eric-haibin-lin commented on a change in pull request #7390: adding ranking
metrics (precision/recall) at position K.
URL: https://github.com/apache/incubator-mxnet/pull/7390#discussion_r136199686
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File path: python/mxnet/metric.py
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@@ -568,6 +568,147 @@ def update(self, labels, preds):
self.sum_metric += f1_score
self.num_inst += 1
+@register
+@alias('top_k_precision')
+class TopKPrecision(EvalMetric):
+ """Computes top k precision metric.
+ top k differs from regular precision in that the score is only
+ computed for the top k predictions. "correct" or "wrong" entries
+ outside the top k are ignored
+ Parameters
+ ----------
+ top_k : int
+ Whether targets are in top k predictions.
+ name : str
+ Name of this metric instance for display.
+ output_names : list of str, or None
+ Name of predictions that should be used when updating with update_dict.
+ By default include all predictions.
+ label_names : list of str, or None
+ Name of labels that should be used when updating with update_dict.
+ By default include all labels.
+
+ Examples
+ --------
+ >>>ytrue = [[1.,0.,1.,0.],[0.,1.,1.,0.]]
+ >>>ytrue = mx.nd.array(ytrue)
+ >>>yhat = [[0.4,0.8,0.1,0.1],[0.4,0.8,0.8,0.4]]
+ >>>yhat = mx.nd.array(yhat)
+ >>>pre = mx.metric.create('top_k_precision',top_k=2)
+ >>>pre.update(preds = [yhat], labels = [ytrue])
+ >>>print pre.get()[1]
+ >>> 0.75
+
+ """
+
+ def __init__(self, top_k=1, name='top_k_precision',
+ output_names=None, label_names=None):
+ super(TopKPrecision, self).__init__(
+ name, top_k=top_k,
+ output_names=output_names, label_names=label_names)
+ self.top_k = top_k
+
+
+ def update(self, labels, preds):
+ """Updates the internal evaluation result.
+ Parameters
+ ----------
+ labels : list of `NDArray`
+ The labels of the data. (binary)
+ preds : list of `NDArray`
+ Predicted values. (float)
+
+ Returns:
+ --------
+ The precision at K (float)
+ """
+ check_label_shapes(labels, preds)
+
+ for label, pred_label in zip(labels, preds):
+ assert(len(pred_label.shape) <= 2), 'Predictions should be no more
than 2 dims'
+ pred_label =
numpy.argsort(-pred_label.asnumpy().astype('float32'), axis=1)
Review comment:
does this work when `len(pred_label.shape) == 1` ??
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