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metrics.R
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metrics.R
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#' Computes the binary focal crossentropy loss.
#'
#' @description
#' According to [Lin et al., 2018](https://arxiv.org/pdf/1708.02002), it
#' helps to apply a focal factor to down-weight easy examples and focus more on
#' hard examples. By default, the focal tensor is computed as follows:
#'
#' `focal_factor = (1 - output)^gamma` for class 1
#' `focal_factor = output^gamma` for class 0
#' where `gamma` is a focusing parameter. When `gamma` = 0, there is no focal
#' effect on the binary crossentropy loss.
#'
#' If `apply_class_balancing == TRUE`, this function also takes into account a
#' weight balancing factor for the binary classes 0 and 1 as follows:
#'
#' `weight = alpha` for class 1 (`target == 1`)
#' `weight = 1 - alpha` for class 0
#' where `alpha` is a float in the range of `[0, 1]`.
#'
#' # Examples
#' ```{r}
#' y_true <- rbind(c(0, 1), c(0, 0))
#' y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6))
#' loss <- loss_binary_focal_crossentropy(y_true, y_pred, gamma=2)
#' loss
#' ```
#'
#' @returns
#' Binary focal crossentropy loss value
#' with shape = `[batch_size, d0, .. dN-1]`.
#'
#' @param y_true
#' Ground truth values, of shape `(batch_size, d0, .. dN)`.
#'
#' @param y_pred
#' The predicted values, of shape `(batch_size, d0, .. dN)`.
#'
#' @param apply_class_balancing
#' A bool, whether to apply weight balancing on the
#' binary classes 0 and 1.
#'
#' @param alpha
#' A weight balancing factor for class 1, default is `0.25` as
#' mentioned in the reference. The weight for class 0 is `1.0 - alpha`.
#'
#' @param gamma
#' A focusing parameter, default is `2.0` as mentioned in the
#' reference.
#'
#' @param from_logits
#' Whether `y_pred` is expected to be a logits tensor. By
#' default, we assume that `y_pred` encodes a probability distribution.
#'
#' @param label_smoothing
#' Float in `[0, 1]`. If > `0` then smooth the labels by
#' squeezing them towards 0.5, that is,
#' using `1. - 0.5 * label_smoothing` for the target class
#' and `0.5 * label_smoothing` for the non-target class.
#'
#' @param axis
#' The axis along which the mean is computed. Defaults to `-1`.
#'
#' @inherit metric_binary_accuracy return
#' @export
#' @family losses
#' @family metrics
# @seealso
# + <https://www.tensorflow.org/api_docs/python/tf/keras/metrics/binary_focal_crossentropy>
#'
#' @tether keras.metrics.binary_focal_crossentropy
metric_binary_focal_crossentropy <-
function (y_true, y_pred, apply_class_balancing = FALSE, alpha = 0.25,
gamma = 2, from_logits = FALSE, label_smoothing = 0, axis = -1L)
{
args <- capture_args(list(axis = as_axis,
y_true = as_py_array,
y_pred = as_py_array))
do.call(keras$metrics$binary_focal_crossentropy, args)
}
#' Computes the categorical focal crossentropy loss.
#'
#' @description
#'
#' # Examples
#' ```{r}
#' y_true <- rbind(c(0, 1, 0), c(0, 0, 1))
#' y_pred <- rbind(c(0.05, 0.9, 0.05), c(0.1, 0.85, 0.05))
#' loss <- loss_categorical_focal_crossentropy(y_true, y_pred)
#' loss
#' ```
#'
#' @returns
#' Categorical focal crossentropy loss value.
#'
#' @param y_true
#' Tensor of one-hot true targets.
#'
#' @param y_pred
#' Tensor of predicted targets.
#'
#' @param alpha
#' A weight balancing factor for all classes, default is `0.25` as
#' mentioned in the reference. It can be a list of floats or a scalar.
#' In the multi-class case, alpha may be set by inverse class
#' frequency by using `compute_class_weight` from `sklearn.utils`.
#'
#' @param gamma
#' A focusing parameter, default is `2.0` as mentioned in the
#' reference. It helps to gradually reduce the importance given to
#' simple examples in a smooth manner. When `gamma` = 0, there is
#' no focal effect on the categorical crossentropy.
#'
#' @param from_logits
#' Whether `y_pred` is expected to be a logits tensor. By
#' default, we assume that `y_pred` encodes a probability
#' distribution.
#'
#' @param label_smoothing
#' Float in `[0, 1].` If > `0` then smooth the labels. For
#' example, if `0.1`, use `0.1 / num_classes` for non-target labels
#' and `0.9 + 0.1 / num_classes` for target labels.
#'
#' @param axis
#' Defaults to `-1`. The dimension along which the entropy is
#' computed.
#'
#' @inherit metric_binary_accuracy return
#' @export
#' @family losses
#' @family metrics
# @seealso
# + <https://www.tensorflow.org/api_docs/python/tf/keras/metrics/categorical_focal_crossentropy>
#'
#' @tether keras.metrics.categorical_focal_crossentropy
metric_categorical_focal_crossentropy <-
function (y_true, y_pred, alpha = 0.25, gamma = 2, from_logits = FALSE,
label_smoothing = 0, axis = -1L)
{
args <- capture_args(list(axis = as_axis,
y_true = as_py_array,
y_pred = as_py_array))
do.call(keras$metrics$categorical_focal_crossentropy, args)
}
#' Computes Huber loss value.
#'
#' @description
#' Formula:
#' ```{r, eval = FALSE}
#' for (x in error) {
#' if (abs(x) <= delta){
#' loss <- c(loss, (0.5 * x^2))
#' } else if (abs(x) > delta) {
#' loss <- c(loss, (delta * abs(x) - 0.5 * delta^2))
#' }
#' }
#' loss <- mean(loss)
#' ```
#' See: [Huber loss](https://en.wikipedia.org/wiki/Huber_loss).
#'
#' # Examples
#' ```{r}
#' y_true <- rbind(c(0, 1), c(0, 0))
#' y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6))
#' loss <- loss_huber(y_true, y_pred)
#' ```
#'
#' @returns
#' Tensor with one scalar loss entry per sample.
#'
#' @param y_true
#' tensor of true targets.
#'
#' @param y_pred
#' tensor of predicted targets.
#'
#' @param delta
#' A float, the point where the Huber loss function changes from a
#' quadratic to linear. Defaults to `1.0`.
#'
#' @inherit metric_binary_accuracy return
#' @export
#' @family losses
#' @family metrics
# @seealso
# + <https://www.tensorflow.org/api_docs/python/tf/keras/metrics/huber>
#'
#' @tether keras.metrics.huber
metric_huber <-
function (y_true, y_pred, delta = 1)
{
args <- capture_args(list(y_true = as_py_array, y_pred = as_py_array))
do.call(keras$metrics$huber, args)
}
#' Logarithm of the hyperbolic cosine of the prediction error.
#'
#' @description
#' Formula:
#' ```{r, eval = FALSE}
#' loss <- mean(log(cosh(y_pred - y_true)), axis=-1)
#' ```
#'
#' Note that `log(cosh(x))` is approximately equal to `(x ** 2) / 2` for small
#' `x` and to `abs(x) - log(2)` for large `x`. This means that 'logcosh' works
#' mostly like the mean squared error, but will not be so strongly affected by
#' the occasional wildly incorrect prediction.
#'
#' # Examples
#' ```{r}
#' y_true <- rbind(c(0., 1.), c(0., 0.))
#' y_pred <- rbind(c(1., 1.), c(0., 0.))
#' loss <- metric_log_cosh(y_true, y_pred)
#' loss
#' ```
#'
#' @returns
#' Logcosh error values with shape = `[batch_size, d0, .. dN-1]`.
#'
#' @param y_true
#' Ground truth values with shape = `[batch_size, d0, .. dN]`.
#'
#' @param y_pred
#' The predicted values with shape = `[batch_size, d0, .. dN]`.
#'
#' @inherit metric_binary_accuracy return
#' @export
#' @family losses
#' @family metrics
# @seealso
# + <https://www.tensorflow.org/api_docs/python/tf/keras/metrics/log_cosh>
#'
#' @tether keras.metrics.log_cosh
metric_log_cosh <-
function (y_true, y_pred)
{
args <- capture_args(list(y_true = as_py_array, y_pred = as_py_array))
do.call(keras$metrics$log_cosh, args)
}
#' Calculates how often predictions match binary labels.
#'
#' @description
#' This metric creates two local variables, `total` and `count` that are used
#' to compute the frequency with which `y_pred` matches `y_true`. This
#' frequency is ultimately returned as `binary accuracy`: an idempotent
#' operation that simply divides `total` by `count`.
#'
#' If `sample_weight` is `NULL`, weights default to 1.
#' Use `sample_weight` of 0 to mask values.
#'
#' # Usage
#' Standalone usage:
#'
#' ```{r}
#' m <- metric_binary_accuracy()
#' m$update_state(rbind(1, 1, 0, 0), rbind(0.98, 1, 0, 0.6))
#' m$result()
#' # 0.75
#' ```
#'
#' ```{r}
#' m$reset_state()
#' m$update_state(rbind(1, 1, 0, 0), rbind(0.98, 1, 0, 0.6),
#' sample_weight = c(1, 0, 0, 1))
#' m$result()
#' # 0.5
#' ```
#'
#' Usage with `compile()` API:
#'
#' ```{r, eval = FALSE}
#' model %>% compile(optimizer='sgd',
#' loss='binary_crossentropy',
#' metrics=list(metric_binary_accuracy()))
#' ```
#'
#' @param name
#' (Optional) string name of the metric instance.
#'
#' @param dtype
#' (Optional) data type of the metric result.
#'
#' @param threshold
#' (Optional) Float representing the threshold for deciding
#' whether prediction values are 1 or 0.
#'
#' @param y_true
#' Tensor of true targets.
#'
#' @param y_pred
#' Tensor of predicted targets.
#'
#' @param ...
#' For forward/backward compatability.
#'
#' @export
#' @family accuracy metrics
#' @family metrics
#' @returns If `y_true` and `y_pred` are missing, a `Metric`
#' instance is returned. The `Metric` instance that can be passed directly to
#' `compile(metrics = )`, or used as a standalone object. See `?Metric` for
#' example usage. If `y_true` and `y_pred` are provided, then a tensor with
#' the computed value is returned.
#' @seealso
#' + <https://keras.io/api/metrics/accuracy_metrics#binaryaccuracy-class>
# + <https://www.tensorflow.org/api_docs/python/tf/keras/metrics/BinaryAccuracy>
#'
#' @tether keras.metrics.BinaryAccuracy
metric_binary_accuracy <-
function (y_true, y_pred, threshold = 0.5, ..., name = "binary_accuracy",
dtype = NULL)
{
args <- capture_args(list(y_true = as_py_array, y_pred = as_py_array))
callable <- if (missing(y_true) && missing(y_pred))
keras$metrics$BinaryAccuracy
else keras$metrics$binary_accuracy
do.call(callable, args)
}
#' Calculates how often predictions match one-hot labels.
#'
#' @description
#' You can provide logits of classes as `y_pred`, since argmax of
#' logits and probabilities are same.
#'
#' This metric creates two local variables, `total` and `count` that are used
#' to compute the frequency with which `y_pred` matches `y_true`. This
#' frequency is ultimately returned as `categorical accuracy`: an idempotent
#' operation that simply divides `total` by `count`.
#'
#' `y_pred` and `y_true` should be passed in as vectors of probabilities,
#' rather than as labels. If necessary, use `op_one_hot` to expand `y_true` as
#' a vector.
#'
#' If `sample_weight` is `NULL`, weights default to 1.
#' Use `sample_weight` of 0 to mask values.
#'
#' # Usage
#' Standalone usage:
#'
#' ```{r}
#' m <- metric_categorical_accuracy()
#' m$update_state(rbind(c(0, 0, 1), c(0, 1, 0)), rbind(c(0.1, 0.9, 0.8),
#' c(0.05, 0.95, 0)))
#' m$result()
#' ```
#'
#' ```{r}
#' m$reset_state()
#' m$update_state(rbind(c(0, 0, 1), c(0, 1, 0)), rbind(c(0.1, 0.9, 0.8),
#' c(0.05, 0.95, 0)),
#' sample_weight = c(0.7, 0.3))
#' m$result()
#' # 0.3
#' ```
#'
#' Usage with `compile()` API:
#'
#' ```{r, eval = FALSE}
#' model %>% compile(optimizer = 'sgd',
#' loss = 'categorical_crossentropy',
#' metrics = list(metric_categorical_accuracy()))
#' ```
#'
#' @param name
#' (Optional) string name of the metric instance.
#'
#' @param dtype
#' (Optional) data type of the metric result.
#'
#' @param y_true
#' Tensor of true targets.
#'
#' @param y_pred
#' Tensor of predicted targets.
#'
#' @param ...
#' For forward/backward compatability.
#'
#' @export
#' @family accuracy metrics
#' @family metrics
#' @inherit metric_binary_accuracy return
#' @seealso
#' + <https://keras.io/api/metrics/accuracy_metrics#categoricalaccuracy-class>
# + <https://www.tensorflow.org/api_docs/python/tf/keras/metrics/CategoricalAccuracy>
#'
#' @tether keras.metrics.CategoricalAccuracy
metric_categorical_accuracy <-
function (y_true, y_pred, ..., name = "categorical_accuracy",
dtype = NULL)
{
args <- capture_args(list(y_true = as_py_array, y_pred = as_py_array))
callable <- if (missing(y_true) && missing(y_pred))
keras$metrics$CategoricalAccuracy
else keras$metrics$categorical_accuracy
do.call(callable, args)
}
#' Calculates how often predictions match integer labels.
#'
#' @description
#' ```{r, eval=FALSE}
#' acc <- sample_weight %*% (y_true == which.max(y_pred))
#' ```
#'
#' You can provide logits of classes as `y_pred`, since argmax of
#' logits and probabilities are same.
#'
#' This metric creates two local variables, `total` and `count` that are used
#' to compute the frequency with which `y_pred` matches `y_true`. This
#' frequency is ultimately returned as `sparse categorical accuracy`: an
#' idempotent operation that simply divides `total` by `count`.
#'
#' If `sample_weight` is `NULL`, weights default to 1.
#' Use `sample_weight` of 0 to mask values.
#'
#' # Usage
#' Standalone usage:
#'
#' ```{r}
#' m <- metric_sparse_categorical_accuracy()
#' m$update_state(rbind(2L, 1L), rbind(c(0.1, 0.6, 0.3), c(0.05, 0.95, 0)))
#' m$result()
#' ```
#'
#' ```{r}
#' m$reset_state()
#' m$update_state(rbind(2L, 1L), rbind(c(0.1, 0.6, 0.3), c(0.05, 0.95, 0)),
#' sample_weight = c(0.7, 0.3))
#' m$result()
#' ```
#'
#' Usage with `compile()` API:
#'
#' ```{r, eval = FALSE}
#' model %>% compile(optimizer = 'sgd',
#' loss = 'sparse_categorical_crossentropy',
#' metrics = list(metric_sparse_categorical_accuracy()))
#' ```
#'
#' @param name
#' (Optional) string name of the metric instance.
#'
#' @param dtype
#' (Optional) data type of the metric result.
#'
#' @param y_true
#' Tensor of true targets.
#'
#' @param y_pred
#' Tensor of predicted targets.
#'
#' @param ...
#' For forward/backward compatability.
#'
#' @export
#' @family accuracy metrics
#' @family metrics
#' @inherit metric_binary_accuracy return
#' @seealso
#' + <https://keras.io/api/metrics/accuracy_metrics#sparsecategoricalaccuracy-class>
# + <https://www.tensorflow.org/api_docs/python/tf/keras/metrics/SparseCategoricalAccuracy>
#'
#' @tether keras.metrics.SparseCategoricalAccuracy
metric_sparse_categorical_accuracy <-
function (y_true, y_pred, ..., name = "sparse_categorical_accuracy",
dtype = NULL)
{
args <- capture_args(list(y_true = as_py_array,
y_pred = as_py_array))
callable <- if (missing(y_true) && missing(y_pred))
keras$metrics$SparseCategoricalAccuracy
else keras$metrics$sparse_categorical_accuracy
do.call(callable, args)
}
#' Computes how often integer targets are in the top `K` predictions.
#'
#' @description
#'
#' # Usage
#' Standalone usage:
#'
#' ```{r}
#' m <- metric_sparse_top_k_categorical_accuracy(k = 1L)
#' m$update_state(
#' rbind(2, 1),
#' op_array(rbind(c(0.1, 0.9, 0.8), c(0.05, 0.95, 0)), dtype = "float32")
#' )
#' m$result()
#' ```
#'
#' ```{r}
#' m$reset_state()
#' m$update_state(
#' rbind(2, 1),
#' op_array(rbind(c(0.1, 0.9, 0.8), c(0.05, 0.95, 0)), dtype = "float32"),
#' sample_weight = c(0.7, 0.3)
#' )
#' m$result()
#' ```
#'
#' Usage with `compile()` API:
#'
#' ```{r, eval = FALSE}
#' model %>% compile(optimizer = 'sgd',
#' loss = 'sparse_categorical_crossentropy',
#' metrics = list(metric_sparse_top_k_categorical_accuracy()))
#' ```
#'
#' @param k
#' (Optional) Number of top elements to look at for computing accuracy.
#' Defaults to `5`.
#'
#' @param name
#' (Optional) string name of the metric instance.
#'
#' @param dtype
#' (Optional) data type of the metric result.
#'
#' @param y_true
#' Tensor of true targets.
#'
#' @param y_pred
#' Tensor of predicted targets.
#'
#' @param ...
#' For forward/backward compatability.
#'
#' @inherit metric_binary_accuracy return
#' @export
#' @family accuracy metrics
#' @family metrics
#' @seealso
#' + <https://keras.io/api/metrics/accuracy_metrics#sparsetopkcategoricalaccuracy-class>
# + <https://www.tensorflow.org/api_docs/python/tf/keras/metrics/SparseTopKCategoricalAccuracy>
#'
#' @tether keras.metrics.SparseTopKCategoricalAccuracy
metric_sparse_top_k_categorical_accuracy <-
function (y_true, y_pred, k = 5L, ..., name = "sparse_top_k_categorical_accuracy",
dtype = NULL)
{
args <- capture_args(list(k = as_integer,
y_true = as_py_array,
y_pred = as_py_array))
callable <- if (missing(y_true) && missing(y_pred))
keras$metrics$SparseTopKCategoricalAccuracy
else keras$metrics$sparse_top_k_categorical_accuracy
do.call(callable, args)
}
#' Computes how often targets are in the top `K` predictions.
#'
#' @description
#'
#' # Usage
#' Standalone usage:
#'
#' ```{r}
#' m <- metric_top_k_categorical_accuracy(k = 1)
#' m$update_state(
#' rbind(c(0, 0, 1), c(0, 1, 0)),
#' op_array(rbind(c(0.1, 0.9, 0.8), c(0.05, 0.95, 0)), dtype = "float32")
#' )
#' m$result()
#' ```
#'
#' ```{r}
#' m$reset_state()
#' m$update_state(
#' rbind(c(0, 0, 1), c(0, 1, 0)),
#' op_array(rbind(c(0.1, 0.9, 0.8), c(0.05, 0.95, 0)), dtype = "float32"),
#' sample_weight = c(0.7, 0.3))
#' m$result()
#' ```
#'
#' Usage with `compile()` API:
#'
#' ```{r, eval = FALSE}
#' model.compile(optimizer = 'sgd',
#' loss = 'categorical_crossentropy',
#' metrics = list(metric_top_k_categorical_accuracy()))
#' ```
#'
#' @param k
#' (Optional) Number of top elements to look at for computing accuracy.
#' Defaults to `5`.
#'
#' @param name
#' (Optional) string name of the metric instance.
#'
#' @param dtype
#' (Optional) data type of the metric result.
#'
#' @param y_true
#' Tensor of true targets.
#'
#' @param y_pred
#' Tensor of predicted targets.
#'
#' @param ...
#' For forward/backward compatability.
#'
#' @inherit metric_binary_accuracy return
#' @export
#' @family accuracy metrics
#' @family metrics
#' @seealso
#' + <https://keras.io/api/metrics/accuracy_metrics#topkcategoricalaccuracy-class>
# + <https://www.tensorflow.org/api_docs/python/tf/keras/metrics/TopKCategoricalAccuracy>
#'
#' @tether keras.metrics.TopKCategoricalAccuracy
metric_top_k_categorical_accuracy <-
function (y_true, y_pred, k = 5L, ..., name = "top_k_categorical_accuracy",
dtype = NULL)
{
args <- capture_args(list(k = as_integer,
y_true = as_py_array,
y_pred = as_py_array))
callable <- if (missing(y_true) && missing(y_pred))
keras$metrics$TopKCategoricalAccuracy
else keras$metrics$top_k_categorical_accuracy
do.call(callable, args)
}
#' Approximates the AUC (Area under the curve) of the ROC or PR curves.
#'
#' @description
#' 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]` (if
#' `from_logits=FALSE`). 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 `NULL`, weights default to 1.
#' Use `sample_weight` of 0 to mask values.
#'
#' # Usage
#' Standalone usage:
#'
#' ```{r}
#' m <- metric_auc(num_thresholds = 3)
#' m$update_state(c(0, 0, 1, 1),
#' c(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$result()
#' ```
#'
#' ```{r}
#' m$reset_state()
#' m$update_state(c(0, 0, 1, 1),
#' c(0, 0.5, 0.3, 0.9),
#' sample_weight=c(1, 0, 0, 1))
#' m$result()
#' ```
#'
#' Usage with `compile()` API:
#'
#' ```{r, eval = FALSE}
#' # Reports the AUC of a model outputting a probability.
#' model |> compile(
#' optimizer = 'sgd',
#' loss = loss_binary_crossentropy(),
#' metrics = list(metric_auc())
#' )
#'
#' # Reports the AUC of a model outputting a logit.
#' model |> compile(
#' optimizer = 'sgd',
#' loss = loss_binary_crossentropy(from_logits = TRUE),
#' metrics = list(metric_auc(from_logits = TRUE))
#' )
#' ```
#'
#' @param num_thresholds
#' (Optional) The number of thresholds to
#' use when discretizing the roc curve. Values must be > 1.
#' Defaults to `200`.
#'
#' @param curve
#' (Optional) Specifies the name of the curve to be computed,
#' `'ROC'` (default) or `'PR'` for the Precision-Recall-curve.
#'
#' @param summation_method
#' (Optional) Specifies the [Riemann summation method](
#' https://en.wikipedia.org/wiki/Riemann_sum) 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.
#'
#' @param name
#' (Optional) string name of the metric instance.
#'
#' @param dtype
#' (Optional) data type of the metric result.
#'
#' @param 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.
#'
#' @param 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.
#'
#' @param 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`.
#'
#' @param 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.
#'
#' @param 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.
#'
#' @param ...
#' For forward/backward compatability.
#'
#' @returns a `Metric` instance is returned. The `Metric` instance can be passed
#' directly to `compile(metrics = )`, or used as a standalone object. See
#' `?Metric` for example usage.
#' @export
#' @family confusion metrics
#' @family metrics
#' @seealso
#' + <https://keras.io/api/metrics/classification_metrics#auc-class>
# + <https://www.tensorflow.org/api_docs/python/tf/keras/metrics/AUC>
#'
#' @tether keras.metrics.AUC
metric_auc <-
function (..., num_thresholds = 200L, curve = "ROC", summation_method = "interpolation",
name = NULL, dtype = NULL, thresholds = NULL, multi_label = FALSE,
num_labels = NULL, label_weights = NULL, from_logits = FALSE)
{
args <- capture_args(list(num_thresholds = as_integer))
do.call(keras$metrics$AUC, args)
}
#' Calculates the number of false negatives.
#'
#' @description
#' If `sample_weight` is given, calculates the sum of the weights of
#' false negatives. This metric creates one local variable, `accumulator`
#' that is used to keep track of the number of false negatives.
#'
#' If `sample_weight` is `NULL`, weights default to 1.
#' Use `sample_weight` of 0 to mask values.
#'
#' # Usage
#' Standalone usage:
#'
#' ```{r}
#' m <- metric_false_negatives()
#' m$update_state(c(0, 1, 1, 1), c(0, 1, 0, 0))
#' m$result()
#' ```
#'
#' ```{r}
#' m$reset_state()
#' m$update_state(c(0, 1, 1, 1), c(0, 1, 0, 0), sample_weight=c(0, 0, 1, 0))
#' m$result()
#' # 1.0
#' ```
#'
#' @param thresholds
#' (Optional) Defaults to `0.5`. A float value, or a Python
#' list of float threshold values in `[0, 1]`. A threshold is
#' compared with prediction values to determine the truth value of
#' predictions (i.e., above the threshold is `TRUE`, below is `FALSE`).
#' If used with a loss function that sets `from_logits=TRUE` (i.e. no
#' sigmoid applied to predictions), `thresholds` should be set to 0.
#' One metric value is generated for each threshold value.
#'
#' @param name
#' (Optional) string name of the metric instance.
#'
#' @param dtype
#' (Optional) data type of the metric result.
#'
#' @param ...
#' For forward/backward compatability.
#'
#' @inherit metric_auc return
#' @export
#' @family confusion metrics
#' @family metrics
#' @seealso
#' + <https://keras.io/api/metrics/classification_metrics#falsenegatives-class>
# + <https://www.tensorflow.org/api_docs/python/tf/keras/metrics/FALSENegatives>
#'
#' @tether keras.metrics.FalseNegatives
metric_false_negatives <-
function (..., thresholds = NULL, name = NULL, dtype = NULL)
{
args <- capture_args()
do.call(keras$metrics$FalseNegatives, args)
}
#' Calculates the number of false positives.
#'
#' @description
#' If `sample_weight` is given, calculates the sum of the weights of
#' false positives. This metric creates one local variable, `accumulator`
#' that is used to keep track of the number of false positives.
#'
#' If `sample_weight` is `NULL`, weights default to 1.
#' Use `sample_weight` of 0 to mask values.
#'
#' # Usage
#' Standalone usage:
#'
#' ```{r}
#' m <- metric_false_positives()
#' m$update_state(c(0, 1, 0, 0), c(0, 0, 1, 1))
#' m$result()
#' ```
#'
#' ```{r}
#' m$reset_state()
#' m$update_state(c(0, 1, 0, 0), c(0, 0, 1, 1), sample_weight = c(0, 0, 1, 0))
#' m$result()
#' ```
#'
#' @param thresholds
#' (Optional) Defaults to `0.5`. A float value, or a Python
#' list of float threshold values in `[0, 1]`. A threshold is
#' compared with prediction values to determine the truth value of
#' predictions (i.e., above the threshold is `TRUE`, below is `FALSE`).
#' If used with a loss function that sets `from_logits=TRUE` (i.e. no
#' sigmoid applied to predictions), `thresholds` should be set to 0.
#' One metric value is generated for each threshold value.
#'
#' @param name
#' (Optional) string name of the metric instance.
#'
#' @param dtype
#' (Optional) data type of the metric result.
#'
#' @param ...
#' For forward/backward compatability.
#'
#' @inherit metric_auc return
#' @export
#' @family confusion metrics
#' @family metrics
#' @seealso
#' + <https://keras.io/api/metrics/classification_metrics#falsepositives-class>
# + <https://www.tensorflow.org/api_docs/python/tf/keras/metrics/FALSEPositives>
#'
#' @tether keras.metrics.FalsePositives
metric_false_positives <-
function (..., thresholds = NULL, name = NULL, dtype = NULL)
{
args <- capture_args()
do.call(keras$metrics$FalsePositives, args)
}
#' Computes the precision of the predictions with respect to the labels.
#'
#' @description
#' The metric creates two local variables, `true_positives` and
#' `false_positives` that are used to compute the precision. This value is
#' ultimately returned as `precision`, an idempotent operation that simply
#' divides `true_positives` by the sum of `true_positives` and
#' `false_positives`.
#'
#' If `sample_weight` is `NULL`, weights default to 1.
#' Use `sample_weight` of 0 to mask values.
#'
#' If `top_k` is set, we'll calculate precision as how often on average a class
#' among the top-k classes with the highest predicted values of a batch entry
#' is correct and can be found in the label for that entry.
#'
#' If `class_id` is specified, we calculate precision by considering only the
#' entries in the batch for which `class_id` is above the threshold and/or in
#' the top-k highest predictions, and computing the fraction of them for which
#' `class_id` is indeed a correct label.
#'
#' # Usage
#' Standalone usage:
#'
#' ```{r}
#' m <- metric_precision()
#' m$update_state(c(0, 1, 1, 1),
#' c(1, 0, 1, 1))
#' m$result() |> as.double() |> signif()
#' ```
#'
#' ```{r}
#' m$reset_state()
#' m$update_state(c(0, 1, 1, 1),
#' c(1, 0, 1, 1),
#' sample_weight = c(0, 0, 1, 0))
#' m$result() |> as.double() |> signif()
#' ```
#'
#' ```{r}
#' # With top_k=2, it will calculate precision over y_true[1:2]
#' # and y_pred[1:2]
#' m <- metric_precision(top_k = 2)
#' m$update_state(c(0, 0, 1, 1), c(1, 1, 1, 1))
#' m$result()
#' ```
#'
#' ```{r}
#' # With top_k=4, it will calculate precision over y_true[1:4]
#' # and y_pred[1:4]
#' m <- metric_precision(top_k = 4)
#' m$update_state(c(0, 0, 1, 1), c(1, 1, 1, 1))
#' m$result()
#' ```
#'
#' Usage with `compile()` API:
#'
#' ```{r, eval=FALSE}
#' model |> compile(
#' optimizer = 'sgd',
#' loss = 'binary_crossentropy',
#' metrics = list(metric_precision())
#' )
#' ```
#'
#' Usage with a loss with `from_logits=TRUE`:
#'
#' ```{r, eval = FALSE}
#' model |> compile(
#' optimizer = 'adam',