1ffa8cb91f
as suggested by justinuang on #5601. Author: Xiangrui Meng <meng@databricks.com> Closes #5873 from mengxr/SPARK-7329 and squashes the following commits: d08f9cf [Xiangrui Meng] simplify tests b7a7b9b [Xiangrui Meng] simplify grid build
85 lines
3 KiB
Python
85 lines
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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import itertools
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__all__ = ['ParamGridBuilder']
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class ParamGridBuilder(object):
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"""
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Builder for a param grid used in grid search-based model selection.
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>>> from classification import LogisticRegression
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>>> lr = LogisticRegression()
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>>> output = ParamGridBuilder().baseOn({lr.labelCol: 'l'}) \
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.baseOn([lr.predictionCol, 'p']) \
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.addGrid(lr.regParam, [1.0, 2.0, 3.0]) \
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.addGrid(lr.maxIter, [1, 5]) \
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.addGrid(lr.featuresCol, ['f']) \
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.build()
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>>> expected = [ \
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{lr.regParam: 1.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \
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{lr.regParam: 2.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \
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{lr.regParam: 3.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \
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{lr.regParam: 1.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}, \
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{lr.regParam: 2.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}, \
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{lr.regParam: 3.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}]
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>>> len(output) == len(expected)
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True
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>>> all([m in expected for m in output])
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True
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"""
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def __init__(self):
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self._param_grid = {}
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def addGrid(self, param, values):
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"""
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Sets the given parameters in this grid to fixed values.
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"""
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self._param_grid[param] = values
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return self
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def baseOn(self, *args):
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"""
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Sets the given parameters in this grid to fixed values.
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Accepts either a parameter dictionary or a list of (parameter, value) pairs.
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"""
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if isinstance(args[0], dict):
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self.baseOn(*args[0].items())
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else:
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for (param, value) in args:
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self.addGrid(param, [value])
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return self
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def build(self):
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"""
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Builds and returns all combinations of parameters specified
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by the param grid.
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"""
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keys = self._param_grid.keys()
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grid_values = self._param_grid.values()
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return [dict(zip(keys, prod)) for prod in itertools.product(*grid_values)]
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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