VectorIndexer

This documentation is for an unreleased version of Apache Flink Machine Learning Library. We recommend you use the latest stable version.

VectorIndexer #

VectorIndexer is an algorithm that implements the vector indexing algorithm. A vector indexer maps each column of the input vector into a continuous/categorical feature. Whether one feature is transformed into a continuous or categorical feature depends on the number of distinct values in this column. If the number of distinct values in one column is greater than a specified parameter (i.e., maxCategories), the corresponding output column is unchanged. Otherwise, it is transformed into a categorical value. For categorical outputs, the indices are in [0, numDistinctValuesInThisColumn].

The output model is organized in ascending order except that 0.0 is always mapped to 0 (for sparsity).

Input Columns #

Param name	Type	Default	Description
inputCol	Vector	`"input"`	Vectors to be indexed.

Output Columns #

Param name	Type	Default	Description
outputCol	Vector	`"output"`	Indexed vectors.

Parameters #

Below are the parameters required by VectorIndexerModel.

Key	Default	Type	Required	Description
inputCol	`"input"`	String	no	Input column name.
outputCol	`"output"`	String	no	Output column name.
handleInvalid	`"error"`	String	no	Strategy to handle invalid entries. Supported values: `'error', 'skip', 'keep'`.

VectorIndexer needs parameters above and also below.

Key	Default	Type	Required	Description
maxCategories	`20`	Integer	no	Threshold for the number of values a categorical feature can take (>= 2). If a feature is found to have > maxCategories values, then it is declared continuous.

Examples #

Java

import org.apache.flink.ml.common.param.HasHandleInvalid;
import org.apache.flink.ml.feature.vectorindexer.VectorIndexer;
import org.apache.flink.ml.feature.vectorindexer.VectorIndexerModel;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;
import org.apache.flink.util.CloseableIterator;

import java.util.Arrays;
import java.util.List;

/** Simple program that creates a VectorIndexer instance and uses it for feature engineering. */
public class VectorIndexerExample {
	public static void main(String[] args) {
		StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
		StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);

		// Generates input data.
		List<Row> trainInput =
			Arrays.asList(
				Row.of(Vectors.dense(1, 1)),
				Row.of(Vectors.dense(2, -1)),
				Row.of(Vectors.dense(3, 1)),
				Row.of(Vectors.dense(4, 0)),
				Row.of(Vectors.dense(5, 0)));

		List<Row> predictInput =
			Arrays.asList(
				Row.of(Vectors.dense(0, 2)),
				Row.of(Vectors.dense(0, 0)),
				Row.of(Vectors.dense(0, -1)));

		Table trainTable = tEnv.fromDataStream(env.fromCollection(trainInput)).as("input");
		Table predictTable = tEnv.fromDataStream(env.fromCollection(predictInput)).as("input");

		// Creates a VectorIndexer object and initializes its parameters.
		VectorIndexer vectorIndexer =
			new VectorIndexer()
				.setInputCol("input")
				.setOutputCol("output")
				.setHandleInvalid(HasHandleInvalid.KEEP_INVALID)
				.setMaxCategories(3);

		// Trains the VectorIndexer Model.
		VectorIndexerModel model = vectorIndexer.fit(trainTable);

		// Uses the VectorIndexer Model for predictions.
		Table outputTable = model.transform(predictTable)[0];

		// Extracts and displays the results.
		for (CloseableIterator<Row> it = outputTable.execute().collect(); it.hasNext(); ) {
			Row row = it.next();
			System.out.printf(
				"Input Value: %s \tOutput Value: %s\n",
				row.getField(vectorIndexer.getInputCol()),
				row.getField(vectorIndexer.getOutputCol()));
		}
	}
}

Python

# Simple program that trains a VectorIndexer model and uses it for feature
# engineering.

from pyflink.common import Types
from pyflink.ml.linalg import Vectors, DenseVectorTypeInfo
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.feature.vectorindexer import VectorIndexer
from pyflink.table import StreamTableEnvironment

# Creates a new StreamExecutionEnvironment.
env = StreamExecutionEnvironment.get_execution_environment()

# Creates a StreamTableEnvironment.
t_env = StreamTableEnvironment.create(env)

# Generates input training and prediction data.
train_table = t_env.from_data_stream(
    env.from_collection([
        (Vectors.dense(1, 1),),
        (Vectors.dense(2, -1),),
        (Vectors.dense(3, 1),),
        (Vectors.dense(4, 0),),
        (Vectors.dense(5, 0),)
    ],
        type_info=Types.ROW_NAMED(
            ['input', ],
            [DenseVectorTypeInfo(), ])))

predict_table = t_env.from_data_stream(
    env.from_collection([
        (Vectors.dense(0, 2),),
        (Vectors.dense(0, 0),),
        (Vectors.dense(0, -1),),
    ],
        type_info=Types.ROW_NAMED(
            ['input', ],
            [DenseVectorTypeInfo(), ])))

# Creates a VectorIndexer object and initializes its parameters.
vector_indexer = VectorIndexer() \
    .set_input_col('input') \
    .set_output_col('output') \
    .set_handle_invalid('keep') \
    .set_max_categories(3)

# Trains the VectorIndexer Model.
model = vector_indexer.fit(train_table)

# Uses the VectorIndexer Model for predictions.
output = model.transform(predict_table)[0]

# Extracts and displays the results.
field_names = output.get_schema().get_field_names()
for result in t_env.to_data_stream(output).execute_and_collect():
    print('Input Value: ' + str(result[field_names.index(vector_indexer.get_input_col())])
          + '\tOutput Value: ' + str(result[field_names.index(vector_indexer.get_output_col())]))