def evaluation(self, test_data, test_label):
scores = []
if self.D > 0:
for d, t in test_data:
score = self.predict(d, t)
if score > 200:
scores.append(1)
elif score < -200:
scores.append(0)
else:
sc = np.exp(score)
scores.append(sc / (1 + sc))
self.scores = scores
x, y = test_data[:, 0], test_data[:, 1]
test_data_T = np.column_stack((y, x))
ndcg = normalized_discounted_cummulative_gain(test_data, test_label,
np.array(scores))
ndcg_inv = normalized_discounted_cummulative_gain(
test_data_T, test_label, np.array(scores))
prec, rec, thr = precision_recall_curve(test_label, scores)
aupr_val = auc(rec, prec)
fpr, tpr, thr = roc_curve(test_label, scores)
auc_val = auc(fpr, tpr)
return aupr_val, auc_val, ndcg, ndcg_inv
def evaluation(self, test_data, test_label,scores):
x, y = test_data[:, 0], test_data[:, 1]
test_data_T = np.column_stack((y,x))
ndcg = normalized_discounted_cummulative_gain(test_data, np.array(test_label), np.array(scores))
ndcg_inv = normalized_discounted_cummulative_gain(test_data_T, np.array(test_label), np.array(scores))
prec, rec, thr = precision_recall_curve(test_label, scores)
aupr_val = auc(rec, prec)
fpr, tpr, thr = roc_curve(test_label, scores)
auc_val = auc(fpr, tpr)
return aupr_val, auc_val, ndcg, ndcg_inv
def evaluation(self, test_data, test_label):
scores = self.predictR[test_data[:, 0], test_data[:, 1]]
self.scores = scores
x, y = test_data[:, 0], test_data[:, 1]
test_data_T = np.column_stack((y,x))
ndcg = normalized_discounted_cummulative_gain(test_data, test_label, np.array(scores))
ndcg_inv = normalized_discounted_cummulative_gain(test_data_T, test_label, np.array(scores))
prec, rec, thr = precision_recall_curve(test_label, scores)
aupr_val = auc(rec, prec)
fpr, tpr, thr = roc_curve(test_label, scores)
auc_val = auc(fpr, tpr)
#!!!!we should distinguish here between inverted and not inverted methods nDCGs!!!!
return aupr_val, auc_val, ndcg, ndcg_inv