| # |
| # Licensed to the Apache Software Foundation (ASF) under one or more |
| # contributor license agreements. See the NOTICE file distributed with |
| # this work for additional information regarding copyright ownership. |
| # The ASF licenses this file to You under the Apache License, Version 2.0 |
| # (the "License"); you may not use this file except in compliance with |
| # the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # |
| |
| import sys |
| from abc import abstractmethod, ABCMeta |
| from typing import Any, Dict, Optional, TYPE_CHECKING |
| |
| from pyspark import since, keyword_only |
| from pyspark.ml.wrapper import JavaParams |
| from pyspark.ml.param import Param, Params, TypeConverters |
| from pyspark.ml.param.shared import ( |
| HasLabelCol, |
| HasPredictionCol, |
| HasProbabilityCol, |
| HasRawPredictionCol, |
| HasFeaturesCol, |
| HasWeightCol, |
| ) |
| from pyspark.ml.common import inherit_doc |
| from pyspark.ml.util import JavaMLReadable, JavaMLWritable, try_remote_evaluate |
| from pyspark.sql.dataframe import DataFrame |
| |
| if TYPE_CHECKING: |
| from pyspark.ml._typing import ( |
| ParamMap, |
| BinaryClassificationEvaluatorMetricType, |
| ClusteringEvaluatorDistanceMeasureType, |
| ClusteringEvaluatorMetricType, |
| MulticlassClassificationEvaluatorMetricType, |
| MultilabelClassificationEvaluatorMetricType, |
| RankingEvaluatorMetricType, |
| RegressionEvaluatorMetricType, |
| ) |
| |
| |
| __all__ = [ |
| "Evaluator", |
| "BinaryClassificationEvaluator", |
| "RegressionEvaluator", |
| "MulticlassClassificationEvaluator", |
| "MultilabelClassificationEvaluator", |
| "ClusteringEvaluator", |
| "RankingEvaluator", |
| ] |
| |
| |
| @inherit_doc |
| class Evaluator(Params, metaclass=ABCMeta): |
| """ |
| Base class for evaluators that compute metrics from predictions. |
| |
| .. versionadded:: 1.4.0 |
| """ |
| |
| @abstractmethod |
| def _evaluate(self, dataset: DataFrame) -> float: |
| """ |
| Evaluates the output. |
| |
| Parameters |
| ---------- |
| dataset : :py:class:`pyspark.sql.DataFrame` |
| a dataset that contains labels/observations and predictions |
| |
| Returns |
| ------- |
| float |
| metric |
| """ |
| raise NotImplementedError() |
| |
| def evaluate(self, dataset: DataFrame, params: Optional["ParamMap"] = None) -> float: |
| """ |
| Evaluates the output with optional parameters. |
| |
| .. versionadded:: 1.4.0 |
| |
| Parameters |
| ---------- |
| dataset : :py:class:`pyspark.sql.DataFrame` |
| a dataset that contains labels/observations and predictions |
| params : dict, optional |
| an optional param map that overrides embedded params |
| |
| Returns |
| ------- |
| float |
| metric |
| """ |
| if params is None: |
| params = dict() |
| if isinstance(params, dict): |
| if params: |
| return self.copy(params)._evaluate(dataset) |
| else: |
| return self._evaluate(dataset) |
| else: |
| raise TypeError("Params must be a param map but got %s." % type(params)) |
| |
| @since("1.5.0") |
| def isLargerBetter(self) -> bool: |
| """ |
| Indicates whether the metric returned by :py:meth:`evaluate` should be maximized |
| (True, default) or minimized (False). |
| A given evaluator may support multiple metrics which may be maximized or minimized. |
| """ |
| return True |
| |
| |
| @inherit_doc |
| class JavaEvaluator(JavaParams, Evaluator, metaclass=ABCMeta): |
| """ |
| Base class for :py:class:`Evaluator`s that wrap Java/Scala |
| implementations. |
| """ |
| |
| @try_remote_evaluate |
| def _evaluate(self, dataset: DataFrame) -> float: |
| """ |
| Evaluates the output. |
| |
| Parameters |
| ---------- |
| dataset : :py:class:`pyspark.sql.DataFrame` |
| a dataset that contains labels/observations and predictions |
| |
| Returns |
| ------- |
| float |
| evaluation metric |
| """ |
| self._transfer_params_to_java() |
| assert self._java_obj is not None |
| return self._java_obj.evaluate(dataset._jdf) |
| |
| def isLargerBetter(self) -> bool: |
| self._transfer_params_to_java() |
| assert self._java_obj is not None |
| return self._java_obj.isLargerBetter() |
| |
| |
| @inherit_doc |
| class BinaryClassificationEvaluator( |
| JavaEvaluator, |
| HasLabelCol, |
| HasRawPredictionCol, |
| HasWeightCol, |
| JavaMLReadable["BinaryClassificationEvaluator"], |
| JavaMLWritable, |
| ): |
| """ |
| Evaluator for binary classification, which expects input columns rawPrediction, label |
| and an optional weight column. |
| The rawPrediction column can be of type double (binary 0/1 prediction, or probability of label |
| 1) or of type vector (length-2 vector of raw predictions, scores, or label probabilities). |
| |
| .. versionadded:: 1.4.0 |
| |
| Examples |
| -------- |
| >>> from pyspark.ml.linalg import Vectors |
| >>> scoreAndLabels = map(lambda x: (Vectors.dense([1.0 - x[0], x[0]]), x[1]), |
| ... [(0.1, 0.0), (0.1, 1.0), (0.4, 0.0), (0.6, 0.0), (0.6, 1.0), (0.6, 1.0), (0.8, 1.0)]) |
| >>> dataset = spark.createDataFrame(scoreAndLabels, ["raw", "label"]) |
| ... |
| >>> evaluator = BinaryClassificationEvaluator() |
| >>> evaluator.setRawPredictionCol("raw") |
| BinaryClassificationEvaluator... |
| >>> evaluator.evaluate(dataset) |
| 0.70... |
| >>> evaluator.evaluate(dataset, {evaluator.metricName: "areaUnderPR"}) |
| 0.83... |
| >>> bce_path = temp_path + "/bce" |
| >>> evaluator.save(bce_path) |
| >>> evaluator2 = BinaryClassificationEvaluator.load(bce_path) |
| >>> str(evaluator2.getRawPredictionCol()) |
| 'raw' |
| >>> scoreAndLabelsAndWeight = map(lambda x: (Vectors.dense([1.0 - x[0], x[0]]), x[1], x[2]), |
| ... [(0.1, 0.0, 1.0), (0.1, 1.0, 0.9), (0.4, 0.0, 0.7), (0.6, 0.0, 0.9), |
| ... (0.6, 1.0, 1.0), (0.6, 1.0, 0.3), (0.8, 1.0, 1.0)]) |
| >>> dataset = spark.createDataFrame(scoreAndLabelsAndWeight, ["raw", "label", "weight"]) |
| ... |
| >>> evaluator = BinaryClassificationEvaluator(rawPredictionCol="raw", weightCol="weight") |
| >>> evaluator.evaluate(dataset) |
| 0.70... |
| >>> evaluator.evaluate(dataset, {evaluator.metricName: "areaUnderPR"}) |
| 0.82... |
| >>> evaluator.getNumBins() |
| 1000 |
| """ |
| |
| metricName: Param["BinaryClassificationEvaluatorMetricType"] = Param( |
| Params._dummy(), |
| "metricName", |
| "metric name in evaluation (areaUnderROC|areaUnderPR)", |
| typeConverter=TypeConverters.toString, # type: ignore[arg-type] |
| ) |
| |
| numBins: Param[int] = Param( |
| Params._dummy(), |
| "numBins", |
| "Number of bins to down-sample the curves " |
| "(ROC curve, PR curve) in area computation. If 0, no down-sampling will " |
| "occur. Must be >= 0.", |
| typeConverter=TypeConverters.toInt, |
| ) |
| |
| _input_kwargs: Dict[str, Any] |
| |
| @keyword_only |
| def __init__( |
| self, |
| *, |
| rawPredictionCol: str = "rawPrediction", |
| labelCol: str = "label", |
| metricName: "BinaryClassificationEvaluatorMetricType" = "areaUnderROC", |
| weightCol: Optional[str] = None, |
| numBins: int = 1000, |
| ): |
| """ |
| __init__(self, \\*, rawPredictionCol="rawPrediction", labelCol="label", \ |
| metricName="areaUnderROC", weightCol=None, numBins=1000) |
| """ |
| super(BinaryClassificationEvaluator, self).__init__() |
| self._java_obj = self._new_java_obj( |
| "org.apache.spark.ml.evaluation.BinaryClassificationEvaluator", self.uid |
| ) |
| self._setDefault(metricName="areaUnderROC", numBins=1000) |
| kwargs = self._input_kwargs |
| self._set(**kwargs) |
| |
| @since("1.4.0") |
| def setMetricName( |
| self, value: "BinaryClassificationEvaluatorMetricType" |
| ) -> "BinaryClassificationEvaluator": |
| """ |
| Sets the value of :py:attr:`metricName`. |
| """ |
| return self._set(metricName=value) |
| |
| @since("1.4.0") |
| def getMetricName(self) -> str: |
| """ |
| Gets the value of metricName or its default value. |
| """ |
| return self.getOrDefault(self.metricName) |
| |
| @since("3.0.0") |
| def setNumBins(self, value: int) -> "BinaryClassificationEvaluator": |
| """ |
| Sets the value of :py:attr:`numBins`. |
| """ |
| return self._set(numBins=value) |
| |
| @since("3.0.0") |
| def getNumBins(self) -> int: |
| """ |
| Gets the value of numBins or its default value. |
| """ |
| return self.getOrDefault(self.numBins) |
| |
| def setLabelCol(self, value: str) -> "BinaryClassificationEvaluator": |
| """ |
| Sets the value of :py:attr:`labelCol`. |
| """ |
| return self._set(labelCol=value) |
| |
| def setRawPredictionCol(self, value: str) -> "BinaryClassificationEvaluator": |
| """ |
| Sets the value of :py:attr:`rawPredictionCol`. |
| """ |
| return self._set(rawPredictionCol=value) |
| |
| @since("3.0.0") |
| def setWeightCol(self, value: str) -> "BinaryClassificationEvaluator": |
| """ |
| Sets the value of :py:attr:`weightCol`. |
| """ |
| return self._set(weightCol=value) |
| |
| @keyword_only |
| @since("1.4.0") |
| def setParams( |
| self, |
| *, |
| rawPredictionCol: str = "rawPrediction", |
| labelCol: str = "label", |
| metricName: "BinaryClassificationEvaluatorMetricType" = "areaUnderROC", |
| weightCol: Optional[str] = None, |
| numBins: int = 1000, |
| ) -> "BinaryClassificationEvaluator": |
| """ |
| setParams(self, \\*, rawPredictionCol="rawPrediction", labelCol="label", \ |
| metricName="areaUnderROC", weightCol=None, numBins=1000) |
| Sets params for binary classification evaluator. |
| """ |
| kwargs = self._input_kwargs |
| return self._set(**kwargs) |
| |
| def isLargerBetter(self) -> bool: |
| """Override this function to make it run on connect""" |
| return True |
| |
| |
| @inherit_doc |
| class RegressionEvaluator( |
| JavaEvaluator, |
| HasLabelCol, |
| HasPredictionCol, |
| HasWeightCol, |
| JavaMLReadable["RegressionEvaluator"], |
| JavaMLWritable, |
| ): |
| """ |
| Evaluator for Regression, which expects input columns prediction, label |
| and an optional weight column. |
| |
| .. versionadded:: 1.4.0 |
| |
| Examples |
| -------- |
| >>> 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 |
| """ |
| |
| metricName: Param["RegressionEvaluatorMetricType"] = Param( |
| Params._dummy(), |
| "metricName", |
| """metric name in evaluation - one of: |
| rmse - root mean squared error (default) |
| mse - mean squared error |
| r2 - r^2 metric |
| mae - mean absolute error |
| var - explained variance.""", |
| typeConverter=TypeConverters.toString, # type: ignore[arg-type] |
| ) |
| |
| throughOrigin: Param[bool] = Param( |
| Params._dummy(), |
| "throughOrigin", |
| "whether the regression is through the origin.", |
| typeConverter=TypeConverters.toBoolean, |
| ) |
| |
| _input_kwargs: Dict[str, Any] |
| |
| @keyword_only |
| def __init__( |
| self, |
| *, |
| predictionCol: str = "prediction", |
| labelCol: str = "label", |
| metricName: "RegressionEvaluatorMetricType" = "rmse", |
| weightCol: Optional[str] = None, |
| throughOrigin: bool = False, |
| ): |
| """ |
| __init__(self, \\*, predictionCol="prediction", labelCol="label", \ |
| metricName="rmse", weightCol=None, throughOrigin=False) |
| """ |
| super(RegressionEvaluator, self).__init__() |
| self._java_obj = self._new_java_obj( |
| "org.apache.spark.ml.evaluation.RegressionEvaluator", self.uid |
| ) |
| self._setDefault(metricName="rmse", throughOrigin=False) |
| kwargs = self._input_kwargs |
| self._set(**kwargs) |
| |
| @since("1.4.0") |
| def setMetricName(self, value: "RegressionEvaluatorMetricType") -> "RegressionEvaluator": |
| """ |
| Sets the value of :py:attr:`metricName`. |
| """ |
| return self._set(metricName=value) |
| |
| @since("1.4.0") |
| def getMetricName(self) -> "RegressionEvaluatorMetricType": |
| """ |
| Gets the value of metricName or its default value. |
| """ |
| return self.getOrDefault(self.metricName) |
| |
| @since("3.0.0") |
| def setThroughOrigin(self, value: bool) -> "RegressionEvaluator": |
| """ |
| Sets the value of :py:attr:`throughOrigin`. |
| """ |
| return self._set(throughOrigin=value) |
| |
| @since("3.0.0") |
| def getThroughOrigin(self) -> bool: |
| """ |
| Gets the value of throughOrigin or its default value. |
| """ |
| return self.getOrDefault(self.throughOrigin) |
| |
| def setLabelCol(self, value: str) -> "RegressionEvaluator": |
| """ |
| Sets the value of :py:attr:`labelCol`. |
| """ |
| return self._set(labelCol=value) |
| |
| def setPredictionCol(self, value: str) -> "RegressionEvaluator": |
| """ |
| Sets the value of :py:attr:`predictionCol`. |
| """ |
| return self._set(predictionCol=value) |
| |
| @since("3.0.0") |
| def setWeightCol(self, value: str) -> "RegressionEvaluator": |
| """ |
| Sets the value of :py:attr:`weightCol`. |
| """ |
| return self._set(weightCol=value) |
| |
| @keyword_only |
| @since("1.4.0") |
| def setParams( |
| self, |
| *, |
| predictionCol: str = "prediction", |
| labelCol: str = "label", |
| metricName: "RegressionEvaluatorMetricType" = "rmse", |
| weightCol: Optional[str] = None, |
| throughOrigin: bool = False, |
| ) -> "RegressionEvaluator": |
| """ |
| setParams(self, \\*, predictionCol="prediction", labelCol="label", \ |
| metricName="rmse", weightCol=None, throughOrigin=False) |
| Sets params for regression evaluator. |
| """ |
| kwargs = self._input_kwargs |
| return self._set(**kwargs) |
| |
| def isLargerBetter(self) -> bool: |
| """Override this function to make it run on connect""" |
| return self.getMetricName() in ["r2", "var"] |
| |
| |
| @inherit_doc |
| class MulticlassClassificationEvaluator( |
| JavaEvaluator, |
| HasLabelCol, |
| HasPredictionCol, |
| HasWeightCol, |
| HasProbabilityCol, |
| JavaMLReadable["MulticlassClassificationEvaluator"], |
| JavaMLWritable, |
| ): |
| """ |
| Evaluator for Multiclass Classification, which expects input |
| columns: prediction, label, weight (optional) and probabilityCol (only for logLoss). |
| |
| .. versionadded:: 1.5.0 |
| |
| Examples |
| -------- |
| >>> scoreAndLabels = [(0.0, 0.0), (0.0, 1.0), (0.0, 0.0), |
| ... (1.0, 0.0), (1.0, 1.0), (1.0, 1.0), (1.0, 1.0), (2.0, 2.0), (2.0, 0.0)] |
| >>> dataset = spark.createDataFrame(scoreAndLabels, ["prediction", "label"]) |
| >>> evaluator = MulticlassClassificationEvaluator() |
| >>> evaluator.setPredictionCol("prediction") |
| MulticlassClassificationEvaluator... |
| >>> evaluator.evaluate(dataset) |
| 0.66... |
| >>> evaluator.evaluate(dataset, {evaluator.metricName: "accuracy"}) |
| 0.66... |
| >>> evaluator.evaluate(dataset, {evaluator.metricName: "truePositiveRateByLabel", |
| ... evaluator.metricLabel: 1.0}) |
| 0.75... |
| >>> evaluator.setMetricName("hammingLoss") |
| MulticlassClassificationEvaluator... |
| >>> evaluator.evaluate(dataset) |
| 0.33... |
| >>> mce_path = temp_path + "/mce" |
| >>> evaluator.save(mce_path) |
| >>> evaluator2 = MulticlassClassificationEvaluator.load(mce_path) |
| >>> str(evaluator2.getPredictionCol()) |
| 'prediction' |
| >>> scoreAndLabelsAndWeight = [(0.0, 0.0, 1.0), (0.0, 1.0, 1.0), (0.0, 0.0, 1.0), |
| ... (1.0, 0.0, 1.0), (1.0, 1.0, 1.0), (1.0, 1.0, 1.0), (1.0, 1.0, 1.0), |
| ... (2.0, 2.0, 1.0), (2.0, 0.0, 1.0)] |
| >>> dataset = spark.createDataFrame(scoreAndLabelsAndWeight, ["prediction", "label", "weight"]) |
| >>> evaluator = MulticlassClassificationEvaluator(predictionCol="prediction", |
| ... weightCol="weight") |
| >>> evaluator.evaluate(dataset) |
| 0.66... |
| >>> evaluator.evaluate(dataset, {evaluator.metricName: "accuracy"}) |
| 0.66... |
| >>> predictionAndLabelsWithProbabilities = [ |
| ... (1.0, 1.0, 1.0, [0.1, 0.8, 0.1]), (0.0, 2.0, 1.0, [0.9, 0.05, 0.05]), |
| ... (0.0, 0.0, 1.0, [0.8, 0.2, 0.0]), (1.0, 1.0, 1.0, [0.3, 0.65, 0.05])] |
| >>> dataset = spark.createDataFrame(predictionAndLabelsWithProbabilities, ["prediction", |
| ... "label", "weight", "probability"]) |
| >>> evaluator = MulticlassClassificationEvaluator(predictionCol="prediction", |
| ... probabilityCol="probability") |
| >>> evaluator.setMetricName("logLoss") |
| MulticlassClassificationEvaluator... |
| >>> evaluator.evaluate(dataset) |
| 0.9682... |
| """ |
| |
| metricName: Param["MulticlassClassificationEvaluatorMetricType"] = Param( |
| Params._dummy(), |
| "metricName", |
| "metric name in evaluation " |
| "(f1|accuracy|weightedPrecision|weightedRecall|weightedTruePositiveRate| " |
| "weightedFalsePositiveRate|weightedFMeasure|truePositiveRateByLabel| " |
| "falsePositiveRateByLabel|precisionByLabel|recallByLabel|fMeasureByLabel| " |
| "logLoss|hammingLoss)", |
| typeConverter=TypeConverters.toString, # type: ignore[arg-type] |
| ) |
| metricLabel: Param[float] = Param( |
| Params._dummy(), |
| "metricLabel", |
| "The class whose metric will be computed in truePositiveRateByLabel|" |
| "falsePositiveRateByLabel|precisionByLabel|recallByLabel|fMeasureByLabel." |
| " Must be >= 0. The default value is 0.", |
| typeConverter=TypeConverters.toFloat, |
| ) |
| beta: Param[float] = Param( |
| Params._dummy(), |
| "beta", |
| "The beta value used in weightedFMeasure|fMeasureByLabel." |
| " Must be > 0. The default value is 1.", |
| typeConverter=TypeConverters.toFloat, |
| ) |
| eps: Param[float] = Param( |
| Params._dummy(), |
| "eps", |
| "log-loss is undefined for p=0 or p=1, so probabilities are clipped to " |
| "max(eps, min(1 - eps, p)). " |
| "Must be in range (0, 0.5). The default value is 1e-15.", |
| typeConverter=TypeConverters.toFloat, |
| ) |
| |
| _input_kwargs: Dict[str, Any] |
| |
| @keyword_only |
| def __init__( |
| self, |
| *, |
| predictionCol: str = "prediction", |
| labelCol: str = "label", |
| metricName: "MulticlassClassificationEvaluatorMetricType" = "f1", |
| weightCol: Optional[str] = None, |
| metricLabel: float = 0.0, |
| beta: float = 1.0, |
| probabilityCol: str = "probability", |
| eps: float = 1e-15, |
| ): |
| """ |
| __init__(self, \\*, predictionCol="prediction", labelCol="label", \ |
| metricName="f1", weightCol=None, metricLabel=0.0, beta=1.0, \ |
| probabilityCol="probability", eps=1e-15) |
| """ |
| super(MulticlassClassificationEvaluator, self).__init__() |
| self._java_obj = self._new_java_obj( |
| "org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator", self.uid |
| ) |
| self._setDefault(metricName="f1", metricLabel=0.0, beta=1.0, eps=1e-15) |
| kwargs = self._input_kwargs |
| self._set(**kwargs) |
| |
| @since("1.5.0") |
| def setMetricName( |
| self, value: "MulticlassClassificationEvaluatorMetricType" |
| ) -> "MulticlassClassificationEvaluator": |
| """ |
| Sets the value of :py:attr:`metricName`. |
| """ |
| return self._set(metricName=value) |
| |
| @since("1.5.0") |
| def getMetricName(self) -> "MulticlassClassificationEvaluatorMetricType": |
| """ |
| Gets the value of metricName or its default value. |
| """ |
| return self.getOrDefault(self.metricName) |
| |
| @since("3.0.0") |
| def setMetricLabel(self, value: float) -> "MulticlassClassificationEvaluator": |
| """ |
| Sets the value of :py:attr:`metricLabel`. |
| """ |
| return self._set(metricLabel=value) |
| |
| @since("3.0.0") |
| def getMetricLabel(self) -> float: |
| """ |
| Gets the value of metricLabel or its default value. |
| """ |
| return self.getOrDefault(self.metricLabel) |
| |
| @since("3.0.0") |
| def setBeta(self, value: float) -> "MulticlassClassificationEvaluator": |
| """ |
| Sets the value of :py:attr:`beta`. |
| """ |
| return self._set(beta=value) |
| |
| @since("3.0.0") |
| def getBeta(self) -> float: |
| """ |
| Gets the value of beta or its default value. |
| """ |
| return self.getOrDefault(self.beta) |
| |
| @since("3.0.0") |
| def setEps(self, value: float) -> "MulticlassClassificationEvaluator": |
| """ |
| Sets the value of :py:attr:`eps`. |
| """ |
| return self._set(eps=value) |
| |
| @since("3.0.0") |
| def getEps(self) -> float: |
| """ |
| Gets the value of eps or its default value. |
| """ |
| return self.getOrDefault(self.eps) |
| |
| def setLabelCol(self, value: str) -> "MulticlassClassificationEvaluator": |
| """ |
| Sets the value of :py:attr:`labelCol`. |
| """ |
| return self._set(labelCol=value) |
| |
| def setPredictionCol(self, value: str) -> "MulticlassClassificationEvaluator": |
| """ |
| Sets the value of :py:attr:`predictionCol`. |
| """ |
| return self._set(predictionCol=value) |
| |
| @since("3.0.0") |
| def setProbabilityCol(self, value: str) -> "MulticlassClassificationEvaluator": |
| """ |
| Sets the value of :py:attr:`probabilityCol`. |
| """ |
| return self._set(probabilityCol=value) |
| |
| @since("3.0.0") |
| def setWeightCol(self, value: str) -> "MulticlassClassificationEvaluator": |
| """ |
| Sets the value of :py:attr:`weightCol`. |
| """ |
| return self._set(weightCol=value) |
| |
| @keyword_only |
| @since("1.5.0") |
| def setParams( |
| self, |
| *, |
| predictionCol: str = "prediction", |
| labelCol: str = "label", |
| metricName: "MulticlassClassificationEvaluatorMetricType" = "f1", |
| weightCol: Optional[str] = None, |
| metricLabel: float = 0.0, |
| beta: float = 1.0, |
| probabilityCol: str = "probability", |
| eps: float = 1e-15, |
| ) -> "MulticlassClassificationEvaluator": |
| """ |
| setParams(self, \\*, predictionCol="prediction", labelCol="label", \ |
| metricName="f1", weightCol=None, metricLabel=0.0, beta=1.0, \ |
| probabilityCol="probability", eps=1e-15) |
| Sets params for multiclass classification evaluator. |
| """ |
| kwargs = self._input_kwargs |
| return self._set(**kwargs) |
| |
| def isLargerBetter(self) -> bool: |
| """Override this function to make it run on connect""" |
| return not self.getMetricName() in [ |
| "weightedFalsePositiveRate", |
| "falsePositiveRateByLabel", |
| "logLoss", |
| "hammingLoss", |
| ] |
| |
| |
| @inherit_doc |
| class MultilabelClassificationEvaluator( |
| JavaEvaluator, |
| HasLabelCol, |
| HasPredictionCol, |
| JavaMLReadable["MultilabelClassificationEvaluator"], |
| JavaMLWritable, |
| ): |
| """ |
| Evaluator for Multilabel Classification, which expects two input |
| columns: prediction and label. |
| |
| .. versionadded:: 3.0.0 |
| |
| Notes |
| ----- |
| Experimental |
| |
| Examples |
| -------- |
| >>> scoreAndLabels = [([0.0, 1.0], [0.0, 2.0]), ([0.0, 2.0], [0.0, 1.0]), |
| ... ([], [0.0]), ([2.0], [2.0]), ([2.0, 0.0], [2.0, 0.0]), |
| ... ([0.0, 1.0, 2.0], [0.0, 1.0]), ([1.0], [1.0, 2.0])] |
| >>> dataset = spark.createDataFrame(scoreAndLabels, ["prediction", "label"]) |
| ... |
| >>> evaluator = MultilabelClassificationEvaluator() |
| >>> evaluator.setPredictionCol("prediction") |
| MultilabelClassificationEvaluator... |
| >>> evaluator.evaluate(dataset) |
| 0.63... |
| >>> evaluator.evaluate(dataset, {evaluator.metricName: "accuracy"}) |
| 0.54... |
| >>> mlce_path = temp_path + "/mlce" |
| >>> evaluator.save(mlce_path) |
| >>> evaluator2 = MultilabelClassificationEvaluator.load(mlce_path) |
| >>> str(evaluator2.getPredictionCol()) |
| 'prediction' |
| """ |
| |
| metricName: Param["MultilabelClassificationEvaluatorMetricType"] = Param( |
| Params._dummy(), |
| "metricName", |
| "metric name in evaluation " |
| "(subsetAccuracy|accuracy|hammingLoss|precision|recall|f1Measure|" |
| "precisionByLabel|recallByLabel|f1MeasureByLabel|microPrecision|" |
| "microRecall|microF1Measure)", |
| typeConverter=TypeConverters.toString, # type: ignore[arg-type] |
| ) |
| metricLabel: Param[float] = Param( |
| Params._dummy(), |
| "metricLabel", |
| "The class whose metric will be computed in precisionByLabel|" |
| "recallByLabel|f1MeasureByLabel. " |
| "Must be >= 0. The default value is 0.", |
| typeConverter=TypeConverters.toFloat, |
| ) |
| |
| _input_kwargs: Dict[str, Any] |
| |
| @keyword_only |
| def __init__( |
| self, |
| *, |
| predictionCol: str = "prediction", |
| labelCol: str = "label", |
| metricName: "MultilabelClassificationEvaluatorMetricType" = "f1Measure", |
| metricLabel: float = 0.0, |
| ) -> None: |
| """ |
| __init__(self, \\*, predictionCol="prediction", labelCol="label", \ |
| metricName="f1Measure", metricLabel=0.0) |
| """ |
| super(MultilabelClassificationEvaluator, self).__init__() |
| self._java_obj = self._new_java_obj( |
| "org.apache.spark.ml.evaluation.MultilabelClassificationEvaluator", self.uid |
| ) |
| self._setDefault(metricName="f1Measure", metricLabel=0.0) |
| kwargs = self._input_kwargs |
| self._set(**kwargs) |
| |
| @since("3.0.0") |
| def setMetricName( |
| self, value: "MultilabelClassificationEvaluatorMetricType" |
| ) -> "MultilabelClassificationEvaluator": |
| """ |
| Sets the value of :py:attr:`metricName`. |
| """ |
| return self._set(metricName=value) |
| |
| @since("3.0.0") |
| def getMetricName(self) -> "MultilabelClassificationEvaluatorMetricType": |
| """ |
| Gets the value of metricName or its default value. |
| """ |
| return self.getOrDefault(self.metricName) |
| |
| @since("3.0.0") |
| def setMetricLabel(self, value: float) -> "MultilabelClassificationEvaluator": |
| """ |
| Sets the value of :py:attr:`metricLabel`. |
| """ |
| return self._set(metricLabel=value) |
| |
| @since("3.0.0") |
| def getMetricLabel(self) -> float: |
| """ |
| Gets the value of metricLabel or its default value. |
| """ |
| return self.getOrDefault(self.metricLabel) |
| |
| @since("3.0.0") |
| def setLabelCol(self, value: str) -> "MultilabelClassificationEvaluator": |
| """ |
| Sets the value of :py:attr:`labelCol`. |
| """ |
| return self._set(labelCol=value) |
| |
| @since("3.0.0") |
| def setPredictionCol(self, value: str) -> "MultilabelClassificationEvaluator": |
| """ |
| Sets the value of :py:attr:`predictionCol`. |
| """ |
| return self._set(predictionCol=value) |
| |
| @keyword_only |
| @since("3.0.0") |
| def setParams( |
| self, |
| *, |
| predictionCol: str = "prediction", |
| labelCol: str = "label", |
| metricName: "MultilabelClassificationEvaluatorMetricType" = "f1Measure", |
| metricLabel: float = 0.0, |
| ) -> "MultilabelClassificationEvaluator": |
| """ |
| setParams(self, \\*, predictionCol="prediction", labelCol="label", \ |
| metricName="f1Measure", metricLabel=0.0) |
| Sets params for multilabel classification evaluator. |
| """ |
| kwargs = self._input_kwargs |
| return self._set(**kwargs) |
| |
| def isLargerBetter(self) -> bool: |
| """Override this function to make it run on connect""" |
| return self.getMetricName() != "hammingLoss" |
| |
| |
| @inherit_doc |
| class ClusteringEvaluator( |
| JavaEvaluator, |
| HasPredictionCol, |
| HasFeaturesCol, |
| HasWeightCol, |
| JavaMLReadable["ClusteringEvaluator"], |
| JavaMLWritable, |
| ): |
| """ |
| Evaluator for Clustering results, which expects two input |
| columns: prediction and features. The metric computes the Silhouette |
| measure using the squared Euclidean distance. |
| |
| The Silhouette is a measure for the validation of the consistency |
| within clusters. It ranges between 1 and -1, where a value close to |
| 1 means that the points in a cluster are close to the other points |
| in the same cluster and far from the points of the other clusters. |
| |
| .. versionadded:: 2.3.0 |
| |
| Examples |
| -------- |
| >>> from pyspark.ml.linalg import Vectors |
| >>> featureAndPredictions = map(lambda x: (Vectors.dense(x[0]), x[1]), |
| ... [([0.0, 0.5], 0.0), ([0.5, 0.0], 0.0), ([10.0, 11.0], 1.0), |
| ... ([10.5, 11.5], 1.0), ([1.0, 1.0], 0.0), ([8.0, 6.0], 1.0)]) |
| >>> dataset = spark.createDataFrame(featureAndPredictions, ["features", "prediction"]) |
| ... |
| >>> evaluator = ClusteringEvaluator() |
| >>> evaluator.setPredictionCol("prediction") |
| ClusteringEvaluator... |
| >>> evaluator.evaluate(dataset) |
| 0.9079... |
| >>> featureAndPredictionsWithWeight = map(lambda x: (Vectors.dense(x[0]), x[1], x[2]), |
| ... [([0.0, 0.5], 0.0, 2.5), ([0.5, 0.0], 0.0, 2.5), ([10.0, 11.0], 1.0, 2.5), |
| ... ([10.5, 11.5], 1.0, 2.5), ([1.0, 1.0], 0.0, 2.5), ([8.0, 6.0], 1.0, 2.5)]) |
| >>> dataset = spark.createDataFrame( |
| ... featureAndPredictionsWithWeight, ["features", "prediction", "weight"]) |
| >>> evaluator = ClusteringEvaluator() |
| >>> evaluator.setPredictionCol("prediction") |
| ClusteringEvaluator... |
| >>> evaluator.setWeightCol("weight") |
| ClusteringEvaluator... |
| >>> evaluator.evaluate(dataset) |
| 0.9079... |
| >>> ce_path = temp_path + "/ce" |
| >>> evaluator.save(ce_path) |
| >>> evaluator2 = ClusteringEvaluator.load(ce_path) |
| >>> str(evaluator2.getPredictionCol()) |
| 'prediction' |
| """ |
| |
| metricName: Param["ClusteringEvaluatorMetricType"] = Param( |
| Params._dummy(), |
| "metricName", |
| "metric name in evaluation (silhouette)", |
| typeConverter=TypeConverters.toString, # type: ignore[arg-type] |
| ) |
| distanceMeasure: Param["ClusteringEvaluatorDistanceMeasureType"] = Param( |
| Params._dummy(), |
| "distanceMeasure", |
| "The distance measure. " + "Supported options: 'squaredEuclidean' and 'cosine'.", |
| typeConverter=TypeConverters.toString, # type: ignore[arg-type] |
| ) |
| |
| _input_kwargs: Dict[str, Any] |
| |
| @keyword_only |
| def __init__( |
| self, |
| *, |
| predictionCol: str = "prediction", |
| featuresCol: str = "features", |
| metricName: "ClusteringEvaluatorMetricType" = "silhouette", |
| distanceMeasure: str = "squaredEuclidean", |
| weightCol: Optional[str] = None, |
| ): |
| """ |
| __init__(self, \\*, predictionCol="prediction", featuresCol="features", \ |
| metricName="silhouette", distanceMeasure="squaredEuclidean", weightCol=None) |
| """ |
| super(ClusteringEvaluator, self).__init__() |
| self._java_obj = self._new_java_obj( |
| "org.apache.spark.ml.evaluation.ClusteringEvaluator", self.uid |
| ) |
| self._setDefault(metricName="silhouette", distanceMeasure="squaredEuclidean") |
| kwargs = self._input_kwargs |
| self._set(**kwargs) |
| |
| @keyword_only |
| @since("2.3.0") |
| def setParams( |
| self, |
| *, |
| predictionCol: str = "prediction", |
| featuresCol: str = "features", |
| metricName: "ClusteringEvaluatorMetricType" = "silhouette", |
| distanceMeasure: str = "squaredEuclidean", |
| weightCol: Optional[str] = None, |
| ) -> "ClusteringEvaluator": |
| """ |
| setParams(self, \\*, predictionCol="prediction", featuresCol="features", \ |
| metricName="silhouette", distanceMeasure="squaredEuclidean", weightCol=None) |
| Sets params for clustering evaluator. |
| """ |
| kwargs = self._input_kwargs |
| return self._set(**kwargs) |
| |
| @since("2.3.0") |
| def setMetricName(self, value: "ClusteringEvaluatorMetricType") -> "ClusteringEvaluator": |
| """ |
| Sets the value of :py:attr:`metricName`. |
| """ |
| return self._set(metricName=value) |
| |
| @since("2.3.0") |
| def getMetricName(self) -> "ClusteringEvaluatorMetricType": |
| """ |
| Gets the value of metricName or its default value. |
| """ |
| return self.getOrDefault(self.metricName) |
| |
| @since("2.4.0") |
| def setDistanceMeasure( |
| self, value: "ClusteringEvaluatorDistanceMeasureType" |
| ) -> "ClusteringEvaluator": |
| """ |
| Sets the value of :py:attr:`distanceMeasure`. |
| """ |
| return self._set(distanceMeasure=value) |
| |
| @since("2.4.0") |
| def getDistanceMeasure(self) -> "ClusteringEvaluatorDistanceMeasureType": |
| """ |
| Gets the value of `distanceMeasure` |
| """ |
| return self.getOrDefault(self.distanceMeasure) |
| |
| def setFeaturesCol(self, value: "str") -> "ClusteringEvaluator": |
| """ |
| Sets the value of :py:attr:`featuresCol`. |
| """ |
| return self._set(featuresCol=value) |
| |
| def setPredictionCol(self, value: str) -> "ClusteringEvaluator": |
| """ |
| Sets the value of :py:attr:`predictionCol`. |
| """ |
| return self._set(predictionCol=value) |
| |
| @since("3.1.0") |
| def setWeightCol(self, value: str) -> "ClusteringEvaluator": |
| """ |
| Sets the value of :py:attr:`weightCol`. |
| """ |
| return self._set(weightCol=value) |
| |
| def isLargerBetter(self) -> bool: |
| """Override this function to make it run on connect""" |
| return True |
| |
| |
| @inherit_doc |
| class RankingEvaluator( |
| JavaEvaluator, HasLabelCol, HasPredictionCol, JavaMLReadable["RankingEvaluator"], JavaMLWritable |
| ): |
| """ |
| Evaluator for Ranking, which expects two input |
| columns: prediction and label. |
| |
| .. versionadded:: 3.0.0 |
| |
| Notes |
| ----- |
| Experimental |
| |
| Examples |
| -------- |
| >>> scoreAndLabels = [([1.0, 6.0, 2.0, 7.0, 8.0, 3.0, 9.0, 10.0, 4.0, 5.0], |
| ... [1.0, 2.0, 3.0, 4.0, 5.0]), |
| ... ([4.0, 1.0, 5.0, 6.0, 2.0, 7.0, 3.0, 8.0, 9.0, 10.0], [1.0, 2.0, 3.0]), |
| ... ([1.0, 2.0, 3.0, 4.0, 5.0], [])] |
| >>> dataset = spark.createDataFrame(scoreAndLabels, ["prediction", "label"]) |
| ... |
| >>> evaluator = RankingEvaluator() |
| >>> evaluator.setPredictionCol("prediction") |
| RankingEvaluator... |
| >>> evaluator.evaluate(dataset) |
| 0.35... |
| >>> evaluator.evaluate(dataset, {evaluator.metricName: "precisionAtK", evaluator.k: 2}) |
| 0.33... |
| >>> ranke_path = temp_path + "/ranke" |
| >>> evaluator.save(ranke_path) |
| >>> evaluator2 = RankingEvaluator.load(ranke_path) |
| >>> str(evaluator2.getPredictionCol()) |
| 'prediction' |
| """ |
| |
| metricName: Param["RankingEvaluatorMetricType"] = Param( |
| Params._dummy(), |
| "metricName", |
| "metric name in evaluation " |
| "(meanAveragePrecision|meanAveragePrecisionAtK|" |
| "precisionAtK|ndcgAtK|recallAtK)", |
| typeConverter=TypeConverters.toString, # type: ignore[arg-type] |
| ) |
| k: Param[int] = Param( |
| Params._dummy(), |
| "k", |
| "The ranking position value used in meanAveragePrecisionAtK|precisionAtK|" |
| "ndcgAtK|recallAtK. Must be > 0. The default value is 10.", |
| typeConverter=TypeConverters.toInt, |
| ) |
| |
| _input_kwargs: Dict[str, Any] |
| |
| @keyword_only |
| def __init__( |
| self, |
| *, |
| predictionCol: str = "prediction", |
| labelCol: str = "label", |
| metricName: "RankingEvaluatorMetricType" = "meanAveragePrecision", |
| k: int = 10, |
| ): |
| """ |
| __init__(self, \\*, predictionCol="prediction", labelCol="label", \ |
| metricName="meanAveragePrecision", k=10) |
| """ |
| super(RankingEvaluator, self).__init__() |
| self._java_obj = self._new_java_obj( |
| "org.apache.spark.ml.evaluation.RankingEvaluator", self.uid |
| ) |
| self._setDefault(metricName="meanAveragePrecision", k=10) |
| kwargs = self._input_kwargs |
| self._set(**kwargs) |
| |
| @since("3.0.0") |
| def setMetricName(self, value: "RankingEvaluatorMetricType") -> "RankingEvaluator": |
| """ |
| Sets the value of :py:attr:`metricName`. |
| """ |
| return self._set(metricName=value) |
| |
| @since("3.0.0") |
| def getMetricName(self) -> "RankingEvaluatorMetricType": |
| """ |
| Gets the value of metricName or its default value. |
| """ |
| return self.getOrDefault(self.metricName) |
| |
| @since("3.0.0") |
| def setK(self, value: int) -> "RankingEvaluator": |
| """ |
| Sets the value of :py:attr:`k`. |
| """ |
| return self._set(k=value) |
| |
| @since("3.0.0") |
| def getK(self) -> int: |
| """ |
| Gets the value of k or its default value. |
| """ |
| return self.getOrDefault(self.k) |
| |
| @since("3.0.0") |
| def setLabelCol(self, value: str) -> "RankingEvaluator": |
| """ |
| Sets the value of :py:attr:`labelCol`. |
| """ |
| return self._set(labelCol=value) |
| |
| @since("3.0.0") |
| def setPredictionCol(self, value: str) -> "RankingEvaluator": |
| """ |
| Sets the value of :py:attr:`predictionCol`. |
| """ |
| return self._set(predictionCol=value) |
| |
| @keyword_only |
| @since("3.0.0") |
| def setParams( |
| self, |
| *, |
| predictionCol: str = "prediction", |
| labelCol: str = "label", |
| metricName: "RankingEvaluatorMetricType" = "meanAveragePrecision", |
| k: int = 10, |
| ) -> "RankingEvaluator": |
| """ |
| setParams(self, \\*, predictionCol="prediction", labelCol="label", \ |
| metricName="meanAveragePrecision", k=10) |
| Sets params for ranking evaluator. |
| """ |
| kwargs = self._input_kwargs |
| return self._set(**kwargs) |
| |
| def isLargerBetter(self) -> bool: |
| """Override this function to make it run on connect""" |
| return True |
| |
| |
| if __name__ == "__main__": |
| import doctest |
| import tempfile |
| import pyspark.ml.evaluation |
| from pyspark.sql import SparkSession |
| |
| globs = pyspark.ml.evaluation.__dict__.copy() |
| # The small batch size here ensures that we see multiple batches, |
| # even in these small test examples: |
| spark = SparkSession.builder.master("local[2]").appName("ml.evaluation tests").getOrCreate() |
| globs["spark"] = spark |
| temp_path = tempfile.mkdtemp() |
| globs["temp_path"] = temp_path |
| try: |
| (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) |
| spark.stop() |
| finally: |
| from shutil import rmtree |
| |
| try: |
| rmtree(temp_path) |
| except OSError: |
| pass |
| if failure_count: |
| sys.exit(-1) |
