def test_tsvm_get_param_names(self):
"""
It checks the names of the hyper-parameters of TSVM estimator that returned
by its get_param_names
"""
self.assertEqual([
TSVM('linear').get_params_names(),
TSVM('RBF').get_params_names()
], [['C1', 'C2'], ['C1', 'C2', 'gamma']])
def test_tsvm_set_get_params_rbf(self):
"""
It checks that set_params and get_params works correctly for TSVM-RBF
"""
expected_output = {
'C2': 0.25,
'C1': 2,
'rect_kernel': 1,
'gamma': 0.125,
'kernel': 'RBF'
}
tsvm_cls = TSVM('RBF')
tsvm_cls.set_params(**{'C1': 2, 'C2': 0.25, 'gamma': 0.125})
self.assertEqual(tsvm_cls.get_params(), expected_output,
'set_params and get_params output don\'t match')
def test_RBF_Validator_ttsplit(self):
"""
It applies train/test split in non-linear TSVM
"""
tsvm_classifier = TSVM(kernel='RBF')
validate = Validator(self.input.X_train, self.input.y_train, ('t_t_split', \
self.train_set_size), tsvm_classifier)
func = validate.choose_validator()
func({'C1': 1, 'C2': 1, 'gamma': 1})
def test_linear_Validator_CV(self):
"""
It applies cross validation on Linear TSVM
"""
tsvm_classifier = TSVM()
validate = Validator(self.input.X_train, self.input.y_train, ('CV', \
self.k_folds), tsvm_classifier)
func = validate.choose_validator()
func({'C1': 1, 'C2': 1})
def initializer(user_input_obj):
"""
It passes a user's input to the functions and classes for solving a
classification task. The steps that this function performs can be summarized
as follows:
#. Specifies a TwinSVM classifier based on the user's input.
#. Chooses an evaluation method for assessment of the classifier.
#. Computes all the combination of search elements.
#. Computes the evaluation metrics for all the search element using grid search.
#. Saves the detailed classification results in a spreadsheet file (Excel).
Parameters
----------
user_input_obj : object
An instance of :class:`UserInput` class which holds the user input.
"""
if user_input_obj.class_type == 'binary':
tsvm_obj = TSVM(user_input_obj.kernel_type, user_input_obj.rect_kernel)
elif user_input_obj.class_type == 'ovo':
tsvm_obj = OVO_TSVM(user_input_obj.kernel_type)
elif user_input_obj.class_type == 'ova':
tsvm_obj = MCTSVM(user_input_obj.kernel_type)
validate = Validator(user_input_obj.X_train, user_input_obj.y_train, \
user_input_obj.test_method_tuple, tsvm_obj)
search_elements = search_space(user_input_obj.kernel_type, user_input_obj.class_type, \
user_input_obj.lower_b_c, user_input_obj.upper_b_c, user_input_obj.lower_b_u, \
user_input_obj.upper_b_u)
# Display headers
print("%s-%s Dataset: %s Total Search Elements: %d" % (tsvm_obj.cls_name,
user_input_obj.kernel_type, user_input_obj.filename, len(search_elements)))
result = grid_search(search_elements, validate.choose_validator())
try:
return save_result(user_input_obj.filename, validate, result, user_input_obj.result_path)
except FileNotFoundError:
os.makedirs('result')
return save_result(user_input_obj.filename, validate, result, user_input_obj.result_path)
def test_train_TSVM_RBF(self):
"""
It trains TSVM classifier with RBF kernel
"""
# Default arguments
tsvm_classifier = TSVM(kernel='RBF')
tsvm_classifier.fit(self.input.X_train, self.input.y_train)
tsvm_classifier.predict(self.input.X_train)
def test_train_TSVM_linear(self):
"""
It trains TSVM classifier with Linear kernel
"""
# Default arguments
tsvm_classifier = TSVM()
tsvm_classifier.fit(self.input.X_train, self.input.y_train)
tsvm_classifier.predict(self.input.X_train)