def make_use_tfidf_with_results(self):
x_all = self.__vectors_provider.get_tfidf_vectors()
y_all = self.__data_source.get_y_multi_label()
model1 = LabelPowerset(
LogisticRegression(C=1.0, solver='sag', n_jobs=-1))
Evaluator.multi_label_predict_proba_tfidf(
model1, x_all, y_all, data_source=self.__data_source)
def make_use_d2v(self):
x_all = self.__vectors_provider.get_d2v_vectors_dbow()
y_all = self.__data_source.get_y_multi_label()
# TODO here grid search
base_estimators = [
LogisticRegression(C=1.0, solver='sag', n_jobs=-1),
LogisticRegression(n_jobs=-1),
LinearSVC(),
MLPClassifier()
]
model_params = [
"LogisticRegression(C=1.0, solver='sag')", 'LogisticRegression()',
"LinearSVC()", "MLPClassifier()"
]
i = 0
for base_estimator in base_estimators:
logging.warning(str(datetime.now()) + 'Start ' + model_params[i])
try:
model = OneVsRestClassifier(base_estimator, n_jobs=-1)
# cross_val_f1 = Evaluator.evaluate_only_cross_val(model, x_all, y_all)
# self.__visualizer.show_results_briefly(self.__CLASSIFIER_NAME, model_params[i],
# "Word2Vec_CBOW", cross_val_f1)
report, micro, macro, weighted = Evaluator.multi_label_report(
model, x_all, y_all)
self.__visualizer.save_metrics(self.__CLASSIFIER_NAME,
model_params[i], "Doc2Vec_DBOW",
report, micro, macro, weighted)
except:
logging.warning('Error on ' + model_params[i])
logging.warning(str(datetime.now()) + 'End ' + model_params[i])
i += 1
def make_use_w2v_fix(self):
x_all = self.__vectors_provider.get_w2v_vectors_fix()
y_all = self.__data_source.get_y_multi_label()
# TODO here grid search
base_estimators = [
LogisticRegression(C=1.0, solver='sag', n_jobs=-1),
# LogisticRegression(n_jobs=-1),
# LinearSVC(),
# MLPClassifier()
]
model_params = [
"LogisticRegression(C=1.0, solver='sag')",
# "LogisticRegression()",
# "LinearSVC()",
# "MLPClassifier()"
]
i = 0
for base_estimator in base_estimators:
logging.warning(str(datetime.now()) + 'Start ' + model_params[i])
try:
model = LabelPowerset(base_estimator)
cross_val_f1 = Evaluator.evaluate_only_cross_val(
model, x_all, y_all)
self.__visualizer.show_results_briefly(self.__CLASSIFIER_NAME,
model_params[i],
"Word2Vec_fix",
cross_val_f1)
except:
logging.warning('Error on ' + model_params[i])
logging.warning(str(datetime.now()) + 'End ' + model_params[i])
i += 1
def make_use_w2v_big(self):
x_all = self.__vectors_provider.get_w2v_big_vectors()
y_all = self.__data_source.get_y()
# TODO here grid search
estimators = []
part1 = LogisticRegression(C=0.5, solver='liblinear')
estimators.append(('logistic', part1))
part2 = SVC(C=10, kernel='rbf')
estimators.append(('svc', part2))
part3 = KNeighborsClassifier(algorithm='auto',
metric='minkowski',
weights='distance')
estimators.append(('knn', part3))
model1 = VotingClassifier(estimators)
cross_val_accuracy, cross_val_f1, train_accuracy, train_f1, test_accuracy, test_f1, y_true, y_pred \
= Evaluator.evaluate(model1, x_all, y_all)
self.__visualizer.show_results(self.__CLASSIFIER_NAME, "model1",
"Word2VecBig", cross_val_accuracy,
cross_val_f1, train_accuracy, train_f1,
test_accuracy, test_f1, y_true, y_pred)
def make_use_w2v(self):
x_all = self.__vectors_provider.get_w2v_vectors_cbow()
y_all = self.__data_source.get_y_multi_label()
# TODO here grid search
base_estimators = [
LogisticRegression(C=1.0, solver='sag', n_jobs=-1),
LogisticRegression(n_jobs=-1),
# LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
# intercept_scaling=True, max_iter=100, multi_class='ovr', penalty='l2', random_state=None,
# solver='newton-cg',
# tol=0.0001, warm_start=False, n_jobs=-1),
LinearSVC(),
MLPClassifier()
# SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0,
# decision_function_shape='ovr', degree=0, gamma='auto', kernel='linear',
# max_iter=-1, probability=True, random_state=None, shrinking=True,
# tol=0.001, verbose=False)
]
model_params = [
"LogisticRegression(C=1.0, solver='sag')",
'LogisticRegression()',
# "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\
# intercept_scaling=True, max_iter=100, multi_class='ovr', penalty='l2', random_state=None,\
# solver='newton-cg',\
# tol=0.0001, warm_start=False, n_jobs=-1)",
"LinearSVC()",
"MLPClassifier()",
# "SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0,\
# decision_function_shape='ovr', degree=0, gamma='auto', kernel='linear',\
# max_iter=-1, probability=True, random_state=None, shrinking=True,\
# tol=0.001, verbose=False)"
]
i = 0
for base_estimator in base_estimators:
logging.warning(str(datetime.now()) + 'Start ' + model_params[i])
try:
model = OneVsRestClassifier(base_estimator, n_jobs=-1)
# cross_val_f1 = Evaluator.evaluate_only_cross_val(model, x_all, y_all)
# self.__visualizer.show_results_briefly(self.__CLASSIFIER_NAME, model_params[i],
# "Word2Vec_CBOW", cross_val_f1)
report, micro, macro, weighted = Evaluator.multi_label_report(
model, x_all, y_all)
self.__visualizer.save_metrics(self.__CLASSIFIER_NAME,
model_params[i],
"Word2Vec_CBOW", report, micro,
macro, weighted)
except:
logging.warning('Error on ' + model_params[i])
logging.warning(str(datetime.now()) + 'End ' + model_params[i])
i += 1
def make_use_w2v(self):
x_all = self.__vectors_provider.get_w2v_vectors()
y_all = self.__data_source.get_y()
# TODO here grid search
model1 = LogisticRegression(C=1.0, solver='sag', n_jobs=-1)
Evaluator.cross_probabilities(model=model1,
x_all=x_all,
y_all=y_all,
data_source=self.__data_source,
method='w2v')
cross_val_accuracy, cross_val_f1, train_accuracy, train_f1, test_accuracy, test_f1, y_true, y_pred \
= Evaluator.evaluate(model1, x_all, y_all)
self.__visualizer.show_results(self.__CLASSIFIER_NAME,
"(C=1.0, solver='sag')", "Word2Vec",
cross_val_accuracy, cross_val_f1,
train_accuracy, train_f1, test_accuracy,
test_f1, y_true, y_pred)
def make_use_w2v_with_tfidf(self):
x_all = self.__vectors_provider.get_w2v_tfidf_vectors()
y_all = self.__data_source.get_y()
# TODO here grid search
model1 = SVC(C=1, kernel='linear', probability=True)
cross_val_accuracy, cross_val_f1, train_accuracy, train_f1, test_accuracy, test_f1, y_true, y_pred \
= Evaluator.evaluate(model1, x_all, y_all)
self.__visualizer.show_results(
self.__CLASSIFIER_NAME, "(C=1, kernel='linear', probability=True)",
"Word2Vec&TF-IDF", cross_val_accuracy, cross_val_f1,
train_accuracy, train_f1, test_accuracy, test_f1, y_true, y_pred)
def make_use_w2v_with_tfidf(self):
x_all = self.__vectors_provider.get_w2v_tfidf_vectors()
y_all = self.__data_source.get_y()
# TODO here grid search
model1 = LogisticRegression(C=1.0, solver='sag', n_jobs=-1)
cross_val_accuracy, cross_val_f1, train_accuracy, train_f1, test_accuracy, test_f1, y_true, y_pred \
= Evaluator.evaluate(model1, x_all, y_all)
self.__visualizer.show_results(self.__CLASSIFIER_NAME,
"(C=1.0, solver='sag')",
"Word2Vec&TF-IDF", cross_val_accuracy,
cross_val_f1, train_accuracy, train_f1,
test_accuracy, test_f1, y_true, y_pred)
def make_use_w2v_with_tfidf(self):
x_all = self.__vectors_provider.get_w2v_tfidf_vectors()
y_all = self.__data_source.get_y()
# TODO here grid search
model1 = KNeighborsClassifier(algorithm='auto', metric='minkowski', weights='distance')
cross_val_accuracy, cross_val_f1, train_accuracy, train_f1, test_accuracy, test_f1, y_true, y_pred \
= Evaluator.evaluate(model1, x_all, y_all)
self.__visualizer.show_results(self.__CLASSIFIER_NAME, "(algorithm='auto', metric='minkowski', weights='distance')",
"Word2Vec&TF-IDF",
cross_val_accuracy, cross_val_f1,
train_accuracy, train_f1,
test_accuracy, test_f1, y_true, y_pred)
def make_use_w2v_old(self):
""" Обучает старый датасет с использованием w2v обученного на новом """
x_all = self.__vectors_provider.get_w2v_old_vectors()
y_all = self.__data_source.get_y()
# TODO here grid search
model1 = SVC(C=1, kernel='linear', probability=True)
cross_val_accuracy, cross_val_f1, train_accuracy, train_f1, test_accuracy, test_f1, y_true, y_pred \
= Evaluator.evaluate(model1, x_all, y_all)
self.__visualizer.show_results(
self.__CLASSIFIER_NAME, "(C=1, kernel='linear', probability=True)",
"Word2VecNewOld", cross_val_accuracy, cross_val_f1, train_accuracy,
train_f1, test_accuracy, test_f1, y_true, y_pred)
def make_use_w2v_old(self):
""" Обучает старый датасет с использованием w2v обученного на новом """
x_all = self.__vectors_provider.get_w2v_old_vectors()
y_all = self.__data_source.get_y()
# TODO here grid search
model1 = LogisticRegression(C=1.0, solver='sag', n_jobs=-1)
cross_val_accuracy, cross_val_f1, train_accuracy, train_f1, test_accuracy, test_f1, y_true, y_pred \
= Evaluator.evaluate(model1, x_all, y_all)
self.__visualizer.show_results(self.__CLASSIFIER_NAME,
"(C=1.0, solver='sag')",
"Word2VecNewOld", cross_val_accuracy,
cross_val_f1, train_accuracy, train_f1,
test_accuracy, test_f1, y_true, y_pred)
def make_use_w2v_old(self):
""" Обучает старый датасет с использованием w2v обученного на новом """
x_all = self.__vectors_provider.get_w2v_old_vectors()
y_all = self.__data_source.get_y()
# TODO here grid search
model1 = KNeighborsClassifier(algorithm='auto', metric='minkowski', weights='distance')
cross_val_accuracy, cross_val_f1, train_accuracy, train_f1, test_accuracy, test_f1, y_true, y_pred \
= Evaluator.evaluate(model1, x_all, y_all)
self.__visualizer.show_results(self.__CLASSIFIER_NAME, "(algorithm='auto', metric='minkowski', weights='distance')",
"Word2VecNewOld",
cross_val_accuracy, cross_val_f1,
train_accuracy, train_f1,
test_accuracy, test_f1, y_true, y_pred)
def make_use_tfidf(self):
x_all = self.__vectors_provider.get_tfidf_vectors()
y_all = self.__data_source.get_y_multi_label()
# binarizer = MultiLabelBinarizer()
# y_all = binarizer.fit_transform(y_all)
#
# default = LabelPowerset()
# parameters = [
# {
# 'classifier': [SVC()],
# 'classifier__C': np.logspace(-1, 5, 7),
# 'classifier__kernel': ['linear', 'poly', 'rbf', 'sigmoid'],
# 'classifier__degree': range(0, 5, 1),
# 'classifier__probability': [True, False],
# 'classifier__shrinking': [True, False],
#
# }
# ]
#
# grid = GridSearchCV(default, parameters, scoring='f1_weighted', cv=5, n_jobs=-1)
# grid.fit(x_all, y_all)
# best_param = grid.best_params_
# self.__visualizer.save_best_params(self.__CLASSIFIER_NAME, 'SVC()', "tfidf", best_param)
#
# logging.warning(str(datetime.now()) + 'End SVC')
#
# default = LabelPowerset()
#
# parameters = [
# {
# 'classifier': [LogisticRegression()],
# 'classifier__fit_intercept': (True, False),
# 'classifier__C': np.logspace(-1, 5, 7),
# 'classifier__intercept_scaling': (True, False),
# 'classifier__class_weight': (None, 'balanced'),
# 'classifier__solver': ('newton-cg', 'lbfgs', 'sag', 'saga'),
# 'classifier__multi_class': ('ovr', 'multinomial')
# }]
#
# grid = GridSearchCV(default, parameters, scoring='f1_weighted', cv=5, n_jobs=-1)
# grid.fit(x_all, y_all)
# best_param = grid.best_params_
# self.__visualizer.save_best_params(self.__CLASSIFIER_NAME, 'LogisticRegression()', "tfidf", best_param)
#
# logging.warning(str(datetime.now()) + 'End LogisticRegression')
base_estimators = [
LogisticRegression(C=1.0, solver='sag', n_jobs=-1),
# LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
# intercept_scaling=True, max_iter=100, multi_class='ovr', penalty='l2', random_state=None,
# solver='newton-cg',
# tol=0.0001, warm_start=False, n_jobs=-1),
# LinearSVC(),
# MLPClassifier(),
# SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0,
# decision_function_shape='ovr', degree=0, gamma='auto', kernel='linear',
# max_iter=-1, probability=True, random_state=None, shrinking=True,
# tol=0.001, verbose=False)
]
model_params = [
"LogisticRegression(C=1.0, solver='sag')",
# "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\
# intercept_scaling=True, max_iter=100, multi_class='ovr', penalty='l2', random_state=None,\
# solver='newton-cg',\
# tol=0.0001, warm_start=False, n_jobs=-1)",
# "LinearSVC()",
# "MLPClassifier()",
# "SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0,\
# decision_function_shape='ovr', degree=0, gamma='auto', kernel='linear',\
# max_iter=-1, probability=True, random_state=None, shrinking=True,\
# tol=0.001, verbose=False)"
]
i = 0
for base_estimator in base_estimators:
logging.warning(str(datetime.now()) + 'Start ' + model_params[i])
try:
model = LabelPowerset(base_estimator)
# cross_val_f1 = Evaluator.evaluate_only_cross_val(model, x_all, y_all)
# self.__visualizer.show_results_briefly(self.__CLASSIFIER_NAME, model_params[i],
# "Word2Vec_CBOW", cross_val_f1)
report, micro, macro, weighted = Evaluator.multi_label_report(
model, x_all, y_all, True)
self.__visualizer.save_metrics(self.__CLASSIFIER_NAME,
model_params[i], "tfidf",
report, micro, macro, weighted)
except:
logging.warning('Error on ' + model_params[i])
logging.warning(str(datetime.now()) + 'End ' + model_params[i])
i += 1
