#
# 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()
    >>> print model.transform(test0).head().prediction
    0.0
    >>> test1 = sc.parallelize([Row(features=Vectors.sparse(1, [0], [1.0]))]).toDF()
    >>> print 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__()
        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_params(**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.feature 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)
