RegressionEvaluator¶
-
class
pyspark.ml.evaluation.
RegressionEvaluator
(predictionCol='prediction', labelCol='label', metricName='rmse', weightCol=None, throughOrigin=False)[source]¶ Evaluator for Regression, which expects input columns prediction, label and an optional weight column.
>>> scoreAndLabels = [(-28.98343821, -27.0), (20.21491975, 21.5), ... (-25.98418959, -22.0), (30.69731842, 33.0), (74.69283752, 71.0)] >>> dataset = spark.createDataFrame(scoreAndLabels, ["raw", "label"]) ... >>> evaluator = RegressionEvaluator() >>> evaluator.setPredictionCol("raw") RegressionEvaluator... >>> evaluator.evaluate(dataset) 2.842... >>> evaluator.evaluate(dataset, {evaluator.metricName: "r2"}) 0.993... >>> evaluator.evaluate(dataset, {evaluator.metricName: "mae"}) 2.649... >>> re_path = temp_path + "/re" >>> evaluator.save(re_path) >>> evaluator2 = RegressionEvaluator.load(re_path) >>> str(evaluator2.getPredictionCol()) 'raw' >>> scoreAndLabelsAndWeight = [(-28.98343821, -27.0, 1.0), (20.21491975, 21.5, 0.8), ... (-25.98418959, -22.0, 1.0), (30.69731842, 33.0, 0.6), (74.69283752, 71.0, 0.2)] >>> dataset = spark.createDataFrame(scoreAndLabelsAndWeight, ["raw", "label", "weight"]) ... >>> evaluator = RegressionEvaluator(predictionCol="raw", weightCol="weight") >>> evaluator.evaluate(dataset) 2.740... >>> evaluator.getThroughOrigin() False
New in version 1.4.0.
Methods
Attributes
Methods Documentation
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clear
(param)¶ Clears a param from the param map if it has been explicitly set.
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copy
(extra=None)¶ 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
extra – Extra parameters to copy to the new instance
- Returns
Copy of this instance
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evaluate
(dataset, params=None)¶ Evaluates the output with optional parameters.
- Parameters
dataset – a dataset that contains labels/observations and predictions
params – an optional param map that overrides embedded params
- Returns
metric
New in version 1.4.0.
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explainParam
(param)¶ Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
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explainParams
()¶ Returns the documentation of all params with their optionally default values and user-supplied values.
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extractParamMap
(extra=None)¶ 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
extra – extra param values
- Returns
merged param map
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getLabelCol
()¶ Gets the value of labelCol or its default value.
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getOrDefault
(param)¶ 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)¶ Gets a param by its name.
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getPredictionCol
()¶ Gets the value of predictionCol or its default value.
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getThroughOrigin
()[source]¶ Gets the value of throughOrigin or its default value.
New in version 3.0.0.
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getWeightCol
()¶ Gets the value of weightCol or its default value.
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hasDefault
(param)¶ Checks whether a param has a default value.
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hasParam
(paramName)¶ Tests whether this instance contains a param with a given (string) name.
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isDefined
(param)¶ Checks whether a param is explicitly set by user or has a default value.
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isLargerBetter
()¶ Indicates whether the metric returned by
evaluate()
should be maximized (True, default) or minimized (False). A given evaluator may support multiple metrics which may be maximized or minimized.New in version 1.5.0.
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isSet
(param)¶ Checks whether a param is explicitly set by user.
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classmethod
load
(path)¶ Reads an ML instance from the input path, a shortcut of read().load(path).
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classmethod
read
()¶ Returns an MLReader instance for this class.
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save
(path)¶ Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
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set
(param, value)¶ Sets a parameter in the embedded param map.
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setMetricName
(value)[source]¶ Sets the value of
metricName
.New in version 1.4.0.
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setParams
(self, predictionCol='prediction', labelCol='label', metricName='rmse', weightCol=None, throughOrigin=False)[source]¶ Sets params for regression evaluator.
New in version 1.4.0.
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setPredictionCol
(value)[source]¶ Sets the value of
predictionCol
.
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setThroughOrigin
(value)[source]¶ Sets the value of
throughOrigin
.New in version 3.0.0.
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write
()¶ Returns an MLWriter instance for this ML instance.
Attributes Documentation
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labelCol
= Param(parent='undefined', name='labelCol', doc='label column name.')¶
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metricName
= Param(parent='undefined', name='metricName', doc='metric name in evaluation - one of:\n rmse - root mean squared error (default)\n mse - mean squared error\n r2 - r^2 metric\n mae - mean absolute error\n var - explained variance.')¶
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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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throughOrigin
= Param(parent='undefined', name='throughOrigin', doc='whether the regression is through the origin.')¶
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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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