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Diffstat (limited to 'python/pyspark/ml/classification.py')
-rw-r--r-- | python/pyspark/ml/classification.py | 76 |
1 files changed, 76 insertions, 0 deletions
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py new file mode 100644 index 0000000000..6bd2aa8e47 --- /dev/null +++ b/python/pyspark/ml/classification.py @@ -0,0 +1,76 @@ +# +# 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) |