Local ok

config_files/config_smote.yml

+# The base configuration of the benchmark
+log: True
+name: ["digits",]
+label: "_"
+file_type: ".hdf5"
+views:
+pathf: "/home/baptiste/Documents/Datasets/Digits/"
+nice: 0
+random_state: 42
+nb_cores: 1
+full: True
+debug: True
+add_noise: False
+noise_std: 0.0
+res_dir: "../results/"
+track_tracebacks: False
+
+# All the classification-realted configuration options
+multiclass_method: "oneVersusOne"
+split: 0.25
+nb_folds: 2
+nb_class:
+classes:
+type: ["monoview"]
+algos_monoview: ["decision_tree" ]
+algos_multiview: ["weighted_linear_early_fusion","weighted_linear_late_fusion"]
+stats_iter: 3
+metrics:
+  accuracy_score: {}
+  f1_score: {}
+metric_princ: "accuracy_score"
+hps_type: "None"
+hps_args:
+  n_iter: 10
+  equivalent_draws: False
+
+
+weighted_linear_early_fusion:
+  view_weights: null
+  monoview_classifier_name: "decision_tree"
+  monoview_classifier_config:
+    decision_tree:
+      max_depth: 12
+      criterion: "gini"
+      splitter: "best"
+weighted_linear_late_fusion:
+  weights: null
+  classifiers_names: "decision_tree"
+  classifier_configs:
+    decision_tree:
+      max_depth: 3
+      criterion: "gini"
+      splitter: "best"
+decision_tree:
+  max_depth: 3
+
+
+
+######################################
+## The Monoview Classifier arguments #
+######################################
+mumbo:
+  base_estimator__criterion: 'gini'
+  base_estimator__max_depth: 3
+  base_estimator__random_state: None
+  base_estimator__splitter: 'best'
+  best_view_mode: 'edge'
+  base_estimator: 'decision_tree'
+  n_estimators: 10
+
+mucombo:
+  base_estimator__criterion: 'gini'
+  base_estimator__max_depth: 3
+  base_estimator__random_state: None
+  base_estimator__splitter: 'best'
+  best_view_mode: 'edge'
+  base_estimator: 'decision_tree'
+  n_estimators: 10
+#
+#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_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: [null]
+#  monoview_classifier_name: ["decision_tree"]
+#  monoview_classifier_config:
+#    decision_tree:
+#      max_depth: [1]
+#      criterion: ["gini"]
+#      splitter: ["best"]
+#
+#entropy_fusion:
+#  classifiers_names: [["decision_tree"]]
+#  classifier_configs:
+#    decision_tree:
+#      max_depth: [1]
+#      criterion: ["gini"]
+#      splitter: ["best"]
+#
+#disagree_fusion:
+#  classifiers_names: [["decision_tree"]]
+#  classifier_configs:
+#    decision_tree:
+#      max_depth: [1]
+#      criterion: ["gini"]
+#      splitter: ["best"]
+#
+#
+#double_fault_fusion:
+#  classifiers_names: [["decision_tree"]]
+#  classifier_configs:
+#    decision_tree:
+#      max_depth: [1]
+#      criterion: ["gini"]
+#      splitter: ["best"]
+#
+#difficulty_fusion:
+#  classifiers_names: [["decision_tree"]]
+#  classifier_configs:
+#    decision_tree:
+#      max_depth: [1]
+#      criterion: ["gini"]
+#      splitter: ["best"]
+#
+#scm_late_fusion:
+#  classifiers_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:
+#  classifiers_names: [["decision_tree", "decision_tree", "decision_tree", ]]
+#  classifier_configs:
+#    decision_tree:
+#      max_depth: [1]
+#      criterion: ["gini"]
+#      splitter: ["best"]
+#
+#bayesian_inference_fusion:
+#  classifiers_names: [["decision_tree", "decision_tree", "decision_tree", ]]
+#  classifier_configs:
+#    decision_tree:
+#      max_depth: [1]
+#      criterion: ["gini"]
+#      splitter: ["best"]
+#
+#weighted_linear_late_fusion:
+#  classifiers_names: [["decision_tree", "decision_tree", "decision_tree", ]]
+#  classifier_configs:
+#    decision_tree:
+#      max_depth: [1]
+#      criterion: ["gini"]
+#      splitter: ["best"]
+#
+#mumbo:
+#  base_estimator: [null]
+#  n_estimators: [10]
+#  best_view_mode: ["edge"]
+#
+#lp_norm_mkl:
+#  lmbda: [0.1]
+#  n_loops: [50]
+#  precision: [0.0001]
+#  kernel: ["rbf"]
+#  kernel_params:
+#    gamma: [0.1]
+#
+#mvml:
+#  reg_params: [[0,1]]
+#  nystrom_param: [1]
+#  learn_A: [1]
+#  learn_w: [0]
+#  n_loops: [6]
+#  kernel_types: ["rbf_kernel"]
+#  kernel_configs:
+#    gamma: [0.1]
+
+

multiview_platform/mono_multi_view_classifiers/exec_classif.py

@@ -903,7 +903,7 @@ def exec_classif(arguments):
             args["full"],
             )
         args["name"] = datasetname
-        splits = execution.gen_splits(dataset_var.get_labels(),
+        splits = execution.gen_splits(dataset_var,
                                       args["split"],
                                       stats_iter_random_states)
 

multiview_platform/mono_multi_view_classifiers/utils/execution.py

@@ -213,7 +213,7 @@ def gen_splits(dataset_var, split_ratio, stats_iter_random_states):
         for ind in test_fold:
             if not example_ids[ind].startswith("new_"):
                 test_indices.append(indices[ind])
-        splits.append([train_indices, test_indices])
+        splits.append([train_indices, np.array(test_indices)])
 
     return splits