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Category: Python

Fraud Detection With Python

Published 2017-06-19 by Kevin Feasel

Kevin Jacobs has a walkthrough of how to use Pandas and scikit-learn to perform fraud detection against a sample set of credit card transactions:

Apparently, the data consists of 28 variables (V1, …, V28), an “Amount” field a “Class” field and the “Time” field. We do not know the exact meanings of the variables (due to privacy concerns). The Class field takes values 0 (when the transaction is not fraudulent) and value 1 (when a transaction is fraudulent). The data is unbalanced: the number of non-fraudulent transactions (where Class equals 0) is way more than the number of fraudulent transactions (where Class equals 1). Furthermore, there is a Time field. Further inspection shows that these are integers, starting from 0.

There is a small trick for getting more information than only the raw records. We can use the following code:

print(df.describe())

This code will give a statistically summary of all the columns. It shows for example that the Amount field ranges between 0.00 and 25691.16. Thus, there are no negative transactions in the data.

The Kaggle competition data set is available, so you can follow along.

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Quantile Regression With Python

Published 2017-06-09 by Kevin Feasel

Gopi Subramanian discusses one of my favorite regression concepts, heteroskedasticity:

With variance score of 0.43 linear regression did not do a good job overall. When the x values are close to 0, linear regression is giving a good estimate of y, but we near end of x values the predicted y is far way from the actual values and hence becomes completely meaningless.

Here is where Quantile Regression comes to rescue. I have used the python package statsmodels 0.8.0 for Quantile Regression.

Let us begin with finding the regression coefficients for the conditioned median, 0.5 quantile.

The article doesn’t render the code very well at all, but Gopi does have the example code on Github, so you can follow along that way.

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Kafka For Pythonistas

Published 2017-06-09 by Kevin Feasel

Matt Howlett has an introduction to Apache Kafka, designed for Python developers:

In the call to the produce method, both the key and value parameters need to be either a byte-like object (in Python 2.x this includes strings), a Unicode object, or None. In Python 3.x, strings are Unicode and will be converted to a sequence of bytes using the UTF-8 encoding. In Python 2.x, objects of type unicode will be encoded using the default encoding. Often, you will want to serialize objects of a particular type before writing them to Kafka. A common pattern for doing this is to subclass Producer and override the produce method with one that performs the required serialization.

The produce method returns immediately without waiting for confirmation that the message has been successfully produced to Kafka (or otherwise). The flush method blocks until all outstanding produce commands have completed, or the optional timeout (specified as a number of seconds) has been exceeded. You can test to see whether all produce commands have completed by checking the value returned by the flush method: if it is greater than zero, there are still produce commands that have yet to complete. Note that you should typically call flush only at application teardown, not during normal flow of execution, as it will prevent requests from being streamlined in a performant manner.

This is a fairly gentle introduction to the topic if you’re already familiar with Python and have a familiarity with message broker systems.

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Riddler Nation: Game Theory In Action

Published 2017-06-07 by Kevin Feasel

Curtis Miller goes over a multi-phase distribution game with no known information:

The winning strategy of the last round, submitted by Vince Vatter, was (0, 1, 2, 16, 21, 3, 2, 1, 32, 22), with an official record1 of 751 wins, 175 losses, and 5 ties. Naturally, the top-performing strategies look similar. This should not be surprising; winning strategies exploit common vulnerabilities among submissions.

I’ve downloaded the submitted strategies for the second round (I already have the first round’s strategies). Lets load them in and start analyzing them.

This is a great blog post, which looks at using evolutionary algorithms to evolve a winning strategy.

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Using mssql-scripter

Published 2017-05-24 by Kevin Feasel

David Alcock looks at a new Python-based command line tool, mssql-scripter:

Earlier today two new command line tools were announced for SQL Server, one an experimental Linux tools DBFS which enables access to live DMVs without using a UI like SSMS and secondly a tool that enables script generation of objects within SQL rather like the Generate SQL Scripts option in SSMS.

In this post I’m going to run through the installation of the script generator tool and provide a very quick demo. The reason I’m going through this is because in order to install the tool we need to use something called PIP. PIP is a package management system that enables us install and use packages written in Python. Yeah, Python again!

I’m pretty interested in DBFS, as it seems well-placed to make crusty Linux sysadmins happier with SQL Server, and that’s a big positive in my book.

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R And Python Support In VS 2017

Published 2017-05-16 by Kevin Feasel

David Smith announces that Visual Studio 2017 now supports R Tools for Visual Studio and Python Tools for Visual Studio:

The new Visual Studio 2017 has built-in support for programming in R and Python. For older versions of Visual Studio, support for these languages has been available via the RTVS and PTVS add-ins, but the new Data Science Workloads in Visual Studio 2017 make them available without a separate add-in. Just choose the “Data Science and analytical applications” option during installation to install everything you need, including Microsoft R Client and the Anaconda Python distribution.

I’m personally going to wait a little bit before jumping onto Visual Studio 2017, but I’m glad that RTVS is now available.

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Installing Anaconda

Published 2017-05-04 by Kevin Feasel

Max Trinidad shows how to install Anaconda in Windows and Ubuntu:

Installing Anaconda in Windows

In Windows the installation is simply done through the SQL Server 2017 setup process. During the SQL Server installation process, select the “Machine Learning Services (In-Database)” option and this will automatically install both “R” and *”Anaconda” on your system.

Max also shows some Python with SQL Server 2017 basics.

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PySpark Persistence

Published 2017-04-26 by Kevin Feasel

David Crook shows how to save data to disk from PySpark:

This is working on HDInsight v3.5 w/Spark 2.0 and Azure Data Lake Storage as the underlying storage system.  What is nice about this is that my cluster only has access to its cluster section of the folder structure.  I have the structure root/clusters/dasciencecluster.  This particular cluster starts at dasciencecluster, while other clusters may start somewhere else.  Therefor my data is saved to root/clusters/dasciencecluster/data/open_data/RF_Model.txt

It’s pretty easy to do, and the Scala code would look suspiciously similar.  The Java version of the code would be seven pages long.

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SQL Server ML Services

Published 2017-04-20 by Kevin Feasel

SQL Server R Services is now SQL Server Machine Learning Services and supports Python.  First, Nagesh Pabbisetty and Sumit Kumar talk about Python support:

The addition of Python builds on the foundation laid for R Services in SQL Server 2016 and extends that mechanism to include Python support for in-database analytics and machine learning. We are renaming R Services to Machine Learning Services, and R and Python are two options under this feature.

The Python integration in SQL Server provides several advantages:

  • Elimination of data movement: You no longer need to move data from the database to your Python application or model. Instead, you can build Python applications in the database. This eliminates barriers of security, compliance, governance, integrity, and a host of similar issues related to moving vast amounts of data around. This new capability brings Python to the data and runs code inside secure SQL Server using the proven extensibility mechanism built in SQL Server 2016.

  • Easy deployment: Once you have the Python model ready, deploying it in production is now as easy as embedding it in a T-SQL script, and then any SQL client application can take advantage of Python-based models and intelligence by a simple stored procedure call.

  • Enterprise-grade performance and scale: You can use SQL Server’s advanced capabilities like in-memory table and column store indexes with the high-performance scalable APIs in RevoScalePy package. RevoScalePy is modeled after RevoScaleR package in SQL Server R Services. Using these with the latest innovations in the open source Python world allows you to bring unparalleled selection, performance, and scale to your SQL Python applications.

  • Rich extensibility: You can install and run any of the latest open source Python packages in SQL Server to build deep learning and AI applications on huge amounts of data in SQL Server. Installing a Python package in SQL Server is as simple as installing a Python package on your local machine.

  • Wide availability at no additional costs: Python integration is available in all editions of SQL Server 2017, including the Express edition.

Nagesh Pabbisetty also announces Microsoft R Server 9.1:

We took the first step with Microsoft R Server 9.0, and this follow on release includes significant innovations such as:

  • New machine learning enhancements and inclusion of pre-trained cognitive models such as sentiment analysis & image featurizers

  • SQL Server Machine Learning Services with integrated Python in Preview

  • Enterprise grade operationalization with real-time scoring and dynamic scaling of VMs

  • Deep customer & ISV partnerships to deliver the right solutions to customers

  • A panoply of sources to help you get started with ease

And Joseph Sirosh indicates that AI is where the money is:

So today it’s my pleasure to announce the first RDBMS with built-in AI—a production-quality Community Technology Preview (CTP 2.0) of SQL Server 2017. In this preview release, we are introducing in-database support for a rich library of machine learning functions, and now for the first time Python support (in addition to R). SQL Server can also leverage NVIDIA GPU-accelerated computing through the Python/R interface to power even the most intensive deep-learning jobs on images, text, and other unstructured data. Developers can implement NVIDIA GPU-accelerated analytics and very sophisticated AI directly in the database server as stored procedures and gain orders of magnitude higher throughput. In addition, developers can use all the rich features of the database management system for concurrency, high-availability, encryption, security, and compliance to build and deploy robust enterprise-grade AI applications.

There’s a lot to digest here.

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