Spaces:
Runtime error
Runtime error
File size: 3,278 Bytes
de3df6f | 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 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | import numpy as np
from datetime import datetime
from typing import List, Dict, Optional
class EngineRecommandationGroupe:
def __init__(
self,
t_half: float = 365.0,
lambda_r: float = 1.0,
alpha: float = 0.65,
omega_hist: float = 0.4,
omega_mood: float = 0.6,
omega_pen: float = 0.3,
delta: float = 0.7,
gamma_pop: float = 0.15,
):
self.t_half = t_half
self.lambda_r = lambda_r
self.alpha = alpha
self.omega_hist = omega_hist
self.omega_mood = omega_mood
self.omega_pen = omega_pen
self.delta = delta
self.gamma_pop = gamma_pop
def calcul_profil_utilisateur(
self,
history: List[Dict],
embeddings_catalogue: Dict[str, np.ndarray],
date_reference: Optional[datetime] = None,
) -> np.ndarray:
if not date_reference:
date_reference = datetime.now()
valid_records = [r for r in history if r["key"] in embeddings_catalogue]
if not valid_records:
dim = next(iter(embeddings_catalogue.values())).shape
return np.zeros(dim, dtype=np.float64)
ratings = [r["rating"] for r in valid_records]
user_mean = np.mean(ratings) if len(ratings) >= 5 else 3.0
sample = next(iter(embeddings_catalogue.values()))
p_u = np.zeros(sample.shape, dtype=np.float64)
for record in valid_records:
v_m = embeddings_catalogue[record["key"]]
w_r = np.exp((record["rating"] - user_mean) / self.lambda_r) - 1.0
delta_t = (date_reference - record["date_viewed"]).days
decay = 2.0 ** (-max(0.0, delta_t) / self.t_half)
p_u += (w_r * decay) * v_m
norm = np.linalg.norm(p_u)
if norm > 1e-9:
p_u = p_u / norm
return p_u
def _cosinus_normalise(self, vec_a: np.ndarray, vec_b: np.ndarray) -> float:
na, nb = np.linalg.norm(vec_a), np.linalg.norm(vec_b)
if na < 1e-9 or nb < 1e-9:
return 0.5
return float(0.5 * (np.dot(vec_a, vec_b) / (na * nb) + 1.0))
def scoring_film_candidat(
self,
v_m: np.ndarray,
genres_m: List[str],
pop_m: float,
profils_groupe: List[np.ndarray],
v_mood: np.ndarray,
genres_mood: List[str],
) -> float:
N = len(profils_groupe)
if N == 0:
raise ValueError("Groupe vide.")
prefs = np.array([self._cosinus_normalise(p, v_m) for p in profils_groupe])
if N == 1:
r_hybrid = float(prefs[0])
sigma_p = 0.0
else:
r_hybrid = self.alpha * np.mean(prefs) + (1.0 - self.alpha) * np.min(prefs)
sigma_p = float(np.std(prefs))
sim_sem = self._cosinus_normalise(v_m, v_mood)
if genres_mood:
sim_genre = len(set(genres_m) & set(genres_mood)) / len(genres_mood)
else:
sim_genre = 1.0
sim_mood = self.delta * sim_sem + (1.0 - self.delta) * sim_genre
score = self.omega_hist * r_hybrid + self.omega_mood * sim_mood - self.omega_pen * sigma_p
facteur_pop = 1.0 / (np.log(10 + pop_m) ** self.gamma_pop)
return float(score * facteur_pop)
|