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CustomMapping<TSrc,TDst>(TransformsCatalog, Action<TSrc,TDst>, String, SchemaDefinition, SchemaDefinition)
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Create a CustomMappingEstimator<TSrc,TDst>, which applies a custom mapping of input columns to output columns.
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StatefulCustomMapping<TSrc,TDst,TState>(TransformsCatalog, Action<TSrc,TDst,TState>, Action<TState>, String)
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Create a StatefulCustomMappingEstimator<TSrc,TDst,TState>, which applies a custom mapping of input columns to output columns,
while allowing a per-cursor state.
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CalculateFeatureContribution(TransformsCatalog, ISingleFeaturePredictionTransformer<ICalculateFeatureContribution>, Int32, Int32, Boolean)
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Create a FeatureContributionCalculatingEstimator that computes model-specific contribution scores for
each feature of the input vector.
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CalculateFeatureContribution<TModelParameters,TCalibrator>(TransformsCatalog,
ISingleFeaturePredictionTransformer<CalibratedModelParametersBase<TModelParameters,
TCalibrator>>, Int32, Int32, Boolean)
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Create a FeatureContributionCalculatingEstimator that computes model-specific contribution scores for
each feature of the input vector. Supports calibrated models.
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Expression(TransformsCatalog, String, String, String[])
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Creates an ExpressionEstimator.
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IndicateMissingValues(TransformsCatalog, InputOutputColumnPair[])
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Create a MissingValueIndicatorEstimator, which copies the data from the column specified in InputColumnName
to a new column: OutputColumnName.
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IndicateMissingValues(TransformsCatalog, String, String)
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Create a MissingValueIndicatorEstimator, which scans the data from the column specified in inputColumnName
and fills new column specified in outputColumnName with vector of bools where i-th bool has value of true
if i-th element in column data has missing value and false otherwise.
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ReplaceMissingValues(TransformsCatalog, InputOutputColumnPair[], MissingValueReplacingEstimator+ReplacementMode, Boolean)
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Create a ColumnCopyingEstimator, which copies the data from the column specified in InputColumnName
to a new column: OutputColumnName and replaces missing values in it according to replacementMode.
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ReplaceMissingValues(TransformsCatalog, String, String, MissingValueReplacingEstimator+ReplacementMode, Boolean)
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Create a MissingValueReplacingEstimator, which copies the data from the column specified in inputColumnName
to a new column: outputColumnName and replaces missing values in it according to replacementMode.
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ConvertToGrayscale(TransformsCatalog, String, String)
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Create a ImageGrayscalingEstimator, which converts images in the column specified in InputColumnName
to grayscale images in a new column: OutputColumnName.
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ConvertToImage(TransformsCatalog, Int32, Int32, String, String,
ImagePixelExtractingEstimator+ColorBits, ImagePixelExtractingEstimator+ColorsOrder,
Boolean, Single, Single, Int32, Int32, Int32, Int32)
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Create a VectorToImageConvertingEstimator, which creates image from the data from the column specified in inputColumnName
to a new column: outputColumnName.
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ExtractPixels(TransformsCatalog, String, String, ImagePixelExtractingEstimator+ColorBits,
ImagePixelExtractingEstimator+ColorsOrder, Boolean, Single, Single,
Boolean)
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Create a ImagePixelExtractingEstimator, which extracts pixels values from the data specified in column: inputColumnName
to a new column: outputColumnName.
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LoadImages(TransformsCatalog, String, String, String)
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Create a ImageLoadingEstimator, which loads the data from the column specified in inputColumnName
as an image to a new column: outputColumnName.
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LoadRawImageBytes(TransformsCatalog, String, String, String)
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Create a ImageLoadingEstimator, which loads the data from the column specified in inputColumnName
as an image of raw bytes to a new column: outputColumnName.
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ResizeImages(TransformsCatalog, String, Int32, Int32, String, ImageResizingEstimator+ResizingKind, ImageResizingEstimator+Anchor)
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Create a ImageResizingEstimator, which resize the image from the column specified in inputColumnName
to a new column: outputColumnName.
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ApproximatedKernelMap(TransformsCatalog, String, String, Int32, Boolean, KernelBase, Nullable<Int32>)
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Create an ApproximatedKernelMappingEstimator that maps input vectors to a low dimensional
feature space where inner products approximate a shift-invariant kernel function.
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VectorWhiten(TransformsCatalog, String, String, WhiteningKind, Single, Int32, Int32)
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Takes column filled with a vector of random variables with a known covariance matrix into a set of new variables whose covariance is the identity matrix,
meaning that they are uncorrelated and each have variance 1.
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NormalizeBinning(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, Int32)
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Create a NormalizingEstimator, which normalizes by assigning the data into bins with equal density.
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NormalizeBinning(TransformsCatalog, String, String, Int64, Boolean, Int32)
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Create a NormalizingEstimator, which normalizes by assigning the data into bins with equal density.
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NormalizeGlobalContrast(TransformsCatalog, String, String, Boolean, Boolean, Single)
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Create a GlobalContrastNormalizingEstimator, which normalizes columns individually applying global contrast normalization.
Setting ensureZeroMean to true, will apply a pre-processing step to make the specified column's mean be the zero vector.
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NormalizeLogMeanVariance(TransformsCatalog, InputOutputColumnPair[], Boolean, Int64, Boolean)
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Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the logarithm of the data.
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NormalizeLogMeanVariance(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean)
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Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the logarithm of the data.
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NormalizeLogMeanVariance(TransformsCatalog, String, Boolean, String, Int64, Boolean)
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Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the logarithm of the data.
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NormalizeLogMeanVariance(TransformsCatalog, String, String, Int64, Boolean)
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Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the logarithm of the data.
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NormalizeLpNorm(TransformsCatalog, String, String, LpNormNormalizingEstimatorBase+NormFunction, Boolean)
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Create a LpNormNormalizingEstimator, which normalizes (scales) vectors in the input column to the unit norm.
The type of norm that is used is defined by norm. Setting ensureZeroMean to true,
will apply a pre-processing step to make the specified column's mean be a zero vector.
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NormalizeMeanVariance(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, Boolean)
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Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the data.
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NormalizeMeanVariance(TransformsCatalog, String, String, Int64, Boolean, Boolean)
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Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the data.
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NormalizeMinMax(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean)
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Create a NormalizingEstimator, which normalizes based on the observed minimum and maximum values of the data.
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NormalizeMinMax(TransformsCatalog, String, String, Int64, Boolean)
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Create a NormalizingEstimator, which normalizes based on the observed minimum and maximum values of the data.
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NormalizeRobustScaling(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, UInt32, UInt32)
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Create a NormalizingEstimator, which normalizes using statistics that are robust to outliers by centering the data around 0 (removing the median) and scales
the data according to the quantile range (defaults to the interquartile range).
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NormalizeRobustScaling(TransformsCatalog, String, String, Int64, Boolean, UInt32, UInt32)
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Create a NormalizingEstimator, which normalizes using statistics that are robust to outliers by centering the data around 0 (removing the median) and scales
the data according to the quantile range (defaults to the interquartile range).
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NormalizeSupervisedBinning(TransformsCatalog, InputOutputColumnPair[], String, Int64, Boolean, Int32, Int32)
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Create a NormalizingEstimator, which normalizes by assigning the data into bins based on correlation with the labelColumnName column.
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NormalizeSupervisedBinning(TransformsCatalog, String, String, String, Int64, Boolean, Int32, Int32)
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Create a NormalizingEstimator, which normalizes by assigning the data into bins based on correlation with the labelColumnName column.
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ApplyOnnxModel(TransformsCatalog, OnnxOptions)
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Create a OnnxScoringEstimator using the specified OnnxOptions.
Please refer to OnnxScoringEstimator to learn more about the necessary dependencies,
and how to run it on a GPU.
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ApplyOnnxModel(TransformsCatalog, String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean)
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Create a OnnxScoringEstimator, which applies a pre-trained Onnx model to the input column.
Input/output columns are determined based on the input/output columns of the provided ONNX model.
Please refer to OnnxScoringEstimator to learn more about the necessary dependencies,
and how to run it on a GPU.
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ApplyOnnxModel(TransformsCatalog, String, Nullable<Int32>, Boolean)
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Create a OnnxScoringEstimator, which applies a pre-trained Onnx model to the input column.
Input/output columns are determined based on the input/output columns of the provided ONNX model.
Please refer to OnnxScoringEstimator to learn more about the necessary dependencies,
and how to run it on a GPU.
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ApplyOnnxModel(TransformsCatalog, String, String, String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean)
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Create a OnnxScoringEstimator, which applies a pre-trained Onnx model to the inputColumnName column.
Please refer to OnnxScoringEstimator to learn more about the necessary dependencies,
and how to run it on a GPU.
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ApplyOnnxModel(TransformsCatalog, String, String, String, Nullable<Int32>, Boolean)
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Create a OnnxScoringEstimator, which applies a pre-trained Onnx model to the inputColumnName column.
Please refer to OnnxScoringEstimator to learn more about the necessary dependencies,
and how to run it on a GPU.
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ApplyOnnxModel(TransformsCatalog, String[], String[], String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean, Int32)
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Create a OnnxScoringEstimator, which applies a pre-trained Onnx model to the inputColumnNames columns.
Please refer to OnnxScoringEstimator to learn more about the necessary dependencies,
and how to run it on a GPU.
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ApplyOnnxModel(TransformsCatalog, String[], String[], String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean)
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Create a OnnxScoringEstimator, which applies a pre-trained Onnx model to the inputColumnNames columns.
Please refer to OnnxScoringEstimator to learn more about the necessary dependencies,
and how to run it on a GPU.
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ApplyOnnxModel(TransformsCatalog, String[], String[], String, Nullable<Int32>, Boolean)
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Create a OnnxScoringEstimator, which applies a pre-trained Onnx model to the inputColumnNames columns.
Please refer to OnnxScoringEstimator to learn more about the necessary dependencies,
and how to run it on a GPU.
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DnnFeaturizeImage(TransformsCatalog, String, Func<DnnImageFeaturizerInput,EstimatorChain<ColumnCopyingTransformer>>, String)
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Create DnnImageFeaturizerEstimator, which applies one of the pre-trained DNN models in
DnnImageModelSelector to featurize an image.
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ProjectToPrincipalComponents(TransformsCatalog, String, String, String, Int32, Int32, Boolean, Nullable<Int32>)
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Initializes a new instance of PrincipalComponentAnalyzer.
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DetectAnomalyBySrCnn(TransformsCatalog, String, String, Int32, Int32, Int32, Int32, Int32, Double)
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Create SrCnnAnomalyEstimator, which detects timeseries anomalies using SRCNN algorithm.
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DetectChangePointBySsa(TransformsCatalog, String, String, Double, Int32, Int32, Int32, ErrorFunction, MartingaleType, Double)
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Create SsaChangePointEstimator, which predicts change points in time series
using Singular Spectrum Analysis (SSA).
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DetectChangePointBySsa(TransformsCatalog, String, String, Int32, Int32, Int32, Int32, ErrorFunction, MartingaleType, Double)
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Obsolete.
Create SsaChangePointEstimator, which predicts change points in time series
using Singular Spectrum Analysis (SSA).
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DetectIidChangePoint(TransformsCatalog, String, String, Double, Int32, MartingaleType, Double)
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Create IidChangePointEstimator, which predicts change points in an
independent identically distributed (i.i.d.)
time series based on adaptive kernel density estimations and martingale scores.
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DetectIidChangePoint(TransformsCatalog, String, String, Int32, Int32, MartingaleType, Double)
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Obsolete.
Create IidChangePointEstimator, which predicts change points in an
independent identically distributed (i.i.d.)
time series based on adaptive kernel density estimations and martingale scores.
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DetectIidSpike(TransformsCatalog, String, String, Double, Int32, AnomalySide)
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Create IidSpikeEstimator, which predicts spikes in
independent identically distributed (i.i.d.)
time series based on adaptive kernel density estimations and martingale scores.
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DetectIidSpike(TransformsCatalog, String, String, Int32, Int32, AnomalySide)
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Obsolete.
Create IidSpikeEstimator, which predicts spikes in
independent identically distributed (i.i.d.)
time series based on adaptive kernel density estimations and martingale scores.
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DetectSpikeBySsa(TransformsCatalog, String, String, Double, Int32, Int32, Int32, AnomalySide, ErrorFunction)
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Create SsaSpikeEstimator, which predicts spikes in time series
using Singular Spectrum Analysis (SSA).
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DetectSpikeBySsa(TransformsCatalog, String, String, Int32, Int32, Int32, Int32, AnomalySide, ErrorFunction)
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Obsolete.
Create SsaSpikeEstimator, which predicts spikes in time series
using Singular Spectrum Analysis (SSA).
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Concatenate(TransformsCatalog, String, String[])
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Create a ColumnConcatenatingEstimator, which concatenates one or more input columns into a new output column.
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CopyColumns(TransformsCatalog, String, String)
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Create a ColumnCopyingEstimator, which copies the data from the column specified in inputColumnName
to a new column: outputColumnName.
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DropColumns(TransformsCatalog, String[])
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Create a ColumnSelectingEstimator, which drops a given list of columns from an IDataView. Any column not specified will
be maintained in the output.
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SelectColumns(TransformsCatalog, String[], Boolean)
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Create a ColumnSelectingEstimator, which keeps a given list of columns in an IDataView and drops the others.
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SelectColumns(TransformsCatalog, String[])
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Create a ColumnSelectingEstimator, which keeps a given list of columns in an IDataView and drops the others.
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FeaturizeByFastForestBinary(TransformsCatalog, FastForestBinaryFeaturizationEstimator+Options)
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Create FastForestBinaryFeaturizationEstimator, which uses FastForestBinaryTrainer to train TreeEnsembleModelParameters to create tree-based features.
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FeaturizeByFastForestRegression(TransformsCatalog, FastForestRegressionFeaturizationEstimator+Options)
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Create FastForestRegressionFeaturizationEstimator, which uses FastForestRegressionTrainer to train TreeEnsembleModelParameters to create tree-based features.
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FeaturizeByFastTreeBinary(TransformsCatalog, FastTreeBinaryFeaturizationEstimator+Options)
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Create FastTreeBinaryFeaturizationEstimator, which uses FastTreeBinaryTrainer to train TreeEnsembleModelParameters to create tree-based features.
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FeaturizeByFastTreeRanking(TransformsCatalog, FastTreeRankingFeaturizationEstimator+Options)
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Create FastTreeRankingFeaturizationEstimator, which uses FastTreeRankingTrainer to train TreeEnsembleModelParameters to create tree-based features.
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FeaturizeByFastTreeRegression(TransformsCatalog, FastTreeRegressionFeaturizationEstimator+Options)
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Create FastTreeRegressionFeaturizationEstimator, which uses FastTreeRegressionTrainer to train TreeEnsembleModelParameters to create tree-based features.
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FeaturizeByFastTreeTweedie(TransformsCatalog, FastTreeTweedieFeaturizationEstimator+Options)
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Create FastTreeTweedieFeaturizationEstimator, which uses FastTreeTweedieTrainer to train TreeEnsembleModelParameters to create tree-based features.
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FeaturizeByPretrainTreeEnsemble(TransformsCatalog, PretrainedTreeFeaturizationEstimator+Options)
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Create PretrainedTreeFeaturizationEstimator, which produces tree-based features given a TreeEnsembleModelParameters.
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