689c294102
### What changes were proposed in this pull request? use `maxBlockSizeInMB` instead of `blockSize` (#rows) to control the stacking of vectors; ### Why are the changes needed? the performance gain is mainly related to the nnz of block. ### Does this PR introduce _any_ user-facing change? yes, param blockSize -> blockSizeInMB in master ### How was this patch tested? updated testsuites Closes #30355 from zhengruifeng/adaptively_blockify_aft_lir_lor. Lead-authored-by: zhengruifeng <ruifengz@foxmail.com> Co-authored-by: Ruifeng Zheng <ruifengz@foxmail.com> Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
180 lines
9.3 KiB
Python
180 lines
9.3 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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header = """#
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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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# Code generator for shared params (shared.py). Run under this folder with:
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# python _shared_params_code_gen.py > shared.py
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def _gen_param_header(name, doc, defaultValueStr, typeConverter):
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"""
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Generates the header part for shared variables
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:param name: param name
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:param doc: param doc
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"""
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template = '''class Has$Name(Params):
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"""
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Mixin for param $name: $doc
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"""
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$name = Param(Params._dummy(), "$name", "$doc", typeConverter=$typeConverter)
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def __init__(self):
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super(Has$Name, self).__init__()'''
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if defaultValueStr is not None:
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template += '''
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self._setDefault($name=$defaultValueStr)'''
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Name = name[0].upper() + name[1:]
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if typeConverter is None:
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typeConverter = str(None)
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return template \
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.replace("$name", name) \
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.replace("$Name", Name) \
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.replace("$doc", doc) \
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.replace("$defaultValueStr", str(defaultValueStr)) \
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.replace("$typeConverter", typeConverter)
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def _gen_param_code(name, doc, defaultValueStr):
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"""
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Generates Python code for a shared param class.
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:param name: param name
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:param doc: param doc
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:param defaultValueStr: string representation of the default value
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:return: code string
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"""
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# TODO: How to correctly inherit instance attributes?
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template = '''
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def get$Name(self):
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"""
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Gets the value of $name or its default value.
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"""
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return self.getOrDefault(self.$name)'''
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Name = name[0].upper() + name[1:]
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return template \
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.replace("$name", name) \
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.replace("$Name", Name) \
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.replace("$doc", doc) \
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.replace("$defaultValueStr", str(defaultValueStr))
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if __name__ == "__main__":
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print(header)
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print("\n# DO NOT MODIFY THIS FILE! It was generated by _shared_params_code_gen.py.\n")
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print("from pyspark.ml.param import *\n\n")
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shared = [
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("maxIter", "max number of iterations (>= 0).", None, "TypeConverters.toInt"),
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("regParam", "regularization parameter (>= 0).", None, "TypeConverters.toFloat"),
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("featuresCol", "features column name.", "'features'", "TypeConverters.toString"),
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("labelCol", "label column name.", "'label'", "TypeConverters.toString"),
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("predictionCol", "prediction column name.", "'prediction'", "TypeConverters.toString"),
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("probabilityCol", "Column name for predicted class conditional probabilities. " +
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"Note: Not all models output well-calibrated probability estimates! These probabilities " +
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"should be treated as confidences, not precise probabilities.", "'probability'",
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"TypeConverters.toString"),
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("rawPredictionCol", "raw prediction (a.k.a. confidence) column name.", "'rawPrediction'",
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"TypeConverters.toString"),
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("inputCol", "input column name.", None, "TypeConverters.toString"),
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("inputCols", "input column names.", None, "TypeConverters.toListString"),
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("outputCol", "output column name.", "self.uid + '__output'", "TypeConverters.toString"),
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("outputCols", "output column names.", None, "TypeConverters.toListString"),
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("numFeatures", "Number of features. Should be greater than 0.", "262144",
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"TypeConverters.toInt"),
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("checkpointInterval", "set checkpoint interval (>= 1) or disable checkpoint (-1). " +
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"E.g. 10 means that the cache will get checkpointed every 10 iterations. Note: " +
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"this setting will be ignored if the checkpoint directory is not set in the SparkContext.",
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None, "TypeConverters.toInt"),
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("seed", "random seed.", "hash(type(self).__name__)", "TypeConverters.toInt"),
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("tol", "the convergence tolerance for iterative algorithms (>= 0).", None,
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"TypeConverters.toFloat"),
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("relativeError", "the relative target precision for the approximate quantile " +
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"algorithm. Must be in the range [0, 1]", "0.001", "TypeConverters.toFloat"),
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("stepSize", "Step size to be used for each iteration of optimization (>= 0).", None,
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"TypeConverters.toFloat"),
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("handleInvalid", "how to handle invalid entries. Options are skip (which will filter " +
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"out rows with bad values), or error (which will throw an error). More options may be " +
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"added later.", None, "TypeConverters.toString"),
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("elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, " +
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"the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.", "0.0",
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"TypeConverters.toFloat"),
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("fitIntercept", "whether to fit an intercept term.", "True", "TypeConverters.toBoolean"),
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("standardization", "whether to standardize the training features before fitting the " +
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"model.", "True", "TypeConverters.toBoolean"),
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("thresholds", "Thresholds in multi-class classification to adjust the probability of " +
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"predicting each class. Array must have length equal to the number of classes, with " +
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"values > 0, excepting that at most one value may be 0. " +
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"The class with largest value p/t is predicted, where p is the original " +
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"probability of that class and t is the class's threshold.", None,
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"TypeConverters.toListFloat"),
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("threshold", "threshold in binary classification prediction, in range [0, 1]",
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"0.5", "TypeConverters.toFloat"),
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("weightCol", "weight column name. If this is not set or empty, we treat " +
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"all instance weights as 1.0.", None, "TypeConverters.toString"),
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("solver", "the solver algorithm for optimization. If this is not set or empty, " +
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"default value is 'auto'.", "'auto'", "TypeConverters.toString"),
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("varianceCol", "column name for the biased sample variance of prediction.",
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None, "TypeConverters.toString"),
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("aggregationDepth", "suggested depth for treeAggregate (>= 2).", "2",
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"TypeConverters.toInt"),
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("parallelism", "the number of threads to use when running parallel algorithms (>= 1).",
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"1", "TypeConverters.toInt"),
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("collectSubModels", "Param for whether to collect a list of sub-models trained during " +
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"tuning. If set to false, then only the single best sub-model will be available after " +
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"fitting. If set to true, then all sub-models will be available. Warning: For large " +
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"models, collecting all sub-models can cause OOMs on the Spark driver.",
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"False", "TypeConverters.toBoolean"),
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("loss", "the loss function to be optimized.", None, "TypeConverters.toString"),
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("distanceMeasure", "the distance measure. Supported options: 'euclidean' and 'cosine'.",
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"'euclidean'", "TypeConverters.toString"),
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("validationIndicatorCol", "name of the column that indicates whether each row is for " +
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"training or for validation. False indicates training; true indicates validation.",
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None, "TypeConverters.toString"),
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("blockSize", "block size for stacking input data in matrices. Data is stacked within "
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"partitions. If block size is more than remaining data in a partition then it is "
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"adjusted to the size of this data.", None, "TypeConverters.toInt"),
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("maxBlockSizeInMB", "maximum memory in MB for stacking input data into blocks. Data is " +
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"stacked within partitions. If more than remaining data size in a partition then it " +
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"is adjusted to the data size. Default 0.0 represents choosing optimal value, depends " +
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"on specific algorithm. Must be >= 0.", "0.0", "TypeConverters.toFloat")]
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code = []
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for name, doc, defaultValueStr, typeConverter in shared:
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param_code = _gen_param_header(name, doc, defaultValueStr, typeConverter)
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code.append(param_code + "\n" + _gen_param_code(name, doc, defaultValueStr))
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print("\n\n\n".join(code))
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