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from sklearn.base import clone

from itertools import combinations

import numpy as np

from sklearn.cross_validation import train_test_split

from sklearn.metrics import accuracy_score

class SBS():

def __init__(self, estimator, k_features, scoring=accuracy_score, test_size=0.25, random_state=1):

self.scoring = scoring

self.estimator = clone(estimator)

self.k_features = k_features

self.test_size = test_size

self.random_state = random_state

def fit(self, X, y):

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=self.test_size, random_state=self.random_state)

dim = X_train.shape[1]

self.indices_ = tuple(range(dim))

self.subsets_ = [self.indices_]

score = self._calc_score(X_train, y_train, X_test, y_test, self.indices_)

self.scores_ = [score]

while dim > self.k_features:

scores = []

subsets = []

for p in combinations(self.indices_, r=dim-1):

score = self._calc_score(X_train, y_train, X_test, y_test, p)

scores.append(score)

subsets.append(p)

best = np.argmax(score)

self.indices_ = subsets[best]

self.subsets_.append(self.indices_)

dim -= 1

self.scores_.append(scores[best])

self.k_score_ = self.scores_[-1]

return self

def transform(self, X):

return X[:, self.indices_]

def _calc_score(self, X_train, y_train, X_test, y_test, indices):

self.estimator.fit(X_train[:, indices], y_train)

y_pred = self.estimator.predict(X_test[:, indices])

score = self.scoring(y_test, y_pred)

return score