cur_features = min_max_scaler.fit_transform(cur_features)
cur_feature_name = feature_names[i]
### split data into training set and label set
X_train, X_test, y_train, y_test = train_test_split(cur_features,
onehotlabels,
test_size=0.4,
random_state=42)
#X_train, X_test, y_train, y_test = train_test_split(features_minmax, labels, test_size=0.4, random_state=42)
### adjust the dataset dimension
# reshape X to be [samples, time steps, features]
X_train_LSTM = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1]))
X_test_LSTM = np.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1]))
### create the deep learning models
epochs = 15
batch_size = 195 #195 best now #190 # 100 # 250
dropoutRate = 0.2
## stacked LSTM
model6, hist6 = deep_learning_models.stacked_LSTM(X_train_LSTM, y_train,
X_test_LSTM, y_test,
batch_size, epochs)
prediction6 = model6.predict(X_test_LSTM)
prediction6_re = np.argmax(prediction6, axis=1)
stacked_LSTM_precision = deep_learning_models.getAccuracy(
prediction6, y_test)
print("Precision for stacked LSTM")
print(cur_feature_name)
print(stacked_LSTM_precision)
features_analysis_result[cur_feature_name] = stacked_LSTM_precision
"""
y_test_re = np.argmax(y_test, axis=1)
accuracies = list()
precisions = list()
Fs = list()
TNRs = list()
recalls = list()
### Bidirectional LSTM
#start_time0 = time.clock()
model0, hist0 = deep_learning_models.Bidirectional_LSTM(X_train_LSTM, y_train, X_test_LSTM, y_test, batch_size, epochs)
prediction0 = model0.predict(X_test_LSTM)
#end_time0 = time.clock()
#Bi_LSTM_performance = end_time0 - start_time0
prediction0_re = np.argmax(prediction0, axis=1)
Bidirectional_LSTM_accuracy, bi_precision, bi_recall, bi_TNR, bi_F = deep_learning_models.getAccuracy(prediction0, y_test)
keys = hist0.history.keys()
print(keys)
print("Precision for bidirectional LSTM")
print(Bidirectional_LSTM_accuracy)
print(bi_precision)
print(bi_recall)
print(bi_TNR)
print(bi_F)
accuracy_pair = list()
accuracy_pair.append("bidirectional LSTM")
accuracy_pair.append(Bidirectional_LSTM_accuracy)
accuracies.append(accuracy_pair)
#start_time1 = time.clock()
model1, hist1 = deep_learning_models.basic_LSTM(X_train_LSTM, y_train, X_test_LSTM, y_test, batch_size, epochs)
"""
epochs = 200
batch_size = 20
dropoutRate = 0.3
"""
y_test_re = np.argmax(y_test, axis=1)
### Bidirectional LSTM
start_time0 = time.clock()
model0, hist0 = deep_learning_models.Bidirectional_LSTM(
X_train_LSTM, y_train, X_test_LSTM, y_test, batch_size, epochs)
prediction0 = model0.predict(X_test_LSTM)
end_time0 = time.clock()
Bi_LSTM_performance = end_time0 - start_time0
prediction0_re = np.argmax(prediction0, axis=1)
Bidirectional_LSTM_accuracy = deep_learning_models.getAccuracy(
prediction0, y_test)
keys = hist0.history.keys()
print(keys)
print("Precision for bidirectional LSTM")
print(Bidirectional_LSTM_accuracy)
"""
start_time1 = time.clock()
model1, hist1 = deep_learning_models.basic_LSTM(X_train_LSTM, y_train, X_test_LSTM, y_test, batch_size, epochs)
prediction1 = model1.predict(X_test_LSTM)
end_time1 = time.clock()
basic_LSTM_performance = end_time1 - start_time1
prediction1_re = np.argmax(prediction1, axis=1)
basic_LSTM_accuracy = deep_learning_models.getAccuracy(prediction1, y_test)
keys = hist1.history.keys()
print(keys)
print("Precision for basic LSTM")