Machine Learning Models in Python - How long does it take

We keep hearing from people about all the computing resources needed for machine learning. Sometimes it can put people off from trying it as they will think I don't have those kind of resources.

This is another blog post in my series on 'How long does it take to create a machine learning model?'

Check out my previous blog post that used data sets containing 72K, 210K, 660K, 2M and 10M records.

• Creating Machine Learning Models in Oracle Cloud Database service
• Creating Machine Learning Models using Oracle Autonomous Data Warehouse (ADW)

There was some surprising results in those these.

In this test, I'll be using Python and SciKitLearn package to create models using the same algorithms. There are a few things to keep in mind. Firstly, although they maybe based on the same algorithms, the actual implementation of them will be different in each environment (SQL vs Python).

With using Python for machine learning, one of the challenges we have is getting access to the data. Assuming the data lives in a Database then time is needed to extract that data to the local Python environment. Secondly, when using Python you will be using a computer with significantly less computing resources than a Database server. In this test I used my laptop (MacBook Pro). Thirdly, when extracting the data from the database, what method should be used.

I've addressed these below and the Oracle Database I used was the DBaaS I used in my first experiment. This is a Database hosted on Oracle Cloud.

Extracting Data to CSV File
This kind of depends on how you do this. There are hundreds of possibilities available to you, but if you are working with an Oracle Database you will probably be using SQL Developer. I used the 'export' option to create a CSV file for each of the data sets. The following table shows how long it took for each data set.

As you can see this is an incredibly slow way of exporting this data. Like I said, there are quicker ways of doing this.

After downloading the data sets, the next step is to see how load it takes to load these CSV files into a pandas data frame in Python. The following table show the timings in seconds.

You can see that Python is very efficient at loading these data sets into a pandas data frame in my Python environment.

Extracting Data using cx_Oracle Python package
As I'll be using Python to create the models and the data exists in an Oracle Database (on Oracle Cloud), I can use the cx_Oracle package to download the data sets into my Python environment. After using the cx_Oracle package to download the data I then converted it into a pandas data frame.

You can see that using cx_Oracle to download the data is a very efficient way of accessing the data.  But if the data already exists in CSV files, then the previous method would be quicker to use.

I had the array fetch size set to 10,000.  I also experimented with smaller and larger numbers for the array fetch size, but 10,000 seemed to give a quickest results.

How long to create Machine Learning Models in Python
Now we get onto checking out the timings of how long it takes to create a number of machine learning models using different algorithms and using the default settings. The algorithms include Naive Bayes, Decision Tree, GLM, SVM and Neural Networks.

I had to stop including SVM in the tests as it was taking way too long to run. For example I killed the SVM model build on the 210K data set after it was running for 5 hours.

The Neural Network models created had 3 hidden layers.

In addition to creating the models, there was some minor data preparation steps performed including factorizing, normalization and one-hot-coding. This data preparation would be comparable to the automatic data preparation steps performed by Oracle, although Oracle Automatic Data Preparation does a bit of extra work.


At the point I would encourage you to look back at my previous blog posts on timings using Oracle DBaaS and ADW.  You will see that Python, in these test cases, was quicker at creating the machine learning models. But with Python the data needed to be extracted from the database and that can take time!

A separate consideration is being able to deploy the models. The time it takes to build models is perhaps not the main consideration. You need to consider ease of deployment and use of the models.

Machine Learning on Oracle Autonomous Data Warehouse

Last week I wrote a blog post about how long it took to create machine learning models on Oracle Database Cloud service. There was some impressive results and some surprising results too.

I decided to try out the exact same tests, using the exact same data on the Oracle Autonomous Data Warehouse Cloud service (ADW).

When creating the ADW service I took the basic configuration and didn't change anything. The inbuilt machine learning for the Autonomous service will magically workout my needs and make the necessary adjustments, Right? It can handle any data volume and any data processing requirements, Right?

Here are the results.

* You will notice that there is no time given for creating a SVM model for the 10M record data set.  After waiting for 4 hours I got bored and gave up waiting (I actually did this three time to make sure it wasn't a once off) 

Update: The SVM Model for the 10M record data set eventually finished after 547 minutes! That's 9.1 hours !

[I also had a 50M record data set. I just didn't waste time trying that.]

[Neural Networks algorithm hasn't been ported onto ADW at this point in time]

If you look back at the results from using the DBaaS you will see it was significantly quicker than the ADW.  (for some it would be quicker using Python on my laptop)

Before you believe the hype, go test it yourself and make sure it measures up.

I re-ran my test cases over a number of days to see if the machine learning aspect of the Autonomous kicked in to learn from the processing and make any performance improvements. Sadly the results were basically the same or slightly slower. Disappointing.

When some tells you, you should be using this, ask them have they actually used and tested it themselves. And more importantly, don't believe them. Go test it yourself.

Oracle Machine Learning notebooks

In this blog post I'll have a look at Oracle Machine Learning notebooks, some of the example notebooks and then how to create a new one.

Check out my previous blog posts on ADWC.

- Create an Autonomous Data Warehouse Cloud Service

- Creating and Managing OML user on ADWC

On entering Oracle Machine Learning on your ADWC service, you will get the following.

Our starting point is to example what is listed in the Examples section. Click on the Examples link. The following lists the example notebooks.

Here we have examples that demonstrate how to build Anomaly Detection, Association Rules, Attribute Importance, Classification, Regression, Clustering and one that contains examples of various statistical function.

Click on one of these to see the notebook. The following is the notebook demoing the Statistical Functions. When you select a notebook it might take a few seconds to setup and open. There is some setup needed in the background and to make sure you have access to the demo data and then runs the notebook, generating the results. Most of the demo data is based on the SH schema.

Now let us create our first notebook.

From the screen shown above lift on the menu icon on the top left of the screen.

And then click on Notebooks from the pop-out menu.

In the Notebooks screen click on the Create button to create your first notebook.

And give it a meaningful name.

The Notebook shell will be created and then opened for you.

In the grey box, just under the name the name of your Notebook, is where you can enter your first SQL statement. Then over on the right hand side of this Cell you will see a triangle on its side. This is the run button.

For now you can only run SQL statements, but you also have other notebooks features such as different charting options and these are listed under the grey cell, where your SQL is located.

Here you can create Bar, Pie, Area, Line and Scatter charts. Here is an example of a Bar chart.

Warning: You do need to be careful of your syntax, as minimal details are given on what is wrong with your code. Not even the error numbers.

Go give it a good and see how far you can take these OML Notebooks.

Oracle Machine Learning Users on ADWC

One of the new features of the Autonomous Data Warehouse Cloud (ADWC) service is Oracle Machine Learning. This is a Zeppelin based notebook for your machine learning on ADWC. Check out my previous blog post about this.

In order to be able to use this new product and the in-database machine learning in ADWC, you will need your database user to have certain privileges. The first step in this is to create a typical user for accessing the ADWC and grant it the necessary OML privileges.

To do this open the ADWC console and then open the Service Console.



This will then open a new admin page which contains a link for 'Manage Oracle ML User'. Click on this.



You can then enter the Username, Password and other details for the user, and then click Create.
This will then create a new user that is specific for Oracle Machine Learning. This new user will be granted the DWROLE, that contains the basic schema privileges and the privileges required to run the in-database machine learning algorithms. For those that a familiar with Oracle Data Mining/Oracle Advanced Analytics option in the Enterprise Edition of the Oracle database, you will see that these privileges are very similar.

You can examine the privileges granted to this DWROLE in the database as an administrator. When you do you will see the following:

CREATE ANALYTIC VIEW 
CREATE ATTRIBUTE DIMENSION 
ALTER SESSION 
CREATE HIERARCHY 
CREATE JOB 
CREATE MINING MODEL 
CREATE PROCEDURE 
CREATE SEQUENCE 
CREATE SESSION 
CREATE SYNONYM 
CREATE TABLE 
CREATE TRIGGER 
CREATE TYPE 
CREATE VIEW READ,WRITE ON directory DATA_PUMP_DIR 
EXECUTE privilege on the PL/SQL package DBMS_CLOUD

Oracle Machine Learning notebooks

With the recent release of Oracle's Autonomous Data Warehouse Cloud (ADWC), Oracle has given data scientists a new tool for data discovery and machine learning on the ADWC. Oracle Machine Learning is based on Apache Zeppelin and gives us a new machine learning tool for accessing the in-database machine learning algorithms and in-database statistical functions.

Oracle Machine Learning (OML) SQL notebooks provide easy access to Oracle's parallelized, scalable in-database implementations of a library of Oracle Advanced Analytics' machine learning algorithms (classification, regression, anomaly detection, clustering, associations, attribute importance, feature extraction, times series, etc.), SQL, PL/SQL and Oracle's statistical and analytical SQL functions. Oracle Machine Learning SQL notebooks and Oracle Advanced Analytics' library of machine learning SQL functions combined with PL/SQL allow companies to automate their discovery of new insights, generate predictions and add "AI" to data viz dashboards and enterprise applications.

The key features of Oracle Machine Learning include:

• Collaborative SQL notebook UI for data scientists
• Packaged with Oracle Autonomous Data Warehouse Cloud
• Easy access to shared notebooks, templates, permissions, scheduler, etc.
• Access to 30+ parallel, scalable in-database implementations of machine learning algorithms
• SQL and PL/SQL scripting language supported
• Enables and Supports Deployments of Enterprise Machine Learning Methodologies in ADWC

Here is a list of key resources for Oracle Machine Learning:

• Oracle Machine Learning Notebooks
• Video overview of Oracle Machine Learning
• Download sample Oracle Machine Learning notebooks
• Quick Start Tutorial for getting started with Oracle Machine Learning
• Documentation: Using Oracle Machine Learning