def get_eval(scores, items, index, top_n=10):
"""
if the last element is the correct one, then
index = len(scores[0])-1
"""
# print('>>>>>>>>>>>>>>>> scores:', scores)
# print('>>>>>>>>>>>>>>>> items:', items)
eps = 1e-15
ndcg = 0.0
custom_hr = 0.0
weighted_hr = 0.0
hr = 0.0
hits = np.array([0, 0, 0])
assert len(scores[0]) > index and index >= 0
# print(items)
items_to_guess = np.array(items)[:, index]
# print(items_to_guess.shape)
assert len(items) == items_to_guess.shape[0]
n_low = 0
n_medium = 0
n_high = 0
for j in range(len(items_to_guess)):
current_item = items_to_guess[j]
current_popularity = Settings.normalized_popularity[current_item]
if current_popularity <= Settings.low_popularity_threshold:
n_low += 1
elif Settings.low_popularity_threshold < current_popularity <= Settings.high_popularity_threshold:
n_medium += 1
else:
n_high += 1
# print('len(scores):', len(scores))
assert n_low + n_medium + n_high == len(scores)
# for score in scores:
for i in range(len(scores)):
score = scores[i]
item_array = items[i]
# print('index:', i, len(scores))
# print('score:', score)
# print('item:', item_array)
# Get the top n indices
arg_index = np.argsort(-score)[:top_n]
if index in arg_index:
# print('index, arg_index:', index, '-', arg_index)
current_item = item_array[index]
current_popularity = Settings.normalized_popularity[current_item]
current_position = np.where(arg_index == index)[0][0]
'''
print('current_item:', current_item)
print('current_popularity:', current_popularity)
print('current_position:', current_position)
print('arg_index:', arg_index)
'''
# Get the position
ndcg += np.log(2.0) / np.log(arg_index.tolist().index(index) + 2.0)
# Increment
hr += 1.0
# Custom HR
custom_hr += y_custom(current_popularity, current_position, top_n)
# Custom HR
weighted_hr += (1 - current_popularity)
# weighted_hr += sigmoid(1 / (current_popularity + eps))
if current_popularity <= Settings.low_popularity_threshold:
hits[0] += 1
elif Settings.low_popularity_threshold < current_popularity <= Settings.high_popularity_threshold:
hits[1] += 1
else:
hits[2] += 1
# print('HITS:', hr)
# print('len(scores):', len(scores))
return hr / len(scores), \
custom_hr / len(scores), \
weighted_hr / len(scores), \
ndcg / len(scores), \
hits, \
np.around(hits / np.sum(hits), 2), \
hits[0] / n_low, \
hits[1] / n_medium, \
hits[2] / n_high, \
[n_low, n_medium, n_high]
def get_eval(scores, items, index, top_n=10):
"""
if the last element is the correct one, then
index = len(scores[0])-1
"""
# print('>>>>>>>>>>>>>>>> scores:', scores)
# print('>>>>>>>>>>>>>>>> items:', items)
eps = 1e-15
ndcg = 0.0
custom_hr = 0.0
weighted_hr = 0.0
hr = 0.0
hits = np.array([0, 0, 0])
assert len(scores[0]) > index and index >= 0
# for score in scores:
for i in range(len(scores)):
score = scores[i]
item_array = items[i]
# print('index:', i, len(scores))
# print('score:', score)
# print('item:', item_array)
# Get the top n indices
arg_index = np.argsort(-score)[:top_n]
if index in arg_index:
# print('index, arg_index:', index, '-', arg_index)
current_item = item_array[index]
current_popularity = Settings.normalized_popularity[current_item]
current_position = np.where(arg_index == index)[0][0]
'''
print('current_item:', current_item)
print('current_popularity:', current_popularity)
print('current_position:', current_position)
print('arg_index:', arg_index)
'''
# Get the position
ndcg += np.log(2.0) / np.log(arg_index.tolist().index(index) + 2.0)
# Increment
hr += 1.0
# Custom HR
custom_hr += y_custom(current_popularity, current_position, top_n)
# Custom HR
weighted_hr += (1 - current_popularity)
# weighted_hr += sigmoid(1 / (current_popularity + eps))
if current_popularity <= 0.33:
hits[0] += 1
elif current_popularity > 0.33 and current_popularity <= 0.66:
hits[1] += 1
else:
hits[2] += 1
# print('HITS:', hr)
# print('len(scores):', len(scores))
return hr / len(scores), custom_hr / len(scores), weighted_hr / len(
scores), ndcg / len(scores), hits, np.around(hits / np.sum(hits), 2)