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