diff options
Diffstat (limited to 'python/pyspark/ml/regression.py')
| -rw-r--r-- | python/pyspark/ml/regression.py | 25 |
1 files changed, 16 insertions, 9 deletions
diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py index 664a44bc47..898260879d 100644 --- a/python/pyspark/ml/regression.py +++ b/python/pyspark/ml/regression.py @@ -189,10 +189,11 @@ class IsotonicRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti isotonic = \ Param(Params._dummy(), "isotonic", "whether the output sequence should be isotonic/increasing (true) or" + - "antitonic/decreasing (false).") + "antitonic/decreasing (false).", typeConverter=TypeConverters.toBoolean) featureIndex = \ Param(Params._dummy(), "featureIndex", - "The index of the feature if featuresCol is a vector column, no effect otherwise.") + "The index of the feature if featuresCol is a vector column, no effect otherwise.", + typeConverter=TypeConverters.toInt) @keyword_only def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", @@ -278,7 +279,8 @@ class TreeEnsembleParams(DecisionTreeParams): """ subsamplingRate = Param(Params._dummy(), "subsamplingRate", "Fraction of the training data " + - "used for learning each decision tree, in range (0, 1].") + "used for learning each decision tree, in range (0, 1].", + typeConverter=TypeConverters.toFloat) def __init__(self): super(TreeEnsembleParams, self).__init__() @@ -335,11 +337,13 @@ class RandomForestParams(TreeEnsembleParams): """ supportedFeatureSubsetStrategies = ["auto", "all", "onethird", "sqrt", "log2"] - numTrees = Param(Params._dummy(), "numTrees", "Number of trees to train (>= 1).") + numTrees = Param(Params._dummy(), "numTrees", "Number of trees to train (>= 1).", + typeConverter=TypeConverters.toInt) featureSubsetStrategy = \ Param(Params._dummy(), "featureSubsetStrategy", "The number of features to consider for splits at each tree node. Supported " + - "options: " + ", ".join(supportedFeatureSubsetStrategies)) + "options: " + ", ".join(supportedFeatureSubsetStrategies), + typeConverter=TypeConverters.toString) def __init__(self): super(RandomForestParams, self).__init__() @@ -653,7 +657,8 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, lossType = Param(Params._dummy(), "lossType", "Loss function which GBT tries to minimize (case-insensitive). " + - "Supported options: " + ", ".join(GBTParams.supportedLossTypes)) + "Supported options: " + ", ".join(GBTParams.supportedLossTypes), + typeConverter=TypeConverters.toString) @keyword_only def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", @@ -767,14 +772,16 @@ class AFTSurvivalRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi censorCol = Param(Params._dummy(), "censorCol", "censor column name. The value of this column could be 0 or 1. " + "If the value is 1, it means the event has occurred i.e. " + - "uncensored; otherwise censored.") + "uncensored; otherwise censored.", typeConverter=TypeConverters.toString) quantileProbabilities = \ Param(Params._dummy(), "quantileProbabilities", "quantile probabilities array. Values of the quantile probabilities array " + - "should be in the range (0, 1) and the array should be non-empty.") + "should be in the range (0, 1) and the array should be non-empty.", + typeConverter=TypeConverters.toListFloat) quantilesCol = Param(Params._dummy(), "quantilesCol", "quantiles column name. This column will output quantiles of " + - "corresponding quantileProbabilities if it is set.") + "corresponding quantileProbabilities if it is set.", + typeConverter=TypeConverters.toString) @keyword_only def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", |