def setUp(self):
self.outer_hyperpipe = Hyperpipe('outer_pipe', KFold(n_splits=2))
# set up inner pipeline
self.inner_hyperpipe = Hyperpipe(
'inner_pipe',
KFold(n_splits=2),
optimizer=self.outer_hyperpipe.optimizer,
local_search=False)
self.inner_pipeline_test_element = PipelineElement.create(
'test_wrapper')
self.inner_hyperpipe += self.inner_pipeline_test_element
self.pipeline_fusion = PipelineStacking('fusion_element',
[self.inner_hyperpipe])
# set up outer pipeline
self.outer_pipeline_test_element = PipelineElement.create(
'test_wrapper')
self.outer_hyperpipe += self.outer_pipeline_test_element
self.outer_hyperpipe += self.pipeline_fusion
self.X = np.arange(1, 101)
self.y = np.ones((100, ))
self.inner_hyperpipe.debug_cv_mode = True
self.outer_hyperpipe.debug_cv_mode = True
def setUp(self):
self.pca_pipe_element = PipelineElement.create(
'pca', {'n_components': [1, 2]}, test_disabled=True)
self.svc_pipe_element = PipelineElement.create('svc', {
'C': [0.1, 1],
'kernel': ['rbf', 'sigmoid']
})
self.cv_object = KFold(n_splits=3)
self.hyperpipe = Hyperpipe('god', self.cv_object)
self.hyperpipe += self.pca_pipe_element
self.hyperpipe.add(self.svc_pipe_element)
def testCaseA(self):
pca_n_components = [2, 5]
svc_c = [.1, 1, 5]
#svc_kernel = ['rbf']
svc_kernel = ['rbf', 'linear']
# SET UP HYPERPIPE
my_pipe = Hyperpipe('primary_pipe',
optimizer='grid_search',
optimizer_params={},
metrics=['accuracy', 'precision', 'f1_score'],
inner_cv=KFold(n_splits=2, random_state=3),
eval_final_performance=False)
my_pipe += PipelineElement.create('standard_scaler')
my_pipe += PipelineElement.create('pca',
{'n_components': pca_n_components})
my_pipe += PipelineElement.create('svc', {
'C': svc_c,
'kernel': svc_kernel
})
# START HYPERPARAMETER SEARCH
my_pipe.fit(self.__X, self.__y)
print(my_pipe._test_performances)
pipe_results = {'train': [], 'test': []}
for i in range(len(my_pipe._performance_history_list)):
pipe_results['train'].extend(my_pipe._performance_history_list[i]
['accuracy_folds']['train'])
pipe_results['test'].extend(
my_pipe._performance_history_list[i]['accuracy_folds']['test'])
print('\n\n')
print('Running sklearn version...')
#cv_outer = KFold(n_splits=2, random_state=3)
cv_inner_1 = KFold(n_splits=2, random_state=3)
sk_results = {'train': [], 'test': []}
for n_comp in pca_n_components:
for c in svc_c:
for current_kernel in svc_kernel:
tr_acc = []
val_acc = []
for train_2, val_1 in cv_inner_1.split(self.__X):
data_train_2 = self.__X[train_2]
print(data_train_2.shape)
data_val_1 = self.__X[val_1]
y_train_2 = self.__y[train_2]
y_val_1 = self.__y[val_1]
my_scaler = StandardScaler()
my_scaler.fit(data_train_2)
data_train_2 = my_scaler.transform(data_train_2)
data_val_1 = my_scaler.transform(data_val_1)
# Run PCA
my_pca = PCA(n_components=n_comp)
my_pca.fit(data_train_2)
data_tr_2_pca = my_pca.transform(data_train_2)
data_val_1_pca = my_pca.transform(data_val_1)
# Run SVC
my_svc = SVC(kernel=current_kernel, C=c)
my_svc.fit(data_tr_2_pca, y_train_2)
tr_acc.append(my_svc.score(data_tr_2_pca, y_train_2))
val_acc.append(my_svc.score(data_val_1_pca, y_val_1))
print('n_components: ', n_comp, 'kernel:',
current_kernel, 'c:', c)
print('Training 2:', tr_acc[-1], 'validation 1:',
val_acc[-1])
sk_results['train'].extend(tr_acc)
sk_results['test'].extend(val_acc)
print('\nCompare results of last iteration (outer cv)...')
print('SkL Train:', sk_results['train'])
print('Pipe Train:', pipe_results['train'])
print('SkL test: ', sk_results['test'])
print('Pipe test: ', pipe_results['test'])
self.assertEqual(sk_results['test'], pipe_results['test'])
self.assertEqual(sk_results['train'], pipe_results['train'])
def testCaseA(self):
pca_n_components = [2, 5]
svc_c = [.1, 1]
svc_kernel = ['rbf']
# svc_kernel = ['rbf','linear']
# SET UP HYPERPIPE
my_pipe = Hyperpipe('primary_pipe', optimizer='grid_search',
optimizer_params={},
inner_cv=KFold(
n_splits=2, random_state=3),
outer_cv=KFold(
n_splits=2, random_state=3), verbose=2, eval_final_performance=True)
my_pipe += PipelineElement.create('standard_scaler')
my_pipe += PipelineElement.create('pca', {'n_components': pca_n_components})
my_pipe += PipelineElement.create('svc', {'C': svc_c, 'kernel': svc_kernel})
# START HYPERPARAMETER SEARCH
my_pipe.fit(self.__X, self.__y)
from Framework import LogExtractor
log_ex = LogExtractor.LogExtractor(my_pipe.result_tree)
log_ex.extract_csv("test_case_A2.csv")
# print(my_pipe.test_performances)
# pipe_results = {'train': [], 'test': []}
# for i in range(len(my_pipe.performance_history_list)):
# pipe_results['train'].extend(
# my_pipe.performance_history_list[i]['accuracy_folds']['train'])
# pipe_results['test'].extend(
# my_pipe.performance_history_list[i]['accuracy_folds']['test'])
print('\n\n')
print('Running sklearn version...')
cv_outer = KFold(n_splits=2, random_state=3)
cv_inner_1 = KFold(n_splits=2, random_state=3)
for train_1, test in cv_outer.split(self.__X):
data_train_1 = self.__X[train_1]
data_test = self.__X[test]
y_train_1 = self.__y[train_1]
y_test = self.__y[test]
sk_results = {'train': [], 'test': []}
for n_comp in pca_n_components:
for current_kernel in svc_kernel:
for c in svc_c:
tr_acc = []
val_acc = []
for train_2, val_1 in cv_inner_1.split(
data_train_1):
data_train_2 = data_train_1[train_2]
data_val_1 = data_train_1[val_1]
y_train_2 = y_train_1[train_2]
y_val_1 = y_train_1[val_1]
my_scaler = StandardScaler()
my_scaler.fit(data_train_2)
data_train_2 = my_scaler.transform(data_train_2)
data_val_1 = my_scaler.transform(data_val_1)
# Run PCA
my_pca = PCA(n_components=n_comp)
my_pca.fit(data_train_2)
data_tr_2_pca = my_pca.transform(data_train_2)
data_val_1_pca = my_pca.transform(data_val_1)
# Run SVC
my_svc = SVC(kernel=current_kernel, C=c)
my_svc.fit(data_tr_2_pca, y_train_2)
tr_acc.append(my_svc.score(data_tr_2_pca, y_train_2))
val_acc.append(my_svc.score(data_val_1_pca, y_val_1))
print('n_components: ', n_comp, 'kernel:',
current_kernel, 'c:', c)
print('Training 2:', tr_acc[-1],
'validation 1:', val_acc[-1])
sk_results['train'].extend(tr_acc)
sk_results['test'].extend(val_acc)
print('\nCompare results of last iteration (outer cv)...')
print('SkL Train:', sk_results['train'])
print('Pipe Train:', pipe_results['train'])
print('SkL test: ', sk_results['test'])
print('Pipe test: ', pipe_results['test'])
self.assertEqual(sk_results['test'], pipe_results['test'])
self.assertEqual(sk_results['train'], pipe_results['train'])
def testCaseB(self):
pca_n_components = [7, 15, 10]
svc_c = [.1, 1]
#svc_kernel = ['rbf']
svc_kernel = ['rbf', 'linear']
cv_outer = ShuffleSplit(n_splits=1, test_size=0.2, random_state=3)
cv_inner_1 = ShuffleSplit(n_splits=1, test_size=0.2, random_state=3)
cv_inner_2 = ShuffleSplit(n_splits=1, test_size=0.2, random_state=3)
# SET UP HYPERPIPE
outer_pipe = Hyperpipe('outer_pipe',
optimizer='grid_search',
metrics=['accuracy'],
inner_cv=cv_inner_1,
outer_cv=cv_outer,
eval_final_performance=True)
inner_pipe = Hyperpipe('pca_pipe',
optimizer='grid_search',
inner_cv=cv_inner_2,
eval_final_performance=False)
inner_pipe.add(PipelineElement.create('standard_scaler'))
inner_pipe.add(
PipelineElement.create('ae_pca',
{'n_components': pca_n_components}))
pipeline_fusion = PipelineStacking('fusion_element', [inner_pipe])
outer_pipe.add(pipeline_fusion)
outer_pipe.add(
PipelineElement.create('svc', {
'C': svc_c,
'kernel': svc_kernel
}))
# START HYPERPARAMETER SEARCH
outer_pipe.fit(self.__X, self.__y)
print(outer_pipe._test_performances)
pipe_results = {'train': [], 'test': []}
for i in range(len(outer_pipe._performance_history_list)):
pipe_results['train'].extend(
outer_pipe._performance_history_list[i]['accuracy_folds']
['train'])
pipe_results['test'].extend(outer_pipe._performance_history_list[i]
['accuracy_folds']['test'])
print(outer_pipe._test_performances['accuracy'])
print('\n\n')
print('Running sklearn version...\n')
opt_tr_acc = []
opt_test_acc = []
for train_1, test in cv_outer.split(self.__X):
data_train_1 = self.__X[train_1]
data_test = self.__X[test]
y_train_1 = self.__y[train_1]
y_test = self.__y[test]
config_inner_1 = {'C': [], 'kernel': []}
sk_results_inner1 = {
'train_2': [],
'val_1': [],
'train_2_mean': [],
'val_1_mean': []
}
print('Outer Split')
print('n train_1:', data_train_1.shape[0], '\n')
for c in svc_c:
for current_kernel in svc_kernel:
config_inner_1['C'].extend([c])
config_inner_1['kernel'].extend([current_kernel])
print('C:', c, 'Kernel:', current_kernel, '\n')
svc_score_tr = []
svc_score_te = []
fold_cnt = 1
for train_2, val_1 in cv_inner_1.split(data_train_1):
print('\n\nSklearn Outer Pipe FoldMetrics', fold_cnt)
data_train_2 = data_train_1[train_2]
data_val_1 = data_train_1[val_1]
y_train_2 = y_train_1[train_2]
y_val_1 = y_train_1[val_1]
print('n train_2:', data_train_2.shape[0], '\n')
config_inner_2 = {'n_comp': []}
print('Sklearn PCA Pipe')
sk_results_inner2 = {
'train_3': [],
'val_2': [],
'train_3_mean': [],
'val_2_mean': []
}
for n_comp in pca_n_components:
config_inner_2['n_comp'].extend([n_comp])
tr_acc = []
val_acc = []
# print('Some training data:',
# data_train_2[0:2, 0:2])
for train_3, val_2 in cv_inner_2.split(
data_train_2):
data_train_3 = data_train_2[train_3]
data_val_2 = data_train_2[val_2]
my_scaler = StandardScaler()
my_scaler.fit(data_train_3)
data_train_3 = my_scaler.transform(
data_train_3)
data_val_2 = my_scaler.transform(data_val_2)
# Run PCA
my_pca = PCA_AE_Wrapper(n_components=n_comp)
my_pca.fit(data_train_3)
mae_tr = my_pca.score(data_train_3)
mae_te = my_pca.score(data_val_2)
tr_acc.append(mae_tr)
val_acc.append(mae_te)
sk_results_inner2['train_3'].extend(tr_acc)
sk_results_inner2['val_2'].extend(val_acc)
sk_results_inner2['train_3_mean'].extend(
[np.mean(tr_acc)])
sk_results_inner2['val_2_mean'].extend(
[np.mean(val_acc)])
print('n_comp:', n_comp)
print('n train_3 fold 1:', data_train_3.shape[0])
print('Training 3 mean:', [np.mean(tr_acc)],
'validation 2 mean:', [np.mean(val_acc)])
# find best config for val 2
best_config_id = np.argmin(
sk_results_inner2['val_2_mean'])
print('Best PCA config:',
config_inner_2['n_comp'][best_config_id], '\n')
# fit optimum pipe
my_scaler = StandardScaler()
my_scaler.fit(data_train_2)
data_train_2 = my_scaler.transform(data_train_2)
data_val_1 = my_scaler.transform(data_val_1)
# Run PCA
my_pca = PCA_AE_Wrapper(
n_components=config_inner_2['n_comp']
[best_config_id])
my_pca.fit(data_train_2)
data_tr_2_pca = my_pca.transform(data_train_2)
data_val_1_pca = my_pca.transform(data_val_1)
# Run SVC
my_svc = SVC(kernel=current_kernel, C=c)
my_svc.fit(data_tr_2_pca, y_train_2)
svc_score_tr.append(
my_svc.score(data_tr_2_pca, y_train_2))
svc_score_te.append(
my_svc.score(data_val_1_pca, y_val_1))
print('Fit Optimum PCA Config and train with SVC')
print('n train 2:', data_train_2.shape[0])
print('n_comp:',
config_inner_2['n_comp'][best_config_id])
print('SVC Train:', svc_score_tr[-1])
print('SVC test:', svc_score_te[-1], '\n\n')
sk_results_inner1['train_2'].append(svc_score_tr[-1])
sk_results_inner1['val_1'].append(svc_score_te[-1])
fold_cnt += 1
sk_results_inner1['train_2_mean'].append(
np.mean(svc_score_tr))
sk_results_inner1['val_1_mean'].append(
np.mean(svc_score_te))
print('\nNow find best config for SVC...')
best_config_id_inner_1 = np.argmax(sk_results_inner1['val_1_mean'])
print('Some test data:')
print(data_test.shape)
print(data_test[0:2, 0:2])
# fit optimum pipe
my_scaler = StandardScaler()
my_scaler.fit(data_train_1)
data_train_1 = my_scaler.transform(data_train_1)
data_test = my_scaler.transform(data_test)
# Run PCA
my_pca = PCA_AE_Wrapper(
n_components=config_inner_2['n_comp'][best_config_id])
my_pca.fit(data_train_1)
data_tr_1_pca = my_pca.transform(data_train_1)
data_test_pca = my_pca.transform(data_test)
# Run SVC
my_svc = SVC(
kernel=config_inner_1['kernel'][best_config_id_inner_1],
C=config_inner_1['C'][best_config_id_inner_1])
print('Best overall config:...')
print('C = ', config_inner_1['C'][best_config_id_inner_1])
print('kernel=', config_inner_1['kernel'][best_config_id_inner_1])
print('pca_n_comp=', config_inner_2['n_comp'][best_config_id])
print('n train 1:', data_train_1.shape[0])
my_svc.fit(data_tr_1_pca, y_train_1)
opt_tr_acc.append(my_svc.score(data_tr_1_pca, y_train_1))
opt_test_acc.append(my_svc.score(data_test_pca, y_test))
print('Train Acc:', opt_tr_acc[-1])
print('test Acc:', opt_test_acc[-1])
print('\nCompare results of last iteration (outer cv)...')
print('SkL Train:', sk_results_inner1['train_2'])
print('Pipe Train:', pipe_results['train'])
print('SkL test: ', sk_results_inner1['val_1'])
print('Pipe test: ', pipe_results['test'])
print('\nEval final performance:')
print('Pipe final perf:', outer_pipe._test_performances['accuracy'])
print('Sklearn final perf:', opt_test_acc)
self.assertEqual(sk_results_inner1['train_2'], pipe_results['train'])
self.assertEqual(sk_results_inner1['val_1'], pipe_results['test'])
self.assertEqual(opt_test_acc,
outer_pipe._test_performances['accuracy'])
def testCaseC2(self):
pca_n_components = [5, 10]
svc_c = [0.1]
svc_c_2 = [1]
#svc_kernel = ['rbf']
svc_kernel = ['linear']
# SET UP HYPERPIPE
outer_pipe = Hyperpipe('outer_pipe',
optimizer='grid_search',
metrics=['accuracy'],
inner_cv=ShuffleSplit(n_splits=1,
test_size=0.2,
random_state=3),
outer_cv=ShuffleSplit(n_splits=1,
test_size=0.2,
random_state=3),
eval_final_performance=True)
# Create pipe for first data source
pipe_source_1 = Hyperpipe('source_1',
optimizer='grid_search',
inner_cv=ShuffleSplit(n_splits=1,
test_size=0.2,
random_state=3),
eval_final_performance=False)
pipe_source_1.add(
PipelineElement.create('SourceSplitter',
{'column_indices': [np.arange(0, 10)]}))
pipe_source_1.add(
PipelineElement.create('pca', {'n_components': pca_n_components}))
pipe_source_1.add(
PipelineElement.create('svc', {
'C': svc_c,
'kernel': svc_kernel
}))
# Create pipe for second data source
pipe_source_2 = Hyperpipe('source_2',
optimizer='grid_search',
inner_cv=ShuffleSplit(n_splits=1,
test_size=0.2,
random_state=3),
eval_final_performance=False)
pipe_source_2.add(
PipelineElement.create('SourceSplitter',
{'column_indices': [np.arange(10, 20)]}))
pipe_source_2.add(
PipelineElement.create('pca', {'n_components': pca_n_components}))
pipe_source_2.add(
PipelineElement.create('svc', {
'C': svc_c,
'kernel': svc_kernel
}))
# Create pipe for third data source
pipe_source_3 = Hyperpipe('source_3',
optimizer='grid_search',
inner_cv=ShuffleSplit(n_splits=1,
test_size=0.2,
random_state=3),
eval_final_performance=False)
pipe_source_3.add(
PipelineElement.create('SourceSplitter',
{'column_indices': [np.arange(20, 30)]}))
pipe_source_3.add(
PipelineElement.create('pca', {'n_components': pca_n_components}))
pipe_source_3.add(
PipelineElement.create('svc', {
'C': svc_c,
'kernel': svc_kernel
}))
# pipeline_fusion = PipelineStacking('multiple_source_pipes',[pipe_source_1, pipe_source_2, pipe_source_3], voting=False)
pipeline_fusion = PipelineStacking(
'multiple_source_pipes',
[pipe_source_1, pipe_source_2, pipe_source_3])
outer_pipe.add(pipeline_fusion)
#outer_pipe.add(PipelineElement.create('svc', {'C': svc_c_2, 'kernel': svc_kernel}))
#outer_pipe.add(PipelineElement.create('knn',{'n_neighbors':[15]}))
outer_pipe.add(
PipelineElement.create('kdnn', {
'target_dimension': [2],
'nb_epoch': [10]
}))
# START HYPERPARAMETER SEARCH
outer_pipe.fit(self.__X, self.__y)
print(outer_pipe._test_performances)
pipe_results = {'train': [], 'test': []}
for i in range(int(len(outer_pipe._performance_history_list) / 2)):
pipe_results['train'].extend(
outer_pipe._performance_history_list[i]['accuracy_folds']
['train'])
pipe_results['test'].extend(outer_pipe._performance_history_list[i]
['accuracy_folds']['test'])
print(outer_pipe._test_performances['accuracy'])
def testCaseA(self):
pca_n_components = 10
svc_c = 1
svc_kernel = "rbf"
# SET UP HYPERPIPE
my_pipe = Hyperpipe('primary_pipe', optimizer='grid_search', optimizer_params={},
metrics=['accuracy', 'precision', 'f1_score'],
inner_cv=KFold(n_splits=3),
outer_cv=KFold(n_splits=3),
eval_final_performance=True)
my_pipe += PipelineElement.create('standard_scaler')
my_pipe += PipelineElement.create('pca', {'n_components': [pca_n_components]})
my_pipe += PipelineElement.create('svc', {'C': [svc_c], 'kernel': [svc_kernel]})
# START HYPERPARAMETER SEARCH
my_pipe.fit(self.__X, self.__y)
print(my_pipe._test_performances)
from Framework import LogExtractor
log_ex = LogExtractor.LogExtractor(my_pipe.result_tree)
log_ex.extract_csv("test_case_A.csv")
# Das muss noch weg! ToDo
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.svm import SVC
from sklearn.pipeline import Pipeline
from sklearn.metrics import f1_score, accuracy_score, precision_score
# Now we are using the native Scikit-learn methods
sk_pipeline = Pipeline([("standard_scaler", StandardScaler()), ("pca", PCA(n_components=pca_n_components)),
("svc", SVC(C=svc_c, kernel=svc_kernel))])
my_pipe._generate_outer_cv_indices()
tmp_counter = 0
for train_idx_arr, test_idx_arr in my_pipe.data_test_cases:
sk_results = {'accuracy': [], 'precision': [], 'f1_score': [], 'default': []}
outer_train_X = self.__X[train_idx_arr]
outer_train_y = self.__y[train_idx_arr]
outer_test_X = self.__X[test_idx_arr]
outer_test_y = self.__y[test_idx_arr]
sk_config_cv = KFold(n_splits=3)
# Todo: test other configs and select best!
for sub_train_idx, sub_test_idx in sk_config_cv.split(outer_train_X, outer_train_y):
inner_train_X = self.__X[sub_train_idx]
inner_train_y = self.__y[sub_train_idx]
#test_X = self.__X[sub_test_idx]
#test_y = self.__y[sub_test_idx]
# sk_pipeline.fit(inner_train_X, inner_train_y)
fit_and_predict_score = _fit_and_score(sk_pipeline, outer_train_X, outer_train_y, self.score,
sub_train_idx, sub_test_idx, verbose=0, parameters={},
fit_params={},
return_train_score=True,
return_n_test_samples=True,
return_times=True, return_parameters=True,
error_score='raise')
sk_pipeline.fit(outer_train_X, outer_train_y)
sk_prediction = sk_pipeline.predict(outer_test_X)
sk_results['default'].append(fit_and_predict_score[1])
sk_results['accuracy'].append(accuracy_score(outer_test_y, sk_prediction))
sk_results['precision'].append(precision_score(outer_test_y, sk_prediction))
sk_results['f1_score'].append(f1_score(outer_test_y, sk_prediction))
# bestItem = np.argmax(sk_results['default'])
# print([str(k)+':'+str(i[bestItem]) for k, i in sk_results.items()])
self.assertEqual(sk_results['accuracy'], my_pipe._test_performances['accuracy'][tmp_counter])
self.assertEqual(sk_results['precision'], my_pipe._test_performances['precision'][tmp_counter])
self.assertEqual(sk_results['f1_score'], my_pipe._test_performances['f1_score'][tmp_counter])
tmp_counter += 1
"""
Test Feature Selection
"""
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import KFold
from Framework.PhotonBase import Hyperpipe, PipelineElement
dataset = load_breast_cancer()
X = dataset.data
y = dataset.target
# create cross-validation object first
cv_object = KFold(n_splits=3, shuffle=True, random_state=0)
# create a hyperPipe
manager = Hyperpipe('god', cv_object, optimizer='random_grid_search')
manager += PipelineElement.create('f_classif_select_percentile',
{'percentile': [10, 20, 30, 100]},
test_disabled=True)
# SVMs (linear and rbf)
manager += PipelineElement.create('svc', {}, kernel='linear')
manager.fit(X, y)
dataset_files = oasis_dataset.gray_matter_maps
targets = oasis_dataset.ext_vars['age'].astype(float) # age
# # data
# from sklearn.datasets import load_breast_cancer
# dataset = load_breast_cancer()
# dataset_files = dataset.data
# targets = dataset.target
print(BrainAtlas._getAtlasDict())
# setup photonai HP
my_pipe = Hyperpipe('primary_pipe',
optimizer='grid_search',
optimizer_params={},
metrics=['mean_squared_error', 'mean_absolute_error'],
inner_cv=KFold(n_splits=2, shuffle=True, random_state=3),
outer_cv=KFold(n_splits=2, shuffle=True, random_state=3),
eval_final_performance=True)
my_pipe += PipelineElement.create('SmoothImgs',
{'fwhr': [[8, 8, 8], [12, 12, 12]]})
my_pipe += PipelineElement.create('ResampleImgs', {'voxel_size': [[5, 5, 5]]})
atlas_info = AtlasInfo(atlas_name='mni_icbm152_t1_tal_nlin_sym_09a_mask',
mask_threshold=.5,
roi_names='all',
extraction_mode='vec')
#atlas_info = AtlasInfo(atlas_name='AAL', roi_names='all', extraction_mode='box')
my_pipe += PipelineElement.create('BrainAtlas', {},
atlas_info_object=atlas_info)
# -----------> calculate something ------------------- #
# LOAD DATA
dataset = load_breast_cancer()
X = dataset.data
y = dataset.target
print(np.sum(y)/len(y))
from pymodm import connect
connect("mongodb://localhost:27017/photon_db")
# BUILD PIPELINE
manager = Hyperpipe('test_manager',
optimizer='timeboxed_random_grid_search', optimizer_params={'limit_in_minutes': 1},
outer_cv=ShuffleSplit(test_size=0.2, n_splits=3),
inner_cv=KFold(n_splits=10, shuffle=True), best_config_metric='accuracy',
metrics=['accuracy', 'precision', 'recall', "f1_score"],
logging=False, eval_final_performance=True,
calculate_metrics_across_folds=True,
verbose=2)
manager.add(PipelineElement.create('standard_scaler', test_disabled=True))
manager += PipelineElement.create('pca', hyperparameters={'n_components': [None, 1, 10000]})
# tmp_lasso = Lasso()
# manager.add(PipelineElement.create('SelectModelWrapper', estimator_obj=tmp_lasso))
svm = PipelineElement.create('svc', hyperparameters={'C': [0.5, 1], 'kernel': ['linear']})
manager.add(svm)
manager.fit(X, y)
# -----------> Result Tree generated ------------------- #
result_tree = manager.result_tree