n",
+        "    1. [Advanced feature transformations](../../tabular-data/advanced-feature-transformations-explain-local.ipynb)\n",
+        "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../remote-explanation/explain-model-on-amlcompute.ipynb)\n",
+        "1. Inferencing time: deploy a classification model and explainer:\n",
+        "    1. [Deploy a locally-trained model and explainer](../scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",
+        "    1. [Deploy a remotely-trained model and explainer](../scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "mesameki"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

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

@@ -0,0 +1,6 @@
+name: save-retrieve-explanations-run-history
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-explain-model
+  - azureml-contrib-explain-model

how-to-use-azureml/explain-model/azure-integration/scoring-time/score.py

@@ -0,0 +1,33 @@
+import json
+import numpy as np
+import pandas as pd
+import os
+import pickle
+from sklearn.externals import joblib
+from sklearn.linear_model import LogisticRegression
+from azureml.core.model import Model
+
+
+def init():
+
+    global original_model
+    global scoring_explainer
+
+    # Retrieve the path to the model file using the model name
+    # Assume original model is named original_prediction_model
+    original_model_path = Model.get_model_path('original_model')
+    scoring_explainer_path = Model.get_model_path('IBM_attrition_explainer')
+
+    original_model = joblib.load(original_model_path)
+    scoring_explainer = joblib.load(scoring_explainer_path)
+
+
+def run(raw_data):
+    # Get predictions and explanations for each data point
+    data = pd.read_json(raw_data)
+    # Make prediction
+    predictions = original_model.predict(data)
+    # Retrieve model explanations
+    local_importance_values = scoring_explainer.explain(data)
+    # You can return any data type as long as it is JSON-serializable
+    return {'predictions': predictions.tolist(), 'local_importance_values': local_importance_values}

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

@@ -0,0 +1,513 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Train and explain models locally and deploy model and scoring explainer\n",
+        "\n",
+        "\n",
+        "_**This notebook illustrates how to use the Azure Machine Learning Interpretability SDK to deploy a locally-trained model and its corresponding scoring explainer to Azure Container Instances (ACI) as a web service.**_\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "Problem: IBM employee attrition classification with scikit-learn (train and explain a model locally and use Azure Container Instances (ACI) for deploying your model and its corresponding scoring explainer as a web service.)\n",
+        "\n",
+        "---\n",
+        "\n",
+        "## Table of Contents\n",
+        "\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Run model explainer locally at training time](#Explain)\n",
+        "    1. Apply feature transformations\n",
+        "    1. Train a binary classification model\n",
+        "    1. Explain the model on raw features\n",
+        "        1. Generate global explanations\n",
+        "        1. Generate local explanations\n",
+        "1. [Visualize explanations](#Visualize)\n",
+        "1. [Deploy model and scoring explainer](#Deploy)\n",
+        "1. [Next steps](#Next)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "\n",
+        "\n",
+        "This notebook showcases how to train and explain a classification model locally, and deploy the trained model and its corresponding explainer to Azure Container Instances (ACI).\n",
+        "It demonstrates the API calls that you need to make to submit a run for training and explaining a model to AMLCompute, download the compute explanations remotely, and visualizing the global and local explanations via a visualization dashboard that provides an interactive way of discovering patterns in model predictions and downloaded explanations. It also demonstrates how to use Azure Machine Learning MLOps capabilities to deploy your model and its corresponding explainer.\n",
+        "\n",
+        "We will showcase one of the tabular data explainers: TabularExplainer (SHAP) and follow these steps:\n",
+        "1.\tDevelop a machine learning script in Python which involves the training script and the explanation script.\n",
+        "2.\tRun the script locally.\n",
+        "3.\tUse the interpretability toolkit\u00e2\u20ac\u2122s visualization dashboard to visualize predictions and their explanation. If the metrics and explanations don't indicate a desired outcome, loop back to step 1 and iterate on your scripts.\n",
+        "5.\tAfter a satisfactory run is found, create a scoring explainer and register the persisted model and its corresponding explainer in the model registry.\n",
+        "6.\tDevelop a scoring script.\n",
+        "7.\tCreate an image and register it in the image registry.\n",
+        "8.\tDeploy the image as a web service in Azure.\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup\n",
+        "Make sure you go through the [configuration notebook](../../../../configuration.ipynb) first if you haven't."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Check core SDK version number\n",
+        "import azureml.core\n",
+        "\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize a Workspace\n",
+        "\n",
+        "Initialize a workspace object from persisted configuration"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "create workspace"
+        ]
+      },
+      "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": [
+        "## Explain\n",
+        "Create An Experiment: **Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "experiment_name = 'explain_model_at_scoring_time'\n",
+        "experiment = Experiment(workspace=ws, name=experiment_name)\n",
+        "run = experiment.start_logging()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# get IBM attrition data\n",
+        "import os\n",
+        "import pandas as pd\n",
+        "\n",
+        "outdirname = 'dataset.6.21.19'\n",
+        "try:\n",
+        "    from urllib import urlretrieve\n",
+        "except ImportError:\n",
+        "    from urllib.request import urlretrieve\n",
+        "import zipfile\n",
+        "zipfilename = outdirname + '.zip'\n",
+        "urlretrieve('https://publictestdatasets.blob.core.windows.net/data/' + zipfilename, zipfilename)\n",
+        "with zipfile.ZipFile(zipfilename, 'r') as unzip:\n",
+        "    unzip.extractall('.')\n",
+        "attritionData = pd.read_csv('./WA_Fn-UseC_-HR-Employee-Attrition.csv')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.externals import joblib\n",
+        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn_pandas import DataFrameMapper\n",
+        "\n",
+        "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
+        "\n",
+        "os.makedirs('./outputs', exist_ok=True)\n",
+        "\n",
+        "# Dropping Employee count as all values are 1 and hence attrition is independent of this feature\n",
+        "attritionData = attritionData.drop(['EmployeeCount'], axis=1)\n",
+        "# Dropping Employee Number since it is merely an identifier\n",
+        "attritionData = attritionData.drop(['EmployeeNumber'], axis=1)\n",
+        "attritionData = attritionData.drop(['Over18'], axis=1)\n",
+        "# Since all values are 80\n",
+        "attritionData = attritionData.drop(['StandardHours'], axis=1)\n",
+        "\n",
+        "# Converting target variables from string to numerical values\n",
+        "target_map = {'Yes': 1, 'No': 0}\n",
+        "attritionData[\"Attrition_numerical\"] = attritionData[\"Attrition\"].apply(lambda x: target_map[x])\n",
+        "target = attritionData[\"Attrition_numerical\"]\n",
+        "\n",
+        "attritionXData = attritionData.drop(['Attrition_numerical', 'Attrition'], axis=1)\n",
+        "\n",
+        "# Creating dummy columns for each categorical feature\n",
+        "categorical = []\n",
+        "for col, value in attritionXData.iteritems():\n",
+        "    if value.dtype == 'object':\n",
+        "        categorical.append(col)\n",
+        "\n",
+        "# Store the numerical columns in a list numerical\n",
+        "numerical = attritionXData.columns.difference(categorical)\n",
+        "\n",
+        "numeric_transformations = [([f], Pipeline(steps=[\n",
+        "    ('imputer', SimpleImputer(strategy='median')),\n",
+        "    ('scaler', StandardScaler())])) for f in numerical]\n",
+        "\n",
+        "categorical_transformations = [([f], OneHotEncoder(handle_unknown='ignore', sparse=False)) for f in categorical]\n",
+        "\n",
+        "transformations = numeric_transformations + categorical_transformations\n",
+        "\n",
+        "# Append classifier to preprocessing pipeline.\n",
+        "# Now we have a full prediction pipeline.\n",
+        "clf = Pipeline(steps=[('preprocessor', DataFrameMapper(transformations)),\n",
+        "                      ('classifier', RandomForestClassifier())])\n",
+        "\n",
+        "# Split data into train and test\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "x_train, x_test, y_train, y_test = train_test_split(attritionXData,\n",
+        "                                                    target,\n",
+        "                                                    test_size = 0.2,\n",
+        "                                                    random_state=0,\n",
+        "                                                    stratify=target)\n",
+        "\n",
+        "# preprocess the data and fit the classification model\n",
+        "clf.fit(x_train, y_train)\n",
+        "model = clf.steps[-1][1]\n",
+        "\n",
+        "model_file_name = 'log_reg.pkl'\n",
+        "\n",
+        "# save model in the outputs folder so it automatically get uploaded\n",
+        "with open(model_file_name, 'wb') as file:\n",
+        "    joblib.dump(value=clf, filename=os.path.join('./outputs/',\n",
+        "                                                 model_file_name))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Explain predictions on your local machine\n",
+        "tabular_explainer = TabularExplainer(model, \n",
+        "                                     initialization_examples=x_train, \n",
+        "                                     features=attritionXData.columns, \n",
+        "                                     classes=[\"Not leaving\", \"leaving\"], \n",
+        "                                     transformations=transformations)\n",
+        "\n",
+        "# Explain overall model predictions (global explanation)\n",
+        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
+        "# x_train can be passed as well, but with more examples explanations it will\n",
+        "# take longer although they may be more accurate\n",
+        "global_explanation = tabular_explainer.explain_global(x_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.explain.model.scoring.scoring_explainer import TreeScoringExplainer, save\n",
+        "# ScoringExplainer\n",
+        "scoring_explainer = TreeScoringExplainer(tabular_explainer)\n",
+        "# Pickle scoring explainer locally\n",
+        "save(scoring_explainer, exist_ok=True)\n",
+        "\n",
+        "# Register original model\n",
+        "run.upload_file('original_model.pkl', os.path.join('./outputs/', model_file_name))\n",
+        "original_model = run.register_model(model_name='original_model', model_path='original_model.pkl')\n",
+        "\n",
+        "# Register scoring explainer\n",
+        "run.upload_file('IBM_attrition_explainer.pkl', 'scoring_explainer.pkl')\n",
+        "scoring_explainer_model = run.register_model(model_name='IBM_attrition_explainer', model_path='IBM_attrition_explainer.pkl')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Visualize\n",
+        "Visualize the explanations"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.explain.model.visualize import ExplanationDashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ExplanationDashboard(global_explanation, clf, x_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Deploy \n",
+        "\n",
+        "Deploy Model and ScoringExplainer"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import AciWebservice\n",
+        "\n",
+        "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
+        "                                               memory_gb=1, \n",
+        "                                               tags={\"data\": \"IBM_Attrition\",  \n",
+        "                                                     \"method\" : \"local_explanation\"}, \n",
+        "                                               description='Get local explanations for IBM Employee Attrition data')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.conda_dependencies import CondaDependencies \n",
+        "\n",
+        "# WARNING: to install this, g++ needs to be available on the Docker image and is not by default (look at the next cell)\n",
+        "azureml_pip_packages = [\n",
+        "    'azureml-defaults', 'azureml-contrib-explain-model', 'azureml-core', 'azureml-telemetry',\n",
+        "    'azureml-explain-model'\n",
+        "]\n",
+        " \n",
+        "\n",
+        "# specify CondaDependencies obj\n",
+        "myenv = CondaDependencies.create(conda_packages=['scikit-learn', 'pandas'],\n",
+        "                                 pip_packages=['sklearn-pandas', 'pyyaml'] + azureml_pip_packages,\n",
+        "                                 pin_sdk_version=False)\n",
+        "\n",
+        "with open(\"myenv.yml\",\"w\") as f:\n",
+        "    f.write(myenv.serialize_to_string())\n",
+        "\n",
+        "with open(\"myenv.yml\",\"r\") as f:\n",
+        "    print(f.read())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%writefile dockerfile\n",
+        "RUN apt-get update && apt-get install -y g++ "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.model import Model\n",
+        "# retrieve scoring explainer for deployment\n",
+        "scoring_explainer_model = Model(ws, 'IBM_attrition_explainer')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import Webservice\n",
+        "from azureml.core.image import ContainerImage\n",
+        "\n",
+        "# Use the custom scoring, docker, and conda files we created above\n",
+        "image_config = ContainerImage.image_configuration(execution_script=\"score.py\",\n",
+        "                                                  docker_file=\"dockerfile\", \n",
+        "                                                  runtime=\"python\", \n",
+        "                                                  conda_file=\"myenv.yml\")\n",
+        "\n",
+        "# Use configs and models generated above\n",
+        "service = Webservice.deploy_from_model(workspace=ws,\n",
+        "                                       name='model-scoring',\n",
+        "                                       deployment_config=aciconfig,\n",
+        "                                       models=[scoring_explainer_model, original_model],\n",
+        "                                       image_config=image_config)\n",
+        "\n",
+        "service.wait_for_deployment(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import requests\n",
+        "import json\n",
+        "\n",
+        "\n",
+        "# Create data to test service with\n",
+        "sample_data = '{\"Age\":{\"899\":49},\"BusinessTravel\":{\"899\":\"Travel_Rarely\"},\"DailyRate\":{\"899\":1098},\"Department\":{\"899\":\"Research & Development\"},\"DistanceFromHome\":{\"899\":4},\"Education\":{\"899\":2},\"EducationField\":{\"899\":\"Medical\"},\"EnvironmentSatisfaction\":{\"899\":1},\"Gender\":{\"899\":\"Male\"},\"HourlyRate\":{\"899\":85},\"JobInvolvement\":{\"899\":2},\"JobLevel\":{\"899\":5},\"JobRole\":{\"899\":\"Manager\"},\"JobSatisfaction\":{\"899\":3},\"MaritalStatus\":{\"899\":\"Married\"},\"MonthlyIncome\":{\"899\":18711},\"MonthlyRate\":{\"899\":12124},\"NumCompaniesWorked\":{\"899\":2},\"OverTime\":{\"899\":\"No\"},\"PercentSalaryHike\":{\"899\":13},\"PerformanceRating\":{\"899\":3},\"RelationshipSatisfaction\":{\"899\":3},\"StockOptionLevel\":{\"899\":1},\"TotalWorkingYears\":{\"899\":23},\"TrainingTimesLastYear\":{\"899\":2},\"WorkLifeBalance\":{\"899\":4},\"YearsAtCompany\":{\"899\":1},\"YearsInCurrentRole\":{\"899\":0},\"YearsSinceLastPromotion\":{\"899\":0},\"YearsWithCurrManager\":{\"899\":0}}'\n",
+        "\n",
+        "\n",
+        "\n",
+        "headers = {'Content-Type':'application/json'}\n",
+        "\n",
+        "# send request to service\n",
+        "resp = requests.post(service.scoring_uri, sample_data, headers=headers)\n",
+        "\n",
+        "print(\"POST to url\", service.scoring_uri)\n",
+        "# can covert back to Python objects from json string if desired\n",
+        "print(\"prediction:\", resp.text)\n",
+        "result = json.loads(resp.text)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#plot the feature importance for the prediction\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt; plt.rcdefaults()\n",
+        "\n",
+        "labels = json.loads(sample_data)\n",
+        "labels = labels.keys()\n",
+        "objects = labels\n",
+        "y_pos = np.arange(len(objects))\n",
+        "performance = result[\"local_importance_values\"][0][0]\n",
+        "\n",
+        "plt.bar(y_pos, performance, align='center', alpha=0.5)\n",
+        "plt.xticks(y_pos, objects)\n",
+        "locs, labels = plt.xticks()\n",
+        "plt.setp(labels, rotation=90)\n",
+        "plt.ylabel('Feature impact - leaving vs not leaving')\n",
+        "plt.title('Local feature importance for prediction')\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "service.delete()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Next\n",
+        "Learn about other use cases of the explain package on a:\n",
+        "1. [Training time: regression problem](../../tabular-data/explain-binary-classification-local.ipynb)       \n",
+        "1. [Training time: binary classification problem](../../tabular-data/explain-binary-classification-local.ipynb)\n",
+        "1. [Training time: multiclass classification problem](../../tabular-data/explain-multiclass-classification-local.ipynb)\n",
+        "1. Explain models with engineered features:\n",
+        "    1. [Simple feature transformations](../../tabular-data/simple-feature-transformations-explain-local.ipynb)\n",
+        "    1. [Advanced feature transformations](../../tabular-data/advanced-feature-transformations-explain-local.ipynb)\n",
+        "1. [Save model explanations via Azure Machine Learning Run History](../run-history/save-retrieve-explanations-run-history.ipynb)\n",
+        "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../remote-explanation/explain-model-on-amlcompute.ipynb)\n",
+        "1. [Inferencing time: deploy a remotely-trained model and explainer](./train-explain-model-on-amlcompute-and-deploy.ipynb)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "mesameki"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

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

@@ -0,0 +1,7 @@
+name: train-explain-model-locally-and-deploy
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-explain-model
+  - azureml-contrib-explain-model
+  - sklearn-pandas

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

@@ -13,33 +13,66 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/explain-on-amlcompute/regression-sklearn-on-amlcompute.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.png)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Train using Azure Machine Learning Compute\n",
+        "# Train and explain models remotely via Azure Machine Learning Compute and deploy model and scoring explainer\n",
         "\n",
-        "* Initialize a Workspace\n",
-        "* Create an Experiment\n",
-        "* Introduction to AmlCompute\n",
-        "* Submit an AmlCompute run in a few different ways\n",
-        "    - Provision as a run based compute target \n",
-        "    - Provision as a persistent compute target (Basic)\n",
-        "    - Provision as a persistent compute target (Advanced)\n",
-        "* Additional operations to perform on AmlCompute\n",
-        "* Download model explanation data from the Run History Portal\n",
-        "* Print the explanation data"
+        "\n",
+        "_**This notebook illustrates how to use the Azure Machine Learning Interpretability SDK to train and explain a classification model remotely on an Azure Machine Leanrning Compute Target (AMLCompute), and use Azure Container Instances (ACI) for deploying your model and its corresponding scoring explainer as a web service.**_\n",
+        "\n",
+        "Problem: IBM employee attrition classification with scikit-learn (train a model and run an explainer remotely via AMLCompute, and deploy model and its corresponding explainer.)\n",
+        "\n",
+        "---\n",
+        "\n",
+        "## Table of Contents\n",
+        "\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Run model explainer locally at training time](#Explain)\n",
+        "    1. Apply feature transformations\n",
+        "    1. Train a binary classification model\n",
+        "    1. Explain the model on raw features\n",
+        "        1. Generate global explanations\n",
+        "        1. Generate local explanations\n",
+        "1. [Visualize results](#Visualize)\n",
+        "1. [Deploy model and scoring explainer](#Deploy)\n",
+        "1. [Next steps](#Next)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Prerequisites\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration notebook](../../../configuration.ipynb) first if you haven't."
+        "## Introduction\n",
+        "\n",
+        "This notebook showcases how to train and explain a classification model remotely via Azure Machine Learning Compute (AMLCompute), download the calculated explanations locally for visualization and inspection, and deploy the final model and its corresponding explainer to Azure Container Instances (ACI).\n",
+        "It demonstrates the API calls that you need to make to submit a run for training and explaining a model to AMLCompute, download the compute explanations remotely, and visualizing the global and local explanations via a visualization dashboard that provides an interactive way of discovering patterns in model predictions and downloaded explanations, and using Azure Machine Learning MLOps capabilities to deploy your model and its corresponding explainer.\n",
+        "\n",
+        "We will showcase one of the tabular data explainers: TabularExplainer (SHAP) and follow these steps:\n",
+        "1.\tDevelop a machine learning script in Python which involves the training script and the explanation script.\n",
+        "2.\tCreate and configure a compute target.\n",
+        "3.\tSubmit the scripts to the configured compute target to run in that environment. During training, the scripts can read from or write to datastore. And the records of execution (e.g., model, metrics, prediction explanations) are saved as runs in the workspace and grouped under experiments.\n",
+        "4.\tQuery the experiment for logged metrics and explanations from the current and past runs. Use the interpretability toolkit\u00e2\u20ac\u2122s visualization dashboard to visualize predictions and their explanation. If the metrics and explanations don't indicate a desired outcome, loop back to step 1 and iterate on your scripts.\n",
+        "5.\tAfter a satisfactory run is found, create a scoring explainer and register the persisted model and its corresponding explainer in the model registry.\n",
+        "6.\tDevelop a scoring script.\n",
+        "7.\tCreate an image and register it in the image registry.\n",
+        "8.\tDeploy the image as a web service in Azure.\n",
+        "\n",
+        "| ![azure-machine-learning-cycle](./img/azure-machine-learning-cycle.PNG) |\n",
+        "|:--:|"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup\n",
+        "Make sure you go through the [configuration notebook](../../../../configuration.ipynb) first if you haven't."
       ]
     },
     {
@@ -83,9 +116,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Create An Experiment\n",
+        "## Explain\n",
         "\n",
-        "**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
+        "Create An Experiment: **Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
       ]
     },
     {
@@ -162,7 +195,7 @@
         "\n",
         "project_folder = './explainer-remote-run-on-amlcompute'\n",
         "os.makedirs(project_folder, exist_ok=True)\n",
-        "shutil.copy('run_explainer.py', project_folder)"
+        "shutil.copy('train_explain.py', project_folder)"
       ]
     },
     {
@@ -203,20 +236,25 @@
         "# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
         "run_config.environment.python.user_managed_dependencies = False\n",
         "\n",
+        "# auto-prepare the Docker image when used for execution (if it is not already prepared)\n",
+        "run_config.auto_prepare_environment = True\n",
+        "\n",
         "azureml_pip_packages = [\n",
         "    'azureml-defaults', 'azureml-contrib-explain-model', 'azureml-core', 'azureml-telemetry',\n",
         "    'azureml-explain-model'\n",
         "]\n",
+        " \n",
+        "\n",
         "\n",
         "# specify CondaDependencies obj\n",
         "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'],\n",
-        "                                                                            pip_packages=azureml_pip_packages)\n",
-        "\n",
+        "                                pip_packages=['sklearn_pandas', 'pyyaml'] + azureml_pip_packages,\n",
+        "                                pin_sdk_version=False)\n",
         "# Now submit a run on AmlCompute\n",
         "from azureml.core.script_run_config import ScriptRunConfig\n",
         "\n",
         "script_run_config = ScriptRunConfig(source_directory=project_folder,\n",
-        "                                    script='run_explainer.py',\n",
+        "                                    script='train_explain.py',\n",
         "                                    run_config=run_config)\n",
         "\n",
         "run = experiment.submit(script_run_config)\n",
@@ -243,262 +281,6 @@
         "run.wait_for_completion(show_output=True)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Provision as a persistent compute target (Basic)\n",
-        "\n",
-        "You can provision a persistent AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n",
-        "\n",
-        "* `vm_size`: VM family of the nodes provisioned by AmlCompute. Simply choose from the supported_vmsizes() above\n",
-        "* `max_nodes`: Maximum nodes to autoscale to while running a job on AmlCompute"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "# Choose a name for your CPU cluster\n",
-        "cpu_cluster_name = \"cpu-cluster\"\n",
-        "\n",
-        "# Verify that cluster does not exist already\n",
-        "try:\n",
-        "    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
-        "    print('Found existing cluster, use it.')\n",
-        "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
-        "                                                           max_nodes=4)\n",
-        "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
-        "\n",
-        "cpu_cluster.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Configure & Run"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.runconfig import RunConfiguration\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "\n",
-        "# create a new RunConfig object\n",
-        "run_config = RunConfiguration(framework=\"python\")\n",
-        "\n",
-        "# Set compute target to AmlCompute target created in previous step\n",
-        "run_config.target = cpu_cluster.name\n",
-        "\n",
-        "# enable Docker \n",
-        "run_config.environment.docker.enabled = True\n",
-        "\n",
-        "azureml_pip_packages = [\n",
-        "    'azureml-defaults', 'azureml-contrib-explain-model', 'azureml-core', 'azureml-telemetry',\n",
-        "    'azureml-explain-model'\n",
-        "]\n",
-        "\n",
-        "# specify CondaDependencies obj\n",
-        "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'],\n",
-        "                                                                            pip_packages=azureml_pip_packages)\n",
-        "\n",
-        "from azureml.core import Run\n",
-        "from azureml.core import ScriptRunConfig\n",
-        "\n",
-        "src = ScriptRunConfig(source_directory=project_folder, \n",
-        "                      script='run_explainer.py', \n",
-        "                      run_config=run_config) \n",
-        "run = experiment.submit(config=src)\n",
-        "run"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "# Shows output of the run on stdout.\n",
-        "run.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run.get_metrics()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Provision as a persistent compute target (Advanced)\n",
-        "\n",
-        "You can also specify additional properties or change defaults while provisioning AmlCompute using a more advanced configuration. This is useful when you want a dedicated cluster of 4 nodes (for example you can set the min_nodes and max_nodes to 4), or want the compute to be within an existing VNet in your subscription.\n",
-        "\n",
-        "In addition to `vm_size` and `max_nodes`, you can specify:\n",
-        "* `min_nodes`: Minimum nodes (default 0 nodes) to downscale to while running a job on AmlCompute\n",
-        "* `vm_priority`: Choose between 'dedicated' (default) and 'lowpriority' VMs when provisioning AmlCompute. Low Priority VMs use Azure's excess capacity and are thus cheaper but risk your run being pre-empted\n",
-        "* `idle_seconds_before_scaledown`: Idle time (default 120 seconds) to wait after run completion before auto-scaling to min_nodes\n",
-        "* `vnet_resourcegroup_name`: Resource group of the **existing** VNet within which AmlCompute should be provisioned\n",
-        "* `vnet_name`: Name of VNet\n",
-        "* `subnet_name`: Name of SubNet within the VNet"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "# Choose a name for your CPU cluster\n",
-        "cpu_cluster_name = \"cpu-cluster\"\n",
-        "\n",
-        "# Verify that cluster does not exist already\n",
-        "try:\n",
-        "    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
-        "    print('Found existing cluster, use it.')\n",
-        "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
-        "                                                           vm_priority='lowpriority',\n",
-        "                                                           min_nodes=2,\n",
-        "                                                           max_nodes=4,\n",
-        "                                                           idle_seconds_before_scaledown='300',\n",
-        "                                                           vnet_resourcegroup_name='<my-resource-group>',\n",
-        "                                                           vnet_name='<my-vnet-name>',\n",
-        "                                                           subnet_name='<my-subnet-name>')\n",
-        "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
-        "\n",
-        "cpu_cluster.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Configure & Run"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.runconfig import RunConfiguration\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "\n",
-        "# create a new RunConfig object\n",
-        "run_config = RunConfiguration(framework=\"python\")\n",
-        "\n",
-        "# Set compute target to AmlCompute target created in previous step\n",
-        "run_config.target = cpu_cluster.name\n",
-        "\n",
-        "# enable Docker \n",
-        "run_config.environment.docker.enabled = True\n",
-        "\n",
-        "azureml_pip_packages = [\n",
-        "    'azureml-defaults', 'azureml-contrib-explain-model', 'azureml-core', 'azureml-telemetry',\n",
-        "    'azureml-explain-model'\n",
-        "]\n",
-        "\n",
-        "# specify CondaDependencies obj\n",
-        "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'],\n",
-        "                                                                            pip_packages=azureml_pip_packages)\n",
-        "\n",
-        "from azureml.core import Run\n",
-        "from azureml.core import ScriptRunConfig\n",
-        "\n",
-        "src = ScriptRunConfig(source_directory=project_folder, \n",
-        "                      script='run_explainer.py', \n",
-        "                      run_config=run_config) \n",
-        "run = experiment.submit(config=src)\n",
-        "run"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "# Shows output of the run on stdout.\n",
-        "run.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run.get_metrics()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient\n",
-        "\n",
-        "client = ExplanationClient.from_run(run)\n",
-        "# Get the top k (e.g., 4) most important features with their importance values\n",
-        "explanation = client.download_model_explanation(top_k=4)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Additional operations to perform on AmlCompute\n",
-        "\n",
-        "You can perform more operations on AmlCompute such as updating the node counts or deleting the compute. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Get_status () gets the latest status of the AmlCompute target\n",
-        "cpu_cluster.get_status().serialize()\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Update () takes in the min_nodes, max_nodes and idle_seconds_before_scaledown and updates the AmlCompute target\n",
-        "# cpu_cluster.update(min_nodes=1)\n",
-        "# cpu_cluster.update(max_nodes=10)\n",
-        "cpu_cluster.update(idle_seconds_before_scaledown=300)\n",
-        "# cpu_cluster.update(min_nodes=2, max_nodes=4, idle_seconds_before_scaledown=600)"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -506,7 +288,7 @@
       "outputs": [],
       "source": [
         "# Delete () is used to deprovision and delete the AmlCompute target. Useful if you want to re-use the compute name \n",
-        "# 'cpu-cluster' in this case but use a different VM family for instance.\n",
+        "# 'cpucluster' in this case but use a different VM family for instance.\n",
         "\n",
         "# cpu_cluster.delete()"
       ]
@@ -515,7 +297,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Download Model Explanation Data"
+        "## Download Model Explanation, Model, and Data"
       ]
     },
     {
@@ -524,13 +306,26 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "# retrieve model for visualization and deployment\n",
+        "from azureml.core.model import Model\n",
+        "from sklearn.externals import joblib\n",
+        "original_model = Model(ws, 'original_model')\n",
+        "model_path = original_model.download(exist_ok=True)\n",
+        "original_svm_model = joblib.load(model_path)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# retrieve global explanation for visualization\n",
         "from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient\n",
         "\n",
-        "# Get model explanation data\n",
+        "# get model explanation data\n",
         "client = ExplanationClient.from_run(run)\n",
-        "explanation = client.download_model_explanation()\n",
-        "local_importance_values = explanation.local_importance_values\n",
-        "expected_values = explanation.expected_values\n"
+        "global_explanation = client.download_model_explanation()"
       ]
     },
     {
@@ -539,42 +334,189 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# Or you can use the saved run.id to retrive the feature importance values\n",
-        "client = ExplanationClient.from_run_id(ws, experiment_name, run.id)\n",
-        "explanation = client.download_model_explanation()\n",
-        "local_importance_values = explanation.local_importance_values\n",
-        "expected_values = explanation.expected_values"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Get the top k (e.g., 4) most important features with their importance values\n",
-        "explanation = client.download_model_explanation(top_k=4)\n",
-        "global_importance_values = explanation.get_ranked_global_values()\n",
-        "global_importance_names = explanation.get_ranked_global_names()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print('global importance values: {}'.format(global_importance_values))\n",
-        "print('global importance names: {}'.format(global_importance_names))"
+        "# retrieve x_test for visualization\n",
+        "from sklearn.externals import joblib\n",
+        "x_test_path = './x_test.pkl'\n",
+        "run.download_file('x_test_ibm.pkl', output_file_path=x_test_path)\n",
+        "x_test = joblib.load(x_test_path)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Success!\n",
-        "Great, you are ready to move on to the remaining notebooks."
+        "## Visualize\n",
+        "Visualize the explanations"
       ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.explain.model.visualize import ExplanationDashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ExplanationDashboard(global_explanation, original_svm_model, x_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Deploy\n",
+        "Deploy Model and ScoringExplainer"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import AciWebservice\n",
+        "\n",
+        "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
+        "                                               memory_gb=1, \n",
+        "                                               tags={\"data\": \"IBM_Attrition\",  \n",
+        "                                                     \"method\" : \"local_explanation\"}, \n",
+        "                                               description='Get local explanations for IBM Employee Attrition data')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.conda_dependencies import CondaDependencies \n",
+        "\n",
+        "# WARNING: to install this, g++ needs to be available on the Docker image and is not by default (look at the next cell)\n",
+        "azureml_pip_packages = [\n",
+        "    'azureml-defaults', 'azureml-contrib-explain-model', 'azureml-core', 'azureml-telemetry',\n",
+        "    'azureml-explain-model'\n",
+        "]\n",
+        " \n",
+        "\n",
+        "# specify CondaDependencies obj\n",
+        "myenv = CondaDependencies.create(conda_packages=['scikit-learn', 'pandas'],\n",
+        "                                 pip_packages=['sklearn-pandas', 'pyyaml'] + azureml_pip_packages,\n",
+        "                                 pin_sdk_version=False)\n",
+        "\n",
+        "with open(\"myenv.yml\",\"w\") as f:\n",
+        "    f.write(myenv.serialize_to_string())\n",
+        "\n",
+        "with open(\"myenv.yml\",\"r\") as f:\n",
+        "    print(f.read())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%writefile dockerfile\n",
+        "RUN apt-get update && apt-get install -y g++ "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# retrieve scoring explainer for deployment\n",
+        "scoring_explainer_model = Model(ws, 'IBM_attrition_explainer')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import Webservice\n",
+        "from azureml.core.image import ContainerImage\n",
+        "\n",
+        "# Use the custom scoring, docker, and conda files we created above\n",
+        "image_config = ContainerImage.image_configuration(execution_script=\"score.py\",\n",
+        "                                                  docker_file=\"dockerfile\", \n",
+        "                                                  runtime=\"python\", \n",
+        "                                                  conda_file=\"myenv.yml\")\n",
+        "\n",
+        "# Use configs and models generated above\n",
+        "service = Webservice.deploy_from_model(workspace=ws,\n",
+        "                                       name='model-scoring-service',\n",
+        "                                       deployment_config=aciconfig,\n",
+        "                                       models=[scoring_explainer_model, original_model],\n",
+        "                                       image_config=image_config)\n",
+        "\n",
+        "service.wait_for_deployment(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import requests\n",
+        "\n",
+        "# create data to test service with\n",
+        "examples = x_test[:4]\n",
+        "input_data = examples.to_json()\n",
+        "\n",
+        "headers = {'Content-Type':'application/json'}\n",
+        "\n",
+        "# send request to service\n",
+        "resp = requests.post(service.scoring_uri, input_data, headers=headers)\n",
+        "\n",
+        "print(\"POST to url\", service.scoring_uri)\n",
+        "# can covert back to Python objects from json string if desired\n",
+        "print(\"prediction:\", resp.text)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "service.delete()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Next\n",
+        "Learn about other use cases of the explain package on a:\n",
+        "1. [Training time: regression problem](../../tabular-data/explain-binary-classification-local.ipynb)       \n",
+        "1. [Training time: binary classification problem](../../tabular-data/explain-binary-classification-local.ipynb)\n",
+        "1. [Training time: multiclass classification problem](../../tabular-data/explain-multiclass-classification-local.ipynb)\n",
+        "1. Explain models with engineered features:\n",
+        "    1. [Simple feature transformations](../../tabular-data/simple-feature-transformations-explain-local.ipynb)\n",
+        "    1. [Advanced feature transformations](../../tabular-data/advanced-feature-transformations-explain-local.ipynb)\n",
+        "1. [Save model explanations via Azure Machine Learning Run History](../run-history/save-retrieve-explanations-run-history.ipynb)\n",
+        "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../remote-explanation/explain-model-on-amlcompute.ipynb)\n",
+        "1. [Inferencing time: deploy a locally-trained model and explainer](./train-explain-model-locally-and-deploy.ipynb)\n",
+        "   "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
     }
   ],
   "metadata": {

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

@@ -0,0 +1,8 @@
+name: train-explain-model-on-amlcompute-and-deploy
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-explain-model
+  - azureml-contrib-explain-model
+  - azureml-dataprep
+  - sklearn-pandas

how-to-use-azureml/explain-model/azure-integration/scoring-time/train_explain.py

@@ -0,0 +1,128 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+import os
+import pandas as pd
+import zipfile
+from sklearn.model_selection import train_test_split
+from sklearn.externals import joblib
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.impute import SimpleImputer
+from sklearn.pipeline import Pipeline
+from sklearn.linear_model import LogisticRegression
+from sklearn_pandas import DataFrameMapper
+
+from azureml.core.run import Run
+from azureml.explain.model.tabular_explainer import TabularExplainer
+from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient
+from azureml.explain.model.scoring.scoring_explainer import LinearScoringExplainer, save
+
+OUTPUT_DIR = './outputs/'
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+# get the IBM employee attrition dataset
+outdirname = 'dataset.6.21.19'
+try:
+    from urllib import urlretrieve
+except ImportError:
+    from urllib.request import urlretrieve
+zipfilename = outdirname + '.zip'
+urlretrieve('https://publictestdatasets.blob.core.windows.net/data/' + zipfilename, zipfilename)
+with zipfile.ZipFile(zipfilename, 'r') as unzip:
+    unzip.extractall('.')
+attritionData = pd.read_csv('./WA_Fn-UseC_-HR-Employee-Attrition.csv')
+
+# dropping Employee count as all values are 1 and hence attrition is independent of this feature
+attritionData = attritionData.drop(['EmployeeCount'], axis=1)
+# dropping Employee Number since it is merely an identifier
+attritionData = attritionData.drop(['EmployeeNumber'], axis=1)
+attritionData = attritionData.drop(['Over18'], axis=1)
+# since all values are 80
+attritionData = attritionData.drop(['StandardHours'], axis=1)
+
+# converting target variables from string to numerical values
+target_map = {'Yes': 1, 'No': 0}
+attritionData["Attrition_numerical"] = attritionData["Attrition"].apply(lambda x: target_map[x])
+target = attritionData["Attrition_numerical"]
+
+attritionXData = attritionData.drop(['Attrition_numerical', 'Attrition'], axis=1)
+
+# creating dummy columns for each categorical feature
+categorical = []
+for col, value in attritionXData.iteritems():
+    if value.dtype == 'object':
+        categorical.append(col)
+
+# store the numerical columns
+numerical = attritionXData.columns.difference(categorical)
+
+numeric_transformations = [([f], Pipeline(steps=[
+    ('imputer', SimpleImputer(strategy='median')),
+    ('scaler', StandardScaler())])) for f in numerical]
+
+categorical_transformations = [([f], OneHotEncoder(handle_unknown='ignore', sparse=False)) for f in categorical]
+
+transformations = numeric_transformations + categorical_transformations
+
+# append classifier to preprocessing pipeline
+clf = Pipeline(steps=[('preprocessor', DataFrameMapper(transformations)),
+                      ('classifier', LogisticRegression(solver='lbfgs'))])
+
+# get the run this was submitted from to interact with run history
+run = Run.get_context()
+
+# create an explanation client to store the explanation (contrib API)
+client = ExplanationClient.from_run(run)
+
+# Split data into train and test
+x_train, x_test, y_train, y_test = train_test_split(attritionXData,
+                                                    target,
+                                                    test_size=0.2,
+                                                    random_state=0,
+                                                    stratify=target)
+
+# write x_test out as a pickle file for later visualization
+x_test_pkl = 'x_test.pkl'
+with open(x_test_pkl, 'wb') as file:
+    joblib.dump(value=x_test, filename=os.path.join(OUTPUT_DIR, x_test_pkl))
+run.upload_file('x_test_ibm.pkl', os.path.join(OUTPUT_DIR, x_test_pkl))
+
+# preprocess the data and fit the classification model
+clf.fit(x_train, y_train)
+model = clf.steps[-1][1]
+
+# save model for use outside the script
+model_file_name = 'log_reg.pkl'
+with open(model_file_name, 'wb') as file:
+    joblib.dump(value=clf, filename=os.path.join(OUTPUT_DIR, model_file_name))
+
+# register the model with the model management service for later use
+run.upload_file('original_model.pkl', os.path.join(OUTPUT_DIR, model_file_name))
+original_model = run.register_model(model_name='original_model', model_path='original_model.pkl')
+
+# create an explainer to validate or debug the model
+tabular_explainer = TabularExplainer(model,
+                                     initialization_examples=x_train,
+                                     features=attritionXData.columns,
+                                     classes=["Not leaving", "leaving"],
+                                     transformations=transformations)
+
+# explain overall model predictions (global explanation)
+# passing in test dataset for evaluation examples - note it must be a representative sample of the original data
+# more data (e.g. x_train) will likely lead to higher accuracy, but at a time cost
+global_explanation = tabular_explainer.explain_global(x_test)
+
+# uploading model explanation data for storage or visualization
+comment = 'Global explanation on classification model trained on IBM employee attrition dataset'
+client.upload_model_explanation(global_explanation, comment=comment)
+
+# also create a lightweight explainer for scoring time
+scoring_explainer = LinearScoringExplainer(tabular_explainer)
+# pickle scoring explainer locally
+save(scoring_explainer, directory=OUTPUT_DIR, exist_ok=True)
+
+# register scoring explainer
+run.upload_file('IBM_attrition_explainer.pkl', os.path.join(OUTPUT_DIR, 'scoring_explainer.pkl'))
+scoring_explainer_model = run.register_model(model_name='IBM_attrition_explainer',
+                                             model_path='IBM_attrition_explainer.pkl')

how-to-use-azureml/explain-model/explain-on-amlcompute/run_explainer.py

@@ -1,52 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-# Licensed under the MIT license.
-
-from sklearn import datasets
-from sklearn.linear_model import Ridge
-from azureml.explain.model.tabular_explainer import TabularExplainer
-from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient
-from sklearn.model_selection import train_test_split
-from azureml.core.run import Run
-from sklearn.externals import joblib
-import os
-import numpy as np
-
-os.makedirs('./outputs', exist_ok=True)
-
-boston_data = datasets.load_boston()
-
-run = Run.get_context()
-client = ExplanationClient.from_run(run)
-
-X_train, X_test, y_train, y_test = train_test_split(boston_data.data,
-                                                    boston_data.target,
-                                                    test_size=0.2,
-                                                    random_state=0)
-
-alpha = 0.5
-# Use Ridge algorithm to create a regression model
-reg = Ridge(alpha)
-model = reg.fit(X_train, y_train)
-
-preds = reg.predict(X_test)
-run.log('alpha', alpha)
-
-model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)
-# save model in the outputs folder so it automatically get uploaded
-with open(model_file_name, 'wb') as file:
-    joblib.dump(value=reg, filename=os.path.join('./outputs/',
-                                                 model_file_name))
-
-# Explain predictions on your local machine
-tabular_explainer = TabularExplainer(model, X_train, features=boston_data.feature_names)
-
-# Explain overall model predictions (global explanation)
-# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data
-# x_train can be passed as well, but with more examples explanations it will
-# take longer although they may be more accurate
-global_explanation = tabular_explainer.explain_global(X_test)
-
-# Uploading model explanation data for storage or visualization in webUX
-# The explanation can then be downloaded on any compute
-comment = 'Global explanation on regression model trained on boston dataset'
-client.upload_model_explanation(global_explanation, comment=comment)

how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-binary-classification.ipynb

@@ -1,279 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Breast cancer diagnosis classification with scikit-learn (run model explainer locally)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-binary-classification.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": [
-        "Explain a model with the AML explain-model package\n",
-        "\n",
-        "1. Train a SVM classification model using Scikit-learn\n",
-        "2. Run 'explain_model' with full data in local mode, which doesn't contact any Azure services\n",
-        "3. Run 'explain_model' with summarized data in local mode, which doesn't contact any Azure services\n",
-        "4. Visualize the global and local explanations with the visualization dashboard."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.datasets import load_breast_cancer\n",
-        "from sklearn import svm\n",
-        "from azureml.explain.model.tabular_explainer import TabularExplainer"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 1. Run model explainer locally with full data"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Load the breast cancer diagnosis data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "breast_cancer_data = load_breast_cancer()\n",
-        "classes = breast_cancer_data.target_names.tolist()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Split data into train and test\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "x_train, x_test, y_train, y_test = train_test_split(breast_cancer_data.data, breast_cancer_data.target, test_size=0.2, random_state=0)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train a SVM classification model, which you want to explain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "clf = svm.SVC(gamma=0.001, C=100., probability=True)\n",
-        "model = clf.fit(x_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain predictions on your local machine"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tabular_explainer = TabularExplainer(model, x_train, features=breast_cancer_data.feature_names, classes=classes)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain overall model predictions (global explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
-        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
-        "global_explanation = tabular_explainer.explain_global(x_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Sorted SHAP values\n",
-        "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
-        "# Corresponding feature names\n",
-        "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
-        "# feature ranks (based on original order of features)\n",
-        "print('global importance rank: {}'.format(global_explanation.global_importance_rank))\n",
-        "# per class feature names\n",
-        "print('ranked per class feature names: {}'.format(global_explanation.get_ranked_per_class_names()))\n",
-        "# per class feature importance values\n",
-        "print('ranked per class feature values: {}'.format(global_explanation.get_ranked_per_class_values()))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dict(zip(global_explanation.get_ranked_global_names(), global_explanation.get_ranked_global_values()))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain overall model predictions as a collection of local (instance-level) explanations"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# feature shap values for all features and all data points in the training data\n",
-        "print('local importance values: {}'.format(global_explanation.local_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain local data points (individual instances)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# explain the first member of the test set\n",
-        "instance_num = 0\n",
-        "local_explanation = tabular_explainer.explain_local(x_test[instance_num,:])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# get the prediction for the first member of the test set and explain why model made that prediction\n",
-        "prediction_value = clf.predict(x_test)[instance_num]\n",
-        "\n",
-        "sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
-        "sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
-        "\n",
-        "\n",
-        "dict(zip(sorted_local_importance_names, sorted_local_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 2. Load visualization dashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Note you will need to have extensions enabled prior to jupyter kernel starting\n",
-        "!jupyter nbextension install --py --sys-prefix azureml.contrib.explain.model.visualize\n",
-        "!jupyter nbextension enable --py --sys-prefix azureml.contrib.explain.model.visualize\n",
-        "# Or, in Jupyter Labs, uncomment below\n",
-        "# jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
-        "# jupyter labextension install microsoft-mli-widget"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.contrib.explain.model.visualize import ExplanationDashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ExplanationDashboard(global_explanation, model, x_test)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "mesameki"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.8"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-multiclass-classification.ipynb

@@ -1,280 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Iris flower classification with scikit-learn (run model explainer locally)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-multiclass-classification.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": [
-        "Explain a model with the AML explain-model package\n",
-        "\n",
-        "1. Train a SVM classification model using Scikit-learn\n",
-        "2. Run 'explain_model' with full data in local mode, which doesn't contact any Azure services\n",
-        "3. Run 'explain_model' with summarized data in local mode, which doesn't contact any Azure services\n",
-        "4. Visualize the global and local explanations with the visualization dashboard."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.datasets import load_iris\n",
-        "from sklearn import svm\n",
-        "from azureml.explain.model.tabular_explainer import TabularExplainer"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 1. Run model explainer locally with full data"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Load the breast cancer diagnosis data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "iris = load_iris()\n",
-        "X = iris['data']\n",
-        "y = iris['target']\n",
-        "classes = iris['target_names']\n",
-        "feature_names = iris['feature_names']"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Split data into train and test\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train a SVM classification model, which you want to explain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "clf = svm.SVC(gamma=0.001, C=100., probability=True)\n",
-        "model = clf.fit(x_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain predictions on your local machine"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tabular_explainer = TabularExplainer(model, x_train, features = feature_names, classes=classes)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain overall model predictions (global explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "global_explanation = tabular_explainer.explain_global(x_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Sorted SHAP values\n",
-        "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
-        "# Corresponding feature names\n",
-        "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
-        "# feature ranks (based on original order of features)\n",
-        "print('global importance rank: {}'.format(global_explanation.global_importance_rank))\n",
-        "# per class feature names\n",
-        "print('ranked per class feature names: {}'.format(global_explanation.get_ranked_per_class_names()))\n",
-        "# per class feature importance values\n",
-        "print('ranked per class feature values: {}'.format(global_explanation.get_ranked_per_class_values()))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dict(zip(global_explanation.get_ranked_global_names(), global_explanation.get_ranked_global_values()))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain overall model predictions as a collection of local (instance-level) explanations"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# feature shap values for all features and all data points in the training data\n",
-        "print('local importance values: {}'.format(global_explanation.local_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain local data points (individual instances)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# explain the first member of the test set\n",
-        "instance_num = 0\n",
-        "local_explanation = tabular_explainer.explain_local(x_test[instance_num,:])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# get the prediction for the first member of the test set and explain why model made that prediction\n",
-        "prediction_value = clf.predict(x_test)[instance_num]\n",
-        "\n",
-        "sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
-        "sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
-        "\n",
-        "\n",
-        "dict(zip(sorted_local_importance_names, sorted_local_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Load visualization dashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Note you will need to have extensions enabled prior to jupyter kernel starting\n",
-        "!jupyter nbextension install --py --sys-prefix azureml.contrib.explain.model.visualize\n",
-        "!jupyter nbextension enable --py --sys-prefix azureml.contrib.explain.model.visualize\n",
-        "# Or, in Jupyter Labs, uncomment below\n",
-        "# jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
-        "# jupyter labextension install microsoft-mli-widget"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.contrib.explain.model.visualize import ExplanationDashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ExplanationDashboard(global_explanation, model, x_test)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "mesameki"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.8"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-regression.ipynb

@@ -1,272 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Boston Housing Price Prediction with scikit-learn (run model explainer locally)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-sklearn-regression.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": [
-        "Explain a model with the AML explain-model package\n",
-        "\n",
-        "1. Train a GradientBoosting regression model using Scikit-learn\n",
-        "2. Run 'explain_model' with full dataset in local mode, which doesn't contact any Azure services.\n",
-        "3. Run 'explain_model' with summarized dataset in local mode, which doesn't contact any Azure services.\n",
-        "4. Visualize the global and local explanations with the visualization dashboard."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn import datasets\n",
-        "from sklearn.ensemble import GradientBoostingRegressor\n",
-        "from azureml.explain.model.tabular_explainer import TabularExplainer"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 1. Run model explainer locally with full data"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Load the Boston house price data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "boston_data = datasets.load_boston()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Split data into train and test\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "x_train, x_test, y_train, y_test = train_test_split(boston_data.data, boston_data.target, test_size=0.2, random_state=0)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train a GradientBoosting Regression model, which you want to explain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "reg = GradientBoostingRegressor(n_estimators=100, max_depth=4,\n",
-        "                                learning_rate=0.1, loss='huber',\n",
-        "                                random_state=1)\n",
-        "model = reg.fit(x_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain predictions on your local machine"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tabular_explainer = TabularExplainer(model, x_train, features = boston_data.feature_names)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain overall model predictions (global explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
-        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
-        "global_explanation = tabular_explainer.explain_global(x_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Sorted SHAP values \n",
-        "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
-        "# Corresponding feature names\n",
-        "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
-        "# feature ranks (based on original order of features)\n",
-        "print('global importance rank: {}'.format(global_explanation.global_importance_rank))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dict(zip(global_explanation.get_ranked_global_names(), global_explanation.get_ranked_global_values()))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain overall model predictions as a collection of local (instance-level) explanations"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# feature shap values for all features and all data points in the training data\n",
-        "print('local importance values: {}'.format(global_explanation.local_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain local data points (individual instances)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_explanation = tabular_explainer.explain_local(x_test[0,:])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# sorted local feature importance information; reflects the original feature order\n",
-        "sorted_local_importance_names = local_explanation.get_ranked_local_names()\n",
-        "sorted_local_importance_values = local_explanation.get_ranked_local_values()\n",
-        "\n",
-        "print('sorted local importance names: {}'.format(sorted_local_importance_names))\n",
-        "print('sorted local importance values: {}'.format(sorted_local_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Load visualization dashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Note you will need to have extensions enabled prior to jupyter kernel starting\n",
-        "!jupyter nbextension install --py --sys-prefix azureml.contrib.explain.model.visualize\n",
-        "!jupyter nbextension enable --py --sys-prefix azureml.contrib.explain.model.visualize\n",
-        "# Or, in Jupyter Labs, uncomment below\n",
-        "# jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
-        "# jupyter labextension install microsoft-mli-widget"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.contrib.explain.model.visualize import ExplanationDashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ExplanationDashboard(global_explanation, model, x_test)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "mesameki"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.8"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/explain-model/explain-tabular-data-raw-features/explain-sklearn-raw-features.ipynb

@@ -1,337 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Summary\n",
-        "From raw data that is a mixture of categoricals and numeric, featurize the categoricals using one hot encoding. Use tabular explainer to get explain object and then get raw feature importances"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/explain-tabular-data-raw-features/explain-sklearn-raw-features.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Explain a model with the AML explain-model package on raw features\n",
-        "\n",
-        "1. Train a Logistic Regression model using Scikit-learn\n",
-        "2. Run 'explain_model' with full dataset in local mode, which doesn't contact any Azure services.\n",
-        "3. Run 'explain_model' with summarized dataset in local mode, which doesn't contact any Azure services.\n",
-        "4. Visualize the global and local explanations with the visualization dashboard."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.pipeline import Pipeline\n",
-        "from sklearn.impute import SimpleImputer\n",
-        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
-        "from sklearn.linear_model import LogisticRegression\n",
-        "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
-        "import pandas as pd\n",
-        "import numpy as np"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "titanic_url = ('https://raw.githubusercontent.com/amueller/'\n",
-        "               'scipy-2017-sklearn/091d371/notebooks/datasets/titanic3.csv')\n",
-        "data = pd.read_csv(titanic_url)\n",
-        "# fill missing values\n",
-        "data = data.fillna(method=\"ffill\")\n",
-        "data = data.fillna(method=\"bfill\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 1. Run model explainer locally with full data"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Similar to example [here](https://scikit-learn.org/stable/auto_examples/compose/plot_column_transformer_mixed_types.html#sphx-glr-auto-examples-compose-plot-column-transformer-mixed-types-py), use a subset of columns"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.model_selection import train_test_split\n",
-        "\n",
-        "numeric_features = ['age', 'fare']\n",
-        "categorical_features = ['embarked', 'sex', 'pclass']\n",
-        "\n",
-        "y = data['survived'].values\n",
-        "X = data[categorical_features + numeric_features]\n",
-        "\n",
-        "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "sklearn imports"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.pipeline import Pipeline\n",
-        "from sklearn.impute import SimpleImputer\n",
-        "from sklearn.preprocessing import StandardScaler, OneHotEncoder"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can explain raw features by either using a `sklearn.compose.ColumnTransformer` or a list of fitted transformer tuples. The cell below uses `sklearn.compose.ColumnTransformer`. In case you want to run the example with the list of fitted transformer tuples, comment the cell below and uncomment the cell that follows after. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.compose import ColumnTransformer\n",
-        "\n",
-        "transformations = ColumnTransformer([\n",
-        "    (\"age_fare\", Pipeline(steps=[\n",
-        "        ('imputer', SimpleImputer(strategy='median')),\n",
-        "        ('scaler', StandardScaler())\n",
-        "    ]), [\"age\", \"fare\"]),\n",
-        "    (\"embarked\", Pipeline(steps=[\n",
-        "        (\"imputer\", SimpleImputer(strategy='constant', fill_value='missing')), \n",
-        "        (\"encoder\", OneHotEncoder(sparse=False))]), [\"embarked\"]),\n",
-        "    (\"sex_pclass\", OneHotEncoder(sparse=False), [\"sex\", \"pclass\"])    \n",
-        "])\n",
-        "\n",
-        "\n",
-        "# Append classifier to preprocessing pipeline.\n",
-        "# Now we have a full prediction pipeline.\n",
-        "clf = Pipeline(steps=[('preprocessor', transformations),\n",
-        "                      ('classifier', LogisticRegression(solver='lbfgs'))])\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "'''\n",
-        "# Uncomment below if sklearn-pandas is not installed\n",
-        "#!pip install sklearn-pandas\n",
-        "from sklearn_pandas import DataFrameMapper\n",
-        "\n",
-        "# Impute, standardize the numeric features and one-hot encode the categorical features.    \n",
-        "\n",
-        "transformations = [\n",
-        "    ([\"age\", \"fare\"], Pipeline(steps=[\n",
-        "        ('imputer', SimpleImputer(strategy='median')),\n",
-        "        ('scaler', StandardScaler())\n",
-        "    ])),\n",
-        "    ([\"embarked\"], Pipeline(steps=[\n",
-        "        (\"imputer\", SimpleImputer(strategy='constant', fill_value='missing')), \n",
-        "        (\"encoder\", OneHotEncoder(sparse=False))])),\n",
-        "    ([\"sex\", \"pclass\"], OneHotEncoder(sparse=False))    \n",
-        "]\n",
-        "\n",
-        "\n",
-        "# Append classifier to preprocessing pipeline.\n",
-        "# Now we have a full prediction pipeline.\n",
-        "clf = Pipeline(steps=[('preprocessor', DataFrameMapper(transformations)),\n",
-        "                      ('classifier', LogisticRegression(solver='lbfgs'))])\n",
-        "'''"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train a Logistic Regression model, which you want to explain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model = clf.fit(x_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain predictions on your local machine"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tabular_explainer = TabularExplainer(clf.steps[-1][1], initialization_examples=x_train, features=x_train.columns, transformations=transformations)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
-        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
-        "global_explanation = tabular_explainer.explain_global(x_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "sorted_global_importance_values = global_explanation.get_ranked_global_values()\n",
-        "sorted_global_importance_names = global_explanation.get_ranked_global_names()\n",
-        "dict(zip(sorted_global_importance_names, sorted_global_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain overall model predictions as a collection of local (instance-level) explanations"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# explain the first member of the test set\n",
-        "local_explanation = tabular_explainer.explain_local(x_test[:1])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# get the prediction for the first member of the test set and explain why model made that prediction\n",
-        "prediction_value = clf.predict(x_test)[0]\n",
-        "\n",
-        "sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
-        "sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
-        "\n",
-        "# Sorted local SHAP values\n",
-        "print('ranked local importance values: {}'.format(sorted_local_importance_values))\n",
-        "# Corresponding feature names\n",
-        "print('ranked local importance names: {}'.format(sorted_local_importance_names))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 2. Load visualization dashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Note you will need to have extensions enabled prior to jupyter kernel starting\n",
-        "!jupyter nbextension install --py --sys-prefix azureml.contrib.explain.model.visualize\n",
-        "!jupyter nbextension enable --py --sys-prefix azureml.contrib.explain.model.visualize\n",
-        "# Or, in Jupyter Labs, uncomment below\n",
-        "# jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
-        "# jupyter labextension install microsoft-mli-widget"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.contrib.explain.model.visualize import ExplanationDashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ExplanationDashboard(global_explanation, model, x_test)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "mesameki"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.8"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/explain-model/explain-tabular-data-run-history/explain-run-history-sklearn-classification.ipynb

@@ -1,262 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Breast cancer diagnosis classification with scikit-learn (save model explanations via AML Run History)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/explain-tabular-data-run-history/explain-run-history-sklearn-classification.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": [
-        "Explain a model with the AML explain-model package\n",
-        "\n",
-        "1. Train a SVM classification model using Scikit-learn\n",
-        "2. Run 'explain_model' with AML Run History, which leverages run history service to store and manage the explanation data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.datasets import load_breast_cancer\n",
-        "from sklearn import svm\n",
-        "from azureml.explain.model.tabular_explainer import TabularExplainer"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 1. Run model explainer locally with full data"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Load the breast cancer diagnosis data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "breast_cancer_data = load_breast_cancer()\n",
-        "classes = breast_cancer_data.target_names.tolist()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Split data into train and test\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "x_train, x_test, y_train, y_test = train_test_split(breast_cancer_data.data, breast_cancer_data.target, test_size=0.2, random_state=0)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train a SVM classification model, which you want to explain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "clf = svm.SVC(gamma=0.001, C=100., probability=True)\n",
-        "model = clf.fit(x_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain predictions on your local machine"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tabular_explainer = TabularExplainer(model, x_train, features=breast_cancer_data.feature_names, classes=classes)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain overall model predictions (global explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
-        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
-        "global_explanation = tabular_explainer.explain_global(x_test)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 2. Save Model Explanation With AML Run History"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.core\n",
-        "from azureml.core import Workspace, Experiment, Run\n",
-        "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
-        "from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient\n",
-        "# Check core SDK version number\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\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": [
-        "experiment_name = 'explain_model'\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "run = experiment.start_logging()\n",
-        "client = ExplanationClient.from_run(run)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Uploading model explanation data for storage or visualization in webUX\n",
-        "# The explanation can then be downloaded on any compute\n",
-        "client.upload_model_explanation(global_explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Get model explanation data\n",
-        "explanation = client.download_model_explanation()\n",
-        "local_importance_values = explanation.local_importance_values\n",
-        "expected_values = explanation.expected_values"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Get the top k (e.g., 4) most important features with their importance values\n",
-        "explanation = client.download_model_explanation(top_k=4)\n",
-        "global_importance_values = explanation.get_ranked_global_values()\n",
-        "global_importance_names = explanation.get_ranked_global_names()\n",
-        "per_class_names = explanation.get_ranked_per_class_names()[0]\n",
-        "per_class_values = explanation.get_ranked_per_class_values()[0]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print('per class feature importance values: {}'.format(per_class_values))\n",
-        "print('per class feature importance names: {}'.format(per_class_names))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dict(zip(per_class_names, per_class_values))"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "mesameki"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.8"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/explain-model/explain-tabular-data-run-history/explain-run-history-sklearn-regression.ipynb

@@ -1,276 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Boston Housing Price Prediction with scikit-learn (save model explanations via AML Run History)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/explain-tabular-data-run-history/explain-run-history-sklearn-regression.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": [
-        "Explain a model with the AML explain-model package\n",
-        "\n",
-        "1. Train a GradientBoosting regression model using Scikit-learn\n",
-        "2. Run 'explain_model' with AML Run History, which leverages run history service to store and manage the explanation data"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Save Model Explanation With AML Run History"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#Import Iris dataset\n",
-        "from sklearn import datasets\n",
-        "from sklearn.ensemble import GradientBoostingRegressor\n",
-        "\n",
-        "import azureml.core\n",
-        "from azureml.core import Workspace, Experiment, Run\n",
-        "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
-        "from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient\n",
-        "# Check core SDK version number\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\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": [
-        "experiment_name = 'explain_model'\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "run = experiment.start_logging()\n",
-        "client = ExplanationClient.from_run(run)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Load the Boston house price data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "boston_data = datasets.load_boston()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Split data into train and test\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "x_train, x_test, y_train, y_test = train_test_split(boston_data.data, boston_data.target, test_size=0.2, random_state=0)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train a GradientBoosting Regression model, which you want to explain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "clf = GradientBoostingRegressor(n_estimators=100, max_depth=4,\n",
-        "                                learning_rate=0.1, loss='huber',\n",
-        "                                random_state=1)\n",
-        "model = clf.fit(x_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain predictions on your local machine"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tabular_explainer = TabularExplainer(model, x_train, features=boston_data.feature_names)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain overall model predictions (global explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
-        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
-        "global_explanation = tabular_explainer.explain_global(x_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Uploading model explanation data for storage or visualization in webUX\n",
-        "# The explanation can then be downloaded on any compute\n",
-        "client.upload_model_explanation(global_explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Get model explanation data\n",
-        "explanation = client.download_model_explanation()\n",
-        "local_importance_values = explanation.local_importance_values\n",
-        "expected_values = explanation.expected_values"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Print the values\n",
-        "print('expected values: {}'.format(expected_values))\n",
-        "print('local importance values: {}'.format(local_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Get the top k (e.g., 4) most important features with their importance values\n",
-        "explanation = client.download_model_explanation(top_k=4)\n",
-        "global_importance_values = explanation.get_ranked_global_values()\n",
-        "global_importance_names = explanation.get_ranked_global_names()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print('global importance values: {}'.format(global_importance_values))\n",
-        "print('global importance names: {}'.format(global_importance_names))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain individual instance predictions (local explanation) ##### needs to get updated with the new build"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_explanation = tabular_explainer.explain_local(x_test[0,:])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# local feature importance information\n",
-        "local_importance_values = local_explanation.local_importance_values\n",
-        "print('local importance values: {}'.format(local_importance_values))"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "mesameki"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.8"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/explain-model/tabular-data/advanced-feature-transformations-explain-local.ipynb

@@ -0,0 +1,523 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/tabular-data/advanced-feature-transformations-explain-local.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Explain binary classification model predictions with raw feature transformations\n",
+        "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to explain and visualize a binary classification model that uses advanced many to one or many to many feature transformations.**_\n",
+        "\n",
+        "\n",
+        "\n",
+        "## Table of Contents\n",
+        "\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Run model explainer locally at training time](#Explain)\n",
+        "    1. Apply feature transformations\n",
+        "    1. Train a binary classification model\n",
+        "    1. Explain the model on raw features\n",
+        "        1. Generate global explanations\n",
+        "        1. Generate local explanations\n",
+        "1. [Visualize results](#Visualize)\n",
+        "1. [Next steps](#Next)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "\n",
+        "This notebook illustrates creating explanations for a binary classification model, Titanic passenger data classification, that uses many to one and many to many feature transformations from raw data to engineered features. For the many to one transformation, we sum 2 features `age` and `fare`. For many to many transformations two features are computed: one that is product of `age` and `fare` and another that is square of this product. Our tabular data explainer is then used to get the explanation object with the flag `allow_all_transformations` passed. The object is then used to get raw feature importances.\n",
+        "\n",
+        "\n",
+        "We will showcase raw feature transformations with three tabular data explainers: TabularExplainer (SHAP), MimicExplainer (global surrogate), and PFIExplainer.\n",
+        "\n",
+        "| ![Interpretability Toolkit Architecture](./img/interpretability-architecture.PNG) |\n",
+        "|:--:|\n",
+        "| *Interpretability Toolkit Architecture* |\n",
+        "\n",
+        "Problem: Titanic passenger data classification with scikit-learn (run model explainer locally)\n",
+        "\n",
+        "1. Transform raw features to engineered features\n",
+        "2. Train a Logistic Regression model using Scikit-learn\n",
+        "3. Run 'explain_model' globally and locally with full dataset in local mode, which doesn't contact any Azure services.\n",
+        "4. Visualize the global and local explanations with the visualization dashboard.\n",
+        "---\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "You will need to have extensions enabled prior to jupyter kernel starting to see the visualization dashboard.\n",
+        "```\n",
+        "(myenv) $ jupyter nbextension install --py --sys-prefix azureml.contrib.explain.model.visualize\n",
+        "(myenv) $ jupyter nbextension enable --py --sys-prefix azureml.contrib.explain.model.visualize\n",
+        "```\n",
+        "Or\n",
+        "\n",
+        "```\n",
+        "(myenv) $ jupyter nbextension install azureml.contrib.explain.model.visualize --user --py\n",
+        "(myenv) $ jupyter nbextension enable azureml.contrib.explain.model.visualize --user --py\n",
+        "```\n",
+        "\n",
+        "If you are using Jupyter Labs run the following commands instead:\n",
+        "```\n",
+        "(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
+        "(myenv) $ jupyter labextension install microsoft-mli-widget\n",
+        "```\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Explain\n",
+        "\n",
+        "### Run model explainer locally at training time"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "# Explainers:\n",
+        "# 1. SHAP Tabular Explainer\n",
+        "from azureml.explain.model.tabular_explainer import TabularExplainer\n",
+        "\n",
+        "# OR\n",
+        "\n",
+        "# 2. Mimic Explainer\n",
+        "from azureml.explain.model.mimic.mimic_explainer import MimicExplainer\n",
+        "# You can use one of the following four interpretable models as a global surrogate to the black box model\n",
+        "from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel\n",
+        "from azureml.explain.model.mimic.models.linear_model import LinearExplainableModel\n",
+        "from azureml.explain.model.mimic.models.linear_model import SGDExplainableModel\n",
+        "from azureml.explain.model.mimic.models.tree_model import DecisionTreeExplainableModel\n",
+        "\n",
+        "# OR\n",
+        "\n",
+        "# 3. PFI Explainer\n",
+        "from azureml.explain.model.permutation.permutation_importance import PFIExplainer "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Load the Titanic passenger data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "titanic_url = ('https://raw.githubusercontent.com/amueller/'\n",
+        "               'scipy-2017-sklearn/091d371/notebooks/datasets/titanic3.csv')\n",
+        "data = pd.read_csv(titanic_url)\n",
+        "# fill missing values\n",
+        "data = data.fillna(method=\"ffill\")\n",
+        "data = data.fillna(method=\"bfill\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Similar to example [here](https://scikit-learn.org/stable/auto_examples/compose/plot_column_transformer_mixed_types.html#sphx-glr-auto-examples-compose-plot-column-transformer-mixed-types-py), use a subset of columns"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "numeric_features = ['age', 'fare']\n",
+        "categorical_features = ['embarked', 'sex', 'pclass']\n",
+        "\n",
+        "y = data['survived'].values\n",
+        "X = data[categorical_features + numeric_features]\n",
+        "\n",
+        "# Split data into train and test\n",
+        "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Transform raw features"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can explain raw features by either using a `sklearn.compose.ColumnTransformer` or a list of fitted transformer tuples. The cell below uses `sklearn.compose.ColumnTransformer`. In case you want to run the example with the list of fitted transformer tuples, comment the cell below and uncomment the cell that follows after. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# We add many to one and many to many transformations for illustration purposes.\n",
+        "# The support for raw feature explanations with many to one and many to many transformations are only supported \n",
+        "# When allow_all_transformations is set to True on explainer creation\n",
+        "from sklearn.preprocessing import FunctionTransformer\n",
+        "many_to_one_transformer = FunctionTransformer(lambda x: x.sum(axis=1).reshape(-1, 1))\n",
+        "many_to_many_transformer = FunctionTransformer(lambda x: np.hstack(\n",
+        "    (np.prod(x, axis=1).reshape(-1, 1), (np.prod(x, axis=1)**2).reshape(-1, 1))\n",
+        "))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.compose import ColumnTransformer\n",
+        "\n",
+        "transformations = ColumnTransformer([\n",
+        "    (\"age_fare_1\", Pipeline(steps=[\n",
+        "        ('imputer', SimpleImputer(strategy='median')),\n",
+        "        ('scaler', StandardScaler())\n",
+        "    ]), [\"age\", \"fare\"]),\n",
+        "    (\"age_fare_2\", many_to_one_transformer, [\"age\", \"fare\"]),\n",
+        "    (\"age_fare_3\", many_to_many_transformer, [\"age\", \"fare\"]),\n",
+        "    (\"embarked\", Pipeline(steps=[\n",
+        "        (\"imputer\", SimpleImputer(strategy='constant', fill_value='missing')), \n",
+        "        (\"encoder\", OneHotEncoder(sparse=False))]), [\"embarked\"]),\n",
+        "    (\"sex_pclass\", OneHotEncoder(sparse=False), [\"sex\", \"pclass\"])    \n",
+        "])\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "'''\n",
+        "# Uncomment below if sklearn-pandas is not installed\n",
+        "#!pip install sklearn-pandas\n",
+        "from sklearn_pandas import DataFrameMapper\n",
+        "\n",
+        "# Impute, standardize the numeric features and one-hot encode the categorical features.    \n",
+        "\n",
+        "transformations = [\n",
+        "    ([\"age\", \"fare\"], Pipeline(steps=[\n",
+        "        ('imputer', SimpleImputer(strategy='median')),\n",
+        "        ('scaler', StandardScaler())\n",
+        "    ])),\n",
+        "    ([\"age\", \"fare\"], many_to_one_transformer),\n",
+        "    ([\"age\", \"fare\"], many_to_many_transformer),\n",
+        "    ([\"embarked\"], Pipeline(steps=[\n",
+        "        (\"imputer\", SimpleImputer(strategy='constant', fill_value='missing')), \n",
+        "        (\"encoder\", OneHotEncoder(sparse=False))])),\n",
+        "    ([\"sex\", \"pclass\"], OneHotEncoder(sparse=False))    \n",
+        "]\n",
+        "\n",
+        "\n",
+        "# Append classifier to preprocessing pipeline.\n",
+        "# Now we have a full prediction pipeline.\n",
+        "clf = Pipeline(steps=[('preprocessor', DataFrameMapper(transformations)),\n",
+        "                      ('classifier', LogisticRegression(solver='lbfgs'))])\n",
+        "'''"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Train a Logistic Regression model, which you want to explain"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Append classifier to preprocessing pipeline.\n",
+        "# Now we have a full prediction pipeline.\n",
+        "clf = Pipeline(steps=[('preprocessor', transformations),\n",
+        "                      ('classifier', LogisticRegression(solver='lbfgs'))])\n",
+        "model = clf.fit(x_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Explain predictions on your local machine"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# 1. Using SHAP TabularExplainer\n",
+        "# When the last parameter allow_all_transformations is passed, we handle many to one and many to many transformations to \n",
+        "# generate approximations to raw feature importances. When this flag is passed, for transformations not recognized as one to \n",
+        "# many, we distribute feature importances evenly to raw features generating them.\n",
+        "# clf.steps[-1][1] returns the trained classification model\n",
+        "explainer = TabularExplainer(clf.steps[-1][1], \n",
+        "                             initialization_examples=x_train, \n",
+        "                             features=x_train.columns, \n",
+        "                             transformations=transformations, \n",
+        "                             allow_all_transformations=True)\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "# 2. Using MimicExplainer\n",
+        "# augment_data is optional and if true, oversamples the initialization examples to improve surrogate model accuracy to fit original model.  Useful for high-dimensional data where the number of rows is less than the number of columns. \n",
+        "# max_num_of_augmentations is optional and defines max number of times we can increase the input data size.\n",
+        "# LGBMExplainableModel can be replaced with LinearExplainableModel, SGDExplainableModel, or DecisionTreeExplainableModel\n",
+        "# explainer = MimicExplainer(clf.steps[-1][1], \n",
+        "#                            x_train, \n",
+        "#                            LGBMExplainableModel, \n",
+        "#                            augment_data=True, \n",
+        "#                            max_num_of_augmentations=10, \n",
+        "#                            features=x_train.columns, \n",
+        "#                            transformations=transformations, \n",
+        "#                            allow_all_transformations=True)\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "# 3. Using PFIExplainer\n",
+        "\n",
+        "# Use the parameter \"metric\" to pass a metric name or function to evaluate the permutation. \n",
+        "# Note that if a metric function is provided a higher value must be better.\n",
+        "# Otherwise, take the negative of the function or set the parameter \"is_error_metric\" to True.\n",
+        "# Default metrics: \n",
+        "# F1 Score for binary classification, F1 Score with micro average for multiclass classification and\n",
+        "# Mean absolute error for regression\n",
+        "\n",
+        "\n",
+        "# explainer = PFIExplainer(clf.steps[-1][1], \n",
+        "#                          features=x_train.columns, \n",
+        "#                          transformations=transformations)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Generate global explanations\n",
+        "Explain overall model predictions (global explanation)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
+        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
+        "\n",
+        "global_explanation = explainer.explain_global(x_test)\n",
+        "\n",
+        "# Note: if you used the PFIExplainer in the previous step, use the next line of code instead\n",
+        "# global_explanation = explainer.explain_global(x_test, true_labels=y_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Sorted SHAP values\n",
+        "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
+        "# Corresponding feature names\n",
+        "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
+        "# Feature ranks (based on original order of features)\n",
+        "print('global importance rank: {}'.format(global_explanation.global_importance_rank))\n",
+        "# Per class feature names\n",
+        "print('ranked per class feature names: {}'.format(global_explanation.get_ranked_per_class_names()))\n",
+        "# Per class feature importance values\n",
+        "print('ranked per class feature values: {}'.format(global_explanation.get_ranked_per_class_values()))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Print out a dictionary that holds the sorted feature importance names and values\n",
+        "print('global importance rank: {}'.format(global_explanation.get_feature_importance_dict()))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Explain overall model predictions as a collection of local (instance-level) explanations"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# feature shap values for all features and all data points in the training data\n",
+        "print('local importance values: {}'.format(global_explanation.local_importance_values))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Generate local explanations\n",
+        "Explain local data points (individual instances)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Note: PFIExplainer does not support local explanations\n",
+        "# You can pass a specific data point or a group of data points to the explain_local function\n",
+        "\n",
+        "# E.g., Explain the first data point in the test set\n",
+        "instance_num = 1\n",
+        "local_explanation = explainer.explain_local(x_test[:instance_num])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Get the prediction for the first member of the test set and explain why model made that prediction\n",
+        "prediction_value = clf.predict(x_test)[instance_num]\n",
+        "\n",
+        "sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
+        "sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
+        "\n",
+        "print('local importance values: {}'.format(sorted_local_importance_values))\n",
+        "print('local importance names: {}'.format(sorted_local_importance_names))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Visualize\n",
+        "Load the visualization dashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.explain.model.visualize import ExplanationDashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ExplanationDashboard(global_explanation, model, x_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Next\n",
+        "Learn about other use cases of the explain package on a:\n",
+        "       \n",
+        "1. [Training time: regression problem](./explain-regression-local.ipynb)\n",
+        "1. [Training time: binary classification problem](./explain-binary-classification-local.ipynb)\n",
+        "1. [Training time: multiclass classification problem](./explain-multiclass-classification-local.ipynb)\n",
+        "1. [Explain models with simple feature transformations](./simple-feature-transformations-explain-local.ipynb)\n",
+        "1. [Save model explanations via Azure Machine Learning Run History](../azure-integration/run-history/save-retrieve-explanations-run-history.ipynb)\n",
+        "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb)\n",
+        "1. Inferencing time: deploy a classification model and explainer:\n",
+        "    1. [Deploy a locally-trained model and explainer](../azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",
+        "    1. [Deploy a remotely-trained model and explainer](../azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "mesameki"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}