microsoftml (Python module in SQL Server)

APPLIES TO: yesSQL Server noAzure SQL Database noAzure SQL Data Warehouse noParallel Data Warehouse

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:

Note

Full product release versions are Windows-only, starting with SQL Server 2017. Linux support for microsoftml is new in SQL Server 2019 Preview.

Package dependencies

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.

1-Training functions

Function Description
microsoftml.rx_ensemble Train an ensemble of models.
microsoftml.rx_fast_forest Random Forest.
microsoftml.rx_fast_linear Linear Model. with Stochastic Dual Coordinate Ascent.
microsoftml.rx_fast_trees Boosted Trees.
microsoftml.rx_logistic_regression Logistic Regression.
microsoftml.rx_neural_network Neural Network.
microsoftml.rx_oneclass_svm Anomaly Detection.

2-Transform functions

Categorical variable handling

Function Description
microsoftml.categorical Converts a text column into categories.
microsoftml.categorical_hash Hashes and converts a text column into categories.

Schema manipulation

Function Description
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.

Variable selection

Function Description
microsoftml.count_select Feature selection based on counts.
microsoftml.mutualinformation_select Feature selection based on mutual information.

Text analytics

Function Description
microsoftml.featurize_text Converts text columns into numerical features.
microsoftml.get_sentiment Sentiment analysis.

Image analytics

Function Description
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.

Featurization functions

Function Description
microsoftml.rx_featurize Data transformation for data sources

3-Scoring functions

Function Description
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

See also