def evaluateCvOuter(self, X, y, folds):
"""
Computer the average AUC using k-fold cross validation and the linear kernel.
"""
Parameter.checkInt(folds, 2, float('inf'))
idx = cross_val.StratifiedKFold(y, folds)
metricMethods = [Evaluator.auc2, Evaluator.roc]
if self.kernel == "linear":
logging.debug("Running linear rank SVM ")
trainMetrics, testMetrics = AbstractPredictor.evaluateLearn2(
X, y, idx, self.modelSelectLinear, self.predict, metricMethods)
elif self.kernel == "rbf":
logging.debug("Running RBF rank SVM")
trainMetrics, testMetrics = AbstractPredictor.evaluateLearn2(
X, y, idx, self.modelSelectRBF, self.predict, metricMethods)
bestTrainAUCs = trainMetrics[0]
bestTrainROCs = trainMetrics[1]
bestTestAUCs = testMetrics[0]
bestTestROCs = testMetrics[1]
bestParams = {}
bestMetaDicts = {}
allMetrics = [bestTrainAUCs, bestTrainROCs, bestTestAUCs, bestTestROCs]
return (bestParams, allMetrics, bestMetaDicts)
def evaluateCvOuter(self, X, y, folds):
"""
Computer the average AUC using k-fold cross validation and the linear kernel.
"""
Parameter.checkInt(folds, 2, float('inf'))
idx = cross_val.StratifiedKFold(y, folds)
metricMethods = [Evaluator.auc2, Evaluator.roc]
if self.kernel == "linear":
logging.debug("Running linear rank SVM ")
trainMetrics, testMetrics = AbstractPredictor.evaluateLearn2(X, y, idx, self.modelSelectLinear, self.predict, metricMethods)
elif self.kernel == "rbf":
logging.debug("Running RBF rank SVM")
trainMetrics, testMetrics = AbstractPredictor.evaluateLearn2(X, y, idx, self.modelSelectRBF, self.predict, metricMethods)
bestTrainAUCs = trainMetrics[0]
bestTrainROCs = trainMetrics[1]
bestTestAUCs = testMetrics[0]
bestTestROCs = testMetrics[1]
bestParams = {}
bestMetaDicts = {}
allMetrics = [bestTrainAUCs, bestTrainROCs, bestTestAUCs, bestTestROCs]
return (bestParams, allMetrics, bestMetaDicts)
def evaluateCvOuter(self, X, y, folds):
"""
Computer the average AUC using k-fold cross validation and the linear kernel.
"""
Parameter.checkInt(folds, 2, float('inf'))
idx = StratifiedKFold(y, folds)
metricMethods = [Evaluator.auc2, Evaluator.roc]
trainMetrics, testMetrics = AbstractPredictor.evaluateLearn2(X, y, idx, self.modelSelect, self.predict, metricMethods)
bestTrainAUCs = trainMetrics[0]
bestTrainROCs = trainMetrics[1]
bestTestAUCs = testMetrics[0]
bestTestROCs = testMetrics[1]
bestParams = {}
bestMetaDicts = {}
allMetrics = [bestTrainAUCs, bestTrainROCs, bestTestAUCs, bestTestROCs]
return (bestParams, allMetrics, bestMetaDicts)
