Category: Python

Multi-Class Text Classification In Python

Published 2018-03-22 by Kevin Feasel

Susan Li has a series on multi-class text classification in Python. First up is analysis with PySpark:

Our task is to classify San Francisco Crime Description into 33 pre-defined categories. The data can be downloaded from Kaggle.

Given a new crime description comes in, we want to assign it to one of 33 categories. The classifier makes the assumption that each new crime description is assigned to one and only one category. This is multi-class text classification problem.

* Input: Descript

* Example: “STOLEN AUTOMOBILE”

* Output: Category

* Example: VEHICLE THEFT

To solve this problem, we will use a variety of feature extraction technique along with different supervised machine learning algorithms in Spark. Let’s get started!

Then, she looks at multi-class text classification with scikit-learn:

The classifiers and learning algorithms can not directly process the text documents in their original form, as most of them expect numerical feature vectors with a fixed size rather than the raw text documents with variable length. Therefore, during the preprocessing step, the texts are converted to a more manageable representation.

One common approach for extracting features from the text is to use the bag of words model: a model where for each document, a complaint narrative in our case, the presence (and often the frequency) of words is taken into consideration, but the order in which they occur is ignored.

Specifically, for each term in our dataset, we will calculate a measure called Term Frequency, Inverse Document Frequency, abbreviated to tf-idf.

This is a nice pair of articles on the topic. Natural Language Processing (and dealing with text in general) is one place where Python is well ahead of R in terms of functionality and ease of use.

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XGBoost With Python

Published 2018-03-13 by Kevin Feasel

Fisseha Berhane looked at Extreme Gradient Boosting with R and now covers it in Python:

In both R and Python, the default base learners are trees (gbtree) but we can also specify gblinear for linear models and dart for both classification and regression problems.
In this post, I will optimize only three of the parameters shown above and you can try optimizing the other parameters. You can see the list of parameters and their details from the website.

It’s hard to overstate just how valuable XGBoost is as an algorithm.

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Using Python In SQL Server 2017

Published 2018-03-12 by Kevin Feasel

Emma Stewart has a post covering setup and configuration of SQL Server 2017 Machine Learning Services and using Python within SQL Server:

One of the new features of SQL Server 2017 was the ability to execute Python Scripts within SQL Server. For anyone who hasn’t heard of Python, it is the language of choice for data analysis. It has a lot of libraries for data analysis and predictive modelling, offers power and flexibility for various machine learning tasks and is also a much simpler language to learn than others.

The release of SQL Server 2016, saw the integration of the database engine with R Services, a data science language. By extending this support to Python, Microsoft have renamed R Services to ‘Machine Learning Services’ to include both R and Python.

The benefits of being able to run Python from SQL Server are that you can keep analytics close to the data (if your data is held within a SQL Server database) and reduce any unnecessary data movement. In a production environment you can simply execute your Python solution via a T-SQL Stored Procedure and you can also deploy the solution using the familiar development tool, Visual Studio.

ML Services is a great addition to SQL Server.

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Microsoft ML Server 9.3 Released

Published 2018-02-28 by Kevin Feasel

Nagesh Pabbisetty announces Microsoft Machine Learning Server 9.3:

In ML Server 9.3, we have added support for SQL compute context in ML Server and in R Client running on Linux platforms, so data scientists who work on Linux workstations can directly use in-database analytics with SQL Server compute context. Additionally, the SQLRUtils package can now be used to package the R scripts into T-SQL stored procedures and run them from R environment on Linux clients.

An interesting scenario enabled by the addition of SQL Server Compute context in ML Server running on Linux is that organizations can now provide a browser-based interface for accessing SQL Server compute context with R Studio Server and ML Server running on a Linux machine connecting to SQL Server.

Since introducing revoscalepy library in the last release of ML Server and SQL Server 2017, we have shipped several additions and improvements in the Python APIs as part of CU releases of SQL Server 2017. We have added APIs like rx_create_col_info, rx_get_var_info etc. that make it easier to get column information, esp. with large number of columns. We added rx_serialize_model for easy model serialization. We have also improved performance when working with string data in different scenarios.

This also gets you up to R 3.4.3. H/T David Smith

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Looping In Python And R

Published 2018-02-28 by Kevin Feasel

Dmitry Kisler has a quick comparison of looping speed in Python and R:

This post is about R versus Python in terms of the time they require to loop and generate pseudo-random numbers. To accomplish the task, the following steps were performed in Python and R (1) loop 100k times ( $i$ is the loop index) (2) generate a random integer number out of the array of integers from 1 to the current loop index $i$ ( $i$ +1 for Python) (3) output elapsed time at the probe loop steps: $i$ ( $i$ +1 for Python) in [10, 100, 1000, 5000, 10000, 25000, 50000, 75000, 100000]

The findings were mostly unsurprising to me, though there was one unexpected twist.

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Visual Studio Code In Anaconda 5.1

Published 2018-02-20 by Kevin Feasel

George Leopold reports that Anaconda 5.1 will now include Visual Studio Code as an optional IDE:

Microsoft and Python data science platform vendor Anaconda have extended their partnership by adding the software giant’s code editor to the latest Anaconda distribution.

The addition of Microsoft’s Visual Studio Code (VS Code) expands its support for the latest release of the Python data science platform, Anaconda 5.1. The Python platform has attracted more than 4.5 million users running the programming language on Windows, Mac and Linux.

Along with editing and debugging features, the partners said the cross-platform code editor includes custom features for Anaconda users. For example, a Python extension customizes VS Code for the Python development environment.

Read on for more information.

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Optimal Image Colorization With Python

Published 2018-02-15 by Kevin Feasel

Sandipan Dey walks through a paper on colorization and shows some examples:

Colorization is a computer-assisted process of adding color to a monochrome image or movie. In the paper the authors presented an optimization-based colorization method that is based on a simple premise: neighboring pixels in space-time that have similar intensities should have similar colors.

This premise is formulated using a quadratic cost function and as an optimization problem. In this approach an artist only needs to annotate the image with a few color scribbles, and the indicated colors are automatically propagated in both space and time to produce a fully colorized image or sequence.

In this article the optimization problem formulation and the way to solve it to obtain the automatically colored image will be described for the images only.

It’s an interesting approach.

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PySpark DataFrame Transformations

Published 2018-02-01 by Kevin Feasel

Vincent-Philippe Lauzon shows how to perform data frame transformations using PySpark:

We wanted to look at some more Data Frames, with a bigger data set, more precisely some transformation techniques. We often say that most of the leg work in Machine learning in data cleansing. Similarly we can affirm that the clever & insightful aggregation query performed on a large dataset can only be executed after a considerable amount of work has been done into formatting, filtering & massaging data: data wrangling.

Here, we’ll look at an interesting dataset, the H-1B Visa Petitions 2011-2016 (from Kaggle) and find some good insights with just a few queries, but also some data wrangling.

It is important to note that about everything in this article isn’t specific to Azure Databricks and would work with any distribution of Apache Spark.

The notebook used for this article is persisted on GitHub.

Read on for explanation, or check out the notebook to work on it at your own pace.

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R Or Python

Published 2018-01-29 by Kevin Feasel

Tomaz Kastrun shares his thoughts on the topic of R versus Python:

Imag[in]e I ask you, would you prefer Apple iPhone over Samsung Galaxy, respectively? Or if I would ask you, would you prefer BMW over Audi, respectively? In all the cases, both phones or both cars will get the job done. So will Python or R, R or Python. So instead of asking which one I prefer, ask your self, which one suits my environment better? If your background is more statistics and less programming, take R, if you are more into programming and less into statistics, take Python; in both cases you will have faster time to accomplish results with your preferred language. If you ask me, can I do gradient boosting or ANOVA or MDS in Python or in R, the answer will be yes, you can do both in any of the languages.

This graf hits the crux of my opinion on the topic, but as I’ve gone deeper into the topic over the past year, I think the correct answer is probably “both” for a mature organization and “pick the one which suits you better” for beginners.

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Leveraging Hive In Pyspark

Published 2018-01-23 by Kevin Feasel

Fisseha Berhane shows how to use Spark to connect Python to Hive:

If we are using earlier Spark versions, we have to use HiveContext which is variant of Spark SQL that integrates with data stored in Hive. Even when we do not have an existing Hive deployment, we can still enable Hive support.
In this tutorial, I am using standalone Spark. When not configured by the Hive-site.xml, the context automatically creates metastore_db in the current directory.

As shown below, initially, we do not have metastore_db but after we instantiate SparkSession with Hive support, we see that metastore_db has been created. Further, when we execute create database command, spark-warehouse is created.

Click through for a bunch of examples.

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