From f43706305e23d04185a53c71994f182633212b9f Mon Sep 17 00:00:00 2001
From: bbauvin <baptiste.bauvin@centrale-marseille.fr>
Date: Thu, 5 Oct 2017 16:38:12 -0400
Subject: [PATCH] Corrected SCM crossval
---
.../MonoviewClassifiers/SCM.py | 76 +++++++++----------
1 file changed, 38 insertions(+), 38 deletions(-)
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SCM.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SCM.py
index 62c5b170..065ee1b0 100644
--- a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SCM.py
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SCM.py
@@ -68,44 +68,44 @@ def randomizedSearch(X_train, y_train, randomState, KFolds=None, metric=["accura
baseScore = 1000.0
isBetter = "lower"
config = []
- # for iterIndex in range(nIter):
- max_attributes = randomState.randint(1, 20)
- p = randomState.random_sample()
- model = randomState.choice(["conjunction", "disjunction"])
- classifier = pyscm.scm.SetCoveringMachine(p=p, max_attributes=max_attributes, model_type=model, verbose=False)
- # if nbFolds != 1:
- # kFolds = DB.getKFoldIndices(nbFolds, y_train, len(set(y_train)), range(len(y_train)), randomState)
- # else:
- # kFolds = [[], range(len(y_train))]
- scores = []
- KFolds = KFolds.split(X_train, y_train)
- for foldIdx, (trainIndices, testIndices) in enumerate(KFolds):
- # if fold != range(len(y_train)):
- # fold.sort()
- # trainIndices = [index for index in range(len(y_train)) if (index not in fold)]
- attributeClassification, binaryAttributes, dsetFile, name = transformData(X_train[trainIndices])
- try:
- classifier.fit(binaryAttributes, y_train[trainIndices], X=None,
- attribute_classifications=attributeClassification, iteration_callback=None)
-
- predictedLabels = classifier.predict(X_train[testIndices])
- score = metricModule.score(y_train[testIndices], predictedLabels)
- scores.append(score)
- except:
- pass
- dsetFile.close()
- os.remove(name)
- if scores==[]:
- score = baseScore
- else:
- score = np.mean(np.array(scores))
-
- if isBetter=="higher" and score>baseScore:
- baseScore = score
- config = [max_attributes, p, model]
- if isBetter=="lower" and score<baseScore:
- baseScore = score
- config = [max_attributes, p, model]
+ for iterIndex in range(nIter):
+ max_attributes = randomState.randint(1, 20)
+ p = randomState.random_sample()
+ model = randomState.choice(["conjunction", "disjunction"])
+ classifier = pyscm.scm.SetCoveringMachine(p=p, max_attributes=max_attributes, model_type=model, verbose=False)
+ # if nbFolds != 1:
+ # kFolds = DB.getKFoldIndices(nbFolds, y_train, len(set(y_train)), range(len(y_train)), randomState)
+ # else:
+ # kFolds = [[], range(len(y_train))]
+ scores = []
+ KFolds = KFolds.split(X_train, y_train)
+ for foldIdx, (trainIndices, testIndices) in enumerate(KFolds):
+ # if fold != range(len(y_train)):
+ # fold.sort()
+ # trainIndices = [index for index in range(len(y_train)) if (index not in fold)]
+ attributeClassification, binaryAttributes, dsetFile, name = transformData(X_train[trainIndices])
+ try:
+ classifier.fit(binaryAttributes, y_train[trainIndices], X=None,
+ attribute_classifications=attributeClassification, iteration_callback=None)
+
+ predictedLabels = classifier.predict(X_train[testIndices])
+ score = metricModule.score(y_train[testIndices], predictedLabels)
+ scores.append(score)
+ except:
+ pass
+ dsetFile.close()
+ os.remove(name)
+ if scores==[]:
+ score = baseScore
+ else:
+ score = np.mean(np.array(scores))
+
+ if isBetter=="higher" and score > baseScore:
+ baseScore = score
+ config = [max_attributes, p, model]
+ if isBetter=="lower" and score < baseScore:
+ baseScore = score
+ config = [max_attributes, p, model]
assert config!=[], "No good configuration found for SCM"
return config
--
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