Monday, January 8, 2018

Azure Machine Learning Workbench: Getting Started

Today, we're going to take a look at one of the newest Data Science offerings from Microsoft.  Of course, we're talking about the Azure Machine Learning (AML) Workbench!  Join us as we dive in and see what this new tool is all about.

Before we install the AML Workbench, let's talk about what it is.  The AML Workbench is a local environment for developing data science solutions that can be easily deployed and managed using Microsoft Azure.  It doesn't appear to be related to AML Studio in any way.  Throughout this series, we'll walk through all of the different things we can do with the AML Workbench.  For today, we're just going to get our feet wet.

Now, we need to create an Azure Machine Learning Experimentation resource in the Azure portal.  You can find complete instructions here.  We will also include a Workspace and a Model Management Account.  This appears to be free for the first two users.  However, we're not sure whether they charge separately for the storage account.  Maybe someone can let us know in the comments.  Now, let's boot this baby up!
Azure Machine Learning Workbench
New Project
In the top-left corner, we can see the Workspace we created in the Azure portal.  Let's add a new Project to this.
Create New Project
Now, we have to add the details for our new project.  Strangely, the project name can't include spaces.  We felt like we were past the point where names had to be simple, but maybe it's a Git thing.  Either way, we'll call our new project "Classifying_Iris" and use the "Classifying Iris" template at the bottom of the screen.  Let's see what's inside this project.
Project Dashboard
The first thing we see is the Project Dashboard.  This is a great place to create (or read) quality documentation on exactly what the project does, links to external resources, etc.
iris_sklearn
Following the QuickStart instructions, we were able to run the "iris_sklearn.py" code.  Unfortunately, it's not immediately obvious what this does.  Fortunately, the Exploring Results section tells us to check the Run History.  We can find this icon on the left side of the screen.
Run History
iris_sklearn Run History
This is pretty cool stuff actually.  This view would let us know how long our code is taking to run, as well as what parameters are being input.  This would be extremely helpful if we were running repeated experiments.  In our case, it doesn't show much though.
Job History
If we click on the Job Name in the Jobs section on the right side of the screen, we can see a more detailed result set.
Run Properties
This is what we were looking for!  This gives us all kinds of information about the run.  This could be extremely useful for showing the results of an experiment to bosses or colleagues.
Logs
Further down the page, we see the Logs section.  This is where we can access all the granular information we would need if we needed to debug a particular issue.

The next section of the instructions is the Quick CLI Reference.  This gives us a bunch of code we can use to run these scripts from the Command Line (or Powershell).  Let's open a new command line window.
Open Command Prompt
In the top-left corner of the window, we can select "Open Command Prompt" from the "File" menu.
Command Prompt
In the command prompt, we can copy the first line of code from the instructions.

pip install matplotlib
This code will install the Python library "matplotlib".  This library contains quite a few functions for creating graphs in Python.  You can read more about it here.  Now that we have the library installed, let's copy the next line of code.

az login
This code will help us log the Command Line Interface into Azure.  When we run this command, we get the following response.
To sign in, use a web browser to open the page https://aka.ms/devicelogin and enter the code ######### to authenticate.
When we follow the instructions, we can log into our Azure subscription.
Azure Login
The next piece of code we need to run is as follows.

python run.py
This piece of code will run the "run.py" script from our project.  We'll look at this script in a later post.  For now, let's see the output from this script.  Please note that the "run.py" script is iterative and creates a large amount of output.  You can skip to the OUTPUT END header if you don't want to see the output.

OUTPUT BEGIN

 RunId: Classifying_Iris_1509457170414

Executing user inputs .....
===========================

Python version: 3.5.2 |Continuum Analytics, Inc.| (default, Jul  5 2016, 11:41:13) [MSC v.1900 64 bit (AMD64)]

Iris dataset shape: (150, 5)
Regularization rate is 10.0
LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)
Accuracy is 0.6415094339622641

==========================================
Serialize and deserialize using the outputs folder.

Export the model to model.pkl
Import the model from model.pkl
New sample: [[3.0, 3.6, 1.3, 0.25]]
Predicted class is ['Iris-setosa']
Plotting confusion matrix...
Confusion matrix in text:
[[50  0  0]
 [ 0 31 19]
 [ 0  4 46]]
Confusion matrix plotted.
Plotting ROC curve....
ROC curve plotted.
Confusion matrix and ROC curve plotted. See them in Run History details page.

Execution Details
=================
RunId: Classifying_Iris_1509457170414

RunId: Classifying_Iris_1509457188739

Executing user inputs .....
===========================

Python version: 3.5.2 |Continuum Analytics, Inc.| (default, Jul  5 2016, 11:41:13) [MSC v.1900 64 bit (AMD64)]

Iris dataset shape: (150, 5)
Regularization rate is 5.0
LogisticRegression(C=0.2, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)
Accuracy is 0.6415094339622641

==========================================
Serialize and deserialize using the outputs folder.

Export the model to model.pkl
Import the model from model.pkl
New sample: [[3.0, 3.6, 1.3, 0.25]]
Predicted class is ['Iris-setosa']
Plotting confusion matrix...
Confusion matrix in text:
[[50  0  0]
 [ 0 32 18]
 [ 0  4 46]]
Confusion matrix plotted.
Plotting ROC curve....
ROC curve plotted.
Confusion matrix and ROC curve plotted. See them in Run History details page.

Execution Details
=================
RunId: Classifying_Iris_1509457188739

RunId: Classifying_Iris_1509457195895

Executing user inputs .....
===========================

Python version: 3.5.2 |Continuum Analytics, Inc.| (default, Jul  5 2016, 11:41:13) [MSC v.1900 64 bit (AMD64)]

Iris dataset shape: (150, 5)
Regularization rate is 2.5
LogisticRegression(C=0.4, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)
Accuracy is 0.660377358490566

==========================================
Serialize and deserialize using the outputs folder.

Export the model to model.pkl
Import the model from model.pkl
New sample: [[3.0, 3.6, 1.3, 0.25]]
Predicted class is ['Iris-setosa']
Plotting confusion matrix...
Confusion matrix in text:
[[50  0  0]
 [ 0 33 17]
 [ 0  4 46]]
Confusion matrix plotted.
Plotting ROC curve....
ROC curve plotted.
Confusion matrix and ROC curve plotted. See them in Run History details page.

Execution Details
=================
RunId: Classifying_Iris_1509457195895

RunId: Classifying_Iris_1509457203051

Executing user inputs .....
===========================

Python version: 3.5.2 |Continuum Analytics, Inc.| (default, Jul  5 2016, 11:41:13) [MSC v.1900 64 bit (AMD64)]

Iris dataset shape: (150, 5)
Regularization rate is 1.25
LogisticRegression(C=0.8, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)
Accuracy is 0.6415094339622641

==========================================
Serialize and deserialize using the outputs folder.

Export the model to model.pkl
Import the model from model.pkl
New sample: [[3.0, 3.6, 1.3, 0.25]]
Predicted class is ['Iris-setosa']
Plotting confusion matrix...
Confusion matrix in text:
[[50  0  0]
 [ 1 33 16]
 [ 0  5 45]]
Confusion matrix plotted.
Plotting ROC curve....
ROC curve plotted.
Confusion matrix and ROC curve plotted. See them in Run History details page.

Execution Details
=================
RunId: Classifying_Iris_1509457203051

RunId: Classifying_Iris_1509457210237

Executing user inputs .....
===========================

Python version: 3.5.2 |Continuum Analytics, Inc.| (default, Jul  5 2016, 11:41:13) [MSC v.1900 64 bit (AMD64)]

Iris dataset shape: (150, 5)
Regularization rate is 0.625
LogisticRegression(C=1.6, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)
Accuracy is 0.660377358490566

==========================================
Serialize and deserialize using the outputs folder.

Export the model to model.pkl
Import the model from model.pkl
New sample: [[3.0, 3.6, 1.3, 0.25]]
Predicted class is ['Iris-setosa']
Plotting confusion matrix...
Confusion matrix in text:
[[50  0  0]
 [ 1 36 13]
 [ 0  5 45]]
Confusion matrix plotted.
Plotting ROC curve....
ROC curve plotted.
Confusion matrix and ROC curve plotted. See them in Run History details page.

Execution Details
=================
RunId: Classifying_Iris_1509457210237

RunId: Classifying_Iris_1509457217482

Executing user inputs .....
===========================

Python version: 3.5.2 |Continuum Analytics, Inc.| (default, Jul  5 2016, 11:41:13) [MSC v.1900 64 bit (AMD64)]

Iris dataset shape: (150, 5)
Regularization rate is 0.3125
LogisticRegression(C=3.2, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)
Accuracy is 0.660377358490566

==========================================
Serialize and deserialize using the outputs folder.

Export the model to model.pkl
Import the model from model.pkl
New sample: [[3.0, 3.6, 1.3, 0.25]]
Predicted class is ['Iris-setosa']
Plotting confusion matrix...
Confusion matrix in text:
[[50  0  0]
 [ 1 36 13]
 [ 0  4 46]]
Confusion matrix plotted.
Plotting ROC curve....
ROC curve plotted.
Confusion matrix and ROC curve plotted. See them in Run History details page.

Execution Details
=================
RunId: Classifying_Iris_1509457217482

RunId: Classifying_Iris_1509457225704

Executing user inputs .....
===========================

Python version: 3.5.2 |Continuum Analytics, Inc.| (default, Jul  5 2016, 11:41:13) [MSC v.1900 64 bit (AMD64)]

Iris dataset shape: (150, 5)
Regularization rate is 0.15625
LogisticRegression(C=6.4, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)
Accuracy is 0.6792452830188679

==========================================
Serialize and deserialize using the outputs folder.

Export the model to model.pkl
Import the model from model.pkl
New sample: [[3.0, 3.6, 1.3, 0.25]]
Predicted class is ['Iris-setosa']
Plotting confusion matrix...
Confusion matrix in text:
[[50  0  0]
 [ 1 36 13]
 [ 0  3 47]]
Confusion matrix plotted.
Plotting ROC curve....
ROC curve plotted.
Confusion matrix and ROC curve plotted. See them in Run History details page.

Execution Details
=================
RunId: Classifying_Iris_1509457225704

RunId: Classifying_Iris_1509457234132

Executing user inputs .....
===========================

Python version: 3.5.2 |Continuum Analytics, Inc.| (default, Jul  5 2016, 11:41:13) [MSC v.1900 64 bit (AMD64)]

Iris dataset shape: (150, 5)
Regularization rate is 0.078125
LogisticRegression(C=12.8, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)
Accuracy is 0.6792452830188679

==========================================
Serialize and deserialize using the outputs folder.

Export the model to model.pkl
Import the model from model.pkl
New sample: [[3.0, 3.6, 1.3, 0.25]]
Predicted class is ['Iris-setosa']
Plotting confusion matrix...
Confusion matrix in text:
[[50  0  0]
 [ 1 36 13]
 [ 0  3 47]]
Confusion matrix plotted.
Plotting ROC curve....
ROC curve plotted.
Confusion matrix and ROC curve plotted. See them in Run History details page.

Execution Details
=================
RunId: Classifying_Iris_1509457234132

RunId: Classifying_Iris_1509457242301

Executing user inputs .....
===========================

Python version: 3.5.2 |Continuum Analytics, Inc.| (default, Jul  5 2016, 11:41:13) [MSC v.1900 64 bit (AMD64)]

Iris dataset shape: (150, 5)
Regularization rate is 0.0390625
LogisticRegression(C=25.6, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)
Accuracy is 0.6981132075471698

==========================================
Serialize and deserialize using the outputs folder.

Export the model to model.pkl
Import the model from model.pkl
New sample: [[3.0, 3.6, 1.3, 0.25]]
Predicted class is ['Iris-setosa']
Plotting confusion matrix...
Confusion matrix in text:
[[50  0  0]
 [ 1 37 12]
 [ 0  3 47]]
Confusion matrix plotted.
Plotting ROC curve....
ROC curve plotted.
Confusion matrix and ROC curve plotted. See them in Run History details page.

Execution Details
=================
RunId: Classifying_Iris_1509457242301

RunId: Classifying_Iris_1509457249742

Executing user inputs .....
===========================

Python version: 3.5.2 |Continuum Analytics, Inc.| (default, Jul  5 2016, 11:41:13) [MSC v.1900 64 bit (AMD64)]

Iris dataset shape: (150, 5)
Regularization rate is 0.01953125
LogisticRegression(C=51.2, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)
Accuracy is 0.6981132075471698

==========================================
Serialize and deserialize using the outputs folder.

Export the model to model.pkl
Import the model from model.pkl
New sample: [[3.0, 3.6, 1.3, 0.25]]
Predicted class is ['Iris-setosa']
Plotting confusion matrix...
Confusion matrix in text:
[[50  0  0]
 [ 1 37 12]
 [ 0  3 47]]
Confusion matrix plotted.
Plotting ROC curve....
ROC curve plotted.
Confusion matrix and ROC curve plotted. See them in Run History details page.

Execution Details
=================
RunId: Classifying_Iris_1509457249742

RunId: Classifying_Iris_1509457257076

Executing user inputs .....
===========================

Python version: 3.5.2 |Continuum Analytics, Inc.| (default, Jul  5 2016, 11:41:13) [MSC v.1900 64 bit (AMD64)]

Iris dataset shape: (150, 5)
Regularization rate is 0.009765625
LogisticRegression(C=102.4, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)
Accuracy is 0.6792452830188679

==========================================
Serialize and deserialize using the outputs folder.

Export the model to model.pkl
Import the model from model.pkl
New sample: [[3.0, 3.6, 1.3, 0.25]]
Predicted class is ['Iris-setosa']
Plotting confusion matrix...
Confusion matrix in text:
[[50  0  0]
 [ 1 37 12]
 [ 0  4 46]]
Confusion matrix plotted.
Plotting ROC curve....
ROC curve plotted.
Confusion matrix and ROC curve plotted. See them in Run History details page.

Execution Details
=================

RunId: Classifying_Iris_1509457257076

OUTPUT END

 Like we said before, we'll dig more into this code in a later post.  For now, let's take a look at the run history again.

Run History 2
Now, we can see all of the runs that just took place.  This is a really easy way to get a visual of what our code was accomplishing.

This seems like a good place to stop for today.  At first glance, the AML Workbench is much more developer-oriented than its Studio counterpart.  There's a ton of information here, but it's going to take some more time for us to get comfortable here.  Stay tuned for the next post where we'll dig into the rest of the pre-built code focusing on executing our code in different environments.  Thanks for reading.  We hope you found this informative.

Brad Llewellyn
Data Science Consultant
Valorem
@BreakingBI
www.linkedin.com/in/bradllewellyn
llewellyn.wb@gmail.com
Posted by at

No comments:

Post a Comment

Subscribe to: Post Comments (Atom)