feature-stuff 0.0.dev3

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

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feature_stuff: a python algorithms distribution for advanced feature extraction, processing and interpretation in machine learning, data science, AI.

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What is it

feature_stuff is a Python package providing fast and flexible algorithms and functions for extracting, processing and interpreting features. It includes functions like feature interaction extraction from from boosted decision tree based models, generic target encoding and memory efficient enrichment of features dataframe with group values.

How to get it

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

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

```sh
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.