IsotonicRegression¶
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class
pyspark.ml.regression.
IsotonicRegression
(*, featuresCol: str = 'features', labelCol: str = 'label', predictionCol: str = 'prediction', weightCol: Optional[str] = None, isotonic: bool = True, featureIndex: int = 0)¶ Currently implemented using parallelized pool adjacent violators algorithm. Only univariate (single feature) algorithm supported.
Examples
>>> from pyspark.ml.linalg import Vectors >>> df = spark.createDataFrame([ ... (1.0, Vectors.dense(1.0)), ... (0.0, Vectors.sparse(1, [], []))], ["label", "features"]) >>> ir = IsotonicRegression() >>> model = ir.fit(df) >>> model.setFeaturesCol("features") IsotonicRegressionModel... >>> model.numFeatures 1 >>> test0 = spark.createDataFrame([(Vectors.dense(-1.0),)], ["features"]) >>> model.transform(test0).head().prediction 0.0 >>> model.predict(test0.head().features[model.getFeatureIndex()]) 0.0 >>> model.boundaries DenseVector([0.0, 1.0]) >>> ir_path = temp_path + "/ir" >>> ir.save(ir_path) >>> ir2 = IsotonicRegression.load(ir_path) >>> ir2.getIsotonic() True >>> model_path = temp_path + "/ir_model" >>> model.save(model_path) >>> model2 = IsotonicRegressionModel.load(model_path) >>> model.boundaries == model2.boundaries True >>> model.predictions == model2.predictions True >>> model.transform(test0).take(1) == model2.transform(test0).take(1) True
Methods
clear
(param)Clears a param from the param map if it has been explicitly set.
copy
([extra])Creates a copy of this instance with the same uid and some extra params.
explainParam
(param)Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
Returns the documentation of all params with their optionally default values and user-supplied values.
extractParamMap
([extra])Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.
fit
(dataset[, params])Fits a model to the input dataset with optional parameters.
fitMultiple
(dataset, paramMaps)Fits a model to the input dataset for each param map in paramMaps.
Gets the value of featureIndex or its default value.
Gets the value of featuresCol or its default value.
Gets the value of isotonic or its default value.
Gets the value of labelCol or its default value.
getOrDefault
(param)Gets the value of a param in the user-supplied param map or its default value.
getParam
(paramName)Gets a param by its name.
Gets the value of predictionCol or its default value.
Gets the value of weightCol or its default value.
hasDefault
(param)Checks whether a param has a default value.
hasParam
(paramName)Tests whether this instance contains a param with a given (string) name.
isDefined
(param)Checks whether a param is explicitly set by user or has a default value.
isSet
(param)Checks whether a param is explicitly set by user.
load
(path)Reads an ML instance from the input path, a shortcut of read().load(path).
read
()Returns an MLReader instance for this class.
save
(path)Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
set
(param, value)Sets a parameter in the embedded param map.
setFeatureIndex
(value)Sets the value of
featureIndex
.setFeaturesCol
(value)Sets the value of
featuresCol
.setIsotonic
(value)Sets the value of
isotonic
.setLabelCol
(value)Sets the value of
labelCol
.setParams
(*[, featuresCol, labelCol, …])setParams(self, *, featuresCol=”features”, labelCol=”label”, predictionCol=”prediction”, weightCol=None, isotonic=True, featureIndex=0): Set the params for IsotonicRegression.
setPredictionCol
(value)Sets the value of
predictionCol
.setWeightCol
(value)Sets the value of
weightCol
.write
()Returns an MLWriter instance for this ML instance.
Attributes
Returns all params ordered by name.
Methods Documentation
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clear
(param: pyspark.ml.param.Param) → None¶ Clears a param from the param map if it has been explicitly set.
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copy
(extra: Optional[ParamMap] = None) → JP¶ Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.
- Parameters
- extradict, optional
Extra parameters to copy to the new instance
- Returns
JavaParams
Copy of this instance
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explainParam
(param: Union[str, pyspark.ml.param.Param]) → str¶ Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
-
explainParams
() → str¶ Returns the documentation of all params with their optionally default values and user-supplied values.
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extractParamMap
(extra: Optional[ParamMap] = None) → ParamMap¶ Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.
- Parameters
- extradict, optional
extra param values
- Returns
- dict
merged param map
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fit
(dataset: pyspark.sql.dataframe.DataFrame, params: Union[ParamMap, List[ParamMap], Tuple[ParamMap], None] = None) → Union[M, List[M]]¶ Fits a model to the input dataset with optional parameters.
- Parameters
- dataset
pyspark.sql.DataFrame
input dataset.
- paramsdict or list or tuple, optional
an optional param map that overrides embedded params. If a list/tuple of param maps is given, this calls fit on each param map and returns a list of models.
- dataset
- Returns
Transformer
or a list ofTransformer
fitted model(s)
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fitMultiple
(dataset: pyspark.sql.dataframe.DataFrame, paramMaps: Sequence[ParamMap]) → Iterator[Tuple[int, M]]¶ Fits a model to the input dataset for each param map in paramMaps.
- Parameters
- dataset
pyspark.sql.DataFrame
input dataset.
- paramMaps
collections.abc.Sequence
A Sequence of param maps.
- dataset
- Returns
_FitMultipleIterator
A thread safe iterable which contains one model for each param map. Each call to next(modelIterator) will return (index, model) where model was fit using paramMaps[index]. index values may not be sequential.
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getFeatureIndex
() → int¶ Gets the value of featureIndex or its default value.
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getFeaturesCol
() → str¶ Gets the value of featuresCol or its default value.
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getIsotonic
() → bool¶ Gets the value of isotonic or its default value.
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getLabelCol
() → str¶ Gets the value of labelCol or its default value.
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getOrDefault
(param: Union[str, pyspark.ml.param.Param[T]]) → Union[Any, T]¶ Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.
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getParam
(paramName: str) → pyspark.ml.param.Param¶ Gets a param by its name.
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getPredictionCol
() → str¶ Gets the value of predictionCol or its default value.
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getWeightCol
() → str¶ Gets the value of weightCol or its default value.
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hasDefault
(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶ Checks whether a param has a default value.
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hasParam
(paramName: str) → bool¶ Tests whether this instance contains a param with a given (string) name.
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isDefined
(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶ Checks whether a param is explicitly set by user or has a default value.
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isSet
(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶ Checks whether a param is explicitly set by user.
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classmethod
load
(path: str) → RL¶ Reads an ML instance from the input path, a shortcut of read().load(path).
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classmethod
read
() → pyspark.ml.util.JavaMLReader[RL]¶ Returns an MLReader instance for this class.
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save
(path: str) → None¶ Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
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set
(param: pyspark.ml.param.Param, value: Any) → None¶ Sets a parameter in the embedded param map.
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setFeatureIndex
(value: int) → pyspark.ml.regression.IsotonicRegression¶ Sets the value of
featureIndex
.
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setFeaturesCol
(value: str) → pyspark.ml.regression.IsotonicRegression¶ Sets the value of
featuresCol
.
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setIsotonic
(value: bool) → pyspark.ml.regression.IsotonicRegression¶ Sets the value of
isotonic
.
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setLabelCol
(value: str) → pyspark.ml.regression.IsotonicRegression¶ Sets the value of
labelCol
.
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setParams
(*, featuresCol: str = 'features', labelCol: str = 'label', predictionCol: str = 'prediction', weightCol: Optional[str] = None, isotonic: bool = True, featureIndex: int = 0) → pyspark.ml.regression.IsotonicRegression¶ setParams(self, *, featuresCol=”features”, labelCol=”label”, predictionCol=”prediction”, weightCol=None, isotonic=True, featureIndex=0): Set the params for IsotonicRegression.
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setPredictionCol
(value: str) → pyspark.ml.regression.IsotonicRegression¶ Sets the value of
predictionCol
.
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setWeightCol
(value: str) → pyspark.ml.regression.IsotonicRegression¶ Sets the value of
weightCol
.
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write
() → pyspark.ml.util.JavaMLWriter¶ Returns an MLWriter instance for this ML instance.
Attributes Documentation
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featureIndex
= Param(parent='undefined', name='featureIndex', doc='The index of the feature if featuresCol is a vector column, no effect otherwise.')¶
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featuresCol
= Param(parent='undefined', name='featuresCol', doc='features column name.')¶
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isotonic
= Param(parent='undefined', name='isotonic', doc='whether the output sequence should be isotonic/increasing (true) orantitonic/decreasing (false).')¶
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labelCol
= Param(parent='undefined', name='labelCol', doc='label column name.')¶
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params
¶ Returns all params ordered by name. The default implementation uses
dir()
to get all attributes of typeParam
.
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predictionCol
= Param(parent='undefined', name='predictionCol', doc='prediction column name.')¶
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weightCol
= Param(parent='undefined', name='weightCol', doc='weight column name. If this is not set or empty, we treat all instance weights as 1.0.')¶
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