87 lines
2.3 KiB
Python
87 lines
2.3 KiB
Python
# Load libraries ---------------------------------------------
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
from collections import defaultdict
|
|
|
|
# ------------------------------------------------------------
|
|
|
|
|
|
def rmse(r_pred, r_real):
|
|
return np.sqrt(np.sum(np.power(r_pred - r_real, 2)) / len(r_pred))
|
|
|
|
|
|
def mape(r_pred, r_real):
|
|
return 1 / len(r_pred) * np.sum(np.abs(r_pred - r_real) / np.abs(r_real))
|
|
|
|
|
|
def tre(r_pred, r_real):
|
|
return np.sum(np.abs(r_pred - r_real)) / np.sum(np.abs(r_real))
|
|
|
|
|
|
def hr(recommendations, real_interactions, n=1):
|
|
"""
|
|
Assumes recommendations are ordered by user_id and then by score.
|
|
:param pd.DataFrame recommendations:
|
|
:param pd.DataFrame real_interactions:
|
|
:param int n:
|
|
"""
|
|
# Transform real_interactions to a dict for a large speed-up
|
|
rui = defaultdict(lambda: 0)
|
|
|
|
for idx, row in real_interactions.iterrows():
|
|
rui[(row['user_id'], row['item_id'])] = 1
|
|
|
|
result = 0.0
|
|
|
|
previous_user_id = -1
|
|
rank = 0
|
|
for idx, row in recommendations.iterrows():
|
|
if previous_user_id == row['user_id']:
|
|
rank += 1
|
|
else:
|
|
rank = 1
|
|
|
|
if rank <= n:
|
|
result += rui[(row['user_id'], row['item_id'])]
|
|
|
|
previous_user_id = row['user_id']
|
|
|
|
if len(recommendations['user_id'].unique()) > 0:
|
|
result /= len(recommendations['user_id'].unique())
|
|
|
|
return result
|
|
|
|
|
|
def ndcg(recommendations, real_interactions, n=1):
|
|
"""
|
|
Assumes recommendations are ordered by user_id and then by score.
|
|
:param pd.DataFrame recommendations:
|
|
:param pd.DataFrame real_interactions:
|
|
:param int n:
|
|
"""
|
|
# Transform real_interactions to a dict for a large speed-up
|
|
rui = defaultdict(lambda: 0)
|
|
|
|
for idx, row in real_interactions.iterrows():
|
|
rui[(row['user_id'], row['item_id'])] = 1
|
|
|
|
result = 0.0
|
|
|
|
previous_user_id = -1
|
|
rank = 0
|
|
for idx, row in recommendations.iterrows():
|
|
if previous_user_id == row['user_id']:
|
|
rank += 1
|
|
else:
|
|
rank = 1
|
|
|
|
if rank <= n:
|
|
result += rui[(row['user_id'], row['item_id'])] / np.log2(1 + rank)
|
|
|
|
previous_user_id = row['user_id']
|
|
|
|
if len(recommendations['user_id'].unique()) > 0:
|
|
result /= len(recommendations['user_id'].unique())
|
|
|
|
return result |