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Category: Python

K-Nearest Neighbors in Python

Published 2019-04-11 by Kevin Feasel

Hardik Jaroli shows how to use the k-Nearest Neighbors algorithm using scikit-learn:

K Nearest Neighbors is a classification algorithm that operates on a very simple principle. It is best shown through example! Imagine we had some imaginary data on Dogs and Horses, with heights and weights.

Training Algorithm:
1. Store all the Data

Prediction Algorithm:
1.Calculate the distance from x to all points in your data
2. Sort the points in your data by increasing distance from x
3. Predict the majority label of the “k” closest points

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Solving Logistic Regression Problems with Python

Published 2019-04-08 by Kevin Feasel

Hardik Jaroli shows how we can solve logistic regression problems using Python, using the Titanic data set as an example:

We will be working with the Titanic Data Set from Kaggle. We’ll be trying to predict a classification- survival or deceased.

Let’s begin by implementing Logistic Regression in Python for classification. We’ll use a “semi-cleaned” version of the titanic data set, if you use the data set hosted directly on Kaggle, you may need to do some additional cleaning.

Click through for the demo.

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Python Natural Language Processing Tools

Published 2019-04-03 by Kevin Feasel

Sandeep Aspari takes us through some of the tooling available in Python around Natural Language Processing:

TextBlob
TextBlob is a python library tool and extension of NLTK. It provides a simple API approach to its methods and executes a large number of NLTK functions, and it also includes the pattern library functionality. You are just at the beginning, this might be an excellent tool to learning, and we can use it in applications production those don’t require heavy performant. TextBlob libraries are similar to python strings, so we can quickly transform and play similarly we performed in python. Finally, TextBlob is used in everywhere, and it is best suitable for smaller projects.

There are several tools from which you can choose. Sandeep also gives us some Node- and Java-based tools as well.

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Power BI IntelliSense For Python and R

Published 2019-02-26 by Kevin Feasel

David Eldersveld makes me wonder about the value of Power BI’s IntelliSense for R and Python:

If I type the letter a into the R Script editor, my code completion options are acts, always, and, and as. Power BI’s editor is not offering any IntelliSense options from a Python or R dictionary. Instead, it’s pulling from the text already in the editor. Note the comment in Line 1 and the inclusion of words beginning with the letter a — always, and, acts, as.

By comparison, the DAX editor contains a detailed function list and helpful annotations for code completion. Can we get something similar for R and Python? Not exactly… But there’s a workaround that I’m almost embarrassed to suggest. If you are a user who codes directly into the script editor, the following hack could be helpful. If you use the option to Edit script in External IDE, keep doing that and ignore the following guidance.

As-is, this is worse than no IntelliSense because at least with no IntelliSense, it’ll never steal a mouse click or keystroke. I wouldn’t expect RStudio level quality out of the gate but unless I’m missing something, that’s pretty bad.

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Parsing HL7 Messages With Python

Published 2019-02-25 by Kevin Feasel

Cristian Satnic has HL7 formatted messages in SQL Server and wishes to parse them using Python:

Each line in the HL7 message is called a segment and then each segment is split into individual fields by | (pipe) characters (typically). HL7 fields have well-defined names and meanings … for example in the example above PID-3 (the 3rd field in the PID segment where the identifier ‘PID’ is not counted) is 12001 and that represents the patient identifier.

For this particular project I’m working on we have HL7 messages stored in a SQL Server 2016 database table where each row in the table contains the raw HL7 2.x message in a particular column. I need to be able to intelligently filter over this HL7 data by looking at values in particular HL7 fields (as shown above). Since this HL7 data is stored in a varchar(MAX) column I could certainly attempt to play games using LIKE comparisons in SQL but that would not get me very far. SQL simply does not understand the complex structure of HL7 and I have no native SQL Server functions at my disposal that I could quickly use to parse this data and filter it.

Cristian has a Jupyter Notebook which takes us through the solution. With SQL Server 2017, there’s the possibility of solving this in a stored procedure using Machine Learning Services.

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The Zen Of Airflow

Published 2019-02-22 by Kevin Feasel

Bas Harenslak shows how you can think of The Zen of Python as it applies to Apache Airflow:

Apache Airflow is a Python framework for programmatically creating workflows in DAGs, e.g. ETL processes, generating reports, and retraining models on a daily basis. This allows for concise and flexible scripts but can also be the downside of Airflow; since it’s Python code there are infinite ways to define your pipelines. The Zen of Python is a list of 19 Python design principles and in this blog post I point out some of these principles on four Airflow examples. This blog was written with Airflow 1.10.2.

My favorite of the Zen of Python principles is a combination of two: “simple is better than complex; complex is better than complicated.” That’s something I don’t always get right, but it is critical for a stable architecture.

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Bayesian Modeling Of Hardware Failure Rates

Published 2019-02-20 by Kevin Feasel

Sean Owen shows how you can use Bayesian statistical approaches with Spark Streaming, using the example of hard drive failure rates:

This data doesn’t arrive all at once, in reality. It arrives in a stream, and so it’s natural to run these kind of queries continuously. This is simple with Apache Spark’s Structured Streaming, and proceeds almost identically.

Of course, on the first day this streaming analysis is rolled out, it starts from nothing. Even after two quarters of data here, there’s still significant uncertainty about failure rates, because failures are rare.

An organization that’s transitioning this kind of offline data science to an online streaming context probably does have plenty of historical data. This is just the kind of prior belief about failure rates that can be injected as a prior distribution on failure rates!

Bayesian approaches work really well with streaming data if you think of the streams as sampling events used to update your priors to a new posterior distribution.

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Using Convolutional Neural Networks To Recognize Features In Images

Published 2019-02-19 by Kevin Feasel

Michael Grogan shows how you can use Keras to perform image recognition with a convolutional neural network:

VGG16 is a built-in neural network in Keras that is pre-trained for image recognition.

Technically, it is possible to gather training and test data independently to build the classifier. However, this would necessitate at least 1,000 images, with 10,000 or greater being preferable.

In this regard, it is much easier to use a pre-trained neural network that has already been designed for image classification purposes.

This is probably the best generally available technique for image classification.

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No-Code ML On Cloudera Data Science Workbench

Published 2019-02-12 by Kevin Feasel

Tim Spann has a post covering ML on the Cloudera Data Science Workbench:

Using Cloudera Data Science Workbench with Apache NiFi, we can easily call functions within our deployed models from Apache NiFi as part of flows. I am working against CDSW on HDP (https://www.cloudera.com/documentation/data-science-workbench/latest/topics/cdsw_hdp.html),  but it will work for all CDSW regardless of install type.
In my simple example, I built a Python model that uses TextBlob to run sentiment analysis against a passed-in sentence. It returns Sentiment Polarity and Subjectivity, which we can immediately act upon in our flow.
CDSW is extremely easy to work with and I was up and running in a few minutes. For my model, I created a python 3 script and a shell script for install details. Both of these artifacts are available here: https://github.com/tspannhw/nifi-cdsw.

The “no code” portion was less interesting to me than the scalable ML portion, as “no code” either drops into tedium or ends up being replaced by code.

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