com.salesforce.op.stages.impl.feature
OPCollectionHashingVectorizer
Related Doc: package feature
Input Features type
Input Features type
Feature name (key) -> value lookup, e.g Row, Map etc.
Feature name (key) -> value lookup, e.g Row, Map etc.
Checks the input length
Checks the input length
input features
true is input size as expected, false otherwise
Check if the stage is serializable
Check if the stage is serializable
Failure if not serializable
This method is used to make a copy of the instance with new parameters in several methods in spark internals Default will find the constructor and make a copy for any class (AS LONG AS ALL CONSTRUCTOR PARAMS ARE VALS, this is why type tags are written as implicit vals in base classes).
This method is used to make a copy of the instance with new parameters in several methods in spark internals Default will find the constructor and make a copy for any class (AS LONG AS ALL CONSTRUCTOR PARAMS ARE VALS, this is why type tags are written as implicit vals in base classes).
Note: that the convention in spark is to have the uid be a constructor argument, so that copies will share a uid with the original (developers should follow this convention).
new parameters want to add to instance
a new instance with the same uid
Gets an input feature Note: this method IS NOT safe to use outside the driver, please use getTransientFeature method instead
Gets an input feature Note: this method IS NOT safe to use outside the driver, please use getTransientFeature method instead
array of features
NoSuchElementException if the features are not set
RuntimeException in case one of the features is null
Gets the input features Note: this method IS NOT safe to use outside the driver, please use getTransientFeatures method instead
Gets the input features Note: this method IS NOT safe to use outside the driver, please use getTransientFeatures method instead
array of features
NoSuchElementException if the features are not set
RuntimeException in case one of the features is null
Output features that will be created by this stage
Output features that will be created by this stage
feature of type OutputFeatures
Name of output feature (i.e.
Name of output feature (i.e. column created by this stage)
Gets an input feature at index i
Gets an input feature at index i
input index
maybe an input feature
Gets the input Features
Hashes input sequence of values into OPVector using the supplied hashing params
Hashes input sequence of values into OPVector using the supplied hashing params
Get the underlying hashing transformer
Get the underlying hashing transformer
HashingTF
HashingTF instance
HashingTF instance
Function to convert InputFeatures to an Array of FeatureLike
Function to convert InputFeatures to an Array of FeatureLike
an Array of FeatureLike
Determine if the transformer should use a shared hash space for all features or not
Determine if the transformer should use a shared hash space for all features or not
true if the shared hashing space to be used, false otherwise
Determine if the transformer should use a shared hash space for all features or not
Determine if the transformer should use a shared hash space for all features or not
true if the shared hashing space to be used, false otherwise
Function to be called on getMetadata
Function to be called on getMetadata
Function to be called on setInput
Function to be called on setInput
unique name of the operation this stage performs
unique name of the operation this stage performs
Function to convert OutputFeatures to an Array of FeatureLike
Function to convert OutputFeatures to an Array of FeatureLike
an Array of FeatureLike
Should output feature be a response? Yes, if any of the input features are.
Should output feature be a response? Yes, if any of the input features are.
true if the the output feature should be a response
Get the metadata describing the output vector
Get the metadata describing the output vector
This does not trigger onGetMetadata()
Metadata of output vector
Function that prepares the input columns to be hashed Note that MurMur3 hashing algorithm only defined for primitive types so need to convert tuples to strings.
Function that prepares the input columns to be hashed Note that MurMur3 hashing algorithm only defined for primitive types so need to convert tuples to strings. MultiPickList sets are hashed as is since there is no meaningful order in the selected choices. Lists and vectors can be hashed with or without their indices, since order may be important. Maps are hashed as (key,value) strings.
element we are hashing (eg. an OPList, OPMap, etc.)
an Iterable object corresponding to the hashed element
Input features that will be used by the stage
Input features that will be used by the stage
feature of type InputFeatures
Sets input features
Sets input features
feature like type
array of input features
this stage
Stage unique name consisting of the stage operation name and uid
Stage unique name consisting of the stage operation name and uid
stage name
Spark operation on dataset to produce new output feature column using defined function
Spark operation on dataset to produce new output feature column using defined function
input data for this stage
a new dataset containing a column for the transformed feature
Function used to convert input to output
Function used to convert input to output
Creates a transform function to transform any key/value to a value
Creates a transform function to transform any key/value to a value
a transform function to transform any key/value to a value
Creates a transform function to transform Map to a value
Creates a transform function to transform Map to a value
a transform function to transform Map to a value
Creates a transform function to transform Row to a value
Creates a transform function to transform Row to a value
a transform function to transform Row to a value
This function translates the input and output features into spark schema checks and changes that will occur on the underlying data frame
This function translates the input and output features into spark schema checks and changes that will occur on the underlying data frame
schema of the input data frame
a new schema with the output features added
type tag for input
type tag for input
type tag for output
type tag for output
type tag for output value
type tag for output value
uid for instance
uid for instance
Compute the output vector metadata only from the input features.
Compute the output vector metadata only from the input features. Vectorizers use this to derive the full vector, including pivot columns or indicator features.
Vector metadata from input features
Get the name of the output vector
Get the name of the output vector
Output vector name as a string
Generic hashing vectorizer to convert features of type OPCollection into Vectors
In more details: It tries to hash entries in the collection using the specified hashing algorithm to build a single vector. If the desired number of features (= hash space size) for all features combined is larger than Integer.Max (the maximal index for a vector), then all the features use the same hash space. There are also options for the user to hash indices with collections where that makes sense (OPLists and OPVectors), and to force a shared hash space, even if the number of feature is not high enough to require it.