parameters = deepMirCut.load_parameters(opts)
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])
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)