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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 <https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.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 Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: Multiclass case .. testcode:: 1 metric = ClassificationReport(output_dict=True) metric.attach(default_evaluator, "cr") y_true = torch.tensor([2, 0, 2, 1, 0, 1]) y_pred = torch.tensor([ [0.0266, 0.1719, 0.3055], [0.6886, 0.3978, 0.8176], [0.9230, 0.0197, 0.8395], [0.1785, 0.2670, 0.6084], [0.8448, 0.7177, 0.7288], [0.7748, 0.9542, 0.8573], ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics["cr"].keys()) print(state.metrics["cr"]["0"]) print(state.metrics["cr"]["1"]) print(state.metrics["cr"]["2"]) print(state.metrics["cr"]["macro avg"]) .. testoutput:: 1 dict_keys(['0', '1', '2', 'macro avg']) {'precision': 0.5, 'recall': 0.5, 'f1-score': 0.4999...} {'precision': 1.0, 'recall': 0.5, 'f1-score': 0.6666...} {'precision': 0.3333..., 'recall': 0.5, 'f1-score': 0.3999...} {'precision': 0.6111..., 'recall': 0.5, 'f1-score': 0.5222...} Multilabel case, the shapes must be (batch_size, num_categories, ...) .. testcode:: 2 metric = ClassificationReport(output_dict=True, is_multilabel=True) metric.attach(default_evaluator, "cr") y_true = torch.tensor([ [0, 0, 1], [0, 0, 0], [0, 0, 0], [1, 0, 0], [0, 1, 1], ]).unsqueeze(0) y_pred = torch.tensor([ [1, 1, 0], [1, 0, 1], [1, 0, 0], [1, 0, 1], [1, 1, 0], ]).unsqueeze(0) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics["cr"].keys()) print(state.metrics["cr"]["0"]) print(state.metrics["cr"]["1"]) print(state.metrics["cr"]["2"]) print(state.metrics["cr"]["macro avg"]) .. testoutput:: 2 dict_keys(['0', '1', '2', 'macro avg']) {'precision': 0.2, 'recall': 1.0, 'f1-score': 0.3333...} {'precision': 0.5, 'recall': 1.0, 'f1-score': 0.6666...} {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0} {'precision': 0.2333..., 'recall': 0.6666..., 'f1-score': 0.3333...} """ # 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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