diff options
-rw-r--r-- | python/docs/pyspark.ml.rst | 16 | ||||
-rw-r--r-- | python/pyspark/ml/evaluation.py | 107 | ||||
-rw-r--r-- | python/pyspark/ml/param/_shared_params_code_gen.py | 1 | ||||
-rw-r--r-- | python/pyspark/ml/param/shared.py | 29 | ||||
-rw-r--r-- | python/pyspark/ml/pipeline.py | 23 | ||||
-rw-r--r-- | python/pyspark/ml/wrapper.py | 17 | ||||
-rw-r--r-- | python/pyspark/sql/_types.py | 2 | ||||
-rwxr-xr-x | python/run-tests | 1 |
8 files changed, 193 insertions, 3 deletions
diff --git a/python/docs/pyspark.ml.rst b/python/docs/pyspark.ml.rst index 4da6d4a74a..220ea595df 100644 --- a/python/docs/pyspark.ml.rst +++ b/python/docs/pyspark.ml.rst @@ -24,3 +24,19 @@ pyspark.ml.classification module :members: :undoc-members: :inherited-members: + +pyspark.ml.tuning module +-------------------------------- + +.. automodule:: pyspark.ml.tuning + :members: + :undoc-members: + :inherited-members: + +pyspark.ml.evaluation module +-------------------------------- + +.. automodule:: pyspark.ml.evaluation + :members: + :undoc-members: + :inherited-members: diff --git a/python/pyspark/ml/evaluation.py b/python/pyspark/ml/evaluation.py new file mode 100644 index 0000000000..02020ebff9 --- /dev/null +++ b/python/pyspark/ml/evaluation.py @@ -0,0 +1,107 @@ +# +# 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.wrapper import JavaEvaluator +from pyspark.ml.param import Param, Params +from pyspark.ml.param.shared import HasLabelCol, HasRawPredictionCol +from pyspark.ml.util import keyword_only +from pyspark.mllib.common import inherit_doc + +__all__ = ['BinaryClassificationEvaluator'] + + +@inherit_doc +class BinaryClassificationEvaluator(JavaEvaluator, HasLabelCol, HasRawPredictionCol): + """ + Evaluator for binary classification, which expects two input + columns: rawPrediction and label. + + >>> from pyspark.mllib.linalg import Vectors + >>> scoreAndLabels = map(lambda x: (Vectors.dense([1.0 - x[0], x[0]]), x[1]), + ... [(0.1, 0.0), (0.1, 1.0), (0.4, 0.0), (0.6, 0.0), (0.6, 1.0), (0.6, 1.0), (0.8, 1.0)]) + >>> dataset = sqlContext.createDataFrame(scoreAndLabels, ["raw", "label"]) + ... + >>> evaluator = BinaryClassificationEvaluator(rawPredictionCol="raw") + >>> evaluator.evaluate(dataset) + 0.70... + >>> evaluator.evaluate(dataset, {evaluator.metricName: "areaUnderPR"}) + 0.83... + """ + + _java_class = "org.apache.spark.ml.evaluation.BinaryClassificationEvaluator" + + # a placeholder to make it appear in the generated doc + metricName = Param(Params._dummy(), "metricName", + "metric name in evaluation (areaUnderROC|areaUnderPR)") + + @keyword_only + def __init__(self, rawPredictionCol="rawPrediction", labelCol="label", + metricName="areaUnderROC"): + """ + __init__(self, rawPredictionCol="rawPrediction", labelCol="label", \ + metricName="areaUnderROC") + """ + super(BinaryClassificationEvaluator, self).__init__() + #: param for metric name in evaluation (areaUnderROC|areaUnderPR) + self.metricName = Param(self, "metricName", + "metric name in evaluation (areaUnderROC|areaUnderPR)") + self._setDefault(rawPredictionCol="rawPrediction", labelCol="label", + metricName="areaUnderROC") + kwargs = self.__init__._input_kwargs + self._set(**kwargs) + + def setMetricName(self, value): + """ + Sets the value of :py:attr:`metricName`. + """ + self.paramMap[self.metricName] = value + return self + + def getMetricName(self): + """ + Gets the value of metricName or its default value. + """ + return self.getOrDefault(self.metricName) + + @keyword_only + def setParams(self, rawPredictionCol="rawPrediction", labelCol="label", + metricName="areaUnderROC"): + """ + setParams(self, rawPredictionCol="rawPrediction", labelCol="label", \ + metricName="areaUnderROC") + Sets params for binary classification evaluator. + """ + kwargs = self.setParams._input_kwargs + return self._set(**kwargs) + + +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.evaluation 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) diff --git a/python/pyspark/ml/param/_shared_params_code_gen.py b/python/pyspark/ml/param/_shared_params_code_gen.py index 6a3192465d..c71c823db2 100644 --- a/python/pyspark/ml/param/_shared_params_code_gen.py +++ b/python/pyspark/ml/param/_shared_params_code_gen.py @@ -93,6 +93,7 @@ if __name__ == "__main__": ("featuresCol", "features column name", "'features'"), ("labelCol", "label column name", "'label'"), ("predictionCol", "prediction column name", "'prediction'"), + ("rawPredictionCol", "raw prediction column name", "'rawPrediction'"), ("inputCol", "input column name", None), ("outputCol", "output column name", None), ("numFeatures", "number of features", None)] diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py index 13b6749998..4f243844f8 100644 --- a/python/pyspark/ml/param/shared.py +++ b/python/pyspark/ml/param/shared.py @@ -165,6 +165,35 @@ class HasPredictionCol(Params): return self.getOrDefault(self.predictionCol) +class HasRawPredictionCol(Params): + """ + Mixin for param rawPredictionCol: raw prediction column name. + """ + + # a placeholder to make it appear in the generated doc + rawPredictionCol = Param(Params._dummy(), "rawPredictionCol", "raw prediction column name") + + def __init__(self): + super(HasRawPredictionCol, self).__init__() + #: param for raw prediction column name + self.rawPredictionCol = Param(self, "rawPredictionCol", "raw prediction column name") + if 'rawPrediction' is not None: + self._setDefault(rawPredictionCol='rawPrediction') + + def setRawPredictionCol(self, value): + """ + Sets the value of :py:attr:`rawPredictionCol`. + """ + self.paramMap[self.rawPredictionCol] = value + return self + + def getRawPredictionCol(self): + """ + Gets the value of rawPredictionCol or its default value. + """ + return self.getOrDefault(self.rawPredictionCol) + + class HasInputCol(Params): """ Mixin for param inputCol: input column name. diff --git a/python/pyspark/ml/pipeline.py b/python/pyspark/ml/pipeline.py index 7c1ec3026d..7b875e4b71 100644 --- a/python/pyspark/ml/pipeline.py +++ b/python/pyspark/ml/pipeline.py @@ -22,7 +22,7 @@ from pyspark.ml.util import keyword_only from pyspark.mllib.common import inherit_doc -__all__ = ['Estimator', 'Transformer', 'Pipeline', 'PipelineModel'] +__all__ = ['Estimator', 'Transformer', 'Pipeline', 'PipelineModel', 'Evaluator'] @inherit_doc @@ -168,3 +168,24 @@ class PipelineModel(Transformer): for t in self.transformers: dataset = t.transform(dataset, paramMap) return dataset + + +class Evaluator(object): + """ + Base class for evaluators that compute metrics from predictions. + """ + + __metaclass__ = ABCMeta + + @abstractmethod + def evaluate(self, dataset, params={}): + """ + Evaluates the output. + + :param dataset: a dataset that contains labels/observations and + predictions + :param params: an optional param map that overrides embedded + params + :return: metric + """ + raise NotImplementedError() diff --git a/python/pyspark/ml/wrapper.py b/python/pyspark/ml/wrapper.py index 394f23c5e9..73741c4b40 100644 --- a/python/pyspark/ml/wrapper.py +++ b/python/pyspark/ml/wrapper.py @@ -20,7 +20,7 @@ from abc import ABCMeta from pyspark import SparkContext from pyspark.sql import DataFrame from pyspark.ml.param import Params -from pyspark.ml.pipeline import Estimator, Transformer +from pyspark.ml.pipeline import Estimator, Transformer, Evaluator from pyspark.mllib.common import inherit_doc @@ -147,3 +147,18 @@ class JavaModel(JavaTransformer): def _java_obj(self): return self._java_model + + +@inherit_doc +class JavaEvaluator(Evaluator, JavaWrapper): + """ + Base class for :py:class:`Evaluator`s that wrap Java/Scala + implementations. + """ + + __metaclass__ = ABCMeta + + def evaluate(self, dataset, params={}): + java_obj = self._java_obj() + self._transfer_params_to_java(params, java_obj) + return java_obj.evaluate(dataset._jdf, self._empty_java_param_map()) diff --git a/python/pyspark/sql/_types.py b/python/pyspark/sql/_types.py index 95fb91ad43..fd98e116d2 100644 --- a/python/pyspark/sql/_types.py +++ b/python/pyspark/sql/_types.py @@ -652,7 +652,7 @@ def _python_to_sql_converter(dataType): if isinstance(dataType, StructType): names, types = zip(*[(f.name, f.dataType) for f in dataType.fields]) - converters = map(_python_to_sql_converter, types) + converters = [_python_to_sql_converter(t) for t in types] def converter(obj): if isinstance(obj, dict): diff --git a/python/run-tests b/python/run-tests index 0e0eee3564..f9ca26467f 100755 --- a/python/run-tests +++ b/python/run-tests @@ -100,6 +100,7 @@ function run_ml_tests() { run_test "pyspark/ml/classification.py" run_test "pyspark/ml/tuning.py" run_test "pyspark/ml/tests.py" + run_test "pyspark/ml/evaluation.py" } function run_streaming_tests() { |