WeichenXu123 commented on code in PR #37734: URL: https://github.com/apache/spark/pull/37734#discussion_r1015422322
########## python/pyspark/ml/functions.py: ########## @@ -106,6 +117,543 @@ 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( + 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 len(preds[field.name]) != num_input_rows: + raise ValueError("Prediction results must have same length as input data.") + if field.dataType == ArrayType and preds[field.name].shape != 2: + raise ValueError("Prediction results for ArrayType must be two-dimensional.") + + 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.") + else: + raise ValueError( + "Prediction results for StructType must be a dictionary or " + "a list of dictionary, got: {}".format(type(preds)) + ) + + # check column names + if set(predNames) != set(fieldNames): + raise ValueError( + "Prediction result columns did not match expected return_type " + "columns: expected {}, got: {}".format(fieldNames, predNames) + ) + + return pd.DataFrame(preds) + elif isinstance(return_type, ArrayType): + if isinstance(preds, np.ndarray): + if len(preds) != num_input_rows: + raise ValueError("Prediction results must have same length as input data.") + if len(preds.shape) != 2: + raise ValueError("Prediction results for ArrayType must be two-dimensional.") + else: + raise ValueError("Prediction results for ArrayType must be an ndarray.") + + return pd.Series(list(preds)) + else: # scalar + if len(preds) != num_input_rows: + raise ValueError("Prediction results must have same length as input data.") + + return pd.Series(np.squeeze(preds)) # type: ignore + + +def predict_batch_udf( + predict_batch_fn: Callable[ + [], + Callable[ + [np.ndarray | List[np.ndarray]], + np.ndarray | Mapping[str, np.ndarray] | List[Mapping[str, np.dtype]], + ], + ], + *, + return_type: DataType, + batch_size: int, + input_tensor_shapes: List[List[int] | None] | Mapping[int, List[int]] | None = None, +) -> UserDefinedFunctionLike: + """Given a function which loads a model, returns a pandas_udf for inferencing over that model. + Review Comment: Add a line here say `.. versionadded:: 3.4.0`, like other APIs. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org