tried to do the right hp search for earlyfusion

multiview_platform/mono_multi_view_classifiers/multiview/multiview_utils.py

@@ -2,6 +2,7 @@ from sklearn.base import BaseEstimator, ClassifierMixin
 import numpy as np
 
 from .. import multiview_classifiers
+from .. import monoview_classifiers
 
 
 
@@ -92,3 +93,29 @@ def get_train_views_indices(dataset, train_indices, view_indices,):
     if train_indices is None:
         train_indices = range(dataset["Metadata"].attrs["datasetLength"])
     return train_indices, view_indices
+
+
+class ConfigGenerator():
+
+    def __init__(self):
+        self.distribs = {}
+        for name in dir(monoview_classifiers):
+            if not name.startswith("__"):
+                module = getattr(monoview_classifiers, name)
+                classifier_class = getattr(module,
+                                           module.classifier_class_name)()
+                self.distribs[name] = dict((param_name, param_distrib)
+                                      for param_name, param_distrib in
+                                      zip(classifier_class().param_names,
+                                          classifier_class().distribs))
+
+    def rvs(self, random_state=None):
+        config_sample = {}
+        for classifier_name, classifier_config in self.distribs.items():
+            for param_name, param_distrib in classifier_config.items():
+                if hasattr(param_distrib, "rvs"):
+                    config_sample[classifier_name][param_name]=param_distrib.rvs(random_state=random_state)
+                else:
+                    config_sample[classifier_name][
+                        param_name] = param_distrib[random_state.randint(len(param_distrib))]
+        return config_sample

multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_early_fusion.py

@@ -2,7 +2,7 @@ import numpy as np
 import pkgutil
 
 from ..utils.dataset import getV
-from ..multiview.multiview_utils import BaseMultiviewClassifier, get_train_views_indices
+from ..multiview.multiview_utils import BaseMultiviewClassifier, get_train_views_indices, ConfigGenerator
 from .. import monoview_classifiers
 
 classifier_class_name = "WeightedLinearEarlyFusion"
@@ -24,19 +24,27 @@ class WeightedLinearEarlyFusion(BaseMultiviewClassifier):
             self.monoview_classifier = monoview_classifier_class(random_state=random_state,
                                                                  **monoview_classifier_config)
         else:
-            self.monoview_classifier = monoview_classifier
+            self.monoview_classifier = monoview_classifier(monoview_classifier_config)
             self.short_name = "early fusion "+self.monoview_classifier.__class__.__name__
-        self.param_names = ["monoview_classifier","random_state"]
+
+        self.param_names = ["monoview_classifier","random_state", "monoview_classifier_config"]
         classifier_classes = []
         for name in dir(monoview_classifiers):
             if not name.startswith("__"):
                 module = getattr(monoview_classifiers, name)
-                classifier_class = getattr(module, module.classifier_class_name)()
+                classifier_class = getattr(module, module.classifier_class_name)
                 classifier_classes.append(classifier_class)
-        self.distribs = [classifier_classes, [self.random_state]]
+        self.distribs = [classifier_classes, [self.random_state], ConfigGenerator()]
         self.classed_params = ["monoview_classifier"]
         self.weird_strings={"monoview_classifier":["class_name", "config"]}
 
+    def set_params(self, monoview_classifier=None, monoview_classifier_config=None, **params):
+        monoview_classifier_name = monoview_classifier.__module__
+        self.monoview_classifier = monoview_classifier()
+        self.set_monoview_classifier_config(monoview_classifier_name,
+                                       monoview_classifier_config)
+
+
     def fit(self, X, y, train_indices=None, view_indices=None):
         train_indices, X = self.transform_data_to_monoview(X, train_indices, view_indices)
         self.monoview_classifier.fit(X, y[train_indices])
@@ -70,6 +78,12 @@ class WeightedLinearEarlyFusion(BaseMultiviewClassifier):
             , axis=1)
         return monoview_data
 
+    def set_monoview_classifier_config(self, monoview_classifier_name, monoview_classifier_config):
+        if monoview_classifier_name in monoview_classifier_config:
+            self.monoview_classifier.set_params(monoview_classifier_config[monoview_classifier_name])
+        else:
+            self.monoview_classifier.set_params(monoview_classifier_config)
+