Update 12/2 2

Update 12/2
Update 12-1-19
2025-12-20 09:37:04 -05:00 · 2018-12-02 19:21:15 -05:00 · 2018-12-02 14:46:52 -05:00 · 2018-12-01 20:37:45 -05:00 · 2018-11-29 11:32:16 -05:00
612 changed files with 111988 additions and 126072 deletions
--- a/.github/ISSUE_TEMPLATE/bug_report.md
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -1,30 +0,0 @@
 ---
 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.
--- a/.github/ISSUE_TEMPLATE/notebook-issue.md
+++ b/.github/ISSUE_TEMPLATE/notebook-issue.md
@@ -1,43 +0,0 @@
 ---
 name: Notebook issue
 about: Describe your notebook issue
 title: "[Notebook] DESCRIPTIVE TITLE"
 labels: notebook
 assignees: ''
 ---
 ### DESCRIPTION: Describe clearly + concisely
 .
 ### REPRODUCIBLE: Steps
 .
 ### EXPECTATION: Clear description 
 .
 ### CONFIG/ENVIRONMENT: 
 ```Provide where applicable
 ## Your Python & SDK version:
 ## Environment definition:
 ## Notebook name or Python scripts:
 ## Run and Workspace Id:
 ## Pipeline definition:
 ## Example data:
 ## Any log files:
 ```
--- a/Dockerfiles/1.0.10/Dockerfile
+++ b/Dockerfiles/1.0.10/Dockerfile
@@ -1,29 +0,0 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.10"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.10" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.15/Dockerfile
+++ b/Dockerfiles/1.0.15/Dockerfile
@@ -1,29 +0,0 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.15"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.15" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.17/Dockerfile
+++ b/Dockerfiles/1.0.17/Dockerfile
@@ -1,29 +0,0 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.17"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.17" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.18/Dockerfile
+++ b/Dockerfiles/1.0.18/Dockerfile
@@ -1,29 +0,0 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.18"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.18" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.2/Dockerfile
+++ b/Dockerfiles/1.0.2/Dockerfile
@@ -1,29 +0,0 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.2"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.2" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.21/Dockerfile
+++ b/Dockerfiles/1.0.21/Dockerfile
@@ -1,29 +0,0 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.21"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.21" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.23/Dockerfile
+++ b/Dockerfiles/1.0.23/Dockerfile
@@ -1,29 +0,0 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.23"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.23" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.30/Dockerfile
+++ b/Dockerfiles/1.0.30/Dockerfile
@@ -1,29 +0,0 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.30"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.30" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.33/Dockerfile
+++ b/Dockerfiles/1.0.33/Dockerfile
@@ -1,29 +0,0 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.33"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.33" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.41/Dockerfile
+++ b/Dockerfiles/1.0.41/Dockerfile
@@ -1,29 +0,0 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.41"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.41" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.43/Dockerfile
+++ b/Dockerfiles/1.0.43/Dockerfile
@@ -1,29 +0,0 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.43"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.43" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.6/Dockerfile
+++ b/Dockerfiles/1.0.6/Dockerfile
@@ -1,29 +0,0 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.6"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.6" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.8/Dockerfile
+++ b/Dockerfiles/1.0.8/Dockerfile
@@ -1,29 +0,0 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.8"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.8" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Licenses/sdk-license/LICENSE
+++ b/Licenses/sdk-license/LICENSE
@@ -1,14 +0,0 @@
 This software is made available to you on the condition that you agree to
 [your agreement][1] governing your use of Azure.
 If you do not have an existing agreement governing your use of Azure, you agree that 
 your agreement governing use of Azure is the [Microsoft Online Subscription Agreement][2]
 (which incorporates the [Online Services Terms][3]).
 By using the software you agree to these terms. This software may collect data
 that is transmitted to Microsoft. Please see the [Microsoft Privacy Statement][4]
 to learn more about how Microsoft processes personal data.
 [1]: https://azure.microsoft.com/en-us/support/legal/
 [2]: https://azure.microsoft.com/en-us/support/legal/subscription-agreement/
 [3]: http://www.microsoftvolumelicensing.com/DocumentSearch.aspx?Mode=3&DocumentTypeId=46
 [4]: http://go.microsoft.com/fwlink/?LinkId=248681 
--- a/NBSETUP.md
+++ b/NBSETUP.md
@@ -1,95 +0,0 @@
 # Set up your notebook environment for Azure Machine Learning
 To run the notebooks in this repository use one of following options.
 ## **Option 1: Use Azure Notebooks**
 Azure Notebooks is a hosted Jupyter-based notebook service in the Azure cloud. Azure Machine Learning Python SDK is already pre-installed in the Azure Notebooks `Python 3.6` kernel.
 1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
 [Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks
 1. Follow the instructions in the [Configuration](configuration.ipynb) notebook to create and connect to a workspace
 1. Open one of the sample notebooks
    **Make sure the Azure Notebook kernel is set to `Python 3.6`** when you open a notebook by choosing Kernel > Change Kernel > Python 3.6 from the menus.
 ## **Option 2: Use your own notebook server**
 ### Quick installation
 We recommend you create a Python virtual environment ([Miniconda](https://conda.io/miniconda.html) preferred but [virtualenv](https://virtualenv.pypa.io/en/latest/) works too) and install the SDK in it.
 ```sh
 # install just the base SDK
 pip install azureml-sdk
 # clone the sample repoistory
 git clone https://github.com/Azure/MachineLearningNotebooks.git
 # below steps are optional
 # install the base SDK, Jupyter notebook server and tensorboard
 pip install azureml-sdk[notebooks,tensorboard]
 # install model explainability component
 pip install azureml-sdk[explain]
 # install automated ml components
 pip install azureml-sdk[automl]
 # install experimental features (not ready for production use)
 pip install azureml-sdk[contrib]
 ```
 Note the _extras_ (the keywords inside the square brackets) can be combined. For example:
 ```sh
 # install base SDK, Jupyter notebook and automated ml components
 pip install azureml-sdk[notebooks,automl]
 ```
 ### Full instructions
 [Install the Azure Machine Learning SDK](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python)
 Please make sure you start with the [Configuration](configuration.ipynb) notebook to create and connect to a workspace.
 ### Video walkthrough:
 [!VIDEO https://youtu.be/VIsXeTuW3FU]
 ## **Option 3: Use Docker**
 You need to have Docker engine installed locally and running. Open a command line window and type the following command. 
 __Note:__ We use version `1.0.10` below as an exmaple, but you can replace that with any available version number you like.
 ```sh
 # clone the sample repoistory
 git clone https://github.com/Azure/MachineLearningNotebooks.git
 # change current directory to the folder 
 # where Dockerfile of the specific SDK version is located.
 cd MachineLearningNotebooks/Dockerfiles/1.0.10
 # build a Docker image with the a name (azuremlsdk for example) 
 # and a version number tag (1.0.10 for example).
 # this can take several minutes depending on your computer speed and network bandwidth.
 docker build . -t azuremlsdk:1.0.10
 # launch the built Docker container which also automatically starts
 # a Jupyter server instance listening on port 8887 of the host machine
 docker run -it -p 8887:8887 azuremlsdk:1.0.10
 ```
 Now you can point your browser to http://localhost:8887. We recommend that you start from the `configuration.ipynb` notebook at the root directory.
 If you need additional Azure ML SDK components, you can either modify the Docker files before you build the Docker images to add additional steps, or install them through command line in the live container after you build the Docker image. For example:
 ```sh
 # install the core SDK and automated ml components
 pip install azureml-sdk[automl]
 # install the core SDK and model explainability component
 pip install azureml-sdk[explain]
 # install the core SDK and experimental components
 pip install azureml-sdk[contrib]
 ```
 Drag and Drop
 The image will be downloaded by Fatkun
--- a/README.md
+++ b/README.md
@@ -1,70 +1,53 @@
-# Azure Machine Learning service example notebooks
+For full documentation for Azure Machine Learning service, visit **https://aka.ms/aml-docs**.
 # Sample Notebooks for Azure Machine Learning service
-This repository contains example notebooks demonstrating the [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning-service/) Python SDK which allows you to build, train, deploy and manage machine learning solutions using Azure.  The AML SDK allows you the choice of using local or cloud compute resources, while managing and maintaining the complete data science workflow from the cloud.
+To run the notebooks in this repository use one of these methods:
-![Azure ML workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/service/media/overview-what-is-azure-ml/aml.png)
+## Use Azure Notebooks - Jupyter based notebooks in the Azure cloud
-## Quick installation
+1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
-```sh
+[Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks.
-pip install azureml-sdk
+1. Follow the instructions in the [00.configuration](00.configuration.ipynb) notebook to create and connect to a workspace.
 1. Open one of the sample notebooks.
    **Make sure the Azure Notebook kernel is set to `Python 3.6`** when you open a notebook.  
    ![set kernel to Python 3.6](images/python36.png)
 ## **Use your own notebook server**
 Video walkthrough:
 [![get started video](images/yt_cover.png)](https://youtu.be/VIsXeTuW3FU)
 1. Setup a Jupyter Notebook server and [install the Azure Machine Learning SDK](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python).
 1. Clone [this repository](https://aka.ms/aml-notebooks).
 1. You may need to install other packages for specific notebook. 
    - For example, to run the Azure Machine Learning Data Prep notebooks, install the extra dataprep SDK:
    ```
-Read more detailed instructions on [how to set up your environment](./NBSETUP.md) using Azure Notebook service, your own Jupyter notebook server, or Docker.
+     pip install --upgrade azureml-dataprep
 ## How to navigate and use the example notebooks?
 If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, you should always run the [Configuration](./configuration.ipynb) notebook first when setting up a notebook library on a new machine or in a new environment. It configures your notebook library to connect to an Azure Machine Learning workspace, and sets up your workspace and compute to be used by many of the other examples. 
 If you want to...
 * ...try out and explore Azure ML, start with image classification tutorials: [Part 1 (Training)](./tutorials/img-classification-part1-training.ipynb) and [Part 2 (Deployment)](./tutorials/img-classification-part2-deploy.ipynb).
 * ...prepare your data and do automated machine learning, start with regression tutorials: [Part 1 (Data Prep)](./tutorials/regression-part1-data-prep.ipynb) and [Part 2 (Automated ML)](./tutorials/regression-part2-automated-ml.ipynb).
 * ...learn about experimentation and tracking run history, first [train within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then try [training on remote VM](./how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb) and [using logging APIs](./how-to-use-azureml/training/logging-api/logging-api.ipynb).
 * ...train deep learning models at scale, first learn about [Machine Learning Compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and then try [distributed hyperparameter tuning](./how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) and [distributed training](./how-to-use-azureml/training-with-deep-learning/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb).
 * ...deploy models as a realtime scoring service, first learn the basics by [training within Notebook and deploying to Azure Container Instance](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then learn how to [register and manage models, and create Docker images](./how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb), and [production deploy models on Azure Kubernetes Cluster](./how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb).
 * ...deploy models as a batch scoring service, first [train a model within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), learn how to [register and manage models](./how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb), then [create Machine Learning Compute for scoring compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and [use Machine Learning Pipelines to deploy your model](https://aka.ms/pl-batch-scoring).
 * ...monitor your deployed models, learn about using [App Insights](./how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb) and [model data collection](./how-to-use-azureml/deployment/enable-data-collection-for-models-in-aks/enable-data-collection-for-models-in-aks.ipynb).
 ## Tutorials
 The [Tutorials](./tutorials) folder contains notebooks for the tutorials described in the [Azure Machine Learning documentation](https://aka.ms/aml-docs).
 ## How to use Azure ML
 The [How to use Azure ML](./how-to-use-azureml) folder contains specific examples demonstrating the features of the Azure Machine Learning SDK
 - [Training](./how-to-use-azureml/training) - Examples of how to build models using Azure ML's logging and execution capabilities on local and remote compute targets
 - [Training with Deep Learning](./how-to-use-azureml/training-with-deep-learning) - Examples demonstrating how to build deep learning models using estimators and parameter sweeps
 - [Manage Azure ML Service](./how-to-use-azureml/manage-azureml-service) - Examples how to perform tasks, such as authenticate against Azure ML service in different ways.
 - [Automated Machine Learning](./how-to-use-azureml/automated-machine-learning) - Examples using Automated Machine Learning to automatically generate optimal machine learning pipelines and models
 - [Machine Learning Pipelines](./how-to-use-azureml/machine-learning-pipelines) - Examples showing how to create and use reusable pipelines for training and batch scoring
 - [Deployment](./how-to-use-azureml/deployment) - Examples showing how to deploy and manage machine learning models and solutions
 - [Azure Databricks](./how-to-use-azureml/azure-databricks) - Examples showing how to use Azure ML with Azure Databricks
 - [Monitor Models](./how-to-use-azureml/monitor-models) - Examples showing how to enable model monitoring services such as DataDrift
 ---
 ## Documentation
 * Quickstarts, end-to-end tutorials, and how-tos on the [official documentation site for Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/).
 * [Python SDK reference](https://docs.microsoft.com/en-us/python/api/overview/azure/ml/intro?view=azure-ml-py)
 * Azure ML Data Prep SDK [overview](https://aka.ms/data-prep-sdk), [Python SDK reference](https://aka.ms/aml-data-prep-apiref), and [tutorials and how-tos](https://aka.ms/aml-data-prep-notebooks).
 ---
 ## Projects using Azure Machine Learning
 Visit following repos to see projects contributed by Azure ML users:
 - [AMLSamples](https://github.com/Azure/AMLSamples) Number of end-to-end examples, including face recognition, predictive maintenance, customer churn and sentiment analysis.
 - [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)
 ## Data/Telemetry 
 This repository collects usage data and sends it to Mircosoft to help improve our products and services. Read Microsoft's [privacy statement to learn more](https://privacy.microsoft.com/en-US/privacystatement)
 To opt out of tracking, please go to the raw markdown or .ipynb files and remove the following line of code:
 ```sh
    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/README.png)"
    ```
 This URL will be slightly different depending on the file. 
- ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/README.png)
+1. Start your notebook server.
 1. Follow the instructions in the [00.configuration](00.configuration.ipynb) notebook to create and connect to a workspace.
 1. Open one of the sample notebooks.
 > Note: **Looking for automated machine learning samples?**
 > For your convenience, you can use an installation script instead of the steps below for the automated ML notebooks. Go to the [automl folder README](automl/README.md) and follow the instructions.  The script installs all  packages needed for notebooks in that folder.
 # Contributing
 This project welcomes contributions and suggestions.  Most contributions require you to agree to a
 Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
 the rights to use your contribution. For details, visit https://cla.microsoft.com.
 When you submit a pull request, a CLA-bot will automatically determine whether you need to provide
 a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions
 provided by the bot. You will only need to do this once across all repos using our CLA.
 This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
 For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
 contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
--- a/configuration.ipynb
+++ b/configuration.ipynb
@@ -9,13 +9,6 @@
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/configuration.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -58,7 +51,7 @@
        "\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, inference, 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, inferencing, and the monitoring of deployed models."
      ]
    },
    {
@@ -103,7 +96,7 @@
      "source": [
        "import azureml.core\n",
        "\n",
-        "print(\"This notebook was created using version 1.0.57 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.0.2 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
@@ -258,7 +251,7 @@
        "```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 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",
+        "* 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",
        "* 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",
@@ -275,14 +268,14 @@
        "from azureml.core.compute_target import ComputeTargetException\n",
        "\n",
        "# Choose a name for your CPU cluster\n",
-        "cpu_cluster_name = \"cpu-cluster\"\n",
+        "cpu_cluster_name = \"cpucluster\"\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 cpu-cluster\")\n",
+        "    print(\"Found existing cpucluster\")\n",
        "except ComputeTargetException:\n",
-        "    print(\"Creating new cpu-cluster\")\n",
+        "    print(\"Creating new cpucluster\")\n",
        "    \n",
        "    # Specify the configuration for the new cluster\n",
        "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_D2_V2\",\n",
@@ -313,14 +306,14 @@
        "from azureml.core.compute_target import ComputeTargetException\n",
        "\n",
        "# Choose a name for your GPU cluster\n",
-        "gpu_cluster_name = \"gpu-cluster\"\n",
+        "gpu_cluster_name = \"gpucluster\"\n",
        "\n",
        "# Verify that cluster does not exist already\n",
        "try:\n",
        "    gpu_cluster = ComputeTarget(workspace=ws, name=gpu_cluster_name)\n",
        "    print(\"Found existing gpu cluster\")\n",
        "except ComputeTargetException:\n",
-        "    print(\"Creating new gpu-cluster\")\n",
+        "    print(\"Creating new gpucluster\")\n",
        "    \n",
        "    # Specify the configuration for the new cluster\n",
        "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_NC6\",\n",
@@ -343,7 +336,7 @@
        "\n",
        "In this notebook you configured this notebook library to connect easily to an Azure ML workspace.  You can copy this notebook to your own libraries to connect them to you workspace, or use it to bootstrap new workspaces completely.\n",
        "\n",
-        "If you came here from another notebook, you can return there and complete that exercise, or you can try out the [Tutorials](./tutorials) or jump into \"how-to\" notebooks and start creating and deploying models.  A good place to start is the [train within notebook](./how-to-use-azureml/training/train-within-notebook) example that walks through a simplified but complete end to end machine learning process."
+        "If you came here from another notebook, you can return there and complete that exercise, or you can try out the [Tutorials](./tutorials) or jump into \"how-to\" notebooks and start creating and deploying models.  A good place to start is the [train in notebook](./how-to-use-azureml/training/train-in-notebook) example that walks through a simplified but complete end to end machine learning process."
      ]
    },
    {
@@ -375,7 +368,7 @@
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
-      "version": "3.6.5"
+      "version": "3.6.7"
    }
  },
  "nbformat": 4,
--- a/configuration.yml
+++ b/configuration.yml
@@ -1,4 +0,0 @@
 name: configuration
 dependencies:
 - pip:
  - azureml-sdk
--- a/contrib/RAPIDS/README.md
+++ b/contrib/RAPIDS/README.md
@@ -1,307 +0,0 @@
 ## How to use the RAPIDS on AzureML materials
 ### Setting up requirements
 The material requires the use of the Azure ML SDK and of the Jupyter Notebook Server to run the interactive execution. Please refer to instructions to [setup the environment.](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#local "Local Computer Set Up") Follow the instructions under **Local Computer**, make sure to run the last step: <span style="font-family: Courier New;">pip install \<new package\></span> with <span style="font-family: Courier New;">new package = progressbar2  (pip install progressbar2)</span>
 After following the directions, the user should end up setting a conda environment (<span style="font-family: Courier New;">myenv</span>)that can be activated in an Anaconda prompt
 The user would also require an Azure Subscription with a Machine Learning Services quota on the desired region for 24 nodes or more (to be able to select a vmSize with 4 GPUs as it is used on the Notebook) on the desired VM family ([NC\_v3](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv3-series),  [NC\_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv2-series), [ND](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#nd-series) or [ND_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ndv2-series-preview)), the specific vmSize to be used within the chosen family would also need to be whitelisted for Machine Learning Services usage.  
 &nbsp;  
 ### Getting and running the material 
 Clone the AzureML Notebooks repository in GitHub by running the following command on a local_directory: 
 * C:\local_directory>git clone https://github.com/Azure/MachineLearningNotebooks.git
 On a conda prompt navigate to the local directory, activate the conda environment (<span style="font-family: Courier New;">myenv</span>), where the Azure ML SDK was installed and launch Jupyter Notebook. 
 * (<span style="font-family: Courier New;">myenv</span>) C:\local_directory>jupyter notebook
 From the resulting browser at http://localhost:8888/tree, navigate to the master notebook: 
 * http://localhost:8888/tree/MachineLearningNotebooks/contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb
 &nbsp;  
 The following notebook will appear:  
 ![](imgs/NotebookHome.png)
 &nbsp;  
 ### Master Jupyter Notebook
 The notebook can be executed interactively step by step, by pressing the Run button (In a red circle in the above image.)
 The first couple of functional steps import the necessary AzureML libraries.  If you experience any errors please refer back to the [setup the environment.](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#local "Local Computer Set Up") instructions.
 &nbsp;  
 #### Setting up a Workspace
 The following step gathers the information necessary to set up a workspace to execute the RAPIDS script. This needs to be done only once, or not at all if you already have a workspace you can use set up on the Azure Portal:
 ![](imgs/WorkSpaceSetUp.png)
 It is important to be sure to set the correct values for the subscription\_id, resource\_group, workspace\_name, and region before executing the step. An example is:
    subscription_id = os.environ.get("SUBSCRIPTION_ID", "1358e503-xxxx-4043-xxxx-65b83xxxx32d")
    resource_group = os.environ.get("RESOURCE_GROUP", "AML-Rapids-Testing")
    workspace_name = os.environ.get("WORKSPACE_NAME", "AML_Rapids_Tester")
    workspace_region = os.environ.get("WORKSPACE_REGION", "West US 2")
 &nbsp;  
 The resource\_group and workspace_name could take any value, the region should match the region for which the subscription has the required Machine Learning Services node quota.
 The first time the code is executed it will redirect to the Azure Portal to validate subscription credentials. After the workspace is created, its related information is stored on a local file so that this step can be subsequently skipped. The immediate step will just load the saved workspace
 ![](imgs/saved_workspace.png)
 Once a workspace has been created the user could skip its creation and just jump to this step. The configuration file resides in:
 * C:\local_directory\\MachineLearningNotebooks\contrib\RAPIDS\aml_config\config.json
 &nbsp;  
 #### Creating an AML Compute Target 
 Following step, creates an AML Compute Target 
 ![](imgs/target_creation.png)
 Parameter vm\_size on function call AmlCompute.provisioning\_configuration() has to be a member of the VM families ([NC\_v3](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv3-series),  [NC\_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv2-series), [ND](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#nd-series) or [ND_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ndv2-series-preview)) that are the ones provided with P40 or V100 GPUs, that are the ones supported by RAPIDS. In this particular case an Standard\_NC24s\_V2 was used.
 &nbsp;  
 If the output of running the step has an error of the form:
 ![](imgs/targeterror1.png)
 It is an indication that even though the subscription has a node quota for VMs for that family, it does not have a node quota for Machine Learning Services for that family. 
 You will need to request an increase node quota for that family in that region for **Machine Learning Services**.
 &nbsp;  
 Another possible error is the following: 
 ![](imgs/targeterror2.png)
 Which indicates that specified vmSize has not been whitelisted for usage on Machine Learning Services and a request to do so should be filled.
 The successful creation of the compute target would have an output like the following:
 ![](imgs/targetsuccess.png)
 &nbsp;  
 #### RAPIDS script uploading and viewing
 The next step copies the RAPIDS script process_data.py, which is a slightly modified implementation of the [RAPIDS E2E example](https://github.com/rapidsai/notebooks/blob/master/mortgage/E2E.ipynb), into a script processing folder and it presents its contents to the user. (The script is discussed in the next section in detail). 
 If the user wants to use a different RAPIDS script, the references to the  <span style="font-family: Courier New;">process_data.py</span> script have to be changed
 ![](imgs/scriptuploading.png)
 &nbsp;  
 #### Data Uploading
 The RAPIDS script loads and extracts features from the Fannie Mae’s Mortgage Dataset to train an XGBoost prediction model. The script uses two years of data
 The next few steps download and decompress the data and is made available to the  script as an [Azure Machine Learning Datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data).
 &nbsp;  
 The following functions are used to download and decompress the input data
 ![](imgs/dcf1.png)
 ![](imgs/dcf2.png)
 ![](imgs/dcf3.png)
 ![](imgs/dcf4.png)
 &nbsp;  
 The next step uses those functions to download locally file: 
 http://rapidsai-data.s3-website.us-east-2.amazonaws.com/notebook-mortgage-data/mortgage_2000-2001.tgz'
 And to decompress it, into local folder path = .\mortgage_2000-2001
 The step takes several minutes, the intermediate outputs provide progress indicators.
 ![](imgs/downamddecom.png)
 &nbsp;  
 The decompressed data should have the following structure:
 * .\mortgage_2000-2001\acq\Acquisition_<year>Q<num>.txt 
 * .\mortgage_2000-2001\perf\Performance_<year>Q<num>.txt 
 * .\mortgage_2000-2001\names.csv
 The data is divided in partitions that roughly correspond to yearly quarters. RAPIDS includes support for multi-node, multi-GPU deployments, enabling scaling up and out on much larger dataset sizes. The user will be able to verify that the number of partitions that the script is able to process increases with the number of GPUs used. The RAPIDS script is implemented for single-machine scenarios. An example supporting multiple nodes will be published later. 
 &nbsp;  
 The next step upload the data into the [Azure Machine Learning Datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data) under reference <span style="font-family: Courier New;">fileroot = mortgage_2000-2001</span>
 The step takes several minutes to load the data, the output provides a progress indicator.
 ![](imgs/datastore.png)
 Once the data has been loaded into the Azure Machine LEarning Data Store, in subsequent run, the user can comment out the ds.upload line and just make reference to the <span style="font-family: Courier New;">mortgage_2000-2001</blog> data store reference  
 &nbsp;  
 #### Setting up required libraries and environment to run RAPIDS code
 There are two options to setup the environment to run RAPIDS code. The following steps shows how to ues a prebuilt conda environment. A recommended alternative is to specify a base Docker image and package dependencies. You can find sample code for that in the notebook.
 ![](imgs/install2.png)
 &nbsp;  
 #### Wrapper function to submit the RAPIDS script as an Azure Machine Learning experiment
 The next step consists of the definition of a wrapper function to be used when the user attempts to run the RAPIDS script with different arguments. It takes as arguments: <span style="font-family: Times New Roman;">*cpu\_training*</span>;  a flag that indicates if the run is meant to be processed with CPU-only, <span style="font-family: Times New Roman;">*gpu\_count*</span>; the number of GPUs to be used if they are meant to be used and part_count: the number of data partitions to be used
 ![](imgs/wrapper.png)
 &nbsp;  
 The core of the function resides in configuring the run by the instantiation of a ScriptRunConfig object, which defines the source_directory for the script to be executed, the name of the script and the arguments to be passed to the script.
 In addition to the wrapper function arguments, two other arguments are passed: <span style="font-family: Times New Roman;">*data\_dir*</span>, the directory where the data is stored and <span style="font-family: Times New Roman;">*end_year*</span> is the largest year to use partition from.
 As mentioned earlier the size of the data that can be processed increases with the number of gpus, in the function, dictionary <span style="font-family: Times New Roman;">*max\_gpu\_count\_data\_partition_mapping*</span> maps the maximum number of partitions that we empirically found that the system can handle given the number of GPUs used. The function throws a warning when the number of partitions for a given number of gpus exceeds the maximum but the script is still executed, however the user should expect an error as an out of memory situation would be encountered
 If the user wants to use a different RAPIDS script, the reference to the process_data.py script has to be changed
 &nbsp;  
 #### Submitting Experiments
 We are ready to submit experiments: launching the RAPIDS script with different sets of parameters.
 &nbsp;  
 The following couple of steps submit experiments under different conditions. 
 ![](imgs/submission1.png)
 &nbsp;  
 The user can change variable num\_gpu between one and the number of GPUs supported by the chosen vmSize. Variable part\_count can take any value between 1 and 11, but if it exceeds the maximum for num_gpu, the run would result in an error
 &nbsp;  
 If the experiment is successfully submitted, it would be placed on a queue for processing, its status would appeared as Queued and an output like the following would appear 
 ![](imgs/queue.png)
 &nbsp;  
 When the experiment starts running, its status would appeared as Running and the output would change to something like this:
 ![](imgs/running.png)
 &nbsp;  
 #### Reproducing the performance gains plot results on the Blog Post
 When the run has finished successfully, its status would appeared as Completed and the output would change to something like this:
 &nbsp; 
 ![](imgs/completed.png)
 Which is the output for an experiment run with three partitions and one GPU, notice that the reported processing time is 49.16 seconds just as depicted on the performance gains plot on the blog post
 &nbsp;  
 ![](imgs/2GPUs.png)
 This output corresponds to a run with three partitions and two GPUs, notice that the reported processing time is 37.50 seconds just as depicted on the performance gains plot on the blog post
 &nbsp;  
 ![](imgs/3GPUs.png)
 This output corresponds to an experiment run with three partitions and three GPUs, notice that the reported processing time is 24.40 seconds just as depicted on the performance gains plot on the blog post
 &nbsp;  
 ![](imgs/4gpus.png)
 This output corresponds to an experiment run with three partitions and four GPUs, notice that the reported processing time is 23.33 seconds just as depicted on the performance gains plot on the blogpost
 &nbsp;  
 ![](imgs/CPUBase.png)
 This output corresponds to an experiment run with three partitions and using only CPU, notice that the reported processing time is 9 minutes and 1.21 seconds or 541.21 second just as depicted on the performance gains plot on the blog post
 &nbsp;  
 ![](imgs/OOM.png)
 This output corresponds to an experiment run with nine partitions and four GPUs, notice that the notebook throws a warning signaling that the number of partitions exceed the maximum that the system can handle with those many GPUs and the run ends up failing, hence having and status of Failed. 
 &nbsp;  
 ##### Freeing Resources
 In the last step the notebook deletes the compute target. (This step is optional especially if the min_nodes in the cluster is set to 0 with which the cluster will scale down to 0 nodes when there is no usage.)
 ![](imgs/clusterdelete.png)
 &nbsp;  
 ### RAPIDS Script
 The Master Notebook runs experiments by launching a RAPIDS script with different sets of parameters. In this section, the RAPIDS script, process_data.py in the material, is analyzed
 The script first imports all the necessary libraries and parses the arguments passed by the Master Notebook.
 The all internal functions to be used by the script are defined.
 &nbsp;  
 #### Wrapper Auxiliary Functions:
 The below functions are wrappers for a configuration module for librmm, the RAPIDS Memory Manager python interface:
 ![](imgs/wap1.png)![](imgs/wap2.png)
 &nbsp;  
 A couple of other functions are wrappers for the submission of jobs to the DASK client:
 ![](imgs/wap3.png)
 ![](imgs/wap4.png)
 &nbsp;  
 #### Data Loading Functions:
 The data is loaded through the use of the following three functions 
 ![](imgs/DLF1.png)![](imgs/DLF2.png)![](imgs/DLF3.png)
 All three functions use library function cudf.read_csv(), cuDF version for the well known counterpart on Pandas.
 &nbsp;  
 #### Data Transformation and Feature Extraction Functions:
 The raw data is transformed and processed to extract features by joining, slicing, grouping, aggregating, factoring, etc, the original dataframes just as is done with Pandas. The following functions in the script are used for that purpose:
 ![](imgs/fef1.png)![](imgs/fef2.png)![](imgs/fef3.png)![](imgs/fef4.png)![](imgs/fef5.png)
 ![](imgs/fef6.png)![](imgs/fef7.png)![](imgs/fef8.png)![](imgs/fef9.png)
 &nbsp;  
 #### Main() Function
 The previous functions are used in the Main function to accomplish several steps: Set up the Dask client, do all ETL operations, set up and train an XGBoost model, the function also assigns which data needs to be processed by each Dask client
 &nbsp;  
 ##### Setting Up DASK client:
 The following lines:
 ![](imgs/daskini.png)
 &nbsp;  
 Initialize and set up a DASK client with a number of workers corresponding to the number of GPUs to be used on the run. A successful execution of the set up will result on the following output:
 ![](imgs/daskoutput.png)
 ##### All ETL functions are used on single calls to process\_quarter_gpu, one per data partition
 ![](imgs/ETL.png)
 &nbsp;  
 ##### Concentrating the data assigned to each DASK worker
 The partitions assigned to each worker are concatenated and set up for training.
 ![](imgs/Dask2.png)
 &nbsp;  
 ##### Setting Training Parameters
 The parameters used for the training of a gradient boosted decision tree model are set up in the following code block:
 ![](imgs/PArameters.png)
 Notice how the parameters are modified when using the CPU-only mode.
 &nbsp;  
 ##### Launching the training of a gradient boosted decision tree model using XGBoost.
 ![](imgs/training.png)
 The outputs of the script can be observed in the master notebook as the script is executed
 ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/contrib/RAPIDS/README.png)
--- a/contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb
+++ b/contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb
@@ -1,559 +0,0 @@
 {
  "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": [
        "# NVIDIA RAPIDS in Azure Machine Learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The [RAPIDS](https://www.developer.nvidia.com/rapids) suite of software libraries from NVIDIA enables the execution of end-to-end data science and analytics pipelines entirely on GPUs. In many machine learning projects, a significant portion of the model training time is spent in setting up the data; this stage of the process is known as Extraction, Transformation and Loading, or ETL. By using the DataFrame API for ETL\u00c3\u201a\u00c2\u00a0and GPU-capable ML algorithms in RAPIDS, data preparation and training models can be done in GPU-accelerated end-to-end pipelines without incurring serialization costs between the pipeline stages. This notebook demonstrates how to use NVIDIA RAPIDS to prepare data and train model\u00c2\u00a0in Azure.\n",
        " \n",
        "In this notebook, we will do the following:\n",
        " \n",
        "* Create an Azure Machine Learning Workspace\n",
        "* Create an AMLCompute target\n",
        "* Use a script to process our data and train a model\n",
        "* Obtain the data required to run this sample\n",
        "* Create an AML run configuration to launch a machine learning job\n",
        "* Run the script to prepare data for training and train the model\n",
        " \n",
        "Prerequisites:\n",
        "* An Azure subscription to create a Machine Learning Workspace\n",
        "* Familiarity with the Azure ML SDK (refer to [notebook samples](https://github.com/Azure/MachineLearningNotebooks))\n",
        "* A Jupyter notebook environment with Azure Machine Learning SDK installed. Refer to instructions to [setup the environment](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#local)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Verify if Azure ML SDK is installed"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import azureml.core\n",
        "print(\"SDK version:\", azureml.core.VERSION)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "from azureml.core import Workspace, Experiment\n",
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "from azureml.core.compute import AmlCompute, ComputeTarget\n",
        "from azureml.data.data_reference import DataReference\n",
        "from azureml.core.runconfig import RunConfiguration\n",
        "from azureml.core import ScriptRunConfig\n",
        "from azureml.widgets import RunDetails"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create Azure ML Workspace"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The following step is optional if you already have a workspace. If you want to use an existing workspace, then\n",
        "skip this workspace creation step and move on to the next step to load the workspace.\n",
        " \n",
        "<font color='red'>Important</font>: in the code cell below, be sure to set the correct values for the subscription_id, \n",
        "resource_group, workspace_name, region before executing this code cell."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "subscription_id = os.environ.get(\"SUBSCRIPTION_ID\", \"<subscription_id>\")\n",
        "resource_group = os.environ.get(\"RESOURCE_GROUP\", \"<resource_group>\")\n",
        "workspace_name = os.environ.get(\"WORKSPACE_NAME\", \"<workspace_name>\")\n",
        "workspace_region = os.environ.get(\"WORKSPACE_REGION\", \"<region>\")\n",
        "\n",
        "ws = Workspace.create(workspace_name, subscription_id=subscription_id, resource_group=resource_group, location=workspace_region)\n",
        "\n",
        "# write config to a local directory for future use\n",
        "ws.write_config()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Load existing Workspace"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "# if a locally-saved configuration file for the workspace is not available, use the following to load workspace\n",
        "# ws = Workspace(subscription_id=subscription_id, resource_group=resource_group, workspace_name=workspace_name)\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')\n",
        "\n",
        "scripts_folder = \"scripts_folder\"\n",
        "\n",
        "if not os.path.isdir(scripts_folder):\n",
        "    os.mkdir(scripts_folder)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create AML Compute Target"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Because NVIDIA RAPIDS requires P40 or V100 GPUs, the user needs to specify compute targets from one of [NC_v3](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv3-series), [NC_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv2-series), [ND](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#nd-series) or [ND_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ndv2-series-preview) virtual machine types in Azure; these are the families of virtual machines in Azure that are provisioned with these GPUs.\n",
        " \n",
        "Pick one of the supported VM SKUs based on the number of GPUs you want to use for ETL and training in RAPIDS.\n",
        " \n",
        "The script in this notebook is implemented for single-machine scenarios. An example supporting multiple nodes will be published later."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "gpu_cluster_name = \"gpucluster\"\n",
        "\n",
        "if gpu_cluster_name in ws.compute_targets:\n",
        "    gpu_cluster = ws.compute_targets[gpu_cluster_name]\n",
        "    if gpu_cluster and type(gpu_cluster) is AmlCompute:\n",
        "        print('found compute target. just use it. ' + gpu_cluster_name)\n",
        "else:\n",
        "    print(\"creating new cluster\")\n",
        "    # vm_size parameter below could be modified to one of the RAPIDS-supported VM types\n",
        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"Standard_NC6s_v2\", min_nodes=1, max_nodes = 1)\n",
        "\n",
        "    # create the cluster\n",
        "    gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, provisioning_config)\n",
        "    gpu_cluster.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Script to process data and train model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The _process&#95;data.py_ script used in the step below is a slightly modified implementation of [RAPIDS E2E example](https://github.com/rapidsai/notebooks/blob/master/mortgage/E2E.ipynb)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# copy process_data.py into the script folder\n",
        "import shutil\n",
        "shutil.copy('./process_data.py', os.path.join(scripts_folder, 'process_data.py'))\n",
        "\n",
        "with open(os.path.join(scripts_folder, './process_data.py'), 'r') as process_data_script:\n",
        "    print(process_data_script.read())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Data required to run this sample"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This sample uses [Fannie Mae's Single-Family Loan Performance Data](http://www.fanniemae.com/portal/funding-the-market/data/loan-performance-data.html). Once you obtain access to the data, you will need to make this data available in an [Azure Machine Learning Datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data), for use in this sample. The following code shows how to do that."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Downloading Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<font color='red'>Important</font>: Python package progressbar2 is necessary to run the following cell. If it is not available in your environment where this notebook is running, please install it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import tarfile\n",
        "import hashlib\n",
        "from urllib.request import urlretrieve\n",
        "from progressbar import ProgressBar\n",
        "\n",
        "def validate_downloaded_data(path):\n",
        "    if(os.path.isdir(path) and os.path.exists(path + '//names.csv')) :\n",
        "        if(os.path.isdir(path + '//acq' ) and len(os.listdir(path + '//acq')) == 8):\n",
        "            if(os.path.isdir(path + '//perf' ) and len(os.listdir(path + '//perf')) == 11):\n",
        "                print(\"Data has been downloaded and decompressed at: {0}\".format(path))\n",
        "                return True\n",
        "    print(\"Data has not been downloaded and decompressed\")\n",
        "    return False\n",
        "\n",
        "def show_progress(count, block_size, total_size):\n",
        "    global pbar\n",
        "    global processed\n",
        "    \n",
        "    if count == 0:\n",
        "        pbar = ProgressBar(maxval=total_size)\n",
        "        processed = 0\n",
        "    \n",
        "    processed += block_size\n",
        "    processed = min(processed,total_size)\n",
        "    pbar.update(processed)\n",
        "\n",
        "        \n",
        "def download_file(fileroot):\n",
        "    filename = fileroot + '.tgz'\n",
        "    if(not os.path.exists(filename) or hashlib.md5(open(filename, 'rb').read()).hexdigest() != '82dd47135053303e9526c2d5c43befd5' ):\n",
        "        url_format = 'http://rapidsai-data.s3-website.us-east-2.amazonaws.com/notebook-mortgage-data/{0}.tgz'\n",
        "        url = url_format.format(fileroot)\n",
        "        print(\"...Downloading file :{0}\".format(filename))\n",
        "        urlretrieve(url, filename,show_progress)\n",
        "        pbar.finish()\n",
        "        print(\"...File :{0} finished downloading\".format(filename))\n",
        "    else:\n",
        "        print(\"...File :{0} has been downloaded already\".format(filename))\n",
        "    return filename\n",
        "\n",
        "def decompress_file(filename,path):\n",
        "    tar = tarfile.open(filename)\n",
        "    print(\"...Getting information from {0} about files to decompress\".format(filename))\n",
        "    members = tar.getmembers()\n",
        "    numFiles = len(members)\n",
        "    so_far = 0\n",
        "    for member_info in members:\n",
        "        tar.extract(member_info,path=path)\n",
        "        show_progress(so_far, 1, numFiles)\n",
        "        so_far += 1\n",
        "    pbar.finish()\n",
        "    print(\"...All {0} files have been decompressed\".format(numFiles))\n",
        "    tar.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "fileroot = 'mortgage_2000-2001'\n",
        "path = '.\\\\{0}'.format(fileroot)\n",
        "pbar = None\n",
        "processed = 0\n",
        "\n",
        "if(not validate_downloaded_data(path)):\n",
        "    print(\"Downloading and Decompressing Input Data\")\n",
        "    filename = download_file(fileroot)\n",
        "    decompress_file(filename,path)\n",
        "    print(\"Input Data has been Downloaded and Decompressed\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Uploading Data to Workspace"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ds = ws.get_default_datastore()\n",
        "\n",
        "# download and uncompress data in a local directory before uploading to data store\n",
        "# directory specified in src_dir parameter below should have the acq, perf directories with data and names.csv file\n",
        "ds.upload(src_dir=path, target_path=fileroot, overwrite=True, show_progress=True)\n",
        "\n",
        "# data already uploaded to the datastore\n",
        "data_ref = DataReference(data_reference_name='data', datastore=ds, path_on_datastore=fileroot)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create AML run configuration to launch a machine learning job"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "RunConfiguration is used to submit jobs to Azure Machine Learning service. When creating RunConfiguration for a job, users can either \n",
        "1. specify a Docker image with prebuilt conda environment and use it without any modifications to run the job, or \n",
        "2. specify a Docker image as the base image and conda or pip packages as dependnecies to let AML build a new Docker image with a conda environment containing specified dependencies to use in the job\n",
        "\n",
        "The second option is the recommended option in AML. \n",
        "The following steps have code for both options. You can pick the one that is more appropriate for your requirements. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Specify prebuilt conda environment"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The following code shows how to use an existing image from [Docker Hub](https://hub.docker.com/r/rapidsai/rapidsai/) that has a prebuilt conda environment named 'rapids' when creating a RunConfiguration. Note that this conda environment does not include azureml-defaults package that is required for using AML functionality like metrics tracking, model management etc. This package is automatically installed when you use 'Specify package dependencies' option and that is why it is the recommended option to create RunConfiguraiton in AML."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "run_config = RunConfiguration()\n",
        "run_config.framework = 'python'\n",
        "run_config.environment.python.user_managed_dependencies = True\n",
        "run_config.environment.python.interpreter_path = '/conda/envs/rapids/bin/python'\n",
        "run_config.target = gpu_cluster_name\n",
        "run_config.environment.docker.enabled = True\n",
        "run_config.environment.docker.gpu_support = True\n",
        "run_config.environment.docker.base_image = \"rapidsai/rapidsai:cuda9.2-runtime-ubuntu18.04\"\n",
        "# run_config.environment.docker.base_image_registry.address = '<registry_url>' # not required if the base_image is in Docker hub\n",
        "# run_config.environment.docker.base_image_registry.username = '<user_name>' # needed only for private images\n",
        "# run_config.environment.docker.base_image_registry.password = '<password>' # needed only for private images\n",
        "run_config.environment.spark.precache_packages = False\n",
        "run_config.data_references={'data':data_ref.to_config()}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Specify package dependencies"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The following code shows how to list package dependencies in a conda environment definition file (rapids.yml) when creating a RunConfiguration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# cd = CondaDependencies(conda_dependencies_file_path='rapids.yml')\n",
        "# run_config = RunConfiguration(conda_dependencies=cd)\n",
        "# run_config.framework = 'python'\n",
        "# run_config.target = gpu_cluster_name\n",
        "# run_config.environment.docker.enabled = True\n",
        "# run_config.environment.docker.gpu_support = True\n",
        "# run_config.environment.docker.base_image = \"<image>\"\n",
        "# run_config.environment.docker.base_image_registry.address = '<registry_url>' # not required if the base_image is in Docker hub\n",
        "# run_config.environment.docker.base_image_registry.username = '<user_name>' # needed only for private images\n",
        "# run_config.environment.docker.base_image_registry.password = '<password>' # needed only for private images\n",
        "# run_config.environment.spark.precache_packages = False\n",
        "# run_config.data_references={'data':data_ref.to_config()}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Wrapper function to submit Azure Machine Learning experiment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# parameter cpu_predictor indicates if training should be done on CPU. If set to true, GPUs are used *only* for ETL and *not* for training\n",
        "# parameter num_gpu indicates number of GPUs to use among the GPUs available in the VM for ETL and if cpu_predictor is false, for training as well \n",
        "def run_rapids_experiment(cpu_training, gpu_count, part_count):\n",
        "    # any value between 1-4 is allowed here depending the type of VMs available in gpu_cluster\n",
        "    if gpu_count not in [1, 2, 3, 4]:\n",
        "        raise Exception('Value specified for the number of GPUs to use {0} is invalid'.format(gpu_count))\n",
        "\n",
        "    # following data partition mapping is empirical (specific to GPUs used and current data partitioning scheme) and may need to be tweaked\n",
        "    max_gpu_count_data_partition_mapping = {1: 3, 2: 4, 3: 6, 4: 8}\n",
        "    \n",
        "    if part_count > max_gpu_count_data_partition_mapping[gpu_count]:\n",
        "        print(\"Too many partitions for the number of GPUs, exceeding memory threshold\")\n",
        "        \n",
        "    if part_count > 11:\n",
        "        print(\"Warning: Maximum number of partitions available is 11\")\n",
        "        part_count = 11\n",
        "        \n",
        "    end_year = 2000\n",
        "    \n",
        "    if part_count > 4:\n",
        "        end_year = 2001 # use more data with more GPUs\n",
        "\n",
        "    src = ScriptRunConfig(source_directory=scripts_folder, \n",
        "                          script='process_data.py', \n",
        "                          arguments = ['--num_gpu', gpu_count, '--data_dir', str(data_ref),\n",
        "                                      '--part_count', part_count, '--end_year', end_year,\n",
        "                                      '--cpu_predictor', cpu_training\n",
        "                                      ],\n",
        "                          run_config=run_config\n",
        "                         )\n",
        "\n",
        "    exp = Experiment(ws, 'rapidstest')\n",
        "    run = exp.submit(config=src)\n",
        "    RunDetails(run).show()\n",
        "    return run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Submit experiment (ETL & training on GPU)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "cpu_predictor = False\n",
        "# the value for num_gpu should be less than or equal to the number of GPUs available in the VM\n",
        "num_gpu = 1\n",
        "data_part_count = 1\n",
        "# train using CPU, use GPU for both ETL and training\n",
        "run = run_rapids_experiment(cpu_predictor, num_gpu, data_part_count)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Submit experiment (ETL on GPU, training on CPU)\n",
        "\n",
        "To observe performance difference between GPU-accelerated RAPIDS based training with CPU-only training, set 'cpu_predictor' predictor to 'True' and rerun the experiment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "cpu_predictor = True\n",
        "# the value for num_gpu should be less than or equal to the number of GPUs available in the VM\n",
        "num_gpu = 1\n",
        "data_part_count = 1\n",
        "# train using CPU, use GPU for ETL\n",
        "run = run_rapids_experiment(cpu_predictor, num_gpu, data_part_count)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Delete cluster"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# delete the cluster\n",
        "# gpu_cluster.delete()"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "ksivas"
      }
    ],
    "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
 }
--- a/contrib/RAPIDS/imgs/2GPUs.png
+++ b/contrib/RAPIDS/imgs/2GPUs.png
--- a/contrib/RAPIDS/imgs/3GPUs.png
+++ b/contrib/RAPIDS/imgs/3GPUs.png
--- a/contrib/RAPIDS/imgs/4gpus.png
+++ b/contrib/RAPIDS/imgs/4gpus.png
--- a/contrib/RAPIDS/imgs/CPUBase.png
+++ b/contrib/RAPIDS/imgs/CPUBase.png
--- a/contrib/RAPIDS/imgs/DLF1.png
+++ b/contrib/RAPIDS/imgs/DLF1.png
--- a/contrib/RAPIDS/imgs/DLF2.png
+++ b/contrib/RAPIDS/imgs/DLF2.png
--- a/contrib/RAPIDS/imgs/DLF3.png
+++ b/contrib/RAPIDS/imgs/DLF3.png
--- a/contrib/RAPIDS/imgs/Dask2.png
+++ b/contrib/RAPIDS/imgs/Dask2.png
--- a/contrib/RAPIDS/imgs/ETL.png
+++ b/contrib/RAPIDS/imgs/ETL.png
--- a/contrib/RAPIDS/imgs/NotebookHome.png
+++ b/contrib/RAPIDS/imgs/NotebookHome.png
--- a/contrib/RAPIDS/imgs/OOM.png
+++ b/contrib/RAPIDS/imgs/OOM.png
--- a/contrib/RAPIDS/imgs/PArameters.png
+++ b/contrib/RAPIDS/imgs/PArameters.png
--- a/contrib/RAPIDS/imgs/WorkSpaceSetUp.png
+++ b/contrib/RAPIDS/imgs/WorkSpaceSetUp.png
--- a/contrib/RAPIDS/imgs/clusterdelete.png
+++ b/contrib/RAPIDS/imgs/clusterdelete.png
--- a/contrib/RAPIDS/imgs/completed.png
+++ b/contrib/RAPIDS/imgs/completed.png
--- a/contrib/RAPIDS/imgs/daskini.png
+++ b/contrib/RAPIDS/imgs/daskini.png
--- a/contrib/RAPIDS/imgs/daskoutput.png
+++ b/contrib/RAPIDS/imgs/daskoutput.png
--- a/contrib/RAPIDS/imgs/datastore.png
+++ b/contrib/RAPIDS/imgs/datastore.png
--- a/contrib/RAPIDS/imgs/dcf1.png
+++ b/contrib/RAPIDS/imgs/dcf1.png
--- a/contrib/RAPIDS/imgs/dcf2.png
+++ b/contrib/RAPIDS/imgs/dcf2.png
--- a/contrib/RAPIDS/imgs/dcf3.png
+++ b/contrib/RAPIDS/imgs/dcf3.png
--- a/contrib/RAPIDS/imgs/dcf4.png
+++ b/contrib/RAPIDS/imgs/dcf4.png
--- a/contrib/RAPIDS/imgs/downamddecom.png
+++ b/contrib/RAPIDS/imgs/downamddecom.png
--- a/contrib/RAPIDS/imgs/fef1.png
+++ b/contrib/RAPIDS/imgs/fef1.png
--- a/contrib/RAPIDS/imgs/fef2.png
+++ b/contrib/RAPIDS/imgs/fef2.png
--- a/contrib/RAPIDS/imgs/fef3.png
+++ b/contrib/RAPIDS/imgs/fef3.png
--- a/contrib/RAPIDS/imgs/fef4.png
+++ b/contrib/RAPIDS/imgs/fef4.png
--- a/contrib/RAPIDS/imgs/fef5.png
+++ b/contrib/RAPIDS/imgs/fef5.png
--- a/contrib/RAPIDS/imgs/fef6.png
+++ b/contrib/RAPIDS/imgs/fef6.png
--- a/contrib/RAPIDS/imgs/fef7.png
+++ b/contrib/RAPIDS/imgs/fef7.png
--- a/contrib/RAPIDS/imgs/fef8.png
+++ b/contrib/RAPIDS/imgs/fef8.png
--- a/contrib/RAPIDS/imgs/fef9.png
+++ b/contrib/RAPIDS/imgs/fef9.png
--- a/contrib/RAPIDS/imgs/install2.png
+++ b/contrib/RAPIDS/imgs/install2.png
--- a/contrib/RAPIDS/imgs/installation.png
+++ b/contrib/RAPIDS/imgs/installation.png
--- a/contrib/RAPIDS/imgs/queue.png
+++ b/contrib/RAPIDS/imgs/queue.png
--- a/contrib/RAPIDS/imgs/running.png
+++ b/contrib/RAPIDS/imgs/running.png
--- a/contrib/RAPIDS/imgs/saved_workspace.png
+++ b/contrib/RAPIDS/imgs/saved_workspace.png
--- a/contrib/RAPIDS/imgs/scriptuploading.png
+++ b/contrib/RAPIDS/imgs/scriptuploading.png
--- a/contrib/RAPIDS/imgs/submission1.png
+++ b/contrib/RAPIDS/imgs/submission1.png
--- a/contrib/RAPIDS/imgs/target_creation.png
+++ b/contrib/RAPIDS/imgs/target_creation.png
--- a/contrib/RAPIDS/imgs/targeterror1.png
+++ b/contrib/RAPIDS/imgs/targeterror1.png
--- a/contrib/RAPIDS/imgs/targeterror2.png
+++ b/contrib/RAPIDS/imgs/targeterror2.png
--- a/contrib/RAPIDS/imgs/targetsuccess.png
+++ b/contrib/RAPIDS/imgs/targetsuccess.png
--- a/contrib/RAPIDS/imgs/training.png
+++ b/contrib/RAPIDS/imgs/training.png
--- a/contrib/RAPIDS/imgs/wap1.png
+++ b/contrib/RAPIDS/imgs/wap1.png
--- a/contrib/RAPIDS/imgs/wap2.png
+++ b/contrib/RAPIDS/imgs/wap2.png
--- a/contrib/RAPIDS/imgs/wap3.png
+++ b/contrib/RAPIDS/imgs/wap3.png
--- a/contrib/RAPIDS/imgs/wap4.png
+++ b/contrib/RAPIDS/imgs/wap4.png
--- a/contrib/RAPIDS/imgs/wrapper.png
+++ b/contrib/RAPIDS/imgs/wrapper.png
--- a/contrib/RAPIDS/process_data.py
+++ b/contrib/RAPIDS/process_data.py
@@ -1,495 +0,0 @@
 import numpy as np
 import datetime
 import dask_xgboost as dxgb_gpu
 import dask
 import dask_cudf
 from dask_cuda import LocalCUDACluster
 from dask.delayed import delayed
 from dask.distributed import Client, wait
 import xgboost as xgb
 import cudf
 from cudf.dataframe import DataFrame
 from collections import OrderedDict
 import gc
 from glob import glob
 import os
 import argparse
 def initialize_rmm_pool():
    from librmm_cffi import librmm_config as rmm_cfg
    rmm_cfg.use_pool_allocator = True
    #rmm_cfg.initial_pool_size = 2<<30 # set to 2GiB. Default is 1/2 total GPU memory
    import cudf
    return cudf._gdf.rmm_initialize()
 def initialize_rmm_no_pool():
    from librmm_cffi import librmm_config as rmm_cfg
    rmm_cfg.use_pool_allocator = False
    import cudf
    return cudf._gdf.rmm_initialize()
 def run_dask_task(func, **kwargs):
    task = func(**kwargs)
    return task
 def process_quarter_gpu(client, col_names_path, acq_data_path, year=2000, quarter=1, perf_file=""):
    dask_client = client
    ml_arrays = run_dask_task(delayed(run_gpu_workflow),
                                          col_path=col_names_path,
                                          acq_path=acq_data_path,
                                          quarter=quarter,
                                          year=year,
                                          perf_file=perf_file)
    return dask_client.compute(ml_arrays,
                          optimize_graph=False,
                          fifo_timeout="0ms")
 def null_workaround(df, **kwargs):
    for column, data_type in df.dtypes.items():
        if str(data_type) == "category":
            df[column] = df[column].astype('int32').fillna(-1)
        if str(data_type) in ['int8', 'int16', 'int32', 'int64', 'float32', 'float64']:
            df[column] = df[column].fillna(-1)
    return df
 def run_gpu_workflow(col_path, acq_path, quarter=1, year=2000, perf_file="", **kwargs):
    names = gpu_load_names(col_path=col_path)
    acq_gdf = gpu_load_acquisition_csv(acquisition_path= acq_path + "/Acquisition_"
                                      + str(year) + "Q" + str(quarter) + ".txt")
    acq_gdf = acq_gdf.merge(names, how='left', on=['seller_name'])
    acq_gdf.drop_column('seller_name')
    acq_gdf['seller_name'] = acq_gdf['new']
    acq_gdf.drop_column('new')
    perf_df_tmp = gpu_load_performance_csv(perf_file)
    gdf = perf_df_tmp
    everdf = create_ever_features(gdf)
    delinq_merge = create_delinq_features(gdf)
    everdf = join_ever_delinq_features(everdf, delinq_merge)
    del(delinq_merge)
    joined_df = create_joined_df(gdf, everdf)
    testdf = create_12_mon_features(joined_df)
    joined_df = combine_joined_12_mon(joined_df, testdf)
    del(testdf)
    perf_df = final_performance_delinquency(gdf, joined_df)
    del(gdf, joined_df)
    final_gdf = join_perf_acq_gdfs(perf_df, acq_gdf)
    del(perf_df)
    del(acq_gdf)
    final_gdf = last_mile_cleaning(final_gdf)
    return final_gdf
 def gpu_load_performance_csv(performance_path, **kwargs):
    """ Loads performance data
    Returns
    -------
    GPU DataFrame
    """
    cols = [
        "loan_id", "monthly_reporting_period", "servicer", "interest_rate", "current_actual_upb",
        "loan_age", "remaining_months_to_legal_maturity", "adj_remaining_months_to_maturity",
        "maturity_date", "msa", "current_loan_delinquency_status", "mod_flag", "zero_balance_code",
        "zero_balance_effective_date", "last_paid_installment_date", "foreclosed_after",
        "disposition_date", "foreclosure_costs", "prop_preservation_and_repair_costs",
        "asset_recovery_costs", "misc_holding_expenses", "holding_taxes", "net_sale_proceeds",
        "credit_enhancement_proceeds", "repurchase_make_whole_proceeds", "other_foreclosure_proceeds",
        "non_interest_bearing_upb", "principal_forgiveness_upb", "repurchase_make_whole_proceeds_flag",
        "foreclosure_principal_write_off_amount", "servicing_activity_indicator"
    ]
    dtypes = OrderedDict([
        ("loan_id", "int64"),
        ("monthly_reporting_period", "date"),
        ("servicer", "category"),
        ("interest_rate", "float64"),
        ("current_actual_upb", "float64"),
        ("loan_age", "float64"),
        ("remaining_months_to_legal_maturity", "float64"),
        ("adj_remaining_months_to_maturity", "float64"),
        ("maturity_date", "date"),
        ("msa", "float64"),
        ("current_loan_delinquency_status", "int32"),
        ("mod_flag", "category"),
        ("zero_balance_code", "category"),
        ("zero_balance_effective_date", "date"),
        ("last_paid_installment_date", "date"),
        ("foreclosed_after", "date"),
        ("disposition_date", "date"),
        ("foreclosure_costs", "float64"),
        ("prop_preservation_and_repair_costs", "float64"),
        ("asset_recovery_costs", "float64"),
        ("misc_holding_expenses", "float64"),
        ("holding_taxes", "float64"),
        ("net_sale_proceeds", "float64"),
        ("credit_enhancement_proceeds", "float64"),
        ("repurchase_make_whole_proceeds", "float64"),
        ("other_foreclosure_proceeds", "float64"),
        ("non_interest_bearing_upb", "float64"),
        ("principal_forgiveness_upb", "float64"),
        ("repurchase_make_whole_proceeds_flag", "category"),
        ("foreclosure_principal_write_off_amount", "float64"),
        ("servicing_activity_indicator", "category")
    ])
    print(performance_path)
    return cudf.read_csv(performance_path, names=cols, delimiter='|', dtype=list(dtypes.values()), skiprows=1)
 def gpu_load_acquisition_csv(acquisition_path, **kwargs):
    """ Loads acquisition data
    Returns
    -------
    GPU DataFrame
    """
    cols = [
        'loan_id', 'orig_channel', 'seller_name', 'orig_interest_rate', 'orig_upb', 'orig_loan_term', 
        'orig_date', 'first_pay_date', 'orig_ltv', 'orig_cltv', 'num_borrowers', 'dti', 'borrower_credit_score', 
        'first_home_buyer', 'loan_purpose', 'property_type', 'num_units', 'occupancy_status', 'property_state',
        'zip', 'mortgage_insurance_percent', 'product_type', 'coborrow_credit_score', 'mortgage_insurance_type', 
        'relocation_mortgage_indicator'
    ]
    dtypes = OrderedDict([
        ("loan_id", "int64"),
        ("orig_channel", "category"),
        ("seller_name", "category"),
        ("orig_interest_rate", "float64"),
        ("orig_upb", "int64"),
        ("orig_loan_term", "int64"),
        ("orig_date", "date"),
        ("first_pay_date", "date"),
        ("orig_ltv", "float64"),
        ("orig_cltv", "float64"),
        ("num_borrowers", "float64"),
        ("dti", "float64"),
        ("borrower_credit_score", "float64"),
        ("first_home_buyer", "category"),
        ("loan_purpose", "category"),
        ("property_type", "category"),
        ("num_units", "int64"),
        ("occupancy_status", "category"),
        ("property_state", "category"),
        ("zip", "int64"),
        ("mortgage_insurance_percent", "float64"),
        ("product_type", "category"),
        ("coborrow_credit_score", "float64"),
        ("mortgage_insurance_type", "float64"),
        ("relocation_mortgage_indicator", "category")
    ])
    print(acquisition_path)
    return cudf.read_csv(acquisition_path, names=cols, delimiter='|', dtype=list(dtypes.values()), skiprows=1)
 def gpu_load_names(col_path):
    """ Loads names used for renaming the banks
    Returns
    -------
    GPU DataFrame
    """
    cols = [
        'seller_name', 'new'
    ]
    dtypes = OrderedDict([
        ("seller_name", "category"),
        ("new", "category"),
    ])
    return cudf.read_csv(col_path, names=cols, delimiter='|', dtype=list(dtypes.values()), skiprows=1)
 def create_ever_features(gdf, **kwargs):
    everdf = gdf[['loan_id', 'current_loan_delinquency_status']]
    everdf = everdf.groupby('loan_id', method='hash').max()
    del(gdf)
    everdf['ever_30'] = (everdf['max_current_loan_delinquency_status'] >= 1).astype('int8')
    everdf['ever_90'] = (everdf['max_current_loan_delinquency_status'] >= 3).astype('int8')
    everdf['ever_180'] = (everdf['max_current_loan_delinquency_status'] >= 6).astype('int8')
    everdf.drop_column('max_current_loan_delinquency_status')
    return everdf
 def create_delinq_features(gdf, **kwargs):
    delinq_gdf = gdf[['loan_id', 'monthly_reporting_period', 'current_loan_delinquency_status']]
    del(gdf)
    delinq_30 = delinq_gdf.query('current_loan_delinquency_status >= 1')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min()
    delinq_30['delinquency_30'] = delinq_30['min_monthly_reporting_period']
    delinq_30.drop_column('min_monthly_reporting_period')
    delinq_90 = delinq_gdf.query('current_loan_delinquency_status >= 3')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min()
    delinq_90['delinquency_90'] = delinq_90['min_monthly_reporting_period']
    delinq_90.drop_column('min_monthly_reporting_period')
    delinq_180 = delinq_gdf.query('current_loan_delinquency_status >= 6')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min()
    delinq_180['delinquency_180'] = delinq_180['min_monthly_reporting_period']
    delinq_180.drop_column('min_monthly_reporting_period')
    del(delinq_gdf)
    delinq_merge = delinq_30.merge(delinq_90, how='left', on=['loan_id'], type='hash')
    delinq_merge['delinquency_90'] = delinq_merge['delinquency_90'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]'))
    delinq_merge = delinq_merge.merge(delinq_180, how='left', on=['loan_id'], type='hash')
    delinq_merge['delinquency_180'] = delinq_merge['delinquency_180'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]'))
    del(delinq_30)
    del(delinq_90)
    del(delinq_180)
    return delinq_merge
 def join_ever_delinq_features(everdf_tmp, delinq_merge, **kwargs):
    everdf = everdf_tmp.merge(delinq_merge, on=['loan_id'], how='left', type='hash')
    del(everdf_tmp)
    del(delinq_merge)
    everdf['delinquency_30'] = everdf['delinquency_30'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]'))
    everdf['delinquency_90'] = everdf['delinquency_90'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]'))
    everdf['delinquency_180'] = everdf['delinquency_180'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]'))
    return everdf
 def create_joined_df(gdf, everdf, **kwargs):
    test = gdf[['loan_id', 'monthly_reporting_period', 'current_loan_delinquency_status', 'current_actual_upb']]
    del(gdf)
    test['timestamp'] = test['monthly_reporting_period']
    test.drop_column('monthly_reporting_period')
    test['timestamp_month'] = test['timestamp'].dt.month
    test['timestamp_year'] = test['timestamp'].dt.year
    test['delinquency_12'] = test['current_loan_delinquency_status']
    test.drop_column('current_loan_delinquency_status')
    test['upb_12'] = test['current_actual_upb']
    test.drop_column('current_actual_upb')
    test['upb_12'] = test['upb_12'].fillna(999999999)
    test['delinquency_12'] = test['delinquency_12'].fillna(-1)
    joined_df = test.merge(everdf, how='left', on=['loan_id'], type='hash')
    del(everdf)
    del(test)
    joined_df['ever_30'] = joined_df['ever_30'].fillna(-1)
    joined_df['ever_90'] = joined_df['ever_90'].fillna(-1)
    joined_df['ever_180'] = joined_df['ever_180'].fillna(-1)
    joined_df['delinquency_30'] = joined_df['delinquency_30'].fillna(-1)
    joined_df['delinquency_90'] = joined_df['delinquency_90'].fillna(-1)
    joined_df['delinquency_180'] = joined_df['delinquency_180'].fillna(-1)
    joined_df['timestamp_year'] = joined_df['timestamp_year'].astype('int32')
    joined_df['timestamp_month'] = joined_df['timestamp_month'].astype('int32')
    return joined_df
 def create_12_mon_features(joined_df, **kwargs):
    testdfs = []
    n_months = 12
    for y in range(1, n_months + 1):
        tmpdf = joined_df[['loan_id', 'timestamp_year', 'timestamp_month', 'delinquency_12', 'upb_12']]
        tmpdf['josh_months'] = tmpdf['timestamp_year'] * 12 + tmpdf['timestamp_month']
        tmpdf['josh_mody_n'] = ((tmpdf['josh_months'].astype('float64') - 24000 - y) / 12).floor()
        tmpdf = tmpdf.groupby(['loan_id', 'josh_mody_n'], method='hash').agg({'delinquency_12': 'max','upb_12': 'min'})
        tmpdf['delinquency_12'] = (tmpdf['max_delinquency_12']>3).astype('int32')
        tmpdf['delinquency_12'] +=(tmpdf['min_upb_12']==0).astype('int32')
        tmpdf.drop_column('max_delinquency_12')
        tmpdf['upb_12'] = tmpdf['min_upb_12']
        tmpdf.drop_column('min_upb_12')
        tmpdf['timestamp_year'] = (((tmpdf['josh_mody_n'] * n_months) + 24000 + (y - 1)) / 12).floor().astype('int16')
        tmpdf['timestamp_month'] = np.int8(y)
        tmpdf.drop_column('josh_mody_n')
        testdfs.append(tmpdf)
        del(tmpdf)
    del(joined_df)
    return cudf.concat(testdfs)
 def combine_joined_12_mon(joined_df, testdf, **kwargs):
    joined_df.drop_column('delinquency_12')
    joined_df.drop_column('upb_12')
    joined_df['timestamp_year'] = joined_df['timestamp_year'].astype('int16')
    joined_df['timestamp_month'] = joined_df['timestamp_month'].astype('int8')
    return joined_df.merge(testdf, how='left', on=['loan_id', 'timestamp_year', 'timestamp_month'], type='hash')
 def final_performance_delinquency(gdf, joined_df, **kwargs):
    merged = null_workaround(gdf)
    joined_df = null_workaround(joined_df)
    merged['timestamp_month'] = merged['monthly_reporting_period'].dt.month
    merged['timestamp_month'] = merged['timestamp_month'].astype('int8')
    merged['timestamp_year'] = merged['monthly_reporting_period'].dt.year
    merged['timestamp_year'] = merged['timestamp_year'].astype('int16')
    merged = merged.merge(joined_df, how='left', on=['loan_id', 'timestamp_year', 'timestamp_month'], type='hash')
    merged.drop_column('timestamp_year')
    merged.drop_column('timestamp_month')
    return merged
 def join_perf_acq_gdfs(perf, acq, **kwargs):
    perf = null_workaround(perf)
    acq = null_workaround(acq)
    return perf.merge(acq, how='left', on=['loan_id'], type='hash')
 def last_mile_cleaning(df, **kwargs):
    drop_list = [
        'loan_id', 'orig_date', 'first_pay_date', 'seller_name',
        'monthly_reporting_period', 'last_paid_installment_date', 'maturity_date', 'ever_30', 'ever_90', 'ever_180',
        'delinquency_30', 'delinquency_90', 'delinquency_180', 'upb_12',
        'zero_balance_effective_date','foreclosed_after', 'disposition_date','timestamp'
    ]
    for column in drop_list:
        df.drop_column(column)
    for col, dtype in df.dtypes.iteritems():
        if str(dtype)=='category':
            df[col] = df[col].cat.codes
        df[col] = df[col].astype('float32')
    df['delinquency_12'] = df['delinquency_12'] > 0
    df['delinquency_12'] = df['delinquency_12'].fillna(False).astype('int32')
    for column in df.columns:
        df[column] = df[column].fillna(-1)
    return df.to_arrow(preserve_index=False)
 def main():
    #print('XGBOOST_BUILD_DOC is ' + os.environ['XGBOOST_BUILD_DOC'])
    parser = argparse.ArgumentParser("rapidssample")
    parser.add_argument("--data_dir", type=str, help="location of data")
    parser.add_argument("--num_gpu", type=int, help="Number of GPUs to use", default=1)
    parser.add_argument("--part_count", type=int, help="Number of data files to train against", default=2)
    parser.add_argument("--end_year", type=int, help="Year to end the data load", default=2000)
    parser.add_argument("--cpu_predictor", type=str, help="Flag to use CPU for prediction", default='False')
    parser.add_argument('-f', type=str, default='') # added for notebook execution scenarios
    args = parser.parse_args()
    data_dir = args.data_dir
    num_gpu = args.num_gpu
    part_count = args.part_count
    end_year = args.end_year
    cpu_predictor = args.cpu_predictor.lower() in ('yes', 'true', 't', 'y', '1')
    if cpu_predictor:
        print('Training with CPUs require num gpu = 1')
        num_gpu = 1
    print('data_dir = {0}'.format(data_dir))
    print('num_gpu = {0}'.format(num_gpu))
    print('part_count = {0}'.format(part_count))
    #part_count = part_count + 1 # adding one because the usage below is not inclusive
    print('end_year = {0}'.format(end_year))
    print('cpu_predictor = {0}'.format(cpu_predictor))
    import subprocess
    cmd = "hostname --all-ip-addresses"
    process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE)
    output, error = process.communicate()
    IPADDR = str(output.decode()).split()[0]
    cluster = LocalCUDACluster(ip=IPADDR,n_workers=num_gpu)
    client = Client(cluster)
    client
    print(client.ncores())
 # to download data for this notebook, visit https://rapidsai.github.io/demos/datasets/mortgage-data and update the following paths accordingly
    acq_data_path = "{0}/acq".format(data_dir) #"/rapids/data/mortgage/acq"
    perf_data_path = "{0}/perf".format(data_dir) #"/rapids/data/mortgage/perf"
    col_names_path = "{0}/names.csv".format(data_dir) # "/rapids/data/mortgage/names.csv"
    start_year = 2000
 #end_year = 2000 # end_year is inclusive -- converted to parameter
 #part_count = 2 # the number of data files to train against -- converted to parameter
    client.run(initialize_rmm_pool)
    client
    print(client.ncores())
 # NOTE: The ETL calculates additional features which are then dropped before creating the XGBoost DMatrix.
 # This can be optimized to avoid calculating the dropped features.
    print("Reading ...")
    t1 = datetime.datetime.now()
    gpu_dfs = []
    gpu_time = 0
    quarter = 1
    year = start_year
    count = 0
    while year <= end_year:
        for file in glob(os.path.join(perf_data_path + "/Performance_" + str(year) + "Q" + str(quarter) + "*")):
            if count < part_count:
                gpu_dfs.append(process_quarter_gpu(client, col_names_path, acq_data_path, year=year, quarter=quarter, perf_file=file))
                count += 1
                print('file: {0}'.format(file))
                print('count: {0}'.format(count))
        quarter += 1
        if quarter == 5:
            year += 1
            quarter = 1
    wait(gpu_dfs)
    t2 = datetime.datetime.now()
    print("Reading time ...")
    print(t2-t1)
    print('len(gpu_dfs) is {0}'.format(len(gpu_dfs)))
    client.run(cudf._gdf.rmm_finalize)
    client.run(initialize_rmm_no_pool)
    client
    print(client.ncores())
    dxgb_gpu_params = {
        'nround':            100,
        'max_depth':         8,
        'max_leaves':        2**8,
        'alpha':             0.9,
        'eta':               0.1,
        'gamma':             0.1,
        'learning_rate':     0.1,
        'subsample':         1,
        'reg_lambda':        1,
        'scale_pos_weight':  2,
        'min_child_weight':  30,
        'tree_method':       'gpu_hist',
        'n_gpus':            1, 
        'distributed_dask':  True,
        'loss':              'ls',
        'objective':         'gpu:reg:linear',
        'max_features':      'auto',
        'criterion':         'friedman_mse',
        'grow_policy':       'lossguide',
        'verbose':           True
    }
    if cpu_predictor:
        print('Training using CPUs')
        dxgb_gpu_params['predictor'] = 'cpu_predictor'
        dxgb_gpu_params['tree_method'] = 'hist'
        dxgb_gpu_params['objective'] = 'reg:linear'
    else:
        print('Training using GPUs')
    print('Training parameters are {0}'.format(dxgb_gpu_params))
    gpu_dfs = [delayed(DataFrame.from_arrow)(gpu_df) for gpu_df in gpu_dfs[:part_count]]
    gpu_dfs = [gpu_df for gpu_df in gpu_dfs]
    wait(gpu_dfs)
    tmp_map = [(gpu_df, list(client.who_has(gpu_df).values())[0]) for gpu_df in gpu_dfs]
    new_map = {}
    for key, value in tmp_map:
        if value not in new_map:
            new_map[value] = [key]
        else:
            new_map[value].append(key)
    del(tmp_map)
    gpu_dfs = []
    for list_delayed in new_map.values():
        gpu_dfs.append(delayed(cudf.concat)(list_delayed))
    del(new_map)
    gpu_dfs = [(gpu_df[['delinquency_12']], gpu_df[delayed(list)(gpu_df.columns.difference(['delinquency_12']))]) for gpu_df in gpu_dfs]
    gpu_dfs = [(gpu_df[0].persist(), gpu_df[1].persist()) for gpu_df in gpu_dfs]
    gpu_dfs = [dask.delayed(xgb.DMatrix)(gpu_df[1], gpu_df[0]) for gpu_df in gpu_dfs]
    gpu_dfs = [gpu_df.persist() for gpu_df in gpu_dfs]
    gc.collect()
    wait(gpu_dfs)
    labels = None
    t1 = datetime.datetime.now()
    bst = dxgb_gpu.train(client, dxgb_gpu_params, gpu_dfs, labels, num_boost_round=dxgb_gpu_params['nround'])
    t2 = datetime.datetime.now()
    print("Training time ...")
    print(t2-t1)
    print('str(bst) is {0}'.format(str(bst)))
    print('Exiting script')
 if __name__ == '__main__':
    main()
--- a/contrib/RAPIDS/rapids.yml
+++ b/contrib/RAPIDS/rapids.yml
@@ -1,35 +0,0 @@
 name: rapids
 channels:
 - nvidia
 - numba
 - conda-forge
 - rapidsai
 - defaults
 - pytorch
 dependencies:
 - arrow-cpp=0.12.0
 - bokeh
 - cffi=1.11.5
 - cmake=3.12
 - cuda92
 - cython==0.29
 - dask=1.1.1
 - distributed=1.25.3
 - faiss-gpu=1.5.0
 - numba=0.42
 - numpy=1.15.4
 - nvstrings
 - pandas=0.23.4
 - pyarrow=0.12.0
 - scikit-learn
 - scipy
 - cudf
 - cuml
 - python=3.6.2
 - jupyterlab
 - pip:
  - file:/rapids/xgboost/python-package/dist/xgboost-0.81-py3-none-any.whl
  - git+https://github.com/rapidsai/dask-xgboost@dask-cudf
  - git+https://github.com/rapidsai/dask-cudf@master
  - git+https://github.com/rapidsai/dask-cuda@master
--- a/contrib/datadrift/azure-ml-datadrift.ipynb
+++ b/contrib/datadrift/azure-ml-datadrift.ipynb
@@ -1,723 +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-opendatasets and lightgbm packages before running this notebook.\n",
        "```\n",
        "pip install azureml-contrib-datadrift\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.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",
        "\n",
        "def enrich_weather_noaa_data(noaa_df):\n",
        "    hours_in_day = 23\n",
        "    week_in_year = 52\n",
        "    \n",
        "    noaa_df[\"hour\"] = noaa_df[\"datetime\"].dt.hour\n",
        "    noaa_df[\"weekofyear\"] = noaa_df[\"datetime\"].dt.week\n",
        "    \n",
        "    noaa_df[\"sine_weekofyear\"] = noaa_df['datetime'].transform(lambda x: np.sin((2*np.pi*x.dt.week-1)/week_in_year))\n",
        "    noaa_df[\"cosine_weekofyear\"] = noaa_df['datetime'].transform(lambda x: np.cos((2*np.pi*x.dt.week-1)/week_in_year))\n",
        "\n",
        "    noaa_df[\"sine_hourofday\"] = noaa_df['datetime'].transform(lambda x: np.sin(2*np.pi*x.dt.hour/hours_in_day))\n",
        "    noaa_df[\"cosine_hourofday\"] = noaa_df['datetime'].transform(lambda x: np.cos(2*np.pi*x.dt.hour/hours_in_day))\n",
        "    \n",
        "    return noaa_df\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['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",
        "datasets = [(Dataset.Scenario.TRAINING, trainingDataset)]\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": [
        "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": {},
      "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
 }
--- a/contrib/datadrift/azure-ml-datadrift.yml
+++ b/contrib/datadrift/azure-ml-datadrift.yml
@@ -1,8 +0,0 @@
 name: azure-ml-datadrift
 dependencies:
 - pip:
  - azureml-sdk
  - azureml-contrib-datadrift
  - azureml-opendatasets
  - lightgbm
  - azureml-widgets
--- a/contrib/datadrift/score.py
+++ b/contrib/datadrift/score.py
@@ -1,58 +0,0 @@
 import pickle
 import json
 import numpy
 import azureml.train.automl
 from sklearn.externals import joblib
 from sklearn.linear_model import Ridge
 from azureml.core.model import Model
 from azureml.core.run import Run
 from azureml.monitoring import ModelDataCollector
 import time
 import pandas as pd
 def init():
    global model, inputs_dc, prediction_dc, feature_names, categorical_features
    print("Model is initialized" + time.strftime("%H:%M:%S"))
    model_path = Model.get_model_path(model_name="driftmodel")
    model = joblib.load(model_path)
    feature_names = ["usaf", "wban", "latitude", "longitude", "station_name", "p_k",
                     "sine_weekofyear", "cosine_weekofyear", "sine_hourofday", "cosine_hourofday",
                     "temperature-7"]
    categorical_features = ["usaf", "wban", "p_k", "station_name"]
    inputs_dc = ModelDataCollector(model_name="driftmodel",
                                   identifier="inputs",
                                   feature_names=feature_names)
    prediction_dc = ModelDataCollector("driftmodel",
                                       identifier="predictions",
                                       feature_names=["temperature"])
 def run(raw_data):
    global inputs_dc, prediction_dc
    try:
        data = json.loads(raw_data)["data"]
        data = pd.DataFrame(data)
        # Remove the categorical features as the model expects OHE values
        input_data = data.drop(categorical_features, axis=1)
        result = model.predict(input_data)
        # Collect the non-OHE dataframe
        collected_df = data[feature_names]
        inputs_dc.collect(collected_df.values)
        prediction_dc.collect(result)
        return result.tolist()
    except Exception as e:
        error = str(e)
        print(error + time.strftime("%H:%M:%S"))
        return error
--- a/end-to-end-samples/README.md
+++ b/end-to-end-samples/README.md
--- a/how-to-use-azureml/README.md
+++ b/how-to-use-azureml/README.md
@@ -4,14 +4,13 @@ Learn how to use Azure Machine Learning services for experimentation and model m
 As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) notebook first to set up your Azure ML Workspace. Then, run the notebooks in following recommended order.
-* [train-within-notebook](./training/train-within-notebook): Train a model hile tracking run history, and learn how to deploy the model as web service to Azure Container Instance.
+ * [train-within-notebook](train-within-notebook/train-within-notebook.ipynb): Train a model hile tracking run history, and learn how to deploy the model as web service to Azure Container Instance.
-* [train-on-local](./training/train-on-local): Learn how to submit a run to local computer and use Azure ML managed run configuration.
+ * [train-on-local](train-on-local/train-on-local.ipynb): Learn how to submit a run and use Azure ML managed run configuration.
-* [train-on-amlcompute](./training/train-on-amlcompute): Use a 1-n node Azure ML managed compute cluster for remote runs on Azure CPU or GPU infrastructure.
+* [train-on-aci](train-on-aci/train-on-aci.ipynb): Submit a remote run on serverless Docker-based compute.
-* [train-on-remote-vm](./training/train-on-remote-vm): Use Data Science Virtual Machine as a target for remote runs.
+* [train-on-remote-vm](train-on-remote-vm/train-on-remote-vm.ipynb): Use Data Science Virtual Machine as a target for remote runs.
-* [logging-api](./track-and-monitor-experiments/logging-api): Learn about the details of logging metrics to run history.
+* [logging-api](logging-api/logging-api.ipynb): Learn about the details of logging metrics to run history.
-* [register-model-create-image-deploy-service](./deployment/register-model-create-image-deploy-service): Learn about the details of model management.
+* [register-model-create-image-deploy-service](register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb): Learn about the details of model management.
-* [production-deploy-to-aks](./deployment/production-deploy-to-aks) Deploy a model to production at scale on Azure Kubernetes Service.
+* [production-deploy-to-aks](production-deploy-to-aks/production-deploy-to-aks.ipynb) Deploy a model to production at scale on Azure Kubernetes Service.
-* [enable-data-collection-for-models-in-aks](./deployment/enable-data-collection-for-models-in-aks) Learn about data collection APIs for deployed model.
+* [enable-data-collection-for-models-in-aks](enable-data-collection-for-models-in-aks/enable-data-collection-for-models-in-aks.ipynb) Learn about data collection APIs for deployed model.
-* [enable-app-insights-in-production-service](./deployment/enable-app-insights-in-production-service) Learn how to use App Insights with production web service.
+* [enable-app-insights-in-production-service](enable-app-insights-in-production-serviceenable-app-insights-in-production-service.ipynb) Learn how to use App Insights with production web service.
 Find quickstarts, end-to-end tutorials, and how-tos on the [official documentation site for Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/).
--- a/how-to-use-azureml/automated-machine-learning/01.auto-ml-classification.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/01.auto-ml-classification.ipynb
@@ -0,0 +1,414 @@
 {
 "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": [
    "# AutoML 01: Classification with Local Compute\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",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you will learn how to:\n",
    "1. Create an `Experiment` in an existing `Workspace`.\n",
    "2. Configure AutoML using `AutoMLConfig`.\n",
    "3. Train the model using local compute.\n",
    "4. Explore the results.\n",
    "5. Test the best fitted model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an Experiment\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 random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\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 the experiment and specify the project folder.\n",
    "experiment_name = 'automl-local-classification'\n",
    "project_folder = './sample_projects/automl-local-classification'\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "\n",
    "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",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Training Data\n",
    "\n",
    "This uses scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "digits = datasets.load_digits()\n",
    "\n",
    "# Exclude the first 100 rows from training so that they can be used for test.\n",
    "X_train = digits.data[100:,:]\n",
    "y_train = digits.target[100:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure AutoML\n",
    "\n",
    "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**task**|classification 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, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\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.|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             primary_metric = 'AUC_weighted',\n",
    "                             iteration_timeout_minutes = 60,\n",
    "                             iterations = 25,\n",
    "                             n_cross_validations = 3,\n",
    "                             verbosity = logging.INFO,\n",
    "                             X = X_train, \n",
    "                             y = y_train,\n",
    "                             path = project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Models\n",
    "\n",
    "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": [
    "local_run = experiment.submit(automl_config, show_output = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optionally, you can continue an interrupted local run by calling `continue_experiment` without the `iterations` parameter, or run more iterations for a completed run by specifying the `iterations` parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = local_run.continue_experiment(X = X_train, \n",
    "                                          y = y_train, \n",
    "                                          show_output = True,\n",
    "                                          iterations = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the 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(local_run).show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(local_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. 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*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = local_run.get_output()\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model Based on Any Other Metric\n",
    "Show the run and the model that has the smallest `log_loss` value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup_metric = \"log_loss\"\n",
    "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a Specific Iteration\n",
    "Show the run and the model from the third iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 3\n",
    "third_run, third_model = local_run.get_output(iteration = iteration)\n",
    "print(third_run)\n",
    "print(third_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Best Fitted Model\n",
    "\n",
    "#### Load Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_test = digits.data[:10, :]\n",
    "y_test = digits.target[:10]\n",
    "images = digits.images[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing Our Best Fitted Model\n",
    "We will try to predict 2 digits and see how our model works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Randomly select digits and test.\n",
    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
    "    print(index)\n",
    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
    "    label = y_test[index]\n",
    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
    "    fig = plt.figure(1, figsize = (3,3))\n",
    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
    "    ax1.set_title(title)\n",
    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
    "    plt.show()"
   ]
  }
 ],
 "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
 }
--- a/how-to-use-azureml/automated-machine-learning/02.auto-ml-regression.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/02.auto-ml-regression.ipynb
@@ -0,0 +1,415 @@
 {
 "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": [
    "# AutoML 02: Regression with Local Compute\n",
    "\n",
    "In this example we use the scikit-learn's [diabetes dataset](http://scikit-learn.org/stable/datasets/index.html#diabetes-dataset) to showcase how you can use AutoML for a simple regression problem.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you will learn how to:\n",
    "1. Create an `Experiment` in an existing `Workspace`.\n",
    "2. Configure AutoML using `AutoMLConfig`.\n",
    "3. Train the model using local compute.\n",
    "4. Explore the results.\n",
    "5. Test the best fitted model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an Experiment\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 random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\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 the experiment and specify the project folder.\n",
    "experiment_name = 'automl-local-regression'\n",
    "project_folder = './sample_projects/automl-local-regression'\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",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Training Data\n",
    "This uses scikit-learn's [load_diabetes](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html) method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the diabetes dataset, a well-known built-in small dataset that comes with scikit-learn.\n",
    "from sklearn.datasets import load_diabetes\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X, y = load_diabetes(return_X_y = True)\n",
    "\n",
    "columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n",
    "\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": [
    "## Configure AutoML\n",
    "\n",
    "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**task**|classification or regression|\n",
    "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
    "|**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, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\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.|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_config = AutoMLConfig(task = 'regression',\n",
    "                             iteration_timeout_minutes = 10,\n",
    "                             iterations = 10,\n",
    "                             primary_metric = 'spearman_correlation',\n",
    "                             n_cross_validations = 5,\n",
    "                             debug_log = 'automl.log',\n",
    "                             verbosity = logging.INFO,\n",
    "                             X = X_train, \n",
    "                             y = y_train,\n",
    "                             path = project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Models\n",
    "\n",
    "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": [
    "local_run = experiment.submit(automl_config, show_output = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the 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(local_run).show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(local_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. 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*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = local_run.get_output()\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model Based on Any Other Metric\n",
    "Show the run and the model that has the smallest `root_mean_squared_error` value (which turned out to be the same as the one with largest `spearman_correlation` value):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup_metric = \"root_mean_squared_error\"\n",
    "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a Specific Iteration\n",
    "Show the run and the model from the third iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 3\n",
    "third_run, third_model = local_run.get_output(iteration = iteration)\n",
    "print(third_run)\n",
    "print(third_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Best Fitted Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predict on training and test set, and calculate residual values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_train = fitted_model.predict(X_train)\n",
    "y_residual_train = y_train - y_pred_train\n",
    "\n",
    "y_pred_test = fitted_model.predict(X_test)\n",
    "y_residual_test = y_test - y_pred_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "# Set up a multi-plot chart.\n",
    "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw = {'width_ratios':[1, 1], 'wspace':0, 'hspace': 0})\n",
    "f.suptitle('Regression Residual Values', fontsize = 18)\n",
    "f.set_figheight(6)\n",
    "f.set_figwidth(16)\n",
    "\n",
    "# Plot residual values of training set.\n",
    "a0.axis([0, 360, -200, 200])\n",
    "a0.plot(y_residual_train, 'bo', alpha = 0.5)\n",
    "a0.plot([-10,360],[0,0], 'r-', lw = 3)\n",
    "a0.text(16,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_train, y_pred_train))), fontsize = 12)\n",
    "a0.text(16,140,'R2 score = {0:.2f}'.format(r2_score(y_train, y_pred_train)), fontsize = 12)\n",
    "a0.set_xlabel('Training samples', fontsize = 12)\n",
    "a0.set_ylabel('Residual Values', fontsize = 12)\n",
    "\n",
    "# Plot a histogram.\n",
    "a0.hist(y_residual_train, orientation = 'horizontal', color = 'b', bins = 10, histtype = 'step');\n",
    "a0.hist(y_residual_train, orientation = 'horizontal', color = 'b', alpha = 0.2, bins = 10);\n",
    "\n",
    "# Plot residual values of test set.\n",
    "a1.axis([0, 90, -200, 200])\n",
    "a1.plot(y_residual_test, 'bo', alpha = 0.5)\n",
    "a1.plot([-10,360],[0,0], 'r-', lw = 3)\n",
    "a1.text(5,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_test, y_pred_test))), fontsize = 12)\n",
    "a1.text(5,140,'R2 score = {0:.2f}'.format(r2_score(y_test, y_pred_test)), fontsize = 12)\n",
    "a1.set_xlabel('Test samples', fontsize = 12)\n",
    "a1.set_yticklabels([])\n",
    "\n",
    "# Plot a histogram.\n",
    "a1.hist(y_residual_test, orientation = 'horizontal', color = 'b', bins = 10, histtype = 'step')\n",
    "a1.hist(y_residual_test, orientation = 'horizontal', color = 'b', alpha = 0.2, bins = 10)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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
 }
--- a/how-to-use-azureml/automated-machine-learning/03.auto-ml-remote-execution.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/03.auto-ml-remote-execution.ipynb
@@ -0,0 +1,507 @@
 {
 "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": [
    "# AutoML 03: Remote Execution using DSVM (Ubuntu)\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",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you wiil learn how to:\n",
    "1. Create an `Experiment` in an existing `Workspace`.\n",
    "2. Attach an existing DSVM to a workspace.\n",
    "3. Configure AutoML using `AutoMLConfig`.\n",
    "4. Train the model using the DSVM.\n",
    "5. Explore the results.\n",
    "6. Test the best fitted 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`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an Experiment\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 random\n",
    "import time\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\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 the run history container in the workspace.\n",
    "experiment_name = 'automl-remote-dsvm4'\n",
    "project_folder = './sample_projects/automl-remote-dsvm4'\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",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Remote Linux DSVM\n",
    "**Note:** If creation fails with a message about Marketplace purchase eligibilty, start creation of a DSVM through the [Azure portal](https://portal.azure.com), and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled this setting, you can exit the portal without actually creating the DSVM, and creation of the DSVM through the notebook should work.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.compute import DsvmCompute\n",
    "\n",
    "dsvm_name = 'mydsvma'\n",
    "try:\n",
    "    dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
    "    print('Found an existing DSVM.')\n",
    "except:\n",
    "    print('Creating a new DSVM.')\n",
    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
    "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
    "    dsvm_compute.wait_for_completion(show_output = True)\n",
    "    print(\"Waiting one minute for ssh to be accessible\")\n",
    "    time.sleep(60) # Wait for ssh to be accessible"
   ]
  },
  {
   "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 the Linux DSVM\n",
    "conda_run_config.target = dsvm_compute\n",
    "\n",
    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
    "conda_run_config.environment.python.conda_dependencies = cd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Get Data File\n",
    "For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
    "In this example, the `get_data()` function returns data using scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(project_folder):\n",
    "    os.makedirs(project_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile $project_folder/get_data.py\n",
    "\n",
    "from sklearn import datasets\n",
    "from scipy import sparse\n",
    "import numpy as np\n",
    "\n",
    "def get_data():\n",
    "    \n",
    "    digits = datasets.load_digits()\n",
    "    X_train = digits.data[100:,:]\n",
    "    y_train = digits.target[100:]\n",
    "\n",
    "    return { \"X\" : X_train, \"y\" : y_train }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure AutoML <a class=\"anchor\" id=\"Instantiate-AutoML-Remote-DSVM\"></a>\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:** When using Remote DSVM, 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 to execute in parallel. This should be less than the number of cores on the DSVM.|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_settings = {\n",
    "    \"iteration_timeout_minutes\": 10,\n",
    "    \"iterations\": 20,\n",
    "    \"n_cross_validations\": 5,\n",
    "    \"primary_metric\": 'AUC_weighted',\n",
    "    \"preprocess\": False,\n",
    "    \"max_concurrent_iterations\": 2,\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",
    "                             data_script = project_folder + \"/get_data.py\",\n",
    "                             **automl_settings\n",
    "                            )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** The first run on a new DSVM may take several minutes to prepare the environment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Models\n",
    "\n",
    "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",
    "\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": "markdown",
   "metadata": {},
   "source": [
    "## Explore the 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_480d3ed6-fc94-44aa-8f4e-0b945db9d3ef')"
   ]
  },
  {
   "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": [
    "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": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(remote_run.get_children())\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",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "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 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*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = remote_run.get_output()\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model Based on Any Other Metric\n",
    "Show the run and the model which has the smallest `log_loss` value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup_metric = \"log_loss\"\n",
    "best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a Specific Iteration\n",
    "Show the run and the model from the third iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 3\n",
    "third_run, third_model = remote_run.get_output(iteration = iteration)\n",
    "print(third_run)\n",
    "print(third_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Best Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n",
    "\n",
    "#### Load Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_test = digits.data[:10, :]\n",
    "y_test = digits.target[:10]\n",
    "images = digits.images[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test Our Best Fitted Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Randomly select digits and test.\n",
    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
    "    print(index)\n",
    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
    "    label = y_test[index]\n",
    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
    "    fig = plt.figure(1, figsize=(3,3))\n",
    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
    "    ax1.set_title(title)\n",
    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
    "    plt.show()"
   ]
  }
 ],
 "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
 }
--- a/how-to-use-azureml/automated-machine-learning/03b.auto-ml-remote-batchai.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/03b.auto-ml-remote-batchai.ipynb
@@ -0,0 +1,528 @@
 {
 "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": [
    "# AutoML 03: Remote Execution using Batch AI\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",
    "\n",
    "Make sure you have executed the [00.configuration](00.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. Attach an existing Batch AI compute to a workspace.\n",
    "3. Configure AutoML using `AutoMLConfig`.\n",
    "4. Train the model using Batch AI.\n",
    "5. Explore the results.\n",
    "6. Test the best fitted 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`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an Experiment\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 random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\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 the run history container in the workspace.\n",
    "experiment_name = 'automl-remote-batchai'\n",
    "project_folder = './sample_projects/automl-remote-batchai'\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",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Batch AI Cluster\n",
    "The cluster is created as Machine Learning Compute and will appear under your workspace.\n",
    "\n",
    "**Note:** The creation of the Batch AI cluster can take over 10 minutes, please be patient.\n",
    "\n",
    "As with other Azure services, there are limits on certain resources (e.g. Batch AI cluster size) 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",
    "batchai_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 batchai_cluster_name in cts and cts[batchai_cluster_name].type == 'BatchAI':\n",
    "    found = True\n",
    "    print('Found existing compute target.')\n",
    "    compute_target = cts[batchai_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, batchai_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 Batch AI cluster status, use the 'status' property."
   ]
  },
  {
   "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 the Batch AI cluster\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",
    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
    "conda_run_config.environment.python.conda_dependencies = cd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Get Data File\n",
    "For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
    "In this example, the `get_data()` function returns data using scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(project_folder):\n",
    "    os.makedirs(project_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile $project_folder/get_data.py\n",
    "\n",
    "from sklearn import datasets\n",
    "from scipy import sparse\n",
    "import numpy as np\n",
    "\n",
    "def get_data():\n",
    "    \n",
    "    digits = datasets.load_digits()\n",
    "    X_train = digits.data\n",
    "    y_train = digits.target\n",
    "\n",
    "    return { \"X\" : X_train, \"y\" : y_train }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\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:** When using Batch AI, 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.|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_settings = {\n",
    "    \"iteration_timeout_minutes\": 2,\n",
    "    \"iterations\": 20,\n",
    "    \"n_cross_validations\": 5,\n",
    "    \"primary_metric\": 'AUC_weighted',\n",
    "    \"preprocess\": False,\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",
    "                             data_script = project_folder + \"/get_data.py\",\n",
    "                             **automl_settings\n",
    "                            )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Models\n",
    "\n",
    "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": "markdown",
   "metadata": {},
   "source": [
    "## Explore the 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": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(remote_run.get_children())\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",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "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 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*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = remote_run.get_output()\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model Based on Any Other Metric\n",
    "Show the run and the model which has the smallest `log_loss` value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup_metric = \"log_loss\"\n",
    "best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a Specific Iteration\n",
    "Show the run and the model from the third iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 3\n",
    "third_run, third_model = remote_run.get_output(iteration=iteration)\n",
    "print(third_run)\n",
    "print(third_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n",
    "\n",
    "#### Load Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_test = digits.data[:10, :]\n",
    "y_test = digits.target[:10]\n",
    "images = digits.images[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing Our Best Fitted Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Randomly select digits and test.\n",
    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
    "    print(index)\n",
    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
    "    label = y_test[index]\n",
    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
    "    fig = plt.figure(1, figsize=(3,3))\n",
    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
    "    ax1.set_title(title)\n",
    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
    "    plt.show()"
   ]
  }
 ],
 "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
 }
--- a/how-to-use-azureml/automated-machine-learning/04.auto-ml-remote-execution-text-data-blob-store.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/04.auto-ml-remote-execution-text-data-blob-store.ipynb
@@ -0,0 +1,539 @@
 {
 "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": [
    "# Auto ML 04: Remote Execution with Text Data from Azure Blob Storage\n",
    "\n",
    "In this example we use the [Burning Man 2016 dataset](https://innovate.burningman.org/datasets-page/) to showcase how you can use AutoML to handle text data from Azure Blob Storage.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you will learn how to:\n",
    "1. Create an `Experiment` in an existing `Workspace`.\n",
    "2. Attach an existing DSVM to a workspace.\n",
    "3. Configure AutoML using `AutoMLConfig`.\n",
    "4. Train the model using the DSVM.\n",
    "5. Explore the results.\n",
    "6. Test the best fitted 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`\n",
    "- Handling **text** data using the `preprocess` flag\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an Experiment\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 random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\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 the run history container in the workspace.\n",
    "experiment_name = 'automl-remote-dsvm-blobstore'\n",
    "project_folder = './sample_projects/automl-remote-dsvm-blobstore'\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",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attach a Remote Linux DSVM\n",
    "To use a remote Docker compute target:\n",
    "1. Create a Linux DSVM in Azure, following these [quick instructions](https://docs.microsoft.com/en-us/azure/machine-learning/desktop-workbench/how-to-create-dsvm-hdi). Make sure you use the Ubuntu flavor (not CentOS). Make sure that disk space is available under `/tmp` because AutoML creates files under `/tmp/azureml_run`s. The DSVM should have more cores than the number of parallel runs that you plan to enable. It should also have at least 4GB per core.\n",
    "2. Enter the IP address, user name and password below.\n",
    "\n",
    "**Note:** By default, SSH runs on port 22 and you don't need to change the port number below. If you've configured SSH to use a different port, change `dsvm_ssh_port` accordinglyaddress. [Read more](https://render.githubusercontent.com/documentation/sdk/ssh-issue.md) on changing SSH ports for security reasons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.compute import ComputeTarget, RemoteCompute\n",
    "import time\n",
    "\n",
    "# Add your VM information below\n",
    "# If a compute with the specified compute_name already exists, it will be used and the dsvm_ip_addr, dsvm_ssh_port, \n",
    "# dsvm_username and dsvm_password will be ignored.\n",
    "compute_name  = 'mydsvmb'\n",
    "dsvm_ip_addr  = '<<ip_addr>>'\n",
    "dsvm_ssh_port = 22\n",
    "dsvm_username = '<<username>>'\n",
    "dsvm_password = '<<password>>'\n",
    "\n",
    "if compute_name in ws.compute_targets:\n",
    "    print('Using existing compute.')\n",
    "    dsvm_compute = ws.compute_targets[compute_name]\n",
    "else:\n",
    "    attach_config = RemoteCompute.attach_configuration(address=dsvm_ip_addr, username=dsvm_username, password=dsvm_password, ssh_port=dsvm_ssh_port)\n",
    "    ComputeTarget.attach(workspace=ws, name=compute_name, attach_configuration=attach_config)\n",
    "\n",
    "    while ws.compute_targets[compute_name].provisioning_state == 'Creating':\n",
    "        time.sleep(1)\n",
    "\n",
    "    dsvm_compute = ws.compute_targets[compute_name]\n",
    "    \n",
    "    if dsvm_compute.provisioning_state == 'Failed':\n",
    "        print('Attached failed.')\n",
    "        print(dsvm_compute.provisioning_errors)\n",
    "        dsvm_compute.detach()"
   ]
  },
  {
   "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 the Linux DSVM\n",
    "conda_run_config.target = dsvm_compute\n",
    "\n",
    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
    "conda_run_config.environment.python.conda_dependencies = cd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Get Data File\n",
    "For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
    "In this example, the `get_data()` function returns a [dictionary](README.md#getdata)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(project_folder):\n",
    "    os.makedirs(project_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile $project_folder/get_data.py\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "def get_data():\n",
    "    # Load Burning Man 2016 data.\n",
    "    df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
    "                     delimiter=\"\\t\", quotechar='\"')\n",
    "    # Get integer labels.\n",
    "    le = LabelEncoder()\n",
    "    le.fit(df[\"Label\"].values)\n",
    "    y = le.transform(df[\"Label\"].values)\n",
    "    X = df.drop([\"Label\"], axis=1)\n",
    "\n",
    "    X_train, _, y_train, _ = train_test_split(X, y, test_size = 0.1, random_state = 42)\n",
    "\n",
    "    return { \"X\" : X_train, \"y\" : y_train }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View data\n",
    "\n",
    "You can execute the `get_data()` function locally to view the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run  $project_folder/get_data.py\n",
    "data_dict = get_data()\n",
    "df = data_dict[\"X\"]\n",
    "y = data_dict[\"y\"]\n",
    "pd.set_option('display.max_colwidth', 15)\n",
    "df['Label'] = pd.Series(y, index=df.index)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\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:** When using Remote DSVM, 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",
    "|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.|\n",
    "|**enable_cache**|Setting this to *True* enables preprocess done once and reuse the same preprocessed data for all the iterations. Default value is True.\n",
    "|**max_cores_per_iteration**|Indicates how many cores on the compute target would be used to train a single pipeline.<br>Default is *1*; you can set it to *-1* to use all cores.|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_settings = {\n",
    "    \"iteration_timeout_minutes\": 60,\n",
    "    \"iterations\": 4,\n",
    "    \"n_cross_validations\": 5,\n",
    "    \"primary_metric\": 'AUC_weighted',\n",
    "    \"preprocess\": True,\n",
    "    \"max_cores_per_iteration\": 2\n",
    "}\n",
    "\n",
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             path = project_folder,\n",
    "                             run_configuration=conda_run_config,\n",
    "                             data_script = project_folder + \"/get_data.py\",\n",
    "                             **automl_settings\n",
    "                            )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Models <a class=\"anchor\" id=\"Training-the-model-Remote-DSVM\"></a>\n",
    "\n",
    "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run = experiment.submit(automl_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
    "#### 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": [
    "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": [
    "## Pre-process cache cleanup\n",
    "The preprocess data gets cache at user default file store. When the run is completed the cache can be cleaned by running below cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run.clean_preprocessor_cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(remote_run.get_children())\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",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cancelling Runs\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 Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = remote_run.get_output()\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model Based on Any Other Metric\n",
    "Show the run and the model which has the smallest `accuracy` value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lookup_metric = \"accuracy\"\n",
    "# best_run, fitted_model = remote_run.get_output(metric = lookup_metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a Specific Iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 0\n",
    "zero_run, zero_model = remote_run.get_output(iteration = iteration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from pandas_ml import ConfusionMatrix\n",
    "\n",
    "df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
    "                     delimiter=\"\\t\", quotechar='\"')\n",
    "\n",
    "# get integer labels\n",
    "le = LabelEncoder()\n",
    "le.fit(df[\"Label\"].values)\n",
    "y = le.transform(df[\"Label\"].values)\n",
    "X = df.drop([\"Label\"], axis=1)\n",
    "\n",
    "_, X_test, _, y_test = train_test_split(X, y, test_size=0.1, random_state=42)\n",
    "\n",
    "\n",
    "ypred = fitted_model.predict(X_test.values)\n",
    "\n",
    "\n",
    "ypred_strings = le.inverse_transform(ypred)\n",
    "ytest_strings = le.inverse_transform(y_test)\n",
    "\n",
    "cm = ConfusionMatrix(ytest_strings, ypred_strings)\n",
    "\n",
    "print(cm)\n",
    "\n",
    "cm.plot()"
   ]
  }
 ],
 "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
 }
--- a/how-to-use-azureml/automated-machine-learning/05.auto-ml-missing-data-Blacklist-Early-Termination.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/05.auto-ml-missing-data-Blacklist-Early-Termination.ipynb
@@ -0,0 +1,381 @@
 {
 "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": [
    "# AutoML 05: Blacklisting Models, Early Termination, and Handling Missing Data\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 handling missing values in data. We also provide a stopping metric indicating a target for the primary metrics so that AutoML can terminate the run without necessarly going through all the iterations. Finally, if you want to avoid a certain pipeline, we allow you to specify a blacklist of algorithms that AutoML will ignore for this run.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you will learn how to:\n",
    "1. Create an `Experiment` in an existing `Workspace`.\n",
    "2. Configure AutoML using `AutoMLConfig`.\n",
    "4. Train the model.\n",
    "5. Explore the results.\n",
    "6. Test the best fitted model.\n",
    "\n",
    "In addition this notebook showcases the following features\n",
    "- **Blacklisting** certain pipelines\n",
    "- Specifying **target metrics** to indicate stopping criteria\n",
    "- Handling **missing data** in the input\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an Experiment\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 random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\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 the experiment.\n",
    "experiment_name = 'automl-local-missing-data'\n",
    "project_folder = './sample_projects/automl-local-missing-data'\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",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import sparse\n",
    "\n",
    "digits = datasets.load_digits()\n",
    "X_train = digits.data[10:,:]\n",
    "y_train = digits.target[10:]\n",
    "\n",
    "# Add missing values in 75% of the lines.\n",
    "missing_rate = 0.75\n",
    "n_missing_samples = int(np.floor(X_train.shape[0] * missing_rate))\n",
    "missing_samples = np.hstack((np.zeros(X_train.shape[0] - n_missing_samples, dtype=np.bool), np.ones(n_missing_samples, dtype=np.bool)))\n",
    "rng = np.random.RandomState(0)\n",
    "rng.shuffle(missing_samples)\n",
    "missing_features = rng.randint(0, X_train.shape[1], n_missing_samples)\n",
    "X_train[np.where(missing_samples)[0], missing_features] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data = X_train)\n",
    "df['Label'] = pd.Series(y_train, index=df.index)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure AutoML\n",
    "\n",
    "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.  This includes setting `experiment_exit_score`, which should cause the run to complete before the `iterations` count is reached.\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",
    "|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.|\n",
    "|**experiment_exit_score**|*double* value indicating the target for *primary_metric*. <br>Once the target is surpassed the run terminates.|\n",
    "|**blacklist_models**|*List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run.<br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGD</i><br><i>MultinomialNaiveBayes</i><br><i>BernoulliNaiveBayes</i><br><i>SVM</i><br><i>LinearSVM</i><br><i>KNN</i><br><i>DecisionTree</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>GradientBoosting</i><br><i>TensorFlowDNN</i><br><i>TensorFlowLinearClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i>|\n",
    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers.|\n",
    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             primary_metric = 'AUC_weighted',\n",
    "                             iteration_timeout_minutes = 60,\n",
    "                             iterations = 20,\n",
    "                             n_cross_validations = 5,\n",
    "                             preprocess = True,\n",
    "                             experiment_exit_score = 0.9984,\n",
    "                             blacklist_models = ['KNN','LinearSVM'],\n",
    "                             verbosity = logging.INFO,\n",
    "                             X = X_train, \n",
    "                             y = y_train,\n",
    "                             path = project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Models\n",
    "\n",
    "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": [
    "local_run = experiment.submit(automl_config, show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the 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(local_run).show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(local_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. 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*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = local_run.get_output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model Based on Any Other Metric\n",
    "Show the run and the model which has the smallest `accuracy` value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lookup_metric = \"accuracy\"\n",
    "# best_run, fitted_model = local_run.get_output(metric = lookup_metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a Specific Iteration\n",
    "Show the run and the model from the third iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# iteration = 3\n",
    "# best_run, fitted_model = local_run.get_output(iteration = iteration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the best Fitted Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_test = digits.data[:10, :]\n",
    "y_test = digits.target[:10]\n",
    "images = digits.images[:10]\n",
    "\n",
    "# Randomly select digits and test.\n",
    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
    "    print(index)\n",
    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
    "    label = y_test[index]\n",
    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
    "    fig = plt.figure(1, figsize=(3,3))\n",
    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
    "    ax1.set_title(title)\n",
    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
    "    plt.show()\n"
   ]
  }
 ],
 "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
 }
--- a/how-to-use-azureml/automated-machine-learning/06.auto-ml-sparse-data-train-test-split.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/06.auto-ml-sparse-data-train-test-split.ipynb
@@ -0,0 +1,384 @@
 {
 "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": [
    "# AutoML 06: Train Test Split and Handling Sparse Data\n",
    "\n",
    "In this example we use the scikit-learn's [20newsgroup](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_20newsgroups.html) to showcase how you can use AutoML for handling sparse data and how to specify custom cross validations splits.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you will learn how to:\n",
    "1. Create an `Experiment` in an existing `Workspace`.\n",
    "2. Configure AutoML using `AutoMLConfig`.\n",
    "4. Train the model.\n",
    "5. Explore the results.\n",
    "6. Test the best fitted model.\n",
    "\n",
    "In addition this notebook showcases the following features\n",
    "- Explicit train test splits \n",
    "- Handling **sparse data** in the input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an Experiment\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 random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\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 the experiment\n",
    "experiment_name = 'automl-local-missing-data'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-local-missing-data'\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",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Sparse Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_20newsgroups\n",
    "from sklearn.feature_extraction.text import HashingVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "remove = ('headers', 'footers', 'quotes')\n",
    "categories = [\n",
    "    'alt.atheism',\n",
    "    'talk.religion.misc',\n",
    "    'comp.graphics',\n",
    "    'sci.space',\n",
    "]\n",
    "data_train = fetch_20newsgroups(subset = 'train', categories = categories,\n",
    "                                shuffle = True, random_state = 42,\n",
    "                                remove = remove)\n",
    "\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(data_train.data, data_train.target, test_size = 0.33, random_state = 42)\n",
    "\n",
    "\n",
    "vectorizer = HashingVectorizer(stop_words = 'english', alternate_sign = False,\n",
    "                               n_features = 2**16)\n",
    "X_train = vectorizer.transform(X_train)\n",
    "X_valid = vectorizer.transform(X_valid)\n",
    "\n",
    "summary_df = pd.DataFrame(index = ['No of Samples', 'No of Features'])\n",
    "summary_df['Train Set'] = [X_train.shape[0], X_train.shape[1]]\n",
    "summary_df['Validation Set'] = [X_valid.shape[0], X_valid.shape[1]]\n",
    "summary_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure AutoML\n",
    "\n",
    "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**task**|classification 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",
    "|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.<br>**Note:** If input data is sparse, you cannot use *True*.|\n",
    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
    "|**X_valid**|(sparse) array-like, shape = [n_samples, n_features] for the custom validation set.|\n",
    "|**y_valid**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification for the custom validation set.|\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.|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             primary_metric = 'AUC_weighted',\n",
    "                             iteration_timeout_minutes = 60,\n",
    "                             iterations = 5,\n",
    "                             preprocess = False,\n",
    "                             verbosity = logging.INFO,\n",
    "                             X = X_train, \n",
    "                             y = y_train,\n",
    "                             X_valid = X_valid, \n",
    "                             y_valid = y_valid, \n",
    "                             path = project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Models\n",
    "\n",
    "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": [
    "local_run = experiment.submit(automl_config, show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the 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(local_run).show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(local_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "    \n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. 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*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = local_run.get_output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model Based on Any Other Metric\n",
    "Show the run and the model which has the smallest `accuracy` value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lookup_metric = \"accuracy\"\n",
    "# best_run, fitted_model = local_run.get_output(metric = lookup_metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a Specific Iteration\n",
    "Show the run and the model from the third iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# iteration = 3\n",
    "# best_run, fitted_model = local_run.get_output(iteration = iteration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the Best Fitted Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load test data.\n",
    "from pandas_ml import ConfusionMatrix\n",
    "\n",
    "data_test = fetch_20newsgroups(subset = 'test', categories = categories,\n",
    "                               shuffle = True, random_state = 42,\n",
    "                               remove = remove)\n",
    "\n",
    "X_test = vectorizer.transform(data_test.data)\n",
    "y_test = data_test.target\n",
    "\n",
    "# Test our best pipeline.\n",
    "\n",
    "y_pred = fitted_model.predict(X_test)\n",
    "y_pred_strings = [data_test.target_names[i] for i in y_pred]\n",
    "y_test_strings = [data_test.target_names[i] for i in y_test]\n",
    "\n",
    "cm = ConfusionMatrix(y_test_strings, y_pred_strings)\n",
    "print(cm)\n",
    "cm.plot()"
   ]
  }
 ],
 "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
 }
--- a/how-to-use-azureml/automated-machine-learning/07.auto-ml-exploring-previous-runs.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/07.auto-ml-exploring-previous-runs.ipynb
@@ -0,0 +1,336 @@
 {
 "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": [
    "# AutoML 07: Exploring Previous Runs\n",
    "\n",
    "In this example we present some examples on navigating previously executed runs. We also show how you can download a fitted model for any previous run.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you will learn how to:\n",
    "1. List all experiments in a workspace.\n",
    "2. List all AutoML runs in an experiment.\n",
    "3. Get details for an AutoML run, including settings, run widget, and all metrics.\n",
    "4. Download a fitted pipeline for any iteration.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# List all AutoML Experiments in a Workspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "import re\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\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.run import Run\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",
    "experiment_list = Experiment.list(workspace=ws)\n",
    "\n",
    "summary_df = pd.DataFrame(index = ['No of Runs'])\n",
    "pattern = re.compile('^AutoML_[^_]*$')\n",
    "for experiment in experiment_list:\n",
    "    all_runs = list(experiment.get_runs())\n",
    "    automl_runs = []\n",
    "    for run in all_runs:\n",
    "        if(pattern.match(run.id)):\n",
    "            automl_runs.append(run)    \n",
    "    summary_df[experiment.name] = [len(automl_runs)]\n",
    "    \n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "summary_df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# List AutoML runs for an experiment\n",
    "Set `experiment_name` to any experiment name from the result of the Experiment.list cell to load the AutoML runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_name = 'automl-local-classification' # Replace this with any project name from previous cell.\n",
    "\n",
    "proj = ws.experiments[experiment_name]\n",
    "summary_df = pd.DataFrame(index = ['Type', 'Status', 'Primary Metric', 'Iterations', 'Compute', 'Name'])\n",
    "pattern = re.compile('^AutoML_[^_]*$')\n",
    "all_runs = list(proj.get_runs(properties={'azureml.runsource': 'automl'}))\n",
    "automl_runs_project = []\n",
    "for run in all_runs:\n",
    "    if(pattern.match(run.id)):\n",
    "        properties = run.get_properties()\n",
    "        tags = run.get_tags()\n",
    "        amlsettings = eval(properties['RawAMLSettingsString'])\n",
    "        if 'iterations' in tags:\n",
    "            iterations = tags['iterations']\n",
    "        else:\n",
    "            iterations = properties['num_iterations']\n",
    "        summary_df[run.id] = [amlsettings['task_type'], run.get_details()['status'], properties['primary_metric'], iterations, properties['target'], amlsettings['name']]\n",
    "        if run.get_details()['status'] == 'Completed':\n",
    "            automl_runs_project.append(run.id)\n",
    "    \n",
    "from IPython.display import HTML\n",
    "projname_html = HTML(\"<h3>{}</h3>\".format(proj.name))\n",
    "\n",
    "from IPython.display import display\n",
    "display(projname_html)\n",
    "display(summary_df.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get details for an AutoML run\n",
    "\n",
    "Copy the project name and run id from the previous cell output to find more details on a particular run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_id = automl_runs_project[0]  # Replace with your own run_id from above run ids\n",
    "assert (run_id in summary_df.keys()), \"Run id not found! Please set run id to a value from above run ids\"\n",
    "\n",
    "from azureml.widgets import RunDetails\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "ml_run = AutoMLRun(experiment = experiment, run_id = run_id)\n",
    "\n",
    "summary_df = pd.DataFrame(index = ['Type', 'Status', 'Primary Metric', 'Iterations', 'Compute', 'Name', 'Start Time', 'End Time'])\n",
    "properties = ml_run.get_properties()\n",
    "tags = ml_run.get_tags()\n",
    "status = ml_run.get_details()\n",
    "amlsettings = eval(properties['RawAMLSettingsString'])\n",
    "if 'iterations' in tags:\n",
    "    iterations = tags['iterations']\n",
    "else:\n",
    "    iterations = properties['num_iterations']\n",
    "start_time = None\n",
    "if 'startTimeUtc' in status:\n",
    "    start_time = status['startTimeUtc']\n",
    "end_time = None\n",
    "if 'endTimeUtc' in status:\n",
    "    end_time = status['endTimeUtc']\n",
    "summary_df[ml_run.id] = [amlsettings['task_type'], status['status'], properties['primary_metric'], iterations, properties['target'], amlsettings['name'], start_time, end_time]\n",
    "display(HTML('<h3>Runtime Details</h3>'))\n",
    "display(summary_df)\n",
    "\n",
    "#settings_df = pd.DataFrame(data = amlsettings, index = [''])\n",
    "display(HTML('<h3>AutoML Settings</h3>'))\n",
    "display(amlsettings)\n",
    "\n",
    "display(HTML('<h3>Iterations</h3>'))\n",
    "RunDetails(ml_run).show() \n",
    "\n",
    "children = list(ml_run.get_children())\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",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "display(HTML('<h3>Metrics</h3>'))\n",
    "display(rundata)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download fitted models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download the Best Model for Any Given Metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metric = 'AUC_weighted' # Replace with a metric name.\n",
    "best_run, fitted_model = ml_run.get_output(metric = metric)\n",
    "fitted_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download the Model for Any Given Iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 1 # Replace with an iteration number.\n",
    "best_run, fitted_model = ml_run.get_output(iteration = iteration)\n",
    "fitted_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Register 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'\n",
    "tags = None\n",
    "ml_run.register_model(description = description, tags = tags)\n",
    "ml_run.model_id # Use this id to deploy the model as a web service in Azure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Register the Best Model for Any Given Metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metric = 'AUC_weighted' # Replace with a metric name.\n",
    "description = 'AutoML Model'\n",
    "tags = None\n",
    "ml_run.register_model(description = description, tags = tags, metric = metric)\n",
    "print(ml_run.model_id) # Use this id to deploy the model as a web service in Azure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Register the Model for Any Given Iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 1 # Replace with an iteration number.\n",
    "description = 'AutoML Model'\n",
    "tags = None\n",
    "ml_run.register_model(description = description, tags = tags, iteration = iteration)\n",
    "print(ml_run.model_id) # Use this id to deploy the model as a web service in Azure."
   ]
  }
 ],
 "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
 }
--- a/how-to-use-azureml/automated-machine-learning/08.auto-ml-remote-execution-with-DataStore.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/08.auto-ml-remote-execution-with-DataStore.ipynb
@@ -0,0 +1,588 @@
 {
 "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": [
    "# AutoML 08: Remote Execution with DataStore\n",
    "\n",
    "This sample accesses a data file on a remote DSVM through DataStore. Advantages of using data store are:\n",
    "1. DataStore secures the access details.\n",
    "2. DataStore supports read, write to blob and file store\n",
    "3. AutoML natively supports copying data from DataStore to DSVM\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you would see\n",
    "1. Storing data in DataStore.\n",
    "2. get_data returning data from DataStore.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Experiment\n",
    "\n",
    "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "import time\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.compute import DsvmCompute\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-remote-datastore-file'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-remote-dsvm-file'\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",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Remote Linux DSVM\n",
    "Note: If creation fails with a message about Marketplace purchase eligibilty, go to portal.azure.com, start creating DSVM there, and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled it, you can exit without actually creating VM.\n",
    "\n",
    "**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you can switch to a different port (such as 5022), you can append the port number to the address. [Read more](https://render.githubusercontent.com/documentation/sdk/ssh-issue.md) on this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "compute_target_name = 'mydsvmc'\n",
    "\n",
    "try:\n",
    "    while ws.compute_targets[compute_target_name].provisioning_state == 'Creating':\n",
    "        time.sleep(1)\n",
    "        \n",
    "    dsvm_compute = DsvmCompute(workspace=ws, name=compute_target_name)\n",
    "    print('found existing:', dsvm_compute.name)\n",
    "except:\n",
    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size=\"Standard_D2_v2\")\n",
    "    dsvm_compute = DsvmCompute.create(ws, name=compute_target_name, provisioning_configuration=dsvm_config)\n",
    "    dsvm_compute.wait_for_completion(show_output=True)\n",
    "    print(\"Waiting one minute for ssh to be accessible\")\n",
    "    time.sleep(60) # Wait for ssh to be accessible"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Copy data file to local\n",
    "\n",
    "Download the data file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mkdir data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
    "                     delimiter=\"\\t\", quotechar='\"')\n",
    "df.to_csv(\"data/data.tsv\", sep=\"\\t\", quotechar='\"', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upload data to the cloud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now make the data accessible remotely by uploading that data from your local machine into Azure so it can be accessed for remote training. The datastore is a convenient construct associated with your workspace for you to upload/download data, and interact with it from your remote compute targets. It is backed by Azure blob storage account.\n",
    "\n",
    "The data.tsv files are uploaded into a directory named data at the root of the datastore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Workspace, Datastore\n",
    "#blob_datastore = Datastore(ws, blob_datastore_name)\n",
    "ds = ws.get_default_datastore()\n",
    "print(ds.datastore_type, ds.account_name, ds.container_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ds.upload_files(\"data.tsv\")\n",
    "ds.upload(src_dir='./data', target_path='data', overwrite=True, show_progress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure & Run\n",
    "\n",
    "First let's create a DataReferenceConfigruation object to inform the system what data folder to download to the compute target.\n",
    "The path_on_compute should be an absolute path to ensure that the data files are downloaded only once.   The get_data method should use this same path to access the data files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.runconfig import DataReferenceConfiguration\n",
    "dr = DataReferenceConfiguration(datastore_name=ds.name, \n",
    "                   path_on_datastore='data', \n",
    "                   path_on_compute='/tmp/azureml_runs',\n",
    "                   mode='download', # download files from datastore to compute target\n",
    "                   overwrite=False)"
   ]
  },
  {
   "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 the Linux DSVM\n",
    "conda_run_config.target = dsvm_compute\n",
    "# set the data reference of the run coonfiguration\n",
    "conda_run_config.data_references = {ds.name: dr}\n",
    "\n",
    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
    "conda_run_config.environment.python.conda_dependencies = cd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Get Data File\n",
    "For remote executions you should author a get_data.py file containing a get_data() function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
    "\n",
    "The *get_data()* function returns a [dictionary](README.md#getdata).\n",
    "\n",
    "The read_csv uses the path_on_compute value specified in the DataReferenceConfiguration call plus the path_on_datastore folder and then the actual file name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(project_folder):\n",
    "    os.makedirs(project_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile $project_folder/get_data.py\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import os\n",
    "from os.path import expanduser, join, dirname\n",
    "\n",
    "def get_data():\n",
    "    # Burning man 2016 data\n",
    "    df = pd.read_csv(\"/tmp/azureml_runs/data/data.tsv\", delimiter=\"\\t\", quotechar='\"')\n",
    "    # get integer labels\n",
    "    le = LabelEncoder()\n",
    "    le.fit(df[\"Label\"].values)\n",
    "    y = le.transform(df[\"Label\"].values)\n",
    "    X = df.drop([\"Label\"], axis=1)\n",
    "\n",
    "    X_train, _, y_train, _ = train_test_split(X, y, test_size=0.1, random_state=42)\n",
    "\n",
    "    return { \"X\" : X_train.values, \"y\" : y_train }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
    "\n",
    "You can specify automl_settings as **kwargs** as well. Also note that you can use the get_data() symantic for local excutions too. \n",
    "\n",
    "<i>Note: For Remote DSVM and Batch AI you cannot pass Numpy arrays directly to AutoMLConfig.</i>\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 Auto ML trains a specific pipeline with the data|\n",
    "|**n_cross_validations**|Number of cross validation splits|\n",
    "|**max_concurrent_iterations**|Max number of iterations that would be executed in parallel.  This should be less than the number of cores on the DSVM\n",
    "|**preprocess**| *True/False* <br>Setting this to *True* enables Auto ML to perform preprocessing <br>on the input to handle *missing data*, and perform some common *feature extraction*|\n",
    "|**enable_cache**|Setting this to *True* enables preprocess done once and reuse the same preprocessed data for all the iterations. Default value is True.|\n",
    "|**max_cores_per_iteration**| Indicates how many cores on the compute target would be used to train a single pipeline.<br> Default is *1*, you can set it to *-1* to use all cores|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_settings = {\n",
    "    \"iteration_timeout_minutes\": 60,\n",
    "    \"iterations\": 4,\n",
    "    \"n_cross_validations\": 5,\n",
    "    \"primary_metric\": 'AUC_weighted',\n",
    "    \"preprocess\": True,\n",
    "    \"max_cores_per_iteration\": 1,\n",
    "    \"verbosity\": logging.INFO\n",
    "}\n",
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             path=project_folder,\n",
    "                             run_configuration=conda_run_config,\n",
    "                             #compute_target = dsvm_compute,\n",
    "                             data_script = project_folder + \"/get_data.py\",\n",
    "                             **automl_settings\n",
    "                            )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Models <a class=\"anchor\" id=\"Training-the-model-Remote-DSVM\"></a>\n",
    "\n",
    "For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets/models even when the experiment is running to retreive the best model up to that point. Once you are satisfied with the model you can cancel a particular iteration or the whole run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run = experiment.submit(automl_config, show_output=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
    "#### Widget for monitoring runs\n",
    "\n",
    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed 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. This links to a web-ui 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": "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": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use sdk methods to fetch all the child runs and see individual metrics that we log. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(remote_run.get_children())\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",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Canceling Runs\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": [
    "## Pre-process cache cleanup\n",
    "The preprocess data gets cache at user default file store. When the run is completed the cache can be cleaned by running below cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run.clean_preprocessor_cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The *get_output* method returns the best run and the fitted model. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or 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": [
    "#### Best Model based on any other metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lookup_metric = \"accuracy\"\n",
    "# best_run, fitted_model = remote_run.get_output(metric=lookup_metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a specific iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# iteration = 1\n",
    "# best_run, fitted_model = remote_run.get_output(iteration=iteration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the Best Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from pandas_ml import ConfusionMatrix\n",
    "\n",
    "df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
    "                     delimiter=\"\\t\", quotechar='\"')\n",
    "\n",
    "# get integer labels\n",
    "le = LabelEncoder()\n",
    "le.fit(df[\"Label\"].values)\n",
    "y = le.transform(df[\"Label\"].values)\n",
    "X = df.drop([\"Label\"], axis=1)\n",
    "\n",
    "_, X_test, _, y_test = train_test_split(X, y, test_size=0.1, random_state=42)\n",
    "\n",
    "ypred = fitted_model.predict(X_test.values)\n",
    "\n",
    "ypred_strings = le.inverse_transform(ypred)\n",
    "ytest_strings = le.inverse_transform(y_test)\n",
    "\n",
    "cm = ConfusionMatrix(ytest_strings, ypred_strings)\n",
    "\n",
    "print(cm)\n",
    "\n",
    "cm.plot()"
   ]
  }
 ],
 "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
 }
--- a/how-to-use-azureml/automated-machine-learning/08b.auto-ml-remote-execution-with-dataprep.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/08b.auto-ml-remote-execution-with-dataprep.ipynb
@@ -0,0 +1,568 @@
 {
 "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": [
    "# AutoML 08b: Remote Execution with DataPrep\n",
    "\n",
    "This sample accesses a data file on a remote DSVM through Datastore using DataPrep. Advantages of using DataPrep are:\n",
    "1. DataPrep supports reading from and writing to datastores.\n",
    "2. DataPrep supports automatic file type and column type detection.\n",
    "3. DataPrep makes passing data into AutoML really simple.\n",
    "\n",
    "More DataPrep documentation and examples can be found [here](https://github.com/Microsoft/AMLDataPrepDocs).\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you would see\n",
    "1. Storing data in DataStore.\n",
    "2. Doing some basic data preparation using DataPrep and passing the prepared data (DataFlow) to AutoML for training (classficiation).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Experiment\n",
    "\n",
    "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "import time\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.compute import DsvmCompute\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-remote-datastore-file'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-remote-dsvm-file'\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",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Remote Linux DSVM\n",
    "Note: If creation fails with a message about Marketplace purchase eligibilty, go to portal.azure.com, start creating DSVM there, and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled it, you can exit without actually creating VM.\n",
    "\n",
    "**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you can switch to a different port (such as 5022), you can append the port number to the address. [Read more](https://render.githubusercontent.com/documentation/sdk/ssh-issue.md) on this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "compute_target_name = 'automl-dataprep'\n",
    "\n",
    "try:\n",
    "    while ws.compute_targets[compute_target_name].provisioning_state == 'Creating':\n",
    "        time.sleep(1)\n",
    "        \n",
    "    dsvm_compute = DsvmCompute(workspace=ws, name=compute_target_name)\n",
    "    print('found existing:', dsvm_compute.name)\n",
    "except:\n",
    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size=\"Standard_D2_v2\")\n",
    "    dsvm_compute = DsvmCompute.create(ws, name=compute_target_name, provisioning_configuration=dsvm_config)\n",
    "    dsvm_compute.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Copy data file to local\n",
    "\n",
    "We will download a 1MB simple random sample of the Chicago Crime data into a local temporary directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tempfile\n",
    "import requests\n",
    "\n",
    "temp_folder = tempfile.mkdtemp()\n",
    "temp_tsv = os.path.join(temp_folder, 'crime0.csv')\n",
    "\n",
    "request = requests.get('https://dprepdata.blob.core.windows.net/demo/crime0-random.csv')\n",
    "with open(temp_tsv, 'w', encoding='utf-8') as f:\n",
    "    f.write(request.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upload data to the cloud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's make the data available in your datastore. Datastore is a convenient construct associated with your workspace for you to  reference different types of cloud storage locations (e.g. Azure Blob Containers, Azure File Shares, Azure Data Lake Stores, etc.). The benefit Datastore brings is you only need to register datastores once and you will be able to access them by name and will not need to expose secrets in your code. When you first create a workspace, a default datastore is registered for you which references the Azure Blob Container that was provisioned with the workspace. Let's upload the data we just got from the public location to the default datastore.\n",
    "\n",
    "The `csv` file is uploaded into a directory named `datasets` at the root of the datastore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Workspace, Datastore\n",
    "\n",
    "ds = ws.get_default_datastore()\n",
    "print(ds.datastore_type, ds.account_name, ds.container_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.upload(src_dir=temp_folder, target_path='datasets', overwrite=True, show_progress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Dataflow using DataPrep\n",
    "Let's use DataPrep to read the `csv` file from the datastore we just uploaded to and get the data profile to make sure our data looks good. We will predict the type of the offense (`Primary Type`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import azureml.dataprep as dprep\n",
    "\n",
    "dflow = dprep.read_csv(path=ds.path('datasets/crime0.csv'))\n",
    "dflow.get_profile()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also take a look at the first 5 rows of the data to give ourselves an idea of what the data looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dflow.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the first 5 rows, we see that there are some rows that have no value in the label column (`Primary Type`). Let's remove those rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dflow = dflow.drop_nulls('Primary Type')\n",
    "dflow.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've removed those rows, let's split the dataflow into a features dataflow and a label dataflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dflow.drop_columns(columns=['Primary Type', 'FBI Code'])\n",
    "y = dflow.keep_columns(columns=['Primary Type'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
    "\n",
    "You can specify automl_settings as **kwargs** as well. Also note that you can use the get_data() symantic for local excutions too. \n",
    "\n",
    "<i>Note: For Remote DSVM and Batch AI you cannot pass Numpy arrays directly to AutoMLConfig.</i>\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 Auto ML trains a specific pipeline with the data|\n",
    "|**n_cross_validations**|Number of cross validation splits|\n",
    "|**max_concurrent_iterations**|Max number of iterations that would be executed in parallel.  This should be less than the number of cores on the DSVM\n",
    "|**preprocess**| *True/False* <br>Setting this to *True* enables Auto ML to perform preprocessing <br>on the input to handle *missing data*, and perform some common *feature extraction*|\n",
    "|**enable_cache**|Setting this to *True* enables preprocess done once and reuse the same preprocessed data for all the iterations. Default value is True.|\n",
    "|**max_cores_per_iteration**| Indicates how many cores on the compute target would be used to train a single pipeline.<br> Default is *1*, you can set it to *-1* to use all cores|"
   ]
  },
  {
   "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",
    "conda_run_config = RunConfiguration(framework=\"python\")\n",
    "\n",
    "conda_run_config.target = dsvm_compute\n",
    "\n",
    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]==0.1.0.1918169'], conda_packages=['numpy'], pin_sdk_version=False, pip_indexurl='https://azuremlsdktestpypi.azureedge.net/sdk-release/master/588E708E0DF342C4A80BD954289657CF')\n",
    "conda_run_config.environment.python.conda_dependencies = cd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_settings = {\n",
    "    \"iteration_timeout_minutes\": 60,\n",
    "    \"iterations\": 4,\n",
    "    \"n_cross_validations\": 5,\n",
    "    \"primary_metric\": 'accuracy',\n",
    "    \"preprocess\": True,\n",
    "    \"max_cores_per_iteration\": 1,\n",
    "    \"verbosity\": logging.INFO\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",
    "                             **automl_settings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Models <a class=\"anchor\" id=\"Training-the-model-Remote-DSVM\"></a>\n",
    "\n",
    "For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets/models even when the experiment is running to retreive the best model up to that point. Once you are satisfied with the model you can cancel a particular iteration or the whole run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run = experiment.submit(automl_config, show_output=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
    "#### Widget for monitoring runs\n",
    "\n",
    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed 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. This links to a web-ui 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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wait until the run finishes.\n",
    "remote_run.wait_for_completion(show_output = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use sdk methods to fetch all the child runs and see individual metrics that we log. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(remote_run.get_children())\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",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Canceling Runs\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": [
    "## Pre-process cache cleanup\n",
    "The preprocess data gets cache at user default file store. When the run is completed the cache can be cleaned by running below cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run.clean_preprocessor_cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The *get_output* method returns the best run and the fitted model. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or 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": [
    "#### Best Model based on any other metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lookup_metric = \"accuracy\"\n",
    "# best_run, fitted_model = remote_run.get_output(metric=lookup_metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a specific iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# iteration = 1\n",
    "# best_run, fitted_model = remote_run.get_output(iteration=iteration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the Best Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dflow = dprep.read_csv(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv')\n",
    "dflow.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas_ml import ConfusionMatrix\n",
    "\n",
    "y_test = dflow.keep_columns(columns=['Primary Type']).to_pandas_dataframe()\n",
    "X_test = dflow.drop_columns(columns=['Primary Type', 'FBI Code']).to_pandas_dataframe()\n",
    "\n",
    "ypred = fitted_model.predict(X_test.values)\n",
    "\n",
    "cm = ConfusionMatrix(y_test['Primary Type'], ypred)\n",
    "\n",
    "print(cm)\n",
    "\n",
    "cm.plot()"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "savitam"
   }
  ],
  "kernelspec": {
   "display_name": "Python [conda env:cli_dev]",
   "language": "python",
   "name": "conda-env-cli_dev-py"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/09.auto-ml-classification-with-deployment.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/09.auto-ml-classification-with-deployment.ipynb
@@ -0,0 +1,501 @@
 {
 "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": [
    "# AutoML 09: Classification with Deployment\n",
    "\n",
    "In this example we use the scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) to showcase how you can use AutoML for a simple classification problem and deploy it to an Azure Container Instance (ACI).\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\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.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an Experiment\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",
    "import os\n",
    "import random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\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-local-classification'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-local-classification'\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",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure AutoML\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, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\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.|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_train = digits.data[10:,:]\n",
    "y_train = digits.target[10:]\n",
    "\n",
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             name = experiment_name,\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             primary_metric = 'AUC_weighted',\n",
    "                             iteration_timeout_minutes = 20,\n",
    "                             iterations = 10,\n",
    "                             n_cross_validations = 2,\n",
    "                             verbosity = logging.INFO,\n",
    "                             X = X_train, \n",
    "                             y = y_train,\n",
    "                             path = project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Models\n",
    "\n",
    "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": [
    "local_run = experiment.submit(automl_config, show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 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 = local_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'\n",
    "tags = None\n",
    "model = local_run.register_model(description = description, tags = tags)\n",
    "local_run.model_id # This will be written to the script file later in the notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Scoring Script"
   ]
  },
  {
   "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": [
    "experiment_name = 'automl-local-classification'\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "ml_run = AutoMLRun(experiment = experiment, run_id = local_run.id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dependencies = ml_run.get_run_sdk_dependencies(iteration = 7)"
   ]
  },
  {
   "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'], 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>>', local_run.model_id))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a Container Image"
   ]
  },
  {
   "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': \"digits\", '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"
   ]
  },
  {
   "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': \"digits\", '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-01'\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#aci_service.delete()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get 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 a Web Service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomly select digits and test\n",
    "digits = datasets.load_digits()\n",
    "X_test = digits.data[:10, :]\n",
    "y_test = digits.target[:10]\n",
    "images = digits.images[:10]\n",
    "\n",
    "for index in np.random.choice(len(y_test), 3, replace = False):\n",
    "    print(index)\n",
    "    test_sample = json.dumps({'data':X_test[index:index + 1].tolist()})\n",
    "    predicted = aci_service.run(input_data = test_sample)\n",
    "    label = y_test[index]\n",
    "    predictedDict = json.loads(predicted)\n",
    "    title = \"Label value = %d  Predicted value = %s \" % ( label,predictedDict['result'][0])\n",
    "    fig = plt.figure(1, figsize = (3,3))\n",
    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
    "    ax1.set_title(title)\n",
    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
    "    plt.show()"
   ]
  }
 ],
 "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
 }
--- a/how-to-use-azureml/automated-machine-learning/10.auto-ml-multi-output-example.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/10.auto-ml-multi-output-example.ipynb
@@ -0,0 +1,294 @@
 {
 "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": [
    "# AutoML 10: Multi-output\n",
    "\n",
    "This notebook shows how to use AutoML to train multi-output problems by leveraging the correlation between the outputs using indicator vectors.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\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": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformer Functions\n",
    "The transformations of inputs `X` and `y` are happening as follows, e.g. `y = {y_1, y_2}`, then `X` becomes\n",
    "    \n",
    "`X 1 0`\n",
    "     \n",
    "`X 0 1`\n",
    "\n",
    "and `y` becomes,\n",
    "\n",
    "`y_1`\n",
    "\n",
    "`y_2`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import sparse\n",
    "from scipy import linalg\n",
    "\n",
    "#Transformer functions\n",
    "def multi_output_transform_x_y(X, y):\n",
    "    X_new = multi_output_transformer_x(X, y.shape[1])\n",
    "    y_new = multi_output_transform_y(y)\n",
    "    return X_new, y_new\n",
    "\n",
    "def multi_output_transformer_x(X, number_of_columns_y):\n",
    "    indicator_vecs = linalg.block_diag(*([np.ones((X.shape[0], 1))] * number_of_columns_y))\n",
    "    if sparse.issparse(X):\n",
    "        X_new = sparse.vstack(np.tile(X, number_of_columns_y))\n",
    "        indicator_vecs = sparse.coo_matrix(indicator_vecs)\n",
    "        X_new = sparse.hstack((X_new, indicator_vecs))\n",
    "    else:\n",
    "        X_new = np.tile(X, (number_of_columns_y, 1))\n",
    "        X_new = np.hstack((X_new, indicator_vecs))\n",
    "    return X_new\n",
    "\n",
    "def multi_output_transform_y(y):\n",
    "    return y.reshape(-1, order=\"F\")\n",
    "\n",
    "def multi_output_inverse_transform_y(y, number_of_columns_y):\n",
    "    return y.reshape((-1, number_of_columns_y), order = \"F\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AutoML Experiment Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# Choose a name for the experiment and specify the project folder.\n",
    "experiment_name = 'automl-local-multi-output'\n",
    "project_folder = './sample_projects/automl-local-multi-output'\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",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Random Dataset for Test Purposes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.random.RandomState(1)\n",
    "X_train = np.sort(200 * rng.rand(600, 1) - 100, axis = 0)\n",
    "y_train = np.array([np.pi * np.sin(X_train).ravel(), np.pi * np.cos(X_train).ravel()]).T\n",
    "y_train += (0.5 - rng.rand(*y_train.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform X and y transformation using the transformer function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_transformed, y_train_transformed = multi_output_transform_x_y(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Configure AutoML using the transformed results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_config = AutoMLConfig(task = 'regression',\n",
    "                             debug_log = 'automl_errors_multi.log',\n",
    "                             primary_metric = 'r2_score',\n",
    "                             iterations = 10,\n",
    "                             n_cross_validations = 2,\n",
    "                             verbosity = logging.INFO,\n",
    "                             X = X_train_transformed,\n",
    "                             y = y_train_transformed,\n",
    "                             path = project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit the Transformed Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = experiment.submit(automl_config, show_output = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the best fit model.\n",
    "best_run, fitted_model = local_run.get_output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate random data set for predicting.\n",
    "X_test = np.sort(200 * rng.rand(200, 1) - 100, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform predict data.\n",
    "X_test_transformed = multi_output_transformer_x(X_test, y_train.shape[1])\n",
    "\n",
    "# Predict and inverse transform the prediction.\n",
    "y_predict = fitted_model.predict(X_test_transformed)\n",
    "y_predict = multi_output_inverse_transform_y(y_predict, y_train.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(y_predict)"
   ]
  }
 ],
 "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
 }
--- a/how-to-use-azureml/automated-machine-learning/11.auto-ml-sample-weight.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/11.auto-ml-sample-weight.ipynb
@@ -0,0 +1,251 @@
 {
 "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": [
    "# AutoML 11: Sample Weight\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 sample weight with AutoML. Sample weight is used where some sample values are more important than others.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you will learn how to configure AutoML to use `sample_weight` and you will see the difference sample weight makes to the test results.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an Experiment\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 random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\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 names for the regular and the sample weight experiments.\n",
    "experiment_name = 'non_sample_weight_experiment'\n",
    "sample_weight_experiment_name = 'sample_weight_experiment'\n",
    "\n",
    "project_folder = './sample_projects/automl-local-classification'\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "sample_weight_experiment=Experiment(ws, sample_weight_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",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure AutoML\n",
    "\n",
    "Instantiate two `AutoMLConfig` objects. One will be used with `sample_weight` and one without."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_train = digits.data[100:,:]\n",
    "y_train = digits.target[100:]\n",
    "\n",
    "# The example makes the sample weight 0.9 for the digit 4 and 0.1 for all other digits.\n",
    "# This makes the model more likely to classify as 4 if the image it not clear.\n",
    "sample_weight = np.array([(0.9 if x == 4 else 0.01) for x in y_train])\n",
    "\n",
    "automl_classifier = AutoMLConfig(task = 'classification',\n",
    "                                 debug_log = 'automl_errors.log',\n",
    "                                 primary_metric = 'AUC_weighted',\n",
    "                                 iteration_timeout_minutes = 60,\n",
    "                                 iterations = 10,\n",
    "                                 n_cross_validations = 2,\n",
    "                                 verbosity = logging.INFO,\n",
    "                                 X = X_train, \n",
    "                                 y = y_train,\n",
    "                                 path = project_folder)\n",
    "\n",
    "automl_sample_weight = AutoMLConfig(task = 'classification',\n",
    "                                    debug_log = 'automl_errors.log',\n",
    "                                    primary_metric = 'AUC_weighted',\n",
    "                                    iteration_timeout_minutes = 60,\n",
    "                                    iterations = 10,\n",
    "                                    n_cross_validations = 2,\n",
    "                                    verbosity = logging.INFO,\n",
    "                                    X = X_train, \n",
    "                                    y = y_train,\n",
    "                                    sample_weight = sample_weight,\n",
    "                                    path = project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Models\n",
    "\n",
    "Call the `submit` method on the experiment objects 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": [
    "local_run = experiment.submit(automl_classifier, show_output = True)\n",
    "sample_weight_run = sample_weight_experiment.submit(automl_sample_weight, show_output = True)\n",
    "\n",
    "best_run, fitted_model = local_run.get_output()\n",
    "best_run_sample_weight, fitted_model_sample_weight = sample_weight_run.get_output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Best Fitted Model\n",
    "\n",
    "#### Load Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_test = digits.data[:100, :]\n",
    "y_test = digits.target[:100]\n",
    "images = digits.images[:100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compare the Models\n",
    "The prediction from the sample weight model is more likely to correctly predict 4's.  However, it is also more likely to predict 4 for some images that are not labelled as 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Randomly select digits and test.\n",
    "for index in range(0,len(y_test)):\n",
    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
    "    predicted_sample_weight = fitted_model_sample_weight.predict(X_test[index:index + 1])[0]\n",
    "    label = y_test[index]\n",
    "    if predicted == 4 or predicted_sample_weight == 4 or label == 4:\n",
    "        title = \"Label value = %d  Predicted value = %d Prediced with sample weight = %d\" % (label, predicted, predicted_sample_weight)\n",
    "        fig = plt.figure(1, figsize=(3,3))\n",
    "        ax1 = fig.add_axes((0,0,.8,.8))\n",
    "        ax1.set_title(title)\n",
    "        plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
    "        plt.show()"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/12.auto-ml-retrieve-the-training-sdk-versions.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/12.auto-ml-retrieve-the-training-sdk-versions.ipynb
@@ -0,0 +1,227 @@
 {
 "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": [
    "# AutoML 12: Retrieving Training SDK Versions\n",
    "\n",
    "This example shows how to find the SDK versions used for an experiment.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\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\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train models using AutoML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# Choose a name for the experiment and specify the project folder.\n",
    "experiment_name = 'automl-local-classification'\n",
    "project_folder = './sample_projects/automl-local-classification'\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "\n",
    "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",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_train = digits.data[10:,:]\n",
    "y_train = digits.target[10:]\n",
    "\n",
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             primary_metric = 'AUC_weighted',\n",
    "                             iterations = 3,\n",
    "                             n_cross_validations = 2,\n",
    "                             verbosity = logging.INFO,\n",
    "                             X = X_train, \n",
    "                             y = y_train,\n",
    "                             path = project_folder)\n",
    "\n",
    "local_run = experiment.submit(automl_config, show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Retrieve the SDK versions from RunHistory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the SDK versions from RunHistory, first the run id needs to be recorded. This can either be done by copying it from the output message or by retrieving it after each run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use a run id copied from an output message.\n",
    "#run_id = 'AutoML_c0585b1f-a0e6-490b-84c7-3a099468b28e'\n",
    "\n",
    "# Retrieve the run id from a run.\n",
    "run_id = local_run.id\n",
    "print(run_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize a new `AutoMLRun` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_name = 'automl-local-classification'\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "ml_run = AutoMLRun(experiment = experiment, run_id = run_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get parent training SDK versions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_run.get_run_sdk_dependencies()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the traning SDK versions of a specific run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_run.get_run_sdk_dependencies(iteration = 2)"
   ]
  }
 ],
 "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
 }
--- a/how-to-use-azureml/automated-machine-learning/13a.auto-ml-dataprep.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/13a.auto-ml-dataprep.ipynb
@@ -0,0 +1,446 @@
 {
 "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": [
    "# AutoML 13: Prepare Data using `azureml.dataprep` for Local Execution\n",
    "In this example we showcase how you can use the `azureml.dataprep` SDK to load and prepare data for AutoML. `azureml.dataprep` can also be used standalone; full documentation can be found [here](https://github.com/Microsoft/PendletonDocs).\n",
    "\n",
    "Make sure you have executed the [setup](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you will learn how to:\n",
    "1. Define data loading and preparation steps in a `Dataflow` using `azureml.dataprep`.\n",
    "2. Pass the `Dataflow` to AutoML for a local run.\n",
    "3. Pass the `Dataflow` to AutoML for a remote run."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an Experiment\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",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "import azureml.dataprep as dprep\n",
    "from azureml.train.automl import AutoMLConfig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    " \n",
    "# choose a name for experiment\n",
    "experiment_name = 'automl-dataprep-local'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-dataprep-local'\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",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data using DataPrep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
    "# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
    "simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
    "X = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1)  # Remove the header row.\n",
    "\n",
    "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
    "# and convert column types manually.\n",
    "# Here we read a comma delimited file and convert all columns to integers.\n",
    "y = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Review the Data Preparation Result\n",
    "\n",
    "You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.skip(1).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure AutoML\n",
    "\n",
    "This creates a general AutoML settings object applicable for both local and remote runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_settings = {\n",
    "    \"iteration_timeout_minutes\" : 10,\n",
    "    \"iterations\" : 2,\n",
    "    \"primary_metric\" : 'AUC_weighted',\n",
    "    \"preprocess\" : False,\n",
    "    \"verbosity\" : logging.INFO,\n",
    "    \"n_cross_validations\": 3\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Local Run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pass Data with `Dataflow` Objects\n",
    "\n",
    "The `Dataflow` objects captured above can be passed to the `submit` method for a local run. AutoML will retrieve the results from the `Dataflow` for model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             X = X,\n",
    "                             y = y,\n",
    "                             **automl_settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = experiment.submit(automl_config, show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the 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(local_run).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retrieve All Child Runs\n",
    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(local_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "    \n",
    "import pandas as pd\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = local_run.get_output()\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model Based on Any Other Metric\n",
    "Show the run and the model that has the smallest `log_loss` value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup_metric = \"log_loss\"\n",
    "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a Specific Iteration\n",
    "Show the run and the model from the first iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 0\n",
    "best_run, fitted_model = local_run.get_output(iteration = iteration)\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Best Fitted Model\n",
    "\n",
    "#### Load Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "digits = datasets.load_digits()\n",
    "X_test = digits.data[:10, :]\n",
    "y_test = digits.target[:10]\n",
    "images = digits.images[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing Our Best Fitted Model\n",
    "We will try to predict 2 digits and see how our model works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomly select digits and test\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import random\n",
    "import numpy as np\n",
    "\n",
    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
    "    print(index)\n",
    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
    "    label = y_test[index]\n",
    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
    "    fig = plt.figure(1, figsize=(3,3))\n",
    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
    "    ax1.set_title(title)\n",
    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Appendix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Capture the `Dataflow` Objects for Later Use in AutoML\n",
    "\n",
    "`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object can be branched at any point for further usage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sklearn.digits.data + target\n",
    "digits_complete = dprep.auto_read_file('https://dprepdata.blob.core.windows.net/automl-notebook-data/digits-complete.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`digits_complete` (sourced from `sklearn.datasets.load_digits()`) is forked into `dflow_X` to capture all the feature columns and `dflow_y` to capture the label column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits_complete.to_pandas_dataframe().shape\n",
    "labels_column = 'Column64'\n",
    "dflow_X = digits_complete.drop_columns(columns = [labels_column])\n",
    "dflow_y = digits_complete.keep_columns(columns = [labels_column])"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/13b.auto-ml-dataprep-remote-execution.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/13b.auto-ml-dataprep-remote-execution.ipynb
@@ -0,0 +1,497 @@
 {
 "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": [
    "# AutoML 13: Prepare Data using `azureml.dataprep` for Remote Execution (DSVM)\n",
    "In this example we showcase how you can use the `azureml.dataprep` SDK to load and prepare data for AutoML. `azureml.dataprep` can also be used standalone; full documentation can be found [here](https://github.com/Microsoft/PendletonDocs).\n",
    "\n",
    "Make sure you have executed the [setup](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you will learn how to:\n",
    "1. Define data loading and preparation steps in a `Dataflow` using `azureml.dataprep`.\n",
    "2. Pass the `Dataflow` to AutoML for a local run.\n",
    "3. Pass the `Dataflow` to AutoML for a remote run."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an Experiment\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 time\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.compute import DsvmCompute\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "import azureml.dataprep as dprep\n",
    "from azureml.train.automl import AutoMLConfig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    " \n",
    "# choose a name for experiment\n",
    "experiment_name = 'automl-dataprep-remote-dsvm'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-dataprep-remote-dsvm'\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",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data using DataPrep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
    "# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
    "simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
    "X = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1)  # Remove the header row.\n",
    "\n",
    "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
    "# and convert column types manually.\n",
    "# Here we read a comma delimited file and convert all columns to integers.\n",
    "y = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Review the Data Preparation Result\n",
    "\n",
    "You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.skip(1).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure AutoML\n",
    "\n",
    "This creates a general AutoML settings object applicable for both local and remote runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_settings = {\n",
    "    \"iteration_timeout_minutes\" : 10,\n",
    "    \"iterations\" : 2,\n",
    "    \"primary_metric\" : 'AUC_weighted',\n",
    "    \"preprocess\" : False,\n",
    "    \"verbosity\" : logging.INFO,\n",
    "    \"n_cross_validations\": 3\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remote Run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create or Attach a Remote Linux DSVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsvm_name = 'mydsvmc'\n",
    "\n",
    "try:\n",
    "    while ws.compute_targets[dsvm_name].provisioning_state == 'Creating':\n",
    "        time.sleep(1)\n",
    "        \n",
    "    dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
    "    print('Found existing DVSM.')\n",
    "except:\n",
    "    print('Creating a new DSVM.')\n",
    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
    "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
    "    dsvm_compute.wait_for_completion(show_output = True)\n",
    "    print(\"Waiting one minute for ssh to be accessible\")\n",
    "    time.sleep(60) # Wait for ssh to be accessible"
   ]
  },
  {
   "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",
    "conda_run_config = RunConfiguration(framework=\"python\")\n",
    "\n",
    "conda_run_config.target = dsvm_compute\n",
    "\n",
    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
    "conda_run_config.environment.python.conda_dependencies = cd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pass Data with `Dataflow` Objects\n",
    "\n",
    "The `Dataflow` objects captured above can also be passed to the `submit` method for a remote run. AutoML will serialize the `Dataflow` object and send it to the remote compute target. The `Dataflow` will not be evaluated locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "                             **automl_settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run = experiment.submit(automl_config, show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the 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": [
    "#### Retrieve All Child Runs\n",
    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(remote_run.get_children())\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",
    "    \n",
    "import pandas as pd\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = remote_run.get_output()\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model Based on Any Other Metric\n",
    "Show the run and the model that has the smallest `log_loss` value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup_metric = \"log_loss\"\n",
    "best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a Specific Iteration\n",
    "Show the run and the model from the first iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 0\n",
    "best_run, fitted_model = remote_run.get_output(iteration = iteration)\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Best Fitted Model\n",
    "\n",
    "#### Load Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "digits = datasets.load_digits()\n",
    "X_test = digits.data[:10, :]\n",
    "y_test = digits.target[:10]\n",
    "images = digits.images[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing Our Best Fitted Model\n",
    "We will try to predict 2 digits and see how our model works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomly select digits and test\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import random\n",
    "import numpy as np\n",
    "\n",
    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
    "    print(index)\n",
    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
    "    label = y_test[index]\n",
    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
    "    fig = plt.figure(1, figsize=(3,3))\n",
    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
    "    ax1.set_title(title)\n",
    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Appendix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Capture the `Dataflow` Objects for Later Use in AutoML\n",
    "\n",
    "`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object can be branched at any point for further usage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sklearn.digits.data + target\n",
    "digits_complete = dprep.auto_read_file('https://dprepdata.blob.core.windows.net/automl-notebook-data/digits-complete.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`digits_complete` (sourced from `sklearn.datasets.load_digits()`) is forked into `dflow_X` to capture all the feature columns and `dflow_y` to capture the label column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits_complete.to_pandas_dataframe().shape\n",
    "labels_column = 'Column64'\n",
    "dflow_X = digits_complete.drop_columns(columns = [labels_column])\n",
    "dflow_y = digits_complete.keep_columns(columns = [labels_column])"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/14.auto-ml-model-explanation.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/14.auto-ml-model-explanation.ipynb
@@ -0,0 +1,348 @@
 {
 "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": [
    "# AutoML 14: Explain classification model and visualize the explanation\n",
    "\n",
    "In this example we use the sklearn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) to showcase how you can use the AutoML Classifier for a simple classification problem.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you would see\n",
    "1. Creating an Experiment in an existing Workspace\n",
    "2. Instantiating AutoMLConfig\n",
    "3. Training the Model using local compute and explain the model\n",
    "4. Visualization model's feature importance in widget\n",
    "5. Explore best model's explanation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Experiment\n",
    "\n",
    "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "import pandas as pd\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-local-classification'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-local-classification-model-explanation'\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",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Iris Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "y = iris.target\n",
    "X = iris.data\n",
    "\n",
    "features = iris.feature_names\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    test_size=0.1,\n",
    "                                                    random_state=100,\n",
    "                                                    stratify=y)\n",
    "\n",
    "X_train = pd.DataFrame(X_train, columns=features)\n",
    "X_test = pd.DataFrame(X_test, columns=features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate Auto ML Config\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",
    "|**max_time_sec**|Time limit in minutes for each iterations|\n",
    "|**iterations**|Number of iterations. In each iteration Auto ML trains the data with a specific pipeline|\n",
    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
    "|**X_valid**|(sparse) array-like, shape = [n_samples, n_features]|\n",
    "|**y_valid**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]|\n",
    "|**model_explainability**|Indicate to explain each trained pipeline or not |\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. |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             primary_metric = 'AUC_weighted',\n",
    "                             iteration_timeout_minutes = 200,\n",
    "                             iterations = 10,\n",
    "                             verbosity = logging.INFO,\n",
    "                             X = X_train, \n",
    "                             y = y_train,\n",
    "                             X_valid = X_test,\n",
    "                             y_valid = y_test,\n",
    "                             model_explainability=True,\n",
    "                             path=project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Model\n",
    "\n",
    "You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
    "You will see the currently running iterations printing to the console."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = experiment.submit(automl_config, show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Widget for monitoring runs\n",
    "\n",
    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed 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. This links to a web-ui to explore the individual run details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.widgets import RunDetails\n",
    "RunDetails(local_run).show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 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 *fit* invocation. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = local_run.get_output()\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best Model 's explanation\n",
    "\n",
    "Retrieve the explanation from the best_run. And explanation information includes:\n",
    "\n",
    "1.\tshap_values: The explanation information generated by shap lib\n",
    "2.\texpected_values: The expected value of the model applied to set of X_train data.\n",
    "3.\toverall_summary: The model level feature importance values sorted in descending order\n",
    "4.\toverall_imp: The feature names sorted in the same order as in overall_summary\n",
    "5.\tper_class_summary: The class level feature importance values sorted in descending order. Only available for the classification case\n",
    "6.\tper_class_imp: The feature names sorted in the same order as in per_class_summary. Only available for the classification case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.train.automl.automlexplainer import retrieve_model_explanation\n",
    "\n",
    "shap_values, expected_values, overall_summary, overall_imp, per_class_summary, per_class_imp = \\\n",
    "    retrieve_model_explanation(best_run)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(overall_summary)\n",
    "print(overall_imp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(per_class_summary)\n",
    "print(per_class_imp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Beside retrieve the existed model explanation information, explain the model with different train/test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.train.automl.automlexplainer import explain_model\n",
    "\n",
    "shap_values, expected_values, overall_summary, overall_imp, per_class_summary, per_class_imp = \\\n",
    "    explain_model(fitted_model, X_train, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(overall_summary)\n",
    "print(overall_imp)"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "xif"
   }
  ],
  "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
 }
--- a/how-to-use-azureml/automated-machine-learning/17.auto-ml-classification_with_tensorflow.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/17.auto-ml-classification_with_tensorflow.ipynb
@@ -0,0 +1,390 @@
 {
 "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": [
    "# AutoML 17: Classification with Local Compute with Tesnorflow DNNClassifier and LinearClassifier using whitelist models feature.\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",
    "\n",
    "Make sure you have executed the [00.configuration](00.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 trains the model exclusively on tensorflow based models.\n",
    "\n",
    "In this notebook you will learn how to:\n",
    "1. Create an `Experiment` in an existing `Workspace`.\n",
    "2. Configure AutoML using `AutoMLConfig`.\n",
    "3. Train the model on a whilelisted models using local compute. \n",
    "4. Explore the results.\n",
    "5. Test the best fitted model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an Experiment\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 random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\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 the experiment and specify the project folder.\n",
    "experiment_name = 'automl-local-classification'\n",
    "project_folder = './sample_projects/automl-local-classification'\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "\n",
    "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",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Training Data\n",
    "\n",
    "This uses scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "digits = datasets.load_digits()\n",
    "\n",
    "# Exclude the first 100 rows from training so that they can be used for test.\n",
    "X_train = digits.data[100:,:]\n",
    "y_train = digits.target[100:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure AutoML\n",
    "\n",
    "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**task**|classification 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>balanced_accuracy</i><br><i>average_precision_score_weighted</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, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\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.|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             primary_metric = 'AUC_weighted',\n",
    "                             iteration_timeout_minutes = 60,\n",
    "                             iterations = 10,\n",
    "                             n_cross_validations = 3,\n",
    "                             verbosity = logging.INFO,\n",
    "                             X = X_train, \n",
    "                             y = y_train,\n",
    "                             enable_tf=True,\n",
    "                             whitelist_models=[\"TensorFlowLinearClassifier\", \"TensorFlowDNN\"],\n",
    "                             path = project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Models\n",
    "\n",
    "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": [
    "local_run = experiment.submit(automl_config, show_output = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the 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(local_run).show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(local_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. 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*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = local_run.get_output()\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model Based on Any Other Metric\n",
    "Show the run and the model that has the smallest `log_loss` value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup_metric = \"log_loss\"\n",
    "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a Specific Iteration\n",
    "Show the run and the model from the third iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 3\n",
    "third_run, third_model = local_run.get_output(iteration = iteration)\n",
    "print(third_run)\n",
    "print(third_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Best Fitted Model\n",
    "\n",
    "#### Load Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_test = digits.data[:10, :]\n",
    "y_test = digits.target[:10]\n",
    "images = digits.images[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing Our Best Fitted Model\n",
    "We will try to predict 2 digits and see how our model works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Randomly select digits and test.\n",
    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
    "    print(index)\n",
    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
    "    label = y_test[index]\n",
    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
    "    fig = plt.figure(1, figsize = (3,3))\n",
    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
    "    ax1.set_title(title)\n",
    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
    "    plt.show()"
   ]
  }
 ],
 "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
 }
--- a/how-to-use-azureml/automated-machine-learning/18a.auto-ml-forecasting.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/18a.auto-ml-forecasting.ipynb
@@ -0,0 +1,397 @@
 {
 "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": [
    "# AutoML 18: Energy Demand Forecasting\n",
    "\n",
    "In this example, we show how AutoML can be used for energy demand forecasting.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you would see\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. Testing the fitted model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Experiment\n",
    "\n",
    "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import azureml.core\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import logging\n",
    "import warnings\n",
    "warnings.showwarning = lambda *args, **kwargs: None\n",
    "\n",
    "\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
   ]
  },
  {
   "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-energydemandforecasting'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-local-energydemandforecasting'\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['Run History Name'] = experiment_name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Data\n",
    "Read energy demanding data from file, and preview data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"nyc_energy.csv\", parse_dates=['timeStamp'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the data to train and test\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = data[data['timeStamp'] < '2017-02-01']\n",
    "test = data[data['timeStamp'] >= '2017-02-01']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare the test data, we will feed X_test to the fitted model and get prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = test.pop('demand').values\n",
    "X_test = test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the train data to train and valid\n",
    "\n",
    "Use one month's data as valid data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train[train['timeStamp'] < '2017-01-01']\n",
    "X_valid = train[train['timeStamp'] >= '2017-01-01']\n",
    "y_train = X_train.pop('demand').values\n",
    "y_valid = X_valid.pop('demand').values\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_valid.shape)\n",
    "print(y_valid.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate Auto ML Config\n",
    "\n",
    "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**task**|forecasting|\n",
    "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>\n",
    "|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
    "|**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, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
    "|**X_valid**|Data used to evaluate a model in a iteration. (sparse) array-like, shape = [n_samples, n_features]|\n",
    "|**y_valid**|Data used to evaluate a model in a iteration. (sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
    "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_column_name = 'timeStamp'\n",
    "automl_settings = {\n",
    "    \"time_column_name\": time_column_name,\n",
    "}\n",
    "\n",
    "\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",
    "                             X_valid = X_valid,\n",
    "                             y_valid = y_valid,\n",
    "                             path=project_folder,\n",
    "                             verbosity = logging.INFO,\n",
    "                            **automl_settings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Model\n",
    "\n",
    "You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
    "You will see the currently running iterations printing to the console."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = experiment.submit(automl_config, show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\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 fit invocation. There are overloads on get_output that allow you to retrieve the best run and fitted model for any logged metric or a particular iteration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = local_run.get_output()\n",
    "fitted_model.steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Best Fitted Model\n",
    "\n",
    "Predict on training and test set, and calculate residual values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = fitted_model.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define a Check Data Function\n",
    "\n",
    "Remove the nan values from y_test to avoid error when calculate metrics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _check_calc_input(y_true, y_pred, rm_na=True):\n",
    "    \"\"\"\n",
    "    Check that 'y_true' and 'y_pred' are non-empty and\n",
    "    have equal length.\n",
    "\n",
    "    :param y_true: Vector of actual values\n",
    "    :type y_true: array-like\n",
    "\n",
    "    :param y_pred: Vector of predicted values\n",
    "    :type y_pred: array-like\n",
    "\n",
    "    :param rm_na:\n",
    "        If rm_na=True, remove entries where y_true=NA and y_pred=NA.\n",
    "    :type rm_na: boolean\n",
    "\n",
    "    :return:\n",
    "        Tuple (y_true, y_pred). if rm_na=True,\n",
    "        the returned vectors may differ from their input values.\n",
    "    :rtype: Tuple with 2 entries\n",
    "    \"\"\"\n",
    "    if len(y_true) != len(y_pred):\n",
    "        raise ValueError(\n",
    "            'the true values and prediction values do not have equal length.')\n",
    "    elif len(y_true) == 0:\n",
    "        raise ValueError(\n",
    "            'y_true and y_pred are empty.')\n",
    "    # if there is any non-numeric element in the y_true or y_pred,\n",
    "    # the ValueError exception will be thrown.\n",
    "    y_true = np.array(y_true).astype(float)\n",
    "    y_pred = np.array(y_pred).astype(float)\n",
    "    if rm_na:\n",
    "        # remove entries both in y_true and y_pred where at least\n",
    "        # one element in y_true or y_pred is missing\n",
    "        y_true_rm_na = y_true[~(np.isnan(y_true) | np.isnan(y_pred))]\n",
    "        y_pred_rm_na = y_pred[~(np.isnan(y_true) | np.isnan(y_pred))]\n",
    "        return (y_true_rm_na, y_pred_rm_na)\n",
    "    else:\n",
    "        return y_true, y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use the Check Data Function to remove the nan values from y_test to avoid error when calculate metrics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test,y_pred =  _check_calc_input(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate metrics for the prediction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % np.sqrt(mean_squared_error(y_test, y_pred)))\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('mean_absolute_error score: %.2f' % mean_absolute_error(y_test, y_pred))\n",
    "print('R2 score: %.2f' % r2_score(y_test, y_pred))\n",
    "\n",
    "\n",
    "\n",
    "# Plot outputs\n",
    "%matplotlib notebook\n",
    "test_pred = plt.scatter(y_test, y_pred, 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()"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "xiaga"
   }
  ],
  "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
 }
--- a/how-to-use-azureml/automated-machine-learning/18b.auto-ml-forecasting.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/18b.auto-ml-forecasting.ipynb
@@ -0,0 +1,390 @@
 {
 "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": [
    "# AutoML 18B: Orange Juice Sales Forecasting\n",
    "\n",
    "In this example, we use AutoML to find and tune a time-series forecasting model.\n",
    "\n",
    "Make sure you have executed the [configuration notebook](00.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",
    "## Sample Data\n",
    "The examples in the follow code samples use the [University of Chicago's Dominick's Finer Foods dataset](https://research.chicagobooth.edu/kilts/marketing-databases/dominicks) to forecast orange juice sales. Dominick's was a grocery chain in the Chicago metropolitan area."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Experiment\n",
    "\n",
    "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. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import azureml.core\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import logging\n",
    "\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error"
   ]
  },
  {
   "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-ojsalesforecasting'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-local-ojsalesforecasting'\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['Run History Name'] = experiment_name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Data\n",
    "You are now ready to load the historical orange juice sales data. We will load the CSV file into a plain pandas DataFrame; the time column in the CSV is called _WeekStarting_, so it will be specially parsed into the datetime type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_column_name = 'WeekStarting'\n",
    "data = pd.read_csv(\"dominicks_OJ.csv\", parse_dates=[time_column_name])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each row in the DataFrame holds a quantity of weekly sales for an OJ brand at a single store. The data also includes the sales price, a flag indicating if the OJ brand was advertised in the store that week, and some customer demographic information based on the store location. For historical reasons, the data also include the logarithm of the sales quantity. The Dominick's grocery data is commonly used to illustrate econometric modeling techniques where logarithms of quantities are generally preferred.    \n",
    "\n",
    "The task is now to build a time-series model for the _Quantity_ column. It is important to note that this dataset is comprised of many individual time-series - one for each unique combination of _Store_ and _Brand_. To distinguish the individual time-series, we thus define the **grain** - the columns whose values determine the boundaries between time-series: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grain_column_names = ['Store', 'Brand']\n",
    "nseries = data.groupby(grain_column_names).ngroups\n",
    "print('Data contains {0} individual time-series.'.format(nseries))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Splitting\n",
    "For the purposes of demonstration and later forecast evaluation, we now split the data into a training and a testing set. The test set will contain the final 20 weeks of observed sales for each time-series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ntest_periods = 20\n",
    "\n",
    "def split_last_n_by_grain(df, n):\n",
    "    \"\"\"\n",
    "    Group df by grain and split on last n rows for each group\n",
    "    \"\"\"\n",
    "    df_grouped = (df.sort_values(time_column_name) # Sort by ascending time\n",
    "                  .groupby(grain_column_names, group_keys=False))\n",
    "    df_head = df_grouped.apply(lambda dfg: dfg.iloc[:-n])\n",
    "    df_tail = df_grouped.apply(lambda dfg: dfg.iloc[-n:])\n",
    "    return df_head, df_tail\n",
    "\n",
    "X_train, X_test = split_last_n_by_grain(data, ntest_periods)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "\n",
    "For forecasting tasks, AutoML uses pre-processing and estimation steps that are specific to time-series. AutoML will undertake the following pre-processing steps:\n",
    "* Detect time-series sample frequency (e.g. hourly, daily, weekly) and create new records for absent time points to make the series regular. A regular time series has a well-defined frequency and has a value at every sample point in a contiguous time span \n",
    "* Impute missing values in the target (via forward-fill) and feature columns (using median column values) \n",
    "* Create grain-based features to enable fixed effects across different series\n",
    "* Create time-based features to assist in learning seasonal patterns\n",
    "* Encode categorical variables to numeric quantities\n",
    "\n",
    "AutoML will currently train a single, regression-type model across **all** time-series in a given training set. This allows the model to generalize across related series.\n",
    "\n",
    "You are almost ready to start an AutoML training job. We will first need to create a validation set from the existing training set (i.e. for hyper-parameter tuning): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nvalidation_periods = 20\n",
    "X_train, X_validate = split_last_n_by_grain(X_train, nvalidation_periods)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to separate the target column from the rest of the DataFrame: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_column_name = 'Quantity'\n",
    "y_train = X_train.pop(target_column_name).values\n",
    "y_validate = X_validate.pop(target_column_name).values "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an AutoMLConfig\n",
    "\n",
    "The AutoMLConfig object defines the settings and data for an AutoML training job. Here, we set necessary inputs like the task type, the number of AutoML iterations to try, and the training and validation data. \n",
    "\n",
    "For forecasting tasks, there are some additional parameters that can be set: the name of the input data column, holding the date/time and the grain column names. A time column is required for forecasting, while the grain is optional. If a grain is not given, the forecaster 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",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**task**|forecasting|\n",
    "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>\n",
    "|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
    "|**X**|Training matrix of features, shape = [n_training_samples, n_features]|\n",
    "|**y**|Target values, shape = [n_training_samples, ]|\n",
    "|**X_valid**|Validation matrix of features, shape = [n_validation_samples, n_features]|\n",
    "|**y_valid**|Target values for validation, shape = [n_validation_samples, ]\n",
    "|**enable_ensembling**|Allow AutoML to create ensembles of the best performing models\n",
    "|**debug_log**|Log file path for writing debugging information\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. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_settings = {\n",
    "    'time_column_name': time_column_name,\n",
    "    'grain_column_names': grain_column_names,\n",
    "    'drop_column_names': ['logQuantity']\n",
    "}\n",
    "\n",
    "automl_config = AutoMLConfig(task='forecasting',\n",
    "                             debug_log='automl_oj_sales_errors.log',\n",
    "                             primary_metric='normalized_root_mean_squared_error',\n",
    "                             iterations=10,\n",
    "                             X=X_train,\n",
    "                             y=y_train,\n",
    "                             X_valid=X_validate,\n",
    "                             y_valid=y_validate,\n",
    "                             enable_ensembling=False,\n",
    "                             path=project_folder,\n",
    "                             verbosity=logging.INFO,\n",
    "                            **automl_settings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Model\n",
    "\n",
    "You can now submit a new training run. For local runs, the execution is synchronous. Depending on the data and number of iterations this operation may take several minutes.\n",
    "Information from each iteration will be printed to the console."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = experiment.submit(automl_config, show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "Each run within an Experiment stores serialized (i.e. pickled) pipelines from the AutoML iterations. We can now retrieve the pipeline with the best performance on the validation dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_pipeline = local_run.get_output()\n",
    "fitted_pipeline.steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make Predictions from the Best Fitted Model\n",
    "Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. First, we remove the target values from the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = X_test.pop(target_column_name).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To produce predictions on the test set, we need to know the feature values at all dates in the test set. This requirement is somewhat reasonable for the OJ sales data since the features mainly consist of price, which is usually set in advance, and customer demographics which are approximately constant for each store over the 20 week forecast horizon in the testing data. \n",
    "\n",
    "The target predictions can be retrieved by calling the `predict` method on the best model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = fitted_pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate evaluation metrics for the prediction\n",
    "To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MAPE(actual, pred):\n",
    "    \"\"\"\n",
    "    Calculate mean absolute percentage error.\n",
    "    Remove NA and values where actual is close to zero\n",
    "    \"\"\"\n",
    "    not_na = ~(np.isnan(actual) | np.isnan(pred))\n",
    "    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)\n",
    "\n",
    "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % np.sqrt(mean_squared_error(y_test, y_pred)))\n",
    "print('mean_absolute_error score: %.2f' % mean_absolute_error(y_test, y_pred))\n",
    "print('MAPE: %.2f' % MAPE(y_test, y_pred))"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "erwright"
   }
  ],
  "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
 }
--- a/how-to-use-azureml/automated-machine-learning/README.md
+++ b/how-to-use-azureml/automated-machine-learning/README.md
@@ -1,8 +1,8 @@
 # Table of Contents
 1. [Automated ML Introduction](#introduction)
-1. [Setup using Azure Notebooks](#jupyter)
+1. [Running samples in Azure Notebooks](#jupyter)
-1. [Setup using Azure Databricks](#databricks)
+1. [Running samples in Azure Databricks](#databricks)
-1. [Setup using a Local Conda environment](#localconda)
+1. [Running samples in a Local Conda environment](#localconda)
 1. [Automated ML SDK Sample Notebooks](#samples)
 1. [Documentation](#documentation)
 1. [Running using python command](#pythoncommand)
@@ -13,116 +13,128 @@
 Automated machine learning (automated ML) builds high quality machine learning models for you by automating model and hyperparameter selection. Bring a labelled dataset that you want to build a model for, automated ML will give you a high quality machine learning model that you can use for predictions.
-If you are new to Data Science, automated ML will help you get jumpstarted by simplifying machine learning model building. It abstracts you from needing to perform model selection, hyperparameter selection and in one step creates a high quality trained model for you to use.
+If you are new to Data Science, AutoML will help you get jumpstarted by simplifying machine learning model building. It abstracts you from needing to perform model selection, hyperparameter selection and in one step creates a high quality trained model for you to use.
-If you are an experienced data scientist, automated ML will help increase your productivity by intelligently performing the model and hyperparameter selection for your training and generates high quality models much quicker than manually specifying several combinations of the parameters and running training jobs. Automated ML provides visibility and access to all the training jobs and the performance characteristics of the models to help you further tune the pipeline if you desire.
+If you are an experienced data scientist, AutoML will help increase your productivity by intelligently performing the model and hyperparameter selection for your training and generates high quality models much quicker than manually specifying several combinations of the parameters and running training jobs. AutoML provides visibility and access to all the training jobs and the performance characteristics of the models to help you further tune the pipeline if you desire.
-Below are the three execution environments supported by automated ML.
+Below are the three execution environments supported by AutoML.
 <a name="jupyter"></a>
-## Setup using Azure Notebooks - Jupyter based notebooks in the Azure cloud
+## Running samples in Azure Notebooks - Jupyter based notebooks in the Azure cloud
 1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
 [Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks.
-1. Follow the instructions in the [configuration](../../configuration.ipynb) notebook to create and connect to a workspace.
+1. Follow the instructions in the [configuration](configuration.ipynb) notebook to create and connect to a workspace.
 1. Open one of the sample notebooks.
 <a name="databricks"></a>
-## Setup using Azure Databricks
+## Running samples in Azure Databricks
 **NOTE**: Please create your Azure Databricks cluster as v4.x (high concurrency preferred) with **Python 3** (dropdown).
 **NOTE**: You should at least have contributor access to your Azure subcription to run the notebook.
 - Please remove the previous SDK version if there is any and install the latest SDK by installing **azureml-sdk[automl_databricks]** as a PyPi library in Azure Databricks workspace.
- You can find the detail Readme instructions at [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks).
+- Download the sample notebook 16a.auto-ml-classification-local-azuredatabricks from [GitHub](https://github.com/Azure/MachineLearningNotebooks) and import into the Azure databricks workspace.
 - Download the sample notebook automl-databricks-local-01.ipynb from [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks) and import into the Azure databricks workspace.
 - Attach the notebook to the cluster.
 <a name="localconda"></a>
-## Setup using a Local Conda environment
+## Running samples in a Local Conda environment
 To run these notebook on your own notebook server, use these installation instructions.
 The instructions below will install everything you need and then start a Jupyter notebook.
-### 1. Install mini-conda from [here](https://conda.io/miniconda.html), choose 64-bit Python 3.7 or higher.
+The instructions below will install everything you need and then start a Jupyter notebook.  To start your Jupyter notebook manually, use:
 - **Note**: if you already have conda installed, you can keep using it but it should be version 4.4.10 or later (as shown by: conda -V).  If you have a previous version installed, you can update it using the command: conda update conda.
 There's no need to install mini-conda specifically.
 ### 2. Downloading the sample notebooks
 - Download the sample notebooks from [GitHub](https://github.com/Azure/MachineLearningNotebooks) as zip and extract the contents to a local directory.  The automated ML sample notebooks are in the "automated-machine-learning" folder.
 ### 3. Setup a new conda environment
 The **automl_setup** script creates a new conda environment, installs the necessary packages, configures the widget and starts a jupyter notebook. It takes the conda environment name as an optional parameter.  The default conda environment name is azure_automl.  The exact command depends on the operating system.  See the specific sections below for Windows, Mac and Linux.  It can take about 10 minutes to execute.
 Packages installed by the **automl_setup** script:
    <ul><li>python</li><li>nb_conda</li><li>matplotlib</li><li>numpy</li><li>cython</li><li>urllib3</li><li>scipy</li><li>scikit-learn</li><li>pandas</li><li>tensorflow</li><li>py-xgboost</li><li>azureml-sdk</li><li>azureml-widgets</li><li>pandas-ml</li></ul>
 For more details refer to the [automl_env.yml](./automl_env.yml)
 ## Windows
 Start an **Anaconda Prompt** window, cd to the **how-to-use-azureml/automated-machine-learning** folder where the sample notebooks were extracted and then run:
 ```
 automl_setup
 ```
 ## Mac
 Install "Command line developer tools" if it is not already installed (you can use the command: `xcode-select --install`).
 Start a Terminal windows, cd to the **how-to-use-azureml/automated-machine-learning** folder where the sample notebooks were extracted and then run:
 ```
 bash automl_setup_mac.sh
 ```
 ## Linux
 cd to the **how-to-use-azureml/automated-machine-learning** folder where the sample notebooks were extracted and then run:
 ```
 bash automl_setup_linux.sh
 ```
 ### 4. Running configuration.ipynb
 - Before running any samples you next need to run the configuration notebook. Click on [configuration](../../configuration.ipynb) notebook
 - Execute the cells in the notebook to Register Machine Learning Services Resource Provider and create a workspace. (*instructions in notebook*)
 ### 5. Running Samples
 - Please make sure you use the Python [conda env:azure_automl] kernel when trying the sample Notebooks.
 - Follow the instructions in the individual notebooks to explore various features in automated ML.
 ### 6. Starting jupyter notebook manually
 To start your Jupyter notebook manually, use:
 ```
 conda activate azure_automl
 jupyter notebook
 ```
-or on Mac or Linux:
+or on Mac:
 ```
 source activate azure_automl
 jupyter notebook
 ```
 ### 1. Install mini-conda from [here](https://conda.io/miniconda.html), choose Python 3.7 or higher.
 - **Note**: if you already have conda installed, you can keep using it but it should be version 4.4.10 or later (as shown by: conda -V).  If you have a previous version installed, you can update it using the command: conda update conda.
 There's no need to install mini-conda specifically.
 ### 2. Downloading the sample notebooks
 - Download the sample notebooks from [GitHub](https://github.com/Azure/MachineLearningNotebooks) as zip and extract the contents to a local directory.  The AutoML sample notebooks are in the "automl" folder.
 ### 3. Setup a new conda environment
 The **automl/automl_setup** script creates a new conda environment, installs the necessary packages, configures the widget and starts a jupyter notebook.
 It takes the conda environment name as an optional parameter.  The default conda environment name is azure_automl.  The exact command depends on the operating system.  See the specific sections below for Windows, Mac and Linux.  It can take about 10 minutes to execute.
 ## Windows
 Start an **Anaconda Prompt** window, cd to the **automl** folder where the sample notebooks were extracted and then run:
 ```
 automl_setup
 ```
 ## Mac
 Install "Command line developer tools" if it is not already installed (you can use the command: `xcode-select --install`).
 Start a Terminal windows, cd to the **automl** folder where the sample notebooks were extracted and then run:
 ```
 bash automl_setup_mac.sh
 ```
 ## Linux
 cd to the **automl** folder where the sample notebooks were extracted and then run:
 ```
 bash automl_setup_linux.sh
 ```
 ### 4. Running configuration.ipynb
 - Before running any samples you next need to run the configuration notebook. Click on configuration.ipynb notebook
 - Execute the cells in the notebook to Register Machine Learning Services Resource Provider and create a workspace. (*instructions in notebook*)
 ### 5. Running Samples
 - Please make sure you use the Python [conda env:azure_automl] kernel when trying the sample Notebooks.
 - Follow the instructions in the individual notebooks to explore various features in AutoML
 <a name="samples"></a>
 # Automated ML SDK Sample Notebooks
 - [configuration.ipynb](configuration.ipynb)
    - Create new Azure ML Workspace
    - Save Workspace configuration file
 - [auto-ml-classification.ipynb](classification/auto-ml-classification.ipynb)
    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
-    - Simple example of using automated ML for classification
+    - Simple example of using Auto ML for classification
    - Uses local compute for training
 - [auto-ml-regression.ipynb](regression/auto-ml-regression.ipynb)
    - Dataset: scikit learn's [diabetes dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html)
-    - Simple example of using automated ML for regression
+    - Simple example of using Auto ML for regression
    - Uses local compute for training
- [auto-ml-remote-amlcompute.ipynb](remote-amlcompute/auto-ml-remote-amlcompute.ipynb)
+- [auto-ml-remote-execution.ipynb](remote-execution/auto-ml-remote-execution.ipynb)
    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
-    - Example of using automated ML for classification using remote AmlCompute for training
+    - Example of using Auto ML for classification using a remote linux DSVM for training
    - Parallel execution of iterations
    - Async tracking of progress
    - Cancelling individual iterations or entire run
    - Retrieving models for any iteration or logged metric
-    - Specify automated ML settings as kwargs
+    - Specify automl settings as kwargs
 - [auto-ml-remote-batchai.ipynb](remote-batchai/auto-ml-remote-batchai.ipynb)
    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
    - Example of using automated ML for classification using a remote Batch AI compute for training
    - Parallel execution of iterations
    - Async tracking of progress
    - Cancelling individual iterations or entire run
    - Retrieving models for any iteration or logged metric
    - Specify automl settings as kwargs
 - [auto-ml-remote-attach.ipynb](remote-attach/auto-ml-remote-attach.ipynb)
    - Dataset: [Burning Man 2016 dataset](https://innovate.burningman.org/datasets-page/)
    - handling text data with preprocess flag
    - Reading data from a blob store for remote executions
    - using pandas dataframes for reading data
 - [auto-ml-missing-data-blacklist-early-termination.ipynb](missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.ipynb)
    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
@@ -137,13 +149,17 @@ jupyter notebook
 - [auto-ml-exploring-previous-runs.ipynb](exploring-previous-runs/auto-ml-exploring-previous-runs.ipynb)
    - List all projects for the workspace
-    - List all automated ML Runs for a given project
+    - List all AutoML Runs for a given project
-    - Get details for a automated ML Run. (automated ML settings, run widget & all metrics)
+    - Get details for a AutoML Run. (Automl settings, run widget & all metrics)
    - Download fitted pipeline for any iteration
 - [auto-ml-remote-execution-with-datastore.ipynb](remote-execution-with-datastore/auto-ml-remote-execution-with-datastore.ipynb)
    - Dataset: scikit learn's [digit dataset](https://innovate.burningman.org/datasets-page/)
    - Download the data and store it in DataStore.
 - [auto-ml-classification-with-deployment.ipynb](classification-with-deployment/auto-ml-classification-with-deployment.ipynb)
    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
-    - Simple example of using automated ML for classification
+    - Simple example of using Auto ML for classification
    - Registering the model
    - Creating Image and creating aci service
    - Testing the aci service
@@ -152,64 +168,120 @@ jupyter notebook
    - How to specifying sample_weight
    - The difference that it makes to test results
- [auto-ml-subsampling-local.ipynb](subsampling/auto-ml-subsampling-local.ipynb)
+- [auto-ml-dataprep.ipynb](dataprep/auto-ml-dataprep.ipynb)
-    - How to enable subsampling
+    - Using DataPrep for reading data
- [auto-ml-dataset.ipynb](dataprep/auto-ml-dataset.ipynb)
+- [auto-ml-dataprep-remote-execution.ipynb](dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb)
-    - Using Dataset for reading data
+    - Using DataPrep for reading data with remote execution
- [auto-ml-dataset-remote-execution.ipynb](dataprep-remote-execution/auto-ml-dataset-remote-execution.ipynb)
+- [auto-ml-classification-local-azuredatabricks.ipynb](classification-local-azuredatabricks/auto-ml-classification-local-azuredatabricks.ipynb)
-    - Using Dataset for reading data with remote execution
+    - Dataset: scikit learn's [digit dataset](https://innovate.burningman.org/datasets-page/)
    - Example of using AutoML for classification using Azure Databricks as the platform for training
- [auto-ml-classification-with-whitelisting.ipynb](classification-with-whitelisting/auto-ml-classification-with-whitelisting.ipynb)
+- [auto-ml-classification_with_tensorflow.ipynb](classification_with_tensorflow/auto-ml-classification_with_tensorflow.ipynb)
    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
-    - Simple example of using automated ML for classification with whitelisting tensorflow models.
+    - Simple example of using Auto ML for classification with whitelisting tensorflow models.checkout
    - Uses local compute for training
- [auto-ml-forecasting-energy-demand.ipynb](forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb)
+- [auto-ml-timeseries.ipynb](timeseries/auto-ml-timeseries.ipynb)
-    - Dataset: [NYC energy demand data](forecasting-a/nyc_energy.csv)
+    - Dataset: NYC energy demanding data
-    - Example of using automated ML for training a forecasting model
+    - Example of using AutoML for timeseries data training
 - [auto-ml-forecasting-orange-juice-sales.ipynb](forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb)
    - Dataset: [Dominick's grocery sales of orange juice](forecasting-b/dominicks_OJ.csv)
    - Example of training an automated ML forecasting model on multiple time-series
 - [auto-ml-classification-with-onnx.ipynb](classification-with-onnx/auto-ml-classification-with-onnx.ipynb)
    - 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
    - Uses azure compute for training
 - [auto-ml-creditcard-with-deployment.ipynb](credit-card-fraud-detection-with-deployment/auto-ml-creditcard-with-deployment.ipynb)
    - Dataset: Kaggle's [credit card fraud detection dataset](https://www.kaggle.com/mlg-ulb/creditcardfraud)
    - Simple example of using automated ML for classification to fraudulent credit card transactions
    - Uses azure compute for training
 - [auto-ml-hardware-performance-with-deployment.ipynb](hardware-performance-prediction-with-deployment/auto-ml-hardware-performance-with-deployment.ipynb)
    - Dataset: UCI's [computer hardware dataset](https://archive.ics.uci.edu/ml/datasets/Computer+Hardware)
    - Simple example of using automated ML for regression to predict the performance of certain combinations of hardware components
    - Uses azure compute for training
 - [auto-ml-concrete-strength-with-deployment.ipynb](predicting-concrete-strength-with-deployment/auto-ml-concrete-strength-with-deployment.ipynb)
    - Dataset: UCI's [concrete compressive strength dataset](https://www.kaggle.com/pavanraj159/concrete-compressive-strength-data-set)
    - Simple example of using automated ML for regression to predict the strength  predict the compressive strength of concrete based off of different ingredient combinations and quantities of those ingredients
    - Uses azure compute for training
 <a name="documentation"></a>
-See [Configure automated machine learning experiments](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-auto-train) to learn how more about the the settings and features available for automated machine learning experiments.
+# Documentation
 ## Table of Contents
 1. [Automated ML Settings ](#automlsettings)
 1. [Cross validation split options](#cvsplits)
 1. [Get Data Syntax](#getdata)
 1. [Data pre-processing and featurization](#preprocessing)
 <a name="automlsettings"></a>
 ## Automated ML Settings
 |Property|Description|Default|
 |-|-|-|
 |**primary_metric**|This is the metric that you want to optimize.<br><br> 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><br><br> Regression supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i><br><i>normalized_root_mean_squared_log_error</i>| Classification: accuracy <br><br> Regression: spearman_correlation
 |**iteration_timeout_minutes**|Time limit in minutes for each iteration|None|
 |**iterations**|Number of iterations. In each iteration trains the data with a specific pipeline.  To get the best result, use at least 100. |100|
 |**n_cross_validations**|Number of cross validation splits|None|
 |**validation_size**|Size of validation set as percentage of all training samples|None|
 |**max_concurrent_iterations**|Max number of iterations that would be executed in parallel|1|
 |**preprocess**|*True/False* <br>Setting this to *True* enables preprocessing <br>on the input to handle missing data, and perform some common feature extraction<br>*Note: If input data is Sparse you cannot use preprocess=True*|False|
 |**max_cores_per_iteration**| Indicates how many cores on the compute target would be used to train a single pipeline.<br> You can set it to *-1* to use all cores|1|
 |**experiment_exit_score**|*double* value indicating the target for *primary_metric*. <br> Once the target is surpassed the run terminates|None|
 |**blacklist_models**|*Array* of *strings* indicating models to ignore for Auto ML from the list of models.|None|
 |**whitelist_models**|*Array* of *strings* use only models listed for Auto ML from the list of models..|None|
 <a name="cvsplits"></a>
 ## List of models for white list/blacklist
 **Classification**
 <br><i>LogisticRegression</i>
 <br><i>SGD</i>
 <br><i>MultinomialNaiveBayes</i>
 <br><i>BernoulliNaiveBayes</i>
 <br><i>SVM</i>
 <br><i>LinearSVM</i>
 <br><i>KNN</i>
 <br><i>DecisionTree</i>
 <br><i>RandomForest</i>
 <br><i>ExtremeRandomTrees</i>
 <br><i>LightGBM</i>
 <br><i>GradientBoosting</i>
 <br><i>TensorFlowDNN</i>
 <br><i>TensorFlowLinearClassifier</i>
 <br><br>**Regression**
 <br><i>ElasticNet</i>
 <br><i>GradientBoosting</i>
 <br><i>DecisionTree</i>
 <br><i>KNN</i>
 <br><i>LassoLars</i>
 <br><i>SGD</i>
 <br><i>RandomForest</i>
 <br><i>ExtremeRandomTrees</i>
 <br><i>LightGBM</i>
 <br><i>TensorFlowLinearRegressor</i>
 <br><i>TensorFlowDNN</i>
 ## Cross validation split options
 ### K-Folds Cross Validation
 Use *n_cross_validations* setting to specify the number of cross validations. The training data set will be randomly split into *n_cross_validations* folds of equal size. During each cross validation round, one of the folds will be used for validation of the model trained on the remaining folds. This process repeats for *n_cross_validations* rounds until each fold is used once as validation set. Finally, the average scores accross all *n_cross_validations* rounds will be reported, and the corresponding model will be retrained on the whole training data set.
 ### Monte Carlo Cross Validation (a.k.a. Repeated Random Sub-Sampling)
 Use *validation_size* to specify the percentage of the training data set that should be used for validation, and use *n_cross_validations* to specify the number of cross validations. During each cross validation round, a subset of size *validation_size* will be randomly selected for validation of the model trained on the remaining data. Finally, the average scores accross all *n_cross_validations* rounds will be reported, and the corresponding model will be retrained on the whole training data set.
 ### Custom train and validation set
 You can specify seperate train and validation set either through the get_data() or directly to the fit method.
 <a name="getdata"></a>
 ## get_data() syntax
 The *get_data()* function can be used to return a dictionary with these values:
 |Key|Type|Dependency|Mutually Exclusive with|Description|
 |:-|:-|:-|:-|:-|
 |X|Pandas Dataframe or Numpy Array|y|data_train, label, columns|All features to train with|
 |y|Pandas Dataframe or Numpy Array|X|label|Label data to train with.  For classification, this should be an array of integers. |
 |X_valid|Pandas Dataframe or Numpy Array|X, y, y_valid|data_train, label|*Optional* All features to validate with.  If this is not specified, X is split between train and validate|
 |y_valid|Pandas Dataframe or Numpy Array|X, y, X_valid|data_train, label|*Optional* The label data to validate with.  If this is not specified, y is split between train and validate|
 |sample_weight|Pandas Dataframe or Numpy Array|y|data_train, label, columns|*Optional* A weight value for each label. Higher values indicate that the sample is more important.|
 |sample_weight_valid|Pandas Dataframe or Numpy Array|y_valid|data_train, label, columns|*Optional* A weight value for each validation label. Higher values indicate that the sample is more important.  If this is not specified, sample_weight is split between train and validate|
 |data_train|Pandas Dataframe|label|X, y, X_valid, y_valid|All data (features+label) to train with|
 |label|string|data_train|X, y, X_valid, y_valid|Which column in data_train represents the label|
 |columns|Array of strings|data_train||*Optional* Whitelist of columns to use for features|
 |cv_splits_indices|Array of integers|data_train||*Optional* List of indexes to split the data for cross validation|
 <a name="preprocessing"></a>
 ## Data pre-processing and featurization
 If you use `preprocess=True`, the following data preprocessing steps are performed automatically for you:
 1. Dropping high cardinality or no variance features
    - Features with no useful information are dropped from training and validation sets. These include features with all values missing, same value across all rows or with extremely high cardinality (e.g., hashes, IDs or GUIDs).
 2. Missing value imputation
    - For numerical features, missing values are imputed with average of values in the column.
    - For categorical features, missing values are imputed with most frequent value.
 3. Generating additional features
    - For DateTime features: Year, Month, Day, Day of week, Day of year, Quarter, Week of the year, Hour, Minute, Second.
    - For Text features: Term frequency based on bi-grams and tri-grams, Count vectorizer.
 4. Transformations and encodings
    - Numeric features with very few unique values are transformed into categorical features.
 <a name="pythoncommand"></a>
 # Running using python command
@@ -224,76 +296,11 @@ The main code of the file must be indented so that it is under this condition.
 <a name="troubleshooting"></a>
 # Troubleshooting
-## automl_setup fails
+## Iterations fail and the log contains "MemoryError"
-1. On Windows, make sure that you are running automl_setup from an Anconda Prompt window rather than a regular cmd window.  You can launch the "Anaconda Prompt" window by hitting the Start button and typing "Anaconda Prompt".  If you don't see the application "Anaconda Prompt", you might not have conda or mini conda installed.  In that case, you can install it [here](https://conda.io/miniconda.html)
+This can be caused by insufficient memory on the DSVM.  AutoML loads all training data into memory.  So, the available memory should be more than the training data size.
 2. Check that you have conda 64-bit installed rather than 32-bit.  You can check this with the command `conda info`.  The `platform` should be `win-64` for Windows or `osx-64` for Mac.
 3. Check that you have conda 4.4.10 or later.  You can check the version with the command `conda -V`.  If you have a previous version installed, you can update it using the command: `conda update conda`.
 4. On Linux, if the error is `gcc: error trying to exec 'cc1plus': execvp: No such file or directory`, install build essentials using the command `sudo apt-get install build-essential`.
 5. Pass a new name as the first parameter to automl_setup so that it creates a new conda environment. You can view existing conda environments using `conda env list` and remove them with `conda env remove -n <environmentname>`.
 ## automl_setup_linux.sh fails
 If automl_setup_linux.sh fails on Ubuntu Linux with the error: `unable to execute 'gcc': No such file or directory`
 1. Make sure that outbound ports 53 and 80 are enabled.  On an Azure VM, you can do this from the Azure Portal by selecting the VM and clicking on Networking.
 2. Run the command: `sudo apt-get update`
 3. Run the command: `sudo apt-get install build-essential --fix-missing`
 4. Run `automl_setup_linux.sh` again.
 ## configuration.ipynb fails
 1) For local conda, make sure that you have susccessfully run automl_setup first.
 2) Check that the subscription_id is correct.  You can find the subscription_id in the Azure Portal by selecting All Service and then Subscriptions. The characters "<" and ">" should not be included in the subscription_id value.  For example, `subscription_id = "12345678-90ab-1234-5678-1234567890abcd"` has the valid format.
 3) Check that you have Contributor or Owner access to the Subscription.
 4) Check that the region is one of the supported regions: `eastus2`, `eastus`, `westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus`
 5) Check that you have access to the region using the Azure Portal.
 ## workspace.from_config fails
 If the call `ws = Workspace.from_config()` fails:
 1) Make sure that you have run the `configuration.ipynb` notebook successfully.
 2) If you are running a notebook from a folder that is not under the folder where you ran `configuration.ipynb`, copy the folder aml_config and the file config.json that it contains to the new folder.  Workspace.from_config reads the config.json for the notebook folder or it parent folder.
 3) If you are switching to a new subscription, resource group, workspace or region, make sure that you run the `configuration.ipynb` notebook again.  Changing config.json directly will only work if the workspace already exists in the specified resource group under the specified subscription.
 4) If you want to change the region, please change the workspace, resource group or subscription.  `Workspace.create` will not create or update a workspace if it already exists, even if the region specified is different.
 ## Sample notebook fails
 If a sample notebook fails with an error that property, method or library does not exist:
 1) Check that you have selected correct kernel in jupyter notebook.  The kernel is displayed in the top right of the notebook page.  It can be changed using the `Kernel | Change Kernel` menu option.  For Azure Notebooks, it should be `Python 3.6`.  For local conda environments, it should be the conda envioronment name that you specified in automl_setup.  The default is azure_automl.  Note that the kernel is saved as part of the notebook.  So, if you switch to a new conda environment, you will have to select the new kernel in the notebook.
 2) Check that the notebook is for the SDK version that you are using.  You can check the SDK version by executing `azureml.core.VERSION` in a jupyter notebook cell.  You can download previous version of the sample notebooks from GitHub by clicking the `Branch` button, selecting the `Tags` tab and then selecting the version.
 ## Numpy import fails on Windows
 Some Windows environments see an error loading numpy with the latest Python version 3.6.8.  If you see this issue, try with Python version 3.6.7.
 ## Numpy import fails
 Check the tensorflow version in the automated ml conda environment. Supported versions are < 1.13. Uninstall tensorflow from the environment if version is >= 1.13
 You may check the version of tensorflow and uninstall as follows
 1) start a command shell, activate conda environment where automated ml packages are installed
 2) enter `pip freeze` and look for `tensorflow` , if found, the version listed should be < 1.13
 3) If the listed version is a not a supported version,  `pip uninstall tensorflow` in the command shell and enter y for confirmation.
 ## Remote run: DsvmCompute.create fails
 There are several reasons why the DsvmCompute.create can fail.  The reason is usually in the error message but you have to look at the end of the error message for the detailed reason.  Some common reasons are:
 1) `Compute name is invalid, it should start with a letter, be between 2 and 16 character, and only include letters (a-zA-Z), numbers (0-9) and \'-\'.`  Note that underscore is not allowed in the name.
 2) `The requested VM size xxxxx is not available in the current region.`  You can select a different region or vm_size.
 ## Remote run: Unable to establish SSH connection
 Automated ML uses the SSH protocol to communicate with remote DSVMs.  This defaults to port 22.  Possible causes for this error are:
 1) The DSVM is not ready for SSH connections.  When DSVM creation completes, the DSVM might still not be ready to acceept SSH connections.  The sample notebooks have a one minute delay to allow for this.
 2) Your Azure Subscription may restrict the IP address ranges that can access the DSVM on port 22.  You can check this in the Azure Portal by selecting the Virtual Machine and then clicking Networking.  The Virtual Machine name is the name that you provided in the notebook plus 10 alpha numeric characters to make the name unique.  The Inbound Port Rules define what can access the VM on specific ports.  Note that there is a priority priority order.  So, a Deny entry with a low priority number will override a Allow entry with a higher priority number.
 ## Remote run: setup iteration fails
 This is often an issue with the `get_data` method.
 1) Check that the `get_data` method is valid by running it locally.
 2) Make sure that `get_data` isn't referring to any local files.  `get_data` is executed on the remote DSVM.  So, it doesn't have direct access to local data files.  Instead you can store the data files with DataStore.  See [auto-ml-remote-execution-with-datastore.ipynb](remote-execution-with-datastore/auto-ml-remote-execution-with-datastore.ipynb)
 3) You can get to the error log for the setup iteration by clicking the `Click here to see the run in Azure portal` link, click `Back to Experiment`, click on the highest run number and then click on Logs.
 ## Remote run: disk full
 Automated ML creates files under /tmp/azureml_runs for each iteration that it runs.  It creates a folder with the iteration id.  For example: AutoML_9a038a18-77cc-48f1-80fb-65abdbc33abe_93.  Under this, there is a azureml-logs folder, which contains logs.  If you run too many iterations on the same DSVM, these files can fill the disk.
 You can delete the files under /tmp/azureml_runs or just delete the VM and create a new one.
 If your get_data downloads files, make sure the delete them or they can use disk space as well.
 When using DataStore, it is good to specify an absolute path for the files so that they are downloaded just once.  If you specify a relative path, it will download a file for each iteration.
 ## Remote run: Iterations fail and the log contains "MemoryError"
 This can be caused by insufficient memory on the DSVM.  Automated ML loads all training data into memory.  So, the available memory should be more than the training data size.
 If you are using a remote DSVM, memory is needed for each concurrent iteration.  The max_concurrent_iterations setting specifies the maximum concurrent iterations.  For example, if the training data size is 8Gb and max_concurrent_iterations is set to 10, the minimum memory required is at least 80Gb.
 To resolve this issue, allocate a DSVM with more memory or reduce the value specified for max_concurrent_iterations.
-## Remote run: Iterations show as "Not Responding" in the RunDetails widget.
+## Iterations show as "Not Responding" in the RunDetails widget.
 This can be caused by too many concurrent iterations for a remote DSVM.  Each concurrent iteration usually takes 100% of a core when it is running.  Some iterations can use multiple cores.  So, the max_concurrent_iterations setting should always be less than the number of cores of the DSVM.
 To resolve this issue, try reducing the value specified for the max_concurrent_iterations setting.
--- a/how-to-use-azureml/automated-machine-learning/automl_env.yml
+++ b/how-to-use-azureml/automated-machine-learning/automl_env.yml
@@ -2,24 +2,31 @@ name: azure_automl
 dependencies:
  # The python interpreter version.
  # Currently Azure ML only supports 3.5.2 and later.
- pip
+- python=3.6
 - python>=3.5.2,<3.6.8
 - nb_conda
 - matplotlib==2.1.0
- numpy>=1.11.0,<=1.16.2
+- numpy>=1.11.0,<1.15.0
 - cython
 - urllib3<1.24
 - scipy>=1.0.0,<=1.1.0
- scikit-learn>=0.19.0,<=0.20.3
+- scikit-learn>=0.18.0,<=0.19.1
- pandas>=0.22.0,<=0.23.4
+- pandas>=0.22.0,<0.23.0
- py-xgboost<=0.80
+- tensorflow>=1.12.0
- pyarrow>=0.11.0
+
 # Required for azuremlftk
 - dill
 - pyodbc
 - statsmodels 
 - numexpr  
 - keras
 - distributed>=1.21.5,<1.24
 - pip:
  # Required for azuremlftk
  - https://azuremlpackages.blob.core.windows.net/forecasting/azuremlftk-0.1.18323.5a1-py3-none-any.whl
  # Required packages for AzureML execution, history, and data preparation.
-  - azureml-defaults
+  - azureml-sdk[automl,notebooks,explain]
  - azureml-train-automl
  - azureml-widgets
  - azureml-explain-model
  - pandas_ml
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
rastala	ce59d79133	Update 12/2 2	2018-12-02 19:21:15 -05:00
rastala	13a5d0baac	Update 12/2	2018-12-02 14:46:52 -05:00
rastala	74309f91f7	Update 12-1-19	2018-12-01 20:37:45 -05:00
Roope Astala	96523ec751	Notebook folder restructuring	2018-11-29 11:32:16 -05:00