FCA lazy classifier

Binary classifier based on lazy learning approach and FCA (Formal Concept Analysis).

Contents of the repository:

• report.pdf - report of development
• fca_lazy_clf - source code
• lazyfca_heart_desease.ipynb - analysis of heart_desease dataset
• lazyfca_tic_tac_toe.ipynb - analysis of tic_tac_toe dataset
• tic-tac-toe - tic_tac_toe dataset
• heart-disease-uci.zip - heart_desease dataset

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'), numpy.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='negative')
>>> clf.fit(X_train, y_train)
>>> clf.score(X_test, y_test)

0.9716088328075709

Parameters of the classifier

• bias — the decision to make if Support+ is equal to Support−. There are three options: 'positive' (always set a positive class), 'negative' (always set a negative class), and 'random' (set a random class). Read more in the report.pdf.

• threshold — threshold numeric value from 0 to 1. Read more in the report.pdf.

• random — True to enable a mode that uses only a randomly selected portion of the training sample, False — to disable the mode.

• sample_share — if random mode is used, this parameter sets the percentage of entries from the positive and negative set. Valid values in the range from 0 to 1.