Modifier and Type | Interface and Description |
---|---|
interface |
Stage<T extends Stage<T>>
Base class for a node in a Pipeline or Graph.
|
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 |
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 | 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 | Field and Description |
---|---|
Stage<?> |
GraphNode.stage |
Constructor and Description |
---|
GraphNode(int nodeId,
Stage<?> stage,
GraphNode.StageType stageType,
TableId[] estimatorInputIds,
TableId[] algoOpInputIds,
TableId[] outputIds,
TableId[] inputModelDataIds,
TableId[] outputModelDataIds) |
Constructor and Description |
---|
Pipeline(List<Stage<?>> stages) |
PipelineModel(List<Stage<?>> stages) |
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 | 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 | 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 |
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 | 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 | Class and Description |
---|---|
class |
AgglomerativeClustering
An AlgoOperator that performs a hierarchical clustering using a bottom-up approach.
|
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 | Class and Description |
---|---|
class |
BinaryClassificationEvaluator
An AlgoOperator which calculates the evaluation metrics for binary classification.
|
Modifier and Type | Class and Description |
---|---|
class |
Binarizer
A Transformer that binarizes the columns of continuous features by the given thresholds.
|
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 | 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 | Class and Description |
---|---|
class |
DCT
A Transformer that takes the 1D discrete cosine transform of a real vector.
|
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 | 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 | 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 | 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 | 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 | 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 | 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 | 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 | 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 | 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 | 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 | Class and Description |
---|---|
class |
Normalizer
A Transformer that normalizes a vector to have unit norm using the given p-norm.
|
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 | Class and Description |
---|---|
class |
PolynomialExpansion
A Transformer that expands the input vectors in polynomial space.
|
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 | 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 | 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 | Class and Description |
---|---|
class |
SQLTransformer
SQLTransformer implements the transformations that are defined by SQL statement.
|
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 | Class and Description |
---|---|
class |
StopWordsRemover
A feature transformer that filters out stop words from input.
|
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 | Class and Description |
---|---|
class |
Tokenizer
A Transformer which converts the input string to lowercase and then splits it by white spaces.
|
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 | 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 | Class and Description |
---|---|
class |
VectorAssembler
A Transformer which combines a given list of input columns into a vector column.
|
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 | 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 | Class and Description |
---|---|
class |
Swing
An AlgoOperator which implements the Swing algorithm.
|
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 | Class and Description |
---|---|
class |
ANOVATest
An AlgoOperator which implements the ANOVA test algorithm.
|
Modifier and Type | Class and Description |
---|---|
class |
ChiSqTest
An AlgoOperator which implements the Chi-square test algorithm.
|
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 Stage<T>> |
ReadWriteUtils.loadStageParam(String path)
Loads the stage with the saved parameters from the given path.
|
Modifier and Type | Method and Description |
---|---|
static Stage<?> |
ReadWriteUtils.loadGraph(org.apache.flink.table.api.bridge.java.StreamTableEnvironment tEnv,
String path,
String expectedClassName)
Loads a Graph or GraphModel from the given path.
|
static Stage<?> |
ReadWriteUtils.loadStage(org.apache.flink.table.api.bridge.java.StreamTableEnvironment tEnv,
String path)
Loads the stage from the given path by invoking the static load() method of the stage.
|
Modifier and Type | Method and Description |
---|---|
static List<Stage<?>> |
ReadWriteUtils.loadPipeline(org.apache.flink.table.api.bridge.java.StreamTableEnvironment tEnv,
String path,
String expectedClassName)
Loads the stages of a Pipeline or PipelineModel from the given path.
|
Modifier and Type | Method and Description |
---|---|
static void |
ReadWriteUtils.saveGraph(Stage<?> graph,
GraphData graphData,
String path)
Saves a Graph or GraphModel with the given GraphData to the given path.
|
static void |
ReadWriteUtils.saveMetadata(Stage<?> stage,
String path)
Saves the metadata of the given stage to a file named `metadata` under the given path.
|
static void |
ReadWriteUtils.saveMetadata(Stage<?> stage,
String path,
Map<String,?> extraMetadata)
Saves the metadata of the given stage and the extra metadata to a file named `metadata` under
the given path.
|
static void |
ReadWriteUtils.savePipeline(Stage<?> pipeline,
List<Stage<?>> stages,
String path)
Saves a Pipeline or PipelineModel with the given list of stages to the given path.
|
Modifier and Type | Method and Description |
---|---|
static void |
ReadWriteUtils.savePipeline(Stage<?> pipeline,
List<Stage<?>> stages,
String path)
Saves a Pipeline or PipelineModel with the given list of stages to the given path.
|
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