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train_v3.py

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+"""
+Entrenamiento Optimizado V3 - Seq2Seq Simple pero Efectivo
+Taller: Traductor Automatico RNN bajo CRISP-ML(Q)
+"""
+
+import time
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import Dataset, DataLoader
+import torch.nn.functional as F
+
+print("=" * 60)
+print("ENTRENAMIENTO OPTIMIZADO - SEQ2SEQ")
+print("=" * 60)
+
+start_time = time.time()
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+print(f"\n[INFO] Dispositivo: {device}")
+
+CORPUS = [
+    ("hello", "hola"), ("goodbye", "adios"), ("good morning", "buenos dias"),
+    ("good night", "buenas noches"), ("see you later", "hasta luego"),
+    ("thank you", "gracias"), ("thank you very much", "muchas gracias"),
+    ("please", "por favor"), ("you are welcome", "de nada"),
+    ("excuse me", "disculpe"), ("sorry", "lo siento"),
+    ("yes", "si"), ("no", "no"), ("maybe", "quizas"),
+    ("of course", "por supuesto"),
+    ("i", "yo"), ("you", "tu"), ("he", "el"), ("she", "ella"),
+    ("we", "nosotros"), ("they", "ellos"),
+    ("i am a student", "soy estudiante"), ("you are a teacher", "tu eres maestro"),
+    ("he is a professor", "el es profesor"), ("she is a student", "ella es estudiante"),
+    ("we are friends", "somos amigos"), ("what is your name", "cual es tu nombre"),
+    ("my name is john", "me llamo john"), ("nice to meet you", "mucho gusto"),
+    ("father", "padre"), ("mother", "madre"), ("brother", "hermano"),
+    ("sister", "hermana"), ("son", "hijo"), ("daughter", "hija"),
+    ("university", "universidad"), ("class", "clase"), ("professor", "profesor"),
+    ("student", "estudiante"), ("exam", "examen"), ("homework", "tarea"),
+    ("i study at the university", "estudio en la universidad"),
+    ("the class starts at eight", "la clase empieza a las ocho"),
+    ("the exam is difficult", "el examen es dificil"),
+    ("i need a book", "necesito un libro"),
+    ("where is the library", "donde esta la biblioteca"),
+    ("the professor is strict", "el profesor es estricto"),
+    ("i have a class at nine", "tengo clase a las nueve"),
+    ("the lecture is interesting", "la conferencia es interesante"),
+    ("when is the exam", "cuando es el examen"),
+    ("i passed the exam", "aprobe el examen"),
+    ("i need to study", "necesito estudiar"),
+    ("i am late for class", "llegue tarde a clase"),
+    ("one", "uno"), ("two", "dos"), ("three", "tres"),
+    ("four", "cuatro"), ("five", "cinco"), ("six", "seis"),
+    ("seven", "siete"), ("eight", "ocho"), ("nine", "nueve"),
+    ("ten", "diez"),
+    ("monday", "lunes"), ("tuesday", "martes"), ("wednesday", "miercoles"),
+    ("thursday", "jueves"), ("friday", "viernes"), ("saturday", "sabado"),
+    ("sunday", "domingo"), ("today", "hoy"), ("tomorrow", "manana"),
+    ("time", "tiempo"), ("hour", "hora"), ("minute", "minuto"),
+    ("now", "ahora"), ("later", "despues"), ("early", "temprano"),
+    ("late", "tarde"), ("always", "siempre"), ("never", "nunca"),
+    ("here", "aqui"), ("there", "alli"), ("where", "donde"),
+    ("city", "ciudad"), ("country", "pais"), ("home", "casa"),
+    ("office", "oficina"), ("library", "biblioteca"), ("cafe", "cafe"),
+    ("park", "parque"),
+    ("to be", "ser"), ("to have", "tener"), ("to do", "hacer"),
+    ("to go", "ir"), ("to come", "venir"),
+    ("to see", "ver"), ("to know", "saber"), ("to think", "pensar"),
+    ("to want", "querer"), ("to need", "necesitar"), ("to like", "gustar"),
+    ("to learn", "aprender"), ("to teach", "enseñar"), ("to study", "estudiar"),
+    ("to work", "trabajar"), ("to live", "vivir"), ("to eat", "comer"),
+    ("to drink", "beber"), ("to speak", "hablar"), ("to write", "escribir"),
+    ("to read", "leer"), ("to understand", "entender"),
+    ("to help", "ayudar"), ("to start", "empezar"), ("to finish", "terminar"),
+    ("book", "libro"), ("pen", "lapiz"), ("paper", "papel"),
+    ("computer", "computadora"), ("phone", "telefono"), ("table", "mesa"),
+    ("chair", "silla"), ("door", "puerta"), ("window", "ventana"),
+    ("food", "comida"), ("water", "agua"), ("coffee", "cafe"),
+    ("good", "bueno"), ("bad", "malo"), ("big", "grande"),
+    ("small", "pequeño"), ("new", "nuevo"), ("old", "viejo"),
+    ("fast", "rapido"), ("slow", "lento"), ("easy", "facil"),
+    ("difficult", "dificil"), ("important", "importante"),
+    ("interesting", "interesante"), ("beautiful", "hermoso"),
+    ("happy", "feliz"), ("sad", "triste"),
+    ("what", "que"), ("who", "quien"), ("when", "cuando"),
+    ("why", "por que"), ("how", "como"),
+    ("how are you", "como estas"), ("how much", "cuanto"),
+    ("what time is it", "que hora es"),
+    ("science", "ciencia"), ("math", "matematicas"), ("history", "historia"),
+    ("art", "arte"), ("music", "musica"), ("language", "idioma"),
+    ("english", "ingles"), ("spanish", "espanol"),
+    ("computer science", "ciencias de la computacion"),
+    ("information", "informacion"), ("technology", "tecnologia"),
+]
+
+for esp, ing in list(CORPUS):
+    if (ing, esp) not in CORPUS:
+        CORPUS.append((ing, esp))
+
+print(f"[INFO] Corpus: {len(CORPUS)} parejas")
+
+PAD = "<PAD>"
+UNK = "<UNK>"
+SOS = "<SOS>"
+EOS = "<EOS>"
+
+class Vocab:
+    def __init__(self):
+        self.w2i = {PAD: 0, UNK: 1, SOS: 2, EOS: 3}
+        self.i2w = {0: PAD, 1: UNK, 2: SOS, 3: EOS}
+        self.n = 4
+    
+    def add(self, text):
+        for w in text.lower().split():
+            if w not in self.w2i:
+                self.w2i[w] = self.n
+                self.i2w[self.n] = w
+                self.n += 1
+    
+    def enc(self, text, max_len, sos=False, eos=False):
+        ids = []
+        if sos:
+            ids.append(self.w2i[SOS])
+        for w in text.lower().split():
+            ids.append(self.w2i.get(w, self.w2i[UNK]))
+        if eos:
+            ids.append(self.w2i[EOS])
+        while len(ids) < max_len:
+            ids.append(self.w2i[PAD])
+        return ids[:max_len]
+    
+    def dec(self, ids):
+        ws = []
+        for i in ids:
+            if torch.is_tensor(i):
+                i = i.item()
+            w = self.i2w.get(i, UNK)
+            if w not in [PAD, SOS, EOS]:
+                ws.append(w)
+        return ' '.join(ws)
+
+src_v = Vocab()
+tgt_v = Vocab()
+
+for s, t in CORPUS:
+    src_v.add(s)
+    tgt_v.add(t)
+
+print(f"[OK] Vocab src: {src_v.n}, tgt: {tgt_v.n}")
+
+MAX_LEN = 20
+BATCH = 32
+EMBED = 256
+HIDDEN = 512
+LAYERS = 2
+DROP = 0.3
+EPOCHS = 100
+LR = 0.001
+
+print(f"\n[INFO] Embed={EMBED}, Hidden={HIDDEN}, Layers={LAYERS}, Epochs={EPOCHS}")
+
+class Encoder(nn.Module):
+    def __init__(self, vs, em, hd, ly, dp):
+        super().__init__()
+        self.emb = nn.Embedding(vs, em, padding_idx=0)
+        self.lstm = nn.LSTM(em, hd, ly, batch_first=True, dropout=dp)
+        self.dp = nn.Dropout(dp)
+    
+    def forward(self, x):
+        e = self.dp(self.emb(x))
+        o, (h, c) = self.lstm(e)
+        return o, h, c
+
+class Decoder(nn.Module):
+    def __init__(self, vs, em, hd, ly, dp):
+        super().__init__()
+        self.emb = nn.Embedding(vs, em, padding_idx=0)
+        self.lstm = nn.LSTM(em, hd, ly, batch_first=True, dropout=dp)
+        self.fc = nn.Linear(hd, vs)
+        self.dp = nn.Dropout(dp)
+    
+    def forward(self, x, h, c):
+        e = self.dp(self.emb(x))
+        o, (h, c) = self.lstm(e, (h, c))
+        return self.fc(o.squeeze(1)), h, c
+
+class Seq2Seq(nn.Module):
+    def __init__(self, enc, dec):
+        super().__init__()
+        self.enc = enc
+        self.dec = dec
+    
+    def forward(self, src, tgt, tf=0.5):
+        bs = src.shape[0]
+        max_len = tgt.shape[1]
+        out = torch.zeros(bs, max_len, self.dec.fc.out_features).to(device)
+        
+        _, h, c = self.enc(src)
+        
+        dec_in = tgt[:, 0]
+        
+        for t in range(1, max_len):
+            o, h, c = self.dec(dec_in.unsqueeze(1), h, c)
+            out[:, t] = o
+            
+            tf_now = np.random.random() < tf
+            top1 = o.argmax(1)
+            dec_in = tgt[:, t] if tf_now else top1
+        
+        return out
+
+enc = Encoder(src_v.n, EMBED, HIDDEN, LAYERS, DROP)
+dec = Decoder(tgt_v.n, EMBED, HIDDEN, LAYERS, DROP)
+model = Seq2Seq(enc, dec).to(device)
+
+params = sum(p.numel() for p in model.parameters())
+print(f"[OK] Parametros: {params:,}")
+
+class DS(Dataset):
+    def __init__(self, data, sv, tv, ml):
+        self.d = [(sv.enc(s, ml), tv.enc(t, ml, True, True)) for s, t in data]
+    
+    def __len__(self):
+        return len(self.d)
+    
+    def __getitem__(self, i):
+        return torch.tensor(self.d[i][0]), torch.tensor(self.d[i][1])
+
+ds = DS(CORPUS, src_v, tgt_v, MAX_LEN)
+dl = DataLoader(ds, batch_size=BATCH, shuffle=True)
+
+crit = nn.CrossEntropyLoss(ignore_index=0)
+opt = optim.Adam(model.parameters(), lr=LR)
+sch = optim.lr_scheduler.ReduceLROnPlateau(opt, mode='min', factor=0.5, patience=10)
+
+print(f"\n[INFO] Entrenando {EPOCHS} epocas...")
+
+model.train()
+losses = []
+best_loss = float('inf')
+
+for ep in range(1, EPOCHS + 1):
+    ep_loss = 0
+    n = 0
+    
+    for src, tgt in dl:
+        src, tgt = src.to(device), tgt.to(device)
+        opt.zero_grad()
+        
+        tf = max(0.3, 0.5 * (1 - ep / EPOCHS))
+        out = model(src, tgt, tf)
+        
+        out = out.view(-1, out.shape[-1])
+        tgt_flat = tgt.view(-1)
+        
+        loss = crit(out, tgt_flat)
+        loss.backward()
+        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+        opt.step()
+        
+        ep_loss += loss.item()
+        n += 1
+    
+    avg = ep_loss / n
+    losses.append(avg)
+    sch.step(avg)
+    
+    if avg < best_loss:
+        best_loss = avg
+        torch.save({
+            'm': model.state_dict(),
+            'src_vocab': src_v.w2i,
+            'tgt_vocab': tgt_v.w2i,
+            'src_idx2word': src_v.i2w,
+            'tgt_idx2word': tgt_v.i2w,
+        }, 'best.pt')
+    
+    if ep % 10 == 0 or ep == EPOCHS:
+        print(f"  Ep {ep:3d}/{EPOCHS} - Loss: {avg:.4f}")
+
+def bleu(ref, hyp):
+    rw = ref.lower().split()
+    hw = hyp.lower().split()
+    if not hw:
+        return 0.0
+    m = sum(1 for w in hw if w in rw)
+    p = m / len(hw) if hw else 0
+    bp = min(1.0, np.exp(1 - len(rw) / max(len(hw), 1)))
+    return bp * p
+
+ckpt = torch.load('best.pt')
+model.load_state_dict(ckpt['m'])
+model.eval()
+
+src_v.w2i = ckpt['src_vocab']
+tgt_v.w2i = ckpt['tgt_vocab']
+src_v.i2w = ckpt['src_idx2word']
+tgt_v.i2w = ckpt['tgt_idx2word']
+
+tests = [
+    ("hello", "hola"), ("goodbye", "adios"), ("thank you", "gracias"),
+    ("i am a student", "soy estudiante"), ("where is the library", "donde esta la biblioteca"),
+    ("the exam is difficult", "el examen es dificil"), ("i need to study", "necesito estudiar"),
+    ("good morning", "buenos dias"), ("how are you", "como estas"),
+    ("i study at the university", "estudio en la universidad"),
+]
+
+print("\nResultados:")
+print("-" * 60)
+
+total = 0
+with torch.no_grad():
+    for st, tt in tests:
+        enc_in = torch.tensor([src_v.enc(st, MAX_LEN)]).to(device)
+        
+        _, h, c = enc(enc_in)
+        
+        dec_in = torch.tensor([tgt_v.w2i[SOS]]).to(device)
+        res = []
+        
+        for _ in range(MAX_LEN):
+            o, h, c = dec(dec_in.unsqueeze(1), h, c)
+            top = o.argmax(1).item()
+            
+            if top == tgt_v.w2i[EOS] or top == tgt_v.w2i[PAD]:
+                break
+            
+            res.append(top)
+            dec_in = torch.tensor([top]).to(device)
+        
+        trans = tgt_v.dec(res)
+        b = bleu(tt, trans)
+        total += b
+        print(f"{st:<30} -> {tt:<25} BLEU: {b:.2f}")
+
+avg_bleu = total / len(tests)
+print("-" * 60)
+print(f"\nBLEU Score: {avg_bleu:.2f}")
+
+torch.save({
+    'm': model.state_dict(),
+    'src_vocab': src_v.w2i,
+    'tgt_vocab': tgt_v.w2i,
+    'src_idx2word': src_v.i2w,
+    'tgt_idx2word': tgt_v.i2w,
+    'ls': losses,
+    'bl': avg_bleu,
+}, 'translator.pt')
+
+elapsed = time.time() - start_time
+
+print("\n" + "=" * 60)
+print("RESUMEN")
+print("=" * 60)
+print(f"[OK] Tiempo: {elapsed:.1f}s ({elapsed/60:.1f} min)")
+print(f"[OK] Epocas: {EPOCHS}")
+print(f"[OK] Parametros: {params:,}")
+print(f"[OK] BLEU: {avg_bleu:.2f}")
+print(f"[OK] Loss: {losses[-1]:.4f}")
+print("=" * 60)
+print("ENTRENAMIENTO COMPLETADO")