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Category: Python

Installing SQL Server 2017 Machine Learning Services

Published 2017-11-29 by Kevin Feasel

Ginger Grant shows how to install SQL Server 2017 Machine Learning Services:

There are two installation options:  In-Database or Standalone.  If you are evaluating Machine Learning Services and you have no knowledge of what the load may be, start by selecting the Machine Learning Service In-Database.  There are several reasons why by default you want to select the In-Database option. One of the problems that Microsoft was looking to solve by incorporating advanced data analytics was to improve performance of the native code by greatly reducing data latency.  If you are analyzing a lot of data which is stored within SQL Server, the performance will be improved if the data does not need to be moved around on a network. Also, the licensing costs of installing R Server standalone also need to be evaluated with a Microsoft representative as well. An evaluation of the resource load on the network, as well as analysis of the code running on SQL Server should be performed prior to the decision to install the Machine Learning Server Standalone.

Read the whole thing.

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Running PySpark In Visual Studio Code

Published 2017-11-27 by Kevin Feasel

Jenny Jiang shows how to run PySpark on HDInsight in VSCode:

We are excited to introduce the integration of HDInsight PySpark into Visual Studio Code (VSCode), which allows developers to easily edit Python scripts and submit PySpark statements to HDInsight clusters. For PySpark developers who value productivity of Python language, VSCode HDInsight Tools offer you a quick Python editor with simple getting started experiences, and enable you to submit PySpark statements to HDInsight clusters with interactive responses. This interactivity brings the best properties of Python and Spark to developers and empowers you to gain faster insights.

Click through to see how it’s done.

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Pandas Basics

Published 2017-11-24 by Kevin Feasel

Kevin Jacobs has a tutorial on Python’s Pandas library:

There are a few things worth mentioning. Often, Pandas is abbreviated as pd (like Numpy which is often abbreviated as np). If you look at other code, you will see that DataFrames are often abbreviated by df. Here, the DataFrame is constructed using data from a list of lists. The columns argument specifies the keys of the data.

This is a high-level intro, but helps you get your feet wet if you’ve not played with the library.

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Housing Prices In Ames, Iowa: A Kaggle Competition

Published 2017-11-21 by Kevin Feasel

Kathryn Bryant and M. Aaron Owen share their Kaggle experiences.  First, Kathryn, et al:

The lifecycle of our project was a typical one. We started with data cleaning and basic exploratory data analysis, then proceeded to feature engineering, individual model training, and ensembling/stacking. Of course, the process in practice was not quite so linear and the results of our individual models alerted us to areas in data cleaning and feature engineering that needed improvement. We used root mean squared error (RMSE) of log Sale Price to evaluate model fit as this was the metric used by Kaggle to evaluate submitted models.

Data cleaning, EDA, feature engineering, and private train/test splitting (and one spline model!) were all done in R but  we used Python for individual model training and ensembling/stacking. Using R and Python in these ways worked well, but the decision to split work in this manner was driven more by timing than anything else.

Then, Aaron, et al, share their process and findings:

Some variables had a moderate amount of missingness. For example, about 17% of the houses were missing the continuous variable, Lot Frontage, the linear feet of street connected to the property. Intuitively, attributes related to the size of a house are likely important factors regarding the price of the house. Therefore, dropping these variables seems ill-advised.

Our solution was based on the assumption that houses in the same neighborhood likely have similar features. Thus, we imputed the missing Lot Frontage values based on the median Lot Frontage for the neighborhood in which the house with missing value was located.

This is the major upside to Kaggle:  it gives you the ability to work in a controlled environment with real data sets, which include real data problems.  Yeah, the data’s much cleaner than you’d experience in production pretty much anywhere, but that lets you practice technique with a relatively low barrier to entry.  H/T R-Bloggers (Kathryn | Aaron)

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Picking A Python IDE

Published 2017-11-20 by Kevin Feasel

Kevin Jacobs reviews a few Python IDEs from the perspective of a data scientist:

Ladies and gentlemens, this is one of the most perfect IDEs for editing your Python code! At least in my opinion. Jupyter notebook is a web based code editor and can quickly generate visualizations. You can mix up code and text containing no, simple or complex mathematics. One thing I am missing here, is the support for code completion, but there are tons of plugins available so this should be no problem. It is also easy to turn your notebook into a presentation. For collaboration with non-technical teams, this is a great tool.

Conclusion: perfect Python IDE for data science! Less support for code inspection.

Click through for reviews of three IDEs.

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Defining Result Sets With ML Services

Published 2017-11-16 by Kevin Feasel

Dave Mason covers a pain point in SQL Server Machine Learning Services:

The example above is so simple, defining the RESULT SETS poses no problems. But what if the format of the output isn’t known at design time? R (or Python) might take the input data set and add, remove, or change columns conditionally. Further, the input data set might not even be known at design time. How would you define the RESULT SETS at run time?

WITH RESULT SETS needs a MAKE_A_GUESS or FIGURE_IT_OUT option. If there’s some other type of “easy button” for this, I haven’t found it.

It would be nice if the service could the ability to read the data frame columns and use those by default.

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Scraping SQL Saturday Statistics

Published 2017-11-14 by Kevin Feasel

Tomaz Kastrun shows how to use rvest to read the SQL Saturday website and parse schedule details:

I wanted to check a simple query: How many times has a particular topic been presented and from how many different presenters.

Sounds interesting, tackling the problem should not be a problem, just that the end numbers may vary, since there will be some text analysis included.

Read on for the code and some analysis.

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Quickly Computing Area Under The Curve

Published 2017-11-13 by Kevin Feasel

Jean-Francois Puget has a fast method for computing Area Under the Curve in Python:

When the target only takes two values we have a binary classification problem at hand.  Example of binary classification are very common. For instance fraud detection where examples are credit card transactions, features are time, location, amount, merchant id, etc., and target is fraud or not fraud.  Spam detection is also a binary classification where examples are emails, features are the email content as a string of words, and target is spam or not spam.  Without loss of generality we can assume that the target values are 0 and 1, for instance 0 means no fraud or no spam, whiloe 1 means fraud or spam.

For binary classification, predictions are also binary.  Therefore, a prediction is either equal to the target, or is off the mark.  A simple way to evaluate model performance is accuracy: how many predictions are right? For instance, if our test set has 100 examples in it, how many times is the prediction correct?  Accuracy seems a logical way to evaluate performance: a higher accuracy obviously means a better model.  At least this is what people think when they are exposed to the first time to binary classification problems.  Issue is that accuracy can be extremely misleading.

Read Jean-Francois’ explanation and scroll down for the Python sample.

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Offline Installation Of SQL Server 2017 ML Services

Published 2017-11-13 by Kevin Feasel

Jan Mulkens shows how to install SQL Server 2017 Machine Learning Services when your the server hosting SQL Server doesn’t have outbound internet access:

That’s when you remember it… Your server isn’t connected to the internet!
Pretty normal, but in your enthusiasm you completely forgot that SQL Server needs to download some binaries for the R and Python components you so desperately want on your precious machine!

Luckily, the installer comes to your rescue and shows you where to download those binaries it needs.
Turns out however… This link only is for one R component and the installer won’t let you pass to the next screen!

Read on for the answer.

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TensorFlow Tutorial

Published 2017-11-10 by Kevin Feasel

Ashish Bakshi has a TensorFlow tutorial:

As shown in the image above, tensors are just multidimensional arrays, that allows you to represent data having higher dimensions. In general, Deep Learning you deal with high dimensional data sets where dimensions refer to different features present in the data set. In fact, the name “TensorFlow” has been derived from the operations which neural networks perform on tensors. It’s literally a flow of tensors. Since, you have understood what are tensors, let us move ahead in this TensorFlow tutorial and understand – what is TensorFlow?

The sample here is Python, though there is an R library as well.

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