flink-ml-python/pyflink/examples/ml/classification/knn_example.py

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# Simple program that trains a Knn model and uses it for classification.

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.linalg import Vectors, DenseVectorTypeInfo
from pyflink.ml.classification.knn import KNN
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_data = t_env.from_data_stream(
    env.from_collection([
        (Vectors.dense([2.0, 3.0]), 1.0),
        (Vectors.dense([2.1, 3.1]), 1.0),
        (Vectors.dense([200.1, 300.1]), 2.0),
        (Vectors.dense([200.2, 300.2]), 2.0),
        (Vectors.dense([200.3, 300.3]), 2.0),
        (Vectors.dense([200.4, 300.4]), 2.0),
        (Vectors.dense([200.4, 300.4]), 2.0),
        (Vectors.dense([200.6, 300.6]), 2.0),
        (Vectors.dense([2.1, 3.1]), 1.0),
        (Vectors.dense([2.1, 3.1]), 1.0),
        (Vectors.dense([2.1, 3.1]), 1.0),
        (Vectors.dense([2.1, 3.1]), 1.0),
        (Vectors.dense([2.3, 3.2]), 1.0),
        (Vectors.dense([2.3, 3.2]), 1.0),
        (Vectors.dense([2.8, 3.2]), 3.0),
        (Vectors.dense([300., 3.2]), 4.0),
        (Vectors.dense([2.2, 3.2]), 1.0),
        (Vectors.dense([2.4, 3.2]), 5.0),
        (Vectors.dense([2.5, 3.2]), 5.0),
        (Vectors.dense([2.5, 3.2]), 5.0),
        (Vectors.dense([2.1, 3.1]), 1.0)
    ],
        type_info=Types.ROW_NAMED(
            ['features', 'label'],
            [DenseVectorTypeInfo(), Types.DOUBLE()])))

predict_data = t_env.from_data_stream(
    env.from_collection([
        (Vectors.dense([4.0, 4.1]), 5.0),
        (Vectors.dense([300, 42]), 2.0),
    ],
        type_info=Types.ROW_NAMED(
            ['features', 'label'],
            [DenseVectorTypeInfo(), Types.DOUBLE()])))

# create a knn object and initialize its parameters
knn = KNN().set_k(4)

# train the knn model
model = knn.fit(train_data)

# use the knn model for predictions
output = model.transform(predict_data)[0]

# extract and display the results
field_names = output.get_schema().get_field_names()
for result in t_env.to_data_stream(output).execute_and_collect():
    features = result[field_names.index(knn.get_features_col())]
    expected_result = result[field_names.index(knn.get_label_col())]
    actual_result = result[field_names.index(knn.get_prediction_col())]
    print('Features: ' + str(features) + ' \tExpected Result: ' + str(expected_result)
          + ' \tActual Result: ' + str(actual_result))