Recall¶
-
class
paddle.fluid.metrics.
Recall
(name=None)[source] Recall (also known as sensitivity) is the fraction of relevant instances that have been retrieved over the total amount of relevant instances
Refer to: https://en.wikipedia.org/wiki/Precision_and_recall
Noted that this class manages the recall score only for binary classification task.
- Parameters
name (str, optional) – Metric name. For details, please refer to Name. Default is None.
Examples
import paddle.fluid as fluid import numpy as np metric = fluid.metrics.Recall() # generate the preds and labels preds = [[0.1], [0.7], [0.8], [0.9], [0.2], [0.2], [0.3], [0.5], [0.8], [0.6]] labels = [[0], [1], [1], [1], [1], [0], [0], [0], [0], [0]] preds = np.array(preds) labels = np.array(labels) metric.update(preds=preds, labels=labels) numpy_recall = metric.eval() print("expect recall: %.2f and got %.2f" % ( 3.0 / 4.0, numpy_recall))
-
update
(preds, labels) Update the recall based on the current mini-batch prediction results.
- Parameters
preds (numpy.array) – prediction results of current mini-batch, the output of two-class sigmoid function. Shape: [batch_size, 1]. Dtype: ‘float64’ or ‘float32’.
labels (numpy.array) – ground truth (labels) of current mini-batch, the shape should keep the same as preds. Shape: [batch_size, 1], Dtype: ‘int32’ or ‘int64’.
-
eval
() Calculate the final recall.
- Returns
results of the calculated Recall. Scalar output with float dtype.
- Return type
float
-
get_config
() Get the metric and current states. The states are the members who do not has “_” prefix.
- Parameters
None –
- Returns
a python dict, which contains the inner states of the metric instance
- Return types:
a python dict
-
reset
() reset function empties the evaluation memory for previous mini-batches.
- Parameters
None –
- Returns
None
- Return types:
None