class VotingEnsemble(BaseClassifier):
def __init__(self, feature_length, num_classes, x=10):
super().__init__(feature_length, num_classes)
self.model = VotingClassifier(estimators=[
('gba',
GradientBoostingClassifier(n_estimators=100,
learning_rate=1.0,
max_depth=1,
random_state=0)),
('knn',
KNeighborsClassifier(metric='manhattan',
weights='distance',
n_neighbors=3)),
('Nc', NearestCentroid(metric='manhattan')), ('nvb', GaussianNB()),
('rf', RandomForestClassifier(n_estimators=10,
criterion='entropy')),
('svmlin', svm.SVC(kernel='linear')),
('svmpol', svm.SVC(kernel='poly')),
('svmrbf', svm.SVC(kernel='rbf'))
],
voting='hard')
self.num_classes = num_classes
def train(self, features, labels):
"""
Using a set of features and labels, trains the classifier and returns the training accuracy.
:param features: An MxN matrix of features to use in prediction
:param labels: An M row list of labels to train to predict
:return: Prediction accuracy, as a float between 0 and 1
"""
labels = self.labels_to_categorical(labels)
self.model.fit(features, labels)
accuracy = self.model.score(features, labels)
return accuracy
# make sure you save model using the same library as we used in machine learning price-predictor
def predict(self, features, labels):
"""
Using a set of features and labels, predicts the labels from the features,
and returns the accuracy of predicted vs actual labels.
:param features: An MxN matrix of features to use in prediction
:param labels: An M row list of labels to test prediction accuracy on
:return: Prediction accuracy, as a float between 0 and 1
"""
label_train = self.labels_to_categorical(labels)
labels = self.model.predict(features)
accuracy = self.model.score(features, label_train)
return accuracy
def get_prediction(self, features):
return self.model.predict(features)
def reset(self):
"""
Resets the trained weights / parameters to initial state
:return:
"""
pass
def labels_to_categorical(self, labels):
_, IDs = unique(labels, return_inverse=True)
return IDs
voting_classifiers = [("perceptron", model_perceptron), ("svc", model_svc),
("bayes", model_bayes), ("tree", model_tree),
("knn", model_knn)]
model_voting = VotingClassifier(estimators=voting_classifiers).fit(
X_train, y_train)
# Initializing the DS techniques
knorau = KNORAU(pool_classifiers)
kne = KNORAE(pool_classifiers)
desp = DESP(pool_classifiers)
# DCS techniques
ola = OLA(pool_classifiers)
mcb = MCB(pool_classifiers)
# Fitting the techniques
knorau.fit(X_dsel, y_dsel)
kne.fit(X_dsel, y_dsel)
desp.fit(X_dsel, y_dsel)
ola.fit(X_dsel, y_dsel)
mcb.fit(X_dsel, y_dsel)
# Calculate classification accuracy of each technique
print('Evaluating DS techniques:')
print('Classification accuracy of Majority voting the pool: ',
model_voting.score(X_test, y_test))
print('Classification accuracy of KNORA-U: ', knorau.score(X_test, y_test))
print('Classification accuracy of KNORA-E: ', kne.score(X_test, y_test))
print('Classification accuracy of DESP: ', desp.score(X_test, y_test))
print('Classification accuracy of OLA: ', ola.score(X_test, y_test))