def parse_previous(s):
# do PREVIOUS SCORE
df_previous = DataFrameOperations.import_df(fn=Name_functions.S_recent_test_predictions(s))
predicted_label = df_previous['Predicted_label']
true_label = df_previous['True_label']
acc_score = Metrics.accuracy(true_label=true_label, predicted_label=predicted_label)
with open(Name_functions.S_recent_score(s, 'accuracy'), 'w+') as wf:
wf.write('{}\n'.format(acc_score))
f1_score = Metrics.f1(true_label=true_label, predicted_label=predicted_label)
with open(Name_functions.S_recent_score(s, 'f1'), 'w+') as wf:
wf.write('{}\n'.format(f1_score))
def undo():
for S in Parameters.S_values:
Filefunctions.delete(Name_functions.S_GRAEC_enumeration_folder(S))
Filefunctions.delete(Name_functions.S_naive_test_predictions(S))
Filefunctions.delete(Name_functions.S_recent_test_predictions(S))
STEP6_Global_Scoring.undo()
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')
