feature-stuff 0.0.dev5

Feature extraction, processing and interpretation algorithms and functions for machine learning and data science.

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

Latest Release	see on pypi.org
Package Status	see on pypi.org
License	see on github
Build Status	see on travis

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

add_interactions	generic function for adding interaction features to a data frame either by passing them as a list or by passing a boosted trees model to extract the interactions from.
target_encoding	target encoding of a feature column using exponential prior smoothing or mean prior smoothing
cv_target_encoding	target encoding of a feature column taking cross-validation folds as input
add_knn_values	creates a new feature with the K-nearest-neighbours of the values of a given feature
add_group_values	generic and memory efficient enrichment of features dataframe with group values

Model feature insights extraction

get_xgboost_interactions	takes a trained xgboost model and returns a list of interactions between features, to the order of maximum depth of all trees.

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

< see the attached API of each function/ algorithm >

Example on extracting interactions form 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']]

Contributing to feature-stuff

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