WeichenXu123 commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r1020332942
##########
python/pyspark/ml/functions.py:
##########
@@ -106,6 +117,601 @@ def array_to_vector(col: Column) -> Column:
return
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
+def _batched(
+ data: pd.Series | pd.DataFrame | Tuple[pd.Series], batch_size: int
+) -> Iterator[pd.DataFrame]:
+ """Generator that splits a pandas dataframe/series into batches."""
+ if isinstance(data, pd.DataFrame):
+ df = data
+ elif isinstance(data, pd.Series):
+ df = pd.concat((data,), axis=1)
+ else: # isinstance(data, Tuple[pd.Series]):
+ df = pd.concat(data, axis=1)
+
+ index = 0
+ data_size = len(df)
+ while index < data_size:
+ yield df.iloc[index : index + batch_size]
+ index += batch_size
+
+
+def _is_tensor_col(data: pd.Series | pd.DataFrame) -> bool:
+ if isinstance(data, pd.Series):
+ return data.dtype == np.object_ and isinstance(data.iloc[0],
(np.ndarray, list))
+ elif isinstance(data, pd.DataFrame):
+ return any(data.dtypes == np.object_) and any(
+ [isinstance(d, (np.ndarray, list)) for d in data.iloc[0]]
+ )
+ else:
+ raise ValueError(
+ "Unexpected data type: {}, expected pd.Series or
pd.DataFrame.".format(type(data))
+ )
+
+
+def _has_tensor_cols(data: pd.Series | pd.DataFrame | Tuple[pd.Series]) ->
bool:
+ """Check if input Series/DataFrame/Tuple contains any tensor-valued
columns."""
+ if isinstance(data, (pd.Series, pd.DataFrame)):
+ return _is_tensor_col(data)
+ else: # isinstance(data, Tuple):
+ return any(_is_tensor_col(elem) for elem in data)
+
+
+def _validate_and_transform_multiple_inputs(
+ batch: pd.DataFrame, input_shapes: List[List[int] | None], num_input_cols:
int
+) -> List[np.ndarray]:
+ multi_inputs = [batch[col].to_numpy() for col in batch.columns]
+ if input_shapes:
+ if len(input_shapes) == num_input_cols:
+ multi_inputs = [
+ np.vstack(v).reshape([-1] + input_shapes[i]) # type: ignore
+ if input_shapes[i]
+ else v
+ for i, v in enumerate(multi_inputs)
+ ]
+ if not all([len(x) == len(batch) for x in multi_inputs]):
+ raise ValueError("Input data does not match expected shape.")
+ else:
+ raise ValueError("input_tensor_shapes must match columns")
+
+ return multi_inputs
+
+
+def _validate_and_transform_single_input(
+ batch: pd.DataFrame,
+ input_shapes: List[List[int] | None],
+ has_tensors: bool,
+ has_tuple: bool,
+) -> np.ndarray:
+ # multiple input columns for single expected input
+ if has_tensors:
+ # tensor columns
+ if len(batch.columns) == 1:
+ # one tensor column and one expected input, vstack rows
+ single_input = np.vstack(batch.iloc[:, 0])
+ else:
+ raise ValueError(
+ "Multiple input columns found, but model expected a single "
+ "input, use `struct` or `array` to combine columns into
tensors."
+ )
+ else:
+ # scalar columns
+ if len(batch.columns) == 1:
+ # single scalar column, remove extra dim
+ single_input = np.squeeze(batch.to_numpy())
+ if input_shapes and input_shapes[0] not in [None, [], [1]]:
+ raise ValueError("Invalid input_tensor_shape for scalar
column.")
+ elif not has_tuple:
+ # columns grouped via struct/array, convert to single tensor
+ single_input = batch.to_numpy()
+ if input_shapes and input_shapes[0] != [len(batch.columns)]:
+ raise ValueError("Input data does not match expected shape.")
+ else:
+ raise ValueError(
+ "Multiple input columns found, but model expected a single "
+ "input, use `struct` or `array` to combine columns into
tensors."
+ )
+
+ # if input_tensor_shapes provided, try to reshape input
+ if input_shapes:
+ if len(input_shapes) == 1:
+ single_input = single_input.reshape([-1] + input_shapes[0]) #
type: ignore
+ if len(single_input) != len(batch):
+ raise ValueError("Input data does not match expected shape.")
+ else:
+ raise ValueError("Multiple input_tensor_shapes found, but model
expected one input")
+
+ return single_input
+
+
+def _validate_and_transform_prediction_result(
+ preds: np.ndarray | Mapping[str, np.ndarray] | List[Mapping[str, Any]],
+ num_input_rows: int,
+ return_type: DataType,
+) -> pd.DataFrame | pd.Series:
+ """Validate numpy-based model predictions against the expected pandas_udf
return_type and
+ transforms the predictions into an equivalent pandas DataFrame or
Series."""
+ if isinstance(return_type, StructType):
+ struct_rtype: StructType = return_type
+ fieldNames = struct_rtype.names
+ if isinstance(preds, dict):
+ # dictionary of columns
+ predNames = list(preds.keys())
+ for field in struct_rtype.fields:
+ if isinstance(field.dataType, ArrayType):
+ if len(preds[field.name].shape) == 2:
+ preds[field.name] = list(preds[field.name])
+ else:
+ raise ValueError(
+ "Prediction results for ArrayType must be
two-dimensional."
+ )
+ else:
+ if len(preds[field.name].shape) != 1:
+ raise ValueError(
+ "Prediction results for scalar types must be
one-dimensional."
+ )
+ if len(preds[field.name]) != num_input_rows:
+ raise ValueError("Prediction results must have same length
as input data")
+
+ elif isinstance(preds, list) and isinstance(preds[0], dict):
+ # rows of dictionaries
+ predNames = list(preds[0].keys())
+ if len(preds) != num_input_rows:
+ raise ValueError("Prediction results must have same length as
input data.")
+ for field in struct_rtype.fields:
+ if isinstance(field.dataType, ArrayType):
+ if len(preds[0][field.name].shape) != 2:
+ raise ValueError(
+ "Prediction results for ArrayType must be
two-dimensional."
+ )
+ else:
+ if (
+ isinstance(preds[0][field.name], np.ndarray)
+ and preds[0][field.name].shape != ()
Review Comment:
If the value is a python number, it don't have "shape" property.
Let's check `np.isscalar(preds[0][field.name])` instead.
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