microsoftml (Python module in SQL Server)
microsoftml is a Python35-compatible module from Microsoft providing high-performance machine learning algorithms. It includes functions for training and transformations, scoring, text and image analysis, and feature extraction for deriving values from existing data.
The machine learning APIs were developed by Microsoft for internal machine learning applications and have been refined over the years to support high performance on big data, using multicore processing and fast data streaming. This package originated as a Python equivalent of an R version, MicrosoftML, that has similar functions.
Full reference documentation
The microsoftml library is distributed in multiple Microsoft products, but usage is the same whether you get the library in SQL Server or another product. Because the functions are the same, documentation for individual microsoftml functions is published to just one location under the Python reference for Microsoft Machine Learning Server. Should any product-specific behaviors exist, discrepancies will be noted in the function help page.
Versions and platforms
The microsoftml module is based on Python 3.5 and available only when you install one of the following Microsoft products or downloads:
- SQL Server 2017 Machine Learning Services
- Microsoft Machine Learning Server 9.2.0 or later
- Python client libraries for a data science client
Full product release versions are Windows-only, starting with SQL Server 2017. Linux support for microsoftml is new in SQL Server 2019 Preview.
Algorithms in microsoftml depend on revoscalepy for:
- Data source objects. Data consumed by microsoftml functions are created using revoscalepy functions.
- Remote computing (shifting function execution to a remote SQL Server instance). The revoscalepy library provides functions for creating and activating a remote compute context for SQL server.
In most cases, you will load the packages together whenever you are using microsoftml.
Functions by category
This section lists the functions by category to give you an idea of how each one is used. You can also use the table of contents to find functions in alphabetical order.
|microsoftml.rx_ensemble||Train an ensemble of models.|
|microsoftml.rx_fast_linear||Linear Model. with Stochastic Dual Coordinate Ascent.|
Categorical variable handling
|microsoftml.categorical||Converts a text column into categories.|
|microsoftml.categorical_hash||Hashes and converts a text column into categories.|
|microsoftml.concat||Concatenates multiple columns into a single vector.|
|microsoftml.drop_columns||Drops columns from a dataset.|
|microsoftml.select_columns||Retains columns of a dataset.|
|microsoftml.count_select||Feature selection based on counts.|
|microsoftml.mutualinformation_select||Feature selection based on mutual information.|
|microsoftml.featurize_text||Converts text columns into numerical features.|
|microsoftml.load_image||Loads an image.|
|microsoftml.resize_image||Resizes an Image.|
|microsoftml.extract_pixels||Extracts pixels from an image.|
|microsoftml.featurize_image||Converts an image into features.|
|microsoftml.rx_featurize||Data transformation for data sources|
|microsoftml.rx_predict||Scores using a Microsoft machine learning model|
How to call microsoftml
Functions in microsoftml are callable in Python code encapsulated in stored procedures. Most developers build microsoftml solutions locally, and then migrate finished Python code to stored procedures as a deployment exercise.
The microsoftml package for Python is installed by default, but unlike revoscalepy, it is not loaded by default when you start a Python session using the Python executables installed with SQL Server.
As a first step, import the microsoftml package, and import revoscalepy if you need to use remote compute contexts or related connectivity or data source objects. Then, reference the individual functions you need.
from microsoftml.modules.logistic_regression.rx_logistic_regression import rx_logistic_regression from revoscalepy.functions.RxSummary import rx_summary from revoscalepy.etl.RxImport import rx_import_datasource
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