Python LogisticRegression.setThreshold方法代码示例

# 需要导入模块: from pyspark.ml.classification import LogisticRegression [as 别名]
# 或者: from pyspark.ml.classification.LogisticRegression import setThreshold [as 别名]
    def test_default_read_write(self):
        temp_path = tempfile.mkdtemp()

        lr = LogisticRegression()
        lr.setMaxIter(50)
        lr.setThreshold(.75)
        writer = DefaultParamsWriter(lr)

        savePath = temp_path + "/lr"
        writer.save(savePath)

        reader = DefaultParamsReadable.read()
        lr2 = reader.load(savePath)

        self.assertEqual(lr.uid, lr2.uid)
        self.assertEqual(lr.extractParamMap(), lr2.extractParamMap())

        # test overwrite
        lr.setThreshold(.8)
        writer.overwrite().save(savePath)

        reader = DefaultParamsReadable.read()
        lr3 = reader.load(savePath)

        self.assertEqual(lr.uid, lr3.uid)
        self.assertEqual(lr.extractParamMap(), lr3.extractParamMap())

# 需要导入模块: from pyspark.ml.classification import LogisticRegression [as 别名]
# 或者: from pyspark.ml.classification.LogisticRegression import setThreshold [as 别名]
    def test_default_read_write_default_params(self):
        lr = LogisticRegression()
        self.assertFalse(lr.isSet(lr.getParam("threshold")))

        lr.setMaxIter(50)
        lr.setThreshold(.75)

        # `threshold` is set by user, default param `predictionCol` is not set by user.
        self.assertTrue(lr.isSet(lr.getParam("threshold")))
        self.assertFalse(lr.isSet(lr.getParam("predictionCol")))
        self.assertTrue(lr.hasDefault(lr.getParam("predictionCol")))

        writer = DefaultParamsWriter(lr)
        metadata = json.loads(writer._get_metadata_to_save(lr, self.sc))
        self.assertTrue("defaultParamMap" in metadata)

        reader = DefaultParamsReadable.read()
        metadataStr = json.dumps(metadata, separators=[',',  ':'])
        loadedMetadata = reader._parseMetaData(metadataStr, )
        reader.getAndSetParams(lr, loadedMetadata)

        self.assertTrue(lr.isSet(lr.getParam("threshold")))
        self.assertFalse(lr.isSet(lr.getParam("predictionCol")))
        self.assertTrue(lr.hasDefault(lr.getParam("predictionCol")))

        # manually create metadata without `defaultParamMap` section.
        del metadata['defaultParamMap']
        metadataStr = json.dumps(metadata, separators=[',',  ':'])
        loadedMetadata = reader._parseMetaData(metadataStr, )
        with self.assertRaisesRegexp(AssertionError, "`defaultParamMap` section not found"):
            reader.getAndSetParams(lr, loadedMetadata)

        # Prior to 2.4.0, metadata doesn't have `defaultParamMap`.
        metadata['sparkVersion'] = '2.3.0'
        metadataStr = json.dumps(metadata, separators=[',',  ':'])
        loadedMetadata = reader._parseMetaData(metadataStr, )
        reader.getAndSetParams(lr, loadedMetadata)

# 需要导入模块: from pyspark.ml.classification import LogisticRegression [as 别名]
# 或者: from pyspark.ml.classification.LogisticRegression import setThreshold [as 别名]
    lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)

    # Fit the model
    lrModel = lr.fit(training)

    # $example on$
    # Extract the summary from the returned LogisticRegressionModel instance trained
    # in the earlier example
    trainingSummary = lrModel.summary

    # Obtain the objective per iteration
    objectiveHistory = trainingSummary.objectiveHistory
    print("objectiveHistory:")
    for objective in objectiveHistory:
        print(objective)

    # Obtain the receiver-operating characteristic as a dataframe and areaUnderROC.
    trainingSummary.roc.show()
    print("areaUnderROC: " + str(trainingSummary.areaUnderROC))

    # Set the model threshold to maximize F-Measure
    fMeasure = trainingSummary.fMeasureByThreshold
    maxFMeasure = fMeasure.groupBy().max('F-Measure').select('max(F-Measure)').head()
    bestThreshold = fMeasure.where(fMeasure['F-Measure'] == maxFMeasure['max(F-Measure)']) \
        .select('threshold').head()['threshold']
    lr.setThreshold(bestThreshold)
    # $example off$

    spark.stop()