tf.keras.metrics.AUC

TensorFlow 2 version View source on GitHub

Computes the approximate AUC (Area under the curve) via a Riemann sum.

Inherits From: Metric

tf.keras.metrics.AUC(
    num_thresholds=200, curve='ROC', summation_method='interpolation', name=None,
    dtype=None, thresholds=None
)

This metric creates four local variables, true_positives, true_negatives, false_positives and false_negatives that are used to compute the AUC. To discretize the AUC curve, a linearly spaced set of thresholds is used to compute pairs of recall and precision values. The area under the ROC-curve is therefore computed using the height of the recall values by the false positive rate, while the area under the PR-curve is the computed using the height of the precision values by the recall.

This value is ultimately returned as auc, an idempotent operation that computes the area under a discretized curve of precision versus recall values (computed using the aforementioned variables). The num_thresholds variable controls the degree of discretization with larger numbers of thresholds more closely approximating the true AUC. The quality of the approximation may vary dramatically depending on num_thresholds. The thresholds parameter can be used to manually specify thresholds which split the predictions more evenly.

For best results, predictions should be distributed approximately uniformly in the range [0, 1] and not peaked around 0 or 1. The quality of the AUC approximation may be poor if this is not the case. Setting summation_method to 'minoring' or 'majoring' can help quantify the error in the approximation by providing lower or upper bound estimate of the AUC.

If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values.

Usage:

m = tf.keras.metrics.AUC(num_thresholds=3)
m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9])

# threshold values are [0 - 1e-7, 0.5, 1 + 1e-7]
# tp = [2, 1, 0], fp = [2, 0, 0], fn = [0, 1, 2], tn = [0, 2, 2]
# recall = [1, 0.5, 0], fp_rate = [1, 0, 0]
# auc = ((((1+0.5)/2)*(1-0))+ (((0.5+0)/2)*(0-0))) = 0.75

print('Final result: ', m.result().numpy())  # Final result: 0.75

Usage with tf.keras API:

model = tf.keras.Model(inputs, outputs)
model.compile('sgd', loss='mse', metrics=[tf.keras.metrics.AUC()])

num_thresholds (Optional) Defaults to 200. The number of thresholds to use when discretizing the roc curve. Values must be > 1.
curve (Optional) Specifies the name of the curve to be computed, 'ROC' [default] or 'PR' for the Precision-Recall-curve.
summation_method (Optional) Specifies the Riemann summation method used (https://en.wikipedia.org/wiki/Riemann_sum): 'interpolation' [default], applies mid-point summation scheme for ROC. For PR-AUC, interpolates (true/false) positives but not the ratio that is precision (see Davis & Goadrich 2006 for details); 'minoring' that applies left summation for increasing intervals and right summation for decreasing intervals; 'majoring' that does the opposite.
name (Optional) string name of the metric instance.
dtype (Optional) data type of the metric result.
thresholds (Optional) A list of floating point values to use as the thresholds for discretizing the curve. If set, the num_thresholds parameter is ignored. Values should be in [0, 1]. Endpoint thresholds equal to {-epsilon, 1+epsilon} for a small positive epsilon value will be automatically included with these to correctly handle predictions equal to exactly 0 or 1.

Methods

interpolate_pr_auc

View source

interpolate_pr_auc()

Interpolation formula inspired by section 4 of Davis & Goadrich 2006.

https://www.biostat.wisc.edu/~page/rocpr.pdf

Note here we derive & use a closed formula not present in the paper as follows:

Precision = TP / (TP + FP) = TP / P

Modeling all of TP (true positive), FP (false positive) and their sum P = TP + FP (predicted positive) as varying linearly within each interval [A, B] between successive thresholds, we get

Precision slope = dTP / dP = (TP_B - TP_A) / (P_B - P_A) = (TP - TP_A) / (P - P_A) Precision = (TP_A + slope * (P - P_A)) / P

The area within the interval is (slope / total_pos_weight) times

int_A^B{Precision.dP} = int_A^B{(TP_A + slope * (P - P_A)) * dP / P} int_A^B{Precision.dP} = int_A^B{slope * dP + intercept * dP / P}

where intercept = TP_A - slope * P_A = TP_B - slope * P_B, resulting in

int_A^B{Precision.dP} = TP_B - TP_A + intercept * log(P_B / P_A)

Bringing back the factor (slope / total_pos_weight) we'd put aside, we get

slope * [dTP + intercept * log(P_B / P_A)] / total_pos_weight

where dTP == TP_B - TP_A.

Note that when P_A == 0 the above calculation simplifies into

int_A^B{Precision.dTP} = int_A^B{slope * dTP} = slope * (TP_B - TP_A)

which is really equivalent to imputing constant precision throughout the first bucket having >0 true positives.

Returns
pr_auc an approximation of the area under the P-R curve.

reset_states

View source

reset_states()

Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

result

View source

result()

Computes and returns the metric value tensor.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.

update_state

View source

update_state(
    y_true, y_pred, sample_weight=None
)

Accumulates confusion matrix statistics.

Args
y_true The ground truth values.
y_pred The predicted values.
sample_weight Optional weighting of each example. Defaults to 1. Can be a Tensor whose rank is either 0, or the same rank as y_true, and must be broadcastable to y_true.

Returns
Update op.