diff --git a/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py b/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py
index 191029796b4b38a951643351c60d69803a61edf7..80d7ee63c269cc58917e961872e42b6555c186c6 100644
--- a/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py
+++ b/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py
@@ -16,59 +16,35 @@ from .base import get_metric
from .. import metrics
-def search_best_settings(dataset_var, labels, classifier_module,
- classifier_name,
- metrics, learning_indices, i_k_folds, random_state,
- directory, views_indices=None, nb_cores=1,
- searching_tool="randomized_search-equiv", n_iter=1,
- classifier_config=None):
- """Used to select the right hyper-parameter optimization function
- to optimize hyper parameters"""
- if views_indices is None:
- views_indices = list(range(dataset_var.get_nb_view))
- output_file_name = directory
- thismodule = sys.modules[__name__]
- if searching_tool is not "None":
- searching_tool_method = getattr(thismodule,
- searching_tool.split("-")[0])
- best_settings, scores, params = searching_tool_method(
- dataset_var, labels, "multiview", random_state, output_file_name,
- classifier_module, classifier_name, i_k_folds,
- nb_cores, metrics, n_iter, classifier_config,
- learning_indices=learning_indices, view_indices=views_indices,
- equivalent_draws=searching_tool.endswith("equiv"))
- gen_report(params, scores, directory, )
- else:
- best_settings = classifier_config
- return best_settings # or well set clasifier ?
+# def search_best_settings(dataset_var, labels, classifier_module,
+# classifier_name,
+# metrics, learning_indices, i_k_folds, random_state,
+# directory, views_indices=None, nb_cores=1,
+# searching_tool="randomized_search-equiv", n_iter=1,
+# classifier_config=None):
+# """Used to select the right hyper-parameter optimization function
+# to optimize hyper parameters"""
+# if views_indices is None:
+# views_indices = list(range(dataset_var.get_nb_view))
+# output_file_name = directory
+# thismodule = sys.modules[__name__]
+# if searching_tool is not "None":
+# searching_tool_method = getattr(thismodule,
+# searching_tool.split("-")[0])
+# best_settings, scores, params = searching_tool_method(
+# dataset_var, labels, "multiview", random_state, output_file_name,
+# classifier_module, classifier_name, i_k_folds,
+# nb_cores, metrics, n_iter, classifier_config,
+# learning_indices=learning_indices, view_indices=views_indices,
+# equivalent_draws=searching_tool.endswith("equiv"))
+# gen_report(params, scores, directory, )
+# else:
+# best_settings = classifier_config
+# return best_settings # or well set clasifier ?
class HPSearch:
- # def __init__(self, y, framework, random_state, output_file_name,
- # classifier_module,
- # classifier_name, folds=4, nb_cores=1,
- # metric = [["accuracy_score", None]],
- # classifier_kwargs={}, learning_indices=None,
- # view_indices=None,
- # track_tracebacks=True):
- # estimator = getattr(classifier_module, classifier_name)(
- # random_state=random_state,
- # **classifier_kwargs)
- # self.init_params()
- # self.estimator = get_mc_estim(estimator, random_state,
- # multiview=(framework == "multiview"),
- # y=y)
- # self.folds = folds
- # self.nb_cores = nb_cores
- # self.clasifier_kwargs = classifier_kwargs
- # self.learning_indices = learning_indices
- # self.view_indices = view_indices
- # self.output_file_name = output_file_name
- # metric_module, metric_kwargs = get_metric(metric)
- # self.scorer = metric_module.get_scorer(**metric_kwargs)
- # self.track_tracebacks = track_tracebacks
-
def get_scoring(self, metric):
if isinstance(metric, list):
metric_module, metric_kwargs = get_metric(metric)
@@ -137,11 +113,6 @@ class HPSearch:
self.n_splits_ = n_splits
return self
- @abstractmethod
- def init_params(self):
- self.params_dict = {}
- raise NotImplementedError
-
@abstractmethod
def get_candidate_params(self, X):
raise NotImplementedError
@@ -203,9 +174,6 @@ class Random(RandomizedSearchCV, HPSearch):
return HPSearch.fit_multiview(self, X, y=y, groups=groups,
**fit_params)
- # def init_params(self,):
- # self.params_dict = self.estimator.gen_distribs()
-
def get_candidate_params(self, X):
if self.equivalent_draws:
self.n_iter = self.n_iter * X.nb_view
diff --git a/multiview_platform/tests/test_utils/test_hyper_parameter_search.py b/multiview_platform/tests/test_utils/test_hyper_parameter_search.py
index 01a4230fe6c8b1e1b9ef0e0fd3bc9eca58de48a3..41287784af397b9db1246c513d257bf8c8716407 100644
--- a/multiview_platform/tests/test_utils/test_hyper_parameter_search.py
+++ b/multiview_platform/tests/test_utils/test_hyper_parameter_search.py
@@ -49,7 +49,7 @@ class FakeEstimMV(BaseEstimator):
-class Test_MultiviewCompatibleRandomizedSearchCV(unittest.TestCase):
+class Test_Random(unittest.TestCase):
@classmethod
def setUpClass(cls):
@@ -142,6 +142,27 @@ class Test_MultiviewCompatibleRandomizedSearchCV(unittest.TestCase):
self.assertEqual(RSCV.best_params_["param1"], "return exact")
+class Test_Grid(unittest.TestCase):
+
+ @classmethod
+ def setUpClass(cls):
+ cls.parameter_grid = {"param1":[5,6], "param2":[7,8]}
+ cls.estimator = FakeEstim()
+
+ def test_simple(self):
+ grid = hyper_parameter_search.Grid(self.estimator,
+ param_grid=self.parameter_grid)
+
+ def test_get_candidate_params(self):
+ grid = hyper_parameter_search.Grid(self.estimator,
+ param_grid=self.parameter_grid)
+ grid.get_candidate_params(None)
+ self.assertEqual(grid.candidate_params, [{"param1": 5, "param2": 7},
+ {"param1": 5, "param2": 8},
+ {"param1": 6, "param2": 7},
+ {"param1": 6, "param2": 8}])
+
+
# if __name__ == '__main__':
# # unittest.main()
# suite = unittest.TestLoader().loadTestsFromTestCase(Test_randomized_search)