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Lazy binary classifier based on Formal Concept Analysis

Project description

Installation

$ pip install fca_lazy_clf

Requirements

The train and test datasets must be represented as pandas.DataFrame. The classifier uses only attributes of types numpy.dtype('O'), np.dtype('int64') and attributes with 2 any values. Other attributes will not be used. The target attribute must be binary.

Example

>>> import fca_lazy_clf as fca
>>> import pandas as pd
>>> from sklearn import model_selection

>>> data = pd.read_csv('https://datahub.io/machine-learning/tic-tac-toe-endgame/r/tic-tac-toe.csv')
>>> data.head()

  TL TM TR ML MM MR BL BM BR  class
0  x  x  x  x  o  o  x  o  o   True
1  x  x  x  x  o  o  o  x  o   True
2  x  x  x  x  o  o  o  o  x   True
3  x  x  x  x  o  o  o  b  b   True
4  x  x  x  x  o  o  b  o  b   True

>>> X = data.iloc[:, :-1] # All attributes except the last one
>>> y = data.iloc[:, -1] # Last attribute
>>> X_train, X_test, y_train, y_test      = model_selection.train_test_split(X, y, test_size=0.33, random_state=0)

>>> clf = fca.LazyClassifier(threshold=0.000001, bias='false')
>>> clf.fit(X_train, y_train)
>>> clf.score(X_test, y_test)

0.9716088328075709

Project details


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Files for fca-lazy-clf, version 0.3
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