except getopt.GetoptError as err:
print(err)
print_usage()
exit()
if '--help' in opts:
print_usage()
exit()
parameters = deepMirCut.load_parameters(opts)
parameters = load_input_output_file_parameters(parameters, opts)
validationSet = deepMirCut.readDataset(parameters["validation_file"],
parameters)
new_validationSet = deepMirCut.dropLongSequences(validationSet, parameters)
X_vl, y_vl = deepMirCut.prepareData(new_validationSet, parameters)
validation_labels = deepMirCut.pred2label(y_vl, parameters)
if "ensemble" in parameters:
validation_pred = []
for i in range(0, len(parameters["ensemble"])):
print("loading model %d: %s\n" % (i, parameters["ensemble"][i]))
model = load_model(parameters["ensemble"][i],
custom_objects={'prox': avg_proximity_metric()})
validation_pred.append(
model.predict(X_vl, verbose=parameters["verbose"]))
#model.summary()
validation_pred_avg = apply_weights(validation_pred,
w=parameters["ensemble_weights"])
pred_labels = deepMirCut.pred2label(validation_pred_avg, parameters)
deepMirCut.print_validation_output_file(new_validationSet, X_vl,
validation_labels, pred_labels,
parameters["predictions_file"],
def hyperopt_train_test(params):
epsilon = 10**params['epsilon_exp']
optimizer = optimizers.adam(lr=params['learning_rate'], epsilon=epsilon)
if dmc_parameters["use_embedding_layer"]:
input = Input(shape=(dmc_parameters["max_seq_len"], ))
model = Embedding(input_dim=dmc_parameters["one_hot_vector_len"],
output_dim=params['embedding_layer_output'],
input_length=dmc_parameters["max_seq_len"])(input)
model = Dropout(rate=params['embedding_dropout'])(model)
else:
input = Input(shape=(dmc_parameters["max_seq_len"],
dmc_parameters["one_hot_vector_len"]))
model = input
if params['bi_lstm1_units'] > 0:
model = Bidirectional(
CuDNNLSTM(units=params['bi_lstm1_units'],
return_sequences=True))(model)
if params['bi_lstm2_units'] > 0:
model = Bidirectional(
CuDNNLSTM(units=params['bi_lstm2_units'],
return_sequences=True))(model)
if dmc_parameters["use_crf_layer"]:
crf = CRF(dmc_parameters["num_tags"]) # CRF layer
out = crf(model) # output
model = Model(input, out)
model.compile(optimizer=optimizer,
loss=losses.crf_loss,
metrics=[metrics.crf_accuracy,
avg_proximity_metric()])
else:
out = TimeDistributed(
Dense(dmc_parameters["num_tags"], activation="softmax"))(model)
model = Model(input, out)
model.compile(optimizer=optimizer,
loss="categorical_crossentropy",
metrics=["accuracy", avg_proximity_metric()])
model.summary()
es = EarlyStopping(monitor='val_loss',
min_delta=0,
patience=dmc_parameters["patience"],
verbose=False,
mode='min',
restore_best_weights=True)
history = model.fit(X_tr,
np.array(y_tr),
batch_size=dmc_parameters['batch_size'],
epochs=dmc_parameters["epochs"],
validation_data=(X_vl, np.array(y_vl)),
verbose=False,
shuffle=True,
callbacks=[es])
loss, acc, prox = model.evaluate(x=X_vl,
y=np.array(y_vl),
batch_size=dmc_parameters['batch_size'],
verbose=False)
validation_labels = deepMirCut.pred2label(y_vl, dmc_parameters)
validation_pred = model.predict(X_vl, verbose=False)
pred_labels = deepMirCut.pred2label(validation_pred, dmc_parameters)
fScore = f1_score(validation_labels, pred_labels)
return loss, acc, prox, fScore
opts.update({o: a for o, a in opts_array if o not in longopts_map})
opts["--validation_file"] = validation_file
except getopt.GetoptError as err:
print(err)
print_usage()
exit()
if '--help' in opts:
print_usage()
exit()
parameters = deepMirCut.load_parameters(opts)
parameters = load_input_output_file_parameters(parameters, opts)
model = load_model(parameters["model"],
custom_objects={
'CRF': CRF,
'crf_loss': losses.crf_loss,
'crf_accuracy': metrics.crf_accuracy,
'prox': avg_proximity_metric()
})
model.summary()
validationSet = deepMirCut.readDataset(parameters["validation_file"],
parameters)
new_validationSet = deepMirCut.dropLongSequences(validationSet, parameters)
X_vl, y_vl = deepMirCut.prepareData(new_validationSet, parameters)
validation_labels = deepMirCut.pred2label(y_vl, parameters)
validation_pred = model.predict(X_vl, verbose=parameters["verbose"])
print_cutsite_scores(validationSet, validation_labels, validation_pred,
parameters)