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[SPARK-7022] [PYSPARK] [ML] Add ML.Tuning.ParamGridBuilder to PySpark
Author: Omede Firouz <[email protected]> Author: Omede <[email protected]> Closes apache#5601 from oefirouz/paramgrid and squashes the following commits: c9e2481 [Omede Firouz] Make test a doctest 9a8ce22 [Omede] Fix linter issues 8b8a6d2 [Omede Firouz] [SPARK-7022][PySpark][ML] Add ML.Tuning.ParamGridBuilder to PySpark
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# | ||
# Licensed to the Apache Software Foundation (ASF) under one or more | ||
# contributor license agreements. See the NOTICE file distributed with | ||
# this work for additional information regarding copyright ownership. | ||
# The ASF licenses this file to You under the Apache License, Version 2.0 | ||
# (the "License"); you may not use this file except in compliance with | ||
# the License. You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
# | ||
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__all__ = ['ParamGridBuilder'] | ||
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class ParamGridBuilder(object): | ||
""" | ||
Builder for a param grid used in grid search-based model selection. | ||
>>> from classification import LogisticRegression | ||
>>> lr = LogisticRegression() | ||
>>> output = ParamGridBuilder().baseOn({lr.labelCol: 'l'}) \ | ||
.baseOn([lr.predictionCol, 'p']) \ | ||
.addGrid(lr.regParam, [1.0, 2.0, 3.0]) \ | ||
.addGrid(lr.maxIter, [1, 5]) \ | ||
.addGrid(lr.featuresCol, ['f']) \ | ||
.build() | ||
>>> expected = [ \ | ||
{lr.regParam: 1.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \ | ||
{lr.regParam: 2.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \ | ||
{lr.regParam: 3.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \ | ||
{lr.regParam: 1.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}, \ | ||
{lr.regParam: 2.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}, \ | ||
{lr.regParam: 3.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}] | ||
>>> fail_count = 0 | ||
>>> for e in expected: | ||
... if e not in output: | ||
... fail_count += 1 | ||
>>> if len(expected) != len(output): | ||
... fail_count += 1 | ||
>>> fail_count | ||
0 | ||
""" | ||
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def __init__(self): | ||
self._param_grid = {} | ||
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def addGrid(self, param, values): | ||
""" | ||
Sets the given parameters in this grid to fixed values. | ||
""" | ||
self._param_grid[param] = values | ||
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return self | ||
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def baseOn(self, *args): | ||
""" | ||
Sets the given parameters in this grid to fixed values. | ||
Accepts either a parameter dictionary or a list of (parameter, value) pairs. | ||
""" | ||
if isinstance(args[0], dict): | ||
self.baseOn(*args[0].items()) | ||
else: | ||
for (param, value) in args: | ||
self.addGrid(param, [value]) | ||
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return self | ||
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def build(self): | ||
""" | ||
Builds and returns all combinations of parameters specified | ||
by the param grid. | ||
""" | ||
param_maps = [{}] | ||
for (param, values) in self._param_grid.items(): | ||
new_param_maps = [] | ||
for value in values: | ||
for old_map in param_maps: | ||
copied_map = old_map.copy() | ||
copied_map[param] = value | ||
new_param_maps.append(copied_map) | ||
param_maps = new_param_maps | ||
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return param_maps | ||
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if __name__ == "__main__": | ||
import doctest | ||
doctest.testmod() |
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