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evaluation.pyi
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evaluation.pyi
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#
# 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
#
# https://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.
from typing import List, Optional, Tuple, TypeVar
from pyspark.rdd import RDD
from pyspark.mllib.common import JavaModelWrapper
from pyspark.mllib.linalg import Matrix
T = TypeVar("T")
class BinaryClassificationMetrics(JavaModelWrapper):
def __init__(self, scoreAndLabels: RDD[Tuple[float, float]]) -> None: ...
@property
def areaUnderROC(self) -> float: ...
@property
def areaUnderPR(self) -> float: ...
def unpersist(self) -> None: ...
class RegressionMetrics(JavaModelWrapper):
def __init__(self, predictionAndObservations: RDD[Tuple[float, float]]) -> None: ...
@property
def explainedVariance(self) -> float: ...
@property
def meanAbsoluteError(self) -> float: ...
@property
def meanSquaredError(self) -> float: ...
@property
def rootMeanSquaredError(self) -> float: ...
@property
def r2(self) -> float: ...
class MulticlassMetrics(JavaModelWrapper):
def __init__(self, predictionAndLabels: RDD[Tuple[float, float]]) -> None: ...
def confusionMatrix(self) -> Matrix: ...
def truePositiveRate(self, label: float) -> float: ...
def falsePositiveRate(self, label: float) -> float: ...
def precision(self, label: float = ...) -> float: ...
def recall(self, label: float = ...) -> float: ...
def fMeasure(self, label: float = ..., beta: Optional[float] = ...) -> float: ...
@property
def accuracy(self) -> float: ...
@property
def weightedTruePositiveRate(self) -> float: ...
@property
def weightedFalsePositiveRate(self) -> float: ...
@property
def weightedRecall(self) -> float: ...
@property
def weightedPrecision(self) -> float: ...
def weightedFMeasure(self, beta: Optional[float] = ...) -> float: ...
class RankingMetrics(JavaModelWrapper):
def __init__(self, predictionAndLabels: RDD[Tuple[List[T], List[T]]]) -> None: ...
def precisionAt(self, k: int) -> float: ...
@property
def meanAveragePrecision(self) -> float: ...
def meanAveragePrecisionAt(self, k: int) -> float: ...
def ndcgAt(self, k: int) -> float: ...
def recallAt(self, k: int) -> float: ...
class MultilabelMetrics(JavaModelWrapper):
def __init__(
self, predictionAndLabels: RDD[Tuple[List[float], List[float]]]
) -> None: ...
def precision(self, label: Optional[float] = ...) -> float: ...
def recall(self, label: Optional[float] = ...) -> float: ...
def f1Measure(self, label: Optional[float] = ...) -> float: ...
@property
def microPrecision(self) -> float: ...
@property
def microRecall(self) -> float: ...
@property
def microF1Measure(self) -> float: ...
@property
def hammingLoss(self) -> float: ...
@property
def subsetAccuracy(self) -> float: ...
@property
def accuracy(self) -> float: ...