model.add(LeakyReLU())
model.add(Dropout(.15))
model.add(BatchNormalization())
# model.add(GaussianNoise(1))
model.add(Dense(64))
model.add(LeakyReLU())
model.add(BatchNormalization())
model.add(Dense(6, activation='softmax'))
# In[85]:
# Let's use a different optimizer this time
noisy = add_gradient_noise(RMSprop)
model.compile(
optimizer="Adamax",
# model.compile(optimizer=noisy(),
loss='categorical_crossentropy',
metrics=['accuracy'])
# In[86]:
callbacks = [
EarlyStopping(monitor='val_loss', patience=16),
ModelCheckpoint(filepath='best_model.h5',
monitor='val_loss',
save_best_only=True)
]
# In[87]:
history = model.fit(x_train,
def perform_learning(train_features,
test_features,
f_output,
local,
gglobal,
deep=False):
print(train_features[0])
if (local and gglobal):
train = [x[1:-1] for x in train_features]
test = [x[1:-1] for x in test_features]
elif (local and not gglobal):
train = [x[1:5] for x in train_features]
test = [x[1:5] for x in test_features]
elif (not local and gglobal):
train = [x[5:-1] for x in train_features]
test = [x[5:-1] for x in test_features]
# print train[0]
train_tags = [x[-1] for x in train_features]
test_tags = [x[-1] for x in test_features]
train = z_scoring(train)
test = z_scoring(test)
print(len(train[0]))
print(train[0])
if not deep:
algos = ['adaBoost', 'RF', 'L-SVM', 'RBF-SVM', 'SGD']
# algos = ['RBF-SVM']
for algo in algos:
print(algo)
f_output.writelines(algo + '\n')
if algo == 'adaBoost':
clf = AdaBoostClassifier(n_estimators=100)
if algo == 'RF':
clf = RandomForestClassifier(n_estimators=1000,
criterion="gini",
min_samples_split=15,
oob_score=True,
class_weight='balanced',
max_depth=3)
if algo == 'L-SVM':
clf = SVC(kernel='linear',
class_weight="balanced",
C=0.01,
probability=True)
if algo == 'RBF-SVM':
clf = SVC(class_weight="balanced", C=0.01, probability=True)
if algo == 'SGD':
clf = SGDClassifier(alpha=0.0001,
average=False,
class_weight=None,
epsilon=0.1,
eta0=0.0,
fit_intercept=True,
l1_ratio=0.15,
learning_rate='optimal',
loss='hinge',
n_iter=5,
n_jobs=1,
penalty='l2',
power_t=0.5,
random_state=None,
shuffle=True,
verbose=0,
warm_start=False)
# print train
clf.fit(train, train_tags)
if (algo == 'RF'):
print(len(clf.feature_importances_))
print(clf.feature_importances_)
f_output.writelines(str(clf.feature_importances_) + '\n')
evaluate_auc(clf, test, test_tags, train, train_tags, f_output)
else:
print(train[0])
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.regularizers import l2, l1_l2
clf = Sequential()
clf.add(
Dense(100,
activation="relu",
kernel_initializer="he_normal",
input_dim=train.shape[1]))
# self.classifier.add(Dropout(0.5))
# self.classifier.add(Dense(100, init='he_normal', activation='relu', W_regularizer=l2(0.5)))
clf.add(Dropout(0.1))
clf.add(
Dense(1,
init='uniform',
activation='sigmoid',
W_regularizer=l1_l2(0.2)))
clf.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
clf.fit(train,
train_tags,
validation_data=[test, test_tags],
epochs=100,
batch_size=10,
verbose=2)
evaluate_auc(clf, test, test_tags, train, train_tags, f_output)