def fashion_linear_svc_rff(data_train, data_test, target_train, target_test): # C_value = 50 C_values = [0.5, 1, 5, 20, 50] tunning_params = {'C': C_values} # model_params = {'C': C_value} # tunning_params = {} model_params = {} model_info = { 'model_name': 'linear_svc', 'model_params': model_params, 'rbfsampler_gamma': None, 'nystroem_gamma': None, 'sampler_name': 'rbf', 'pca_bool': False, 'pca_first': None, 'n_estim': None, 'box_type': 'none', } print('Empieza el experimento') d = exp(model_info=model_info, tunning_params=tunning_params, data_train=data_train, data_test=data_test, target_train=target_train, target_test=target_test, description='A linear SVM with RFF with fashion mnist') print('Termina el experimento') store_exp(d, exp_code='fashion', dts_name='linear_svc_rff')
def fashion_dt_rff_grey_bag(data_train, data_test, target_train, target_test): # min_id = 0.2 # min_id_values = [0, 0.1, 0.2, 0.5] # tunning_params = {'min_impurity_decrease': min_id_values} # model_params = {'min_impurity_decrease': min_id} model_params = {} tunning_params = {} model_info = { 'model_name': 'dt', 'model_params': model_params, 'rbfsampler_gamma': None, 'nystroem_gamma': None, 'sampler_name': 'rbf', 'pca_bool': False, 'pca_first': None, 'n_estim': 50, 'box_type': 'grey_bag', } print('Empieza el experimento') d = exp(model_info=model_info, tunning_params=tunning_params, data_train=data_train, data_test=data_test, target_train=target_train, target_test=target_test, description='A DT with RFF Grey Bag with fashion mnist') print('Termina el experimento') store_exp(d, exp_code='fashion', dts_name='dt_rff_grey_bag')
def fashion_logit_rff_grey_bag(data_train, data_test, target_train, target_test): C_value = 1000 # C_values = [0.5, 1, 5, 20, 50] # tunning_params = {'C': C_values} model_params = {'C': C_value} tunning_params = {} model_info = { 'model_name': 'logit', 'model_params': model_params, 'rbfsampler_gamma': None, 'nystroem_gamma': None, 'sampler_name': 'rbf', 'pca_bool': False, 'pca_first': None, 'n_estim': 50, 'box_type': 'grey_bag', } print('Empieza el experimento') d = exp(model_info=model_info, tunning_params=tunning_params, data_train=data_train, data_test=data_test, target_train=target_train, target_test=target_test, description='A Logit with RFF Grey Bag with fashion mnist') print('Termina el experimento') store_exp(d, exp_code='fashion', dts_name='logit_rff_grey_bag')
def exp4_1(dts_name): exp_code = '4_1' model_name = 'dt' box_type = 'none' # dt_params = { # 'splitter': 'best', # 'max_features': 'sqrt', # } n_estim = None min_id = [0, .1, .2, .5, 1] tunning_params = {'min_impurity_decrease': min_id} model1_info = { 'model_name': model_name, 'model_params': {}, 'rbfsampler_gamma': None, 'nystroem_gamma': None, 'sampler_name': 'identity', 'pca_bool': False, 'pca_first': None, 'n_estim': None, 'box_type': 'none', } model2_info = { 'model_name': model_name, 'model_params': {}, 'rbfsampler_gamma': None, 'nystroem_gamma': None, 'sampler_name': 'rbf', 'pca_bool': False, 'pca_first': None, 'n_estim': n_estim, 'box_type': box_type, } model3_info = { 'model_name': model_name, 'model_params': {}, 'rbfsampler_gamma': None, 'nystroem_gamma': None, 'sampler_name': 'nystroem', 'pca_bool': False, 'pca_first': None, 'n_estim': n_estim, 'box_type': box_type, } data = get_data(dataset_name=dts_name, prop_train=2 / 3, n_ins=5000) data_train = data['data_train'] data_test = data['data_test'] target_train = data['target_train'] target_test = data['target_test'] d1 = exp(model_info=model1_info, tunning_params=tunning_params, data_train=data_train, data_test=data_test, target_train=target_train, target_test=target_test, description=f'DT ({dts_name})') d2 = exp(model_info=model2_info, tunning_params=tunning_params, data_train=data_train, data_test=data_test, target_train=target_train, target_test=target_test, description=f'DT with RFF ({dts_name})') d3 = exp(model_info=model3_info, tunning_params=tunning_params, data_train=data_train, data_test=data_test, target_train=target_train, target_test=target_test, description=f'DT with Nystroem ({dts_name})') store_exp(d1, d2, d3, exp_code=exp_code, dts_name=dts_name)
def exp1_1(dts_name): exp_code = '1_1' # overfitting_gamma = 1000 # C_values = [10**i for i in range(4)] C_values = [0.5, 1, 5, 20, 50] tunning_params = {'C': C_values} box_type = 'none' model1_info = { 'model_name': 'rbf_svc', 'model_params': {}, 'rbfsampler_gamma': None, 'nystroem_gamma': None, 'sampler_name': 'identity', 'pca_bool': False, 'pca_first': None, 'n_estim': None, 'box_type': box_type, } model2_info = { 'model_name': 'linear_svc', 'model_params': {}, 'rbfsampler_gamma': None, 'nystroem_gamma': None, 'sampler_name': 'rbf', 'pca_bool': False, 'pca_first': None, 'n_estim': None, 'box_type': box_type, } model3_info = { 'model_name': 'linear_svc', 'model_params': {}, 'rbfsampler_gamma': None, 'nystroem_gamma': None, 'sampler_name': 'nystroem', 'pca_bool': False, 'pca_first': None, 'n_estim': None, 'box_type': box_type, } data = get_data(dataset_name=dts_name, prop_train=2 / 3, n_ins=5000) data_train = data['data_train'] data_test = data['data_test'] target_train = data['target_train'] target_test = data['target_test'] d1 = exp(model_info=model1_info, tunning_params=tunning_params, data_train=data_train, data_test=data_test, target_train=target_train, target_test=target_test, description=f'A normal RBF-SVC with gamest ({dts_name})') d2 = exp(model_info=model2_info, tunning_params=tunning_params, data_train=data_train, data_test=data_test, target_train=target_train, target_test=target_test, description=f'A normal linear-SVC with RFF ({dts_name})') d3 = exp(model_info=model3_info, tunning_params=tunning_params, data_train=data_train, data_test=data_test, target_train=target_train, target_test=target_test, description=f'A normal linear-SVC with Nystroem ({dts_name})') store_exp(d1, d2, d3, exp_code=exp_code, dts_name=dts_name)
def exp3_4(dts_name): exp_code = '3_4' model_name = 'linear_svc' # C_value = {'C': 1000} # box_type = 'black_bag' n_estim = 1 C_values = [10**i for i in range(4)] tunning_params = {'C': C_values} # tunning_params = {} model1_info = { 'model_name': model_name, 'model_params': {}, 'rbfsampler_gamma': None, 'nystroem_gamma': None, 'sampler_name': 'rbf', 'pca_bool': False, 'pca_first': None, 'n_estim': n_estim, 'box_type': 'black_bag', } model2_info = { 'model_name': model_name, 'model_params': {}, 'rbfsampler_gamma': None, 'nystroem_gamma': None, 'sampler_name': 'nystroem', 'pca_bool': False, 'pca_first': None, 'n_estim': n_estim, 'box_type': 'black_bag', } model3_info = { 'model_name': model_name, 'model_params': {}, 'rbfsampler_gamma': None, 'nystroem_gamma': None, 'sampler_name': 'rbf', 'pca_bool': False, 'pca_first': None, 'n_estim': n_estim, 'box_type': 'black_ens', } model4_info = { 'model_name': model_name, 'model_params': {}, 'rbfsampler_gamma': None, 'nystroem_gamma': None, 'sampler_name': 'nystroem', 'pca_bool': False, 'pca_first': None, 'n_estim': n_estim, 'box_type': 'black_ens', } data = get_data(dataset_name=dts_name, prop_train=2 / 3, n_ins=5000) data_train = data['data_train'] data_test = data['data_test'] target_train = data['target_train'] target_test = data['target_test'] d1 = exp( model_info=model1_info, tunning_params=tunning_params, data_train=data_train, data_test=data_test, target_train=target_train, target_test=target_test, description=f'Linear-SVC black_bag with RFF without regul. ({dts_name})' ) d2 = exp( model_info=model2_info, tunning_params=tunning_params, data_train=data_train, data_test=data_test, target_train=target_train, target_test=target_test, description=f'Linear-SVC black_bag with Nys without regul. ({dts_name})' ) d3 = exp( model_info=model3_info, tunning_params=tunning_params, data_train=data_train, data_test=data_test, target_train=target_train, target_test=target_test, description=f'Linear-SVC black_ens with RFF without regul. ({dts_name})' ) d4 = exp( model_info=model4_info, tunning_params=tunning_params, data_train=data_train, data_test=data_test, target_train=target_train, target_test=target_test, description=f'Linear-SVC black_ens with Nys without regul. ({dts_name})' ) store_exp(d1, d2, d3, d4, exp_code=exp_code, dts_name=dts_name)
def exp2_1(dts_name): exp_code = '2_1' model_name = 'logit' C_value = {'C': 1000} box_type = 'none' n_estim = None # C_values = [10**i for i in range(4)] # tunning_params = {'C': C_values} tunning_params = {} model1_info = { 'model_name': model_name, 'model_params': C_value, 'rbfsampler_gamma': None, 'nystroem_gamma': None, 'sampler_name': 'identity', 'pca_bool': False, 'pca_first': None, 'n_estim': n_estim, 'box_type': box_type, } model2_info = { 'model_name': model_name, 'model_params': C_value, 'rbfsampler_gamma': None, 'nystroem_gamma': None, 'sampler_name': 'rbf', 'pca_bool': False, 'pca_first': None, 'n_estim': n_estim, 'box_type': box_type, } model3_info = { 'model_name': model_name, 'model_params': C_value, 'rbfsampler_gamma': None, 'nystroem_gamma': None, 'sampler_name': 'nystroem', 'pca_bool': False, 'pca_first': None, 'n_estim': n_estim, 'box_type': box_type, } data = get_data(dataset_name=dts_name, prop_train=2 / 3, n_ins=5000) data_train = data['data_train'] data_test = data['data_test'] target_train = data['target_train'] target_test = data['target_test'] d1 = exp(model_info=model1_info, tunning_params=tunning_params, data_train=data_train, data_test=data_test, target_train=target_train, target_test=target_test, description=f'Normal logit without regularization ({dts_name})') d2 = exp(model_info=model2_info, tunning_params=tunning_params, data_train=data_train, data_test=data_test, target_train=target_train, target_test=target_test, description=f'Logit with RFF without regularization ({dts_name})') d3 = exp( model_info=model3_info, tunning_params=tunning_params, data_train=data_train, data_test=data_test, target_train=target_train, target_test=target_test, description=f'Logit with Nystroem without regularization ({dts_name})') store_exp(d1, d2, d3, exp_code=exp_code, dts_name=dts_name)
def exp2_8(dts_name): exp_code = '2_8' model_name = 'linear_svc' C_value = {'C': 1000} box_type = 'grey_ens' n_estim = 50 C_values = [10**i for i in range(4)] tunning_params = {'C': C_values} # tunning_params = {} model1_info = { 'model_name': model_name, 'model_params': {}, 'rbfsampler_gamma': None, 'nystroem_gamma': None, 'sampler_name': 'identity', 'pca_bool': False, 'pca_first': None, 'n_estim': None, 'box_type': 'none', } model2_info = { 'model_name': model_name, 'model_params': C_value, 'rbfsampler_gamma': None, 'nystroem_gamma': None, 'sampler_name': 'rbf', 'pca_bool': False, 'pca_first': None, 'n_estim': n_estim, 'box_type': box_type, } model3_info = { 'model_name': model_name, 'model_params': C_value, 'rbfsampler_gamma': None, 'nystroem_gamma': None, 'sampler_name': 'nystroem', 'pca_bool': False, 'pca_first': None, 'n_estim': n_estim, 'box_type': box_type, } data = get_data(dataset_name=dts_name, prop_train=2 / 3, n_ins=5000) data_train = data['data_train'] data_test = data['data_test'] target_train = data['target_train'] target_test = data['target_test'] d1 = exp(model_info=model1_info, tunning_params=tunning_params, data_train=data_train, data_test=data_test, target_train=target_train, target_test=target_test, description=f'Linear SVM ({dts_name})') d2 = exp(model_info=model2_info, tunning_params={}, data_train=data_train, data_test=data_test, target_train=target_train, target_test=target_test, description=f'Linear SVM with RFF Black Bag ({dts_name})') d3 = exp(model_info=model3_info, tunning_params={}, data_train=data_train, data_test=data_test, target_train=target_train, target_test=target_test, description=f'Linear SVM with Nystroem Black Bag ({dts_name})') store_exp(d1, d2, d3, exp_code=exp_code, dts_name=dts_name)