claudson/ai/lib/python3.12/site-packages/keras/src/metrics/accuracy_metrics.py

from keras.src import backend
from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.losses.loss import squeeze_or_expand_to_same_rank
from keras.src.metrics import reduction_metrics


def accuracy(y_true, y_pred):
    y_pred = ops.convert_to_tensor(y_pred)
    y_true = ops.convert_to_tensor(y_true, dtype=y_pred.dtype)
    y_true, y_pred = squeeze_or_expand_to_same_rank(y_true, y_pred)
    return ops.cast(ops.equal(y_true, y_pred), dtype=backend.floatx())


@keras_export("keras.metrics.Accuracy")
class Accuracy(reduction_metrics.MeanMetricWrapper):
    """Calculates how often predictions equal labels.

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

    Args:
        name: (Optional) string name of the metric instance.
        dtype: (Optional) data type of the metric result.

    Examples:

    >>> m = keras.metrics.Accuracy()
    >>> m.update_state([[1], [2], [3], [4]], [[0], [2], [3], [4]])
    >>> m.result()
    0.75

    >>> m.reset_state()
    >>> m.update_state([[1], [2], [3], [4]], [[0], [2], [3], [4]],
    ...                sample_weight=[1, 1, 0, 0])
    >>> m.result()
    0.5

    Usage with `compile()` API:

    ```python
    model.compile(optimizer='sgd',
                  loss='binary_crossentropy',
                  metrics=[keras.metrics.Accuracy()])
    ```
    """

    def __init__(self, name="accuracy", dtype=None):
        super().__init__(fn=accuracy, name=name, dtype=dtype)
        # Metric should be maximized during optimization.
        self._direction = "up"

    def get_config(self):
        return {"name": self.name, "dtype": self.dtype}


@keras_export("keras.metrics.binary_accuracy")
def binary_accuracy(y_true, y_pred, threshold=0.5):
    y_pred = ops.convert_to_tensor(y_pred)
    y_true = ops.convert_to_tensor(y_true)
    threshold = ops.convert_to_tensor(threshold)
    y_true, y_pred = squeeze_or_expand_to_same_rank(y_true, y_pred)
    y_pred = ops.cast(ops.greater(y_pred, threshold), y_true.dtype)
    return ops.cast(ops.equal(y_true, y_pred), dtype=backend.floatx())


@keras_export("keras.metrics.BinaryAccuracy")
class BinaryAccuracy(reduction_metrics.MeanMetricWrapper):
    """Calculates how often predictions match binary labels.

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

    Args:
        name: (Optional) string name of the metric instance.
        dtype: (Optional) data type of the metric result.
        threshold: (Optional) Float representing the threshold for deciding
        whether prediction values are 1 or 0.

    Example:

    >>> m = keras.metrics.BinaryAccuracy()
    >>> m.update_state([[1], [1], [0], [0]], [[0.98], [1], [0], [0.6]])
    >>> m.result()
    0.75

    >>> m.reset_state()
    >>> m.update_state([[1], [1], [0], [0]], [[0.98], [1], [0], [0.6]],
    ...                sample_weight=[1, 0, 0, 1])
    >>> m.result()
    0.5

    Usage with `compile()` API:

    ```python
    model.compile(optimizer='sgd',
                  loss='binary_crossentropy',
                  metrics=[keras.metrics.BinaryAccuracy()])
    ```
    """

    def __init__(self, name="binary_accuracy", dtype=None, threshold=0.5):
        super().__init__(
            fn=binary_accuracy, name=name, dtype=dtype, threshold=threshold
        )
        self.threshold = threshold
        # Metric should be maximized during optimization.
        self._direction = "up"

    def get_config(self):
        return {
            "name": self.name,
            "dtype": self.dtype,
            "threshold": self.threshold,
        }


@keras_export("keras.metrics.categorical_accuracy")
def categorical_accuracy(y_true, y_pred):
    y_true = ops.argmax(y_true, axis=-1)

    reshape_matches = False
    y_pred = ops.convert_to_tensor(y_pred)
    y_true = ops.convert_to_tensor(y_true, dtype=y_pred.dtype)

    y_true_org_shape = ops.shape(y_true)
    y_pred_rank = len(y_pred.shape)
    y_true_rank = len(y_true.shape)

    # If the shape of y_true is (num_samples, 1), squeeze to (num_samples,)
    if (
        (y_true_rank is not None)
        and (y_pred_rank is not None)
        and (len(y_true.shape) == len(y_pred.shape))
    ):
        y_true = ops.squeeze(y_true, -1)
        reshape_matches = True
    y_pred = ops.argmax(y_pred, axis=-1)

    # If the predicted output and actual output types don't match, force cast
    # them to match.
    if y_pred.dtype is not y_true.dtype:
        y_pred = ops.cast(y_pred, dtype=y_true.dtype)
    matches = ops.cast(ops.equal(y_true, y_pred), backend.floatx())
    if reshape_matches:
        matches = ops.reshape(matches, y_true_org_shape)
    return matches


@keras_export("keras.metrics.CategoricalAccuracy")
class CategoricalAccuracy(reduction_metrics.MeanMetricWrapper):
    """Calculates how often predictions match one-hot labels.

    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 `ops.one_hot` to expand `y_true` as
    a vector.

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

    Args:
        name: (Optional) string name of the metric instance.
        dtype: (Optional) data type of the metric result.

    Example:

    >>> m = keras.metrics.CategoricalAccuracy()
    >>> m.update_state([[0, 0, 1], [0, 1, 0]], [[0.1, 0.9, 0.8],
    ...                 [0.05, 0.95, 0]])
    >>> m.result()
    0.5

    >>> m.reset_state()
    >>> m.update_state([[0, 0, 1], [0, 1, 0]], [[0.1, 0.9, 0.8],
    ...                 [0.05, 0.95, 0]],
    ...                sample_weight=[0.7, 0.3])
    >>> m.result()
    0.3

    Usage with `compile()` API:

    ```python
    model.compile(optimizer='sgd',
                  loss='categorical_crossentropy',
                  metrics=[keras.metrics.CategoricalAccuracy()])
    ```
    """

    def __init__(self, name="categorical_accuracy", dtype=None):
        super().__init__(fn=categorical_accuracy, name=name, dtype=dtype)
        # Metric should be maximized during optimization.
        self._direction = "up"

    def get_config(self):
        return {"name": self.name, "dtype": self.dtype}


@keras_export("keras.metrics.sparse_categorical_accuracy")
def sparse_categorical_accuracy(y_true, y_pred):
    reshape_matches = False
    y_pred = ops.convert_to_tensor(y_pred)
    y_true = ops.convert_to_tensor(y_true, dtype=y_pred.dtype)
    y_true_org_shape = ops.shape(y_true)
    y_pred_rank = len(y_pred.shape)
    y_true_rank = len(y_true.shape)

    # If the shape of y_true is (num_samples, 1), squeeze to (num_samples,)
    if (
        (y_true_rank is not None)
        and (y_pred_rank is not None)
        and (len(y_true.shape) == len(y_pred.shape))
        and ops.shape(y_true)[-1] == 1
    ):
        y_true = ops.squeeze(y_true, -1)
        reshape_matches = True
    y_pred = ops.argmax(y_pred, axis=-1)

    # If the predicted output and actual output types don't match, force cast
    # them to match.
    if y_pred.dtype is not y_true.dtype:
        y_pred = ops.cast(y_pred, y_true.dtype)
    matches = ops.cast(ops.equal(y_true, y_pred), backend.floatx())
    if reshape_matches:
        matches = ops.reshape(matches, y_true_org_shape)
    # if shape is (num_samples, 1) squeeze
    if len(matches.shape) > 1 and matches.shape[-1] == 1:
        matches = ops.squeeze(matches, -1)
    return matches


@keras_export("keras.metrics.SparseCategoricalAccuracy")
class SparseCategoricalAccuracy(reduction_metrics.MeanMetricWrapper):
    """Calculates how often predictions match integer labels.

    ```python
    acc = np.dot(sample_weight, np.equal(y_true, np.argmax(y_pred, axis=1))
    ```

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

    Args:
        name: (Optional) string name of the metric instance.
        dtype: (Optional) data type of the metric result.

    Example:

    >>> m = keras.metrics.SparseCategoricalAccuracy()
    >>> m.update_state([[2], [1]], [[0.1, 0.6, 0.3], [0.05, 0.95, 0]])
    >>> m.result()
    0.5

    >>> m.reset_state()
    >>> m.update_state([[2], [1]], [[0.1, 0.6, 0.3], [0.05, 0.95, 0]],
    ...                sample_weight=[0.7, 0.3])
    >>> m.result()
    0.3

    Usage with `compile()` API:

    ```python
    model.compile(optimizer='sgd',
                  loss='sparse_categorical_crossentropy',
                  metrics=[keras.metrics.SparseCategoricalAccuracy()])
    ```
    """

    def __init__(self, name="sparse_categorical_accuracy", dtype=None):
        super().__init__(fn=sparse_categorical_accuracy, name=name, dtype=dtype)
        # Metric should be maximized during optimization.
        self._direction = "up"

    def get_config(self):
        return {"name": self.name, "dtype": self.dtype}


@keras_export("keras.metrics.top_k_categorical_accuracy")
def top_k_categorical_accuracy(y_true, y_pred, k=5):
    reshape_matches = False
    y_pred = ops.convert_to_tensor(y_pred)
    y_true = ops.convert_to_tensor(y_true, dtype=y_pred.dtype)
    y_true = ops.argmax(y_true, axis=-1)
    y_true_rank = len(y_true.shape)
    y_pred_rank = len(y_pred.shape)
    y_true_org_shape = ops.shape(y_true)

    # Flatten y_pred to (batch_size, num_samples) and y_true to (num_samples,)
    if (y_true_rank is not None) and (y_pred_rank is not None):
        if y_pred_rank > 2:
            y_pred = ops.reshape(y_pred, [-1, y_pred.shape[-1]])
        if y_true_rank > 1:
            reshape_matches = True
            y_true = ops.reshape(y_true, [-1])

    matches = ops.cast(
        ops.in_top_k(ops.cast(y_true, "int32"), y_pred, k=k),
        dtype=backend.floatx(),
    )

    # returned matches is expected to have same shape as y_true input
    if reshape_matches:
        matches = ops.reshape(matches, y_true_org_shape)

    return matches


@keras_export("keras.metrics.TopKCategoricalAccuracy")
class TopKCategoricalAccuracy(reduction_metrics.MeanMetricWrapper):
    """Computes how often targets are in the top `K` predictions.

    Args:
        k: (Optional) Number of top elements to look at for computing accuracy.
            Defaults to `5`.
        name: (Optional) string name of the metric instance.
        dtype: (Optional) data type of the metric result.

    Example:

    >>> m = keras.metrics.TopKCategoricalAccuracy(k=1)
    >>> m.update_state([[0, 0, 1], [0, 1, 0]],
    ...                [[0.1, 0.9, 0.8], [0.05, 0.95, 0]])
    >>> m.result()
    0.5

    >>> m.reset_state()
    >>> m.update_state([[0, 0, 1], [0, 1, 0]],
    ...                [[0.1, 0.9, 0.8], [0.05, 0.95, 0]],
    ...                sample_weight=[0.7, 0.3])
    >>> m.result()
    0.3

    Usage with `compile()` API:

    ```python
    model.compile(optimizer='sgd',
                  loss='categorical_crossentropy',
                  metrics=[keras.metrics.TopKCategoricalAccuracy()])
    ```
    """

    def __init__(self, k=5, name="top_k_categorical_accuracy", dtype=None):
        super().__init__(
            fn=top_k_categorical_accuracy,
            name=name,
            dtype=dtype,
            k=k,
        )
        self.k = k
        # Metric should be maximized during optimization.
        self._direction = "up"

    def get_config(self):
        return {"name": self.name, "dtype": self.dtype, "k": self.k}


@keras_export("keras.metrics.sparse_top_k_categorical_accuracy")
def sparse_top_k_categorical_accuracy(
    y_true, y_pred, k=5, from_sorted_ids=False
):
    """Computes how often integer targets are in the top `K` predictions.

    Args:
        y_true: A tensor of shape `(batch_size)` representing indices or IDs of
            true categories.
        y_pred: If `from_sorted_ids=False`, a tensor of shape
            `(batch_size, num_categories)` containing the scores for each sample
            for all possible categories. If `from_sorted_ids=True`, a tensor of
            shape `(batch_size, N)` containing indices or IDs of the top `N`
            categories in order from highest score to lowest score.
        k: (Optional) Number of top elements to look at for computing accuracy.
            Defaults to `5`.
        from_sorted_ids: (Optional) Whether `y_pred` is sorted category IDs or
            scores for all categories (the default).

    Returns:
        A tensor with the same shape as `y_true` containing ones where `y_true`
        is in the top `k` and zeros elsewhere.
    """
    reshape_matches = False
    y_pred = ops.convert_to_tensor(y_pred)
    y_true_dtype = y_pred.dtype if from_sorted_ids else "int32"
    y_true = ops.convert_to_tensor(y_true, dtype=y_true_dtype)
    y_true_rank = len(y_true.shape)
    y_pred_rank = len(y_pred.shape)
    y_true_org_shape = ops.shape(y_true)

    # Flatten y_pred to (batch_size, num_samples) and y_true to (num_samples,)
    if (y_true_rank is not None) and (y_pred_rank is not None):
        if y_pred_rank > 2:
            y_pred = ops.reshape(y_pred, [-1, y_pred.shape[-1]])
        if y_true_rank > 1:
            reshape_matches = True
            y_true = ops.reshape(y_true, [-1])

    if from_sorted_ids:
        # By slicing the first k items, we assume they are sorted by score.
        # Reduce with `any` to count multiple matches only once.
        matches = ops.any(
            ops.equal(ops.expand_dims(y_true, axis=1), y_pred[:, :k]), axis=1
        )
    else:
        matches = ops.in_top_k(y_true, y_pred, k=k)

    matches = ops.cast(matches, dtype=backend.floatx())

    # returned matches is expected to have same shape as y_true input
    if reshape_matches:
        matches = ops.reshape(matches, y_true_org_shape)

    return matches


@keras_export("keras.metrics.SparseTopKCategoricalAccuracy")
class SparseTopKCategoricalAccuracy(reduction_metrics.MeanMetricWrapper):
    """Computes how often integer targets are in the top `K` predictions.

    By default, the arguments expected by `update_state()` are:
    - `y_true`: a tensor of shape `(batch_size)` representing indices of true
        categories.
    - `y_pred`: a tensor of shape `(batch_size, num_categories)` containing the
        scores for each sample for all possible categories.

    With `from_sorted_ids=True`, the arguments expected by `update_state` are:
    - `y_true`: a tensor of shape `(batch_size)` representing indices or IDs of
        true categories.
    - `y_pred`: a tensor of shape `(batch_size, N)` containing the indices or
        IDs of the top `N` categories sorted in order from highest score to
        lowest score. `N` must be greater or equal to `k`.

    The `from_sorted_ids=True` option can be more efficient when the set of
    categories is very large and the model has an optimized way to retrieve the
    top ones either without scoring or without maintaining the scores for all
    the possible categories.

    Args:
        k: (Optional) Number of top elements to look at for computing accuracy.
            Defaults to `5`.
        name: (Optional) string name of the metric instance.
        dtype: (Optional) data type of the metric result.
        from_sorted_ids: (Optional) When `False`, the default, the tensor passed
            in `y_pred` contains the unsorted scores of all possible categories.
            When `True`, `y_pred` contains a the indices or IDs for the top
            categories.

    Example:

    >>> m = keras.metrics.SparseTopKCategoricalAccuracy(k=1)
    >>> m.update_state([2, 1], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]])
    >>> m.result()
    0.5

    >>> m.reset_state()
    >>> m.update_state([2, 1], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]],
    ...                sample_weight=[0.7, 0.3])
    >>> m.result()
    0.3

    >>> m = keras.metrics.SparseTopKCategoricalAccuracy(k=1,
    ...                                                from_sorted_ids=True)
    >>> m.update_state([2, 1], [[1, 0, 3], [1, 2, 3]])
    >>> m.result()
    0.5

    Usage with `compile()` API:

    ```python
    model.compile(optimizer='sgd',
                  loss='sparse_categorical_crossentropy',
                  metrics=[keras.metrics.SparseTopKCategoricalAccuracy()])
    ```
    """

    def __init__(
        self,
        k=5,
        name="sparse_top_k_categorical_accuracy",
        dtype=None,
        from_sorted_ids=False,
    ):
        super().__init__(
            fn=sparse_top_k_categorical_accuracy,
            name=name,
            dtype=dtype,
            k=k,
            from_sorted_ids=from_sorted_ids,
        )
        self.k = k
        self.from_sorted_ids = from_sorted_ids
        # Metric should be maximized during optimization.
        self._direction = "up"

    def get_config(self):
        config = {"name": self.name, "dtype": self.dtype, "k": self.k}
        if self.from_sorted_ids:
            config["from_sorted_ids"] = True
        return config