python/pyspark/ml/connect/functions.py

#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

from typing import Any, TYPE_CHECKING

from pyspark.ml import functions as PyMLFunctions
from pyspark.sql.column import Column


if TYPE_CHECKING:
    from pyspark.sql._typing import UserDefinedFunctionLike


def vector_to_array(col: Column, dtype: str = "float64") -> Column:
    from pyspark.sql.connect.functions.builtin import _invoke_function, _to_col, lit

    return _invoke_function("vector_to_array", _to_col(col), lit(dtype))


vector_to_array.__doc__ = PyMLFunctions.vector_to_array.__doc__


def array_to_vector(col: Column) -> Column:
    from pyspark.sql.connect.functions.builtin import _invoke_function, _to_col

    return _invoke_function("array_to_vector", _to_col(col))


array_to_vector.__doc__ = PyMLFunctions.array_to_vector.__doc__


def predict_batch_udf(*args: Any, **kwargs: Any) -> "UserDefinedFunctionLike":
    return PyMLFunctions.predict_batch_udf(*args, **kwargs)


predict_batch_udf.__doc__ = PyMLFunctions.predict_batch_udf.__doc__


def _test() -> None:
    import os
    import sys

    if os.environ.get("PYTHON_GIL", "?") == "0":
        print("Not supported in no-GIL mode", file=sys.stderr)
        sys.exit(0)

    from pyspark.testing.utils import should_test_connect

    if not should_test_connect:
        print("Skipping pyspark.ml.connect.functions doctests", file=sys.stderr)
        sys.exit(0)

    import doctest
    from pyspark.sql import SparkSession as PySparkSession
    import pyspark.ml.connect.functions

    globs = pyspark.ml.connect.functions.__dict__.copy()

    globs["spark"] = (
        PySparkSession.builder.appName("ml.connect.functions tests")
        .remote(os.environ.get("SPARK_CONNECT_TESTING_REMOTE", "local[4]"))
        .getOrCreate()
    )

    (failure_count, test_count) = doctest.testmod(
        pyspark.ml.connect.functions,
        globs=globs,
        optionflags=doctest.ELLIPSIS
        | doctest.NORMALIZE_WHITESPACE
        | doctest.IGNORE_EXCEPTION_DETAIL,
    )
    globs["spark"].stop()
    if failure_count:
        sys.exit(-1)


if __name__ == "__main__":
    _test()