def run(self):
# text = codecs.open('test.csv', 'r', 'utf-8').read()
self.get_keywords_num()
train_data = table2df(self.train_data)
if train_data.index.size == 0:
raise AttributesError_new(
"Missing data, the input dataset should have two rows and one column "
"(the first row is column name, the second row is the single text.)"
)
elif train_data.columns.size > 1:
raise AttributesError_new("Input data should have only one column")
elif train_data.index.size > 1:
raise AttributesError_new(
"The single text should be filled into one row")
""" use 1 col and 1 row data """
train_data = str(train_data.values[0][0])
#use Textrank to get the keywords
tr4w = TextRank4Keyword()
tr4w.analyze(text=train_data, lower=True, window=5)
list1 = []
list2 = []
list3 = []
list4 = []
list5 = []
list6 = []
result = []
for item in tr4w.get_keywords(self.keywords_num * 2, word_min_len=2):
list1.append([item.word, item.weight])
list6.append(item.word)
self.keywords_set = list(set(list6))
# print('keywords_set:',self.keywords_set)
# print(list1)
#use TF_IDF to get the keywords
tfidfer = TF_IDF()
word_dict, candi_dict = tfidfer.build_wordsdict(train_data)
word_dict1 = tfidfer.build_wordsdict1(train_data)
for keyword in tfidfer.extract_keywords(train_data,
self.keywords_num * 2):
list2.append(list(keyword))
# print(list2)
for i in range(len(list1)):
for j in range(len(list2)):
if list1[i][0] == list2[j][0]:
list3.append(list1[i])
for i in list1:
if i not in list3:
list4.append(i)
length = len(list3)
if length >= self.keywords_num:
for i in range(self.keywords_num):
result.append(list3[i])
else:
result = list3
if len(list4) >= self.keywords_num - length:
for i in range(self.keywords_num - length):
list3.append(list4[i])
result = list3
result.sort(key=lambda k: k[1], reverse=True)
for i in range(len(result)):
for word, word_tf in word_dict1.items():
if result[i][0] == word:
result[i].append(int(word_tf))
# print('result1：',result)
for i in range(len(result)):
list7 = []
list7.append(result[i][0])
# print('list7:',list7)
mapping = list(map(lambda x: self.keywords_set.index(x), list7))
# print('mapping:',mapping)
result[i][0] = mapping[0]
# print('result2：',result)
metas = [
DiscreteVariable('keywords', self.keywords_set),
ContinuousVariable('weight'),
ContinuousVariable('word_frequency')
]
# print('Domain(metas):',Domain(metas))
# listma=[[1,2,3],[4,5,6],[3,5,6]]
# print(listma)
domain = Domain(metas)
# print('domain.attributes:',domain.attributes)
# print('domain.class_vars:',domain.class_vars)
final_result = Table.from_list(domain, result)
# final_result=Table.from_list(Domain(metas),listma)
print('final_result:', final_result)
self.send('News', final_result)
self.send("Metas", metas)
def run(self):
train_data = table2df(self.train_data)
if train_data.index.size == 0:
raise AttributesError_new(
"Missing data, the input dataset should have two rows and one column "
"(the first row is column name, the second row is the single text.)"
)
elif train_data.columns.size > 1:
raise AttributesError_new("Input data should have only one column")
elif train_data.index.size > 1:
raise AttributesError_new(
"The single text should be filled into one row")
""" use 1 col and 1 row data """
train_data = str(train_data.values[0][0])
# use Textrank to get the keywords
tr4s = TextRank4Sentence()
tr4s.analyze(text=train_data, lower=True, source='all_filters')
self.proportion_set = ['20%', '30%', '40%', '50%']
list1 = []
list2 = []
list3 = []
list4 = []
list5 = []
list6 = []
result = []
for item in tr4s.get_key_sentences(num=4):
#print(item.index, item.weight, item.sentence)
list1.append([item.index, item.weight, item.sentence])
# print('list1',list1)
list6.append(['20%', list1[0][2]])
list5.append(list1[0][2])
for i in range(2):
list2.append(list1[i])
list2.sort(key=lambda k: k[0], reverse=False)
list6.append(['30%', list2[0][2] + list2[1][2]])
list5.append(list2[0][2] + list2[1][2])
for i in range(3):
list3.append(list1[i])
list3.sort(key=lambda k: k[0], reverse=False)
list6.append(['40%', list3[0][2] + list3[1][2] + list3[2][2]])
list5.append(list3[0][2] + list3[1][2] + list3[2][2])
for i in range(4):
list4.append(list1[i])
list4.sort(key=lambda k: k[0], reverse=False)
list6.append(
['50%', list4[0][2] + list4[1][2] + list4[2][2] + list4[3][2]])
list5.append(list4[0][2] + list4[1][2] + list4[2][2] + list4[3][2])
self.abstract_set = list5
# print('list5',list5)
# print('list6',list6)
for i in range(len(list6)):
list7 = []
list8 = []
list7.append(list6[i][0])
list8.append(list6[i][1])
mapping1 = list(map(lambda x: self.proportion_set.index(x), list7))
mapping2 = list(map(lambda x: self.abstract_set.index(x), list8))
# print('mapping1',mapping1[0])
# print('mapping2',mapping2[0])
result.append([mapping1[0], mapping2[0]])
# print('list6:',list6)
# print('result:',result)
metas = [
DiscreteVariable('proportion', self.proportion_set),
DiscreteVariable('abstract', self.abstract_set)
]
domain = Domain(metas)
# print('domain.attributes:',domain.attributes)
# print('domain.class_vars:',domain.class_vars)
final_result = Table.from_list(domain, result)
self.send('News', final_result)
self.send("Metas", metas)
# print('result:',result)
# print('final_result:',final_result)
json_res = {}
temp_lst = []
fields = ['proportion', 'abstract']
for i in result:
temp_dir = {}
for j, k in enumerate(i):
if j == 0:
temp_dir[fields[j]] = self.proportion_set[k]
else:
temp_dir[fields[j]] = self.abstract_set[k]
temp_lst.insert(0, temp_dir)
json_res['visualization_type'] = "summary"
json_res['results'] = temp_lst
json_res["chartXName"] = 'proportion'
json_res["chartYName"] = 'abstract'
# json_res["tableCols"] = ['name', 'count']
# print('json_res:',json_res)
self.send('Jsondata', json_res)
def run(self):
# text = codecs.open('test.csv', 'r', 'utf-8').read()
self.get_keywords_num()
train_data = table2df(self.train_data)
if train_data.index.size == 0:
raise AttributesError_new("Missing data, the input dataset should have two rows and one column "
"(the first row is column name,the second row is the single text.)")
elif train_data.columns.size > 1:
raise AttributesError_new("Input data should have only one column")
elif train_data.index.size > 1:
raise AttributesError_new("The single text should be filled into one row")
""" use 1 col and 1 row data """
train_data = str(train_data.values[0][0])
# use Textrank to get the keywords
tr4w = TextRank4Keyword()
tr4w.analyze(text=train_data, lower=True, window=5)
list1 = []
list2 = []
list3 = []
list4 = []
list5 = []
list6 = []
result = []
for item in tr4w.get_keywords(self.keywords_num * 2, word_min_len=2):
list1.append([item.word, item.weight])
list6.append(item.word)
self.keywords_set = list(set(list6))
# print('keywords_set:',self.keywords_set)
# print(list1)
# use TF_IDF to get the keywords
tfidfer = TF_IDF()
word_dict, candi_dict = tfidfer.build_wordsdict(train_data)
""" add the build_wordsdict1 to figure out words frequence by chenjing """
word_dict1=tfidfer.build_wordsdict1(train_data)
for keyword in tfidfer.extract_keywords(train_data, self.keywords_num * 2):
list2.append(list(keyword))
# print(list2)
for i in range(len(list1)):
for j in range(len(list2)):
if list1[i][0] == list2[j][0]:
list3.append(list1[i])
for i in list1:
if i not in list3:
list4.append(i)
length = len(list3)
if length >= self.keywords_num:
for i in range(self.keywords_num):
result.append(list3[i])
else:
result = list3
if len(list4) >= self.keywords_num - length:
for i in range(self.keywords_num - length):
list3.append(list4[i])
result = list3
result.sort(key=lambda k: k[1], reverse=True)
for i in range(len(result)):
for word, word_tf in word_dict1.items():
if result[i][0] == word:
result[i].append(int(word_tf))
# print('result1：',result)
for i in range(len(result)):
list7 = []
list7.append(result[i][0])
# print('list7:',list7)
mapping = list(map(lambda x: self.keywords_set.index(x), list7))
# print('mapping:',mapping)
result[i][0] = mapping[0]
# print('result2：',result)
metas = [DiscreteVariable('keywords', self.keywords_set),
ContinuousVariable('weight'),
ContinuousVariable('word_frequency')]
# print('Domain(metas):',Domain(metas))
# listma=[[1,2,3],[4,5,6],[3,5,6]]
# print(listma)
domain = Domain(metas)
# print('domain.attributes:',domain.attributes)
# print('domain.class_vars:',domain.class_vars)
final_result = Table.from_list(domain, result)
# final_result=Table.from_list(Domain(metas),listma)
# print('final_result:',final_result)
json_res = {}
temp_lst = []
fields = ['name', 'weight', 'count']
for i in result:
temp_dir = {}
for j, k in enumerate(i):
if j != 0:
temp_dir[fields[j]] = k
else:
temp_dir[fields[j]] = self.keywords_set[k]
temp_lst.insert(0, temp_dir)
json_res['visualization_type'] = "keywords"
json_res['results'] = temp_lst
json_res["chartXName"] = 'weight'
json_res["chartYName"] = 'name'
json_res["tableCols"] = ['name', 'count']
# print(json_res)
self.send('Jsondata', json_res)
# json_dicts = json.dumps(json_res, indent=4)
# with open('c.json', 'w') as f:
# json.dump(json_res, f, ensure_ascii=False, indent=4)
# f.close()
# print("加载入文件完成...")
# print(json_dicts)
self.send('News', final_result)
self.send("Metas", metas)
def rerun(self):
try:
self.root_path = self.get_dataset_path()
"""--------add by wwang29---------"""
if self.temporary_dir is not None:
self.temporary_dir.cleanup()
self.set_file_path()
# os.chdir(self.root_path)
self.remove_files(self.file_path, 'fasttext_*.txt',
show=True) # in case did not removed
test_data = table2df(self.test_data)
test_data = self.add_commentid(test_data)
# test_data = test_data.dropna(subset=[self.label_name])
text_split = test_data
test_data_raw = test_data # test data may delete some records
# text_split= test_data.drop([self.label_name], axis=1)
self.text_split(text_split, 'test_split')
legal_test_data_id = list(test_data.index)
test_set = open(os.path.join(self.file_path,
'fasttext_test_split.txt'),
'r',
encoding='utf-8-sig')
predict_label = self.classifier.predict(test_set)
predict_label_df = pd.DataFrame(predict_label)
int_index = []
for index in predict_label:
int_index.append(int(index[0]))
test_set1 = open(os.path.join(self.file_path,
'fasttext_test_split.txt'),
'r',
encoding='utf-8-sig')
wind_pre_prob = self.classifier.predict_proba(test_set1)
wind_pre_prob_index = []
for index in wind_pre_prob:
wind_pre_prob_index.append(index[0][1])
"""-----------calculate probility-------------------------------"""
if len(self.label_domain) == 2:
metas = [
DiscreteVariable('predict', self.label_domain),
ContinuousVariable('negative probability'),
ContinuousVariable('positive probability')
]
else:
metas = [
DiscreteVariable('predict', self.label_domain),
ContinuousVariable('others'),
ContinuousVariable('selected class prob')
]
new_two_prob_positive = []
new_two_prob_negative = []
for index, item in enumerate(wind_pre_prob_index):
new_two_prob_negative.append(1 - item)
new_two_prob_positive.append(item)
wind_pre_prob = np.array([
np.array(new_two_prob_negative),
np.array(new_two_prob_positive)
])
wind_pre_prob = wind_pre_prob.T
total_pre_prob = self.merge_possibility(len(test_data_raw),
wind_pre_prob,
legal_test_data_id)
cols = [np.array(int_index).reshape(-1, 1), wind_pre_prob]
aa = np.array(int_index).reshape(-1, 1)
tbl = np.column_stack(
(np.array(int_index).reshape(-1, 1), wind_pre_prob))
res = Table.from_numpy(Domain(metas), tbl)
final_result = self.merge_data(self.test_data, res)
self.send("News", final_result)
self.send("Metric Score", None)
self.send("Metas", metas)
self.send("Columns", cols)
# self.remove_files(self.file_path, '*', show=True)
self.remove_files(self.file_path, 'fasttext_*.txt', show=True)
print('rerun')
test_set.close()
test_set1.close()
# shutil.rmtree(self.file_path)
# self.classifier = None # in order to pickle the model model
self.train_data = None
self.test_data = None
finally:
self.temporary_dir.cleanup()
self.temporary_dir = None
def run(self):
try:
self.root_path = self.get_dataset_path()
self.text_clomn_name = self.train_data.domain.attributes[0].name
# os.chdir(self.root_path)
self.get_name()
self.set_file_path()
test_data = table2df(self.test_data)
# try:
# test_data.iloc[:, :-len(self.test_data.domain.class_vars)] = \
# test_data.iloc[:, :-len(self.test_data.domain.class_vars)].astype('float')
# except Exception as e:
# print(e)
# raise Error('data must be numeric')
test_data = test_data.dropna(subset=[self.label_name])
test_data, test_label_domain = self.label_str2number(test_data)
test_data = self.add_commentid(test_data)
if len(test_data) != len(self.test_data):
raise Error("test data missing label")
test_data_raw = test_data # test data may delete some records
y_test_visual = test_data_raw[self.label_name]
train_data = table2df(self.train_data)
train_data = self.add_commentid(train_data)
# try:
# train_data.iloc[:, :-len(self.train_data.domain.class_vars)] = \
# train_data.iloc[:, :-len(self.train_data.domain.class_vars)].astype('float')
# except Exception as e:
# print(e)
# raise Error('data must be numeric')
if len(test_data.columns) != len(train_data.columns):
raise Error('train data and test data must match')
train_data = train_data.dropna(subset=[self.label_name])
train_data, self.label_domain = self.label_str2number(train_data)
# print("self.label_domain",self.label_domain)
Traina, validationa = train_test_split(train_data,
train_size=0.8,
random_state=1234)
Traina = self.add_commentid(Traina)
validationa = self.add_commentid(validationa)
self.text_split(Traina, 'trainingset')
legal_test_data_id = list(test_data.index)
test_split = test_data.drop([self.label_name], axis=1)
self.text_split(test_data, 'testset')
self.text_split(test_split, 'test_split')
self.text_split(validationa, 'validationset')
# training the model
"""---------add by wwang29----"""
try:
classifier_model = NamedTemporaryFile(suffix=".model.bin",
dir=self.file_path)
self.classifier = fasttext.supervised(
os.path.join(self.file_path, 'fasttext_trainingset.txt'),
os.path.splitext(classifier_model.name)[0],
label_prefix='__label__',
epoch=200,
min_count=3,
word_ngrams=3,
ws=10,
thread=32,
lr=0.1,
dim=200,
bucket=5000000)
self.classifier_code = classifier_model.read()
finally:
classifier_model.close()
# self.classifier = fasttext.supervised(os.path.join(self.file_path, 'fasttext_trainingset.txt'),
# os.path.join(self.model_path, 'fasttext_test.model'), label_prefix='__label__',
# epoch=200, min_count=3, word_ngrams=3, ws=10, thread=32, lr=0.1, dim=200,
# bucket=5000000)
"""--------end wwang29---------"""
train_result = self.classifier.test(
os.path.join(self.file_path, 'fasttext_trainingset.txt'))
train_score = train_result.precision
validation_result = self.classifier.test(
os.path.join(self.file_path, 'fasttext_validationset.txt'))
val_score = validation_result.precision
test_set = open(os.path.join(self.file_path,
'fasttext_test_split.txt'),
'r',
encoding='utf-8-sig')
predict_label = self.classifier.predict(test_set)
int_index = []
for index in predict_label:
int_index.append(int(index[0]))
test_set1 = open(os.path.join(self.file_path,
'fasttext_test_split.txt'),
'r',
encoding='utf-8-sig')
wind_pre_prob = self.classifier.predict_proba(test_set1)
wind_pre_prob_two_c = wind_pre_prob
wind_pre_prob_index = []
for index in wind_pre_prob:
wind_pre_prob_index.append(index[0][1])
"""-----------calculate probility-------------------------------"""
if len(self.label_domain) == 2:
metas = [
DiscreteVariable('predict', self.label_domain),
ContinuousVariable('negative probability'),
ContinuousVariable('positive probability')
]
else:
metas = [
DiscreteVariable('predict', self.label_domain),
ContinuousVariable('others'),
ContinuousVariable('selected class prob')
]
new_two_prob_positive = []
new_two_prob_negative = []
for index, item in enumerate(wind_pre_prob_index):
new_two_prob_negative.append(1 - item)
new_two_prob_positive.append(item)
wind_pre_prob = np.array([
np.array(new_two_prob_negative),
np.array(new_two_prob_positive)
])
wind_pre_prob = wind_pre_prob.T
total_pre_prob = self.merge_possibility(len(test_data_raw),
wind_pre_prob,
legal_test_data_id)
cols = [np.array(int_index).reshape(-1, 1), wind_pre_prob]
aa = np.array(int_index).reshape(-1, 1)
tbl = np.column_stack(
(np.array(int_index).reshape(-1, 1), wind_pre_prob))
res = Table.from_numpy(Domain(metas), tbl)
final_result = self.merge_data(self.test_data, res)
results = None
N = len(test_data) # note that self.test_data is orange Table
results = Results(self.test_data[legal_test_data_id],
store_data=True)
results.folds = None
results.row_indices = np.arange(N)
results.actual = np.array(y_test_visual[legal_test_data_id])
results.predicted = np.array([int_index])
results.probabilities = np.array([wind_pre_prob])
# results.probabilities = wind_pre_prob
"""-----changed by wwang29-------"""
# in sentiment classifier we add "illegal data" in domain, so -1, here, we do not use that
if len(self.label_domain) > 2:
results.probabilities = None
elif len(self.label_domain) == 2:
# test_set2 = open(os.path.join(self.file_path, 'fasttext_test_split.txt'), 'r', encoding='utf-8-sig')
# wind_pre_prob = self.classifier.predict_proba(test_set2)
pro_two_class = []
for p_index in range(len(wind_pre_prob_two_c)):
local_ = [0, 1]
local_[results.predicted[0]
[p_index]] = wind_pre_prob_two_c[p_index][0][1]
local_[results.predicted[0][p_index] -
1] = 1 - wind_pre_prob_two_c[p_index][0][1]
pro_two_class.append(local_)
pro_two_class = np.array(pro_two_class)
results.probabilities[0] = pro_two_class
"""-----------change end by wwang29-----------------"""
print("predicted", results.predicted[0])
print("actual", results.actual)
results.learner_names = ['Multiple_text_Classifier']
# self.send("Predictions", predictions)
self.send("Evaluation Results", results)
metric_frame = MetricFrame(
[[val_score], [train_score]],
index=["hyperparam_searcher", "modeler"],
columns=[MetricType.ACCURACY.name])
self.send("Metric Score", metric_frame)
self.send("Columns", cols)
self.send("Metas", metas)
self.send("Metric", MetricType.ACCURACY)
# print(MetricType.ACCURACY)
print("metric_frame", metric_frame)
print("result:", results.predicted, results.predicted.shape)
self.send('News', final_result)
self.remove_files(self.file_path, 'fasttext_*.txt', show=True)
test_set.close()
test_set1.close()
# self.classifier = None # in order to pickle the model model
self.train_data = None
self.test_data = None
finally:
self.temporary_dir.cleanup()
self.temporary_dir = None
def rerun(self):
test_data = table2df(self.test_data)
test_data = self.add_commentid(test_data)
# test_data = test_data.dropna(subset=[self.label_name])
text_split = test_data
test_data_raw = test_data # test data may delete some records
# text_split= test_data.drop([self.label_name], axis=1)
self.text_split(text_split, 'test_split')
legal_test_data_id = list(test_data.index)
#loading the fasttext model
classifier = fasttext.load_model("fasttext_test.model.bin",
label_prefix="__label__")
test_set = open('fasttext_test_split.txt', 'r', encoding='utf-8-sig')
predict_label = classifier.predict(test_set)
predict_label_df = pd.DataFrame(predict_label)
int_index = []
for index in predict_label:
int_index.append(int(index[0]))
test_set1 = open('fasttext_test_split.txt', 'r', encoding='utf-8-sig')
wind_pre_prob = self.classifier.predict_proba(test_set1)
wind_pre_prob_index = []
for index in wind_pre_prob:
wind_pre_prob_index.append(index[0][1])
"""-----------calculate probility-------------------------------"""
metas = [
DiscreteVariable('predict', self.label_domain),
ContinuousVariable('negative probability'),
ContinuousVariable('positive probability')
]
new_two_prob_positive = []
new_two_prob_negative = []
for index, item in enumerate(wind_pre_prob_index):
new_two_prob_negative.append(1 - item)
new_two_prob_positive.append(item)
wind_pre_prob = np.array(
[np.array(new_two_prob_negative),
np.array(new_two_prob_positive)])
wind_pre_prob = wind_pre_prob.T
total_pre_prob = self.merge_possibility(len(test_data_raw),
wind_pre_prob,
legal_test_data_id)
cols = [np.array(int_index).reshape(-1, 1), wind_pre_prob]
aa = np.array(int_index).reshape(-1, 1)
tbl = np.column_stack((np.array(int_index).reshape(-1,
1), wind_pre_prob))
res = Table.from_numpy(Domain(metas), tbl)
final_result = self.merge_data(self.test_data, res)
self.send("News", final_result)
self.send("Metric Score", None)
self.send("Metas", metas)
self.send("Columns", cols)
print('rerun')
self.remove_files('.', 'fasttext_*.txt', show=True)
def run(self):
self.get_name()
test_data = table2df(self.test_data)
# try:
# test_data.iloc[:, :-len(self.test_data.domain.class_vars)] = \
# test_data.iloc[:, :-len(self.test_data.domain.class_vars)].astype('float')
# except Exception as e:
# print(e)
# raise Error('data must be numeric')
test_data = test_data.dropna(subset=[self.label_name])
test_data, test_label_domain = self.label_str2number(test_data)
test_data = self.add_commentid(test_data)
if len(test_data) != len(self.test_data):
raise Error("test data missing label")
test_data_raw = test_data # test data may delete some records
y_test_visual = test_data_raw[self.label_name]
train_data = table2df(self.train_data)
train_data = self.add_commentid(train_data)
# try:
# train_data.iloc[:, :-len(self.train_data.domain.class_vars)] = \
# train_data.iloc[:, :-len(self.train_data.domain.class_vars)].astype('float')
# except Exception as e:
# print(e)
# raise Error('data must be numeric')
if len(test_data.columns) != len(train_data.columns):
raise Error('train data and test data must match')
train_data = train_data.dropna(subset=[self.label_name])
train_data, self.label_domain = self.label_str2number(train_data)
# print("self.label_domain",self.label_domain)
Traina, validationa = train_test_split(train_data,
train_size=0.8,
random_state=1234)
Traina = self.add_commentid(Traina)
validationa = self.add_commentid(validationa)
self.text_split(Traina, 'trainingset')
legal_test_data_id = list(test_data.index)
test_split = test_data.drop([self.label_name], axis=1)
self.text_split(test_data, 'testset')
self.text_split(test_split, 'test_split')
self.text_split(validationa, 'validationset')
#training the model
self.classifier = fasttext.supervised('fasttext_trainingset.txt',
'fasttext_test.model',
label_prefix='__label__',
thread=32,
epoch=50,
lr=0.1,
dim=200,
bucket=5000000)
train_result = self.classifier.test('fasttext_trainingset.txt')
train_score = train_result.precision
validation_result = self.classifier.test('fasttext_validationset.txt')
val_score = validation_result.precision
test_set = open('fasttext_test_split.txt', 'r', encoding='utf-8-sig')
predict_label = self.classifier.predict(test_set)
int_index = []
for index in predict_label:
int_index.append(int(index[0]))
test_set1 = open('fasttext_test_split.txt', 'r', encoding='utf-8-sig')
wind_pre_prob = self.classifier.predict_proba(test_set1)
wind_pre_prob_index = []
for index in wind_pre_prob:
wind_pre_prob_index.append(index[0][1])
"""-----------calculate probility-------------------------------"""
metas = [
DiscreteVariable('predict', self.label_domain),
ContinuousVariable('negative probability'),
ContinuousVariable('positive probability')
]
new_two_prob_positive = []
new_two_prob_negative = []
for index, item in enumerate(wind_pre_prob_index):
new_two_prob_negative.append(1 - item)
new_two_prob_positive.append(item)
wind_pre_prob = np.array(
[np.array(new_two_prob_negative),
np.array(new_two_prob_positive)])
wind_pre_prob = wind_pre_prob.T
total_pre_prob = self.merge_possibility(len(test_data_raw),
wind_pre_prob,
legal_test_data_id)
cols = [np.array(int_index).reshape(-1, 1), wind_pre_prob]
aa = np.array(int_index).reshape(-1, 1)
tbl = np.column_stack((np.array(int_index).reshape(-1,
1), wind_pre_prob))
res = Table.from_numpy(Domain(metas), tbl)
final_result = self.merge_data(self.test_data, res)
results = None
N = len(test_data) # note that self.test_data is orange Table
results = Results(self.test_data[legal_test_data_id], store_data=True)
results.folds = None
results.row_indices = np.arange(N)
results.actual = np.array(y_test_visual[legal_test_data_id])
results.predicted = np.array([int_index])
results.probabilities = np.array([wind_pre_prob])
# results.probabilities = wind_pre_prob
if len(self.label_domain) - 1 > 2:
results.probabilities = None
print("predicted", results.predicted[0])
print("actual", results.actual)
results.learner_names = ['Multiple_text_Classifier']
# self.send("Predictions", predictions)
self.send("Evaluation Results", results)
metric_frame = MetricFrame([[val_score], [train_score]],
index=["hyperparam_searcher", "modeler"],
columns=[MetricType.ACCURACY.name])
self.send("Metric Score", metric_frame)
self.send("Columns", cols)
self.send("Metas", metas)
self.send("Metric", MetricType.ACCURACY)
# print(MetricType.ACCURACY)
print("metric_frame", metric_frame)
print("result:", results.predicted, results.predicted.shape)
self.send('News', final_result)
self.remove_files('.', 'fasttext_*.txt', show=True)