20750a3f9e
### What changes were proposed in this pull request? This PR proposes to use a proper built-in exceptions instead of the plain `Exception` in Python. While I am here, I fixed another minor issue at `DataFrams.schema` together: ```diff - except AttributeError as e: - raise Exception( - "Unable to parse datatype from schema. %s" % e) + except Exception as e: + raise ValueError( + "Unable to parse datatype from schema. %s" % e) from e ``` Now it catches all exceptions during schema parsing, chains the exception with `ValueError`. Previously it only caught `AttributeError` that does not catch all cases. ### Why are the changes needed? For users to expect the proper exceptions. ### Does this PR introduce _any_ user-facing change? Yeah, the exception classes became different but should be compatible because previous exception was plain `Exception` which other exceptions inherit. ### How was this patch tested? Existing unittests should cover, Closes #31238 Closes #32650 from HyukjinKwon/SPARK-32194. Authored-by: Hyukjin Kwon <gurwls223@apache.org> Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
34 lines
1.3 KiB
Python
34 lines
1.3 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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RDD-based machine learning APIs for Python (in maintenance mode).
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The `pyspark.mllib` package is in maintenance mode as of the Spark 2.0.0 release to encourage
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migration to the DataFrame-based APIs under the `pyspark.ml` package.
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"""
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# MLlib currently needs NumPy 1.4+, so complain if lower
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import numpy
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ver = [int(x) for x in numpy.version.version.split('.')[:2]]
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if ver < [1, 4]:
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raise RuntimeError("MLlib requires NumPy 1.4+")
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__all__ = ['classification', 'clustering', 'feature', 'fpm', 'linalg', 'random',
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'recommendation', 'regression', 'stat', 'tree', 'util']
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