def run(self):
from ml_statistics import BaseStatistics
X = self.model.get_model_X_rows()
Y = self.model.get_model_Y_rows()
self.folds.get_n_splits(X, Y)
for train_index, test_index in self.folds.split(X, Y):
# TRAIN instances
X_train_inputs, y_train = self.model.get_model_data(train_index)
# TEST instances
X_test_inputs, y_test = self.model.get_model_data(test_index)
self.model.model.compile(loss=self.error_metric, optimizer=self.optimizer, metrics=['accuracy'])
print(self.model.model.summary())
history = self.model.model.fit(X_train_inputs, y_train, validation_split=self.validation_split, epochs=self.epochs, batch_size=self.batch_size, callbacks=self.callbacks, class_weight=self.class_weigths, verbose=0)
print(history.history.keys())
self.print_history(history)
Y_pred = self.model.model.predict(X_test_inputs)
stats = BaseStatistics(y_test, Y_pred)
print stats
print '='*30
def test(model, data):
from ml_statistics import BaseStatistics
x_test, y_test = data
Y = np.zeros(y_test.shape)
y_pred = model.predict(x=x_test, batch_size=8)
stats = BaseStatistics(y_test, y_pred)
return stats, y_pred
def gen_synthetic_stats(num_samples=100, similarity=.8):
y_true = np.random.randint(2, size=num_samples)
y_pred = np.array(
[x if np.random.random() < similarity else 1 - x for x in y_true])
statistics = BaseStatistics(y_true, y_pred)
print(statistics)
return statistics
def test(model, data, args):
from ml_statistics import BaseStatistics
x_test, y_test = data
y_pred, x_recon = model.predict(x_test, batch_size=100)
print('-'*30 + 'Begin: test' + '-'*30)
print('Test acc:', np.sum(np.argmax(y_pred, 1) == np.argmax(y_test, 1))/y_test.shape[0])
img = combine_images(np.concatenate([x_test[:50],x_recon[:50]]))
image = img * 255
Image.fromarray(image.astype(np.uint8)).save(args.save_dir + "/real_and_recon.png")
print()
print('Reconstructed images are saved to %s/real_and_recon.png' % args.save_dir)
print('-' * 30 + 'End: test' + '-' * 30)
plt.imshow(plt.imread(args.save_dir + "/real_and_recon.png"))
plt.show()
stats = BaseStatistics(y_test, y_pred)
print stats
def test_model(self):
from ml_statistics import BaseStatistics
Y = np.zeros(self.y_test.shape)
y_pred = self.model.predict(x=self.x_test, batch_size=8)
stats = BaseStatistics(self.y_test, y_pred)
return stats, y_pred
model = get_mergerd_model(X_train, X2_train)
opt = get_options()
loss_function = ['mse', 'binary_crossentropy']
model.compile(loss=loss_function[1], optimizer=opt, metrics=['accuracy'])
tensorboard = TensorBoard(log_dir="logs/{}".format(time()))
if cnt==0:
print(model.summary())
# calls = [tensorboard, earlyStopping, reduceLR]
calls = [tensorboard, earlyStopping]
weights={0:.3, 1:.7}
history = model.fit([X_train, X2_train], y_train, validation_split=0.1, epochs=250, batch_size=32, callbacks=calls, class_weight=weights, verbose=0)
Y_pred = model.predict([X_test, X2_test])
stats = BaseStatistics(y_test, Y_pred)
print stats
scores = model.evaluate([X_test, X2_test], y_test, verbose=0)
print("Accuracy: %.2f%%" % (scores[1]*100))
cvscores.append(scores[1] * 100)
print scores
print '='*30
cnt+=1
print("%.2f%% (+/- %.2f%%)" % (numpy.mean(cvscores), numpy.std(cvscores)))
print('<<< END >>>')
def run(npath, ppath):
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_score
from keras.wrappers.scikit_learn import KerasClassifier
from keras.utils import plot_model
from keras.callbacks import EarlyStopping
from ml_statistics import BaseStatistics
from keras import optimizers
import numpy as np
# Params
weightTrue = 0.8
class_weight = {0: (1 - weightTrue), 1: weightTrue}
earlyStopping = EarlyStopping(monitor='val_loss',
patience=10,
verbose=1,
mode='auto')
opt = optimizers.Nadam(lr=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
schedule_decay=0.004)
# opt = optimizers.RMSprop(lr=0.001, decay=0.0)
# Setup all data for inputs
setup_data(npath, ppath)
# Retrieve data for model
X, Y = get_model_data_full(data)
X = X * 100000
print X
print Y.shape
# plot_model(create_model(), show_shapes=True)
# evaluate using 10-fold cross validation
kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=17)
# results = cross_val_score(model, X, Y, cv=kfold)
# print('='*30)
# print(results.mean())
kfold.get_n_splits(X, Y)
for train_index, test_index in kfold.split(X, Y):
# TRAIN DATA
X_train, y_train = X[train_index, :], Y[train_index]
# TEST DATA
X_test, y_test = X[test_index, :], Y[test_index]
# create model
# model = KerasClassifier(build_fn=create_model, epochs=50, batch_size=32, verbose=1)
model = create_model()
# Compile Model
model.compile(loss='binary_crossentropy',
optimizer=opt,
metrics=['accuracy'])
print(model.summary())
history = model.fit(X_train,
y_train,
validation_split=0.3,
epochs=100,
batch_size=128,
callbacks=[earlyStopping],
class_weight=class_weight,
verbose=0)
print(history.history.keys())
Y_pred = model.predict(X_test, verbose=0)
stats = BaseStatistics(y_test, Y_pred)
print stats
def eval(self, model, data):
from ml_statistics import BaseStatistics
x_test, y_test = data
y_pred, aux = model.predict(x_test, batch_size=32)
stats = BaseStatistics(y_test[0], y_pred)
return stats
def get_test_stats(model, X, y):
from ml_statistics import BaseStatistics
y_pred = model.predict(x=X, batch_size=8)
stats = BaseStatistics(y, y_pred)
return stats, y_pred
def eval(self, model, data):
x_test, y_test = data
y_pred = model.predict(x_test, batch_size=32)
stats = BaseStatistics(y_test, y_pred)
return stats
def test(model, data):
from ml_statistics import BaseStatistics
x_test, y_test = data
y_pred, x_recon = model.predict([x_test, y_test], batch_size=100)
stats = BaseStatistics(y_test, y_pred)
return stats
