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new 99a979df0de [SPARK-45130][CONNECT][ML][PYTHON] Avoid Spark connect ML
model to change input pandas dataframe
99a979df0de is described below
commit 99a979df0de95a966e1b0d780aa5329d4e62cbf7
Author: Weichen Xu <weichen...@databricks.com>
AuthorDate: Mon Sep 18 12:54:17 2023 +0800
[SPARK-45130][CONNECT][ML][PYTHON] Avoid Spark connect ML model to change
input pandas dataframe
### What changes were proposed in this pull request?
Currently, to avoid data copy, Spark connect ML model directly changes
input pandas dataframe for appending prediction columns. But we can use
`pandas_df.copy(deep=False)` to shallow copy it and then append prediction
columns in copied dataframe. This is easier for user to use it.
### Why are the changes needed?
This makes `pyspark.ml.connect` model `transform` method has more similar
behavior with `pyspark.ml` model, i.e., the input dataframe is intact after
`transform` is called. Otherwise user might be surprise at the new behavior and
have to change more code to migrate their workload to `pyspark.ml.connect`
### Does this PR introduce _any_ user-facing change?
Yes.
Previous behavior:
In `pyspark.ml.connect`, `model.transform` will append new columns into
input pandas dataframe, and return input dataframe object.
Changed behavior:
In `pyspark.ml.connect`, `model.transform` will shallow copy input pandas
dataframe and append new columns into shallow copied pandas dataframe, then
return copied pandas dataframe.
### How was this patch tested?
Unit tests.
### Was this patch authored or co-authored using generative AI tooling?
No.
Closes #42887 from
WeichenXu123/spark-ml-connect-model-avoid-change-input-dataframe.
Authored-by: Weichen Xu <weichen...@databricks.com>
Signed-off-by: Ruifeng Zheng <ruife...@apache.org>
---
python/pyspark/ml/connect/base.py | 8 ++++----
python/pyspark/ml/connect/util.py | 1 +
.../ml/tests/connect/test_legacy_mode_classification.py | 7 ++++++-
python/pyspark/ml/tests/connect/test_legacy_mode_feature.py | 10 ++++++++--
python/pyspark/ml/tests/connect/test_legacy_mode_pipeline.py | 7 ++++++-
5 files changed, 25 insertions(+), 8 deletions(-)
diff --git a/python/pyspark/ml/connect/base.py
b/python/pyspark/ml/connect/base.py
index f8ce0cb6962..fdfcddf601c 100644
--- a/python/pyspark/ml/connect/base.py
+++ b/python/pyspark/ml/connect/base.py
@@ -151,11 +151,11 @@ class Transformer(Params, metaclass=ABCMeta):
) -> Union[DataFrame, pd.DataFrame]:
"""
Transforms the input dataset.
- The dataset can be either pandas dataframe or spark dataframe,
+ The dataset can be either pandas dataframe or spark dataframeļ¼
if it is a spark DataFrame, the result of transformation is a new
spark DataFrame
- that contains all existing columns and output columns with names.
- if it is a pandas DataFrame, the input pandas dataframe is appended
with output
- columns in place.
+ that contains all existing columns and output columns with names,
+ If it is a pandas DataFrame, the result of transformation is a shallow
copy
+ of the input pandas dataframe with output columns with names.
Note: Transformers does not allow output column having the same name
with
existing columns.
diff --git a/python/pyspark/ml/connect/util.py
b/python/pyspark/ml/connect/util.py
index a543a16d1fc..d05893ad2a0 100644
--- a/python/pyspark/ml/connect/util.py
+++ b/python/pyspark/ml/connect/util.py
@@ -147,6 +147,7 @@ def transform_dataframe_column(
output_col_name, spark_udf_return_type = output_cols[0]
if isinstance(dataframe, pd.DataFrame):
+ dataframe = dataframe.copy(deep=False)
result_data = transform_fn(*[dataframe[col_name] for col_name in
input_cols])
if isinstance(result_data, pd.Series):
assert len(output_cols) == 1
diff --git a/python/pyspark/ml/tests/connect/test_legacy_mode_classification.py
b/python/pyspark/ml/tests/connect/test_legacy_mode_classification.py
index 5e5f1b64a33..bc48b4bddd4 100644
--- a/python/pyspark/ml/tests/connect/test_legacy_mode_classification.py
+++ b/python/pyspark/ml/tests/connect/test_legacy_mode_classification.py
@@ -33,6 +33,7 @@ if should_test_connect:
LogisticRegression as LORV2,
LogisticRegressionModel as LORV2Model,
)
+ import pandas as pd
class ClassificationTestsMixin:
@@ -81,7 +82,11 @@ class ClassificationTestsMixin:
result = model.transform(eval_df1).toPandas()
self._check_result(result, expected_predictions,
expected_probabilities)
- local_transform_result = model.transform(eval_df1.toPandas())
+ pandas_eval_df1 = eval_df1.toPandas()
+ pandas_eval_df1_copy = pandas_eval_df1.copy()
+ local_transform_result = model.transform(pandas_eval_df1)
+ # assert that `transform` doesn't mutate the input dataframe.
+ pd.testing.assert_frame_equal(pandas_eval_df1, pandas_eval_df1_copy)
self._check_result(local_transform_result, expected_predictions,
expected_probabilities)
model.set(model.probabilityCol, "")
diff --git a/python/pyspark/ml/tests/connect/test_legacy_mode_feature.py
b/python/pyspark/ml/tests/connect/test_legacy_mode_feature.py
index 4f8b74e1f70..7315590cdd3 100644
--- a/python/pyspark/ml/tests/connect/test_legacy_mode_feature.py
+++ b/python/pyspark/ml/tests/connect/test_legacy_mode_feature.py
@@ -25,6 +25,7 @@ import unittest
from pyspark.sql import SparkSession
from pyspark.testing.connectutils import should_test_connect,
connect_requirement_message
+
if should_test_connect:
from pyspark.ml.connect.feature import (
MaxAbsScaler,
@@ -32,6 +33,7 @@ if should_test_connect:
StandardScaler,
StandardScalerModel,
)
+ import pandas as pd
class FeatureTestsMixin:
@@ -57,8 +59,10 @@ class FeatureTestsMixin:
local_df1 = df1.toPandas()
local_fit_model = scaler.fit(local_df1)
+ local_df1_copy = local_df1.copy()
local_transform_result = local_fit_model.transform(local_df1)
- assert id(local_transform_result) == id(local_df1)
+ # assert that `transform` doesn't mutate the input dataframe.
+ pd.testing.assert_frame_equal(local_df1, local_df1_copy)
assert list(local_transform_result.columns) == ["features",
"scaled_features"]
np.testing.assert_allclose(list(local_transform_result.scaled_features),
expected_result)
@@ -110,8 +114,10 @@ class FeatureTestsMixin:
local_df1 = df1.toPandas()
local_fit_model = scaler.fit(local_df1)
+ local_df1_copy = local_df1.copy()
local_transform_result = local_fit_model.transform(local_df1)
- assert id(local_transform_result) == id(local_df1)
+ # assert that `transform` doesn't mutate the input dataframe.
+ pd.testing.assert_frame_equal(local_df1, local_df1_copy)
assert list(local_transform_result.columns) == ["features",
"scaled_features"]
np.testing.assert_allclose(list(local_transform_result.scaled_features),
expected_result)
diff --git a/python/pyspark/ml/tests/connect/test_legacy_mode_pipeline.py
b/python/pyspark/ml/tests/connect/test_legacy_mode_pipeline.py
index 009c17e5b05..34a2ed851bb 100644
--- a/python/pyspark/ml/tests/connect/test_legacy_mode_pipeline.py
+++ b/python/pyspark/ml/tests/connect/test_legacy_mode_pipeline.py
@@ -26,6 +26,7 @@ if should_test_connect:
from pyspark.ml.connect.feature import StandardScaler
from pyspark.ml.connect.classification import LogisticRegression as LORV2
from pyspark.ml.connect.pipeline import Pipeline
+ import pandas as pd
class PipelineTestsMixin:
@@ -81,7 +82,11 @@ class PipelineTestsMixin:
model2 = pipeline2.fit(train_dataset)
result2 = model2.transform(eval_dataset).toPandas()
self._check_result(result2, expected_predictions,
expected_probabilities)
- local_transform_result2 = model2.transform(eval_dataset.toPandas())
+ local_eval_dataset = eval_dataset.toPandas()
+ local_eval_dataset_copy = local_eval_dataset.copy()
+ local_transform_result2 = model2.transform(local_eval_dataset)
+ # assert that `transform` doesn't mutate the input dataframe.
+ pd.testing.assert_frame_equal(local_eval_dataset,
local_eval_dataset_copy)
self._check_result(local_transform_result2, expected_predictions,
expected_probabilities)
with tempfile.TemporaryDirectory() as tmp_dir:
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