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 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_CrossValidation_outer_folds():
test_value = None
assert (Hyperpipe.CrossValidation(
3, 1, True, 0.3, 4,
test_value).calculate_metrics_across_folds == test_value)
def test_CrossValidation_calculate_metrics_per_fold():
test_value = 3
assert (Hyperpipe.CrossValidation(
test_value, 1, True, 0.3, test_value,
True).calculate_metrics_per_fold == test_value)
def test_CrossValidation_test_size():
test_value = 0.37
assert (Hyperpipe.CrossValidation(test_value, 1, True, test_value, 4,
True).test_size == test_value)
def test_CrossValidation_eval_final_performance():
test_value = True
assert (Hyperpipe.CrossValidation(
test_value, 1, test_value, 0.3, 4,
True).eval_final_performance == test_value)
def test_CrossValidation_outer_cv():
test_value = 3
assert (Hyperpipe.CrossValidation(test_value, test_value, True, 0.3, 4,
True).outer_cv == test_value)
def test_CrossValidation_inner_cv():
test_value = 3
assert (Hyperpipe.CrossValidation(test_value, 1, True, 0.3, 4,
True).inner_cv == test_value)
def test_CrossValidation_inner_folds():
test_value = dict()
assert (Hyperpipe.CrossValidation(3, 1, True, 0.3, 4,
test_value).inner_folds == test_value)
