Using filters

Filters are a part of transforms and gives a DSL for you to keep parts of your dataset. Filters can be one-liners for single conditions or include complex boolean logic.

TransformProcess tp = new TransformProcess.Builder(inputDataSchema)
    .filter(new ConditionFilter(new CategoricalColumnCondition("MerchantCountryCode", ConditionOp.NotInSet, new HashSet<>(Arrays.asList("USA","CAN")))))
    .build();

You can also write your own filters by implementing the Filter interface, though it is much more often that you may want to create a custom condition instead.

Available filters

ConditionFilter

[source]

If condition is satisfied (returns true): remove the example or sequence If condition is not satisfied (returns false): keep the example or sequence

removeExample

public boolean removeExample(Object writables)

• param writables Example

• return true if example should be removed, false to keep

removeSequence

public boolean removeSequence(Object sequence)

• param sequence sequence example

• return true if example should be removed, false to keep

transform

public Schema transform(Schema inputSchema)

Get the output schema for this transformation, given an input schema

• param inputSchema

outputColumnName

public String outputColumnName()

The output column name after the operation has been applied

• return the output column name

columnName

public String columnName()

The output column names This will often be the same as the input

• return the output column names

Filter

[source]

Filter: a method of removing examples (or sequences) according to some condition

FilterInvalidValues

[source]

FilterInvalidValues: a filter operation that removes any examples (or sequences) if the examples/sequences contains invalid values in any of a specified set of columns. Invalid values are determined with respect to the schema

transform

public Schema transform(Schema inputSchema)

• param columnsToFilterIfInvalid Columns to check for invalid values

removeExample

public boolean removeExample(Object writables)

• param writables Example

• return true if example should be removed, false to keep

removeSequence

public boolean removeSequence(Object sequence)

• param sequence sequence example

• return true if example should be removed, false to keep

outputColumnName

public String outputColumnName()

The output column name after the operation has been applied

• return the output column name

columnName

public String columnName()

The output column names This will often be the same as the input

• return the output column names

InvalidNumColumns

[source]

Remove invalid records of a certain size.

removeExample

public boolean removeExample(Object writables)

• param writables Example

• return true if example should be removed, false to keep

removeSequence

public boolean removeSequence(Object sequence)

• param sequence sequence example

• return true if example should be removed, false to keep

removeExample

public boolean removeExample(List<Writable> writables)

• param writables Example

• return true if example should be removed, false to keep

removeSequence

public boolean removeSequence(List<List<Writable>> sequence)

• param sequence sequence example

• return true if example should be removed, false to keep

transform

public Schema transform(Schema inputSchema)

Get the output schema for this transformation, given an input schema

• param inputSchema

outputColumnName

public String outputColumnName()

The output column name after the operation has been applied

• return the output column name

columnName

public String columnName()

The output column names This will often be the same as the input

• return the output column names

Local or remote execution?

Because datasets are commonly large by nature, you can decide on an execution mechanism that best suits your needs. For example, if you are vectorizing a large training dataset, you can process it in a distributed Spark cluster. However, if you need to do real-time inference, DataVec also provides a local executor that doesn't require any additional setup.

Executing a transform process

Once you've created your TransformProcess using your Schema, and you've either loaded your dataset into a Apache Spark JavaRDD or have a RecordReader that load your dataset, you can execute a transform.

Locally this looks like:

When using Spark this looks like:

Available executors

LocalTransformExecutor

Local transform executor

isTryCatch

Execute the specified TransformProcess with the given input data Note: this method can only be used if the TransformProcess returns non-sequence data. For TransformProcesses that return a sequence, use {- link #executeToSequence(List, TransformProcess)}

• param inputWritables Input data to process

• param transformProcess TransformProcess to execute

• return Processed data

SparkTransformExecutor

Execute a datavec transform process on spark rdds.

isTryCatch

• deprecated Use static methods instead of instance methods on SparkTransformExecutor

BooleanCondition

BooleanCondition: used for creating compound conditions, such as AND(ConditionA, ConditionB, …) As a BooleanCondition is a condition, these can be chained together, like NOT(OR(AND(…),AND(…)))

outputColumnName

The output column name after the operation has been applied

• return the output column name

columnName

The output column names This will often be the same as the input

• return the output column names

condition

Condition on arbitrary input

• param input the input to return the condition for

• return true if the condition is met false otherwise

conditionSequence

Condition on arbitrary input

• param sequence the sequence to do a condition on

• return true if the condition for the sequence is met false otherwise

transform

Get the output schema for this transformation, given an input schema

• param inputSchema

AND

And of all the given conditions

• param conditions the conditions to and

• return a joint and of all these conditions

OR

Or of all the given conditions

• param conditions the conditions to or

• return a joint and of all these conditions

NOT

Not of the given condition

• param condition the conditions to and

• return a joint and of all these condition

XOR

And of all the given conditions

• param first the first condition

• param second the second condition for xor

• return the xor of these 2 conditions

SequenceConditionMode

For certain single-column conditions: how should we apply these to sequences? And: Condition applies to sequence only if it applies to ALL time steps Or: Condition applies to sequence if it applies to ANY time steps NoSequencMode: Condition cannot be applied to sequences at all (error condition)

BooleanColumnCondition

Created by agibsonccc on 11/26/16.

columnCondition

Returns whether the given element meets the condition set by this operation

• param writable the element to test

• return true if the condition is met false otherwise

condition

Condition on arbitrary input

• param input the input to return the condition for

• return true if the condition is met false otherwise

CategoricalColumnCondition

columnCondition

Constructor for conditions equal or not equal. Uses default sequence condition mode, {- link BaseColumnCondition#DEFAULT_SEQUENCE_CONDITION_MODE}

• param columnName Column to check for the condition

• param op Operation (== or != only)

• param value Value to use in the condition

condition

Condition on arbitrary input

• param input the input to return the condition for

• return true if the condition is met false otherwise

DoubleColumnCondition

columnCondition

Constructor for operations such as less than, equal to, greater than, etc. Uses default sequence condition mode, {- link BaseColumnCondition#DEFAULT_SEQUENCE_CONDITION_MODE}

• param columnName Column to check for the condition

• param op Operation (<, >=, !=, etc)

• param value Value to use in the condition

condition

Condition on arbitrary input

• param input the input to return the condition for

• return true if the condition is met false otherwise

InfiniteColumnCondition

A column condition that simply checks whether a floating point value is infinite

columnCondition

• param columnName Column check for the condition

IntegerColumnCondition

columnCondition

Constructor for operations such as less than, equal to, greater than, etc. Uses default sequence condition mode, {- link BaseColumnCondition#DEFAULT_SEQUENCE_CONDITION_MODE}

• param columnName Column to check for the condition

• param op Operation (<, >=, !=, etc)

• param value Value to use in the condition

condition

Condition on arbitrary input

• param input the input to return the condition for

• return true if the condition is met false otherwise

InvalidValueColumnCondition

A Condition that applies to a single column. Whenever the specified value is invalid according to the schema, the condition applies.

For example, if a Writable contains String values in an Integer column (and these cannot be parsed to an integer), then the condition would return true, as these values are invalid according to the schema.

condition

Condition on arbitrary input

• param input the input to return the condition for

• return true if the condition is met false otherwise

LongColumnCondition

columnCondition

Constructor for operations such as less than, equal to, greater than, etc. Uses default sequence condition mode, {- link BaseColumnCondition#DEFAULT_SEQUENCE_CONDITION_MODE}

• param columnName Column to check for the condition

• param op Operation (<, >=, !=, etc)

• param value Value to use in the condition

condition

Condition on arbitrary input

• param input the input to return the condition for

• return true if the condition is met false otherwise

NaNColumnCondition

A column condition that simply checks whether a floating point value is NaN

columnCondition

• param columnName Name of the column to check the condition for

NullWritableColumnCondition

Condition that applies to the values in any column. Specifically, condition is true if the Writable value is a NullWritable, and false for any other value

condition

Condition on arbitrary input

• param input the input to return the condition for

• return true if the condition is met false otherwise

StringColumnCondition

columnCondition

Constructor for conditions equal or not equal Uses default sequence condition mode, {- link BaseColumnCondition#DEFAULT_SEQUENCE_CONDITION_MODE}

• param columnName Column to check for the condition

• param op Operation (== or != only)

• param value Value to use in the condition

condition

Condition on arbitrary input

• param input the input to return the condition for

• return true if the condition is met false otherwise

TimeColumnCondition

Condition that applies to the values

columnCondition

Constructor for operations such as less than, equal to, greater than, etc. Uses default sequence condition mode, {- link BaseColumnCondition#DEFAULT_SEQUENCE_CONDITION_MODE}

• param columnName Column to check for the condition

• param op Operation (<, >=, !=, etc)

• param value Time value (in epoch millisecond format) to use in the condition

condition

Condition on arbitrary input

• param input the input to return the condition for

• return true if the condition is met false otherwise

TrivialColumnCondition

Created by huitseeker on 5/17/17.

SequenceLengthCondition

A condition on sequence lengths

StringRegexColumnCondition

Condition that applies to the values in a String column, using a provided regex. Condition return true if the String matches the regex, or false otherwise Note: Uses Writable.toString(), hence can potentially be applied to non-String columns

condition

Condition on arbitrary input

• param input the input to return the condition for

• return true if the condition is met false otherwise

Analysis of data

Sometimes datasets are too large or too abstract in their format to manually analyze and estimate statistics on certain columns or patterns. DataVec comes with some helper utilities for performing a data analysis, and maximums, means, minimums, and other useful metrics.

Using Spark for analysis

If you have loaded your data into Apache Spark, DataVec has a special AnalyzeSpark class which can generate histograms, collect statistics, and return information about the quality of the data. Assuming you have already loaded your data into a Spark RDD, pass the JavaRDD and Schema to the class.

If you are using DataVec in Scala and your data was loaded into a regular RDD class, you can convert it by calling .toJavaRDD() which returns a JavaRDD. If you need to convert it back, call rdd().

The code below demonstrates some of many analyses for a 2D dataset in Spark analysis using the RDD javaRdd and the schema mySchema:

Note that if you have sequence data, there are special methods for that as well:

Analyzing locally

The AnalyzeLocal class works very similarly to its Spark counterpart and has a similar API. Instead of passing an RDD, it accepts a RecordReader which allows it to iterate over the dataset.

Utilities

AnalyzeLocal

Analyse the specified data - returns a DataAnalysis object with summary information about each column

analyze

Analyse the specified data - returns a DataAnalysis object with summary information about each column

• param schema Schema for data

• param rr Data to analyze

• return DataAnalysis for data

analyzeQualitySequence

Analyze the data quality of sequence data - provides a report on missing values, values that don’t comply with schema, etc

• param schema Schema for data

• param data Data to analyze

• return DataQualityAnalysis object

analyzeQuality

Analyze the data quality of data - provides a report on missing values, values that don’t comply with schema, etc

• param schema Schema for data

• param data Data to analyze

• return DataQualityAnalysis object

AnalyzeSpark

AnalizeSpark: static methods for analyzing and

analyzeSequence

• param schema

• param data

• param maxHistogramBuckets

• return

analyze

Analyse the specified data - returns a DataAnalysis object with summary information about each column

• param schema Schema for data

• param data Data to analyze

• return DataAnalysis for data

analyzeQualitySequence

Randomly sample values from a single column

• param count Number of values to sample

• param columnName Name of the column to sample from

• param schema Schema

• param data Data to sample from

• return A list of random samples

analyzeQuality

Analyze the data quality of data - provides a report on missing values, values that don’t comply with schema, etc

• param schema Schema for data

• param data Data to analyze

• return DataQualityAnalysis object

min

Randomly sample a set of invalid values from a specified column. Values are considered invalid according to the Schema / ColumnMetaData

• param numToSample Maximum number of invalid values to sample

• param columnName Same of the column from which to sample invalid values

• param schema Data schema

• param data Data

• return List of invalid examples

max

Get the maximum value for the specified column

• param allData All data

• param columnName Name of the column to get the minimum value for

• param schema Schema of the data

• return Maximum value for the column

Usage

Operations, such as a Function, help execute transforms and load data into DataVec. The concept of operations is low-level, meaning that most of the time you will not need to worry about them.

Loading data into Spark

If you're using Apache Spark, functions will iterate over the dataset and load it into a Spark RDD and convert the raw data format into a Writable.

import org.datavec.api.writable.Writable;
import org.datavec.api.records.reader.impl.csv.CSVRecordReader;
import org.datavec.spark.transform.misc.StringToWritablesFunction;

SparkConf conf = new SparkConf();
JavaSparkContext sc = new JavaSparkContext(conf)

String customerInfoPath = new ClassPathResource("CustomerInfo.csv").getFile().getPath();
JavaRDD<List<Writable>> customerInfo = sc.textFile(customerInfoPath).map(new StringToWritablesFunction(rr));

The above code loads a CSV file into a 2D java RDD. Once your RDD is loaded, you can transform it, perform joins and use reducers to wrangle the data any way you want.

Available ops

AggregableCheckingOp

[source]

Created by huitseeker on 5/8/17.

AggregableMultiOp

[source]

It is used to execute many reduction operations in parallel on the same column, datavec#238

Created by huitseeker on 5/8/17.

ByteWritableOp

[source]

supports a conversion to Byte.

Created by huitseeker on 5/14/17.

DispatchOp

[source]

Created by huitseeker on 5/14/17.

DispatchWithConditionOp

[source]

before dispatching the appropriate column of this element to its operation.

Created by huitseeker on 5/14/17.

DoubleWritableOp

[source]

supports a conversion to Double.

Created by huitseeker on 5/14/17.

FloatWritableOp

[source]

supports a conversion to Float.

Created by huitseeker on 5/14/17.

IntWritableOp

[source]

supports a conversion to Integer.

Created by huitseeker on 5/14/17.

LongWritableOp

[source]

supports a conversion to Long.

Created by huitseeker on 5/14/17.

StringWritableOp

[source]

supports a conversion to TextWritable. Created by huitseeker on 5/14/17.

CalculateSortedRank

[source]

CalculateSortedRank: calculate the rank of each example, after sorting example. For example, we might have some numerical “score” column, and we want to know for the rank (sort order) for each example, according to that column. The rank of each example (after sorting) will be added in a new Long column. Indexing is done from 0; examples will have values 0 to dataSetSize - 1.

Currently, CalculateSortedRank can only be applied on standard (i.e., non-sequence) data. Furthermore, the current implementation can only sort on one column

transform

public Schema transform(Schema inputSchema)

• param newColumnName Name of the new column (will contain the rank for each example)

• param sortOnColumn Name of the column to sort on

• param comparator Comparator used to sort examples

outputColumnName

public String outputColumnName()

The output column name after the operation has been applied

• return the output column name

columnName

public String columnName()

The output column names This will often be the same as the input

• return the output column names

What is a record?

In the DataVec world a Record represents a single entry in a dataset. DataVec differentiates types of records to make data manipulation easier with built-in APIs. Sequences and 2D records are distinguishable.

Using records

Most of the time you do not need to interact with the record classes directly, unless you are manually iterating records for the purpose of forwarding through a neural network.

Types of records

Record

[source]

A standard implementation of the Record interface

SequenceRecord

[source]

A standard implementation of the SequenceRecord interface.

Data wrangling

One of the key tools in DataVec is transformations. DataVec helps the user map a dataset from one schema to another, and provides a list of operations to convert types, format data, and convert a 2D dataset to sequence data.

Building a transform process

A transform process requires a Schema to successfully transform data. Both schema and transform process classes come with a helper Builder class which are useful for organizing code and avoiding complex constructors.

When both are combined together they look like the sample code below. Note how inputDataSchema is passed into the Builder constructor. Your transform process will fail to compile without it.

import org.datavec.api.transform.TransformProcess;

TransformProcess tp = new TransformProcess.Builder(inputDataSchema)
    .removeColumns("CustomerID","MerchantID")
    .filter(new ConditionFilter(new CategoricalColumnCondition("MerchantCountryCode", ConditionOp.NotInSet, new HashSet<>(Arrays.asList("USA","CAN")))))
    .conditionalReplaceValueTransform(
        "TransactionAmountUSD",     //Column to operate on
        new DoubleWritable(0.0),    //New value to use, when the condition is satisfied
        new DoubleColumnCondition("TransactionAmountUSD",ConditionOp.LessThan, 0.0)) //Condition: amount < 0.0
    .stringToTimeTransform("DateTimeString","YYYY-MM-DD HH:mm:ss.SSS", DateTimeZone.UTC)
    .renameColumn("DateTimeString", "DateTime")
    .transform(new DeriveColumnsFromTimeTransform.Builder("DateTime").addIntegerDerivedColumn("HourOfDay", DateTimeFieldType.hourOfDay()).build())
    .removeColumns("DateTime")
    .build();

Executing a transformation

Different "backends" for executors are available. Using the tp transform process above, here's how you can execute it locally using plain DataVec.

import org.datavec.local.transforms.LocalTransformExecutor;

List<List<Writable>> processedData = LocalTransformExecutor.execute(originalData, tp);

Debugging

Each operation in a transform process represents a "step" in schema changes. Sometimes, the resulting transformation is not the intended result. You can debug this by printing each step in the transform tp with the following:

//Now, print the schema after each time step:
int numActions = tp.getActionList().size();

for(int i=0; i<numActions; i++ ){
    System.out.println("\n\n==================================================");
    System.out.println("-- Schema after step " + i + " (" + tp.getActionList().get(i) + ") --");

    System.out.println(tp.getSchemaAfterStep(i));
}

Available transformations and conversions

TransformProcess

[source]

A TransformProcess defines an ordered list of transformations to be executed on some data

getFinalSchema

public Schema getFinalSchema()

Get the action list that this transform process will execute

• return

getSchemaAfterStep

public Schema getSchemaAfterStep(int step)

Return the schema after executing all steps up to and including the specified step. Steps are indexed from 0: so getSchemaAfterStep(0) is after one transform has been executed.

• param step Index of the step

• return Schema of the data, after that (and all prior) steps have been executed

toJson

public String toJson()

Execute the full sequence of transformations for a single example. May return null if example is filtered NOTE: Some TransformProcess operations cannot be done on examples individually. Most notably, ConvertToSequence and ConvertFromSequence operations require the full data set to be processed at once

• param input

• return

toYaml

public String toYaml()

Convert the TransformProcess to a YAML string

• return TransformProcess, as YAML

fromJson

public static TransformProcess fromJson(String json)

Deserialize a JSON String (created by {- link #toJson()}) to a TransformProcess

• return TransformProcess, from JSON

fromYaml

public static TransformProcess fromYaml(String yaml)

Deserialize a JSON String (created by {- link #toJson()}) to a TransformProcess

• return TransformProcess, from JSON

transform

public Builder transform(Transform transform)

Infer the categories for the given record reader for a particular column Note that each “column index” is a column in the context of: List record = ...; record.get(columnIndex);

Note that anything passed in as a column will be automatically converted to a string for categorical purposes.

The expected input is strings or numbers (which have sensible toString() representations)

Note that the returned categories will be sorted alphabetically

• param recordReader the record reader to iterate through

• param columnIndex te column index to get categories for

• return

filter

public Builder filter(Filter filter)

Add a filter operation to be executed after the previously-added operations have been executed

• param filter Filter operation to execute

filter

public Builder filter(Condition condition)

Add a filter operation, based on the specified condition.

If condition is satisfied (returns true): remove the example or sequence If condition is not satisfied (returns false): keep the example or sequence

• param condition Condition to filter on

removeColumns

public Builder removeColumns(String... columnNames)

Remove all of the specified columns, by name

• param columnNames Names of the columns to remove

removeColumns

public Builder removeColumns(Collection<String> columnNames)

Remove all of the specified columns, by name

• param columnNames Names of the columns to remove

removeAllColumnsExceptFor

public Builder removeAllColumnsExceptFor(String... columnNames)

Remove all columns, except for those that are specified here

• param columnNames Names of the columns to keep

removeAllColumnsExceptFor

public Builder removeAllColumnsExceptFor(Collection<String> columnNames)

Remove all columns, except for those that are specified here

• param columnNames Names of the columns to keep

renameColumn

public Builder renameColumn(String oldName, String newName)

Rename a single column

• param oldName Original column name

• param newName New column name

renameColumns

public Builder renameColumns(List<String> oldNames, List<String> newNames)

Rename multiple columns

• param oldNames List of original column names

• param newNames List of new column names

reorderColumns

public Builder reorderColumns(String... newOrder)

Reorder the columns using a partial or complete new ordering. If only some of the column names are specified for the new order, the remaining columns will be placed at the end, according to their current relative ordering

• param newOrder Names of the columns, in the order they will appear in the output

duplicateColumn

public Builder duplicateColumn(String column, String newName)

Duplicate a single column

• param column Name of the column to duplicate

• param newName Name of the new (duplicate) column

duplicateColumns

public Builder duplicateColumns(List<String> columnNames, List<String> newNames)

Duplicate a set of columns

• param columnNames Names of the columns to duplicate

• param newNames Names of the new (duplicated) columns

integerMathOp

public Builder integerMathOp(String column, MathOp mathOp, int scalar)

Perform a mathematical operation (add, subtract, scalar max etc) on the specified integer column, with a scalar

• param column The integer column to perform the operation on

• param mathOp The mathematical operation

• param scalar The scalar value to use in the mathematical operation

integerColumnsMathOp

public Builder integerColumnsMathOp(String newColumnName, MathOp mathOp, String... columnNames)

Calculate and add a new integer column by performing a mathematical operation on a number of existing columns. New column is added to the end.

• param newColumnName Name of the new/derived column

• param mathOp Mathematical operation to execute on the columns

• param columnNames Names of the columns to use in the mathematical operation

longMathOp

public Builder longMathOp(String columnName, MathOp mathOp, long scalar)

Perform a mathematical operation (add, subtract, scalar max etc) on the specified long column, with a scalar

• param columnName The long column to perform the operation on

• param mathOp The mathematical operation

• param scalar The scalar value to use in the mathematical operation

longColumnsMathOp

public Builder longColumnsMathOp(String newColumnName, MathOp mathOp, String... columnNames)

Calculate and add a new long column by performing a mathematical operation on a number of existing columns. New column is added to the end.

• param newColumnName Name of the new/derived column

• param mathOp Mathematical operation to execute on the columns

• param columnNames Names of the columns to use in the mathematical operation

floatMathOp

public Builder floatMathOp(String columnName, MathOp mathOp, float scalar)

Perform a mathematical operation (add, subtract, scalar max etc) on the specified double column, with a scalar

• param columnName The float column to perform the operation on

• param mathOp The mathematical operation

• param scalar The scalar value to use in the mathematical operation

floatColumnsMathOp

public Builder floatColumnsMathOp(String newColumnName, MathOp mathOp, String... columnNames)

Calculate and add a new float column by performing a mathematical operation on a number of existing columns. New column is added to the end.

• param newColumnName Name of the new/derived column

• param mathOp Mathematical operation to execute on the columns

• param columnNames Names of the columns to use in the mathematical operation

floatMathFunction

public Builder floatMathFunction(String columnName, MathFunction mathFunction)

Perform a mathematical operation (such as sin(x), ceil(x), exp(x) etc) on a column

• param columnName Column name to operate on

• param mathFunction MathFunction to apply to the column

doubleMathOp

public Builder doubleMathOp(String columnName, MathOp mathOp, double scalar)

Perform a mathematical operation (add, subtract, scalar max etc) on the specified double column, with a scalar

• param columnName The double column to perform the operation on

• param mathOp The mathematical operation

• param scalar The scalar value to use in the mathematical operation

doubleColumnsMathOp

public Builder doubleColumnsMathOp(String newColumnName, MathOp mathOp, String... columnNames)

Calculate and add a new double column by performing a mathematical operation on a number of existing columns. New column is added to the end.

• param newColumnName Name of the new/derived column

• param mathOp Mathematical operation to execute on the columns

• param columnNames Names of the columns to use in the mathematical operation

doubleMathFunction

public Builder doubleMathFunction(String columnName, MathFunction mathFunction)

Perform a mathematical operation (such as sin(x), ceil(x), exp(x) etc) on a column

• param columnName Column name to operate on

• param mathFunction MathFunction to apply to the column

timeMathOp

public Builder timeMathOp(String columnName, MathOp mathOp, long timeQuantity, TimeUnit timeUnit)

Perform a mathematical operation (add, subtract, scalar min/max only) on the specified time column

• param columnName The integer column to perform the operation on

• param mathOp The mathematical operation

• param timeQuantity The quantity used in the mathematical op

• param timeUnit The unit that timeQuantity is specified in

categoricalToOneHot

public Builder categoricalToOneHot(String... columnNames)

Convert the specified column(s) from a categorical representation to a one-hot representation. This involves the creation of multiple new columns each.

• param columnNames Names of the categorical column(s) to convert to a one-hot representation

categoricalToInteger

public Builder categoricalToInteger(String... columnNames)

Convert the specified column(s) from a categorical representation to an integer representation. This will replace the specified categorical column(s) with an integer repreesentation, where each integer has the value 0 to numCategories-1.

• param columnNames Name of the categorical column(s) to convert to an integer representation

integerToCategorical

public Builder integerToCategorical(String columnName, List<String> categoryStateNames)

Convert the specified column from an integer representation (assume values 0 to numCategories-1) to a categorical representation, given the specified state names

• param columnName Name of the column to convert

• param categoryStateNames Names of the states for the categorical column

integerToCategorical

public Builder integerToCategorical(String columnName, Map<Integer, String> categoryIndexNameMap)

Convert the specified column from an integer representation to a categorical representation, given the specified mapping between integer indexes and state names

• param columnName Name of the column to convert

• param categoryIndexNameMap Names of the states for the categorical column

integerToOneHot

public Builder integerToOneHot(String columnName, int minValue, int maxValue)

Convert an integer column to a set of 1 hot columns, based on the value in integer column

• param columnName Name of the integer column

• param minValue Minimum value possible for the integer column (inclusive)

• param maxValue Maximum value possible for the integer column (inclusive)

addConstantColumn

public Builder addConstantColumn(String newColumnName, ColumnType newColumnType, Writable fixedValue)

Add a new column, where all values in the column are identical and as specified.

• param newColumnName Name of the new column

• param newColumnType Type of the new column

• param fixedValue Value in the new column for all records

addConstantDoubleColumn

public Builder addConstantDoubleColumn(String newColumnName, double value)

Add a new double column, where the value for that column (for all records) are identical

• param newColumnName Name of the new column

• param value Value in the new column for all records

addConstantIntegerColumn

public Builder addConstantIntegerColumn(String newColumnName, int value)

Add a new integer column, where th e value for that column (for all records) are identical

• param newColumnName Name of the new column

• param value Value of the new column for all records

addConstantLongColumn

public Builder addConstantLongColumn(String newColumnName, long value)

Add a new integer column, where the value for that column (for all records) are identical

• param newColumnName Name of the new column

• param value Value in the new column for all records

convertToString

public Builder convertToString(String inputColumn)

Convert the specified column to a string.

• param inputColumn the input column to convert

• return builder pattern

convertToDouble

public Builder convertToDouble(String inputColumn)

Convert the specified column to a double.

• param inputColumn the input column to convert

• return builder pattern

convertToInteger

public Builder convertToInteger(String inputColumn)

Convert the specified column to an integer.

• param inputColumn the input column to convert

• return builder pattern

normalize

public Builder normalize(String column, Normalize type, DataAnalysis da)

Normalize the specified column with a given type of normalization

• param column Column to normalize

• param type Type of normalization to apply

• param da DataAnalysis object

convertToSequence

public Builder convertToSequence(String keyColumn, SequenceComparator comparator)

Convert a set of independent records/examples into a sequence, according to some key. Within each sequence, values are ordered using the provided {- link SequenceComparator}

• param keyColumn Column to use as a key (values with the same key will be combined into sequences)

• param comparator A SequenceComparator to order the values within each sequence (for example, by time or String order)

convertToSequence

public Builder convertToSequence()

Convert a set of independent records/examples into a sequence; each example is simply treated as a sequence of length 1, without any join/group operations. Note that more commonly, joining/grouping is required; use {- link #convertToSequence(List, SequenceComparator)} for this functionality

convertToSequence

public Builder convertToSequence(List<String> keyColumns, SequenceComparator comparator)

Convert a set of independent records/examples into a sequence, where each sequence is grouped according to one or more key values (i.e., the values in one or more columns) Within each sequence, values are ordered using the provided {- link SequenceComparator}

• param keyColumns Column to use as a key (values with the same key will be combined into sequences)

• param comparator A SequenceComparator to order the values within each sequence (for example, by time or String order)

convertFromSequence

public Builder convertFromSequence()

Convert a sequence to a set of individual values (by treating each value in each sequence as a separate example)

splitSequence

public Builder splitSequence(SequenceSplit split)

Split sequences into 1 or more other sequences. Used for example to split large sequences into a set of smaller sequences

• param split SequenceSplit that defines how splits will occur

trimSequence

public Builder trimSequence(int numStepsToTrim, boolean trimFromStart)

SequenceTrimTranform removes the first or last N values in a sequence. Note that the resulting sequence may be of length 0, if the input sequence is less than or equal to N.

• param numStepsToTrim Number of time steps to trim from the sequence

• param trimFromStart If true: Trim values from the start of the sequence. If false: trim values from the end.

offsetSequence

public Builder offsetSequence(List<String> columnsToOffset, int offsetAmount,
                                      SequenceOffsetTransform.OperationType operationType)

Perform a sequence of operation on the specified columns. Note that this also truncates sequences by the specified offset amount by default. Use {- code transform(new SequenceOffsetTransform(…)} to change this. See {- link SequenceOffsetTransform} for details on exactly what this operation does and how.

• param columnsToOffset Columns to offset

• param offsetAmount Amount to offset the specified columns by (positive offset: ‘columnsToOffset’ are moved to later time steps)

• param operationType Whether the offset should be done in-place or by adding a new column

reduce

public Builder reduce(IAssociativeReducer reducer)

Reduce (i.e., aggregate/combine) a set of examples (typically by key). Note: In the current implementation, reduction operations can be performed only on standard (i.e., non-sequence) data

• param reducer Reducer to use

reduceSequence

public Builder reduceSequence(IAssociativeReducer reducer)

Reduce (i.e., aggregate/combine) a set of sequence examples - for each sequence individually. Note: This method results in non-sequence data. If you would instead prefer sequences of length 1 after the reduction, use {- code transform(new ReduceSequenceTransform(reducer))}.

• param reducer Reducer to use to reduce each window

reduceSequenceByWindow

public Builder reduceSequenceByWindow(IAssociativeReducer reducer, WindowFunction windowFunction)

Reduce (i.e., aggregate/combine) a set of sequence examples - for each sequence individually - using a window function. For example, take all records/examples in each 24-hour period (i.e., using window function), and convert them into a singe value (using the reducer). In this example, the output is a sequence, with time period of 24 hours.

• param reducer Reducer to use to reduce each window

• param windowFunction Window function to find apply on each sequence individually

sequenceMovingWindowReduce

public Builder sequenceMovingWindowReduce(String columnName, int lookback, ReduceOp op)

SequenceMovingWindowReduceTransform: Adds a new column, where the value is derived by: (a) using a window of the last N values in a single column, (b) Apply a reduction op on the window to calculate a new value for example, this transformer can be used to implement a simple moving average of the last N values, or determine the minimum or maximum values in the last N time steps.

For example, for a simple moving average, length 20: {- code new SequenceMovingWindowReduceTransform(“myCol”, 20, ReduceOp.Mean)}

• param columnName Column name to perform windowing on

• param lookback Look back period for windowing

• param op Reduction operation to perform on each window

calculateSortedRank

public Builder calculateSortedRank(String newColumnName, String sortOnColumn, WritableComparator comparator)

CalculateSortedRank: calculate the rank of each example, after sorting example. For example, we might have some numerical “score” column, and we want to know for the rank (sort order) for each example, according to that column. The rank of each example (after sorting) will be added in a new Long column. Indexing is done from 0; examples will have values 0 to dataSetSize-1.

Currently, CalculateSortedRank can only be applied on standard (i.e., non-sequence) data Furthermore, the current implementation can only sort on one column

• param newColumnName Name of the new column (will contain the rank for each example)

• param sortOnColumn Column to sort on

• param comparator Comparator used to sort examples

calculateSortedRank

public Builder calculateSortedRank(String newColumnName, String sortOnColumn, WritableComparator comparator,
                                           boolean ascending)

CalculateSortedRank: calculate the rank of each example, after sorting example. For example, we might have some numerical “score” column, and we want to know for the rank (sort order) for each example, according to that column. The rank of each example (after sorting) will be added in a new Long column. Indexing is done from 0; examples will have values 0 to dataSetSize-1.

Currently, CalculateSortedRank can only be applied on standard (i.e., non-sequence) data Furthermore, the current implementation can only sort on one column

• param newColumnName Name of the new column (will contain the rank for each example)

• param sortOnColumn Column to sort on

• param comparator Comparator used to sort examples

• param ascending If true: sort ascending. False: descending

stringToCategorical

public Builder stringToCategorical(String columnName, List<String> stateNames)

Convert the specified String column to a categorical column. The state names must be provided.

• param columnName Name of the String column to convert to categorical

• param stateNames State names of the category

stringRemoveWhitespaceTransform

public Builder stringRemoveWhitespaceTransform(String columnName)

Remove all whitespace characters from the values in the specified String column

• param columnName Name of the column to remove whitespace from

stringMapTransform

public Builder stringMapTransform(String columnName, Map<String, String> mapping)

Replace one or more String values in the specified column with new values.

Keys in the map are the original values; the Values in the map are their replacements. If a String appears in the data but does not appear in the provided map (as a key), that String values will not be modified.

• param columnName Name of the column in which to do replacement

• param mapping Map of oldValues -> newValues

stringToTimeTransform

public Builder stringToTimeTransform(String column, String format, DateTimeZone dateTimeZone)

Convert a String column (containing a date/time String) to a time column (by parsing the date/time String)

• param column String column containing the date/time Strings

• param format Format of the strings. Time format is specified as per http://www.joda.org/joda-time/apidocs/org/joda/time/format/DateTimeFormat.html

• param dateTimeZone Timezone of the column

stringToTimeTransform

public Builder stringToTimeTransform(String column, String format, DateTimeZone dateTimeZone, Locale locale)

Convert a String column (containing a date/time String) to a time column (by parsing the date/time String)

• param column String column containing the date/time Strings

• param format Format of the strings. Time format is specified as per http://www.joda.org/joda-time/apidocs/org/joda/time/format/DateTimeFormat.html

• param dateTimeZone Timezone of the column

• param locale Locale of the column

appendStringColumnTransform

public Builder appendStringColumnTransform(String column, String toAppend)

Append a String to a specified column

• param column Column to append the value to

• param toAppend String to append to the end of each writable

conditionalReplaceValueTransform

public Builder conditionalReplaceValueTransform(String column, Writable newValue, Condition condition)

Replace the values in a specified column with a specified new value, if some condition holds. If the condition does not hold, the original values are not modified.

• param column Column to operate on

• param newValue Value to use as replacement, if condition is satisfied

• param condition Condition that must be satisfied for replacement

conditionalReplaceValueTransformWithDefault

public Builder conditionalReplaceValueTransformWithDefault(String column, Writable yesVal, Writable noVal, Condition condition)

Replace the values in a specified column with a specified “yes” value, if some condition holds. Replace it with a “no” value, otherwise.

• param column Column to operate on

• param yesVal Value to use as replacement, if condition is satisfied

• param noVal Value to use as replacement, if condition is not satisfied

• param condition Condition that must be satisfied for replacement

conditionalCopyValueTransform

public Builder conditionalCopyValueTransform(String columnToReplace, String sourceColumn, Condition condition)

Replace the value in a specified column with a new value taken from another column, if a condition is satisfied/true. Note that the condition can be any generic condition, including on other column(s), different to the column that will be modified if the condition is satisfied/true.

• param columnToReplace Name of the column in which values will be replaced (if condition is satisfied)

• param sourceColumn Name of the column from which the new values will be

• param condition Condition to use

replaceStringTransform

public Builder replaceStringTransform(String columnName, Map<String, String> mapping)

Replace one or more String values in the specified column that match regular expressions.

Keys in the map are the regular expressions; the Values in the map are their String replacements. For example:

• param columnName Name of the column in which to do replacement

• param mapping Map of old values or regular expression to new values

ndArrayScalarOpTransform

public Builder ndArrayScalarOpTransform(String columnName, MathOp op, double value)

Element-wise NDArray math operation (add, subtract, etc) on an NDArray column

• param columnName Name of the NDArray column to perform the operation on

• param op Operation to perform

• param value Value for the operation

ndArrayColumnsMathOpTransform

public Builder ndArrayColumnsMathOpTransform(String newColumnName, MathOp mathOp, String... columnNames)

Perform an element wise mathematical operation (such as add, subtract, multiply) on NDArray columns. The existing columns are unchanged, a new NDArray column is added

• param newColumnName Name of the new NDArray column

• param mathOp Operation to perform

• param columnNames Name of the columns used as input to the operation

ndArrayMathFunctionTransform

public Builder ndArrayMathFunctionTransform(String columnName, MathFunction mathFunction)

Apply an element wise mathematical function (sin, tanh, abs etc) to an NDArray column. This operation is performed in place.

• param columnName Name of the column to perform the operation on

• param mathFunction Mathematical function to apply

ndArrayDistanceTransform

public Builder ndArrayDistanceTransform(String newColumnName, Distance distance, String firstCol,
                                                String secondCol)

Calculate a distance (cosine similarity, Euclidean, Manhattan) on two equal-sized NDArray columns. This operation adds a new Double column (with the specified name) with the result.

• param newColumnName Name of the new column (result) to add

• param distance Distance to apply

• param firstCol first column to use in the distance calculation

• param secondCol second column to use in the distance calculation

firstDigitTransform

public Builder firstDigitTransform(String inputColumn, String outputColumn)

FirstDigitTransform converts a column to a categorical column, with values being the first digit of the number. For example, “3.1415” becomes “3” and “2.0” becomes “2”. Negative numbers ignore the sign: “-7.123” becomes “7”. Note that two {- link FirstDigitTransform.Mode}s are supported, which determines how non-numerical entries should be handled: EXCEPTION_ON_INVALID: output has 10 category values (“0”, …, “9”), and any non-numerical values result in an exception INCLUDE_OTHER_CATEGORY: output has 11 category values (“0”, …, “9”, “Other”), all non-numerical values are mapped to “Other”

FirstDigitTransform is useful (combined with {- link CategoricalToOneHotTransform} and Reductions) to implement Benford’s law.

• param inputColumn Input column name

• param outputColumn Output column name. If same as input, input column is replaced

firstDigitTransform

public Builder firstDigitTransform(String inputColumn, String outputColumn, FirstDigitTransform.Mode mode)

FirstDigitTransform converts a column to a categorical column, with values being the first digit of the number. For example, “3.1415” becomes “3” and “2.0” becomes “2”. Negative numbers ignore the sign: “-7.123” becomes “7”. Note that two {- link FirstDigitTransform.Mode}s are supported, which determines how non-numerical entries should be handled: EXCEPTION_ON_INVALID: output has 10 category values (“0”, …, “9”), and any non-numerical values result in an exception INCLUDE_OTHER_CATEGORY: output has 11 category values (“0”, …, “9”, “Other”), all non-numerical values are mapped to “Other”

FirstDigitTransform is useful (combined with {- link CategoricalToOneHotTransform} and Reductions) to implement Benford’s law.

• param inputColumn Input column name

• param outputColumn Output column name. If same as input, input column is replaced

• param mode See {- link FirstDigitTransform.Mode}

build

public TransformProcess build()

Create the TransformProcess object

CategoricalToIntegerTransform

[source]

Created by Alex on 4/03/2016.

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

mapSequence

public Object mapSequence(Object sequence)

Transform a sequence

• param sequence

outputColumnName

public String outputColumnName()

The output column name after the operation has been applied

• return the output column name

columnName

public String columnName()

The output column names This will often be the same as the input

• return the output column names

CategoricalToOneHotTransform

[source]

Created by Alex on 4/03/2016.

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

mapSequence

public Object mapSequence(Object sequence)

Transform a sequence

• param sequence

outputColumnName

public String outputColumnName()

The output column name after the operation has been applied

• return the output column name

columnName

public String columnName()

The output column names This will often be the same as the input

• return the output column names

IntegerToCategoricalTransform

[source]

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

mapSequence

public Object mapSequence(Object sequence)

Transform a sequence

• param sequence

PivotTransform

[source]

Pivot transform operates on two columns:

• a categorical column that operates as a key, and

• Another column that contains a value Essentially, Pivot transform takes keyvalue pairs and breaks them out into separate columns.

For example, with schema [col0, key, value, col3] and values with key in {a,b,c} Output schema is [col0, key[a], key[b], key[c], col3] and input (col0Val, b, x, col3Val) gets mapped to (col0Val, 0, x, 0, col3Val).

When expanding columns, a default value is used - for example 0 for numerical columns.

transform

public Schema transform(Schema inputSchema)

• param keyColumnName Key column to expand

• param valueColumnName Name of the column that contains the value

StringToCategoricalTransform

[source]

Convert a String column to a categorical column

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

mapSequence

public Object mapSequence(Object sequence)

Transform a sequence

• param sequence

AddConstantColumnTransform

[source]

Add a new column, where the values in that column for all records are identical (according to the specified value)

DuplicateColumnsTransform

[source]

Duplicate one or more columns. The duplicated columns are placed immediately after the original columns

transform

public Schema transform(Schema inputSchema)

• param columnsToDuplicate List of columns to duplicate

• param newColumnNames List of names for the new (duplicate) columns

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

mapSequence

public Object mapSequence(Object sequence)

Transform a sequence

• param sequence

outputColumnName

public String outputColumnName()

The output column name after the operation has been applied

• return the output column name

columnName

public String columnName()

The output column names This will often be the same as the input

• return the output column names

RemoveAllColumnsExceptForTransform

[source]

Transform that removes all columns except for those that are explicitly specified as ones to keep To specify only the columns

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

mapSequence

public Object mapSequence(Object sequence)

Transform a sequence

• param sequence

outputColumnName

public String outputColumnName()

The output column name after the operation has been applied

• return the output column name

columnName

public String columnName()

The output column names This will often be the same as the input

• return the output column names

RemoveColumnsTransform

[source]

Remove the specified columns from the data. To specify only the columns to keep,

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

mapSequence

public Object mapSequence(Object sequence)

Transform a sequence

• param sequence

outputColumnName

public String outputColumnName()

The output column name after the operation has been applied

• return the output column name

columnName

public String columnName()

The output column names This will often be the same as the input

• return the output column names

RenameColumnsTransform

[source]

Rename one or more columns

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

mapSequence

public Object mapSequence(Object sequence)

Transform a sequence

• param sequence

outputColumnName

public String outputColumnName()

The output column name after the operation has been applied

• return the output column name

columnName

public String columnName()

The output column names This will often be the same as the input

• return the output column names

ReorderColumnsTransform

[source]

Rearrange the order of the columns. Note: A partial list of columns can be used here. Any columns that are not explicitly mentioned will be placed after those that are in the output, without changing their relative order.

transform

public Schema transform(Schema inputSchema)

• param newOrder A partial or complete order of the columns in the output

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

mapSequence

public Object mapSequence(Object sequence)

Transform a sequence

• param sequence

outputColumnName

public String outputColumnName()

The output column name after the operation has been applied

• return the output column name

columnName

public String columnName()

The output column names This will often be the same as the input

• return the output column names

ConditionalCopyValueTransform

[source]

Replace the value in a specified column with a new value taken from another column, if a condition is satisfied/true. Note that the condition can be any generic condition, including on other column(s), different to the column that will be modified if the condition is satisfied/true.

Note: For sequences, this transform use the convention that each step in the sequence is passed to the condition, and replaced (or not) separately (i.e., Condition.condition(List) is used on each time step individually)

transform

public Schema transform(Schema inputSchema)

• param columnToReplace Name of the column in which to replace the old value

• param sourceColumn Name of the column to get the new value from

• param condition Condition

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

mapSequence

public Object mapSequence(Object sequence)

Transform a sequence

• param sequence

outputColumnName

public String outputColumnName()

The output column name after the operation has been applied

• return the output column name

columnName

public String columnName()

The output column names This will often be the same as the input

• return the output column names

ConditionalReplaceValueTransform

[source]

Replace the value in a specified column with a new value, if a condition is satisfied/true. Note that the condition can be any generic condition, including on other column(s), different to the column that will be modified if the condition is satisfied/true.

Note: For sequences, this transform use the convention that each step in the sequence is passed to the condition, and replaced (or not) separately (i.e., Condition.condition(List) is used on each time step individually)

transform

public Schema transform(Schema inputSchema)

• param columnToReplace Name of the column in which to replace the old value with ‘newValue’, if the condition holds

• param newValue New value to use

• param condition Condition

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

mapSequence

public Object mapSequence(Object sequence)

Transform a sequence

• param sequence

outputColumnName

public String outputColumnName()

The output column name after the operation has been applied

• return the output column name

columnName

public String columnName()

The output column names This will often be the same as the input

• return the output column names

ConditionalReplaceValueTransformWithDefault

[source]

Replace the value in a specified column with a ‘yes’ value, if a condition is satisfied/true. Replace the value of this same column with a ‘no’ value otherwise. Note that the condition can be any generic condition, including on other column(s), different to the column that will be modified if the condition is satisfied/true.

Note: For sequences, this transform use the convention that each step in the sequence is passed to the condition, and replaced (or not) separately (i.e., Condition.condition(List) is used on each time step individually)

ConvertToDouble

[source]

Convert any value to an Double

map

public DoubleWritable map(Writable writable)

• param column Name of the column to convert to a Double column

DoubleColumnsMathOpTransform

[source]

Add a new double column, calculated from one or more other columns. A new column (with the specified name) is added as the final column of the output. No other columns are modified. For example, if newColumnName==”newCol”, mathOp==Add, and columns=={“col1”,”col2”}, then the output column with name “newCol” has value col1+col2.

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

mapSequence

public Object mapSequence(Object sequence)

Transform a sequence

• param sequence

DoubleMathFunctionTransform

[source]

A simple transform to do common mathematical operations, such as sin(x), ceil(x), etc.

DoubleMathOpTransform

[source]

Double mathematical operation. This is an in-place operation of the double column value and a double scalar.

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

mapSequence

public Object mapSequence(Object sequence)

Transform a sequence

• param sequence

Log2Normalizer

[source]

Normalize by taking scale log2((in-columnMin)/(mean-columnMin) + 1) Maps values in range (columnMin to infinity) to (0 to infinity) Most suitable for values with a geometric/negative exponential type distribution.

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

MinMaxNormalizer

[source]

Normalizer to map (min to max) -> (newMin-to newMax) linearly.

Mathematically: (newMax-newMin)/(max-min) (x-min) + newMin

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

StandardizeNormalizer

[source]

Normalize using (x-mean)/stdev. Also known as a standard score, standardization etc.

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

SubtractMeanNormalizer

[source]

Normalize by substracting the mean

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

ConvertToInteger

[source]

Convert any value to an Integer.

map

public IntWritable map(Writable writable)

• param column Name of the column to convert to an integer

IntegerColumnsMathOpTransform

[source]

Add a new integer column, calculated from one or more other columns. A new column (with the specified name) is added as the final column of the output. No other columns are modified. For example, if newColumnName==”newCol”, mathOp==MathOp.Add, and columns=={“col1”,”col2”}, then the output column with name “newCol” has value col1+col2. NOTE: Division here is using if a decimal output value is required.

toString

public String toString()

• param newColumnName Name of the new column (output column)

• param mathOp Mathematical operation. Only Add/Subtract/Multiply/Divide/Modulus is allowed here

• param columns Columns to use in the mathematical operation

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

mapSequence

public Object mapSequence(Object sequence)

Transform a sequence

• param sequence

IntegerMathOpTransform

[source]

Integer mathematical operation. This is an in-place operation of the integer column value and an integer scalar.

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

IntegerToOneHotTransform

[source]

Convert an integer column to a set of one-hot columns.

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

mapSequence

public Object mapSequence(Object sequence)

Transform a sequence

• param sequence

outputColumnName

public String outputColumnName()

The output column name after the operation has been applied

• return the output column name

columnName

public String columnName()

The output column names This will often be the same as the input

• return the output column names

ReplaceEmptyIntegerWithValueTransform

[source]

Replace an empty/missing integer with a certain value.

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

ReplaceInvalidWithIntegerTransform

[source]

Replace an invalid (non-integer) value in a column with a specified integer

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

LongColumnsMathOpTransform

[source]

Add a new long column, calculated from one or more other columns. A new column (with the specified name) is added as the final column of the output. No other columns are modified. For example, if newColumnName==”newCol”, mathOp==MathOp.Add, and columns=={“col1”,”col2”}, then the output column with name “newCol” has value col1+col2. if a decimal output value is required.

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

mapSequence

public Object mapSequence(Object sequence)

Transform a sequence

• param sequence

LongMathOpTransform

[source]

Long mathematical operation. This is an in-place operation of the long column value and an long scalar.

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

TextToCharacterIndexTransform

[source]

Convert each text value in a sequence to a longer sequence of integer indices. For example, “abc” would be converted to [1, 2, 3]. Values in other columns will be duplicated.

TextToTermIndexSequenceTransform

[source]

Convert each text value in a sequence to a longer sequence of integer indices. For example, “zero one two” would be converted to [0, 1, 2]. Values in other columns will be duplicated.

SequenceDifferenceTransform

[source]

SequenceDifferenceTransform: for an input sequence, calculate the difference on one column. For each time t, calculate someColumn(t) - someColumn(t-s), where s >= 1 is the ‘lookback’ period.

Note: at t=0 (i.e., the first step in a sequence; or more generally, for all times t < s), there is no previous value these time steps:

1. Default: output = someColumn(t) - someColumn(max(t-s, 0))

2. SpecifiedValue: output = someColumn(t) - someColumn(t-s) if t-s >= 0, or a custom Writable object (for example, a DoubleWritable(0) or NullWritable).

Note: this is an in-place operation: i.e., the values in each column are modified. If the original values are and apply the difference operation in-place on the copy.

outputColumnName

public String outputColumnName()

Create a SequenceDifferenceTransform with default lookback of 1, and using FirstStepMode.Default. Output column name is the same as the input column name.

• param columnName Name of the column to perform the operation on.

columnName

public String columnName()

The output column names This will often be the same as the input

• return the output column names

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

mapSequence

public Object mapSequence(Object sequence)

Transform a sequence

• param sequence

SequenceMovingWindowReduceTransform

[source]

SequenceMovingWindowReduceTransform Adds a new column, where the value is derived by: (a) using a window of the last N values in a single column, (b) Apply a reduction op on the window to calculate a new value for example, this transformer can be used to implement a simple moving average of the last N values, or determine the minimum or maximum values in the last N time steps.

defaultOutputColumnName

public static String defaultOutputColumnName(String originalName, int lookback, ReduceOp op)

Enumeration to specify how each cases are handled: For example, for a look back period of 20, how should the first 19 output values be calculated? Default: Perform your former reduction as normal, with as many values are available SpecifiedValue: use the given/specified value instead of the actual output value. For example, you could assign values of 0 or NullWritable to positions 0 through 18 of the output.

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

mapSequence

public Object mapSequence(Object sequence)

Transform a sequence

• param sequence

outputColumnName

public String outputColumnName()

The output column name after the operation has been applied

• return the output column name

columnName

public String columnName()

The output column names This will often be the same as the input

• return the output column names

SequenceOffsetTransform

[source]

Sequence offset transform takes a sequence, and shifts The values in one or more columns by a specified number of times steps. It has 2 modes of operation (OperationType enum), with respect to the columns it operates on: InPlace: operations may be performed in-place, modifying the values in the specified columns NewColumn: operations may produce new columns, with the original (source) columns remaining unmodified

Additionally, there are 2 modes for handling values outside the original sequence (EdgeHandling enum): TrimSequence: the entire sequence is trimmed (start or end) by a specified number of steps SpecifiedValue: for any values outside of the original sequence, they are given a specified value

Note 1: When specifying offsets, they are done as follows: Positive offsets: move the values in the specified columns to a later time. Earlier time steps are either be trimmed or Given specified values; the last values in these columns will be truncated/removed.

Note 2: Care must be taken when using TrimSequence: for example, if we chain multiple sequence offset transforms on the one dataset, we may end up trimming much more than we want. In this case, it may be better to use SpecifiedValue, at the end.

AppendStringColumnTransform

[source]

Append a String to the values in a single column

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

ChangeCaseStringTransform

[source]

Change case (to, e.g, all lower case) of String column.

ConcatenateStringColumns

[source]

Concatenate values of one or more String columns into a new String column. Retains the constituent String columns so user must remove those manually, if desired.

TODO: use new String Reduce functionality in DataVec?

transform

public Schema transform(Schema inputSchema)

• param columnsToConcatenate A partial or complete order of the columns in the output

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

mapSequence

public Object mapSequence(Object sequence)

Transform a sequence

• param sequence

outputColumnName

public String outputColumnName()

The output column name after the operation has been applied

• return the output column name

columnName

public String columnName()

The output column names This will often be the same as the input

• return the output column names

ConvertToString

[source]

Convert any value to a string.

map

public Text map(Writable writable)

Transform the writable in to a string

• param writable the writable to transform

• return the string form of this writable

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

MapAllStringsExceptListTransform

[source]

This method maps all String values, except those is the specified list, to a single String value

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

RemoveWhiteSpaceTransform

[source]

String transform that removes all whitespace charaters

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

ReplaceEmptyStringTransform

[source]

Replace empty String values with the specified String

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

ReplaceStringTransform

[source]

Replaces String values that match regular expressions.

map

public Text map(final Writable writable)

Constructs a new ReplaceStringTransform using the specified

• param columnName Name of the column

• param map Key: regular expression; Value: replacement value

StringListToCategoricalSetTransform

[source]

Convert a delimited String to a list of binary categorical columns. Suppose the possible String values were {“a”,”b”,”c”,”d”} and the String column value to be converted contained the String “a,c”, then the 4 output columns would have values [“true”,”false”,”true”,”false”]

transform

public Schema transform(Schema inputSchema)

• param columnName The name of the column to convert

• param newColumnNames The names of the new columns to create

• param categoryTokens The possible tokens that may be present. Note this list must have the same length and order as the newColumnNames list

• param delimiter The delimiter for the Strings to convert

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

mapSequence

public Object mapSequence(Object sequence)

Transform a sequence

• param sequence

outputColumnName

public String outputColumnName()

The output column name after the operation has been applied

• return the output column name

columnName

public String columnName()

The output column names This will often be the same as the input

• return the output column names

StringListToCountsNDArrayTransform

[source]

Converts String column into a bag-of-words (BOW) represented as an NDArray of “counts.” Note that the original column is removed in the process

transform

public Schema transform(Schema inputSchema)

• param columnName The name of the column to convert

• param vocabulary The possible tokens that may be present.

• param delimiter The delimiter for the Strings to convert

• param ignoreUnknown Whether to ignore unknown tokens

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

mapSequence

public Object mapSequence(Object sequence)

Transform a sequence

• param sequence

outputColumnName

public String outputColumnName()

The output column name after the operation has been applied

• return the output column name

columnName

public String columnName()

The output column names This will often be the same as the input

• return the output column names

StringListToIndicesNDArrayTransform

[source]

Converts String column into a sparse bag-of-words (BOW) represented as an NDArray of indices. Appropriate for embeddings or as efficient storage before being expanded into a dense array.

StringMapTransform

[source]

A simple String -> String map function.

Keys in the map are the original values; the Values in the map are their replacements. If a String appears in the data but does not appear in the provided map (as a key), that String values will not be modified.

map

public Text map(Writable writable)

• param columnName Name of the column

• param map Key: From. Value: To

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

DeriveColumnsFromTimeTransform

[source]

Create a number of new columns by deriving their values from a Time column. Can be used for example to create new columns with the year, month, day, hour, minute, second etc.

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

mapSequence

public Object mapSequence(Object sequence)

Transform a sequence

• param sequence

toString

public String toString()

The output column name after the operation has been applied

• return the output column name

StringToTimeTransform

[source]

Convert a String column to a time column by parsing the date/time String, using a JodaTime.

Time format is specified as per http://www.joda.org/joda-time/apidocs/org/joda/time/format/DateTimeFormat.html

getNewColumnMetaData

public ColumnMetaData getNewColumnMetaData(String newName, ColumnMetaData oldColumnType)

Instantiate this without a time format specified. If this constructor is used, this transform will be allowed to handle several common transforms as defined in the static formats array.

• param columnName Name of the String column

• param timeZone Timezone for time parsing

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

TimeMathOpTransform

[source]

Transform math op on a time column

Note: only the following MathOps are supported: Add, Subtract, ScalarMin, ScalarMax For ScalarMin/Max, the TimeUnit must be milliseconds - i.e., value must be in epoch millisecond format

map

public Object map(Object input)

Transform an object in to another object

• param input the record to transform

• return the transformed writable

Available reductions

GeographicMidpointReduction

[source]

delimiter is configurable), determine the geographic midpoint. See “geographic midpoint” at: http://www.geomidpoint.com/methods.html For implementation algorithm, see: http://www.geomidpoint.com/calculation.html

transform

public Schema transform(Schema inputSchema)

• param delim Delimiter for the coordinates in text format. For example, if format is “lat,long” use “,”

StringReducer

[source]

A StringReducer is used to take a set of examples and reduce them. The idea: suppose you have a large number of columns, and you want to combine/reduce the values in each column. StringReducer allows you to specify different reductions for differently for different columns: min, max, sum, mean etc.

Uses are: (1) Reducing examples by a key (2) Reduction operations in time series (windowing ops, etc)

transform

public Schema transform(Schema schema)

Get the output schema, given the input schema

outputColumnName

public Builder outputColumnName(String outputColumnName)

Create a StringReducer builder, and set the default column reduction operation. For any columns that aren’t specified explicitly, they will use the default reduction operation. If a column does have a reduction operation explicitly specified, then it will override the default specified here.

• param defaultOp Default reduction operation to perform

appendColumns

public Builder appendColumns(String... columns)

Reduce the specified columns by taking the minimum value

prependColumns

public Builder prependColumns(String... columns)

Reduce the specified columns by taking the maximum value

mergeColumns

public Builder mergeColumns(String... columns)

Reduce the specified columns by taking the sum of values

replaceColumn

public Builder replaceColumn(String... columns)

Reduce the specified columns by taking the mean of the values

customReduction

public Builder customReduction(String column, ColumnReduction columnReduction)

Reduce the specified column using a custom column reduction functionality.

• param column Column to execute the custom reduction functionality on

• param columnReduction Column reduction to execute on that column

setIgnoreInvalid

public Builder setIgnoreInvalid(String... columns)

When doing the reduction: set the specified columns to ignore any invalid values. Invalid: defined as being not valid according to the ColumnMetaData: {- link ColumnMetaData#isValid(Writable)}. For numerical columns, this typically means being unable to parse the Writable. For example, Writable.toLong() failing for a Long column. If the column has any restrictions (min/max values, regex for Strings etc) these will also be taken into account.

• param columns Columns to set ‘ignore invalid’ for

Why normalize?

Neural networks work best when the data they’re fed is normalized, constrained to a range between -1 and 1. There are several reasons for that. One is that nets are trained using gradient descent, and their activation functions usually having an active range somewhere between -1 and 1. Even when using an activation function that doesn’t saturate quickly, it is still good practice to constrain your values to this range to improve performance.

Available preprocessors

NormalizerMinMaxScaler

[source]

Pre processor for DataSets that normalizes feature values (and optionally label values) to lie between a minimum and maximum value (by default between 0 and 1)

NormalizerMinMaxScaler

public NormalizerMinMaxScaler(double minRange, double maxRange)

Preprocessor can take a range as minRange and maxRange

• param minRange

• param maxRange

load

public void load(File... statistics) throws IOException

Load the given min and max

• param statistics the statistics to load

• throws IOException

save

public void save(File... files) throws IOException

Save the current min and max

• param files the statistics to save

• throws IOException

• deprecated use {- link NormalizerSerializer instead}

Normalizer

[source]

Base interface for all normalizers

ImageFlatteningDataSetPreProcessor

[source]

A DataSetPreProcessor used to flatten a 4d CNN features array to a flattened 2d format (for use in networks such as a DenseLayer/multi-layer perceptron)

MinMaxStrategy

[source]

statistics of the upper and lower bounds of the population

MinMaxStrategy

public MinMaxStrategy(double minRange, double maxRange)

• param minRange the target range lower bound

• param maxRange the target range upper bound

preProcess

public void preProcess(INDArray array, INDArray maskArray, MinMaxStats stats)

Normalize a data array

• param array the data to normalize

• param stats statistics of the data population

revert

public void revert(INDArray array, INDArray maskArray, MinMaxStats stats)

Denormalize a data array

• param array the data to denormalize

• param stats statistics of the data population

ImagePreProcessingScaler

[source]

Created by susaneraly on 6/23/16. A preprocessor specifically for images that applies min max scaling Can take a range, so pixel values can be scaled from 0->255 to minRange->maxRange default minRange = 0 and maxRange = 1; If pixel values are not 8 bits, you can specify the number of bits as the third argument in the constructor For values that are already floating point, specify the number of bits as 1

ImagePreProcessingScaler

public ImagePreProcessingScaler(double a, double b, int maxBits)

Preprocessor can take a range as minRange and maxRange

• param a, default = 0

• param b, default = 1

• param maxBits in the image, default = 8

fit

public void fit(DataSet dataSet)

Fit a dataset (only compute based on the statistics from this dataset0

• param dataSet the dataset to compute on

fit

public void fit(DataSetIterator iterator)

Iterates over a dataset accumulating statistics for normalization

• param iterator the iterator to use for collecting statistics.

transform

public void transform(DataSet toPreProcess)

Transform the data

• param toPreProcess the dataset to transform

CompositeMultiDataSetPreProcessor

[source]

A simple Composite MultiDataSetPreProcessor - allows you to apply multiple MultiDataSetPreProcessors sequentially on the one MultiDataSet, in the order they are passed to the constructor

CompositeMultiDataSetPreProcessor

public CompositeMultiDataSetPreProcessor(MultiDataSetPreProcessor... preProcessors)

• param preProcessors Preprocessors to apply. They will be applied in this order

MultiNormalizerMinMaxScaler

[source]

Pre processor for MultiDataSet that normalizes feature values (and optionally label values) to lie between a minimum and maximum value (by default between 0 and 1)

MultiNormalizerMinMaxScaler

public MultiNormalizerMinMaxScaler(double minRange, double maxRange)

Preprocessor can take a range as minRange and maxRange

• param minRange the target range lower bound

• param maxRange the target range upper bound

MultiDataNormalization

[source]

An interface for multi dataset normalizers. Data normalizers compute some sort of statistics over a MultiDataSet and scale the data in some way.

ImageMultiPreProcessingScaler

[source]

A preprocessor specifically for images that applies min max scaling to one or more of the feature arrays in a MultiDataSet. Can take a range, so pixel values can be scaled from 0->255 to minRange->maxRange default minRange = 0 and maxRange = 1; If pixel values are not 8 bits, you can specify the number of bits as the third argument in the constructor For values that are already floating point, specify the number of bits as 1

ImageMultiPreProcessingScaler

public ImageMultiPreProcessingScaler(double a, double b, int maxBits, int[] featureIndices)

Preprocessor can take a range as minRange and maxRange

• param a, default = 0

• param b, default = 1

• param maxBits in the image, default = 8

• param featureIndices Indices of feature arrays to process. If only one feature array is present, this should always be 0

NormalizerStandardize

[source]

Created by susaneraly, Ede Meijer variance and mean Pre processor for DataSet that normalizes feature values (and optionally label values) to have 0 mean and a standard deviation of 1

load

public void load(File... files) throws IOException

Load the means and standard deviations from the file system

• param files the files to load from. Needs 4 files if normalizing labels, otherwise 2.

save

public void save(File... files) throws IOException

• param files the files to save to. Needs 4 files if normalizing labels, otherwise 2.

• deprecated use {- link NormalizerSerializer} instead

Save the current means and standard deviations to the file system

StandardizeStrategy

[source]

of the means and standard deviations of the population

preProcess

public void preProcess(INDArray array, INDArray maskArray, DistributionStats stats)

Normalize a data array

• param array the data to normalize

• param stats statistics of the data population

revert

public void revert(INDArray array, INDArray maskArray, DistributionStats stats)

Denormalize a data array

• param array the data to denormalize

• param stats statistics of the data population

NormalizerStrategy

[source]

Interface for strategies that can normalize and denormalize data arrays based on statistics of the population

MultiNormalizerHybrid

[source]

Pre processor for MultiDataSet that can be configured to use different normalization strategies for different inputs and outputs, or none at all. Can be used for example when one input should be normalized, but a different one should be untouched because it’s the input for an embedding layer. Alternatively, one might want to mix standardization and min-max scaling for different inputs and outputs.

By default, no normalization is applied. There are methods to configure the desired normalization strategy for inputs and outputs either globally or on an individual input/output level. Specific input/output strategies will override global ones.

MultiNormalizerHybrid

public MultiNormalizerHybrid standardizeAllInputs()

Apply standardization to all inputs, except the ones individually configured

• return the normalizer

minMaxScaleAllInputs

public MultiNormalizerHybrid minMaxScaleAllInputs()

Apply min-max scaling to all inputs, except the ones individually configured

• return the normalizer

minMaxScaleAllInputs

public MultiNormalizerHybrid minMaxScaleAllInputs(double rangeFrom, double rangeTo)

Apply min-max scaling to all inputs, except the ones individually configured

• param rangeFrom lower bound of the target range

• param rangeTo upper bound of the target range

• return the normalizer

standardizeInput

public MultiNormalizerHybrid standardizeInput(int input)

Apply standardization to a specific input, overriding the global input strategy if any

• param input the index of the input

• return the normalizer

minMaxScaleInput

public MultiNormalizerHybrid minMaxScaleInput(int input)

Apply min-max scaling to a specific input, overriding the global input strategy if any

• param input the index of the input

• return the normalizer

minMaxScaleInput

public MultiNormalizerHybrid minMaxScaleInput(int input, double rangeFrom, double rangeTo)

Apply min-max scaling to a specific input, overriding the global input strategy if any

• param input the index of the input

• param rangeFrom lower bound of the target range

• param rangeTo upper bound of the target range

• return the normalizer

standardizeAllOutputs

public MultiNormalizerHybrid standardizeAllOutputs()

Apply standardization to all outputs, except the ones individually configured

• return the normalizer

minMaxScaleAllOutputs

public MultiNormalizerHybrid minMaxScaleAllOutputs()

Apply min-max scaling to all outputs, except the ones individually configured

• return the normalizer

minMaxScaleAllOutputs

public MultiNormalizerHybrid minMaxScaleAllOutputs(double rangeFrom, double rangeTo)

Apply min-max scaling to all outputs, except the ones individually configured

• param rangeFrom lower bound of the target range

• param rangeTo upper bound of the target range

• return the normalizer

standardizeOutput

public MultiNormalizerHybrid standardizeOutput(int output)

Apply standardization to a specific output, overriding the global output strategy if any

• param output the index of the input

• return the normalizer

minMaxScaleOutput

public MultiNormalizerHybrid minMaxScaleOutput(int output)

Apply min-max scaling to a specific output, overriding the global output strategy if any

• param output the index of the input

• return the normalizer

minMaxScaleOutput

public MultiNormalizerHybrid minMaxScaleOutput(int output, double rangeFrom, double rangeTo)

Apply min-max scaling to a specific output, overriding the global output strategy if any

• param output the index of the input

• param rangeFrom lower bound of the target range

• param rangeTo upper bound of the target range

• return the normalizer

getInputStats

public NormalizerStats getInputStats(int input)

Get normalization statistics for a given input.

• param input the index of the input

• return implementation of NormalizerStats corresponding to the normalization strategy selected

getOutputStats

public NormalizerStats getOutputStats(int output)

Get normalization statistics for a given output.

• param output the index of the output

• return implementation of NormalizerStats corresponding to the normalization strategy selected

fit

public void fit(@NonNull MultiDataSet dataSet)

Get the map of normalization statistics per input

• return map of input indices pointing to NormalizerStats instances

fit

public void fit(@NonNull MultiDataSetIterator iterator)