HyukjinKwon commented on code in PR #39628:
URL: https://github.com/apache/spark/pull/39628#discussion_r1082211081
##########
python/pyspark/ml/functions.py:
##########
@@ -647,37 +386,369 @@ def predict_columnar(x1: np.ndarray, x2: np.ndarray) ->
Mapping[str, np.ndarray]
Function which is responsible for loading a model and returning a
:py:class:`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).
+
+ * 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 dimension arrays are not supported. For a row-oriented list of
dictionaries, each
+ 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_type : :class:`pspark.sql.types.DataType` or str.
+ return_type : :py:class:`pyspark.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](https://huggingface.co/docs/transformers/quicktour#pipeline-usage] #
noqa: E501
+
+ * 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
+
<https://huggingface.co/docs/transformers/quicktour#pipeline-usage>`_.
+
batch_size : int
- Batch size to use for inference, note that this is typically a
limitation of the model
- and/or the hardware resources and is usually smaller than the Spark
partition size.
- input_tensor_shapes: List[List[int] | None] | Mapping[int, List[int]] |
None
- Optional input tensor shapes for models with tensor inputs. This can
be a list of shapes,
+ 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_shapes : List[List[int] | None] | Mapping[int, List[int]] |
None, optional
+ 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
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.
+ `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
-------
- A pandas_udf for predicting a batch.
+ :py:class:`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
Review Comment:
If this example cannot be ran, should better do it with `.. code-block::
python`
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