flink-ml-python/pyflink/examples/ml/feature/vectorindexer_example.py

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# 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())]))