[SPARK-45130][CONNECT][ML][PYTHON] Avoid Spark connect ML model to change input pandas dataframe#42887
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WeichenXu123 wants to merge 8 commits intoapache:masterfrom
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zhengruifeng
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harupy
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harupy
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harupy
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harupy
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HyukjinKwon
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| import tempfile | ||
| import unittest | ||
| import numpy as np | ||
| import pandas as pd |
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can you import this within test_binary_classes_logistic_regression or under should_test_connect so the tests can be skipped fine when pandas is not installed?
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isn't pandas always installed in CI image ?
HyukjinKwon
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| import os | ||
| import pickle | ||
| import numpy as np | ||
| import pandas as pd |
HyukjinKwon
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Sep 13, 2023
| import tempfile | ||
| import unittest | ||
| import numpy as np | ||
| import pandas as pd |
HyukjinKwon
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Sep 18, 2023
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merged to master |
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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.connectmodeltransformmethod has more similar behavior withpyspark.mlmodel, i.e., the input dataframe is intact aftertransformis called. Otherwise user might be surprise at the new behavior and have to change more code to migrate their workload topyspark.ml.connectDoes this PR introduce any user-facing change?
Yes.
Previous behavior:
In
pyspark.ml.connect,model.transformwill append new columns into input pandas dataframe, and return input dataframe object.Changed behavior:
In
pyspark.ml.connect,model.transformwill 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.