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:
.. 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]).long()
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)
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
.. code-block:: python
def process_function(engine, batch):
# ...
return y_pred, y
engine = Engine(process_function)
metric = ClassificationReport()
metric.attach(engine, "cr")
engine.run...
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,
)