def test_fit(self): #file_path = "../Dataset/00-91-Drugs-All-In-One-File.csv" #loaded_data = FileManager.load_file(file_path) read_data = ReadData() loaded_data = read_data.read_data_and_set_variable_settings("../Dataset/00-91-Drugs-All-In-One-File.csv", "../Dataset/VariableSetting.csv") data_manager = DataManager(normalizer=None) data_manager.set_data(loaded_data) data_manager.split_data_into_train_valid_test_sets() model = svm.SVR() velocity = Velocity() velocity_matrix = velocity.create_first_velocity() # define the first population # validation of a row generating random row for population = Population(velocity_matrix=velocity_matrix) population.create_first_population() debpso = DEBPSO(population.population_matrix[1]) debpso.fit(data_manager.inputs[SplitTypes.Train], data_manager.targets[SplitTypes.Train])
def test_transform(self): read_data = ReadData() loaded_data = read_data.read_data_and_set_variable_settings("../Dataset/00-91-Drugs-All-In-One-File.csv", "../Dataset/VariableSetting.csv") data_manager = DataManager(normalizer=None) data_manager.set_data(loaded_data) data_manager.split_data_into_train_valid_test_sets() model = svm.SVR() velocity = Velocity() velocity_matrix = velocity.create_first_velocity() # define the first population # validation of a row generating random row for population = Population(velocity_matrix=velocity_matrix) population.create_first_population() debpso = DEBPSO(population.population_matrix[0]) debpso.fit(data_manager.inputs[SplitTypes.Train], data_manager.targets[SplitTypes.Train]) data_manager.transformed_input[SplitTypes.Train] = debpso.transform(data_manager.inputs[SplitTypes.Train]) print("Population 0 row sum ", population.population_matrix[0].sum()) print("Selected feature descriptors",debpso.sel_descriptors_for_curr_population) print("Transformed array", data_manager.transformed_input[SplitTypes.Train])
def test_transform(self): read_data = ReadData() loaded_data = read_data.read_data_and_set_variable_settings( "../Dataset/00-91-Drugs-All-In-One-File.csv", "../Dataset/VariableSetting.csv") data_manager = DataManager(normalizer=None) data_manager.set_data(loaded_data) data_manager.split_data_into_train_valid_test_sets() model = svm.SVR() velocity = Velocity() velocity_matrix = velocity.create_first_velocity() # define the first population # validation of a row generating random row for population = Population(velocity_matrix=velocity_matrix) population.create_first_population() debpso = DEBPSO(population.population_matrix[0]) debpso.fit(data_manager.inputs[SplitTypes.Train], data_manager.targets[SplitTypes.Train]) data_manager.transformed_input[SplitTypes.Train] = debpso.transform( data_manager.inputs[SplitTypes.Train]) print("Population 0 row sum ", population.population_matrix[0].sum()) print("Selected feature descriptors", debpso.sel_descriptors_for_curr_population) print("Transformed array", data_manager.transformed_input[SplitTypes.Train])
def test_fit(self): #file_path = "../Dataset/00-91-Drugs-All-In-One-File.csv" #loaded_data = FileManager.load_file(file_path) read_data = ReadData() loaded_data = read_data.read_data_and_set_variable_settings( "../Dataset/00-91-Drugs-All-In-One-File.csv", "../Dataset/VariableSetting.csv") data_manager = DataManager(normalizer=None) data_manager.set_data(loaded_data) data_manager.split_data_into_train_valid_test_sets() model = svm.SVR() velocity = Velocity() velocity_matrix = velocity.create_first_velocity() # define the first population # validation of a row generating random row for population = Population(velocity_matrix=velocity_matrix) population.create_first_population() debpso = DEBPSO(population.population_matrix[1]) debpso.fit(data_manager.inputs[SplitTypes.Train], data_manager.targets[SplitTypes.Train])
def generate_population_matrix(self, current_alpha): self.old_population_matrix = np.copy(self.population_matrix) for row_index in range(0, self.selective_section): for col_index in range(0, VariableSetting.No_of_Descriptors): if current_alpha < self.velocity_matrix[row_index][ col_index] and self.velocity_matrix[row_index][ col_index] <= (0.5 * (1 + current_alpha)): self.population_matrix[row_index][ col_index] = self.local_best_matrix[row_index][ col_index] elif ((0.5 * (1 + current_alpha)) < self.velocity_matrix[row_index][col_index]) and ( self.velocity_matrix[row_index][col_index] <= (1 - VariableSetting.Beta)): self.population_matrix[row_index][ col_index] = self.global_best_row[col_index] elif ((1 - VariableSetting.Beta) < self.velocity_matrix[row_index][col_index]) and ( self.velocity_matrix[row_index][col_index] <= 1): self.population_matrix[row_index][ col_index] = 1 - self.population_matrix[row_index][ col_index] for row_index in range(self.selective_section, VariableSetting.Population_Size): velocity_object = Velocity() random_velocity_row = velocity_object.get_valid_row() self.population_matrix[ row_index] = Population.create_valid_random_population_row( random_velocity_row)
def test_fit(self): file_path = "../Dataset/00-91-Drugs-All-In-One-File.csv" loaded_data = FileManager.load_file(file_path) data_manager = DataManager(normalizer=None) data_manager.set_data(loaded_data) data_manager.split_data_into_train_valid_test_sets(test_split=0.15, train_split=0.70) model = svm.SVR() velocity = Velocity() velocity_matrix = velocity.create_first_velocity() # define the first population # validation of a row generating random row for population = Population(velocity_matrix=velocity_matrix) population.create_first_population() debpso = DEBPSO(population.population_matrix[1]) debpso.fit(data_manager.inputs[SplitTypes.Train], data_manager.targets[SplitTypes.Train]) print("Population 1 row sum ", population.population_matrix[1].sum()) print("Selected feature descriptors", debpso.sel_descriptors_for_curr_population)
def generate_population_matrix(self, current_alpha): self.old_population_matrix = np.copy(self.population_matrix) for row_index in range(0, self.selective_section): for col_index in range(0, VariableSetting.No_of_Descriptors): if current_alpha< self.velocity_matrix[row_index][col_index] and self.velocity_matrix[row_index][col_index] <= (0.5 * (1+ current_alpha)): self.population_matrix[row_index][col_index] = self.local_best_matrix[row_index][col_index] elif ((0.5 * (1+ current_alpha)) < self.velocity_matrix[row_index][col_index]) and (self.velocity_matrix[row_index][col_index] <= (1 - VariableSetting.Beta)): self.population_matrix[row_index][col_index] = self.global_best_row[col_index] elif ((1 - VariableSetting.Beta ) < self.velocity_matrix[row_index][col_index]) and (self.velocity_matrix[row_index][col_index] <= 1): self.population_matrix[row_index][col_index] = 1 - self.population_matrix[row_index][col_index] for row_index in range(self.selective_section, VariableSetting.Population_Size): velocity_object = Velocity() random_velocity_row = velocity_object.get_valid_row() self.population_matrix[row_index] = Population.create_valid_random_population_row(random_velocity_row)
def test_fit(self): file_path = "../Dataset/00-91-Drugs-All-In-One-File.csv" loaded_data = FileManager.load_file(file_path) data_manager = DataManager(normalizer=None) data_manager.set_data(loaded_data) data_manager.split_data_into_train_valid_test_sets(test_split=0.15, train_split=0.70) model = svm.SVR() velocity = Velocity() velocity_matrix = velocity.create_first_velocity() # define the first population # validation of a row generating random row for population = Population(velocity_matrix=velocity_matrix) population.create_first_population() debpso = DEBPSO(population.population_matrix[1]) debpso.fit(data_manager.inputs[SplitTypes.Train], data_manager.targets[SplitTypes.Train]) print("Population 1 row sum ", population.population_matrix[1].sum()) print("Selected feature descriptors",debpso.sel_descriptors_for_curr_population)
def create_first_velocity(self): velocity = Velocity() self.velocity_matrix = velocity.create_first_velocity()