diff --git a/config_files/config_test.yml b/config_files/config_test.yml
index 1914f94ebfaadab87ac21602dd576eb61e3a83ba..d2e9a17174375f7837c25e70584cf9c297170450 100644
--- a/config_files/config_test.yml
+++ b/config_files/config_test.yml
@@ -22,13 +22,13 @@ Classification:
nb_folds: 2
nb_class: 2
classes:
- type: ["monoview"]
+ type: ["monoview", "multiview"]
algos_monoview: ["adaboost",]
algos_multiview: ["weighted_linear_early_fusion"]
stats_iter: 2
metrics: ["accuracy_score", "f1_score"]
metric_princ: "f1_score"
- hps_type: "randomized_search"
+ hps_type: "randomized_search-equiv"
hps_iter: 2
diff --git a/docs/source/tutorials/example2.rst b/docs/source/tutorials/example2.rst
index 12bdb6bd0c6a4902e25d0d956844f7971de2a5c5..16f8381363b9ef182ea3d1fe68f6dfe16e1966c2 100644
--- a/docs/source/tutorials/example2.rst
+++ b/docs/source/tutorials/example2.rst
@@ -264,5 +264,9 @@ The figure below represents the duration of the execution on a personal computer
The duration is in seconds, and we used 2,5,10,15,20 as values for ``nb_folds`` and 2,5,10,20,30,50,100 for ``hps_iter`` with two monoview classifiers and one multiview classifier on simulated data.
+.. note::
+ In order to compensate the fact that the multiview classifiers have more complex problems to solve, it is possible to use ``"randomized_search-equiv"`` as the HPS optimization method to allow
+ ``hps_iter`` draws for the monoview classifiers and ``hps_iter * nb_view`` draws for the ones that are multiview.
+
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview/exec_classif_mono_view.py b/multiview_platform/mono_multi_view_classifiers/monoview/exec_classif_mono_view.py
index bb24ae0dc99c82a2c6913166bfe5129ff7aff0f1..e2f18a0af4c2396246fe7bee3dfc7f388b5fbabb 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview/exec_classif_mono_view.py
+++ b/multiview_platform/mono_multi_view_classifiers/monoview/exec_classif_mono_view.py
@@ -187,7 +187,7 @@ def getHPs(classifierModule, hyper_param_search, nIter, classifier_module_name,
logging.debug(
"Start:\t " + hyper_param_search + " best settings with " + str(
nIter) + " iterations for " + classifier_module_name)
- classifierHPSearch = getattr(hyper_parameter_search, hyper_param_search)
+ classifierHPSearch = getattr(hyper_parameter_search, hyper_param_search.split("-")[0])
clKWARGS, testFoldsPreds = classifierHPSearch(X_train, y_train, "monoview",
random_state,
outputFileName,
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 d1ad6272f0c55dad2c14a54972b0388f9415dbb4..bc27b3d32e93497d1da672d93bad0697660eced4 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
@@ -13,7 +13,7 @@ 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", n_iter=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"""
@@ -22,12 +22,12 @@ def search_best_settings(dataset_var, labels, classifier_module, classifier_name
output_file_name = directory
thismodule = sys.modules[__name__]
if searching_tool is not "None":
- searching_tool_method = getattr(thismodule, searching_tool)
+ searching_tool_method = getattr(thismodule, searching_tool.split("-")[0])
best_settings, test_folds_preds = 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,)
+ learning_indices=learning_indices, view_indices=views_indices, equivalent_draws=searching_tool.endswith("equiv"))
else:
best_settings = classifier_config
return best_settings # or well set clasifier ?
@@ -108,7 +108,8 @@ def get_test_folds_preds(X, y, cv, estimator, framework, available_indices=None)
def randomized_search(X, y, framework, random_state, output_file_name, classifier_module,
classifier_name, folds=4, nb_cores=1, metric=["accuracy_score", None],
- n_iter=30, classifier_kwargs =None, learning_indices=None, view_indices=None):
+ n_iter=30, classifier_kwargs =None, learning_indices=None, view_indices=None,
+ equivalent_draws=True):
estimator = getattr(classifier_module, classifier_name)(random_state=random_state,
**classifier_kwargs)
params_dict = estimator.genDistribs()
@@ -132,7 +133,8 @@ def randomized_search(X, y, framework, random_state, output_file_name, classifie
cv=folds, random_state=random_state,
learning_indices=learning_indices,
view_indices=view_indices,
- framework = framework)
+ framework = framework,
+ equivalent_draws=equivalent_draws)
random_search.fit(X, y)
best_params = random_search.best_params_
if "random_state" in best_params:
@@ -157,7 +159,8 @@ class MultiviewCompatibleRandomizedSearchCV(RandomizedSearchCV):
def __init__(self, estimator, param_distributions, n_iter=10,
refit=True, n_jobs=1, scoring=None, cv=None,
- random_state=None, learning_indices=None, view_indices=None, framework="monoview"):
+ random_state=None, learning_indices=None, view_indices=None, framework="monoview",
+ equivalent_draws=True):
super(MultiviewCompatibleRandomizedSearchCV, self).__init__(estimator,
n_iter=n_iter,
param_distributions=param_distributions,
@@ -167,6 +170,7 @@ class MultiviewCompatibleRandomizedSearchCV(RandomizedSearchCV):
self.framework = framework
self.available_indices = learning_indices
self.view_indices = view_indices
+ self.equivalent_draws = equivalent_draws
def fit(self, X, y=None, groups=None, **fit_params):
if self.framework == "monoview":
@@ -180,6 +184,8 @@ class MultiviewCompatibleRandomizedSearchCV(RandomizedSearchCV):
candidate_params = list(self._get_param_iterator())
base_estimator = clone(self.estimator)
results = {}
+ if self.equivalent_draws:
+ self.n_iter = self.n_iter*X.nb_view
self.cv_results_ = dict(("param_"+param_name, []) for param_name in candidate_params[0].keys())
self.cv_results_["mean_test_score"] = []
for candidate_param_idx, candidate_param in enumerate(candidate_params):