def test_linear(): hyperparameter_elm = {'reg': [10**i for i in range(-3, 4)]} value_dict = check_algorithm(folder='data', dataset='iris', algorithm='LinearRegressor', hyperparameter=hyperparameter_elm, metric_list=['accuracy', 'rmse'])
def test_ols_regression(): value_dict = check_algorithm(folder='data_regression', dataset='housing', algorithm='OLS', hyperparameter=None, metric_list=['rmse'], classification=False)
def test_linear_regression(): hyperparameter_elm = {'reg': [10**i for i in range(-2, 3)]} value_dict = check_algorithm(folder='data_regression', dataset='housing', algorithm='LinearRegressor', hyperparameter=hyperparameter_elm, metric_list=['rmse'], classification=False)
def test_bagging(): hyperparameter_bagging = {'activation': ['sigmoid'], 'reg': [10 ** i for i in range(-1, 2)], 'hidden_neurons': [10], 'size': [5]} value_dict = check_algorithm(folder='data', dataset='iris', algorithm='BaggingELM', hyperparameter=hyperparameter_bagging, metric_list=['accuracy', 'rmse'])
def test_kelm(): hyperparameter_kelm = { 'kernel': ['rbf', 'linear'], 'reg': [10**i for i in range(-1, 2)], 'gamma': [10] } value_dict = check_algorithm(folder='data', dataset='iris', algorithm='KernelELM', hyperparameter=hyperparameter_kelm, metric_list=['accuracy', 'rmse'])
def test_pl(): hyperparameter_elm = { 'activation': ['sigmoid'], 'reg': [10**i for i in range(-1, 2)], 'hidden_neurons': [10] } value_dict = check_algorithm(folder='data', dataset='iris', algorithm='ParallelLayerELM', hyperparameter=hyperparameter_elm, metric_list=['accuracy', 'rmse'])
def test_pcaldaelm(): hyperparameter_elm = { 'activation': ['sigmoid'], 'reg': [10**i for i in range(-1, 2)], 'pca_perc': [0.9] } value_dict = check_algorithm(folder='data', dataset='iris', algorithm='PCALDAELM', hyperparameter=hyperparameter_elm, metric_list=['accuracy'])
def test_bagging_regression(): hyperparameter_bagging = {'activation': ['sigmoid'], 'reg': [10 ** i for i in range(-1, 2)], 'hidden_neurons': [10], 'size': [5]} value_dict = check_algorithm(folder='data_regression', dataset='housing', algorithm='BaggingELM', hyperparameter=hyperparameter_bagging, metric_list=['rmse'], classification=False)
def test_diverse_elm(): hyperparameter_div = {'activation': ['sigmoid'], 'reg': [10 ** i for i in range(-1, 2)], 'div': [10 ** i for i in range(-1, 2)], 'hidden_neurons': [10], 'size': [5]} value_dict = check_algorithm(folder='data', dataset='iris', algorithm='DiverseELM', hyperparameter=hyperparameter_div, metric_list=['accuracy', 'rmse', 'diversity'])
def test_ncelm(): hyperparameter_inc = {'activation': ['sigmoid'], 'reg': [10 ** i for i in range(-2, 3)], 'lambda_': [10 ** i for i in range(-4, -2)], 'max_iter_': [5], 'hidden_neurons': [10], 'size': [5]} value_dict = check_algorithm(folder='data', dataset='iris', algorithm='NegativeCorrelationELM', hyperparameter=hyperparameter_inc, metric_list=['accuracy', 'rmse'])
def test_adaboost(): hyperparameter_boost = { 'activation': ['sigmoid'], 'reg': [10**i for i in range(-1, 2)], 'hidden_neurons': [10], 'size': [5] } value_dict = check_algorithm(folder='data', dataset='iris', algorithm='AdaBoostELM', hyperparameter=hyperparameter_boost, metric_list=['accuracy'])
def test_pl_regression(): hyperparameter_elm = { 'activation': ['sigmoid'], 'reg': [0.001], 'hidden_neurons': [10] } value_dict = check_algorithm(folder='data_regression', dataset='housing', algorithm='ParallelLayerELM', hyperparameter=hyperparameter_elm, metric_list=['rmse'], classification=False)
def test_kelm_regression(): hyperparameter_kelm = { 'kernel': ['rbf', 'linear'], 'reg': [10**i for i in range(-1, 2)], 'gamma': [10] } value_dict = check_algorithm(folder='data_regression', dataset='housing', algorithm='KernelELM', hyperparameter=hyperparameter_kelm, metric_list=['rmse'], classification=False)
def test_pcaelm_regression(): hyperparameter_elm = { 'activation': ['sigmoid'], 'reg': [10**i for i in range(-1, 2)], 'pca_perc': [0.9] } value_dict = check_algorithm(folder='data_regression', dataset='housing', algorithm='PCAELM', hyperparameter=hyperparameter_elm, metric_list=['rmse'], classification=False)
def test_ncelm_regression(): hyperparameter_inc = {'activation': ['sigmoid'], 'reg': [10 ** i for i in range(-1, 2)], 'lambda_': [10 ** i for i in range(-2, -1)], 'max_iter_': [5], 'hidden_neurons': [10], 'size': [5]} value_dict = check_algorithm(folder='data_regression', dataset='housing', algorithm='NegativeCorrelationELM', hyperparameter=hyperparameter_inc, metric_list=['rmse'], classification=False)
def test_ancelm(): hyperparameter_anc = { 'activation': ['sigmoid'], 'reg': [10**i for i in range(-1, 2)], 'hidden_neurons': [10], 'lambda_': [0.5, 1.0, 5.0], 'size': [5] } value_dict = check_algorithm(folder='data', dataset='iris', algorithm='AdaBoostNCELM', hyperparameter=hyperparameter_anc, metric_list=['accuracy'])
def test_adaboost_regression(): hyperparameter_boost = { 'activation': ['sigmoid'], 'reg': [10**i for i in range(-1, 2)], 'hidden_neurons': [10], 'size': [5] } with pytest.raises(ValueError): value_dict = check_algorithm(folder='data_regression', dataset='housing', algorithm='AdaBoostELM', hyperparameter=hyperparameter_boost, metric_list=['rmse'], classification=False)
def test_nn(): hyperparameter_nn = { 'max_iter': [200], 'activation': ['sigmoid'], 'hidden_neurons': [10 * i for i in range(1, 3)], 'solver': ['irprop'], 'batch_size': [50], 'learning_rate': [0.01] } value_dict = check_algorithm(folder='data', dataset='iris', algorithm='NeuralNetwork', hyperparameter=hyperparameter_nn, metric_list=['accuracy', 'rmse'])
def test_rnn(): hyperparameter_nn = { 'max_iter': [200], 'activation': ['sigmoid'], 'hidden_neurons': [5, 10, 15], 'solver': ['backpropagation'], 'batch_size': [75], 'learning_rate': [0.01] } value_dict = check_algorithm(folder='data', dataset='iris', algorithm='RandomNeuralNetwork', hyperparameter=hyperparameter_nn, metric_list=['accuracy', 'rmse'])
def test_nn_autoencoder(): hyperparameter_nn = { 'max_iter': [200], 'activation': ['sigmoid'], 'hidden_neurons': [2], 'solver': ['irprop'], 'batch_size': [50], 'learning_rate': [0.01] } value_dict = check_algorithm(folder='data', dataset='iris', algorithm='NeuralNetwork', hyperparameter=hyperparameter_nn, metric_list=['rmse'], autoencoder=True)
def test_nc_nn(): hyperparameter_nn = { 'max_iter': [100], 'activation': ['sigmoid'], 'solver': ['irprop'], 'batch_size': [25], 'hidden_neurons': [5, 10], 'learning_rate': [0.01], 'size': [5], 'lambda_': [0.01]} value_dict = check_algorithm(folder='data', dataset='iris', algorithm='NegativeCorrelationNN', hyperparameter=hyperparameter_nn, metric_list=['accuracy', 'rmse'])
def test_rnn_regression(): hyperparameter_nn = { 'max_iter': [200], 'activation': ['sigmoid'], 'hidden_neurons': [5, 10, 15], 'solver': ['irprop'], 'batch_size': [150], 'learning_rate': [0.001] } value_dict = check_algorithm(folder='data_regression', dataset='housing', algorithm='RandomNeuralNetwork', hyperparameter=hyperparameter_nn, metric_list=['rmse'], classification=False)
def test_nc_nn_regression(): hyperparameter_nn = {'max_iter': [100], 'activation': ['sigmoid'], 'hidden_neurons': [5, 10], 'learning_rate': [0.001], 'solver': ['irprop'], 'batch_size': [50], 'size': [5], 'lambda_': [0.001]} value_dict = check_algorithm(folder='data_regression', dataset='housing', algorithm='NegativeCorrelationNN', hyperparameter=hyperparameter_nn, metric_list=['rmse'], classification=False)
def test_activation(): hyperparameter_elm = { 'activation': ['sigmoid'], 'reg': [10**i for i in range(-1, 2)], 'hidden_neurons': [10] } for activation in activation_dict.keys(): hyperparameter_elm['activation'] = [activation] value_dict = check_algorithm(folder='data_regression', dataset='housing', algorithm='ELM', hyperparameter=hyperparameter_elm, metric_list=['rmse'], classification=False) if value_dict['rmse'] > 0.4: raise ValueError( 'Activation function %s does not ' 'provide good results', activation)
def test_nn_activation(): hyperparameter_nn = { 'max_iter': [100], 'activation': ['sigmoid'], 'hidden_neurons': [5], 'solver': ['irprop'], 'batch_size': [150], 'learning_rate': [0.001] } for activation in nn_activation_dict.keys(): hyperparameter_nn['activation'] = [activation] value_dict = check_algorithm(folder='data_regression', dataset='housing', algorithm='NeuralNetwork', hyperparameter=hyperparameter_nn, metric_list=['rmse'], classification=False) if value_dict['rmse'] > 0.4: raise ValueError( 'Activation function %s does not ' 'provide good results', activation)
def test_ols(): value_dict = check_algorithm(folder='data', dataset='iris', algorithm='OLS', hyperparameter=None, metric_list=['accuracy', 'rmse'])