diff options
Diffstat (limited to 'python/pyspark/ml/param/shared.py')
| -rw-r--r-- | python/pyspark/ml/param/shared.py | 144 |
1 files changed, 72 insertions, 72 deletions
diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py index 4d96080150..23f9431484 100644 --- a/python/pyspark/ml/param/shared.py +++ b/python/pyspark/ml/param/shared.py @@ -26,18 +26,18 @@ class HasMaxIter(Params): """ # a placeholder to make it appear in the generated doc - maxIter = Param(Params._dummy(), "maxIter", "max number of iterations (>= 0).") + maxIter = Param(Params._dummy(), "maxIter", "max number of iterations (>= 0).", int) def __init__(self): super(HasMaxIter, self).__init__() #: param for max number of iterations (>= 0). - self.maxIter = Param(self, "maxIter", "max number of iterations (>= 0).") + self.maxIter = Param(self, "maxIter", "max number of iterations (>= 0).", int) def setMaxIter(self, value): """ Sets the value of :py:attr:`maxIter`. """ - self._paramMap[self.maxIter] = value + self._set(maxIter=value) return self def getMaxIter(self): @@ -53,18 +53,18 @@ class HasRegParam(Params): """ # a placeholder to make it appear in the generated doc - regParam = Param(Params._dummy(), "regParam", "regularization parameter (>= 0).") + regParam = Param(Params._dummy(), "regParam", "regularization parameter (>= 0).", float) def __init__(self): super(HasRegParam, self).__init__() #: param for regularization parameter (>= 0). - self.regParam = Param(self, "regParam", "regularization parameter (>= 0).") + self.regParam = Param(self, "regParam", "regularization parameter (>= 0).", float) def setRegParam(self, value): """ Sets the value of :py:attr:`regParam`. """ - self._paramMap[self.regParam] = value + self._set(regParam=value) return self def getRegParam(self): @@ -80,19 +80,19 @@ class HasFeaturesCol(Params): """ # a placeholder to make it appear in the generated doc - featuresCol = Param(Params._dummy(), "featuresCol", "features column name.") + featuresCol = Param(Params._dummy(), "featuresCol", "features column name.", str) def __init__(self): super(HasFeaturesCol, self).__init__() #: param for features column name. - self.featuresCol = Param(self, "featuresCol", "features column name.") + self.featuresCol = Param(self, "featuresCol", "features column name.", str) self._setDefault(featuresCol='features') def setFeaturesCol(self, value): """ Sets the value of :py:attr:`featuresCol`. """ - self._paramMap[self.featuresCol] = value + self._set(featuresCol=value) return self def getFeaturesCol(self): @@ -108,19 +108,19 @@ class HasLabelCol(Params): """ # a placeholder to make it appear in the generated doc - labelCol = Param(Params._dummy(), "labelCol", "label column name.") + labelCol = Param(Params._dummy(), "labelCol", "label column name.", str) def __init__(self): super(HasLabelCol, self).__init__() #: param for label column name. - self.labelCol = Param(self, "labelCol", "label column name.") + self.labelCol = Param(self, "labelCol", "label column name.", str) self._setDefault(labelCol='label') def setLabelCol(self, value): """ Sets the value of :py:attr:`labelCol`. """ - self._paramMap[self.labelCol] = value + self._set(labelCol=value) return self def getLabelCol(self): @@ -136,19 +136,19 @@ class HasPredictionCol(Params): """ # a placeholder to make it appear in the generated doc - predictionCol = Param(Params._dummy(), "predictionCol", "prediction column name.") + predictionCol = Param(Params._dummy(), "predictionCol", "prediction column name.", str) def __init__(self): super(HasPredictionCol, self).__init__() #: param for prediction column name. - self.predictionCol = Param(self, "predictionCol", "prediction column name.") + self.predictionCol = Param(self, "predictionCol", "prediction column name.", str) self._setDefault(predictionCol='prediction') def setPredictionCol(self, value): """ Sets the value of :py:attr:`predictionCol`. """ - self._paramMap[self.predictionCol] = value + self._set(predictionCol=value) return self def getPredictionCol(self): @@ -164,19 +164,19 @@ class HasProbabilityCol(Params): """ # a placeholder to make it appear in the generated doc - probabilityCol = Param(Params._dummy(), "probabilityCol", "Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.") + probabilityCol = Param(Params._dummy(), "probabilityCol", "Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.", str) def __init__(self): super(HasProbabilityCol, self).__init__() #: param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities. - self.probabilityCol = Param(self, "probabilityCol", "Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.") + self.probabilityCol = Param(self, "probabilityCol", "Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.", str) self._setDefault(probabilityCol='probability') def setProbabilityCol(self, value): """ Sets the value of :py:attr:`probabilityCol`. """ - self._paramMap[self.probabilityCol] = value + self._set(probabilityCol=value) return self def getProbabilityCol(self): @@ -192,19 +192,19 @@ class HasRawPredictionCol(Params): """ # a placeholder to make it appear in the generated doc - rawPredictionCol = Param(Params._dummy(), "rawPredictionCol", "raw prediction (a.k.a. confidence) column name.") + rawPredictionCol = Param(Params._dummy(), "rawPredictionCol", "raw prediction (a.k.a. confidence) column name.", str) def __init__(self): super(HasRawPredictionCol, self).__init__() #: param for raw prediction (a.k.a. confidence) column name. - self.rawPredictionCol = Param(self, "rawPredictionCol", "raw prediction (a.k.a. confidence) column name.") + self.rawPredictionCol = Param(self, "rawPredictionCol", "raw prediction (a.k.a. confidence) column name.", str) self._setDefault(rawPredictionCol='rawPrediction') def setRawPredictionCol(self, value): """ Sets the value of :py:attr:`rawPredictionCol`. """ - self._paramMap[self.rawPredictionCol] = value + self._set(rawPredictionCol=value) return self def getRawPredictionCol(self): @@ -220,18 +220,18 @@ class HasInputCol(Params): """ # a placeholder to make it appear in the generated doc - inputCol = Param(Params._dummy(), "inputCol", "input column name.") + inputCol = Param(Params._dummy(), "inputCol", "input column name.", str) def __init__(self): super(HasInputCol, self).__init__() #: param for input column name. - self.inputCol = Param(self, "inputCol", "input column name.") + self.inputCol = Param(self, "inputCol", "input column name.", str) def setInputCol(self, value): """ Sets the value of :py:attr:`inputCol`. """ - self._paramMap[self.inputCol] = value + self._set(inputCol=value) return self def getInputCol(self): @@ -247,18 +247,18 @@ class HasInputCols(Params): """ # a placeholder to make it appear in the generated doc - inputCols = Param(Params._dummy(), "inputCols", "input column names.") + inputCols = Param(Params._dummy(), "inputCols", "input column names.", None) def __init__(self): super(HasInputCols, self).__init__() #: param for input column names. - self.inputCols = Param(self, "inputCols", "input column names.") + self.inputCols = Param(self, "inputCols", "input column names.", None) def setInputCols(self, value): """ Sets the value of :py:attr:`inputCols`. """ - self._paramMap[self.inputCols] = value + self._set(inputCols=value) return self def getInputCols(self): @@ -274,19 +274,19 @@ class HasOutputCol(Params): """ # a placeholder to make it appear in the generated doc - outputCol = Param(Params._dummy(), "outputCol", "output column name.") + outputCol = Param(Params._dummy(), "outputCol", "output column name.", str) def __init__(self): super(HasOutputCol, self).__init__() #: param for output column name. - self.outputCol = Param(self, "outputCol", "output column name.") + self.outputCol = Param(self, "outputCol", "output column name.", str) self._setDefault(outputCol=self.uid + '__output') def setOutputCol(self, value): """ Sets the value of :py:attr:`outputCol`. """ - self._paramMap[self.outputCol] = value + self._set(outputCol=value) return self def getOutputCol(self): @@ -302,18 +302,18 @@ class HasNumFeatures(Params): """ # a placeholder to make it appear in the generated doc - numFeatures = Param(Params._dummy(), "numFeatures", "number of features.") + numFeatures = Param(Params._dummy(), "numFeatures", "number of features.", int) def __init__(self): super(HasNumFeatures, self).__init__() #: param for number of features. - self.numFeatures = Param(self, "numFeatures", "number of features.") + self.numFeatures = Param(self, "numFeatures", "number of features.", int) def setNumFeatures(self, value): """ Sets the value of :py:attr:`numFeatures`. """ - self._paramMap[self.numFeatures] = value + self._set(numFeatures=value) return self def getNumFeatures(self): @@ -329,18 +329,18 @@ class HasCheckpointInterval(Params): """ # a placeholder to make it appear in the generated doc - checkpointInterval = Param(Params._dummy(), "checkpointInterval", "set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations.") + checkpointInterval = Param(Params._dummy(), "checkpointInterval", "set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations.", int) def __init__(self): super(HasCheckpointInterval, self).__init__() #: param for set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations. - self.checkpointInterval = Param(self, "checkpointInterval", "set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations.") + self.checkpointInterval = Param(self, "checkpointInterval", "set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations.", int) def setCheckpointInterval(self, value): """ Sets the value of :py:attr:`checkpointInterval`. """ - self._paramMap[self.checkpointInterval] = value + self._set(checkpointInterval=value) return self def getCheckpointInterval(self): @@ -356,19 +356,19 @@ class HasSeed(Params): """ # a placeholder to make it appear in the generated doc - seed = Param(Params._dummy(), "seed", "random seed.") + seed = Param(Params._dummy(), "seed", "random seed.", int) def __init__(self): super(HasSeed, self).__init__() #: param for random seed. - self.seed = Param(self, "seed", "random seed.") + self.seed = Param(self, "seed", "random seed.", int) self._setDefault(seed=hash(type(self).__name__)) def setSeed(self, value): """ Sets the value of :py:attr:`seed`. """ - self._paramMap[self.seed] = value + self._set(seed=value) return self def getSeed(self): @@ -384,18 +384,18 @@ class HasTol(Params): """ # a placeholder to make it appear in the generated doc - tol = Param(Params._dummy(), "tol", "the convergence tolerance for iterative algorithms.") + tol = Param(Params._dummy(), "tol", "the convergence tolerance for iterative algorithms.", float) def __init__(self): super(HasTol, self).__init__() #: param for the convergence tolerance for iterative algorithms. - self.tol = Param(self, "tol", "the convergence tolerance for iterative algorithms.") + self.tol = Param(self, "tol", "the convergence tolerance for iterative algorithms.", float) def setTol(self, value): """ Sets the value of :py:attr:`tol`. """ - self._paramMap[self.tol] = value + self._set(tol=value) return self def getTol(self): @@ -411,18 +411,18 @@ class HasStepSize(Params): """ # a placeholder to make it appear in the generated doc - stepSize = Param(Params._dummy(), "stepSize", "Step size to be used for each iteration of optimization.") + stepSize = Param(Params._dummy(), "stepSize", "Step size to be used for each iteration of optimization.", float) def __init__(self): super(HasStepSize, self).__init__() #: param for Step size to be used for each iteration of optimization. - self.stepSize = Param(self, "stepSize", "Step size to be used for each iteration of optimization.") + self.stepSize = Param(self, "stepSize", "Step size to be used for each iteration of optimization.", float) def setStepSize(self, value): """ Sets the value of :py:attr:`stepSize`. """ - self._paramMap[self.stepSize] = value + self._set(stepSize=value) return self def getStepSize(self): @@ -438,18 +438,18 @@ class HasHandleInvalid(Params): """ # a placeholder to make it appear in the generated doc - handleInvalid = Param(Params._dummy(), "handleInvalid", "how to handle invalid entries. Options are skip (which will filter out rows with bad values), or error (which will throw an errror). More options may be added later.") + handleInvalid = Param(Params._dummy(), "handleInvalid", "how to handle invalid entries. Options are skip (which will filter out rows with bad values), or error (which will throw an errror). More options may be added later.", str) def __init__(self): super(HasHandleInvalid, self).__init__() #: param for how to handle invalid entries. Options are skip (which will filter out rows with bad values), or error (which will throw an errror). More options may be added later. - self.handleInvalid = Param(self, "handleInvalid", "how to handle invalid entries. Options are skip (which will filter out rows with bad values), or error (which will throw an errror). More options may be added later.") + self.handleInvalid = Param(self, "handleInvalid", "how to handle invalid entries. Options are skip (which will filter out rows with bad values), or error (which will throw an errror). More options may be added later.", str) def setHandleInvalid(self, value): """ Sets the value of :py:attr:`handleInvalid`. """ - self._paramMap[self.handleInvalid] = value + self._set(handleInvalid=value) return self def getHandleInvalid(self): @@ -465,19 +465,19 @@ class HasElasticNetParam(Params): """ # a placeholder to make it appear in the generated doc - elasticNetParam = Param(Params._dummy(), "elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.") + elasticNetParam = Param(Params._dummy(), "elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.", float) def __init__(self): super(HasElasticNetParam, self).__init__() #: param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty. - self.elasticNetParam = Param(self, "elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.") + self.elasticNetParam = Param(self, "elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.", float) self._setDefault(elasticNetParam=0.0) def setElasticNetParam(self, value): """ Sets the value of :py:attr:`elasticNetParam`. """ - self._paramMap[self.elasticNetParam] = value + self._set(elasticNetParam=value) return self def getElasticNetParam(self): @@ -493,19 +493,19 @@ class HasFitIntercept(Params): """ # a placeholder to make it appear in the generated doc - fitIntercept = Param(Params._dummy(), "fitIntercept", "whether to fit an intercept term.") + fitIntercept = Param(Params._dummy(), "fitIntercept", "whether to fit an intercept term.", bool) def __init__(self): super(HasFitIntercept, self).__init__() #: param for whether to fit an intercept term. - self.fitIntercept = Param(self, "fitIntercept", "whether to fit an intercept term.") + self.fitIntercept = Param(self, "fitIntercept", "whether to fit an intercept term.", bool) self._setDefault(fitIntercept=True) def setFitIntercept(self, value): """ Sets the value of :py:attr:`fitIntercept`. """ - self._paramMap[self.fitIntercept] = value + self._set(fitIntercept=value) return self def getFitIntercept(self): @@ -521,19 +521,19 @@ class HasStandardization(Params): """ # a placeholder to make it appear in the generated doc - standardization = Param(Params._dummy(), "standardization", "whether to standardize the training features before fitting the model.") + standardization = Param(Params._dummy(), "standardization", "whether to standardize the training features before fitting the model.", bool) def __init__(self): super(HasStandardization, self).__init__() #: param for whether to standardize the training features before fitting the model. - self.standardization = Param(self, "standardization", "whether to standardize the training features before fitting the model.") + self.standardization = Param(self, "standardization", "whether to standardize the training features before fitting the model.", bool) self._setDefault(standardization=True) def setStandardization(self, value): """ Sets the value of :py:attr:`standardization`. """ - self._paramMap[self.standardization] = value + self._set(standardization=value) return self def getStandardization(self): @@ -549,18 +549,18 @@ class HasThresholds(Params): """ # a placeholder to make it appear in the generated doc - thresholds = Param(Params._dummy(), "thresholds", "Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.") + thresholds = Param(Params._dummy(), "thresholds", "Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.", None) def __init__(self): super(HasThresholds, self).__init__() #: param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold. - self.thresholds = Param(self, "thresholds", "Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.") + self.thresholds = Param(self, "thresholds", "Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.", None) def setThresholds(self, value): """ Sets the value of :py:attr:`thresholds`. """ - self._paramMap[self.thresholds] = value + self._set(thresholds=value) return self def getThresholds(self): @@ -576,18 +576,18 @@ class HasWeightCol(Params): """ # a placeholder to make it appear in the generated doc - weightCol = Param(Params._dummy(), "weightCol", "weight column name. If this is not set or empty, we treat all instance weights as 1.0.") + weightCol = Param(Params._dummy(), "weightCol", "weight column name. If this is not set or empty, we treat all instance weights as 1.0.", str) def __init__(self): super(HasWeightCol, self).__init__() #: param for weight column name. If this is not set or empty, we treat all instance weights as 1.0. - self.weightCol = Param(self, "weightCol", "weight column name. If this is not set or empty, we treat all instance weights as 1.0.") + self.weightCol = Param(self, "weightCol", "weight column name. If this is not set or empty, we treat all instance weights as 1.0.", str) def setWeightCol(self, value): """ Sets the value of :py:attr:`weightCol`. """ - self._paramMap[self.weightCol] = value + self._set(weightCol=value) return self def getWeightCol(self): @@ -603,19 +603,19 @@ class HasSolver(Params): """ # a placeholder to make it appear in the generated doc - solver = Param(Params._dummy(), "solver", "the solver algorithm for optimization. If this is not set or empty, default value is 'auto'.") + solver = Param(Params._dummy(), "solver", "the solver algorithm for optimization. If this is not set or empty, default value is 'auto'.", str) def __init__(self): super(HasSolver, self).__init__() #: param for the solver algorithm for optimization. If this is not set or empty, default value is 'auto'. - self.solver = Param(self, "solver", "the solver algorithm for optimization. If this is not set or empty, default value is 'auto'.") + self.solver = Param(self, "solver", "the solver algorithm for optimization. If this is not set or empty, default value is 'auto'.", str) self._setDefault(solver='auto') def setSolver(self, value): """ Sets the value of :py:attr:`solver`. """ - self._paramMap[self.solver] = value + self._set(solver=value) return self def getSolver(self): @@ -658,7 +658,7 @@ class DecisionTreeParams(Params): """ Sets the value of :py:attr:`maxDepth`. """ - self._paramMap[self.maxDepth] = value + self._set(maxDepth=value) return self def getMaxDepth(self): @@ -671,7 +671,7 @@ class DecisionTreeParams(Params): """ Sets the value of :py:attr:`maxBins`. """ - self._paramMap[self.maxBins] = value + self._set(maxBins=value) return self def getMaxBins(self): @@ -684,7 +684,7 @@ class DecisionTreeParams(Params): """ Sets the value of :py:attr:`minInstancesPerNode`. """ - self._paramMap[self.minInstancesPerNode] = value + self._set(minInstancesPerNode=value) return self def getMinInstancesPerNode(self): @@ -697,7 +697,7 @@ class DecisionTreeParams(Params): """ Sets the value of :py:attr:`minInfoGain`. """ - self._paramMap[self.minInfoGain] = value + self._set(minInfoGain=value) return self def getMinInfoGain(self): @@ -710,7 +710,7 @@ class DecisionTreeParams(Params): """ Sets the value of :py:attr:`maxMemoryInMB`. """ - self._paramMap[self.maxMemoryInMB] = value + self._set(maxMemoryInMB=value) return self def getMaxMemoryInMB(self): @@ -723,7 +723,7 @@ class DecisionTreeParams(Params): """ Sets the value of :py:attr:`cacheNodeIds`. """ - self._paramMap[self.cacheNodeIds] = value + self._set(cacheNodeIds=value) return self def getCacheNodeIds(self): |