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 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 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 test_run_experiment_for_DEBPSO_population_With_Velocity(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") #output_filename = FileManager.create_output_file() #rescaling_normalizer = RescalingNormalizer() #scikit_normalizer = ScikitNormalizer() #data_manager = DataManager(normalizer=scikit_normalizer) data_manager = DataManager(normalizer=None) data_manager.set_data(loaded_data) data_manager.split_data_into_train_valid_test_sets() #data_manager.feature_selector = debpso feature_selection_algo = None model = None if VariableSetting.Feature_Selection_Algorithm == 'GA' and VariableSetting.Model == 'SVM': #feature_selection_algo = GA() model = svm.SVR() elif VariableSetting.Feature_Selection_Algorithm == 'DEBPSO' and VariableSetting.Model == 'SVM': feature_selection_algo = DEBPSO() model = svm.SVR() experiment = Experiment(data_manager, model, feature_selection_algo) experiment.run_experiment()
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)
"../Dataset/VariableSetting.csv") output_filename = FileManager.create_output_file() #normalizer = ZeroOneMinMaxNormalizer() #normalizer = MinMaxScaler() normalizer = None data_manager = DataManager(normalizer=normalizer) data_manager.set_data(loaded_data) data_manager.split_data_into_train_valid_test_sets() #data_manager.feature_selector = debpso #set feature selection algorithm based on variable settings feature_selection_algo = None if VariableSetting.Feature_Selection_Algorithm == 'DEBPSO': feature_selection_algo = DEBPSO() if VariableSetting.Feature_Selection_Algorithm == 'LinearSVC': feature_selection_algo = LinearSVC() #set model based on variable settings if VariableSetting.Model == 'SVM': model = svm.SVR() elif VariableSetting.Model == 'BayesianRidge': model = linear_model.BayesianRidge() elif VariableSetting.Model == 'GradientBoostingRegressor': model = GradientBoostingRegressor() elif VariableSetting.Model == 'RandomizedLasso': model = RandomizedLasso() elif VariableSetting.Model == 'LinearRegression': model = LinearRegression()