def test_load_from_file(self):
X, y = load_breast_cancer(True)
my_pipe = Hyperpipe(
'load_results_file_test',
metrics=['accuracy'],
best_config_metric='accuracy',
output_settings=OutputSettings(project_folder='./tmp'))
my_pipe += PipelineElement("StandardScaler")
my_pipe += PipelineElement("SVC")
my_pipe.fit(X, y)
results_file = os.path.join(my_pipe.output_settings.results_folder,
"photon_result_file.p")
my_result_handler = ResultsHandler()
my_result_handler.load_from_file(results_file)
self.assertIsInstance(my_result_handler.results, MDBHyperpipe)
def test_inverse_transform(self):
settings = OutputSettings(
project_folder=self.tmp_folder_path, overwrite_results=True
)
# DESIGN YOUR PIPELINE
pipe = Hyperpipe(
"Limbic_System",
optimizer="grid_search",
metrics=["mean_absolute_error"],
best_config_metric="mean_absolute_error",
outer_cv=ShuffleSplit(n_splits=1, test_size=0.2),
inner_cv=ShuffleSplit(n_splits=1, test_size=0.2),
verbosity=2,
cache_folder=self.cache_folder_path,
eval_final_performance=True,
output_settings=settings,
)
# PICK AN ATLAS
atlas = PipelineElement(
"BrainAtlas",
rois=["Hippocampus_L", "Amygdala_L"],
atlas_name="AAL",
extract_mode="vec",
batch_size=20,
)
# EITHER ADD A NEURO BRANCH OR THE ATLAS ITSELF
neuro_branch = NeuroBranch("NeuroBranch")
neuro_branch += atlas
pipe += neuro_branch
pipe += PipelineElement("LinearSVR")
pipe.fit(self.X, self.y)
# GET IMPORTANCE SCORES
handler = ResultsHandler(pipe.results)
importance_scores_optimum_pipe = handler.results.best_config_feature_importances
manual_img, _, _ = pipe.optimum_pipe.inverse_transform(
importance_scores_optimum_pipe, None
)
img = image.load_img(
os.path.join(
self.tmp_folder_path,
"Limbic_System_results/optimum_pipe_feature_importances_backmapped.nii.gz",
)
)
self.assertTrue(np.array_equal(manual_img.get_data(), img.get_data()))
def fit(self, X, y=None, **kwargs):
if len(self.hyperpipes_to_fit) == 0:
raise Exception(
"No hyperpipes to fit. Did you call 'generate_mappings'?")
# Get data from BrainAtlas first and save to .npz
# ToDo: currently not supported for hyperparameters inside neurobranch
self.neuro_element.fit(X)
# extract regions
X_extracted, _, _ = self.neuro_element.transform(X)
X_extracted = self._reshape_roi_data(X_extracted)
# save neuro branch to file
joblib.dump(
self.neuro_element,
os.path.join(self.folder, "neuro_element.pkl"),
compress=1,
)
hyperpipe_infos = dict()
hyperpipe_results = dict()
for roi_name, hyperpipe in self.hyperpipes_to_fit.items():
hyperpipe.verbosity = self.verbosity
hyperpipe.fit(X_extracted[self.roi_indices[roi_name]], y, **kwargs)
hyperpipe_infos[roi_name] = {
"hyperpipe_name":
hyperpipe.name,
"model_filename":
os.path.join(
os.path.basename(hyperpipe.output_settings.results_folder),
"photon_best_model.photon",
),
"roi_index":
self.roi_indices[roi_name],
}
hyperpipe_results[roi_name] = ResultsHandler(
hyperpipe.results).get_performance_outer_folds()
self.hyperpipe_infos = hyperpipe_infos
# write results
with open(
os.path.join(
self.folder,
self.original_hyperpipe_name + "_atlas_mapper_meta.json"),
"w",
) as fp:
json.dump(self.hyperpipe_infos, fp)
df = pd.DataFrame(hyperpipe_results)
df.to_csv(
os.path.join(
self.folder,
self.original_hyperpipe_name + "_atlas_mapper_results.csv"))
# write performance to atlas niftis
performances = list()
for roi_name, roi_res in hyperpipe_results.items():
n_voxels = len(X_extracted[self.roi_indices[roi_name]][0])
performances.append(
np.repeat(roi_res[self.best_config_metric], n_voxels))
backmapped_img, _, _ = self.neuro_element.inverse_transform(
performances)
backmapped_img.to_filename(
os.path.join(self.folder, "atlas_mapper_performances.nii.gz"))
if self.create_surface_plots:
self.surface_plots(backmapped_img)
rois=["Hippocampus_L", "Amygdala_L"],
atlas_name="AAL",
extract_mode="vec",
batch_size=20,
)
# EITHER ADD A NEURO BRANCH OR THE ATLAS ITSELF
neuro_branch = NeuroBranch("NeuroBranch")
neuro_branch += atlas
# ADD NEURO ELEMENTS TO HYPERPIPE
pipe += neuro_branch
pipe += PipelineElement("LinearSVR")
pipe.fit(X, y)
# GET IMPORTANCE SCORES
handler = ResultsHandler(pipe.results)
# get feature importances (training set) for your best configuration (for all outer folds)
# this function returns the importance scores for the best configuration of each outer fold in a list
importance_scores_outer_folds = handler.get_importance_scores()
importance_scores_optimum_pipe = handler.results.best_config_feature_importances
img, _, _ = pipe.optimum_pipe.inverse_transform(importance_scores_optimum_pipe, None)
img.to_filename("./tmp/best_config_feature_importances.nii.gz")
debug = True
def fit(self, X, y=None, **kwargs):
"""
Transform data on NeuroElement and fit hyperpipes.
:param X: input data
:param y: targets
:param kwargs:
:return:
"""
# disable fitting with loading from file/folder
if not self.hyperpipes_to_fit and self.hyperpipe:
self._generate_mappings()
else:
msg = "Cannot fit AtlasMapper with hyperpipe as NoneType."
logger.error(msg)
raise ValueError(msg)
# Get data from BrainAtlas first and save to .npz
# ToDo: currently not supported for hyperparameters inside neurobranch
self.neuro_element.fit(X)
# extract regions
X_extracted, _, _ = self.neuro_element.transform(X)
X_extracted = AtlasMapper._reshape_roi_data(X_extracted)
# save neuro element to file
joblib.dump(self.neuro_element,
os.path.join(self.folder, 'neuro_element.pkl'),
compress=1)
hyperpipe_infos = dict()
hyperpipe_results = dict()
# ToDo: parallel fitting
for roi_name, hyperpipe in self.hyperpipes_to_fit.items():
hyperpipe.verbosity = self.hyperpipe.verbosity
hyperpipe.fit(X_extracted[self.roi_indices[roi_name]], y, **kwargs)
hyperpipe_infos[roi_name] = {
'hyperpipe_name':
hyperpipe.name,
'model_filename':
os.path.join(
os.path.basename(hyperpipe.output_settings.results_folder),
'photon_best_model.photon'),
'roi_index':
self.roi_indices[roi_name]
}
hyperpipe_results[roi_name] = ResultsHandler(
hyperpipe.results).get_performance_outer_folds()
self.hyperpipe_infos = hyperpipe_infos
# write results
with open(
os.path.join(self.folder,
self.hyperpipe.name + '_atlas_mapper_meta.json'),
'w') as fp:
json.dump(self.hyperpipe_infos, fp)
df = pd.DataFrame(hyperpipe_results)
df.to_csv(
os.path.join(self.folder,
self.hyperpipe.name + '_atlas_mapper_results.csv'))
# write performance to atlas niftis
performances = list()
for roi_name, roi_res in hyperpipe_results.items():
n_voxels = len(X_extracted[self.roi_indices[roi_name]][0])
performances.append(
np.repeat(
roi_res[self.hyperpipe.optimization.best_config_metric],
n_voxels))
backmapped_img, _, _ = self.neuro_element.inverse_transform(
performances)
backmapped_img.to_filename(
os.path.join(self.folder, 'atlas_mapper_performances.nii.gz'))
if self.create_surface_plots:
self.surface_plots(backmapped_img)