google register code to crawl and index

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"

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"

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"

NBSETUP.md

@@ -1,4 +1,6 @@
-# Set up your notebook environment for Azure Machine Learning
+# Setting up environment
+
+---
 
 To run the notebooks in this repository use one of following options.
 
@@ -10,7 +12,9 @@ Azure Notebooks is a hosted Jupyter-based notebook service in the Azure cloud. A
 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.
+    **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)
 
 ## **Option 2: Use your own notebook server**
 
@@ -27,6 +31,9 @@ git clone https://github.com/Azure/MachineLearningNotebooks.git
 # install the base SDK and a Jupyter notebook server
 pip install azureml-sdk[notebooks]
 
+# install the data prep component
+pip install azureml-dataprep
+
 # install model explainability component
 pip install azureml-sdk[explain]
 
@@ -51,7 +58,8 @@ Please make sure you start with the [Configuration](configuration.ipynb) noteboo
 
 ### Video walkthrough:
 
-[!VIDEO https://youtu.be/VIsXeTuW3FU]
+[![Get Started video](images/yt_cover.png)](https://youtu.be/VIsXeTuW3FU)
+
 
 ## **Option 3: Use Docker**
 
@@ -82,6 +90,9 @@ Now you can point your browser to http://localhost:8887. We recommend that you s
 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 dataprep components
+pip install azureml-dataprep
+
 # install the core SDK and automated ml components
 pip install azureml-sdk[automl]
 

configuration.ipynb

@@ -96,7 +96,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.0.23 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.0.21 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

contrib/RAPIDS/README.md

@@ -287,8 +287,6 @@ Notice how the parameters are modified when using the CPU-only mode.
 
 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)
-
 
 
 

googled8147fb6c0788258.html

@@ -0,0 +1 @@
+google-site-verification: googled8147fb6c0788258.html

how-to-use-azureml/README.md

@@ -15,6 +15,3 @@ As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) not
 * [enable-app-insights-in-production-service](./deployment/enable-app-insights-in-production-service) 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/).
-
-
- ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/README.png)

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)
@@ -13,15 +13,15 @@
 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.
@@ -29,7 +29,7 @@ Below are the three execution environments supported by automated ML.
 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.
@@ -39,7 +39,7 @@ Below are the three execution environments supported by automated ML.
 - 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.
@@ -49,15 +49,11 @@ The instructions below will install everything you need and then start a Jupyter
 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.
+- 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_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.
+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.
-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:
 ```
@@ -85,7 +81,7 @@ bash automl_setup_linux.sh
 
 ### 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.
+- Follow the instructions in the individual notebooks to explore various features in AutoML
 
 ### 6. Starting jupyter notebook manually
 To start your Jupyter notebook manually, use:
@@ -107,22 +103,22 @@ jupyter notebook
 
 - [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-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 a remote linux DSVM 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-amlcompute.ipynb](remote-batchai/auto-ml-remote-amlcompute.ipynb)
     - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
@@ -131,7 +127,7 @@ jupyter notebook
     - 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-attach.ipynb](remote-attach/auto-ml-remote-attach.ipynb)
     - Dataset: Scikit learn's [20newsgroup](http://scikit-learn.org/stable/datasets/twenty_newsgroups.html)
@@ -152,8 +148,8 @@ 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)
@@ -162,7 +158,7 @@ jupyter notebook
 
 - [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
@@ -182,21 +178,16 @@ jupyter notebook
 
 - [auto-ml-classification-with-whitelisting.ipynb](classification-with-whitelisting/auto-ml-classification-with-whitelisting.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.
     - Uses local compute for training
 
 - [auto-ml-forecasting-energy-demand.ipynb](forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb)
     - Dataset: [NYC energy demand data](forecasting-a/nyc_energy.csv)
-    - Example of using automated ML for training a forecasting model
+    - Example of using AutoML for training a forecasting model
 
 - [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
+    - Example of training an AutoML forecasting model on multiple time-series
-
-- [auto-ml-classification-with-onnx.ipynb](classification-with-onnx/auto-ml-classification-with-onnx.ipynb)
-    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
-    - Simple example of using automated ML for classification with ONNX models
-    - Uses local 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.
@@ -215,18 +206,10 @@ The main code of the file must be indented so that it is under this condition.
 <a name="troubleshooting"></a>
 # Troubleshooting
 ## automl_setup fails
-1. On Windows, make sure that you are running automl_setup from an Anconda Prompt window rather than a regular cmd window.  You can launch the "Anaconda Prompt" window by hitting the Start button and typing "Anaconda Prompt".  If you don't see the application "Anaconda Prompt", you might not have conda or mini conda installed.  In that case, you can install it [here](https://conda.io/miniconda.html)
+1. On windows, make sure that you are running automl_setup from an Anconda Prompt window rather than a regular cmd window.  You can launch the "Anaconda Prompt" window by hitting the Start button and typing "Anaconda Prompt".  If you don't see the application "Anaconda Prompt", you might not have conda or mini conda installed.  In that case, you can install it [here](https://conda.io/miniconda.html)
 2. Check that you have conda 64-bit installed rather than 32-bit.  You can check this with the command `conda info`.  The `platform` should be `win-64` for Windows or `osx-64` for Mac.
 3. Check that you have conda 4.4.10 or later.  You can check the version with the command `conda -V`.  If you have a previous version installed, you can update it using the command: `conda update conda`.
-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`.
+4. 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>`. 
-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.
@@ -250,20 +233,13 @@ If a sample notebook fails with an error that property, method or library does n
 ## 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:
+AutoML 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.
 
@@ -274,13 +250,13 @@ This is often an issue with the `get_data` method.
 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.
+AutoML 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.
+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.
 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.
 

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

@@ -1,21 +1,22 @@
-name: azure_automl
+name: azure_automl
-dependencies:
+dependencies:
-  # The python interpreter version.
+  # The python interpreter version.
-  # Currently Azure ML only supports 3.5.2 and later.
+  # Currently Azure ML only supports 3.5.2 and later.
-- python>=3.5.2,<3.6.8
+- python>=3.5.2,<3.6.8
-- nb_conda
+- nb_conda
-- matplotlib==2.1.0
+- matplotlib==2.1.0
-- numpy>=1.11.0,<=1.16.2
+- numpy>=1.11.0,<1.15.0
-- cython
+- cython
-- urllib3<1.24
+- urllib3<1.24
-- scipy>=1.0.0,<=1.1.0
+- 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,<0.23.0
+- pandas>=0.22.0,<0.23.0
-- py-xgboost<=0.80
+- tensorflow>=1.12.0
-
+- py-xgboost<=0.80
-- pip:
+
-  # Required packages for AzureML execution, history, and data preparation.
+- pip:
-  - azureml-sdk[automl,explain]
+  # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets
+  - azureml-sdk[automl,explain]
-  - pandas_ml
+  - azureml-widgets
-  
+  - pandas_ml
+  

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

@@ -0,0 +1,23 @@
+name: azure_automl
+dependencies:
+  # The python interpreter version.
+  # Currently Azure ML only supports 3.5.2 and later.
+- python>=3.5.2,<3.6.8
+- nb_conda
+- matplotlib==2.1.0
+- numpy>=1.15.3
+- cython
+- urllib3<1.24
+- scipy>=1.0.0,<=1.1.0
+- scikit-learn>=0.18.0,<=0.19.1
+- pandas>=0.22.0,<0.23.0
+- tensorflow>=1.12.0
+- py-xgboost<=0.80
+
+- pip:
+  # Required packages for AzureML execution, history, and data preparation.
+  - azureml-sdk[automl,explain]
+  - azureml-widgets
+  - pandas_ml
+  
+

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

@@ -1,51 +1,51 @@
-@echo off
+@echo off
-set conda_env_name=%1
+set conda_env_name=%1
-set automl_env_file=%2
+set automl_env_file=%2
-set options=%3
+set options=%3
-set PIP_NO_WARN_SCRIPT_LOCATION=0
+set PIP_NO_WARN_SCRIPT_LOCATION=0
-
+
-IF "%conda_env_name%"=="" SET conda_env_name="azure_automl"
+IF "%conda_env_name%"=="" SET conda_env_name="azure_automl"
-IF "%automl_env_file%"=="" SET automl_env_file="automl_env.yml"
+IF "%automl_env_file%"=="" SET automl_env_file="automl_env.yml"
-
+
-IF NOT EXIST %automl_env_file% GOTO YmlMissing
+IF NOT EXIST %automl_env_file% GOTO YmlMissing
-
+
-call conda activate %conda_env_name% 2>nul:
+call conda activate %conda_env_name% 2>nul:
-
+
-if not errorlevel 1 (
+if not errorlevel 1 (
-  echo Upgrading azureml-sdk[automl,notebooks,explain] in existing conda environment %conda_env_name%
+  echo Upgrading azureml-sdk[automl,notebooks,explain] in existing conda environment %conda_env_name%
-  call pip install --upgrade azureml-sdk[automl,notebooks,explain]
+  call pip install --upgrade azureml-sdk[automl,notebooks,explain]
-  if errorlevel 1 goto ErrorExit
+  if errorlevel 1 goto ErrorExit
-) else (
+) else (
-  call conda env create -f %automl_env_file% -n %conda_env_name%
+  call conda env create -f %automl_env_file% -n %conda_env_name%
-)
+)
-
+
-call conda activate %conda_env_name% 2>nul:
+call conda activate %conda_env_name% 2>nul:
-if errorlevel 1 goto ErrorExit
+if errorlevel 1 goto ErrorExit
-
+
-call python -m ipykernel install --user --name %conda_env_name% --display-name "Python (%conda_env_name%)"
+call python -m ipykernel install --user --name %conda_env_name% --display-name "Python (%conda_env_name%)"
-
+
-REM azureml.widgets is now installed as part of the pip install under the conda env.
+REM azureml.widgets is now installed as part of the pip install under the conda env.
-REM Removing the old user install so that the notebooks will use the latest widget.
+REM Removing the old user install so that the notebooks will use the latest widget.
-call jupyter nbextension uninstall --user --py azureml.widgets
+call jupyter nbextension uninstall --user --py azureml.widgets
-
+
-echo.
+echo.
-echo.
+echo.
-echo ***************************************
+echo ***************************************
-echo * AutoML setup completed successfully *
+echo * AutoML setup completed successfully *
-echo ***************************************
+echo ***************************************
-IF NOT "%options%"=="nolaunch" (
+IF NOT "%options%"=="nolaunch" (
-  echo.
+  echo.
-  echo Starting jupyter notebook - please run the configuration notebook 
+  echo Starting jupyter notebook - please run the configuration notebook 
-  echo.
+  echo.
-  jupyter notebook --log-level=50 --notebook-dir='..\..'
+  jupyter notebook --log-level=50 --notebook-dir='..\..'
-)
+)
-
+
-goto End
+goto End
-
+
-:YmlMissing
+:YmlMissing
-echo File %automl_env_file% not found.
+echo File %automl_env_file% not found.
-
+
-:ErrorExit
+:ErrorExit
-echo Install failed
+echo Install failed
-
+
 :End

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

@@ -12,7 +12,7 @@ fi
 
 if [ "$AUTOML_ENV_FILE" == "" ]
 then
-  AUTOML_ENV_FILE="automl_env.yml"
+  AUTOML_ENV_FILE="automl_env_mac.yml"
 fi
 
 if [ ! -f $AUTOML_ENV_FILE ]; then
@@ -31,6 +31,7 @@ else
    conda install lightgbm -c conda-forge -y &&
    python -m ipykernel install --user --name $CONDA_ENV_NAME --display-name "Python ($CONDA_ENV_NAME)" &&
    jupyter nbextension uninstall --user --py azureml.widgets &&
+   pip install numpy==1.15.3 &&
    echo "" &&
    echo "" &&
    echo "***************************************" &&

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

@@ -139,6 +139,7 @@
         "                             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",
@@ -262,7 +263,7 @@
       "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. The following cells create a file, myenv.yml, which specifies the dependencies from the run."
+        "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)."
       ]
     },
     {
@@ -302,8 +303,7 @@
       "source": [
         "from azureml.core.conda_dependencies import CondaDependencies\n",
         "\n",
-        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost<=0.80'],\n",
+        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-sdk[automl]'])\n",
-        "                                 pip_packages=['azureml-sdk[automl]'])\n",
         "\n",
         "conda_env_file_name = 'myenv.yml'\n",
         "myenv.save_to_file('.', conda_env_file_name)"

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

@@ -1,284 +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": [
-        "# Automated Machine Learning\n",
-        "_**Classification with Local Compute**_\n",
-        "\n",
-        "## Contents\n",
-        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#Setup)\n",
-        "1. [Data](#Data)\n",
-        "1. [Train](#Train)\n",
-        "1. [Results](#Results)\n",
-        "1. [Test](#Test)\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Introduction\n",
-        "\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 [configuration](../../../configuration.ipynb) before running this notebook.\n",
-        "\n",
-        "Please find the ONNX related documentations [here](https://github.com/onnx/onnx).\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 with ONNX compatible config on.\n",
-        "4. Explore the results and save the ONNX model."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Setup\n",
-        "\n",
-        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import logging\n",
-        "\n",
-        "from matplotlib import pyplot as plt\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "from sklearn import datasets\n",
-        "\n",
-        "import azureml.core\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.core.workspace import Workspace\n",
-        "from azureml.train.automl import AutoMLConfig"
-      ]
-    },
-    {
-      "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-classification-onnx'\n",
-        "project_folder = './sample_projects/automl-classification-onnx'\n",
-        "\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "\n",
-        "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace Name'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
-        "outputDf.T"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 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": [
-        "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": [
-        "## Train with enable ONNX compatible models config on\n",
-        "\n",
-        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
-        "\n",
-        "Set the parameter enable_onnx_compatible_models=True, if you also want to generate the ONNX compatible models. Please note, the forecasting task and TensorFlow models are not ONNX compatible yet.\n",
-        "\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",
-        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
-        "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
-        "|**enable_onnx_compatible_models**|Enable the ONNX compatible models in the experiment.|\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",
-        "                             verbosity = logging.INFO,\n",
-        "                             X = X_train, \n",
-        "                             y = y_train,\n",
-        "                             enable_onnx_compatible_models=True,\n",
-        "                             path = project_folder)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
-        "In this example, we specify `show_output = True` to print currently running iterations to the console."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "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": [
-        "## 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 the Best ONNX Model\n",
-        "\n",
-        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*.\n",
-        "\n",
-        "Set the parameter return_onnx_model=True to retrieve the best ONNX model, instead of the Python model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "best_run, onnx_mdl = local_run.get_output(return_onnx_model=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Save the best ONNX model"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.automl._vendor.automl.client.core.common.onnx_convert import OnnxConverter\n",
-        "onnx_fl_path = \"./best_model.onnx\"\n",
-        "OnnxConverter.save_onnx_model(onnx_mdl, onnx_fl_path)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "savitam"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

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

@@ -71,17 +71,11 @@
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-        "import sys\n",
+        "try:\n",
-        "whitelist_models=[\"LightGBM\"]\n",
+        "    import tensorflow as tf1\n",
-        "if \"3.7\" != sys.version[0:3]:\n",
+        "except ImportError:\n",
-        "    try:\n",
+        "    from pip._internal import main\n",
-        "        import tensorflow as tf1\n",
+        "    main(['install', 'tensorflow>=1.10.0,<=1.12.0'])\n",
-        "    except ImportError:\n",
-        "        from pip._internal import main\n",
-        "        main(['install', 'tensorflow>=1.10.0,<=1.12.0'])\n",
-        "        logging.getLogger().setLevel(logging.ERROR)\n",
-        "    whitelist_models=[\"TensorFlowLinearClassifier\", \"TensorFlowDNN\"]\n",
-        "\n",
         "from azureml.train.automl import AutoMLConfig"
       ]
     },
@@ -166,11 +160,12 @@
         "                             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=whitelist_models,\n",
+        "                             whitelist_models=[\"TensorFlowLinearClassifier\", \"TensorFlowDNN\"],\n",
         "                             path = project_folder)"
       ]
     },

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

@@ -72,32 +72,6 @@
         "from azureml.train.automl import AutoMLConfig"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Accessing the Azure ML workspace requires authentication with Azure.\n",
-        "\n",
-        "The default authentication is interactive authentication using the default tenant.  Executing the `ws = Workspace.from_config()` line in the cell below will prompt for authentication the first time that it is run.\n",
-        "\n",
-        "If you have multiple Azure tenants, you can specify the tenant by replacing the `ws = Workspace.from_config()` line in the cell below with the following:\n",
-        "\n",
-        "```\n",
-        "from azureml.core.authentication import InteractiveLoginAuthentication\n",
-        "auth = InteractiveLoginAuthentication(tenant_id = 'mytenantid')\n",
-        "ws = Workspace.from_config(auth = auth)\n",
-        "```\n",
-        "\n",
-        "If you need to run in an environment where interactive login is not possible, you can use Service Principal authentication by replacing the `ws = Workspace.from_config()` line in the cell below with the following:\n",
-        "\n",
-        "```\n",
-        "from azureml.core.authentication import ServicePrincipalAuthentication\n",
-        "auth = auth = ServicePrincipalAuthentication('mytenantid', 'myappid', 'mypassword')\n",
-        "ws = Workspace.from_config(auth = auth)\n",
-        "```\n",
-        "For more details, see [aka.ms/aml-notebook-auth](http://aka.ms/aml-notebook-auth)"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -159,17 +133,12 @@
         "|-|-|\n",
         "|**task**|classification or regression|\n",
         "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
+        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+        "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
         "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
-        "|**n_cross_validations**|Number of cross validation splits.|\n",
+        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
-        "|\n",
-        "\n",
-        "Automated machine learning trains multiple machine learning pipelines.  Each pipelines training is known as an iteration.\n",
-        "* You can specify a maximum number of iterations using the `iterations` parameter.\n",
-        "* You can specify a maximum time for the run using the `experiment_timeout_minutes` parameter.\n",
-        "* If you specify neither the `iterations` nor the `experiment_timeout_minutes`, automated ML keeps running iterations while it continues to see improvements in the scores.\n",
-        "\n",
-        "The following example doesn't specify `iterations` or `experiment_timeout_minutes` and so runs until the scores stop improving.\n"
       ]
     },
     {
@@ -179,10 +148,15 @@
       "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",
-        "                             n_cross_validations = 3)"
+        "                             path = project_folder)"
       ]
     },
     {

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

@@ -163,7 +163,8 @@
         "    \"iterations\" : 2,\n",
         "    \"primary_metric\" : 'AUC_weighted',\n",
         "    \"preprocess\" : False,\n",
-        "    \"verbosity\" : logging.INFO\n",
+        "    \"verbosity\" : logging.INFO,\n",
+        "    \"n_cross_validations\": 3\n",
         "}"
       ]
     },

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

@@ -1,493 +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": [
-        "# Automated Machine Learning\n",
-        "**BikeShare Demand Forecasting**\n",
-        "\n",
-        "## Contents\n",
-        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#Setup)\n",
-        "1. [Data](#Data)\n",
-        "1. [Train](#Train)\n",
-        "1. [Evaluate](#Evaluate)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Introduction\n",
-        "In this example, we show how AutoML can be used for bike share forecasting.\n",
-        "\n",
-        "The purpose is to demonstrate how to take advantage of the built-in holiday featurization, access the feature names, and further demonstrate how to work with the `forecast` function. Please also look at the additional forecasting notebooks, which document lagging, rolling windows, forecast quantiles, other ways to use the forecast function, and forecaster deployment.\n",
-        "\n",
-        "Make sure you have executed the [configuration](../../../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. Viewing the engineered names for featurized data and featurization summary for all raw features\n",
-        "6. Testing the fitted model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Setup\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.core\n",
-        "import pandas as pd\n",
-        "import numpy as np\n",
-        "import logging\n",
-        "import warnings\n",
-        "# Squash warning messages for cleaner output in the notebook\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 matplotlib import pyplot as plt\n",
-        "from sklearn.metrics import mean_absolute_error, mean_squared_error"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
-      ]
-    },
-    {
-      "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-bikeshareforecasting'\n",
-        "# project folder\n",
-        "project_folder = './sample_projects/automl-local-bikeshareforecasting'\n",
-        "\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "\n",
-        "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['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",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
-        "outputDf.T"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Data\n",
-        "Read bike share demand data from file, and preview data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "data = pd.read_csv('bike-no.csv', parse_dates=['date'])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's set up what we know abou the dataset. \n",
-        "\n",
-        "**Target column** is what we want to forecast.\n",
-        "\n",
-        "**Time column** is the time axis along which to predict.\n",
-        "\n",
-        "**Grain** is another word for an individual time series in your dataset. Grains are identified by values of the columns listed `grain_column_names`, for example \"store\" and \"item\" if your data has multiple time series of sales, one series for each combination of store and item sold.\n",
-        "\n",
-        "This dataset has only one time series. Please see the [orange juice notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales) for an example of a multi-time series dataset."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "target_column_name = 'cnt'\n",
-        "time_column_name = 'date'\n",
-        "grain_column_names = []"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Split the data\n",
-        "\n",
-        "The first split we make is into train and test sets. Note we are splitting on time."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "train = data[data[time_column_name] < '2012-09-01']\n",
-        "test = data[data[time_column_name] >= '2012-09-01']\n",
-        "\n",
-        "X_train = train.copy()\n",
-        "y_train = X_train.pop(target_column_name).values\n",
-        "\n",
-        "X_test = test.copy()\n",
-        "y_test = X_test.pop(target_column_name).values\n",
-        "\n",
-        "print(X_train.shape)\n",
-        "print(y_train.shape)\n",
-        "print(X_test.shape)\n",
-        "print(y_test.shape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Setting forecaster maximum horizon \n",
-        "\n",
-        "Assuming your test data forms a full and regular time series(regular time intervals and no holes), \n",
-        "the maximum horizon you will need to forecast is the length of the longest grain in your test set."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "if len(grain_column_names) == 0:\n",
-        "    max_horizon = len(X_test)\n",
-        "else:\n",
-        "    max_horizon = X_test.groupby(grain_column_names)[time_column_name].count().max()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train\n",
-        "\n",
-        "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
-        "\n",
-        "|Property|Description|\n",
-        "|-|-|\n",
-        "|**task**|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, ], targets values.|\n",
-        "|**n_cross_validations**|Number of cross validation splits.|\n",
-        "|**country**|The country used to generate holiday features. These should be ISO 3166 two-letter country codes (i.e. 'US', 'GB').|\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 = 'date'\n",
-        "automl_settings = {\n",
-        "    \"time_column_name\": time_column_name,\n",
-        "    # these columns are a breakdown of the total and therefore a leak\n",
-        "    \"drop_column_names\": ['casual', 'registered'],\n",
-        "    # knowing the country allows Automated ML to bring in holidays\n",
-        "    \"country\" : 'US',\n",
-        "    \"max_horizon\" : max_horizon,\n",
-        "    \"target_lags\": 1    \n",
-        "}\n",
-        "\n",
-        "automl_config = AutoMLConfig(task = 'forecasting',                             \n",
-        "                             primary_metric='normalized_root_mean_squared_error',\n",
-        "                             iterations = 10,\n",
-        "                             iteration_timeout_minutes = 5,\n",
-        "                             X = X_train,\n",
-        "                             y = y_train,\n",
-        "                             n_cross_validations = 3,                             \n",
-        "                             path=project_folder,\n",
-        "                             verbosity = logging.INFO,\n",
-        "                            **automl_settings)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We will now run the experiment, starting with 10 iterations of model search. Experiment can be continued for more iterations if the results are not yet good. You will see the currently running iterations printing to the console."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_run = experiment.submit(automl_config, show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Displaying the run objects gives you links to the visual tools in the Azure Portal. Go try them!"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_run"
-      ]
-    },
-    {
-      "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": [
-        "### View the engineered names for featurized data\n",
-        "\n",
-        "You can accees the engineered feature names generated in time-series featurization. Note that a number of named holiday periods are represented. We recommend that you have at least one year of data when using this feature to ensure that all yearly holidays are captured in the training featurization."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "fitted_model.named_steps['timeseriestransformer'].get_engineered_feature_names()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### View the featurization summary\n",
-        "\n",
-        "You can also see what featurization steps were performed on different raw features in the user data. For each raw feature in the user data, the following information is displayed:\n",
-        "\n",
-        "- Raw feature name\n",
-        "- Number of engineered features formed out of this raw feature\n",
-        "- Type detected\n",
-        "- If feature was dropped\n",
-        "- List of feature transformations for the raw feature"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "fitted_model.named_steps['timeseriestransformer'].get_featurization_summary()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Test the Best Fitted Model\n",
-        "\n",
-        "Predict on training and test set, and calculate residual values.\n",
-        "\n",
-        "We always score on the original dataset whose schema matches the scheme of the training dataset."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "X_test.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "y_query = y_test.copy().astype(np.float)\n",
-        "y_query.fill(np.NaN)\n",
-        "y_fcst, X_trans = fitted_model.forecast(X_test, y_query)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "It is a good practice to always align the output explicitly to the input, as the count and order of the rows may have changed during transformations that span multiple rows."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "def align_outputs(y_predicted, X_trans, X_test, y_test, predicted_column_name = 'predicted'):\n",
-        "    \"\"\"\n",
-        "    Demonstrates how to get the output aligned to the inputs\n",
-        "    using pandas indexes. Helps understand what happened if\n",
-        "    the output's shape differs from the input shape, or if\n",
-        "    the data got re-sorted by time and grain during forecasting.\n",
-        "    \n",
-        "    Typical causes of misalignment are:\n",
-        "    * we predicted some periods that were missing in actuals -> drop from eval\n",
-        "    * model was asked to predict past max_horizon -> increase max horizon\n",
-        "    * data at start of X_test was needed for lags -> provide previous periods\n",
-        "    \"\"\"\n",
-        "    df_fcst = pd.DataFrame({predicted_column_name : y_predicted})\n",
-        "    # y and X outputs are aligned by forecast() function contract\n",
-        "    df_fcst.index = X_trans.index\n",
-        "    \n",
-        "    # align original X_test to y_test    \n",
-        "    X_test_full = X_test.copy()\n",
-        "    X_test_full[target_column_name] = y_test\n",
-        "\n",
-        "    # X_test_full's index does not include origin, so reset for merge\n",
-        "    df_fcst.reset_index(inplace=True)\n",
-        "    X_test_full = X_test_full.reset_index().drop(columns='index')\n",
-        "    together = df_fcst.merge(X_test_full, how='right')\n",
-        "    \n",
-        "    # drop rows where prediction or actuals are nan \n",
-        "    # happens because of missing actuals \n",
-        "    # or at edges of time due to lags/rolling windows\n",
-        "    clean = together[together[[target_column_name, predicted_column_name]].notnull().all(axis=1)]\n",
-        "    return(clean)\n",
-        "\n",
-        "df_all = align_outputs(y_fcst, X_trans, X_test, y_test)\n"
-      ]
-    },
-    {
-      "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)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(\"Simple forecasting model\")\n",
-        "rmse = np.sqrt(mean_squared_error(df_all[target_column_name], df_all['predicted']))\n",
-        "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % rmse)\n",
-        "mae = mean_absolute_error(df_all[target_column_name], df_all['predicted'])\n",
-        "print('mean_absolute_error score: %.2f' % mae)\n",
-        "print('MAPE: %.2f' % MAPE(df_all[target_column_name], df_all['predicted']))\n",
-        "\n",
-        "# Plot outputs\n",
-        "%matplotlib notebook\n",
-        "test_pred = plt.scatter(df_all[target_column_name], df_all['predicted'], color='b')\n",
-        "test_test = plt.scatter(y_test, y_test, color='g')\n",
-        "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
-        "plt.show()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "xiaga@microsoft.com, tosingli@microsoft.com"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.7"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/automated-machine-learning/forecasting-bike-share/bike-no.csv

@@ -1,732 +0,0 @@
-instant,date,season,yr,mnth,weekday,weathersit,temp,atemp,hum,windspeed,casual,registered,cnt
-1,1/1/2011,1,0,1,6,2,0.344167,0.363625,0.805833,0.160446,331,654,985
-2,1/2/2011,1,0,1,0,2,0.363478,0.353739,0.696087,0.248539,131,670,801
-3,1/3/2011,1,0,1,1,1,0.196364,0.189405,0.437273,0.248309,120,1229,1349
-4,1/4/2011,1,0,1,2,1,0.2,0.212122,0.590435,0.160296,108,1454,1562
-5,1/5/2011,1,0,1,3,1,0.226957,0.22927,0.436957,0.1869,82,1518,1600
-6,1/6/2011,1,0,1,4,1,0.204348,0.233209,0.518261,0.0895652,88,1518,1606
-7,1/7/2011,1,0,1,5,2,0.196522,0.208839,0.498696,0.168726,148,1362,1510
-8,1/8/2011,1,0,1,6,2,0.165,0.162254,0.535833,0.266804,68,891,959
-9,1/9/2011,1,0,1,0,1,0.138333,0.116175,0.434167,0.36195,54,768,822
-10,1/10/2011,1,0,1,1,1,0.150833,0.150888,0.482917,0.223267,41,1280,1321
-11,1/11/2011,1,0,1,2,2,0.169091,0.191464,0.686364,0.122132,43,1220,1263
-12,1/12/2011,1,0,1,3,1,0.172727,0.160473,0.599545,0.304627,25,1137,1162
-13,1/13/2011,1,0,1,4,1,0.165,0.150883,0.470417,0.301,38,1368,1406
-14,1/14/2011,1,0,1,5,1,0.16087,0.188413,0.537826,0.126548,54,1367,1421
-15,1/15/2011,1,0,1,6,2,0.233333,0.248112,0.49875,0.157963,222,1026,1248
-16,1/16/2011,1,0,1,0,1,0.231667,0.234217,0.48375,0.188433,251,953,1204
-17,1/17/2011,1,0,1,1,2,0.175833,0.176771,0.5375,0.194017,117,883,1000
-18,1/18/2011,1,0,1,2,2,0.216667,0.232333,0.861667,0.146775,9,674,683
-19,1/19/2011,1,0,1,3,2,0.292174,0.298422,0.741739,0.208317,78,1572,1650
-20,1/20/2011,1,0,1,4,2,0.261667,0.25505,0.538333,0.195904,83,1844,1927
-21,1/21/2011,1,0,1,5,1,0.1775,0.157833,0.457083,0.353242,75,1468,1543
-22,1/22/2011,1,0,1,6,1,0.0591304,0.0790696,0.4,0.17197,93,888,981
-23,1/23/2011,1,0,1,0,1,0.0965217,0.0988391,0.436522,0.2466,150,836,986
-24,1/24/2011,1,0,1,1,1,0.0973913,0.11793,0.491739,0.15833,86,1330,1416
-25,1/25/2011,1,0,1,2,2,0.223478,0.234526,0.616957,0.129796,186,1799,1985
-26,1/26/2011,1,0,1,3,3,0.2175,0.2036,0.8625,0.29385,34,472,506
-27,1/27/2011,1,0,1,4,1,0.195,0.2197,0.6875,0.113837,15,416,431
-28,1/28/2011,1,0,1,5,2,0.203478,0.223317,0.793043,0.1233,38,1129,1167
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-670,10/31/2012,4,1,10,3,2,0.3575,0.3611,0.666667,0.166667,419,5147,5566
-671,11/1/2012,4,1,11,4,2,0.365833,0.369942,0.581667,0.157346,466,5520,5986
-672,11/2/2012,4,1,11,5,1,0.355,0.356042,0.522083,0.266175,618,5229,5847
-673,11/3/2012,4,1,11,6,2,0.343333,0.323846,0.49125,0.270529,1029,4109,5138
-674,11/4/2012,4,1,11,0,1,0.325833,0.329538,0.532917,0.179108,1201,3906,5107
-675,11/5/2012,4,1,11,1,1,0.319167,0.308075,0.494167,0.236325,378,4881,5259
-676,11/6/2012,4,1,11,2,1,0.280833,0.281567,0.567083,0.173513,466,5220,5686
-677,11/7/2012,4,1,11,3,2,0.295833,0.274621,0.5475,0.304108,326,4709,5035
-678,11/8/2012,4,1,11,4,1,0.352174,0.341891,0.333478,0.347835,340,4975,5315
-679,11/9/2012,4,1,11,5,1,0.361667,0.355413,0.540833,0.214558,709,5283,5992
-680,11/10/2012,4,1,11,6,1,0.389167,0.393937,0.645417,0.0578458,2090,4446,6536
-681,11/11/2012,4,1,11,0,1,0.420833,0.421713,0.659167,0.1275,2290,4562,6852
-682,11/12/2012,4,1,11,1,1,0.485,0.475383,0.741667,0.173517,1097,5172,6269
-683,11/13/2012,4,1,11,2,2,0.343333,0.323225,0.662917,0.342046,327,3767,4094
-684,11/14/2012,4,1,11,3,1,0.289167,0.281563,0.552083,0.199625,373,5122,5495
-685,11/15/2012,4,1,11,4,2,0.321667,0.324492,0.620417,0.152987,320,5125,5445
-686,11/16/2012,4,1,11,5,1,0.345,0.347204,0.524583,0.171025,484,5214,5698
-687,11/17/2012,4,1,11,6,1,0.325,0.326383,0.545417,0.179729,1313,4316,5629
-688,11/18/2012,4,1,11,0,1,0.3425,0.337746,0.692917,0.227612,922,3747,4669
-689,11/19/2012,4,1,11,1,2,0.380833,0.375621,0.623333,0.235067,449,5050,5499
-690,11/20/2012,4,1,11,2,2,0.374167,0.380667,0.685,0.082725,534,5100,5634
-691,11/21/2012,4,1,11,3,1,0.353333,0.364892,0.61375,0.103246,615,4531,5146
-692,11/22/2012,4,1,11,4,1,0.34,0.350371,0.580417,0.0528708,955,1470,2425
-693,11/23/2012,4,1,11,5,1,0.368333,0.378779,0.56875,0.148021,1603,2307,3910
-694,11/24/2012,4,1,11,6,1,0.278333,0.248742,0.404583,0.376871,532,1745,2277
-695,11/25/2012,4,1,11,0,1,0.245833,0.257583,0.468333,0.1505,309,2115,2424
-696,11/26/2012,4,1,11,1,1,0.313333,0.339004,0.535417,0.04665,337,4750,5087
-697,11/27/2012,4,1,11,2,2,0.291667,0.281558,0.786667,0.237562,123,3836,3959
-698,11/28/2012,4,1,11,3,1,0.296667,0.289762,0.50625,0.210821,198,5062,5260
-699,11/29/2012,4,1,11,4,1,0.28087,0.298422,0.555652,0.115522,243,5080,5323
-700,11/30/2012,4,1,11,5,1,0.298333,0.323867,0.649583,0.0584708,362,5306,5668
-701,12/1/2012,4,1,12,6,2,0.298333,0.316904,0.806667,0.0597042,951,4240,5191
-702,12/2/2012,4,1,12,0,2,0.3475,0.359208,0.823333,0.124379,892,3757,4649
-703,12/3/2012,4,1,12,1,1,0.4525,0.455796,0.7675,0.0827208,555,5679,6234
-704,12/4/2012,4,1,12,2,1,0.475833,0.469054,0.73375,0.174129,551,6055,6606
-705,12/5/2012,4,1,12,3,1,0.438333,0.428012,0.485,0.324021,331,5398,5729
-706,12/6/2012,4,1,12,4,1,0.255833,0.258204,0.50875,0.174754,340,5035,5375
-707,12/7/2012,4,1,12,5,2,0.320833,0.321958,0.764167,0.1306,349,4659,5008
-708,12/8/2012,4,1,12,6,2,0.381667,0.389508,0.91125,0.101379,1153,4429,5582
-709,12/9/2012,4,1,12,0,2,0.384167,0.390146,0.905417,0.157975,441,2787,3228
-710,12/10/2012,4,1,12,1,2,0.435833,0.435575,0.925,0.190308,329,4841,5170
-711,12/11/2012,4,1,12,2,2,0.353333,0.338363,0.596667,0.296037,282,5219,5501
-712,12/12/2012,4,1,12,3,2,0.2975,0.297338,0.538333,0.162937,310,5009,5319
-713,12/13/2012,4,1,12,4,1,0.295833,0.294188,0.485833,0.174129,425,5107,5532
-714,12/14/2012,4,1,12,5,1,0.281667,0.294192,0.642917,0.131229,429,5182,5611
-715,12/15/2012,4,1,12,6,1,0.324167,0.338383,0.650417,0.10635,767,4280,5047
-716,12/16/2012,4,1,12,0,2,0.3625,0.369938,0.83875,0.100742,538,3248,3786
-717,12/17/2012,4,1,12,1,2,0.393333,0.4015,0.907083,0.0982583,212,4373,4585
-718,12/18/2012,4,1,12,2,1,0.410833,0.409708,0.66625,0.221404,433,5124,5557
-719,12/19/2012,4,1,12,3,1,0.3325,0.342162,0.625417,0.184092,333,4934,5267
-720,12/20/2012,4,1,12,4,2,0.33,0.335217,0.667917,0.132463,314,3814,4128
-721,12/21/2012,1,1,12,5,2,0.326667,0.301767,0.556667,0.374383,221,3402,3623
-722,12/22/2012,1,1,12,6,1,0.265833,0.236113,0.44125,0.407346,205,1544,1749
-723,12/23/2012,1,1,12,0,1,0.245833,0.259471,0.515417,0.133083,408,1379,1787
-724,12/24/2012,1,1,12,1,2,0.231304,0.2589,0.791304,0.0772304,174,746,920
-725,12/25/2012,1,1,12,2,2,0.291304,0.294465,0.734783,0.168726,440,573,1013
-726,12/26/2012,1,1,12,3,3,0.243333,0.220333,0.823333,0.316546,9,432,441
-727,12/27/2012,1,1,12,4,2,0.254167,0.226642,0.652917,0.350133,247,1867,2114
-728,12/28/2012,1,1,12,5,2,0.253333,0.255046,0.59,0.155471,644,2451,3095
-729,12/29/2012,1,1,12,6,2,0.253333,0.2424,0.752917,0.124383,159,1182,1341
-730,12/30/2012,1,1,12,0,1,0.255833,0.2317,0.483333,0.350754,364,1432,1796
-731,12/31/2012,1,1,12,1,2,0.215833,0.223487,0.5775,0.154846,439,2290,2729

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

@@ -37,8 +37,7 @@
         "2. Instantiating AutoMLConfig with new task type \"forecasting\" for timeseries data training, and other timeseries related settings: for this dataset we use the basic one: \"time_column_name\" \n",
         "3. Training the Model using local compute\n",
         "4. Exploring the results\n",
-        "5. Viewing the engineered names for featurized data and featurization summary for all raw features\n",
+        "5. Testing the fitted model"
-        "6. Testing the fitted model"
       ]
     },
     {
@@ -123,22 +122,29 @@
         "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": [
-        "# let's take note of what columns means what in the data\n",
+        "train = data[data['timeStamp'] < '2017-02-01']\n",
-        "time_column_name = 'timeStamp'\n",
+        "test = data[data['timeStamp'] >= '2017-02-01']\n"
-        "target_column_name = 'demand'"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Split the data into train and test sets\n"
+        "### Prepare the test data, we will feed X_test to the fitted model and get prediction"
       ]
     },
     {
@@ -147,10 +153,33 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "X_train = data[data[time_column_name] < '2017-02-01']\n",
+        "y_test = test.pop('demand').values\n",
-        "X_test = data[data[time_column_name] >= '2017-02-01']\n",
+        "X_test = test"
-        "y_train = X_train.pop(target_column_name).values\n",
+      ]
-        "y_test = X_test.pop(target_column_name).values"
+    },
+    {
+      "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)"
       ]
     },
     {
@@ -169,7 +198,8 @@
         "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
         "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
         "|**y**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
-        "|**n_cross_validations**|Number of cross validation splits.|\n",
+        "|**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, ], targets values.|\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. "
       ]
     },
@@ -179,8 +209,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "time_column_name = 'timeStamp'\n",
         "automl_settings = {\n",
-        "    \"time_column_name\": time_column_name    \n",
+        "    \"time_column_name\": time_column_name,\n",
         "}\n",
         "\n",
         "\n",
@@ -191,7 +222,8 @@
         "                             iteration_timeout_minutes = 5,\n",
         "                             X = X_train,\n",
         "                             y = y_train,\n",
-        "                             n_cross_validations = 3,\n",
+        "                             X_valid = X_valid,\n",
+        "                             y_valid = y_valid,\n",
         "                             path=project_folder,\n",
         "                             verbosity = logging.INFO,\n",
         "                            **automl_settings)"
@@ -201,8 +233,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Submitting the configuration will start a new run in this experiment. For local runs, the execution is synchronous. Depending on the data and number of iterations, this can run for a while. Parameters controlling concurrency may speed up the process, depending on your hardware.\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",
-        "\n",
         "You will see the currently running iterations printing to the console."
       ]
     },
@@ -242,34 +273,13 @@
         "fitted_model.steps"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### View the engineered names for featurized data\n",
-        "Below we display the engineered feature names generated for the featurized data using the time-series featurization."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "fitted_model.named_steps['timeseriestransformer'].get_engineered_feature_names()"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
         "### Test the Best Fitted Model\n",
         "\n",
-        "For forecasting, we will use the `forecast` function instead of the `predict` function. There are two reasons for this.\n",
+        "Predict on training and test set, and calculate residual values."
-        "\n",
-        "We need to pass the recent values of the target variable `y`, whereas the scikit-compatible `predict` function only takes the non-target variables `X`. In our case, the test data immediately follows the training data, and we fill the `y` variable with `NaN`. The `NaN` serves as a question mark for the forecaster to fill with the actuals. Using the forecast function will produce forecasts using the shortest possible forecast horizon. The last time at which a definite (non-NaN) value is seen is the _forecast origin_ - the last time when the value of the target is known. \n",
-        "\n",
-        "Using the `predict` method would result in getting predictions for EVERY horizon the forecaster can predict at. This is useful when training and evaluating the performance of the forecaster at various horizons, but the level of detail is excessive for normal use."
       ]
     },
     {
@@ -278,64 +288,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# Replace ALL values in y_pred by NaN. \n",
+        "y_pred = fitted_model.predict(X_test)\n",
-        "# The forecast origin will be at the beginning of the first forecast period\n",
+        "y_pred"
-        "# (which is the same time as the end of the last training period).\n",
-        "y_query = y_test.copy().astype(np.float)\n",
-        "y_query.fill(np.nan)\n",
-        "# The featurized data, aligned to y, will also be returned.\n",
-        "# This contains the assumptions that were made in the forecast\n",
-        "# and helps align the forecast to the original data\n",
-        "y_fcst, X_trans = fitted_model.forecast(X_test, y_query)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# limit the evaluation to data where y_test has actuals\n",
-        "def align_outputs(y_predicted, X_trans, X_test, y_test, predicted_column_name = 'predicted'):\n",
-        "    \"\"\"\n",
-        "    Demonstrates how to get the output aligned to the inputs\n",
-        "    using pandas indexes. Helps understand what happened if\n",
-        "    the output's shape differs from the input shape, or if\n",
-        "    the data got re-sorted by time and grain during forecasting.\n",
-        "    \n",
-        "    Typical causes of misalignment are:\n",
-        "    * we predicted some periods that were missing in actuals -> drop from eval\n",
-        "    * model was asked to predict past max_horizon -> increase max horizon\n",
-        "    * data at start of X_test was needed for lags -> provide previous periods\n",
-        "    \"\"\"\n",
-        "    df_fcst = pd.DataFrame({predicted_column_name : y_predicted})\n",
-        "    # y and X outputs are aligned by forecast() function contract\n",
-        "    df_fcst.index = X_trans.index\n",
-        "    \n",
-        "    # align original X_test to y_test    \n",
-        "    X_test_full = X_test.copy()\n",
-        "    X_test_full[target_column_name] = y_test\n",
-        "\n",
-        "    # X_test_full's does not include origin, so reset for merge\n",
-        "    df_fcst.reset_index(inplace=True)\n",
-        "    X_test_full = X_test_full.reset_index().drop(columns='index')\n",
-        "    together = df_fcst.merge(X_test_full, how='right')\n",
-        "    \n",
-        "    # drop rows where prediction or actuals are nan \n",
-        "    # happens because of missing actuals \n",
-        "    # or at edges of time due to lags/rolling windows\n",
-        "    clean = together[together[[target_column_name, predicted_column_name]].notnull().all(axis=1)]\n",
-        "    return(clean)\n",
-        "\n",
-        "df_all = align_outputs(y_fcst, X_trans, X_test, y_test)\n",
-        "df_all.head()"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Looking at `X_trans` is also useful to see what featurization happened to the data."
+        "### Use the Check Data Function to remove the nan values from y_test to avoid error when calculate metrics "
       ]
     },
     {
@@ -344,14 +305,29 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "X_trans"
+        "if len(y_test) != len(y_pred):\n",
+        "    raise ValueError(\n",
+        "        'the true values and prediction values do not have equal length.')\n",
+        "elif len(y_test) == 0:\n",
+        "    raise ValueError(\n",
+        "        'y_true and y_pred are empty.')\n",
+        "\n",
+        "# if there is any non-numeric element in the y_true or y_pred,\n",
+        "# the ValueError exception will be thrown.\n",
+        "y_test_f = np.array(y_test).astype(float)\n",
+        "y_pred_f = np.array(y_pred).astype(float)\n",
+        "\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_test = y_test_f[~(np.isnan(y_test_f) | np.isnan(y_pred_f))]\n",
+        "y_pred = y_pred_f[~(np.isnan(y_test_f) | np.isnan(y_pred_f))]"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Calculate accuracy metrics\n"
+        "### Calculate metrics for the prediction\n"
       ]
     },
     {
@@ -360,180 +336,26 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "def MAPE(actual, pred):\n",
+        "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % np.sqrt(mean_squared_error(y_test, y_pred)))\n",
-        "    \"\"\"\n",
+        "# Explained variance score: 1 is perfect prediction\n",
-        "    Calculate mean absolute percentage error.\n",
+        "print('mean_absolute_error score: %.2f' % mean_absolute_error(y_test, y_pred))\n",
-        "    Remove NA and values where actual is close to zero\n",
+        "print('R2 score: %.2f' % r2_score(y_test, y_pred))\n",
-        "    \"\"\"\n",
+        "\n",
-        "    not_na = ~(np.isnan(actual) | np.isnan(pred))\n",
+        "\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)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(\"Simple forecasting model\")\n",
-        "rmse = np.sqrt(mean_squared_error(df_all[target_column_name], df_all['predicted']))\n",
-        "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % rmse)\n",
-        "mae = mean_absolute_error(df_all[target_column_name], df_all['predicted'])\n",
-        "print('mean_absolute_error score: %.2f' % mae)\n",
-        "print('MAPE: %.2f' % MAPE(df_all[target_column_name], df_all['predicted']))\n",
         "\n",
         "# Plot outputs\n",
         "%matplotlib notebook\n",
-        "test_pred = plt.scatter(df_all[target_column_name], df_all['predicted'], color='b')\n",
+        "test_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()"
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The distribution looks a little heavy tailed: we underestimate the excursions of the extremes. A normal-quantile transform of the target might help, but let's first try using some past data with the lags and rolling window transforms.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Using lags and rolling window features to improve the forecast"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, grain and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data.\n",
-        "\n",
-        "Now that we configured target lags, that is the previous values of the target variables, and the prediction is no longer horizon-less. We therefore must specify the `max_horizon` that the model will learn to forecast. The `target_lags` keyword specifies how far back we will construct the lags of the target variable, and the `target_rolling_window_size` specifies the size of the rolling window over which we will generate the `max`, `min` and `sum` features."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "automl_settings_lags = {\n",
-        "    'time_column_name': time_column_name,\n",
-        "    'target_lags': 1,\n",
-        "    'target_rolling_window_size': 5,\n",
-        "    # you MUST set the max_horizon when using lags and rolling windows\n",
-        "    # it is optional when looking-back features are not used \n",
-        "    'max_horizon': len(y_test), # only one grain\n",
-        "}\n",
-        "\n",
-        "\n",
-        "automl_config_lags = AutoMLConfig(task = 'forecasting',\n",
-        "                             debug_log = 'automl_nyc_energy_errors.log',\n",
-        "                             primary_metric='normalized_root_mean_squared_error',\n",
-        "                             iterations = 10,\n",
-        "                             iteration_timeout_minutes = 5,\n",
-        "                             X = X_train,\n",
-        "                             y = y_train,\n",
-        "                             n_cross_validations = 3,\n",
-        "                             path=project_folder,\n",
-        "                             verbosity = logging.INFO,\n",
-        "                            **automl_settings_lags)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_run_lags = experiment.submit(automl_config_lags, show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "best_run_lags, fitted_model_lags = local_run_lags.get_output()\n",
-        "y_fcst_lags, X_trans_lags = fitted_model_lags.forecast(X_test, y_query)\n",
-        "df_lags = align_outputs(y_fcst_lags, X_trans_lags, X_test, y_test)\n",
-        "df_lags.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "X_trans_lags"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(\"Forecasting model with lags\")\n",
-        "rmse = np.sqrt(mean_squared_error(df_lags[target_column_name], df_lags['predicted']))\n",
-        "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % rmse)\n",
-        "mae = mean_absolute_error(df_lags[target_column_name], df_lags['predicted'])\n",
-        "print('mean_absolute_error score: %.2f' % mae)\n",
-        "print('MAPE: %.2f' % MAPE(df_lags[target_column_name], df_lags['predicted']))\n",
-        "\n",
-        "# Plot outputs\n",
-        "%matplotlib notebook\n",
-        "test_pred = plt.scatter(df_lags[target_column_name], df_lags['predicted'], color='b')\n",
-        "test_test = plt.scatter(y_test, y_test, color='g')\n",
-        "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### What features matter for the forecast?"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.automl.automlexplainer import explain_model\n",
-        "\n",
-        "# feature names are everything in the transformed data except the target\n",
-        "features = X_trans.columns[:-1]\n",
-        "expl = explain_model(fitted_model, X_train, X_test, features = features, best_run=best_run_lags, y_train = y_train)\n",
-        "# unpack the tuple\n",
-        "shap_values, expected_values, feat_overall_imp, feat_names, per_class_summary, per_class_imp = expl\n",
-        "best_run_lags"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Please go to the Azure Portal's best run to see the top features chart.\n",
-        "\n",
-        "The informative features make all sorts of intuitive sense. Temperature is a strong driver of heating and cooling demand in NYC. Apart from that, the daily life cycle, expressed by `hour`, and the weekly cycle, expressed by `wday` drives people's energy use habits."
-      ]
     }
   ],
   "metadata": {
     "authors": [
       {
-        "name": "xiaga, tosingli"
+        "name": "xiaga"
       }
     ],
     "kernelspec": {
@@ -551,7 +373,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.6.8"
     }
   },
   "nbformat": 4,

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

@@ -20,9 +20,7 @@
         "1. [Introduction](#Introduction)\n",
         "1. [Setup](#Setup)\n",
         "1. [Data](#Data)\n",
-        "1. [Train](#Train)\n",
+        "1. [Train](#Train)"
-        "1. [Predict](#Predict)\n",
-        "1. [Operationalize](#Operationalize)"
       ]
     },
     {
@@ -87,9 +85,9 @@
         "ws = Workspace.from_config()\n",
         "\n",
         "# choose a name for the run history container in the workspace\n",
-        "experiment_name = 'automl-ojforecasting'\n",
+        "experiment_name = 'automl-ojsalesforecasting'\n",
         "# project folder\n",
-        "project_folder = './sample_projects/automl-local-ojforecasting'\n",
+        "project_folder = './sample_projects/automl-local-ojsalesforecasting'\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
@@ -262,12 +260,12 @@
         "    'time_column_name': time_column_name,\n",
         "    'grain_column_names': grain_column_names,\n",
         "    'drop_column_names': ['logQuantity'],\n",
-        "    'max_horizon': n_test_periods # optional\n",
+        "    'max_horizon': n_test_periods\n",
         "}\n",
         "\n",
         "automl_config = AutoMLConfig(task='forecasting',\n",
         "                             debug_log='automl_oj_sales_errors.log',\n",
-        "                             primary_metric='normalized_mean_absolute_error',\n",
+        "                             primary_metric='normalized_root_mean_squared_error',\n",
         "                             iterations=10,\n",
         "                             X=X_train,\n",
         "                             y=y_train,\n",
@@ -295,6 +293,15 @@
         "local_run = experiment.submit(automl_config, show_output=True)"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "local_run"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -317,7 +324,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Predict\n",
+        "### 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:"
       ]
     },
@@ -345,7 +352,7 @@
       "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",
-        "We will first create a query `y_query`, which is aligned index-for-index to `X_test`. This is a vector of target values where each `NaN` serves the function of the question mark to be replaced by forecast. Passing definite values in the `y` argument allows the `forecast` function to make predictions on data that does not immediately follow the train data which contains `y`. In each grain, the last time point where the model sees a definite value of `y` is that grain's _forecast origin_."
+        "The target predictions can be retrieved by calling the `predict` method on the best model:"
       ]
     },
     {
@@ -354,76 +361,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# Replace ALL values in y_pred by NaN.\n",
+        "y_pred = fitted_pipeline.predict(X_test)"
-        "# The forecast origin will be at the beginning of the first forecast period.\n",
-        "# (Which is the same time as the end of the last training period.)\n",
-        "y_query = y_test.copy().astype(np.float)\n",
-        "y_query.fill(np.nan)\n",
-        "# The featurized data, aligned to y, will also be returned.\n",
-        "# This contains the assumptions that were made in the forecast\n",
-        "# and helps align the forecast to the original data\n",
-        "y_pred, X_trans = fitted_pipeline.forecast(X_test, y_query)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "If you are used to scikit pipelines, perhaps you expected `predict(X_test)`. However, forecasting requires a more general interface that also supplies the past target `y` values. Please use `forecast(X,y)` as `predict(X)` is reserved for internal purposes on forecasting models.\n",
+        "### Calculate evaluation metrics for the prediction\n",
-        "\n",
+        "To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE)."
-        "The [energy demand forecasting notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand) demonstrates the use of the forecast function in more detail in the context of using lags and rolling window features. "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Evaluate\n",
-        "\n",
-        "To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE). \n",
-        "\n",
-        "It is a good practice to always align the output explicitly to the input, as the count and order of the rows may have changed during transformations that span multiple rows."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "def align_outputs(y_predicted, X_trans, X_test, y_test, predicted_column_name = 'predicted'):\n",
-        "    \"\"\"\n",
-        "    Demonstrates how to get the output aligned to the inputs\n",
-        "    using pandas indexes. Helps understand what happened if\n",
-        "    the output's shape differs from the input shape, or if\n",
-        "    the data got re-sorted by time and grain during forecasting.\n",
-        "    \n",
-        "    Typical causes of misalignment are:\n",
-        "    * we predicted some periods that were missing in actuals -> drop from eval\n",
-        "    * model was asked to predict past max_horizon -> increase max horizon\n",
-        "    * data at start of X_test was needed for lags -> provide previous periods in y\n",
-        "    \"\"\"\n",
-        "    \n",
-        "    df_fcst = pd.DataFrame({predicted_column_name : y_predicted})\n",
-        "    # y and X outputs are aligned by forecast() function contract\n",
-        "    df_fcst.index = X_trans.index\n",
-        "    \n",
-        "    # align original X_test to y_test    \n",
-        "    X_test_full = X_test.copy()\n",
-        "    X_test_full[target_column_name] = y_test\n",
-        "\n",
-        "    # X_test_full's index does not include origin, so reset for merge\n",
-        "    df_fcst.reset_index(inplace=True)\n",
-        "    X_test_full = X_test_full.reset_index().drop(columns='index')\n",
-        "    together = df_fcst.merge(X_test_full, how='right')\n",
-        "    \n",
-        "    # drop rows where prediction or actuals are nan \n",
-        "    # happens because of missing actuals \n",
-        "    # or at edges of time due to lags/rolling windows\n",
-        "    clean = together[together[[target_column_name, predicted_column_name]].notnull().all(axis=1)]\n",
-        "    return(clean)\n",
-        "\n",
-        "df_all = align_outputs(y_pred, X_trans, X_test, y_test)"
       ]
     },
     {
@@ -442,392 +388,18 @@
         "    actual_safe = actual[not_na & not_zero]\n",
         "    pred_safe = pred[not_na & not_zero]\n",
         "    APE = 100*np.abs((actual_safe - pred_safe)/actual_safe)\n",
-        "    return np.mean(APE)"
+        "    return np.mean(APE)\n",
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(\"Simple forecasting model\")\n",
-        "rmse = np.sqrt(mean_squared_error(df_all[target_column_name], df_all['predicted']))\n",
-        "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % rmse)\n",
-        "mae = mean_absolute_error(df_all[target_column_name], df_all['predicted'])\n",
-        "print('mean_absolute_error score: %.2f' % mae)\n",
-        "print('MAPE: %.2f' % MAPE(df_all[target_column_name], df_all['predicted']))\n",
         "\n",
-        "# Plot outputs\n",
+        "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % np.sqrt(mean_squared_error(y_test, y_pred)))\n",
-        "import matplotlib.pyplot as plt\n",
+        "print('mean_absolute_error score: %.2f' % mean_absolute_error(y_test, y_pred))\n",
-        "\n",
+        "print('MAPE: %.2f' % MAPE(y_test, y_pred))"
-        "%matplotlib notebook\n",
-        "test_pred = plt.scatter(df_all[target_column_name], df_all['predicted'], color='b')\n",
-        "test_test = plt.scatter(y_test, y_test, color='g')\n",
-        "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Operationalize"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "_Operationalization_ means getting the model into the cloud so that other can run it after you close the notebook. We will create a docker running on Azure Container Instances with the model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "description = 'AutoML OJ forecaster'\n",
-        "tags = None\n",
-        "model = local_run.register_model(description = description, tags = tags)\n",
-        "\n",
-        "print(local_run.model_id)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Develop the scoring script\n",
-        "\n",
-        "Serializing and deserializing complex data frames may be tricky. We first develop the `run()` function of the scoring script locally, then write it into a scoring script. It is much easier to debug any quirks of the scoring function without crossing two compute environments. For this exercise, we handle a common quirk of how pandas dataframes serialize time stamp values."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# this is where we test the run function of the scoring script interactively\n",
-        "# before putting it in the scoring script\n",
-        "\n",
-        "timestamp_columns = ['WeekStarting']\n",
-        "\n",
-        "def run(rawdata, test_model = None):\n",
-        "    \"\"\"\n",
-        "    Intended to process 'rawdata' string produced by\n",
-        "    \n",
-        "    {'X': X_test.to_json(), y' : y_test.to_json()}\n",
-        "    \n",
-        "    Don't convert the X payload to numpy.array, use it as pandas.DataFrame\n",
-        "    \"\"\"\n",
-        "    try:\n",
-        "        # unpack the data frame with timestamp        \n",
-        "        rawobj = json.loads(rawdata)                    # rawobj is now a dict of strings        \n",
-        "        X_pred = pd.read_json(rawobj['X'], convert_dates=False)   # load the pandas DF from a json string\n",
-        "        for col in timestamp_columns:                             # fix timestamps\n",
-        "            X_pred[col] = pd.to_datetime(X_pred[col], unit='ms') \n",
-        "        \n",
-        "        y_pred = np.array(rawobj['y'])                    # reconstitute numpy array from serialized list\n",
-        "        \n",
-        "        if test_model is None:\n",
-        "            result = model.forecast(X_pred, y_pred)       # use the global model from init function\n",
-        "        else:\n",
-        "            result = test_model.forecast(X_pred, y_pred)  # use the model on which we are testing\n",
-        "        \n",
-        "    except Exception as e:\n",
-        "        result = str(e)\n",
-        "        return json.dumps({\"error\": result})\n",
-        "    \n",
-        "    forecast_as_list = result[0].tolist()\n",
-        "    index_as_df = result[1].index.to_frame().reset_index(drop=True)\n",
-        "    \n",
-        "    return json.dumps({\"forecast\": forecast_as_list,   # return the minimum over the wire: \n",
-        "                       \"index\": index_as_df.to_json()  # no forecast and its featurized values\n",
-        "                      })"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# test the run function here before putting in the scoring script\n",
-        "import json\n",
-        "\n",
-        "test_sample = json.dumps({'X': X_test.to_json(), 'y' : y_query.tolist()})\n",
-        "response = run(test_sample, fitted_pipeline)\n",
-        "\n",
-        "# unpack the response, dealing with the timestamp serialization again\n",
-        "res_dict = json.loads(response)\n",
-        "y_fcst_all = pd.read_json(res_dict['index'])\n",
-        "y_fcst_all[time_column_name] = pd.to_datetime(y_fcst_all[time_column_name], unit = 'ms')\n",
-        "y_fcst_all['forecast'] = res_dict['forecast']\n",
-        "y_fcst_all.head()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now that the function works locally in the notebook, let's write it down into the scoring script. The scoring script is authored by the data scientist. Adjust it to taste, adding inputs, outputs and processing as needed."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile score_fcast.py\n",
-        "import pickle\n",
-        "import json\n",
-        "import numpy as np\n",
-        "import pandas as pd\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",
-        "timestamp_columns = ['WeekStarting']\n",
-        "\n",
-        "def run(rawdata, test_model = None):\n",
-        "    \"\"\"\n",
-        "    Intended to process 'rawdata' string produced by\n",
-        "    \n",
-        "    {'X': X_test.to_json(), y' : y_test.to_json()}\n",
-        "    \n",
-        "    Don't convert the X payload to numpy.array, use it as pandas.DataFrame\n",
-        "    \"\"\"\n",
-        "    try:\n",
-        "        # unpack the data frame with timestamp        \n",
-        "        rawobj = json.loads(rawdata)                    # rawobj is now a dict of strings        \n",
-        "        X_pred = pd.read_json(rawobj['X'], convert_dates=False)   # load the pandas DF from a json string\n",
-        "        for col in timestamp_columns:                             # fix timestamps\n",
-        "            X_pred[col] = pd.to_datetime(X_pred[col], unit='ms') \n",
-        "        \n",
-        "        y_pred = np.array(rawobj['y'])                    # reconstitute numpy array from serialized list\n",
-        "        \n",
-        "        if test_model is None:\n",
-        "            result = model.forecast(X_pred, y_pred)       # use the global model from init function\n",
-        "        else:\n",
-        "            result = test_model.forecast(X_pred, y_pred)  # use the model on which we are testing\n",
-        "        \n",
-        "    except Exception as e:\n",
-        "        result = str(e)\n",
-        "        return json.dumps({\"error\": result})\n",
-        "    \n",
-        "    # prepare to send over wire as json\n",
-        "    forecast_as_list = result[0].tolist()\n",
-        "    index_as_df = result[1].index.to_frame().reset_index(drop=True)\n",
-        "    \n",
-        "    return json.dumps({\"forecast\": forecast_as_list,   # return the minimum over the wire: \n",
-        "                       \"index\": index_as_df.to_json()  # no forecast and its featurized values\n",
-        "                      })"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# get the model\n",
-        "from azureml.train.automl.run import AutoMLRun\n",
-        "\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "ml_run = AutoMLRun(experiment = experiment, run_id = local_run.id)\n",
-        "best_iteration = int(str.split(best_run.id,'_')[-1])      # the iteration number is a postfix of the run ID."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# get the best model's dependencies and write them into this file\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "\n",
-        "conda_env_file_name = 'fcast_env.yml'\n",
-        "\n",
-        "dependencies = ml_run.get_run_sdk_dependencies(iteration = best_iteration)\n",
-        "for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
-        "    print('{}\\t{}'.format(p, dependencies[p]))\n",
-        "\n",
-        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-sdk[automl]'])\n",
-        "\n",
-        "myenv.save_to_file('.', conda_env_file_name)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# this is the script file name we wrote a few cells above\n",
-        "script_file_name = 'score_fcast.py'\n",
-        "\n",
-        "# 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",
-        "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 = {'type': \"automl-forecasting\"},\n",
-        "                                 description = \"Image for automl forecasting sample\")\n",
-        "\n",
-        "image = Image.create(name = \"automl-fcast-image\",\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 = 2, \n",
-        "                                               tags = {'type': \"automl-forecasting\"},\n",
-        "                                               description = \"Automl forecasting sample service\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import Webservice\n",
-        "\n",
-        "aci_service_name = 'automl-forecast-01'\n",
-        "print(aci_service_name)\n",
-        "\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": [
-        "### Call the service"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# we send the data to the service serialized into a json string\n",
-        "test_sample = json.dumps({'X':X_test.to_json(), 'y' : y_query.tolist()})\n",
-        "response = aci_service.run(input_data = test_sample)\n",
-        "\n",
-        "# translate from networkese to datascientese\n",
-        "try: \n",
-        "    res_dict = json.loads(response)\n",
-        "    y_fcst_all = pd.read_json(res_dict['index'])\n",
-        "    y_fcst_all[time_column_name] = pd.to_datetime(y_fcst_all[time_column_name], unit = 'ms')\n",
-        "    y_fcst_all['forecast'] = res_dict['forecast']    \n",
-        "except:\n",
-        "    print(res_dict)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "y_fcst_all.head()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Delete the web service if desired"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "serv = Webservice(ws, 'automl-forecast-01')\n",
-        "# serv.delete()     # don't do it accidentally"
       ]
     }
   ],
   "metadata": {
     "authors": [
       {
-        "name": "erwright, tosingli"
+        "name": "erwright"
       }
     ],
     "kernelspec": {
@@ -845,7 +417,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.6.8"
     }
   },
   "nbformat": 4,

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

@@ -37,9 +37,8 @@
         "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.\n",
+        "4. Train the model.\n",
-        "4. Explore the results.\n",
+        "5. Explore the results.\n",
-        "5. Viewing the engineered names for featurized data and featurization summary for all raw features.\n",
         "6. Test the best fitted model.\n",
         "\n",
         "In addition this notebook showcases the following features\n",
@@ -155,6 +154,7 @@
         "|**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",
@@ -174,6 +174,7 @@
         "                             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",
@@ -317,45 +318,6 @@
         "# best_run, fitted_model = local_run.get_output(iteration = iteration)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### View the engineered names for featurized data\n",
-        "Below we display the engineered feature names generated for the featurized data using the preprocessing featurization."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "fitted_model.named_steps['datatransformer'].get_engineered_feature_names()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### View the featurization summary\n",
-        "Below we display the featurization that was performed on different raw features in the user data. For each raw feature in the user data, the following information is displayed:-\n",
-        "- Raw feature name\n",
-        "- Number of engineered features formed out of this raw feature\n",
-        "- Type detected\n",
-        "- If feature was dropped\n",
-        "- List of feature transformations for the raw feature"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "fitted_model.named_steps['datatransformer'].get_featurization_summary()"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},

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

@@ -254,9 +254,7 @@
         "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\n",
+        "6.\tper_class_imp: The feature names sorted in the same order as in per_class_summary. Only available for the classification case"
-        "\n",
-        "Note:- The **retrieve_model_explanation()** API only works in case AutoML has been configured with **'model_explainability'** flag set to **True**. "
       ]
     },
     {
@@ -307,7 +305,7 @@
         "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, features=features)"
+        "    explain_model(fitted_model, X_train, X_test)"
       ]
     },
     {

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

@@ -40,8 +40,7 @@
         "3. Configure AutoML using `AutoMLConfig`.\n",
         "4. Train the model using the DSVM.\n",
         "5. Explore the results.\n",
-        "6. Viewing the engineered names for featurized data and featurization summary for all raw features.\n",
+        "6. Test the best fitted model.\n",
-        "7. Test the best fitted model.\n",
         "\n",
         "In addition this notebook showcases the following features\n",
         "- **Parallel** executions for iterations\n",
@@ -111,7 +110,7 @@
       "source": [
         "### Attach a Remote Linux DSVM\n",
         "To use a remote Docker compute target:\n",
-        "1. Create a Linux DSVM in Azure, following these [instructions](https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-ubuntu-intro). 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",
+        "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://docs.microsoft.com/en-us/azure/virtual-machines/troubleshooting/detailed-troubleshoot-ssh-connection) on changing SSH ports for security reasons."
@@ -161,7 +160,6 @@
       "source": [
         "from azureml.core.runconfig import RunConfiguration\n",
         "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "import pkg_resources\n",
         "\n",
         "# create a new RunConfig object\n",
         "conda_run_config = RunConfiguration(framework=\"python\")\n",
@@ -169,9 +167,7 @@
         "# Set compute target to the Linux DSVM\n",
         "conda_run_config.target = dsvm_compute\n",
         "\n",
-        "pandas_dependency = 'pandas==' + pkg_resources.get_distribution(\"pandas\").version\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy','py-xgboost<=0.80'])\n",
-        "\n",
-        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy','py-xgboost<=0.80',pandas_dependency])\n",
         "conda_run_config.environment.python.conda_dependencies = cd"
       ]
     },
@@ -411,45 +407,6 @@
         "print(fitted_model)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### View the engineered names for featurized data\n",
-        "Below we display the engineered feature names generated for the featurized data using the preprocessing featurization."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "fitted_model.named_steps['datatransformer'].get_engineered_feature_names()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### View the featurization summary\n",
-        "Below we display the featurization that was performed on different raw features in the user data. For each raw feature in the user data, the following information is displayed:-\n",
-        "- Raw feature name\n",
-        "- Number of engineered features formed out of this raw feature\n",
-        "- Type detected\n",
-        "- If feature was dropped\n",
-        "- List of feature transformations for the raw feature"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "fitted_model.named_steps['datatransformer'].get_featurization_summary()"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},

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

@@ -245,7 +245,6 @@
       "source": [
         "from azureml.core.runconfig import RunConfiguration\n",
         "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "import pkg_resources\n",
         "\n",
         "# create a new RunConfig object\n",
         "conda_run_config = RunConfiguration(framework=\"python\")\n",
@@ -255,9 +254,7 @@
         "# set the data reference of the run coonfiguration\n",
         "conda_run_config.data_references = {ds.name: dr}\n",
         "\n",
-        "pandas_dependency = 'pandas==' + pkg_resources.get_distribution(\"pandas\").version\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy','py-xgboost<=0.80'])\n",
-        "\n",
-        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy','py-xgboost<=0.80',pandas_dependency])\n",
         "conda_run_config.environment.python.conda_dependencies = cd"
       ]
     },

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

@@ -23,8 +23,7 @@
         "3. Configure Automated ML using `AutoMLConfig`.\n",
         "4. Train the model using Azure Databricks.\n",
         "5. Explore the results.\n",
-        "6. Viewing the engineered names for featurized data and featurization summary for all raw features.\n",
+        "6. Test the best fitted model.\n",
-        "7. Test the best fitted model.\n",
         "\n",
         "Before running this notebook, please follow the <a href=\"https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks\" target=\"_blank\">readme for using Automated ML on Azure Databricks</a> for installing necessary libraries to your cluster."
       ]
@@ -557,45 +556,6 @@
         "print(fitted_model)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### View the engineered names for featurized data\n",
-        "Below we display the engineered feature names generated for the featurized data using the preprocessing featurization."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "fitted_model.named_steps['datatransformer'].get_engineered_feature_names()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### View the featurization summary\n",
-        "Below we display the featurization that was performed on different raw features in the user data. For each raw feature in the user data, the following information is displayed:-\n",
-        "- Raw feature name\n",
-        "- Number of engineered features formed out of this raw feature\n",
-        "- Type detected\n",
-        "- If feature was dropped\n",
-        "- List of feature transformations for the raw feature"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "fitted_model.named_steps['datatransformer'].get_featurization_summary()"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},

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

@@ -207,7 +207,6 @@
         "import os\n",
         "import random\n",
         "import time\n",
-        "import json\n",
         "\n",
         "from matplotlib import pyplot as plt\n",
         "from matplotlib.pyplot import imshow\n",
@@ -296,7 +295,7 @@
         "    datastore_name = datastore_name, \n",
         "    container_name = container_name, \n",
         "    account_name = account_name,\n",
-        "     overwrite = True\n",
+        "    overwrite = True\n",
         ")"
       ]
     },
@@ -428,7 +427,7 @@
         "                             debug_log = 'automl_errors.log',\n",
         "                             primary_metric = 'AUC_weighted',\n",
         "                             iteration_timeout_minutes = 10,\n",
-        "                             iterations = 5,\n",
+        "                             iterations = 30,\n",
         "                             preprocess = True,\n",
         "                             n_cross_validations = 10,\n",
         "                             max_concurrent_iterations = 2, #change it based on number of worker nodes\n",
@@ -592,21 +591,22 @@
         "%%writefile score.py\n",
         "import pickle\n",
         "import json\n",
-        "import numpy as np\n",
+        "import numpy\n",
         "import azureml.train.automl\n",
         "from sklearn.externals import joblib\n",
         "from azureml.core.model import Model\n",
-        "import pandas as pd\n",
+        "\n",
         "\n",
         "def init():\n",
         "    global model\n",
-        "    model_path = Model.get_model_path(model_name = '<<model_id>>') # this name is model.id of model that we want to deploy\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(raw_data):\n",
+        "def run(rawdata):\n",
         "    try:\n",
-        "        data = (pd.DataFrame(np.array(json.loads(raw_data)['data']), columns=[str(i) for i in range(0,64)]))\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",
@@ -614,22 +614,6 @@
         "    return json.dumps({\"result\":result.tolist()})"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#Replace <<model_id>>\n",
-        "content = \"\"\n",
-        "with open(\"score.py\", \"r\") as fo:\n",
-        "    content = fo.read()\n",
-        "\n",
-        "new_content = content.replace(\"<<model_id>>\", local_run.model_id)\n",
-        "with open(\"score.py\", \"w\") as fw:\n",
-        "    fw.write(new_content)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -688,19 +672,16 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "\n",
         "# this will take 10-15 minutes to finish\n",
         "\n",
-        "import uuid\n",
+        "service_name = \"<<servicename>>\"\n",
-        "from azureml.core.image import ContainerImage\n",
-        "\n",
-        "guid = str(uuid.uuid4()).split(\"-\")[0]\n",
-        "service_name = \"myservice-{}\".format(guid)\n",
-        "print(\"Creating service with name: {}\".format(service_name))\n",
         "runtime = \"spark-py\" \n",
         "driver_file = \"score.py\"\n",
         "my_conda_file = \"mydeployenv.yml\"\n",
         "\n",
         "# image creation\n",
+        "from azureml.core.image import ContainerImage\n",
         "myimage_config = ContainerImage.image_configuration(execution_script = driver_file, \n",
         "                                    runtime = runtime, \n",
         "                                    conda_file = 'mydeployenv.yml')\n",
@@ -763,39 +744,18 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "import json\n",
         "# Randomly select digits and test.\n",
         "for index in np.random.choice(len(y_test), 2, replace = False):\n",
         "    print(index)\n",
-        "    test_sample = json.dumps({'data':X_test[index:index + 1].values.tolist()})\n",
+        "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
-        "    predicted = myservice.run(input_data = test_sample)\n",
         "    label = y_test.values[index]\n",
-        "    predictedDict = json.loads(predicted)\n",
+        "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
-        "    title = \"Label value = %d  Predicted value = %s \" % ( label,predictedDict['result'][0])    \n",
         "    fig = plt.figure(3, figsize = (5,5))\n",
         "    ax1 = fig.add_axes((0,0,.8,.8))\n",
         "    ax1.set_title(title)\n",
         "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
         "    display(fig)"
       ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "### Delete the service"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "myservice.delete()"
-      ]
     }
   ],
   "metadata": {

how-to-use-azureml/azure-hdi/README.md

@@ -1,55 +0,0 @@
-**Azure HDInsight**
-
-Azure HDInsight is a fully managed cloud Hadoop & Spark offering the gives
-optimized open-source analytic clusters for Spark, Hive, MapReduce, HBase,
-Storm, and Kafka. HDInsight Spark clusters provide kernels that you can use with
-the Jupyter notebook on [Apache Spark](https://spark.apache.org/) for testing
-your applications. 
-
-How Azure HDInsight works with Azure Machine Learning service
-
--   You can train a model using Spark clusters and deploy the model to ACI/AKS
-    from within Azure HDInsight.
-
--   You can also use [automated machine
-    learning](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-automated-ml) capabilities
-    integrated within Azure HDInsight.
-
-You can use Azure HDInsight as a compute target from an [Azure Machine Learning
-pipeline](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines).
-
-**Set up your HDInsight cluster**
-
-Create [HDInsight
-cluster](https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-hadoop-provision-linux-clusters)
-
-**Quick create: Basic cluster setup**
-
-This article walks you through setup in the [Azure
-portal](https://portal.azure.com/), where you can create an HDInsight cluster
-using *Quick create* or *Custom*.
-
-![hdinsight create options custom quick create](media/0a235b34c0b881117e51dc31a232dbe1.png)
-
-Follow instructions on the screen to do a basic cluster setup. Details are
-provided below for:
-
--   [Resource group
-    name](https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-hadoop-provision-linux-clusters#resource-group-name)
-
--   [Cluster types and
-    configuration](https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-hadoop-provision-linux-clusters#cluster-types)
-    (Cluster must be Spark 2.3 (HDI 3.6) or greater)
-
--   Cluster login and SSH username
-
--   [Location](https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-hadoop-provision-linux-clusters#location)
-
-**Import the sample HDI notebook in Jupyter**
-
-**Important links:**
-
-Create HDI cluster:
-<https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-hadoop-provision-linux-clusters>
-
-![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-hdi/README.png)

how-to-use-azureml/azure-hdi/automl_hdi_local_classification.ipynb

@@ -1,624 +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": [
-    "# Automated ML on Azure HDInsight\n",
-    "\n",
-    "In this example we use the scikit-learn's <a href=\"http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset\" target=\"_blank\">digit dataset</a> to showcase how you can use AutoML for a simple classification problem.\n",
-    "\n",
-    "In this notebook you will learn how to:\n",
-    "1. Create Azure Machine Learning Workspace object and initialize your notebook directory to easily reload this object from a configuration file.\n",
-    "2. Create an `Experiment` in an existing `Workspace`.\n",
-    "3. Configure Automated ML using `AutoMLConfig`.\n",
-    "4. Train the model using Azure HDInsight.\n",
-    "5. Explore the results.\n",
-    "6. Test the best fitted model.\n",
-    "\n",
-    "Before running this notebook, please follow the readme for using Automated ML on Azure HDI for installing necessary libraries to your cluster."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Check the Azure ML Core SDK Version to Validate Your Installation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import azureml.core\n",
-    "import pandas as pd\n",
-    "from azureml.core.authentication import ServicePrincipalAuthentication\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",
-    "import logging\n",
-    "\n",
-    "print(\"SDK Version:\", azureml.core.VERSION)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialize an Azure ML Workspace\n",
-    "### What is an Azure ML Workspace and Why Do I Need One?\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, operationalization, and the monitoring of operationalized models.\n",
-    "\n",
-    "\n",
-    "### What do I Need?\n",
-    "\n",
-    "To create or access an Azure ML workspace, you will need to import the Azure ML library and specify following information:\n",
-    "* A name for your workspace. You can choose one.\n",
-    "* Your subscription id. Use the `id` value from the `az account show` command output above.\n",
-    "* The resource group name. The resource group organizes Azure resources and provides a default region for the resources in the group. The resource group will be created if it doesn't exist. Resource groups can be created and viewed in the [Azure portal](https://portal.azure.com)\n",
-    "* Supported regions include `eastus2`, `eastus`,`westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import azureml.core\n",
-    "import pandas as pd\n",
-    "from azureml.core.authentication import ServicePrincipalAuthentication\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",
-    "import logging\n",
-    "\n",
-    "subscription_id = \"<Your SubscriptionId>\" #you should be owner or contributor\n",
-    "resource_group = \"<Resource group - new or existing>\" #you should be owner or contributor\n",
-    "workspace_name = \"<workspace to be created>\" #your workspace name\n",
-    "workspace_region = \"<azureregion>\" #your region\n",
-    "\n",
-    "\n",
-    "tenant_id = \"<tenant_id>\"\n",
-    "app_id = \"<app_id>\"\n",
-    "app_key = \"<app_key>\"\n",
-    "\n",
-    "auth_sp = ServicePrincipalAuthentication(tenant_id = tenant_id,\n",
-    "                                         service_principal_id = app_id,\n",
-    "                                         service_principal_password = app_key)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Creating a Workspace\n",
-    "If you already have access to an Azure ML workspace you want to use, you can skip this cell.  Otherwise, this cell will create an Azure ML workspace for you in the specified subscription, provided you have the correct permissions for the given `subscription_id`.\n",
-    "\n",
-    "This will fail when:\n",
-    "1. The workspace already exists.\n",
-    "2. You do not have permission to create a workspace in the resource group.\n",
-    "3. You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription.\n",
-    "\n",
-    "If workspace creation fails for any reason other than already existing, please work with your IT administrator to provide you with the appropriate permissions or to provision the required resources.\n",
-    "\n",
-    "**Note:** Creation of a new workspace can take several minutes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##TESTONLY\n",
-    "# Import the Workspace class and check the Azure ML SDK version.\n",
-    "from azureml.core import Workspace\n",
-    "\n",
-    "ws = Workspace.create(name = workspace_name,\n",
-    "                      subscription_id = subscription_id,\n",
-    "                      resource_group = resource_group, \n",
-    "                      location = workspace_region,\n",
-    "                      auth = auth_sp,\n",
-    "                      exist_ok=True)\n",
-    "ws.get_details()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Configuring Your Local Environment\n",
-    "You can validate that you have access to the specified workspace and write a configuration file to the default configuration location, `./aml_config/config.json`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core import Workspace\n",
-    "\n",
-    "ws = Workspace(workspace_name = workspace_name,\n",
-    "               subscription_id = subscription_id,\n",
-    "               resource_group = resource_group,\n",
-    "               auth = auth_sp)\n",
-    "\n",
-    "# Persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
-    "ws.write_config()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create a Folder to Host Sample Projects\n",
-    "Finally, create a folder where all the sample projects will be hosted."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "sample_projects_folder = './sample_projects'\n",
-    "\n",
-    "if not os.path.isdir(sample_projects_folder):\n",
-    "    os.mkdir(sample_projects_folder)\n",
-    "    \n",
-    "print('Sample projects will be created in {}.'.format(sample_projects_folder))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create an Experiment\n",
-    "\n",
-    "As part of the setup you have already created an Azure ML `Workspace` object. For Automated ML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
-   ]
-  },
-  {
-   "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",
-    "\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": [
-    "# Choose a name for the experiment and specify the project folder.\n",
-    "experiment_name = 'automl-local-classification-hdi'\n",
-    "project_folder = './sample_projects/automl-local-classification-hdi'\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": [
-    "## Registering Datastore"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Datastore is the way to save connection information to a storage service (e.g. Azure Blob, Azure Data Lake, Azure SQL) information to your workspace so you can access them without exposing credentials in your code. The first thing you will need to do is register a datastore, you can refer to our [python SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore.datastore?view=azure-ml-py) on how to register datastores. __Note: for best security practices, please do not check in code that contains registering datastores with secrets into your source control__\n",
-    "\n",
-    "The code below registers a datastore pointing to a publicly readable blob container."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core import Datastore\n",
-    "\n",
-    "datastore_name = 'demo_training'\n",
-    "container_name = 'digits' \n",
-    "account_name = 'automlpublicdatasets'\n",
-    "Datastore.register_azure_blob_container(\n",
-    "    workspace = ws, \n",
-    "    datastore_name = datastore_name, \n",
-    "    container_name = container_name, \n",
-    "    account_name = account_name,\n",
-    "     overwrite = True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Below is an example on how to register a private blob container\n",
-    "```python\n",
-    "datastore = Datastore.register_azure_blob_container(\n",
-    "    workspace = ws, \n",
-    "    datastore_name = 'example_datastore', \n",
-    "    container_name = 'example-container', \n",
-    "    account_name = 'storageaccount',\n",
-    "    account_key = 'accountkey'\n",
-    ")\n",
-    "```\n",
-    "The example below shows how  to register an Azure Data Lake store. Please make sure you have granted the necessary permissions for the service principal to access the data lake.\n",
-    "```python\n",
-    "datastore = Datastore.register_azure_data_lake(\n",
-    "    workspace = ws,\n",
-    "    datastore_name = 'example_datastore',\n",
-    "    store_name = 'adlsstore',\n",
-    "    tenant_id = 'tenant-id-of-service-principal',\n",
-    "    client_id = 'client-id-of-service-principal',\n",
-    "    client_secret = 'client-secret-of-service-principal'\n",
-    ")\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load Training Data Using DataPrep"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Automated ML takes a Dataflow as input.\n",
-    "\n",
-    "If you are familiar with Pandas and have done your data preparation work in Pandas already, you can use the `read_pandas_dataframe` method in dprep to convert the DataFrame to a Dataflow.\n",
-    "```python\n",
-    "df = pd.read_csv(...)\n",
-    "# apply some transforms\n",
-    "dprep.read_pandas_dataframe(df, temp_folder='/path/accessible/by/both/driver/and/worker')\n",
-    "```\n",
-    "\n",
-    "If you just need to ingest data without doing any preparation, you can directly use AzureML Data Prep (Data Prep) to do so. The code below demonstrates this scenario. Data Prep also has data preparation capabilities, we have many [sample notebooks](https://github.com/Microsoft/AMLDataPrepDocs) demonstrating the capabilities.\n",
-    "\n",
-    "You will get the datastore you registered previously and pass it to Data Prep for reading. The data comes from the digits dataset: `sklearn.datasets.load_digits()`. `DataPath` points to a specific location within a datastore. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import azureml.dataprep as dprep\n",
-    "from azureml.data.datapath import DataPath\n",
-    "\n",
-    "datastore = Datastore.get(workspace = ws, datastore_name = datastore_name)\n",
-    "\n",
-    "X_train = dprep.read_csv(datastore.path('X.csv'))\n",
-    "y_train = dprep.read_csv(datastore.path('y.csv')).to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Review the Data Preparation Result\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_train.get_profile()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_train.get_profile()"
-   ]
-  },
-  {
-   "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",
-    "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
-    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
-    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
-    "|**n_cross_validations**|Number of cross validation splits.|\n",
-    "|**spark_context**|Spark Context object. for HDInsight, use spark_context=sc|\n",
-    "|**max_concurrent_iterations**|Maximum number of iterations to execute in parallel. This should be <= number of worker nodes in your Azure HDInsight cluster.|\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.|\n",
-    "|**preprocess**|set this to True to enable pre-processing of data eg. string to numeric using one-hot encoding|\n",
-    "|**exit_score**|Target score for experiment. It is associated with the metric. eg. exit_score=0.995 will exit experiment after that|"
-   ]
-  },
-  {
-   "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 = 10,\n",
-    "                             iterations = 3,\n",
-    "                             preprocess = True,\n",
-    "                             n_cross_validations = 10,\n",
-    "                             max_concurrent_iterations = 2, #change it based on number of worker nodes\n",
-    "                             verbosity = logging.INFO,\n",
-    "                             spark_context=sc, #HDI /spark related\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."
-   ]
-  },
-  {
-   "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": [
-    "The following will show the child runs and waits for the parent run to complete."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Retrieve All Child Runs after the experiment is completed (in portal)\n",
-    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "children = list(local_run.get_children())\n",
-    "metricslist = {}\n",
-    "for run in children:\n",
-    "    properties = run.get_properties()\n",
-    "    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 after the above run is complete \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 after the above run is complete based on the child run\n",
-    "Show the run and the model that has the smallest `log_loss` value:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lookup_metric = \"log_loss\"\n",
-    "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
-    "print(best_run)\n",
-    "print(fitted_model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Test the Best Fitted Model\n",
-    "\n",
-    "#### Load Test Data - you can split the dataset beforehand & pass Train dataset to AutoML and use Test dataset to evaluate the best model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "blob_location = \"https://{}.blob.core.windows.net/{}\".format(account_name, container_name)\n",
-    "X_test = pd.read_csv(\"{}./X_valid.csv\".format(blob_location), header=0)\n",
-    "y_test = pd.read_csv(\"{}/y_valid.csv\".format(blob_location), header=0)\n",
-    "images  = pd.read_csv(\"{}/images.csv\".format(blob_location), header=None)\n",
-    "images = np.reshape(images.values, (100,8,8))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Testing Our Best Fitted Model\n",
-    "We will try to predict digits and see how our model works. This is just an example to show you."
-   ]
-  },
-  {
-   "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.values[index]\n",
-    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
-    "    fig = plt.figure(3, figsize = (5,5))\n",
-    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
-    "    ax1.set_title(title)\n",
-    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
-    "    display(fig)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When deploying an automated ML trained model, please specify _pippackages=['azureml-sdk[automl]']_ in your CondaDependencies.\n",
-    "\n",
-    "Please refer to only the **Deploy** section in this notebook - <a href=\"https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification-with-deployment\" target=\"_blank\">Deployment of Automated ML trained model</a>"
-   ]
-  }
- ],
- "metadata": {
-  "authors": [
-   {
-    "name": "savitam"
-   },
-   {
-    "name": "sasum"
-   }
-  ],
-  "kernelspec": {
-   "display_name": "Python 3.6",
-   "language": "Python",
-   "name": "Python36"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "python",
-    "version": 3
-   },
-   "mimetype": "text/x-python",
-   "name": "pyspark3",
-   "pygments_lexer": "python3"
-  },
-  "name": "auto-ml-classification-local-adb",
-  "notebookId": 587284549713154
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}

how-to-use-azureml/deploy-to-cloud/README.md

@@ -1,25 +0,0 @@
-# Model Deployment with Azure ML service
-
-You can use Azure Machine Learning to package, debug, validate and deploy inference containers to a variety of compute targets. This process is known as "MLOps" (ML operationalization).
-For more information please check out this article: https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where
-
-## Get Started
-To begin, you will need an ML workspace.
-For more information please check out this article: https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace
-
-## Deploy to the cloud
-You can deploy to the cloud using the Azure ML CLI or the Azure ML SDK.
-
-### Deploy with the CLI
-```
-az extension add -n azure-cli-ml
-az ml folder attach -w myworkspace -g myresourcegroup
-az ml model register -n sklearn_regression_model.pkl -p sklearn_regression_model.pkl -t model.json
-az ml model deploy -n acicicd -f model.json --ic inferenceConfig.yml --dc deploymentConfig.yml
-```
-
-Here is an [Azure DevOps Pipelines model deployment example](./azure-pipelines-model-deploy.yml)
-[![Build Status](https://aidemos.visualstudio.com/azmlcli/_apis/build/status/Azure.MachineLearningNotebooks?branchName=cli-ga)](https://aidemos.visualstudio.com/azmlcli/_build/latest?definitionId=87&branchName=cli-ga)
-
-### Deploy from a notebook
-- Notebook example: [model-register-and-deploy](./model-register-and-deploy.ipynb).

how-to-use-azureml/deploy-to-cloud/azure-pipelines-model-deploy.yml

@@ -1,50 +0,0 @@
-trigger:
-- master
-
-pool:
-  vmImage: 'Ubuntu-16.04'
-
-steps:
-- task: DownloadSecureFile@1
-  inputs:
-    name: config.json
-    secureFile: config.json
- 
-- script: cp $(Agent.TempDirectory)/config.json $(Build.SourcesDirectory)
-
-- task: AzureCLI@1
-  displayName: 'Install the CLI'
-  inputs:
-    azureSubscription: 'azmldemows'
-    scriptLocation: inlineScript
-    inlineScript: 'az extension add -n azure-cli-ml'
-
-- task: AzureCLI@1
-  displayName: 'Attach folder to workspace'
-  inputs:
-    azureSubscription: 'azmldemows'
-    scriptLocation: inlineScript
-    inlineScript: 'az ml folder attach'
-
-- task: AzureCLI@1
-  displayName: 'Register model'
-  inputs:
-    azureSubscription: 'azmldemows'
-    scriptLocation: inlineScript
-    inlineScript: 'az ml model register -n sklearn_regression_model.pkl -p sklearn_regression_model.pkl -t model.json'
-    workingDirectory: 'how-to-use-azureml/deploy-to-cloud/'
-
-- task: AzureCLI@1
-  displayName: 'Deploy model'
-  inputs:
-    azureSubscription: 'azmldemows'
-    scriptLocation: inlineScript
-    inlineScript: 'az ml model deploy -n acicicd -f model.json --ic inferenceConfig.yml --dc deploymentConfig.yml'
-    workingDirectory: 'how-to-use-azureml/deploy-to-cloud/'
-
-- task: AzureCLI@1
-  displayName: 'Delete deployed service'
-  inputs:
-    azureSubscription: 'azmldemows'
-    scriptLocation: inlineScript
-    inlineScript: 'az ml service delete -n acicicd'

how-to-use-azureml/deploy-to-cloud/deploymentConfig.yml

@@ -1,5 +0,0 @@
----
-containerResourceRequirements:
-  cpu: 1
-  memoryInGB: 1
-computeType: ACI

how-to-use-azureml/deploy-to-cloud/deploymentconfig.json

@@ -1,7 +0,0 @@
-{
-   "containerResourceRequirements": {
-      "cpu": 1,
-      "memoryInGB": 1
-   },
-   "computeType": "ACI"
-}

how-to-use-azureml/deploy-to-cloud/helloworld.txt

@@ -1 +0,0 @@
-RUN echo "this is test"

how-to-use-azureml/deploy-to-cloud/inferenceConfig.yml

@@ -1,9 +0,0 @@
-entryScript: score.py
-runtime: python
-condaFile: myenv.yml
-extraDockerfileSteps:
-schemaFile:
-dependencies:
-enableGpu: False
-baseImage:
-baseImageRegistry:

how-to-use-azureml/deploy-to-cloud/inferenceconfig.json

@@ -1,11 +0,0 @@
-{
-   "entryScript": "score.py",
-   "runtime": "python",
-   "condaFile": "myenv.yml",
-   "extraDockerfileSteps": null,
-   "schemaFile": null,
-   "dependencies": null,
-   "enableGpu": false,
-   "baseImage": null,
-   "baseImageRegistry": null
-}

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

@@ -1,275 +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": [
-        "# Register Model and deploy as Webservice\n",
-        "\n",
-        "This example shows how to deploy a Webservice in step-by-step fashion:\n",
-        "\n",
-        " 1. Register Model\n",
-        " 2. Deploy Model as Webservice"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "Make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Check core SDK version number\n",
-        "import azureml.core\n",
-        "\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialize Workspace\n",
-        "\n",
-        "Initialize a workspace object from persisted configuration."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "create workspace"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Register Model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can add tags and descriptions to your Models. Note you need to have a `sklearn_regression_model.pkl` file in the current directory. This file is generated by the 01 notebook. The below call registers that file as a Model with the same name `sklearn_regression_model.pkl` in the workspace.\n",
-        "\n",
-        "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "register model from file"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core.model import Model\n",
-        "\n",
-        "model = Model.register(model_path = \"sklearn_regression_model.pkl\",\n",
-        "                       model_name = \"sklearn_regression_model.pkl\",\n",
-        "                       tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
-        "                       description = \"Ridge regression model to predict diabetes\",\n",
-        "                       workspace = ws)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create Inference Configuration\n",
-        "\n",
-        "There is now support for a source directory, you can upload an entire folder from your local machine as dependencies for the Webservice.\n",
-        "Note: in that case, your entry_script, conda_file, and extra_docker_file_steps paths are relative paths to the source_directory path.\n",
-        "\n",
-        "Sample code for using a source directory:\n",
-        "\n",
-        "```python\n",
-        "inference_config = InferenceConfig(source_directory=\"C:/abc\",\n",
-        "                                   runtime= \"python\", \n",
-        "                                   entry_script=\"x/y/score.py\",\n",
-        "                                   conda_file=\"env/myenv.yml\", \n",
-        "                                   extra_docker_file_steps=\"helloworld.txt\")\n",
-        "```\n",
-        "\n",
-        " - source_directory = holds source path as string, this entire folder gets added in image so its really easy to access any files within this folder or subfolder\n",
-        " - runtime = Which runtime to use for the image. Current supported runtimes are 'spark-py' and 'python\n",
-        " - entry_script = contains logic specific to initializing your model and running predictions\n",
-        " - conda_file = manages conda and python package dependencies.\n",
-        " - extra_docker_file_steps = optional: any extra steps you want to inject into docker file"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "create image"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core.model import InferenceConfig\n",
-        "\n",
-        "inference_config = InferenceConfig(runtime= \"python\", \n",
-        "                                   entry_script=\"score.py\",\n",
-        "                                   conda_file=\"myenv.yml\", \n",
-        "                                   extra_docker_file_steps=\"helloworld.txt\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Deploy Model as Webservice on Azure Container Instance\n",
-        "\n",
-        "Note that the service creation can take few minutes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import AciWebservice, Webservice\n",
-        "from azureml.exceptions import WebserviceException\n",
-        "\n",
-        "deployment_config = AciWebservice.deploy_configuration(cpu_cores = 1, memory_gb = 1)\n",
-        "aci_service_name = 'aciservice1'\n",
-        "\n",
-        "try:\n",
-        "    # if you want to get existing service below is the command\n",
-        "    # since aci name needs to be unique in subscription deleting existing aci if any\n",
-        "    # we use aci_service_name to create azure aci\n",
-        "    service = Webservice(ws, name=aci_service_name)\n",
-        "    if service:\n",
-        "        service.delete()\n",
-        "except WebserviceException as e:\n",
-        "    print()\n",
-        "\n",
-        "service = Model.deploy(ws, aci_service_name, [model], inference_config, deployment_config)\n",
-        "\n",
-        "service.wait_for_deployment(True)\n",
-        "print(service.state)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Test web service"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import json\n",
-        "test_sample = json.dumps({'data': [\n",
-        "    [1,2,3,4,5,6,7,8,9,10], \n",
-        "    [10,9,8,7,6,5,4,3,2,1]\n",
-        "]})\n",
-        "\n",
-        "test_sample_encoded = bytes(test_sample,encoding = 'utf8')\n",
-        "prediction = service.run(input_data=test_sample_encoded)\n",
-        "print(prediction)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Delete ACI to clean up"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "deploy service",
-          "aci"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "service.delete()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Model Profiling\n",
-        "\n",
-        "you can also take advantage of profiling feature for model\n",
-        "\n",
-        "```python\n",
-        "\n",
-        "profile = model.profile(ws, \"profilename\", [model], inference_config, test_sample)\n",
-        "profile.wait_for_profiling(True)\n",
-        "profiling_results = profile.get_results()\n",
-        "print(profiling_results)\n",
-        "\n",
-        "```"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "aashishb"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.7.0"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/deploy-to-cloud/myenv.yml

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

how-to-use-azureml/deploy-to-cloud/mylib.py

@@ -1,8 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-# Licensed under the MIT license.
-
-import numpy as np
-
-def get_alphas():
-    # list of numbers from 0.0 to 1.0 with a 0.05 interval
-    return np.arange(0.0, 1.0, 0.05)

how-to-use-azureml/deploy-to-cloud/score.py

@@ -1,34 +0,0 @@
-import pickle
-import json
-import numpy as np
-from sklearn.externals import joblib
-from sklearn.linear_model import Ridge
-from azureml.core.model import Model
-
-from inference_schema.schema_decorators import input_schema, output_schema
-from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType
-
-
-def init():
-    global model
-    # note here "sklearn_regression_model.pkl" is the name of the model registered under
-    # this is a different behavior than before when the code is run locally, even though the code is the same.
-    model_path = Model.get_model_path('sklearn_regression_model.pkl')
-    # deserialize the model file back into a sklearn model
-    model = joblib.load(model_path)
-
-
-input_sample = np.array([[10, 9, 8, 7, 6, 5, 4, 3, 2, 1]])
-output_sample = np.array([3726.995])
-
-
-@input_schema('data', NumpyParameterType(input_sample))
-@output_schema(NumpyParameterType(output_sample))
-def run(data):
-    try:
-        result = model.predict(data)
-        # you can return any datatype as long as it is JSON-serializable
-        return result.tolist()
-    except Exception as e:
-        error = str(e)
-        return error

how-to-use-azureml/deploy-to-cloud/train.py

@@ -1,45 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-# Licensed under the MIT license.
-
-from sklearn.datasets import load_diabetes
-from sklearn.linear_model import Ridge
-from sklearn.metrics import mean_squared_error
-from sklearn.model_selection import train_test_split
-from azureml.core.run import Run
-from sklearn.externals import joblib
-import os
-import numpy as np
-import mylib
-
-os.makedirs('./outputs', exist_ok=True)
-
-X, y = load_diabetes(return_X_y=True)
-
-run = Run.get_context()
-
-X_train, X_test, y_train, y_test = train_test_split(X, y,
-                                                    test_size=0.2,
-                                                    random_state=0)
-data = {"train": {"X": X_train, "y": y_train},
-        "test": {"X": X_test, "y": y_test}}
-
-# list of numbers from 0.0 to 1.0 with a 0.05 interval
-alphas = mylib.get_alphas()
-
-for alpha in alphas:
-    # Use Ridge algorithm to create a regression model
-    reg = Ridge(alpha=alpha)
-    reg.fit(data["train"]["X"], data["train"]["y"])
-
-    preds = reg.predict(data["test"]["X"])
-    mse = mean_squared_error(preds, data["test"]["y"])
-    run.log('alpha', alpha)
-    run.log('mse', mse)
-
-    model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)
-    # save model in the outputs folder so it automatically get uploaded
-    with open(model_file_name, "wb") as file:
-        joblib.dump(value=reg, filename=os.path.join('./outputs/',
-                                                     model_file_name))
-
-    print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))

how-to-use-azureml/deploy-to-local/README.md

@@ -1,12 +0,0 @@
-# Model Deployment with Azure ML service
-You can use Azure Machine Learning to package, debug, validate and deploy inference containers to a variety of compute targets. This process is known as "MLOps" (ML operationalization).
-For more information please check out this article: https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where
-
-## Get Started
-To begin, you will need an ML workspace.
-For more information please check out this article: https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace
-
-## Deploy locally
-You can deploy a model locally for testing & debugging using the Azure ML CLI or the Azure ML SDK.
-- CLI example: https://aka.ms/azmlcli
-- Notebook example: [register-model-deploy-local](./register-model-deploy-local.ipynb).

how-to-use-azureml/deploy-to-local/helloworld.txt

@@ -1 +0,0 @@
-RUN echo "this is test"

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

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

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

@@ -1,487 +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": [
-        "# Register model and deploy locally with advanced usages\n",
-        "\n",
-        "This example shows how to deploy a web service in step-by-step fashion:\n",
-        "\n",
-        " 1. Register model\n",
-        " 2. Deploy the image as a web service in a local Docker container.\n",
-        " 3. Quickly test changes to your entry script by reloading the local service.\n",
-        " 4. Optionally, you can also make changes to model, conda or extra_docker_file_steps and update local service"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "Make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Check core SDK version number\n",
-        "import azureml.core\n",
-        "\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialize Workspace\n",
-        "\n",
-        "Initialize a workspace object from persisted configuration."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "create workspace"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Register Model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can add tags and descriptions to your models. we are using `sklearn_regression_model.pkl` file in the current directory as a model with the same name `sklearn_regression_model.pkl` in the workspace.\n",
-        "\n",
-        "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model, framework, category, target customer etc. Note that tags must be alphanumeric."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "register model from file"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core.model import Model\n",
-        "\n",
-        "model = Model.register(model_path = \"sklearn_regression_model.pkl\",\n",
-        "                       model_name = \"sklearn_regression_model.pkl\",\n",
-        "                       tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
-        "                       description = \"Ridge regression model to predict diabetes\",\n",
-        "                       workspace = ws)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Manage your dependencies in a folder"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "source_directory = \"C:/abc\"\n",
-        "\n",
-        "os.makedirs(source_directory, exist_ok = True)\n",
-        "os.makedirs(\"C:/abc/x/y\", exist_ok = True)\n",
-        "os.makedirs(\"C:/abc/env\", exist_ok = True)\n",
-        "os.makedirs(\"C:/abc/dockerstep\", exist_ok = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Show `score.py`. Note that the `sklearn_regression_model.pkl` in the `get_model_path` call is referring to a model named `sklearn_regression_model.pkl` registered under the workspace. It is NOT referencing the local file."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile C:/abc/x/y/score.py\n",
-        "import pickle\n",
-        "import json\n",
-        "import numpy as np\n",
-        "from sklearn.externals import joblib\n",
-        "from sklearn.linear_model import Ridge\n",
-        "from azureml.core.model import Model\n",
-        "\n",
-        "from inference_schema.schema_decorators import input_schema, output_schema\n",
-        "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n",
-        "\n",
-        "def init():\n",
-        "    global model\n",
-        "    # note here \"sklearn_regression_model.pkl\" is the name of the model registered under\n",
-        "    # this is a different behavior than before when the code is run locally, even though the code is the same.\n",
-        "    model_path = Model.get_model_path('sklearn_regression_model.pkl')\n",
-        "    # deserialize the model file back into a sklearn model\n",
-        "    model = joblib.load(model_path)\n",
-        "    global name\n",
-        "    # note here, entire source directory on inference config gets added into image\n",
-        "    # bellow is the example how you can use any extra files in image\n",
-        "    with open('./abc/extradata.json') as json_file:  \n",
-        "        data = json.load(json_file)\n",
-        "        name = data[\"people\"][0][\"name\"]\n",
-        "\n",
-        "input_sample = np.array([[10,9,8,7,6,5,4,3,2,1]])\n",
-        "output_sample = np.array([3726.995])\n",
-        "\n",
-        "@input_schema('data', NumpyParameterType(input_sample))\n",
-        "@output_schema(NumpyParameterType(output_sample))\n",
-        "def run(data):\n",
-        "    try:\n",
-        "        result = model.predict(data)\n",
-        "        # you can return any datatype as long as it is JSON-serializable\n",
-        "        return \"Hello \" + name + \" here is your result = \" + str(result)\n",
-        "    except Exception as e:\n",
-        "        error = str(e)\n",
-        "        return error"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile C:/abc/env/myenv.yml\n",
-        "name: project_environment\n",
-        "dependencies:\n",
-        "  - python=3.6.2\n",
-        "  - pip:\n",
-        "    - azureml-defaults\n",
-        "    - scikit-learn\n",
-        "    - numpy\n",
-        "    - inference-schema[numpy-support]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile C:/abc/dockerstep/customDockerStep.txt\n",
-        "RUN echo \"this is test\""
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile C:/abc/extradata.json\n",
-        "{\n",
-        "    \"people\": [\n",
-        "        {\n",
-        "            \"website\": \"microsoft.com\", \n",
-        "            \"from\": \"Seattle\", \n",
-        "            \"name\": \"Mrudula\"\n",
-        "        }\n",
-        "    ]\n",
-        "}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create Inference Configuration\n",
-        "\n",
-        " - source_directory = holds source path as string, this entire folder gets added in image so its really easy to access any files within this folder or subfolder\n",
-        " - runtime = Which runtime to use for the image. Current supported runtimes are 'spark-py' and 'python\n",
-        " - entry_script = contains logic specific to initializing your model and running predictions\n",
-        " - conda_file = manages conda and python package dependencies.\n",
-        " - extra_docker_file_steps = optional: any extra steps you want to inject into docker file"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.model import InferenceConfig\n",
-        "\n",
-        "inference_config = InferenceConfig(source_directory=\"C:/abc\",\n",
-        "                                   runtime= \"python\", \n",
-        "                                   entry_script=\"x/y/score.py\",\n",
-        "                                   conda_file=\"env/myenv.yml\", \n",
-        "                                   extra_docker_file_steps=\"dockerstep/customDockerStep.txt\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Deploy Model as a Local Docker Web Service\n",
-        "\n",
-        "*Make sure you have Docker installed and running.*\n",
-        "\n",
-        "Note that the service creation can take few minutes.\n",
-        "\n",
-        "NOTE:\n",
-        "\n",
-        "we require docker running with linux container. If you are running Docker for Windows, you need to ensure the Linux Engine is running\n",
-        "\n",
-        "    powershell command to switch to linux engine\n",
-        "    & 'C:\\Program Files\\Docker\\Docker\\DockerCli.exe' -SwitchLinuxEngine\n",
-        "\n",
-        "and c drive is shared https://docs.docker.com/docker-for-windows/#shared-drives\n",
-        "sometimes you have to reshare c drive as docker \n",
-        "\n",
-        "<img src=\"./dockerSharedDrive.JPG\" align=\"left\"/>"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "deploy service",
-          "aci"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import LocalWebservice\n",
-        "\n",
-        "#this is optional, if not provided we choose random port\n",
-        "deployment_config = LocalWebservice.deploy_configuration(port=6789)\n",
-        "\n",
-        "local_service = Model.deploy(ws, \"test\", [model], inference_config, deployment_config)\n",
-        "\n",
-        "local_service.wait_for_deployment()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print('Local service port: {}'.format(local_service.port))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Check Status and Get Container Logs\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(local_service.get_logs())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Test Web Service"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Call the web service with some input data to get a prediction."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import json\n",
-        "\n",
-        "sample_input = json.dumps({\n",
-        "    'data': [\n",
-        "        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
-        "        [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]\n",
-        "    ]\n",
-        "})\n",
-        "\n",
-        "sample_input = bytes(sample_input, encoding='utf-8')\n",
-        "\n",
-        "print(local_service.run(input_data=sample_input))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Reload Service\n",
-        "\n",
-        "You can update your score.py file and then call `reload()` to quickly restart the service. This will only reload your execution script and dependency files, it will not rebuild the underlying Docker image. As a result, `reload()` is fast, but if you do need to rebuild the image -- to add a new Conda or pip package, for instance -- you will have to call `update()`, instead (see below)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile C:/abc/x/y/score.py\n",
-        "import pickle\n",
-        "import json\n",
-        "import numpy as np\n",
-        "from sklearn.externals import joblib\n",
-        "from sklearn.linear_model import Ridge\n",
-        "from azureml.core.model import Model\n",
-        "\n",
-        "from inference_schema.schema_decorators import input_schema, output_schema\n",
-        "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n",
-        "\n",
-        "def init():\n",
-        "    global model\n",
-        "    # note here \"sklearn_regression_model.pkl\" is the name of the model registered under\n",
-        "    # this is a different behavior than before when the code is run locally, even though the code is the same.\n",
-        "    model_path = Model.get_model_path('sklearn_regression_model.pkl')\n",
-        "    # deserialize the model file back into a sklearn model\n",
-        "    model = joblib.load(model_path)\n",
-        "    global name, from_location\n",
-        "    # note here, entire source directory on inference config gets added into image\n",
-        "    # bellow is the example how you can use any extra files in image\n",
-        "    with open('./abc/extradata.json') as json_file:  \n",
-        "        data = json.load(json_file)\n",
-        "        name = data[\"people\"][0][\"name\"]\n",
-        "        from_location = data[\"people\"][0][\"from\"]\n",
-        "\n",
-        "input_sample = np.array([[10,9,8,7,6,5,4,3,2,1]])\n",
-        "output_sample = np.array([3726.995])\n",
-        "\n",
-        "@input_schema('data', NumpyParameterType(input_sample))\n",
-        "@output_schema(NumpyParameterType(output_sample))\n",
-        "def run(data):\n",
-        "    try:\n",
-        "        result = model.predict(data)\n",
-        "        # you can return any datatype as long as it is JSON-serializable\n",
-        "        return \"Hello  \" + name + \" from \" + from_location + \" here is your result = \" + str(result)\n",
-        "    except Exception as e:\n",
-        "        error = str(e)\n",
-        "        return error"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_service.reload()\n",
-        "print(\"--------------------------------------------------------------\")\n",
-        "\n",
-        "# after reload now if you call run this will return updated return message\n",
-        "\n",
-        "print(local_service.run(input_data=sample_input))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Update Service\n",
-        "\n",
-        "If you want to change your model(s), Conda dependencies, or deployment configuration, call `update()` to rebuild the Docker image.\n",
-        "\n",
-        "```python\n",
-        "\n",
-        "local_service.update(models = [SomeOtherModelObject],\n",
-        "             deployment_config = local_config,\n",
-        "             inference_config = inference_config)\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Delete Service"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_service.delete()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "raymondl"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.7.0"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

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

@@ -1,342 +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": [
-        "# Register model and deploy locally\n",
-        "\n",
-        "This example shows how to deploy a web service in step-by-step fashion:\n",
-        "\n",
-        " 1. Register model\n",
-        " 2. Deploy the image as a web service in a local Docker container.\n",
-        " 3. Quickly test changes to your entry script by reloading the local service.\n",
-        " 4. Optionally, you can also make changes to model, conda or extra_docker_file_steps and update local service"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "Make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Check core SDK version number\n",
-        "import azureml.core\n",
-        "\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialize Workspace\n",
-        "\n",
-        "Initialize a workspace object from persisted configuration."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Register Model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can add tags and descriptions to your models. we are using `sklearn_regression_model.pkl` file in the current directory as a model with the same name `sklearn_regression_model.pkl` in the workspace.\n",
-        "\n",
-        "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model, framework, category, target customer etc. Note that tags must be alphanumeric."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "register model from file"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core.model import Model\n",
-        "\n",
-        "model = Model.register(model_path = \"sklearn_regression_model.pkl\",\n",
-        "                       model_name = \"sklearn_regression_model.pkl\",\n",
-        "                       tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
-        "                       description = \"Ridge regression model to predict diabetes\",\n",
-        "                       workspace = ws)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create Inference Configuration"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.model import InferenceConfig\n",
-        "\n",
-        "inference_config = InferenceConfig(runtime= \"python\", \n",
-        "                                   entry_script=\"score.py\",\n",
-        "                                   conda_file=\"myenv.yml\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Deploy Model as a Local Docker Web Service\n",
-        "\n",
-        "*Make sure you have Docker installed and running.*\n",
-        "\n",
-        "Note that the service creation can take few minutes.\n",
-        "\n",
-        "NOTE:\n",
-        "\n",
-        "we require docker running with linux container. If you are running Docker for Windows, you need to ensure the Linux Engine is running\n",
-        "\n",
-        "    powershell command to switch to linux engine\n",
-        "    & 'C:\\Program Files\\Docker\\Docker\\DockerCli.exe' -SwitchLinuxEngine\n",
-        "\n",
-        "and c drive is shared https://docs.docker.com/docker-for-windows/#shared-drives\n",
-        "sometimes you have to reshare c drive as docker \n",
-        "\n",
-        "<img src=\"./dockerSharedDrive.JPG\" align=\"left\"/>"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import LocalWebservice\n",
-        "\n",
-        "#this is optional, if not provided we choose random port\n",
-        "deployment_config = LocalWebservice.deploy_configuration(port=6789)\n",
-        "\n",
-        "local_service = Model.deploy(ws, \"test\", [model], inference_config, deployment_config)\n",
-        "\n",
-        "local_service.wait_for_deployment()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print('Local service port: {}'.format(local_service.port))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Check Status and Get Container Logs\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(local_service.get_logs())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Test Web Service"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Call the web service with some input data to get a prediction."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import json\n",
-        "\n",
-        "sample_input = json.dumps({\n",
-        "    'data': [\n",
-        "        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
-        "        [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]\n",
-        "    ]\n",
-        "})\n",
-        "\n",
-        "sample_input = bytes(sample_input, encoding='utf-8')\n",
-        "\n",
-        "print(local_service.run(input_data=sample_input))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Reload Service\n",
-        "\n",
-        "You can update your score.py file and then call `reload()` to quickly restart the service. This will only reload your execution script and dependency files, it will not rebuild the underlying Docker image. As a result, `reload()` is fast, but if you do need to rebuild the image -- to add a new Conda or pip package, for instance -- you will have to call `update()`, instead (see below)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile score.py\n",
-        "import pickle\n",
-        "import json\n",
-        "import numpy as np\n",
-        "from sklearn.externals import joblib\n",
-        "from sklearn.linear_model import Ridge\n",
-        "from azureml.core.model import Model\n",
-        "\n",
-        "from inference_schema.schema_decorators import input_schema, output_schema\n",
-        "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n",
-        "\n",
-        "def init():\n",
-        "    global model\n",
-        "    # note here \"sklearn_regression_model.pkl\" is the name of the model registered under\n",
-        "    # this is a different behavior than before when the code is run locally, even though the code is the same.\n",
-        "    model_path = Model.get_model_path('sklearn_regression_model.pkl')\n",
-        "    # deserialize the model file back into a sklearn model\n",
-        "    model = joblib.load(model_path)\n",
-        "\n",
-        "input_sample = np.array([[10,9,8,7,6,5,4,3,2,1]])\n",
-        "output_sample = np.array([3726.995])\n",
-        "\n",
-        "@input_schema('data', NumpyParameterType(input_sample))\n",
-        "@output_schema(NumpyParameterType(output_sample))\n",
-        "def run(data):\n",
-        "    try:\n",
-        "        result = model.predict(data)\n",
-        "        # you can return any datatype as long as it is JSON-serializable\n",
-        "        return 'hello from updated score.py'\n",
-        "    except Exception as e:\n",
-        "        error = str(e)\n",
-        "        return error"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_service.reload()\n",
-        "print(\"--------------------------------------------------------------\")\n",
-        "\n",
-        "# after reload now if you call run this will return updated return message\n",
-        "\n",
-        "print(local_service.run(input_data=sample_input))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Update Service\n",
-        "\n",
-        "If you want to change your model(s), Conda dependencies, or deployment configuration, call `update()` to rebuild the Docker image.\n",
-        "\n",
-        "```python\n",
-        "\n",
-        "local_service.update(models = [SomeOtherModelObject],\n",
-        "             deployment_config = local_config,\n",
-        "             inference_config = inference_config)\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Delete Service"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_service.delete()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "raymondl"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.7.0"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

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

@@ -1,34 +0,0 @@
-import pickle
-import json
-import numpy as np
-from sklearn.externals import joblib
-from sklearn.linear_model import Ridge
-from azureml.core.model import Model
-
-from inference_schema.schema_decorators import input_schema, output_schema
-from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType
-
-
-def init():
-    global model
-    # note here "sklearn_regression_model.pkl" is the name of the model registered under
-    # this is a different behavior than before when the code is run locally, even though the code is the same.
-    model_path = Model.get_model_path('sklearn_regression_model.pkl')
-    # deserialize the model file back into a sklearn model
-    model = joblib.load(model_path)
-
-
-input_sample = np.array([[10, 9, 8, 7, 6, 5, 4, 3, 2, 1]])
-output_sample = np.array([3726.995])
-
-
-@input_schema('data', NumpyParameterType(input_sample))
-@output_schema(NumpyParameterType(output_sample))
-def run(data):
-    try:
-        result = model.predict(data)
-        # you can return any datatype as long as it is JSON-serializable
-        return result.tolist()
-    except Exception as e:
-        error = str(e)
-        return error

how-to-use-azureml/deployment/onnx/README.md

@@ -6,18 +6,15 @@ These tutorials show how to create and deploy Open Neural Network eXchange ([ONN
 
 0. [Configure your Azure Machine Learning Workspace](../../../configuration.ipynb)
 
-#### Obtain pretrained models from the [ONNX Model Zoo](https://github.com/onnx/models) and deploy with ONNX Runtime
+#### Obtain models from the [ONNX Model Zoo](https://github.com/onnx/models) and deploy with ONNX Runtime Inference
-1. [MNIST - Handwritten Digit Classification with ONNX Runtime](onnx-inference-mnist-deploy.ipynb)
+1. [Handwritten Digit Classification (MNIST)](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb)
-2. [Emotion FER+ - Facial Expression Recognition with ONNX Runtime](onnx-inference-facial-expression-recognition-deploy.ipynb)
+2. [Facial Expression Recognition (Emotion FER+)](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb)
-
-#### Train model on Azure ML, convert to ONNX, and deploy with ONNX Runtime
-3. [MNIST - Train using PyTorch and deploy with ONNX Runtime](onnx-train-pytorch-aml-deploy-mnist.ipynb)
 
 #### Demo Notebooks from Microsoft Ignite 2018
 Note that the following notebooks do not have evaluation sections for the models since they were deployed as part of a live demo. You can find the respective pre-processing and post-processing code linked from the ONNX Model Zoo Github pages ([ResNet](https://github.com/onnx/models/tree/master/models/image_classification/resnet), [TinyYoloV2](https://github.com/onnx/models/tree/master/tiny_yolov2)), or experiment with the ONNX models by [running them in the browser](https://microsoft.github.io/onnxjs-demo/#/).
 
-4. [ResNet50 - Image Recognition with ONNX Runtime](onnx-modelzoo-aml-deploy-resnet50.ipynb)
+3. [Image Recognition (ResNet50)](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb)
-5. [TinyYoloV2 - Convert from CoreML and deploy with ONNX Runtime](onnx-convert-aml-deploy-tinyyolo.ipynb)
+4. [Convert Core ML Model to ONNX and deploy - Real Time Object Detection (TinyYOLO)](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb) 
 
 ## Documentation
 - [ONNX Runtime Python API Documentation](http://aka.ms/onnxruntime-python)
@@ -25,7 +22,7 @@ Note that the following notebooks do not have evaluation sections for the models
 
 ## Related Articles
 - [Building and Deploying ONNX Runtime Models](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-build-deploy-onnx)
-- [Azure AI – Making AI Real for Business](https://aka.ms/aml-blog-overview)
+- [Azure AI – Making AI Real for Business](https://aka.ms/aml-blog-overview) 
 - [What’s new in Azure Machine Learning](https://aka.ms/aml-blog-whats-new)
 
 ## License

how-to-use-azureml/deployment/onnx/mnist.py

@@ -1,124 +0,0 @@
-# This is a modified version of https://github.com/pytorch/examples/blob/master/mnist/main.py which is
-# licensed under BSD 3-Clause (https://github.com/pytorch/examples/blob/master/LICENSE)
-
-from __future__ import print_function
-import argparse
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.optim as optim
-from torchvision import datasets, transforms
-import os
-
-
-class Net(nn.Module):
-    def __init__(self):
-        super(Net, self).__init__()
-        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
-        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
-        self.conv2_drop = nn.Dropout2d()
-        self.fc1 = nn.Linear(320, 50)
-        self.fc2 = nn.Linear(50, 10)
-
-    def forward(self, x):
-        x = F.relu(F.max_pool2d(self.conv1(x), 2))
-        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
-        x = x.view(-1, 320)
-        x = F.relu(self.fc1(x))
-        x = F.dropout(x, training=self.training)
-        x = self.fc2(x)
-        return F.log_softmax(x, dim=1)
-
-
-def train(args, model, device, train_loader, optimizer, epoch, output_dir):
-    model.train()
-    for batch_idx, (data, target) in enumerate(train_loader):
-        data, target = data.to(device), target.to(device)
-        optimizer.zero_grad()
-        output = model(data)
-        loss = F.nll_loss(output, target)
-        loss.backward()
-        optimizer.step()
-        if batch_idx % args.log_interval == 0:
-            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
-                epoch, batch_idx * len(data), len(train_loader.dataset),
-                100. * batch_idx / len(train_loader), loss.item()))
-
-
-def test(args, model, device, test_loader):
-    model.eval()
-    test_loss = 0
-    correct = 0
-    with torch.no_grad():
-        for data, target in test_loader:
-            data, target = data.to(device), target.to(device)
-            output = model(data)
-            test_loss += F.nll_loss(output, target, size_average=False, reduce=True).item()  # sum up batch loss
-            pred = output.max(1, keepdim=True)[1]  # get the index of the max log-probability
-            correct += pred.eq(target.view_as(pred)).sum().item()
-
-    test_loss /= len(test_loader.dataset)
-    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
-        test_loss, correct, len(test_loader.dataset),
-        100. * correct / len(test_loader.dataset)))
-
-
-def main():
-    # Training settings
-    parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
-    parser.add_argument('--batch-size', type=int, default=64, metavar='N',
-                        help='input batch size for training (default: 64)')
-    parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
-                        help='input batch size for testing (default: 1000)')
-    parser.add_argument('--epochs', type=int, default=5, metavar='N',
-                        help='number of epochs to train (default: 5)')
-    parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
-                        help='learning rate (default: 0.01)')
-    parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
-                        help='SGD momentum (default: 0.5)')
-    parser.add_argument('--no-cuda', action='store_true', default=False,
-                        help='disables CUDA training')
-    parser.add_argument('--seed', type=int, default=1, metavar='S',
-                        help='random seed (default: 1)')
-    parser.add_argument('--log-interval', type=int, default=10, metavar='N',
-                        help='how many batches to wait before logging training status')
-    parser.add_argument('--output-dir', type=str, default='outputs')
-    args = parser.parse_args()
-    use_cuda = not args.no_cuda and torch.cuda.is_available()
-
-    torch.manual_seed(args.seed)
-
-    device = torch.device("cuda" if use_cuda else "cpu")
-
-    output_dir = args.output_dir
-    os.makedirs(output_dir, exist_ok=True)
-
-    kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
-    train_loader = torch.utils.data.DataLoader(
-        datasets.MNIST('data', train=True, download=True,
-                       transform=transforms.Compose([transforms.ToTensor(),
-                                                    transforms.Normalize((0.1307,), (0.3081,))])
-                       ),
-        batch_size=args.batch_size, shuffle=True, **kwargs)
-    test_loader = torch.utils.data.DataLoader(
-        datasets.MNIST('data', train=False,
-                       transform=transforms.Compose([transforms.ToTensor(),
-                                                    transforms.Normalize((0.1307,), (0.3081,))])
-                       ),
-        batch_size=args.test_batch_size, shuffle=True, **kwargs)
-
-    model = Net().to(device)
-    optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
-
-    for epoch in range(1, args.epochs + 1):
-        train(args, model, device, train_loader, optimizer, epoch, output_dir)
-        test(args, model, device, test_loader)
-
-    # save model
-    dummy_input = torch.randn(1, 1, 28, 28, device=device)
-    model_path = os.path.join(output_dir, 'mnist.onnx')
-    torch.onnx.export(model, dummy_input, model_path)
-
-
-if __name__ == '__main__':
-    main()

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

@@ -261,31 +261,6 @@
         "image.wait_for_creation(show_output = True)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Use a custom Docker image\n",
-        "\n",
-        "You can also specify a custom Docker image to be used as base image if you don't want to use the default base image provided by Azure ML. Please make sure the custom Docker image has Ubuntu >= 16.04, Conda >= 4.5.\\* and Python(3.5.\\* or 3.6.\\*).\n",
-        "\n",
-        "Only Supported for `ContainerImage`(from azureml.core.image) with `python` runtime.\n",
-        "```python\n",
-        "# use an image available in public Container Registry without authentication\n",
-        "image_config.base_image = \"mcr.microsoft.com/azureml/o16n-sample-user-base/ubuntu-miniconda\"\n",
-        "\n",
-        "# or, use an image available in a private Container Registry\n",
-        "image_config.base_image = \"myregistry.azurecr.io/mycustomimage:1.0\"\n",
-        "image_config.base_image_registry.address = \"myregistry.azurecr.io\"\n",
-        "image_config.base_image_registry.username = \"username\"\n",
-        "image_config.base_image_registry.password = \"password\"\n",
-        "\n",
-        "# or, use an image built during training.\n",
-        "image_config.base_image = run.properties[\"AzureML.DerivedImageName\"]\n",
-        "```\n",
-        "You can get the address of training image from the properties of a Run object. Only new runs submitted with azureml-sdk>=1.0.22 to AMLCompute targets will have the 'AzureML.DerivedImageName' property. Instructions on how to get a Run can be found in [manage-runs](../../training/manage-runs/manage-runs.ipynb). \n"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -420,7 +395,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "aashishb"
+        "name": "raymondl"
       }
     ],
     "kernelspec": {

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

@@ -38,19 +38,18 @@ In this directory, there are two types of notebooks:
 
 * The first type of notebooks will introduce you to core Azure Machine Learning Pipelines features. These notebooks below belong in this category, and are designed to go in sequence; they're all located in the "intro-to-pipelines" folder:
 
-1. [aml-pipelines-getting-started.ipynb](https://aka.ms/pl-get-started): Start with this notebook to understand the concepts of using Azure Machine Learning Pipelines. This notebook will show you how to runs steps in parallel and in sequence.
+1. [aml-pipelines-getting-started.ipynb](https://aka.ms/pl-get-started)
-2. [aml-pipelines-with-data-dependency-steps.ipynb](https://aka.ms/pl-data-dep): This notebooks shows how to connect steps in your pipeline using data. Data produced by one step is used by subsequent steps to force an explicit dependency between steps. 
+2. [aml-pipelines-with-data-dependency-steps.ipynb](https://aka.ms/pl-data-dep)
-3. [aml-pipelines-publish-and-run-using-rest-endpoint.ipynb](https://aka.ms/pl-pub-rep): Once you are satisfied with your iterative runs in, you could publish your pipeline to get a REST endpoint which could be invoked from non-Pythons clients as well. 
+3. [aml-pipelines-publish-and-run-using-rest-endpoint.ipynb](https://aka.ms/pl-pub-rep)
-4. [aml-pipelines-data-transfer.ipynb](https://aka.ms/pl-data-trans): This notebook shows how you transfer data between supported datastores.
+4. [aml-pipelines-data-transfer.ipynb](https://aka.ms/pl-data-trans)
-5. [aml-pipelines-use-databricks-as-compute-target.ipynb](https://aka.ms/pl-databricks): This notebooks shows how you can use Pipelines to send your compute payload to Azure Databricks.
+5. [aml-pipelines-use-databricks-as-compute-target.ipynb](https://aka.ms/pl-databricks)
-6. [aml-pipelines-use-adla-as-compute-target.ipynb](https://aka.ms/pl-adla): This notebook shows how you can use Azure Data Lake Analytics (ADLA) as a compute target.
+6. [aml-pipelines-use-adla-as-compute-target.ipynb](https://aka.ms/pl-adla)
-7. [aml-pipelines-how-to-use-estimatorstep.ipynb](https://aka.ms/pl-estimator): This notebook shows how to use the EstimatorStep.
+7. [aml-pipelines-parameter-tuning-with-hyperdrive.ipynb](https://aka.ms/pl-hyperdrive)
-7. [aml-pipelines-parameter-tuning-with-hyperdrive.ipynb](https://aka.ms/pl-hyperdrive): HyperDriveStep in Pipelines shows how you can do hyper parameter tuning using Pipelines.
+8. [aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb](https://aka.ms/pl-azbatch)
-8. [aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb](https://aka.ms/pl-azbatch): AzureBatchStep can be used to run your custom code in AzureBatch cluster.
+9. [aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb](https://aka.ms/pl-schedule)
-9. [aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb](https://aka.ms/pl-schedule): Once you publish a Pipeline, you can schedule it to trigger based on an interval or on data change in a defined datastore. 
+10. [aml-pipelines-with-automated-machine-learning-step.ipynb](https://aka.ms/pl-automl)
-10. [aml-pipelines-with-automated-machine-learning-step.ipynb](https://aka.ms/pl-automl): AutoMLStep in Pipelines shows how you can do automated machine learning using Pipelines.
 
 * The second type of notebooks illustrate more sophisticated scenarios, and are independent of each other. These notebooks include:
 
-1. [pipeline-batch-scoring.ipynb](https://aka.ms/pl-batch-score): This notebook demonstrates how to run a batch scoring job using Azure Machine Learning pipelines.
+1. [pipeline-batch-scoring.ipynb](https://aka.ms/pl-batch-score)
 2. [pipeline-style-transfer.ipynb](https://aka.ms/pl-style-trans)

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/README.md

@@ -1,15 +0,0 @@
-# Introduction to Azure Machine Learning Pipelines
-
-The following notebooks provide an introduction to a concept in Azure Machine Learning Pipelines. They will introduce you to core Azure Machine Learning Pipelines features. 
-These notebooks below are designed to go in sequence.
-
-1. [aml-pipelines-getting-started.ipynb](https://aka.ms/pl-get-started): Start with this notebook to understand the concepts of using Azure Machine Learning Pipelines. This notebook will show you how to runs steps in parallel and in sequence.
-2. [aml-pipelines-with-data-dependency-steps.ipynb](https://aka.ms/pl-data-dep): This notebooks shows how to connect steps in your pipeline using data. Data produced by one step is used by subsequent steps to force an explicit dependency between steps. 
-3. [aml-pipelines-publish-and-run-using-rest-endpoint.ipynb](https://aka.ms/pl-pub-rep): Once you are satisfied with your iterative runs in, you could publish your pipeline to get a REST endpoint which could be invoked from non-Pythons clients as well. 
-4. [aml-pipelines-data-transfer.ipynb](https://aka.ms/pl-data-trans): This notebook shows how you transfer data between supported datastores.
-5. [aml-pipelines-use-databricks-as-compute-target.ipynb](https://aka.ms/pl-databricks): This notebooks shows how you can use Pipelines to send your compute payload to Azure Databricks.
-6. [aml-pipelines-use-adla-as-compute-target.ipynb](https://aka.ms/pl-adla): This notebook shows how you can use Azure Data Lake Analytics (ADLA) as a compute target.
-7. [aml-pipelines-how-to-use-estimatorstep.ipynb](https://aka.ms/pl-estimator): This notebook shows how to use the EstimatorStep.
-8. [aml-pipelines-parameter-tuning-with-hyperdrive.ipynb](https://aka.ms/pl-hyperdrive): HyperDriveStep in Pipelines shows how you can do hyper parameter tuning using Pipelines.
-9. [aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb](https://aka.ms/pl-azbatch): AzureBatchStep can be used to run your custom code in AzureBatch cluster.
-10. [aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb](https://aka.ms/pl-schedule): Once you publish a Pipeline, you can schedule it to trigger based on an interval or on data change in a defined datastore. 

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

@@ -141,7 +141,7 @@
         "    print(\"registered blob datastore with name: %s\" % blob_datastore_name)\n",
         "\n",
         "# CLI:\n",
-        "# az ml datastore attach-blob -n <datastore-name> -a <account-name> -c <container-name> -k <account-key> [-t <sas-token>]"
+        "# az ml datastore register-blob -n <datastore-name> -a <account-name> -c <container-name> -k <account-key> [-t <sas-token>]"
       ]
     },
     {

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

@@ -303,7 +303,7 @@
         "\n",
         "The following code will create a PythonScriptStep to be executed in the Azure Machine Learning Compute we created above using train.py, one of the files already made available in the project folder.\n",
         "\n",
-        "A **PythonScriptStep** is a basic, built-in step to run a Python Script on a compute target. It takes a script name and optionally other parameters like arguments for the script, compute target, inputs and outputs. If no compute target is specified, default compute target for the workspace is used. You can also use a [**RunConfiguration**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.runconfiguration?view=azure-ml-py) to specify requirements for the PythonScriptStep, such as conda dependencies and docker image."
+        "A **PythonScriptStep** is a basic, built-in step to run a Python Script on a compute target. It takes a script name and optionally other parameters like arguments for the script, compute target, inputs and outputs. If no compute target is specified, default compute target for the workspace is used."
       ]
     },
     {
@@ -369,34 +369,10 @@
         "                         compute_target=aml_compute, \n",
         "                         source_directory=project_folder)\n",
         "\n",
-        "# Use a RunConfiguration to specify some additional requirements for this step.\n",
-        "from azureml.core.runconfig import RunConfiguration\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "from azureml.core.runconfig import DEFAULT_CPU_IMAGE\n",
-        "\n",
-        "# create a new runconfig object\n",
-        "run_config = RunConfiguration()\n",
-        "\n",
-        "# enable Docker \n",
-        "run_config.environment.docker.enabled = True\n",
-        "\n",
-        "# set Docker base image to the default CPU-based image\n",
-        "run_config.environment.docker.base_image = DEFAULT_CPU_IMAGE\n",
-        "\n",
-        "# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
-        "run_config.environment.python.user_managed_dependencies = False\n",
-        "\n",
-        "# auto-prepare the Docker image when used for execution (if it is not already prepared)\n",
-        "run_config.auto_prepare_environment = True\n",
-        "\n",
-        "# specify CondaDependencies obj\n",
-        "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
-        "\n",
         "step3 = PythonScriptStep(name=\"extract_step\",\n",
         "                         script_name=\"extract.py\", \n",
         "                         compute_target=aml_compute, \n",
-        "                         source_directory=project_folder,\n",
+        "                         source_directory=project_folder)\n",
-        "                         runconfig=run_config)\n",
         "\n",
         "# list of steps to run\n",
         "steps = [step1, step2, step3]\n",

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

@@ -1,281 +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": [
-        "# How to use EstimatorStep in AML Pipeline\n",
-        "\n",
-        "This notebook shows how to use the EstimatorStep with Azure Machine Learning Pipelines. Estimator is a convenient object in Azure Machine Learning that wraps run configuration information to help simplify the tasks of specifying how a script is executed.\n",
-        "\n",
-        "\n",
-        "## Prerequisite:\n",
-        "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
-        "* Go through the [configuration notebook](../../../configuration.ipynb) to:\n",
-        "    * install the AML SDK\n",
-        "    * create a workspace and its configuration file (`config.json`)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's get started. First let's import some Python libraries."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.core\n",
-        "# check core SDK version number\n",
-        "print(\"Azure ML SDK Version: \", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialize workspace\n",
-        "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "ws = Workspace.from_config()\n",
-        "print('Workspace name: ' + ws.name, \n",
-        "      'Azure region: ' + ws.location, \n",
-        "      'Subscription id: ' + ws.subscription_id, \n",
-        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create or Attach existing AmlCompute\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "If we could not find the cluster with the given name, then we will create a new cluster here. We will create an `AmlCompute` cluster of `STANDARD_NC6` GPU VMs. This process is broken down into 3 steps:\n",
-        "1. create the configuration (this step is local and only takes a second)\n",
-        "2. create the cluster (this step will take about **20 seconds**)\n",
-        "3. provision the VMs to bring the cluster to the initial size (of 1 in this case). This step will take about **3-5 minutes** and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "# choose a name for your cluster\n",
-        "cluster_name = \"cpucluster\"\n",
-        "\n",
-        "try:\n",
-        "    cpu_cluster = ComputeTarget(workspace=ws, name=cluster_name)\n",
-        "    print('Found existing compute target')\n",
-        "except ComputeTargetException:\n",
-        "    print('Creating a new compute target...')\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', max_nodes=4)\n",
-        "\n",
-        "    # create the cluster\n",
-        "    cpu_cluster = ComputeTarget.create(ws, cluster_name, compute_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 uses the scale settings for the cluster\n",
-        "    cpu_cluster.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
-        "\n",
-        "# use get_status() to get a detailed status for the current cluster. \n",
-        "print(cpu_cluster.get_status().serialize())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now that you have created the compute target, let's see what the workspace's `compute_targets` property returns. You should now see one entry named 'cpucluster' of type `AmlCompute`."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Use a simple script\n",
-        "We have already created a simple \"hello world\" script. This is the script that we will submit through the estimator pattern. It prints a hello-world message, and if Azure ML SDK is installed, it will also logs an array of values ([Fibonacci numbers](https://en.wikipedia.org/wiki/Fibonacci_number))."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Build an Estimator object\n",
-        "Estimator by default will attempt to use Docker-based execution. You can also enable Docker and let estimator pick the default CPU image supplied by Azure ML for execution. You can target an AmlCompute cluster (or any other supported compute target types). You can also customize the conda environment by adding conda and/or pip packages.\n",
-        "\n",
-        "> Note: The arguments to the entry script used in the Estimator object should be specified as *list* using\n",
-        "    'estimator_entry_script_arguments' parameter when instantiating EstimatorStep. Estimator object's parameter\n",
-        "    'script_params' accepts a dictionary. However 'estimator_entry_script_arguments' parameter expects arguments as\n",
-        "    a list.\n",
-        "\n",
-        "> Estimator object initialization involves specifying a list of DataReference objects in its 'inputs' parameter.\n",
-        "    In Pipelines, a step can take another step's output or DataReferences as input. So when creating an EstimatorStep,\n",
-        "    the parameters 'inputs' and 'outputs' need to be set explicitly and that will override 'inputs' parameter\n",
-        "    specified in the Estimator object."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Datastore\n",
-        "from azureml.data.data_reference import DataReference\n",
-        "from azureml.pipeline.core import PipelineData\n",
-        "\n",
-        "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n",
-        "\n",
-        "input_data = DataReference(\n",
-        "    datastore=def_blob_store,\n",
-        "    data_reference_name=\"input_data\",\n",
-        "    path_on_datastore=\"20newsgroups/20news.pkl\")\n",
-        "\n",
-        "output = PipelineData(\"output\", datastore=def_blob_store)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.estimator import Estimator\n",
-        "\n",
-        "est = Estimator(source_directory='.', \n",
-        "                compute_target=cpu_cluster, \n",
-        "                entry_script='dummy_train.py', \n",
-        "                conda_packages=['scikit-learn'])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create an EstimatorStep\n",
-        "[EstimatorStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.estimator_step.estimatorstep?view=azure-ml-py) adds a step to run Estimator in a Pipeline.\n",
-        "\n",
-        "- **name:** Name of the step\n",
-        "- **estimator:** Estimator object\n",
-        "- **estimator_entry_script_arguments:** \n",
-        "- **runconfig_pipeline_params:** Override runconfig properties at runtime using key-value pairs each with name of the runconfig property and PipelineParameter for that property\n",
-        "- **inputs:** Inputs\n",
-        "- **outputs:** Output is list of PipelineData\n",
-        "- **compute_target:** Compute target to use \n",
-        "- **allow_reuse:** Whether the step should reuse previous results when run with the same settings/inputs. If this is false, a new run will always be generated for this step during pipeline execution.\n",
-        "- **version:** Optional version tag to denote a change in functionality for the step"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.pipeline.steps import EstimatorStep\n",
-        "\n",
-        "est_step = EstimatorStep(name=\"Estimator_Train\", \n",
-        "                         estimator=est, \n",
-        "                         estimator_entry_script_arguments=[\"--datadir\", input_data, \"--output\", output],\n",
-        "                         runconfig_pipeline_params=None, \n",
-        "                         inputs=[input_data], \n",
-        "                         outputs=[output], \n",
-        "                         compute_target=cpu_cluster)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Build and Submit the Experiment"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.pipeline.core import Pipeline\n",
-        "from azureml.core import Experiment\n",
-        "pipeline = Pipeline(workspace=ws, steps=[est_step])\n",
-        "pipeline_run = Experiment(ws, 'Estimator_sample').submit(pipeline)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## View Run Details"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.widgets import RunDetails\n",
-        "RunDetails(pipeline_run).show()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sanpil"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.7"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.ipynb

@@ -36,7 +36,7 @@
         "from azureml.exceptions import ComputeTargetException\n",
         "from azureml.data.data_reference import DataReference\n",
         "from azureml.pipeline.steps import HyperDriveStep\n",
-        "from azureml.pipeline.core import Pipeline, PipelineData\n",
+        "from azureml.pipeline.core import Pipeline\n",
         "from azureml.train.dnn import TensorFlow\n",
         "from azureml.train.hyperdrive import *\n",
         "\n",
@@ -310,17 +310,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "metrics_output_name = 'metrics_output'\n",
-        "metirics_data = PipelineData(name='metrics_data',\n",
-        "                             datastore=ds,\n",
-        "                             pipeline_output_name=metrics_output_name)\n",
-        "\n",
         "hd_step = HyperDriveStep(\n",
         "    name=\"hyperdrive_module\",\n",
         "    hyperdrive_run_config=hd_config,\n",
         "    estimator_entry_script_arguments=['--data-folder', data_folder],\n",
-        "    inputs=[data_folder],\n",
+        "    inputs=[data_folder])"
-        "    metrics_output=metirics_data)"
       ]
     },
     {
@@ -372,40 +366,6 @@
       "source": [
         "pipeline_run.wait_for_completion()"
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Retrieve the metrics\n",
-        "Outputs of above run can be used as inputs of other steps in pipeline. In this tutorial, we will show the result metrics."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "metrics_output = pipeline_run.get_pipeline_output(metrics_output_name)\n",
-        "num_file_downloaded = metrics_output.download('.', show_progress=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import pandas as pd\n",
-        "import json\n",
-        "with open(metrics_output._path_on_datastore) as f:  \n",
-        "   metrics_output_result = f.read()\n",
-        "    \n",
-        "deserialized_metrics_output = json.loads(metrics_output_result)\n",
-        "df = pd.DataFrame(deserialized_metrics_output)\n",
-        "df"
-      ]
     }
   ],
   "metadata": {

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb

@@ -33,7 +33,7 @@
       "outputs": [],
       "source": [
         "import azureml.core\n",
-        "from azureml.core import Workspace, Datastore, Experiment\n",
+        "from azureml.core import Workspace, Datastore\n",
         "from azureml.core.compute import AmlCompute\n",
         "from azureml.core.compute import ComputeTarget\n",
         "\n",
@@ -55,7 +55,10 @@
         "print(\"Default datastore's name: {}\".format(def_file_store.name))\n",
         "\n",
         "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n",
-        "print(\"Blobstore's name: {}\".format(def_blob_store.name))"
+        "print(\"Blobstore's name: {}\".format(def_blob_store.name))\n",
+        "\n",
+        "# project folder\n",
+        "project_folder = '.'"
       ]
     },
     {
@@ -157,7 +160,7 @@
         "    inputs=[blob_input_data],\n",
         "    outputs=[processed_data1],\n",
         "    compute_target=aml_compute, \n",
-        "    source_directory='.'\n",
+        "    source_directory=project_folder\n",
         ")\n",
         "print(\"trainStep created\")"
       ]
@@ -188,7 +191,7 @@
         "    inputs=[processed_data1],\n",
         "    outputs=[processed_data2],\n",
         "    compute_target=aml_compute, \n",
-        "    source_directory='.')\n",
+        "    source_directory=project_folder)\n",
         "print(\"extractStep created\")"
       ]
     },
@@ -249,7 +252,7 @@
         "    inputs=[processed_data1, processed_data2],\n",
         "    outputs=[processed_data3],    \n",
         "    compute_target=aml_compute, \n",
-        "    source_directory='.')\n",
+        "    source_directory=project_folder)\n",
         "print(\"compareStep created\")"
       ]
     },
@@ -267,7 +270,10 @@
       "outputs": [],
       "source": [
         "pipeline1 = Pipeline(workspace=ws, steps=[compareStep])\n",
-        "print (\"Pipeline is built\")"
+        "print (\"Pipeline is built\")\n",
+        "\n",
+        "pipeline1.validate()\n",
+        "print(\"Simple validation complete\") "
       ]
     },
     {
@@ -284,38 +290,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "published_pipeline1 = pipeline1.publish(name=\"My_New_Pipeline\", description=\"My Published Pipeline Description\", continue_on_step_failure=True)\n",
+        "published_pipeline1 = pipeline1.publish(name=\"My_New_Pipeline\", description=\"My Published Pipeline Description\")\n",
         "published_pipeline1"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Note: the continue_on_step_failure parameter specifies whether the execution of steps in the Pipeline will continue if one step fails. The default value is False, meaning when one step fails, the Pipeline execution will stop, canceling any running steps."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Publish the pipeline from a submitted PipelineRun\n",
-        "It is also possible to publish a pipeline from a submitted PipelineRun"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# submit a pipeline run\n",
-        "pipeline_run1 = Experiment(ws, 'Pipeline_experiment').submit(pipeline1)\n",
-        "# publish a pipeline from the submitted pipeline run\n",
-        "published_pipeline2 = pipeline_run1.publish_pipeline(name=\"My_New_Pipeline2\", description=\"My Published Pipeline Description\", version=\"0.1\", continue_on_step_failure=True)\n",
-        "published_pipeline2"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -347,8 +325,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Run published pipeline using its REST endpoint\n",
+        "### Run published pipeline using its REST endpoint"
-        "[This notebook](https://aka.ms/pl-restep-auth) shows how to authenticate to AML workspace."
       ]
     },
     {

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb

@@ -107,11 +107,15 @@
       "source": [
         "from azureml.pipeline.steps import PythonScriptStep\n",
         "\n",
+        "\n",
+        "# project folder\n",
+        "project_folder = 'scripts'\n",
+        "\n",
         "trainStep = PythonScriptStep(\n",
         "    name=\"Training_Step\",\n",
         "    script_name=\"train.py\", \n",
         "    compute_target=aml_compute_target, \n",
-        "    source_directory='.'\n",
+        "    source_directory=project_folder\n",
         ")\n",
         "print(\"TrainStep created\")"
       ]
@@ -132,7 +136,9 @@
         "from azureml.pipeline.core import Pipeline\n",
         "\n",
         "pipeline1 = Pipeline(workspace=ws, steps=[trainStep])\n",
-        "print (\"Pipeline is built\")"
+        "print (\"Pipeline is built\")\n",
+        "\n",
+        "pipeline1.validate()"
       ]
     },
     {
@@ -249,12 +255,11 @@
         "schedules = Schedule.get_all(ws, pipeline_id=pub_pipeline_id)\n",
         "\n",
         "# We will iterate through the list of schedules and \n",
-        "# use the last recurrence schedule in the list for further operations: \n",
+        "# use the last ID in the list for further operations: \n",
         "print(\"Found these schedules for the pipeline id {}:\".format(pub_pipeline_id))\n",
         "for schedule in schedules: \n",
         "    print(schedule.id)\n",
-        "    if schedule.recurrence is not None:\n",
+        "    schedule_id = schedule.id\n",
-        "        schedule_id = schedule.id\n",
         "\n",
         "print(\"Schedule id to be used for schedule operations: {}\".format(schedule_id))"
       ]
@@ -375,8 +380,7 @@
       "metadata": {},
       "source": [
         "### Create a schedule for the pipeline using a Datastore\n",
-        "This schedule will run when additions or modifications are made to Blobs in the Datastore.\n",
+        "This schedule will run when additions or modifications are made to Blobs in the Datastore container.\n",
-        "By default, the Datastore container is monitored for changes. Use the path_on_datastore parameter to instead specify a path on the Datastore to monitor for changes. Changes made to subfolders in the container/path will not trigger the schedule.\n",
         "Note: Only Blob Datastores are supported."
       ]
     },
@@ -396,7 +400,6 @@
         "                           datastore=datastore,\n",
         "                           wait_for_provisioning=True,\n",
         "                           description=\"Schedule Run\")\n",
-        "                          #path_on_datastore=\"file/path\") use path_on_datastore to specify a specific folder to monitor for changes.\n",
         "\n",
         "# You may want to make sure that the schedule is provisioned properly\n",
         "# before making any further changes to the schedule\n",

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb

@@ -168,7 +168,7 @@
       "metadata": {},
       "source": [
         "## Data Connections with Inputs and Outputs\n",
-        "The DatabricksStep supports Azure Blob and ADLS for inputs and outputs. You also will need to define a [Secrets](https://docs.azuredatabricks.net/user-guide/secrets/index.html) scope to enable authentication to external data sources such as Blob and ADLS from Databricks.\n",
+        "The DatabricksStep supports Azure Bloband ADLS for inputs and outputs. You also will need to define a [Secrets](https://docs.azuredatabricks.net/user-guide/secrets/index.html) scope to enable authentication to external data sources such as Blob and ADLS from Databricks.\n",
         "\n",
         "- Databricks documentation on [Azure Blob](https://docs.azuredatabricks.net/spark/latest/data-sources/azure/azure-storage.html)\n",
         "- Databricks documentation on [ADLS](https://docs.databricks.com/spark/latest/data-sources/azure/azure-datalake.html)\n",

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb

@@ -1,517 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Azure Machine Learning Pipeline with AutoMLStep\n",
-        "This notebook demonstrates the use of AutoMLStep in Azure Machine Learning Pipeline."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Introduction\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 [configuration](../../../configuration.ipynb) before running this notebook.\n",
-        "\n",
-        "In this notebook you would see\n",
-        "1. Create an `Experiment` in an existing `Workspace`.\n",
-        "2. Create or Attach existing AmlCompute to a workspace.\n",
-        "3. Configure AutoML using `AutoMLConfig`.\n",
-        "4. Use AutoMLStep\n",
-        "5. Train the model using AmlCompute\n",
-        "6. Explore the results.\n",
-        "7. 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",
-        "- Retrieving models for any iteration or logged metric\n",
-        "- Specifying AutoML settings as `**kwargs`"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Azure Machine Learning and Pipeline SDK-specific imports"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import logging\n",
-        "import os\n",
-        "import csv\n",
-        "\n",
-        "from matplotlib import pyplot as plt\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "from sklearn import datasets\n",
-        "\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.core.compute import AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
-        "from azureml.core.runconfig import RunConfiguration\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "\n",
-        "from azureml.train.automl import AutoMLStep\n",
-        "\n",
-        "# Check core SDK version number\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialize Workspace\n",
-        "Initialize a workspace object from persisted configuration. Make sure the config file is present at .\\config.json"
-      ]
-    },
-    {
-      "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": [
-        "## Create an Azure ML experiment\n",
-        "Let's create an experiment named \"automl-classification\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Choose a name for the run history container in the workspace.\n",
-        "experiment_name = 'automlstep-classification'\n",
-        "project_folder = './project'\n",
-        "\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "experiment"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create or Attach existing AmlCompute\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
-        "\n",
-        "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Choose a name for your cluster.\n",
-        "amlcompute_cluster_name = \"cpucluster\"\n",
-        "\n",
-        "found = False\n",
-        "# Check if this compute target already exists in the workspace.\n",
-        "cts = ws.compute_targets\n",
-        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
-        "    found = True\n",
-        "    print('Found existing compute target.')\n",
-        "    compute_target = cts[amlcompute_cluster_name]\n",
-        "    \n",
-        "if not found:\n",
-        "    print('Creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-        "                                                                #vm_priority = 'lowpriority', # optional\n",
-        "                                                                max_nodes = 4)\n",
-        "\n",
-        "    # Create the cluster.\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "    \n",
-        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "    compute_target.wait_for_completion(show_output = True, min_node_count = 1, timeout_in_minutes = 10)\n",
-        "    \n",
-        "     # For a more detailed view of current AmlCompute status, use get_status()."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prepare and Point to Data\n",
-        "For remote executions, you need to make the data accessible from the remote compute.\n",
-        "This can be done by uploading the data to DataStore.\n",
-        "In this example, we upload scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "data_train = datasets.load_digits()\n",
-        "\n",
-        "if not os.path.isdir('data'):\n",
-        "    os.mkdir('data')\n",
-        "    \n",
-        "if not os.path.exists(project_folder):\n",
-        "    os.makedirs(project_folder)\n",
-        "    \n",
-        "pd.DataFrame(data_train.data).to_csv(\"data/X_train.tsv\", index=False, header=False, quoting=csv.QUOTE_ALL, sep=\"\\t\")\n",
-        "pd.DataFrame(data_train.target).to_csv(\"data/y_train.tsv\", index=False, header=False, sep=\"\\t\")\n",
-        "\n",
-        "ds = ws.get_default_datastore()\n",
-        "ds.upload(src_dir='./data', target_path='bai_data', overwrite=True, show_progress=True)\n",
-        "\n",
-        "from azureml.data.data_reference import DataReference      \n",
-        "input_data = DataReference(datastore=ds, \n",
-        "                           data_reference_name=\"input_data_reference\",\n",
-        "                           path_on_datastore='bai_data',\n",
-        "                           mode='download',\n",
-        "                           path_on_compute='/tmp/azureml_runs',\n",
-        "                           overwrite=False)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# create a new RunConfig object\n",
-        "conda_run_config = RunConfiguration(framework=\"python\")\n",
-        "\n",
-        "# Set compute target to AmlCompute\n",
-        "#conda_run_config.target = compute_target\n",
-        "\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]'], \n",
-        "                              conda_packages=['numpy', 'py-xgboost'], \n",
-        "                              pin_sdk_version=False)\n",
-        "conda_run_config.environment.python.conda_dependencies = cd\n",
-        "\n",
-        "print('run config is ready')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile $project_folder/get_data.py\n",
-        "\n",
-        "import pandas as pd\n",
-        "\n",
-        "def get_data():\n",
-        "    X_train = pd.read_csv(\"/tmp/azureml_runs/bai_data/X_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
-        "    y_train = pd.read_csv(\"/tmp/azureml_runs/bai_data/y_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
-        "\n",
-        "    return { \"X\" : X_train.values, \"y\" : y_train[0].values }\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Set up AutoMLConfig for Training\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 AmlCompute, you can't pass Numpy arrays directly to the fit method.\n",
-        "\n",
-        "|Property|Description|\n",
-        "|-|-|\n",
-        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
-        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
-        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
-        "|**n_cross_validations**|Number of cross validation splits.|\n",
-        "|**max_concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of cores on the DSVM.|"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "automl_settings = {\n",
-        "    \"iteration_timeout_minutes\": 5,\n",
-        "    \"iterations\": 20,\n",
-        "    \"n_cross_validations\": 5,\n",
-        "    \"primary_metric\": 'AUC_weighted',\n",
-        "    \"preprocess\": False,\n",
-        "    \"max_concurrent_iterations\": 3,\n",
-        "    \"verbosity\": logging.INFO\n",
-        "}\n",
-        "automl_config = AutoMLConfig(task = 'classification',\n",
-        "                             debug_log = 'automl_errors.log',\n",
-        "                             path = project_folder,\n",
-        "                             compute_target=compute_target,\n",
-        "                             run_configuration=conda_run_config,\n",
-        "                             data_script = project_folder + \"/get_data.py\",\n",
-        "                             **automl_settings\n",
-        "                            )"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run.\n",
-        "In this example, we specify `show_output = False` to suppress console output while the run is in progress."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Define AutoMLStep"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.pipeline.core import PipelineData, TrainingOutput\n",
-        "\n",
-        "metrics_output_name = 'metrics_output'\n",
-        "best_model_output_name = 'best_model_output'\n",
-        "\n",
-        "metirics_data = PipelineData(name='metrics_data',\n",
-        "                           datastore=ds,\n",
-        "                           pipeline_output_name=metrics_output_name,\n",
-        "                           training_output=TrainingOutput(type='Metrics'))\n",
-        "model_data = PipelineData(name='model_data',\n",
-        "                           datastore=ds,\n",
-        "                           pipeline_output_name=best_model_output_name,\n",
-        "                           training_output=TrainingOutput(type='Model'))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "automl_step = AutoMLStep(\n",
-        "    name='automl_module',\n",
-        "    experiment=experiment,\n",
-        "    automl_config=automl_config,\n",
-        "    inputs=[input_data],\n",
-        "    outputs=[metirics_data, model_data],\n",
-        "    allow_reuse=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.pipeline.core import Pipeline\n",
-        "pipeline = Pipeline(\n",
-        "    description=\"pipeline_with_automlstep\",\n",
-        "    workspace=ws,    \n",
-        "    steps=[automl_step])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "pipeline_run = experiment.submit(pipeline)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.widgets import RunDetails\n",
-        "RunDetails(pipeline_run).show()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "pipeline_run.wait_for_completion()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Examine Results\n",
-        "\n",
-        "### Retrieve the metrics of all child runs\n",
-        "Outputs of above run can be used as inputs of other steps in pipeline. In this tutorial, we will examine the outputs by retrieve output data and running some tests."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "metrics_output = pipeline_run.get_pipeline_output(metrics_output_name)\n",
-        "num_file_downloaded = metrics_output.download('.', show_progress=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import json\n",
-        "with open(metrics_output._path_on_datastore) as f:  \n",
-        "    metrics_output_result = f.read()\n",
-        "    \n",
-        "deserialized_metrics_output = json.loads(metrics_output_result)\n",
-        "df = pd.DataFrame(deserialized_metrics_output)\n",
-        "df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Retrieve the Best Model"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "best_model_output = pipeline_run.get_pipeline_output(best_model_output_name)\n",
-        "num_file_downloaded = best_model_output.download('.', show_progress=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        " import pickle\n",
-        "\n",
-        " with open(best_model_output._path_on_datastore, \"rb\" ) as f:\n",
-        "     best_model = pickle.load(f)\n",
-        " best_model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Test the Model\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 Best 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), 3, replace = False):\n",
-        "   print(index)\n",
-        "   predicted = best_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": "sanpil"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.7"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb

@@ -83,10 +83,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# source directory\n",
+        "# project folder\n",
-        "source_directory = '.'\n",
+        "project_folder = '.'\n",
         "    \n",
-        "print('Sample scripts will be created in {} directory.'.format(source_directory))"
+        "print('Sample projects will be created in {}.'.format(project_folder))"
       ]
     },
     {
@@ -259,44 +259,6 @@
         "**Open `train.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.** "
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Specify conda dependencies and a base docker image through a RunConfiguration\n",
-        "\n",
-        "This step uses a docker image and scikit-learn, use a [**RunConfiguration**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.runconfiguration?view=azure-ml-py) to specify these requirements and use when creating the PythonScriptStep. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.runconfig import RunConfiguration\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "from azureml.core.runconfig import DEFAULT_CPU_IMAGE\n",
-        "\n",
-        "# create a new runconfig object\n",
-        "run_config = RunConfiguration()\n",
-        "\n",
-        "# enable Docker \n",
-        "run_config.environment.docker.enabled = True\n",
-        "\n",
-        "# set Docker base image to the default CPU-based image\n",
-        "run_config.environment.docker.base_image = DEFAULT_CPU_IMAGE\n",
-        "\n",
-        "# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
-        "run_config.environment.python.user_managed_dependencies = False\n",
-        "\n",
-        "# auto-prepare the Docker image when used for execution (if it is not already prepared)\n",
-        "run_config.auto_prepare_environment = True\n",
-        "\n",
-        "# specify CondaDependencies obj\n",
-        "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -311,8 +273,7 @@
         "    inputs=[blob_input_data],\n",
         "    outputs=[processed_data1],\n",
         "    compute_target=aml_compute, \n",
-        "    source_directory=source_directory,\n",
+        "    source_directory=project_folder\n",
-        "    runconfig=run_config\n",
         ")\n",
         "print(\"trainStep created\")"
       ]
@@ -343,7 +304,7 @@
         "    inputs=[processed_data1],\n",
         "    outputs=[processed_data2],\n",
         "    compute_target=aml_compute, \n",
-        "    source_directory=source_directory)\n",
+        "    source_directory=project_folder)\n",
         "print(\"extractStep created\")"
       ]
     },
@@ -351,10 +312,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "#### Define a Step that consumes intermediate data and existing data and produces intermediate data\n",
+        "#### Define a Step that consumes multiple intermediate data and produces intermediate data\n",
-        "In this step, we define a step that consumes multiple data types and produces intermediate data.\n",
+        "In this step, we define a step that consumes multiple intermediate data and produces intermediate data.\n",
-        "\n",
-        "This step uses the output generated from the previous step as well as existing data on a DataStore. The location of the existing data is specified using a [**PipelineParameter**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelineparameter?view=azure-ml-py) and a [**DataPath**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.datapath.datapath?view=azure-ml-py). Using a PipelineParameter enables easy modification of the data location when the Pipeline is published and resubmitted.\n",
         "\n",
         "**Open `compare.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.**"
       ]
@@ -365,31 +324,16 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# Reference the data uploaded to blob storage using a PipelineParameter and a DataPath\n",
+        "# Now define step6 that takes two inputs (both intermediate data), and produce an output\n",
-        "from azureml.pipeline.core import PipelineParameter\n",
-        "from azureml.data.datapath import DataPath, DataPathComputeBinding\n",
-        "\n",
-        "datapath = DataPath(datastore=def_blob_store, path_on_datastore='20newsgroups/20news.pkl')\n",
-        "datapath_param = PipelineParameter(name=\"compare_data\", default_value=datapath)\n",
-        "data_parameter1 = (datapath_param, DataPathComputeBinding(mode='mount'))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Now define the compare step which takes two inputs and produces an output\n",
         "processed_data3 = PipelineData(\"processed_data3\", datastore=def_blob_store)\n",
         "\n",
         "compareStep = PythonScriptStep(\n",
         "    script_name=\"compare.py\",\n",
-        "    arguments=[\"--compare_data1\", data_parameter1, \"--compare_data2\", processed_data2, \"--output_compare\", processed_data3],\n",
+        "    arguments=[\"--compare_data1\", processed_data1, \"--compare_data2\", processed_data2, \"--output_compare\", processed_data3],\n",
-        "    inputs=[data_parameter1, processed_data2],\n",
+        "    inputs=[processed_data1, processed_data2],\n",
         "    outputs=[processed_data3],    \n",
         "    compute_target=aml_compute, \n",
-        "    source_directory=source_directory)\n",
+        "    source_directory=project_folder)\n",
         "print(\"compareStep created\")"
       ]
     },
@@ -407,7 +351,10 @@
       "outputs": [],
       "source": [
         "pipeline1 = Pipeline(workspace=ws, steps=[compareStep])\n",
-        "print (\"Pipeline is built\")"
+        "print (\"Pipeline is built\")\n",
+        "\n",
+        "pipeline1.validate()\n",
+        "print(\"Simple validation complete\") "
       ]
     },
     {

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/dummy_train.py

@@ -1,30 +0,0 @@
-# Copyright (c) Microsoft Corporation. All rights reserved.
-# Licensed under the MIT License.
-import argparse
-import os
-
-print("*********************************************************")
-print("Hello Azure ML!")
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--datadir', type=str, help="data directory")
-parser.add_argument('--output', type=str, help="output")
-args = parser.parse_args()
-
-print("Argument 1: %s" % args.datadir)
-print("Argument 2: %s" % args.output)
-
-if not (args.output is None):
-    os.makedirs(args.output, exist_ok=True)
-    print("%s created" % args.output)
-
-try:
-    from azureml.core import Run
-    run = Run.get_context()
-    print("Log Fibonacci numbers.")
-    run.log_list('Fibonacci numbers', [0, 1, 1, 2, 3, 5, 8, 13, 21, 34])
-    run.complete()
-except:
-    print("Warning: you need to install Azure ML SDK in order to log metrics.")
-
-print("*********************************************************")

how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.ipynb

@@ -508,8 +508,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Get AAD token\n",
+        "### Get AAD token"
-        "[This notebook](https://aka.ms/pl-restep-auth) shows how to authenticate to AML workspace."
       ]
     },
     {

how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer.ipynb

@@ -492,8 +492,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Get AAD token\n",
+        "## Get AAD token"
-        "[This notebook](https://aka.ms/pl-restep-auth) shows how to authenticate to AML workspace."
       ]
     },
     {

how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azure-ml.ipynb

@@ -1,253 +1,253 @@
 {
-  "cells": [
+ "cells": [
-    {
+  {
-      "cell_type": "markdown",
+   "cell_type": "markdown",
-      "metadata": {},
+   "metadata": {},
-      "source": [
+   "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
+    "\n",
-        "Licensed under the MIT License.\n",
+    "Licensed under the MIT License.\n",
-        "\n",
+    "\n",
-        "## Authentication in Azure Machine Learning\n",
+    "## Authentication in Azure Machine Learning\n",
-        "\n",
+    "\n",
-        "This notebook shows you how to authenticate to your Azure ML Workspace using\n",
+    "This notebook shows you how to authenticate to your Azure ML Workspace using\n",
-        "\n",
+    "\n",
-        " 1. Interactive Login Authentication\n",
+    " 1. Interactive Login Authentication\n",
-        " 2. Azure CLI Authentication\n",
+    " 2. Azure CLI Authentication\n",
-        " 3. Service Principal Authentication\n",
+    " 3. Service Principal Authentication\n",
-        " \n",
+    " \n",
-        "The interactive authentication is suitable for local experimentation on your own computer. Azure CLI authentication is suitable if you are already using Azure CLI for managing Azure resources, and want to sign in only once. The Service Principal authentication is suitable for automated workflows, for example as part of Azure Devops build."
+    "The interactive authentication is suitable for local experimentation on your own computer. Azure CLI authentication is suitable if you are already using Azure CLI for managing Azure resources, and want to sign in only once. The Service Principal authentication is suitable for automated workflows, for example as part of Azure Devops build."
-      ]
+   ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Interactive Authentication\n",
-        "\n",
-        "Interactive authentication is the default mode when using Azure ML SDK.\n",
-        "\n",
-        "When you connect to your workspace using workspace.from_config, you will get an interactive login dialog."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Also, if you explicitly specify the subscription ID, resource group and resource group, you will get the dialog."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace(subscription_id=\"my-subscription-id\",\n",
-        "               resource_group=\"my-ml-rg\",\n",
-        "               workspace_name=\"my-ml-workspace\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Note the user you're authenticated as must have access to the subscription and resource group. If you receive an error\n",
-        "\n",
-        "```\n",
-        "AuthenticationException: You don't have access to xxxxxx-xxxx-xxx-xxx-xxxxxxxxxx subscription. All the subscriptions that you have access to = ...\n",
-        "```\n",
-        "\n",
-        "check that the you used correct login and entered the correct subscription ID."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In some cases, you may see a version of the error message containing text: ```All the subscriptions that you have access to = []```\n",
-        "\n",
-        "In such a case, you may have to specify the tenant ID of the Azure Active Directory you're using. An example would be accessing a subscription as a guest to a tenant that is not your default. You specify the tenant by explicitly instantiating _InteractiveLoginAuthentication_ with tenant ID as argument ([see instructions how to obtain tenant Id](#get-tenant-id))."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.authentication import InteractiveLoginAuthentication\n",
-        "\n",
-        "interactive_auth = InteractiveLoginAuthentication(tenant_id=\"my-tenant-id\")\n",
-        "\n",
-        "ws = Workspace(subscription_id=\"my-subscription-id\",\n",
-        "               resource_group=\"my-ml-rg\",\n",
-        "               workspace_name=\"my-ml-workspace\",\n",
-        "               auth=interactive_auth)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Azure CLI Authentication\n",
-        "\n",
-        "If you have installed azure-cli package, and used ```az login``` command to log in to your Azure Subscription, you can use _AzureCliAuthentication_ class.\n",
-        "\n",
-        "Note that interactive authentication described above won't use existing Azure CLI auth tokens. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.authentication import AzureCliAuthentication\n",
-        "\n",
-        "cli_auth = AzureCliAuthentication()\n",
-        "\n",
-        "ws = Workspace(subscription_id=\"my-subscription-id\",\n",
-        "               resource_group=\"my-ml-rg\",\n",
-        "               workspace_name=\"my-ml-workspace\",\n",
-        "               auth=cli_auth)\n",
-        "\n",
-        "print(\"Found workspace {} at location {}\".format(ws.name, ws.location))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Service Principal Authentication\n",
-        "\n",
-        "When setting up a machine learning workflow as an automated process, we recommend using Service Principal Authentication. This approach decouples the authentication from any specific user login, and allows managed access control.\n",
-        "\n",
-        "Note that you must have administrator privileges over the Azure subscription to complete these steps.\n",
-        "\n",
-        "The first step is to create a service principal. First, go to [Azure Portal](https://portal.azure.com), select **Azure Active Directory** and **App Registrations**. Then select **+New application registration**, give your service principal a name, for example _my-svc-principal_. You can leave application type as is, and specify a dummy value for Sign-on URL, such as _https://invalid_.\n",
-        "\n",
-        "Then click **Create**.\n",
-        "\n",
-        "![service principal creation]<img src=\"images/svc-pr-1.PNG\">"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The next step is to obtain the _Application ID_ (also called username) and create _password_ for the service principal.\n",
-        "\n",
-        "From the page for your newly created service principal, copy the _Application ID_. Then select **Settings** and **Keys**, write a description for your key, and select duration. Then click **Save**, and copy the _password_ to a secure location.\n",
-        "\n",
-        "![application id and password](images/svc-pr-2.PNG)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a id =\"get-tenant-id\"></a>\n",
-        "\n",
-        "Also, you need to obtain the tenant ID of your Azure subscription. Go back to **Azure Active Directory**, select **Properties** and copy _Directory ID_.\n",
-        "\n",
-        "![tenant id](images/svc-pr-3.PNG)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Finally, you need to give the service principal permissions to access your workspace. Navigate to **Resource Groups**, to the resource group for your Machine Learning Workspace. \n",
-        "\n",
-        "Then select **Access Control (IAM)** and **Add a role assignment**. For _Role_, specify which level of access you need to grant, for example _Contributor_. Start entering your service principal name and once it is found, select it, and click **Save**.\n",
-        "\n",
-        "![add role](images/svc-pr-4.PNG)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now you are ready to use the service principal authentication. For example, to connect to your Workspace, see code below and enter your own values for tenant ID, application ID, subscription ID, resource group and workspace.\n",
-        "\n",
-        "**We strongly recommended that you do not insert the secret password to code**. Instead, you can use environment variables to pass it to your code, for example through Azure Key Vault, or through secret build variables in Azure DevOps. For local testing, you can for example use following PowerShell command to set the environment variable.\n",
-        "\n",
-        "```\n",
-        "$env:AZUREML_PASSWORD = \"my-password\"\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "from azureml.core.authentication import ServicePrincipalAuthentication\n",
-        "\n",
-        "svc_pr_password = os.environ.get(\"AZUREML_PASSWORD\")\n",
-        "\n",
-        "svc_pr = ServicePrincipalAuthentication(\n",
-        "    tenant_id=\"my-tenant-id\",\n",
-        "    service_principal_id=\"my-application-id\",\n",
-        "    service_principal_password=svc_pr_password)\n",
-        "\n",
-        "\n",
-        "ws = Workspace(\n",
-        "    subscription_id=\"my-subscription-id\",\n",
-        "    resource_group=\"my-ml-rg\",\n",
-        "    workspace_name=\"my-ml-workspace\",\n",
-        "    auth=svc_pr\n",
-        "    )\n",
-        "\n",
-        "print(\"Found workspace {} at location {}\".format(ws.name, ws.location))"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "roastala"
-      }
-    ],
-    "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
+   "cell_type": "code",
-}
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interactive Authentication\n",
+    "\n",
+    "Interactive authentication is the default mode when using Azure ML SDK.\n",
+    "\n",
+    "When you connect to your workspace using workspace.from_config, you will get an interactive login dialog."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Also, if you explicitly specify the subscription ID, resource group and resource group, you will get the dialog."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace(subscription_id=\"my-subscription-id\",\n",
+    "               resource_group=\"my-ml-rg\",\n",
+    "               workspace_name=\"my-ml-workspace\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note the user you're authenticated as must have access to the subscription and resource group. If you receive an error\n",
+    "\n",
+    "```\n",
+    "AuthenticationException: You don't have access to xxxxxx-xxxx-xxx-xxx-xxxxxxxxxx subscription. All the subscriptions that you have access to = ...\n",
+    "```\n",
+    "\n",
+    "check that the you used correct login and entered the correct subscription ID."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In some cases, you may see a version of the error message containing text: ```All the subscriptions that you have access to = []```\n",
+    "\n",
+    "In such a case, you may have to specify the tenant ID of the Azure Active Directory you're using. An example would be accessing a subscription as a guest to a tenant that is not your default. You specify the tenant by explicitly instantiating _InteractiveLoginAuthentication_ with tenant ID as argument ([see instructions how to obtain tenant Id](#get-tenant-id))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.authentication import InteractiveLoginAuthentication\n",
+    "\n",
+    "interactive_auth = InteractiveLoginAuthentication(tenant_id=\"my-tenant-id\")\n",
+    "\n",
+    "ws = Workspace(subscription_id=\"my-subscription-id\",\n",
+    "               resource_group=\"my-ml-rg\",\n",
+    "               workspace_name=\"my-ml-workspace\",\n",
+    "               auth=interactive_auth)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Azure CLI Authentication\n",
+    "\n",
+    "If you have installed azure-cli package, and used ```az login``` command to log in to your Azure Subscription, you can use _AzureCliAuthentication_ class.\n",
+    "\n",
+    "Note that interactive authentication described above won't use existing Azure CLI auth tokens. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.authentication import AzureCliAuthentication\n",
+    "\n",
+    "cli_auth = AzureCliAuthentication()\n",
+    "\n",
+    "ws = Workspace(subscription_id=\"my-subscription-id\",\n",
+    "               resource_group=\"my-ml-rg\",\n",
+    "               workspace_name=\"my-ml-workspace\",\n",
+    "               auth=cli_auth)\n",
+    "\n",
+    "print(\"Found workspace {} at location {}\".format(ws.name, ws.location))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Service Principal Authentication\n",
+    "\n",
+    "When setting up a machine learning workflow as an automated process, we recommend using Service Principal Authentication. This approach decouples the authentication from any specific user login, and allows managed access control.\n",
+    "\n",
+    "Note that you must have administrator privileges over the Azure subscription to complete these steps.\n",
+    "\n",
+    "The first step is to create a service principal. First, go to [Azure Portal](https://portal.azure.com), select **Azure Active Directory** and **App Registrations**. Then select **+New application registration**, give your service principal a name, for example _my-svc-principal_. You can leave application type as is, and specify a dummy value for Sign-on URL, such as _https://invalid_.\n",
+    "\n",
+    "Then click **Create**.\n",
+    "\n",
+    "![service principal creation]<img src=\"images/svc-pr-1.PNG\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The next step is to obtain the _Application ID_ (also called username) and create _password_ for the service principal.\n",
+    "\n",
+    "From the page for your newly created service principal, copy the _Application ID_. Then select **Settings** and **Keys**, write a description for your key, and select duration. Then click **Save**, and copy the _password_ to a secure location.\n",
+    "\n",
+    "![application id and password](images/svc-pr-2.PNG)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id =\"get-tenant-id\"></a>\n",
+    "\n",
+    "Also, you need to obtain the tenant ID of your Azure subscription. Go back to **Azure Active Directory**, select **Properties** and copy _Directory ID_.\n",
+    "\n",
+    "![tenant id](images/svc-pr-3.PNG)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, you need to give the service principal permissions to access your workspace. Navigate to **Resource Groups**, to the resource group for your Machine Learning Workspace. \n",
+    "\n",
+    "Then select **Access Control (IAM)** and **Add a role assignment**. For _Role_, specify which level of access you need to grant, for example _Contributor_. Start entering your service principal name and once it is found, select it, and click **Save**.\n",
+    "\n",
+    "![add role](images/svc-pr-4.PNG)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now you are ready to use the service principal authentication. For example, to connect to your Workspace, see code below and enter your own values for tenant ID, application ID, subscription ID, resource group and workspace.\n",
+    "\n",
+    "**We strongly recommended that you do not insert the secret password to code**. Instead, you can use environment variables to pass it to your code, for example through Azure Key Vault, or through secret build variables in Azure DevOps. For local testing, you can for example use following PowerShell command to set the environment variable.\n",
+    "\n",
+    "```\n",
+    "$env:AZUREML_PASSWORD = \"my-password\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from azureml.core.authentication import ServicePrincipalAuthentication\n",
+    "\n",
+    "svc_pr_password = os.environ.get(\"AZUREML_PASSWORD\")\n",
+    "\n",
+    "svc_pr = ServicePrincipalAuthentication(\n",
+    "    tenant_id=\"my-tenant-id\",\n",
+    "    service_principal_id=\"my-application-id\",\n",
+    "    service_principal_password=svc_pr_password)\n",
+    "\n",
+    "\n",
+    "ws = Workspace(\n",
+    "    subscription_id=\"my-subscription-id\",\n",
+    "    resource_group=\"my-ml-rg\",\n",
+    "    workspace_name=\"my-ml-workspace\",\n",
+    "    auth=svc_pr\n",
+    "    )\n",
+    "\n",
+    "print(\"Found workspace {} at location {}\".format(ws.name, ws.location))"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "roastala"
+   }
+  ],
+  "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
+}

how-to-use-azureml/training-with-deep-learning/distributed-chainer/distributed-chainer.ipynb

@@ -220,14 +220,14 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.runconfig import MpiConfiguration\n",
         "from azureml.train.dnn import Chainer\n",
         "\n",
         "estimator = Chainer(source_directory=project_folder,\n",
         "                    compute_target=compute_target,\n",
         "                    entry_script='train_mnist.py',\n",
         "                    node_count=2,\n",
-        "                    distributed_training=MpiConfiguration(),\n",
+        "                    process_count_per_node=1,\n",
+        "                    distributed_backend='mpi',\n",
         "                    use_gpu=True)"
       ]
     },

how-to-use-azureml/training-with-deep-learning/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb

@@ -233,14 +233,14 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.runconfig import MpiConfiguration\n",
         "from azureml.train.dnn import PyTorch\n",
         "\n",
         "estimator = PyTorch(source_directory=project_folder,\n",
         "                    compute_target=compute_target,\n",
         "                    entry_script='pytorch_horovod_mnist.py',\n",
         "                    node_count=2,\n",
-        "                    distributed_training=MpiConfiguration(),\n",
+        "                    process_count_per_node=1,\n",
+        "                    distributed_backend='mpi',\n",
         "                    use_gpu=True)"
       ]
     },

how-to-use-azureml/training-with-deep-learning/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.ipynb

@@ -285,9 +285,7 @@
       "metadata": {},
       "source": [
         "### Create a TensorFlow estimator\n",
-        "The AML SDK's TensorFlow estimator enables you to easily submit TensorFlow training jobs for both single-node and distributed runs. For more information on the TensorFlow estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-tensorflow).\n",
+        "The AML SDK's TensorFlow estimator enables you to easily submit TensorFlow training jobs for both single-node and distributed runs. For more information on the TensorFlow estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-tensorflow)."
-        "\n",
-        "The TensorFlow estimator also takes a `framework_version` parameter -- if no version is provided, the estimator will default to the latest version supported by AzureML. Use `TensorFlow.get_supported_versions()` to get a list of all versions supported by your current SDK version or see the [SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn?view=azure-ml-py) for the versions supported in the most current release."
       ]
     },
     {
@@ -296,7 +294,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.runconfig import MpiConfiguration\n",
         "from azureml.train.dnn import TensorFlow\n",
         "\n",
         "script_params={\n",
@@ -308,8 +305,9 @@
         "                      script_params=script_params,\n",
         "                      entry_script='tf_horovod_word2vec.py',\n",
         "                      node_count=2,\n",
-        "                      distributed_training=MpiConfiguration(),\n",
+        "                      process_count_per_node=1,\n",
-        "                      framework_version='1.13')"
+        "                      distributed_backend='mpi',\n",
+        "                      use_gpu=True)"
       ]
     },
     {

how-to-use-azureml/training-with-deep-learning/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.ipynb

@@ -26,7 +26,7 @@
         "* Go through the [configuration notebook](../../../configuration.ipynb) to:\n",
         "    * install the AML SDK\n",
         "    * create a workspace and its configuration file (`config.json`)\n",
-        "* Review the [tutorial](../train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb) on single-node TensorFlow training using the SDK"
+        "* Review the [tutorial](https://aka.ms/aml-notebook-hyperdrive) on single-node TensorFlow training using the SDK"
       ]
     },
     {
@@ -208,7 +208,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.runconfig import TensorflowConfiguration\n",
         "from azureml.train.dnn import TensorFlow\n",
         "\n",
         "script_params={\n",
@@ -216,15 +215,14 @@
         "    '--train_steps': 500\n",
         "}\n",
         "\n",
-        "distributed_training = TensorflowConfiguration()\n",
-        "distributed_training.worker_count = 2\n",
-        "\n",
         "estimator = TensorFlow(source_directory=project_folder,\n",
         "                       compute_target=compute_target,\n",
         "                       script_params=script_params,\n",
         "                       entry_script='tf_mnist_replica.py',\n",
         "                       node_count=2,\n",
-        "                       distributed_training=distributed_training,\n",
+        "                       worker_count=2,\n",
+        "                       parameter_server_count=1,   \n",
+        "                       distributed_backend='ps',\n",
         "                       use_gpu=True)"
       ]
     },

how-to-use-azureml/training-with-deep-learning/how-to-use-estimator/how-to-use-estimator.ipynb

@@ -291,7 +291,7 @@
       "outputs": [],
       "source": [
         "# use a custom Docker image\n",
-        "from azureml.core.container_registry import ContainerRegistry\n",
+        "from azureml.core.runconfig import ContainerRegistry\n",
         "\n",
         "# this is an image available in Docker Hub\n",
         "image_name = 'continuumio/miniconda3'\n",
@@ -309,8 +309,7 @@
         "est = Estimator(source_directory='.', compute_target='local', \n",
         "                entry_script='dummy_train.py',\n",
         "                custom_docker_image=image_name,\n",
-        "                # uncomment below line to use your private ACR\n",
+        "                image_registry_details=image_registry_details,\n",
-        "                #image_registry_details=image_registry_details,\n",
         "                user_managed=user_managed_dependencies\n",
         "                )\n",
         "\n",
@@ -337,7 +336,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "maxluk"
+        "name": "minxia"
       }
     ],
     "kernelspec": {
@@ -357,7 +356,7 @@
       "pygments_lexer": "ipython3",
       "version": "3.6.8"
     },
-    "msauthor": "minxia"
+    "msauthor": "haining"
   },
   "nbformat": 4,
   "nbformat_minor": 2

how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb

@@ -396,7 +396,7 @@
         "est = TensorFlow(source_directory=script_folder,\n",
         "                 script_params=script_params,\n",
         "                 compute_target=compute_target, \n",
-        "                 pip_packages=['keras', 'matplotlib'],\n",
+        "                 conda_packages=['keras', 'matplotlib'],\n",
         "                 entry_script='keras_mnist.py', \n",
         "                 use_gpu=True)"
       ]
@@ -792,7 +792,7 @@
       "outputs": [],
       "source": [
         "best_run = hdr.get_best_run_by_primary_metric()\n",
-        "print(best_run.get_details()['runDefinition']['arguments'])"
+        "print(best_run.get_details()['runDefinition']['Arguments'])"
       ]
     },
     {
@@ -1144,7 +1144,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "maxluk"
+        "name": "haining"
       }
     ],
     "kernelspec": {
@@ -1164,7 +1164,7 @@
       "pygments_lexer": "ipython3",
       "version": "3.6.7"
     },
-    "msauthor": "maxluk"
+    "msauthor": "haining"
   },
   "nbformat": 4,
   "nbformat_minor": 2

how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb

@@ -396,10 +396,7 @@
       "source": [
         "## Create TensorFlow estimator\n",
         "Next, we construct an `azureml.train.dnn.TensorFlow` estimator object, use the Batch AI cluster as compute target, and pass the mount-point of the datastore to the training code as a parameter.\n",
-        "\n",
+        "The TensorFlow estimator is providing a simple way of launching a TensorFlow training job on a compute target. It will automatically provide a docker image that has TensorFlow installed -- if additional pip or conda packages are required, their names can be passed in via the `pip_packages` and `conda_packages` arguments and they will be included in the resulting docker."
-        "The TensorFlow estimator is providing a simple way of launching a TensorFlow training job on a compute target. It will automatically provide a docker image that has TensorFlow installed -- if additional pip or conda packages are required, their names can be passed in via the `pip_packages` and `conda_packages` arguments and they will be included in the resulting docker.\n",
-        "\n",
-        "The TensorFlow estimator also takes a `framework_version` parameter -- if no version is provided, the estimator will default to the latest version supported by AzureML. Use `TensorFlow.get_supported_versions()` to get a list of all versions supported by your current SDK version or see the [SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn?view=azure-ml-py) for the versions supported in the most current release."
       ]
     },
     {
@@ -422,8 +419,7 @@
         "                 script_params=script_params,\n",
         "                 compute_target=compute_target,\n",
         "                 entry_script='tf_mnist.py', \n",
-        "                 use_gpu=True, \n",
+        "                 use_gpu=True)"
-        "                 framework_version='1.13')"
       ]
     },
     {
@@ -1162,7 +1158,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.6"
+      "version": "3.6.8"
     },
     "msauthor": "minxia"
   },

how-to-use-azureml/training/train-on-local/train-on-local.ipynb

@@ -319,7 +319,9 @@
       "metadata": {},
       "source": [
         "### Submit script to run in the system-managed environment\n",
-        "A new conda environment is built based on the conda dependencies object. If you are running this for the first time, this might take up to 5 minutes. But this conda environment is reused so long as you don't change the conda dependencies."
+        "A new conda environment is built based on the conda dependencies object. If you are running this for the first time, this might take up to 5 mninutes. But this conda environment is reused so long as you don't change the conda dependencies.\n",
+        "\n",
+        "\n"
       ]
     },
     {
@@ -330,9 +332,9 @@
       "source": [
         "import subprocess\n",
         "\n",
-        "# Check if Docker is installed and Linux containers are enabled\n",
+        "# Check if Docker is installed and Linux containers are enables\n",
-        "if subprocess.run(\"docker -v\", shell=True).returncode == 0:\n",
+        "if subprocess.run(\"docker -v\", shell=True) == 0:\n",
-        "    out = subprocess.check_output(\"docker system info\", shell=True).decode('ascii')\n",
+        "    out = subprocess.check_output(\"docker system info\", shell=True, encoding=\"ascii\").split(\"\\n\")\n",
         "    if not \"OSType: linux\" in out:\n",
         "        print(\"Switch Docker engine to use Linux containers.\")\n",
         "    else:\n",
@@ -433,29 +435,6 @@
         "))"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's compare it to the others"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%matplotlib inline\n",
-        "\n",
-        "import matplotlib\n",
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "plt.plot(metrics['alpha'], metrics['mse'], marker='o')\n",
-        "plt.ylabel(\"MSE\")\n",
-        "plt.xlabel(\"Alpha\")"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -476,7 +455,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "We know the model `ridge_0.40.pkl` is the best performing model from the earlier queries. So let's register it with the workspace."
+        "We know the model `ridge_0.40.pkl` is the best performing model from the eariler queries. So let's register it with the workspace."
       ]
     },
     {
@@ -509,7 +488,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "roastala"
+        "name": "haining"
       }
     ],
     "kernelspec": {
@@ -527,7 +506,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.8"
+      "version": "3.6.6"
     }
   },
   "nbformat": 4,

how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb

@@ -110,7 +110,7 @@
       "metadata": {},
       "source": [
         "## Upload data files into datastore\n",
-        "Every workspace comes with a default [datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data) (and you can register more) which is backed by the Azure blob storage account associated with the workspace. We can use it to transfer data from local to the cloud, and access it from the compute target."
+        "Every workspace comes with a default datastore (and you can register more) which is backed by the Azure blob storage account associated with the workspace. We can use it to transfer data from local to the cloud, and access it from the compute target."
       ]
     },
     {
@@ -236,7 +236,7 @@
       "metadata": {},
       "source": [
         "## Configure & Run\n",
-        "First let's create a `DataReferenceConfiguration` object to inform the system what data folder to download to the compute target."
+        "First let's create a `DataReferenceConfiguration` object to inform the system what data folder to download to the copmute target."
       ]
     },
     {
@@ -615,7 +615,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "roastala"
+        "name": "haining"
       }
     ],
     "kernelspec": {

how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb

@@ -673,7 +673,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "roastala"
+        "name": "haining"
       }
     ],
     "kernelspec": {

how-to-use-azureml/work-with-data/dataprep/README.md

@@ -1,201 +0,0 @@
-# Azure Machine Learning Data Prep SDK
-
-The Azure Machine Learning Data Prep SDK helps data scientists explore, cleanse and transform data for machine learning workflows in any Python environment.
-
-Key benefits to the SDK:
-- Cross-platform functionality. Write with a single SDK and run it on Windows, macOS, or Linux.
-- Intelligent transformations powered by AI, including grouping similar values to their canonical form and deriving columns by examples without custom code.
-- Capability to work with large, multiple files of different schema.
-- Scalability on a single machine by streaming data during processing rather than loading into memory.
-- Seamless integration with other Azure Machine Learning services. You can simply pass your prepared data file into `AutoMLConfig` object for automated machine learning training.
-
-You will find in this repo:
-- [Getting Started Tutorial](tutorials/getting-started/getting-started.ipynb) for a quick introduction to the main features of Data Prep SDK.
-- [Case Study Notebooks](case-studies/new-york-taxi) that present an end-to-end data preparation tutorial where users start with small dataset, profile data with statistics summary, cleanse and perform feature engineering. All transformation steps are saved in a dataflow object. Users can easily reapply the same steps on the full dataset, and run it on Spark.
-- [How-To Guide Notebooks](how-to-guides) for more in-depth sample code at feature level.
-
-## Installation
-Here are the [SDK installation steps](https://docs.microsoft.com/python/api/overview/azure/dataprep/intro?view=azure-dataprep-py#install).
-
-## Documentation 
-Here is more information on how to use the new Data Prep SDK:
-- [SDK overview and API reference docs](http://aka.ms/data-prep-sdk) that show different classes, methods, and function parameters for the SDK.
-- [Tutorial: Prep NYC taxi data](https://docs.microsoft.com/azure/machine-learning/service/tutorial-data-prep) for regression modeling and then run automated machine learning to build the model.
-- [How to load data](https://docs.microsoft.com/azure/machine-learning/service/how-to-load-data) is an overview guide on how to load data using the Data Prep SDK.
-- [How to transform data](https://docs.microsoft.com/azure/machine-learning/service/how-to-transform-data) is an overview guide on how to transform data. 
-- [How to write data](https://docs.microsoft.com/azure/machine-learning/service/how-to-write-data) is an overview guide on how to write data to different storage locations. 
-
-## Support
-
-If you have any questions or feedback, send us an email at: [askamldataprep@microsoft.com](mailto:askamldataprep@microsoft.com).
-
-## Release Notes
-
-### 2019-04-08 (version 1.1.1)
-
-New features
-- You can read multiple Datastore/DataPath/DataReference sources using read_* transforms.
-- You can perform the following operations on columns to create a new column: division, floor, modulo, power, length.
-- Data Prep is now part of the Azure ML diagnostics suite and will log diagnostic information by default.
-  - To turn this off, set this environment variable to true: DISABLE_DPREP_LOGGER
-
-Bug fixes and improvements
-- Improved code documentation for commonly used classes and functions.
-- Fixed a bug in auto_read_file that failed to read Excel files.
-- Added option to overwrite the folder in read_pandas_dataframe.
-- Improved performance of dotnetcore2 dependency installation, and added support for Fedora 27/28 and Ubuntu 1804.
-- Improved the performance of reading from Azure Blobs.
-- Column type detection now supports columns of type Long.
-- Fixed a bug where some date values were being displayed as timestamps instead of Python datetime objects.
-- Fixed a bug where some type counts were being displayed as doubles instead of integers.
-
-### 2019-03-25 (version 1.1.0)
-
-Breaking changes
-- The concept of the Data Prep Package has been deprecated and is no longer supported. Instead of persisting multiple Dataflows in one Package, you can persist Dataflows individually.
-  - How-to guide: [Opening and Saving Dataflows notebook](https://aka.ms/aml-data-prep-open-save-dataflows-nb)
-
-New features
-- Data Prep can now recognize columns that match a particular Semantic Type, and split accordingly. The STypes currently supported include: email address, geographic coordinates (latitude & longitude), IPv4 and IPv6 addresses, US phone number, and US zip code.
-  - How-to guide: [Semantic Types notebook](https://aka.ms/aml-data-prep-semantic-types-nb)
-- Data Prep now supports the following operations to generate a resultant column from two numeric columns: subtract, multiply, divide, and modulo.
-- You can call `verify_has_data()` on a Dataflow to check whether the Dataflow would produce records if executed.
-
-Bug fixes and improvements
-- You can now specify the number of bins to use in a histogram for numeric column profiles.
-- The `read_pandas_dataframe` transform now requires the DataFrame to have string- or byte- typed column names.
-- Fixed a bug in the `fill_nulls` transform, where values were not correctly filled in if the column was missing.
-
-### 2019-03-11 (version 1.0.17)
-
-New features
-- Now supports adding two numeric columns to generate a resultant column using the expression language.
-
-Bug fixes and improvements
-- Improved the documentation and parameter checking for random_split.
-
-### 2019-02-27 (version 1.0.16)
-
-Bug fix
-- Fixed a Service Principal authentication issue that was caused by an API change.
-
-### 2019-02-25 (version 1.0.15)
-
-New features
-- Data Prep now supports writing file streams from a dataflow. Also provides the ability to manipulate the file stream names to create new file names.
-  - How-to guide: [Working With File Streams notebook](https://aka.ms/aml-data-prep-file-stream-nb)
- 
-Bug fixes and improvements
-- Improved performance of t-Digest on large data sets.
-- Data Prep now supports reading data from a DataPath.
-- One hot encoding now works on boolean and numeric columns.
-- Other miscellaneous bug fixes.
-
-### 2019-02-11 (version 1.0.12)
-
-New features
-- Data Prep now supports reading from an Azure SQL database using Datastore.
- 
-Changes
-- Significantly improved the memory performance of certain operations on large data.
-- `read_pandas_dataframe()` now requires `temp_folder` to be specified.
-- The `name` property on `ColumnProfile` has been deprecated - use `column_name` instead.
-
-### 2019-01-28 (version 1.0.8)
-
-Bug fixes
-- Significantly improved the performance of getting data profiles.
-- Fixed minor bugs related to error reporting.
-
-### 2019-01-14 (version 1.0.7)
-
-New features
-- Datastore improvements (documented in [Datastore how-to-guide](https://aka.ms/aml-data-prep-datastore-nb))
-  - Added ability to read from and write to Azure File Share and ADLS Datastores in scale-up.
-  - When using Datastores, Data Prep now supports using service principal authentication instead of interactive authentication.
-  - Added support for wasb and wasbs urls.
-
-### 2019-01-09 (version 1.0.6)
-
-Bug fixes
-- Fixed bug with reading from public readable Azure Blob containers on Spark.
-
-### 2018-12-19 (version 1.0.4)
-
-New features
-- `to_bool` function now allows mismatched values to be converted to Error values. This is the new default mismatch behavior for `to_bool` and `set_column_types`, whereas the previous default behavior was to convert mismatched values to False.
-- When calling `to_pandas_dataframe`, there is a new option to interpret null/missing values in numeric columns as NaN.
-- Added ability to check the return type of some expressions to ensure type consistency and fail early.
-- You can now call `parse_json` to parse values in a column as JSON objects and expand them into multiple columns.
-
-Bug fixes
-- Fixed a bug that crashed `set_column_types` in Python 3.5.2.
-- Fixed a bug that crashed when connecting to Datastore using an AML image.
-
-### 2018-12-07 (version 0.5.3)
-
-Fixed missing dependency issue for .NET Core2 on Ubuntu 16.
-
-### 2018-12-03 (version 0.5.2)
-
-Breaking changes
-- `SummaryFunction.N` was renamed to `SummaryFunction.Count`.
-  
-Bug fixes
-- Use latest AML Run Token when reading from and writing to datastores on remote runs. Previously, if the AML Run Token is updated in Python, the Data Prep runtime will not be updated with the updated AML Run Token.
-- Additional clearer error messages
-- to_spark_dataframe() will no longer crash when Spark uses Kryo serialization
-- Value Count Inspector can now show more than 1000 unique values
-- Random Split no longer fails if the original Dataflow doesn’t have a name  
-
-### 2018-11-19 (version 0.5.0)
-
-New features
-- Created a new DataPrep CLI to execute DataPrep packages and view the data profile for a dataset or dataflow
-- Redesigned SetColumnType API to improve usability
-- Renamed smart_read_file to auto_read_file
-- Now includes skew and kurtosis in the Data Profile
-- Can sample with stratified sampling
-- Can read from zip files that contain CSV files
-- Can split datasets row-wise with Random Split (e.g. into test-train sets)
-- Can get all the column data types from a dataflow or a data profile by calling .dtypes
-- Can get the row count from a dataflow or a data profile by calling .row_count
-
-Bug fixes
-- Fixed long to double conversion 
-- Fixed assert after any add column 
-- Fixed an issue with FuzzyGrouping, where it would not detect groups in some cases
-- Fixed sort function to respect multi-column sort order
-- Fixed and/or expressions to be similar to how Pandas handles them
-- Fixed reading from dbfs path.
-- Made error messages more understandable 
-- Now no longer fails when reading on remote compute target using AML token
-- Now no longer fails on Linux DSVM
-- Now no longer crashes when non-string values are in string predicates
-- Now handles assertion errors when Dataflow should fail correctly
-- Now supports dbutils mounted storage locations on Azure Databricks
-
-### 2018-11-05 (version 0.4.0)
-
-New features
-- Type Count added to Data Profile
-- Value Count and Histogram is now available
-- More percentiles in Data Profile
-- The Median is available in Summarize
-- Python 3.7 is now supported
-- When you save a dataflow that contains datastores to a Data Prep package, the datastore information will be persisted as part of the Data Prep package
-- Writing to datastore is now supported
- 
-Bug fixes
-- 64bit unsigned integer overflows are now handled properly on Linux 
-- Fixed incorrect text label for plain text files in smart_read
-- String column type now shows up in metrics view
-- Type count now is fixed to show ValueKinds mapped to single FieldType instead of individual ones
-- Write_to_csv no longer fails when path is provided as a string
-- When using Replace, leaving “find” blank will no longer fail
-
-## Datasets License Information
-
-IMPORTANT: Please read the notice and find out more about this NYC Taxi and Limousine Commission dataset here: http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml 
-
-IMPORTANT: Please read the notice and find out more about this Chicago Police Department dataset here: https://catalog.data.gov/dataset/crimes-2001-to-present-398a4 

how-to-use-azureml/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi.ipynb

@@ -1,508 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Cleaning up New York Taxi Cab data\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's use DataPrep to clean and featurize the data which can then be used to predict taxi trip duration. We will not use the For Hire Vehicle (FHV) datasets as they are not really taxi rides and they don't provide drop-off time and geo-coordinates."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from IPython.display import display\n",
-        "from os import path\n",
-        "from tempfile import mkdtemp\n",
-        "\n",
-        "import pandas as pd\n",
-        "import azureml.dataprep as dprep"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's take a quick peek at yellow cab data and green cab data to see what the data looks like. DataPrep supports globing, so you will notice below that we have added a `*` in the path.\n",
-        "\n",
-        "*We are using a small sample of the taxi data for this demo. You can find a bigger sample ~6GB by changing \"green-small\" to \"green-sample\" and \"yellow-small\" to \"yellow-sample\" in the paths below.*"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "pd.set_option('display.max_columns', None)\n",
-        "\n",
-        "cache_location = mkdtemp()\n",
-        "dataset_root = \"https://dprepdata.blob.core.windows.net/demo\"\n",
-        "\n",
-        "green_path = \"/\".join([dataset_root, \"green-small/*\"])\n",
-        "yellow_path = \"/\".join([dataset_root, \"yellow-small/*\"])\n",
-        "\n",
-        "print(\"Retrieving data from the following two sources:\")\n",
-        "print(green_path)\n",
-        "print(yellow_path)\n",
-        "\n",
-        "green_df = dprep.read_csv(path=green_path, header=dprep.PromoteHeadersMode.GROUPED)\n",
-        "yellow_df = dprep.auto_read_file(path=yellow_path)\n",
-        "\n",
-        "display(green_df.head(5))\n",
-        "display(yellow_df.head(5))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Data Cleanup"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's define some shortcut transforms that will apply to all Dataflows."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "all_columns = dprep.ColumnSelector(term=\".*\", use_regex=True)\n",
-        "drop_if_all_null = [all_columns, dprep.ColumnRelationship(dprep.ColumnRelationship.ALL)]\n",
-        "useful_columns = [\n",
-        "    \"cost\", \"distance\"\"distance\", \"dropoff_datetime\", \"dropoff_latitude\", \"dropoff_longitude\",\n",
-        "    \"passengers\", \"pickup_datetime\", \"pickup_latitude\", \"pickup_longitude\", \"store_forward\", \"vendor\"\n",
-        "]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's first work with the green taxi data and get it into a good shape that then can be combined with the yellow taxi data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tmp_df = (green_df\n",
-        "    .replace_na(columns=all_columns)\n",
-        "    .drop_nulls(*drop_if_all_null)\n",
-        "    .rename_columns(column_pairs={\n",
-        "        \"VendorID\": \"vendor\",\n",
-        "        \"lpep_pickup_datetime\": \"pickup_datetime\",\n",
-        "        \"Lpep_dropoff_datetime\": \"dropoff_datetime\",\n",
-        "        \"lpep_dropoff_datetime\": \"dropoff_datetime\",\n",
-        "        \"Store_and_fwd_flag\": \"store_forward\",\n",
-        "        \"store_and_fwd_flag\": \"store_forward\",\n",
-        "        \"Pickup_longitude\": \"pickup_longitude\",\n",
-        "        \"Pickup_latitude\": \"pickup_latitude\",\n",
-        "        \"Dropoff_longitude\": \"dropoff_longitude\",\n",
-        "        \"Dropoff_latitude\": \"dropoff_latitude\",\n",
-        "        \"Passenger_count\": \"passengers\",\n",
-        "        \"Fare_amount\": \"cost\",\n",
-        "        \"Trip_distance\": \"distance\"\n",
-        "     })\n",
-        "    .keep_columns(columns=useful_columns))\n",
-        "tmp_df.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "green_df = tmp_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's do the same thing to yellow taxi data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tmp_df = (yellow_df\n",
-        "    .replace_na(columns=all_columns)\n",
-        "    .drop_nulls(*drop_if_all_null)\n",
-        "    .rename_columns(column_pairs={\n",
-        "        \"vendor_name\": \"vendor\",\n",
-        "        \"VendorID\": \"vendor\",\n",
-        "        \"vendor_id\": \"vendor\",\n",
-        "        \"Trip_Pickup_DateTime\": \"pickup_datetime\",\n",
-        "        \"tpep_pickup_datetime\": \"pickup_datetime\",\n",
-        "        \"Trip_Dropoff_DateTime\": \"dropoff_datetime\",\n",
-        "        \"tpep_dropoff_datetime\": \"dropoff_datetime\",\n",
-        "        \"store_and_forward\": \"store_forward\",\n",
-        "        \"store_and_fwd_flag\": \"store_forward\",\n",
-        "        \"Start_Lon\": \"pickup_longitude\",\n",
-        "        \"Start_Lat\": \"pickup_latitude\",\n",
-        "        \"End_Lon\": \"dropoff_longitude\",\n",
-        "        \"End_Lat\": \"dropoff_latitude\",\n",
-        "        \"Passenger_Count\": \"passengers\",\n",
-        "        \"passenger_count\": \"passengers\",\n",
-        "        \"Fare_Amt\": \"cost\",\n",
-        "        \"fare_amount\": \"cost\",\n",
-        "        \"Trip_Distance\": \"distance\",\n",
-        "        \"trip_distance\": \"distance\"\n",
-        "    })\n",
-        "    .keep_columns(columns=useful_columns))\n",
-        "tmp_df.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "yellow_df = tmp_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's now append the rows from the `yellow_df` to `green_df`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "combined_df = green_df.append_rows(dataflows=[yellow_df])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's take a look at the pickup and drop-off coordinates' data profile to see how the data is distributed."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "decimal_type = dprep.TypeConverter(data_type=dprep.FieldType.DECIMAL)\n",
-        "combined_df = combined_df.set_column_types(type_conversions={\n",
-        "    \"pickup_longitude\": decimal_type,\n",
-        "    \"pickup_latitude\": decimal_type,\n",
-        "    \"dropoff_longitude\": decimal_type,\n",
-        "    \"dropoff_latitude\": decimal_type\n",
-        "})\n",
-        "combined_df.keep_columns(columns=[\n",
-        "    \"pickup_longitude\", \"pickup_latitude\", \n",
-        "    \"dropoff_longitude\", \"dropoff_latitude\"\n",
-        "]).get_profile()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "From the data profile, we can see that there are coordinates that are missing and coordinates that are not in New York. Let's filter out coordinates not in the [city border](https://mapmakerapp.com?map=5b60a055a191245990310739f658)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tmp_df = (combined_df\n",
-        "    .drop_nulls(\n",
-        "        columns=[\"pickup_longitude\", \"pickup_latitude\", \"dropoff_longitude\", \"dropoff_latitude\"],\n",
-        "        column_relationship=dprep.ColumnRelationship(dprep.ColumnRelationship.ANY)\n",
-        "    ) \n",
-        "    .filter(dprep.f_and(\n",
-        "        dprep.col(\"pickup_longitude\") <= -73.72,\n",
-        "        dprep.col(\"pickup_longitude\") >= -74.09,\n",
-        "        dprep.col(\"pickup_latitude\") <= 40.88,\n",
-        "        dprep.col(\"pickup_latitude\") >= 40.53,\n",
-        "        dprep.col(\"dropoff_longitude\") <= -73.72,\n",
-        "        dprep.col(\"dropoff_longitude\") >= -74.09,\n",
-        "        dprep.col(\"dropoff_latitude\") <= 40.88,\n",
-        "        dprep.col(\"dropoff_latitude\") >= 40.53\n",
-        "    )))\n",
-        "tmp_df.keep_columns(columns=[\n",
-        "    \"pickup_longitude\", \"pickup_latitude\", \n",
-        "    \"dropoff_longitude\", \"dropoff_latitude\"\n",
-        "]).get_profile()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "combined_df = tmp_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's take a look at the data profile for the `store_forward` column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "combined_df.keep_columns(columns='store_forward').get_profile()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "From the data profile of `store_forward` above, we can see that the data is inconsistent and there are missing values. Let's fix them."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "combined_df = combined_df.replace(columns=\"store_forward\", find=\"0\", replace_with=\"N\").fill_nulls(\"store_forward\", \"N\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's now split the pick up and drop off datetimes into a date column and a time column. We will use `split_column_by_example` to perform the split. If the `example` parameter of `split_column_by_example` is omitted, we will automatically try to figure out where to split based on the data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tmp_df = (combined_df\n",
-        "    .split_column_by_example(source_column=\"pickup_datetime\")\n",
-        "    .split_column_by_example(source_column=\"dropoff_datetime\"))\n",
-        "tmp_df.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "combined_df = tmp_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's rename the columns generated by `split_column_by_example` into meaningful names."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tmp_df = (combined_df\n",
-        "    .rename_columns(column_pairs={\n",
-        "        \"pickup_datetime_1\": \"pickup_date\",\n",
-        "        \"pickup_datetime_2\": \"pickup_time\",\n",
-        "        \"dropoff_datetime_1\": \"dropoff_date\",\n",
-        "        \"dropoff_datetime_2\": \"dropoff_time\"\n",
-        "    }))\n",
-        "tmp_df.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "combined_df = tmp_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Feature Engineering"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Datetime features"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's split the pickup and drop-off date further into day of week, day of month, and month. For pickup and drop-off time columns, we will split it into hour, minute, and second."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tmp_df = (combined_df\n",
-        "    .derive_column_by_example(\n",
-        "        source_columns=\"pickup_date\", \n",
-        "        new_column_name=\"pickup_weekday\", \n",
-        "        example_data=[(\"2009-01-04\", \"Sunday\"), (\"2013-08-22\", \"Thursday\")]\n",
-        "    )\n",
-        "    .derive_column_by_example(\n",
-        "        source_columns=\"dropoff_date\",\n",
-        "        new_column_name=\"dropoff_weekday\",\n",
-        "        example_data=[(\"2013-08-22\", \"Thursday\"), (\"2013-11-03\", \"Sunday\")]\n",
-        "    )\n",
-        "    .split_column_by_example(source_column=\"pickup_date\")\n",
-        "    .split_column_by_example(source_column=\"pickup_time\")\n",
-        "    .split_column_by_example(source_column=\"dropoff_date\")\n",
-        "    .split_column_by_example(source_column=\"dropoff_time\")\n",
-        "    .split_column_by_example(source_column=\"pickup_time_1\")\n",
-        "    .split_column_by_example(source_column=\"dropoff_time_1\")\n",
-        "    .drop_columns(columns=[\n",
-        "        \"pickup_date\", \"pickup_time\", \"dropoff_date\", \"dropoff_time\", \n",
-        "        \"pickup_date_1\", \"dropoff_date_1\", \"pickup_time_1\", \"dropoff_time_1\"\n",
-        "    ])\n",
-        "    .rename_columns(column_pairs={\n",
-        "        \"pickup_date_2\": \"pickup_month\",\n",
-        "        \"pickup_date_3\": \"pickup_monthday\",\n",
-        "        \"pickup_time_1_1\": \"pickup_hour\",\n",
-        "        \"pickup_time_1_2\": \"pickup_minute\",\n",
-        "        \"pickup_time_2\": \"pickup_second\",\n",
-        "        \"dropoff_date_2\": \"dropoff_month\",\n",
-        "        \"dropoff_date_3\": \"dropoff_monthday\",\n",
-        "        \"dropoff_time_1_1\": \"dropoff_hour\",\n",
-        "        \"dropoff_time_1_2\": \"dropoff_minute\",\n",
-        "        \"dropoff_time_2\": \"dropoff_second\"\n",
-        "    }))\n",
-        "tmp_df.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "combined_df = tmp_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "From the data above, we can see that the pickup and drop-off date and time components produced from the transforms above looks good. Let's drop the `pickup_datetime` and `dropoff_datetime` columns as they are no longer needed."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tmp_df = combined_df.drop_columns(columns=[\"pickup_datetime\", \"dropoff_datetime\"])\n",
-        "tmp_df.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "combined_df = tmp_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's now save the transformation steps into a DataPrep package so we can use it to to run on spark."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_path = path.join(mkdtemp(), \"new_york_taxi.dprep\")\n",
-        "combined_df.save(file_path=dflow_path)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "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": "3.6.4"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi_scale-out.ipynb

@@ -1,129 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Scale-Out Data Preparation\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Once we are done with preparing and featurizing the data locally, we can run the same steps on the full dataset in scale-out mode. The new york taxi cab data is about 300GB in total, which is perfect for scale-out. Let's start by downloading the package we saved earlier to disk. Feel free to run the `new_york_taxi_cab.ipynb` notebook to generate the package yourself, in which case you may comment out the download code and set the `package_path` to where the package is saved."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from tempfile import mkdtemp\n",
-        "from os import path\n",
-        "from urllib.request import urlretrieve\n",
-        "\n",
-        "dflow_root = mkdtemp()\n",
-        "dflow_path = path.join(dflow_root, \"new_york_taxi.dprep\")\n",
-        "print(\"Downloading Dataflow to: {}\".format(dflow_path))\n",
-        "urlretrieve(\"https://dprepdata.blob.core.windows.net/demo/new_york_taxi_v2.dprep\", dflow_path)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's load the package we just downloaded."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "\n",
-        "df = dprep.Dataflow.open(dflow_path)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's replace the datasources with the full dataset."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from uuid import uuid4\n",
-        "\n",
-        "other_step = df._get_steps()[7].arguments['dataflows'][0]['anonymousSteps'][0]\n",
-        "other_step['id'] = str(uuid4())\n",
-        "other_step['arguments']['path']['target'] = 1\n",
-        "other_step['arguments']['path']['resourceDetails'][0]['path'] = 'https://wranglewestus.blob.core.windows.net/nyctaxi/yellow_tripdata*'"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "green_dsource = dprep.BlobDataSource(\"https://wranglewestus.blob.core.windows.net/nyctaxi/green_tripdata*\")\n",
-        "df = df.replace_datasource(green_dsource)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Once we have replaced the datasource, we can now run the same steps on the full dataset. We will print the first 5 rows of the spark DataFrame. Since we are running on the full dataset, this might take a little while depending on your spark cluster's size."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "spark_df = df.take(5).to_pandas_dataframe()\n",
-        "spark_df.head(5)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "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": "3.6.4"
-    },
-    "skip_execute_as_test": true
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/work-with-data/dataprep/data/adls-dpreptestfiles.crt

@@ -1,45 +0,0 @@
------BEGIN PRIVATE KEY-----
-MIIEvwIBADANBgkqhkiG9w0BAQEFAASCBKkwggSlAgEAAoIBAQDmkkyF0BwipZow
-Wd1AMkRkySx0y079JPxpsYhv4i1xXKdoa9bpFqwoXmJpeQM1JWnU4UeZzFeM86qK
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-ZCNSqkMlcXyA0U1z8C1ILNUsbA==
------END PRIVATE KEY-----
------BEGIN CERTIFICATE-----
-MIICoTCCAYkCAgPoMA0GCSqGSIb3DQEBBQUAMBQxEjAQBgNVBAMMCUNMSS1Mb2dp
-bjAiGA8yMDE4MDcxMzIzMjA0N1oYDzIwMTkwNzEzMjMyMDQ5WjAUMRIwEAYDVQQD
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-GEnRVW/ANpsBZyKrAgMBAAEwDQYJKoZIhvcNAQEFBQADggEBAI4VlaFb9NsXMLdT
-Cw5/pk0Xo2Qi6483RGTy8vzrw88IE7f3juB/JWG+rayjtW5bBRx2fae4/ZIdZ4zg
-N2FDKn2PQPAc9m9pcKyUKUvWOC8ixSkrUmeQew0l1AXU0hsPSlJ7/7ZK4efoyB47
-hj71fsyKdyKbisZDcUFBq/S8PazdPF0YOD1W/4A2tW0cSMg+jmFWynuUTdWt3SU8
-CwBGqdiSKT5faJuYwIWnRXDEQS3ObRn1OFEfFdd4d2sxjxydWKRgnINnGlBdiFAT
-KzCozVr+75cO2ErH6x5C0hLQGG5BxXbaijyxyvaRNokTMVVv6OaDEnjzCGfJ72Yf
-2wgitNc=
------END CERTIFICATE-----

how-to-use-azureml/work-with-data/dataprep/data/chicago-aldermen-2015.csv

@@ -1,54 +0,0 @@
-"Retrieved from https://en.wikipedia.org/wiki/Chicago_City_Council on November 6, 2018"
-
-
-Ward,Name,Took Office,Party
-1,Proco Joe Moreno,2010*,Dem
-2,Brian Hopkins,2015,Dem
-3,Pat Dowell,2007,Dem
-4,Sophia King,2016*,Dem
-5,Leslie Hairston,1999,Dem
-6,Roderick Sawyer,2011,Dem
-7,Gregory Mitchell,2015,Dem
-8,Michelle A. Harris,2006*,Dem
-9,Anthony Beale,1999,Dem
-10,Susie Sadlowski Garza,2015,Dem
-11,Patrick Daley Thompson,2015,Dem
-12,George Cardenas,2003,Dem
-13,Marty Quinn,2011,Dem
-14,Edward M. Burke,1969,Dem
-15,Raymond Lopez,2015,Dem
-16,Toni Foulkes,2007,Dem
-17,David H. Moore,2015,Dem
-18,Derrick Curtis,2015,Dem
-19,Matthew O'Shea,2011,Dem
-20,Willie Cochran,2007,Dem
-21,Howard Brookins Jr.,2003,Dem
-22,Ricardo Muñoz,1993*,Dem
-23,Silvana Tabares,2018*,Dem
-24,"Michael Scott, Jr.",2015,Dem
-25,Daniel Solis,1996*,Dem
-26,Roberto Maldonado,2009*,Dem
-27,"Walter Burnett, Jr.",1995,Dem
-28,Jason Ervin,2011*,Dem
-29,Chris Taliaferro,2015,Dem
-30,Ariel Reboyras,2003,Dem
-31,Milly Santiago,2015,Dem
-32,Scott Waguespack,2007,Dem
-33,Deb Mell,2013*,Dem
-34,Carrie Austin,1994*,Dem
-35,Carlos Ramirez-Rosa,2015,Dem
-36,Gilbert Villegas,2015,Dem
-37,Emma Mitts,2000*,Dem
-38,Nicholas Sposato,2011,Ind
-39,Margaret Laurino,1994*,Dem
-40,Patrick J. O'Connor,1983,Dem
-41,Anthony Napolitano,2015,Rep
-42,Brendan Reilly,2007,Dem
-43,Michele Smith,2011,Dem
-44,Thomas M. Tunney,2002*,Dem
-45,John Arena,2011,Dem
-46,James Cappleman,2011,Dem
-47,Ameya Pawar,2011,Dem
-48,Harry Osterman,2011,Dem
-49,Joe Moore,1991,Dem
-50,Debra Silverstein,2011,Dem

how-to-use-azureml/work-with-data/dataprep/data/crime-dirty.csv

@@ -1,15 +0,0 @@
-File updated 11/2/2018
-
-
-
-ID|Case Number|Date|Block|IUCR|Primary Type|Description|Location Description|Arrest|Domestic|Beat|District|Ward|Community Area|FBI Code|X Coordinate|Y Coordinate|Year|Updated On|Latitude|Longitude|Location
-10140490|HY329907|07/05/2015 11:50:00 PM|050XX N NEWLAND AVE|0820|THEFT|$500 AND UNDER|STREET|false|false|1613|016|41|10|06|1129230|1933315|2015|07/12/2015 12:42:46 PM|41.973309466|-87.800174996|(41.973309466, -87.800174996)
-10139776|HY329265|07/05/2015 11:30:00 PM|011XX W MORSE AVE|0460|BATTERY|SIMPLE|STREET|false|true|2431|024|49|1|08B|1167370|1946271|2015|07/12/2015 12:42:46 PM|42.008124017|-87.65955018|(42.008124017, -87.65955018)
-10140270|HY329253|07/05/2015 11:20:00 PM|121XX S FRONT AVE|0486|BATTERY|DOMESTIC BATTERY SIMPLE|STREET|false|true|0532||9|53|08B|||2015|07/12/2015 12:42:46 PM|||
-10139885|HY329308|07/05/2015 11:19:00 PM|051XX W DIVISION ST|0610|BURGLARY|FORCIBLE ENTRY|SMALL RETAIL STORE|false|false|1531|015|37|25|05|1141721|1907465|2015|07/12/2015 12:42:46 PM|41.902152027|-87.754883404|(41.902152027, -87.754883404)
-10140379|HY329556|07/05/2015 11:00:00 PM|012XX W LAKE ST|0930|MOTOR VEHICLE THEFT|THEFT/RECOVERY: AUTOMOBILE|STREET|false|false|1215|012|27|28|07|1168413|1901632|2015|07/12/2015 12:42:46 PM|41.885610142|-87.657008701|(41.885610142, -87.657008701)
-10140868|HY330421|07/05/2015 10:54:00 PM|118XX S PEORIA ST|1320|CRIMINAL DAMAGE|TO VEHICLE|VEHICLE NON-COMMERCIAL|false|false|0524|005|34|53|14|1172409|1826485|2015|07/12/2015 12:42:46 PM|41.6793109|-87.644545209|(41.6793109, -87.644545209)
-10139762|HY329232|07/05/2015 10:42:00 PM|026XX W 37TH PL|1020|ARSON|BY FIRE|VACANT LOT/LAND|false|false|0911|009|12|58|09|1159436|1879658|2015|07/12/2015 12:42:46 PM|41.825500607|-87.690578042|(41.825500607, -87.690578042)
-10139722|HY329228|07/05/2015 10:30:00 PM|016XX S CENTRAL PARK AVE|1811|NARCOTICS|POSS: CANNABIS 30GMS OR LESS|ALLEY|true|false|1021|010|24|29|18|1152687|1891389|2015|07/12/2015 12:42:46 PM|41.857827814|-87.715028789|(41.857827814, -87.715028789)
-10139774|HY329209|07/05/2015 10:15:00 PM|048XX N ASHLAND AVE|1310|CRIMINAL DAMAGE|TO PROPERTY|APARTMENT|false|false|2032|020|46|3|14|1164821|1932394|2015|07/12/2015 12:42:46 PM|41.970099796|-87.669324377|(41.970099796, -87.669324377)
-10139697|HY329177|07/05/2015 10:10:00 PM|058XX S ARTESIAN AVE|1320|CRIMINAL DAMAGE|TO VEHICLE|ALLEY|false|false|0824|008|16|63|14|1160997|1865851|2015|07/12/2015 12:42:46 PM|41.787580282|-87.685233078|(41.787580282, -87.685233078)

how-to-use-azureml/work-with-data/dataprep/data/crime-spring.csv

@@ -1,11 +0,0 @@
-ID,Case Number,Date,Block,IUCR,Primary Type,Description,Location Description,Arrest,Domestic,Beat,District,Ward,Community Area,FBI Code,X Coordinate,Y Coordinate,Year,Updated On,Latitude,Longitude,Location
-10498554,HZ239907,4/4/2016 23:56,007XX E 111TH ST,1153,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT OVER $ 300,OTHER,FALSE,FALSE,531,5,9,50,11,1183356,1831503,2016,5/11/2016 15:48,41.69283384,-87.60431945,"(41.692833841, -87.60431945)"
-10516598,HZ258664,4/15/2016 17:00,082XX S MARSHFIELD AVE,890,THEFT,FROM BUILDING,RESIDENCE,FALSE,FALSE,614,6,21,71,6,1166776,1850053,2016,5/12/2016 15:48,41.74410697,-87.66449429,"(41.744106973, -87.664494285)"
-10519196,HZ261252,4/15/2016 10:00,104XX S SACRAMENTO AVE,1154,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT $300 AND UNDER,RESIDENCE,FALSE,FALSE,2211,22,19,74,11,,,2016,5/12/2016 15:50,,,
-10519591,HZ261534,4/15/2016 9:00,113XX S PRAIRIE AVE,1120,DECEPTIVE PRACTICE,FORGERY,RESIDENCE,FALSE,FALSE,531,5,9,49,10,,,2016,5/13/2016 15:51,,,
-10534446,HZ277630,4/15/2016 10:00,055XX N KEDZIE AVE,890,THEFT,FROM BUILDING,"SCHOOL, PUBLIC, BUILDING",FALSE,FALSE,1712,17,40,13,6,,,2016,5/25/2016 15:59,,,
-10535059,HZ278872,4/15/2016 4:30,004XX S KILBOURN AVE,810,THEFT,OVER $500,RESIDENCE,FALSE,FALSE,1131,11,24,26,6,,,2016,5/25/2016 15:59,,,
-10499802,HZ240778,4/15/2016 10:00,010XX N MILWAUKEE AVE,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,RESIDENCE,FALSE,FALSE,1213,12,27,24,11,,,2016,5/27/2016 15:45,,,
-10522293,HZ264802,4/15/2016 16:00,019XX W DIVISION ST,1110,DECEPTIVE PRACTICE,BOGUS CHECK,RESTAURANT,FALSE,FALSE,1424,14,1,24,11,1163094,1908003,2016,5/16/2016 15:48,41.90320604,-87.67636193,"(41.903206037, -87.676361925)"
-10523111,HZ265911,4/15/2016 8:00,061XX N SHERIDAN RD,1153,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT OVER $ 300,RESIDENCE,FALSE,FALSE,2433,24,48,77,11,,,2016,5/16/2016 15:50,,,
-10525877,HZ268138,4/15/2016 15:00,023XX W EASTWOOD AVE,1153,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT OVER $ 300,,FALSE,FALSE,1911,19,47,4,11,,,2016,5/18/2016 15:50,,,

how-to-use-azureml/work-with-data/dataprep/data/crime-winter.csv

@@ -1,11 +0,0 @@
-ID,Case Number,Date,Block,IUCR,Primary Type,Description,Location Description,Arrest,Domestic,Beat,District,Ward,Community Area,FBI Code,X Coordinate,Y Coordinate,Year,Updated On,Latitude,Longitude,Location
-10378283,HZ114126,1/10/2016 11:00,033XX W IRVING PARK RD,610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,TRUE,FALSE,1724,17,33,16,5,1153593,1926401,2016,5/22/2016 15:51,41.95388599,-87.71077048,"(41.95388599, -87.710770479)"
-10382154,HZ118288,1/10/2016 21:00,055XX S FRANCISCO AVE,1754,OFFENSE INVOLVING CHILDREN,AGG SEX ASSLT OF CHILD FAM MBR,RESIDENCE,FALSE,TRUE,824,8,14,63,2,1157983,1867874,2016,6/1/2016 15:51,41.79319349,-87.69622926,"(41.793193489, -87.696229255)"
-10374287,HZ110730,1/10/2016 11:50,043XX W ARMITAGE AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,FALSE,TRUE,2522,25,30,20,26,1146917,1912931,2016,6/7/2016 15:55,41.91705356,-87.73565764,"(41.917053561, -87.735657637)"
-10374662,HZ110403,1/10/2016 1:30,073XX S CLAREMONT AVE,497,BATTERY,AGGRAVATED DOMESTIC BATTERY: OTHER DANG WEAPON,STREET,FALSE,TRUE,835,8,18,66,04B,1162007,1855951,2016,2/4/2016 15:44,41.76039236,-87.68180481,"(41.760392356, -87.681804812)"
-10374720,HZ110836,1/10/2016 7:30,079XX S RHODES AVE,890,THEFT,FROM BUILDING,OTHER,FALSE,FALSE,624,6,6,44,6,1181279,1852568,2016,2/4/2016 15:44,41.75068679,-87.61127681,"(41.75068679, -87.611276811)"
-10375178,HZ110832,1/10/2016 14:20,057XX S KEDZIE AVE,460,BATTERY,SIMPLE,RESTAURANT,FALSE,FALSE,824,8,14,63,08B,1156029,1866379,2016,2/4/2016 15:44,41.78913051,-87.7034346,"(41.78913051, -87.703434602)"
-10398695,HZ135279,1/10/2016 23:00,031XX S PARNELL AVE,620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,FALSE,FALSE,915,9,11,60,5,1173138,1884117,2016,2/4/2016 15:44,41.8374442,-87.64017699,"(41.837444199, -87.640176991)"
-10402270,HZ138745,1/10/2016 11:00,051XX S ELIZABETH ST,620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,FALSE,FALSE,934,9,16,61,5,,,2016,2/4/2016 6:53,,,
-10380619,HZ116583,1/10/2016 9:41,091XX S PAXTON AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,RESIDENCE,TRUE,TRUE,413,4,7,48,26,1192434,1844707,2016,2/2/2016 15:56,41.72885134,-87.57065553,"(41.728851343, -87.570655525)"
-10400131,HZ136171,1/10/2016 18:00,0000X W TERMINAL ST,810,THEFT,OVER $500,AIRPORT BUILDING NON-TERMINAL - SECURE AREA,FALSE,FALSE,1651,16,41,76,6,,,2016,2/2/2016 15:58,,,

how-to-use-azureml/work-with-data/dataprep/data/crime.dprep

@@ -1,204 +0,0 @@
-{
-  "id": "75637565-60ad-4baa-87d3-396a7930cfe7",
-  "blocks": [
-    {
-      "id": "ba5a8061-129e-4618-953a-ce3e89c8f2cb",
-      "type": "Microsoft.DPrep.GetFilesBlock",
-      "arguments": {
-        "path": {
-          "target": 0,
-          "resourceDetails": [
-            {
-              "path": "./crime-spring.csv"
-            }
-          ]
-        }
-      },
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "1b345643-6b60-4ca1-99f9-2a64ae932a23",
-      "type": "Microsoft.DPrep.ParseDelimitedBlock",
-      "arguments": {
-        "columnHeadersMode": 1,
-        "fileEncoding": 0,
-        "handleQuotedLineBreaks": false,
-        "preview": false,
-        "separator": ",",
-        "skipRowsMode": 0
-      },
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "12cf73a2-1487-4915-bfa7-c86be7de08c0",
-      "type": "Microsoft.DPrep.SetColumnTypesBlock",
-      "arguments": {
-        "columnConversion": [
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "ID"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "IUCR"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Domestic"
-              }
-            },
-            "typeProperty": 1
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Beat"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "District"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Ward"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Community Area"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Year"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Longitude"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Arrest"
-              }
-            },
-            "typeProperty": 1
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "X Coordinate"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Updated On"
-              }
-            },
-            "typeArguments": {
-              "dateTimeFormats": [
-                "%m/%d/%Y %I:%M:%S %p"
-              ]
-            },
-            "typeProperty": 4
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Date"
-              }
-            },
-            "typeArguments": {
-              "dateTimeFormats": [
-                "%m/%d/%Y %I:%M:%S %p"
-              ]
-            },
-            "typeProperty": 4
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Y Coordinate"
-              }
-            },
-            "typeProperty": 3
-          },
-          {
-            "column": {
-              "type": 2,
-              "details": {
-                "selectedColumn": "Latitude"
-              }
-            },
-            "typeProperty": 3
-          }
-        ]
-      },
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    },
-    {
-      "id": "dfd62543-9285-412b-a930-0aeaaffde699",
-      "type": "Microsoft.DPrep.HandlePathColumnBlock",
-      "arguments": {
-        "pathColumnOperation": 0
-      },
-      "isEnabled": true,
-      "name": null,
-      "annotation": null
-    }
-  ],
-  "inspectors": []
-}

how-to-use-azureml/work-with-data/dataprep/data/crime.txt

@@ -1,10 +0,0 @@
-10140490 HY329907 7/5/2015  23:50                  050XX N NEWLAND AVE 820    THEFT                 
-10139776 HY329265 7/5/2015  23:30                  011XX W MORSE AVE 460    BATTERY
-10140270 HY329253 7/5/2015  23:20                  121XX S FRONT AVE 486    BATTERY
-10139885 HY329308 7/5/2015  23:19                  051XX W DIVISION ST 610    BURGLARY
-10140379 HY329556 7/5/2015  23:00                  012XX W LAKE ST 930    MOTOR VEHICLE THEFT
-10140868 HY330421 7/5/2015  22:54                  118XX S PEORIA ST 1320    CRIMINAL DAMAGE
-10139762 HY329232 7/5/2015  22:42                  026XX W 37TH PL 1020    ARSON
-10139722 HY329228 7/5/2015  22:30                  016XX S CENTRAL PARK AVE 1811    NARCOTICS
-10139774 HY329209 7/5/2015  22:15                  048XX N ASHLAND AVE 1310    CRIMINAL DAMAGE
-10139697 HY329177 7/5/2015  22:10                  058XX S ARTESIAN AVE 1320    CRIMINAL DAMAGE

how-to-use-azureml/work-with-data/dataprep/data/crime_duplicate_headers.csv

@@ -1,12 +0,0 @@
-ID,Case Number,Date,Block,IUCR,Primary Type,Description,Location Description,Arrest,Domestic,Beat,District,Ward,Community Area,FBI Code,X Coordinate,Y Coordinate,Year,Updated On,Latitude,Longitude,Location
-ID,Case Number,Date,Block,IUCR,Primary Type,Description,Location Description,Arrest,Domestic,Beat,District,Ward,Community Area,FBI Code,X Coordinate,Y Coordinate,Year,Updated On,Latitude,Longitude,Location
-10498554,HZ239907,4/15/2016 23:56,007XX E 111TH ST,1153,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT OVER $ 300,OTHER,FALSE,FALSE,531,5,9,50,11,1183356,1831503,2016,5/11/2016 15:48,41.69283384,-87.60431945,"(41.692833841, -87.60431945)"
-10516598,HZ258664,4/15/2016 17:00,082XX S MARSHFIELD AVE,890,THEFT,FROM BUILDING,RESIDENCE,FALSE,FALSE,614,6,21,71,6,1166776,1850053,2016,5/12/2016 15:48,41.74410697,-87.66449429,"(41.744106973, -87.664494285)"
-10519196,HZ261252,4/15/2016 10:00,104XX S SACRAMENTO AVE,1154,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT $300 AND UNDER,RESIDENCE,FALSE,FALSE,2211,22,19,74,11,,,2016,5/12/2016 15:50,,,
-10519591,HZ261534,4/15/2016 9:00,113XX S PRAIRIE AVE,1120,DECEPTIVE PRACTICE,FORGERY,RESIDENCE,FALSE,FALSE,531,5,9,49,10,,,2016,5/13/2016 15:51,,,
-10534446,HZ277630,4/15/2016 10:00,055XX N KEDZIE AVE,890,THEFT,FROM BUILDING,"SCHOOL, PUBLIC, BUILDING",FALSE,FALSE,1712,17,40,13,6,,,2016,5/25/2016 15:59,,,
-10535059,HZ278872,4/15/2016 4:30,004XX S KILBOURN AVE,810,THEFT,OVER $500,RESIDENCE,FALSE,FALSE,1131,11,24,26,6,,,2016,5/25/2016 15:59,,,
-10499802,HZ240778,4/15/2016 10:00,010XX N MILWAUKEE AVE,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,RESIDENCE,FALSE,FALSE,1213,12,27,24,11,,,2016,5/27/2016 15:45,,,
-10522293,HZ264802,4/15/2016 16:00,019XX W DIVISION ST,1110,DECEPTIVE PRACTICE,BOGUS CHECK,RESTAURANT,FALSE,FALSE,1424,14,1,24,11,1163094,1908003,2016,5/16/2016 15:48,41.90320604,-87.67636193,"(41.903206037, -87.676361925)"
-10523111,HZ265911,4/15/2016 8:00,061XX N SHERIDAN RD,1153,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT OVER $ 300,RESIDENCE,FALSE,FALSE,2433,24,48,77,11,,,2016,5/16/2016 15:50,,,
-10525877,HZ268138,4/15/2016 15:00,023XX W EASTWOOD AVE,1153,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT OVER $ 300,,FALSE,FALSE,1911,19,47,4,11,,,2016,5/18/2016 15:50,,,

how-to-use-azureml/work-with-data/dataprep/data/crime_fixed_width_file.txt

@@ -1,10 +0,0 @@
-10140490 HY329907 7/5/2015  23:50                  050XX N NEWLAND AVE 820    THEFT                 
-10139776 HY329265 7/5/2015  23:30                  011XX W MORSE AVE 460    BATTERY
-10140270 HY329253 7/5/2015  23:20                  121XX S FRONT AVE 486    BATTERY
-10139885 HY329308 7/5/2015  23:19                  051XX W DIVISION ST 610    BURGLARY
-10140379 HY329556 7/5/2015  23:00                  012XX W LAKE ST 930    MOTOR VEHICLE THEFT
-10140868 HY330421 7/5/2015  22:54                  118XX S PEORIA ST 1320    CRIMINAL DAMAGE
-10139762 HY329232 7/5/2015  22:42                  026XX W 37TH PL 1020    ARSON
-10139722 HY329228 7/5/2015  22:30                  016XX S CENTRAL PARK AVE 1811    NARCOTICS
-10139774 HY329209 7/5/2015  22:15                  048XX N ASHLAND AVE 1310    CRIMINAL DAMAGE
-10139697 HY329177 7/5/2015  22:10                  058XX S ARTESIAN AVE 1320    CRIMINAL DAMAGE

how-to-use-azureml/work-with-data/dataprep/data/crime_multiple_separators.csv

@@ -1,11 +0,0 @@
-ID |CaseNumber| |Completed|
-10140490 |HY329907| |Y|
-10139776 |HY329265| |Y|
-10140270 |HY329253| |N|
-10139885 |HY329308| |Y|
-10140379 |HY329556| |N|
-10140868 |HY330421| |N|
-10139762 |HY329232| |N|
-10139722 |HY329228| |Y|
-10139774 |HY329209| |N|
-10139697 |HY329177| |N|

how-to-use-azureml/work-with-data/dataprep/data/crime_partfiles/part-00000-0b08e77b-f17a-4c20-972c-aa382e830fca-c000.csv

@@ -1,914 +0,0 @@
-10140382,HY329023,07/05/2015 07:02:00 PM,004XX N CENTRAL PARK BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,false,1123,,27,23,08B,,,2015,07/12/2015 12:42:46 PM,,,
-10139396,HY328581,07/04/2015 11:00:00 PM,004XX N MONTICELLO AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENTIAL YARD (FRONT/BACK),false,false,1122,011,27,23,14,1151917,1902866,2015,07/11/2015 12:39:38 PM,41.889337148,-87.717552947,"(41.889337148, -87.717552947)"
-10137054,HY325533,07/02/2015 11:37:00 PM,005XX N SPRINGFIELD AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,APARTMENT,true,false,1122,011,27,23,15,1150251,1903434,2015,07/09/2015 12:37:51 PM,41.890928444,-87.723656407,"(41.890928444, -87.723656407)"
-10137515,HY326210,07/02/2015 10:00:00 PM,017XX N CICERO AVE,0560,ASSAULT,SIMPLE,APARTMENT,false,false,2533,025,37,25,08A,1144171,1911174,2015,07/09/2015 12:37:51 PM,41.912284239,-87.745790773,"(41.912284239, -87.745790773)"
-10136819,HY325292,07/02/2015 07:54:00 PM,016XX S DRAKE AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,APARTMENT,true,false,1021,010,24,29,15,1153024,1891215,2015,07/09/2015 12:37:51 PM,41.857343671,-87.713796395,"(41.857343671, -87.713796395)"
-10136842,HY325161,07/02/2015 06:50:00 PM,009XX E 131ST ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0533,005,9,54,06,1184854,1818532,2015,07/09/2015 12:37:51 PM,41.657204701,-87.599239629,"(41.657204701, -87.599239629)"
-10135642,HY324210,07/02/2015 02:10:00 AM,009XX E 130TH ST,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,CHA PARKING LOT/GROUNDS,false,false,0532,005,9,54,04B,1184757,1819322,2015,07/09/2015 12:37:51 PM,41.659374843,-87.599569957,"(41.659374843, -87.599569957)"
-10135444,HY323989,07/01/2015 08:40:00 PM,068XX S TALMAN AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0831,008,15,66,18,1159936,1858880,2015,07/08/2015 12:38:00 PM,41.768472756,-87.689314761,"(41.768472756, -87.689314761)"
-10134135,HY322934,07/01/2015 04:00:00 AM,079XX S VINCENNES AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,false,0623,006,17,44,08B,1175024,1852356,2015,07/08/2015 12:38:00 PM,41.750246796,-87.634204288,"(41.750246796, -87.634204288)"
-10147117,HY336163,06/28/2015 07:00:00 AM,020XX N KIMBALL AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENTIAL YARD (FRONT/BACK),false,false,1413,014,26,22,14,1153332,1913617,2015,07/12/2015 12:40:52 PM,41.918810907,-87.712070517,"(41.918810907, -87.712070517)"
-10130196,HY318835,06/28/2015 12:20:00 AM,033XX N HALSTED ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1925,019,44,6,08B,1170364,1922754,2015,07/05/2015 12:38:04 PM,41.943527622,-87.649225604,"(41.943527622, -87.649225604)"
-10129636,HY318033,06/27/2015 12:00:00 PM,044XX N BROADWAY,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,1913,019,46,3,06,1168391,1929931,2015,07/04/2015 12:37:19 PM,41.963264568,-87.656268955,"(41.963264568, -87.656268955)"
-10129573,HY317986,06/26/2015 07:00:00 PM,010XX N WOOD ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1212,012,1,24,06,1164162,1907126,2015,07/03/2015 12:39:14 PM,41.900776974,-87.672463767,"(41.900776974, -87.672463767)"
-10128956,HY315657,06/25/2015 11:16:00 AM,001XX N WOLCOTT AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,OTHER,false,false,1223,012,27,28,26,1163750,1901322,2015,07/02/2015 12:42:40 PM,41.884859047,-87.674140851,"(41.884859047, -87.674140851)"
-10147340,HY336420,06/21/2015 01:30:00 PM,006XX W BARRY AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1934,019,44,6,14,1171641,1920753,2015,07/12/2015 12:42:46 PM,41.93800874,-87.644591096,"(41.93800874, -87.644591096)"
-10120242,HY308991,06/20/2015 12:00:00 PM,022XX E 103RD ST,1780,OFFENSE INVOLVING CHILDREN,OTHER OFFENSE,POLICE FACILITY/VEH PARKING LOT,false,false,0434,004,10,51,26,1193088,1837089,2015,06/27/2015 12:41:22 PM,41.707930876,-87.568507723,"(41.707930876, -87.568507723)"
-10122352,HY308873,06/20/2015 10:20:00 AM,055XX W GLADYS AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,1522,,29,25,14,,,2015,06/27/2015 12:41:22 PM,,,
-10119796,HY308435,06/20/2015 01:28:00 AM,039XX S CALUMET AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0213,002,3,38,14,1179038,1878991,2015,06/27/2015 12:41:22 PM,41.823245374,-87.618684022,"(41.823245374, -87.618684022)"
-10119477,HY307724,06/19/2015 03:00:00 PM,052XX W LAKE ST,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,1523,015,28,25,03,1141587,1902060,2015,06/26/2015 12:42:22 PM,41.887322542,-87.755509318,"(41.887322542, -87.755509318)"
-10118190,HY306860,06/17/2015 07:48:00 PM,003XX S PULASKI RD,2250,LIQUOR LAW VIOLATION,LIQUOR LICENSE VIOLATION,TAVERN/LIQUOR STORE,true,false,1132,011,24,26,22,1149753,1897756,2015,06/24/2015 12:40:31 PM,41.875357085,-87.725632989,"(41.875357085, -87.725632989)"
-10117842,HY306398,06/17/2015 05:00:00 PM,030XX W GUNNISON ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1713,017,33,14,14,1154936,1932019,2015,06/24/2015 12:40:31 PM,41.969275252,-87.705682159,"(41.969275252, -87.705682159)"
-10116440,HY304973,06/16/2015 11:00:00 PM,016XX N HONORE ST,0810,THEFT,OVER $500,STREET,false,false,1434,014,32,24,06,1163721,1911003,2015,06/23/2015 12:51:44 PM,41.91142505,-87.673974124,"(41.91142505, -87.673974124)"
-10112616,HY301842,06/14/2015 09:00:00 PM,104XX S PROSPECT AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2212,022,19,72,14,1167593,1835635,2015,06/21/2015 12:38:51 PM,41.704524331,-87.661912753,"(41.704524331, -87.661912753)"
-10122748,HY311701,06/13/2015 03:30:00 PM,022XX S CENTRAL PARK AVE,0810,THEFT,OVER $500,APARTMENT,false,false,1024,010,22,30,06,1152766,1888660,2015,06/23/2015 12:51:44 PM,41.850337554,-87.714810965,"(41.850337554, -87.714810965)"
-10110096,HY298671,06/12/2015 06:00:00 PM,050XX S WINCHESTER AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0931,009,16,61,14,1164250,1871069,2015,06/19/2015 01:10:05 PM,41.801831215,-87.673158727,"(41.801831215, -87.673158727)"
-10113127,HY302162,06/12/2015 04:30:00 PM,031XX S ABERDEEN ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,0913,009,11,60,05,1169448,1884115,2015,06/19/2015 01:10:05 PM,41.837519626,-87.653717198,"(41.837519626, -87.653717198)"
-10109843,HY298100,06/12/2015 01:05:00 PM,068XX S NORMAL BLVD,0820,THEFT,$500 AND UNDER,"SCHOOL, PUBLIC, BUILDING",true,false,0722,007,6,68,06,1174171,1859468,2015,06/19/2015 01:10:05 PM,41.769781958,-87.63711913,"(41.769781958, -87.63711913)"
-10109480,HY297992,06/12/2015 12:20:00 PM,068XX S PERRY AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,0722,007,6,69,08A,1176533,1859790,2015,06/19/2015 01:10:05 PM,41.770612768,-87.628451443,"(41.770612768, -87.628451443)"
-10133746,HY320661,06/12/2015 11:00:00 AM,069XX S CRANDON AVE,1154,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT $300 AND UNDER,APARTMENT,false,false,0331,003,5,43,11,1192560,1859518,2015,07/05/2015 12:36:37 PM,41.769490914,-87.569712448,"(41.769490914, -87.569712448)"
-10108325,HY296759,06/11/2015 09:00:00 AM,037XX W 26TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,1031,010,22,30,14,1152090,1886433,2015,06/18/2015 12:41:41 PM,41.844239727,-87.717350648,"(41.844239727, -87.717350648)"
-10111213,HY300134,06/10/2015 01:00:00 PM,040XX W MELROSE ST,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,RESIDENCE,false,true,1731,017,31,16,26,1148889,1921264,2015,06/17/2015 12:40:49 PM,41.939882117,-87.728196374,"(41.939882117, -87.728196374)"
-10102842,HY291892,06/07/2015 08:00:00 PM,009XX S SPRINGFIELD AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,1133,011,24,26,14,1150571,1895527,2015,06/14/2015 12:39:55 PM,41.869224524,-87.722687811,"(41.869224524, -87.722687811)"
-10102538,HY291587,06/07/2015 02:40:00 PM,051XX S ROCKWELL ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0923,009,14,63,14,1159944,1870621,2015,06/14/2015 12:39:55 PM,41.800691514,-87.688962913,"(41.800691514, -87.688962913)"
-10102063,HY290911,06/06/2015 08:25:00 PM,054XX W FULTON ST,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,true,1523,015,28,25,26,1139936,1901441,2015,06/13/2015 12:39:41 PM,41.885654275,-87.761587509,"(41.885654275, -87.761587509)"
-10109910,HY298308,06/06/2015 03:00:00 PM,098XX S INDIANA AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,0511,005,6,49,05,1179279,1839603,2015,06/20/2015 12:40:44 PM,41.715155065,-87.61900001,"(41.715155065, -87.61900001)"
-10101757,HY290389,06/06/2015 02:12:00 PM,029XX W FILLMORE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1135,011,28,29,08B,1157137,1895279,2015,06/13/2015 12:39:41 PM,41.868413285,-87.698588955,"(41.868413285, -87.698588955)"
-10103505,HY292428,06/05/2015 06:00:00 PM,048XX S PAULINA ST,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0931,009,20,61,06,1165781,1872809,2015,06/12/2015 12:42:30 PM,41.806573572,-87.667494505,"(41.806573572, -87.667494505)"
-10101148,HY289611,06/05/2015 05:30:00 PM,058XX S DORCHESTER AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0235,002,5,41,07,1186500,1866746,2015,06/12/2015 12:42:30 PM,41.789470647,-87.591696818,"(41.789470647, -87.591696818)"
-10099041,HY287215,06/04/2015 06:50:00 AM,048XX S ASHLAND AVE,031A,ROBBERY,ARMED: HANDGUN,BANK,false,false,0933,009,20,61,03,1166526,1872752,2015,07/07/2015 12:40:12 PM,41.806401287,-87.66476372,"(41.806401287, -87.66476372)"
-10097772,HY286029,06/02/2015 10:30:00 PM,053XX W BYRON ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1634,016,38,15,06,1140003,1925444,2015,06/09/2015 12:37:43 PM,41.951519927,-87.760752962,"(41.951519927, -87.760752962)"
-10096983,HY285435,06/02/2015 02:30:00 PM,023XX N MILWAUKEE AVE,0810,THEFT,OVER $500,VEHICLE NON-COMMERCIAL,false,false,1414,014,35,22,06,1156799,1915514,2015,06/09/2015 12:37:43 PM,41.923946796,-87.699280832,"(41.923946796, -87.699280832)"
-10095244,HY284182,06/01/2015 07:19:00 PM,057XX W MADISON ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,JAIL / LOCK-UP FACILITY,true,false,1513,015,29,25,18,1138127,1899398,2015,06/08/2015 12:48:46 PM,41.880080898,-87.768280017,"(41.880080898, -87.768280017)"
-10093684,HY282559,05/31/2015 06:15:00 PM,071XX S VINCENNES AVE,0460,BATTERY,SIMPLE,GAS STATION,false,false,0731,007,6,69,08B,1176577,1857698,2015,06/07/2015 12:43:36 PM,41.764871097,-87.62835301,"(41.764871097, -87.62835301)"
-10092276,HY280537,05/29/2015 11:57:00 PM,068XX S CORNELL AVE,041A,BATTERY,AGGRAVATED: HANDGUN,STREET,false,false,0332,003,5,43,04B,1188428,1859737,2015,06/05/2015 12:41:44 PM,41.77019154,-87.584851252,"(41.77019154, -87.584851252)"
-10093489,HY282318,05/29/2015 08:58:00 PM,016XX N LARAMIE AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,2532,025,37,25,04B,1141421,1910182,2015,06/05/2015 12:41:44 PM,41.90961333,-87.755918184,"(41.90961333, -87.755918184)"
-10091871,HY279905,05/29/2015 10:00:00 AM,050XX S SPAULDING AVE,0497,BATTERY,AGGRAVATED DOMESTIC BATTERY: OTHER DANG WEAPON,RESIDENCE,false,true,0821,008,14,63,04B,1155187,1871130,2015,06/05/2015 12:41:44 PM,41.802184814,-87.706394917,"(41.802184814, -87.706394917)"
-10088970,HY277782,05/28/2015 01:15:00 AM,015XX N LARAMIE AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,2532,025,37,25,03,1141384,1909974,2015,06/04/2015 12:42:50 PM,41.909043238,-87.75605925,"(41.909043238, -87.75605925)"
-10088660,HY277409,05/27/2015 06:00:00 PM,054XX N CUMBERLAND AVE,0890,THEFT,FROM BUILDING,ATHLETIC CLUB,false,false,1614,016,41,76,06,1119277,1935253,2015,06/03/2015 12:42:25 PM,41.978791706,-87.836734078,"(41.978791706, -87.836734078)"
-10089078,HY277646,05/27/2015 05:52:00 PM,022XX W PERSHING RD,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,OTHER,false,true,0912,009,11,59,26,1162171,1878823,2015,06/03/2015 12:42:25 PM,41.823152685,-87.680567258,"(41.823152685, -87.680567258)"
-10085461,HY274661,05/25/2015 05:04:00 PM,074XX S ST LAWRENCE AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,RESIDENCE,false,false,0323,003,6,69,04B,1181559,1855960,2015,06/01/2015 12:47:00 PM,41.759988347,-87.610146282,"(41.759988347, -87.610146282)"
-10084832,HY273834,05/25/2015 12:49:00 AM,012XX W 73RD PL,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0734,007,17,67,08B,1168941,1855993,2015,06/01/2015 12:47:00 PM,41.760360685,-87.656390252,"(41.760360685, -87.656390252)"
-10083877,HY272579,05/23/2015 10:30:00 PM,048XX N KEYSTONE AVE,0560,ASSAULT,SIMPLE,RESIDENCE PORCH/HALLWAY,false,false,1712,017,39,14,08A,1148471,1932134,2015,05/30/2015 12:39:53 PM,41.969718324,-87.72945115,"(41.969718324, -87.72945115)"
-10086359,HY274268,05/23/2015 04:00:00 AM,026XX S HAMLIN AVE,033A,ROBBERY,ATTEMPT: ARMED-HANDGUN,SIDEWALK,false,false,1031,,22,30,03,,,2015,05/30/2015 12:39:53 PM,,,
-10080813,HY269135,05/20/2015 05:25:00 PM,110XX S MICHIGAN AVE,0860,THEFT,RETAIL THEFT,DRUG STORE,false,false,0513,005,9,49,06,1178749,1831610,2015,05/27/2015 12:41:26 PM,41.693233223,-87.621183177,"(41.693233223, -87.621183177)"
-10078694,HY267487,05/20/2015 05:20:00 AM,001XX W 113TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,SIDEWALK,false,true,0522,005,34,49,14,1177168,1830059,2015,05/27/2015 12:41:26 PM,41.689012774,-87.627018081,"(41.689012774, -87.627018081)"
-10077527,HY266272,05/18/2015 09:00:00 PM,029XX W HARRISON ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1135,011,2,27,14,1156787,1897264,2015,05/25/2015 12:39:09 PM,41.873867422,-87.6998201,"(41.873867422, -87.6998201)"
-10074976,HY264023,05/17/2015 02:20:00 PM,011XX N MAYFIELD AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1511,015,29,25,18,1136783,1906846,2015,05/24/2015 12:39:16 PM,41.90054335,-87.773036525,"(41.90054335, -87.773036525)"
-10074408,HY263293,05/16/2015 11:00:00 PM,002XX W 70TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0731,007,6,69,08B,1176094,1858601,2015,05/23/2015 12:39:33 PM,41.767359881,-87.630096272,"(41.767359881, -87.630096272)"
-10070781,HY259424,05/13/2015 06:00:00 PM,054XX S WABASH AVE,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,0225,002,3,40,06,1177595,1869166,2015,05/21/2015 01:30:34 PM,41.796317515,-87.624275174,"(41.796317515, -87.624275174)"
-10069076,HY257420,05/12/2015 03:55:00 PM,0000X E 83RD ST,0460,BATTERY,SIMPLE,RESTAURANT,false,false,0632,006,6,44,08B,1177781,1849937,2015,05/21/2015 01:30:34 PM,41.743546853,-87.624174513,"(41.743546853, -87.624174513)"
-10068765,HY257238,05/11/2015 02:00:00 PM,074XX S SANGAMON ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0733,007,17,68,14,1171297,1855717,2015,05/21/2015 01:30:34 PM,41.759552106,-87.647763521,"(41.759552106, -87.647763521)"
-10066956,HY255874,05/10/2015 08:30:00 PM,088XX S LUELLA AVE,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,0412,004,8,48,06,1192674,1846539,2015,05/21/2015 01:30:34 PM,41.733872682,-87.569716826,"(41.733872682, -87.569716826)"
-10061067,HY249438,05/06/2015 02:30:00 PM,011XX N WESTERN AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,STREET,false,true,1212,012,1,24,26,1160235,1907766,2015,05/21/2015 01:30:34 PM,41.902615292,-87.686870179,"(41.902615292, -87.686870179)"
-10057558,HY246574,05/04/2015 08:30:00 AM,002XX W 106TH ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0512,005,34,49,05,1176626,1834692,2015,05/11/2015 12:40:43 PM,41.701738583,-87.628863594,"(41.701738583, -87.628863594)"
-10057342,HY246477,05/03/2015 02:00:00 AM,002XX E 49TH ST,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,0224,002,3,38,03,1178647,1872628,2015,05/10/2015 12:43:18 PM,41.805793681,-87.620312128,"(41.805793681, -87.620312128)"
-10055977,HY245326,05/02/2015 09:30:00 PM,059XX N NAVARRE AVE,1360,CRIMINAL TRESPASS,TO VEHICLE,STREET,false,false,1611,016,41,10,26,1131653,1939109,2015,05/09/2015 12:54:37 PM,41.989167023,-87.79113002,"(41.989167023, -87.79113002)"
-10047645,HY237074,04/26/2015 07:17:00 PM,022XX W 71ST ST,4625,OTHER OFFENSE,PAROLE VIOLATION,STREET,true,false,0832,008,17,66,26,1162647,1857583,2015,05/03/2015 12:41:02 PM,41.764857472,-87.679413712,"(41.764857472, -87.679413712)"
-10046013,HY234766,04/24/2015 07:00:00 PM,005XX N MICHIGAN AVE,0890,THEFT,FROM BUILDING,DEPARTMENT STORE,false,false,1834,018,42,8,06,1177300,1903904,2015,05/01/2015 12:39:29 PM,41.891647792,-87.624305286,"(41.891647792, -87.624305286)"
-10045777,HY234407,04/24/2015 12:00:00 PM,082XX S COTTAGE GROVE AVE,1122,DECEPTIVE PRACTICE,COUNTERFEIT CHECK,CURRENCY EXCHANGE,false,false,0631,006,6,44,10,1182961,1850200,2015,05/23/2015 12:37:53 PM,41.744149843,-87.605186658,"(41.744149843, -87.605186658)"
-10043099,HY232289,04/22/2015 10:02:00 PM,077XX S EBERHART AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0624,006,6,69,14,1180950,1854001,2015,04/29/2015 12:46:00 PM,41.754626668,-87.612438413,"(41.754626668, -87.612438413)"
-10044435,HY232550,04/22/2015 08:15:00 PM,093XX S STEWART AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0634,006,21,49,14,1175433,1842805,2015,04/29/2015 12:46:00 PM,41.724028492,-87.632990344,"(41.724028492, -87.632990344)"
-10042666,HY231827,04/22/2015 12:31:00 PM,004XX E 32ND ST,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,APARTMENT,false,false,0211,002,4,35,26,1179471,1883821,2015,04/29/2015 12:46:00 PM,41.836489362,-87.616947764,"(41.836489362, -87.616947764)"
-10042314,HY231616,04/22/2015 12:15:00 PM,068XX S MICHIGAN AVE,4625,OTHER OFFENSE,PAROLE VIOLATION,STREET,true,false,0322,003,20,69,26,1178301,1859745,2015,04/29/2015 12:46:00 PM,41.770449338,-87.621972052,"(41.770449338, -87.621972052)"
-10039516,HY229079,04/20/2015 11:30:00 AM,066XX S LAFLIN ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0725,007,17,67,14,1167437,1860810,2015,04/27/2015 12:45:29 PM,41.773611511,-87.661764615,"(41.773611511, -87.661764615)"
-10038323,HY228207,04/19/2015 03:39:00 PM,079XX S WOOD ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,APARTMENT,true,false,0611,006,21,71,15,1165767,1852106,2015,04/26/2015 12:39:05 PM,41.749762163,-87.668133219,"(41.749762163, -87.668133219)"
-10035528,HY224812,04/16/2015 09:10:00 PM,132XX S BURLEY AVE,0610,BURGLARY,FORCIBLE ENTRY,"SCHOOL, PUBLIC, BUILDING",false,false,0433,004,10,55,05,1199800,1817793,2015,04/23/2015 12:42:02 PM,41.654814497,-87.544575541,"(41.654814497, -87.544575541)"
-10037634,HY227208,04/16/2015 07:00:00 PM,079XX S YATES BLVD,0580,STALKING,SIMPLE,STREET,false,false,0414,004,7,46,08A,1193528,1852987,2015,04/23/2015 12:42:02 PM,41.751545711,-87.566377706,"(41.751545711, -87.566377706)"
-10035186,HY224450,04/16/2015 12:00:00 AM,043XX W 25TH PL,1750,OFFENSE INVOLVING CHILDREN,CHILD ABUSE,RESIDENCE,false,false,1013,010,22,30,20,1147698,1886734,2015,06/28/2015 12:40:29 PM,41.845151022,-87.733460968,"(41.845151022, -87.733460968)"
-10043019,HY232232,04/15/2015 01:45:00 PM,083XX S KERFOOT AVE,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,APARTMENT,false,false,0622,006,21,71,26,1173267,1849190,2015,04/24/2015 12:43:44 PM,41.74159788,-87.640736088,"(41.74159788, -87.640736088)"
-10036700,HY225094,04/15/2015 08:00:00 AM,058XX S BLACKSTONE AVE,1154,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT $300 AND UNDER,RESIDENCE,false,false,0235,002,5,41,11,1186947,1866698,2015,04/22/2015 12:47:10 PM,41.789328339,-87.590059355,"(41.789328339, -87.590059355)"
-10032646,HY222500,04/15/2015 03:24:00 AM,049XX W FULLERTON AVE,041A,BATTERY,AGGRAVATED: HANDGUN,SIDEWALK,false,false,2521,025,31,19,04B,1143266,1915551,2015,04/22/2015 12:47:10 PM,41.924312148,-87.749005973,"(41.924312148, -87.749005973)"
-10031809,HY221582,04/14/2015 12:00:00 PM,036XX W LEXINGTON ST,2093,NARCOTICS,FOUND SUSPECT NARCOTICS,ALLEY,true,false,1133,011,24,27,26,1152082,1896490,2015,04/21/2015 03:59:18 PM,41.871837474,-87.717115121,"(41.871837474, -87.717115121)"
-10027994,HY217421,04/10/2015 08:28:00 PM,003XX N LAMON AVE,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),STREET,true,false,1532,015,28,25,18,1143693,1901637,2015,04/17/2015 12:55:31 PM,41.886122605,-87.747785968,"(41.886122605, -87.747785968)"
-10028354,HY217751,04/10/2015 07:00:00 PM,022XX W 47TH ST,0610,BURGLARY,FORCIBLE ENTRY,COMMERCIAL / BUSINESS OFFICE,false,false,0931,009,12,61,05,1161901,1873418,2015,04/17/2015 12:55:31 PM,41.808326347,-87.681708152,"(41.808326347, -87.681708152)"
-10028175,HY217738,04/10/2015 04:00:00 PM,039XX S LAKE PARK AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,0214,002,4,36,26,1183288,1879279,2015,04/17/2015 12:55:31 PM,41.823937602,-87.603083608,"(41.823937602, -87.603083608)"
-10026268,HY215497,04/08/2015 09:00:00 PM,086XX S SAGINAW AVE,0610,BURGLARY,FORCIBLE ENTRY,OTHER,false,false,0423,004,7,46,05,1195367,1848070,2015,04/15/2015 12:59:16 PM,41.738007887,-87.559800758,"(41.738007887, -87.559800758)"
-10026351,HY215662,04/08/2015 05:00:00 PM,048XX S WESTERN AVE,0820,THEFT,$500 AND UNDER,CTA TRAIN,false,false,0922,009,12,58,06,1161141,1872753,2015,04/15/2015 12:59:16 PM,41.806517283,-87.684514078,"(41.806517283, -87.684514078)"
-10025005,HY214424,04/08/2015 03:48:00 AM,013XX N CAMPBELL AVE,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,APARTMENT,false,false,1423,014,26,24,11,1159529,1909108,2015,04/15/2015 12:59:16 PM,41.906312418,-87.689426439,"(41.906312418, -87.689426439)"
-10023943,HY213442,04/07/2015 12:49:00 PM,015XX S KEELER AVE,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,STREET,true,false,1012,010,24,29,04B,1148671,1892315,2015,04/14/2015 12:54:50 PM,41.860447286,-87.729746171,"(41.860447286, -87.729746171)"
-10023029,HY212757,04/06/2015 06:50:00 PM,063XX S STEWART AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,OTHER,true,false,0722,007,20,68,18,1174672,1862929,2015,04/13/2015 12:58:14 PM,41.779268187,-87.635179701,"(41.779268187, -87.635179701)"
-10023241,HY213007,04/06/2015 04:00:00 PM,036XX W LE MOYNE ST,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,false,2535,025,26,23,14,1151652,1909686,2015,04/13/2015 12:58:14 PM,41.908057114,-87.718346605,"(41.908057114, -87.718346605)"
-10022408,HY212071,04/05/2015 09:00:00 PM,037XX W WRIGHTWOOD AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,2524,025,35,22,06,1151111,1916988,2015,04/12/2015 12:45:09 PM,41.928105088,-87.72014218,"(41.928105088, -87.72014218)"
-10021678,HY211343,04/05/2015 12:00:00 AM,049XX N AVERS AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,"SCHOOL, PUBLIC, GROUNDS",false,false,1712,017,39,14,14,1149775,1932807,2015,04/12/2015 12:45:09 PM,41.97153976,-87.724638691,"(41.97153976, -87.724638691)"
-10019065,HY208218,04/02/2015 07:45:00 PM,025XX E 95TH PL,1320,CRIMINAL DAMAGE,TO VEHICLE,DRIVEWAY - RESIDENTIAL,false,false,0431,004,7,51,14,1194456,1842139,2015,04/09/2015 12:47:19 PM,41.721755113,-87.563332757,"(41.721755113, -87.563332757)"
-10015678,HY204987,03/31/2015 01:17:00 PM,046XX N BROADWAY,1821,NARCOTICS,MANU/DEL:CANNABIS 10GM OR LESS,SIDEWALK,true,false,1914,019,46,3,18,1167959,1931001,2015,04/07/2015 12:49:51 PM,41.966210041,-87.657826246,"(41.966210041, -87.657826246)"
-10015737,HY205013,03/31/2015 09:00:00 AM,027XX E 89TH ST,0560,ASSAULT,SIMPLE,"SCHOOL, PUBLIC, BUILDING",true,false,0423,004,7,46,08A,1195945,1846528,2015,04/12/2015 12:43:21 PM,41.733762235,-87.557734065,"(41.733762235, -87.557734065)"
-10014530,HY204114,03/30/2015 05:35:00 PM,013XX N HUDSON AVE,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,SIDEWALK,false,false,1821,018,27,8,04B,1173106,1909574,2015,04/06/2015 12:56:12 PM,41.907300651,-87.639539378,"(41.907300651, -87.639539378)"
-10012385,HY202038,03/28/2015 06:50:00 PM,036XX W GRAND AVE,0460,BATTERY,SIMPLE,GROCERY FOOD STORE,false,false,1112,011,27,23,08B,1152105,1907552,2015,04/04/2015 12:43:24 PM,41.902192292,-87.716738854,"(41.902192292, -87.716738854)"
-10020751,HY210143,03/27/2015 05:00:00 PM,105XX S SAWYER AVE,1153,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT OVER $ 300,RESIDENCE,false,false,2211,022,19,74,11,1156598,1834160,2015,04/05/2015 12:44:22 PM,41.700704845,-87.702214708,"(41.700704845, -87.702214708)"
-10011486,HY200686,03/27/2015 04:30:00 AM,078XX S COLES AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0421,004,7,43,05,1197539,1853730,2015,04/03/2015 12:48:39 PM,41.753485501,-87.551654884,"(41.753485501, -87.551654884)"
-10008464,HY197715,03/25/2015 10:30:00 AM,049XX W CONCORD PL,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,RESIDENCE,false,false,2533,025,37,25,04A,1142971,1910472,2015,04/01/2015 12:59:33 PM,41.910380348,-87.750216831,"(41.910380348, -87.750216831)"
-10007608,HY197439,03/25/2015 02:02:00 AM,006XX S CICERO AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1533,015,24,25,18,1144445,1896879,2015,04/01/2015 12:59:33 PM,41.873051963,-87.745144168,"(41.873051963, -87.745144168)"
-10005857,HY195687,03/23/2015 02:45:00 PM,127XX S PEORIA ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0523,005,34,53,26,1172691,1820353,2015,03/30/2015 12:50:30 PM,41.662477478,-87.643692601,"(41.662477478, -87.643692601)"
-10005950,HY196073,03/23/2015 12:25:00 PM,016XX W NELSON ST,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,1931,019,32,6,06,1164709,1920349,2015,03/30/2015 12:50:30 PM,41.93705014,-87.670078974,"(41.93705014, -87.670078974)"
-10004311,HY194163,03/22/2015 12:05:00 PM,104XX S HOXIE AVE,1360,CRIMINAL TRESPASS,TO VEHICLE,STREET,false,false,0434,004,10,51,26,1195232,1835974,2015,03/29/2015 12:46:38 PM,41.704818682,-87.56069309,"(41.704818682, -87.56069309)"
-10010662,HY194007,03/22/2015 09:10:00 AM,0000X W TERMINAL ST,1350,CRIMINAL TRESPASS,TO STATE SUP LAND,AIRPORT TERMINAL LOWER LEVEL - NON-SECURE AREA,true,false,1651,016,41,76,26,1100317,1935189,2015,03/29/2015 12:46:38 PM,41.978896531,-87.906463888,"(41.978896531, -87.906463888)"
-10003161,HY192560,03/20/2015 11:00:00 PM,003XX W 60TH PL,0496,BATTERY,AGGRAVATED DOMESTIC BATTERY: KNIFE/CUTTING INST,RESIDENCE,true,true,0711,007,20,68,04B,1174990,1864872,2015,03/27/2015 12:43:43 PM,41.784592899,-87.633955949,"(41.784592899, -87.633955949)"
-10001206,HY190733,03/19/2015 03:15:00 PM,076XX S PERRY AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0623,006,6,69,14,1176677,1854255,2015,03/26/2015 12:42:14 PM,41.75542086,-87.628089942,"(41.75542086, -87.628089942)"
-9998224,HY188343,03/17/2015 04:28:00 PM,048XX W ROOSEVELT RD,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,CTA BUS,false,false,1533,015,24,25,08B,1144517,1894311,2015,03/24/2015 12:41:57 PM,41.866003693,-87.744944445,"(41.866003693, -87.744944445)"
-9995182,HY185943,03/15/2015 08:00:00 PM,040XX S INDIANA AVE,0560,ASSAULT,SIMPLE,APARTMENT,false,false,0213,002,3,38,08A,1178229,1878171,2015,03/22/2015 12:40:27 PM,41.821013654,-87.621676823,"(41.821013654, -87.621676823)"
-9994150,HY184507,03/14/2015 01:00:00 PM,040XX W GEORGE ST,0820,THEFT,$500 AND UNDER,STREET,false,false,2523,025,31,21,06,1148712,1919018,2015,03/21/2015 12:41:38 PM,41.933722327,-87.728905109,"(41.933722327, -87.728905109)"
-9993498,HY183509,03/13/2015 10:30:00 PM,009XX E 80TH ST,0920,MOTOR VEHICLE THEFT,ATT: AUTOMOBILE,STREET,false,false,0624,006,8,44,07,1184065,1852200,2015,03/20/2015 12:42:30 PM,41.749612347,-87.60107917,"(41.749612347, -87.60107917)"
-9993401,HY183397,03/13/2015 05:45:00 PM,0000X N STATE ST,0460,BATTERY,SIMPLE,STREET,false,false,0112,001,42,32,08B,1176403,1900554,2015,03/20/2015 12:42:30 PM,41.882475504,-87.627700686,"(41.882475504, -87.627700686)"
-9994352,HY184735,03/12/2015 06:30:00 PM,0000X E OHIO ST,0810,THEFT,OVER $500,PARKING LOT/GARAGE(NON.RESID.),false,false,1834,018,42,8,06,1176451,1904161,2015,03/19/2015 12:40:58 PM,41.892372222,-87.627415473,"(41.892372222, -87.627415473)"
-9991562,HY181458,03/12/2015 12:20:00 PM,029XX W ADDISON ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,false,false,1733,017,33,21,06,1155968,1923741,2015,03/19/2015 12:40:58 PM,41.946539101,-87.702111692,"(41.946539101, -87.702111692)"
-10004818,HY179615,03/10/2015 11:21:00 PM,079XX S COTTAGE GROVE AVE,0291,CRIM SEXUAL ASSAULT,ATTEMPT NON-AGGRAVATED,STREET,false,false,0624,,8,44,02,,,2015,03/23/2015 12:42:19 PM,,,
-9989176,HY179410,03/10/2015 07:24:00 PM,064XX W HIGGINS AVE,2890,PUBLIC PEACE VIOLATION,OTHER VIOLATION,STREET,false,false,1613,016,41,10,26,1132462,1934423,2015,03/17/2015 12:53:42 PM,41.976294114,-87.788264031,"(41.976294114, -87.788264031)"
-9985998,HY175947,03/08/2015 12:38:00 AM,036XX S MICHIGAN AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,APARTMENT,false,false,0212,002,3,35,04A,1177827,1881117,2015,03/15/2015 12:40:37 PM,41.829106844,-87.623062197,"(41.829106844, -87.623062197)"
-9985765,HY175547,03/07/2015 06:00:00 PM,016XX W SHERWIN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,2423,024,49,1,08B,1164303,1948764,2015,03/14/2015 12:40:59 PM,42.015030538,-87.670763363,"(42.015030538, -87.670763363)"
-9991010,HY173529,03/06/2015 07:15:00 AM,0000X W CHECKPOINT 7 ST,5007,OTHER OFFENSE,OTHER WEAPONS VIOLATION,AIRPORT TERMINAL UPPER LEVEL - SECURE AREA,false,false,1653,016,41,76,26,1101708,1934266,2015,03/13/2015 03:58:05 PM,41.976344553,-87.901365347,"(41.976344553, -87.901365347)"
-9984641,HY173965,03/06/2015 12:15:00 AM,050XX N SHERIDAN RD,0810,THEFT,OVER $500,SIDEWALK,false,false,2024,020,46,3,06,1168685,1933669,2015,03/13/2015 03:58:05 PM,41.973515376,-87.655079227,"(41.973515376, -87.655079227)"
-9982348,HY172088,03/04/2015 08:00:00 PM,016XX W 47TH ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0931,009,20,61,08B,1166097,1873507,2015,03/11/2015 12:43:59 PM,41.80848224,-87.666315654,"(41.80848224, -87.666315654)"
-9980903,HY170838,03/03/2015 06:46:00 PM,035XX W CHICAGO AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),STREET,true,false,1121,011,27,23,18,1152272,1905061,2015,03/10/2015 12:50:41 PM,41.89535345,-87.716191254,"(41.89535345, -87.716191254)"
-9980703,HY170468,03/03/2015 01:30:00 PM,010XX N LAWNDALE AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),RESIDENCE PORCH/HALLWAY,true,false,1112,011,27,23,18,1151566,1906611,2015,03/10/2015 12:50:41 PM,41.89962071,-87.718743456,"(41.89962071, -87.718743456)"
-9975095,HY164313,02/25/2015 06:00:00 PM,035XX W PALMER ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,1413,014,26,22,05,1152200,1914429,2015,03/04/2015 12:46:43 PM,41.921061534,-87.716208154,"(41.921061534, -87.716208154)"
-9972872,HY162326,02/24/2015 01:30:00 PM,022XX W 19TH ST,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,SIDEWALK,false,true,1234,012,25,31,26,1161755,1890664,2015,03/03/2015 12:38:55 PM,41.855654326,-87.681763869,"(41.855654326, -87.681763869)"
-9970647,HY160602,02/22/2015 11:40:00 PM,0000X E GRAND AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,HOTEL/MOTEL,false,true,1834,018,42,8,08B,1176699,1903953,2015,03/01/2015 12:38:30 PM,41.891795857,-87.626510975,"(41.891795857, -87.626510975)"
-9979724,HY157398,02/20/2015 08:10:00 AM,072XX S RACINE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0734,,17,67,08B,,,2015,03/03/2015 12:38:55 PM,,,
-9968252,HY157292,02/19/2015 11:40:00 PM,052XX S CALIFORNIA AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,0923,009,14,63,03,1158550,1869986,2015,02/26/2015 12:47:04 PM,41.798977552,-87.694092519,"(41.798977552, -87.694092519)"
-9966895,HY156125,02/18/2015 04:30:00 PM,0000X W 95TH ST,0560,ASSAULT,SIMPLE,CTA BUS STOP,false,false,0634,006,21,49,08A,1177744,1841988,2015,02/25/2015 12:47:36 PM,41.721734632,-87.624549931,"(41.721734632, -87.624549931)"
-9965540,HY155265,02/17/2015 11:45:00 PM,001XX N CENTRAL AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1523,015,29,25,08B,1139065,1900502,2015,02/24/2015 12:49:00 PM,41.883093419,-87.764808886,"(41.883093419, -87.764808886)"
-9963921,HY153624,02/15/2015 09:12:00 PM,019XX W 103RD ST,0620,BURGLARY,UNLAWFUL ENTRY,RESTAURANT,false,false,2212,022,19,72,05,1165579,1836330,2015,02/22/2015 12:52:34 PM,41.706474378,-87.66926812,"(41.706474378, -87.66926812)"
-9962798,HY151920,02/14/2015 07:50:00 AM,059XX S CARPENTER ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,RESIDENCE,true,false,0712,007,16,68,18,1170291,1865347,2015,02/21/2015 12:48:00 PM,41.785999946,-87.651170542,"(41.785999946, -87.651170542)"
-9969490,HY159017,02/14/2015 12:00:00 AM,070XX S MERRILL AVE,1750,OFFENSE INVOLVING CHILDREN,CHILD ABUSE,APARTMENT,false,false,0331,003,5,43,20,1191721,1858717,2015,02/23/2015 12:43:57 PM,41.767313308,-87.572813747,"(41.767313308, -87.572813747)"
-9961582,HY150020,02/13/2015 10:22:00 AM,050XX W ADAMS ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,true,true,1533,015,28,25,08B,1142531,1898814,2015,02/20/2015 12:46:33 PM,41.878397624,-87.752123357,"(41.878397624, -87.752123357)"
-9960121,HY148185,02/11/2015 03:45:00 PM,013XX S ASHLAND AVE,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",true,false,1233,012,2,28,08B,1165893,1893995,2015,02/18/2015 01:01:53 PM,41.864707716,-87.666480488,"(41.864707716, -87.666480488)"
-9959747,HY147844,02/11/2015 11:00:00 AM,080XX S HALSTED ST,0334,ROBBERY,ATTEMPT: ARMED-KNIFE/CUT INSTR,SIDEWALK,false,false,0621,006,21,71,03,1172334,1851289,2015,02/18/2015 01:01:53 PM,41.747378368,-87.644092954,"(41.747378368, -87.644092954)"
-9960689,HY149104,02/11/2015 09:00:00 AM,044XX W CONGRESS PKWY,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1131,011,24,26,05,1146683,1897357,2015,02/18/2015 01:01:53 PM,41.874321274,-87.736915119,"(41.874321274, -87.736915119)"
-9957195,HY146047,02/09/2015 08:15:00 PM,038XX W MONROE ST,2170,NARCOTICS,POSSESSION OF DRUG EQUIPMENT,ALLEY,true,false,1122,011,28,26,18,1150474,1899339,2015,02/16/2015 12:49:01 PM,41.879686974,-87.722944403,"(41.879686974, -87.722944403)"
-9957141,HY145623,02/09/2015 06:30:00 AM,073XX S BLACKSTONE AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,0324,003,5,43,05,1187445,1856254,2015,02/16/2015 12:49:01 PM,41.760657299,-87.588565015,"(41.760657299, -87.588565015)"
-9955125,HY143913,02/08/2015 03:15:00 AM,001XX S KENTON AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1113,011,28,25,16,1145635,1899142,2015,02/15/2015 12:43:39 PM,41.879239448,-87.740717694,"(41.879239448, -87.740717694)"
-9954997,HY143724,02/07/2015 10:30:00 PM,045XX W MONROE ST,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1113,011,28,26,16,1145901,1899233,2015,02/14/2015 12:46:15 PM,41.879484118,-87.739738665,"(41.879484118, -87.739738665)"
-9955338,HY144033,02/07/2015 07:30:00 PM,046XX N BROADWAY,0610,BURGLARY,FORCIBLE ENTRY,COMMERCIAL / BUSINESS OFFICE,false,false,1914,019,46,3,05,1167897,1931128,2015,02/14/2015 12:46:15 PM,41.966559874,-87.658050527,"(41.966559874, -87.658050527)"
-9954491,HY142587,02/06/2015 11:20:00 PM,092XX S JUSTINE ST,0460,BATTERY,SIMPLE,APARTMENT,false,false,2221,022,21,73,08B,1167666,1843431,2015,02/13/2015 12:43:08 PM,41.725916207,-87.661422654,"(41.725916207, -87.661422654)"
-9953753,HY141810,02/06/2015 01:15:00 PM,046XX W 59TH ST,1340,CRIMINAL DAMAGE,TO STATE SUP PROP,CTA STATION,false,false,0813,008,23,62,14,1146459,1865053,2015,02/13/2015 12:43:08 PM,41.785678796,-87.738558722,"(41.785678796, -87.738558722)"
-9953242,HY141398,02/06/2015 07:10:00 AM,025XX N PARKSIDE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,2515,025,30,19,14,1138314,1916618,2015,02/13/2015 12:43:08 PM,41.92733127,-87.767176057,"(41.92733127, -87.767176057)"
-9952676,HY140948,02/05/2015 05:02:00 PM,052XX N SHERIDAN RD,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,2023,020,48,77,06,1168740,1934786,2015,02/12/2015 12:43:51 PM,41.976579259,-87.654844448,"(41.976579259, -87.654844448)"
-9952089,HY140529,02/04/2015 03:15:00 PM,030XX S DR MARTIN LUTHER KING JR DR,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0133,001,2,35,08B,1179263,1885133,2015,02/11/2015 12:40:51 PM,41.840094342,-87.617670874,"(41.840094342, -87.617670874)"
-9951209,HY139612,02/03/2015 05:00:00 PM,046XX S SACRAMENTO AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0922,009,14,58,07,1157194,1873605,2015,02/10/2015 12:37:41 PM,41.808936137,-87.698967366,"(41.808936137, -87.698967366)"
-9960871,HY149190,02/02/2015 09:03:00 AM,035XX N LONG AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1634,016,38,15,26,1139776,1922962,2015,02/13/2015 12:43:08 PM,41.944713239,-87.761648277,"(41.944713239, -87.761648277)"
-9950021,HY138936,02/01/2015 03:00:00 PM,006XX E 84TH ST,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,0632,006,6,44,08A,1182016,1849383,2015,02/08/2015 12:39:37 PM,41.741929795,-87.60867441,"(41.741929795, -87.60867441)"
-9947532,HY136043,02/01/2015 03:30:00 AM,013XX N RITCHIE CT,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,1824,018,43,8,06,1176592,1909199,2015,02/08/2015 12:39:37 PM,41.906193549,-87.626745256,"(41.906193549, -87.626745256)"
-9949528,HY136813,01/31/2015 01:02:00 PM,068XX S CRANDON AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,0331,003,5,43,26,1192551,1859796,2015,02/07/2015 12:41:58 PM,41.770253986,-87.569736391,"(41.770253986, -87.569736391)"
-9946865,HY135120,01/31/2015 12:00:00 AM,055XX S LA SALLE ST,0820,THEFT,$500 AND UNDER,CTA TRAIN,false,false,0225,002,3,68,06,1176193,1868334,2015,02/07/2015 12:41:58 PM,41.794066043,-87.629441364,"(41.794066043, -87.629441364)"
-9945866,HY133776,01/30/2015 09:23:00 AM,076XX S COTTAGE GROVE AVE,4651,OTHER OFFENSE,SEX OFFENDER: FAIL REG NEW ADD,APARTMENT,true,false,0624,006,6,69,26,1182926,1854545,2015,04/22/2015 12:45:18 PM,41.756073797,-87.605180167,"(41.756073797, -87.605180167)"
-9945059,HY133480,01/29/2015 09:00:00 PM,049XX W NEWPORT AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,true,1634,016,38,15,26,1142659,1922440,2015,02/05/2015 12:49:48 PM,41.943227569,-87.751064454,"(41.943227569, -87.751064454)"
-9944712,HY132979,01/29/2015 02:30:00 PM,030XX W 26TH ST,0330,ROBBERY,AGGRAVATED,ALLEY,false,false,1033,010,12,30,03,1156719,1886639,2015,02/05/2015 12:49:48 PM,41.844712652,-87.700357361,"(41.844712652, -87.700357361)"
-9943063,HY131303,01/27/2015 11:00:00 PM,063XX N CALIFORNIA AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,2413,024,50,2,06,1156547,1942203,2015,02/03/2015 12:50:43 PM,41.9971881,-87.699481376,"(41.9971881, -87.699481376)"
-9940929,HY129670,01/26/2015 05:27:00 PM,008XX E 40TH ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0214,002,4,36,08B,1182966,1878533,2015,02/02/2015 12:52:04 PM,41.82189803,-87.604288123,"(41.82189803, -87.604288123)"
-9939680,HY128781,01/25/2015 11:20:00 PM,023XX S BLUE ISLAND AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,SIDEWALK,false,false,1034,010,25,31,04B,1164548,1888723,2015,02/01/2015 12:44:26 PM,41.85026942,-87.671567136,"(41.85026942, -87.671567136)"
-9938486,HY127235,01/24/2015 01:00:00 PM,0000X E LAKE ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0111,001,42,32,06,1176901,1901784,2015,01/31/2015 12:47:54 PM,41.88583944,-87.625834807,"(41.88583944, -87.625834807)"
-9936457,HY125274,01/22/2015 11:00:00 PM,014XX W 115TH ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0524,005,34,53,08B,1168509,1828436,2015,01/29/2015 12:53:50 PM,41.684749447,-87.658764946,"(41.684749447, -87.658764946)"
-9927294,HY115893,01/15/2015 12:05:00 PM,0000X N KILBOURN AVE,4625,OTHER OFFENSE,PAROLE VIOLATION,STREET,true,false,1113,011,28,26,26,1146342,1899948,2015,01/22/2015 12:50:54 PM,41.881437781,-87.738101155,"(41.881437781, -87.738101155)"
-9923758,HY113006,01/13/2015 03:00:00 AM,077XX S EXCHANGE AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,0421,004,7,43,04B,1196415,1854165,2015,01/20/2015 12:47:56 PM,41.754707122,-87.555759435,"(41.754707122, -87.555759435)"
-9922352,HY111851,01/11/2015 06:00:00 PM,039XX N WAYNE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1923,019,47,6,06,1166483,1926426,2015,01/18/2015 12:44:12 PM,41.953687864,-87.663384725,"(41.953687864, -87.663384725)"
-9921456,HY110645,01/10/2015 09:19:00 PM,035XX W 26TH ST,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,1032,010,22,30,06,1152863,1886453,2015,01/17/2015 12:47:56 PM,41.844279361,-87.714513323,"(41.844279361, -87.714513323)"
-9921209,HY110227,01/10/2015 02:33:00 PM,066XX S MICHIGAN AVE,0460,BATTERY,SIMPLE,SIDEWALK,true,false,0322,003,20,69,08B,1178131,1860693,2015,01/17/2015 12:47:56 PM,41.773054608,-87.622566481,"(41.773054608, -87.622566481)"
-9920010,HY108705,01/09/2015 09:00:00 AM,054XX S ELLIS AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,false,0233,002,5,41,08B,1183904,1869756,2015,01/16/2015 12:48:48 PM,41.797791393,-87.60112141,"(41.797791393, -87.60112141)"
-9919121,HY107948,01/08/2015 01:49:00 PM,018XX W 21ST ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,ALLEY,true,false,1234,012,25,31,18,1164368,1890143,2015,01/15/2015 12:46:55 PM,41.854169841,-87.672187639,"(41.854169841, -87.672187639)"
-9917982,HY107286,01/07/2015 06:24:00 PM,045XX S ASHLAND AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,0924,009,3,61,06,1166395,1874589,2015,01/14/2015 12:49:41 PM,41.811445018,-87.665191803,"(41.811445018, -87.665191803)"
-9917893,HY107151,01/06/2015 08:20:00 PM,072XX S COLES AVE,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,0334,003,7,43,03,1194421,1857630,2015,01/13/2015 12:50:18 PM,41.764264564,-87.562952988,"(41.764264564, -87.562952988)"
-9913396,HY102423,01/03/2015 05:05:00 AM,059XX S MORGAN ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,0712,007,16,68,08B,1170624,1865338,2015,01/10/2015 12:39:37 PM,41.785967993,-87.649949865,"(41.785967993, -87.649949865)"
-9926946,HY102381,01/03/2015 12:15:00 AM,0000X W TERMINAL ST,0820,THEFT,$500 AND UNDER,AIRPORT TERMINAL LOWER LEVEL - NON-SECURE AREA,false,false,1653,016,41,76,06,1101811,1934379,2015,01/17/2015 12:47:56 PM,41.976653215,-87.900984463,"(41.976653215, -87.900984463)"
-9915311,HY105038,01/02/2015 05:00:00 PM,095XX S YALE AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,STREET,false,true,0511,005,21,49,26,1176335,1841584,2015,01/09/2015 12:40:58 PM,41.72065772,-87.629722924,"(41.72065772, -87.629722924)"
-9910855,HX560918,12/31/2014 03:30:00 PM,016XX S CLARK ST,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,0131,001,3,33,06,1175811,1892220,2014,01/07/2015 12:42:19 PM,41.859619819,-87.630125199,"(41.859619819, -87.630125199)"
-9911568,HY100634,12/31/2014 09:00:00 AM,019XX E 79TH ST,0820,THEFT,$500 AND UNDER,OTHER,false,false,0414,004,8,46,06,1190480,1852935,2014,01/07/2015 12:42:19 PM,41.751477033,-87.577548654,"(41.751477033, -87.577548654)"
-9905525,HX556019,12/26/2014 11:05:00 PM,033XX W 19TH ST,1812,NARCOTICS,POSS: CANNABIS MORE THAN 30GMS,STREET,true,false,1024,010,24,29,18,1154543,1890475,2014,01/02/2015 12:40:27 PM,41.855282815,-87.708240531,"(41.855282815, -87.708240531)"
-9900039,HX550748,12/21/2014 07:00:00 PM,048XX N HARDING AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1712,017,39,14,14,1149138,1932092,2014,12/28/2014 12:47:37 PM,41.969590146,-87.726999648,"(41.969590146, -87.726999648)"
-9898956,HX549362,12/20/2014 01:20:00 PM,023XX W JACKSON BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CHA APARTMENT,false,true,1225,012,2,28,08B,1160776,1898600,2014,12/27/2014 12:41:59 PM,41.877451805,-87.685137328,"(41.877451805, -87.685137328)"
-9898429,HX548657,12/19/2014 08:27:00 PM,014XX W MADISON ST,2820,OTHER OFFENSE,TELEPHONE THREAT,OTHER,false,false,1224,012,2,28,26,1166919,1900092,2014,12/26/2014 12:47:30 PM,41.881416445,-87.662539142,"(41.881416445, -87.662539142)"
-9897672,HX547786,12/19/2014 07:30:00 AM,080XX S BURNHAM AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0422,004,7,46,08B,1196277,1852033,2014,12/26/2014 12:47:30 PM,41.748860177,-87.556335712,"(41.748860177, -87.556335712)"
-9898261,HX548370,12/19/2014 07:15:00 AM,013XX W ROSEDALE AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,2013,020,48,77,05,1166367,1939263,2014,12/26/2014 12:47:30 PM,41.988915519,-87.663442157,"(41.988915519, -87.663442157)"
-9896028,HX546312,12/17/2014 02:13:00 PM,008XX N WOLCOTT AVE,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,1212,012,1,24,06,1163616,1905871,2014,12/24/2014 12:48:14 PM,41.897344685,-87.674504657,"(41.897344685, -87.674504657)"
-9894751,HX544921,12/16/2014 07:40:00 PM,002XX W 63RD ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,CTA PLATFORM,true,false,0711,007,20,68,11,1175833,1863236,2014,12/23/2014 12:50:08 PM,41.780084684,-87.630914183,"(41.780084684, -87.630914183)"
-9891938,HX542544,12/14/2014 10:05:00 PM,069XX S MARSHFIELD AVE,031A,ROBBERY,ARMED: HANDGUN,ALLEY,false,false,0735,007,17,67,03,1166589,1858376,2014,12/21/2014 12:44:13 PM,41.766950424,-87.664942543,"(41.766950424, -87.664942543)"
-9894321,HX544462,12/14/2014 08:00:00 PM,048XX S DORCHESTER AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0222,002,4,39,07,1186425,1872929,2014,12/21/2014 12:44:13 PM,41.806439038,-87.591776239,"(41.806439038, -87.591776239)"
-9892945,HX543306,12/13/2014 12:01:00 AM,008XX N KEDVALE AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,VACANT LOT/LAND,false,false,1111,011,37,23,14,1148602,1905391,2014,12/20/2014 12:40:24 PM,41.896330653,-87.729661846,"(41.896330653, -87.729661846)"
-9886850,HX537621,12/10/2014 05:22:00 PM,003XX N MICHIGAN AVE,3300,PUBLIC PEACE VIOLATION,PUBLIC DEMONSTRATION,STREET,true,false,0114,001,42,32,26,1177284,1902460,2014,12/17/2014 12:53:10 PM,41.887685745,-87.624407859,"(41.887685745, -87.624407859)"
-9886725,HX537414,12/10/2014 05:00:00 PM,106XX S MICHIGAN AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0512,005,9,49,26,1178778,1834510,2014,12/17/2014 12:53:10 PM,41.701190574,-87.62098919,"(41.701190574, -87.62098919)"
-9892333,HX542910,12/08/2014 01:00:00 PM,021XX E 87TH ST,0560,ASSAULT,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0412,004,8,45,08A,1191717,1847744,2014,12/16/2014 12:50:48 PM,41.737202561,-87.573183761,"(41.737202561, -87.573183761)"
-9898752,HX549109,12/08/2014 08:00:00 AM,029XX S ARCH ST,1153,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT OVER $ 300,RESIDENCE,false,false,0913,009,11,60,11,1168235,1884726,2014,12/21/2014 12:42:21 PM,41.839222519,-87.658150589,"(41.839222519, -87.658150589)"
-9880507,HX531080,12/05/2014 11:00:00 AM,033XX N WESTERN AVE,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,false,false,1921,019,47,5,06,1159720,1922380,2014,12/12/2014 12:38:27 PM,41.942727784,-87.688358118,"(41.942727784, -87.688358118)"
-9880169,HX530851,12/05/2014 09:15:00 AM,045XX W GEORGE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,2521,025,31,20,08B,1145651,1918935,2014,12/12/2014 12:38:27 PM,41.933553234,-87.740156372,"(41.933553234, -87.740156372)"
-9880034,HX530796,12/05/2014 07:50:00 AM,053XX S SPAULDING AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,true,false,0822,008,14,63,07,1155333,1868802,2014,12/17/2014 12:51:14 PM,41.795793533,-87.705921787,"(41.795793533, -87.705921787)"
-9881039,HX530696,12/05/2014 04:00:00 AM,123XX S WALLACE ST,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,STREET,false,true,0523,005,34,53,04A,1174584,1823040,2014,12/12/2014 12:38:27 PM,41.669809275,-87.636685763,"(41.669809275, -87.636685763)"
-9876435,HX527370,12/01/2014 05:30:00 PM,024XX W ALTGELD ST,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,false,false,1431,014,35,22,06,1159423,1916577,2014,12/08/2014 12:48:35 PM,41.926810096,-87.689609883,"(41.926810096, -87.689609883)"
-9875501,HX526486,12/01/2014 02:30:00 PM,034XX W FLOURNOY ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,RESIDENCE PORCH/HALLWAY,true,false,1133,011,24,27,18,1153382,1896775,2014,12/08/2014 12:48:35 PM,41.872593836,-87.712334692,"(41.872593836, -87.712334692)"
-9875481,HX526344,12/01/2014 12:30:00 PM,089XX S BUFFALO AVE,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",false,false,0424,004,10,46,08B,1199555,1846379,2014,12/08/2014 12:48:35 PM,41.733263337,-87.544514098,"(41.733263337, -87.544514098)"
-9873984,HX524656,11/30/2014 12:10:00 AM,018XX W AUGUSTA BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1212,012,32,24,08B,1163731,1906655,2014,12/07/2014 12:49:14 PM,41.899493615,-87.67406015,"(41.899493615, -87.67406015)"
-9872781,HX523051,11/28/2014 02:39:00 PM,043XX W 26TH ST,1330,CRIMINAL TRESPASS,TO LAND,RESTAURANT,true,false,1013,010,22,30,26,1147940,1886407,2014,12/05/2014 12:46:44 PM,41.844249049,-87.732581242,"(41.844249049, -87.732581242)"
-9871926,HX522328,11/27/2014 03:30:00 PM,078XX S BURNHAM AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0421,004,7,43,14,1196037,1853716,2014,12/04/2014 12:50:41 PM,41.7534844,-87.557159507,"(41.7534844, -87.557159507)"
-9871360,HX521607,11/26/2014 04:45:00 PM,004XX N WABASH AVE,0820,THEFT,$500 AND UNDER,MEDICAL/DENTAL OFFICE,false,false,1834,018,42,8,06,1176706,1903104,2014,12/03/2014 12:41:59 PM,41.889465999,-87.626510952,"(41.889465999, -87.626510952)"
-9869821,HX520159,11/25/2014 08:00:00 AM,068XX S KILBOURN AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,OTHER,false,true,0833,008,13,65,26,1147595,1858910,2014,12/02/2014 12:51:55 PM,41.768799751,-87.734550378,"(41.768799751, -87.734550378)"
-9867852,HX518460,11/23/2014 09:00:00 AM,049XX S ST LAWRENCE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,RESIDENTIAL YARD (FRONT/BACK),false,false,0223,002,4,38,14,1181132,1872374,2014,11/30/2014 12:40:35 PM,41.805039751,-87.611206014,"(41.805039751, -87.611206014)"
-9866997,HX517284,11/22/2014 09:45:00 PM,047XX S LAKE PARK AVE,0810,THEFT,OVER $500,STREET,false,false,0222,002,4,39,06,1186257,1874027,2014,11/29/2014 12:42:24 PM,41.809456003,-87.592357676,"(41.809456003, -87.592357676)"
-9871091,HX521295,11/22/2014 05:18:00 PM,027XX W 71ST ST,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,ATM (AUTOMATIC TELLER MACHINE),false,false,0831,008,18,66,11,1158968,1857400,2014,11/29/2014 12:42:24 PM,41.764431252,-87.692903372,"(41.764431252, -87.692903372)"
-9861563,HX511192,11/17/2014 08:44:00 PM,035XX W MONTROSE AVE,1582,OFFENSE INVOLVING CHILDREN,CHILD PORNOGRAPHY,RESIDENCE,false,false,1723,017,33,14,17,1152230,1929059,2014,11/24/2014 12:37:49 PM,41.961206786,-87.715710628,"(41.961206786, -87.715710628)"
-9860968,HX510651,11/17/2014 06:20:00 PM,092XX S MAY ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,2222,022,21,73,08B,1170245,1843258,2014,11/24/2014 12:37:49 PM,41.725385841,-87.651980618,"(41.725385841, -87.651980618)"
-9858939,HX508481,11/15/2014 08:57:00 PM,048XX N WINTHROP AVE,1360,CRIMINAL TRESPASS,TO VEHICLE,STREET,true,false,2033,020,46,3,26,1167964,1932162,2014,11/22/2014 12:39:04 PM,41.969395755,-87.657774202,"(41.969395755, -87.657774202)"
-9899074,HX549382,11/12/2014 07:00:00 PM,034XX W BELMONT AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,1412,014,35,21,03,1153132,1921033,2014,12/21/2014 12:44:13 PM,41.939164986,-87.712608085,"(41.939164986, -87.712608085)"
-9855970,HX504637,11/12/2014 03:48:00 PM,085XX S COLFAX AVE,3710,INTERFERENCE WITH PUBLIC OFFICER,RESIST/OBSTRUCT/DISARM OFFICER,SIDEWALK,true,false,0423,004,7,46,24,1194946,1848557,2014,11/19/2014 12:39:51 PM,41.739354629,-87.561327161,"(41.739354629, -87.561327161)"
-9854870,HX503963,11/11/2014 04:30:00 PM,017XX E 74TH ST,0820,THEFT,$500 AND UNDER,VEHICLE NON-COMMERCIAL,false,false,0324,003,8,43,06,1189052,1856215,2014,11/18/2014 12:38:30 PM,41.760511949,-87.582676629,"(41.760511949, -87.582676629)"
-9853749,HX502584,11/10/2014 09:00:00 PM,099XX S LA SALLE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,0511,005,9,49,08B,1177069,1838933,2014,11/17/2014 12:40:31 PM,41.713366524,-87.627114115,"(41.713366524, -87.627114115)"
-9851182,HX499999,11/08/2014 04:45:00 PM,008XX N STATE ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,CTA STATION,true,false,1832,018,42,8,18,1176184,1905758,2014,11/15/2014 12:38:17 PM,41.896760495,-87.62834785,"(41.896760495, -87.62834785)"
-9850584,HX499243,11/08/2014 01:20:00 AM,0000X W DIVISION ST,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,STREET,true,false,1824,018,42,8,24,1175834,1908327,2014,11/15/2014 12:38:17 PM,41.903817841,-87.629555923,"(41.903817841, -87.629555923)"
-9850838,HX499494,11/06/2014 03:00:00 PM,045XX N MALDEN ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1913,019,46,3,06,1166767,1930333,2014,11/13/2014 12:42:15 PM,41.964402729,-87.662228235,"(41.964402729, -87.662228235)"
-9914350,HY103574,11/05/2014 09:00:00 AM,018XX W HURON ST,1153,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT OVER $ 300,OTHER,false,false,1221,012,1,24,11,1163988,1904752,2014,01/06/2015 12:40:42 PM,41.894266217,-87.673169976,"(41.894266217, -87.673169976)"
-9846021,HX495345,11/04/2014 10:20:00 PM,001XX E 71ST ST,0560,ASSAULT,SIMPLE,RESIDENCE,true,false,0323,003,6,69,08A,1178683,1857942,2014,11/11/2014 12:41:33 PM,41.765493035,-87.620626567,"(41.765493035, -87.620626567)"
-9840961,HX490074,10/31/2014 09:00:00 PM,069XX N CAMPBELL AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,2411,024,50,2,14,1158361,1946121,2014,11/07/2014 12:42:06 PM,42.007902189,-87.692700532,"(42.007902189, -87.692700532)"
-9843414,HX492917,10/31/2014 05:30:00 PM,002XX W 63RD ST,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,STREET,false,false,0711,007,20,68,11,1175833,1863236,2014,11/07/2014 12:42:06 PM,41.780084684,-87.630914183,"(41.780084684, -87.630914183)"
-9839306,HX488498,10/30/2014 02:30:00 PM,016XX W SHERWIN AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2423,024,49,1,14,1164125,1948677,2014,11/06/2014 12:41:16 PM,42.014795587,-87.671420811,"(42.014795587, -87.671420811)"
-9838081,HX487366,10/29/2014 06:00:00 PM,054XX W DAKIN ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1633,016,38,15,05,1139528,1925704,2014,11/05/2014 12:45:59 PM,41.952242087,-87.76249271,"(41.952242087, -87.76249271)"
-9837898,HX486896,10/29/2014 12:30:00 AM,005XX W 87TH ST,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,0622,006,21,71,03,1174455,1847316,2014,11/05/2014 12:45:59 PM,41.73642906,-87.636438922,"(41.73642906, -87.636438922)"
-9834815,HX484914,10/27/2014 11:57:00 PM,029XX W AUGUSTA BLVD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1211,012,26,24,18,1156839,1906479,2014,11/03/2014 12:40:32 PM,41.8991532,-87.699379232,"(41.8991532, -87.699379232)"
-9832556,HX482550,10/26/2014 02:15:00 AM,057XX W MADISON ST,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,POLICE FACILITY/VEH PARKING LOT,true,false,1513,015,29,25,24,1138127,1899398,2014,11/02/2014 12:37:16 PM,41.880080898,-87.768280017,"(41.880080898, -87.768280017)"
-9832596,HX482655,10/25/2014 10:00:00 PM,015XX N NORTH PARK AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,false,1821,018,27,8,14,1173845,1910754,2014,11/01/2014 12:39:11 PM,41.910522194,-87.636789532,"(41.910522194, -87.636789532)"
-9829059,HX478613,10/22/2014 10:30:00 PM,042XX W VAN BUREN ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE PORCH/HALLWAY,false,false,1132,011,24,26,05,1148336,1897729,2014,10/29/2014 12:37:07 PM,41.875310407,-87.730836402,"(41.875310407, -87.730836402)"
-9829669,HX479074,10/21/2014 04:30:00 PM,105XX S MORGAN ST,0460,BATTERY,SIMPLE,PARK PROPERTY,false,false,2232,022,34,73,08B,1171485,1834726,2014,10/28/2014 12:36:38 PM,41.701945742,-87.64768733,"(41.701945742, -87.64768733)"
-9826811,HX476501,10/21/2014 03:00:00 PM,063XX S ARTESIAN AVE,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,RESIDENCE,false,false,0825,008,15,66,04B,1161176,1862291,2014,10/28/2014 12:36:38 PM,41.777807452,-87.684675225,"(41.777807452, -87.684675225)"
-9824950,HX474998,10/20/2014 01:14:00 PM,062XX S PARK SHORE EAST CT,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,SIDEWALK,true,false,0314,003,5,42,24,1187522,1864274,2014,10/27/2014 12:36:31 PM,41.782663033,-87.58802809,"(41.782663033, -87.58802809)"
-9832261,HX482192,10/20/2014 10:00:00 AM,014XX W 47TH ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,APARTMENT,false,false,0933,009,20,61,11,1167243,1873534,2014,10/27/2014 12:36:31 PM,41.808531847,-87.662111611,"(41.808531847, -87.662111611)"
-9824158,HX474355,10/19/2014 08:50:00 PM,027XX E 95TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0431,004,10,51,18,1195951,1842451,2014,10/26/2014 12:37:49 PM,41.722574445,-87.557846645,"(41.722574445, -87.557846645)"
-9843649,HX493152,10/19/2014 02:56:00 PM,004XX N STATE ST,1206,DECEPTIVE PRACTICE,"THEFT BY LESSEE,MOTOR VEH",VEHICLE-COMMERCIAL,false,false,1834,018,42,8,11,1176341,1903042,2014,11/04/2014 12:43:25 PM,41.88930411,-87.627853241,"(41.88930411, -87.627853241)"
-9817656,HX467320,10/13/2014 05:15:00 PM,010XX W VAN BUREN ST,0810,THEFT,OVER $500,STREET,false,false,1232,012,2,28,06,1169285,1898385,2014,10/20/2014 12:41:20 PM,41.876681237,-87.653900961,"(41.876681237, -87.653900961)"
-9816325,HX466018,10/13/2014 01:55:00 PM,041XX S SACRAMENTO AVE,3100,PUBLIC PEACE VIOLATION,MOB ACTION,STREET,true,false,0921,009,14,58,24,1157079,1877024,2014,10/20/2014 12:41:20 PM,41.818320643,-87.699296642,"(41.818320643, -87.699296642)"
-9815967,HX465548,10/13/2014 06:25:00 AM,110XX S SANGAMON ST,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,2234,022,34,75,06,1172033,1831656,2014,10/20/2014 12:41:20 PM,41.693509196,-87.645770458,"(41.693509196, -87.645770458)"
-9815913,HX465489,10/13/2014 04:35:00 AM,049XX W SUPERIOR ST,0560,ASSAULT,SIMPLE,APARTMENT,false,false,1531,015,37,25,08A,1142973,1904469,2014,10/20/2014 12:41:20 PM,41.893907401,-87.750359347,"(41.893907401, -87.750359347)"
-9814725,HX464040,10/11/2014 02:00:00 PM,047XX S VINCENNES AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,true,true,0223,002,3,38,26,1180441,1873370,2014,11/10/2014 12:41:06 PM,41.807788763,-87.613709697,"(41.807788763, -87.613709697)"
-9814775,HX464088,10/11/2014 02:00:00 AM,014XX W WEBSTER AVE,0810,THEFT,OVER $500,STREET,false,false,1811,018,32,7,06,1165880,1914689,2014,10/18/2014 12:37:02 PM,41.921493844,-87.665937306,"(41.921493844, -87.665937306)"
-9814093,HX463139,10/11/2014 12:45:00 AM,031XX N WESTERN AVE,0337,ROBBERY,ATTEMPT: ARMED-OTHER DANG WEAP,GAS STATION,true,false,1931,019,1,5,03,1159894,1921024,2014,10/18/2014 12:37:02 PM,41.939003242,-87.687756119,"(41.939003242, -87.687756119)"
-9813903,HX462973,10/10/2014 08:40:00 PM,024XX S TRUMBULL AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1024,010,22,30,18,1153788,1887773,2014,10/17/2014 12:40:52 PM,41.84788326,-87.71108361,"(41.84788326, -87.71108361)"
-9813941,HX462999,10/10/2014 08:05:00 PM,043XX N CLARENDON AVE,0810,THEFT,OVER $500,APARTMENT,false,true,1915,019,46,3,06,1170166,1929352,2014,10/17/2014 12:40:52 PM,41.961637102,-87.649759923,"(41.961637102, -87.649759923)"
-9813848,HX462620,10/10/2014 04:00:00 PM,076XX S PRAIRIE AVE,0810,THEFT,OVER $500,VEHICLE-COMMERCIAL,false,false,0623,006,6,69,06,1179405,1854139,2014,10/17/2014 12:40:52 PM,41.755040742,-87.618096124,"(41.755040742, -87.618096124)"
-9813323,HX462198,10/10/2014 09:50:00 AM,034XX S PRAIRIE AVE,0560,ASSAULT,SIMPLE,OTHER,false,false,0211,002,2,35,08A,1178543,1881954,2014,10/17/2014 12:40:52 PM,41.831387362,-87.620409767,"(41.831387362, -87.620409767)"
-9820287,HX469854,10/10/2014 12:01:00 AM,051XX S MICHIGAN AVE,1154,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT $300 AND UNDER,RESIDENCE,false,false,0225,002,3,40,11,1177999,1870570,2014,10/17/2014 12:40:52 PM,41.800161069,-87.622751126,"(41.800161069, -87.622751126)"
-9812395,HX461105,10/09/2014 12:00:00 PM,002XX W 63RD ST,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,CTA PLATFORM,false,false,0711,007,20,68,04A,1175833,1863236,2014,10/16/2014 12:41:58 PM,41.780084684,-87.630914183,"(41.780084684, -87.630914183)"
-9817458,HX467003,10/09/2014 10:00:00 AM,017XX W PRYOR AVE,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",true,false,2212,022,19,75,08B,1166552,1831113,2014,10/16/2014 12:41:58 PM,41.69213742,-87.66585308,"(41.69213742, -87.66585308)"
-9810084,HX459088,10/07/2014 06:35:00 PM,017XX S MICHIGAN AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0131,001,2,33,06,1177453,1891985,2014,10/14/2014 12:38:29 PM,41.858937901,-87.624105092,"(41.858937901, -87.624105092)"
-9817684,HX467316,10/06/2014 08:30:00 AM,026XX W NORTH AVE,0820,THEFT,$500 AND UNDER,OTHER,false,false,1421,014,1,24,06,1158234,1910572,2014,10/15/2014 12:40:17 PM,41.91035633,-87.694143404,"(41.91035633, -87.694143404)"
-9799224,HX448227,09/29/2014 01:15:00 PM,103XX S COTTAGE GROVE AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,STREET,false,false,0512,005,9,50,04A,1182666,1836790,2014,10/06/2014 12:37:42 PM,41.707358059,-87.60668233,"(41.707358059, -87.60668233)"
-9796745,HX445627,09/26/2014 07:00:00 PM,035XX N BELL AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1921,019,47,5,06,1160780,1923416,2014,10/03/2014 12:39:14 PM,41.945548677,-87.684433289,"(41.945548677, -87.684433289)"
-9797462,HX445594,09/26/2014 06:30:00 AM,041XX W CRYSTAL ST,0820,THEFT,$500 AND UNDER,ALLEY,false,false,2534,025,37,23,06,1148681,1907977,2014,10/03/2014 12:39:14 PM,41.903425379,-87.729304819,"(41.903425379, -87.729304819)"
-9794344,HX443056,09/25/2014 04:48:00 PM,020XX S HARDING AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1014,010,24,29,18,1150421,1889624,2014,10/02/2014 12:40:26 PM,41.853028916,-87.723392466,"(41.853028916, -87.723392466)"
-9793210,HX442170,09/24/2014 10:45:00 AM,040XX W CONGRESS PKWY,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,1132,011,24,26,03,1149557,1897346,2014,10/01/2014 12:43:13 PM,41.874235804,-87.726363276,"(41.874235804, -87.726363276)"
-9791274,HX439250,09/22/2014 09:00:00 PM,048XX W POTOMAC AVE,0313,ROBBERY,ARMED: OTHER DANGEROUS WEAPON,SIDEWALK,false,false,2533,025,37,25,03,1144065,1908256,2014,09/29/2014 12:37:44 PM,41.904278924,-87.746253564,"(41.904278924, -87.746253564)"
-9789624,HX439217,09/22/2014 08:00:00 PM,005XX N HALSTED ST,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,1214,012,27,24,14,1170990,1903827,2014,09/29/2014 12:37:44 PM,41.891577242,-87.647481089,"(41.891577242, -87.647481089)"
-9790746,HX439202,09/22/2014 06:00:00 PM,057XX W LAWRENCE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1622,,45,15,14,,,2014,09/29/2014 12:37:44 PM,,,
-9788911,HX438418,09/22/2014 09:32:00 AM,097XX S COTTAGE GROVE AVE,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,GOVERNMENT BUILDING/PROPERTY,true,false,0511,005,8,50,04A,1183369,1840641,2014,09/29/2014 12:37:44 PM,41.71790937,-87.603988507,"(41.71790937, -87.603988507)"
-9787064,HX436180,09/20/2014 02:08:00 PM,003XX E 63RD ST,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,0311,003,20,40,06,1179603,1863354,2014,09/27/2014 12:37:59 PM,41.780323149,-87.617089297,"(41.780323149, -87.617089297)"
-9786469,HX435495,09/19/2014 11:00:00 PM,023XX W FULLERTON AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1432,014,1,22,14,1160212,1915931,2014,09/26/2014 12:39:34 PM,41.925021137,-87.686728554,"(41.925021137, -87.686728554)"
-9785189,HX434311,09/18/2014 06:00:00 PM,033XX W LE MOYNE ST,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENCE-GARAGE,false,false,1422,014,26,23,14,1153836,1909729,2014,09/25/2014 12:42:39 PM,41.908131862,-87.710322526,"(41.908131862, -87.710322526)"
-9784201,HX433214,09/18/2014 11:00:00 AM,088XX S DOBSON AVE,0890,THEFT,FROM BUILDING,COMMERCIAL / BUSINESS OFFICE,false,false,0412,004,8,47,06,1184626,1846385,2014,09/25/2014 12:42:39 PM,41.733642248,-87.599205239,"(41.733642248, -87.599205239)"
-9787167,HX436428,09/17/2014 08:45:00 PM,0000X E LOWER WACKER DR,0460,BATTERY,SIMPLE,STREET,false,false,0111,001,42,32,08B,1176702,1902144,2014,09/24/2014 12:40:15 PM,41.886831799,-87.62655468,"(41.886831799, -87.62655468)"
-9781473,HX431358,09/17/2014 01:31:00 AM,029XX W WILCOX ST,051A,ASSAULT,AGGRAVATED: HANDGUN,RESIDENCE PORCH/HALLWAY,false,false,1124,011,2,27,04A,1156810,1899174,2014,09/24/2014 12:40:15 PM,41.879108186,-87.699683883,"(41.879108186, -87.699683883)"
-9779790,HX429994,09/15/2014 11:50:00 PM,017XX S STATE ST,2890,PUBLIC PEACE VIOLATION,OTHER VIOLATION,POLICE FACILITY/VEH PARKING LOT,true,false,0131,001,3,33,26,1176569,1891773,2014,09/22/2014 12:39:52 PM,41.858376151,-87.627356324,"(41.858376151, -87.627356324)"
-9779735,HX429906,09/15/2014 09:27:00 PM,006XX W GARFIELD BLVD,0320,ROBBERY,STRONGARM - NO WEAPON,CTA BUS,true,false,0935,009,3,61,03,1172959,1868459,2014,09/22/2014 12:39:52 PM,41.794481108,-87.641296534,"(41.794481108, -87.641296534)"
-9777994,HX428283,09/14/2014 04:30:00 PM,038XX W VAN BUREN ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1133,011,24,26,08B,1150745,1897706,2014,09/21/2014 12:37:13 PM,41.875200548,-87.721992032,"(41.875200548, -87.721992032)"
-9778804,HX429064,09/14/2014 11:40:00 AM,060XX N TALMAN AVE,0890,THEFT,FROM BUILDING,RESIDENCE PORCH/HALLWAY,false,false,2413,024,50,2,06,1157604,1940249,2014,09/21/2014 12:37:13 PM,41.991804708,-87.695646609,"(41.991804708, -87.695646609)"
-9776421,HX426318,09/12/2014 11:06:00 PM,095XX S OGLESBY AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,OTHER,true,false,0431,004,7,51,18,1193714,1842377,2014,09/19/2014 12:39:17 PM,41.722426388,-87.566042754,"(41.722426388, -87.566042754)"
-9774786,HX424565,09/11/2014 04:30:00 PM,091XX S MARQUETTE AVE,041A,BATTERY,AGGRAVATED: HANDGUN,SIDEWALK,false,false,0423,004,7,48,04B,1195895,1844559,2014,09/18/2014 12:40:09 PM,41.728360367,-87.557982215,"(41.728360367, -87.557982215)"
-9782076,HX422991,09/09/2014 09:00:00 PM,027XX N CAMPBELL AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1411,014,1,22,26,1159181,1918143,2014,09/18/2014 12:40:09 PM,41.931112291,-87.690455982,"(41.931112291, -87.690455982)"
-9771220,HX421925,09/09/2014 02:47:00 PM,035XX W FLOURNOY ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1133,011,24,27,18,1152955,1896845,2014,09/16/2014 12:44:17 PM,41.87279439,-87.713900557,"(41.87279439, -87.713900557)"
-9771010,HX421841,09/09/2014 01:30:00 PM,051XX S HERMITAGE AVE,0554,ASSAULT,AGG PO HANDS NO/MIN INJURY,RESIDENCE PORCH/HALLWAY,true,false,0932,009,16,61,08A,1165508,1870630,2014,09/16/2014 12:44:17 PM,41.80059994,-87.668557591,"(41.80059994, -87.668557591)"
-9768137,HX418879,09/07/2014 07:40:00 AM,068XX S MICHIGAN AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,APARTMENT,false,true,0322,003,20,69,26,1178304,1859653,2014,09/14/2014 12:35:10 PM,41.770196812,-87.621963844,"(41.770196812, -87.621963844)"
-9766561,HX416651,09/05/2014 02:00:00 PM,073XX S UNION AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,true,false,0732,007,17,68,03,1172858,1856409,2014,09/12/2014 12:40:40 PM,41.761416758,-87.642022119,"(41.761416758, -87.642022119)"
-9767408,HX417854,09/05/2014 01:00:00 AM,001XX W GARFIELD BLVD,0820,THEFT,$500 AND UNDER,CTA TRAIN,false,false,0225,002,3,37,06,1176054,1868561,2014,09/12/2014 12:40:40 PM,41.794692076,-87.629944256,"(41.794692076, -87.629944256)"
-9765269,HX415148,09/04/2014 01:00:00 AM,013XX W 118TH ST,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,0524,005,34,53,06,1169461,1826552,2014,09/11/2014 12:39:33 PM,41.679558925,-87.655334203,"(41.679558925, -87.655334203)"
-9766288,HX416321,09/03/2014 01:12:00 PM,073XX S PHILLIPS AVE,1154,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT $300 AND UNDER,OTHER,false,false,0334,003,7,43,11,1193885,1856511,2014,09/10/2014 12:38:25 PM,41.761207101,-87.564954158,"(41.761207101, -87.564954158)"
-9759917,HX410132,08/31/2014 03:00:00 PM,013XX S CLINTON ST,1220,DECEPTIVE PRACTICE,THEFT OF LOST/MISLAID PROP,DEPARTMENT STORE,false,false,0124,001,2,28,11,1172883,1894339,2014,09/07/2014 12:35:09 PM,41.865499815,-87.640810193,"(41.865499815, -87.640810193)"
-9759486,HX409540,08/31/2014 06:30:00 AM,084XX S MACKINAW AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,0424,004,10,46,08B,1199894,1849752,2014,09/07/2014 12:35:09 PM,41.742510561,-87.543158753,"(41.742510561, -87.543158753)"
-9773776,HX409715,08/30/2014 09:00:00 AM,026XX W FRANCIS PL,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1431,014,1,22,14,1158394,1914043,2014,09/12/2014 12:40:40 PM,41.91987775,-87.693460491,"(41.91987775, -87.693460491)"
-9756933,HX406714,08/29/2014 02:35:00 AM,011XX W GRANVILLE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESTAURANT,false,false,2433,024,48,77,14,1167586,1941293,2014,09/05/2014 12:35:08 PM,41.994459632,-87.658899737,"(41.994459632, -87.658899737)"
-9755483,HX405270,08/27/2014 10:30:00 PM,031XX W LELAND AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1713,017,33,14,08B,1154402,1931093,2014,10/31/2014 03:20:56 PM,41.966744971,-87.70767055,"(41.966744971, -87.70767055)"
-9771082,HX421772,08/27/2014 09:00:00 PM,0000X W ELM ST,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,1824,018,42,8,03,1175926,1908130,2014,09/10/2014 12:38:25 PM,41.903275192,-87.629223928,"(41.903275192, -87.629223928)"
-9751837,HX401955,08/25/2014 02:00:00 PM,055XX S CALIFORNIA AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0824,008,16,63,08B,1158693,1867775,2014,09/01/2014 12:37:37 PM,41.792907351,-87.69362844,"(41.792907351, -87.69362844)"
-9751452,HX401715,08/24/2014 01:00:00 AM,064XX S RICHMOND ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,DRIVEWAY - RESIDENTIAL,false,false,0823,008,15,66,07,1157858,1861956,2014,08/31/2014 12:37:21 PM,41.776956199,-87.696848262,"(41.776956199, -87.696848262)"
-9744280,HX394241,08/19/2014 08:30:00 AM,016XX N PULASKI RD,0334,ROBBERY,ATTEMPT: ARMED-KNIFE/CUT INSTR,CTA BUS STOP,false,false,2535,025,30,23,03,1149495,1910935,2014,08/26/2014 12:39:33 PM,41.911526656,-87.726237895,"(41.911526656, -87.726237895)"
-9742984,HX393429,08/19/2014 03:48:00 AM,057XX W BERENICE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,RESIDENCE,false,false,1633,016,38,15,14,1137601,1924979,2014,08/26/2014 12:39:33 PM,41.950287617,-87.76959404,"(41.950287617, -87.76959404)"
-9758516,HX392623,08/18/2014 03:40:00 PM,0000X W TERMINAL ST,5007,OTHER OFFENSE,OTHER WEAPONS VIOLATION,AIRPORT TERMINAL UPPER LEVEL - SECURE AREA,false,false,1651,016,41,76,26,1100317,1935189,2014,10/31/2014 03:20:56 PM,41.978896531,-87.906463888,"(41.978896531, -87.906463888)"
-9741073,HX391490,08/17/2014 04:18:00 PM,036XX S RHODES AVE,0560,ASSAULT,SIMPLE,CHA HALLWAY/STAIRWELL/ELEVATOR,false,false,0212,002,4,35,08A,1180140,1880977,2014,08/24/2014 12:37:19 PM,41.828669884,-87.614580294,"(41.828669884, -87.614580294)"
-9740756,HX390940,08/17/2014 06:00:00 AM,063XX S ASHLAND AVE,0820,THEFT,$500 AND UNDER,CTA TRAIN,false,false,0725,007,16,67,06,1166712,1862870,2014,08/24/2014 12:37:19 PM,41.77927992,-87.664363567,"(41.77927992, -87.664363567)"
-9740047,HX390250,08/15/2014 07:00:00 PM,092XX S BLACKSTONE AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,true,0413,004,8,48,26,1187771,1843919,2014,08/22/2014 12:36:05 PM,41.726801095,-87.587761923,"(41.726801095, -87.587761923)"
-9738616,HX388445,08/15/2014 10:59:00 AM,110XX S MICHIGAN AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0513,005,9,49,06,1178749,1831610,2014,08/22/2014 12:36:05 PM,41.693233223,-87.621183177,"(41.693233223, -87.621183177)"
-9736288,HX386331,08/13/2014 06:15:00 PM,102XX S MICHIGAN AVE,1330,CRIMINAL TRESPASS,TO LAND,OTHER,true,false,0511,005,9,49,26,1179016,1836736,2014,08/20/2014 12:42:23 PM,41.707293618,-87.62005021,"(41.707293618, -87.62005021)"
-9734857,HX384998,08/12/2014 07:15:00 PM,117XX S PEORIA ST,0820,THEFT,$500 AND UNDER,SIDEWALK,true,true,0524,005,34,53,06,1172476,1826876,2014,08/19/2014 12:37:52 PM,41.680382398,-87.644288515,"(41.680382398, -87.644288515)"
-9733500,HX383572,08/11/2014 06:00:00 PM,056XX N CENTRAL AVE,0460,BATTERY,SIMPLE,ALLEY,false,false,1622,016,45,11,08B,1137828,1937425,2014,08/18/2014 12:38:20 PM,41.984436467,-87.768457961,"(41.984436467, -87.768457961)"
-9732121,HX382303,08/10/2014 07:31:00 PM,100XX S WESTERN AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,2211,022,19,72,18,1162121,1837770,2014,08/17/2014 12:37:26 PM,41.710498462,-87.681891359,"(41.710498462, -87.681891359)"
-9731541,HX381526,08/09/2014 08:45:00 PM,087XX S LAFAYETTE AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0634,006,21,44,06,1177419,1847206,2014,08/16/2014 12:37:25 PM,41.736060832,-87.625583193,"(41.736060832, -87.625583193)"
-9731079,HX380994,08/09/2014 07:50:00 PM,059XX S LOOMIS BLVD,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0713,007,16,67,08B,1167971,1865431,2014,08/16/2014 12:37:25 PM,41.786280647,-87.65967438,"(41.786280647, -87.65967438)"
-9731005,HX380917,08/09/2014 04:00:00 PM,022XX N CICERO AVE,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,2522,025,31,19,08A,1143994,1914282,2014,08/16/2014 12:37:25 PM,41.92081624,-87.746362884,"(41.92081624, -87.746362884)"
-9732914,HX382841,08/08/2014 09:00:00 AM,104XX S SANGAMON ST,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,RESIDENCE,false,false,2232,022,34,73,11,1171787,1835651,2014,08/15/2014 12:34:08 PM,41.70447748,-87.646554485,"(41.70447748, -87.646554485)"
-9728761,HX378475,08/07/2014 10:50:00 PM,050XX W WASHINGTON BLVD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1533,015,28,25,18,1142638,1899990,2014,08/14/2014 12:38:24 PM,41.881622725,-87.751701207,"(41.881622725, -87.751701207)"
-9726019,HX375967,08/05/2014 04:00:00 PM,030XX W 24TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1033,010,12,30,14,1156374,1887862,2014,08/12/2014 12:35:59 PM,41.848075675,-87.70159045,"(41.848075675, -87.70159045)"
-9724865,HX374809,08/05/2014 12:00:00 AM,005XX S CLAREMONT AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1225,012,2,28,06,1160856,1897515,2014,08/12/2014 12:35:59 PM,41.874472807,-87.684873688,"(41.874472807, -87.684873688)"
-9724920,HX374891,08/04/2014 01:30:00 PM,002XX N KEDZIE AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,1123,011,28,27,03,1154955,1901787,2014,08/11/2014 12:39:47 PM,41.886315899,-87.706425058,"(41.886315899, -87.706425058)"
-9722510,HX372885,08/03/2014 07:30:00 PM,075XX S JEFFERY BLVD,0820,THEFT,$500 AND UNDER,PARK PROPERTY,false,false,0414,004,8,43,06,1190802,1855567,2014,08/10/2014 12:34:42 PM,41.758691699,-87.576283827,"(41.758691699, -87.576283827)"
-9720689,HX370279,08/01/2014 11:53:00 PM,003XX W HILL ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1823,018,27,8,08B,1174098,1907721,2014,08/08/2014 12:34:16 PM,41.90219385,-87.635950699,"(41.90219385, -87.635950699)"
-9715033,HX365227,07/28/2014 05:30:00 PM,031XX N HALSTED ST,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,1934,019,44,6,14,1170406,1921370,2014,08/04/2014 12:42:16 PM,41.939728951,-87.649111837,"(41.939728951, -87.649111837)"
-9712916,HX363368,07/28/2014 02:38:00 AM,046XX S FAIRFIELD AVE,2022,NARCOTICS,POSS: COCAINE,STREET,true,false,0922,009,12,58,18,1158781,1873576,2014,08/04/2014 12:42:16 PM,41.808824264,-87.693147323,"(41.808824264, -87.693147323)"
-9712145,HX362361,07/27/2014 10:12:00 AM,002XX E 32ND ST,1025,ARSON,AGGRAVATED,CHA APARTMENT,false,false,0211,002,3,35,09,1178601,1883799,2014,08/03/2014 12:37:30 PM,41.836448859,-87.620140758,"(41.836448859, -87.620140758)"
-9718124,HX360798,07/26/2014 03:00:00 AM,013XX W 79TH ST,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,APARTMENT,false,true,0612,,17,71,04A,,,2014,08/02/2014 12:35:35 PM,,,
-9711292,HX361045,07/25/2014 08:00:00 AM,058XX S MARYLAND AVE,1156,DECEPTIVE PRACTICE,ATTEMPT - FINANCIAL IDENTITY THEFT,OTHER,false,false,0235,002,5,41,11,1182938,1866306,2014,08/01/2014 12:38:57 PM,41.788346824,-87.604771066,"(41.788346824, -87.604771066)"
-9705965,HX356119,07/22/2014 06:40:00 PM,030XX N ROCKWELL ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1411,014,1,21,14,1158472,1920082,2014,07/29/2014 12:41:54 PM,41.936447598,-87.693008205,"(41.936447598, -87.693008205)"
-9705299,HX355475,07/22/2014 11:00:00 AM,047XX S LAKE PARK AVE,0820,THEFT,$500 AND UNDER,OTHER,false,false,0222,002,4,39,06,1186257,1874027,2014,07/29/2014 12:41:54 PM,41.809456003,-87.592357676,"(41.809456003, -87.592357676)"
-9703206,HX353776,07/21/2014 08:28:00 AM,016XX S KOMENSKY AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1012,010,24,29,08B,1149694,1891491,2014,07/28/2014 12:40:00 PM,41.858166334,-87.72601235,"(41.858166334, -87.72601235)"
-9702075,HX352569,07/19/2014 08:00:00 PM,004XX W 24TH ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0914,009,25,34,14,1173230,1888308,2014,07/26/2014 12:40:58 PM,41.848942609,-87.639715206,"(41.848942609, -87.639715206)"
-9700756,HX350786,07/18/2014 09:13:00 PM,102XX S NORMAL AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,2232,022,9,73,18,1174737,1836882,2014,07/25/2014 12:40:34 PM,41.707790457,-87.635715544,"(41.707790457, -87.635715544)"
-9700541,HX350526,07/18/2014 05:25:00 PM,044XX S LAPORTE AVE,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,0814,008,23,56,18,1144029,1874968,2014,07/25/2014 12:40:34 PM,41.812932932,-87.747220515,"(41.812932932, -87.747220515)"
-9700483,HX350110,07/18/2014 12:20:00 PM,070XX S OGLESBY AVE,0650,BURGLARY,HOME INVASION,APARTMENT,true,false,0331,003,5,43,05,1193021,1858906,2014,10/31/2014 03:20:56 PM,41.767800297,-87.568042624,"(41.767800297, -87.568042624)"
-9700827,HX350780,07/17/2014 09:30:00 PM,0000X N WALLER AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1513,015,29,25,06,1138202,1899683,2014,07/24/2014 12:40:50 PM,41.88086162,-87.767997728,"(41.88086162, -87.767997728)"
-9699350,HX349080,07/17/2014 02:11:00 AM,009XX S CENTRAL AVE,0610,BURGLARY,FORCIBLE ENTRY,OTHER,true,false,1522,015,29,25,05,1139294,1895407,2014,10/31/2014 03:20:56 PM,41.8691079,-87.764092057,"(41.8691079, -87.764092057)"
-9697540,HX347063,07/16/2014 12:00:00 PM,104XX S CORLISS AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0512,005,9,50,08B,1183456,1836137,2014,07/23/2014 12:42:44 PM,41.705547814,-87.603809628,"(41.705547814, -87.603809628)"
-9696774,HX347074,07/16/2014 09:10:00 AM,075XX N WESTERN AVE,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,2411,024,50,2,06,1158976,1949813,2014,07/23/2014 12:42:44 PM,42.018020494,-87.690335774,"(42.018020494, -87.690335774)"
-9721392,HX371097,07/16/2014 08:30:00 AM,017XX E 72ND ST,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,true,0324,003,8,43,26,1189168,1857546,2014,10/31/2014 03:20:56 PM,41.764161549,-87.582208896,"(41.764161549, -87.582208896)"
-9694631,HX345022,07/14/2014 04:25:00 PM,044XX S DREXEL BLVD,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),ALLEY,true,false,0221,002,4,39,18,1182899,1875739,2014,07/21/2014 12:50:42 PM,41.814232649,-87.604620838,"(41.814232649, -87.604620838)"
-9692664,HX342799,07/12/2014 10:45:00 PM,123XX S UNION AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENTIAL YARD (FRONT/BACK),true,false,0523,005,34,53,26,1173849,1822967,2014,07/19/2014 12:41:41 PM,41.669625225,-87.639377922,"(41.669625225, -87.639377922)"
-9691906,HX341698,07/12/2014 02:55:00 AM,054XX S ABERDEEN ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,RESIDENTIAL YARD (FRONT/BACK),true,false,0934,009,16,61,15,1169943,1868877,2014,07/19/2014 12:41:41 PM,41.795694239,-87.652343958,"(41.795694239, -87.652343958)"
-9690050,HX339536,07/10/2014 04:20:00 PM,004XX W WINNECONNA PKWY,0820,THEFT,$500 AND UNDER,OTHER,false,false,0621,006,17,69,06,1174482,1853160,2014,07/17/2014 12:40:06 PM,41.75246514,-87.63616654,"(41.75246514, -87.63616654)"
-9689887,HX339616,07/10/2014 08:45:00 AM,002XX E RANDOLPH ST,0810,THEFT,OVER $500,SIDEWALK,false,false,0114,001,42,32,06,1177892,1901352,2014,07/17/2014 12:40:06 PM,41.884631532,-87.622208838,"(41.884631532, -87.622208838)"
-9688545,HX338746,07/10/2014 12:15:00 AM,071XX S YALE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,0731,007,6,69,08B,1175904,1857440,2014,07/17/2014 12:40:06 PM,41.764178225,-87.630827456,"(41.764178225, -87.630827456)"
-9700089,HX349974,07/08/2014 04:00:00 PM,032XX N SEMINARY AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,1924,019,44,6,05,1168412,1921843,2014,07/19/2014 12:41:41 PM,41.941070323,-87.656426615,"(41.941070323, -87.656426615)"
-9685806,HX336184,07/08/2014 09:30:00 AM,001XX W 79TH ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0623,006,17,69,06,1176831,1852649,2014,07/15/2014 12:40:16 PM,41.75101034,-87.627573854,"(41.75101034, -87.627573854)"
-9683505,HX334152,07/06/2014 09:35:00 PM,064XX S WOLCOTT AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0726,007,15,67,14,1164828,1862070,2014,07/13/2014 12:37:47 PM,41.777124626,-87.671293105,"(41.777124626, -87.671293105)"
-9683433,HX334071,07/06/2014 07:20:00 PM,024XX W PERSHING RD,0820,THEFT,$500 AND UNDER,RESIDENCE,false,false,0911,009,12,58,06,1160578,1878777,2014,07/13/2014 12:37:47 PM,41.823059518,-87.686412651,"(41.823059518, -87.686412651)"
-9683248,HX333952,07/06/2014 06:35:00 PM,064XX S LOOMIS BLVD,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,0725,007,17,67,03,1168057,1862246,2014,07/13/2014 12:37:47 PM,41.777538767,-87.65945058,"(41.777538767, -87.65945058)"
-9681413,HX331657,07/04/2014 11:00:00 PM,003XX E ERIE ST,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,1834,018,42,8,04A,1178633,1904814,2014,07/11/2014 12:39:31 PM,41.894114543,-87.619382042,"(41.894114543, -87.619382042)"
-9736098,HX386130,07/03/2014 09:44:00 PM,043XX S ARTESIAN AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0922,009,12,58,26,1160704,1875820,2014,08/14/2014 12:38:24 PM,41.814942552,-87.686032127,"(41.814942552, -87.686032127)"
-9678427,HX328336,07/02/2014 02:40:00 PM,051XX W OAKDALE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,2521,025,31,19,08B,1141741,1919172,2014,07/09/2014 12:39:14 PM,41.934276921,-87.75451971,"(41.934276921, -87.75451971)"
-9679165,HX328128,07/02/2014 12:25:00 PM,122XX S WALLACE ST,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,RESIDENCE,false,false,0523,005,34,53,11,1174568,1823519,2014,07/09/2014 12:39:14 PM,41.671124084,-87.63673015,"(41.671124084, -87.63673015)"
-9676375,HX326244,06/30/2014 09:00:00 PM,054XX S CORNELL AVE,0810,THEFT,OVER $500,STREET,false,false,0234,002,5,41,06,1188135,1869647,2014,07/07/2014 12:43:15 PM,41.797392345,-87.585609361,"(41.797392345, -87.585609361)"
-9727785,HX377498,06/29/2014 02:38:00 PM,111XX S HALSTED ST,1582,OFFENSE INVOLVING CHILDREN,CHILD PORNOGRAPHY,APARTMENT,false,false,2233,022,34,49,17,1173009,1830995,2014,08/08/2014 12:34:16 PM,41.691673874,-87.642216524,"(41.691673874, -87.642216524)"
-9674893,HX323740,06/29/2014 12:45:00 PM,020XX E 79TH ST,0810,THEFT,OVER $500,SMALL RETAIL STORE,false,false,0414,,8,46,06,,,2014,07/06/2014 12:39:22 PM,,,
-9670786,HX320544,06/27/2014 06:20:00 AM,014XX W HURON ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,1215,012,27,24,04B,1166287,1904820,2014,07/04/2014 12:37:50 PM,41.894403963,-87.664724537,"(41.894403963, -87.664724537)"
-9671481,HX321007,06/27/2014 01:30:00 AM,009XX W WRIGHTWOOD AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1935,019,43,7,07,1169293,1917497,2014,07/04/2014 12:37:50 PM,41.929125564,-87.653315326,"(41.929125564, -87.653315326)"
-9670356,HX320302,06/26/2014 10:00:00 AM,028XX W WILCOX ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1124,011,2,27,05,1157348,1899266,2014,07/03/2014 12:45:31 PM,41.879349726,-87.697705934,"(41.879349726, -87.697705934)"
-9665087,HX315300,06/23/2014 10:59:00 AM,034XX W AUGUSTA BLVD,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1121,011,27,23,18,1153093,1906405,2014,06/30/2014 12:37:16 PM,41.899025271,-87.713140222,"(41.899025271, -87.713140222)"
-9663886,HX313975,06/21/2014 07:00:00 PM,018XX N TRIPP AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2534,025,30,20,07,1147795,1912188,2014,06/28/2014 12:36:34 PM,41.91499786,-87.732450961,"(41.91499786, -87.732450961)"
-9663074,HX312925,06/21/2014 12:30:00 PM,015XX N CLYBOURN AVE,0460,BATTERY,SIMPLE,CTA TRAIN,false,false,1822,018,27,8,08B,1170846,1910482,2014,06/28/2014 12:36:34 PM,41.909842113,-87.64781463,"(41.909842113, -87.64781463)"
-9663018,HX312850,06/21/2014 11:40:00 AM,115XX S YALE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0522,005,34,53,08B,1176635,1828469,2014,06/28/2014 12:36:34 PM,41.684661551,-87.629016959,"(41.684661551, -87.629016959)"
-9662643,HX312494,06/21/2014 04:36:00 AM,062XX W CUYLER AVE,1090,ARSON,ATTEMPT ARSON,RESIDENCE-GARAGE,false,false,1624,016,38,15,09,1133886,1926223,2014,06/28/2014 12:36:34 PM,41.953767525,-87.783220921,"(41.953767525, -87.783220921)"
-9662638,HX312474,06/21/2014 04:10:00 AM,074XX S GREEN ST,033A,ROBBERY,ATTEMPT: ARMED-HANDGUN,STREET,false,false,0733,007,17,68,03,1171884,1855553,2014,06/28/2014 12:36:34 PM,41.759089212,-87.645616987,"(41.759089212, -87.645616987)"
-9662569,HX312440,06/21/2014 01:20:00 AM,040XX W WILCOX ST,041A,BATTERY,AGGRAVATED: HANDGUN,RESIDENCE PORCH/HALLWAY,false,false,1115,011,28,26,04B,1149683,1899074,2014,06/28/2014 12:36:34 PM,41.878975185,-87.725855753,"(41.878975185, -87.725855753)"
-9661658,HX311366,06/20/2014 11:20:00 AM,069XX S ASHLAND AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,0735,007,17,67,03,1166833,1858894,2014,06/27/2014 12:37:10 PM,41.768366678,-87.664033409,"(41.768366678, -87.664033409)"
-9658359,HX308295,06/18/2014 02:45:00 AM,091XX S BEVERLY AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2221,022,21,73,14,1166214,1844254,2014,06/25/2014 12:38:46 PM,41.728205623,-87.666718093,"(41.728205623, -87.666718093)"
-9657339,HX307593,06/17/2014 04:30:00 PM,053XX W QUINCY ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1522,015,29,25,14,1140920,1898446,2014,06/24/2014 12:40:13 PM,41.877417569,-87.758047739,"(41.877417569, -87.758047739)"
-9654848,HX305558,06/16/2014 09:12:00 AM,004XX W 105TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,OTHER,true,false,2233,022,34,49,14,1175278,1835240,2014,06/23/2014 12:44:45 PM,41.703272527,-87.633783236,"(41.703272527, -87.633783236)"
-9654231,HX304988,06/15/2014 08:25:00 PM,027XX W CATALPA AVE,0460,BATTERY,SIMPLE,RESIDENCE,true,false,2011,020,40,4,08B,1157179,1936460,2014,06/22/2014 12:39:11 PM,41.981416194,-87.697313387,"(41.981416194, -87.697313387)"
-9654346,HX305220,06/15/2014 05:00:00 PM,056XX S MAPLEWOOD AVE,4388,OTHER OFFENSE,VIO BAIL BOND: DOM VIOLENCE,RESIDENCE,true,true,0824,008,16,63,26,1160379,1866998,2014,06/22/2014 12:39:11 PM,41.790740566,-87.687467443,"(41.790740566, -87.687467443)"
-9802938,HX452071,06/15/2014 12:00:00 PM,006XX S CENTRAL PARK AVE,1153,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT OVER $ 300,APARTMENT,false,false,1133,011,24,27,11,1152525,1896827,2014,10/03/2014 12:39:14 PM,41.8727535,-87.715479771,"(41.8727535, -87.715479771)"
-9653006,HX303514,06/14/2014 05:24:00 PM,036XX E 106TH ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,SMALL RETAIL STORE,true,false,0432,004,10,52,04B,1201982,1835237,2014,06/21/2014 12:36:33 PM,41.702627447,-87.536001073,"(41.702627447, -87.536001073)"
-9651540,HX301643,06/12/2014 07:30:00 PM,002XX W MONROE ST,0810,THEFT,OVER $500,OTHER,false,false,0122,001,2,32,06,1174707,1899918,2014,07/11/2014 12:37:03 PM,41.880768379,-87.633947393,"(41.880768379, -87.633947393)"
-9651273,HX301372,06/12/2014 05:30:00 PM,034XX W 31ST ST,0620,BURGLARY,UNLAWFUL ENTRY,RESTAURANT,false,false,1032,010,22,30,05,1154155,1883902,2014,06/19/2014 12:43:10 PM,41.837253462,-87.709839834,"(41.837253462, -87.709839834)"
-9678453,HX298334,06/10/2014 07:49:00 PM,008XX E 91ST ST,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,0633,,8,47,03,,,2014,07/03/2014 12:45:31 PM,,,
-9645261,HX296258,06/09/2014 10:00:00 AM,028XX N AUSTIN AVE,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,2514,025,30,19,06,1135941,1918388,2014,06/16/2014 12:51:59 PM,41.932231013,-87.775853712,"(41.932231013, -87.775853712)"
-9644523,HX295659,06/08/2014 09:35:00 PM,002XX S TROY ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1124,011,28,27,18,1155379,1898669,2014,06/15/2014 12:40:25 PM,41.877751287,-87.704951861,"(41.877751287, -87.704951861)"
-9643705,HX294660,06/08/2014 03:20:00 AM,037XX S WOOD ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0912,009,11,59,14,1165002,1880015,2014,06/15/2014 12:40:25 PM,41.82636417,-87.670147645,"(41.82636417, -87.670147645)"
-9643858,HX294683,06/08/2014 01:00:00 AM,066XX N NORTHWEST HWY,0460,BATTERY,SIMPLE,STREET,false,false,1612,016,41,9,08B,1124915,1943895,2014,06/15/2014 12:40:25 PM,42.00241466,-87.81580751,"(42.00241466, -87.81580751)"
-9642121,HX292493,06/05/2014 08:00:00 PM,073XX S ASHLAND AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,ALLEY,false,false,0734,007,17,67,14,1166994,1856032,2014,06/12/2014 12:41:22 PM,41.760509529,-87.663524955,"(41.760509529, -87.663524955)"
-9641152,HX287518,06/03/2014 12:10:00 AM,024XX W 45TH ST,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,STREET,false,false,0922,009,12,58,24,1161087,1874736,2014,06/10/2014 12:45:15 PM,41.811959999,-87.684657237,"(41.811959999, -87.684657237)"
-9934688,HY123629,06/01/2014 12:00:00 AM,065XX S MINERVA AVE,1153,DECEPTIVE PRACTICE,FINANCIAL IDENTITY THEFT OVER $ 300,RESIDENCE,false,false,0321,003,20,42,11,1185052,1861728,2014,01/26/2015 12:53:30 PM,41.775734979,-87.597163634,"(41.775734979, -87.597163634)"
-9633194,HX284351,05/31/2014 05:55:00 PM,076XX S CICERO AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,false,false,0833,008,13,65,06,1145766,1853739,2014,06/07/2014 12:40:43 PM,41.754644364,-87.741385133,"(41.754644364, -87.741385133)"
-9632434,HX283223,05/30/2014 09:50:00 PM,107XX S YATES AVE,0460,BATTERY,SIMPLE,SIDEWALK,true,false,0434,004,10,51,08B,1194304,1834510,2014,06/06/2014 12:40:22 PM,41.700824113,-87.564139118,"(41.700824113, -87.564139118)"
-9632364,HX283153,05/30/2014 08:30:00 PM,074XX S INDIANA AVE,0460,BATTERY,SIMPLE,STREET,false,false,0323,003,6,69,08B,1178934,1855397,2014,06/06/2014 12:40:22 PM,41.758503566,-87.619783957,"(41.758503566, -87.619783957)"
-9631862,HX282478,05/30/2014 12:21:00 PM,049XX W WEST END AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1532,015,28,25,18,1143435,1900569,2014,06/06/2014 12:40:22 PM,41.883196711,-87.74876013,"(41.883196711, -87.74876013)"
-9631861,HX282458,05/30/2014 11:00:00 AM,063XX S KOSTNER AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0813,008,13,65,14,1148169,1862253,2014,06/06/2014 12:40:22 PM,41.777962512,-87.732360716,"(41.777962512, -87.732360716)"
-9631036,HX281898,05/29/2014 10:40:00 PM,084XX S VERNON AVE,0560,ASSAULT,SIMPLE,RESIDENCE,true,true,0632,006,6,44,08A,1180874,1848896,2014,06/10/2014 12:41:59 PM,41.740619724,-87.612873573,"(41.740619724, -87.612873573)"
-9630714,HX281512,05/29/2014 10:20:00 AM,026XX S CALIFORNIA AVE,0810,THEFT,OVER $500,STREET,false,false,1033,010,12,30,06,1158082,1886563,2014,06/05/2014 12:38:31 PM,41.844476425,-87.695357404,"(41.844476425, -87.695357404)"
-9630844,HX281710,05/28/2014 10:00:00 PM,096XX S LA SALLE ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0511,005,21,49,14,1177015,1841086,2014,06/04/2014 12:43:29 PM,41.719275863,-87.627247216,"(41.719275863, -87.627247216)"
-9629323,HX279878,05/28/2014 10:34:00 AM,017XX W ALBION AVE,0810,THEFT,OVER $500,APARTMENT,false,false,2432,024,40,1,06,1163702,1943935,2014,06/04/2014 12:43:29 PM,42.001792401,-87.673111851,"(42.001792401, -87.673111851)"
-9630401,HX281029,05/28/2014 12:48:00 AM,089XX S PARNELL AVE,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,RESIDENCE,false,true,2223,022,21,71,26,1174237,1845760,2014,06/04/2014 12:43:29 PM,41.73216403,-87.637283701,"(41.73216403, -87.637283701)"
-9627038,HX277879,05/27/2014 04:14:00 AM,057XX W GRAND AVE,2022,NARCOTICS,POSS: COCAINE,STREET,true,false,2515,025,37,19,18,1137626,1913796,2014,06/03/2014 12:44:29 PM,41.919599824,-87.769772365,"(41.919599824, -87.769772365)"
-9669357,HX275028,05/24/2014 06:35:00 PM,030XX W VAN BUREN ST,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),PARK PROPERTY,true,false,1134,011,28,27,18,1156013,1897963,2014,10/31/2014 03:20:56 PM,41.87580119,-87.702643013,"(41.87580119, -87.702643013)"
-9624246,HX274031,05/23/2014 10:30:00 PM,041XX W CONGRESS PKWY,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,true,1132,011,24,26,14,1149055,1897334,2014,05/30/2014 12:40:32 PM,41.874212601,-87.728206718,"(41.874212601, -87.728206718)"
-9624539,HX274436,05/23/2014 07:00:00 PM,002XX S LAVERGNE AVE,0460,BATTERY,SIMPLE,STREET,false,false,1533,015,28,25,08B,1143330,1898753,2014,05/30/2014 12:40:32 PM,41.878215347,-87.74919109,"(41.878215347, -87.74919109)"
-9622445,HX272206,05/22/2014 03:05:00 PM,015XX W HOWARD ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,OTHER,true,false,2422,024,49,1,15,1164940,1950388,2014,05/29/2014 12:39:18 PM,42.019473264,-87.66837302,"(42.019473264, -87.66837302)"
-9667124,HX316825,05/22/2014 02:11:00 AM,0000X S STATE ST,0810,THEFT,OVER $500,DEPARTMENT STORE,true,false,0112,001,42,32,06,1176423,1900356,2014,07/15/2014 12:37:55 PM,41.881931729,-87.627633225,"(41.881931729, -87.627633225)"
-9616841,HX267003,05/18/2014 08:07:00 PM,030XX N NEWCASTLE AVE,4625,OTHER OFFENSE,PAROLE VIOLATION,PARK PROPERTY,true,false,2511,025,36,18,26,1130191,1919247,2014,05/25/2014 12:39:09 PM,41.934688863,-87.796964877,"(41.934688863, -87.796964877)"
-9615128,HX264609,05/16/2014 10:30:00 PM,072XX S LAWNDALE AVE,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0833,008,13,65,08B,1152912,1856469,2014,05/23/2014 12:38:03 PM,41.761998025,-87.715124936,"(41.761998025, -87.715124936)"
-9615221,HX264704,05/16/2014 07:40:00 PM,007XX N CLARK ST,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,1832,018,42,8,06,1175363,1905385,2014,05/23/2014 12:38:03 PM,41.895755436,-87.631374423,"(41.895755436, -87.631374423)"
-9660802,HX310550,05/16/2014 06:00:00 PM,022XX N KARLOV AVE,0810,THEFT,OVER $500,STREET,false,false,2525,025,31,20,06,1148715,1914816,2014,06/20/2014 12:39:30 PM,41.922191603,-87.729002923,"(41.922191603, -87.729002923)"
-9664302,HX314663,05/13/2014 12:00:00 PM,108XX S EWING AVE,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,RESIDENCE,false,false,0432,004,10,52,26,1202234,1833580,2014,06/23/2014 12:44:45 PM,41.698074092,-87.535134571,"(41.698074092, -87.535134571)"
-9606666,HX256705,05/10/2014 08:20:00 PM,010XX N OAKLEY BLVD,0460,BATTERY,SIMPLE,APARTMENT,true,false,1212,012,1,24,08B,1160840,1907072,2014,06/14/2014 12:41:49 PM,41.900698369,-87.684667185,"(41.900698369, -87.684667185)"
-9605704,HX253729,05/08/2014 01:00:00 AM,071XX S SANGAMON ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0733,007,17,68,06,1171258,1857197,2014,05/15/2014 12:39:28 PM,41.763614265,-87.647863232,"(41.763614265, -87.647863232)"
-9602797,HX252929,05/08/2014 12:13:00 AM,009XX N LAVERGNE AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1531,015,37,25,26,1142815,1905711,2014,05/15/2014 12:39:28 PM,41.897318537,-87.750908671,"(41.897318537, -87.750908671)"
-9599431,HX250048,05/05/2014 08:00:00 PM,069XX S HALSTED ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0733,007,6,68,07,1172120,1859040,2014,05/07/2014 12:40:24 AM,41.768652788,-87.644649741,"(41.768652788, -87.644649741)"
-9600076,HX249518,05/04/2014 11:00:00 PM,012XX S MICHIGAN AVE,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,0131,001,2,33,06,1177378,1894927,2014,10/31/2014 03:20:56 PM,41.867012636,-87.6242912,"(41.867012636, -87.6242912)"
-9597140,HX247894,05/04/2014 12:00:00 AM,019XX W LUNT AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2424,024,49,1,07,1161952,1946467,2014,05/07/2014 12:40:24 AM,42.008777125,-87.679478726,"(42.008777125, -87.679478726)"
-9596923,HX247607,05/03/2014 10:30:00 PM,084XX S DANTE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0412,004,8,45,06,1187382,1849199,2014,05/06/2014 12:39:53 AM,41.741299201,-87.589019593,"(41.741299201, -87.589019593)"
-9597020,HX247812,05/03/2014 06:30:00 PM,037XX N SAWYER AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,RESIDENCE-GARAGE,false,false,1733,017,33,16,07,1154094,1924763,2014,05/07/2014 12:40:24 AM,41.949381205,-87.708972604,"(41.949381205, -87.708972604)"
-9593242,HX243752,05/01/2014 12:15:00 AM,0000X S KENTON AVE,1506,PROSTITUTION,SOLICIT ON PUBLIC WAY,STREET,true,false,1113,011,28,25,16,1145622,1899504,2014,05/04/2014 12:39:40 AM,41.880233065,-87.740756251,"(41.880233065, -87.740756251)"
-9592330,HX242881,04/30/2014 10:15:00 AM,008XX E 103RD ST,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",true,false,0512,005,9,50,08B,1183714,1836812,2014,05/03/2014 12:40:14 AM,41.707394098,-87.602843897,"(41.707394098, -87.602843897)"
-9591786,HX242257,04/29/2014 06:30:00 PM,052XX S RICHMOND ST,0820,THEFT,$500 AND UNDER,APARTMENT,false,false,0923,009,14,63,06,1157640,1869646,2014,05/02/2014 12:40:03 AM,41.798063068,-87.697438946,"(41.798063068, -87.697438946)"
-9591241,HX241733,04/29/2014 01:00:00 PM,086XX S CICERO AVE,0860,THEFT,RETAIL THEFT,DRUG STORE,false,false,0834,008,18,70,06,1145964,1846687,2014,05/01/2014 12:39:47 AM,41.735288641,-87.740837457,"(41.735288641, -87.740837457)"
-9590499,HX241039,04/28/2014 06:00:00 PM,073XX S CHAPPEL AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,0333,003,5,43,05,1191193,1856740,2014,05/07/2014 12:40:24 AM,41.761901057,-87.574812957,"(41.761901057, -87.574812957)"
-9589211,HX239851,04/27/2014 08:00:00 PM,056XX W WEST END AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,RESIDENCE,false,true,1512,015,29,25,26,1138966,1900877,2014,04/30/2014 12:39:48 AM,41.884124266,-87.765163308,"(41.884124266, -87.765163308)"
-9589392,HX239092,04/26/2014 11:00:00 PM,036XX N SHEFFIELD AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE,false,false,1923,,44,6,26,,,2014,04/30/2014 12:39:48 AM,,,
-21347,HX237678,04/26/2014 01:17:00 AM,054XX S WINCHESTER AVE,0110,HOMICIDE,FIRST DEGREE MURDER,APARTMENT,true,false,0932,009,16,61,01A,1164317,1868727,2014,02/27/2015 12:38:45 PM,41.795403068,-87.672978994,"(41.795403068, -87.672978994)"
-21343,HX233807,04/23/2014 06:18:00 AM,078XX S INGLESIDE AVE,0110,HOMICIDE,FIRST DEGREE MURDER,VESTIBULE,false,false,0624,006,8,69,01A,1183884,1853127,2014,04/23/2014 12:06:22 PM,41.752160356,-87.601713536,"(41.752160356, -87.601713536)"
-9583054,HX233226,04/22/2014 04:15:00 PM,005XX N MICHIGAN AVE,0890,THEFT,FROM BUILDING,COMMERCIAL / BUSINESS OFFICE,false,false,1834,018,42,8,06,1177300,1903904,2014,05/05/2014 12:38:28 AM,41.891647792,-87.624305286,"(41.891647792, -87.624305286)"
-9600374,HX233046,04/22/2014 01:59:00 PM,006XX S CALIFORNIA AVE,2018,NARCOTICS,MANU/DELIVER:SYNTHETIC DRUGS,VEHICLE NON-COMMERCIAL,true,false,1135,011,2,27,18,1157762,1897188,2014,06/17/2014 12:41:24 PM,41.873639068,-87.696242425,"(41.873639068, -87.696242425)"
-9579259,HX229835,04/19/2014 06:05:00 PM,083XX S STEWART AVE,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,false,false,0622,006,21,44,06,1175120,1849560,2014,04/22/2014 12:38:23 AM,41.742572081,-87.633935781,"(41.742572081, -87.633935781)"
-9646964,HX227294,04/17/2014 05:15:33 PM,040XX W WILCOX ST,2017,NARCOTICS,MANU/DELIVER:CRACK,APARTMENT,true,false,1115,011,28,26,18,1149354,1898987,2014,08/19/2014 12:37:52 PM,41.878742831,-87.727066047,"(41.878742831, -87.727066047)"
-9576335,HX226601,04/17/2014 01:00:00 AM,050XX W HURON ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1532,015,28,25,06,1142630,1904137,2014,04/20/2014 12:40:33 AM,41.893002742,-87.751627356,"(41.893002742, -87.751627356)"
-9578552,HX222468,04/13/2014 04:50:00 PM,0000X E MONROE ST,0870,THEFT,POCKET-PICKING,HOTEL/MOTEL,false,false,0112,,42,32,06,,,2014,04/21/2014 12:38:45 AM,,,
-9570986,HX221704,04/13/2014 02:40:00 AM,038XX N KENMORE AVE,0460,BATTERY,SIMPLE,STREET,true,false,1923,019,44,6,08B,1168625,1925785,2014,04/15/2014 12:40:31 AM,41.951882717,-87.655529192,"(41.951882717, -87.655529192)"
-9569616,HX220020,04/11/2014 07:00:00 PM,0000X E MADISON ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0112,001,42,32,18,1176636,1900450,2014,04/16/2014 12:38:21 AM,41.882184862,-87.62684826,"(41.882184862, -87.62684826)"
-9570147,HX218847,04/10/2014 09:20:00 PM,001XX S KENTON AVE,1513,PROSTITUTION,SOLICIT FOR BUSINESS,STREET,true,false,1113,011,28,25,16,1145635,1899142,2014,04/16/2014 12:38:21 AM,41.879239448,-87.740717694,"(41.879239448, -87.740717694)"
-9567953,HX218479,04/10/2014 04:00:00 PM,037XX W FULLERTON AVE,0810,THEFT,OVER $500,VEHICLE-COMMERCIAL,false,false,2524,025,35,22,06,1151278,1915742,2014,04/13/2014 12:40:06 AM,41.924682678,-87.719561286,"(41.924682678, -87.719561286)"
-9564306,HX215589,04/08/2014 01:30:00 PM,008XX E 79TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,GROCERY FOOD STORE,true,false,0624,006,8,44,18,1183306,1852770,2014,04/13/2014 12:40:06 AM,41.751194179,-87.603842721,"(41.751194179, -87.603842721)"
-9574602,HX225257,04/02/2014 10:00:00 PM,020XX W CHICAGO AVE,1220,DECEPTIVE PRACTICE,THEFT OF LOST/MISLAID PROP,PARKING LOT/GARAGE(NON.RESID.),false,false,1221,012,1,24,11,1162677,1905295,2014,05/02/2014 12:40:03 AM,41.895783831,-87.677969636,"(41.895783831, -87.677969636)"
-9555357,HX207194,04/01/2014 05:30:00 PM,013XX S CALIFORNIA BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1023,010,28,29,08B,1157936,1893781,2014,04/09/2014 12:42:18 AM,41.86428637,-87.695696497,"(41.86428637, -87.695696497)"
-9554659,HX206518,04/01/2014 08:30:00 AM,075XX N RIDGE BLVD,0460,BATTERY,SIMPLE,RESIDENCE PORCH/HALLWAY,false,false,2411,024,49,2,08B,1160500,1950133,2014,04/27/2014 12:38:22 AM,42.018867029,-87.684718825,"(42.018867029, -87.684718825)"
-9553632,HX205472,03/31/2014 12:10:00 PM,077XX N EASTLAKE TER,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,LAKEFRONT/WATERFRONT/RIVERBANK,false,false,2422,024,49,1,04A,1165634,1951414,2014,04/12/2014 12:41:09 AM,42.022273797,-87.665789739,"(42.022273797, -87.665789739)"
-9550879,HX202382,03/28/2014 06:30:00 PM,037XX S ELLIS AVE,1020,ARSON,BY FIRE,VEHICLE NON-COMMERCIAL,false,false,0212,002,4,36,09,1182248,1880304,2014,05/11/2014 12:36:24 PM,41.826774468,-87.606867167,"(41.826774468, -87.606867167)"
-9550165,HX201779,03/28/2014 10:30:00 AM,050XX S WINCHESTER AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,STREET,true,false,0931,009,16,61,15,1164166,1871195,2014,03/31/2014 12:39:14 AM,41.802178744,-87.673463242,"(41.802178744, -87.673463242)"
-9549444,HX201405,03/27/2014 10:00:00 PM,0000X W ONTARIO ST,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,DRUG STORE,false,false,1832,018,42,8,11,1175596,1904429,2014,03/30/2014 12:39:29 AM,41.893126888,-87.630547435,"(41.893126888, -87.630547435)"
-9556693,HX208455,03/27/2014 08:00:00 PM,001XX N KILBOURN AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1113,011,28,26,26,1146325,1900456,2014,04/09/2014 12:42:18 AM,41.882832116,-87.738150643,"(41.882832116, -87.738150643)"
-9546928,HX199334,03/26/2014 12:00:00 PM,055XX S SHIELDS AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,RESIDENTIAL YARD (FRONT/BACK),true,false,0711,007,3,68,18,1174958,1868001,2014,03/30/2014 12:39:29 AM,41.793179917,-87.633979955,"(41.793179917, -87.633979955)"
-9546860,HX199339,03/26/2014 11:45:00 AM,035XX W 66TH PL,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0831,008,15,66,07,1153698,1860248,2014,03/28/2014 12:40:24 AM,41.772352662,-87.712144089,"(41.772352662, -87.712144089)"
-9546313,HX199061,03/26/2014 07:50:00 AM,027XX W 56TH ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0824,008,16,63,07,1159138,1867452,2014,03/28/2014 12:40:24 AM,41.792011897,-87.692005502,"(41.792011897, -87.692005502)"
-9545566,HX198242,03/25/2014 02:00:00 PM,132XX S BALTIMORE AVE,0460,BATTERY,SIMPLE,STREET,false,false,0433,004,10,55,08B,1199056,1817972,2014,04/03/2014 12:40:46 AM,41.655324343,-87.547291826,"(41.655324343, -87.547291826)"
-9847079,HX495791,03/24/2014 10:00:00 PM,014XX N ASTOR ST,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,1824,018,43,8,06,1176323,1909900,2014,12/24/2014 12:46:30 PM,41.9081232,-87.6277122,"(41.9081232, -87.6277122)"
-9542532,HX196008,03/23/2014 03:00:00 PM,089XX S ESSEX AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,0423,004,7,48,08A,1194342,1846245,2014,04/12/2014 12:41:09 AM,41.733025148,-87.563615828,"(41.733025148, -87.563615828)"
-9543954,HX197071,03/22/2014 05:00:00 PM,034XX W 53RD PL,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0822,008,14,63,05,1154278,1868889,2014,04/07/2014 12:40:59 AM,41.796053338,-87.709788246,"(41.796053338, -87.709788246)"
-9540644,HX193878,03/21/2014 06:30:00 PM,032XX W 13TH ST,4650,OTHER OFFENSE,SEX OFFENDER: FAIL TO REGISTER,SIDEWALK,true,false,1022,010,24,29,26,1155199,1893905,2014,03/29/2014 12:39:43 AM,41.864681984,-87.70574067,"(41.864681984, -87.70574067)"
-9539574,HX192950,03/21/2014 06:55:00 AM,093XX S BALTIMORE AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,SIDEWALK,false,false,0424,004,10,46,04A,1198571,1843332,2014,04/12/2014 12:41:09 AM,41.724926816,-87.548220696,"(41.724926816, -87.548220696)"
-9602243,HX252212,03/20/2014 12:00:00 AM,002XX N PINE AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,APARTMENT,false,false,1523,015,28,25,26,1139416,1901226,2014,05/08/2014 01:20:21 PM,41.885073779,-87.763502323,"(41.885073779, -87.763502323)"
-9537477,HX191040,03/19/2014 04:00:00 PM,001XX W 83RD ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0622,006,21,44,14,1176867,1849911,2014,03/22/2014 12:39:11 AM,41.743496127,-87.627524234,"(41.743496127, -87.627524234)"
-9537987,HX190836,03/19/2014 01:30:00 PM,062XX S STEWART AVE,0545,ASSAULT,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",false,false,0711,007,20,68,08A,1174731,1863728,2014,03/22/2014 12:39:11 AM,41.781459416,-87.634939611,"(41.781459416, -87.634939611)"
-9539073,HX192372,03/19/2014 12:36:00 PM,028XX W DEVON AVE,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,ATM (AUTOMATIC TELLER MACHINE),false,false,2413,024,50,2,11,1156470,1942298,2014,03/29/2014 12:39:43 AM,41.997450348,-87.699762044,"(41.997450348, -87.699762044)"
-9537702,HX191355,03/19/2014 12:12:00 PM,053XX N OKETO AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1613,016,41,10,26,1125970,1934755,2014,03/27/2014 12:41:25 AM,41.977316027,-87.812130818,"(41.977316027, -87.812130818)"
-9536490,HX190346,03/19/2014 02:00:00 AM,072XX S CARPENTER ST,1320,CRIMINAL DAMAGE,TO VEHICLE,VEHICLE NON-COMMERCIAL,false,true,0733,007,17,68,14,1170525,1856823,2014,04/02/2014 12:41:31 AM,41.762603959,-87.65056071,"(41.762603959, -87.65056071)"
-9535418,HX189234,03/18/2014 09:00:00 AM,059XX N GLENWOOD AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,"SCHOOL, PUBLIC, BUILDING",true,false,2013,020,48,77,18,1165871,1939326,2014,03/23/2014 12:39:11 AM,41.989099025,-87.665264694,"(41.989099025, -87.665264694)"
-9535811,HX189169,03/18/2014 08:45:00 AM,057XX S MICHIGAN AVE,0560,ASSAULT,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0232,002,20,40,08A,1178101,1866823,2014,03/21/2014 12:39:35 AM,41.78987663,-87.622490691,"(41.78987663, -87.622490691)"
-9535770,HX189465,03/17/2014 05:00:00 PM,112XX S COTTAGE GROVE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,OTHER,false,false,0531,005,9,50,14,1181767,1830734,2014,03/21/2014 12:39:35 AM,41.690760356,-87.610160661,"(41.690760356, -87.610160661)"
-9532540,HX186429,03/15/2014 09:05:00 PM,071XX S ABERDEEN ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,false,0733,007,17,68,08B,1170178,1857422,2014,03/28/2014 12:40:24 AM,41.764255242,-87.651815123,"(41.764255242, -87.651815123)"
-9531229,HX184578,03/14/2014 10:15:00 AM,051XX W CRYSTAL ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,2533,025,37,25,05,1141869,1907798,2014,03/30/2014 12:39:29 AM,41.903063077,-87.754331516,"(41.903063077, -87.754331516)"
-9528024,HX182097,03/12/2014 09:30:00 AM,071XX S HALSTED ST,0497,BATTERY,AGGRAVATED DOMESTIC BATTERY: OTHER DANG WEAPON,RESIDENCE,false,true,0733,007,6,68,04B,1172169,1857259,2014,03/25/2014 12:39:08 AM,41.76376443,-87.644522411,"(41.76376443, -87.644522411)"
-9577540,HX227919,03/10/2014 11:00:00 PM,008XX N CALIFORNIA AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1211,012,26,24,14,1157524,1905715,2014,04/24/2014 12:40:08 AM,41.897042799,-87.696884056,"(41.897042799, -87.696884056)"
-9528027,HX181023,03/10/2014 06:00:00 PM,002XX W MARQUETTE RD,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0722,007,6,68,08B,1175548,1860575,2014,03/17/2014 12:39:52 AM,41.772788992,-87.632038586,"(41.772788992, -87.632038586)"
-9524636,HX179370,03/10/2014 04:13:00 PM,051XX S WINCHESTER AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0932,009,16,61,08B,1164184,1870532,2014,03/28/2014 12:40:24 AM,41.800359012,-87.673415896,"(41.800359012, -87.673415896)"
-9522632,HX177750,03/09/2014 05:15:00 AM,007XX W 61ST ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0711,007,16,68,08B,1172424,1864460,2014,03/13/2014 12:40:30 AM,41.783519213,-87.643376039,"(41.783519213, -87.643376039)"
-9520000,HX175139,03/06/2014 10:27:00 PM,007XX N LAMON AVE,3731,INTERFERENCE WITH PUBLIC OFFICER,OBSTRUCTING IDENTIFICATION,SIDEWALK,true,false,1531,015,37,25,24,1143524,1904225,2014,03/09/2014 12:39:51 AM,41.893227545,-87.74834179,"(41.893227545, -87.74834179)"
-9519949,HX175085,03/06/2014 09:30:00 PM,086XX S KENWOOD AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0412,004,8,45,26,1186710,1847771,2014,03/21/2014 12:39:35 AM,41.737396545,-87.591526827,"(41.737396545, -87.591526827)"
-9519659,HX174630,03/06/2014 02:00:00 PM,065XX W DIVERSEY AVE,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,2512,025,36,19,06,1132343,1917856,2014,03/09/2014 12:39:51 AM,41.930834579,-87.789088549,"(41.930834579, -87.789088549)"
-9518522,HX174018,03/05/2014 11:50:00 PM,0000X E 87TH ST,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,SIDEWALK,true,true,0632,006,6,44,04B,1177946,1847344,2014,03/08/2014 12:40:21 AM,41.736427612,-87.623648296,"(41.736427612, -87.623648296)"
-9516681,HX172093,03/04/2014 01:40:00 PM,052XX N MILWAUKEE AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,1623,016,45,11,06,1138091,1934091,2014,03/07/2014 12:40:58 AM,41.975282927,-87.767571657,"(41.975282927, -87.767571657)"
-9511742,HX166413,02/27/2014 01:26:00 PM,034XX W CHICAGO AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1121,011,27,23,18,1153532,1905166,2014,03/02/2014 12:39:31 AM,41.895616617,-87.711560762,"(41.895616617, -87.711560762)"
-9510829,HX165902,02/27/2014 12:21:00 AM,073XX S WOLCOTT AVE,051A,ASSAULT,AGGRAVATED: HANDGUN,RESIDENCE,true,true,0735,007,17,67,04A,1164996,1856090,2014,05/16/2014 12:37:19 PM,41.760711147,-87.670846078,"(41.760711147, -87.670846078)"
-9508671,HX163921,02/25/2014 11:15:00 AM,001XX N STATE ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,true,false,0111,001,42,32,06,1176392,1900920,2014,02/27/2014 12:42:14 AM,41.883480076,-87.627730028,"(41.883480076, -87.627730028)"
-9507959,HX163577,02/25/2014 01:11:00 AM,020XX W 63RD ST,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0726,007,15,67,06,1163901,1862818,2014,02/27/2014 12:42:14 AM,41.779196771,-87.674670479,"(41.779196771, -87.674670479)"
-9505515,HX160953,02/22/2014 04:15:00 PM,044XX W MONROE ST,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1113,011,28,26,18,1146819,1899334,2014,02/26/2014 12:40:16 AM,41.879743802,-87.736365296,"(41.879743802, -87.736365296)"
-9503518,HX158340,02/20/2014 01:30:00 PM,050XX S BLACKSTONE AVE,0890,THEFT,FROM BUILDING,"SCHOOL, PUBLIC, BUILDING",false,false,0222,002,4,39,06,1186813,1871938,2014,02/23/2014 12:41:45 AM,41.803710468,-87.590384628,"(41.803710468, -87.590384628)"
-9500504,HX155544,02/18/2014 07:00:00 AM,087XX S MERRILL AVE,0810,THEFT,OVER $500,STREET,false,false,0412,004,8,48,06,1191989,1847296,2014,02/21/2014 12:39:35 AM,41.735966611,-87.572201766,"(41.735966611, -87.572201766)"
-9499318,HX154389,02/17/2014 02:07:00 PM,007XX N TRUMBULL AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),OTHER,true,false,1121,011,27,23,18,1153204,1904794,2014,02/19/2014 12:39:37 AM,41.894602329,-87.712775319,"(41.894602329, -87.712775319)"
-9498714,HX153398,02/15/2014 05:00:00 PM,037XX N LAKE SHORE DR,0820,THEFT,$500 AND UNDER,STREET,false,false,1925,019,46,6,06,1171600,1925416,2014,02/19/2014 12:39:37 AM,41.950805085,-87.644604078,"(41.950805085, -87.644604078)"
-9508314,HX163806,02/15/2014 03:00:00 AM,006XX N CLARK ST,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,TAXICAB,false,false,1832,018,42,8,11,1175394,1904200,2014,03/09/2014 12:39:51 AM,41.892503037,-87.631296179,"(41.892503037, -87.631296179)"
-9499928,HX149804,02/11/2014 09:00:00 AM,035XX W BELDEN AVE,0630,BURGLARY,ATTEMPT FORCIBLE ENTRY,RESIDENCE,false,false,1413,014,26,22,05,1152338,1915099,2014,04/12/2014 12:41:09 AM,41.922897345,-87.715683376,"(41.922897345, -87.715683376)"
-9485217,HX137525,02/03/2014 11:10:00 AM,061XX N TALMAN AVE,0460,BATTERY,SIMPLE,OTHER,false,false,2413,024,50,2,08B,1157588,1940774,2014,02/07/2014 12:38:58 AM,41.993245657,-87.695691083,"(41.993245657, -87.695691083)"
-9484288,HX137883,02/03/2014 06:00:00 AM,008XX W WELLINGTON AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1933,019,44,6,14,1170047,1920093,2014,02/06/2014 12:39:04 AM,41.936232663,-87.650468647,"(41.936232663, -87.650468647)"
-9482033,HX135312,02/01/2014 10:15:00 AM,037XX S WINCHESTER AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0912,009,11,59,14,1164016,1879590,2014,02/03/2014 12:39:04 AM,41.825218752,-87.673777053,"(41.825218752, -87.673777053)"
-9481922,HX135119,02/01/2014 01:45:00 AM,024XX N LINCOLN AVE,0460,BATTERY,SIMPLE,BAR OR TAVERN,false,false,1932,019,43,7,08B,1170293,1916346,2014,02/04/2014 12:39:03 AM,41.925945344,-87.649674382,"(41.925945344, -87.649674382)"
-9480375,HX133597,01/30/2014 08:45:00 PM,071XX S ARTESIAN AVE,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,RESIDENCE,true,false,0832,008,18,66,15,1161245,1856944,2014,02/02/2014 12:39:28 AM,41.763133096,-87.684570132,"(41.763133096, -87.684570132)"
-9480165,HX133324,01/30/2014 04:20:00 PM,013XX W THORNDALE AVE,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,2013,020,48,77,03,1166205,1939678,2014,02/26/2014 12:40:16 AM,41.990057766,-87.664026082,"(41.990057766, -87.664026082)"
-9496144,HX129986,01/28/2014 02:00:00 AM,002XX S WELLS ST,0810,THEFT,OVER $500,SIDEWALK,false,false,0122,001,2,32,06,1174725,1899304,2014,02/16/2014 12:39:09 AM,41.879083123,-87.633899673,"(41.879083123, -87.633899673)"
-9475081,HX128795,01/26/2014 09:55:00 PM,055XX S JUSTINE ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,true,true,0713,007,16,67,14,1167002,1867662,2014,01/29/2014 12:39:43 AM,41.792423561,-87.663163463,"(41.792423561, -87.663163463)"
-9475067,HX128696,01/26/2014 07:00:00 PM,017XX E 70TH ST,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,OTHER,false,false,0332,003,5,43,04B,1189262,1858957,2014,04/28/2014 12:39:03 AM,41.768031199,-87.581819184,"(41.768031199, -87.581819184)"
-9470521,HX123587,01/20/2014 05:00:00 PM,086XX S HONORE ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0614,006,18,71,14,1165484,1847286,2014,01/25/2014 12:39:51 AM,41.736541375,-87.669306547,"(41.736541375, -87.669306547)"
-9467720,HX120666,01/19/2014 09:55:00 PM,067XX S OAKLEY AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0832,008,17,66,07,1162192,1859765,2014,01/23/2014 12:40:19 AM,41.770854668,-87.68102077,"(41.770854668, -87.68102077)"
-9467146,HX120110,01/19/2014 10:46:00 AM,007XX N CHRISTIANA AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1121,011,27,23,26,1153876,1904532,2014,01/22/2014 12:39:52 AM,41.893870012,-87.710314231,"(41.893870012, -87.710314231)"
-9466913,HX119798,01/19/2014 12:14:00 AM,029XX W 63RD ST,3730,INTERFERENCE WITH PUBLIC OFFICER,OBSTRUCTING JUSTICE,OTHER,true,false,0823,008,15,66,24,1157682,1862760,2014,01/21/2014 12:39:39 AM,41.779166065,-87.697471684,"(41.779166065, -87.697471684)"
-9469352,HX119210,01/17/2014 10:30:00 PM,016XX S ALLPORT ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1233,012,25,31,08B,1168275,1891647,2014,03/13/2014 12:40:30 AM,41.858213501,-87.657804026,"(41.858213501, -87.657804026)"
-9466006,HX118511,01/17/2014 08:57:00 PM,044XX W GLADYS AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,1131,011,24,26,05,1146809,1897945,2014,02/12/2014 12:40:02 AM,41.875932413,-87.736437486,"(41.875932413, -87.736437486)"
-9465524,HX117944,01/17/2014 11:55:00 AM,002XX S LA SALLE ST,0870,THEFT,POCKET-PICKING,CTA BUS,false,false,0122,001,42,32,06,1175123,1899399,2014,01/20/2014 12:40:05 AM,41.879334899,-87.632435454,"(41.879334899, -87.632435454)"
-9465089,HX117534,01/17/2014 07:23:00 AM,021XX E 71ST ST,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,0331,003,5,43,06,1191473,1858372,2014,01/20/2014 12:40:05 AM,41.766372615,-87.573733923,"(41.766372615, -87.573733923)"
-9465710,HX118038,01/16/2014 01:15:00 PM,009XX N ASHLAND AVE,0560,ASSAULT,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,1213,012,1,24,08A,1165513,1906501,2014,01/20/2014 12:40:05 AM,41.899033264,-87.667519286,"(41.899033264, -87.667519286)"
-9473883,HX116295,01/16/2014 06:33:48 AM,0000X W CHECKPOINT 5 ST,5007,OTHER OFFENSE,OTHER WEAPONS VIOLATION,AIRPORT TERMINAL UPPER LEVEL - SECURE AREA,false,false,1652,016,41,76,26,1100690,1934276,2014,01/29/2014 12:39:43 AM,41.97638602,-87.905108897,"(41.97638602, -87.905108897)"
-9463375,HX116168,01/15/2014 10:45:00 PM,064XX N WASHTENAW AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,2412,024,50,2,08B,1157108,1942956,2014,01/21/2014 12:39:39 AM,41.999242948,-87.697397101,"(41.999242948, -87.697397101)"
-9463073,HX115842,01/15/2014 04:58:00 PM,051XX W ROSCOE ST,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,RESIDENCE,false,false,1634,016,38,15,26,1141295,1922075,2014,01/23/2014 12:40:19 AM,41.94225129,-87.756086939,"(41.94225129, -87.756086939)"
-9562703,HX214341,01/14/2014 08:00:00 AM,082XX S MARSHFIELD AVE,0820,THEFT,$500 AND UNDER,APARTMENT,false,false,0614,006,21,71,06,1166811,1850232,2014,04/10/2014 12:40:25 AM,41.744597428,-87.664360942,"(41.744597428, -87.664360942)"
-9463358,HX116176,01/13/2014 10:30:00 PM,021XX N PULASKI RD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,2525,025,30,22,08B,1149412,1913709,2014,01/19/2014 12:40:13 AM,41.919140386,-87.726470703,"(41.919140386, -87.726470703)"
-9458937,HX112171,01/12/2014 08:00:00 PM,015XX S SANGAMON ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1232,012,25,28,14,1170483,1892698,2014,01/15/2014 12:39:35 AM,41.861049572,-87.64966861,"(41.861049572, -87.64966861)"
-9458123,HX111210,01/11/2014 09:17:00 PM,069XX S STONY ISLAND AVE,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE PORCH/HALLWAY,true,false,0332,003,5,43,26,1188013,1859500,2014,01/20/2014 12:40:05 AM,41.769551093,-87.586379992,"(41.769551093, -87.586379992)"
-9455812,HX108637,01/09/2014 05:00:00 PM,024XX W CONGRESS PKWY,0810,THEFT,OVER $500,CTA BUS,false,false,1135,011,2,28,06,1160449,1897663,2014,01/22/2014 12:39:52 AM,41.874887361,-87.686363918,"(41.874887361, -87.686363918)"
-9461014,HX107512,01/08/2014 06:40:51 PM,070XX S CAMPBELL AVE,2017,NARCOTICS,MANU/DELIVER:CRACK,RESIDENCE,false,false,0832,008,18,66,18,1160881,1858057,2014,03/22/2014 12:39:11 AM,41.766194856,-87.685873561,"(41.766194856, -87.685873561)"
-9450517,HX103614,01/04/2014 04:35:00 PM,0000X E 8TH ST,0560,ASSAULT,SIMPLE,OTHER,true,false,0123,001,2,32,08A,1176501,1896707,2014,01/07/2014 12:39:59 AM,41.871916899,-87.627457011,"(41.871916899, -87.627457011)"
-9452542,HX105729,01/04/2014 02:00:00 PM,055XX W WAVELAND AVE,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE,false,false,1633,016,38,15,26,1138414,1924083,2014,01/15/2014 12:39:35 AM,41.947814192,-87.766627259,"(41.947814192, -87.766627259)"
-9447548,HX101036,01/01/2014 06:00:00 PM,069XX S PRAIRIE AVE,0460,BATTERY,SIMPLE,RESIDENCE,false,false,0322,003,6,69,08B,1179274,1859254,2014,01/05/2014 12:39:48 AM,41.769079844,-87.61842041,"(41.769079844, -87.61842041)"
-9447499,HX100946,01/01/2014 02:30:00 PM,055XX W BELMONT AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,2514,025,30,19,05,1139087,1920691,2014,01/30/2014 12:39:55 AM,41.938493968,-87.764236211,"(41.938493968, -87.764236211)"
-9446798,HW590618,12/31/2013 09:30:00 PM,045XX N MULLIGAN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,true,1622,016,38,15,14,1133162,1929412,2013,01/03/2014 11:11:01 AM,41.962531191,-87.785807568,"(41.962531191, -87.785807568)"
-9463485,HX116303,12/31/2013 11:49:00 AM,017XX W DIVERSEY PKWY,0810,THEFT,OVER $500,RESIDENCE,false,false,1931,019,32,7,06,1164115,1918593,2013,02/10/2014 10:57:46 AM,41.932244166,-87.672311764,"(41.932244166, -87.672311764)"
-9445506,HW589726,12/31/2013 03:30:00 AM,082XX S MARYLAND AVE,0810,THEFT,OVER $500,RESIDENCE,false,false,0631,006,8,44,06,1183387,1850211,2013,12/31/2013 06:37:38 AM,41.744170126,-87.603625425,"(41.744170126, -87.603625425)"
-9443677,HW588194,12/29/2013 02:00:00 PM,018XX S WENTWORTH AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0914,009,25,34,06,1175295,1891529,2013,12/30/2013 11:58:17 AM,41.857735248,-87.632039982,"(41.857735248, -87.632039982)"
-9441369,HW585335,12/27/2013 10:00:00 AM,069XX S VERNON AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENTIAL YARD (FRONT/BACK),false,false,0322,003,6,69,14,1180491,1858955,2013,12/27/2013 12:11:46 PM,41.768231511,-87.613968674,"(41.768231511, -87.613968674)"
-9438988,HW583050,12/24/2013 04:15:00 PM,047XX S CALUMET AVE,031A,ROBBERY,ARMED: HANDGUN,OTHER,false,false,0224,002,3,38,03,1179280,1873373,2013,12/30/2013 10:06:52 PM,41.807823592,-87.617967803,"(41.807823592, -87.617967803)"
-9445303,HW589380,12/24/2013 11:00:00 AM,001XX E CHESTNUT ST,0460,BATTERY,SIMPLE,HOTEL/MOTEL,false,false,1833,018,42,8,08B,1176968,1906390,2013,01/12/2014 08:03:55 AM,41.898477019,-87.625449228,"(41.898477019, -87.625449228)"
-9438193,HW582421,12/22/2013 06:00:00 AM,004XX W BRIAR PL,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1934,019,44,6,05,1172707,1920990,2013,03/31/2014 09:32:37 AM,41.938635508,-87.640666327,"(41.938635508, -87.640666327)"
-9436007,HW580185,12/22/2013 04:20:00 AM,022XX N LINCOLN AVE,0460,BATTERY,SIMPLE,BAR OR TAVERN,false,false,1812,018,43,7,08B,1171535,1915285,2013,01/01/2014 08:26:11 AM,41.923006647,-87.645141966,"(41.923006647, -87.645141966)"
-9435371,HW579047,12/21/2013 06:35:00 AM,081XX S EAST END AVE,0880,THEFT,PURSE-SNATCHING,SIDEWALK,false,false,0414,004,8,45,06,1189056,1851233,2013,01/01/2014 05:42:14 PM,41.746840795,-87.58282125,"(41.746840795, -87.58282125)"
-9443033,HW587304,12/20/2013 03:00:00 PM,023XX W SCHOOL ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1921,019,32,5,05,1160178,1921820,2013,01/22/2014 12:00:33 PM,41.941181641,-87.686690277,"(41.941181641, -87.686690277)"
-9430964,HW574873,12/18/2013 02:45:00 AM,121XX S STEWART AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0523,005,34,53,08B,1175855,1824383,2013,12/27/2013 05:32:41 PM,41.673466392,-87.631994066,"(41.673466392, -87.631994066)"
-9430928,HW574834,12/18/2013 12:25:00 AM,044XX S DREXEL BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0221,002,4,39,08B,1182896,1875885,2013,01/15/2014 12:28:54 PM,41.814633354,-87.6046273,"(41.814633354, -87.6046273)"
-9427754,HW571866,12/15/2013 03:00:00 PM,037XX W LE MOYNE ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2535,025,26,23,14,1151055,1909670,2013,12/16/2013 11:27:32 AM,41.908024933,-87.720540109,"(41.908024933, -87.720540109)"
-9422258,HW566091,12/09/2013 05:45:00 PM,024XX W THOMAS ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1212,012,1,24,06,1159993,1907286,2013,12/11/2013 10:38:33 AM,41.901303134,-87.687772355,"(41.901303134, -87.687772355)"
-9419862,HW563956,12/08/2013 08:15:00 PM,045XX S LOWE AVE,0650,BURGLARY,HOME INVASION,RESIDENCE,false,false,0925,009,11,61,05,1172767,1874641,2013,01/25/2014 02:12:32 PM,41.811449389,-87.641818209,"(41.811449389, -87.641818209)"
-9419440,HW563282,12/08/2013 12:40:00 PM,011XX N AVERS AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1112,011,27,23,18,1150467,1907365,2013,12/08/2013 01:55:00 PM,41.901711295,-87.722760401,"(41.901711295, -87.722760401)"
-9417989,HW561291,12/06/2013 05:15:00 PM,086XX S LOOMIS BLVD,0495,BATTERY,AGGRAVATED OF A SENIOR CITIZEN,RESIDENCE,false,true,0614,006,21,71,04B,1168464,1847493,2013,12/20/2013 12:35:23 PM,41.7370458,-87.658382843,"(41.7370458, -87.658382843)"
-9418209,HW560520,12/06/2013 03:45:00 AM,071XX W HIGGINS AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1613,016,41,10,08B,1127721,1936004,2013,12/08/2013 09:43:21 AM,41.980713995,-87.805663146,"(41.980713995, -87.805663146)"
-9417783,HW560249,12/05/2013 07:30:00 PM,026XX W CORTEZ ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1211,012,1,24,08B,1158569,1906844,2013,01/15/2014 12:25:13 PM,41.900119535,-87.69301497,"(41.900119535, -87.69301497)"
-9413386,HW556650,12/03/2013 09:20:00 AM,022XX N MILWAUKEE AVE,0870,THEFT,POCKET-PICKING,BANK,false,false,1431,014,1,22,06,1157424,1915108,2013,12/04/2013 08:36:57 AM,41.922819997,-87.696995399,"(41.922819997, -87.696995399)"
-9422734,HW555393,11/30/2013 05:00:00 PM,053XX N ELSTON AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,GROCERY FOOD STORE,false,false,1621,016,45,11,14,1140926,1935350,2013,12/12/2013 10:14:16 AM,41.978685873,-87.757115096,"(41.978685873, -87.757115096)"
-9409073,HW552271,11/28/2013 04:30:00 PM,030XX W NORTH AVE,0820,THEFT,$500 AND UNDER,APARTMENT,false,false,1421,014,26,23,06,1155601,1910520,2013,12/02/2013 11:28:35 AM,41.910267073,-87.703817467,"(41.910267073, -87.703817467)"
-9408740,HW551993,11/27/2013 02:30:00 PM,004XX E MC FETRIDGE DR,0890,THEFT,FROM BUILDING,SPORTS ARENA/STADIUM,false,false,0132,001,2,33,06,1179306,1894170,2013,11/30/2013 07:00:12 AM,41.864891468,-87.617236533,"(41.864891468, -87.617236533)"
-9406044,HW549485,11/26/2013 09:15:00 PM,056XX N CLARK ST,1330,CRIMINAL TRESPASS,TO LAND,RESTAURANT,true,false,2012,020,40,77,26,1164868,1937443,2013,11/27/2013 11:00:28 AM,41.983953427,-87.669007577,"(41.983953427, -87.669007577)"
-9401846,HW544563,11/22/2013 09:39:00 PM,095XX S GREENWOOD AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0511,005,8,50,14,1185261,1841750,2013,11/23/2013 06:00:53 AM,41.720908403,-87.597024187,"(41.720908403, -87.597024187)"
-9405301,HW548719,11/22/2013 01:30:00 PM,027XX E 89TH ST,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",false,false,0423,004,7,46,08B,1195945,1846528,2013,11/27/2013 08:46:04 AM,41.733762235,-87.557734065,"(41.733762235, -87.557734065)"
-9398511,HW541664,11/20/2013 08:30:00 PM,079XX S VINCENNES AVE,0460,BATTERY,SIMPLE,RESTAURANT,false,false,0621,006,17,44,08B,1174965,1852481,2013,11/21/2013 07:36:32 AM,41.750591127,-87.634416769,"(41.750591127, -87.634416769)"
-9397579,HW540801,11/20/2013 11:00:00 AM,069XX S STEWART AVE,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE,true,false,0731,007,6,68,26,1174869,1858736,2013,11/21/2013 07:38:40 AM,41.767757733,-87.634582377,"(41.767757733, -87.634582377)"
-9396480,HW540058,11/19/2013 05:00:00 PM,040XX W LAKE ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,CTA PLATFORM,false,false,1114,011,28,26,11,1149614,1901482,2013,11/20/2013 10:49:30 AM,41.885584343,-87.726046543,"(41.885584343, -87.726046543)"
-9396178,HW539680,11/19/2013 01:53:00 PM,027XX W FLOURNOY ST,2017,NARCOTICS,MANU/DELIVER:CRACK,SIDEWALK,true,false,1135,011,2,27,18,1157878,1896874,2013,11/19/2013 02:51:35 PM,41.872775057,-87.695825091,"(41.872775057, -87.695825091)"
-9395154,HW538677,11/18/2013 09:30:00 AM,021XX N ST LOUIS AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1413,014,26,22,05,1152957,1913981,2013,12/30/2013 10:35:27 AM,41.919817201,-87.713438645,"(41.919817201, -87.713438645)"
-9388727,HW532069,11/13/2013 04:15:00 PM,031XX N CLARK ST,1310,CRIMINAL DAMAGE,TO PROPERTY,SMALL RETAIL STORE,false,false,1933,019,44,6,14,1170331,1920956,2013,11/14/2013 09:31:28 AM,41.93859456,-87.649399622,"(41.93859456, -87.649399622)"
-9388289,HW531527,11/12/2013 05:30:00 PM,008XX N MICHIGAN AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,1833,018,42,8,06,1177379,1906245,2013,11/28/2013 07:03:12 AM,41.898069817,-87.623944075,"(41.898069817, -87.623944075)"
-9387653,HW531130,11/12/2013 12:00:00 PM,016XX N CLARK ST,0930,MOTOR VEHICLE THEFT,THEFT/RECOVERY: AUTOMOBILE,STREET,true,false,1814,018,43,7,07,1175174,1911062,2013,11/13/2013 10:58:10 AM,41.911337641,-87.631898083,"(41.911337641, -87.631898083)"
-9386279,HW530040,11/12/2013 12:02:00 AM,019XX W LAWRENCE AVE,0460,BATTERY,SIMPLE,SIDEWALK,true,false,1912,019,47,4,08B,1162205,1931845,2013,11/12/2013 07:07:42 AM,41.968648549,-87.678958932,"(41.968648549, -87.678958932)"
-9386177,HW529878,11/11/2013 07:31:00 PM,015XX E 55TH ST,0460,BATTERY,SIMPLE,GROCERY FOOD STORE,false,false,0234,002,4,41,08B,1187435,1868879,2013,11/14/2013 10:15:21 AM,41.795301576,-87.58820073,"(41.795301576, -87.58820073)"
-9385960,HW529741,11/11/2013 05:45:00 PM,001XX W MONROE ST,0860,THEFT,RETAIL THEFT,DEPARTMENT STORE,false,false,0122,001,42,32,06,1175444,1899856,2013,11/12/2013 08:01:46 AM,41.880581734,-87.63124308,"(41.880581734, -87.63124308)"
-9382909,HW525552,11/08/2013 11:30:00 AM,068XX S PERRY AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,0722,007,6,69,05,1176620,1859576,2013,11/21/2013 02:50:51 PM,41.770023572,-87.628138968,"(41.770023572, -87.628138968)"
-9380477,HW523608,11/06/2013 08:00:00 PM,020XX W VAN BUREN ST,0460,BATTERY,SIMPLE,STREET,false,false,1225,012,2,28,08B,1163092,1898204,2013,11/07/2013 08:51:00 AM,41.876316834,-87.676644735,"(41.876316834, -87.676644735)"
-9378817,HW522179,11/05/2013 07:00:00 PM,064XX W IRVING PARK RD,0810,THEFT,OVER $500,GROCERY FOOD STORE,false,false,1632,016,38,17,06,1132665,1925939,2013,12/12/2013 12:32:58 PM,41.953009618,-87.787716178,"(41.953009618, -87.787716178)"
-9381262,HW524192,11/05/2013 01:15:00 PM,036XX N MILWAUKEE AVE,0810,THEFT,OVER $500,"SCHOOL, PUBLIC, BUILDING",false,false,1731,017,38,16,06,1146826,1923648,2013,11/10/2013 10:57:29 AM,41.94646373,-87.735717521,"(41.94646373, -87.735717521)"
-9378444,HW521658,11/04/2013 02:30:00 AM,040XX W NORTH AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,VEHICLE NON-COMMERCIAL,false,true,2534,025,30,23,08B,1149440,1910376,2013,11/07/2013 01:55:29 PM,41.909993773,-87.726454479,"(41.909993773, -87.726454479)"
-9375841,HW519378,11/03/2013 03:00:00 PM,033XX W 55TH ST,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,0822,008,14,63,03,1155215,1867996,2013,12/22/2013 03:42:52 PM,41.793584117,-87.706376059,"(41.793584117, -87.706376059)"
-9375532,HW517470,11/02/2013 06:40:00 AM,073XX S DANTE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0324,003,5,43,14,1187106,1856500,2013,11/04/2013 11:58:09 AM,41.761340392,-87.589799654,"(41.761340392, -87.589799654)"
-9373859,HW516405,11/01/2013 01:40:00 PM,118XX S UNION AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0524,005,34,53,14,1173732,1826567,2013,11/03/2013 08:03:38 AM,41.679506796,-87.639700066,"(41.679506796, -87.639700066)"
-9373995,HW516673,11/01/2013 08:00:00 AM,082XX S HALSTED ST,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0622,006,21,71,05,1172449,1850113,2013,11/06/2013 12:32:16 AM,41.744148741,-87.643706097,"(41.744148741, -87.643706097)"
-9372591,HW515482,10/31/2013 08:30:00 PM,022XX N ORCHARD ST,0890,THEFT,FROM BUILDING,RESTAURANT,false,false,1812,018,43,7,06,1171187,1915438,2013,11/01/2013 07:36:06 AM,41.923434143,-87.646416123,"(41.923434143, -87.646416123)"
-9369795,HW512980,10/30/2013 09:30:00 AM,091XX S STONY ISLAND AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0413,004,8,48,06,1188343,1844881,2013,10/31/2013 08:50:52 AM,41.72942731,-87.585636041,"(41.72942731, -87.585636041)"
-9369259,HW512671,10/30/2013 12:03:00 AM,011XX S PULASKI RD,2027,NARCOTICS,POSS: CRACK,STREET,true,false,1132,011,24,29,18,1149930,1894710,2013,10/30/2013 01:13:38 AM,41.86699507,-87.72506236,"(41.86699507, -87.72506236)"
-9374961,HW517969,10/29/2013 08:00:00 PM,017XX N CENTRAL PARK AVE,0890,THEFT,FROM BUILDING,GAS STATION,false,false,2535,025,26,23,06,1152074,1911121,2013,11/07/2013 12:45:33 PM,41.911986574,-87.716758491,"(41.911986574, -87.716758491)"
-9367633,HW511020,10/28/2013 03:00:00 PM,091XX S STEWART AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0634,006,21,49,26,1175435,1844197,2013,11/02/2013 10:54:45 AM,41.727848282,-87.632941532,"(41.727848282, -87.632941532)"
-9367453,HW510727,10/28/2013 11:20:00 AM,048XX W IRVING PARK RD,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,1634,016,45,15,06,1143518,1926115,2013,10/30/2013 11:17:00 AM,41.953296043,-87.747814885,"(41.953296043, -87.747814885)"
-9365490,HW508655,10/26/2013 07:30:00 PM,0000X E 112TH ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0531,005,9,49,06,1178300,1830750,2013,10/27/2013 08:44:45 AM,41.690883434,-87.622853021,"(41.690883434, -87.622853021)"
-9364471,HW507300,10/25/2013 07:00:00 PM,012XX N WELLS ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1821,018,43,8,14,1174531,1908411,2013,10/26/2013 07:48:48 AM,41.904077575,-87.634339591,"(41.904077575, -87.634339591)"
-9368192,HW510471,10/25/2013 05:00:00 PM,040XX W GRENSHAW ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,1132,011,24,29,05,1149546,1894773,2013,11/03/2013 01:02:50 PM,41.867175407,-87.726470451,"(41.867175407, -87.726470451)"
-9362844,HW505807,10/24/2013 04:01:00 PM,010XX W 83RD ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0613,006,21,71,18,1170959,1849745,2013,10/24/2013 05:35:36 PM,41.743171538,-87.649176324,"(41.743171538, -87.649176324)"
-9362926,HW505629,10/24/2013 02:30:00 AM,002XX W 91ST ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0634,006,21,49,08B,1176260,1844567,2013,10/29/2013 10:56:45 AM,41.72884515,-87.629908349,"(41.72884515, -87.629908349)"
-9360142,HW503466,10/22/2013 06:24:00 PM,053XX W DIVISION ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,STREET,true,false,2532,025,37,25,15,1140246,1907512,2013,10/23/2013 07:13:58 AM,41.902308165,-87.760300215,"(41.902308165, -87.760300215)"
-9358780,HW502274,10/21/2013 06:45:00 PM,0000X E LAKE ST,0870,THEFT,POCKET-PICKING,SIDEWALK,false,false,0111,001,42,32,06,1176958,1901785,2013,10/27/2013 06:49:04 AM,41.885840894,-87.625625463,"(41.885840894, -87.625625463)"
-9357846,HW501395,10/20/2013 01:00:00 AM,027XX S HAMLIN AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1031,010,22,30,06,1151439,1885627,2013,10/21/2013 01:14:04 PM,41.84204075,-87.719760856,"(41.84204075, -87.719760856)"
-9356701,HW500367,10/18/2013 07:00:00 PM,015XX N HUDSON AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,RESIDENCE,false,false,1821,018,43,8,14,1173068,1910776,2013,10/21/2013 11:21:48 AM,41.910599843,-87.639643258,"(41.910599843, -87.639643258)"
-9355772,HW499307,10/18/2013 03:00:00 PM,054XX S MICHIGAN AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0231,002,3,40,06,1178124,1868891,2013,10/20/2013 10:27:44 AM,41.795550899,-87.622343646,"(41.795550899, -87.622343646)"
-9354975,HW498169,10/18/2013 03:00:00 PM,004XX E 42ND ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0214,002,3,38,08B,1180011,1877216,2013,10/22/2013 07:36:27 AM,41.818352371,-87.615168922,"(41.818352371, -87.615168922)"
-9351995,HW495731,10/16/2013 07:50:00 PM,067XX S UNION AVE,502P,OTHER OFFENSE,FALSE/STOLEN/ALTERED TRP,STREET,true,false,0723,007,6,68,26,1172857,1860296,2013,10/17/2013 04:28:08 AM,41.772083175,-87.641911265,"(41.772083175, -87.641911265)"
-9351560,HW495145,10/15/2013 06:09:00 PM,033XX W 26TH ST,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,1024,010,22,30,06,1154210,1886567,2013,11/05/2013 01:58:17 PM,41.844565451,-87.709566994,"(41.844565451, -87.709566994)"
-9350227,HW493843,10/15/2013 02:00:00 PM,004XX S STATE ST,1330,CRIMINAL TRESPASS,TO LAND,LIBRARY,true,false,0113,001,2,32,26,1176394,1898485,2013,10/16/2013 07:04:32 AM,41.876798252,-87.62779619,"(41.876798252, -87.62779619)"
-9350128,HW493773,10/15/2013 01:15:00 PM,060XX W NORTH AVE,0560,ASSAULT,SIMPLE,SMALL RETAIL STORE,false,false,2513,025,29,25,08A,1135975,1910053,2013,10/16/2013 08:07:07 AM,41.909358198,-87.775927859,"(41.909358198, -87.775927859)"
-9347997,HW491754,10/13/2013 05:15:00 PM,014XX S TRUMBULL AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,SIDEWALK,false,false,1021,010,24,29,04B,1153565,1892742,2013,10/14/2013 12:49:24 PM,41.861523204,-87.711770024,"(41.861523204, -87.711770024)"
-9347204,HW490756,10/12/2013 10:00:00 PM,025XX W MOFFAT ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1434,014,1,22,08B,1159251,1912170,2013,10/20/2013 10:03:29 AM,41.914720508,-87.690363324,"(41.914720508, -87.690363324)"
-9347060,HW490607,10/12/2013 06:38:00 PM,053XX W BELMONT AVE,0460,BATTERY,SIMPLE,BAR OR TAVERN,false,false,2514,025,30,19,08B,1139891,1920710,2013,10/13/2013 09:32:00 AM,41.938531422,-87.761280817,"(41.938531422, -87.761280817)"
-9346247,HW489698,10/12/2013 12:00:00 AM,033XX N HALSTED ST,0820,THEFT,$500 AND UNDER,BAR OR TAVERN,false,false,1925,019,44,6,06,1170364,1922754,2013,10/14/2013 09:23:06 AM,41.943527622,-87.649225604,"(41.943527622, -87.649225604)"
-9345820,HW489083,10/11/2013 03:30:00 PM,036XX N CENTRAL AVE,0820,THEFT,$500 AND UNDER,CONVENIENCE STORE,false,false,1633,016,38,15,06,1138423,1923411,2013,10/16/2013 02:31:08 PM,41.945969994,-87.766610509,"(41.945969994, -87.766610509)"
-9344416,HW488030,10/10/2013 08:25:00 PM,039XX S LAKE PARK AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0214,002,4,36,18,1183461,1878927,2013,10/10/2013 10:03:33 PM,41.822967653,-87.602459924,"(41.822967653, -87.602459924)"
-9360698,HW503849,10/10/2013 06:00:00 PM,020XX N KENMORE AVE,0810,THEFT,OVER $500,STREET,false,false,1811,018,43,7,06,1168932,1913720,2013,10/23/2013 11:11:05 AM,41.918769129,-87.654751715,"(41.918769129, -87.654751715)"
-9345919,HW489237,10/10/2013 03:00:00 PM,013XX S ST LOUIS AVE,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1021,010,24,29,08B,1153223,1893084,2013,10/16/2013 10:12:48 AM,41.862468479,-87.71301638,"(41.862468479, -87.71301638)"
-9348839,HW492862,10/10/2013 10:00:00 AM,0000X W 114TH ST,0841,THEFT,FINANCIAL ID THEFT:$300 &UNDER,OTHER,false,false,0522,005,34,49,06,1177998,1829418,2013,10/15/2013 12:19:52 PM,41.687235061,-87.623998815,"(41.687235061, -87.623998815)"
-9341149,HW485223,10/08/2013 05:30:00 PM,068XX S UNION AVE,0880,THEFT,PURSE-SNATCHING,SIDEWALK,false,false,0723,007,6,68,06,1172873,1859725,2013,10/09/2013 06:30:18 AM,41.770515931,-87.641869442,"(41.770515931, -87.641869442)"
-9340907,HW484734,10/08/2013 01:39:00 PM,001XX E 35TH ST,3731,INTERFERENCE WITH PUBLIC OFFICER,OBSTRUCTING IDENTIFICATION,STREET,true,false,0211,002,3,35,24,1178047,1881868,2013,10/09/2013 07:27:48 AM,41.831162653,-87.622232227,"(41.831162653, -87.622232227)"
-9340372,HW484367,10/07/2013 08:00:00 PM,011XX N CENTRAL PARK AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,1112,011,27,23,07,1152130,1907216,2013,11/01/2013 10:35:50 AM,41.901269783,-87.716655897,"(41.901269783, -87.716655897)"
-9337231,HW481152,10/05/2013 05:23:00 PM,010XX W WILSON AVE,0460,BATTERY,SIMPLE,SIDEWALK,true,false,1914,019,46,3,08B,1168209,1930671,2013,10/06/2013 08:30:55 AM,41.965299098,-87.656916624,"(41.965299098, -87.656916624)"
-9335258,HW479058,10/03/2013 07:30:00 PM,010XX W VERNON PARK PL,0820,THEFT,$500 AND UNDER,RESIDENCE-GARAGE,false,false,1232,012,25,28,06,1169708,1897023,2013,10/08/2013 12:43:36 PM,41.872934609,-87.652387521,"(41.872934609, -87.652387521)"
-9334585,HW478184,10/03/2013 02:25:00 PM,066XX S GREENWOOD AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE PORCH/HALLWAY,true,false,0321,003,5,42,26,1184397,1861361,2013,10/04/2013 07:23:17 AM,41.774743259,-87.599576249,"(41.774743259, -87.599576249)"
-9332022,HW476133,10/02/2013 04:55:00 AM,054XX W HARRISON ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,1522,015,29,25,08B,1140298,1896829,2013,10/03/2013 08:50:25 AM,41.872991728,-87.760371219,"(41.872991728, -87.760371219)"
-9331199,HW474837,10/01/2013 09:15:00 AM,0000X S STATE ST,0890,THEFT,FROM BUILDING,OTHER,false,false,0112,001,42,32,06,1176348,1900301,2013,10/02/2013 07:21:10 AM,41.881782498,-87.627910281,"(41.881782498, -87.627910281)"
-9329027,HW472960,09/29/2013 10:00:00 PM,035XX W HIRSCH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,1422,014,26,23,14,1152377,1909122,2013,10/02/2013 09:47:51 AM,41.906495151,-87.715698231,"(41.906495151, -87.715698231)"
-9327302,HW470730,09/28/2013 08:30:00 AM,058XX W SCHOOL ST,0560,ASSAULT,SIMPLE,RESIDENCE,false,false,1633,016,38,15,08A,1136934,1921306,2013,10/01/2013 08:34:04 AM,41.940220541,-87.772134309,"(41.940220541, -87.772134309)"
-9329276,HW473218,09/27/2013 11:00:00 PM,018XX W FULLERTON AVE,0820,THEFT,$500 AND UNDER,BAR OR TAVERN,false,false,1432,014,32,22,06,1163234,1916006,2013,10/01/2013 10:32:17 AM,41.925163873,-87.675622245,"(41.925163873, -87.675622245)"
-9333098,HW475335,09/25/2013 08:00:00 AM,051XX W DICKENS AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2522,025,37,19,14,1141835,1913519,2013,10/03/2013 07:28:36 AM,41.918762764,-87.754314544,"(41.918762764, -87.754314544)"
-9320262,HW464419,09/23/2013 10:22:00 PM,026XX E 77TH ST,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,0421,004,7,43,18,1195449,1854467,2013,09/23/2013 11:26:40 PM,41.755559736,-87.559289506,"(41.755559736, -87.559289506)"
-9322704,HW466341,09/23/2013 09:00:00 PM,013XX W LOYOLA AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,2432,024,40,1,06,1166125,1943764,2013,09/26/2013 02:30:26 PM,42.001271558,-87.664202897,"(42.001271558, -87.664202897)"
-9318532,HW462977,09/22/2013 07:30:00 PM,011XX N AUSTIN BLVD,0312,ROBBERY,ARMED:KNIFE/CUTTING INSTRUMENT,SIDEWALK,false,false,1511,015,29,25,03,1136195,1907104,2013,09/27/2013 01:49:23 PM,41.901261852,-87.775190151,"(41.901261852, -87.775190151)"
-9318253,HW462564,09/22/2013 12:00:00 PM,083XX S WOOD ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0614,006,18,71,14,1165849,1849067,2013,09/23/2013 06:38:20 AM,41.741420967,-87.667918852,"(41.741420967, -87.667918852)"
-9318111,HW462324,09/22/2013 11:40:00 AM,054XX S EAST VIEW PARK,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0234,002,5,41,14,1189105,1869607,2013,09/22/2013 12:36:18 PM,41.797259374,-87.582053577,"(41.797259374, -87.582053577)"
-9316582,HW460471,09/20/2013 10:15:00 PM,027XX W 44TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0922,009,12,58,18,1158733,1875413,2013,06/14/2014 12:41:49 PM,41.813866207,-87.693273196,"(41.813866207, -87.693273196)"
-9311752,HW455833,09/17/2013 03:07:00 PM,022XX N RACINE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1811,018,32,7,06,1167986,1914852,2013,09/18/2013 06:57:03 AM,41.921895888,-87.658194649,"(41.921895888, -87.658194649)"
-9310874,HW455337,09/17/2013 10:00:00 AM,001XX N CLARK ST,0860,THEFT,RETAIL THEFT,DRUG STORE,true,false,0111,001,42,32,06,1175544,1901630,2013,09/17/2013 11:55:51 AM,41.885447447,-87.63082257,"(41.885447447, -87.63082257)"
-9308270,HW452815,09/15/2013 11:10:00 AM,015XX N CENTRAL AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,2532,025,37,25,08A,1138753,1909725,2013,09/18/2013 11:16:41 AM,41.908408135,-87.765730529,"(41.908408135, -87.765730529)"
-9308151,HW452734,09/15/2013 08:00:00 AM,044XX W GLADYS AVE,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,RESIDENCE,false,false,1131,011,24,26,26,1146753,1897944,2013,09/17/2013 02:50:20 PM,41.875930737,-87.736643125,"(41.875930737, -87.736643125)"
-9307352,HW450765,09/13/2013 03:00:00 PM,062XX N RICHMOND ST,0560,ASSAULT,SIMPLE,APARTMENT,false,true,2413,024,50,2,08A,1155594,1941095,2013,09/27/2013 03:55:58 PM,41.994167003,-87.703017101,"(41.994167003, -87.703017101)"
-9305945,HW450171,09/13/2013 12:15:00 PM,006XX N HOMAN AVE,1330,CRIMINAL TRESPASS,TO LAND,ABANDONED BUILDING,true,false,1121,011,27,23,26,1153638,1904044,2013,09/18/2013 12:28:29 PM,41.892535632,-87.711201326,"(41.892535632, -87.711201326)"
-9314694,HW458575,09/12/2013 04:00:00 PM,080XX S HALSTED ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0621,006,21,71,07,1172408,1851475,2013,10/08/2013 10:06:51 AM,41.747887149,-87.643816335,"(41.747887149, -87.643816335)"
-9304727,HW448997,09/12/2013 03:10:00 PM,002XX E 75TH ST,0460,BATTERY,SIMPLE,BARBERSHOP,false,false,0623,006,6,69,08B,1179087,1855296,2013,09/17/2013 01:08:52 PM,41.758222927,-87.619226301,"(41.758222927, -87.619226301)"
-9304482,HW448781,09/12/2013 12:30:00 PM,012XX N LAWNDALE AVE,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,RESIDENCE,false,false,2535,025,26,23,26,1151287,1908142,2013,09/23/2013 01:42:04 PM,41.903827404,-87.719728008,"(41.903827404, -87.719728008)"
-9303427,HW448094,09/11/2013 03:30:00 PM,040XX W NELSON ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2523,025,31,21,07,1149128,1919939,2013,09/12/2013 11:21:18 AM,41.936241572,-87.727352391,"(41.936241572, -87.727352391)"
-9303778,HW448369,09/11/2013 11:00:00 AM,017XX W HOWARD ST,0820,THEFT,$500 AND UNDER,STREET,false,false,2422,024,49,1,06,1163125,1950307,2013,09/12/2013 09:45:30 AM,42.019289497,-87.675054297,"(42.019289497, -87.675054297)"
-9301023,HW445780,09/10/2013 01:00:00 PM,061XX S COTTAGE GROVE AVE,1330,CRIMINAL TRESPASS,TO LAND,SIDEWALK,true,false,0313,003,20,42,26,1182662,1864400,2013,09/11/2013 11:15:39 AM,41.783123003,-87.605842173,"(41.783123003, -87.605842173)"
-9301033,HW445823,09/10/2013 10:15:00 AM,023XX N GREENVIEW AVE,0810,THEFT,OVER $500,STREET,false,false,1811,018,32,7,06,1165955,1915825,2013,09/10/2013 04:11:01 PM,41.924609492,-87.665629237,"(41.924609492, -87.665629237)"
-9297658,HW442865,09/08/2013 11:30:00 AM,050XX S BISHOP ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0933,009,16,61,18,1167489,1870990,2013,09/09/2013 04:08:50 PM,41.801545549,-87.661282298,"(41.801545549, -87.661282298)"
-9297353,HW442554,09/08/2013 04:10:00 AM,0000X N MASON AVE,0496,BATTERY,AGGRAVATED DOMESTIC BATTERY: KNIFE/CUTTING INST,RESIDENCE,true,true,1513,015,29,25,04B,1136732,1899680,2013,09/09/2013 07:31:07 AM,41.880879833,-87.773395618,"(41.880879833, -87.773395618)"
-9296152,HW440966,09/06/2013 11:18:00 PM,028XX W 71ST ST,0915,MOTOR VEHICLE THEFT,"TRUCK, BUS, MOTOR HOME",GAS STATION,false,false,0831,008,18,66,07,1158835,1857396,2013,09/09/2013 11:53:26 AM,41.764422991,-87.693390964,"(41.764422991, -87.693390964)"
-9298158,HW443520,09/03/2013 09:00:00 AM,087XX S DANTE AVE,0810,THEFT,OVER $500,RESIDENTIAL YARD (FRONT/BACK),false,false,0412,004,8,48,06,1187357,1847169,2013,09/09/2013 06:55:41 AM,41.735729267,-87.589175504,"(41.735729267, -87.589175504)"
-9289930,HW435033,09/02/2013 10:10:00 PM,059XX S MAPLEWOOD AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0824,008,16,66,05,1160359,1864856,2013,09/06/2013 02:02:19 PM,41.78486304,-87.687599767,"(41.78486304, -87.687599767)"
-9288722,HW433610,09/01/2013 09:15:00 PM,031XX W MADISON ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1222,012,28,27,18,1155636,1899894,2013,09/01/2013 10:17:17 PM,41.881107642,-87.703975234,"(41.881107642, -87.703975234)"
-9289606,HW433509,09/01/2013 04:30:00 PM,069XX S CRANDON AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0331,003,5,43,05,1192486,1859296,2013,09/21/2013 07:34:06 PM,41.768883532,-87.569990916,"(41.768883532, -87.569990916)"
-9290660,HW435606,08/31/2013 11:55:00 PM,053XX S BLACKSTONE AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,0234,002,4,41,06,1186856,1870264,2013,09/03/2013 01:20:53 PM,41.799115871,-87.590280001,"(41.799115871, -87.590280001)"
-9287829,HW432489,08/31/2013 11:44:00 PM,022XX S ALBANY AVE,1710,OFFENSE INVOLVING CHILDREN,ENDANGERING LIFE/HEALTH CHILD,STREET,true,false,1033,010,24,30,26,1156084,1888859,2013,09/02/2013 02:27:18 PM,41.850817406,-87.702627894,"(41.850817406, -87.702627894)"
-9326423,HW469599,08/31/2013 09:30:00 AM,081XX S WOOD ST,0810,THEFT,OVER $500,STREET,false,false,0614,006,18,71,06,1165811,1850449,2013,09/28/2013 06:54:39 AM,41.745214183,-87.668018933,"(41.745214183, -87.668018933)"
-9291789,HW436861,08/30/2013 03:15:00 PM,042XX W THOMAS ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1111,011,37,23,14,1148112,1907042,2013,09/04/2013 09:38:08 AM,41.900870618,-87.731418997,"(41.900870618, -87.731418997)"
-9285193,HW429459,08/29/2013 08:06:00 PM,059XX S DR MARTIN LUTHER KING JR DR,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0232,002,20,40,08B,1179902,1865589,2013,09/01/2013 01:23:27 PM,41.786449366,-87.615924759,"(41.786449366, -87.615924759)"
-9281946,HW426728,08/27/2013 10:59:00 PM,063XX N MOZART ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,2413,024,50,2,08B,1156226,1941941,2013,08/28/2013 08:36:51 AM,41.996475675,-87.700669333,"(41.996475675, -87.700669333)"
-9280854,HW424692,08/26/2013 10:00:00 AM,078XX S YATES BLVD,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0414,004,7,43,14,1193523,1853283,2013,08/28/2013 07:40:53 AM,41.752358081,-87.566386359,"(41.752358081, -87.566386359)"
-9278699,HW423644,08/25/2013 01:00:00 AM,082XX W IRVING PARK RD,1310,CRIMINAL DAMAGE,TO PROPERTY,VACANT LOT/LAND,false,false,1631,016,36,17,14,1120290,1925502,2013,08/26/2013 09:23:11 AM,41.952017615,-87.833217939,"(41.952017615, -87.833217939)"
-9274671,HW419043,08/22/2013 03:15:00 PM,011XX S JEFFERSON ST,0860,THEFT,RETAIL THEFT,TAVERN/LIQUOR STORE,false,false,0124,001,2,28,06,1172454,1895218,2013,08/23/2013 07:32:33 AM,41.867921341,-87.642359085,"(41.867921341, -87.642359085)"
-9270198,HW415275,08/20/2013 12:40:00 AM,036XX N CLARK ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,PARKING LOT/GARAGE(NON.RESID.),true,false,1923,019,44,6,18,1168211,1924262,2013,08/20/2013 03:30:24 AM,41.947712517,-87.657095221,"(41.947712517, -87.657095221)"
-9268585,HW413851,08/18/2013 02:00:00 PM,106XX S CHAMPLAIN AVE,1790,OFFENSE INVOLVING CHILDREN,CHILD ABDUCTION,RESIDENCE,false,true,0512,005,9,50,20,1182539,1834278,2013,08/24/2013 06:57:52 AM,41.700467745,-87.607224962,"(41.700467745, -87.607224962)"
-9271584,HW416058,08/18/2013 05:00:00 AM,108XX S AVENUE H,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0432,004,10,52,08B,1202895,1833453,2013,08/31/2013 02:06:33 PM,41.697708757,-87.532718716,"(41.697708757, -87.532718716)"
-9275579,HW419823,08/17/2013 12:00:00 PM,028XX W TAYLOR ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,1135,011,28,27,26,1157337,1895616,2013,08/23/2013 11:52:43 AM,41.869333987,-87.697845551,"(41.869333987, -87.697845551)"
-9266861,HW411607,08/17/2013 07:00:00 AM,003XX N CENTRAL PARK AVE,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,RESIDENCE,true,false,1123,011,28,27,26,1152370,1901862,2013,09/03/2013 12:55:11 PM,41.886573136,-87.715915864,"(41.886573136, -87.715915864)"
-9274267,HW417282,08/14/2013 04:30:00 PM,059XX S LOWE AVE,0265,CRIM SEXUAL ASSAULT,AGGRAVATED: OTHER,OTHER,false,true,0711,007,20,68,02,1172948,1865675,2013,04/18/2014 09:34:42 PM,41.786841754,-87.641419032,"(41.786841754, -87.641419032)"
-9259779,HW405080,08/11/2013 07:19:00 PM,002XX E 131ST PL,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0533,005,9,54,05,1180405,1818201,2013,08/24/2013 04:49:01 PM,41.656399208,-87.615529218,"(41.656399208, -87.615529218)"
-9256443,HW401407,08/10/2013 08:30:00 AM,0000X E 111TH ST,0520,ASSAULT,AGGRAVATED:KNIFE/CUTTING INSTR,OTHER,false,true,0531,005,9,49,04A,1178617,1831339,2013,08/27/2013 07:56:49 AM,41.692492554,-87.621674651,"(41.692492554, -87.621674651)"
-9256014,HW400932,08/09/2013 10:10:00 PM,014XX W 47TH ST,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,PARKING LOT/GARAGE(NON.RESID.),true,false,0924,009,3,61,04B,1167491,1873621,2013,08/10/2013 11:07:06 AM,41.808765266,-87.661199508,"(41.808765266, -87.661199508)"
-9255251,HW400104,08/09/2013 11:58:00 AM,002XX S LOTUS AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,1522,015,29,25,26,1139956,1898540,2013,08/09/2013 01:09:47 PM,41.877693194,-87.761585036,"(41.877693194, -87.761585036)"
-9255880,HW400654,08/08/2013 11:00:00 AM,018XX E 72ND ST,0820,THEFT,$500 AND UNDER,APARTMENT,false,false,0324,003,5,43,06,1189563,1857635,2013,08/10/2013 07:00:16 AM,41.764396299,-87.580758295,"(41.764396299, -87.580758295)"
-9253099,HW398015,08/07/2013 09:20:00 PM,079XX S NORMAL AVE,0460,BATTERY,SIMPLE,ALLEY,false,false,0621,006,17,44,08B,1174311,1851915,2013,08/13/2013 09:45:58 AM,41.749052505,-87.636830113,"(41.749052505, -87.636830113)"
-9250995,HW396526,08/06/2013 09:05:00 PM,071XX S ARTESIAN AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0832,008,18,66,18,1161231,1857440,2013,08/06/2013 10:36:15 PM,41.764494482,-87.684607734,"(41.764494482, -87.684607734)"
-9249286,HW395064,08/05/2013 08:13:00 PM,064XX S DR MARTIN LUTHER KING JR DR,0460,BATTERY,SIMPLE,STREET,false,false,0312,003,20,69,08B,1179983,1862579,2013,08/16/2013 07:36:58 AM,41.778187779,-87.615719878,"(41.778187779, -87.615719878)"
-9248845,HW394509,08/04/2013 03:00:00 PM,071XX S CONSTANCE AVE,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,APARTMENT,false,true,0324,003,5,43,26,1189567,1858033,2013,08/08/2013 11:41:47 AM,41.765488348,-87.58073087,"(41.765488348, -87.58073087)"
-9244376,HW389774,08/02/2013 03:50:00 AM,055XX S PARKSIDE AVE,0560,ASSAULT,SIMPLE,STREET,false,false,0811,008,23,56,08A,1139598,1867373,2013,08/05/2013 10:15:52 AM,41.792172764,-87.763658484,"(41.792172764, -87.763658484)"
-9309840,HW454411,07/31/2013 10:00:00 AM,132XX S RIVERDALE AVE,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,0533,005,9,54,06,1182090,1817252,2013,09/17/2013 07:29:24 AM,41.653756335,-87.609392811,"(41.653756335, -87.609392811)"
-9238374,HW384927,07/29/2013 09:45:00 PM,005XX E 47TH ST,041A,BATTERY,AGGRAVATED: HANDGUN,SIDEWALK,false,false,0223,002,3,38,04B,1180856,1873939,2013,08/28/2013 05:58:10 PM,41.8093406,-87.612170092,"(41.8093406, -87.612170092)"
-9238138,HW384399,07/29/2013 02:30:00 PM,005XX E 115TH ST,2017,NARCOTICS,MANU/DELIVER:CRACK,STREET,true,false,0532,005,9,54,18,1182061,1828766,2013,07/29/2013 08:16:36 PM,41.685353114,-87.609144885,"(41.685353114, -87.609144885)"
-9238988,HW385225,07/29/2013 08:15:00 AM,076XX S MORGAN ST,0810,THEFT,OVER $500,VEHICLE NON-COMMERCIAL,false,false,0621,006,17,71,06,1171011,1854036,2013,07/31/2013 07:05:36 AM,41.754945475,-87.648860721,"(41.754945475, -87.648860721)"
-9236482,HW383571,07/29/2013 02:45:00 AM,023XX S ALBANY AVE,2022,NARCOTICS,POSS: COCAINE,ALLEY,true,false,1033,010,24,30,18,1156105,1888191,2013,07/29/2013 04:14:27 AM,41.848983915,-87.702568828,"(41.848983915, -87.702568828)"
-9231548,HW377783,07/24/2013 10:23:00 PM,029XX W 71ST ST,0340,ROBBERY,ATTEMPT: STRONGARM-NO WEAPON,STREET,false,false,0831,008,18,66,03,1157634,1857360,2013,09/02/2013 02:41:57 PM,41.764348637,-87.69779395,"(41.764348637, -87.69779395)"
-9229928,HW376308,07/23/2013 11:17:00 PM,001XX N LECLAIRE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1532,015,28,25,14,1142303,1900934,2013,07/24/2013 01:19:42 PM,41.884219401,-87.752907889,"(41.884219401, -87.752907889)"
-9229636,HW375966,07/23/2013 08:25:00 AM,009XX N KINGSBURY ST,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,1822,018,27,8,14,1171875,1906421,2013,07/24/2013 10:47:19 AM,41.898675875,-87.644154395,"(41.898675875, -87.644154395)"
-9225920,HW372833,07/21/2013 06:00:00 PM,027XX S WASHTENAW AVE,0460,BATTERY,SIMPLE,OTHER,false,false,1034,010,12,30,08B,1158754,1885829,2013,07/22/2013 01:39:46 PM,41.842448522,-87.69291135,"(41.842448522, -87.69291135)"
-9225609,HW372478,07/20/2013 11:00:00 PM,0000X E DIVISION ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENTIAL YARD (FRONT/BACK),false,false,1824,018,42,8,14,1176267,1908340,2013,07/22/2013 09:33:58 AM,41.903843754,-87.627965036,"(41.903843754, -87.627965036)"
-9224788,HW371354,07/20/2013 04:20:00 PM,037XX W 27TH ST,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,SIDEWALK,true,false,1031,010,22,30,24,1151841,1885757,2013,07/21/2013 01:38:28 PM,41.842389596,-87.718282208,"(41.842389596, -87.718282208)"
-9224725,HW371229,07/20/2013 02:53:00 PM,030XX W 63RD ST,0890,THEFT,FROM BUILDING,CLEANING STORE,false,false,0823,008,15,66,06,1157345,1862671,2013,07/21/2013 11:18:38 AM,41.778928668,-87.698709577,"(41.778928668, -87.698709577)"
-9223621,HW369682,07/19/2013 02:34:00 PM,012XX S INDEPENDENCE BLVD,0460,BATTERY,SIMPLE,STREET,false,false,1011,010,24,29,08B,1151252,1893995,2013,07/20/2013 10:03:14 AM,41.86500722,-87.720227835,"(41.86500722, -87.720227835)"
-9222372,HW368911,07/18/2013 09:30:00 PM,012XX W 119TH ST,1562,SEX OFFENSE,AGG CRIMINAL SEXUAL ABUSE,OTHER,false,false,0524,005,34,53,17,1169947,1825901,2013,10/15/2013 05:07:46 PM,41.677761963,-87.65357403,"(41.677761963, -87.65357403)"
-9221120,HW367655,07/18/2013 07:25:00 AM,017XX W MONTVALE AVE,0820,THEFT,$500 AND UNDER,RESIDENTIAL YARD (FRONT/BACK),false,false,2234,022,34,75,06,1166791,1829632,2013,07/20/2013 09:41:34 AM,41.688068218,-87.665020129,"(41.688068218, -87.665020129)"
-9241022,HW386903,07/17/2013 12:00:00 PM,097XX S JEFFERY AVE,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,0431,004,7,51,06,1191256,1840695,2013,08/01/2013 07:47:00 AM,41.717870574,-87.575100186,"(41.717870574, -87.575100186)"
-9221658,HW366033,07/17/2013 04:40:00 AM,001XX N MASON AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,1512,015,29,25,14,1136785,1900556,2013,07/19/2013 09:23:35 AM,41.883282745,-87.773180007,"(41.883282745, -87.773180007)"
-9217203,HW363954,07/15/2013 05:50:00 PM,055XX S HALSTED ST,0320,ROBBERY,STRONGARM - NO WEAPON,STREET,false,false,0712,007,20,68,03,1171871,1868201,2013,07/24/2013 09:29:29 AM,41.793797093,-87.64529376,"(41.793797093, -87.64529376)"
-9216316,HW362123,07/14/2013 01:27:00 PM,061XX S VERNON AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE PORCH/HALLWAY,false,true,0313,003,20,42,08B,1180348,1864290,2013,07/19/2013 10:47:41 AM,41.782874563,-87.614329336,"(41.782874563, -87.614329336)"
-9215223,HW361982,07/14/2013 11:35:00 AM,003XX S CICERO AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1533,015,24,25,18,1144396,1898214,2013,07/14/2013 12:21:10 PM,41.876716289,-87.745290492,"(41.876716289, -87.745290492)"
-9217885,HW363147,07/13/2013 04:30:00 PM,055XX S RACINE AVE,0313,ROBBERY,ARMED: OTHER DANGEROUS WEAPON,STREET,false,false,0713,007,16,67,03,1169222,1868128,2013,08/02/2013 12:27:23 PM,41.793654541,-87.655009565,"(41.793654541, -87.655009565)"
-9214023,HW360287,07/13/2013 05:25:00 AM,025XX S HOMAN AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,1024,010,22,30,14,1154074,1886680,2013,08/28/2013 07:23:18 AM,41.844878246,-87.710063086,"(41.844878246, -87.710063086)"
-9212997,HW358993,07/12/2013 10:57:00 AM,068XX S DAMEN AVE,031A,ROBBERY,ARMED: HANDGUN,STREET,false,false,0726,007,17,67,03,1164161,1859529,2013,08/16/2013 08:06:23 PM,41.770165845,-87.67380976,"(41.770165845, -87.67380976)"
-9216312,HW363056,07/12/2013 08:30:00 AM,0000X S WACKER DR,0890,THEFT,FROM BUILDING,OTHER,false,false,0122,001,2,32,06,1173977,1900284,2013,07/15/2013 12:49:42 PM,41.881788999,-87.636616958,"(41.881788999, -87.636616958)"
-9212351,HW358420,07/11/2013 07:31:00 PM,011XX W 66TH ST,1330,CRIMINAL TRESPASS,TO LAND,"SCHOOL, PUBLIC, GROUNDS",false,false,0724,007,17,68,26,1169672,1861069,2013,07/13/2013 07:45:55 AM,41.774274054,-87.65356409,"(41.774274054, -87.65356409)"
-9217432,HW357808,07/11/2013 02:45:00 PM,058XX S CALIFORNIA AVE,0460,BATTERY,SIMPLE,STREET,false,false,0824,008,16,63,08B,1158662,1866003,2013,07/22/2013 10:05:23 AM,41.788045372,-87.69379045,"(41.788045372, -87.69379045)"
-9211669,HW357693,07/11/2013 01:08:00 PM,002XX E 111TH ST,4650,OTHER OFFENSE,SEX OFFENDER: FAIL TO REGISTER,PARK PROPERTY,true,false,0531,005,9,49,26,1179618,1831366,2013,07/29/2013 09:58:40 AM,41.692543893,-87.618009026,"(41.692543893, -87.618009026)"
-9208756,HW354909,07/09/2013 01:52:00 PM,083XX S PHILLIPS AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,true,false,0423,004,7,46,05,1194003,1849875,2013,07/12/2013 01:00:09 AM,41.742994495,-87.564738956,"(41.742994495, -87.564738956)"
-9207821,HW353861,07/08/2013 06:00:00 PM,034XX W PETERSON AVE,0820,THEFT,$500 AND UNDER,OTHER,false,false,1711,017,50,13,06,1152420,1939708,2013,07/10/2013 10:09:45 AM,41.990424518,-87.714729357,"(41.990424518, -87.714729357)"
-9215757,HW353540,07/08/2013 02:06:00 PM,132XX S BUFFALO AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,SIDEWALK,false,false,0433,004,10,55,04B,1200052,1817538,2013,09/25/2013 06:44:57 AM,41.654108417,-87.543662034,"(41.654108417, -87.543662034)"
-9248873,HW394366,07/07/2013 11:00:00 AM,004XX W 107TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE-GARAGE,false,false,2233,022,34,49,14,1175109,1833990,2013,08/06/2013 06:29:21 AM,41.699846109,-87.634439219,"(41.699846109, -87.634439219)"
-9204968,HW350693,07/06/2013 02:05:00 PM,012XX N LUIS MUNOZ MARIN DR E,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,PARK PROPERTY,true,false,1423,014,26,24,18,1157251,1908052,2013,07/06/2013 03:05:23 PM,41.903461284,-87.697823163,"(41.903461284, -87.697823163)"
-9206684,HW352801,07/05/2013 04:00:00 PM,033XX N AVONDALE AVE,0810,THEFT,OVER $500,CONSTRUCTION SITE,false,false,1732,017,35,21,06,1152705,1922495,2013,07/08/2013 11:54:47 AM,41.943185298,-87.714138621,"(41.943185298, -87.714138621)"
-9202921,HW348249,07/04/2013 10:00:00 PM,101XX S DR MARTIN LUTHER KING JR DR,0810,THEFT,OVER $500,RESIDENTIAL YARD (FRONT/BACK),false,false,0511,005,9,49,06,1180739,1837929,2013,07/05/2013 07:19:50 AM,41.710528029,-87.613704109,"(41.710528029, -87.613704109)"
-9204919,HW350585,07/04/2013 10:00:00 PM,028XX N MASON AVE,0810,THEFT,OVER $500,RESIDENCE-GARAGE,false,false,2514,025,30,19,06,1136275,1918287,2013,07/08/2013 12:20:59 PM,41.931947891,-87.774628703,"(41.931947891, -87.774628703)"
-9223105,HW369249,07/03/2013 10:00:00 AM,033XX W WARREN BLVD,1310,CRIMINAL DAMAGE,TO PROPERTY,ABANDONED BUILDING,false,false,1123,011,28,27,14,1153964,1900103,2013,08/05/2013 09:06:10 AM,41.881714642,-87.710109173,"(41.881714642, -87.710109173)"
-9200732,HW345900,07/03/2013 12:30:00 AM,033XX N SHEFFIELD AVE,0920,MOTOR VEHICLE THEFT,ATT: AUTOMOBILE,PARKING LOT/GARAGE(NON.RESID.),false,false,1924,019,44,6,07,1168975,1922434,2013,07/03/2013 11:23:30 AM,41.942679834,-87.654340184,"(41.942679834, -87.654340184)"
-9198516,HW344014,07/01/2013 08:00:00 PM,034XX W 25TH ST,5002,OTHER OFFENSE,OTHER VEHICLE OFFENSE,STREET,false,false,1024,010,22,30,26,1153466,1887213,2013,07/23/2013 09:03:54 AM,41.846352949,-87.712280232,"(41.846352949, -87.712280232)"
-9198766,HW344368,06/30/2013 08:00:00 PM,097XX S PEORIA ST,0820,THEFT,$500 AND UNDER,STREET,false,false,2223,022,21,73,06,1172072,1840194,2013,07/02/2013 08:04:59 AM,41.716937907,-87.645377948,"(41.716937907, -87.645377948)"
-9196391,HW341658,06/30/2013 12:00:00 AM,038XX N KENNETH AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,RESIDENTIAL YARD (FRONT/BACK),false,false,1731,017,38,16,04B,1145743,1925481,2013,08/30/2013 04:51:17 PM,41.951514289,-87.739651662,"(41.951514289, -87.739651662)"
-9195089,HW340083,06/29/2013 01:35:00 AM,057XX S CARPENTER ST,0460,BATTERY,SIMPLE,STREET,false,true,0712,007,16,68,08B,1170260,1866528,2013,07/06/2013 04:20:37 PM,41.789241421,-87.651249851,"(41.789241421, -87.651249851)"
-9193441,HW338281,06/27/2013 08:20:00 PM,076XX N MARSHFIELD AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,2422,024,49,1,18,1164017,1950501,2013,06/27/2013 09:43:38 PM,42.019802964,-87.671766335,"(42.019802964, -87.671766335)"
-9193297,HW337998,06/27/2013 05:20:00 PM,077XX S ESSEX AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0421,004,7,43,08B,1194162,1854200,2013,07/06/2013 04:20:21 PM,41.754858746,-87.564014697,"(41.754858746, -87.564014697)"
-9191400,HW336273,06/26/2013 03:25:00 PM,0000X W WASHINGTON ST,1330,CRIMINAL TRESPASS,TO LAND,CHURCH/SYNAGOGUE/PLACE OF WORSHIP,true,false,0112,001,42,32,26,1175690,1900776,2013,06/27/2013 07:57:01 AM,41.883100742,-87.630312127,"(41.883100742, -87.630312127)"
-9188322,HW333653,06/24/2013 07:26:00 PM,013XX S AVERS AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),ALLEY,true,false,1011,010,24,29,18,1150974,1893170,2013,06/14/2014 12:41:49 PM,41.862748767,-87.721269978,"(41.862748767, -87.721269978)"
-9188191,HW333242,06/24/2013 03:13:00 PM,075XX S KINGSTON AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0421,004,7,43,14,1194551,1855130,2013,06/25/2013 07:03:15 AM,41.757401183,-87.562558617,"(41.757401183, -87.562558617)"
-9188305,HW333052,06/24/2013 04:42:00 AM,025XX E 79TH ST,0610,BURGLARY,FORCIBLE ENTRY,APPLIANCE STORE,false,false,0422,004,7,46,05,1194715,1853045,2013,08/02/2013 12:50:52 PM,41.751675749,-87.562026098,"(41.751675749, -87.562026098)"
-9185682,HW330763,06/22/2013 06:30:00 PM,062XX S DORCHESTER AVE,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,0314,003,20,42,08A,1186574,1863800,2013,06/30/2013 07:02:03 AM,41.781384833,-87.591518683,"(41.781384833, -87.591518683)"
-9184955,HW329687,06/21/2013 08:30:00 PM,032XX S NORMAL AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,0915,009,11,60,08A,1173527,1883516,2013,06/25/2013 10:21:41 AM,41.835786381,-87.638767424,"(41.835786381, -87.638767424)"
-9184484,HW329145,06/21/2013 03:57:00 PM,017XX W HOWARD ST,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,2422,024,49,1,06,1163125,1950307,2013,06/24/2013 07:11:00 AM,42.019289497,-87.675054297,"(42.019289497, -87.675054297)"
-9189839,HW334835,06/20/2013 08:00:00 PM,036XX S UNION AVE,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,APARTMENT,false,false,0915,009,11,60,04B,1172190,1880784,2013,07/23/2013 01:11:40 PM,41.82831909,-87.643753772,"(41.82831909, -87.643753772)"
-9179963,HW325198,06/18/2013 11:13:00 PM,051XX N LA CROSSE AVE,1821,NARCOTICS,MANU/DEL:CANNABIS 10GM OR LESS,ALLEY,true,false,1621,016,45,12,18,1143109,1934039,2013,06/19/2013 12:16:46 AM,41.975047812,-87.749119806,"(41.975047812, -87.749119806)"
-9179991,HW325187,06/18/2013 03:00:00 PM,050XX S MARSHFIELD AVE,0460,BATTERY,SIMPLE,SIDEWALK,true,false,0931,009,16,61,08B,1166147,1871453,2013,06/19/2013 09:29:19 AM,41.802844763,-87.666190742,"(41.802844763, -87.666190742)"
-9176155,HW320657,06/15/2013 09:30:00 PM,006XX E GRAND AVE,0917,MOTOR VEHICLE THEFT,"CYCLE, SCOOTER, BIKE W-VIN",OTHER,false,false,1834,018,42,8,07,1180773,1904096,2013,06/17/2013 09:39:20 AM,41.892095189,-87.611544796,"(41.892095189, -87.611544796)"
-9176135,HW320790,06/15/2013 09:30:00 AM,039XX S ELLIS AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE-GARAGE,false,false,0214,002,4,36,05,1183000,1878659,2013,07/13/2013 10:05:52 PM,41.822242991,-87.604159472,"(41.822242991, -87.604159472)"
-9175075,HW319332,06/15/2013 12:32:00 AM,121XX S HARVARD AVE,1512,PROSTITUTION,SOLICIT FOR PROSTITUTE,STREET,true,false,0523,005,34,53,16,1176182,1824444,2013,06/15/2013 02:21:40 AM,41.673626476,-87.630795402,"(41.673626476, -87.630795402)"
-9628844,HX273082,06/14/2013 09:33:00 PM,043XX W DIVERSEY AVE,1122,DECEPTIVE PRACTICE,COUNTERFEIT CHECK,CURRENCY EXCHANGE,true,false,2524,,31,20,10,,,2013,05/29/2014 12:39:18 PM,,,
-9181321,HW326307,06/14/2013 06:00:00 PM,114XX S SPAULDING AVE,1725,OFFENSE INVOLVING CHILDREN,CONTRIBUTE CRIM DELINQUENCY JUVENILE,"SCHOOL, PUBLIC, GROUNDS",false,false,2211,022,19,74,26,1156373,1828648,2013,06/29/2013 06:42:39 PM,41.685583396,-87.703186236,"(41.685583396, -87.703186236)"
-9174786,HW318851,06/14/2013 06:00:00 PM,078XX S KINGSTON AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0421,004,7,43,08B,1194592,1853385,2013,07/01/2013 04:30:11 PM,41.752611761,-87.562465663,"(41.752611761, -87.562465663)"
-9171134,HW316051,06/12/2013 08:30:00 PM,109XX S MICHIGAN AVE,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,0513,005,9,49,18,1178806,1832645,2013,06/12/2013 10:13:06 PM,41.696072117,-87.620943149,"(41.696072117, -87.620943149)"
-9171140,HW316046,06/12/2013 07:20:00 PM,073XX S RICHMOND ST,3730,INTERFERENCE WITH PUBLIC OFFICER,OBSTRUCTING JUSTICE,APARTMENT,true,false,0835,008,18,66,24,1157945,1855601,2013,06/13/2013 10:52:15 AM,41.759515361,-87.696701762,"(41.759515361, -87.696701762)"
-9171032,HW315703,06/12/2013 04:30:00 PM,069XX S SANGAMON ST,0820,THEFT,$500 AND UNDER,STREET,false,false,0733,007,17,68,06,1171217,1858668,2013,06/13/2013 07:08:10 AM,41.767651767,-87.647970546,"(41.767651767, -87.647970546)"
-9170962,HW315686,06/11/2013 10:00:00 PM,069XX N OTTAWA AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,1611,016,41,9,06,1124454,1945564,2013,06/13/2013 10:07:18 AM,42.007002188,-87.817466529,"(42.007002188, -87.817466529)"
-9167610,HW313022,06/10/2013 06:00:00 AM,067XX N NEWGARD AVE,0810,THEFT,OVER $500,RESIDENCE,false,false,2432,024,40,1,06,1165416,1944842,2013,06/11/2013 03:39:55 PM,42.004244788,-87.666780311,"(42.004244788, -87.666780311)"
-9163290,HW308086,06/06/2013 02:00:00 PM,036XX W GRENSHAW ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,1133,011,24,29,26,1152089,1894753,2013,06/10/2013 02:47:20 PM,41.867070814,-87.7171352,"(41.867070814, -87.7171352)"
-9162152,HW306814,06/06/2013 11:50:00 AM,005XX W HARRISON ST,0560,ASSAULT,SIMPLE,PARKING LOT/GARAGE(NON.RESID.),false,false,0124,001,2,28,08A,1172629,1897533,2013,06/12/2013 10:00:18 AM,41.874269995,-87.64164815,"(41.874269995, -87.64164815)"
-9162380,HW307138,06/06/2013 07:00:00 AM,050XX S DR MARTIN LUTHER KING JR DR,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0223,002,3,38,14,1179753,1871765,2013,06/07/2013 11:15:20 AM,41.803400285,-87.616282185,"(41.803400285, -87.616282185)"
-9160245,HW305229,06/04/2013 10:58:00 AM,001XX W ROOSEVELT RD,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0123,001,2,32,06,1175197,1895070,2013,06/05/2013 01:18:16 PM,41.867454191,-87.632293564,"(41.867454191, -87.632293564)"
-9158235,HW303611,06/03/2013 05:37:00 PM,008XX W NORTH AVE,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,SMALL RETAIL STORE,false,false,1822,018,43,8,11,1170686,1910833,2013,06/06/2013 09:09:48 AM,41.910808784,-87.648392103,"(41.910808784, -87.648392103)"
-9156124,HW301954,06/03/2013 05:34:00 AM,064XX S LECLAIRE AVE,0460,BATTERY,SIMPLE,RESIDENCE,true,false,0813,008,13,64,08B,1143611,1861564,2013,06/14/2014 12:41:49 PM,41.776157974,-87.749087922,"(41.776157974, -87.749087922)"
-9160865,HW304695,06/02/2013 03:50:00 PM,063XX S HALSTED ST,1750,OFFENSE INVOLVING CHILDREN,CHILD ABUSE,RESIDENCE,false,true,0723,007,20,68,20,1172012,1863016,2013,06/23/2013 02:13:39 PM,41.779565774,-87.644928954,"(41.779565774, -87.644928954)"
-9154075,HW299357,06/01/2013 01:55:00 AM,014XX N WESTERN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1423,014,1,24,08B,1160104,1909656,2013,06/05/2013 02:08:47 PM,41.90780431,-87.687299081,"(41.90780431, -87.687299081)"
-9156958,HW299827,05/31/2013 07:00:00 AM,072XX S MICHIGAN AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0323,003,6,69,05,1178460,1856858,2013,06/29/2013 09:45:53 AM,41.762523488,-87.621476808,"(41.762523488, -87.621476808)"
-9150070,HW295819,05/29/2013 06:05:00 PM,033XX W 61ST PL,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true,0823,008,15,66,08B,1155382,1863614,2013,05/30/2013 09:23:57 AM,41.781555929,-87.705880943,"(41.781555929, -87.705880943)"
-9150142,HW295826,05/29/2013 05:50:00 PM,025XX N LONG AVE,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,STREET,true,false,2515,025,31,19,24,1139985,1916603,2013,05/30/2013 07:38:43 AM,41.927259668,-87.761036097,"(41.927259668, -87.761036097)"
-9149287,HW295100,05/28/2013 04:10:00 PM,083XX S ASHLAND AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0614,006,18,71,14,1167080,1849613,2013,11/08/2013 08:05:05 AM,41.742893061,-87.663392947,"(41.742893061, -87.663392947)"
-9146439,HW292102,05/26/2013 08:00:00 PM,013XX W TAYLOR ST,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,1231,012,2,28,05,1167641,1895666,2013,06/05/2013 03:27:41 PM,41.869255639,-87.660015494,"(41.869255639, -87.660015494)"
-9146120,HW291662,05/26/2013 02:25:00 PM,011XX S STATE ST,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,CTA TRAIN,true,false,0123,001,2,32,24,1176558,1895307,2013,06/14/2014 12:41:49 PM,41.868073926,-87.627290019,"(41.868073926, -87.627290019)"
-9154481,HW299716,05/25/2013 02:00:00 PM,084XX S BRANDON AVE,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0424,004,10,46,05,1198983,1849794,2013,06/13/2013 06:52:12 AM,41.742648719,-87.546495178,"(41.742648719, -87.546495178)"
-9144290,HW289353,05/24/2013 04:45:00 PM,048XX S LAMON AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,STREET,false,true,0814,008,23,56,08B,1144554,1871928,2013,05/27/2013 02:55:59 PM,41.804580858,-87.745371057,"(41.804580858, -87.745371057)"
-9143805,HW288615,05/24/2013 09:00:00 AM,065XX S MOZART ST,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,0831,008,15,66,03,1158553,1860816,2013,07/29/2013 09:50:13 AM,41.77381373,-87.694331445,"(41.77381373, -87.694331445)"
-9142866,HW287719,05/23/2013 03:00:00 PM,068XX S PERRY AVE,0820,THEFT,$500 AND UNDER,APARTMENT,false,true,0722,007,6,69,06,1176536,1859701,2013,05/15/2014 12:36:49 PM,41.770368475,-87.62844312,"(41.770368475, -87.62844312)"
-9141631,HW286473,05/22/2013 03:30:00 PM,063XX S DR MARTIN LUTHER KING JR DR,1310,CRIMINAL DAMAGE,TO PROPERTY,CHA PARKING LOT/GROUNDS,false,false,0312,003,20,69,14,1179976,1862780,2013,06/18/2013 05:06:54 PM,41.778739503,-87.615739391,"(41.778739503, -87.615739391)"
-9140376,HW285731,05/22/2013 09:55:00 AM,064XX N RIDGE BLVD,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,true,2412,024,50,2,26,1162639,1942586,2013,05/23/2013 02:49:19 PM,41.998113133,-87.677060522,"(41.998113133, -87.677060522)"
-9140699,HW285570,05/21/2013 10:00:00 PM,027XX W ROOSEVELT RD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,1135,011,28,29,08B,1157985,1894634,2013,05/24/2013 02:10:56 PM,41.866626091,-87.695493353,"(41.866626091, -87.695493353)"
-9150666,HW296406,05/21/2013 07:59:00 PM,008XX S WOOD ST,0890,THEFT,FROM BUILDING,HOSPITAL BUILDING/GROUNDS,false,false,1231,012,2,28,06,1164480,1896066,2013,06/09/2013 09:45:02 AM,41.870420725,-87.671608993,"(41.870420725, -87.671608993)"
-9139399,HW284567,05/21/2013 02:30:00 PM,062XX N TROY ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,2413,024,50,2,26,1154260,1941488,2013,05/30/2013 01:08:21 PM,41.995272253,-87.707913591,"(41.995272253, -87.707913591)"
-9200265,HW345507,05/21/2013 12:01:00 AM,061XX S TROY ST,0850,THEFT,ATTEMPT THEFT,RESIDENCE,false,false,0823,008,15,66,06,1156485,1863563,2013,07/03/2013 07:51:42 AM,41.78139382,-87.701838422,"(41.78139382, -87.701838422)"
-9136040,HW281044,05/19/2013 04:30:00 AM,042XX W DIVISION ST,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,STREET,false,false,2534,025,37,23,04B,1148092,1907707,2013,05/22/2013 12:52:57 PM,41.902695832,-87.731475322,"(41.902695832, -87.731475322)"
-9138856,HW284161,05/19/2013 02:00:00 AM,010XX W BELMONT AVE,0890,THEFT,FROM BUILDING,BAR OR TAVERN,false,false,1924,019,44,6,06,1168911,1921462,2013,05/23/2013 03:31:36 PM,41.940014015,-87.6546037,"(41.940014015, -87.6546037)"
-9136464,HW281646,05/18/2013 02:00:00 PM,105XX S OGLESBY AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0434,004,10,51,14,1193818,1835718,2013,05/20/2013 07:59:39 AM,41.704150888,-87.565879225,"(41.704150888, -87.565879225)"
-9137395,HW282668,05/18/2013 12:30:00 PM,040XX E 106TH ST,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0432,004,10,52,07,1204462,1835244,2013,07/28/2013 07:05:06 PM,41.70258329,-87.526919961,"(41.70258329, -87.526919961)"
-9138860,HW284195,05/18/2013 11:38:00 AM,036XX N BROADWAY,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,BANK,false,false,1925,019,46,6,11,1170904,1924204,2013,05/22/2013 08:05:07 AM,41.947494633,-87.647198177,"(41.947494633, -87.647198177)"
-9159962,HW279894,05/18/2013 10:50:00 AM,079XX S HALSTED ST,1821,NARCOTICS,MANU/DEL:CANNABIS 10GM OR LESS,STREET,true,false,0621,006,17,71,18,1172303,1852301,2013,07/11/2013 02:15:23 PM,41.750156108,-87.644176845,"(41.750156108, -87.644176845)"
-9134618,HW278767,05/17/2013 12:45:00 PM,030XX N MANGO AVE,0460,BATTERY,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,2514,025,30,19,08B,1137481,1919428,2013,05/19/2013 06:52:55 AM,41.935057267,-87.770169215,"(41.935057267, -87.770169215)"
-9134160,HW278200,05/17/2013 02:30:00 AM,027XX W POLK ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1135,011,2,27,06,1158078,1896294,2013,05/20/2013 09:49:28 AM,41.871179401,-87.695106631,"(41.871179401, -87.695106631)"
-9213479,HW359690,05/17/2013 12:01:00 AM,018XX W ROSCOE ST,0840,THEFT,FINANCIAL ID THEFT: OVER $300,RESIDENCE,false,false,1922,019,32,5,06,1163583,1922647,2013,07/15/2013 11:50:47 AM,41.943379809,-87.674152237,"(41.943379809, -87.674152237)"
-9129516,HW274386,05/14/2013 06:30:00 PM,022XX W 18TH ST,2826,OTHER OFFENSE,HARASSMENT BY ELECTRONIC MEANS,OTHER,false,false,1234,012,25,31,26,1161699,1891330,2013,05/20/2013 10:29:27 AM,41.857483062,-87.681950871,"(41.857483062, -87.681950871)"
-9129370,HW272824,05/13/2013 04:58:00 PM,075XX S SANGAMON ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0621,006,17,71,26,1171244,1854744,2013,06/11/2013 08:01:06 AM,41.756883228,-87.647986174,"(41.756883228, -87.647986174)"
-9128054,HW272670,05/13/2013 04:20:00 PM,003XX S KEDZIE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,CTA BUS,false,false,1134,011,28,27,14,1155052,1898458,2013,05/14/2013 07:17:19 AM,41.877178845,-87.706158189,"(41.877178845, -87.706158189)"
-9136086,HW272284,05/13/2013 11:00:00 AM,047XX S UNION AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,"SCHOOL, PUBLIC, BUILDING",false,false,0935,009,11,61,26,1172475,1873205,2013,05/19/2013 10:59:42 AM,41.807515304,-87.642931557,"(41.807515304, -87.642931557)"
-9127449,HW272097,05/12/2013 03:30:00 PM,123XX S LA SALLE ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENTIAL YARD (FRONT/BACK),false,false,0523,005,9,53,14,1177537,1823178,2013,05/14/2013 06:39:38 AM,41.670121952,-87.625874034,"(41.670121952, -87.625874034)"
-9131698,HW276693,05/10/2013 12:00:00 PM,022XX E 68TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0331,003,5,43,14,1192169,1860273,2013,05/16/2013 10:38:03 AM,41.771572208,-87.571121124,"(41.771572208, -87.571121124)"
-9123045,HW267491,05/09/2013 04:24:00 PM,007XX W 79TH ST,051A,ASSAULT,AGGRAVATED: HANDGUN,STREET,false,false,0621,006,17,71,04A,1172637,1852527,2013,06/23/2013 09:50:30 PM,41.750768934,-87.642946278,"(41.750768934, -87.642946278)"
-9120662,HW265424,05/08/2013 11:40:00 AM,013XX S ASHLAND AVE,0484,BATTERY,PRO EMP HANDS NO/MIN INJURY,"SCHOOL, PUBLIC, BUILDING",true,false,1233,012,2,28,08B,1165893,1893995,2013,05/08/2013 02:18:35 PM,41.864707716,-87.666480488,"(41.864707716, -87.666480488)"
-9116986,HW262048,05/06/2013 12:15:00 AM,068XX S LANGLEY AVE,0261,CRIM SEXUAL ASSAULT,AGGRAVATED: HANDGUN,ALLEY,false,false,0321,003,6,42,02,1182024,1860012,2013,06/29/2013 04:35:59 PM,41.771096702,-87.608316896,"(41.771096702, -87.608316896)"
-9116423,HW261455,05/05/2013 11:05:00 AM,009XX N MICHIGAN AVE,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,1833,018,42,8,06,1177285,1906688,2013,05/06/2013 12:35:12 PM,41.899287561,-87.624275876,"(41.899287561, -87.624275876)"
-9115988,HW260851,05/05/2013 02:14:00 AM,070XX S PARNELL AVE,0560,ASSAULT,SIMPLE,RESIDENCE,true,true,0732,007,6,68,08A,1173791,1858424,2013,05/05/2013 06:20:42 AM,41.766925532,-87.638542952,"(41.766925532, -87.638542952)"
-9115437,HW260187,05/04/2013 03:40:00 PM,009XX W 65TH ST,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,0723,007,17,68,18,1171290,1861695,2013,05/04/2013 04:30:52 PM,41.775956625,-87.647614506,"(41.775956625, -87.647614506)"
-9114984,HW259600,05/04/2013 04:45:00 AM,042XX W CONGRESS PKWY,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,1132,011,24,26,03,1148404,1897400,2013,05/08/2013 03:38:38 PM,41.874406282,-87.730595213,"(41.874406282, -87.730595213)"
-9115014,HW259633,05/04/2013 01:00:00 AM,060XX S MAY ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0712,007,16,68,08B,1169725,1864741,2013,05/21/2013 02:21:15 PM,41.784349315,-87.653263346,"(41.784349315, -87.653263346)"
-9112219,HW256823,05/01/2013 10:00:00 PM,071XX S RIDGELAND AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,0324,003,5,43,05,1189062,1858051,2013,05/11/2013 09:43:03 AM,41.765549852,-87.582581251,"(41.765549852, -87.582581251)"
-9110002,HW254930,04/30/2013 10:55:00 PM,050XX W IRVING PARK RD,0560,ASSAULT,SIMPLE,RESTAURANT,true,false,1624,016,45,15,08A,1142279,1926169,2013,05/01/2013 07:12:52 AM,41.953467362,-87.752368259,"(41.953467362, -87.752368259)"
-9109890,HW254690,04/30/2013 07:26:00 PM,024XX W 72ND ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,SIDEWALK,true,false,0832,008,18,66,15,1161389,1856805,2013,05/01/2013 02:49:07 PM,41.762748679,-87.684046188,"(41.762748679, -87.684046188)"
-9108423,HW252877,04/29/2013 03:00:00 PM,001XX N DEARBORN ST,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,false,false,0111,001,42,32,06,1175967,1901030,2013,04/30/2013 10:03:02 AM,41.883791501,-87.629287328,"(41.883791501, -87.629287328)"
-9107683,HW252437,04/27/2013 09:00:00 PM,032XX N LAKE SHORE DR SB,0486,BATTERY,DOMESTIC BATTERY SIMPLE,VEHICLE NON-COMMERCIAL,false,true,1925,019,44,6,08B,1173234,1921685,2013,05/06/2013 01:48:49 PM,41.940530912,-87.638708797,"(41.940530912, -87.638708797)"
-9105741,HW250287,04/27/2013 02:35:00 PM,092XX S MICHIGAN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0634,006,6,49,08B,1178744,1843430,2013,05/13/2013 07:43:13 PM,41.725669014,-87.620843411,"(41.725669014, -87.620843411)"
-9105987,HW250437,04/27/2013 01:00:00 PM,066XX S ROCKWELL ST,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,false,0831,008,15,66,05,1160150,1860287,2013,07/21/2013 01:00:33 PM,41.772329368,-87.688491674,"(41.772329368, -87.688491674)"
-9102244,HW246692,04/25/2013 01:15:00 AM,005XX S STATE ST,0460,BATTERY,SIMPLE,COLLEGE/UNIVERSITY RESIDENCE HALL,false,false,0123,001,2,32,08B,1176487,1897892,2013,07/24/2013 08:50:59 AM,41.875168926,-87.627472632,"(41.875168926, -87.627472632)"
-9102820,HW246613,04/24/2013 11:45:00 PM,091XX S STONY ISLAND AVE,0560,ASSAULT,SIMPLE,GAS STATION,false,false,0413,004,8,48,08A,1188557,1844370,2013,05/05/2013 03:13:45 PM,41.728019969,-87.58486839,"(41.728019969, -87.58486839)"
-9101969,HW246211,04/24/2013 05:50:00 PM,051XX S MORGAN ST,0460,BATTERY,SIMPLE,SIDEWALK,false,false,0934,009,20,61,08B,1170555,1870817,2013,04/25/2013 01:21:22 PM,41.801004486,-87.650043184,"(41.801004486, -87.650043184)"
-9101721,HW245973,04/24/2013 03:30:00 PM,064XX S DR MARTIN LUTHER KING JR DR,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0312,003,20,69,08B,1179988,1862397,2013,05/06/2013 02:15:52 PM,41.777688239,-87.615707116,"(41.777688239, -87.615707116)"
-9099852,HW244419,04/23/2013 11:45:00 AM,070XX S DAMEN AVE,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,0735,007,17,67,06,1164286,1857782,2013,04/24/2013 06:38:15 AM,41.765369206,-87.673400694,"(41.765369206, -87.673400694)"
-9097276,HW242018,04/21/2013 06:00:00 PM,045XX W NORTH AVE,0860,THEFT,RETAIL THEFT,SMALL RETAIL STORE,true,false,2533,025,37,23,06,1145935,1910208,2013,04/22/2013 06:26:20 AM,41.909600093,-87.739334828,"(41.909600093, -87.739334828)"
-9124498,HW242997,04/21/2013 01:49:00 PM,031XX S HALSTED ST,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENCE,false,false,0913,009,11,60,14,1171440,1884079,2013,05/11/2013 08:12:44 AM,41.837377356,-87.64640877,"(41.837377356, -87.64640877)"
-9096188,HW240498,04/20/2013 12:10:00 PM,019XX E 71ST ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0332,003,5,43,18,1190377,1858351,2013,04/20/2013 01:54:55 PM,41.766341479,-87.577751774,"(41.766341479, -87.577751774)"
-9096034,HW240298,04/20/2013 09:45:00 AM,061XX S CAMPBELL AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,true,0825,008,15,66,08B,1160804,1863629,2013,04/24/2013 06:55:44 AM,41.781486802,-87.686002068,"(41.781486802, -87.686002068)"
-9094519,HW238821,04/19/2013 07:00:00 AM,008XX E 82ND ST,1365,CRIMINAL TRESPASS,TO RESIDENCE,APARTMENT,true,false,0631,006,8,44,26,1183188,1850845,2013,04/20/2013 05:37:53 AM,41.745914519,-87.604334893,"(41.745914519, -87.604334893)"
-9095079,HW238865,04/18/2013 07:15:00 PM,040XX W MONROE ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1115,011,28,26,06,1149638,1899319,2013,04/21/2013 07:41:23 AM,41.879648367,-87.72601462,"(41.879648367, -87.72601462)"
-9093809,HW238106,04/18/2013 03:50:00 PM,004XX S CENTRAL PARK AVE,0530,ASSAULT,AGGRAVATED: OTHER DANG WEAPON,STREET,false,true,1133,011,28,27,04A,1152411,1897881,2013,05/01/2013 02:27:46 PM,41.875648042,-87.715870482,"(41.875648042, -87.715870482)"
-9092071,HW236897,04/17/2013 02:45:00 PM,038XX N CALIFORNIA AVE,2850,PUBLIC PEACE VIOLATION,BOMB THREAT,PARK PROPERTY,false,false,1733,017,33,16,26,1157066,1925582,2013,05/16/2013 02:26:15 PM,41.951568663,-87.698025611,"(41.951568663, -87.698025611)"
-9089544,HW234630,04/15/2013 09:40:00 PM,035XX N RACINE AVE,1330,CRIMINAL TRESPASS,TO LAND,RESIDENCE PORCH/HALLWAY,true,false,1924,019,44,6,26,1167694,1923791,2013,04/16/2013 06:46:55 AM,41.94643125,-87.659009199,"(41.94643125, -87.659009199)"
-9089553,HW234605,04/15/2013 09:15:00 PM,003XX S CICERO AVE,1330,CRIMINAL TRESPASS,TO LAND,GAS STATION,true,false,1533,015,24,25,26,1144396,1898214,2013,04/16/2013 06:41:58 AM,41.876716289,-87.745290492,"(41.876716289, -87.745290492)"
-9093500,HW237885,04/15/2013 12:00:00 AM,073XX S DORCHESTER AVE,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,APARTMENT,false,false,0324,003,5,43,11,1186858,1856368,2013,04/19/2013 09:37:46 AM,41.760984051,-87.590712758,"(41.760984051, -87.590712758)"
-9087954,HW232859,04/14/2013 01:20:00 PM,047XX S ASHLAND AVE,0810,THEFT,OVER $500,STREET,false,false,0931,009,20,61,06,1166424,1873487,2013,04/15/2013 11:38:43 AM,41.808420388,-87.665116861,"(41.808420388, -87.665116861)"
-9086602,HW231156,04/12/2013 06:30:00 PM,001XX S SANGAMON ST,0810,THEFT,OVER $500,STREET,false,false,1232,012,2,28,06,1170081,1899476,2013,04/14/2013 01:55:56 PM,41.879657691,-87.650946469,"(41.879657691, -87.650946469)"
-9087178,HW231905,04/12/2013 05:00:00 PM,035XX N KOSTNER AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,1731,017,30,16,26,1146463,1923263,2013,04/17/2013 11:06:35 AM,41.945414194,-87.737061654,"(41.945414194, -87.737061654)"
-9085913,HW230261,04/12/2013 04:16:00 PM,103XX S MICHIGAN AVE,2170,NARCOTICS,POSSESSION OF DRUG EQUIPMENT,CTA BUS STOP,true,false,0512,005,9,49,18,1178930,1836678,2013,04/12/2013 05:31:34 PM,41.707136412,-87.620366899,"(41.707136412, -87.620366899)"
-9084425,HW228700,04/11/2013 02:00:00 PM,033XX W 25TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1024,010,22,30,18,1154461,1887233,2013,04/11/2013 04:36:22 PM,41.846388028,-87.708628079,"(41.846388028, -87.708628079)"
-9083647,HW227107,04/09/2013 06:00:00 PM,0000X E 75TH ST,1150,DECEPTIVE PRACTICE,CREDIT CARD FRAUD,DRUG STORE,false,false,0623,006,6,69,11,1177736,1855240,2013,04/12/2013 07:40:30 AM,41.758099925,-87.624179245,"(41.758099925, -87.624179245)"
-9080749,HW225436,04/09/2013 07:00:00 AM,049XX S KEDVALE AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,RESIDENCE,true,false,0815,008,14,57,07,1149569,1871599,2013,04/17/2013 02:31:32 PM,41.803582419,-87.726986553,"(41.803582419, -87.726986553)"
-9089099,HW233021,04/08/2013 08:30:00 PM,026XX N MELVINA AVE,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,2512,025,29,19,08A,1134574,1917167,2013,04/25/2013 10:19:11 AM,41.928904731,-87.780906258,"(41.928904731, -87.780906258)"
-9080856,HW225580,04/08/2013 07:00:00 PM,072XX S EAST END AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,0324,003,8,43,08B,1188748,1857345,2013,04/12/2013 12:15:57 PM,41.763620042,-87.583754694,"(41.763620042, -87.583754694)"
-9080119,HW224505,04/08/2013 01:45:00 PM,132XX S INDIANA AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0533,005,9,54,07,1179955,1817599,2013,06/07/2013 01:54:36 PM,41.654757495,-87.617194112,"(41.654757495, -87.617194112)"
-9079746,HW224647,04/08/2013 08:00:00 AM,083XX S RHODES AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENTIAL YARD (FRONT/BACK),false,false,0632,006,6,44,14,1181219,1849481,2013,04/09/2013 07:02:44 AM,41.742217098,-87.611591562,"(41.742217098, -87.611591562)"
-9079183,HW223969,04/08/2013 07:20:00 AM,050XX S CORNELL AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,GAS STATION,false,false,0222,002,4,39,14,1187850,1871631,2013,04/08/2013 12:41:19 PM,41.802843379,-87.586591273,"(41.802843379, -87.586591273)"
-9078158,HW223047,04/07/2013 12:30:00 AM,0000X W DIVISION ST,0890,THEFT,FROM BUILDING,BAR OR TAVERN,false,false,1824,018,42,8,06,1175957,1908331,2013,04/07/2013 02:24:22 PM,41.903826047,-87.629103999,"(41.903826047, -87.629103999)"
-9077116,HW221868,04/06/2013 12:05:00 PM,032XX W NORTH AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1422,014,26,23,05,1154215,1910485,2013,05/05/2013 11:37:00 AM,41.910198831,-87.70891005,"(41.910198831, -87.70891005)"
-9076982,HW221682,04/05/2013 07:00:00 PM,057XX S KENWOOD AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0235,002,5,41,14,1186123,1867281,2013,04/06/2013 12:05:08 PM,41.790947645,-87.593062248,"(41.790947645, -87.593062248)"
-9076211,HW220435,04/05/2013 11:15:00 AM,024XX E 79TH ST,0810,THEFT,OVER $500,RESTAURANT,false,false,0422,004,7,46,06,1193846,1853022,2013,04/16/2013 01:20:46 AM,41.751633969,-87.565211265,"(41.751633969, -87.565211265)"
-9081081,HW225549,04/05/2013 11:00:00 AM,038XX W LEXINGTON ST,0810,THEFT,OVER $500,APARTMENT,false,false,1133,011,24,26,06,1150627,1896456,2013,04/10/2013 08:34:07 AM,41.871772713,-87.722457947,"(41.871772713, -87.722457947)"
-9074727,HW219477,04/04/2013 02:45:00 PM,010XX N LARAMIE AVE,1750,OFFENSE INVOLVING CHILDREN,CHILD ABUSE,"SCHOOL, PUBLIC, BUILDING",false,false,1524,015,37,25,20,1141465,1906395,2013,04/21/2013 12:34:53 PM,41.899220555,-87.755850198,"(41.899220555, -87.755850198)"
-9074623,HW219388,04/03/2013 04:30:00 PM,005XX W SUPERIOR ST,1320,CRIMINAL DAMAGE,TO VEHICLE,RESIDENCE-GARAGE,false,false,1831,018,42,8,14,1172377,1905224,2013,04/04/2013 06:27:22 PM,41.895380157,-87.642346001,"(41.895380157, -87.642346001)"
-9071659,HW216846,04/02/2013 10:00:00 AM,027XX S KOSTNER AVE,1305,CRIMINAL DAMAGE,CRIMINAL DEFACEMENT,RESIDENCE-GARAGE,false,false,1031,010,22,30,14,1147538,1885623,2013,04/03/2013 07:40:25 AM,41.842105356,-87.734076604,"(41.842105356, -87.734076604)"
-9070533,HW215888,04/02/2013 08:05:00 AM,021XX W CORTEZ ST,0810,THEFT,OVER $500,STREET,true,false,1212,012,32,24,06,1161650,1907002,2013,05/13/2013 09:38:40 AM,41.900489439,-87.681693959,"(41.900489439, -87.681693959)"
-9090581,HW235594,04/02/2013 07:00:00 AM,009XX N LEAMINGTON AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,1531,015,37,25,05,1141890,1905919,2013,05/14/2013 11:14:36 AM,41.897906495,-87.754300962,"(41.897906495, -87.754300962)"
-9070013,HW215174,04/01/2013 03:50:00 PM,063XX S MORGAN ST,0820,THEFT,$500 AND UNDER,SIDEWALK,false,false,0724,007,16,68,06,1170688,1862978,2013,04/02/2013 06:39:25 AM,41.779490481,-87.649783997,"(41.779490481, -87.649783997)"
-9071172,HW216235,03/31/2013 10:00:00 AM,043XX S WENTWORTH AVE,1121,DECEPTIVE PRACTICE,COUNTERFEITING DOCUMENT,STREET,false,false,0925,009,3,37,10,1175609,1876459,2013,04/26/2013 10:39:54 AM,41.81637492,-87.631339504,"(41.81637492, -87.631339504)"
-9068088,HW213295,03/31/2013 12:19:00 AM,025XX N SPRINGFIELD AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,2524,025,30,22,18,1149920,1916406,2013,03/31/2013 02:49:30 AM,41.926531317,-87.724533886,"(41.926531317, -87.724533886)"
-9066571,HW211257,03/29/2013 02:00:00 PM,062XX N CLAREMONT AVE,0820,THEFT,$500 AND UNDER,STREET,false,false,2413,024,50,2,06,1159557,1941410,2013,03/31/2013 09:11:26 AM,41.994950405,-87.688430729,"(41.994950405, -87.688430729)"
-9061084,HW206073,03/25/2013 01:00:00 PM,003XX E 47TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0215,002,3,38,18,1179017,1873966,2013,03/25/2013 02:03:45 PM,41.809456838,-87.618914329,"(41.809456838, -87.618914329)"
-9077264,HW222126,03/25/2013 09:00:00 AM,088XX S LUELLA AVE,0810,THEFT,OVER $500,RESIDENCE,false,true,0412,004,8,48,06,1192749,1846725,2013,04/24/2013 06:37:23 PM,41.734381257,-87.56943602,"(41.734381257, -87.56943602)"
-9083816,HW228168,03/25/2013 01:00:00 AM,0000X W DIVISION ST,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,OTHER,false,false,1824,018,42,8,11,1175751,1908405,2013,04/12/2013 12:17:46 PM,41.904033744,-87.629858449,"(41.904033744, -87.629858449)"
-9060406,HW205350,03/24/2013 06:47:00 PM,069XX N GREENVIEW AVE,0820,THEFT,$500 AND UNDER,STREET,true,false,2431,024,49,1,06,1165164,1945801,2013,03/26/2013 08:31:31 AM,42.006881683,-87.667679987,"(42.006881683, -87.667679987)"
-9059825,HW204857,03/24/2013 11:00:00 AM,024XX W GLADYS AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1125,011,2,28,08B,1160283,1898322,2013,03/28/2013 02:41:23 PM,41.87669915,-87.686955179,"(41.87669915, -87.686955179)"
-9059326,HW204214,03/23/2013 10:00:00 AM,103XX S AVENUE G,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE,false,true,0432,004,10,52,05,1203112,1836868,2013,03/25/2013 06:51:56 PM,41.707074259,-87.531807828,"(41.707074259, -87.531807828)"
-9071447,HW216570,03/22/2013 03:10:00 PM,033XX W 71ST ST,0560,ASSAULT,SIMPLE,"SCHOOL, PUBLIC, BUILDING",false,false,0831,008,18,66,08A,1155297,1857375,2013,04/03/2013 11:33:22 AM,41.764436863,-87.70635933,"(41.764436863, -87.70635933)"
-9054964,HW199729,03/20/2013 11:31:00 AM,057XX S ADA ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0713,007,16,67,18,1168276,1866364,2013,03/20/2013 01:31:09 PM,41.788834348,-87.658529247,"(41.788834348, -87.658529247)"
-9054429,HW196935,03/18/2013 09:29:00 AM,048XX S PAULINA ST,1570,SEX OFFENSE,PUBLIC INDECENCY,RESIDENCE,false,false,0931,009,20,61,17,1165781,1872809,2013,04/02/2013 02:20:52 AM,41.806573572,-87.667494505,"(41.806573572, -87.667494505)"
-9051424,HW196816,03/18/2013 08:50:00 AM,026XX N HOYNE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,CHA APARTMENT,false,true,1931,019,1,7,08B,1162397,1917830,2013,03/21/2013 11:14:38 AM,41.930186621,-87.678646594,"(41.930186621, -87.678646594)"
-9049988,HW195184,03/16/2013 08:05:00 PM,011XX W ADDISON ST,0460,BATTERY,SIMPLE,SIDEWALK,true,false,1924,019,44,6,08B,1168041,1924023,2013,03/17/2013 11:23:09 AM,41.94706037,-87.657727021,"(41.94706037, -87.657727021)"
-9048547,HW193160,03/15/2013 12:13:00 PM,001XX W VAN BUREN ST,0460,BATTERY,SIMPLE,SIDEWALK,true,false,0122,001,2,32,08B,1175483,1898467,2013,03/16/2013 09:11:20 AM,41.876769359,-87.631141605,"(41.876769359, -87.631141605)"
-9048620,HW193267,03/15/2013 12:00:00 PM,006XX W 35TH ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,0915,009,11,60,26,1172360,1881726,2013,03/23/2013 02:22:03 PM,41.830900277,-87.643102292,"(41.830900277, -87.643102292)"
-9047753,HW192045,03/14/2013 02:48:00 PM,029XX E 78TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0421,004,7,43,14,1196937,1853930,2013,03/15/2013 06:06:48 AM,41.754049303,-87.553854304,"(41.754049303, -87.553854304)"
-9046903,HW191731,03/14/2013 12:00:00 PM,029XX N CALIFORNIA AVE,0810,THEFT,OVER $500,OTHER,false,false,1411,014,1,21,06,1157241,1919449,2013,03/15/2013 01:49:09 PM,41.934735743,-87.697549531,"(41.934735743, -87.697549531)"
-9044518,HW190109,03/13/2013 08:15:00 AM,008XX N STATE ST,1210,DECEPTIVE PRACTICE,THEFT OF LABOR/SERVICES,CTA PLATFORM,true,false,1832,018,42,8,11,1176184,1905758,2013,03/13/2013 10:46:18 AM,41.896760495,-87.62834785,"(41.896760495, -87.62834785)"
-9043531,HW189099,03/12/2013 01:00:00 PM,075XX S MAY ST,1330,CRIMINAL TRESPASS,TO LAND,CONVENIENCE STORE,true,false,0612,006,17,71,26,1169927,1854496,2013,03/13/2013 06:04:50 AM,41.756231373,-87.652819939,"(41.756231373, -87.652819939)"
-9042054,HW188077,03/11/2013 02:00:00 PM,016XX W 95TH ST,0820,THEFT,$500 AND UNDER,RESTAURANT,false,false,2221,022,19,72,06,1166876,1841743,2013,03/12/2013 06:46:06 AM,41.721300961,-87.664364532,"(41.721300961, -87.664364532)"
-9039993,HW186374,03/09/2013 08:45:00 PM,015XX W MORSE AVE,0810,THEFT,OVER $500,STREET,false,false,2431,024,49,1,06,1164714,1946111,2013,03/11/2013 07:28:47 AM,42.007741918,-87.669326759,"(42.007741918, -87.669326759)"
-9049578,HW194495,03/09/2013 10:00:00 AM,069XX S CLYDE AVE,0610,BURGLARY,FORCIBLE ENTRY,VACANT LOT/LAND,false,false,0331,003,5,43,05,1191378,1859098,2013,03/26/2013 04:26:09 PM,41.768367118,-87.574058636,"(41.768367118, -87.574058636)"
-9038460,HW184610,03/08/2013 08:59:00 PM,096XX S HALSTED ST,1812,NARCOTICS,POSS: CANNABIS MORE THAN 30GMS,STREET,true,false,2223,022,21,73,18,1172638,1840858,2013,03/08/2013 10:30:11 PM,41.718747589,-87.643285453,"(41.718747589, -87.643285453)"
-9038211,HW184346,03/08/2013 05:40:00 PM,019XX N MILWAUKEE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1434,014,1,22,14,1160282,1912690,2013,03/10/2013 09:19:08 AM,41.916126154,-87.686561159,"(41.916126154, -87.686561159)"
-9037585,HW183828,03/08/2013 11:25:00 AM,065XX S FAIRFIELD AVE,0454,BATTERY,AGG PO HANDS NO/MIN INJURY,STREET,true,false,0831,008,15,66,08B,1159125,1861348,2013,03/09/2013 03:12:31 PM,41.775261933,-87.692220057,"(41.775261933, -87.692220057)"
-9052012,HW197372,03/08/2013 09:00:00 AM,038XX W 82ND PL,0841,THEFT,FINANCIAL ID THEFT:$300 &UNDER,STREET,false,false,0834,008,18,70,06,1152355,1849562,2013,03/19/2013 11:09:20 AM,41.743055023,-87.717347677,"(41.743055023, -87.717347677)"
-9034777,HW181955,03/06/2013 09:30:00 PM,049XX W NORTH AVE,0820,THEFT,$500 AND UNDER,ATHLETIC CLUB,false,false,2533,025,37,25,06,1143341,1910146,2013,03/07/2013 07:09:42 AM,41.909478858,-87.748865737,"(41.909478858, -87.748865737)"
-9034845,HW182127,03/06/2013 03:00:00 PM,039XX S WESTERN AVE,0810,THEFT,OVER $500,STREET,false,false,0921,009,12,58,06,1160989,1878147,2013,03/07/2013 09:58:33 AM,41.821322222,-87.68492229,"(41.821322222, -87.68492229)"
-9032370,HW180288,03/05/2013 04:07:00 PM,036XX W CONGRESS PKWY,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,1133,011,28,27,18,1152343,1897501,2013,03/05/2013 06:04:36 PM,41.874606623,-87.716130188,"(41.874606623, -87.716130188)"
-9032230,HW180137,03/05/2013 01:50:00 PM,021XX E 83RD ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,0412,004,8,46,08B,1192261,1850330,2013,03/16/2013 09:27:22 AM,41.744285573,-87.571106823,"(41.744285573, -87.571106823)"
-9031719,HW179778,03/04/2013 03:30:00 PM,046XX S WASHTENAW AVE,0890,THEFT,FROM BUILDING,ABANDONED BUILDING,false,false,0922,009,12,58,06,1159193,1873606,2013,03/05/2013 11:19:53 AM,41.808898157,-87.691635362,"(41.808898157, -87.691635362)"
-9029384,HW176994,03/02/2013 09:00:00 PM,019XX W MADISON ST,0460,BATTERY,SIMPLE,OTHER,false,false,1223,012,27,28,08B,1163705,1900003,2013,03/03/2013 07:14:30 PM,41.88124055,-87.674343291,"(41.88124055, -87.674343291)"
-9027267,HW174500,03/01/2013 12:30:00 AM,021XX N CLARK ST,0460,BATTERY,SIMPLE,BAR OR TAVERN,false,false,1814,018,43,7,08B,1173584,1914628,2013,03/07/2013 07:45:30 AM,41.921158441,-87.637632895,"(41.921158441, -87.637632895)"
-9028017,HW175099,02/28/2013 03:00:00 PM,011XX W DIVISION ST,0560,ASSAULT,SIMPLE,WAREHOUSE,true,false,1822,018,32,8,08A,1168614,1908122,2013,03/10/2013 07:38:53 AM,41.903414787,-87.656082473,"(41.903414787, -87.656082473)"
-9034124,HW172770,02/27/2013 04:16:35 PM,012XX W 73RD PL,1812,NARCOTICS,POSS: CANNABIS MORE THAN 30GMS,RESIDENCE,true,false,0734,007,17,67,18,1169065,1855997,2013,04/10/2013 11:47:01 AM,41.760368983,-87.655935674,"(41.760368983, -87.655935674)"
-9023328,HW170363,02/25/2013 08:03:00 PM,019XX E 71ST ST,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,0333,003,5,43,03,1190310,1858206,2013,03/01/2013 08:33:01 PM,41.765945202,-87.578002016,"(41.765945202, -87.578002016)"
-9022699,HW170319,02/25/2013 06:30:00 PM,003XX S WESTERN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,1225,012,2,28,14,1160508,1898174,2013,02/26/2013 08:25:44 AM,41.876288371,-87.686133147,"(41.876288371, -87.686133147)"
-9022889,HW170516,02/25/2013 04:00:00 PM,080XX S CHAPPEL AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,0414,004,8,46,05,1191304,1851864,2013,04/05/2013 09:55:57 AM,41.748518214,-87.574563752,"(41.748518214, -87.574563752)"
-9026296,HW173462,02/25/2013 12:00:00 PM,019XX W NORTH AVE,0890,THEFT,FROM BUILDING,ATHLETIC CLUB,false,false,1434,014,32,24,06,1163045,1910702,2013,02/28/2013 12:24:02 PM,41.910613318,-87.676465994,"(41.910613318, -87.676465994)"
-9020820,HW168391,02/24/2013 12:30:00 AM,054XX W EDDY ST,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1633,016,38,15,14,1139666,1923034,2013,02/25/2013 06:32:32 AM,41.944912826,-87.762050833,"(41.944912826, -87.762050833)"
-9013249,HW161197,02/18/2013 02:00:00 PM,049XX W KAMERLING AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2533,025,37,25,07,1142893,1908481,2013,03/11/2013 01:05:25 PM,41.904918279,-87.75055307,"(41.904918279, -87.75055307)"
-9011044,HW158167,02/15/2013 07:10:00 PM,053XX S KIMBARK AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,0234,002,4,41,03,1185604,1870253,2013,03/04/2013 10:43:42 PM,41.799115293,-87.594871663,"(41.799115293, -87.594871663)"
-9058660,HW202916,02/15/2013 03:00:00 PM,030XX W PALMER BLVD,0820,THEFT,$500 AND UNDER,RESIDENCE,false,false,1414,014,35,22,06,1155762,1914653,2013,04/28/2013 08:14:51 AM,41.921605121,-87.703114443,"(41.921605121, -87.703114443)"
-9010117,HW156490,02/14/2013 02:37:00 PM,085XX S COTTAGE GROVE AVE,1130,DECEPTIVE PRACTICE,FRAUD OR CONFIDENCE GAME,SIDEWALK,false,false,0632,006,6,44,11,1183020,1848266,2013,02/15/2013 02:40:42 PM,41.738841364,-87.605030451,"(41.738841364, -87.605030451)"
-9008368,HW155749,02/13/2013 09:40:00 PM,049XX W WALTON ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,SIDEWALK,true,false,1531,015,37,25,15,1142993,1905814,2013,02/14/2013 07:35:20 AM,41.897597864,-87.750252322,"(41.897597864, -87.750252322)"
-9006217,HW153678,02/12/2013 08:00:00 AM,076XX W ADDISON ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1631,016,36,17,06,1124228,1923095,2013,02/13/2013 07:36:41 AM,41.945348542,-87.818794589,"(41.945348542, -87.818794589)"
-9007607,HW153414,02/12/2013 07:44:00 AM,068XX S ST LAWRENCE AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0321,003,20,42,14,1181376,1859881,2013,02/18/2013 08:44:34 AM,41.770752191,-87.610696241,"(41.770752191, -87.610696241)"
-9004775,HW152149,02/11/2013 07:50:00 AM,069XX S VERNON AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0322,003,6,69,07,1180413,1858861,2013,02/11/2013 10:36:06 AM,41.767975356,-87.614257459,"(41.767975356, -87.614257459)"
-9000744,HW147488,02/06/2013 05:00:00 PM,058XX W WASHINGTON BLVD,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,true,1513,015,29,25,26,1137493,1900114,2013,02/23/2013 08:14:36 PM,41.882057128,-87.770590787,"(41.882057128, -87.770590787)"
-8998290,HW145657,02/05/2013 10:25:00 PM,080XX S INGLESIDE AVE,2017,NARCOTICS,MANU/DELIVER:CRACK,APARTMENT,true,false,0631,006,8,44,18,1183997,1851981,2013,02/06/2013 12:22:58 AM,41.749012976,-87.601335174,"(41.749012976, -87.601335174)"
-8996938,HW144451,02/04/2013 08:25:00 PM,031XX S PULASKI RD,0560,ASSAULT,SIMPLE,SIDEWALK,false,false,1031,010,22,30,08A,1150163,1883671,2013,03/21/2013 05:24:35 PM,41.836698151,-87.724494316,"(41.836698151, -87.724494316)"
-8996688,HW144024,02/04/2013 05:15:00 PM,121XX S PRINCETON AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,false,0523,005,34,53,14,1176435,1824280,2013,02/05/2013 06:35:29 AM,41.67317077,-87.629874301,"(41.67317077, -87.629874301)"
-8996793,HW144123,02/04/2013 07:00:00 AM,079XX S INGLESIDE AVE,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,0624,006,8,44,03,1183894,1852756,2013,02/15/2013 12:58:07 AM,41.751142061,-87.601688452,"(41.751142061, -87.601688452)"
-8995157,HW142539,02/03/2013 10:34:00 AM,038XX W JACKSON BLVD,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,1122,011,28,26,18,1150946,1898455,2013,02/03/2013 12:12:29 PM,41.877251956,-87.721234423,"(41.877251956, -87.721234423)"
-8993669,HW140577,02/01/2013 02:56:00 PM,010XX E 47TH ST,0890,THEFT,FROM BUILDING,"SCHOOL, PRIVATE, BUILDING",true,false,0222,002,4,39,06,1184100,1874102,2013,02/02/2013 07:29:10 AM,41.809712569,-87.600266727,"(41.809712569, -87.600266727)"
-8992364,HW139341,01/31/2013 04:05:00 PM,0000X W 95TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,CTA GARAGE / OTHER PROPERTY,true,false,0634,006,21,49,18,1177744,1841988,2013,01/31/2013 06:26:31 PM,41.721734632,-87.624549931,"(41.721734632, -87.624549931)"
-8992227,HW139175,01/31/2013 12:00:00 PM,079XX S ASHLAND AVE,1330,CRIMINAL TRESPASS,TO LAND,PARKING LOT/GARAGE(NON.RESID.),true,false,0611,006,21,71,26,1167010,1852247,2013,02/01/2013 08:10:13 AM,41.750122626,-87.663574309,"(41.750122626, -87.663574309)"
-8990994,HW138279,01/30/2013 08:20:00 PM,052XX S BISHOP ST,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,0934,009,16,61,18,1167598,1869903,2013,01/30/2013 10:00:57 PM,41.798560357,-87.660913739,"(41.798560357, -87.660913739)"
-8990552,HW137681,01/30/2013 01:35:00 PM,095XX S JEFFERY AVE,0820,THEFT,$500 AND UNDER,OTHER,false,false,0431,004,7,51,06,1191138,1842112,2013,01/31/2013 05:05:00 AM,41.721761818,-87.575486681,"(41.721761818, -87.575486681)"
-8990681,HW137895,01/30/2013 02:00:00 AM,032XX S HALSTED ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0913,009,11,60,14,1171460,1883306,2013,01/31/2013 07:08:33 AM,41.835255736,-87.646358067,"(41.835255736, -87.646358067)"
-8989530,HW136892,01/29/2013 09:30:00 PM,015XX S HOMAN AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,false,true,1021,010,24,29,08B,1153913,1892140,2013,01/30/2013 03:37:15 PM,41.859864328,-87.710508603,"(41.859864328, -87.710508603)"
-8990672,HW137671,01/29/2013 08:00:00 PM,004XX W 129TH PL,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,0523,005,9,53,07,1175628,1818856,2013,02/03/2013 11:31:00 AM,41.65830447,-87.632989292,"(41.65830447, -87.632989292)"
-8986960,HW134560,01/28/2013 11:15:00 AM,044XX W VAN BUREN ST,2027,NARCOTICS,POSS: CRACK,ALLEY,true,false,1131,011,24,26,18,1146763,1897692,2013,01/28/2013 12:10:26 PM,41.875239029,-87.736612841,"(41.875239029, -87.736612841)"
-8985714,HW133357,01/27/2013 06:00:00 AM,029XX W SHAKESPEARE AVE,0560,ASSAULT,SIMPLE,RESIDENCE PORCH/HALLWAY,false,true,1414,014,35,22,08A,1156469,1914257,2013,01/30/2013 03:25:18 PM,41.920504179,-87.700527469,"(41.920504179, -87.700527469)"
-8985371,HW132935,01/26/2013 08:57:00 PM,051XX S DREXEL AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0233,002,4,41,18,1183086,1870792,2013,01/26/2013 10:58:23 PM,41.800653342,-87.604088881,"(41.800653342, -87.604088881)"
-8984770,HW132070,01/26/2013 09:00:00 AM,060XX S CARPENTER ST,4387,OTHER OFFENSE,VIOLATE ORDER OF PROTECTION,APARTMENT,false,true,0712,007,16,68,26,1170384,1864906,2013,01/28/2013 04:53:39 PM,41.784787766,-87.650842393,"(41.784787766, -87.650842393)"
-8984117,HW131241,01/25/2013 04:34:00 PM,027XX W DEVON AVE,0460,BATTERY,SIMPLE,GROCERY FOOD STORE,false,false,2412,024,50,2,08B,1156696,1942384,2013,01/28/2013 11:34:11 AM,41.997681744,-87.698928329,"(41.997681744, -87.698928329)"
-8978349,HW125975,01/21/2013 02:15:00 PM,046XX W ROSCOE ST,0810,THEFT,OVER $500,STREET,false,false,1731,017,30,15,06,1144678,1922239,2013,01/22/2013 06:21:53 AM,41.942638133,-87.743648613,"(41.942638133, -87.743648613)"
-8981092,HW128360,01/20/2013 07:00:00 PM,011XX N DEARBORN ST,0820,THEFT,$500 AND UNDER,STREET,false,false,1824,018,42,8,06,1175689,1907957,2013,01/24/2013 01:26:08 PM,41.902805806,-87.630099679,"(41.902805806, -87.630099679)"
-8975632,HW122697,01/18/2013 06:40:00 PM,058XX S LAFLIN ST,143A,WEAPONS VIOLATION,UNLAWFUL POSS OF HANDGUN,STREET,true,false,0713,007,16,67,15,1167373,1866083,2013,01/20/2013 06:11:06 PM,41.788082651,-87.661848281,"(41.788082651, -87.661848281)"
-8970992,HW118789,01/15/2013 07:15:00 PM,0000X W 79TH ST,0340,ROBBERY,ATTEMPT: STRONGARM-NO WEAPON,SIDEWALK,false,false,0623,006,6,69,03,1177642,1852676,2013,02/17/2013 06:09:30 PM,41.751066138,-87.624601153,"(41.751066138, -87.624601153)"
-8970568,HW118275,01/15/2013 12:01:00 AM,071XX S MAY ST,0560,ASSAULT,SIMPLE,RESIDENCE,false,true,0733,007,17,68,08A,1169850,1857322,2013,02/02/2013 06:25:16 AM,41.763987954,-87.653020222,"(41.763987954, -87.653020222)"
-8970587,HW118183,01/15/2013 12:00:00 AM,043XX W AINSLIE ST,0890,THEFT,FROM BUILDING,CHURCH/SYNAGOGUE/PLACE OF WORSHIP,false,false,1712,017,39,14,06,1146191,1932245,2013,01/16/2013 09:43:24 AM,41.97006671,-87.73783201,"(41.97006671, -87.73783201)"
-8969173,HW117366,01/14/2013 06:34:00 PM,005XX E 50TH PL,1365,CRIMINAL TRESPASS,TO RESIDENCE,RESIDENCE PORCH/HALLWAY,true,false,0223,002,3,38,26,1180678,1871611,2013,01/15/2013 11:04:53 AM,41.80295647,-87.612894532,"(41.80295647, -87.612894532)"
-8963545,HW111688,01/10/2013 01:00:00 AM,008XX E 87TH ST,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,SIDEWALK,false,false,0632,006,8,44,04B,1183116,1847509,2013,01/19/2013 09:20:22 AM,41.736761842,-87.604702211,"(41.736761842, -87.604702211)"
-8987839,HW134962,01/09/2013 05:00:00 PM,071XX S STONY ISLAND AVE,1152,DECEPTIVE PRACTICE,ILLEGAL USE CASH CARD,BANK,false,false,0324,003,5,43,11,1188000,1858165,2013,01/29/2013 01:30:49 PM,41.765888048,-87.586470135,"(41.765888048, -87.586470135)"
-8962609,HW110972,01/08/2013 11:30:00 PM,016XX E 69TH ST,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,0332,003,5,43,14,1188296,1859599,2013,01/10/2013 09:22:16 AM,41.769816008,-87.585339501,"(41.769816008, -87.585339501)"
-8956430,HW104433,01/04/2013 01:35:00 PM,030XX N CLYBOURN AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,PARKING LOT/GARAGE(NON.RESID.),false,false,1931,019,1,5,14,1160603,1920381,2013,01/06/2013 08:36:54 AM,41.937224129,-87.685168228,"(41.937224129, -87.685168228)"
-8956589,HW105718,01/03/2013 11:00:00 PM,025XX N NEVA AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,false,false,2512,025,36,18,07,1128039,1915946,2013,01/15/2013 10:12:35 AM,41.92566718,-87.804948431,"(41.92566718, -87.804948431)"
-8950257,HV622625,12/30/2012 08:30:00 PM,080XX S KENWOOD AVE,0810,THEFT,OVER $500,STREET,false,false,0411,004,8,45,06,1186572,1851692,2012,01/01/2013 07:26:21 AM,41.74815943,-87.591908643,"(41.74815943, -87.591908643)"
-8948936,HV621249,12/29/2012 08:50:00 PM,0000X S HOYNE AVE,0470,PUBLIC PEACE VIOLATION,RECKLESS CONDUCT,STREET,true,false,1223,012,2,28,24,1162382,1899663,2012,01/01/2013 02:50:39 PM,41.880335335,-87.679210786,"(41.880335335, -87.679210786)"
-8943965,HV616299,12/25/2012 04:00:00 PM,055XX W WASHINGTON BLVD,0460,BATTERY,SIMPLE,SIDEWALK,false,false,1522,015,29,25,08B,1139122,1900154,2012,12/27/2012 12:37:24 PM,41.882137425,-87.764608045,"(41.882137425, -87.764608045)"
-8954009,HV616983,12/25/2012 12:04:00 PM,009XX N HOMAN AVE,2014,NARCOTICS,MANU/DELIVER: HEROIN (WHITE),VEHICLE NON-COMMERCIAL,true,false,1121,011,27,23,18,1153499,1905991,2012,01/10/2013 08:24:15 AM,41.897881152,-87.711660008,"(41.897881152, -87.711660008)"
-8944826,HV617170,12/24/2012 05:00:00 PM,001XX E CHESTNUT ST,0460,BATTERY,SIMPLE,RESTAURANT,false,false,1833,018,42,8,08B,1176968,1906390,2012,12/30/2012 09:07:05 AM,41.898477019,-87.625449228,"(41.898477019, -87.625449228)"
-8942797,HV614869,12/24/2012 01:15:00 AM,055XX N CLARK ST,0860,THEFT,RETAIL THEFT,GROCERY FOOD STORE,true,false,2012,020,40,77,06,1164970,1936797,2012,12/24/2012 07:34:57 AM,41.982178611,-87.668650876,"(41.982178611, -87.668650876)"
-8942616,HV614636,12/23/2012 03:00:00 PM,012XX S FAIRFIELD AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1023,010,28,29,05,1158191,1894112,2012,12/26/2012 02:04:24 PM,41.865189467,-87.694751358,"(41.865189467, -87.694751358)"
-8941478,HV613170,12/22/2012 03:25:00 PM,031XX W 15TH ST,2024,NARCOTICS,POSS: HEROIN(WHITE),SIDEWALK,true,false,1022,010,24,29,18,1155375,1892717,2012,12/22/2012 04:15:20 PM,41.861418451,-87.705126484,"(41.861418451, -87.705126484)"
-8941877,HV613667,12/22/2012 01:30:00 PM,078XX S ELLIS AVE,0620,BURGLARY,UNLAWFUL ENTRY,APARTMENT,false,false,0624,006,8,69,05,1184215,1853079,2012,01/16/2013 03:02:59 PM,41.752020909,-87.60050208,"(41.752020909, -87.60050208)"
-8942103,HV614034,12/22/2012 12:00:00 PM,060XX W NELSON ST,5000,OTHER OFFENSE,OTHER CRIME AGAINST PERSON,RESIDENCE,false,false,2511,025,29,19,26,1135501,1919686,2012,01/03/2013 09:41:19 AM,41.935800708,-87.777439753,"(41.935800708, -87.777439753)"
-8977239,HV615005,12/21/2012 09:30:00 PM,038XX S ELLIS AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,0212,002,4,36,14,1182574,1879753,2012,01/20/2013 01:50:56 PM,41.825254915,-87.605688269,"(41.825254915, -87.605688269)"
-8939087,HV610608,12/20/2012 02:00:00 PM,026XX W WASHINGTON BLVD,0320,ROBBERY,STRONGARM - NO WEAPON,SIDEWALK,false,false,1222,012,2,27,03,1158585,1900620,2012,01/10/2013 01:04:53 PM,41.88304,-87.693126773,"(41.88304, -87.693126773)"
-8938768,HV610334,12/20/2012 12:50:00 PM,009XX E 132ND ST,0560,ASSAULT,SIMPLE,CHA PARKING LOT/GROUNDS,false,false,0533,005,9,54,08A,1184927,1818054,2012,12/21/2012 09:05:01 AM,41.655891292,-87.598987414,"(41.655891292, -87.598987414)"
-8939390,HV610836,12/20/2012 11:30:00 AM,009XX N ORLEANS ST,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,1823,018,27,8,06,1173710,1906560,2012,12/21/2012 07:48:39 AM,41.899016651,-87.63741046,"(41.899016651, -87.63741046)"
-8938409,HV610040,12/20/2012 08:00:00 AM,064XX S ARTESIAN AVE,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,SIDEWALK,true,false,0825,008,15,66,04B,1161113,1861643,2012,01/08/2013 08:45:32 PM,41.776030554,-87.684924098,"(41.776030554, -87.684924098)"
-8934500,HV606305,12/17/2012 02:15:00 PM,063XX W ROSCOE ST,0630,BURGLARY,ATTEMPT FORCIBLE ENTRY,RESIDENCE,false,false,1633,016,36,17,05,1133418,1921895,2012,12/26/2012 02:25:15 PM,41.941899258,-87.785043149,"(41.941899258, -87.785043149)"
-8932488,HV604420,12/16/2012 12:00:00 AM,038XX N BROADWAY,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,1923,019,46,6,08B,1170189,1925866,2012,12/18/2012 03:17:59 PM,41.952070898,-87.649777594,"(41.952070898, -87.649777594)"
-8932415,HV604268,12/15/2012 08:00:00 PM,004XX W 59TH ST,2027,NARCOTICS,POSS: CRACK,STREET,true,false,0711,007,20,68,18,1174197,1865844,2012,12/16/2012 12:03:37 AM,41.787277835,-87.636834528,"(41.787277835, -87.636834528)"
-8931281,HV602682,12/14/2012 06:50:00 PM,027XX W 68TH ST,0560,ASSAULT,SIMPLE,HOSPITAL BUILDING/GROUNDS,true,false,0831,008,15,66,08A,1159516,1859408,2012,12/15/2012 10:55:44 AM,41.769930285,-87.690839819,"(41.769930285, -87.690839819)"
-8936102,HV607615,12/14/2012 08:00:00 AM,010XX W MADISON ST,0560,ASSAULT,SIMPLE,CTA BUS,false,false,1224,012,27,28,08A,1169746,1900256,2012,12/19/2012 12:53:51 PM,41.881805369,-87.6521538,"(41.881805369, -87.6521538)"
-8929871,HV601276,12/13/2012 07:00:00 PM,007XX N DRAKE AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,true,false,1121,011,27,23,08B,1152626,1904577,2012,12/14/2012 08:53:22 AM,41.894018312,-87.714903904,"(41.894018312, -87.714903904)"
-8926381,HV598340,12/11/2012 02:38:00 PM,023XX S HOMAN AVE,1710,OFFENSE INVOLVING CHILDREN,ENDANGERING LIFE/HEALTH CHILD,APARTMENT,false,false,1024,010,22,30,26,1154025,1888288,2012,12/22/2012 03:32:31 PM,41.849291765,-87.710200092,"(41.849291765, -87.710200092)"
-8925652,HV597834,12/10/2012 11:00:00 PM,013XX S KOMENSKY AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,STREET,true,false,1011,010,24,29,07,1149559,1893412,2012,01/16/2013 10:56:16 AM,41.863440409,-87.726458047,"(41.863440409, -87.726458047)"
-8944565,HV597396,12/10/2012 09:00:00 PM,056XX S MAY ST,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0712,007,16,68,26,1169580,1867154,2012,12/27/2012 03:15:12 PM,41.790974014,-87.653725041,"(41.790974014, -87.653725041)"
-8924953,HV597314,12/10/2012 07:25:00 PM,039XX N SHERIDAN RD,2022,NARCOTICS,POSS: COCAINE,SIDEWALK,true,false,1923,019,46,6,18,1168934,1926424,2012,12/10/2012 09:02:05 PM,41.953629446,-87.654374706,"(41.953629446, -87.654374706)"
-8924197,HV596201,12/10/2012 12:20:00 AM,064XX N WESTERN AVE,0420,BATTERY,AGGRAVATED:KNIFE/CUTTING INSTR,STREET,false,false,2412,024,50,2,04B,1159114,1942452,2012,12/17/2012 11:14:04 PM,41.997818835,-87.6900315,"(41.997818835, -87.6900315)"
-8913011,HV586561,12/03/2012 05:20:00 AM,016XX S HOMAN AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,APARTMENT,false,false,1021,010,24,29,14,1154008,1891667,2012,12/07/2012 10:38:38 AM,41.858564471,-87.710172484,"(41.858564471, -87.710172484)"
-8908591,HV581713,11/29/2012 03:30:00 PM,118XX S WESTERN AVE,1330,CRIMINAL TRESPASS,TO LAND,DRUG STORE,true,false,2212,022,19,75,26,1162569,1825927,2012,12/02/2012 10:35:22 AM,41.677989869,-87.680579369,"(41.677989869, -87.680579369)"
-8907239,HV580907,11/28/2012 09:25:00 PM,040XX W 21ST PL,2027,NARCOTICS,POSS: CRACK,SIDEWALK,true,false,1012,010,24,29,18,1149805,1889414,2012,10/31/2014 03:20:56 PM,41.852464635,-87.725658863,"(41.852464635, -87.725658863)"
-8907796,HV581139,11/28/2012 11:30:00 AM,022XX S ST LOUIS AVE,0620,BURGLARY,UNLAWFUL ENTRY,RESIDENCE,false,false,1024,010,22,30,05,1153350,1888675,2012,12/02/2012 02:00:54 PM,41.850367153,-87.71266717,"(41.850367153, -87.71266717)"
-8905818,HV579789,11/28/2012 09:30:00 AM,011XX S STATE ST,1330,CRIMINAL TRESPASS,TO LAND,PARKING LOT/GARAGE(NON.RESID.),true,false,0123,001,2,32,26,1176547,1895698,2012,11/28/2012 10:23:47 AM,41.869147103,-87.627318596,"(41.869147103, -87.627318596)"
-8906677,HV580211,11/26/2012 11:00:00 PM,100XX S YATES AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,0431,004,7,51,26,1194191,1838551,2012,11/29/2012 05:02:42 PM,41.711915796,-87.564420757,"(41.711915796, -87.564420757)"
-8902768,HV576942,11/26/2012 08:00:00 AM,016XX S CALIFORNIA BLVD,0486,BATTERY,DOMESTIC BATTERY SIMPLE,SIDEWALK,false,true,1023,010,28,29,08B,1158001,1891592,2012,11/29/2012 11:02:10 PM,41.858278201,-87.695517581,"(41.858278201, -87.695517581)"
-20712,HV575223,11/25/2012 08:47:00 PM,055XX S EMERALD AVE,0110,HOMICIDE,FIRST DEGREE MURDER,HALLWAY,false,false,0711,007,3,68,01A,1172212,1867877,2012,05/11/2015 12:38:40 PM,41.792900506,-87.644052865,"(41.792900506, -87.644052865)"
-8901791,HV576185,11/25/2012 02:45:00 PM,019XX W FARGO AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,true,true,2424,024,49,1,08B,1162178,1949372,2012,11/26/2012 11:50:43 AM,42.016743771,-87.678565457,"(42.016743771, -87.678565457)"
-8901115,HV575538,11/25/2012 12:20:00 AM,051XX W CHICAGO AVE,0460,BATTERY,SIMPLE,SIDEWALK,true,false,1531,015,28,25,08B,1142191,1904807,2012,11/27/2012 09:21:49 AM,41.894849457,-87.75322302,"(41.894849457, -87.75322302)"
-8997924,HW144989,11/21/2012 02:00:00 PM,068XX S MAY ST,1120,DECEPTIVE PRACTICE,FORGERY,RESIDENCE,false,false,0724,007,17,68,10,1169876,1859257,2012,02/06/2013 10:56:56 AM,41.769297274,-87.652868818,"(41.769297274, -87.652868818)"
-8897548,HV571030,11/20/2012 04:00:00 PM,047XX N KILBOURN AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,1722,017,45,16,14,1145182,1931135,2012,11/26/2012 08:34:41 AM,41.96703997,-87.741570378,"(41.96703997, -87.741570378)"