Repository: spark Updated Branches: refs/heads/master 860ea0d38 -> 88a3fdcc7
[SPARK-10280][MLLIB][PYSPARK][DOCS] Add @since annotation to pyspark.ml.classification Author: Yu ISHIKAWA <yuu.ishik...@gmail.com> Closes #8690 from yu-iskw/SPARK-10280. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/88a3fdcc Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/88a3fdcc Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/88a3fdcc Branch: refs/heads/master Commit: 88a3fdcc783f880a8d01c7e194ec42fc114bdf8a Parents: 860ea0d Author: Yu ISHIKAWA <yuu.ishik...@gmail.com> Authored: Mon Nov 9 13:16:04 2015 -0800 Committer: Xiangrui Meng <m...@databricks.com> Committed: Mon Nov 9 13:16:04 2015 -0800 ---------------------------------------------------------------------- python/pyspark/ml/classification.py | 56 ++++++++++++++++++++++++++++++++ 1 file changed, 56 insertions(+) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/88a3fdcc/python/pyspark/ml/classification.py ---------------------------------------------------------------------- diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py index 2e468f6..603f2c7 100644 --- a/python/pyspark/ml/classification.py +++ b/python/pyspark/ml/classification.py @@ -67,6 +67,8 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti Traceback (most recent call last): ... TypeError: Method setParams forces keyword arguments. + + .. versionadded:: 1.3.0 """ # a placeholder to make it appear in the generated doc @@ -99,6 +101,7 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti self._checkThresholdConsistency() @keyword_only + @since("1.3.0") def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, threshold=0.5, thresholds=None, probabilityCol="probability", @@ -119,6 +122,7 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti def _create_model(self, java_model): return LogisticRegressionModel(java_model) + @since("1.4.0") def setThreshold(self, value): """ Sets the value of :py:attr:`threshold`. @@ -129,6 +133,7 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti del self._paramMap[self.thresholds] return self + @since("1.4.0") def getThreshold(self): """ Gets the value of threshold or its default value. @@ -144,6 +149,7 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti else: return self.getOrDefault(self.threshold) + @since("1.5.0") def setThresholds(self, value): """ Sets the value of :py:attr:`thresholds`. @@ -154,6 +160,7 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti del self._paramMap[self.threshold] return self + @since("1.5.0") def getThresholds(self): """ If :py:attr:`thresholds` is set, return its value. @@ -185,9 +192,12 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti class LogisticRegressionModel(JavaModel): """ Model fitted by LogisticRegression. + + .. versionadded:: 1.3.0 """ @property + @since("1.4.0") def weights(self): """ Model weights. @@ -205,6 +215,7 @@ class LogisticRegressionModel(JavaModel): return self._call_java("coefficients") @property + @since("1.4.0") def intercept(self): """ Model intercept. @@ -215,6 +226,8 @@ class LogisticRegressionModel(JavaModel): class TreeClassifierParams(object): """ Private class to track supported impurity measures. + + .. versionadded:: 1.4.0 """ supportedImpurities = ["entropy", "gini"] @@ -231,6 +244,7 @@ class TreeClassifierParams(object): "gain calculation (case-insensitive). Supported options: " + ", ".join(self.supportedImpurities)) + @since("1.6.0") def setImpurity(self, value): """ Sets the value of :py:attr:`impurity`. @@ -238,6 +252,7 @@ class TreeClassifierParams(object): self._paramMap[self.impurity] = value return self + @since("1.6.0") def getImpurity(self): """ Gets the value of impurity or its default value. @@ -248,6 +263,8 @@ class TreeClassifierParams(object): class GBTParams(TreeEnsembleParams): """ Private class to track supported GBT params. + + .. versionadded:: 1.4.0 """ supportedLossTypes = ["logistic"] @@ -287,6 +304,8 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"]) >>> model.transform(test1).head().prediction 1.0 + + .. versionadded:: 1.4.0 """ @keyword_only @@ -310,6 +329,7 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred self.setParams(**kwargs) @keyword_only + @since("1.4.0") def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", probabilityCol="probability", rawPredictionCol="rawPrediction", maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, @@ -333,6 +353,8 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred class DecisionTreeClassificationModel(DecisionTreeModel): """ Model fitted by DecisionTreeClassifier. + + .. versionadded:: 1.4.0 """ @@ -371,6 +393,8 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"]) >>> model.transform(test1).head().prediction 1.0 + + .. versionadded:: 1.4.0 """ @keyword_only @@ -396,6 +420,7 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred self.setParams(**kwargs) @keyword_only + @since("1.4.0") def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", probabilityCol="probability", rawPredictionCol="rawPrediction", maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, @@ -419,6 +444,8 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred class RandomForestClassificationModel(TreeEnsembleModels): """ Model fitted by RandomForestClassifier. + + .. versionadded:: 1.4.0 """ @@ -450,6 +477,8 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"]) >>> model.transform(test1).head().prediction 1.0 + + .. versionadded:: 1.4.0 """ # a placeholder to make it appear in the generated doc @@ -482,6 +511,7 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol self.setParams(**kwargs) @keyword_only + @since("1.4.0") def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, @@ -499,6 +529,7 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol def _create_model(self, java_model): return GBTClassificationModel(java_model) + @since("1.4.0") def setLossType(self, value): """ Sets the value of :py:attr:`lossType`. @@ -506,6 +537,7 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol self._paramMap[self.lossType] = value return self + @since("1.4.0") def getLossType(self): """ Gets the value of lossType or its default value. @@ -516,6 +548,8 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol class GBTClassificationModel(TreeEnsembleModels): """ Model fitted by GBTClassifier. + + .. versionadded:: 1.4.0 """ @@ -555,6 +589,8 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, H >>> test1 = sc.parallelize([Row(features=Vectors.sparse(2, [0], [1.0]))]).toDF() >>> model.transform(test1).head().prediction 1.0 + + .. versionadded:: 1.5.0 """ # a placeholder to make it appear in the generated doc @@ -587,6 +623,7 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, H self.setParams(**kwargs) @keyword_only + @since("1.5.0") def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", probabilityCol="probability", rawPredictionCol="rawPrediction", smoothing=1.0, modelType="multinomial"): @@ -602,6 +639,7 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, H def _create_model(self, java_model): return NaiveBayesModel(java_model) + @since("1.5.0") def setSmoothing(self, value): """ Sets the value of :py:attr:`smoothing`. @@ -609,12 +647,14 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, H self._paramMap[self.smoothing] = value return self + @since("1.5.0") def getSmoothing(self): """ Gets the value of smoothing or its default value. """ return self.getOrDefault(self.smoothing) + @since("1.5.0") def setModelType(self, value): """ Sets the value of :py:attr:`modelType`. @@ -622,6 +662,7 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, H self._paramMap[self.modelType] = value return self + @since("1.5.0") def getModelType(self): """ Gets the value of modelType or its default value. @@ -632,9 +673,12 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, H class NaiveBayesModel(JavaModel): """ Model fitted by NaiveBayes. + + .. versionadded:: 1.5.0 """ @property + @since("1.5.0") def pi(self): """ log of class priors. @@ -642,6 +686,7 @@ class NaiveBayesModel(JavaModel): return self._call_java("pi") @property + @since("1.5.0") def theta(self): """ log of class conditional probabilities. @@ -681,6 +726,8 @@ class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, |[0.0,0.0]| 0.0| +---------+----------+ ... + + .. versionadded:: 1.6.0 """ # a placeholder to make it appear in the generated doc @@ -715,6 +762,7 @@ class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, self.setParams(**kwargs) @keyword_only + @since("1.6.0") def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxIter=100, tol=1e-4, seed=None, layers=None, blockSize=128): """ @@ -731,6 +779,7 @@ class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, def _create_model(self, java_model): return MultilayerPerceptronClassificationModel(java_model) + @since("1.6.0") def setLayers(self, value): """ Sets the value of :py:attr:`layers`. @@ -738,12 +787,14 @@ class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, self._paramMap[self.layers] = value return self + @since("1.6.0") def getLayers(self): """ Gets the value of layers or its default value. """ return self.getOrDefault(self.layers) + @since("1.6.0") def setBlockSize(self, value): """ Sets the value of :py:attr:`blockSize`. @@ -751,6 +802,7 @@ class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, self._paramMap[self.blockSize] = value return self + @since("1.6.0") def getBlockSize(self): """ Gets the value of blockSize or its default value. @@ -761,9 +813,12 @@ class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, class MultilayerPerceptronClassificationModel(JavaModel): """ Model fitted by MultilayerPerceptronClassifier. + + .. versionadded:: 1.6.0 """ @property + @since("1.6.0") def layers(self): """ array of layer sizes including input and output layers. @@ -771,6 +826,7 @@ class MultilayerPerceptronClassificationModel(JavaModel): return self._call_java("javaLayers") @property + @since("1.6.0") def weights(self): """ vector of initial weights for the model that consists of the weights of layers. --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
