def normal_mnist_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 normal mnist')
print('Termina el experimento')
store_exp(d, exp_code='normal_mnist', dts_name='linear_svc_rff')
def normal_mnist_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 normal mnist')
print('Termina el experimento')
store_exp(d, exp_code='normal_mnist', dts_name='logit_rff_grey_bag')
def normal_mnist_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 normal mnist')
print('Termina el experimento')
store_exp(d, exp_code='normal_mnist', dts_name='dt_rff_grey_bag')
def fashion_rbf_svc(data_train, data_test, target_train, target_test):
C_value = 20
# C_values = [0.5, 1, 5, 20, 50]
# tunning_params = {'C': C_values}
model_params = {'C': C_value}
tunning_params = {}
model_info = {
'model_name': 'rbf_svc',
'model_params': model_params,
'rbfsampler_gamma': None,
'nystroem_gamma': None,
'sampler_name': 'identity',
'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='An RBF-SVM with fashion mnist')
print('Termina el experimento')
store_exp(d, exp_code='fashion', dts_name='rbf_svc')
def fashion_dt(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': 'identity',
'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 DT with fashion mnist')
print('Termina el experimento')
store_exp(d, exp_code='fashion', dts_name='dt')
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)
