@i `dZddlZddlmZmZmZmZddlZeje Z ddee efde dejfdZdejd ejdefd Z ddejd ejd eedefd Z dd eee efd ejd eededeef dZy)zAPreference scorer for ranking patterns based on user preferences.N)ListDictAnyOptionalmemorydimreturnc|jdg}|jdg}t|dk(r#t|dk(rtj|St|dkDr-tj|}tj |d}ntj|}t|dkDr-tj|}tj |d}ntj|}||z }|S)aDBuild user preference vector from memory. Formula: u = mean(positive_vectors) - mean(negative_vectors) Args: memory: User memory dictionary with 'positive' and 'negative' lists dim: Dimension of the embedding vectors Returns: User preference vector (anchor vector) positivenegativer)axis)getlennpzerosarraymean) rrr r pos_vectorsmean_pos neg_vectorsmean_neg user_vectors H/home/ubuntu/codebase/yexijia/保研/colocation_mvp/preference/scorer.pybuild_user_vectorr szz*b)Hzz*b)H 8}c(mq0xx} 8}qhhx( 77;Q/88C= 8}qhhx( 77;Q/88C=X%K  pattern_vecuser_vecctjj|}tjj|}|dkDr#|dkDrtj||||zz }|Sd}|S)zScore pattern using baseline method (cosine similarity). Args: pattern_vec: Pattern embedding vector user_vec: User preference vector Returns: Cosine similarity score r)rlinalgnormdot)rr pattern_norm user_norm similaritys rscore_with_baseliner&0se99>>+.L x(IaIMVVK2lY6NO   rmodelc|| t||S ddl}|j|jd}|j|jd}|j 5||}||}ddd|j d}|j d} |dkDrK| dkDrF|j |j|j|| zz } | j} | Sd} | S#1swYxYw#t$r/} tjd| dt||cYd} ~ Sd} ~ wwxYw)a+Score pattern using learned preference encoder model. Args: pattern_vec: Pattern embedding vector user_vec: User preference vector model: Learned preference encoder model (PyTorch nn.Module) Returns: Cosine similarity score after projection Nr)rrzError in model-based scoring: z, falling back to baseline) r&torch FloatTensor unsqueezeno_gradr!r"squeezeitem Exceptionloggerwarning) rrr'r*pattern_tensor user_tensor pattern_proj user_projpattern_proj_normuser_proj_normr%es rscore_with_modelr:Es? }";99:**;7AA!D''1;;A> ]]_ 0Lk*I "JJ|J;I15 q ^a%7<#7#7#99;L;L;NO!N2J$*JJ!_$ :7s:TUV";99:s=AD$C75A=D3D7D<D D; $D60D;6D;patternsalphacg}|D]e}|jd}|d}n|t|||}n t||}|jdd}||zd|z |zz} |j| g|S)aScore patterns with preference and confidence fusion. Final score = alpha * preference_score + (1 - alpha) * confidence Args: patterns: List of pattern dictionaries user_vec: User preference vector model: Optional learned preference encoder model alpha: Weight for preference score (default: 0.7) Returns: List of final scores embeddingr confidencer))rr:r&append) r;rr'r<scorespatternrpreference_scorer? final_scores rscore_patternsrEts"Fkk+.  "  #3K5#Q $7{H#M [[s3 ..!e)z1II  k"+. Mr)i)N)Ngffffff?)__doc__numpyrtypingrrrrlogging getLogger__name__r1strintndarrayrfloatr&r:rErrrQsG,,   8 $#d38n#3##LRZZ2::%,-1,:"**,: ,:$SM,:5:,:b*.!$*T$sCx.1*JJ*!#***.e*r