diff --git a/config_files/config_test.yml b/config_files/config_test.yml
index 9118a96bf0f260267f131e0936b839d8a4ac78cc..50c2791fa1f5a0805a3c8f0702aa622cc8a36082 100644
--- a/config_files/config_test.yml
+++ b/config_files/config_test.yml
@@ -22,7 +22,7 @@ nb_folds: 2
nb_class: 3
classes:
type: ["multiview",]
-algos_monoview: ["all" ]
+algos_monoview: ["decision_tree" ]
algos_multiview: ["svm_jumbo_fusion",]
stats_iter: 2
metrics: ["accuracy_score", "f1_score"]
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 9785229897c6e71c0f9ab1485e34f92b7cfa36ff..cc8f791b46e523e07be6b693964fe06bc9133f22 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
@@ -93,8 +93,8 @@ def exec_monoview(directory, X, Y, name, labels_names, classification_indices,
classifier = get_mc_estim(getattr(classifier_module,
classifier_class_name)
(random_state, **cl_kwargs),
- Y,
- random_state)
+ random_state,
+ y=Y)
classifier.fit(X_train, y_train) # NB_CORES=nbCores,
logging.debug("Done:\t Training")
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview/exec_multiview.py b/multiview_platform/mono_multi_view_classifiers/multiview/exec_multiview.py
index d849025027aff2fd13ea8ca9665d76a997d3cbce..192bd3d63ab098338463602dfe56e162c235f23e 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview/exec_multiview.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview/exec_multiview.py
@@ -273,7 +273,8 @@ def exec_multiview(directory, dataset_var, name, classification_indices, k_folds
classifier_config=classifier_config)
classifier = get_mc_estim(getattr(classifier_module, classifier_name)(random_state=random_state,
**classifier_config),
- dataset_var.get_labels(), random_state, multiview=True,)
+ random_state, multiview=True,
+ y=dataset_var.get_labels())
logging.debug("Done:\t Optimizing hyperparameters")
logging.debug("Start:\t Fitting classifier")
classifier.fit(dataset_var, dataset_var.get_labels(), train_indices=learning_indices,
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview/multiview_utils.py b/multiview_platform/mono_multi_view_classifiers/multiview/multiview_utils.py
index 9648943e24f980a5067e768ca344feb67552dc38..57062546fe274e34ead2ea579b8fc8673b44c576 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview/multiview_utils.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview/multiview_utils.py
@@ -125,7 +125,7 @@ def get_available_monoview_classifiers(need_probas=False):
available_classifiers = proba_classifiers
return available_classifiers
-def get_monoview_classifier(classifier_name):
+def get_monoview_classifier(classifier_name, multiclass=False):
classifier_module = getattr(monoview_classifiers, classifier_name)
classifier_class = getattr(classifier_module, classifier_module.classifier_class_name)
return classifier_class
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/diversity_utils.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/diversity_utils.py
index 4013f030aeb4b6f6d0434c50252d4a6e4ff76423..bebc38efdc4130801f0bdd31d5b9ec5350e2b75f 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/diversity_utils.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/diversity_utils.py
@@ -32,6 +32,7 @@ class DiversityFusionClassifier(BaseMultiviewClassifier,
train_indices, view_indices = get_examples_views_indices(X,
train_indices,
view_indices)
+ # TODO : Finer analysis, may support a bit of mutliclass
if np.unique(y[train_indices]).shape[0] > 2:
raise ValueError("Multiclass not supported, classes used : {}".format(np.unique(y[train_indices])))
if self.monoview_estimators is None:
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/fusion_utils.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/fusion_utils.py
index 201ae23eca50f72cdd23b49d3f650f5b4341bb7b..4754b9a96d234f705a60754495be051b6b267f96 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/fusion_utils.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/fusion_utils.py
@@ -2,12 +2,13 @@ import inspect
from ...multiview.multiview_utils import get_monoview_classifier
+from ...utils.multiclass import get_mc_estim
class BaseFusionClassifier():
def init_monoview_estimator(self, classifier_name, classifier_config,
- classifier_index=None,):
+ classifier_index=None, multiclass=False):
if classifier_index is not None:
if classifier_config is not None:
classifier_configs = classifier_config[classifier_name]
@@ -31,4 +32,7 @@ class BaseFusionClassifier():
random_state=self.random_state)
else:
estimator = get_monoview_classifier(classifier_name)()
- return estimator
+
+ return get_mc_estim(estimator, random_state=self.random_state,
+ multiview=False, multiclass=multiclass)
+
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/jumbo_fusion_utils.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/jumbo_fusion_utils.py
index 9cb907df4ef3be3b4a2a38c70369e3e2d45caba4..fd82ff69cf1a896a40110552e0c046ed1bf9b46e 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/jumbo_fusion_utils.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/jumbo_fusion_utils.py
@@ -35,8 +35,13 @@ class BaseJumboFusion(LateFusionClassifier):
return self
def fit_monoview_estimators(self, X, y, train_indices=None, view_indices=None):
+ if np.unique(y).shape[0]>2:
+ multiclass=True
+ else:
+ multiclass=False
self.monoview_estimators = [[self.init_monoview_estimator(classifier_name,
- self.classifier_configs[classifier_index])
+ self.classifier_configs[classifier_index],
+ multiclass=multiclass)
for classifier_index, classifier_name
in enumerate(self.classifiers_names)]
for _ in view_indices]
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/late_fusion_utils.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/late_fusion_utils.py
index 26a3cab6e9a9e113e8350dc916753d562193b64e..f0a67a1988d65b284ae07c2798b2b4bbd90ce3bb 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/late_fusion_utils.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/late_fusion_utils.py
@@ -97,7 +97,11 @@ class LateFusionClassifier(BaseMultiviewClassifier, BaseFusionClassifier):
train_indices, view_indices = get_examples_views_indices(X,
train_indices,
view_indices)
- self.init_params(len(view_indices))
+ if np.unique(y).shape[0]>2:
+ multiclass=True
+ else:
+ multiclass=False
+ self.init_params(len(view_indices), multiclass)
if np.unique(y[train_indices]).shape[0] > 2:
raise ValueError("Multiclass not supported")
self.monoview_estimators = [monoview_estimator.fit(X.get_v(view_index, train_indices),
@@ -107,7 +111,7 @@ class LateFusionClassifier(BaseMultiviewClassifier, BaseFusionClassifier):
self.monoview_estimators)]
return self
- def init_params(self, nb_view):
+ def init_params(self, nb_view, mutliclass=False):
if self.weights is None:
self.weights = np.ones(nb_view) / nb_view
elif isinstance(self.weights, WeightDistribution):
@@ -120,7 +124,8 @@ class LateFusionClassifier(BaseMultiviewClassifier, BaseFusionClassifier):
self.monoview_estimators = [
self.init_monoview_estimator(classifier_name,
self.classifier_configs[classifier_index],
- classifier_index=classifier_index)
+ classifier_index=classifier_index,
+ multiclass=mutliclass)
for classifier_index, classifier_name
in enumerate(self.classifiers_names)]
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_early_fusion.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_early_fusion.py
index 27e94a742f87b293600ab35e82c05f48a20a23ae..4f4eadce3bba170b3833f72b65daa85de633017a 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_early_fusion.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_early_fusion.py
@@ -50,15 +50,12 @@ class WeightedLinearEarlyFusion(BaseMultiviewClassifier, BaseFusionClassifier):
self.classed_params = []
self.weird_strings={}
- def set_params(self, monoview_classifier_name=None, monoview_classifier_config=None, **params):
+ def set_params(self, monoview_classifier_name=None,
+ monoview_classifier_config=None, **params):
self.monoview_classifier_name = monoview_classifier_name
- monoview_classifier_module = getattr(monoview_classifiers,
- self.monoview_classifier_name)
- monoview_classifier_class = getattr(monoview_classifier_module,
- monoview_classifier_module.classifier_class_name)
- self.monoview_classifier = monoview_classifier_class()
- self.init_monoview_estimator(monoview_classifier_name,
+ self.monoview_classifier = self.init_monoview_estimator(monoview_classifier_name,
monoview_classifier_config)
+ self.monoview_classifier_config = self.monoview_classifier.get_params()
self.short_name = "early fusion " + self.monoview_classifier_name
return self
@@ -74,9 +71,9 @@ class WeightedLinearEarlyFusion(BaseMultiviewClassifier, BaseFusionClassifier):
if np.unique(y[train_indices]).shape[0] > 2 and \
not(isinstance(self.monoview_classifier, MultiClassWrapper)):
self.monoview_classifier = get_mc_estim(self.monoview_classifier,
- y[train_indices],
self.random_state,
- multiview=False)
+ multiview=False,
+ y=y[train_indices])
self.monoview_classifier.fit(X, y[train_indices])
return self
@@ -100,10 +97,10 @@ class WeightedLinearEarlyFusion(BaseMultiviewClassifier, BaseFusionClassifier):
X = self.hdf5_to_monoview(dataset, example_indices)
return example_indices, X
- def hdf5_to_monoview(self, dataset, exmaples):
+ def hdf5_to_monoview(self, dataset, examples):
"""Here, we concatenate the views for the asked examples """
monoview_data = np.concatenate(
- [dataset.get_v(view_idx, exmaples)
+ [dataset.get_v(view_idx, examples)
for view_weight, (index, view_idx)
in zip(self.view_weights, enumerate(self.view_indices))]
, axis=1)
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 7d6b2ca4a8eb289b41f4ebc522a851197a560eed..a02d8e0cc121e3e9280d004c502b72bff2c69c59 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
@@ -114,8 +114,9 @@ def randomized_search(X, y, framework, random_state, output_file_name, classifie
estimator = getattr(classifier_module, classifier_name)(random_state=random_state,
**classifier_kwargs)
params_dict = estimator.gen_distribs()
- estimator = get_mc_estim(estimator, y, random_state,
- multiview=(framework=="multiview"))
+ estimator = get_mc_estim(estimator, random_state,
+ multiview=(framework=="multiview"),
+ y=y)
if params_dict:
metric_module = getattr(metrics, metric[0])
if metric[1] is not None:
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/multiclass.py b/multiview_platform/mono_multi_view_classifiers/utils/multiclass.py
index 7988d0cd350bc8e6b7b4e67208fa6016c3cd4202..e41f76ca3d898cb50a5877d0eb021f9ee03208a7 100644
--- a/multiview_platform/mono_multi_view_classifiers/utils/multiclass.py
+++ b/multiview_platform/mono_multi_view_classifiers/utils/multiclass.py
@@ -110,7 +110,8 @@ from .dataset import get_examples_views_indices
# return False
-def get_mc_estim(estimator, y, random_state, multiview=False):
+def get_mc_estim(estimator, random_state, y=None, multiview=False,
+ multiclass=False):
r"""Used to get a multiclass-compatible estimator if the one in param does not natively support multiclass.
If perdict_proba is available in the asked estimator, a One Versus Rest wrapper is returned,
else, a One Versus One wrapper is returned.
@@ -133,7 +134,7 @@ def get_mc_estim(estimator, y, random_state, multiview=False):
estimator : sklearn-like estimator
Either the aksed estimator, or a multiclass-compatible wrapper over the asked estimator
"""
- if np.unique(y).shape[0]>2:
+ if (y is not None and np.unique(y).shape[0]>2) or multiclass :
if not clone(estimator).accepts_multi_class(random_state):
if hasattr(estimator, "predict_proba"):
if multiview:
diff --git a/multiview_platform/tests/test_utils/test_multiclass.py b/multiview_platform/tests/test_utils/test_multiclass.py
index 62de20d75af77607867e219a235af6814d2a0547..178308ad4da87818e2ff388e3c84aa44ea06fd24 100644
--- a/multiview_platform/tests/test_utils/test_multiclass.py
+++ b/multiview_platform/tests/test_utils/test_multiclass.py
@@ -40,34 +40,34 @@ class Test_get_mc_estim(unittest.TestCase):
def test_biclass(self):
y = self.random_state.randint(0,2,10)
estimator="Test"
- returned_estimator = get_mc_estim(estimator, y, self.random_state,)
+ returned_estimator = get_mc_estim(estimator, self.random_state, y=y)
self.assertEqual(returned_estimator, estimator)
def test_multiclass_native(self):
estimator = FakeEstimNative()
- returned_estimator = get_mc_estim(estimator, self.y, self.random_state)
+ returned_estimator = get_mc_estim(estimator, self.random_state, y=self.y)
self.assertIsInstance(returned_estimator, FakeEstimNative)
def test_multiclass_ovo(self):
estimator = FakeNonProbaEstim()
- returned_estimator = get_mc_estim(estimator, self.y, self.random_state)
+ returned_estimator = get_mc_estim(estimator, self.random_state, y=self.y)
self.assertIsInstance(returned_estimator, OVOWrapper)
def test_multiclass_ovr(self):
estimator = FakeProbaEstim()
- returned_estimator = get_mc_estim(estimator, self.y, self.random_state)
+ returned_estimator = get_mc_estim(estimator, self.random_state, y=self.y)
self.assertIsInstance(returned_estimator, OVRWrapper)
def test_multiclass_ovo_multiview(self):
estimator = FakeNonProbaEstim()
- returned_estimator = get_mc_estim(estimator, self.y, self.random_state,
- multiview=True)
+ returned_estimator = get_mc_estim(estimator, self.random_state,
+ multiview=True, y=self.y, )
self.assertIsInstance(returned_estimator, MultiviewOVOWrapper)
def test_multiclass_ovr_multiview(self):
estimator = FakeProbaEstim()
- returned_estimator = get_mc_estim(estimator, self.y, self.random_state,
- multiview=True)
+ returned_estimator = get_mc_estim(estimator, self.random_state,
+ multiview=True, y=self.y,)
self.assertIsInstance(returned_estimator, MultiviewOVRWrapper)
class FakeMVClassifier(BaseEstimator):