parameters = load_input_output_file_parameters(parameters,opts)
if "ensemble" not in parameters:
print("Error: ensemble file is required")
exit()
if "--ensemble_size" not in opts:
print("Error: must enter an --ensemble_size")
exit()
output_file = "new_ensemble_list.txt"
if parameters["output_prefix"]:
output_file = parameters["output_prefix"] + "_ensemble_list.txt"
num = int(opts["--ensemble_size"])
dataSet = deepMirCut.readDataset(parameters["validation_file"],parameters)
new_dataSet = deepMirCut.dropLongSequences(dataSet,parameters)
X_test,y_test = deepMirCut.prepareData(new_dataSet,parameters)
members = []
for i in range(0,len(parameters["ensemble"])):
print("loading model %d: %s\n"%(i, parameters["ensemble"][i]))
members.append(load_model(parameters["ensemble"][i], custom_objects={'prox':avg_proximity_metric()}))
test_pred = []
macro_f1_scores = []
for i in range(0,len(members)):
model = members[i]
test_pred.append(model.predict(X_test, verbose=parameters["verbose"]))
perf = get_classification_metrics(y_test,test_pred[i],parameters)
macro_f1_scores.append(sum([perf[c]["fscore"] for c in ["DR5","DC5","DC3","DR3"]]) / 4)
print(i,macro_f1_scores[i])
f = open(output_file,"w")
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)
predictions_outputFile = parameters[
"output_prefix"] + "_predicted_cutsites.txt"
inputSet = read_predict_set(input_file, parameters)
new_inputSet = deepMirCut.dropLongSequences(inputSet, parameters)
X_vl, _ = deepMirCut.prepareData(new_inputSet, parameters)
if "ensemble" in parameters:
predictions = []
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()})
predictions.append(
model.predict(X_vl, verbose=parameters["verbose"]))
predictions_avg = apply_weights(predictions,
w=parameters["ensemble_weights"])
print_predictions_output_file(new_inputSet, X_vl, predictions_avg,
predictions_outputFile, parameters)
if parameters["print_dvs"]:
deepMirCut.print_classification_values_file(
new_inputSet,
opts = {}
opts['--verbose'] = True
opts['--epochs'] = 20
opts['--patience'] = 3
opts['--use_embedding_layer'] = "True"
opts['--use_crf_layer'] = "False"
opts['--input_setting'] = '1' #USE_SEQ_FOLD
dmc_parameters = deepMirCut.load_parameters(opts)
dmc_parameters['train_file'] = sys.argv[1]
dmc_parameters['validation_file'] = sys.argv[2]
dmc_parameters['batch_size'] = 64
print("input_setting=", dmc_parameters['input_setting'])
trainSet = deepMirCut.readDataset(dmc_parameters["train_file"],
dmc_parameters)
new_trainSet = deepMirCut.dropLongSequences(trainSet, dmc_parameters)
X_tr, y_tr = deepMirCut.prepareData(new_trainSet, dmc_parameters)
validationSet = deepMirCut.readDataset(dmc_parameters["validation_file"],
dmc_parameters)
new_validationSet = deepMirCut.dropLongSequences(validationSet,
dmc_parameters)
X_vl, y_vl = deepMirCut.prepareData(new_validationSet, dmc_parameters)
while (trial_step < new_evals):
run_trials(pickle_file="tuning_adam_seqOnly.pickle",
trials_step=trial_step)
new_evals -= trial_step
run_trials(pickle_file="tuning_adam_seqOnly.pickle", trials_step=new_evals)
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)
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,