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Metrics

danling.metric.metrics

Metrics

Bases: Metric

A comprehensive metric tracking system that maintains the complete history of predictions and labels.

Metrics is designed for computing evaluations that require access to the entire dataset history, such as AUROC, Pearson correlation, or other metrics that cannot be meaningfully averaged batch-by-batch.

Attributes:

Name Type Description
metrics FlatDict[str, Callable]

A dictionary of metric functions to be computed
preprocess Optional[Callable]

Optional preprocessing function to apply to inputs and targets
ignore_index int

Value to ignore in classification tasks (e.g., -100 for padding)
ignore_nan bool

Whether to ignore NaN values in regression tasks
val NestedDict[str, float | flist]

Metrics computed on the current batch only
avg NestedDict[str, float | flist]

Metrics computed on all accumulated data
input Tensor

The input tensor from the latest batch
target Tensor

The target tensor from the latest batch
inputs Tensor

Concatenation of all input tensors seen so far
targets Tensor

Concatenation of all target tensors seen so far

Parameters:

Name Type Description Default

*args

A single mapping of metrics or callable metric functions

 ()

device

device | None

Device to store tensors on

 None

ignore_index

int | None

Value to ignore in classification tasks

 None

ignore_nan

bool | None

Whether to ignore NaN values in regression tasks

 None

preprocess

Callable | None

Function to preprocess inputs before computing metrics

 None

**metrics

Callable

Named metric functions to compute

 {}

Examples:

Python Console Session
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>>> from danling.metric.functional import auroc, auprc, base_preprocess
>>> 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, preprocess=base_preprocess)
>>> 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]))
Notes
  • Metrics stores the complete prediction and target history, which is memory-intensive but necessary for metrics like AUROC that operate on the entire dataset.
  • For metrics that can be meaningfully averaged batch-by-batch (like accuracy), consider using MetricMeter for better memory efficiency.
  • The ignore_index parameter is useful for handling masked or padded values in classification tasks.
  • The ignore_nan parameter helps handle missing values in regression tasks.
  • All metrics are synchronized across devices in distributed training environments.
See Also
  • MetricMeters: Memory-efficient metric tracker that averages multiple metrics batch-by-batch.
Source code in danling/metric/metrics.py
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class Metrics(Metric):
    r"""
    A comprehensive metric tracking system that maintains the complete history of predictions and labels.

    Metrics is designed for computing evaluations that require access to the entire dataset history,
    such as AUROC, Pearson correlation, or other metrics that cannot be meaningfully averaged batch-by-batch.

    Attributes:
        metrics: A dictionary of metric functions to be computed
        preprocess: Optional preprocessing function to apply to inputs and targets
        ignore_index: Value to ignore in classification tasks (e.g., -100 for padding)
        ignore_nan: Whether to ignore NaN values in regression tasks
        val: Metrics computed on the current batch only
        avg: Metrics computed on all accumulated data
        input: The input tensor from the latest batch
        target: The target tensor from the latest batch
        inputs: Concatenation of all input tensors seen so far
        targets: Concatenation of all target tensors seen so far

    Args:
        *args: A single mapping of metrics or callable metric functions
        device: Device to store tensors on
        ignore_index: Value to ignore in classification tasks
        ignore_nan: Whether to ignore NaN values in regression tasks
        preprocess: Function to preprocess inputs before computing metrics
        **metrics: Named metric functions to compute

    Examples:
        >>> from danling.metric.functional import auroc, auprc, base_preprocess
        >>> 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, preprocess=base_preprocess)
        >>> 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]))

    Notes:
        - `Metrics` stores the complete prediction and target history, which is memory-intensive
          but necessary for metrics like AUROC that operate on the entire dataset.
        - For metrics that can be meaningfully averaged batch-by-batch (like accuracy),
          consider using [`MetricMeter`][danling.metric.metric_meter.MetricMeter] for better memory efficiency.
        - The `ignore_index` parameter is useful for handling masked or padded values in classification tasks.
        - The `ignore_nan` parameter helps handle missing values in regression tasks.
        - All metrics are synchronized across devices in distributed training environments.

    See Also:
        - [`MetricMeters`][danling.metric.metric_meter.MetricMeters]:
            Memory-efficient metric tracker that averages multiple metrics batch-by-batch.
    """

    metrics: FlatDict[str, Callable]
    preprocess: Optional[Callable] = None
    ignore_index: int = -100
    ignore_nan: bool = True
    _input: Tensor
    _target: Tensor
    _inputs: Tensor
    _targets: Tensor

    def __init__(
        self,
        *args,
        device: torch.device | None = None,
        ignore_index: int | None = None,
        ignore_nan: bool | None = None,
        preprocess: Callable | None = None,
        **metrics: Callable,
    ):
        super().__init__(device=device)
        self.metrics = FlatDict()
        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()
        if args:
            from .metric_meter import MetricMeters

            if len(args) == 1 and isinstance(args[0], MetricMeters):
                meters = args[0]
                for name, meter in meters.items():
                    metrics.setdefault(name, meter.metric)
                if preprocess is None:
                    preprocess = meters.getattr("preprocess")
                if ignore_index is None:
                    ignore_index = meters.ignore_index
                if ignore_nan is None:
                    ignore_nan = meters.ignore_nan
            else:
                for metric in args:
                    if not callable(metric):
                        raise ValueError(f"Expected metric to be callable, but got {type(metric)}")
                    metrics.setdefault(metric.__name__, metric)
        self.preprocess = preprocess
        if ignore_index is not None:
            self.ignore_index = ignore_index
        if ignore_nan is not None:
            self.ignore_nan = ignore_nan
        for name, metric in metrics.items():
            self.metrics[name] = self._preprocess_callable(metric)

    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}")
        if self.preprocess is not None:
            input, target = self.preprocess(input, target, ignore_index=self.ignore_index, ignore_nan=self.ignore_nan)
        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()
        for name, metric in self.metrics.items():
            score = self._calculate(metric, input, target)
            if isinstance(score, Mapping):
                ret.merge(score)
            else:
                ret[name] = score
        return ret

    @torch.inference_mode()
    def _calculate(self, metric, input: Tensor, target: Tensor) -> flist | float:
        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) -> Tensor:
        return self._input

    @property
    def target(self) -> Tensor:
        return self._target

    @property
    def inputs(self) -> Tensor:
        return self._inputs

    @property
    def targets(self) -> Tensor:
        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

    def _preprocess_callable(self, func: Callable) -> Callable:
        if not callable(func):
            raise ValueError(f"Expected func to be callable, but got {type(func)}")
        if "preprocess" not in signature(func).parameters:
            return func
        return partial(func, preprocess=self.preprocess is None)

reset

Python
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/metric/metrics.py
Python
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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.

 {}
Notes
  • ScoreMetrics adds the ability to designate one metric as the “score” metric
  • The score metric is typically used for model selection or early stopping
  • best_fn determines how to select the “best” score (e.g., max for accuracy, min for loss)
  • Access the score using metrics.batch_score or metrics.average_score
Source code in danling/metric/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.

    Notes:
        - `ScoreMetrics` adds the ability to designate one metric as the "score" metric
        - The score metric is typically used for model selection or early stopping
        - `best_fn` determines how to select the "best" score (e.g., max for accuracy, min for loss)
        - Access the score using `metrics.batch_score` or `metrics.average_score`
    """

    _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

    @property
    def best_fn(self) -> Callable:
        return self._best_fn

    @best_fn.setter
    def best_fn(self, fn: Callable) -> None:
        if not callable(fn):
            raise ValueError(f"best_fn must be callable, but got {type(fn)}")
        self._best_fn = fn

MultiTaskMetrics

Bases: MultiTaskDict

A container for managing multiple Metrics for multiple tasks.

Typically, we have multiple tasks, and each task has multiple metrics. For example, a multi-task model might have a classification task and a regression task. We want to compute auroc and accuracy for the classification task, and pearson and rmse for the regression task.

MultiTaskMetrics is a mapping from task names to Metrics instances.

Examples:

Python Console Session
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>>> from danling.metric.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(...
  ('dataset1'): 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 ...
Notes
  • MultiTaskMetrics is a container for managing multiple Metrics instances
  • Each task has its own Metrics instance for tracking task-specific metrics
  • Updates are passed as a dictionary mapping task names to (input, target) pairs
  • Access values using attribute notation: metrics.task_name.metric_name
See Also
  • MultiTaskMetricMeters: Memory-efficient metric tracker that averages multiple metrics batch-by-batch for multi-task learning.
Source code in danling/metric/metrics.py
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class MultiTaskMetrics(MultiTaskDict):
    r"""
    A container for managing multiple `Metrics` for multiple tasks.

    Typically, we have multiple tasks, and each task has multiple metrics.
    For example, a multi-task model might have a classification task and a regression task.
    We want to compute auroc and accuracy for the classification task,
    and pearson and rmse for the regression task.

    `MultiTaskMetrics` is a mapping from task names to `Metrics` instances.

    Examples:
        >>> from danling.metric.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  # doctest: +ELLIPSIS
        MultiTaskMetrics(...
          ('dataset1'): 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 ...

    Notes:
        - `MultiTaskMetrics` is a container for managing multiple `Metrics` instances
        - Each task has its own `Metrics` instance for tracking task-specific metrics
        - Updates are passed as a dictionary mapping task names to (input, target) pairs
        - Access values using attribute notation: `metrics.task_name.metric_name`

    See Also:
        - [`MultiTaskMetricMeters`][danling.metric.metric_meter.MultiTaskMetricMeters]:
            Memory-efficient metric tracker that averages multiple metrics batch-by-batch for multi-task learning.
    """  # 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
update(
    values: Mapping[
        str, Mapping[str, Tensor | NestedTensor | Sequence]
    ]
) -> None

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/metric/metrics.py
Python
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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])}"
            )