def __init__(self, cm: "metrics.ConfusionMatrix" = None):

        self.cm = metrics.ConfusionMatrix() if cm is None else cm
        self.accuracy = metrics.Accuracy(cm=self.cm)
        self.kappa = metrics.CohenKappa(cm=self.cm)
        self.kappa_m = metrics.KappaM(cm=self.cm)
        self.kappa_t = metrics.KappaT(cm=self.cm)
        self.recall = metrics.Recall(cm=self.cm)
        self.micro_recall = metrics.MicroRecall(cm=self.cm)
        self.macro_recall = metrics.MacroRecall(cm=self.cm)
        self.precision = metrics.Precision(cm=self.cm)
        self.micro_precision = metrics.MicroPrecision(cm=self.cm)
        self.macro_precision = metrics.MacroPrecision(cm=self.cm)
        self.f1 = metrics.F1(cm=self.cm)
        self.micro_f1 = metrics.MicroF1(cm=self.cm)
        self.macro_f1 = metrics.MacroF1(cm=self.cm)
        self.geometric_mean = metrics.GeometricMean(cm=self.cm)
示例#2
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 def __init__(self, step, name):
     self.name = name
     self.optimizer = SynchronousSGD(0.01, name, None)
     self.model = compose.Pipeline(
         preprocessing.StandardScaler(),
         linear_model.LogisticRegression(self.optimizer))
     self.metrics = [
         metrics.Accuracy(),
         metrics.MAE(),
         metrics.RMSE(),
         metrics.Precision(),
         metrics.Recall()
     ]
     self.count = 0
     if step is None:
         self.step = 50
     else:
         self.step = int(step)
示例#3
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def partial(f, **kwargs):
    return functools.update_wrapper(functools.partial(f, **kwargs), f)


TEST_CASES = [
    (metrics.Accuracy(), sk_metrics.accuracy_score),
    (metrics.Precision(), sk_metrics.precision_score),
    (metrics.MacroPrecision(),
     partial(sk_metrics.precision_score, average='macro')),
    (metrics.MicroPrecision(),
     partial(sk_metrics.precision_score, average='micro')),
    (metrics.WeightedPrecision(),
     partial(sk_metrics.precision_score, average='weighted')),
    (metrics.Recall(), sk_metrics.recall_score),
    (metrics.MacroRecall(), partial(sk_metrics.recall_score, average='macro')),
    (metrics.MicroRecall(), partial(sk_metrics.recall_score, average='micro')),
    (metrics.WeightedRecall(),
     partial(sk_metrics.recall_score, average='weighted')),
    (metrics.FBeta(beta=.5), partial(sk_metrics.fbeta_score, beta=.5)),
    (metrics.MacroFBeta(beta=.5),
     partial(sk_metrics.fbeta_score, beta=.5, average='macro')),
    (metrics.MicroFBeta(beta=.5),
     partial(sk_metrics.fbeta_score, beta=.5, average='micro')),
    (metrics.WeightedFBeta(beta=.5),
     partial(sk_metrics.fbeta_score, beta=.5, average='weighted')),
    (metrics.F1(), sk_metrics.f1_score),
    (metrics.MacroF1(), partial(sk_metrics.f1_score, average='macro')),
    (metrics.MicroF1(), partial(sk_metrics.f1_score, average='micro')),
    (metrics.WeightedF1(), partial(sk_metrics.f1_score, average='weighted')),
示例#4
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                               zero_division=0)),
 (
     metrics.MacroPrecision(),
     partial(sk_metrics.precision_score, average="macro", zero_division=0),
 ),
 (
     metrics.MicroPrecision(),
     partial(sk_metrics.precision_score, average="micro", zero_division=0),
 ),
 (
     metrics.WeightedPrecision(),
     partial(sk_metrics.precision_score,
             average="weighted",
             zero_division=0),
 ),
 (metrics.Recall(), partial(sk_metrics.recall_score, zero_division=0)),
 (metrics.MacroRecall(),
  partial(sk_metrics.recall_score, average="macro", zero_division=0)),
 (metrics.MicroRecall(),
  partial(sk_metrics.recall_score, average="micro", zero_division=0)),
 (
     metrics.WeightedRecall(),
     partial(sk_metrics.recall_score, average="weighted", zero_division=0),
 ),
 (metrics.FBeta(beta=0.5),
  partial(sk_metrics.fbeta_score, beta=0.5, zero_division=0)),
 (
     metrics.MacroFBeta(beta=0.5),
     partial(sk_metrics.fbeta_score,
             beta=0.5,
             average="macro",
示例#5
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文件: fbeta.py 项目: Leo-VK/creme
 def __init__(self, beta: float, cm=None, pos_val=True):
     super().__init__(cm, pos_val)
     self.beta = beta
     self.precision = metrics.Precision(self.cm, self.pos_val)
     self.recall = metrics.Recall(self.cm, self.pos_val)