Metrics.py

import numpy as np


class Metrics(object):

	def __init__(self):
		super().__init__()
		self.PAD = 0

	def apk(self, actual, predicted, k=10):
		"""
		Computes the average precision at k.
		This function computes the average prescision at k between two lists of
		items.
		Parameters
		----------
		actual : list
				 A list of elements that are to be predicted (order doesn't matter)
		predicted : list
					A list of predicted elements (order does matter)
		k : int, optional
			The maximum number of predicted elements
		Returns
		-------
		score : double
				The average precision at k over the input lists
		"""
		score = 0.0
		num_hits = 0.0

		for i, p in enumerate(predicted):
			if p in actual and p not in predicted[:i]:
				num_hits += 1.0
				score += num_hits / (i + 1.0)

		# if not actual:
		# 	return 0.0
		return score / min(len(actual), k)


	def compute_metric(self, y_prob, y_true, k_list=[10, 50, 100]):
		'''
			y_true: (#samples, )
			y_pred: (#samples, #users)
		'''
		scores_len = 0
		y_prob = np.array(y_prob)
		y_true = np.array(y_true)

		scores = {'hits@'+str(k):[] for k in k_list}
		scores.update({'map@'+str(k):[] for k in k_list})
		for p_, y_ in zip(y_prob, y_true):
			if y_ != self.PAD:
				scores_len += 1.0
				p_sort = p_.argsort()
				for k in k_list:
					topk = p_sort[-k:][::-1]
					scores['hits@' + str(k)].extend([1. if y_ in topk else 0.])
					scores['map@'+str(k)].extend([self.apk([y_], topk, k)])

		scores = {k: np.mean(v) for k, v in scores.items()}
		return scores, scores_len