pyspark.ml.functions.predict_batch_udf

pyspark.ml.functions.predict_batch_udf(make_predict_fn: Callable[], PredictBatchFunction], *, return_type: DataType, batch_size: int, input_tensor_shapes: Optional[Union[List[Optional[List[int]]], Mapping[int, List[int]]]] = None) → UserDefinedFunctionLike

Given a function which loads a model and returns a predict function for inference over a batch of numpy inputs, returns a Pandas UDF wrapper for inference over a Spark DataFrame.

The returned Pandas UDF does the following on each DataFrame partition:

• calls the make_predict_fn to load the model and cache its predict function.

• batches the input records as numpy arrays and invokes predict on each batch.

Note: this assumes that the make_predict_fn encapsulates all of the necessary dependencies for running the model, or the Spark executor environment already satisfies all runtime requirements.

For the conversion of the Spark DataFrame to numpy arrays, there is a one-to-one mapping between the input arguments of the predict function (returned by the make_predict_fn) and the input columns sent to the Pandas UDF (returned by the predict_batch_udf) at runtime. Each input column will be converted as follows:

• scalar column -> 1-dim np.ndarray

• tensor column + tensor shape -> N-dim np.ndarray

Note that any tensor columns in the Spark DataFrame must be represented as a flattened one-dimensional array, and multiple scalar columns can be combined into a single tensor column using the standard pyspark.sql.functions.array() function.

New in version 3.4.0.

Parameters

make_predict_fncallable

Function which is responsible for loading a model and returning a PredictBatchFunction which takes one or more numpy arrays as input and returns one of the following:

• a numpy array (for a single output)

• a dictionary of named numpy arrays (for multiple outputs)

• a row-oriented list of dictionaries (for multiple outputs).

For a dictionary of named numpy arrays, the arrays can only be one or two dimensional, since higher dimensional arrays are not supported. For a row-oriented list of dictionaries, each element in the dictionary must be either a scalar or one-dimensional array.

return_typepyspark.sql.types.DataType or str.

Spark SQL datatype for the expected output:

• Scalar (e.g. IntegerType, FloatType) –> 1-dim numpy array.

• ArrayType –> 2-dim numpy array.

• StructType –> dict with keys matching struct fields.

• StructType –> list of dict with keys matching struct fields, for models like the Huggingface pipeline for sentiment analysis.

batch_sizeint

Batch size to use for inference. This is typically a limitation of the model and/or available hardware resources and is usually smaller than the Spark partition size.

input_tensor_shapeslist, dict, optional.

A list of ints or a dictionary of ints (key) and list of ints (value). Input tensor shapes for models with tensor inputs. This can be a list of shapes, where each shape is a list of integers or None (for scalar inputs). Alternatively, this can be represented by a “sparse” dictionary, where the keys are the integer indices of the inputs, and the values are the shapes. Each tensor input value in the Spark DataFrame must be represented as a single column containing a flattened 1-D array. The provided input_tensor_shapes will be used to reshape the flattened array into the expected tensor shape. For the list form, the order of the tensor shapes must match the order of the selected DataFrame columns. The batch dimension (typically -1 or None in the first dimension) should not be included, since it will be determined by the batch_size argument. Tabular datasets with scalar-valued columns should not provide this argument.

Returns

UserDefinedFunctionLike

A Pandas UDF for model inference on a Spark DataFrame.

Examples

For a pre-trained TensorFlow MNIST model with two-dimensional input images represented as a flattened tensor value stored in a single Spark DataFrame column of type array<float>.

from pyspark.ml.functions import predict_batch_udf

def make_mnist_fn():
    # load/init happens once per python worker
    import tensorflow as tf
    model = tf.keras.models.load_model('/path/to/mnist_model')

    # predict on batches of tasks/partitions, using cached model
    def predict(inputs: np.ndarray) -> np.ndarray:
        # inputs.shape = [batch_size, 784], see input_tensor_shapes
        # outputs.shape = [batch_size, 10], see return_type
        return model.predict(inputs)

    return predict

mnist_udf = predict_batch_udf(make_mnist_fn,
                              return_type=ArrayType(FloatType()),
                              batch_size=100,
                              input_tensor_shapes=[[784]])

df = spark.read.parquet("/path/to/mnist_data")
df.show(5)
# +--------------------+
# |                data|
# +--------------------+
# |[0.0, 0.0, 0.0, 0...|
# |[0.0, 0.0, 0.0, 0...|
# |[0.0, 0.0, 0.0, 0...|
# |[0.0, 0.0, 0.0, 0...|
# |[0.0, 0.0, 0.0, 0...|
# +--------------------+

df.withColumn("preds", mnist_udf("data")).show(5)
# +--------------------+--------------------+
# |                data|               preds|
# +--------------------+--------------------+
# |[0.0, 0.0, 0.0, 0...|[-13.511008, 8.84...|
# |[0.0, 0.0, 0.0, 0...|[-5.3957458, -2.2...|
# |[0.0, 0.0, 0.0, 0...|[-7.2014456, -8.8...|
# |[0.0, 0.0, 0.0, 0...|[-19.466187, -13....|
# |[0.0, 0.0, 0.0, 0...|[-5.7757926, -7.8...|
# +--------------------+--------------------+

To demonstrate usage with different combinations of input and output types, the following examples just use simple mathematical transforms as the models.

• Single scalar column

  Input DataFrame has a single scalar column, which will be passed to the predict function as a 1-D numpy array.

  >>> import numpy as np
  >>> import pandas as pd
  >>> from pyspark.ml.functions import predict_batch_udf
  >>> from pyspark.sql.types import FloatType
  >>>
  >>> df = spark.createDataFrame(pd.DataFrame(np.arange(100)))
  >>> df.show(5)
  +---+
  |  0|
  +---+
  |  0|
  |  1|
  |  2|
  |  3|
  |  4|
  +---+
  only showing top 5 rows
  

  >>> def make_times_two_fn():
  ...     def predict(inputs: np.ndarray) -> np.ndarray:
  ...         # inputs.shape = [batch_size]
  ...         # outputs.shape = [batch_size]
  ...         return inputs * 2
  ...     return predict
  >>>
  >>> times_two_udf = predict_batch_udf(make_times_two_fn,
  ...                                   return_type=FloatType(),
  ...                                   batch_size=10)
  >>>
  >>> df = spark.createDataFrame(pd.DataFrame(np.arange(100)))
  >>> df.withColumn("x2", times_two_udf("0")).show(5)
  +---+---+
  |  0| x2|
  +---+---+
  |  0|0.0|
  |  1|2.0|
  |  2|4.0|
  |  3|6.0|
  |  4|8.0|
  +---+---+
  only showing top 5 rows
  

• Multiple scalar columns

  Input DataFrame has muliple columns of scalar values. If the user-provided predict function expects a single input, then the user must combine the multiple columns into a single tensor using pyspark.sql.functions.array.

  >>> import numpy as np
  >>> import pandas as pd
  >>> from pyspark.ml.functions import predict_batch_udf
  >>> from pyspark.sql.functions import array
  >>>
  >>> data = np.arange(0, 1000, dtype=np.float64).reshape(-1, 4)
  >>> pdf = pd.DataFrame(data, columns=['a','b','c','d'])
  >>> df = spark.createDataFrame(pdf)
  >>> df.show(5)
  +----+----+----+----+
  |   a|   b|   c|   d|
  +----+----+----+----+
  | 0.0| 1.0| 2.0| 3.0|
  | 4.0| 5.0| 6.0| 7.0|
  | 8.0| 9.0|10.0|11.0|
  |12.0|13.0|14.0|15.0|
  |16.0|17.0|18.0|19.0|
  +----+----+----+----+
  only showing top 5 rows
  

  >>> def make_sum_fn():
  ...     def predict(inputs: np.ndarray) -> np.ndarray:
  ...         # inputs.shape = [batch_size, 4]
  ...         # outputs.shape = [batch_size]
  ...         return np.sum(inputs, axis=1)
  ...     return predict
  >>>
  >>> sum_udf = predict_batch_udf(make_sum_fn,
  ...                             return_type=FloatType(),
  ...                             batch_size=10,
  ...                             input_tensor_shapes=[[4]])
  >>>
  >>> df.withColumn("sum", sum_udf(array("a", "b", "c", "d"))).show(5)
  +----+----+----+----+----+
  |   a|   b|   c|   d| sum|
  +----+----+----+----+----+
  | 0.0| 1.0| 2.0| 3.0| 6.0|
  | 4.0| 5.0| 6.0| 7.0|22.0|
  | 8.0| 9.0|10.0|11.0|38.0|
  |12.0|13.0|14.0|15.0|54.0|
  |16.0|17.0|18.0|19.0|70.0|
  +----+----+----+----+----+
  only showing top 5 rows
  

  If the predict function expects multiple inputs, then the number of selected input columns must match the number of expected inputs.

  >>> def make_sum_fn():
  ...     def predict(x1: np.ndarray,
  ...                 x2: np.ndarray,
  ...                 x3: np.ndarray,
  ...                 x4: np.ndarray) -> np.ndarray:
  ...         # xN.shape = [batch_size]
  ...         # outputs.shape = [batch_size]
  ...         return x1 + x2 + x3 + x4
  ...     return predict
  >>>
  >>> sum_udf = predict_batch_udf(make_sum_fn,
  ...                             return_type=FloatType(),
  ...                             batch_size=10)
  >>>
  >>> df.withColumn("sum", sum_udf("a", "b", "c", "d")).show(5)
  +----+----+----+----+----+
  |   a|   b|   c|   d| sum|
  +----+----+----+----+----+
  | 0.0| 1.0| 2.0| 3.0| 6.0|
  | 4.0| 5.0| 6.0| 7.0|22.0|
  | 8.0| 9.0|10.0|11.0|38.0|
  |12.0|13.0|14.0|15.0|54.0|
  |16.0|17.0|18.0|19.0|70.0|
  +----+----+----+----+----+
  only showing top 5 rows
  

• Multiple tensor columns

  Input DataFrame has multiple columns, where each column is a tensor. The number of columns should match the number of expected inputs for the user-provided predict function.

  >>> import numpy as np
  >>> import pandas as pd
  >>> from pyspark.ml.functions import predict_batch_udf
  >>> from pyspark.sql.types import ArrayType, FloatType, StructType, StructField
  >>> from typing import Mapping
  >>>
  >>> data = np.arange(0, 1000, dtype=np.float64).reshape(-1, 4)
  >>> pdf = pd.DataFrame(data, columns=['a','b','c','d'])
  >>> pdf_tensor = pd.DataFrame()
  >>> pdf_tensor['t1'] = pdf.values.tolist()
  >>> pdf_tensor['t2'] = pdf.drop(columns='d').values.tolist()
  >>> df = spark.createDataFrame(pdf_tensor)
  >>> df.show(5)
  +--------------------+------------------+
  |                  t1|                t2|
  +--------------------+------------------+
  |[0.0, 1.0, 2.0, 3.0]|   [0.0, 1.0, 2.0]|
  |[4.0, 5.0, 6.0, 7.0]|   [4.0, 5.0, 6.0]|
  |[8.0, 9.0, 10.0, ...|  [8.0, 9.0, 10.0]|
  |[12.0, 13.0, 14.0...|[12.0, 13.0, 14.0]|
  |[16.0, 17.0, 18.0...|[16.0, 17.0, 18.0]|
  +--------------------+------------------+
  only showing top 5 rows
  

  >>> def make_multi_sum_fn():
  ...     def predict(x1: np.ndarray, x2: np.ndarray) -> np.ndarray:
  ...         # x1.shape = [batch_size, 4]
  ...         # x2.shape = [batch_size, 3]
  ...         # outputs.shape = [batch_size]
  ...         return np.sum(x1, axis=1) + np.sum(x2, axis=1)
  ...     return predict
  >>>
  >>> multi_sum_udf = predict_batch_udf(
  ...     make_multi_sum_fn,
  ...     return_type=FloatType(),
  ...     batch_size=5,
  ...     input_tensor_shapes=[[4], [3]],
  ... )
  >>>
  >>> df.withColumn("sum", multi_sum_udf("t1", "t2")).show(5)
  +--------------------+------------------+-----+
  |                  t1|                t2|  sum|
  +--------------------+------------------+-----+
  |[0.0, 1.0, 2.0, 3.0]|   [0.0, 1.0, 2.0]|  9.0|
  |[4.0, 5.0, 6.0, 7.0]|   [4.0, 5.0, 6.0]| 37.0|
  |[8.0, 9.0, 10.0, ...|  [8.0, 9.0, 10.0]| 65.0|
  |[12.0, 13.0, 14.0...|[12.0, 13.0, 14.0]| 93.0|
  |[16.0, 17.0, 18.0...|[16.0, 17.0, 18.0]|121.0|
  +--------------------+------------------+-----+
  only showing top 5 rows
  

• Multiple outputs

  Some models can provide multiple outputs. These can be returned as a dictionary of named values, which can be represented in either columnar or row-based formats.

  >>> def make_multi_sum_fn():
  ...     def predict_columnar(x1: np.ndarray, x2: np.ndarray) -> Mapping[str, np.ndarray]:
  ...         # x1.shape = [batch_size, 4]
  ...         # x2.shape = [batch_size, 3]
  ...         return {
  ...             "sum1": np.sum(x1, axis=1),
  ...             "sum2": np.sum(x2, axis=1)
  ...         }
  ...     return predict_columnar
  >>>
  >>> multi_sum_udf = predict_batch_udf(
  ...     make_multi_sum_fn,
  ...     return_type=StructType([
  ...         StructField("sum1", FloatType(), True),
  ...         StructField("sum2", FloatType(), True)
  ...     ]),
  ...     batch_size=5,
  ...     input_tensor_shapes=[[4], [3]],
  ... )
  >>>
  >>> df.withColumn("preds", multi_sum_udf("t1", "t2")).select("t1", "t2", "preds.*").show(5)
  +--------------------+------------------+----+----+
  |                  t1|                t2|sum1|sum2|
  +--------------------+------------------+----+----+
  |[0.0, 1.0, 2.0, 3.0]|   [0.0, 1.0, 2.0]| 6.0| 3.0|
  |[4.0, 5.0, 6.0, 7.0]|   [4.0, 5.0, 6.0]|22.0|15.0|
  |[8.0, 9.0, 10.0, ...|  [8.0, 9.0, 10.0]|38.0|27.0|
  |[12.0, 13.0, 14.0...|[12.0, 13.0, 14.0]|54.0|39.0|
  |[16.0, 17.0, 18.0...|[16.0, 17.0, 18.0]|70.0|51.0|
  +--------------------+------------------+----+----+
  only showing top 5 rows
  

  >>> def make_multi_sum_fn():
  ...     def predict_row(x1: np.ndarray, x2: np.ndarray) -> list[Mapping[str, float]]:
  ...         # x1.shape = [batch_size, 4]
  ...         # x2.shape = [batch_size, 3]
  ...         return [{'sum1': np.sum(x1[i]), 'sum2': np.sum(x2[i])} for i in range(len(x1))]
  ...     return predict_row
  >>>
  >>> multi_sum_udf = predict_batch_udf(
  ...     make_multi_sum_fn,
  ...     return_type=StructType([
  ...         StructField("sum1", FloatType(), True),
  ...         StructField("sum2", FloatType(), True)
  ...     ]),
  ...     batch_size=5,
  ...     input_tensor_shapes=[[4], [3]],
  ... )
  >>>
  >>> df.withColumn("sum", multi_sum_udf("t1", "t2")).select("t1", "t2", "sum.*").show(5)
  +--------------------+------------------+----+----+
  |                  t1|                t2|sum1|sum2|
  +--------------------+------------------+----+----+
  |[0.0, 1.0, 2.0, 3.0]|   [0.0, 1.0, 2.0]| 6.0| 3.0|
  |[4.0, 5.0, 6.0, 7.0]|   [4.0, 5.0, 6.0]|22.0|15.0|
  |[8.0, 9.0, 10.0, ...|  [8.0, 9.0, 10.0]|38.0|27.0|
  |[12.0, 13.0, 14.0...|[12.0, 13.0, 14.0]|54.0|39.0|
  |[16.0, 17.0, 18.0...|[16.0, 17.0, 18.0]|70.0|51.0|
  +--------------------+------------------+----+----+
  only showing top 5 rows
  

  Note that the multiple outputs can be arrays as well.

  >>> def make_multi_times_two_fn():
  ...     def predict(x1: np.ndarray, x2: np.ndarray) -> Mapping[str, np.ndarray]:
  ...         # x1.shape = [batch_size, 4]
  ...         # x2.shape = [batch_size, 3]
  ...         return {"t1x2": x1 * 2, "t2x2": x2 * 2}
  ...     return predict
  >>>
  >>> multi_times_two_udf = predict_batch_udf(
  ...     make_multi_times_two_fn,
  ...     return_type=StructType([
  ...         StructField("t1x2", ArrayType(FloatType()), True),
  ...         StructField("t2x2", ArrayType(FloatType()), True)
  ...     ]),
  ...     batch_size=5,
  ...     input_tensor_shapes=[[4], [3]],
  ... )
  >>>
  >>> df.withColumn("x2", multi_times_two_udf("t1", "t2")).select("t1", "t2", "x2.*").show(5)
  +--------------------+------------------+--------------------+------------------+
  |                  t1|                t2|                t1x2|              t2x2|
  +--------------------+------------------+--------------------+------------------+
  |[0.0, 1.0, 2.0, 3.0]|   [0.0, 1.0, 2.0]|[0.0, 2.0, 4.0, 6.0]|   [0.0, 2.0, 4.0]|
  |[4.0, 5.0, 6.0, 7.0]|   [4.0, 5.0, 6.0]|[8.0, 10.0, 12.0,...| [8.0, 10.0, 12.0]|
  |[8.0, 9.0, 10.0, ...|  [8.0, 9.0, 10.0]|[16.0, 18.0, 20.0...|[16.0, 18.0, 20.0]|
  |[12.0, 13.0, 14.0...|[12.0, 13.0, 14.0]|[24.0, 26.0, 28.0...|[24.0, 26.0, 28.0]|
  |[16.0, 17.0, 18.0...|[16.0, 17.0, 18.0]|[32.0, 34.0, 36.0...|[32.0, 34.0, 36.0]|
  +--------------------+------------------+--------------------+------------------+
  only showing top 5 rows
  

pyspark.ml.functions.vector_to_array Vector