class CVTestsLocalSearchTrue(unittest.TestCase):
def setUp(self):
# set up inner pipeline
self.inner_hyperpipe = Hyperpipe('inner_pipe',
KFold(n_splits=2),
local_search=True)
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_hyperpipe = Hyperpipe('outer_pipe', KFold(n_splits=2))
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, ))
def test_default_split_fit(self):
"""
test default splitting mode: 80% validation and 20% testing
make sure that DURING the optimization the optimum pipeline is fitted with the correct amount of data
"""
self.outer_hyperpipe.debug_cv_mode = True
self.inner_hyperpipe.debug_cv_mode = True
self.outer_hyperpipe.hyperparameter_fitting_cv_object = ShuffleSplit(
n_splits=1, test_size=0.2)
self.inner_hyperpipe.hyperparameter_fitting_cv_object = ShuffleSplit(
n_splits=1, test_size=0.2)
self.outer_hyperpipe.fit(self.X, self.y)
outer_data = self.outer_pipeline_test_element.base_element.data_dict[
'fit_X'].tolist()
inner_data = self.inner_pipeline_test_element.base_element.data_dict[
'fit_X'].tolist()
print('local_search true: outer pipeline data:')
print(sorted(outer_data))
print('local_search true: inner pipeline data:')
print(sorted(inner_data))
# we expect that all items from inner_data are existent in outer_data
validation = set(inner_data) < set(outer_data)
self.assertTrue(validation)
# test that it is only 50% of 80% of original X (n=100) and that there is a test_x of 20% size
self.assertEqual(len(outer_data), 40)
# test that inner data is 50% from 80% of outer
self.assertEqual(len(inner_data), 16)
# we also expect that inner_data is 50% of length from outer_data
self.assertEqual(len(inner_data), 0.5 * 0.8 * len(outer_data))
def test_default_split_predict(self):
"""
test default splitting mode: 80% validation and 20% testing
make sure that AFTER the optimization the optimum pipeline is fitted with the correct amount of data
which means test that the optimum pipe is fitted to the validation data and tested with the test data
"""
self.outer_hyperpipe.debug_cv_mode = False
self.inner_hyperpipe.debug_cv_mode = False
self.outer_hyperpipe.hyperparameter_fitting_cv_object = ShuffleSplit(
n_splits=1, test_size=0.2, train_size=0.8)
self.inner_hyperpipe.hyperparameter_fitting_cv_object = ShuffleSplit(
n_splits=1, test_size=0.2, train_size=0.8)
self.outer_hyperpipe.fit(self.X, self.y)
print('local_search true: outer pipeline data:')
print(self.outer_pipeline_test_element.base_element.data_dict['fit_X'])
print('local_search true: inner pipeline data:')
print(self.inner_pipeline_test_element.base_element.data_dict['fit_X'])
outer_data = self.outer_pipeline_test_element.base_element.data_dict[
'fit_X'].tolist()
inner_data = self.inner_pipeline_test_element.base_element.data_dict[
'fit_X'].tolist()
# we expect that all items from inner_data are existent in outer_data
validation = set(inner_data) < set(outer_data)
self.assertTrue(validation)
# test that it is only 80% of original X (n=100) and that there is a test_x of 20% size
self.assertEqual(len(outer_data), 80)
# test that inner data is 80% from 80% of original
self.assertEqual(len(inner_data), 64)
# we also expect that inner_data is 80% of length from outer_data
self.assertEqual(len(inner_data), 0.8 * len(outer_data))
def test_no_split(self):
"""
test no splitting mode: the data is NOT split into test and validation set
"""
self.outer_hyperpipe.debug_cv_mode = True
self.outer_hyperpipe.eval_final_performance = False
self.inner_hyperpipe.debug_cv_mode = True
self.inner_hyperpipe.eval_final_performance = False
self.outer_hyperpipe.fit(self.X, self.y)
outer_data = self.outer_pipeline_test_element.base_element.data_dict[
'fit_X'].tolist()
inner_data = self.inner_pipeline_test_element.base_element.data_dict[
'fit_X'].tolist()
# we expect that all items from inner_data are existent in outer_data
validation = set(inner_data) < set(outer_data)
self.assertTrue(validation)
# test that it is only 50% of original X (n=100)
self.assertEqual(len(outer_data), 50)
# test that inner data is 50% from 50% of outer = 25% of original
self.assertEqual(len(inner_data), 25)
def test_CV_split(self):
"""
test cv splitting mode: the entire search for hyperparameters is cross validated
"""
self.outer_hyperpipe.debug_cv_mode = True
self.outer_hyperpipe.eval_final_performance = False
self.outer_hyperpipe.hyperparameter_fitting_cv_object = KFold(
n_splits=2)
self.inner_hyperpipe.debug_cv_mode = True
self.inner_hyperpipe.eval_final_performance = False
self.inner_hyperpipe.hyperparameter_fitting_cv_object = KFold(
n_splits=2)
self.outer_hyperpipe.fit(self.X, self.y)
outer_data = self.outer_pipeline_test_element.base_element.data_dict[
'fit_X'].tolist()
inner_data = self.inner_pipeline_test_element.base_element.data_dict[
'fit_X'].tolist()
# we expect that all items from inner_data are existent in outer_data
validation = set(inner_data) < set(outer_data)
self.assertTrue(validation)
# we use KFold = 2 so the original should be 100/2 = 50
# test that it is only 50% of original X (n=50)
self.assertEqual(len(outer_data), 25)
# test that inner data is 25% of outer = 12,5% of original
self.assertTrue((len(inner_data) == 6 or len(inner_data) == 7))
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'])
class CVTestsLocalSearchFalse(unittest.TestCase):
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 test_no_split(self):
self.outer_hyperpipe.eval_final_performance = False
self.inner_hyperpipe.eval_final_performance = False
self.outer_hyperpipe.fit(self.X, self.y)
print('local_search true: outer pipeline data:')
print(self.outer_pipeline_test_element.base_element.data_dict['fit_X'])
print('local_search true: inner pipeline data:')
print(self.inner_pipeline_test_element.base_element.data_dict['fit_X'])
outer_data = self.outer_pipeline_test_element.base_element.data_dict[
'fit_X'].tolist()
inner_data = self.inner_pipeline_test_element.base_element.data_dict[
'fit_X'].tolist()
self.assertTrue(set(outer_data) == set(inner_data))
self.assertEqual(len(outer_data), 50)
def test_default_split(self):
self.outer_hyperpipe.eval_final_performance = True
self.inner_hyperpipe.eval_final_performance = True
self.outer_hyperpipe.fit(self.X, self.y)
outer_data = self.outer_pipeline_test_element.base_element.data_dict[
'fit_X'].tolist()
inner_data = self.inner_pipeline_test_element.base_element.data_dict[
'fit_X'].tolist()
self.assertTrue(set(outer_data) == set(inner_data))
self.assertEqual(len(outer_data), 40)
def test_cv_split(self):
self.outer_hyperpipe.hyperparameter_fitting_cv_object = KFold(
n_splits=2)
# should be ignored:
self.inner_hyperpipe.hyperparameter_fitting_cv_object = KFold(
n_splits=2)
self.outer_hyperpipe.fit(self.X, self.y)
outer_data = self.outer_pipeline_test_element.base_element.data_dict[
'fit_X'].tolist()
inner_data = self.inner_pipeline_test_element.base_element.data_dict[
'fit_X'].tolist()
self.assertTrue(set(outer_data) == set(inner_data))
self.assertEqual(len(outer_data), 25)
self.assertEqual(len(outer_data), len(inner_data))
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)
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)
# my_pipe += PipelineElement('atlas_stacker',
# AtlasStacker(atlas_info, [['SVR', {'kernel': ['rbf', 'linear']}, {}]]),
# {})
my_pipe += PipelineElement.create('SVR', {'kernel': ['linear']})
# START HYPERPARAMETER SEARCH
my_pipe.fit(dataset_files, targets)
# resImg = ResamplingImgs(voxel_size=[5, 5, 5], output_img=True).transform(dataset_files)
# smImg = SmoothImgs(fwhr=[6, 6, 6], output_img=True).transform(resImg)
#
# from Photon_Neuro0.BrainAtlas import BrainAtlas
# myAtlas = BrainAtlas(atlas_name='AAL',
# extract_mode='vec',
# whichROIs='all')
# roi_data = myAtlas.transform(X=smImg)
# myAtlas.getInfo()
print('')