def get_sum_metrics(predictions, metrics=[]):
for i in range(3):
metrics.append(lambda x: x + i)
sum_metrics = 0
for metric in metrics:
sum_metrics += metric(predictions)
return sum_metrics
The function get_sum_metrics takes two arguments: a prediction and a list of metrics to apply to the prediction (say, for instance, the accuracy or the precision). Note that each metric is a function, not a number. The function should compute each of the metrics for the prediction and sum them. It should also add to this sum three default metrics, in this case, adding 0, 1 or 2 to the prediction.
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def get_sum_metrics(predictions, metrics=None):
if metrics is None:
metrics = []
for i in range(0,3):
f = lambda x, i=i: x+i
metrics.append(f)
sum_metrics = 0
for metric in metrics:
sum_metrics += metric(predictions)
return sum_metrics
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