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DSML Toolkit for Python - Documentation and Spotfire® Examples 1.1.0


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Summary

This is the documentation and examples for spotfire-dsml Python package (downloadable from PyPI).

Overview

If you ever create Data Functions in Spotfire, our Python package will be a new, valuable resource for you. The package is called spotfire-dsml v1.0.0 and can be downloaded from PyPI. This release consists of Python functions for the following distinctive sub-modules:

  1. ML Modeling (ml_modeling): Dive into pipeline-centric model training and evaluation. Whether you're a seasoned data scientist or just starting, this module equips you with the tools to build robust machine learning models effortlessly.
  2. DS Module (nlp_preprocessing): For those delving into the world of text analytics, this sub-module offers pipeline-centric preprocessing solutions. It simplifies text data preparation, a critical step in natural language processing tasks. 
  3. DS Module (time_series): Time series can be messy and challenging to work with. This sub-module contains functions for time-series data which specializes in time-series preprocessing and smoothing, ensuring your analyses are fast, accurate, and reliable.
  4. Explainability Module (ml_explain): Uncover the mysteries of model explainability using the XWiN methodology. Gain insights into your models, making your predictions more transparent and trustworthy.
  5. Monitoring Module (ml_drift): Detect and measure drift in your models with ease. Keeping your models up-to-date and accurate is crucial. This module simplifies the process by enabling you to decide when to trigger a new rebasing or retraining process.
  6. Distribution Fitting (distribution_fitting): Distribution fitting and normality testing is useful, and at times, even a critical process across numerous industries. This sub-module aims to simplify the distribution fitting and normality testing processes with functions that can be applied to full datasets, rather than working with one column at a time.

Details

This release consists of Spotfire examples for each sub-module and documentation of the functions (as html files. Select any to view the documentation). The Spotfire examples (dxp) show how these functions can be called through the library via data functions in Spotfire   More details about this important release can be found in this community article.

Release 1.1.0

Published: March 2024

Release Includes:

  • Spotfire dashboard for each sub-module
  • Html documentation of spotfire-dsml 1.0.0 Python library
  • License information

What is new in this release:

  • nlp_preprocessing: Added binary and multi-class classifiers as the next step to the existing nlp_preprocessing pipeline. 
  • time_series: Added decomposition for time series using convolution and Fourier transformation. Additionally, also added univariate forecasting for time series using ARIMA, Holt Winters and LSTM.
  • distribution_fitting: Added distribution fitting and normality testing processes with functions that can be applied to full datasets, rather than just individual columns.

 

Release 1.0.0

Published: October 2023

Release Includes:

  • Spotfire dashboard for each sub-module
  • Html documentation of spotfire-dsml 0.3.3 Python library
  • License information

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