Validate¶
<auto-generated stub>
Related methods¶
def AUC(unsortedROCs: Rep[DenseVector[Tup2[Double,:doc:double]]]): Rep[Double]
def ROC(x: Rep[DenseMatrix[Int]]): Rep[Tup2[Double,:doc:double]]
def ROCCurve(testSet: Rep[TS[T,:doc:boolean]], classify: (Rep[Double]) => (Rep[Int]) => Rep[Boolean], numThresholds: Rep[Int] = 10)(implicit ev0: TrainingSetLike[T,:doc:boolean,TS],ev1: Manifest[TS[T,:doc:boolean]]): Rep[DenseVector[Tup2[Double,:doc:double]]]
def ROCCurveBatch(testSet: Rep[TS[T,:doc:boolean]], classify: (Rep[Double]) => (Rep[IndexVector]) => Rep[DenseVector[Boolean]], numThresholds: Rep[Int] = 10)(implicit ev0: TrainingSetLike[T,:doc:boolean,TS],ev1: Manifest[TS[T,:doc:boolean]]): Rep[DenseVector[Tup2[Double,:doc:double]]]
def accuracy(x: Rep[DenseMatrix[Int]]): Rep[Double]
def confusionMatrix(testSet: Rep[TS[T,:doc:boolean]], classify: (Rep[Int]) => Rep[Boolean], numSamples: Rep[Int] = unit(-1))(implicit ev0: TrainingSetLike[T,:doc:boolean,TS],ev1: Manifest[TS[T,:doc:boolean]]): Rep[DenseMatrix[Int]] Generate a confusion matrix for the given classifier and testSet.
def confusionMatrixBatch(testSet: Rep[TS[T,:doc:boolean]], classify: (Rep[IndexVector]) => Rep[DenseVector[Boolean]], numSamples: Rep[Int] = unit(-1))(implicit ev0: TrainingSetLike[T,:doc:boolean,TS],ev1: Manifest[TS[T,:doc:boolean]]): Rep[DenseMatrix[Int]] The same as confusionMatrix, except process testSamples as a batch.
def crossValidate(dataSet: Rep[TS[T,:doc:boolean]], train: (Rep[TS[T,:doc:boolean]]) => Rep[M], classify: (Rep[M],Rep[TS[T,:doc:boolean]],Rep[Int]) => Rep[Boolean], metric: (Rep[DenseMatrix[Int]]) => Rep[Double], numFolds: Rep[Int] = 10, verbose: Rep[Boolean] = false)(implicit ev0: TrainingSetLike[T,:doc:boolean,TS],ev1: Manifest[TS[T,:doc:boolean]]): Rep[Double] Compute a cross-validated score for the classifier using a user-specified metric from a confusion matrix to a score (e.g. accuracy, precision).
def crossValidateAUC(dataSet: Rep[TS[T,:doc:boolean]], train: (Rep[TS[T,:doc:boolean]]) => Rep[M], classify: (Rep[M],Rep[TS[T,:doc:boolean]],Rep[Int]) => Rep[Double], numFolds: Rep[Int] = 10, numThresholds: Rep[Int] = 10)(implicit ev0: TrainingSetLike[T,:doc:boolean,TS],ev1: Manifest[TS[T,:doc:boolean]]): Rep[Double]
def crossValidateAUCBatch(dataSet: Rep[TS[T,:doc:boolean]], train: (Rep[TS[T,:doc:boolean]]) => Rep[M], classify: (Rep[M],Rep[TS[T,:doc:boolean]],Rep[IndexVector]) => Rep[DenseVector[Double]], numFolds: Rep[Int] = 10, numThresholds: Rep[Int] = 10)(implicit ev0: TrainingSetLike[T,:doc:boolean,TS],ev1: Manifest[TS[T,:doc:boolean]]): Rep[Double] Same as crossValidateAUC, except with a batch of test samples at a time.
def crossValidateBatch(dataSet: Rep[TS[T,:doc:boolean]], train: (Rep[TS[T,:doc:boolean]]) => Rep[M], classify: (Rep[M],Rep[TS[T,:doc:boolean]],Rep[IndexVector]) => Rep[DenseVector[Boolean]], metric: (Rep[DenseMatrix[Int]]) => Rep[Double], numFolds: Rep[Int] = 10, verbose: Rep[Boolean] = false)(implicit ev0: TrainingSetLike[T,:doc:boolean,TS],ev1: Manifest[TS[T,:doc:boolean]]): Rep[Double] The same as crossValidate, except with a batch of test samples at a time.
def crossValidateRaw(dataSet: Rep[TS[T,:doc:boolean]], train: (Rep[TS[T,:doc:boolean]]) => Rep[M], _numFolds: Rep[Int] = 10)(evalTestSet: (Rep[M],Rep[TS[T,:doc:boolean]]) => Rep[R])(implicit ev0: TrainingSetLike[T,:doc:boolean,TS],ev1: Manifest[TS[T,:doc:boolean]]): Rep[DenseVector[R]] A generic cross validate routine that is shared by crossValidate and crossValidateBatch.
def fallout(x: Rep[DenseMatrix[Int]]): Rep[Double]
def fnr(x: Rep[DenseMatrix[Int]]): Rep[Double]
def fpr(x: Rep[DenseMatrix[Int]]): Rep[Double]
def fscore(x: Rep[DenseMatrix[Int]]): Rep[Double]
def holdOut(dataSet: Rep[TS[T,L]], pct: Rep[Double])(implicit ev0: TrainingSetLike[T,L,TS],ev1: Manifest[TS[T,L]]): Rep[Tup2[TS[T,L],TS[T,L]]]
def holdOut2(dataSet: Rep[TS[T,L]], pctValidationSamples: Rep[Double], pctTestSamples: Rep[Double])(implicit ev0: TrainingSetLike[T,L,TS],ev1: Manifest[TS[T,L]]): Rep[Tup3[TS[T,L],TS[T,L],TS[T,L]]]
def precision(x: Rep[DenseMatrix[Int]]): Rep[Double]
def recall(x: Rep[DenseMatrix[Int]]): Rep[Double]
def sensitivity(x: Rep[DenseMatrix[Int]]): Rep[Double]
def specificity(x: Rep[DenseMatrix[Int]]): Rep[Double]
def tnr(x: Rep[DenseMatrix[Int]]): Rep[Double]
def tpr(x: Rep[DenseMatrix[Int]]): Rep[Double]