com.salesforce.op.stages.impl.feature
TextMapPivotVectorizer
Related Doc: package feature
uid for instance
Input Features type
Input Features type
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
Count unique values of each of the sequence & map key components in the dataset using HyperLogLog HLL
Count unique values of each of the sequence & map key components in the dataset using HyperLogLog HLL
value type
dataset to count unique values
size of each sequence component
number of bits for HyperLogLog HLL
kryo serializer to serialize V value into array of bytes
class tag of V - needed by kryo
HyperLogLog HLL of unique values count for each of the sequence components and total rows count
Count unique values of each of the sequence components in the dataset using HyperLogLog HLL
Count unique values of each of the sequence components in the dataset using HyperLogLog HLL
value type
dataset to count unique values
size of each sequence component
number of bits for HyperLogLog HLL
kryo serializer to serialize V value into array of bytes
class tag of V - needed by kryo
HyperLogLog HLL of unique values count for each of the sequence components and total rows count
Throw out values above max percent cardinality as follows:
Throw out values above max percent cardinality as follows:
1. count unique values of each of the sequence & map key components in the dataset using HyperLogLog HLL 2. count total number of rows 3. compute percent filters for each of the sequence & map key components 4. throw out values above max percent cardinality
value type
dataset to count unique values
max percentage of distinct values a categorical feature can have
size of each sequence component
number of bits for HyperLogLog HLL
type tag of V - needed by kryo
values encoder for dataset
Spark operation on dataset to produce Dataset for constructor fit function and then turn output function into a Model
Spark operation on dataset to produce Dataset for constructor fit function and then turn output function into a Model
input data for this stage
a fitted model that will perform the transformation specified by the function defined in constructor fit
Function that fits the sequence model
Function that fits the sequence model
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
Function to convert InputFeatures to an Array of FeatureLike
Function to convert InputFeatures to an Array of FeatureLike
an Array of FeatureLike
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
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
Option to keep track of values that were missing
Option to keep track of values that were missing
Stage unique name consisting of the stage operation name and uid
Stage unique name consisting of the stage operation name and uid
stage name
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 input value
type tag for input value
type tag for input
type tag for input
type tag for output value
type tag for output value
uid for instance
uid for instance
Converts a sequence of KeyString features into a vector keeping the top K most common occurrences of each key in the maps for that feature (ie the final vector has length k * number of keys * number of features). Each key found will also generate an other column which will capture values that do not make the cut or where not seen in training. Note that any keys not seen in training will be ignored.