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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