6ada4f6f52
Use `sqlContext` in PySpark shell, make it consistent with SQL programming guide. `sqlCtx` is also kept for compatibility. Author: Davies Liu <davies@databricks.com> Closes #5425 from davies/sqlCtx and squashes the following commits: af67340 [Davies Liu] sqlCtx -> sqlContext 15a278f [Davies Liu] use sqlContext in python shell
102 lines
3.7 KiB
Python
102 lines
3.7 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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from pyspark.ml.util import keyword_only
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from pyspark.ml.wrapper import JavaEstimator, JavaModel
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from pyspark.ml.param.shared import HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,\
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HasRegParam
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from pyspark.mllib.common import inherit_doc
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__all__ = ['LogisticRegression', 'LogisticRegressionModel']
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@inherit_doc
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class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
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HasRegParam):
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"""
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Logistic regression.
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>>> from pyspark.sql import Row
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>>> from pyspark.mllib.linalg import Vectors
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>>> df = sc.parallelize([
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... Row(label=1.0, features=Vectors.dense(1.0)),
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... Row(label=0.0, features=Vectors.sparse(1, [], []))]).toDF()
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>>> lr = LogisticRegression(maxIter=5, regParam=0.01)
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>>> model = lr.fit(df)
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>>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0))]).toDF()
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>>> print model.transform(test0).head().prediction
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0.0
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>>> test1 = sc.parallelize([Row(features=Vectors.sparse(1, [0], [1.0]))]).toDF()
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>>> print model.transform(test1).head().prediction
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1.0
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>>> lr.setParams("vector")
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Traceback (most recent call last):
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...
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TypeError: Method setParams forces keyword arguments.
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"""
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_java_class = "org.apache.spark.ml.classification.LogisticRegression"
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@keyword_only
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def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
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maxIter=100, regParam=0.1):
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"""
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__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
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maxIter=100, regParam=0.1)
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"""
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super(LogisticRegression, self).__init__()
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kwargs = self.__init__._input_kwargs
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self.setParams(**kwargs)
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@keyword_only
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def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
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maxIter=100, regParam=0.1):
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"""
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setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
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maxIter=100, regParam=0.1)
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Sets params for logistic regression.
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"""
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kwargs = self.setParams._input_kwargs
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return self._set_params(**kwargs)
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def _create_model(self, java_model):
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return LogisticRegressionModel(java_model)
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class LogisticRegressionModel(JavaModel):
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"""
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Model fitted by LogisticRegression.
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"""
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if __name__ == "__main__":
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import doctest
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from pyspark.context import SparkContext
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from pyspark.sql import SQLContext
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globs = globals().copy()
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# The small batch size here ensures that we see multiple batches,
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# even in these small test examples:
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sc = SparkContext("local[2]", "ml.feature tests")
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sqlContext = SQLContext(sc)
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globs['sc'] = sc
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globs['sqlContext'] = sqlContext
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(failure_count, test_count) = doctest.testmod(
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globs=globs, optionflags=doctest.ELLIPSIS)
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sc.stop()
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if failure_count:
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exit(-1)
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