diff --git a/config_files/config_test.yml b/config_files/config_test.yml
new file mode 100644
index 0000000000000000000000000000000000000000..5a4d59df8fc4edc1066c69fb6d7d99b427f66f15
--- /dev/null
+++ b/config_files/config_test.yml
@@ -0,0 +1,205 @@
+# The base configuration of the benchmark
+Base :
+ log: true
+ name: ["Plausible"]
+ label: "_"
+ type: ".hdf5"
+ views:
+ pathf: "../data/"
+ nice: 0
+ random_state: 42
+ nb_cores: 1
+ full: False
+ debug: True
+ add_noise: False
+ noise_std: 0.0
+ res_dir: "../results/"
+
+# All the classification-realted configuration options
+Classification:
+ multiclass_method: "oneVersusOne"
+ split: 0.8
+ nb_folds: 2
+ nb_class: 2
+ classes:
+ type: ["multiview"]
+ algos_monoview: ["all"]
+ algos_multiview: ["mumbo"]
+ stats_iter: 2
+ metrics: ["accuracy_score", "f1_score"]
+ metric_princ: "f1_score"
+ hps_type: "randomized_search"
+ hps_iter: 2
+
+
+#####################################
+# The Monoview Classifier arguments #
+#####################################
+
+random_forest:
+ n_estimators: [25]
+ max_depth: [3]
+ criterion: ["entropy"]
+
+svm_linear:
+ C: [1]
+
+svm_rbf:
+ C: [1]
+
+svm_poly:
+ C: [1]
+ degree: [2]
+
+adaboost:
+ n_estimators: [50]
+ base_estimator: ["DecisionTreeClassifier"]
+
+adaboost_pregen:
+ n_estimators: [50]
+ base_estimator: ["DecisionTreeClassifier"]
+ n_stumps: [1]
+
+adaboost_graalpy:
+ n_iterations: [50]
+ n_stumps: [1]
+
+decision_tree:
+ max_depth: [10]
+ criterion: ["gini"]
+ splitter: ["best"]
+
+decision_tree_pregen:
+ max_depth: [10]
+ criterion: ["gini"]
+ splitter: ["best"]
+ n_stumps: [1]
+
+sgd:
+ loss: ["hinge"]
+ penalty: [l2]
+ alpha: [0.0001]
+
+knn:
+ n_neighbors: [5]
+ weights: ["uniform"]
+ algorithm: ["auto"]
+
+scm:
+ model_type: ["conjunction"]
+ max_rules: [10]
+ p: [0.1]
+
+scm_pregen:
+ model_type: ["conjunction"]
+ max_rules: [10]
+ p: [0.1]
+ n_stumps: [1]
+
+cq_boost:
+ mu: [0.01]
+ epsilon: [1e-06]
+ n_max_iterations: [5]
+ n_stumps: [1]
+
+cg_desc:
+ n_max_iterations: [10]
+ n_stumps: [1]
+
+cb_boost:
+ n_max_iterations: [10]
+ n_stumps: [1]
+
+lasso:
+ alpha: [1]
+ max_iter: [2]
+
+gradient_boosting:
+ n_estimators: [2]
+
+
+######################################
+# The Multiview Classifier arguments #
+######################################
+
+weighted_linear_early_fusion:
+ view_weights: [None]
+ monoview_classifier_name: ["decision_tree"]
+ monoview_classifier_config:
+ decision_tree:
+ max_depth: [1]
+ criterion: ["gini"]
+ splitter: ["best"]
+
+entropy_fusion:
+ classifier_names: [["decision_tree"]]
+ classifier_configs:
+ decision_tree:
+ max_depth: [1]
+ criterion: ["gini"]
+ splitter: ["best"]
+
+disagree_fusion:
+ classifier_names: [["decision_tree"]]
+ classifier_configs:
+ decision_tree:
+ max_depth: [1]
+ criterion: ["gini"]
+ splitter: ["best"]
+
+
+double_fault_fusion:
+ classifier_names: [["decision_tree"]]
+ classifier_configs:
+ decision_tree:
+ max_depth: [1]
+ criterion: ["gini"]
+ splitter: ["best"]
+
+difficulty_fusion:
+ classifier_names: [["decision_tree"]]
+ classifier_configs:
+ decision_tree:
+ max_depth: [1]
+ criterion: ["gini"]
+ splitter: ["best"]
+
+scm_late_fusion:
+ classifier_names: [["decision_tree"]]
+ p: 0.1
+ max_rules: 10
+ model_type: 'conjunction'
+ classifier_configs:
+ decision_tree:
+ max_depth: [1]
+ criterion: ["gini"]
+ splitter: ["best"]
+
+majority_voting_fusion:
+ classifier_names: [["decision_tree", "decision_tree", "decision_tree", ]]
+ classifier_configs:
+ decision_tree:
+ max_depth: [1]
+ criterion: ["gini"]
+ splitter: ["best"]
+
+bayesian_inference_fusion:
+ classifier_names: [["decision_tree", "decision_tree", "decision_tree", ]]
+ classifier_configs:
+ decision_tree:
+ max_depth: [1]
+ criterion: ["gini"]
+ splitter: ["best"]
+
+weighted_linear_late_fusion:
+ classifier_names: [["decision_tree", "decision_tree", "decision_tree", ]]
+ classifier_configs:
+ decision_tree:
+ max_depth: [1]
+ criterion: ["gini"]
+ splitter: ["best"]
+
+mumbo:
+ base_estimator: [None]
+ n_estimators: [10]
+ best_view_mode: ["edge"]
\ No newline at end of file
diff --git a/multiview_platform/mono_multi_view_classifiers/exec_classif.py b/multiview_platform/mono_multi_view_classifiers/exec_classif.py
index 2adbba0dadfc8f75544c2ececd36dcdac488a1e1..aa342b5433e51a1be444df2f7914ef22e6e47fd2 100644
--- a/multiview_platform/mono_multi_view_classifiers/exec_classif.py
+++ b/multiview_platform/mono_multi_view_classifiers/exec_classif.py
@@ -72,10 +72,13 @@ def init_benchmark(cl_type, monoview_algos, multiview_algos, args):
benchmark["monoview"] = monoview_algos
if "multiview" in cl_type:
- benchmark["multiview"] = [name for _, name, isPackage in
- pkgutil.iter_modules([
- "./mono_multi_view_classifiers/multiview_classifiers"])
- if not isPackage]
+ if multiview_algos==["all"]:
+ benchmark["multiview"] = [name for _, name, isPackage in
+ pkgutil.iter_modules([
+ "./mono_multi_view_classifiers/multiview_classifiers"])
+ if not isPackage]
+ else:
+ benchmark["multiview"] = multiview_algos
return benchmark
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 7cca55ea9613cc0b33d16a6524c17789d9f31673..d1ccd57fd4596f269fc4454ebb2b6b8c790763f1 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview/exec_multiview.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview/exec_multiview.py
@@ -272,7 +272,7 @@ def exec_multiview(directory, dataset_var, name, classification_indices, k_folds
searching_tool=hyper_param_search, n_iter=n_iter,
classifier_config=classifier_config)
- classifier = getattr(classifier_module, classifier_name)(random_state,
+ classifier = getattr(classifier_module, classifier_name)(random_state=random_state,
**classifier_config)
logging.debug("Done:\t Optimizing hyperparameters")
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/mumbo.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/mumbo.py
new file mode 100644
index 0000000000000000000000000000000000000000..508d2a94d6c78d86cea917e2ae9164fcec4a8d49
--- /dev/null
+++ b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/mumbo.py
@@ -0,0 +1,41 @@
+from sklearn.tree import DecisionTreeClassifier
+
+
+from multimodalboost.mumbo import MumboClassifier
+from ..multiview.multiview_utils import BaseMultiviewClassifier, \
+ get_examples_views_indices
+from ..utils.hyper_parameter_search import CustomRandint
+
+classifier_class_name = "Mumbo"
+
+class Mumbo(BaseMultiviewClassifier, MumboClassifier):
+
+ def __init__(self, base_estimator=None,
+ n_estimators=50,
+ random_state=None,
+ best_view_mode="edge"):
+ super().__init__(random_state)
+ super(BaseMultiviewClassifier, self).__init__(base_estimator=base_estimator,
+ n_estimators=n_estimators,
+ random_state=random_state,
+ best_view_mode=best_view_mode)
+ self.param_names = ["base_estimator", "n_estimators", "random_state", "best_view_mode"]
+ self.distribs = [[DecisionTreeClassifier(max_depth=1)],
+ CustomRandint(5,200), [random_state], ["edge", "error"]]
+
+ def fit(self, X, y, train_indices=None, view_indices=None):
+ train_indices, view_indices = get_examples_views_indices(X,
+ train_indices,
+ view_indices)
+ numpy_X, view_limits = X.to_numpy_array(example_indices=train_indices,
+ view_indices=view_indices)
+ return super(Mumbo, self).fit(numpy_X, y[train_indices],
+ view_limits)
+
+ def predict(self, X, example_indices=None, view_indices=None):
+ example_indices, view_indices = get_examples_views_indices(X,
+ example_indices,
+ view_indices)
+ numpy_X, view_limits = X.to_numpy_array(example_indices=example_indices,
+ view_indices=view_indices)
+ return super(Mumbo, self).predict(numpy_X)
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/dataset.py b/multiview_platform/mono_multi_view_classifiers/utils/dataset.py
index 6c9301626c5c613775d40dc7b772f241298aed27..6c40d787545f5a155763571d180db58085040ea5 100644
--- a/multiview_platform/mono_multi_view_classifiers/utils/dataset.py
+++ b/multiview_platform/mono_multi_view_classifiers/utils/dataset.py
@@ -295,6 +295,39 @@ class Dataset():
# The following methods are hdf5 free
+ def to_numpy_array(self, example_indices=None, view_indices=None):
+ """
+ To concanteant the needed views in one big numpy array while saving the
+ limits of each view in a list, to be bale to retrieve them later.
+
+ Parameters
+ ----------
+ example_indices : array like,
+ The indices of the examples to extract from the dataset
+
+ view_indices : array like,
+ The indices of the view to concatenate in the numpy array
+
+ Returns
+ -------
+ concat_views : numpy array,
+ The numpy array containing all the needed views.
+
+ view_limits : list of int
+ The limits of each slice used to extract the views.
+
+ """
+ view_limits = [0]
+ for view_index in view_indices:
+ view_data = self.get_v(view_index, example_indices=example_indices)
+ nb_features = view_data.shape[1]
+ view_limits.append(view_limits[-1]+nb_features)
+ concat_views = np.concatenate([self.get_v(view_index,
+ example_indices=example_indices)
+ for view_index in view_indices], axis=1)
+ return concat_views, view_limits
+
+
def select_views_and_labels(self, nb_labels=None,
selected_label_names=None, random_state=None,
view_names = None, path_for_new="../data/"):
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 9872bea62a103fc716c13a64d6042e29d1dd799d..8e3c104268599f2a528e48244ca4aab50eea9f05 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
@@ -93,7 +93,7 @@ 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):
- estimator = getattr(classifier_module, classifier_name)(random_state,
+ estimator = getattr(classifier_module, classifier_name)(random_state=random_state,
**classifier_kwargs)
params_dict = estimator.genDistribs()
if params_dict: