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

Feature Hasher #

Feature Hasher transforms a set of categorical or numerical features into a sparse vector of a specified dimension. The rules of hashing categorical columns and numerical columns are as follows:

  • For numerical columns, the index of this feature in the output vector is the hash value of the column name and its correponding value is the same as the input.
  • For categorical columns, the index of this feature in the output vector is the hash value of the string "column_name=value" and the corresponding value is 1.0.

If multiple features are projected into the same column, the output values are accumulated. For the hashing trick, see https://en.wikipedia.org/wiki/Feature_hashing for details.

Input Columns #

Param name Type Default Description
inputCols Number/String/Boolean null Columns to be hashed.

Output Columns #

Param name Type Default Description
outputCol Vector "output" Output vector.

Parameters #

Key Default Type Required Description
inputCols null String[] yes Input column names.
outputCol "output" String no Output column name.
categoricalCols [] String[] no Categorical column names.
numFeatures 262144 Integer no The number of features.

Examples #

import org.apache.flink.ml.feature.featurehasher.FeatureHasher;
import org.apache.flink.ml.linalg.Vector;
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 creates a FeatureHasher instance and uses it for feature engineering. */
public class FeatureHasherExample {
    public static void main(String[] args) {

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);

        // Generates input data.
        DataStream<Row> dataStream =
                env.fromCollection(
                        Arrays.asList(Row.of(0, "a", 1.0, true), Row.of(1, "c", 1.0, false)));
        Table inputDataTable = tEnv.fromDataStream(dataStream).as("id", "f0", "f1", "f2");

        // Creates a FeatureHasher object and initializes its parameters.
        FeatureHasher featureHash =
                new FeatureHasher()
                        .setInputCols("f0", "f1", "f2")
                        .setCategoricalCols("f0", "f2")
                        .setOutputCol("vec")
                        .setNumFeatures(1000);

        // Uses the FeatureHasher object for feature transformations.
        Table outputTable = featureHash.transform(inputDataTable)[0];

        // Extracts and displays the results.
        for (CloseableIterator<Row> it = outputTable.execute().collect(); it.hasNext(); ) {
            Row row = it.next();

            Object[] inputValues = new Object[featureHash.getInputCols().length];
            for (int i = 0; i < inputValues.length; i++) {
                inputValues[i] = row.getField(featureHash.getInputCols()[i]);
            }
            Vector outputValue = (Vector) row.getField(featureHash.getOutputCol());

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

# Simple program that creates a FeatureHasher instance and uses it for feature
# engineering.

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.lib.feature.featurehasher import FeatureHasher
from pyflink.table import StreamTableEnvironment

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

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

# generate input data
input_data_table = t_env.from_data_stream(
    env.from_collection([
        (0, 'a', 1.0, True),
        (1, 'c', 1.0, False),
    ],
        type_info=Types.ROW_NAMED(
            ['id', 'f0', 'f1', 'f2'],
            [Types.INT(), Types.STRING(), Types.DOUBLE(), Types.BOOLEAN()])))

# create a feature hasher object and initialize its parameters
feature_hasher = FeatureHasher() \
    .set_input_cols('f0', 'f1', 'f2') \
    .set_categorical_cols('f0', 'f2') \
    .set_output_col('vec') \
    .set_num_features(1000)

# use the feature hasher for feature engineering
output = feature_hasher.transform(input_data_table)[0]

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