diff --git a/config_files/config_test.yml b/config_files/config_test.yml
index db2a33152464f8e0b67a0dfa063aa8a73d358bd2..8476b0e445d290f1f25b10898e39a842b9014d9e 100644
--- a/config_files/config_test.yml
+++ b/config_files/config_test.yml
@@ -20,12 +20,12 @@ Classification:
multiclass_method: "oneVersusOne"
split: 0.4
nb_folds: 2
- nb_class: 3
+ nb_class: 2
classes:
- type: ["monoview", "multiview"]
+ type: ["multiview"]
algos_monoview: ["adaboost",]
- algos_multiview: ["weighted_linear_early_fusion"]
- stats_iter: 2
+ algos_multiview: ["svm_jumbo_fusion"]
+ stats_iter: 1
metrics: ["accuracy_score", "f1_score"]
metric_princ: "f1_score"
hps_type: "randomized_search-equiv"
@@ -221,3 +221,6 @@ mvml:
kernel_types: ["rbf_kernel"]
kernel_configs:
gamma: [0.1]
+
+svm_jumbo_fusion:
+ degree: [1]
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 607c2a3d513acc225d2ffa123a7ce4d21d8f0799..abea7e64e38475d671e89dd4bafba594d3892b1a 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview/exec_multiview.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview/exec_multiview.py
@@ -292,9 +292,9 @@ def exec_multiview(directory, dataset_var, name, classification_indices, k_folds
for test_index, index in enumerate(validation_indices):
full_labels[index] = test_labels[test_index]
if test_indices_multiclass != []:
- test_labels_multiclass = classifier.predict_hdf5(dataset_var,
- used_indices=test_indices_multiclass,
- views_indices=views_indices)
+ test_labels_multiclass = classifier.predict(dataset_var,
+ example_indices=test_indices_multiclass,
+ view_indices=views_indices)
else:
test_labels_multiclass = []
logging.info("Done:\t Pertidcting")
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 7b40beaff1366dae890b5d65e99a7cbbea452809..680412c17568cd2b924971a6bf8a62c4972ead78 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
@@ -35,8 +35,8 @@ class DiversityFusionClassifier(BaseMultiviewClassifier,
self.estimator_pool = []
for classifier_idx, classifier_name in enumerate(self.classifier_names):
self.estimator_pool.append([])
- estimator = self.init_monoview_estimator(classifier_name, self.classifier_configs)
for idx, view_idx in enumerate(view_indices):
+ estimator = self.init_monoview_estimator(classifier_name, self.classifier_configs)
estimator.fit(X.get_v(view_idx, train_indices), y[train_indices])
self.estimator_pool[classifier_idx].append(estimator)
else:
@@ -49,14 +49,19 @@ class DiversityFusionClassifier(BaseMultiviewClassifier,
example_indices, view_indices = get_examples_views_indices(X,
example_indices,
view_indices)
- nb_class = X.get_nb_class(example_indices)
+ nb_class = X.get_nb_class()
+ if nb_class>2:
+ nb_class=3
votes = np.zeros((len(example_indices), nb_class), dtype=float)
monoview_predictions = [monoview_estimator.predict(X.get_v(view_idx, example_indices))
for view_idx, monoview_estimator
in zip(view_indices, self.monoview_estimators)]
for idx, example_index in enumerate(example_indices):
for monoview_estimator_index, monoview_prediciton in enumerate(monoview_predictions):
- votes[idx, int(monoview_prediciton[idx])] += 1
+ if int(monoview_prediciton[idx]) == -100:
+ votes[idx, 2] += 1
+ else:
+ votes[idx, int(monoview_prediciton[idx])] += 1
predicted_labels = np.argmax(votes, axis=1)
return predicted_labels
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/jumbo_multiview_utils.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/jumbo_multiview_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..bceee169bf991f92b5a6ab505cac157ab2e9d561
--- /dev/null
+++ b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/jumbo_multiview_utils.py
@@ -0,0 +1,60 @@
+import numpy as np
+
+from ...multiview.multiview_utils import get_examples_views_indices
+from .late_fusion_utils import LateFusionClassifier
+from ...monoview.monoview_utils import CustomRandint
+
+class BaseJumboFusion(LateFusionClassifier):
+
+ def __init__(self, random_state, classifiers_names=None,
+ classifier_configs=None,
+ nb_cores=1, weights=None, nb_monoview_per_view=1):
+ super(BaseJumboFusion, self).__init__(random_state, classifiers_names=classifiers_names,
+ classifier_configs=classifier_configs,
+ nb_cores=nb_cores, weights=weights)
+ self.param_names += ["nb_monoview_per_view", ]
+ self.distribs += [CustomRandint(1,10)]
+ self.nb_monoview_per_view = nb_monoview_per_view
+
+ def set_params(self, **params):
+ self.nb_monoview_per_view = params["nb_monoview_per_view"]
+ super(BaseJumboFusion, self).set_params(**params)
+
+ def predict(self, X, example_indices=None, view_indices=None):
+ example_indices, view_indices = get_examples_views_indices(X, example_indices, view_indices)
+ monoview_decisions = self.predict_monoview(X, example_indices=example_indices, view_indices=view_indices)
+ return self.aggregation_estimator.predict(monoview_decisions)
+
+ def fit(self, X, y, train_indices=None, view_indices=None):
+ train_indices, view_indices = get_examples_views_indices(X, train_indices, view_indices)
+ self.init_classifiers(len(view_indices), nb_monoview_per_view=self.nb_monoview_per_view)
+ print(self.classifiers_names, self.nb_monoview_per_view)
+ self.fit_monoview_estimators(X, y, train_indices=train_indices, view_indices=view_indices)
+ monoview_decisions = self.predict_monoview(X, example_indices=train_indices, view_indices=view_indices)
+ self.aggregation_estimator.fit(monoview_decisions, y[train_indices])
+ return self
+
+ def fit_monoview_estimators(self, X, y, train_indices=None, view_indices=None):
+ self.monoview_estimators = [[self.init_monoview_estimator(classifier_name,
+ self.classifier_configs[classifier_index])
+ for classifier_index, classifier_name
+ in enumerate(self.classifiers_names)]
+ for _ in view_indices]
+
+ self.monoview_estimators = [[estimator.fit(X.get_v(view_indices[idx], train_indices), y[train_indices])
+ for estimator in view_estimators]
+ for idx, view_estimators in enumerate(self.monoview_estimators)]
+ return self
+
+ def predict_monoview(self, X, example_indices=None, view_indices=None):
+ monoview_decisions = np.zeros((len(example_indices), len(view_indices)*len(self.classifiers_names)))
+ for idx, view_estimators in enumerate(self.monoview_estimators):
+ for estimator_index, estimator in enumerate(view_estimators):
+ monoview_decisions[:, len(self.classifiers_names)*idx+estimator_index] = estimator.predict(X.get_v(view_indices[idx],
+ example_indices))
+ return monoview_decisions
+
+
+
+
+
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 487e427600c331646f58a650e3984add30616766..f54fa286e9f25fe9a513cc5f885e56139904342e 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
@@ -73,14 +73,14 @@ class WeightsGenerator:
class LateFusionClassifier(BaseMultiviewClassifier, BaseFusionClassifier):
- def __init__(self, random_state=None, classifier_names=None,
+ def __init__(self, random_state=None, classifiers_names=None,
classifier_configs=None, nb_cores=1, weights=None):
super(LateFusionClassifier, self).__init__(random_state)
- self.classifiers_names = classifier_names
+ self.classifiers_names = classifiers_names
self.classifier_configs = classifier_configs
self.nb_cores = nb_cores
self.weights = weights
- self.param_names = ["classifier_names", "classifier_configs", "weights"]
+ self.param_names = ["classifiers_names", "classifier_configs", "weights"]
self.distribs =[ClassifierCombinator(need_probas=self.need_probas),
MultipleConfigGenerator(),
WeightsGenerator()]
@@ -106,15 +106,7 @@ class LateFusionClassifier(BaseMultiviewClassifier, BaseFusionClassifier):
else:
self.weights = self.weights/np.sum(self.weights)
- if isinstance(self.classifiers_names, ClassifierDistribution):
- self.classifiers_names = self.classifiers_names.draw(nb_view)
- elif self.classifiers_names is None:
- self.classifiers_names = ["decision_tree" for _ in range(nb_view)]
-
- if isinstance(self.classifier_configs, ConfigDistribution):
- self.classifier_configs = self.classifier_configs.draw(nb_view)
- elif isinstance(self.classifier_configs, dict):
- self.classifier_configs = [{classifier_name: self.classifier_configs[classifier_name]} for classifier_name in self.classifiers_names]
+ self.init_classifiers(nb_view)
self.monoview_estimators = [
self.init_monoview_estimator(classifier_name,
@@ -123,7 +115,23 @@ class LateFusionClassifier(BaseMultiviewClassifier, BaseFusionClassifier):
for classifier_index, classifier_name
in enumerate(self.classifiers_names)]
- # def verif_clf_views(self, classifier_names, nb_view):
+ def init_classifiers(self, nb_view, nb_monoview_per_view=None):
+ if nb_monoview_per_view is not None:
+ nb_clfs = nb_monoview_per_view
+ else:
+ nb_clfs = nb_view
+ if isinstance(self.classifiers_names, ClassifierDistribution):
+ self.classifiers_names = self.classifiers_names.draw(nb_clfs)
+ elif self.classifiers_names is None:
+ self.classifiers_names = ["decision_tree" for _ in range(nb_clfs)]
+
+ if isinstance(self.classifier_configs, ConfigDistribution):
+ self.classifier_configs = self.classifier_configs.draw(nb_clfs)
+ elif isinstance(self.classifier_configs, dict):
+ self.classifier_configs = [{classifier_name: self.classifier_configs[classifier_name]} for classifier_name in self.classifiers_names]
+
+
+# def verif_clf_views(self, classifier_names, nb_view):
# if classifier_names is None:
# if nb_view is None:
# raise AttributeError(self.__class__.__name__+" must have either classifier_names or nb_views provided.")
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/svm_jumbo_fusion.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/svm_jumbo_fusion.py
new file mode 100644
index 0000000000000000000000000000000000000000..f88c344d095fa1da6a187c4eea6e787f1fb72c69
--- /dev/null
+++ b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/svm_jumbo_fusion.py
@@ -0,0 +1,27 @@
+from sklearn.svm import SVC
+
+from .additions.jumbo_multiview_utils import BaseJumboFusion
+from ..monoview.monoview_utils import CustomUniform, CustomRandint
+
+classifier_class_name = "SVMJumboFusion"
+
+class SVMJumboFusion(BaseJumboFusion):
+
+ def __init__(self, random_state=None, classifiers_names=None,
+ classifier_configs=None, nb_cores=1, weights=None, nb_monoview_per_view=1, C=1.0, kernel="rbf", degree=2):
+ self.need_probas=False
+ super(SVMJumboFusion, self).__init__(random_state, classifiers_names=classifiers_names,
+ classifier_configs=classifier_configs,
+ nb_cores=nb_cores, weights=weights, nb_monoview_per_view=nb_monoview_per_view)
+ self.param_names += ["C", "kernel", "degree"]
+ self.distribs += [CustomUniform(), ["rbf", "poly", "linear"], CustomRandint(2, 5)]
+ self.aggregation_estimator = SVC(C=C, kernel=kernel, degree=degree)
+
+ def set_params(self, **params):
+ super(SVMJumboFusion, self).set_params(**params)
+ self.aggregation_estimator.set_params(**dict((key, value) for key, value in params.items() if key in ["C", "kernel", "degree"]))
+ return self
+
+
+
+