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

< 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.