View source on GitHub |
Approximates the AUC (Area under the curve) of the ROC or PR curves.
Inherits From: Metric
, Layer
, Module
tf.keras.metrics.AUC(
num_thresholds=200,
curve='ROC',
summation_method='interpolation',
name=None,
dtype=None,
thresholds=None,
multi_label=False,
num_labels=None,
label_weights=None,
from_logits=False
)
The AUC (Area under the curve) of the ROC (Receiver operating characteristic; default) or PR (Precision Recall) curves are quality measures of binary classifiers. Unlike the accuracy, and like cross-entropy losses, ROC-AUC and PR-AUC evaluate all the operational points of a model.
This class approximates AUCs using a Riemann sum. During the metric accumulation phrase, predictions are accumulated within predefined buckets by value. The AUC is then computed by interpolating per-bucket averages. These buckets define the evaluated operational points.
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 a best approximation of the real AUC, predictions
should be
distributed approximately uniformly in the range 0, 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.
Args | |
---|---|
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.
'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' applies left summation for increasing intervals and right
summation for decreasing intervals; 'majoring' 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.
|
multi_label
|
boolean indicating whether multilabel data should be treated as such, wherein AUC is computed separately for each label and then averaged across labels, or (when False) if the data should be flattened into a single label before AUC computation. In the latter case, when multilabel data is passed to AUC, each label-prediction pair is treated as an individual data point. Should be set to False for multi-class data. |
num_labels
|
(Optional) The number of labels, used when multi_label is
True. If num_labels is not specified, then state variables get created
on the first call to update_state .
|
label_weights
|
(Optional) list, array, or tensor of non-negative weights
used to compute AUCs for multilabel data. When multi_label is True,
the weights are applied to the individual label AUCs when they are
averaged to produce the multi-label AUC. When it's False, they are used
to weight the individual label predictions in computing the confusion
matrix on the flattened data. Note that this is unlike class_weights in
that class_weights weights the example depending on the value of its
label, whereas label_weights depends only on the index of that label
before flattening; therefore label_weights should not be used for
multi-class data.
|
from_logits
|
boolean indicating whether the predictions (y_pred in
update_state ) are probabilities or sigmoid logits. As a rule of thumb,
when using a keras loss, the from_logits constructor argument of the
loss should match the AUC from_logits constructor argument.
|
Standalone 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]
# tp_rate = 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
m.result().numpy()
0.75
m.reset_state()
m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9],
sample_weight=[1, 0, 0, 1])
m.result().numpy()
1.0
Usage with compile()
API:
# Reports the AUC of a model outputting a probability.
model.compile(optimizer='sgd',
loss=tf.keras.losses.BinaryCrossentropy(),
metrics=[tf.keras.metrics.AUC()])
# Reports the AUC of a model outputting a logit.
model.compile(optimizer='sgd',
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
metrics=[tf.keras.metrics.AUC(from_logits=True)])
Attributes | |
---|---|
thresholds
|
The thresholds used for evaluating AUC. |
Methods
interpolate_pr_auc
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. |
merge_state
merge_state(
metrics
)
Merges the state from one or more metrics.
This method can be used by distributed systems to merge the state computed by different metric instances. Typically the state will be stored in the form of the metric's weights. For example, a tf.keras.metrics.Mean metric contains a list of two weight values: a total and a count. If there were two instances of a tf.keras.metrics.Accuracy that each independently aggregated partial state for an overall accuracy calculation, these two metric's states could be combined as follows:
m1 = tf.keras.metrics.Accuracy()
_ = m1.update_state([[1], [2]], [[0], [2]])
m2 = tf.keras.metrics.Accuracy()
_ = m2.update_state([[3], [4]], [[3], [4]])
m2.merge_state([m1])
m2.result().numpy()
0.75
Args | |
---|---|
metrics
|
an iterable of metrics. The metrics must have compatible state. |
Raises | |
---|---|
ValueError
|
If the provided iterable does not contain metrics matching the metric's required specifications. |
reset_state
reset_state()
Resets all of the metric state variables.
This function is called between epochs/steps, when a metric is evaluated during training.
result
result()
Computes and returns the scalar metric value tensor or a dict of scalars.
Result computation is an idempotent operation that simply calculates the metric value using the state variables.
Returns | |
---|---|
A scalar tensor, or a dictionary of scalar tensors. |
update_state
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. |