feature_stuff: a python machine learning library for advanced feature extraction, processing and interpretation.

What is it

feature_stuff is a Python package providing fast and flexible algorithms and functions for extracting, processing and interpreting features:

Numeric feature extraction

Model feature insights extraction

Installation

Binary installers for the latest released version are available at the Python package index .

# or PyPI
pip install feature_stuff

The source code is currently hosted on GitHub at: https://github.com/hiflyin/Feature-Stuff

Installation from sources

In the Feature-Stuff directory (same one where you found this file after cloning the git repo), execute:

python setup.py install

or for installing in development mode:

python setup.py develop

Alternatively, you can use pip if you want all the dependencies pulled in automatically (the -e option is for installing it in development mode):

pip install -e .

How to use it

Below are examples for some functions. See the attached API of each function/ algorithm, for a complete documentation.

feature_stuff.add_interactions

Inputs:
    df: a pandas dataframe
    model: boosted trees model (currently xgboost supported only). Can be None in which case the interactions have
    to be provided
    interactions: list in which each element is a list of features/columns in df, default: None

Output: df containing the group values added to it

Example on extracting interactions from tree based models and adding them as new features to your dataset.

import feature_stuff as fs
import pandas as pd
import xgboost as xgb

data = pd.DataFrame({"x0":[0,1,0,1], "x1":range(4), "x2":[1,0,1,0]})
print data
   x0  x1  x2
0   0   0   1
1   1   1   0
2   0   2   1
3   1   3   0

target = data.x0 * data.x1 + data.x2*data.x1
print target.tolist()
[0, 1, 2, 3]

model = xgb.train({'max_depth': 4, "seed": 123}, xgb.DMatrix(data, label=target), num_boost_round=2)
fs.addInteractions(data, model)

# at least one of the interactions in target must have been discovered by xgboost
print data
   x0  x1  x2  inter_0
0   0   0   1        0
1   1   1   0        1
2   0   2   1        0
3   1   3   0        3

# if we want to inspect the interactions extracted
from feature_stuff import model_features_insights_extractions as insights
print insights.get_xgboost_interactions(model)
[['x0', 'x1']]

feature_stuff.target_encoding

Inputs:
    df: a pandas dataframe containing the column for which to calculate target encoding (categ_col)
    ref_df: a pandas dataframe containing the column for which to calculate target encoding and the target (y_col)
        for example we might want to use train data as ref_df to encode test data
    categ_col: the name of the categorical column for which to calculate target encoding
    y_col: the name of the target column, or target variable to predict
    smoothing_func: the name of the function to be used for calculating the weights of the corresponding target
        value inside ref_df. Default: exponentialPriorSmoothing.
    aggr_func: the statistic used to aggregate the target variable values inside each category of the categ_col
    smoothing_prior_weight: a prior weight to put on each category. Default 1.

Output: df containing a new column called <categ_col + "_bayes_" + aggr_func> containing the encodings of categ_col

Example on extracting target encodings from categorical features and adding them as new features to your dataset.

import feature_stuff as fs
import pandas as pd

train_data = pd.DataFrame({"x0":[0,1,0,1]})
test_data = pd.DataFrame({"x0":[1, 0, 0, 1]})
target = range(4)

train_data = fs.target_encoding(train_data, train_data, "x0", target, smoothing_func=fs.exponentialPriorSmoothing,
                                        aggr_func="mean", smoothing_prior_weight=1)
test_data = fs.target_encoding(test_data, train_data, "x0", target, smoothing_func=fs.exponentialPriorSmoothing,
                                        aggr_func="mean", smoothing_prior_weight=1)

#train data with target encoding of "x0"
print(train_data)
   x0  y_xx  g_xx  x0_bayes_mean
0   0     0     0       1.134471
1   1     1     0       1.865529
2   0     2     0       1.134471
3   1     3     0       1.865529

#test data with target encoding of "x0"
print(test_data)
   x0  x0_bayes_mean
0   1       1.865529
1   0       1.134471
2   0       1.134471
3   1       1.865529

feature_stuff.cv_target_encoding

Inputs:
    df: a pandas dataframe containing the column for which to calculate target encoding (categ_col) and the target
    categ_cols: a list or array with the the names of the categorical columns for which to calculate target encoding
    y_col: a numpy array of the target variable to predict
    cv_folds: a list with fold pairs as tuples of numpy arrays for cross-val target encoding
    smoothing_func: the name of the function to be used for calculating the weights of the corresponding target
        value inside ref_df. Default: exponentialPriorSmoothing.
    aggr_func: the statistic used to aggregate the target variable values inside each category of the categ_col
    smoothing_prior_weight: a prior weight to put on each category. Default 1.
    verbosity: 0-none, 1-high_level, 2-detailed

Output: df containing a new column called <categ_col + "_bayes_" + aggr_func> containing the encodings of categ_col

See feature_stuff.target_encoding example above.

Contributing to feature-stuff

All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome.