String Indexer

String Indexer #

String Indexer maps one or more columns (string/numerical value) of the input to one or more indexed output columns (integer value). The output indices of two data points are the same iff their corresponding input columns are the same. The indices are in [0, numDistinctValuesInThisColumn].

IndexToStringModel transforms input index column(s) to string column(s) using the model data computed by StringIndexer. It is a reverse operation of StringIndexerModel.

Input Columns #

Param name	Type	Default	Description
inputCols	Number/String	`null`	string/numerical values to be indexed.

Output Columns #

Param name	Type	Default	Description
outputCols	Double	`null`	Indices of string/numerical values.

Parameters #

Below are the parameters required by StringIndexerModel.

Key	Default	Type	Required	Description
inputCols	`null`	String	yes	Input column names.
outputCols	`null`	String	yes	Output column names.
handleInvalid	`HasHandleInvalid.ERROR_INVALID`	String	No	Strategy to handle invalid entries.

StringIndexer needs parameters above and also below.

Key	Default	Type	Required	Description
stringOrderType	`StringIndexerParams.ARBITRARY_ORDER`	String	no	How to order strings of each column.

Examples #

Java

import org.apache.flink.ml.feature.stringindexer.StringIndexer;
import org.apache.flink.ml.feature.stringindexer.StringIndexerModel;
import org.apache.flink.ml.feature.stringindexer.StringIndexerParams;
import org.apache.flink.streaming.api.datastream.DataStream;
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;

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

        // Generates input training and prediction data.
        DataStream<Row> trainStream =
                env.fromElements(
                        Row.of("a", 1.0),
                        Row.of("b", 1.0),
                        Row.of("b", 2.0),
                        Row.of("c", 0.0),
                        Row.of("d", 2.0),
                        Row.of("a", 2.0),
                        Row.of("b", 2.0),
                        Row.of("b", -1.0),
                        Row.of("a", -1.0),
                        Row.of("c", -1.0));
        Table trainTable = tEnv.fromDataStream(trainStream).as("inputCol1", "inputCol2");

        DataStream<Row> predictStream =
                env.fromElements(Row.of("a", 2.0), Row.of("b", 1.0), Row.of("c", 2.0));
        Table predictTable = tEnv.fromDataStream(predictStream).as("inputCol1", "inputCol2");

        // Creates a StringIndexer object and initializes its parameters.
        StringIndexer stringIndexer =
                new StringIndexer()
                        .setStringOrderType(StringIndexerParams.ALPHABET_ASC_ORDER)
                        .setInputCols("inputCol1", "inputCol2")
                        .setOutputCols("outputCol1", "outputCol2");

        // Trains the StringIndexer Model.
        StringIndexerModel model = stringIndexer.fit(trainTable);

        // Uses the StringIndexer 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();

            Object[] inputValues = new Object[stringIndexer.getInputCols().length];
            double[] outputValues = new double[stringIndexer.getInputCols().length];
            for (int i = 0; i < inputValues.length; i++) {
                inputValues[i] = row.getField(stringIndexer.getInputCols()[i]);
                outputValues[i] = (double) row.getField(stringIndexer.getOutputCols()[i]);
            }

            System.out.printf(
                    "Input Values: %s \tOutput Values: %s\n",
                    Arrays.toString(inputValues), Arrays.toString(outputValues));
        }
    }
}

Python

# Simple program that trains a StringIndexer model and uses it for feature
# engineering.
#
# Before executing this program, please make sure you have followed Flink ML's
# quick start guideline to set up Flink ML and Flink environment. The guideline
# can be found at
#
# https://nightlies.apache.org/flink/flink-ml-docs-master/docs/try-flink-ml/quick-start/

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.lib.feature.stringindexer import StringIndexer
from pyflink.table import StreamTableEnvironment

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

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

# generate input training and prediction data
train_table = t_env.from_data_stream(
    env.from_collection([
        ('a', 1.),
        ('b', 1.),
        ('b', 2.),
        ('c', 0.),
        ('d', 2.),
        ('a', 2.),
        ('b', 2.),
        ('b', -1.),
        ('a', -1.),
        ('c', -1.),
    ],
        type_info=Types.ROW_NAMED(
            ['input_col1', 'input_col2'],
            [Types.STRING(), Types.DOUBLE()])
    ))

predict_table = t_env.from_data_stream(
    env.from_collection([
        ('a', 2.),
        ('b', 1.),
        ('c', 2.),
    ],
        type_info=Types.ROW_NAMED(
            ['input_col1', 'input_col2'],
            [Types.STRING(), Types.DOUBLE()])
    ))

# create a string-indexer object and initialize its parameters
string_indexer = StringIndexer() \
    .set_string_order_type('alphabetAsc') \
    .set_input_cols('input_col1', 'input_col2') \
    .set_output_cols('output_col1', 'output_col2')

# train the string-indexer model
model = string_indexer.fit(train_table)

# use the string-indexer model for feature engineering
output = model.transform(predict_table)[0]

# extract and display the results
field_names = output.get_schema().get_field_names()
input_values = [None for _ in string_indexer.get_input_cols()]
output_values = [None for _ in string_indexer.get_input_cols()]
for result in t_env.to_data_stream(output).execute_and_collect():
    for i in range(len(string_indexer.get_input_cols())):
        input_values[i] = result[field_names.index(string_indexer.get_input_cols()[i])]
        output_values[i] = result[field_names.index(string_indexer.get_output_cols()[i])]
    print('Input Values: ' + str(input_values) + '\tOutput Values: ' + str(output_values))

Index To String #

IndexToStringModel transforms input index column(s) to string column(s) using the model data computed by StringIndexer. It is a reverse operation of StringIndexerModel.