def generate_output_multiclass(model,
input,
gold,
doc_list_sub,
processed_path,
output_pred_path,
pred=True,
data_folder="",
format_abbre=".TimeNorm.system.completed.xml"):
non_operator = read.textfile2list(non_operator_path)
operator = read.textfile2list(operator_path)
labels_index = [non_operator, operator, operator]
classes, probs = output.make_prediction_function_multiclass(
input, model, output_pred_path)
if pred == True:
np.save(output_pred_path + "/y_predict_classes" + data_folder, classes)
read.savein_pickle(output_pred_path + "/y_predict_proba" + data_folder,
probs)
spans = list()
int2labels = list()
for index in range(len(classes)):
class_loc = output.found_location_with_constraint(classes[index])
span = output.loc2span(class_loc, probs[index], post_process=False)
spans.append(span)
one_hot = read.counterList2Dict(list(enumerate(labels_index[index],
1)))
one_hot = {y: x for x, y in one_hot.items()}
int2label = dict((int, char) for char, int in one_hot.items())
int2labels.append(int2label)
n_marks = 3
sent_index = 0
for data_id in range(0, len(doc_list_sub)):
sent_spans = read.readfrom_json(
os.path.join(processed_path, doc_list_sub[data_id],
doc_list_sub[data_id] + "_sent"))
data_span = list()
for sent_span in sent_spans:
for index in range(len(classes)):
span_list = spans[index][sent_index]
if len(span_list[0]) < 1:
pass
else:
for [posi_start, posi_end, label] in span_list:
data_span.append([
posi_start - n_marks + sent_span[1],
posi_end - n_marks + sent_span[1],
int2labels[index][label]
])
sent_index += 1
data = span2xmlfiles(data_span, doc_list_sub[data_id])
output_path = os.path.join(output_pred_path, doc_list_sub[data_id],
doc_list_sub[data_id])
read.create_folder(output_path)
data.to_file(output_path + format_abbre)
del classes, probs, input
def generate_output_multiclass(model,input,gold,doc_list_sub, processed_path,output_pred_path,pred =True,data_folder = "",format_abbre = ".TimeNorm.system.completed.xml"):
non_operator = read.textfile2list(non_operator_path)
operator = read.textfile2list(operator_path)
labels_index = [non_operator,operator,operator]
classes, probs = output.make_prediction_function_multiclass(input, model, output_pred_path)
if pred == True:
np.save(output_pred_path + "/y_predict_classes"+data_folder, classes)
read.savein_pickle(output_pred_path + "/y_predict_proba"+data_folder, probs)
spans = list()
int2labels = list()
for index in range(len(classes)):
class_loc = output.found_location_with_constraint(classes[index])
span = output.loc2span(class_loc, probs[index],post_process = False)
spans.append(span)
one_hot = read.counterList2Dict(list(enumerate(labels_index[index], 1)))
one_hot = {y: x for x, y in one_hot.items()}
int2label = dict((int, char) for char, int in one_hot.items())
int2labels.append(int2label)
n_marks =3
sent_index = 0
for data_id in range(0,len(doc_list_sub)):
sent_spans = read.readfrom_json(os.path.join(processed_path,doc_list_sub[data_id],doc_list_sub[data_id]+"_sent"))
data_span = list()
for sent_span in sent_spans:
for index in range(len(classes)):
span_list = spans[index][sent_index]
if len(span_list[0]) <1:
pass
else:
for [posi_start,posi_end,label] in span_list:
data_span.append([posi_start-n_marks+sent_span[1],posi_end-n_marks+ sent_span[1],int2labels[index][label]])
sent_index += 1
data = span2xmlfiles(data_span,doc_list_sub[data_id])
output_path = os.path.join(output_pred_path,doc_list_sub[data_id],doc_list_sub[data_id])
read.create_folder(output_path)
data.to_file(output_path+format_abbre)
del classes,probs,input
def generate_output_multiclass(sent_len,model,input,doc_list_sub, processed_path,output_pred_path,pred =True,data_folder = "",format_abbre = ".TimeNorm.system.completed.xml"):
non_operator = read.textfile2list(non_operator_path)
print('non_operator')
print(non_operator)
operator = read.textfile2list(operator_path)
print('operator')
print(operator)
labels_index = [non_operator,operator,operator]
print('labels_index')
print(labels_index)
classes, probs = output.make_prediction_function_multiclass(input, model, output_pred_path)
print('sent_len')
print(sent_len)
print('classes, probs - ')
print(classes)
#print(probs)
if pred == True:
np.save(output_pred_path + "/y_predict_classes"+data_folder, classes)
read.savein_pickle(output_pred_path + "/y_predict_proba"+data_folder, probs)
spans = list()
int2labels = list()
for index in range(len(classes)):
class_loc = output.found_location_with_constraint(classes[index], sent_len)
print('class_loc')
print(class_loc)
span = output.loc2span(class_loc, probs[index],post_process = False)
print('span')
print(span)
spans.append(span)
one_hot = read.counterList2Dict(list(enumerate(labels_index[index], 1)))
one_hot = {y: x for x, y in one_hot.items()}
int2label = dict((int, char) for char, int in one_hot.items())
int2labels.append(int2label)
n_marks =3
sent_index = 0
for data_id in range(0,len(doc_list_sub)):
print('HERE %s', doc_list_sub[data_id])
print(os.path.join(processed_path,doc_list_sub[data_id]+"_sent"))
sent_spans = read.readfrom_json(os.path.join(processed_path,doc_list_sub[data_id]+"_sent"))
print('sent_spans %s', sent_spans)
data_span = list()
for sent_span in sent_spans:
""" print('sent_span - ')
print(sent_span)
posi_start = sent_span[1]
posi_end = sent_span[2]
label = sent_span[0]
print('posi_start:%s posi_end:%s, label:%s', (posi_start,posi_end,label))
data_span.append([posi_start-n_marks+sent_span[1],posi_end-n_marks+ sent_span[1],int2labels[index][label]]) """
print('classes - ', range(len(classes)))
for index in range(len(classes)):
#print('index - ' + index + ':' + sent_index)
""" print(index)
print(sent_index) """
span_list = spans[index][sent_index]
#print('span_list - ')
#print(len(span_list[0]))
#print(span_list)
if len(span_list[0]) <1:
pass
else:
for [posi_start,posi_end,label] in span_list:
print('posi_start:%s posi_end:%s, label:%s', (posi_start,posi_end,label))
data_span.append([posi_start-n_marks+sent_span[1],posi_end-n_marks+ sent_span[1],int2labels[index][label]])
sent_index += 1
print('data_span - ', data_span)
data = span2xmlfiles(data_span,doc_list_sub[data_id])
print('data %s', data)
output_path = os.path.join(output_pred_path,doc_list_sub[data_id],doc_list_sub[data_id])
read.create_folder(output_path)
data.to_file(output_path+format_abbre)
del classes,probs,input