def wnd_test(X, y):
auc = []
pr = []
sr = []
# X_val2 = X.tolist()
# for i in range(0, len(X_val2)):
# X_val2[i] = X_val2[i][0:3967]
# X = np.array(X_val2)
for i in range(1, 11):
model = keras.models.load_model(
'../saved_models/WnD/WnD' + str(i) + '.h5',
custom_objects={
'masked_loss_function': masked_loss_function,
'masked_accuracy': masked_accuracy
})
score_for_each_drug = ROC_PR.ROC(model,
X,
y, ("wide-n-deep" + "BO_delete"),
True,
bccdc=True)
spec_recall, prec_recall = ROC_PR.PR(model, X, y, bccdc=True)
# print('AUC-ROC:', score_for_each_drug)
# print("recall at 95 spec: ", spec_recall)
# print("precision recall: ", prec_recall)
auc.append(score_for_each_drug)
pr.append(prec_recall)
sr.append(spec_recall)
print(auc)
print(pr)
print(sr)
def model_256_128_64_2_100Ep(FrameSize, X, X_train, X_test, y_train, y_test):
model = Sequential()
# model.add(Embedding(2, 50, input_length=None))
# model.add(LSTM(256, return_sequences=True))
model.add(
LSTM(256,
input_shape=(FrameSize, X[0].shape[1]),
return_sequences=True,
recurrent_dropout=0.3))
model.add(SpatialDropout1D(0.2))
model.add(LSTM(128, return_sequences=False, recurrent_dropout=0.3))
model.add(Dropout(0.2))
model.add(Dense(64))
model.add(Dropout(0.2))
model.add(Dense(2, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='Adam',
metrics=['accuracy'])
history = model.fit(X_train,
y_train,
epochs=100,
batch_size=128,
shuffle=True,
verbose=2,
validation_data=(X_test, y_test))
plot.plot(history, "One_256_128_64_2_100Ep")
ROC_PR.ROC(model, X_test, y_test, "One_256_128_64_2_100Ep")
def run_one_fold(model):
model.compile(loss=masked_loss_function,
optimizer='Adam',
metrics=[masked_accuracy])
history = model.fit(
X_train,
y_train,
epochs=epochs,
batch_size=128,
# shuffle=True,
verbose=2,
validation_data=(X_val, y_val),
callbacks=[MyCustomCallback()])
score = ROC_PR.ROC_Score(model, X_val, y_val)
score_test = ROC_PR.ROC_Score(model, X_test, y_test)
score_for_each_drug = ROC_PR.ROC(model, X_test, y_test,
("wide-n-deep" + "BO_delete"), True)
spec_recall, prec_recall = ROC_PR.PR(model, X_test, y_test)
print('area under ROC curve for val:', score)
print('area under ROC curve for test:', score_test)
print(score_for_each_drug)
print("recall at 95 spec: ", spec_recall)
print("precision recall: ", prec_recall)
string_random = get_random_string(17)
print(string_random)
model.save('wnd_' + string_random + '.h5')
return score
def lrcn_test(X, y):
auc = []
pr = []
sr = []
for i in range(0, 10):
model = keras.models.load_model(
'../saved_models/LRCN/lrcn_' + str(i) + '.h5',
custom_objects={
'masked_loss_function': masked_loss_function,
'masked_accuracy': masked_accuracy
})
score_for_each_drug = ROC_PR.ROC(model,
X,
y, ("LRCN" + "BO_delete"),
True,
bccdc=True)
spec_recall, prec_recall = ROC_PR.PR(model, X, y, bccdc=True)
# print('AUC-ROC:', score_for_each_drug)
# print("recall at 95 spec: ", spec_recall)
# print("precision recall: ", prec_recall)
auc.append(score_for_each_drug)
pr.append(prec_recall)
sr.append(spec_recall)
print(auc)
print(pr)
print(sr)
def model_CNN_LSTM_limited_1(FrameSize, X, X_train, X_test, y_train, y_test,
epoch, earlyStopping, name):
print(X.shape)
print(FrameSize)
model = Sequential()
model.add(Dropout(0.43369355853937297))
model.add(
Conv1D(filters=5, kernel_size=4, activation='relu', padding='same'))
model.add(MaxPooling1D(pool_size=4, padding='same'))
model.add(
Conv1D(filters=7, kernel_size=7, activation='relu', padding='same'))
model.add(MaxPooling1D(pool_size=7, padding='same'))
model.add(LSTM(398, return_sequences=True, recurrent_dropout=0.3))
model.add(SpatialDropout1D(0.43369355853937297))
model.add(LSTM(106, return_sequences=True, recurrent_dropout=0.3))
model.add(SpatialDropout1D(0.43369355853937297))
model.add(LSTM(475, return_sequences=True, recurrent_dropout=0.3))
model.add(SpatialDropout1D(0.43369355853937297))
model.add(LSTM(264, return_sequences=False, recurrent_dropout=0.3))
model.add(Dropout(0.43369355853937297))
model.add(Dense(352))
model.add(Dropout(0.43369355853937297))
model.add(Dense(378))
model.add(Dropout(0.43369355853937297))
model.add(Dense(7, activation='sigmoid'))
model.compile(loss=masked_loss_function,
optimizer='Adam',
metrics=[masked_accuracy])
history = model.fit(
X_train,
y_train,
epochs=epoch,
batch_size=128,
# shuffle=True,
verbose=2,
validation_data=(X_test, y_test),
callbacks=[
earlyStopping,
ModelCheckpoint('result/CNN_LSTM_limited_1.h5',
monitor='val_masked_accuracy',
mode='max',
save_best_only=True)
])
# plot_model(model, to_file='model_plot.png', show_shapes=True)
plot.plot(history, ("CNN_LSTM_limited_1" + name))
score = ROC_PR.ROC(model,
X_test,
y_test, ("CNN_LSTM_limited_1" + name),
True,
limited=True)
return score
def model_CNN_LSTM_random_data(FrameSize, X, X_train, X_test, y_train, y_test,
epoch, earlyStopping, name):
print(X.shape)
print(FrameSize)
model = Sequential()
model.add(Dropout(0.3311428861138142))
model.add(
Conv1D(filters=4, kernel_size=6, activation='relu', padding='same'))
model.add(MaxPooling1D(pool_size=4, padding='same'))
model.add(Dropout(0.3311428861138142))
model.add(
Conv1D(filters=7, kernel_size=4, activation='relu', padding='same'))
model.add(MaxPooling1D(pool_size=4, padding='same'))
model.add(Dropout(0.3311428861138142))
model.add(
Conv1D(filters=6, kernel_size=6, activation='relu', padding='same'))
model.add(MaxPooling1D(pool_size=4, padding='same'))
model.add(Dropout(0.3311428861138142))
model.add(
Conv1D(filters=4, kernel_size=4, activation='relu', padding='same'))
model.add(MaxPooling1D(pool_size=4, padding='same'))
model.add(LSTM(425, return_sequences=True, recurrent_dropout=0.3))
model.add(Dropout(0.3311428861138142))
model.add(LSTM(189, return_sequences=True, recurrent_dropout=0.3))
model.add(Dropout(0.3311428861138142))
model.add(LSTM(283, return_sequences=True, recurrent_dropout=0.3))
model.add(Dropout(0.3311428861138142))
model.add(LSTM(333, return_sequences=False, recurrent_dropout=0.3))
model.add(Dropout(0.3311428861138142))
model.add(Dense(331))
model.add(Dropout(0.3311428861138142))
model.add(Dense(12, activation='sigmoid'))
model.compile(loss=masked_loss_function,
optimizer='Adam',
metrics=[masked_accuracy])
history = model.fit(X_train,
y_train,
epochs=epoch,
batch_size=128,
verbose=2,
validation_data=(X_test, y_test),
callbacks=[
earlyStopping,
ModelCheckpoint('result/CNN256_LSTM128_64_2.h5',
monitor='val_masked_accuracy',
mode='max',
save_best_only=True)
])
plot.plot(history, ("LRCN" + name))
score = ROC_PR.ROC(model, X_test, y_test, ("LRCN" + name), True)
return score, ROC_PR.ROC_Score(model, X_train, y_train, limited=False)
def model_CNN_LSTM_time(FrameSize, X, X_train, X_test, y_train, y_test, epoch,
earlyStopping, name):
print(X.shape)
print(X_train.shape)
print(X_test.shape)
print(FrameSize)
print(y_train.shape)
print(y_test.shape)
X_train = X_train.reshape(X_train.shape[0], X_train.shape[1],
X_train.shape[2], 1)
X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], X_test.shape[2],
1)
# y_train = y_train.reshape(7060, 12, 1)
# y_test = y_test.reshape(785, 12, 1)
model = Sequential()
model.add(Dropout(0.1))
model.add(
TimeDistributed(
Conv1D(filters=8, kernel_size=3, activation='relu',
padding='same')))
model.add(TimeDistributed(MaxPooling1D(pool_size=3, padding='same')))
model.add(
TimeDistributed(
Conv1D(filters=8, kernel_size=3, activation='relu',
padding='same')))
model.add(TimeDistributed(MaxPooling1D(pool_size=3, padding='same')))
model.add(TimeDistributed(Flatten()))
model.add(LSTM(518, return_sequences=False, recurrent_dropout=0.3))
model.add(Dropout(0.1))
model.add(Dense(64))
model.add(Dropout(0.1))
model.add(Dense(12, activation='sigmoid'))
model.compile(loss=masked_loss_function,
optimizer='Adam',
metrics=[masked_accuracy])
history = model.fit(X_train,
y_train,
epochs=epoch,
batch_size=128,
verbose=2,
validation_data=(X_test, y_test),
callbacks=[
earlyStopping,
ModelCheckpoint('result/CNN256_LSTM128_64_2.h5',
monitor='val_masked_accuracy',
mode='max',
save_best_only=True)
])
plot.plot(history, ("LRCN" + name))
score = ROC_PR.ROC(model, X_test, y_test, ("LRCN" + name), True)
return score, ROC_PR.ROC_Score(model, X_train, y_train, limited=False)
def model_lrcn_simple(FrameSize,
X,
X_train,
X_test,
y_train,
y_test,
epoch,
earlyStopping,
name,
limited=False):
print(X.shape)
print(FrameSize)
model = Sequential()
model.add(Dropout(0.2))
model.add(
Conv1D(filters=5, kernel_size=5, activation='relu', padding='same'))
model.add(MaxPooling1D(pool_size=3, padding='same'))
model.add(LSTM(256, return_sequences=True, recurrent_dropout=0.3))
model.add(SpatialDropout1D(0.2))
model.add(LSTM(128, return_sequences=False, recurrent_dropout=0.3))
model.add(Dropout(0.2))
model.add(Dense(128))
model.add(Dropout(0.2))
if limited:
model.add(Dense(7, activation='sigmoid'))
else:
model.add(Dense(12, activation='sigmoid'))
model.compile(loss=masked_loss_function,
optimizer='Adam',
metrics=[masked_accuracy])
history = model.fit(X_train,
y_train,
epochs=epoch,
batch_size=128,
verbose=2,
validation_data=(X_test, y_test),
callbacks=[
earlyStopping,
ModelCheckpoint('result/CNN256_LSTM128_64_2.h5',
monitor='val_masked_accuracy',
mode='max',
save_best_only=True)
])
plot.plot(history, ("LRCN" + name))
score = ROC_PR.ROC(model,
X_test,
y_test, ("LRCN" + name),
True,
limited=limited)
return score
def model_256_128_64_2BS(FrameSize, X, X_train, X_test, y_train, y_test,
epoch):
model = Sequential()
# model.add(Embedding(2, 50, input_length=None))
# model.add(LSTM(256, return_sequences=True))
model.add(
LSTM(256,
input_shape=(FrameSize, X[0].shape[1]),
return_sequences=True,
recurrent_dropout=0.3))
model.add(SpatialDropout1D(0.2))
model.add(LSTM(128, return_sequences=False, recurrent_dropout=0.3))
model.add(Dropout(0.2))
model.add(Dense(64))
model.add(Dropout(0.2))
model.add(Dense(2, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='Adam',
metrics=['accuracy'])
history = model.fit(X_train,
y_train,
epochs=epoch,
batch_size=128,
verbose=2,
shuffle=True,
validation_data=(X_test, y_test),
callbacks=[
ModelCheckpoint('result/One_256_128_64_2BS.h5',
monitor='val_accuracy',
mode='max',
save_best_only=True)
])
# model.save_weights("result/One_256_128_64_2BS.h5")
plot.plot(history, "One_256_128_64_2BS")
ROC_PR.ROC(model, X_test, y_test, "One_256_128_64_2BS")
def model_CNN256_LSTM128_64_2(FrameSize, X, X_train, X_test, y_train, y_test,
epoch):
model = Sequential()
model.add(Dropout(0.2))
model.add(
Conv1D(filters=5, kernel_size=3, activation='relu', padding='same'))
model.add(MaxPooling1D(pool_size=3))
model.add(LSTM(256, return_sequences=True, recurrent_dropout=0.3))
model.add(SpatialDropout1D(0.2))
model.add(LSTM(128, return_sequences=False, recurrent_dropout=0.3))
model.add(Dropout(0.2))
model.add(Dense(64))
model.add(Dropout(0.2))
model.add(Dense(2, activation='sigmoid'))
model.compile(loss='categorical_crossentropy',
optimizer='Adam',
metrics=['accuracy'])
history = model.fit(X_train,
y_train,
epochs=epoch,
batch_size=128,
shuffle=True,
verbose=2,
validation_data=(X_test, y_test),
callbacks=[
ModelCheckpoint('result/CNN256_LSTM128_64_2.h5',
monitor='accuracy',
mode='max',
save_best_only=True)
])
# plot_model(model, to_file='model_plot.png', show_shapes=True)
plot.plot(history, "One_CNN256_LSTM128_64_2")
ROC_PR.ROC(model, X_test, y_test, "One_CNN256_LSTM128_64_2", False)
def model_256_128_64_2(FrameSize, X, X_train, X_test, y_train, y_test, epoch,
earlyStopping):
model = Sequential()
model.add(
LSTM(256,
input_shape=(FrameSize, X[0].shape[1]),
return_sequences=True,
recurrent_dropout=0.3))
model.add(SpatialDropout1D(0.2))
model.add(LSTM(128, return_sequences=False, recurrent_dropout=0.3))
model.add(Dropout(0.2))
model.add(Dense(64))
model.add(Dropout(0.2))
model.add(Dense(12, activation='sigmoid'))
model.compile(loss=masked_loss_function,
optimizer='Adam',
metrics=[masked_accuracy])
print(model.summary())
history = model.fit(X_train,
y_train,
epochs=epoch,
batch_size=128,
shuffle=True,
verbose=2,
validation_data=(X_test, y_test),
callbacks=[
earlyStopping,
ModelCheckpoint('result/256_128_64_2.h5',
monitor='val_masked_accuracy',
mode='max',
save_best_only=True)
])
plot.plot(history, "256_128_64_2")
ROC_PR.ROC(model, X_test, y_test, "256_128_64_2", True)
def run_k_fold(model):
global X_train, X_test, y_train, y_test
global X, y, check
if check == 0:
check = 1
X = np.append(X_train, X_test, axis=0)
y = np.append(y_train, y_test, axis=0)
X_train = 0
X_test = 0
y_train = 0
y_test = 0
cvscores = []
scores_each_drug = []
for i in range(0, 10):
print("fold:" + str(i))
length = int(len(X) / 10)
if i == 0:
X_train_tmp = X[length:]
X_test_tmp = X[0:length]
y_train_tmp = y[length:]
y_test_tmp = y[0:length]
elif i != 9:
X_train_tmp = np.append(X[0:length * i],
X[length * (i + 1):],
axis=0)
X_test_tmp = X[length * i:length * (i + 1)]
y_train_tmp = np.append(y[0:length * i],
y[length * (i + 1):],
axis=0)
y_test_tmp = y[length * i:length * (i + 1)]
else:
X_train_tmp = X[0:length * i]
X_test_tmp = X[length * i:]
y_train_tmp = y[0:length * i]
y_test_tmp = y[length * i:]
model.compile(loss=masked_loss_function,
optimizer='Adam',
metrics=[masked_accuracy])
# plot_model(model, to_file='model_plot.png', show_shapes=True)
history = model.fit(
X_train_tmp,
y_train_tmp,
epochs=epochs,
batch_size=128,
# shuffle=True,
verbose=2,
validation_data=(X_test_tmp, y_test_tmp))
score = ROC_PR.ROC_Score(model,
X_train_tmp,
y_train_tmp,
limited=limited)
print('area under ROC curve:', score)
cvscores.append(score)
scores_each_drug.append(
ROC_PR.ROC(model, X_test_tmp, y_test_tmp,
("LRCN" + "BO_delete" + str(i)), True))
print(np.mean(cvscores))
if np.mean(cvscores) > 0.97:
model.save()
print(scores_each_drug)
return np.mean(cvscores)
def run_one_fold(model):
#
# X_train2 = tf.cast(X_train, tf.float32)
# X_test2 = tf.cast(X_test, tf.float32)
# X_val2 = tf.cast(X_val, tf.float32)
# y_train2 = tf.cast(y_train, tf.float32)
# y_test2 = tf.cast(y_test, tf.float32)
# y_val2 = tf.cast(y_val, tf.float32)
# X_train2 = np.array(X_train)
# X_train2 = tf.convert_to_tensor(X_train2,dtype=tf.float32)
# X_train2 = tf.cast(X_train2, tf.float32)
# y_train2 = np.array(y_train)
# y_train2 = tf.convert_to_tensor(y_train2, dtype=tf.float32)
# y_train2 = np.array(y_train)
# y_train2 = tf.cast(y_train2, tf.float32)
# print(X_train.)
# Train the model for a specified number of epochs.
X_train2 = np.array(X_train).astype(np.float)
X_test2 = np.array(X_test).astype(np.float)
X_val2 = np.array(X_val).astype(np.float)
y_train2 = np.array(y_train).astype(np.float)
y_test2 = np.array(y_test).astype(np.float)
y_val2 = np.array(y_val).astype(np.float)
model.compile(loss=masked_loss_function,
optimizer='Adam',
metrics=[masked_accuracy])
# Train the model with the train dataset.
history = model.fit(
X_train2,
y_train2,
epochs=epochs,
batch_size=128,
# shuffle=True,
verbose=2,
validation_data=(X_val2, y_val2))
# Evaluate the model with the eval dataset.
# score = model.evaluate(X_test, y_test, steps=10, verbose=0)
# print('Test loss:', score[0])
# print('Test accuracy:', score[1])
# Return the accuracy.
# print(history.history['val_masked_accuracy'])
score = ROC_PR.ROC_Score(model, X_val2, y_val2)
score_test = ROC_PR.ROC_Score(model, X_test2, y_test2)
score_for_each_drug = ROC_PR.ROC(model, X_test2, y_test2,
("LRCN" + "BO_delete"), True)
spec_recall, prec_recall = ROC_PR.PR(model, X_test2, y_test2)
print('area under ROC curve for val:', score)
print('area under ROC curve for test:', score_test)
print(score_for_each_drug)
print("recall at 95 spec: ", spec_recall)
print("precision recall: ", prec_recall)
global scores
global fold_num
global comp
if len(scores) == 0:
# string_random = get_random_string(17)
# print(string_random)
print(scores)
print(score)
model.save('LRCN' + str(comp) + '_' + str(fold_num) + '.h5')
scores.append(score)
else:
br = 0
for iter in range(0, len(scores)):
if scores[iter] > score:
print(scores)
print(score)
br = 1
break
if br == 0:
print(br)
print(scores)
print(score)
model.save('LRCN' + str(comp) + '_' + str(fold_num) + '.h5')
scores.append(score)
# from lime import lime_tabular
# ins = lime_tabular.LimeTabularExplainer
# explainer = lime_tabular.LimeTabularExplainer.explain_instance(self=ins ,data_row=X_train, predict_fn=model, num_samples=6354)
# explainer = lime_tabular.LimeTabularExplainer.explain_instance
# import lime
# import lime.lime_tabular
# import pandas as pd
# explainer = lime.lime_tabular.LimeTabularExplainer(X_train)
# print(len(X_train))
# exp = explainer.explain_instance(len(X_train), model.predict, num_features=len(X_train[0]))
#
# exp.show_in_notebook(show_table=True)
#
# exp.as_list()
# shap.initjs()
#
# explainer = shap.DeepExplainer(model, X_train2[:100])
# shap_values = explainer.shap_values(X_test2[:10])
# shap.summary_plot(shap_values, X_test2, plot_type='bar')
# worked
# TODO this block worked
# def score(X_test, y_test):
# return ROC_PR.ROC_Score(model, X_test, y_test)
#
# from eli5.permutation_importance import get_score_importances
#
# feature_score = []
#
# for i in range(0, len(X_test2[0])):
# lst = []
# lst.append(i)
# base_score, score_decreases = get_score_importances(score, X_test2, y_test2, n_iter=1, columns_to_shuffle=lst)
# feature_importances = np.mean(score_decreases, axis=0)
# feature_score.append(feature_importances[0])
# print(feature_score)
#
# print(feature_score)
# model.save('model_save.h5')
#
# import deeplift
# from deeplift.conversion import kerasapi_conversion as kc
#
# import keras
# # print(keras.__version__)
# # deeplift_model = kc.convert_sequential_model(model)
# deeplift_model = \
# kc.convert_model_from_saved_files(
# 'model_save.h5',
# nonlinear_mxts_mode=deeplift.layers.NonlinearMxtsMode.DeepLIFT_GenomicsDefault)
#
# find_scores_layer_idx = 0
#
# deeplift_contribs_func = deeplift_model.get_target_contribs_func(
# find_scores_layer_idx=find_scores_layer_idx,
# target_layer_idx=-1)
#
# scores = np.array(deeplift_contribs_func(task_idx=0,
# input_data_list=[X],
# batch_size=10,
# progress_update=1000))
# print(scores)
return score
def model_CNN256_LSTM128_64_2(FrameSize,
X,
X_train,
X_test,
y_train,
y_test,
epoch,
earlyStopping,
name,
dropout2_rate,
dense_1,
filterCNN,
kernelCNN,
LSTM1,
LSTM2,
recurrent_dropout,
limited=False):
print(X.shape)
print(FrameSize)
model = Sequential()
# model.add(TimeDistributed(Conv1D(filters=1, kernel_size=3, activation='relu', padding='same', input_shape=(FrameSize, X[0].shape[1], 1))))
# model.add(TimeDistributed(MaxPooling1D(pool_size=3)))
# model.add(TimeDistributed(Flatten()))
model.add(Dropout(dropout2_rate))
# model.add(Conv1D(filters=5, kernel_size=3, activation='relu', padding='same'))
model.add(
Conv1D(filters=filterCNN,
kernel_size=kernelCNN,
activation='relu',
padding='same'))
model.add(MaxPooling1D(pool_size=3, padding='same'))
# model.add(TimeDistributed(Flatten()))
# model.add(LSTM(256, return_sequences=True, recurrent_dropout=0.3))
model.add(
LSTM(LSTM1, return_sequences=True,
recurrent_dropout=recurrent_dropout))
model.add(SpatialDropout1D(dropout2_rate))
# model.add(LSTM(128, return_sequences=False, recurrent_dropout=0.3))
model.add(
LSTM(LSTM2,
return_sequences=False,
recurrent_dropout=recurrent_dropout))
model.add(Dropout(dropout2_rate))
# model.add(Dense(64))
model.add(Dense(dense_1))
model.add(Dropout(dropout2_rate))
if limited:
model.add(Dense(7, activation='sigmoid'))
else:
model.add(Dense(12, activation='sigmoid'))
model.compile(loss=masked_loss_function,
optimizer='Adam',
metrics=[masked_accuracy])
history = model.fit(
X_train,
y_train,
epochs=epoch,
batch_size=128,
# shuffle=True,
verbose=2,
validation_data=(X_test, y_test),
callbacks=[
earlyStopping,
ModelCheckpoint('result/CNN256_LSTM128_64_2.h5',
monitor='val_masked_accuracy',
mode='max',
save_best_only=True)
])
# plot_model(model, to_file='model_plot.png', show_shapes=True)
plot.plot(history, ("LRCN" + name))
score = ROC_PR.ROC(model,
X_test,
y_test, ("LRCN" + name),
True,
limited=limited)
return score
def model_shuffle_index_0(FrameSize, X, X_train, X_test, y_train, y_test,
epoch, earlyStopping, name, index):
print(X.shape)
print(FrameSize)
model = Sequential()
if index == 0:
model.add(Dropout(0.3975303416300372))
model.add(
Conv1D(filters=4, kernel_size=3, activation='relu',
padding='same'))
model.add(MaxPooling1D(pool_size=3, padding='same'))
model.add(Dropout(0.3975303416300372))
model.add(
Conv1D(filters=6, kernel_size=3, activation='relu',
padding='same'))
model.add(MaxPooling1D(pool_size=3, padding='same'))
model.add(Dropout(0.3975303416300372))
model.add(
Conv1D(filters=7, kernel_size=3, activation='relu',
padding='same'))
model.add(MaxPooling1D(pool_size=3, padding='same'))
model.add(LSTM(107, return_sequences=True, recurrent_dropout=0.3))
model.add(Dropout(0.3975303416300372))
model.add(LSTM(161, return_sequences=True, recurrent_dropout=0.3))
model.add(Dropout(0.3975303416300372))
model.add(LSTM(386, return_sequences=False, recurrent_dropout=0.3))
model.add(Dropout(0.3975303416300372))
model.add(Dense(90))
model.add(Dropout(0.3975303416300372))
model.add(Dense(503))
model.add(Dropout(0.3975303416300372))
model.add(Dense(319))
model.add(Dropout(0.3975303416300372))
model.add(Dense(151))
model.add(Dropout(0.3975303416300372))
model.add(Dense(12, activation='sigmoid'))
elif index == 1:
model.add(Dropout(0.1135579744586698))
model.add(
Conv1D(filters=4, kernel_size=5, activation='relu',
padding='same'))
model.add(MaxPooling1D(pool_size=4, padding='same'))
model.add(LSTM(420, return_sequences=True, recurrent_dropout=0.3))
model.add(Dropout(0.1135579744586698))
model.add(LSTM(67, return_sequences=True, recurrent_dropout=0.3))
model.add(Dropout(0.1135579744586698))
model.add(LSTM(231, return_sequences=False, recurrent_dropout=0.3))
model.add(Dropout(0.1135579744586698))
model.add(Dense(112))
model.add(Dropout(0.1135579744586698))
model.add(Dense(92))
model.add(Dropout(0.1135579744586698))
model.add(Dense(12, activation='sigmoid'))
elif index == 2:
model.add(Dropout(0.3211287914743064))
model.add(
Conv1D(filters=7, kernel_size=3, activation='relu',
padding='same'))
model.add(MaxPooling1D(pool_size=3, padding='same'))
model.add(Dropout(0.3211287914743064))
model.add(
Conv1D(filters=4, kernel_size=5, activation='relu',
padding='same'))
model.add(MaxPooling1D(pool_size=4, padding='same'))
model.add(Dropout(0.3211287914743064))
model.add(
Conv1D(filters=5, kernel_size=4, activation='relu',
padding='same'))
model.add(MaxPooling1D(pool_size=4, padding='same'))
model.add(LSTM(404, return_sequences=False, recurrent_dropout=0.3))
model.add(Dropout(0.3211287914743064))
model.add(Dense(69))
model.add(Dropout(0.3211287914743064))
model.add(Dense(70))
model.add(Dropout(0.3211287914743064))
model.add(Dense(171))
model.add(Dropout(0.3211287914743064))
model.add(Dense(453))
model.add(Dropout(0.3211287914743064))
model.add(Dense(12, activation='sigmoid'))
elif index == 3:
model.add(Dropout(0.3584182599371434))
model.add(
Conv1D(filters=8, kernel_size=5, activation='relu',
padding='same'))
model.add(MaxPooling1D(pool_size=5, padding='same'))
model.add(Dropout(0.3584182599371434))
model.add(
Conv1D(filters=7, kernel_size=5, activation='relu',
padding='same'))
model.add(MaxPooling1D(pool_size=5, padding='same'))
model.add(LSTM(438, return_sequences=True, recurrent_dropout=0.3))
model.add(Dropout(0.3584182599371434))
model.add(LSTM(500, return_sequences=False, recurrent_dropout=0.3))
model.add(Dropout(0.3584182599371434))
model.add(Dense(81))
model.add(Dropout(0.3584182599371434))
model.add(Dense(267))
model.add(Dropout(0.3584182599371434))
model.add(Dense(388))
model.add(Dropout(0.3584182599371434))
model.add(Dense(12, activation='sigmoid'))
model.compile(loss=masked_loss_function,
optimizer='Adam',
metrics=[masked_accuracy])
history = model.fit(X_train,
y_train,
epochs=epoch,
batch_size=128,
verbose=2,
validation_data=(X_test, y_test),
callbacks=[
earlyStopping,
ModelCheckpoint('result/CNN256_LSTM128_64_2.h5',
monitor='val_masked_accuracy',
mode='max',
save_best_only=True)
])
plot.plot(history, ("LRCN" + name))
score = ROC_PR.ROC(model, X_test, y_test, ("LRCN" + name), True)
return score, ROC_PR.ROC_Score(model, X_train, y_train, limited=False)
