def parse_graec(s):
# do GRAEC SCORE
enumeration_encoder = Human_Functions.load_dict_from_csv(Name_functions.S_GRAEC_enumeration_dictionary(s))
best_acc_train = -1
best_acc_test = -1
best_acc_enum = -1
best_f1_train = -1
best_f1_test = -1
best_f1_enum = -1
with open(Name_functions.full_GRAEC_table(), 'a+') as wf:
for e in enumeration_encoder:
df_train = DataFrameOperations.import_df(fn=Name_functions.S_GRAEC_train_predictions(s, e))
acc_train = Metrics.accuracy(df_train['True_label'], df_train['Predicted_label'])
f1_train = Metrics.f1(df_train['True_label'], df_train['Predicted_label'])
df_test = DataFrameOperations.import_df(fn=Name_functions.S_GRAEC_test_predictions(s, e))
acc_test = Metrics.accuracy(df_test['True_label'], df_test['Predicted_label'])
f1_test = Metrics.f1(df_test['True_label'], df_test['Predicted_label'])
wf.write(enumeration_encoder[e] + ';{};ACC;{}\n'.format(s, acc_test))
wf.write(enumeration_encoder[e] + ';{};F1;{}\n'.format(s, f1_test))
if acc_train > best_acc_train:
best_acc_train = acc_train
best_acc_test = acc_test
best_acc_enum = e
if f1_train > best_f1_train:
best_f1_train = f1_train
best_f1_test = f1_test
best_f1_enum = e
with open(Name_functions.S_GRAEC_score(s, 'accuracy'), 'w+') as wf:
wf.write(enumeration_encoder[best_acc_enum] + '\n')
wf.write('{}\n'.format(best_acc_test))
with open(Name_functions.S_GRAEC_score(s, 'f1'), 'w+') as wf:
wf.write(enumeration_encoder[best_f1_enum] + '\n')
wf.write('{}\n'.format(best_f1_test))
def parse_ms(s):
print('\tGRAEC ... ', end='', flush=True)
if Filefunctions.exists(Name_functions.S_GRAEC_enumeration_dictionary(s)):
print('Already done')
return
enumeration_encoder = dict()
fn_data = Name_functions.DS_file(s)
fn_train_ids = Name_functions.DS_train_ids(s)
fn_test_ids = Name_functions.DS_test_ids(s)
x_train, labels_train, times_train, ids_train = DI(fn_data).get_data(
fn_subset_ids=fn_train_ids,
return_split_values=True,
return_identifiers=True)
x_test, labels_test, times_test, ids_test = DI(fn_data).get_data(
fn_subset_ids=fn_test_ids,
return_split_values=True,
return_identifiers=True)
enumeration = 0
predictor = Classifiers.BPTSClassifier(s=s, score_function=None)
for B in Parameters.GRAEC_beta:
for T in Parameters.GRAEC_tau:
for P in Parameters.GRAEC_p if not T == 0 else [
0
]: # P has no use for T == 0
enumeration_encoder[enumeration] = '{};{};{}'.format(B, T, P)
predictor.set_scoring_function(
score_function=PeriodScoring(beta=B, p=P, tau=T, s=s))
with open(
Name_functions.S_GRAEC_train_predictions(
s, enumeration), 'w+') as wf:
wf.write('SOID;time;True_label;Predicted_label\n')
for case_id, t, true_label in zip(ids_train, times_train,
labels_train):
predicted_label = predictor.predict(case_id=case_id,
time=t)
wf.write('{};{};{};{}\n'.format(
case_id, t, true_label[0], predicted_label))
with open(
Name_functions.S_GRAEC_test_predictions(
s, enumeration), 'w+') as wf:
wf.write('Case_id;time;True_label;Predicted_label\n')
for case_id, t, true_label in zip(ids_test, times_test,
labels_test):
predicted_label = predictor.predict(case_id=case_id,
time=t)
wf.write('{};{};{};{}\n'.format(
case_id, t, true_label[0], predicted_label))
enumeration += 1
Human_Functions.save_dict_to_csv(
enumeration_encoder, Name_functions.S_GRAEC_enumeration_dictionary(s))
fn_data = Name_functions.DS_file(s)
fn_ids = Name_functions.DS_test_ids(s)
x, labels, times, ids = DI(fn_data).get_data(fn_subset_ids=fn_ids,
return_split_values=True,
return_identifiers=True)
print('Done')
print('\tNaive and Previous ... ', end='', flush=True)
naive_predictor = Classifiers.NaiveClassifier(s)
previous_predictor = Classifiers.PreviousClassifier(s)
with open(Name_functions.S_naive_test_predictions(s), 'w+') as wf_naive:
with open(Name_functions.S_recent_test_predictions(s),
'w+') as wf_previous:
wf_naive.write('{};{};{};{}\n'.format('Case_id', 'time',
'True_label',
'Predicted_label'))
wf_previous.write('{};{};{};{}\n'.format('Case_id', 'time',
'True_label',
'Predicted_label'))
for case_id, t, true_label in zip(ids, times, labels):
predicted_label_naive = naive_predictor.predict(
case_id=case_id, time=t)
if predicted_label_naive is not None:
wf_naive.write('{};{};{};{}\n'.format(
case_id, t, true_label[0], predicted_label_naive))
predicted_label_previous = previous_predictor.predict(
case_id=case_id, time=t)
if predicted_label_previous is not None:
wf_previous.write('{};{};{};{}\n'.format(
case_id, t, true_label[0], predicted_label_previous))
print('Done')
