diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py
index e1f23cc46dffc9c5aeac01b8b83066b3198651ca..9d36b968ad0a93ba7dd881a51192a928ef5b7e04 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py
+++ b/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"
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/gradient_boosting.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/gradient_boosting.py
index bf8cccb2f63c5a3372fe642cd9cc508e84efea23..4a3cae43e94f06ef4bf82d4ec4eeebfe3fbddb36 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/gradient_boosting.py
+++ b/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"
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/random_forest.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/random_forest.py
index 82a442d99c42ac96604ac36e4469fd1288eb0b6f..06bb25af3a61617e529ab43bb745165b7303e877 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/random_forest.py
+++ b/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 = {}
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/base.py b/multiview_platform/mono_multi_view_classifiers/utils/base.py
index 6c2dd88efa3768f414aeeb0ae4bb4a9f124444f6..82e5cc236f569c189a42fb303d6e4e3cf24e09ef 100644
--- a/multiview_platform/mono_multi_view_classifiers/utils/base.py
+++ b/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), ]
\ No newline at end of file
diff --git a/multiview_platform/tests/test_utils/test_base.py b/multiview_platform/tests/test_utils/test_base.py
index c4cd5998232bb92146053abf29cc614892d89a7a..f4f87316308f9fe766341f3068f156c7a47b0117 100644
--- a/multiview_platform/tests/test_utils/test_base.py
+++ b/multiview_platform/tests/test_utils/test_base.py
@@ -1,10 +1,43 @@
-# 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)
\ No newline at end of file