7 examples of 'tf.keras.metrics.auc' in Python

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def roc_auc_score(y_pred, y_true):
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    """ ROC AUC Score.
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    Approximates the Area Under Curve score, using approximation based on
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    the Wilcoxon-Mann-Whitney U statistic.
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    Yan, L., Dodier, R., Mozer, M. C., & Wolniewicz, R. (2003).
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    Optimizing Classifier Performance via an Approximation to the Wilcoxon-Mann-Whitney Statistic.
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    Measures overall performance for a full range of threshold levels.
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    Arguments:
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        y_pred: `Tensor`. Predicted values.
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        y_true: `Tensor` . Targets (labels), a probability distribution.
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    """
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    with tf.name_scope("RocAucScore"):
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        pos = tf.boolean_mask(y_pred, tf.cast(y_true, tf.bool))
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        neg = tf.boolean_mask(y_pred, ~tf.cast(y_true, tf.bool))
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        pos = tf.expand_dims(pos, 0)
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        neg = tf.expand_dims(neg, 1)
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        # original paper suggests performance is robust to exact parameter choice
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        gamma = 0.2
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        p     = 3
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        difference = tf.zeros_like(pos * neg) + pos - neg - gamma
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        masked = tf.boolean_mask(difference, difference < 0.0)
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        return tf.reduce_sum(tf.pow(-masked, p))

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def roc_auc_score(y_pred, y_true):
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    """ ROC AUC Score.
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    Approximates the Area Under Curve score, using approximation based on
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    the Wilcoxon-Mann-Whitney U statistic.
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    Yan, L., Dodier, R., Mozer, M. C., & Wolniewicz, R. (2003).
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    Optimizing Classifier Performance via an Approximation to the Wilcoxon-Mann-Whitney Statistic.
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    Measures overall performance for a full range of threshold levels.
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    Arguments:
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        y_pred: `Tensor`. Predicted values.
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        y_true: `Tensor` . Targets (labels), a probability distribution.
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    """
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    with tf.name_scope("RocAucScore"):
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        pos = tf.boolean_mask(y_pred, tf.cast(y_true, tf.bool))
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        neg = tf.boolean_mask(y_pred, ~tf.cast(y_true, tf.bool))
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        pos = tf.expand_dims(pos, 0)
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        neg = tf.expand_dims(neg, 1)
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        # original paper suggests performance is robust to exact parameter choice
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        gamma = 0.2
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        p     = 3
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        difference = tf.zeros_like(pos * neg) + pos - neg - gamma
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        masked = tf.boolean_mask(difference, difference < 0.0)
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        return tf.reduce_sum(tf.pow(-masked, p))

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def auc_score(y_true, y_pred, positive_label=1):
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    if hasattr(sklearn.metrics, 'roc_auc_score'):
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        return sklearn.metrics.roc_auc_score(y_true, y_pred)
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    fp_rate, tp_rate, thresholds = sklearn.metrics.roc_curve(
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        y_true, y_pred, pos_label=positive_label)
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    return sklearn.metrics.auc(fp_rate, tp_rate)

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def gini_tf_metric(y_true, y_pred):
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    return 2.0 * tf.metrics.auc(y_true, y_pred)[0] - 1.0

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def auc(self):
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    if self.type != DatasetType.binary:
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        # raise ValueError("AUC metric is only supported for binary classification: {}.".format(self.classes))
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        log.warning("AUC metric is only supported for binary classification: %s.", self.classes)
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        return nan
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    return float(roc_auc_score(self.truth, self.probabilities[:, 1]))

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def _add_metrics(self):
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    label_split = fluid.layers.split(
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        self.labels[0], self.num_classes, dim=-1)
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    # metrics change to auc of every class
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    eval_list = []
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    for index, probs in enumerate(self.outputs):
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        current_auc, _, _ = fluid.layers.auc(
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            input=probs, label=label_split[index])
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        eval_list.append(current_auc)
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    return eval_list

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@curry
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def auc_evaluator(test_data: pd.DataFrame,
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                  prediction_column: str = "prediction",
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                  target_column: str = "target",
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                  eval_name: str = None) -> EvalReturnType:
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    """
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    Computes the ROC AUC score, given true label and prediction scores.
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    Parameters
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    ----------
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    test_data : Pandas' DataFrame
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        A Pandas' DataFrame with target and prediction scores.
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    prediction_column : Strings
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        The name of the column in `test_data` with the prediction scores.
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    target_column : String
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        The name of the column in `test_data` with the binary target.
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    eval_name : String, optional (default=None)
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        the name of the evaluator as it will appear in the logs.
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    Returns
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    ----------
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    log: dict
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        A log-like dictionary with the ROC AUC Score
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    """
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    warnings.warn("The method `auc_evaluator` will be renamed to `roc_auc_evaluator` in the next major release 2.0.0."
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                  " Please use `roc_auc_evaluator` instead of `auc_evaluator` for Area Under the Curve of the"
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                  " Receiver Operating Characteristics curve.")
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    return roc_auc_evaluator(test_data, prediction_column, target_column, eval_name)