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f60c555 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | import numpy as np
def cal_recall(pos_index, pos_len):
rec_ret = np.cumsum(pos_index, axis=1) / pos_len.reshape(-1, 1)
return rec_ret.mean(axis=0)
def cal_ndcg(pos_index, pos_len):
len_rank = np.full_like(pos_len, pos_index.shape[1])
idcg_len = np.where(pos_len > len_rank, len_rank, pos_len)
iranks = np.zeros_like(pos_index, dtype=float)
iranks[:, :] = np.arange(1, pos_index.shape[1] + 1)
idcg = np.cumsum(1.0 / np.log2(iranks + 1), axis=1)
for row, idx in enumerate(idcg_len):
idcg[row, idx:] = idcg[row, idx - 1]
ranks = np.zeros_like(pos_index, dtype=float)
ranks[:, :] = np.arange(1, pos_index.shape[1] + 1)
dcg = 1.0 / np.log2(ranks + 1)
dcg = np.cumsum(np.where(pos_index, dcg, 0), axis=1)
result = dcg / idcg
return result.mean(axis=0)
def cal_map(pos_index, pos_len):
pre = pos_index.cumsum(axis=1) / np.arange(1, pos_index.shape[1] + 1)
sum_pre = np.cumsum(pre * pos_index.astype(float), axis=1)
len_rank = np.full_like(pos_len, pos_index.shape[1])
actual_len = np.where(pos_len > len_rank, len_rank, pos_len)
result = np.zeros_like(pos_index, dtype=float)
for row, lens in enumerate(actual_len):
ranges = np.arange(1, pos_index.shape[1] + 1)
ranges[lens:] = ranges[lens - 1]
result[row] = sum_pre[row] / ranges
return result.mean(axis=0)
def cal_precision(pos_index, pos_len):
rec_ret = pos_index.cumsum(axis=1) / np.arange(1, pos_index.shape[1] + 1)
return rec_ret.mean(axis=0)
"""Function name and function mapper.
Useful when we have to serialize evaluation metric names
and call the functions based on deserialized names
"""
metrics_dict = {
'Precision': cal_precision,
'Recall': cal_recall,
'NDCG': cal_ndcg,
'MAP': cal_map,
}
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