#
# 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 inherit_doc
from pyspark.ml.wrapper import JavaEstimator, JavaModel
from pyspark.ml.param.shared import HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,\
HasRegParam
__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
>>> dataset = sqlCtx.inferSchema(sc.parallelize([ \
Row(label=1.0, features=Vectors.dense(1.0)), \
Row(label=0.0, features=Vectors.sparse(1, [], []))]))
>>> lr = LogisticRegression() \
.setMaxIter(5) \
.setRegParam(0.01)
>>> model = lr.fit(dataset)
>>> test0 = sqlCtx.inferSchema(sc.parallelize([Row(features=Vectors.dense(-1.0))]))
>>> print model.transform(test0).head().prediction
0.0
>>> test1 = sqlCtx.inferSchema(sc.parallelize([Row(features=Vectors.sparse(1, [0], [1.0]))]))
>>> print model.transform(test1).head().prediction
1.0
"""
_java_class = "org.apache.spark.ml.classification.LogisticRegression"
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.feature tests")
sqlCtx = SQLContext(sc)
globs['sc'] = sc
globs['sqlCtx'] = sqlCtx
(failure_count, test_count) = doctest.testmod(
globs=globs, optionflags=doctest.ELLIPSIS)
sc.stop()
if failure_count:
exit(-1)
