[SPARK-10280][MLLIB][PYSPARK][DOCS] Add @since annotation to pyspark.ml.classification
Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #8690 from yu-iskw/SPARK-10280.
This commit is contained in:
Yu ISHIKAWA
Xiangrui Meng
parent
860ea0d386
commit
88a3fdcc78
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@ -67,6 +67,8 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
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Traceback (most recent call last):
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...
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TypeError: Method setParams forces keyword arguments.
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.. versionadded:: 1.3.0
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"""
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# a placeholder to make it appear in the generated doc
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@ -99,6 +101,7 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
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self._checkThresholdConsistency()
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@keyword_only
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@since("1.3.0")
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def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
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maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
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threshold=0.5, thresholds=None, probabilityCol="probability",
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@ -119,6 +122,7 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
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def _create_model(self, java_model):
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return LogisticRegressionModel(java_model)
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@since("1.4.0")
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def setThreshold(self, value):
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"""
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Sets the value of :py:attr:`threshold`.
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@ -129,6 +133,7 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
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del self._paramMap[self.thresholds]
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return self
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@since("1.4.0")
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def getThreshold(self):
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"""
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Gets the value of threshold or its default value.
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@ -144,6 +149,7 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
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else:
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return self.getOrDefault(self.threshold)
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@since("1.5.0")
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def setThresholds(self, value):
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"""
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Sets the value of :py:attr:`thresholds`.
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@ -154,6 +160,7 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
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del self._paramMap[self.threshold]
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return self
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@since("1.5.0")
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def getThresholds(self):
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"""
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If :py:attr:`thresholds` is set, return its value.
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@ -185,9 +192,12 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
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class LogisticRegressionModel(JavaModel):
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"""
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Model fitted by LogisticRegression.
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.. versionadded:: 1.3.0
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"""
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@property
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@since("1.4.0")
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def weights(self):
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"""
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Model weights.
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@ -205,6 +215,7 @@ class LogisticRegressionModel(JavaModel):
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return self._call_java("coefficients")
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@property
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@since("1.4.0")
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def intercept(self):
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"""
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Model intercept.
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@ -215,6 +226,8 @@ class LogisticRegressionModel(JavaModel):
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class TreeClassifierParams(object):
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"""
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Private class to track supported impurity measures.
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.. versionadded:: 1.4.0
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"""
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supportedImpurities = ["entropy", "gini"]
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@ -231,6 +244,7 @@ class TreeClassifierParams(object):
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"gain calculation (case-insensitive). Supported options: " +
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", ".join(self.supportedImpurities))
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@since("1.6.0")
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def setImpurity(self, value):
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"""
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Sets the value of :py:attr:`impurity`.
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@ -238,6 +252,7 @@ class TreeClassifierParams(object):
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self._paramMap[self.impurity] = value
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return self
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@since("1.6.0")
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def getImpurity(self):
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"""
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Gets the value of impurity or its default value.
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@ -248,6 +263,8 @@ class TreeClassifierParams(object):
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class GBTParams(TreeEnsembleParams):
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"""
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Private class to track supported GBT params.
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.. versionadded:: 1.4.0
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"""
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supportedLossTypes = ["logistic"]
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@ -287,6 +304,8 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
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>>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
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>>> model.transform(test1).head().prediction
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1.0
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.. versionadded:: 1.4.0
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"""
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@keyword_only
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@ -310,6 +329,7 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
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self.setParams(**kwargs)
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@keyword_only
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@since("1.4.0")
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def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
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probabilityCol="probability", rawPredictionCol="rawPrediction",
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maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
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@ -333,6 +353,8 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
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class DecisionTreeClassificationModel(DecisionTreeModel):
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"""
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Model fitted by DecisionTreeClassifier.
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.. versionadded:: 1.4.0
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"""
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@ -371,6 +393,8 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
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>>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
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>>> model.transform(test1).head().prediction
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1.0
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.. versionadded:: 1.4.0
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"""
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@keyword_only
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@ -396,6 +420,7 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
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self.setParams(**kwargs)
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@keyword_only
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@since("1.4.0")
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def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
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probabilityCol="probability", rawPredictionCol="rawPrediction",
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maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
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@ -419,6 +444,8 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
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class RandomForestClassificationModel(TreeEnsembleModels):
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"""
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Model fitted by RandomForestClassifier.
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.. versionadded:: 1.4.0
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"""
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@ -450,6 +477,8 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol
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>>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
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>>> model.transform(test1).head().prediction
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1.0
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.. versionadded:: 1.4.0
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"""
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# a placeholder to make it appear in the generated doc
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@ -482,6 +511,7 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol
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self.setParams(**kwargs)
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@keyword_only
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@since("1.4.0")
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def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
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maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
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maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
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@ -499,6 +529,7 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol
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def _create_model(self, java_model):
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return GBTClassificationModel(java_model)
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@since("1.4.0")
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def setLossType(self, value):
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"""
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Sets the value of :py:attr:`lossType`.
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@ -506,6 +537,7 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol
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self._paramMap[self.lossType] = value
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return self
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@since("1.4.0")
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def getLossType(self):
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"""
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Gets the value of lossType or its default value.
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@ -516,6 +548,8 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol
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class GBTClassificationModel(TreeEnsembleModels):
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"""
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Model fitted by GBTClassifier.
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.. versionadded:: 1.4.0
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"""
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@ -555,6 +589,8 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, H
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>>> test1 = sc.parallelize([Row(features=Vectors.sparse(2, [0], [1.0]))]).toDF()
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>>> model.transform(test1).head().prediction
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1.0
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.. versionadded:: 1.5.0
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"""
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# a placeholder to make it appear in the generated doc
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@ -587,6 +623,7 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, H
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self.setParams(**kwargs)
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@keyword_only
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@since("1.5.0")
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def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
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probabilityCol="probability", rawPredictionCol="rawPrediction", smoothing=1.0,
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modelType="multinomial"):
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@ -602,6 +639,7 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, H
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def _create_model(self, java_model):
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return NaiveBayesModel(java_model)
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@since("1.5.0")
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def setSmoothing(self, value):
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"""
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Sets the value of :py:attr:`smoothing`.
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@ -609,12 +647,14 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, H
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self._paramMap[self.smoothing] = value
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return self
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@since("1.5.0")
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def getSmoothing(self):
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"""
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Gets the value of smoothing or its default value.
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"""
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return self.getOrDefault(self.smoothing)
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@since("1.5.0")
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def setModelType(self, value):
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"""
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Sets the value of :py:attr:`modelType`.
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@ -622,6 +662,7 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, H
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self._paramMap[self.modelType] = value
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return self
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@since("1.5.0")
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def getModelType(self):
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"""
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Gets the value of modelType or its default value.
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@ -632,9 +673,12 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, H
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class NaiveBayesModel(JavaModel):
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"""
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Model fitted by NaiveBayes.
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.. versionadded:: 1.5.0
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"""
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@property
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@since("1.5.0")
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def pi(self):
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"""
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log of class priors.
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@ -642,6 +686,7 @@ class NaiveBayesModel(JavaModel):
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return self._call_java("pi")
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@property
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@since("1.5.0")
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def theta(self):
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"""
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log of class conditional probabilities.
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@ -681,6 +726,8 @@ class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol,
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|[0.0,0.0]| 0.0|
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+---------+----------+
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...
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.. versionadded:: 1.6.0
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"""
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# a placeholder to make it appear in the generated doc
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@ -715,6 +762,7 @@ class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol,
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self.setParams(**kwargs)
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@keyword_only
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@since("1.6.0")
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def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
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maxIter=100, tol=1e-4, seed=None, layers=None, blockSize=128):
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"""
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@ -731,6 +779,7 @@ class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol,
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def _create_model(self, java_model):
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return MultilayerPerceptronClassificationModel(java_model)
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@since("1.6.0")
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def setLayers(self, value):
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"""
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Sets the value of :py:attr:`layers`.
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@ -738,12 +787,14 @@ class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol,
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self._paramMap[self.layers] = value
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return self
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@since("1.6.0")
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def getLayers(self):
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"""
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Gets the value of layers or its default value.
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"""
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return self.getOrDefault(self.layers)
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@since("1.6.0")
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def setBlockSize(self, value):
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"""
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Sets the value of :py:attr:`blockSize`.
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@ -751,6 +802,7 @@ class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol,
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self._paramMap[self.blockSize] = value
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return self
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@since("1.6.0")
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def getBlockSize(self):
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"""
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Gets the value of blockSize or its default value.
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@ -761,9 +813,12 @@ class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol,
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class MultilayerPerceptronClassificationModel(JavaModel):
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"""
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Model fitted by MultilayerPerceptronClassifier.
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.. versionadded:: 1.6.0
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"""
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@property
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@since("1.6.0")
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def layers(self):
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"""
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array of layer sizes including input and output layers.
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@ -771,6 +826,7 @@ class MultilayerPerceptronClassificationModel(JavaModel):
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return self._call_java("javaLayers")
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@property
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@since("1.6.0")
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def weights(self):
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"""
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vector of initial weights for the model that consists of the weights of layers.
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