#
# 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.
#
from pyspark.ml.util import keyword_only
from pyspark.ml.wrapper import JavaEstimator, JavaModel
from pyspark.ml.param.shared import HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,\
HasRegParam
from pyspark.mllib.common import inherit_doc
__all__ = ['LogisticRegression', 'LogisticRegressionModel']
@inherit_doc
class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
HasRegParam):
"""
Logistic regression.
>>> from pyspark.sql import Row
>>> from pyspark.mllib.linalg import Vectors
>>> df = sc.parallelize([
... Row(label=1.0, features=Vectors.dense(1.0)),
... Row(label=0.0, features=Vectors.sparse(1, [], []))]).toDF()
>>> lr = LogisticRegression(maxIter=5, regParam=0.01)
>>> model = lr.fit(df)
>>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0))]).toDF()
>>> model.transform(test0).head().prediction
0.0
>>> test1 = sc.parallelize([Row(features=Vectors.sparse(1, [0], [1.0]))]).toDF()
>>> model.transform(test1).head().prediction
1.0
>>> lr.setParams("vector")
Traceback (most recent call last):
...
TypeError: Method setParams forces keyword arguments.
"""
_java_class = "org.apache.spark.ml.classification.LogisticRegression"
@keyword_only
def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
maxIter=100, regParam=0.1):
"""
__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
maxIter=100, regParam=0.1)
"""
super(LogisticRegression, self).__init__()
self._setDefault(maxIter=100, regParam=0.1)
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
@keyword_only
def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
maxIter=100, regParam=0.1):
"""
setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
maxIter=100, regParam=0.1)
Sets params for logistic regression.
"""
kwargs = self.setParams._input_kwargs
return self._set(**kwargs)
def _create_model(self, java_model):
return LogisticRegressionModel(java_model)
class LogisticRegressionModel(JavaModel):
"""
Model fitted by LogisticRegression.
"""
if __name__ == "__main__":
import doctest
from pyspark.context import SparkContext
from pyspark.sql import SQLContext
globs = globals().copy()
# The small batch size here ensures that we see multiple batches,
# even in these small test examples:
sc = SparkContext("local[2]", "ml.classification tests")
sqlContext = SQLContext(sc)
globs['sc'] = sc
globs['sqlContext'] = sqlContext
(failure_count, test_count) = doctest.testmod(
globs=globs, optionflags=doctest.ELLIPSIS)
sc.stop()
if failure_count:
exit(-1)
