microsoftml (Python package in SQL Server Machine Learning Services)
Applies to: SQL Server 2017 (14.x) and later
microsoftml is a Python package from Microsoft that provides 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 package is included in SQL Server Machine Learning Services and supports high performance on big data, using multicore processing, and fast data streaming.
|Built on:||Anaconda 4.2 distribution of Python 3.7.1|
|Package distribution:||SQL Server Machine Learning Services version 2017 or 2019.|
How to use microsoftml
The microsoftml module is installed as part of SQL Server Machine Learning Services when you add Python to your installation. You get the full collection of proprietary packages plus a Python distribution with its modules and interpreters. You can use any Python IDE to write Python script calling functions in microsoftml, but the script must run on a computer having SQL Server Machine Learning Services with Python.
Microsoftml and revoscalepy are tightly coupled; data sources used in microsoftml are defined as revoscalepy objects. Compute context limitations in revoscalepy transfer to microsoftml. Namely, all functionality is available for local operations, but switching to a remote compute context requires RxSpark or RxInSQLServer.
Versions and platforms
The microsoftml module is available only when you install one of the following Microsoft products or downloads:
Full product release versions are Windows-only in SQL Server 2017. Both Windows and Linux are supported for microsoftml in SQL Server 2019.
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 package 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