WeichenXu123 commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r1015466019
##########
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:
Review Comment:
For the case `field.dataType == ArrayType`, you need to add an additional
conversion step: `preds[field.name] = list(preds[field.name])`, otherwise
constructing the result pandas dataframe will cause error. (Similar to what
your current code does in L206)
Pls also add a test for the case.
##########
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:
Review Comment:
Issue:
For the case `field.dataType == ArrayType`, you need to add an additional
conversion step: `preds[field.name] = list(preds[field.name])`, otherwise
constructing the result pandas dataframe will cause error. (Similar to what
your current code does in L206)
Pls also add a test for the case.
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