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Metrics

danling.metrics.metrics

Metrics

Bases: Metric

Metric class wraps around multiple metrics that share the same states.

Typically, there are many metrics that we want to compute for a single task. For example, we usually needs to compute pearson and spearman for a regression task. Unlike accuracy, which can uses an average meter to compute the average accuracy, pearson and spearman cannot be computed by averaging the results of multiple batches. They need access to all the data to compute the correct results. And saving all intermediate results for each tasks is quite inefficient.

Metrics solves this problem by maintaining a shared state for multiple metric functions.

Attributes:

Name Type Description
metrics FlatDict[str, Callable]

A dictionary of metrics to be computed.A
ignored_index Optional[int]

Index to be ignored in the computation.
val NestedDict[str, float | flist]

Metric results of current batch on current device.
avg NestedDict[str, float | flist]

Metric results of all results on all devices.
input

The input tensor of latest batch.
target

The target tensor of latest batch.
inputs

All input tensors.
targets

All target tensors.

Parameters:

Name Type Description Default

*args

A single mapping of metrics.

 ()

**metrics

Callable

Metrics.

 {}

Examples:

Python Console Session
>>> from danling.metrics.functional import auroc, auprc
>>> metrics = Metrics(auroc=auroc, auprc=auprc)
>>> metrics
Metrics('auroc', 'auprc')
>>> metrics.update([0.2, 0.3, 0.5, 0.7], [0, 1, 0, 1])
>>> metrics.input  # predicted values of current batch
tensor([0.2000, 0.3000, 0.5000, 0.7000])
>>> metrics.target  # ground truth of current batch
tensor([0, 1, 0, 1])
>>> metrics.inputs  # predicted values of all data
tensor([0.2000, 0.3000, 0.5000, 0.7000])
>>> metrics.targets  # ground truth of all data
tensor([0, 1, 0, 1])
>>> metrics.val  # Metrics of current batch on current device
NestedDict(
  ('auroc'): 0.75
  ('auprc'): 0.8333333730697632
)
>>> metrics.avg  # Metrics of all data on all devices
NestedDict(
  ('auroc'): 0.75
  ('auprc'): 0.8333333730697632
)
>>> metrics.update([0.1, 0.4, 0.6, 0.8], [0, 0, 1, 0])
>>> metrics.input  # predicted values of current batch
tensor([0.1000, 0.4000, 0.6000, 0.8000])
>>> metrics.target  # ground truth of current batch
tensor([0, 0, 1, 0])
>>> metrics.inputs  # predicted values of all data
tensor([0.2000, 0.3000, 0.5000, 0.7000, 0.1000, 0.4000, 0.6000, 0.8000])
>>> metrics.targets  # ground truth of all data
tensor([0, 1, 0, 1, 0, 0, 1, 0])
>>> metrics.val  # Metrics of current batch on current device
NestedDict(
  ('auroc'): 0.6666666666666666
  ('auprc'): 0.5
)
>>> metrics.avg  # Metrics of all data on all devices
NestedDict(
  ('auroc'): 0.6666666666666666
  ('auprc'): 0.5555555820465088
)
>>> f"{metrics:.4f}"
'auroc: 0.6667 (0.6667)\tauprc: 0.5000 (0.5556)'
>>> metrics = Metrics(auroc=auroc, auprc=auprc, ignored_index=-100)
>>> metrics.update([[0.1, 0.4, 0.6, 0.8], [0.1, 0.4, 0.6]], [[0, -100, 1, 0], [0, -100, 1]])
>>> metrics.input, metrics.target
(tensor([0.1000, 0.6000, 0.8000, 0.1000, 0.6000]), tensor([0, 1, 0, 0, 1]))
Source code in danling/metrics/metrics.py
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class Metrics(Metric):
    r"""
    Metric class wraps around multiple metrics that share the same states.

    Typically, there are many metrics that we want to compute for a single task.
    For example, we usually needs to compute `pearson` and `spearman` for a regression task.
    Unlike `accuracy`, which can uses an average meter to compute the average accuracy,
    `pearson` and `spearman` cannot be computed by averaging the results of multiple batches.
    They need access to all the data to compute the correct results.
    And saving all intermediate results for each tasks is quite inefficient.

    `Metrics` solves this problem by maintaining a shared state for multiple metric functions.

    Attributes:
        metrics: A dictionary of metrics to be computed.A
        ignored_index: Index to be ignored in the computation.
        val: Metric results of current batch on current device.
        avg: Metric results of all results on all devices.
        input: The input tensor of latest batch.
        target: The target tensor of latest batch.
        inputs: All input tensors.
        targets: All target tensors.

    Args:
        *args: A single mapping of metrics.
        **metrics: Metrics.

    Examples:
        >>> from danling.metrics.functional import auroc, auprc
        >>> metrics = Metrics(auroc=auroc, auprc=auprc)
        >>> metrics
        Metrics('auroc', 'auprc')
        >>> metrics.update([0.2, 0.3, 0.5, 0.7], [0, 1, 0, 1])
        >>> metrics.input  # predicted values of current batch
        tensor([0.2000, 0.3000, 0.5000, 0.7000])
        >>> metrics.target  # ground truth of current batch
        tensor([0, 1, 0, 1])
        >>> metrics.inputs  # predicted values of all data
        tensor([0.2000, 0.3000, 0.5000, 0.7000])
        >>> metrics.targets  # ground truth of all data
        tensor([0, 1, 0, 1])
        >>> metrics.val  # Metrics of current batch on current device
        NestedDict(
          ('auroc'): 0.75
          ('auprc'): 0.8333333730697632
        )
        >>> metrics.avg  # Metrics of all data on all devices
        NestedDict(
          ('auroc'): 0.75
          ('auprc'): 0.8333333730697632
        )
        >>> metrics.update([0.1, 0.4, 0.6, 0.8], [0, 0, 1, 0])
        >>> metrics.input  # predicted values of current batch
        tensor([0.1000, 0.4000, 0.6000, 0.8000])
        >>> metrics.target  # ground truth of current batch
        tensor([0, 0, 1, 0])
        >>> metrics.inputs  # predicted values of all data
        tensor([0.2000, 0.3000, 0.5000, 0.7000, 0.1000, 0.4000, 0.6000, 0.8000])
        >>> metrics.targets  # ground truth of all data
        tensor([0, 1, 0, 1, 0, 0, 1, 0])
        >>> metrics.val  # Metrics of current batch on current device
        NestedDict(
          ('auroc'): 0.6666666666666666
          ('auprc'): 0.5
        )
        >>> metrics.avg  # Metrics of all data on all devices
        NestedDict(
          ('auroc'): 0.6666666666666666
          ('auprc'): 0.5555555820465088
        )
        >>> f"{metrics:.4f}"
        'auroc: 0.6667 (0.6667)\tauprc: 0.5000 (0.5556)'
        >>> metrics = Metrics(auroc=auroc, auprc=auprc, ignored_index=-100)
        >>> metrics.update([[0.1, 0.4, 0.6, 0.8], [0.1, 0.4, 0.6]], [[0, -100, 1, 0], [0, -100, 1]])
        >>> metrics.input, metrics.target
        (tensor([0.1000, 0.6000, 0.8000, 0.1000, 0.6000]), tensor([0, 1, 0, 0, 1]))
    """

    metrics: FlatDict[str, Callable]
    preprocess: Callable
    ignored_index: Optional[int] = None
    _input: Tensor
    _target: Tensor
    _inputs: Tensor
    _targets: Tensor
    score_name: str
    best_fn: Callable
    merge_dict: bool = True
    return_nested: bool = False
    flatten: bool = False

    def __init__(
        self,
        *args,
        merge_dict: bool | None = None,
        return_nested: bool | None = None,
        device: torch.device | None = None,
        ignored_index: int | None = None,
        preprocess: Callable = default_preprocess,
        **metrics: Callable,
    ):
        super().__init__(device=device)
        self._add_state("_input", torch.empty(0))
        self._add_state("_target", torch.empty(0))
        self._add_state("_inputs", torch.empty(0))
        self._add_state("_targets", torch.empty(0))
        self.world_size = get_world_size()
        self.metrics = FlatDict(*args, **metrics)
        self.preprocess = preprocess
        if merge_dict is not None:
            self.merge_dict = merge_dict
        if return_nested is not None:
            self.return_nested = return_nested
        self.ignored_index = ignored_index

    def update(self, input: Tensor | NestedTensor | Sequence, target: Tensor | NestedTensor | Sequence) -> None:
        # convert input and target to Tensor if they are not
        if not isinstance(input, (Tensor, NestedTensor)):
            try:
                input = torch.tensor(input)
            except ValueError:
                input = NestedTensor(input)
        if not isinstance(target, (Tensor, NestedTensor)):
            try:
                target = torch.tensor(target)
            except ValueError:
                target = NestedTensor(target)
        if input.ndim == target.ndim + 1:
            input = input.squeeze(-1)
        # convert input and target to NestedTensor if one of them is
        if isinstance(input, NestedTensor) or isinstance(target, NestedTensor):
            if isinstance(target, NestedTensor) and isinstance(input, NestedTensor):
                input, target = input.concat, target.concat
            elif isinstance(input, NestedTensor):
                input, mask = input.concat, input.mask
                target = target[mask]
            elif isinstance(target, NestedTensor):
                target, mask = target.concat, target.mask
                input = input[mask]
            else:
                raise ValueError(f"Unknown input and target: {input}, {target}")
            self.flatten = True
        elif self.flatten:
            target = target.flatten()
            input = input.flatten() if input.numel() == target.numel() else input.view(*target.shape, -1)
        # remove ignored index
        if self.ignored_index is not None:
            if isinstance(input, NestedTensor):
                indices = [i != self.ignored_index for i in target.storage()]
                input = NestedTensor([t[i] for t, i in zip(input.storage(), indices)])
                target = NestedTensor([t[i] for t, i in zip(target.storage(), indices)])
            else:
                input, target = input[target != self.ignored_index], target[target != self.ignored_index]
        if self.world_size > 1:
            input, target = self._sync(input), self._sync(target)
        input, target = input.detach().to(self.device), target.detach().to(self.device)
        self._input = input
        self._target = target
        self._inputs = torch.cat([self._inputs, input]).to(input.dtype)
        self._targets = torch.cat([self._targets, target]).to(target.dtype)

    def value(self) -> NestedDict[str, float | flist]:
        return self.calculate(self.input, self.target)

    def average(self) -> NestedDict[str, float | flist]:
        return self.calculate(self.inputs, self.targets)

    def compute(self) -> NestedDict[str, float | flist]:
        return self.average()

    @property
    def val(self) -> NestedDict[str, float | flist]:
        return self.value()

    @property
    def avg(self) -> NestedDict[str, float | flist]:
        return self.average()

    @torch.inference_mode()
    def calculate(self, input: Tensor, target: Tensor) -> NestedDict[str, flist | float]:
        if (
            isinstance(input, (Tensor, NestedTensor))
            and input.numel() == 0 == target.numel()
            or isinstance(input, (list, dict))
            and len(input) == 0 == len(target)
        ):
            return NestedDict({name: nan for name in self.metrics.keys()})
        ret = NestedDict()
        input, target = self.preprocess(input, target, ignored_index=self.ignored_index)
        for name, metric in self.metrics.items():
            score = self._calculate(metric, input, target, preprocess=False)
            if isinstance(score, Mapping):
                if self.merge_dict:
                    ret.merge(score)
                else:
                    for n, s in score.items():
                        ret[f"{name}.{n}"] = s
            else:
                ret[name] = score
        return ret

    @torch.inference_mode()
    def _calculate(self, metric, input: Tensor, target: Tensor, preprocess: bool = True) -> flist | float:
        if preprocess:
            input, target = self.preprocess(input, target, ignored_index=self.ignored_index)
        score = metric(input, target)
        if isinstance(score, Tensor):
            return score.item() if score.numel() == 1 else flist(score.tolist())
        return score

    @torch.inference_mode()
    def merge_state(self, metrics: Iterable):
        raise NotImplementedError()

    def _sync(self, tensor: Tensor):
        local_size = torch.tensor([tensor.shape[0]], dtype=torch.int64, device=tensor.device)
        size_list = [torch.zeros_like(local_size) for _ in range(self.world_size)]
        dist.all_gather(size_list, local_size)
        sizes = torch.cat(size_list)
        max_size = sizes.max()

        padded_tensor = torch.empty((max_size, *tensor.shape[1:]), dtype=tensor.dtype, device=tensor.device)
        padded_tensor[: tensor.shape[0]] = tensor
        gathered_tensors = [torch.empty_like(padded_tensor) for _ in range(self.world_size)]
        dist.all_gather(gathered_tensors, padded_tensor)
        slices = [gathered_tensors[i][: sizes[i]] for i in range(self.world_size) if sizes[i] > 0]
        return torch.cat(slices, dim=0)

    @property
    def input(self):
        return self._input

    @property
    def target(self):
        return self._target

    @property
    def inputs(self):
        return self._inputs

    @property
    def targets(self):
        return self._targets

    def __repr__(self):
        keys = tuple(i for i in self.metrics.keys())
        return f"{self.__class__.__name__}{keys}"

    def __format__(self, format_spec):
        val, avg = self.value(), self.average()
        return "\t".join(
            [f"{key}: {val[key].__format__(format_spec)} ({avg[key].__format__(format_spec)})" for key in self.metrics]
        )

    def reset(self: Self) -> Self:  # pragma: no cover
        r"""
        Reset the metric state variables to their default value.
        The tensors in the default values are also moved to the device of
        the last ``self.to(device)`` call.
        """
        for state_name, default in self._state_name_to_default.items():
            if isinstance(default, Tensor):
                setattr(self, state_name, default.clone().to(self.device))
            elif isinstance(default, list):
                setattr(
                    self,
                    state_name,
                    flist(tensor.clone().to(self.device) for tensor in default),
                )
            elif isinstance(default, dict):
                setattr(
                    self,
                    state_name,
                    DefaultDict(
                        lambda: torch.tensor(0.0, device=self.device),
                        {key: tensor.clone().to(self.device) for key, tensor in default.items()},
                    ),
                )
            elif isinstance(default, (int, float)):
                setattr(self, state_name, default)
            else:
                raise TypeError(
                    f"Invalid type for default value for {state_name}. Received {type(default)},"
                    "but expected ``Tensor``, a list of ``Tensor``,"
                    "a dictionary with ``Tensor``, int, or float."
                )
        return self

reset

Python
1
reset() -> Self

Reset the metric state variables to their default value. The tensors in the default values are also moved to the device of the last self.to(device) call.

Source code in danling/metrics/metrics.py
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def reset(self: Self) -> Self:  # pragma: no cover
    r"""
    Reset the metric state variables to their default value.
    The tensors in the default values are also moved to the device of
    the last ``self.to(device)`` call.
    """
    for state_name, default in self._state_name_to_default.items():
        if isinstance(default, Tensor):
            setattr(self, state_name, default.clone().to(self.device))
        elif isinstance(default, list):
            setattr(
                self,
                state_name,
                flist(tensor.clone().to(self.device) for tensor in default),
            )
        elif isinstance(default, dict):
            setattr(
                self,
                state_name,
                DefaultDict(
                    lambda: torch.tensor(0.0, device=self.device),
                    {key: tensor.clone().to(self.device) for key, tensor in default.items()},
                ),
            )
        elif isinstance(default, (int, float)):
            setattr(self, state_name, default)
        else:
            raise TypeError(
                f"Invalid type for default value for {state_name}. Received {type(default)},"
                "but expected ``Tensor``, a list of ``Tensor``,"
                "a dictionary with ``Tensor``, int, or float."
            )
    return self

ScoreMetrics

Bases: Metrics

ScoreMetrics is a subclass of Metrics that supports scoring.

Score is a single value that best represents the performance of the model. It is the core metrics that we use to compare different models. For example, in classification, we usually use auroc as the score.

ScoreMetrics requires two additional arguments: score_name and best_fn. score_name is the name of the metric that we use to compute the score. best_fn is a function that takes a list of values and returns the best value. best_fn is only not used by ScoreMetrics, it is meant to be accessed by other classes.

Attributes:

Name Type Description
score_name str

The name of the metric that we use to compute the score.
best_fn Callable

A function that takes a list of values and returns the best value.

Parameters:

Name Type Description Default

*args

A single mapping of metrics.

 ()

score_name

str | None

The name of the metric that we use to compute the score. Defaults to the first metric.

 None

best_fn

Callable | None

A function that takes a list of values and returns the best value. Defaults to max.

 max

**metrics

NestedDict[str, Callable]

Metrics.

 {}
Source code in danling/metrics/metrics.py
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class ScoreMetrics(Metrics):  # pylint: disable=abstract-method
    r"""
    `ScoreMetrics` is a subclass of Metrics that supports scoring.

    Score is a single value that best represents the performance of the model.
    It is the core metrics that we use to compare different models.
    For example, in classification, we usually use auroc as the score.

    `ScoreMetrics` requires two additional arguments: `score_name` and `best_fn`.
    `score_name` is the name of the metric that we use to compute the score.
    `best_fn` is a function that takes a list of values and returns the best value.
    `best_fn` is only not used by `ScoreMetrics`, it is meant to be accessed by other classes.

    Attributes:
        score_name: The name of the metric that we use to compute the score.
        best_fn: A function that takes a list of values and returns the best value.

    Args:
        *args: A single mapping of metrics.
        score_name: The name of the metric that we use to compute the score. Defaults to the first metric.
        best_fn: A function that takes a list of values and returns the best value. Defaults to `max`.
        **metrics: Metrics.
    """

    _score_name: str
    best_fn: Callable

    def __init__(
        self, *args, score_name: str | None = None, best_fn: Callable | None = max, **metrics: NestedDict[str, Callable]
    ):
        super().__init__(*args, **metrics)
        self.score_name = score_name or next(iter(self.metrics.keys()))
        self.metric = self.metrics[self.score_name]
        self.best_fn = best_fn or max

    def get_score(self, scope: str) -> float | flist:
        if scope == "batch":
            return self.batch_score
        if scope == "average":
            return self.average_score
        raise ValueError(f"Unknown scope: {scope}")

    @property
    def batch_score(self) -> float | flist:
        return self._calculate(self.metric, self.input, self.target)

    @property
    def average_score(self) -> float | flist:
        return self._calculate(self.metric, self.inputs, self.targets)

    @property
    def score_name(self) -> str:
        return self._score_name

    @score_name.setter
    def score_name(self, name) -> None:
        if name not in self.metrics:
            raise ValueError(f"score_name must be in {self.metrics.keys()}, but got {name}")
        self._score_name = name

MultiTaskMetrics

Bases: MultiTaskDict

Examples:

Python Console Session
>>> from danling.metrics.functional import auroc, auprc, pearson, spearman, accuracy, mcc
>>> metrics = MultiTaskMetrics()
>>> metrics.dataset1.cls = Metrics(auroc=auroc, auprc=auprc)
>>> metrics.dataset1.reg = Metrics(pearson=pearson, spearman=spearman)
>>> metrics.dataset2 = Metrics(auroc=auroc, auprc=auprc)
>>> metrics
MultiTaskMetrics(<class 'danling.metrics.metrics.MultiTaskMetrics'>,
  ('dataset1'): MultiTaskMetrics(<class 'danling.metrics.metrics.MultiTaskMetrics'>,
    ('cls'): Metrics('auroc', 'auprc')
    ('reg'): Metrics('pearson', 'spearman')
  )
  ('dataset2'): Metrics('auroc', 'auprc')
)
>>> metrics.update({"dataset1.cls": {"input": [0.2, 0.4, 0.5, 0.7], "target": [0, 1, 0, 1]}, "dataset1.reg": {"input": [0.1, 0.4, 0.6, 0.8], "target": [0.2, 0.3, 0.5, 0.7]}, "dataset2": {"input": [0.1, 0.4, 0.6, 0.8], "target": [0, 1, 0, 1]}})
>>> f"{metrics:.4f}"
'dataset1.cls: auroc: 0.7500 (0.7500)\tauprc: 0.8333 (0.8333)\ndataset1.reg: pearson: 0.9691 (0.9691)\tspearman: 1.0000 (1.0000)\ndataset2: auroc: 0.7500 (0.7500)\tauprc: 0.8333 (0.8333)'
>>> metrics.setattr("return_average", True)
>>> metrics.update({"dataset1.cls": {"input": [0.1, 0.4, 0.6, 0.8], "target": [0, 0, 1, 0]}, "dataset1.reg": {"input": [0.2, 0.3, 0.5, 0.7], "target": [0.2, 0.4, 0.6, 0.8]}, "dataset2": {"input": [0.2, 0.3, 0.5, 0.7], "target": [0, 0, 1, 0]}})
>>> f"{metrics:.4f}"
'dataset1.cls: auroc: 0.6667 (0.7000)\tauprc: 0.5000 (0.5556)\ndataset1.reg: pearson: 0.9898 (0.9146)\tspearman: 1.0000 (0.9222)\ndataset2: auroc: 0.6667 (0.7333)\tauprc: 0.5000 (0.7000)'
>>> metrics.update({"dataset1": {"cls": {"input": [0.1, 0.4, 0.6, 0.8], "target": [1, 0, 1, 0]}}})
>>> f"{metrics:.4f}"
'dataset1.cls: auroc: 0.2500 (0.5286)\tauprc: 0.5000 (0.4789)\ndataset1.reg: pearson: 0.9898 (0.9146)\tspearman: 1.0000 (0.9222)\ndataset2: auroc: 0.6667 (0.7333)\tauprc: 0.5000 (0.7000)'
>>> metrics.update(dict(loss=""))
Traceback (most recent call last):
ValueError: Metric loss not found in ...
Source code in danling/metrics/metrics.py
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class MultiTaskMetrics(MultiTaskDict):
    r"""
    Examples:
        >>> from danling.metrics.functional import auroc, auprc, pearson, spearman, accuracy, mcc
        >>> metrics = MultiTaskMetrics()
        >>> metrics.dataset1.cls = Metrics(auroc=auroc, auprc=auprc)
        >>> metrics.dataset1.reg = Metrics(pearson=pearson, spearman=spearman)
        >>> metrics.dataset2 = Metrics(auroc=auroc, auprc=auprc)
        >>> metrics
        MultiTaskMetrics(<class 'danling.metrics.metrics.MultiTaskMetrics'>,
          ('dataset1'): MultiTaskMetrics(<class 'danling.metrics.metrics.MultiTaskMetrics'>,
            ('cls'): Metrics('auroc', 'auprc')
            ('reg'): Metrics('pearson', 'spearman')
          )
          ('dataset2'): Metrics('auroc', 'auprc')
        )
        >>> metrics.update({"dataset1.cls": {"input": [0.2, 0.4, 0.5, 0.7], "target": [0, 1, 0, 1]}, "dataset1.reg": {"input": [0.1, 0.4, 0.6, 0.8], "target": [0.2, 0.3, 0.5, 0.7]}, "dataset2": {"input": [0.1, 0.4, 0.6, 0.8], "target": [0, 1, 0, 1]}})
        >>> f"{metrics:.4f}"
        'dataset1.cls: auroc: 0.7500 (0.7500)\tauprc: 0.8333 (0.8333)\ndataset1.reg: pearson: 0.9691 (0.9691)\tspearman: 1.0000 (1.0000)\ndataset2: auroc: 0.7500 (0.7500)\tauprc: 0.8333 (0.8333)'
        >>> metrics.setattr("return_average", True)
        >>> metrics.update({"dataset1.cls": {"input": [0.1, 0.4, 0.6, 0.8], "target": [0, 0, 1, 0]}, "dataset1.reg": {"input": [0.2, 0.3, 0.5, 0.7], "target": [0.2, 0.4, 0.6, 0.8]}, "dataset2": {"input": [0.2, 0.3, 0.5, 0.7], "target": [0, 0, 1, 0]}})
        >>> f"{metrics:.4f}"
        'dataset1.cls: auroc: 0.6667 (0.7000)\tauprc: 0.5000 (0.5556)\ndataset1.reg: pearson: 0.9898 (0.9146)\tspearman: 1.0000 (0.9222)\ndataset2: auroc: 0.6667 (0.7333)\tauprc: 0.5000 (0.7000)'
        >>> metrics.update({"dataset1": {"cls": {"input": [0.1, 0.4, 0.6, 0.8], "target": [1, 0, 1, 0]}}})
        >>> f"{metrics:.4f}"
        'dataset1.cls: auroc: 0.2500 (0.5286)\tauprc: 0.5000 (0.4789)\ndataset1.reg: pearson: 0.9898 (0.9146)\tspearman: 1.0000 (0.9222)\ndataset2: auroc: 0.6667 (0.7333)\tauprc: 0.5000 (0.7000)'
        >>> metrics.update(dict(loss=""))  # doctest: +ELLIPSIS
        Traceback (most recent call last):
        ValueError: Metric loss not found in ...
    """  # noqa: E501

    def __init__(self, *args, **kwargs):
        super().__init__(*args, default_factory=MultiTaskMetrics, **kwargs)

    def update(
        self,
        values: Mapping[str, Mapping[str, Tensor | NestedTensor | Sequence]],
    ) -> None:
        r"""
        Updates the average and current value in all metrics.

        Args:
            values: Dict of values to be added to the average.

        Raises:
            ValueError: If the value is not an instance of (Mapping).
        """

        for metric, value in values.items():
            if metric not in self:
                raise ValueError(f"Metric {metric} not found in {self}")
            if isinstance(self[metric], MultiTaskMetrics):
                for name, met in self[metric].items():
                    if name in value:
                        val = value[name]
                        if isinstance(value, Mapping):
                            met.update(**val)
                        elif isinstance(value, Sequence):
                            met.update(*val)
                        else:
                            raise ValueError(f"Expected value to be a Mapping or Sequence, but got {type(value)}")
            elif isinstance(self[metric], (Metrics, Metric)):
                if isinstance(value, Mapping):
                    self[metric].update(**value)
                elif isinstance(value, Sequence):
                    self[metric].update(*value)
                else:
                    raise ValueError(f"Expected value to be a Mapping or Sequence, but got {type(value)}")
            else:
                raise ValueError(
                    f"Expected {metric} to be an instance of MultiTaskMetrics, Metrics, or Metric, "
                    "but got {type(self[metric])}"
                )

    def set(  # pylint: disable=W0237
        self,
        name: str,
        metric: Metrics | Metric,  # type: ignore[override]
    ) -> None:
        if not isinstance(metric, (Metrics, Metric)):
            raise ValueError(f"Expected {metric} to be an instance of Metrics or Metric, but got {type(metric)}")
        super().set(name, metric)

update

Python
1

Updates the average and current value in all metrics.

Parameters:

Name Type Description Default
values
Mapping[str, Mapping[str, Tensor | NestedTensor | Sequence]]

Dict of values to be added to the average.

 required

Raises:

Type Description
ValueError

If the value is not an instance of (Mapping).
Source code in danling/metrics/metrics.py
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def update(
    self,
    values: Mapping[str, Mapping[str, Tensor | NestedTensor | Sequence]],
) -> None:
    r"""
    Updates the average and current value in all metrics.

    Args:
        values: Dict of values to be added to the average.

    Raises:
        ValueError: If the value is not an instance of (Mapping).
    """

    for metric, value in values.items():
        if metric not in self:
            raise ValueError(f"Metric {metric} not found in {self}")
        if isinstance(self[metric], MultiTaskMetrics):
            for name, met in self[metric].items():
                if name in value:
                    val = value[name]
                    if isinstance(value, Mapping):
                        met.update(**val)
                    elif isinstance(value, Sequence):
                        met.update(*val)
                    else:
                        raise ValueError(f"Expected value to be a Mapping or Sequence, but got {type(value)}")
        elif isinstance(self[metric], (Metrics, Metric)):
            if isinstance(value, Mapping):
                self[metric].update(**value)
            elif isinstance(value, Sequence):
                self[metric].update(*value)
            else:
                raise ValueError(f"Expected value to be a Mapping or Sequence, but got {type(value)}")
        else:
            raise ValueError(
                f"Expected {metric} to be an instance of MultiTaskMetrics, Metrics, or Metric, "
                "but got {type(self[metric])}"
            )