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PreProcess1

Super Easy Way of PreProcessing your Data!

Good News!

Preprocess1 simplifies the preprocessing steps that are some time essential for ML/modelling, such as imputations, one hot encoding. There are over 20 preprocessing steps available. A summary of the options is below:

  • Auto infer data types
  • Impute (simple or with surrogate columns)
  • Ordinal Encoder
  • Drop categorical variables that have zero variance or near-zero variance
  • Club categorical variables levels together as a new level (other_infrequent) that are rare / at the bottom 5% of the variable
    distribution
  • Club unseen levels in test dataset with most/least frequent levels in train dataset
  • Reduce high cardinality in categorical features using clustering or counts
  • Generate sub-features from time feature such as 'month','weekday',is_month_end','is_month_start' & 'hour'
  • Group features by calculating min, max, mean, median & sd of similar features
  • Make nonlinear features (polynomial, sin, cos & tan)
  • Scales & Power Transform (zscore,minmax,yeo-johnson,quantile,maxabs,robust) , including option to transform target variable
  • Apply binning to variables when numeric features are provided as a list
  • Detect & remove outliers using isolation forest, KNN and PCA
  • Apply clusters to segment entire data
  • One Hot / Dummy encoding
  • Remove special characters from column names such as commas, square brackets etc. to make it compatible with Jason dependent models
  • Feature Selection through Random Forest, LightGBM and Pearson Correlation
  • Fix multicollinearity
  • Feature Interaction (DFS), multiply, divided, add and subtract features
  • Apply dimension reduction techniques such as pca_liner, pca_kernal, incremental or Tsne. except for pca_liner, all other methods only take the number of components (as integer) i.e no variance explanation method available

You can install the library as

pip install preprocess1
from preprocess1 import toolkit as t

Although one can use the methods individually (by calling the respective class) , such as:

binn = t.Binning(['feature_tobin'])
binned_data = binn.fit_transform(training_data)
binned_new_data = binn.transform(test_data)

However, there is more power to it. We have made pre-built complete pipelines to deploy all sorts of preprocessing transformers. Path1 is for supervised ML, and Path2 is for unsupervised ML problems. Below is how you use it:

# apply the path to the training dataset while clubbing rare categorical levels & scaling numerical features
# Imputation & One Hot Encoding is automatically applied
data_training_transformed = t.Preprocess_Path_One(training_data, 'target_column', club_rare_levels = True, scale_data= True)
# apply the pipeline to the test data set
data_test_transformed = pipe.fit_transform(test_data)

You can find more information under the docstring of each class/function. Enjoy coding! Please share your ideas, suggestions and critique with me.

License

Copyright 2019-2020 Fahad Akbar fahad.akbar@gmail.com

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