Modifier and Type | Interface and Description |
---|---|
interface |
AlgoOperator<T extends AlgoOperator<T>>
An AlgoOperator takes a list of tables as inputs and produces a list of tables as results.
|
interface |
Estimator<E extends Estimator<E,M>,M extends Model<M>>
Estimators are responsible for training and generating Models.
|
interface |
Model<T extends Model<T>>
A Model is typically generated by invoking
Estimator.fit(Table...) . |
interface |
Stage<T extends Stage<T>>
Base class for a node in a Pipeline or Graph.
|
interface |
Transformer<T extends Transformer<T>>
A Transformer is an AlgoOperator with the semantic difference that it encodes the Transformation
logic, such that a record in the output typically corresponds to one record in the input.
|
Modifier and Type | Interface and Description |
---|---|
interface |
DataGenerator<T extends DataGenerator<T>>
Interface for generating data as table arrays.
|
interface |
InputDataGenerator<T extends InputDataGenerator<T>>
Interface for generating data as input table arrays.
|
Modifier and Type | Class and Description |
---|---|
class |
KMeansModelDataGenerator
A DataGenerator which creates a table containing one
KMeansModel instance. |
Modifier and Type | Class and Description |
---|---|
class |
DenseVectorArrayGenerator
A DataGenerator which creates a table of DenseVector array.
|
class |
DenseVectorGenerator
A DataGenerator which creates a table of DenseVector.
|
class |
DoubleGenerator
A DataGenerator which creates a table of doubles.
|
class |
InputTableGenerator<T extends InputTableGenerator<T>>
Base class for generating data as input table arrays.
|
class |
LabeledPointWithWeightGenerator
A DataGenerator which creates a table of features, label and weight.
|
class |
RandomStringArrayGenerator
A DataGenerator which creates a table of random string arrays.
|
class |
RandomStringGenerator
A DataGenerator which creates a table of random strings.
|
Modifier and Type | Interface and Description |
---|---|
interface |
HasArraySize<T>
Interface for the benchmark array size param.
|
interface |
HasNumDistinctValues<T>
Interface for the benchmark num distinct values param.
|
interface |
HasVectorDim<T>
Interface for the benchmark vector dimension param.
|
Modifier and Type | Class and Description |
---|---|
class |
Graph
A Graph acts as an Estimator.
|
class |
GraphModel
A GraphModel acts as a Model.
|
class |
Pipeline
A Pipeline acts as an Estimator.
|
class |
PipelineModel
A PipelineModel acts as a Model.
|
Modifier and Type | Interface and Description |
---|---|
interface |
KnnModelParams<T>
Params for
KnnModel . |
interface |
KnnParams<T>
Params for
Knn . |
Modifier and Type | Class and Description |
---|---|
class |
Knn
An Estimator which implements the KNN algorithm.
|
class |
KnnModel
A Model which classifies data using the model data computed by
Knn . |
Modifier and Type | Interface and Description |
---|---|
interface |
LinearSVCModelParams<T>
Params for
LinearSVCModel . |
interface |
LinearSVCParams<T>
Params for
LinearSVC . |
Modifier and Type | Class and Description |
---|---|
class |
LinearSVC
An Estimator which implements the linear support vector classification.
|
class |
LinearSVCModel
A Model which classifies data using the model data computed by
LinearSVC . |
Modifier and Type | Interface and Description |
---|---|
interface |
LogisticRegressionModelParams<T>
Params for LogisticRegressionModel and LogisticRegressionModelServable.
|
interface |
LogisticRegressionParams<T>
Params for
LogisticRegression . |
interface |
OnlineLogisticRegressionModelParams<T>
Params for
OnlineLogisticRegressionModel . |
interface |
OnlineLogisticRegressionParams<T>
Params of
OnlineLogisticRegression . |
Modifier and Type | Class and Description |
---|---|
class |
LogisticRegression
An Estimator which implements the logistic regression algorithm.
|
class |
LogisticRegressionModel
A Model which classifies data using the model data computed by
LogisticRegression . |
class |
LogisticRegressionModelServable
A Servable which can be used to classifies data in online inference.
|
class |
OnlineLogisticRegression
An Estimator which implements the online logistic regression algorithm.
|
class |
OnlineLogisticRegressionModel
A Model which classifies data using the model data computed by
OnlineLogisticRegression . |
Modifier and Type | Interface and Description |
---|---|
interface |
NaiveBayesModelParams<T>
Params of
NaiveBayesModel . |
interface |
NaiveBayesParams<T>
Params of
NaiveBayes . |
Modifier and Type | Class and Description |
---|---|
class |
NaiveBayes
An Estimator which implements the naive bayes classification algorithm.
|
class |
NaiveBayesModel
A Model which classifies data using the model data computed by
NaiveBayes . |
Modifier and Type | Interface and Description |
---|---|
interface |
AgglomerativeClusteringParams<T>
Params of
AgglomerativeClustering . |
Modifier and Type | Class and Description |
---|---|
class |
AgglomerativeClustering
An AlgoOperator that performs a hierarchical clustering using a bottom-up approach.
|
Modifier and Type | Interface and Description |
---|---|
interface |
KMeansModelParams<T>
Params of
KMeansModel and OnlineKMeansModel . |
interface |
KMeansParams<T>
Params of
KMeans . |
interface |
OnlineKMeansParams<T>
Params of
OnlineKMeans . |
Modifier and Type | Class and Description |
---|---|
class |
KMeans
An Estimator which implements the k-means clustering algorithm.
|
class |
KMeansModel
A Model which clusters data into k clusters using the model data computed by
KMeans . |
class |
OnlineKMeans
OnlineKMeans extends the function of
KMeans , supporting to train a K-Means model
continuously according to an unbounded stream of train data. |
class |
OnlineKMeansModel
OnlineKMeansModel can be regarded as an advanced
KMeansModel operator which can update
model data in a streaming format, using the model data provided by OnlineKMeans . |
Modifier and Type | Interface and Description |
---|---|
interface |
HasBatchStrategy<T>
Interface for the shared batch strategy param.
|
interface |
HasCategoricalCols<T>
Interface for the shared categoricalCols param.
|
interface |
HasDecayFactor<T>
Interface for the shared decay factor param.
|
interface |
HasDistanceMeasure<T>
Interface for the shared distanceMeasure param.
|
interface |
HasElasticNet<T>
Interface for the shared elasticNet param, which specifies the mixing of L1 and L2 penalty:
If the value is zero, it is L2 penalty.
|
interface |
HasFeaturesCol<T>
Interface for the shared featuresCol param.
|
interface |
HasFlatten<T>
Interface for the shared flatten param.
|
interface |
HasGlobalBatchSize<T>
Interface for the shared global batch size param.
|
interface |
HasHandleInvalid<T>
Interface for the shared handleInvalid param.
|
interface |
HasInputCol<T>
Interface for the shared inputCol param.
|
interface |
HasInputCols<T>
Interface for the shared inputCols param.
|
interface |
HasLabelCol<T>
Interface for the shared label column param.
|
interface |
HasLearningRate<T>
Interface for the shared learning rate param.
|
interface |
HasMaxAllowedModelDelayMs<T>
Interface for the shared max allowed model delay in milliseconds param.
|
interface |
HasMaxIter<T>
Interface for the shared maxIter param.
|
interface |
HasModelVersionCol<T>
Interface for the shared model version column param.
|
interface |
HasMultiClass<T>
Interface for the shared multi-class param.
|
interface |
HasNumFeatures<T>
Interface for the shared num features param.
|
interface |
HasOutputCol<T>
Interface for the shared outputCol param.
|
interface |
HasOutputCols<T>
Interface for the shared outputCols param.
|
interface |
HasPredictionCol<T>
Interface for the shared prediction column param.
|
interface |
HasRawPredictionCol<T>
Interface for the shared raw prediction column param.
|
interface |
HasReg<T>
Interface for the shared regularization param.
|
interface |
HasRelativeError<T>
Interface for shared param relativeError.
|
interface |
HasSeed<T>
Interface for the shared seed param.
|
interface |
HasTol<T>
Interface for the shared tolerance param.
|
interface |
HasWeightCol<T>
Interface for the shared weight column param.
|
interface |
HasWindows<T>
Interface for the shared windows param.
|
Modifier and Type | Interface and Description |
---|---|
interface |
BinaryClassificationEvaluatorParams<T>
Params of BinaryClassificationEvaluator.
|
Modifier and Type | Class and Description |
---|---|
class |
BinaryClassificationEvaluator
An AlgoOperator which calculates the evaluation metrics for binary classification.
|
Modifier and Type | Interface and Description |
---|---|
interface |
BinarizerParams<T>
Params of
Binarizer . |
Modifier and Type | Class and Description |
---|---|
class |
Binarizer
A Transformer that binarizes the columns of continuous features by the given thresholds.
|
Modifier and Type | Interface and Description |
---|---|
interface |
BucketizerParams<T>
Params for
Bucketizer . |
Modifier and Type | Class and Description |
---|---|
class |
Bucketizer
A Transformer that maps multiple columns of continuous features to multiple columns of discrete
features, i.e., buckets indices.
|
Modifier and Type | Interface and Description |
---|---|
interface |
CountVectorizerModelParams<T>
Params for
CountVectorizerModel . |
interface |
CountVectorizerParams<T>
Params of
CountVectorizer . |
Modifier and Type | Class and Description |
---|---|
class |
CountVectorizer
An Estimator which converts a collection of text documents to vectors of token counts.
|
class |
CountVectorizerModel
A Model which transforms data using the model data computed by
CountVectorizer . |
Modifier and Type | Interface and Description |
---|---|
interface |
DCTParams<T>
Params for
DCT . |
Modifier and Type | Class and Description |
---|---|
class |
DCT
A Transformer that takes the 1D discrete cosine transform of a real vector.
|
Modifier and Type | Interface and Description |
---|---|
interface |
ElementwiseProductParams<T>
Params of
ElementwiseProduct . |
Modifier and Type | Class and Description |
---|---|
class |
ElementwiseProduct
A Transformer that multiplies each input vector with a given scaling vector using Hadamard
product.
|
Modifier and Type | Interface and Description |
---|---|
interface |
FeatureHasherParams<T>
Params of
FeatureHasher . |
Modifier and Type | Class and Description |
---|---|
class |
FeatureHasher
A Transformer that transforms a set of categorical or numerical features into a sparse vector of
a specified dimension.
|
Modifier and Type | Interface and Description |
---|---|
interface |
HashingTFParams<T>
Params of
HashingTF . |
Modifier and Type | Class and Description |
---|---|
class |
HashingTF
A Transformer that maps a sequence of terms(strings, numbers, booleans) to a sparse vector with a
specified dimension using the hashing trick.
|
Modifier and Type | Interface and Description |
---|---|
interface |
IDFModelParams<T>
Params for
IDFModel . |
interface |
IDFParams<T>
Params for
IDF . |
Modifier and Type | Class and Description |
---|---|
class |
IDF
An Estimator that computes the inverse document frequency (IDF) for the input documents.
|
class |
IDFModel
A Model which transforms data using the model data computed by
IDF . |
Modifier and Type | Interface and Description |
---|---|
interface |
ImputerModelParams<T>
Params for
ImputerModel . |
interface |
ImputerParams<T>
Params of
Imputer . |
Modifier and Type | Class and Description |
---|---|
class |
Imputer
The imputer for completing missing values of the input columns.
|
class |
ImputerModel
A Model which replaces the missing values using the model data computed by
Imputer . |
Modifier and Type | Interface and Description |
---|---|
interface |
InteractionParams<T>
Params of
Interaction . |
Modifier and Type | Class and Description |
---|---|
class |
Interaction
A Transformer that takes vector or numerical columns, and generates a single vector column that
contains the product of all combinations of one value from each input column.
|
Modifier and Type | Interface and Description |
---|---|
interface |
KBinsDiscretizerModelParams<T>
Params for
KBinsDiscretizerModel . |
interface |
KBinsDiscretizerParams<T>
Params for
KBinsDiscretizer . |
Modifier and Type | Class and Description |
---|---|
class |
KBinsDiscretizer
An Estimator which implements discretization (also known as quantization or binning) to transform
continuous features into discrete ones.
|
class |
KBinsDiscretizerModel
A Model which transforms continuous features into discrete features using the model data computed
by
KBinsDiscretizer . |
Modifier and Type | Interface and Description |
---|---|
interface |
LSHModelParams<T>
Params for
LSHModel . |
interface |
LSHParams<T>
Params for
LSH . |
interface |
MinHashLSHParams<T>
Params for
MinHashLSH . |
Modifier and Type | Class and Description |
---|---|
class |
MinHashLSH
An Estimator that implements the MinHash LSH algorithm, which supports LSH for Jaccard distance.
|
class |
MinHashLSHModel
A Model which generates hash values using the model data computed by
MinHashLSH . |
Modifier and Type | Interface and Description |
---|---|
interface |
MaxAbsScalerParams<T>
Params for
MaxAbsScaler . |
Modifier and Type | Class and Description |
---|---|
class |
MaxAbsScaler
An Estimator which implements the MaxAbsScaler algorithm.
|
class |
MaxAbsScalerModel
A Model which transforms data using the model data computed by
MaxAbsScaler . |
Modifier and Type | Interface and Description |
---|---|
interface |
MinMaxScalerParams<T>
Params for
MinMaxScaler . |
Modifier and Type | Class and Description |
---|---|
class |
MinMaxScaler
An Estimator which implements the MinMaxScaler algorithm.
|
class |
MinMaxScalerModel
A Model which transforms data using the model data computed by
MinMaxScaler . |
Modifier and Type | Interface and Description |
---|---|
interface |
NGramParams<T>
Params of
NGram . |
Modifier and Type | Class and Description |
---|---|
class |
NGram
A Transformer that converts the input string array into an array of n-grams, where each n-gram is
represented by a space-separated string of words.
|
Modifier and Type | Interface and Description |
---|---|
interface |
NormalizerParams<T>
Params of
Normalizer . |
Modifier and Type | Class and Description |
---|---|
class |
Normalizer
A Transformer that normalizes a vector to have unit norm using the given p-norm.
|
Modifier and Type | Interface and Description |
---|---|
interface |
OneHotEncoderParams<T>
Params of OneHotEncoderModel.
|
Modifier and Type | Class and Description |
---|---|
class |
OneHotEncoder
An Estimator which implements the one-hot encoding algorithm.
|
class |
OneHotEncoderModel
A Model which encodes data into one-hot format using the model data computed by
OneHotEncoder . |
Modifier and Type | Interface and Description |
---|---|
interface |
PolynomialExpansionParams<T>
Params of
PolynomialExpansion . |
Modifier and Type | Class and Description |
---|---|
class |
PolynomialExpansion
A Transformer that expands the input vectors in polynomial space.
|
Modifier and Type | Interface and Description |
---|---|
interface |
RandomSplitterParams<T>
Params of
RandomSplitter . |
Modifier and Type | Class and Description |
---|---|
class |
RandomSplitter
An AlgoOperator which splits a Table into N Tables according to the given weights.
|
Modifier and Type | Interface and Description |
---|---|
interface |
RegexTokenizerParams<T>
Params for
RegexTokenizer . |
Modifier and Type | Class and Description |
---|---|
class |
RegexTokenizer
A Transformer which converts the input string to lowercase and then splits it by white spaces
based on regex.
|
Modifier and Type | Interface and Description |
---|---|
interface |
RobustScalerModelParams<T>
Params for
RobustScalerModel . |
interface |
RobustScalerParams<T>
Params for
RobustScaler . |
Modifier and Type | Class and Description |
---|---|
class |
RobustScaler
An Estimator which scales features using statistics that are robust to outliers.
|
class |
RobustScalerModel
A Model which transforms data using the model data computed by
RobustScaler . |
Modifier and Type | Interface and Description |
---|---|
interface |
SQLTransformerParams<T>
Params for
SQLTransformer . |
Modifier and Type | Class and Description |
---|---|
class |
SQLTransformer
SQLTransformer implements the transformations that are defined by SQL statement.
|
Modifier and Type | Interface and Description |
---|---|
interface |
OnlineStandardScalerModelParams<T>
Params for
OnlineStandardScalerModel . |
interface |
OnlineStandardScalerParams<T>
Params for
OnlineStandardScaler . |
interface |
StandardScalerParams<T>
Params for
StandardScaler . |
Modifier and Type | Class and Description |
---|---|
class |
OnlineStandardScaler
An Estimator which implements the online standard scaling algorithm, which is the online version
of
StandardScaler . |
class |
OnlineStandardScalerModel
A Model which transforms data using the model data computed by
OnlineStandardScaler . |
class |
StandardScaler
An Estimator which implements the standard scaling algorithm.
|
class |
StandardScalerModel
A Model which transforms data using the model data computed by
StandardScaler . |
Modifier and Type | Interface and Description |
---|---|
interface |
StopWordsRemoverParams<T>
Params of
StopWordsRemover . |
Modifier and Type | Class and Description |
---|---|
class |
StopWordsRemover
A feature transformer that filters out stop words from input.
|
Modifier and Type | Interface and Description |
---|---|
interface |
IndexToStringModelParams<T>
Params for
IndexToStringModel . |
interface |
StringIndexerModelParams<T>
Params of
StringIndexerModel . |
interface |
StringIndexerParams<T>
Params of
StringIndexer . |
Modifier and Type | Class and Description |
---|---|
class |
IndexToStringModel
A Model which transforms input index column(s) to string column(s) using the model data computed
by
StringIndexer . |
class |
StringIndexer
An Estimator which implements the string indexing algorithm.
|
class |
StringIndexerModel
A Model which transforms input string/numeric column(s) to double column(s) using the model data
computed by
StringIndexer . |
Modifier and Type | Interface and Description |
---|---|
interface |
TokenizerParams<T>
Params of
Tokenizer . |
Modifier and Type | Class and Description |
---|---|
class |
Tokenizer
A Transformer which converts the input string to lowercase and then splits it by white spaces.
|
Modifier and Type | Interface and Description |
---|---|
interface |
UnivariateFeatureSelectorModelParams<T>
Params for
UnivariateFeatureSelectorModel . |
interface |
UnivariateFeatureSelectorParams<T>
Params for
UnivariateFeatureSelector . |
Modifier and Type | Class and Description |
---|---|
class |
UnivariateFeatureSelector
An Estimator which selects features based on univariate statistical tests against labels.
|
class |
UnivariateFeatureSelectorModel
A Model which transforms data using the model data computed by
UnivariateFeatureSelector . |
Modifier and Type | Interface and Description |
---|---|
interface |
VarianceThresholdSelectorModelParams<T>
Params for
VarianceThresholdSelectorModel . |
interface |
VarianceThresholdSelectorParams<T>
Params of VarianceThresholdSelectorModel.
|
Modifier and Type | Class and Description |
---|---|
class |
VarianceThresholdSelector
An Estimator which implements the VarianceThresholdSelector algorithm.
|
class |
VarianceThresholdSelectorModel
A Model which removes low-variance data using the model data computed by
VarianceThresholdSelector . |
Modifier and Type | Interface and Description |
---|---|
interface |
VectorAssemblerParams<T>
Params of
VectorAssembler . |
Modifier and Type | Class and Description |
---|---|
class |
VectorAssembler
A Transformer which combines a given list of input columns into a vector column.
|
Modifier and Type | Interface and Description |
---|---|
interface |
VectorIndexerModelParams<T>
Params for
VectorIndexerModel . |
interface |
VectorIndexerParams<T>
Params of
VectorIndexer . |
Modifier and Type | Class and Description |
---|---|
class |
VectorIndexer
An Estimator which implements the vector indexing algorithm.
|
class |
VectorIndexerModel
A Model which encodes input vector to an output vector using the model data computed by
VectorIndexer . |
Modifier and Type | Interface and Description |
---|---|
interface |
VectorSlicerParams<T>
Params of
VectorSlicer . |
Modifier and Type | Class and Description |
---|---|
class |
VectorSlicer
A Transformer that transforms a vector to a new feature, which is a sub-array of the original
feature.
|
Modifier and Type | Interface and Description |
---|---|
interface |
SwingParams<T>
Params for
Swing . |
Modifier and Type | Class and Description |
---|---|
class |
Swing
An AlgoOperator which implements the Swing algorithm.
|
Modifier and Type | Interface and Description |
---|---|
interface |
LinearRegressionModelParams<T>
Params for
LinearRegressionModel . |
interface |
LinearRegressionParams<T>
Params for
LinearRegression . |
Modifier and Type | Class and Description |
---|---|
class |
LinearRegression
An Estimator which implements the linear regression algorithm.
|
class |
LinearRegressionModel
A Model which predicts data using the model data computed by
LinearRegression . |
Modifier and Type | Interface and Description |
---|---|
interface |
ModelServable<T extends ModelServable<T>>
A ModelServable is a TransformerServable with the extra API to set model data.
|
interface |
TransformerServable<T extends TransformerServable<T>>
A TransformerServable takes a DataFrame as input and produces a DataFrame as the result.
|
Modifier and Type | Class and Description |
---|---|
class |
PipelineModelServable
A PipelineModelServable acts as a
ModelServable . |
Modifier and Type | Interface and Description |
---|---|
interface |
ANOVATestParams<T>
Params for
ANOVATest . |
Modifier and Type | Class and Description |
---|---|
class |
ANOVATest
An AlgoOperator which implements the ANOVA test algorithm.
|
Modifier and Type | Interface and Description |
---|---|
interface |
ChiSqTestParams<T>
Params for
ChiSqTest . |
Modifier and Type | Class and Description |
---|---|
class |
ChiSqTest
An AlgoOperator which implements the Chi-square test algorithm.
|
Modifier and Type | Interface and Description |
---|---|
interface |
FValueTestParams<T>
Params for
FValueTest . |
Modifier and Type | Class and Description |
---|---|
class |
FValueTest
An AlgoOperator which implements the F-value test algorithm.
|
Modifier and Type | Method and Description |
---|---|
static <T extends WithParams<T>> |
ParamUtils.instantiateWithParams(Map<String,?> jsonMap)
Instantiates a WithParams subclass from the provided json map.
|
Modifier and Type | Method and Description |
---|---|
static void |
ParamUtils.initializeMapWithDefaultValues(Map<Param<?>,Object> paramMap,
WithParams<?> instance)
Updates the paramMap with default values of all public final Param-typed fields of the given
instance.
|
static <T> void |
ParamUtils.setParam(WithParams<?> instance,
Param<T> param,
Object value) |
static void |
ParamUtils.updateExistingParams(WithParams<?> instance,
Map<Param<?>,Object> paramOverrides) |
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