Source code for ignite.metrics.classification_report

import json
from typing import Callable, Collection, Dict, List, Optional, Union

import torch

from ignite.metrics.fbeta import Fbeta
from ignite.metrics.metric import Metric
from ignite.metrics.metrics_lambda import MetricsLambda
from ignite.metrics.precision import Precision
from ignite.metrics.recall import Recall

__all__ = ["ClassificationReport"]

[docs]def ClassificationReport( beta: int = 1, output_dict: bool = False, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), is_multilabel: bool = False, labels: Optional[List[str]] = None, ) -> MetricsLambda: r"""Build a text report showing the main classification metrics. The report resembles in functionality to `scikit-learn classification_report <>`_ The underlying implementation doesn't use the sklearn function. Args: beta: weight of precision in harmonic mean output_dict: If True, return output as dict, otherwise return a str output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. is_multilabel: If True, the tensors are assumed to be multilabel. device: optional device specification for internal storage. labels: Optional list of label indices to include in the report .. code-block:: python def process_function(engine, batch): # ... return y_pred, y engine = Engine(process_function) metric = ClassificationReport() metric.attach(engine, "cr") res = engine.state.metrics["cr"] # result should be like { "0": { "precision": 0.4891304347826087, "recall": 0.5056179775280899, "f1-score": 0.497237569060773 }, "1": { "precision": 0.5157232704402516, "recall": 0.4992389649923896, "f1-score": 0.507347254447022 }, "macro avg": { "precision": 0.5024268526114302, "recall": 0.5024284712602398, "f1-score": 0.5022924117538975 } } """ # setup all the underlying metrics precision = Precision(average=False, is_multilabel=is_multilabel, output_transform=output_transform, device=device,) recall = Recall(average=False, is_multilabel=is_multilabel, output_transform=output_transform, device=device,) fbeta = Fbeta(beta, average=False, precision=precision, recall=recall) averaged_precision = precision.mean() averaged_recall = recall.mean() averaged_fbeta = fbeta.mean() def _wrapper( recall_metric: Metric, precision_metric: Metric, f: Metric, a_recall: Metric, a_precision: Metric, a_f: Metric, ) -> Union[Collection[str], Dict]: p_tensor, r_tensor, f_tensor = precision_metric, recall_metric, f if p_tensor.shape != r_tensor.shape: raise ValueError( "Internal error: Precision and Recall have mismatched shapes: " f"{p_tensor.shape} vs {r_tensor.shape}. Please, open an issue " "with a reference on this error. Thank you!" ) dict_obj = {} for idx, p_label in enumerate(p_tensor): dict_obj[_get_label_for_class(idx)] = { "precision": p_label.item(), "recall": r_tensor[idx].item(), "f{0}-score".format(beta): f_tensor[idx].item(), } dict_obj["macro avg"] = { "precision": a_precision.item(), "recall": a_recall.item(), "f{0}-score".format(beta): a_f.item(), } return dict_obj if output_dict else json.dumps(dict_obj) # helper method to get a label for a given class def _get_label_for_class(idx: int) -> str: return labels[idx] if labels else str(idx) return MetricsLambda(_wrapper, recall, precision, fbeta, averaged_recall, averaged_precision, averaged_fbeta,)

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