def validate(files, model, nParticles):
"""
:param files:
:param model:
:return:
"""
utils = Utilities(nParticles)
for file in files:
proc_name = file.split("/")[-1]
X_1, Y_1, _, MVA = utils.BuildValidationDataset(file)
pred = model.predict(X_1)
pred.to_csv("{0}/{1}.csv".format(TRAINING_RES, proc_name), index=False)
#Evaluate Results:
return
Samples = glob(TEST_DATA)
print(Samples)
if config.get("model", "meta_name") == "DeepSets":
# Build Architecture from the
from Trainer_DeepSet import model_build
model = model_build()
model = load_model(config=config, epoch=epoch, model=model)
else:
model = load_model(config=config, epoch=epoch)
print("Model Loaded")
print("Sample", Samples)
for sample in Samples:
print(sample)
X_valid, Y_valid, _, MVA = utils.BuildValidationDataset(sample, None)
#print("MVA:", MVA.shape)
#df_valid = pd.DataFrame({'DY_labels_valid':[i for i in Y_valid]})
#print("Valid shape:", df_valid.shape)
#df_valid.to_csv("{0}.csv".format(sample), index=False)
#del df_valid
predict = model.predict(X_valid, batch_size=1000)
#print(predict)
df_predict = pd.DataFrame({
'valid_pred': [i[0] for i in predict],
'labels_valid': [i for i in Y_valid],
'mva': [i[0] for i in MVA],
'decay_mode': [i[1] for i in MVA],
'mu_match': [i[2] for i in MVA],
'el_match': [i[3] for i in MVA],
'tau_match': [i[4] for i in MVA]
