Delete cnn_bigru/utils/semantic_embeddings.py with huggingface_hub

cnn_bigru/utils/semantic_embeddings.py

@@ -1,144 +0,0 @@
-"""semantic_embeddings.py — Embedder semântico com Sinkhorn OT e Ensemble.
-
-Inspirado em xavante_work/xavante/utils/semantic_embeddings.py, simplificado
-para o projeto CNN-BiGRU. Mantém as 5 técnicas matemáticas:
-    1. Sinkhorn Optimal Transport para alinhamento de tokens
-    2. Spectral clustering init via Laplacian normalization
-    3. Information Bottleneck (beta-VAE style)
-    4. Elastic WMD (smoothed WMD via Sinkhorn)
-    5. Multi-metric ensemble (Minkowski + random projections)
-
-Saída: embeddings L2-normalizados com dimensão configurável (default 128).
-"""
-from __future__ import annotations
-
-import logging
-import math
-from typing import List, Optional, Tuple
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-logger = logging.getLogger(__name__)
-
-
-# ---------------- Sinkhorn OT ----------------
-
-def sinkhorn_knopp(
-    cost_matrix: np.ndarray,
-    p: np.ndarray,
-    q: np.ndarray,
-    epsilon: float = 0.1,
-    n_iters: int = 50,
-    tol: float = 1e-6,
-) -> np.ndarray:
-    """Resolve OT regularizado entropicamente via Sinkhorn-Knopp.
-
-    T = diag(u) exp(-C/eps) diag(v), atualizado iterativamente até convergência.
-    """
-    n, m = cost_matrix.shape
-    K = np.exp(-cost_matrix / max(epsilon, 1e-8))
-    u = np.ones(n) / n
-    v = np.ones(m) / m
-    for _ in range(n_iters):
-        u_prev = u.copy()
-        Kv = K @ v
-        u = p / np.maximum(Kv, 1e-30)
-        Ktu = K.T @ u
-        v = q / np.maximum(Ktu, 1e-30)
-        if np.max(np.abs(u - u_prev)) < tol:
-            break
-    T = u[:, None] * K * v[None, :]
-    return T
-
-
-def elastic_wmd(embs_a: np.ndarray, embs_b: np.ndarray, epsilon: float = 0.1) -> float:
-    """Elastic Word Mover's Distance via Sinkhorn (diferenciável)."""
-    if embs_a.size == 0 or embs_b.size == 0:
-        return 0.0
-    C = np.linalg.norm(
-        embs_a[:, None, :] - embs_b[None, :, :], axis=-1
-    ).astype(np.float64)
-    p = np.ones(len(embs_a)) / len(embs_a)
-    q = np.ones(len(embs_b)) / len(embs_b)
-    T = sinkhorn_knopp(C, p, q, epsilon=epsilon, n_iters=30)
-    return float(np.sum(T * C))
-
-
-class SemanticEmbedder(nn.Module):
-    """Embedder semântico com projeção L2-normalizada.
-
-    Combina:
-        - Embedding token -> dimensão oculta
-        - Mean-pooling com máscara
-        - Projeto linear para dimensão final
-        - L2 normalization para retrieval
-        - Information bottleneck: mínima informação com ruído, máxima com conteúdo
-    """
-
-    def __init__(
-        self,
-        vocab_size: int,
-        embed_dim: int = 64,
-        out_dim: int = 128,
-        pad_idx: int = 1,
-        beta: float = 0.01,
-    ):
-        super().__init__()
-        self.pad_idx = pad_idx
-        self.embed_dim = embed_dim
-        self.out_dim = out_dim
-        self.beta = beta  # Information Bottleneck weight
-
-        self.token_embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=pad_idx)
-        nn.init.orthogonal_(self.token_embedding.weight)
-
-        self.proj = nn.Linear(embed_dim, out_dim)
-        self.out_norm = nn.LayerNorm(out_dim)
-
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        """Returns L2-normalized embeddings [B, out_dim]."""
-        if attention_mask is None:
-            attention_mask = (input_ids != self.pad_idx).float()
-
-        emb = self.token_embedding(input_ids)  # [B, T, D]
-        mask = attention_mask.unsqueeze(-1)  # [B, T, 1]
-
-        # Mean pooling com máscara
-        summed = (emb * mask).sum(dim=1)
-        counts = mask.sum(dim=1).clamp(min=1.0)
-        pooled = summed / counts  # [B, D]
-
-        proj = self.proj(pooled)  # [B, out_dim]
-        proj = self.out_norm(proj)
-
-        # L2 normalize
-        norm = proj.norm(p=2, dim=-1, keepdim=True).clamp(min=1e-8)
-        emb_norm = proj / norm
-
-        # Information Bottleneck regularization (beta-VAE style):
-        # penaliza variância excessiva (encoraja representação comprimida)
-        if self.training and self.beta > 0:
-            var = emb_norm.var(dim=0).mean()
-            ib_loss = self.beta * var
-            # Anexa ao atributo para coleta externa
-            setattr(emb_norm, "ib_loss", ib_loss)
-
-        return emb_norm
-
-    def similarity(self, a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
-        """Similaridade coseno (a já está normalizado)."""
-        return torch.matmul(a, b.T)
-
-
-__all__ = [
-    "sinkhorn_knopp",
-    "elastic_wmd",
-    "SemanticEmbedder",
-]