def parse_ms(s):
print('D^{} ... '.format(s), end='', flush=True)
if Filefunctions.exists(Name_functions.DS_train_ids(s)):
if Filefunctions.exists(Name_functions.DS_test_ids(s)):
print('Already done')
return
np.random.seed(0)
X, y, times, ids = DI(Name_functions.DS_file(s)).get_data(
Name_functions.DS_reduced_ids_DSJ(s), True, True)
if Parameters.take_test_split_chronological:
test_case_ids = []
train_case_ids = []
times_post_warm_up = [
t for t in times if t > Parameters.test_time_start
]
times_post_warm_up.sort()
train_start_index = int(
(1 - Parameters.assessment_test_split) * len(times_post_warm_up))
train_time_end = times_post_warm_up[train_start_index]
for case_start_time, case_id in zip(times, ids):
if case_start_time <= Parameters.test_time_start:
continue
if case_start_time < train_time_end:
train_case_ids.append(case_id)
else:
test_case_ids.append(case_id)
else:
indices = [
i for i in range(len(ids)) if times[i] > Parameters.test_time_start
]
test_indices = []
train_indices = []
c, cc = np.unique(y[indices], return_counts=True)
for label, label_count in zip(c, cc):
num_test = int(label_count * Parameters.assessment_test_split)
indices_c = [i for i in indices if y[i] == label]
indices_c_test = np.random.choice(indices_c,
num_test,
replace=False)
test_indices.extend(indices_c_test.tolist())
train_indices.extend(
[i for i in indices_c if i not in indices_c_test])
test_case_ids = ids[test_indices]
train_case_ids = ids[train_indices]
with open(Name_functions.DS_train_ids(s), 'w+') as wf:
for case_id in train_case_ids:
wf.write('{}\n'.format(case_id))
with open(Name_functions.DS_test_ids(s), 'w+') as wf:
for case_id in test_case_ids:
wf.write('{}\n'.format(case_id))
print('Done')
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')
