diff --git a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/PregenUtils.py b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/PregenUtils.py
new file mode 100644
index 0000000000000000000000000000000000000000..196c97ec893eed64a84dfa955463f216b0d3845a
--- /dev/null
+++ b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/PregenUtils.py
@@ -0,0 +1,18 @@
+
+from ..MonoviewUtils import change_label_to_minus
+from .BoostUtils import StumpsClassifiersGenerator, BaseBoost
+
+class PregenClassifier(BaseBoost):
+
+ def pregen_voters(self, X, y=None):
+ if y is not None:
+ neg_y = change_label_to_minus(y)
+ if self.estimators_generator is None:
+ self.estimators_generator = StumpsClassifiersGenerator(
+ n_stumps_per_attribute=self.n_stumps,
+ self_complemented=self.self_complemented)
+ self.estimators_generator.fit(X, neg_y)
+ else:
+ neg_y=None
+ classification_matrix = self._binary_classification_matrix(X)
+ return classification_matrix, neg_y
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/AdaboostPregen.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/AdaboostPregen.py
index ddb8f8649f0bf9d05707689dd9d07d20d2227016..0eaa134d3ac5e7919bb936547aa8376aa9b13b9e 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/AdaboostPregen.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/AdaboostPregen.py
@@ -8,13 +8,14 @@ from ..Monoview.MonoviewUtils import CustomRandint, BaseMonoviewClassifier, chan
from ..Monoview.Additions.BoostUtils import get_accuracy_graph
from .. import Metrics
from ..Monoview.Additions.BoostUtils import get_accuracy_graph, StumpsClassifiersGenerator, BaseBoost
+from ..Monoview.Additions.PregenUtils import PregenClassifier
# Author-Info
__author__ = "Baptiste Bauvin"
__status__ = "Prototype" # Production, Development, Prototype
-class AdaboostPregen(AdaBoostClassifier, BaseBoost, BaseMonoviewClassifier):
+class AdaboostPregen(AdaBoostClassifier, BaseMonoviewClassifier, PregenClassifier):
def __init__(self, random_state=None, n_estimators=50,
base_estimator=None, n_stumps=1, self_complemeted=True , **kwargs):
@@ -82,17 +83,17 @@ class AdaboostPregen(AdaBoostClassifier, BaseBoost, BaseMonoviewClassifier):
np.savetxt(directory + "times.csv", np.array([self.train_time, self.pred_time]), delimiter=',')
return interpretString
- def pregen_voters(self, X, y=None):
- if y is not None:
- neg_y = change_label_to_minus(y)
- if self.estimators_generator is None:
- self.estimators_generator = StumpsClassifiersGenerator(
- n_stumps_per_attribute=self.n_stumps,
- self_complemented=self.self_complemented)
- self.estimators_generator.fit(X, neg_y)
- else:
- neg_y=None
- classification_matrix = self._binary_classification_matrix(X)
+ # def pregen_voters(self, X, y=None):
+ # if y is not None:
+ # neg_y = change_label_to_minus(y)
+ # if self.estimators_generator is None:
+ # self.estimators_generator = StumpsClassifiersGenerator(
+ # n_stumps_per_attribute=self.n_stumps,
+ # self_complemented=self.self_complemented)
+ # self.estimators_generator.fit(X, neg_y)
+ # else:
+ # neg_y=None
+ # classification_matrix = self._binary_classification_matrix(X)
return classification_matrix, neg_y
def formatCmdArgs(args):
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/SCMPregen.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/SCMPregen.py
index 8c2fb54c63825b85782b050dae2e4f2922064d02..fdd78acbcb076bd0f8563f30ec1f28362e592a9f 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/SCMPregen.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/SCMPregen.py
@@ -5,11 +5,12 @@ import numpy as np
from ..Monoview.MonoviewUtils import CustomRandint, CustomUniform, BaseMonoviewClassifier, change_label_to_minus, change_label_to_zero
from ..Monoview.Additions.BoostUtils import StumpsClassifiersGenerator, BaseBoost
+from ..Monoview.Additions.PregenUtils import PregenClassifier
# Author-Info
__author__ = "Baptiste Bauvin"
__status__ = "Prototype" # Production, Development, Prototype
-class SCMPregen(scm, BaseMonoviewClassifier, BaseBoost):
+class SCMPregen(scm, BaseMonoviewClassifier, PregenClassifier):
def __init__(self, random_state=None, model_type="conjunction",
max_rules=10, p=0.1, n_stumps=10,self_complemented=True, **kwargs):
@@ -35,44 +36,45 @@ class SCMPregen(scm, BaseMonoviewClassifier, BaseBoost):
return self
def predict(self, X):
- h = np.ones(X.shape)
- print('poul')
- pregen_h, _ = self.pregen_voters(h)
- print('from')
- pred = super(SCMPregen, self).predict(pregen_h)
- return pred
-
- def pregen_voters(self, X, y=None):
- if y is not None:
- if self.estimators_generator is None:
- self.estimators_generator = StumpsClassifiersGenerator(
- n_stumps_per_attribute=self.n_stumps,
- self_complemented=self.self_complemented)
- self.estimators_generator.fit(X, y)
- else:
- neg_y=None
- classification_matrix = self._binary_classification_matrix_t(X)
- return classification_matrix, y
-
- def _collect_probas_t(self, X):
- print('jb')
- for est in self.estimators_generator.estimators_:
- print(type(est))
- print(est.predict_proba_t(X))
- print('ha')
- return np.asarray([clf.predict_proba(X) for clf in self.estimators_generator.estimators_])
-
- def _binary_classification_matrix_t(self, X):
- probas = self._collect_probas_t(X)
- predicted_labels = np.argmax(probas, axis=2)
- predicted_labels[predicted_labels == 0] = -1
- values = np.max(probas, axis=2)
- return (predicted_labels * values).T
+ pregen_h, _ = self.pregen_voters(X)
+ return self.classes_[self.model_.predict(X)]
+
+ def get_params(self, deep=True):
+ return {"p": self.p, "model_type": self.model_type,
+ "max_rules": self.max_rules,
+ "random_state": self.random_state, "n_stumps":self.n_stumps}
+
+ # def pregen_voters(self, X, y=None):
+ # if y is not None:
+ # if self.estimators_generator is None:
+ # self.estimators_generator = StumpsClassifiersGenerator(
+ # n_stumps_per_attribute=self.n_stumps,
+ # self_complemented=self.self_complemented)
+ # self.estimators_generator.fit(X, y)
+ # else:
+ # neg_y=None
+ # classification_matrix = self._binary_classification_matrix_t(X)
+ # return classification_matrix, y
+ #
+ # def _collect_probas_t(self, X):
+ # print('jb')
+ # for est in self.estimators_generator.estimators_:
+ # print(type(est))
+ # print(est.predict_proba_t(X))
+ # print('ha')
+ # return np.asarray([clf.predict_proba(X) for clf in self.estimators_generator.estimators_])
+ #
+ # def _binary_classification_matrix_t(self, X):
+ # probas = self._collect_probas_t(X)
+ # predicted_labels = np.argmax(probas, axis=2)
+ # predicted_labels[predicted_labels == 0] = -1
+ # values = np.max(probas, axis=2)
+ # return (predicted_labels * values).T
def canProbas(self):
"""Used to know if the classifier can return label probabilities"""
- return True
+ return False
def getInterpret(self, directory, y_test):
interpretString = "Model used : " + str(self.model_)