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
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