|---| | [ View source on GitHub](https://github.com/tensorflow/probability/blob/v0.25.0/tensorflow_probability/python/stats/calibration.py#L350-L513) | Expected calibration error via `quantiles(exp(pred_log_prob),num_buckets)`. tfp.stats.expected_calibration_error_quantiles( hit, pred_log_prob, num_buckets=20, axis=0, log_space_buckets=False, name=None ) Calibration is a measure of how well a model reports its own uncertainty. A model is said to be "calibrated" if buckets of predicted probabilities have the same within bucket average accurcy. The exected calibration error is the average absolute difference between predicted probability and (bucket) average accuracy. That is: bucket weight = bucket_count / tf.reduce_sum(bucket_count, axis=0) bucket_error = abs(bucket_accuracy - bucket_confidence) ece = tf.reduce_sum(bucket_weight * bucket_error, axis=0) where `bucket_accuracy, bucket_confidence, bucket_count` are statistics aggregated by `num_buckets`-quantiles of [`tf.math.exp(pred_log_prob)`](https://www.tensorflow.org/api_docs/python/tf/math/exp). Note: `bucket_*` always have `num_buckets` size for the zero-th dimension.


| ## Args || |---|---| | `hit` | `bool` `Tensor` where `True` means the model prediction was correct and `False` means the model prediction was incorrect. Shape must broadcast with pred_log_prob. | | `pred_log_prob` | `Tensor` representing the model's predicted log probability for the given `hit`. Shape must broadcast with `hit`. | | `num_buckets` | `int` representing the number of buckets over which to aggregate hits. Buckets are quantiles of `exp(pred_log_prob)`. Default value: `20`. | | `axis` | Dimension over which to compute buckets and aggregate stats. Default value: `0`. | | `log_space_buckets` | When `False` bucket edges are computed from [`tf.math.exp(pred_log_prob)`](https://www.tensorflow.org/api_docs/python/tf/math/exp); when `True` bucket edges are computed from `pred_log_prob`. Default value: `False`. | | `name` | Prefer `str` name used for ops created by this function. Default value: `None` (i.e., `"expected_calibration_error_quantiles"`). |



| ## Returns || |---|---| | `ece` | Expected calibration error; `tf.reduce_sum(abs(bucket_accuracy - bucket_confidence) * bucket_count, axis=0) / tf.reduce_sum(bucket_count, axis=0)`. | | `bucket_accuracy` | `Tensor` representing the within bucket average hits, i.e., total bucket hits divided by bucket count. Has shape `tf.concat([[num_buckets], tf.shape(tf.reduce_sum(pred_log_prob, axis=axis))], axis=0)`. | | `bucket_confidence` | `Tensor` representing the within bucket average probability, i.e., total bucket predicted probability divided by bucket count. Has shape `tf.concat([[num_buckets], tf.shape(tf.reduce_sum(pred_log_prob, axis=axis))], axis=0)`. | | `bucket_count` | `Tensor` representing the total number of obervations in each bucket. Has shape `tf.concat([[num_buckets], tf.shape(tf.reduce_sum(pred_log_prob, axis=axis))], axis=0)`. | | `bucket_pred_log_prob` | `Tensor` representing `pred_log_prob` bucket edges. Always in log space, regardless of the value of `log_space_buckets`. | | `bucket` | `int` `Tensor` representing the bucket within which `pred_log_prob` lies. |
#### Examples # Example 1: Generic use. label = tf.cast([0, 0, 1, 0, 1, 1], dtype=tf.bool) log_pred = tf.math.log([0.1, 0.05, 0.5, 0.2, 0.99, 0.99]) ( ece, acc, conf, cnt, edges, bucket, ) = tfp.stats.expected_calibration_error_quantiles( label, log_pred, num_buckets=3) # ece ==> tf.Tensor(0.145, shape=(), dtype=float32) # acc ==> tf.Tensor([0. 0. 1.], shape=(3,), dtype=float32) # conf ==> tf.Tensor([0.075, 0.2, 0.826665], shape=(3,), dtype=float32) # cnt ==> tf.Tensor([2. 1. 3.], shape=(3,), dtype=float32) # Example 2: Categorgical classification. # Assume we have evidence `x`, targets `y`, and model function `dnn`. d = tfd.Categorical(logits=dnn(x)) def all_categories(d): num_classes = tf.shape(d.logits_parameter())[-1] batch_ndims = tf.size(d.batch_shape_tensor()) expand_shape = tf.pad( [num_classes], paddings=[[0, batch_ndims]], constant_values=1) return tf.reshape(tf.range(num_classes, dtype=d.dtype), expand_shape) all_pred_log_prob = d.log_prob(all_categories(d)) yhat = tf.argmax(all_pred_log_prob, axis=0) def rollaxis(x, shift): return tf.transpose(x, tf.roll(tf.range(tf.rank(x)), shift=shift, axis=0)) pred_log_prob = tf.gather(rollaxis(all_pred_log_prob, shift=-1), yhat, batch_dims=len(d.batch_shape)) hit = tf.equal(y, yhat) ( ece, acc, conf, cnt, edges, bucket, ) = tfp.stats.expected_calibration_error_quantiles( hit, pred_log_prob, num_buckets=10)