Added a base_estimator loader

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py

@@ -7,6 +7,7 @@ from sklearn.tree import DecisionTreeClassifier
 from .. import metrics
 from ..monoview.monoview_utils import CustomRandint, BaseMonoviewClassifier, \
     get_accuracy_graph
+from ..utils.base import base_boosting_estimators
 
 # Author-Info
 __author__ = "Baptiste Bauvin"
@@ -53,11 +54,11 @@ class Adaboost(AdaBoostClassifier, BaseMonoviewClassifier):
     """
 
     def __init__(self, random_state=None, n_estimators=50,
-                 base_estimator=None, **kwargs):
+                 base_estimator=None, base_estimator_config=None, **kwargs):
 
-        if isinstance(base_estimator, str):
-            if base_estimator == "DecisionTreeClassifier":
-                base_estimator = DecisionTreeClassifier()
+        base_estimator = BaseMonoviewClassifier.get_base_estimator(self,
+                                                                   base_estimator,
+                                                  base_estimator_config)
         AdaBoostClassifier.__init__(self,
                                     random_state=random_state,
                                     n_estimators=n_estimators,
@@ -67,7 +68,7 @@ class Adaboost(AdaBoostClassifier, BaseMonoviewClassifier):
         self.param_names = ["n_estimators", "base_estimator"]
         self.classed_params = ["base_estimator"]
         self.distribs = [CustomRandint(low=1, high=500),
-                         [DecisionTreeClassifier(max_depth=1)]]
+                        base_boosting_estimators]
         self.weird_strings = {"base_estimator": "class_name"}
         self.plotted_metric = metrics.zero_one_loss
         self.plotted_metric_name = "zero_one_loss"

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/gradient_boosting.py

@@ -35,9 +35,10 @@ class GradientBoosting(GradientBoostingClassifier, BaseMonoviewClassifier):
                                             init=init,
                                             random_state=random_state
                                             )
-        self.param_names = ["n_estimators", ]
+        self.param_names = ["n_estimators", "max_depth"]
         self.classed_params = []
-        self.distribs = [CustomRandint(low=50, high=500), ]
+        self.distribs = [CustomRandint(low=50, high=500),
+                         CustomRandint(low=1, high=10),]
         self.weird_strings = {}
         self.plotted_metric = metrics.zero_one_loss
         self.plotted_metric_name = "zero_one_loss"

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/random_forest.py

@@ -61,7 +61,7 @@ class RandomForest(RandomForestClassifier, BaseMonoviewClassifier):
                             "random_state"]
         self.classed_params = []
         self.distribs = [CustomRandint(low=1, high=300),
-                         CustomRandint(low=1, high=300),
+                         CustomRandint(low=1, high=10),
                          ["gini", "entropy"], [random_state]]
         self.weird_strings = {}
 

multiview_platform/mono_multi_view_classifiers/utils/base.py

@@ -3,6 +3,9 @@ from sklearn.base import BaseEstimator
 from abc import abstractmethod
 from datetime import timedelta as hms
 
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier
+
 from multiview_platform.mono_multi_view_classifiers import metrics
 
 
@@ -60,6 +63,24 @@ class BaseClassifier(BaseEstimator, ):
         else:
             return self.__class__.__name__ + "with no config."
 
+    def get_base_estimator(self, base_estimator, estimator_config):
+        if base_estimator is None:
+            return DecisionTreeClassifier(**estimator_config)
+        if isinstance(base_estimator, str):
+            if base_estimator == "DecisionTreeClassifier":
+                return DecisionTreeClassifier(**estimator_config)
+            elif base_estimator == "AdaboostClassifier":
+                return AdaBoostClassifier(**estimator_config)
+            elif base_estimator == "RandomForestClassifier":
+                return RandomForestClassifier(**estimator_config)
+            else:
+                raise ValueError('Base estimator string {} does not match an available classifier.'.format(base_estimator))
+        elif isinstance(base_estimator, BaseEstimator):
+            return base_estimator.set_params(**estimator_config)
+        else:
+            raise ValueError('base_estimator must be either a string or a BaseEstimator child class, it is {}'.format(type(base_estimator)))
+
+
     def to_str(self, param_name):
         """
         Formats a parameter into a string
@@ -317,3 +338,10 @@ class ResultAnalyser():
             self.labels[self.test_indices])
         image_analysis = {}
         return string_analysis, image_analysis, self.metric_scores
+
+
+base_boosting_estimators = [DecisionTreeClassifier(max_depth=1),
+                            DecisionTreeClassifier(max_depth=2),
+                            DecisionTreeClassifier(max_depth=3),
+                            DecisionTreeClassifier(max_depth=4),
+                            DecisionTreeClassifier(max_depth=5), ]
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multiview_platform/tests/test_utils/test_base.py

-# import os
-# import unittest
-# import yaml
-# import numpy as np
-#
-# from multiview_platform.tests.utils import rm_tmp, tmp_path
-# from multiview_platform.mono_multi_view_classifiers.utils import base
-#
-#
-# class Test_ResultAnalyzer(unittest.TestCase):
+import os
+import unittest
+import yaml
+import numpy as np
+from sklearn.tree import DecisionTreeClassifier
+
+from multiview_platform.tests.utils import rm_tmp, tmp_path
+from multiview_platform.mono_multi_view_classifiers.utils import base
+
+
+class Test_ResultAnalyzer(unittest.TestCase):
+    pass
+
+class Test_BaseEstimator(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        cls.base_estimator = "DecisionTreeClassifier"
+        cls.base_estimator_config = {"max_depth":10,
+                                     "splitter": "best"}
+        cls.est = base.BaseClassifier()
+
+    def test_simple(self):
+        base_estim = self.est.get_base_estimator(self.base_estimator,
+                                            self.base_estimator_config)
+        self.assertTrue(isinstance(base_estim, DecisionTreeClassifier))
+        self.assertEqual(base_estim.max_depth, 10)
+        self.assertEqual(base_estim.splitter, "best")
+
+    def test_class(self):
+        base_estimator = DecisionTreeClassifier(max_depth=15, splitter="random")
+        base_estim = self.est.get_base_estimator(base_estimator,
+                                            self.base_estimator_config)
+        self.assertTrue(isinstance(base_estim, DecisionTreeClassifier))
+        self.assertEqual(base_estim.max_depth, 10)
+        self.assertEqual(base_estim.splitter, "best")
+
+    def test_wrong_args(self):
+        base_estimator_config = {"n_estimators": 10,
+                                 "splitter": "best"}
+        with self.assertRaises(TypeError):
+            base_estim = self.est.get_base_estimator(self.base_estimator,
+                                                     base_estimator_config)
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