MERGE

summit/examples/data/config_mv_mnist.yml

+# The base configuration of the benchmark
+
+# Enable logging
+log: True
+# The name of each dataset in the directory on which the benchmark should be run
+name: "multiview_mnist"
+# A label for the result directory
+label: "mnist"
+# The type of dataset, currently supported ".hdf5", and ".csv"
+file_type: ".hdf5"
+# The views to use in the banchmark, an empty value will result in using all the views
+views:
+# The path to the directory where the datasets are stored, an absolute path is advised
+pathf: "examples/data/"
+# The niceness of the processes, useful to lower their priority
+nice: 0
+# The random state of the benchmark, useful for reproducibility
+random_state: 42
+# The number of parallel computing threads
+nb_cores: 4
+# Used to run the benchmark on the full dataset
+full: True
+# Used to be able to run more than one benchmark per minute
+debug: False
+# The directory in which the results will be stored, an absolute path is advised
+res_dir: "examples/results/example_3/"
+# If an error occurs in a classifier, if track_tracebacks is set to True, the
+# benchmark saves the traceback and continues, if it is set to False, it will
+# stop the benchmark and raise the error
+track_tracebacks: True
+
+# All the classification-realted configuration options
+
+# If the dataset is multiclass, will use this multiclass-to-biclass method
+multiclass_method: "oneVersusOne"
+# The ratio number of test exmaples/number of train samples
+split: 0.8
+# The nubmer of folds in the cross validation process when hyper-paramter optimization is performed
+nb_folds: 5
+# The number of classes to select in the dataset
+nb_class: 2
+# The name of the classes to select in the dataset
+classes:
+# The type of algorithms to run during the benchmark (monoview and/or multiview)
+type: ["monoview","multiview"]
+# The name of the monoview algorithms to run, ["all"] to run all the available classifiers
+algos_monoview: ["decision_tree", "adaboost", ]
+# The names of the multiview algorithms to run, ["all"] to run all the available classifiers
+algos_multiview: ["early_fusion_decision_tree", "early_fusion_adaboost"]
+# The number of times the benchamrk is repeated with different train/test
+# split, to have more statistically significant results
+stats_iter: 5
+# The metrics that will be use din the result analysis
+metrics:
+  accuracy_score: {}
+  f1_score:
+    average: "micro"
+# The metric that will be used in the hyper-parameter optimization process
+metric_princ: "accuracy_score"
+# The type of hyper-parameter optimization method
+hps_type: 'Random'
+# The number of iteration in the hyper-parameter optimization process
+hps_args:
+  n_iter: 10
+
+decision_tree:
+  max_depth: 3
+
+adaboost:
+  base_estimator: "DecisionTreeClassifier"
+  n_estimators: 10
+
+weighted_linear_late_fusion:
+  classifiers_names: "decision_tree"
+  classifier_configs:
+    decision_tree:
+      max_depth: 2
+
+
+
+# The following arguments are classifier-specific, and are documented in each
+# of the corresponding modules.
+
+# In order to run multiple sets of parameters, use multiple values in the
+# following lists, and set hps_type to None.

summit/multiview_platform/monoview_classifiers/imbalance_bagging.py

@@ -12,9 +12,8 @@ class ImbalanceBagging(BaseMonoviewClassifier, BalancedBaggingClassifier):
 
     def __init__(self, random_state=None, base_estimator="DecisionTreeClassifier",
                  n_estimators=10, sampling_strategy="auto",
-                 replacement=False, base_estimator_config=None):
-        base_estimator = self.get_base_estimator(base_estimator,
-                                                 base_estimator_config)
+                 replacement=False, base_estimator_config=None, **kwargs):
+        base_estimator = self.get_base_estimator(base_estimator, base_estimator_config, **kwargs)
         super(ImbalanceBagging, self).__init__(random_state=random_state, base_estimator=base_estimator,
                                          n_estimators=n_estimators,
                                          sampling_strategy=sampling_strategy,

summit/multiview_platform/multiview/exec_multiview.py

@@ -267,7 +267,6 @@ def exec_multiview(directory, dataset_var, name, classification_indices,
     logging.info("Start:\t Optimizing hyperparameters")
     hps_beg = time.monotonic()
 
-    print(dataset_var.view_dict)
     if hps_method != "None":
         hps_method_class = getattr(hyper_parameter_search, hps_method)
         estimator = getattr(classifier_module, classifier_name)(

summit/multiview_platform/multiview_classifiers/early_fusion_imbalance_bagging.py

+from .additions.early_fusion_from_monoview import BaseEarlyFusion
+from ..utils.base import base_boosting_estimators
+from ..utils.hyper_parameter_search import CustomRandint, CustomUniform
+
+classifier_class_name = "EarlyFusionImbalanceBagging"
+
+
+class EarlyFusionImbalanceBagging(BaseEarlyFusion):
+
+    def __init__(self, random_state=None, base_estimator="DecisionTreeClassifier",
+                 n_estimators=10, sampling_strategy="auto",
+                 replacement=False, base_estimator_config=None, **kwargs):
+        BaseEarlyFusion.__init__(self, random_state=random_state,
+                                 monoview_classifier="imbalance_bagging",
+                                 base_estimator=base_estimator,
+                 n_estimators=n_estimators, sampling_strategy=sampling_strategy,
+                 replacement=replacement, base_estimator_config=base_estimator_config, **kwargs)
+        self.param_names = ["n_estimators", "base_estimator",
+                            "sampling_strategy", ]
+        self.classed_params = ["base_estimator"]
+        self.distribs = [CustomRandint(low=1, high=50),
+                         base_boosting_estimators,
+                         ["auto"]]
+        self.weird_strings = {"base_estimator": "class_name"}
+        self.base_estimator_config = base_estimator_config
\ No newline at end of file

summit/multiview_platform/multiview_classifiers/early_fusion_scm.py

+from .additions.early_fusion_from_monoview import BaseEarlyFusion
+from ..utils.hyper_parameter_search import CustomUniform, CustomRandint
+
+classifier_class_name = "EarlyFusionSCM"
+
+
+class EarlyFusionSCM(BaseEarlyFusion):
+
+    def __init__(self, random_state=None, model_type="conjunction",
+                 max_rules=10, p=0.1, **kwargs):
+        BaseEarlyFusion.__init__(self, random_state=None,
+                                    monoview_classifier="scm",
+                                 model_type=model_type,
+                                 max_rules=max_rules, p=p, **kwargs)
+        self.param_names = ["model_type", "max_rules", "p", "random_state"]
+        self.distribs = [["conjunction", "disjunction"],
+                         CustomRandint(low=1, high=15),
+                         CustomUniform(loc=0, state=1), [random_state]]
+        self.classed_params = []
+        self.weird_strings = {}
\ No newline at end of file

summit/multiview_platform/result_analysis/feature_importances.py

@@ -121,9 +121,9 @@ def plot_feature_importances(file_name, feature_importance,
         hoverinfo=["text"],
         colorscale="Hot",
         reversescale=True))
-    fig.update_layout(
-        xaxis={"showgrid": False, "showticklabels": False, "ticks": ''},
-        yaxis={"showgrid": False, "showticklabels": False, "ticks": ''})
+    # fig.update_layout(
+    #     xaxis={"showgrid": False, "showticklabels": False, "ticks": ''},
+    #     yaxis={"showgrid": False, "showticklabels": False, "ticks": ''})
     fig.update_layout(paper_bgcolor='rgba(0,0,0,0)',
                       plot_bgcolor='rgba(0,0,0,0)')
     plotly.offline.plot(fig, filename=file_name + ".html", auto_open=False)

summit/multiview_platform/utils/base.py

@@ -65,7 +65,14 @@ class BaseClassifier(BaseEstimator, ):
         else:
             return self.__class__.__name__ + " with no config."
 
-    def get_base_estimator(self, base_estimator, estimator_config):
+    def get_base_estimator(self, base_estimator, estimator_config, **kwargs):
+        if not kwargs is None:
+            for key, value in kwargs.items():
+                if key.startswith("base_estimator__"):
+                    if estimator_config is None:
+                        estimator_config = {}
+                    param_name = key.split("base_estimator__")[1]
+                    estimator_config[param_name] = value
         if estimator_config is None:
             estimator_config = {}
         if base_estimator is None:

summit/multiview_platform/utils/compression.py

@@ -43,12 +43,5 @@ def remove_compressed(exp_path):
 
 
 if __name__=="__main__":
-    import os
-    explore_files("/home/baptiste/Documents/Gitwork/summit/results/BioBanQ_mv_status/debug_started_2022_07_01-22_23_04_bal_acc/")
-    # print(os.listdir("/home/baptiste/Documents/Gitwork/summit/results/"))
-    # for dataset in os.listdir("/home/baptiste/Documents/Gitwork/summit/results/"):
-    #     print(dataset)
-    #     dataset_path = os.path.join("/home/baptiste/Documents/Gitwork/summit/results/", dataset)
-    #     for exp_name in os.listdir(dataset_path):
-    #         exp_path = os.path.join(dataset_path, exp_name)
-    #         explore_files(exp_path, compress=False)
\ No newline at end of file
+
+    simplify_plotly("/home/baptiste/Documents/Gitwork/summit/results/hepatitis/debug_started_2022_03_16-15_06_55__/hepatitis-mean_on_10_iter-balanced_accuracy_p.html")

summit/multiview_platform/utils/hyper_parameter_search.py

@@ -166,7 +166,6 @@ class Random(RandomizedSearchCV, HPSearch):
                                     param_distributions=param_distributions,
                                     refit=refit, n_jobs=n_jobs, scoring=scoring,
                                     cv=cv, random_state=random_state)
-
         self.framework = framework
         self.available_indices = learning_indices
         self.view_indices = view_indices
@@ -282,8 +281,6 @@ class CustomUniform(CustomDist):
         return self.multiply(unif)
 
 
-
-
 def format_params(params, pref=""):
     if isinstance(params, dict):
         dictionary = {}