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Project: xam (GitHub Link)

• xam-master
  • datasets
    • titanic
      • train.csv
      • test.csv
    • airline-passengers.csv
  • CHEAT_SHEET.md
  • xam
    • ensemble
      • stacking.py
      • localized_ensemble.py
      • groupby_model.py
      • __init__.py
      • lgbm_cv.py
    • nlp
      • spell_correct.py
      • top_terms.py
      • nb_svm.py
      • __init__.py
    • pipeline.py
    • feature_selection
      • forward_backward.py
      • __init__.py
    • eda.py
    • feature_extraction
      • combinations.py
      • cycle.py
      • __init__.py
      • encoding
        • __init__.py
        • bayesian_target.py
        • count.py
    • model_selection
      • __init__.py
      • ordered_cross_validation.py
    • latex.py
    • __init__.py
    • utils.py
    • linear_model
      • auc_regressor.py
      • __init__.py
    • preprocessing
      • groupby_transformer.py
      • binning
        • equal_width.py
        • equal_frequency.py
        • mdlp.py
        • __init__.py
        • bayesian_blocks.py
        • base.py
      • __init__.py
      • resampling.py
    • tsa
      • __init__.py
      • exponential_smoothing.py
      • frequency_average.py
      • base.py
      • ewm_opt.py
    • error_analysis
      • binary_classification.py
      • __init__.py
      • base.py
  • requirements.dev.txt
  • LICENSE
  • CONTRIBUTING.md
  • .pylintrc
  • setup.py
  • .travis.yml
  • README.md
  • tests
    • test_preprocessing.py
    • test_feature_extraction.py
    • __init__.py
    • test_model_selection.py
  • pytest.ini
  • .gitignore
  • docs
    • pipeline.md
    • preprocessing.md
    • figures
      • latex_example.pgf
    • tsa.md
    • feature-extraction.md
    • linear-models.md
    • various.md
    • eda.md
    • nlp.md
    • ensembling.md
    • plotting.md
    • feature-selection.md
    • model-selection.md

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.