xam

xam is my personal data science and machine learning toolbox. It is written in Python 3 and stands on the shoulders of giants (mainly pandas and scikit-learn). It loosely follows scikit-learn's fit/transform/predict convention.

Installation

• Install Anaconda for Python 3.x >= 3.5
• Run pip install git+https://github.com/MaxHalford/xam --upgrade in a terminal

:warning: Because xam is a personal toolkit, the --upgrade flag will install the latest releases of each dependency (scipy, pandas etc.). I like to stay up-to-date with the latest library versions.

Table of contents

Usage example is available in the docs folder. Each example is tested with doctest.

• Ensembling
  • Groupby model
  • LightGBM with CV
  • Stacking
  • Stacking with bagged test predictions
• Exploratory data analysis (EDA)
  • Feature importance
• Feature extraction
  • Bayesian target encoding
  • Combining features
  • Count encoding
  • Cyclic features
• Feature selection
  • Forward-backward selection
• Linear models
  • AUC regressor
• Model selection
  • Ordered cross-validation
• Natural Language Processing (NLP)
  • NB-SVM
  • Norvig spelling corrector
  • Top-terms classifier
• Pipeline
  • Column selection
  • Series transformer
  • DataFrame transformer
  • Lambda transformer
• Plotting
  • Latex style figures
• Preprocessing
  • Binning
  • Groupby transformer
  • One-hot encoding
  • Resampling
• Time series analysis (TSA)
  • Exponentially weighted average
  • Exponential smoothing
  • Frequency average forecasting
• Various
  • Datetime range
  • Next day of the week
  • Subsequence lengths
  • DataFrame to Vowpal Wabbit
  • Normalized compression distance
  • Skyline querying
  • Fuzzy duplicates

Other Python data science and machine learning toolkits

• fastai/fastai
• Laurae2/Laurae
• rasbt/mlxtend
• reiinakano/scikit-plot
• scikit-learn-contrib
• zygmuntz/phraug2

License

The MIT License (MIT). Please see the license file for more information.