def train_model(X, y, clf):
#split the dataset
crossvalidation = cross_validation.StratifiedKFold(y, n_folds=5)
#fit the model
cms = []
train_scores = []
test_scores = []
for train, test in crossvalidation:
X_train, y_train = X[train], y[train]
X_test, y_test = X[test], y[test]
X_train, X_test = impute_nan(X_train, X_test)
X_train, X_test = normalize_features(X_train, X_test)
#print(X_train[0])
clf.fit(X_train, y_train)
#evaluate the model
train_score = clf.score(X_train, y_train)
train_scores.append(train_score)
test_score = clf.score(X_test, y_test)
test_scores.append(test_score)
y_predict = clf.predict(X_test)
cm = confusion_matrix(y_test, y_predict)
cms.append(cm)
return np.mean(test_scores), np.mean(train_scores), np.asarray(cms)
def train_model(X, y, clf):
#split the dataset
crossvalidation = cross_validation.StratifiedKFold(y, n_folds=5)
#fit the model
cms = []
train_scores = []
test_scores = []
for train, test in crossvalidation:
X_train, y_train = X[train], y[train]
X_test, y_test = X[test], y[test]
X_train, X_test = impute_nan(X_train, X_test)
X_train, X_test = normalize_features(X_train, X_test)
#print(X_train[0])
clf.fit(X_train, y_train)
#evaluate the model
train_score = clf.score(X_train, y_train)
train_scores.append(train_score)
test_score = clf.score(X_test, y_test)
test_scores.append(test_score)
y_predict = clf.predict(X_test)
cm = confusion_matrix(y_test, y_predict)
cms.append(cm)
return np.mean(test_scores), np.mean(train_scores), np.asarray(cms)
def train_sknn(X, y):
'''
NeuralNet with sknn
'''
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.25,
random_state=5)
X_train, X_test = impute_nan(X_train, X_test)
X_train, X_test = normalize_features(X_train, X_test)
nn = Classifier(layers=[Layer("Tanh", units=12),
Layer("Softmax")],
learning_rate=0.005,
n_iter=25)
# gs = GridSearchCV(nn, param_grid={
# 'learning_rate': [0.05, 0.01, 0.005, 0.001],
# 'hidden0__units': [4, 8, 12,100],
# 'hidden0__type': ["Rectifier", "Sigmoid", "Tanh"]})
# gs.fit(X_train, y_train)
# print(gs.best_estimator_)
nn.fit(X_train, y_train)
predicted = nn.predict(X_test).flatten()
labels = y_test
return predicted, labels
def train_nolearn_model(X, y):
'''
NeuralNet with nolearn
'''
X = X.astype(np.float32)
y = y.astype(np.int32)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=5)
X_train, X_test = impute_nan(X_train, X_test)
X_train, X_test = normalize_features(X_train, X_test)
lays = [
('input', layers.InputLayer),
('hidden', layers.DenseLayer),
('output', layers.DenseLayer),
]
net = NeuralNet(
layers=lays,
input_shape=(None, 23),
hidden_num_units=10,
objective_loss_function=lasagne.objectives.categorical_crossentropy,
output_nonlinearity=lasagne.nonlinearities.sigmoid,
output_num_units=10,
update=nesterov_momentum,
update_learning_rate=0.001,
update_momentum=0.9,
max_epochs=10,
verbose=1,
)
#net.fit(X_train, y_train)
#predicted = net.predict(X_test)
test_score = net.predict(X_test, y_test)
train_score = net.score(X_train, y_train)
return train_score, test_score
def train_nolearn_model(X, y):
'''
NeuralNet with nolearn
'''
X = X.astype(np.float32)
y = y.astype(np.int32)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 5)
X_train, X_test = impute_nan(X_train, X_test)
X_train, X_test = normalize_features(X_train, X_test)
lays = [('input', layers.InputLayer),
('hidden', layers.DenseLayer),
('output', layers.DenseLayer),
]
net = NeuralNet(
layers = lays,
input_shape=(None, 23),
hidden_num_units=10,
objective_loss_function=lasagne.objectives.categorical_crossentropy,
output_nonlinearity=lasagne.nonlinearities.sigmoid,
output_num_units=10,
update = nesterov_momentum,
update_learning_rate= 0.001,
update_momentum=0.9,
max_epochs=10,
verbose=1,
)
#net.fit(X_train, y_train)
#predicted = net.predict(X_test)
test_score = net.predict(X_test, y_test)
train_score = net.score(X_train, y_train)
return train_score, test_score
def train_sknn(X, y):
'''
NeuralNet with sknn
'''
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 5)
X_train, X_test = impute_nan(X_train, X_test)
X_train, X_test = normalize_features(X_train, X_test)
nn = Classifier(
layers=[
Layer("Tanh", units=12),
Layer("Softmax")],
learning_rate=0.005,
n_iter=25)
# gs = GridSearchCV(nn, param_grid={
# 'learning_rate': [0.05, 0.01, 0.005, 0.001],
# 'hidden0__units': [4, 8, 12,100],
# 'hidden0__type': ["Rectifier", "Sigmoid", "Tanh"]})
# gs.fit(X_train, y_train)
# print(gs.best_estimator_)
nn.fit(X_train, y_train)
predicted = nn.predict(X_test).flatten()
labels = y_test
return predicted, labels
