def _prepare_data(self, X, y=None, **kwargs):
logger.info(
"Preparing data for outer fold "
+ str(self.cross_validaton_info.outer_folds[self.outer_fold_id].fold_nr)
+ "..."
)
# Prepare Train and validation set data
train_indices = self.cross_validaton_info.outer_folds[
self.outer_fold_id
].train_indices
test_indices = self.cross_validaton_info.outer_folds[
self.outer_fold_id
].test_indices
self._validation_X, self._validation_y, self._validation_kwargs = PhotonDataHelper.split_data(
X, y, kwargs, indices=train_indices
)
self._test_X, self._test_y, self._test_kwargs = PhotonDataHelper.split_data(
X, y, kwargs, indices=test_indices
)
# write numbers to database info object
self.result_object.number_samples_validation = self._validation_y.shape[0]
self.result_object.number_samples_test = self._test_y.shape[0]
if self._pipe._estimator_type == "classifier":
self.result_object.class_distribution_validation = FoldInfo.data_overview(
self._validation_y
)
self.result_object.class_distribution_test = FoldInfo.data_overview(
self._test_y
)
def setUp(self):
super(InnerFoldTests, self).setUp()
self.pipe = PhotonPipeline([
("StandardScaler", PipelineElement("StandardScaler")),
("PCA", PipelineElement("PCA")),
("RidgeClassifier", PipelineElement("RidgeClassifier")),
])
self.config = {
"PCA__n_components": 5,
"RidgeClassifier__solver": "svd",
"RidgeClassifier__random_state": 42,
}
self.outer_fold_id = "TestID"
self.inner_cv = KFold(n_splits=4)
self.X, self.y = load_breast_cancer(True)
self.cross_validation = Hyperpipe.CrossValidation(
self.inner_cv, None, True, 0.2, True, False)
self.cross_validation.inner_folds = {
self.outer_fold_id: {
i: FoldInfo(i, i + 1, train, test)
for i, (train,
test) in enumerate(self.inner_cv.split(self.X, self.y))
}
}
self.optimization = Hyperpipe.Optimization(
"grid_search", {}, ["accuracy", "recall", "specificity"],
"accuracy", None)
def setUp(self):
super(InnerFoldTests, self).setUp()
self.pipe = PhotonPipeline([
('StandardScaler', PipelineElement('StandardScaler')),
('PCA', PipelineElement('PCA')),
('RidgeClassifier', PipelineElement('RidgeClassifier'))
])
self.config = {
'PCA__n_components': 5,
'RidgeClassifier__solver': 'svd',
'RidgeClassifier__random_state': 42
}
self.outer_fold_id = 'TestID'
self.inner_cv = KFold(n_splits=4)
self.X, self.y = load_breast_cancer(return_X_y=True)
self.cross_validation = Hyperpipe.CrossValidation(
self.inner_cv, None, True, 0.2, True, False, False, None)
self.cross_validation.inner_folds = {
self.outer_fold_id: {
i: FoldInfo(i, i + 1, train, test)
for i, (train,
test) in enumerate(self.inner_cv.split(self.X, self.y))
}
}
self.optimization = Hyperpipe.Optimization(
'grid_search', {}, ['accuracy', 'recall', 'specificity'],
'accuracy', None)
def _generate_inner_folds(self):
self.inner_folds = FoldInfo.generate_folds(self.cross_validaton_info.inner_cv,
self._validation_X,
self._validation_y,
self._validation_kwargs)
self.cross_validaton_info.inner_folds[self.outer_fold_id] = {f.fold_id: f for f in self.inner_folds}
def test_no_cv_strategy_eval_final_performance_false(self):
test_size = 0.15
fold_list = FoldInfo.generate_folds(None,
self.X,
self.y,
self.kwargs,
eval_final_performance=False,
test_size=test_size)
# check that we have only one outer fold, that is split in training and test according to test size
self.assertEqual(len(fold_list), 1)
self.assertEqual(len(fold_list[0].train_indices), self.num_subjects)
self.assertEqual(len(fold_list[0].test_indices), 0)
def base_assertions(self, cv, nr_of_folds, eval_final_performance=True):
fold_list = FoldInfo.generate_folds(
cv,
self.X,
self.y,
self.kwargs,
eval_final_performance=eval_final_performance)
self.assertTrue(len(fold_list) == nr_of_folds)
for generated_fold in fold_list:
self.assertEqual(len(generated_fold.train_indices),
(nr_of_folds - 1) *
(self.num_subjects / nr_of_folds))
self.assertEqual(len(generated_fold.test_indices),
(self.num_subjects / nr_of_folds))
# we always start with 1 for the investigator
self.assertEqual(fold_list[0].fold_nr, 1)
return fold_list
def setUp(self):
super(OuterFoldTests, self).setUp()
self.fold_nr_inner_cv = 5
self.inner_cv = ShuffleSplit(n_splits=self.fold_nr_inner_cv,
random_state=42)
self.outer_cv = ShuffleSplit(n_splits=1,
test_size=0.2,
random_state=42)
self.cv_info = Hyperpipe.CrossValidation(
inner_cv=self.inner_cv,
outer_cv=self.outer_cv,
eval_final_performance=True,
test_size=0.2,
calculate_metrics_per_fold=True,
calculate_metrics_across_folds=False,
learning_curves=False,
learning_curves_cut=None)
self.X, self.y = load_boston(return_X_y=True)
self.outer_fold_id = "TestFoldOuter1"
self.cv_info.outer_folds = {
self.outer_fold_id: FoldInfo(0, 1, train, test)
for train, test in self.outer_cv.split(self.X, self.y)
}
self.config_num = 2
self.optimization_info = Hyperpipe.Optimization(
metrics=['mean_absolute_error', 'mean_squared_error'],
best_config_metric='mean_absolute_error',
optimizer_input='grid_search',
optimizer_params={},
performance_constraints=None)
self.elements = [
PipelineElement('StandardScaler'),
PipelineElement('PCA', {'n_components': [4, 7]}),
PipelineElement('DecisionTreeRegressor', random_state=42)
]
self.pipe = PhotonPipeline([(p.name, p) for p in self.elements])
def test_data_overview(self):
expected_outcome = {str(i): 10 for i in range(10)}
data_count = FoldInfo.data_overview(self.kwargs['groups'].astype(int))
self.assertDictEqual(expected_outcome, data_count)
def test_class_distribution_info(self):
unique, counts = np.unique(self.__y, return_counts=True)
nr_dict = FoldInfo.data_overview(self.__y)
self.assertEqual(counts[1], nr_dict["1"])
