Upload 13 files

app.py

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+import os
+import numpy as np
+import torch
+from transformers import AutoTokenizer, AutoModel
+from tensorflow.keras.models import load_model
+import joblib
+import streamlit as st
+
+# ---------- Caminhos ----------
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+MODELS_DIR = os.path.join(BASE_DIR, "models")
+MLB_PATH = os.path.join(BASE_DIR, "data", "mlb_597.pkl")
+
+# ---------- Parâmetros ----------
+TOP_N = 10
+CHUNK_PB = 512
+CHUNK_ESM = 1024
+
+# ---------- Cache dos modelos HuggingFace ----------
+@st.cache_resource
+def load_hf_model(name):
+    tokenizer = AutoTokenizer.from_pretrained(name, do_lower_case=False)
+    model = AutoModel.from_pretrained(name)
+    model.eval()
+    return tokenizer, model
+
+# ---------- Função para gerar embedding por chunk ----------
+def embed_sequence(model_name, seq, chunk_size):
+    tokenizer, model = load_hf_model(model_name)
+
+    def format_seq(s):
+        return " ".join(list(s))
+
+    chunks = [seq[i:i+chunk_size] for i in range(0, len(seq), chunk_size)]
+    embeddings = []
+
+    for chunk in chunks:
+        formatted = format_seq(chunk)
+        inputs = tokenizer(formatted, return_tensors="pt", truncation=True)
+        with torch.no_grad():
+            outputs = model(**inputs)
+        cls = outputs.last_hidden_state[:, 0, :].squeeze().numpy()
+        embeddings.append(cls)
+
+    return np.mean(embeddings, axis=0, keepdims=True)
+
+# ---------- Carregar modelos ----------
+mlp_pb  = load_model(os.path.join(MODELS_DIR, "mlp_protbert.h5"), compile=False)
+mlp_bfd = load_model(os.path.join(MODELS_DIR, "mlp_protbertbfd.h5"), compile=False)
+mlp_esm = load_model(os.path.join(MODELS_DIR, "mlp_esm2.h5"), compile=False)
+stacking = load_model(os.path.join(MODELS_DIR, "modelo_ensemble_stack.h5"), compile=False)
+
+# ---------- Carregar MultiLabelBinarizer ----------
+mlb = joblib.load(MLB_PATH)
+go_terms = mlb.classes_
+
+# ---------- Interface Streamlit ----------
+st.title("Predição de Funções de Proteínas")
+
+seq = st.text_area("Insere a sequência FASTA:", height=200)
+
+# Limpar sequência: remover cabeçalhos (">") e espaços/quebras
+if seq:
+    seq = "\n".join([line for line in seq.splitlines() if not line.startswith(">")])
+    seq = seq.replace(" ", "").replace("\n", "").strip()
+
+if st.button("Prever GO terms"):
+    if not seq:
+        st.warning("Por favor, insere uma sequência válida.")
+    else:
+        st.write("A gerar embeddings por chunks...")
+
+        emb_pb  = embed_sequence("Rostlab/prot_bert", seq, CHUNK_PB)
+        emb_bfd = embed_sequence("Rostlab/prot_bert_bfd", seq, CHUNK_PB)
+        emb_esm = embed_sequence("facebook/esm2_t33_650M_UR50D", seq, CHUNK_ESM)
+
+        st.write("A fazer predições base...")
+
+        y_pb  = mlp_pb.predict(emb_pb)[:, :597]
+        y_bfd = mlp_bfd.predict(emb_bfd)[:, :597]
+        y_esm = mlp_esm.predict(emb_esm)[:, :597]
+
+        X_stack = np.concatenate([y_pb, y_bfd, y_esm], axis=1)
+        y_pred = stacking.predict(X_stack)
+
+        st.subheader("GO terms com probabilidade ≥ 0.5:")
+        predicted = mlb.inverse_transform((y_pred >= 0.5).astype(int))[0]
+        if predicted:
+            st.code("\n".join(predicted))
+        else:
+            st.info("Nenhum GO term com probabilidade ≥ 0.5.")
+
+        st.subheader(f"Top {TOP_N} GO terms mais prováveis:")
+        top_idx = np.argsort(-y_pred[0])[:TOP_N]
+        for i in top_idx:
+            st.write(f"{go_terms[i]} : {y_pred[0][i]:.4f}")
+

data/mlb_597.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:652f2a315accef73abf13ff733cb24aacda95448fe7f1194a63eca9ca7e7dee5
+size 19564

models/mlp_esm2.h5

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+oid sha256:e85d48faa31a582b632f152a48dbb25f84d79e4cb06e4e1bcc9543c92fbf4646
+size 8598312

models/mlp_protbert.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:950ab09eecdb21bedb0d68aae6ed7412722cceac42ed94489887c3e43ecb0598
+size 7539476

models/mlp_protbertbfd.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:562ea1f6667928274a38169d2a25eab49305de0103101599b08404c84a470845
+size 7539476

models/modelo_ensemble_stack.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0a6c06c7c16c30841ce54e3cf6b551e21ec5942daba1f77029f4fe752c17caed
+size 4826900

notebooks/Ensemble.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0fbbb46c-1a00-4585-9ecd-a490a46e8b99",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "go.obo: fmt(1.2) rel(2025-03-16) 43,544 Terms\n",
+      "mlb com 597 GO terms guardado em data/mlb_597.pkl\n",
+      "     ProtBERT  Fmax=0.6616  Thr=0.40  AuPRC=0.7009  Smin=13.9047\n",
+      " ProtBERT-BFD  Fmax=0.6573  Thr=0.41  AuPRC=0.6925  Smin=13.7060\n",
+      "        ESM-2  Fmax=0.6375  Thr=0.39  AuPRC=0.6875  Smin=14.1194\n",
+      "     Ensemble  Fmax=0.6864  Thr=0.37  AuPRC=0.7332  Smin=12.7879\n"
+     ]
+    }
+   ],
+   "source": [
+    "# %%\n",
+    "import numpy as np, joblib, math\n",
+    "from sklearn.metrics import precision_recall_curve, auc\n",
+    "from goatools.obo_parser import GODag\n",
+    "from sklearn.preprocessing import MultiLabelBinarizer\n",
+    "\n",
+    "GO_FILE = \"go.obo\"\n",
+    "dag     = GODag(GO_FILE)\n",
+    "\n",
+    "# ---------- 1. y_true + GO terms (referência ProtBERT) ----------\n",
+    "test_pb = joblib.load(\"embeddings/test_protbert.pkl\")\n",
+    "y_true  = test_pb[\"labels\"]            # (1724, 597)  ← ground-truth\n",
+    "go_ref  = list(test_pb[\"go_terms\"])    # ordem exacta das colunas\n",
+    "\n",
+    "n_go = len(go_ref)                     # 597\n",
+    "\n",
+    "# --- Recriar o MultiLabelBinarizer com os 597 termos corretos ---\n",
+    "mlb = MultiLabelBinarizer(classes=go_ref)\n",
+    "mlb.fit([go_ref])  # necessário para permitir inverse_transform depois\n",
+    "\n",
+    "# ---------- 2. Carregar predições ----------\n",
+    "y_pb   = np.load(\"predictions/mf-protbert-pam1.npy\")        # 1724×597\n",
+    "y_bfd  = np.load(\"predictions/mf-protbertbfd-pam1.npy\")     # 1724×597\n",
+    "y_esm0 = np.load(\"predictions/mf-esm2.npy\")                 # 1724×602\n",
+    "\n",
+    "# ---------- 3. Remapear ESM-2 para ordem ProtBERT ----------\n",
+    "mlb_esm = joblib.load(\"data/mlb.pkl\")                       # 602 GO terms\n",
+    "idx_map = [list(mlb_esm.classes_).index(t) for t in go_ref]\n",
+    "y_esm   = y_esm0[:, idx_map]                               # 1724×597\n",
+    "\n",
+    "# ---------- 4. Garantir shapes iguais ----------\n",
+    "assert (y_true.shape == y_pb.shape == y_bfd.shape\n",
+    "        == y_esm.shape == (1724, n_go)), \"Ainda há desalinhamento!\"\n",
+    "\n",
+    "# ---------- 4. Guardar mlb (y_true) alinhado ----------\n",
+    "joblib.dump(mlb, \"data/mlb_597.pkl\")\n",
+    "print(\"mlb com 597 GO terms guardado em data/mlb_597.pkl\")\n",
+    "\n",
+    "# ---------- 5. Métricas ----------\n",
+    "THR = np.linspace(0,1,101)\n",
+    "def fmax(y_t,y_p):\n",
+    "    best,thr = 0,0\n",
+    "    for t in THR:\n",
+    "        y_b = (y_p>=t).astype(int)\n",
+    "        tp = (y_t*y_b).sum(1); fp=((1-y_t)*y_b).sum(1); fn=(y_t*(1-y_b)).sum(1)\n",
+    "        f1 = 2*tp/(2*tp+fp+fn+1e-8); m=f1.mean()\n",
+    "        if m>best: best,thr = m,t\n",
+    "    return best,thr\n",
+    "\n",
+    "def auprc(y_t,y_p):\n",
+    "    p,r,_ = precision_recall_curve(y_t.ravel(), y_p.ravel()); return auc(r,p)\n",
+    "\n",
+    "def smin(y_t,y_p,thr,alpha=0.5):\n",
+    "    y_b=(y_p>=thr).astype(int)\n",
+    "    ic=-(np.log((y_t+y_b).sum(0)+1e-8)-np.log((y_t+y_b).sum()+1e-8))\n",
+    "    ru=np.logical_and(y_b, np.logical_not(y_t))*ic\n",
+    "    mi=np.logical_and(y_t, np.logical_not(y_b))*ic\n",
+    "    return np.sqrt((alpha*ru.sum(1))**2 + ((1-alpha)*mi.sum(1))**2).mean()\n",
+    "\n",
+    "def show(name,y_p):\n",
+    "    f,thr=fmax(y_true,y_p)\n",
+    "    print(f\"{name:>13s}  Fmax={f:.4f}  Thr={thr:.2f}  \"\n",
+    "          f\"AuPRC={auprc(y_true,y_p):.4f}  Smin={smin(y_true,y_p,thr):.4f}\")\n",
+    "\n",
+    "show(\"ProtBERT\",      y_pb)\n",
+    "show(\"ProtBERT-BFD\",  y_bfd)\n",
+    "show(\"ESM-2\",         y_esm)\n",
+    "show(\"Ensemble\",      (y_pb + y_bfd + y_esm)/3)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f1807404-c2ce-48d0-b87c-a7e0fecc1728",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/50\n",
+      "19/19 [==============================] - 0s 17ms/step - loss: 0.3811 - val_loss: 0.0868\n",
+      "Epoch 2/50\n",
+      "19/19 [==============================] - 0s 8ms/step - loss: 0.0882 - val_loss: 0.0696\n",
+      "Epoch 3/50\n",
+      "19/19 [==============================] - 0s 6ms/step - loss: 0.0628 - val_loss: 0.0563\n",
+      "Epoch 4/50\n",
+      "19/19 [==============================] - 0s 6ms/step - loss: 0.0552 - val_loss: 0.0520\n",
+      "Epoch 5/50\n",
+      "19/19 [==============================] - 0s 5ms/step - loss: 0.0507 - val_loss: 0.0486\n",
+      "Epoch 6/50\n",
+      "19/19 [==============================] - 0s 5ms/step - loss: 0.0473 - val_loss: 0.0455\n",
+      "Epoch 7/50\n",
+      "19/19 [==============================] - 0s 5ms/step - loss: 0.0437 - val_loss: 0.0431\n",
+      "Epoch 8/50\n",
+      "19/19 [==============================] - 0s 8ms/step - loss: 0.0414 - val_loss: 0.0414\n",
+      "Epoch 9/50\n",
+      "19/19 [==============================] - 0s 7ms/step - loss: 0.0391 - val_loss: 0.0395\n",
+      "Epoch 10/50\n",
+      "19/19 [==============================] - 0s 3ms/step - loss: 0.0371 - val_loss: 0.0383\n",
+      "Epoch 11/50\n",
+      "19/19 [==============================] - 0s 3ms/step - loss: 0.0355 - val_loss: 0.0372\n",
+      "Epoch 12/50\n",
+      "19/19 [==============================] - 0s 4ms/step - loss: 0.0341 - val_loss: 0.0362\n",
+      "Epoch 13/50\n",
+      "19/19 [==============================] - 0s 3ms/step - loss: 0.0329 - val_loss: 0.0352\n",
+      "Epoch 14/50\n",
+      "19/19 [==============================] - 0s 3ms/step - loss: 0.0318 - val_loss: 0.0345\n",
+      "Epoch 15/50\n",
+      "19/19 [==============================] - 0s 3ms/step - loss: 0.0307 - val_loss: 0.0341\n",
+      "Epoch 16/50\n",
+      "19/19 [==============================] - 0s 3ms/step - loss: 0.0300 - val_loss: 0.0337\n",
+      "Epoch 17/50\n",
+      "19/19 [==============================] - 0s 3ms/step - loss: 0.0291 - val_loss: 0.0334\n",
+      "Epoch 18/50\n",
+      "19/19 [==============================] - 0s 3ms/step - loss: 0.0281 - val_loss: 0.0331\n",
+      "Epoch 19/50\n",
+      "19/19 [==============================] - 0s 4ms/step - loss: 0.0278 - val_loss: 0.0329\n",
+      "Epoch 20/50\n",
+      "19/19 [==============================] - 0s 5ms/step - loss: 0.0270 - val_loss: 0.0328\n",
+      "Epoch 21/50\n",
+      "19/19 [==============================] - 0s 4ms/step - loss: 0.0266 - val_loss: 0.0329\n",
+      "Epoch 22/50\n",
+      "19/19 [==============================] - 0s 3ms/step - loss: 0.0261 - val_loss: 0.0326\n",
+      "Epoch 23/50\n",
+      "19/19 [==============================] - 0s 3ms/step - loss: 0.0257 - val_loss: 0.0325\n",
+      "Epoch 24/50\n",
+      "19/19 [==============================] - 0s 3ms/step - loss: 0.0249 - val_loss: 0.0324\n",
+      "Epoch 25/50\n",
+      "19/19 [==============================] - 0s 3ms/step - loss: 0.0247 - val_loss: 0.0325\n",
+      "Epoch 26/50\n",
+      "19/19 [==============================] - 0s 3ms/step - loss: 0.0243 - val_loss: 0.0322\n",
+      "Epoch 27/50\n",
+      "19/19 [==============================] - 0s 3ms/step - loss: 0.0239 - val_loss: 0.0325\n",
+      "Epoch 28/50\n",
+      "19/19 [==============================] - 0s 3ms/step - loss: 0.0235 - val_loss: 0.0325\n",
+      "Epoch 29/50\n",
+      "19/19 [==============================] - 0s 5ms/step - loss: 0.0226 - val_loss: 0.0328\n",
+      "Epoch 30/50\n",
+      "19/19 [==============================] - 0s 5ms/step - loss: 0.0227 - val_loss: 0.0326\n",
+      "Epoch 31/50\n",
+      "19/19 [==============================] - 0s 4ms/step - loss: 0.0224 - val_loss: 0.0326\n",
+      "\n",
+      " STACKING (GPU-Keras MLP)\n",
+      "Fmax  = 0.6937\n",
+      "Thr.  = 0.34\n",
+      "AuPRC = 0.7551\n",
+      "Smin  = 12.2407\n"
+     ]
+    }
+   ],
+   "source": [
+    "# %%\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Dropout\n",
+    "from tensorflow.keras.optimizers import Adam\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import precision_recall_curve, auc\n",
+    "import numpy as np\n",
+    "import math\n",
+    "\n",
+    "# --- Preparar dados para stacking ---\n",
+    "# (já com y_pb, y_bfd, y_esm com shape (1724, 597))\n",
+    "X_stack = np.concatenate([y_pb, y_bfd, y_esm], axis=1)  # (1724, 597*3)\n",
+    "y_stack = y_true.copy()                                # (1724, 597)\n",
+    "\n",
+    "# --- Divisão treino/validação ---\n",
+    "X_train, X_val, y_train, y_val = train_test_split(X_stack, y_stack, test_size=0.3, random_state=42)\n",
+    "\n",
+    "# --- Modelo MLP (usa GPU automaticamente se disponível) ---\n",
+    "from tensorflow.keras.callbacks import EarlyStopping\n",
+    "\n",
+    "model = Sequential([\n",
+    "    Dense(512, activation=\"relu\", input_shape=(X_train.shape[1],)),\n",
+    "    Dropout(0.3),\n",
+    "    Dense(256, activation=\"relu\"),\n",
+    "    Dropout(0.3),\n",
+    "    Dense(y_stack.shape[1], activation=\"sigmoid\")\n",
+    "])\n",
+    "\n",
+    "model.compile(optimizer=Adam(1e-3), loss=\"binary_crossentropy\")\n",
+    "\n",
+    "model.fit(X_train, y_train, validation_data=(X_val, y_val),\n",
+    "          epochs=50, batch_size=64, verbose=1,\n",
+    "          callbacks=[EarlyStopping(patience=5, restore_best_weights=True)])\n",
+    "\n",
+    "# --- Prever com stacking ---\n",
+    "y_pred_stack = model.predict(X_stack, batch_size=64)\n",
+    "\n",
+    "# --- Métricas ---\n",
+    "THR = np.linspace(0, 1, 101)\n",
+    "def fmax(y_t, y_p):\n",
+    "    best, thr = 0, 0\n",
+    "    for t in THR:\n",
+    "        y_b = (y_p >= t).astype(int)\n",
+    "        tp = (y_t * y_b).sum(1); fp = ((1 - y_t) * y_b).sum(1); fn = (y_t * (1 - y_b)).sum(1)\n",
+    "        f1 = 2 * tp / (2 * tp + fp + fn + 1e-8); m = f1.mean()\n",
+    "        if m > best: best, thr = m, t\n",
+    "    return best, thr\n",
+    "\n",
+    "def auprc(y_t, y_p):\n",
+    "    p, r, _ = precision_recall_curve(y_t.ravel(), y_p.ravel())\n",
+    "    return auc(r, p)\n",
+    "\n",
+    "def smin(y_t, y_p, thr, alpha=0.5):\n",
+    "    y_b = (y_p >= thr).astype(int)\n",
+    "    ic = -(np.log((y_t + y_b).sum(0) + 1e-8) - np.log((y_t + y_b).sum() + 1e-8))\n",
+    "    ru = np.logical_and(y_b, np.logical_not(y_t)) * ic\n",
+    "    mi = np.logical_and(y_t, np.logical_not(y_b)) * ic\n",
+    "    return np.sqrt((alpha * ru.sum(1))**2 + ((1 - alpha) * mi.sum(1))**2).mean()\n",
+    "\n",
+    "f, thr = fmax(y_stack, y_pred_stack)\n",
+    "print(f\"\\n STACKING (GPU-Keras MLP)\")\n",
+    "print(f\"Fmax  = {f:.4f}\")\n",
+    "print(f\"Thr.  = {thr:.2f}\")\n",
+    "print(f\"AuPRC = {auprc(y_stack, y_pred_stack):.4f}\")\n",
+    "print(f\"Smin  = {smin(y_stack, y_pred_stack, thr):.4f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "00695029-3d24-4803-a6e1-8ac5fd70b710",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "guardado em models/modelo_ensemble_stacking.keras\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.save(\"models/modelo_ensemble_stacking.keras\")\n",
+    "print('guardado em models/modelo_ensemble_stacking.keras')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37629e3a-1c24-4f0f-9d12-dddf48be8724",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/Input.ipynb

@@ -0,0 +1,157 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9eca7d69-3f17-4306-84d0-58a0363144fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A gerar embeddings por chunks...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Melvin\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\huggingface_hub\\file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
+      "  warnings.warn(\n",
+      "C:\\Users\\Melvin\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\huggingface_hub\\file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
+      "  warnings.warn(\n",
+      "Some weights of EsmModel were not initialized from the model checkpoint at facebook/esm2_t33_650M_UR50D and are newly initialized: ['esm.pooler.dense.bias', 'esm.pooler.dense.weight']\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A fazer predições base...\n",
+      "\n",
+      " GO terms com prob ≥ 0.5:\n",
+      "('GO:0003674', 'GO:0003824', 'GO:0005488', 'GO:0016491', 'GO:0036094', 'GO:0043167')\n",
+      "\n",
+      " Top 10 GO terms mais prováveis:\n",
+      "GO:0003674 : 0.9975\n",
+      "GO:0003824 : 0.9156\n",
+      "GO:0036094 : 0.6652\n",
+      "GO:0043167 : 0.6336\n",
+      "GO:0016491 : 0.6327\n",
+      "GO:0005488 : 0.5595\n",
+      "GO:0043169 : 0.4801\n",
+      "GO:0140096 : 0.4790\n",
+      "GO:0051213 : 0.4551\n",
+      "GO:0046872 : 0.4098\n"
+     ]
+    }
+   ],
+   "source": [
+    "# %%\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "from transformers import AutoTokenizer, AutoModel\n",
+    "from tensorflow.keras.models import load_model\n",
+    "import joblib\n",
+    "\n",
+    "# --- Parâmetros ---\n",
+    "SEQ_FASTA = \"MPISSSSSSSTKSMRRAASELERSDSVTSPRFIGRRQSLIEDARKEREAAAAAAEAAEATEQIVFEEEDGKALLNLFFTLRSSKTPALSRSLKVFETFEAKIHHLETRPCRKPRDSLEGLEYFVRCEVHLSDVSTLISSIKRIAEDVKTTKEVKFHWFPKKISELDRCHHLITKFDPDLDQEHPGFTDPVYRQRRKMIGDIAFRYKQGEPIPRVEYTEEEIGTWREVYSTLRDLYTTHACSEHLEAFNLLERHCGYSPENIPQLEDVSRFLRERTGFQLRPVAGLLSARDFLASLAFRVFQCTQYIRHASSPMHSPEPDCVHELLGHVPILADRVFAQFSQNIGLASLGASEEDIEKLSTLYWFTVEFGLCKQGGIVKAYGAGLLSSYGELVHALSDEPERREFDPEAAAIQPYQDQNYQSVYFVSESFTDAKEKLRSYVAGIKRPFSVRFDPYTYSIEVLDNPLKIRGGLESVKDELKMLTDALNVLA\"\n",
+    "TOP_N = 10\n",
+    "\n",
+    "# --- 1. Função para dividir sequência (512 para Protbert e Protbertbfd. 1024 para ESM2) ---\n",
+    "def slice_sequence(seq, chunk_size):\n",
+    "    return [seq[i:i+chunk_size] for i in range(0, len(seq), chunk_size)]\n",
+    "\n",
+    "# --- 2. Função para gerar embeddings médios ---\n",
+    "def get_embedding_mean(model_name, seq, chunk_size):\n",
+    "    tokenizer = AutoTokenizer.from_pretrained(model_name, do_lower_case=False)\n",
+    "    model     = AutoModel.from_pretrained(model_name)\n",
+    "    model.eval()\n",
+    "\n",
+    "    chunks = [seq[i:i+chunk_size] for i in range(0, len(seq), chunk_size)]\n",
+    "    embeddings = []\n",
+    "\n",
+    "    for chunk in chunks:\n",
+    "        seq_chunk = \" \".join(list(chunk))\n",
+    "        # tokenizar SEM truncar\n",
+    "        inputs = tokenizer(seq_chunk,\n",
+    "                           return_tensors=\"pt\",\n",
+    "                           truncation=False,         # ≤ 512 ou 1024 já garantido\n",
+    "                           padding=False)\n",
+    "        with torch.no_grad():\n",
+    "            cls = model(**inputs).last_hidden_state[:, 0, :].squeeze().numpy()\n",
+    "        embeddings.append(cls)\n",
+    "\n",
+    "    return np.mean(embeddings, axis=0, keepdims=True)   # (1, dim)\n",
+    "\n",
+    "print(\"A gerar embeddings por chunks...\")\n",
+    "emb_pb  = get_embedding_mean(\"Rostlab/prot_bert\", SEQ_FASTA, 512)\n",
+    "emb_bfd = get_embedding_mean(\"Rostlab/prot_bert_bfd\", SEQ_FASTA, 512)\n",
+    "emb_esm = get_embedding_mean(\"facebook/esm2_t33_650M_UR50D\", SEQ_FASTA, 1024)\n",
+    "\n",
+    "# --- 3. Carregar os MLPs base ---\n",
+    "mlp_pb  = load_model(\"models/protbert_mlp.keras\")\n",
+    "mlp_bfd = load_model(\"models/protbertbfd_mlp.keras\")\n",
+    "mlp_esm = load_model(\"models/esm2_mlp.keras\")\n",
+    "\n",
+    "# --- 4. Gerar predições base (garantir 597 colunas) ---\n",
+    "print(\"A fazer predições base...\")\n",
+    "y_pb  = mlp_pb.predict(emb_pb)[:, :597]\n",
+    "y_bfd = mlp_bfd.predict(emb_bfd)[:, :597]\n",
+    "y_esm = mlp_esm.predict(emb_esm)[:, :597]\n",
+    "\n",
+    "# --- 5. Concatenar para o stacking ---\n",
+    "X_stack = np.concatenate([y_pb, y_bfd, y_esm], axis=1)\n",
+    "\n",
+    "# --- 6. Carregar modelo de stacking ---\n",
+    "stacking = load_model(\"models/modelo_ensemble_stacking.keras\")\n",
+    "y_pred = stacking.predict(X_stack)\n",
+    "\n",
+    "# --- 7. Carregar binarizador (597 GO terms) ---\n",
+    "mlb = joblib.load(\"data/mlb_597.pkl\")\n",
+    "go_terms = mlb.classes_\n",
+    "\n",
+    "# --- 8. Mostrar resultados ---\n",
+    "print(\"\\n GO terms com prob ≥ 0.5:\")\n",
+    "predicted_terms = mlb.inverse_transform((y_pred >= 0.5).astype(int))\n",
+    "print(predicted_terms[0] if predicted_terms[0] else \"Nenhum GO term acima de 0.5\")\n",
+    "\n",
+    "print(f\"\\n Top {TOP_N} GO terms mais prováveis:\")\n",
+    "top_idx = np.argsort(-y_pred[0])[:TOP_N]\n",
+    "for i in top_idx:\n",
+    "    print(f\"{go_terms[i]} : {y_pred[0][i]:.4f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e959e7d9-15ba-4533-a2bb-ddd7df2a639d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/PAM1_ESM2.ipynb

@@ -0,0 +1,561 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "641053e3-7fec-4f9b-a75e-ddd957af03c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "go.obo: fmt(1.2) rel(2025-03-16) 43,544 Terms\n",
+      "✓ Dataset preparado:\n",
+      " - Training: (31142, 3)\n",
+      " - Validation: (1724, 3)\n",
+      " - Test: (1724, 3)\n",
+      " - GO terms: 602\n"
+     ]
+    }
+   ],
+   "source": [
+    "# %%\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from Bio import SeqIO\n",
+    "from goatools.obo_parser import GODag\n",
+    "from collections import Counter\n",
+    "from sklearn.preprocessing import MultiLabelBinarizer\n",
+    "from iterstrat.ml_stratifiers import MultilabelStratifiedKFold\n",
+    "import os, random\n",
+    "\n",
+    "# --- 1. Carregar ficheiros principais ---\n",
+    "FASTA = \"uniprot_sprot_exp.fasta\"\n",
+    "ANNOT = \"uniprot_sprot_exp.txt\"\n",
+    "GO_OBO = \"go.obo\"\n",
+    "\n",
+    "# --- 2. Ler sequências ---\n",
+    "seqs, ids = [], []\n",
+    "for record in SeqIO.parse(FASTA, \"fasta\"):\n",
+    "    ids.append(record.id)\n",
+    "    seqs.append(str(record.seq))\n",
+    "\n",
+    "df_seq = pd.DataFrame({\"protein_id\": ids, \"sequence\": seqs})\n",
+    "\n",
+    "# --- 3. Ler anotações GO:MF ---\n",
+    "df_ann = pd.read_csv(ANNOT, sep=\"\\t\", names=[\"protein_id\", \"go_term\", \"category\"])\n",
+    "df_ann = df_ann[df_ann[\"category\"] == \"F\"]\n",
+    "\n",
+    "# --- 4. Propagação hierárquica dos GO terms ---\n",
+    "go_dag = GODag(GO_OBO)\n",
+    "mf_terms = {t for t, o in go_dag.items() if o.namespace == \"molecular_function\"}\n",
+    "\n",
+    "def propagate_terms(terms):\n",
+    "    expanded = set()\n",
+    "    for t in terms:\n",
+    "        if t in go_dag:\n",
+    "            expanded |= go_dag[t].get_all_parents()\n",
+    "        expanded.add(t)\n",
+    "    return list(expanded & mf_terms)\n",
+    "\n",
+    "grouped = df_ann.groupby(\"protein_id\")[\"go_term\"].apply(list).reset_index()\n",
+    "grouped[\"go_term\"] = grouped[\"go_term\"].apply(propagate_terms)\n",
+    "\n",
+    "# --- 5. Juntar com sequência ---\n",
+    "df = df_seq.merge(grouped, on=\"protein_id\")\n",
+    "df = df[df[\"go_term\"].str.len() > 0]\n",
+    "\n",
+    "# --- 6. Filtrar GO terms com ≥50 proteínas ---\n",
+    "all_terms = [term for sublist in df[\"go_term\"] for term in sublist]\n",
+    "term_counts = Counter(all_terms)\n",
+    "valid_terms = {t for t, count in term_counts.items() if count >= 50}\n",
+    "\n",
+    "df[\"go_term\"] = df[\"go_term\"].apply(lambda ts: [t for t in ts if t in valid_terms])\n",
+    "df = df[df[\"go_term\"].str.len() > 0]\n",
+    "\n",
+    "# --- 7. Preparar labels e dividir por proteína ---\n",
+    "df[\"go_terms\"] = df[\"go_term\"].apply(lambda x: ';'.join(sorted(set(x))))\n",
+    "df = df[[\"protein_id\", \"sequence\", \"go_terms\"]].drop_duplicates()\n",
+    "\n",
+    "mlb = MultiLabelBinarizer()\n",
+    "Y = mlb.fit_transform(df[\"go_terms\"].str.split(\";\"))\n",
+    "X = df[[\"protein_id\", \"sequence\"]].values\n",
+    "\n",
+    "mskf = MultilabelStratifiedKFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "train_idx, temp_idx = next(mskf.split(X, Y))\n",
+    "val_idx, test_idx = np.array_split(temp_idx, 2)\n",
+    "\n",
+    "df_train = df.iloc[train_idx].copy()\n",
+    "df_val   = df.iloc[val_idx].copy()\n",
+    "df_test  = df.iloc[test_idx].copy()\n",
+    "\n",
+    "os.makedirs(\"data\", exist_ok=True)\n",
+    "df_train.to_csv(\"data/mf-training.csv\", index=False)\n",
+    "df_val.to_csv(\"data/mf-validation.csv\", index=False)\n",
+    "df_test.to_csv(\"data/mf-test.csv\", index=False)\n",
+    "\n",
+    "# --- 8. Guardar o binarizador ---\n",
+    "import joblib\n",
+    "joblib.dump(mlb, \"data/mlb.pkl\")\n",
+    "\n",
+    "print(\"✓ Dataset preparado:\")\n",
+    "print(\" - Training:\", df_train.shape)\n",
+    "print(\" - Validation:\", df_val.shape)\n",
+    "print(\" - Test:\", df_test.shape)\n",
+    "print(\" - GO terms:\", len(mlb.classes_))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "40ba1798-daf8-4649-ae3f-bfe81df6437f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %%\n",
+    "import random\n",
+    "from collections import defaultdict\n",
+    "\n",
+    "# --- PAM1 matrix normalizada ---\n",
+    "pam_data = {\n",
+    "    'A': [9948, 19, 27, 42, 31, 46, 50, 92, 17, 7, 40, 88, 42, 41, 122, 279, 255, 9, 72, 723],\n",
+    "    'R': [14, 9871, 24, 38, 37, 130, 38, 62, 49, 4, 58, 205, 26, 33, 47, 103, 104, 5, 36, 52],\n",
+    "    'N': [20, 22, 9860, 181, 29, 36, 41, 67, 31, 5, 22, 49, 23, 10, 33, 83, 66, 3, 43, 32],\n",
+    "    'D': [40, 34, 187, 9818, 11, 63, 98, 61, 23, 5, 25, 54, 43, 13, 27, 88, 55, 4, 29, 36],\n",
+    "    'C': [20, 16, 26, 9, 9987, 10, 17, 37, 12, 2, 16, 26, 10, 19, 27, 26, 25, 2, 6, 67],\n",
+    "    'Q': [29, 118, 29, 49, 8, 9816, 72, 55, 36, 4, 60, 158, 35, 22, 39, 86, 74, 3, 34, 28],\n",
+    "    'E': [35, 29, 41, 101, 12, 71, 9804, 56, 33, 5, 36, 107, 42, 20, 38, 87, 69, 4, 30, 42],\n",
+    "    'G': [96, 61, 77, 70, 38, 51, 58, 9868, 26, 6, 37, 53, 39, 28, 69, 134, 116, 5, 47, 60],\n",
+    "    'H': [17, 53, 33, 19, 15, 39, 34, 24, 9907, 3, 32, 57, 24, 15, 27, 47, 43, 2, 22, 19],\n",
+    "    'I': [6, 3, 6, 6, 3, 5, 6, 7, 3, 9973, 23, 13, 12, 41, 93, 84, 115, 3, 8, 102],\n",
+    "    'L': [26, 39, 17, 15, 7, 33, 22, 20, 19, 27, 9864, 49, 24, 78, 117, 148, 193, 5, 24, 70],\n",
+    "    'K': [60, 198, 43, 52, 12, 142, 96, 53, 42, 10, 63, 9710, 33, 26, 54, 109, 102, 5, 43, 42],\n",
+    "    'M': [21, 22, 15, 18, 6, 20, 18, 18, 17, 11, 27, 32, 9945, 26, 34, 61, 71, 3, 12, 31],\n",
+    "    'F': [18, 17, 8, 6, 8, 11, 10, 16, 10, 44, 92, 24, 29, 9899, 89, 88, 142, 7, 14, 68],\n",
+    "    'P': [97, 47, 35, 29, 23, 35, 38, 57, 21, 24, 47, 56, 28, 76, 9785, 115, 77, 4, 24, 35],\n",
+    "    'S': [241, 87, 76, 73, 17, 56, 60, 99, 32, 13, 69, 92, 42, 67, 100, 9605, 212, 8, 63, 70],\n",
+    "    'T': [186, 78, 54, 37, 14, 42, 42, 83, 28, 23, 84, 85, 53, 93, 66, 182, 9676, 8, 39, 90],\n",
+    "    'W': [2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 1, 5, 3, 4, 4, 9960, 3, 4],\n",
+    "    'Y': [29, 21, 17, 9, 4, 13, 9, 21, 10, 7, 20, 17, 11, 23, 19, 41, 31, 3, 9935, 23],\n",
+    "    'V': [368, 27, 18, 18, 50, 23, 34, 64, 15, 85, 72, 42, 33, 88, 42, 112, 137, 4, 20, 9514]\n",
+    "}\n",
+    "\n",
+    "pam_raw = pd.DataFrame(pam_data, index=pam_data.keys())\n",
+    "pam_matrix = pam_raw.div(pam_raw.sum(axis=1), axis=0)\n",
+    "pam_dict = {aa: pam_matrix.loc[aa].to_dict() for aa in pam_matrix.index}\n",
+    "\n",
+    "def pam1_substitution(aa):\n",
+    "    if aa not in pam_dict:\n",
+    "        return aa\n",
+    "    subs = list(pam_dict[aa].keys())\n",
+    "    probs = list(pam_dict[aa].values())\n",
+    "    return np.random.choice(subs, p=probs)\n",
+    "\n",
+    "def augment_sequence(seq, sub_prob=0.05):\n",
+    "    return ''.join([pam1_substitution(aa) if random.random() < sub_prob else aa for aa in seq])\n",
+    "\n",
+    "def slice_sequence(seq, win=1024):\n",
+    "    if len(seq) <= win:\n",
+    "        return [seq]\n",
+    "    return [seq[i:i+win] for i in range(0, len(seq), win)]\n",
+    "\n",
+    "def format_seq(seq):\n",
+    "    return \" \".join(seq)\n",
+    "\n",
+    "# --- Carregar labels e datasets ---\n",
+    "import joblib\n",
+    "mlb = joblib.load(\"data/mlb.pkl\")\n",
+    "df_train = pd.read_csv(\"data/mf-training.csv\")\n",
+    "df_val   = pd.read_csv(\"data/mf-validation.csv\")\n",
+    "df_test  = pd.read_csv(\"data/mf-test.csv\")\n",
+    "\n",
+    "# --- Slicing + augmentação no treino ---\n",
+    "X_train, y_train = [], []\n",
+    "\n",
+    "for _, row in df_train.iterrows():\n",
+    "    seq_aug = augment_sequence(row[\"sequence\"], sub_prob=0.05)\n",
+    "    slices = slice_sequence(seq_aug, win=1024)\n",
+    "    label = mlb.transform([row[\"go_terms\"].split(\";\")])[0]\n",
+    "    for sl in slices:\n",
+    "        X_train.append(format_seq(sl))\n",
+    "        y_train.append(label)\n",
+    "\n",
+    "# --- Sem slicing no val/test ---\n",
+    "X_val   = [format_seq(seq) for seq in df_val[\"sequence\"]]\n",
+    "X_test  = [format_seq(seq) for seq in df_test[\"sequence\"]]\n",
+    "\n",
+    "y_val   = mlb.transform(df_val[\"go_terms\"].str.split(\";\"))\n",
+    "y_test  = mlb.transform(df_test[\"go_terms\"].str.split(\";\"))\n",
+    "\n",
+    "np.save(\"embeddings/y_test.npy\", y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "80d5c1fb-9c84-463d-8d8c-bfcc2982afc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Melvin\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\huggingface_hub\\file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
+      "  warnings.warn(\n",
+      "Some weights of EsmModel were not initialized from the model checkpoint at facebook/esm2_t33_650M_UR50D and are newly initialized: ['esm.pooler.dense.bias', 'esm.pooler.dense.weight']\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
+      "100%|██████████| 2189/2189 [1:17:26<00:00,  2.12s/it]\n",
+      "100%|██████████| 108/108 [03:43<00:00,  2.07s/it]\n",
+      "100%|██████████| 108/108 [03:56<00:00,  2.19s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# %%\n",
+    "from transformers import AutoTokenizer, AutoModel\n",
+    "import torch\n",
+    "from tqdm import tqdm\n",
+    "import numpy as np\n",
+    "import os\n",
+    "\n",
+    "# --- Configurações ---\n",
+    "MODEL_NAME = \"facebook/esm2_t33_650M_UR50D\"\n",
+    "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "CHUNK_SIZE = 16\n",
+    "\n",
+    "# --- Carregar modelo ---\n",
+    "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, do_lower_case=False)\n",
+    "model = AutoModel.from_pretrained(MODEL_NAME)\n",
+    "model.to(DEVICE)\n",
+    "model.eval()\n",
+    "\n",
+    "def extract_embeddings(texts):\n",
+    "    embeddings = []\n",
+    "    for i in tqdm(range(0, len(texts), CHUNK_SIZE)):\n",
+    "        batch = texts[i:i+CHUNK_SIZE]\n",
+    "        with torch.no_grad():\n",
+    "            inputs = tokenizer(batch, return_tensors=\"pt\", padding=True, truncation=True, max_length=1024)\n",
+    "            inputs = {k: v.to(DEVICE) for k, v in inputs.items()}\n",
+    "            outputs = model(**inputs).last_hidden_state\n",
+    "            cls_tokens = outputs[:, 0, :]  # token CLS\n",
+    "            embeddings.append(cls_tokens.cpu().numpy())\n",
+    "    return np.vstack(embeddings)\n",
+    "\n",
+    "# --- Extrair e guardar embeddings ---\n",
+    "os.makedirs(\"embeddings\", exist_ok=True)\n",
+    "\n",
+    "emb_train = extract_embeddings(X_train)\n",
+    "emb_val   = extract_embeddings(X_val)\n",
+    "emb_test  = extract_embeddings(X_test)\n",
+    "\n",
+    "np.save(\"embeddings/esm2_train.npy\", emb_train)\n",
+    "np.save(\"embeddings/esm2_val.npy\", emb_val)\n",
+    "np.save(\"embeddings/esm2_test.npy\", emb_test)\n",
+    "\n",
+    "np.save(\"embeddings/y_train.npy\", np.array(y_train))\n",
+    "np.save(\"embeddings/y_val.npy\", np.array(y_val))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "592e4f6c-b871-4f0b-b84c-f3918c698544",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/100\n",
+      "1095/1095 [==============================] - 4s 2ms/step - loss: 0.0552 - val_loss: 0.0455\n",
+      "Epoch 2/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0445 - val_loss: 0.0424\n",
+      "Epoch 3/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0419 - val_loss: 0.0394\n",
+      "Epoch 4/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0403 - val_loss: 0.0381\n",
+      "Epoch 5/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0392 - val_loss: 0.0373\n",
+      "Epoch 6/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0383 - val_loss: 0.0362\n",
+      "Epoch 7/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0374 - val_loss: 0.0358\n",
+      "Epoch 8/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0368 - val_loss: 0.0351\n",
+      "Epoch 9/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0362 - val_loss: 0.0348\n",
+      "Epoch 10/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0357 - val_loss: 0.0344\n",
+      "Epoch 11/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0353 - val_loss: 0.0340\n",
+      "Epoch 12/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0349 - val_loss: 0.0335\n",
+      "Epoch 13/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0344 - val_loss: 0.0334\n",
+      "Epoch 14/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0342 - val_loss: 0.0330\n",
+      "Epoch 15/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0338 - val_loss: 0.0325\n",
+      "Epoch 16/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0337 - val_loss: 0.0327\n",
+      "Epoch 17/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0333 - val_loss: 0.0325\n",
+      "Epoch 18/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0330 - val_loss: 0.0322\n",
+      "Epoch 19/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0328 - val_loss: 0.0321\n",
+      "Epoch 20/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0326 - val_loss: 0.0322\n",
+      "Epoch 21/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0323 - val_loss: 0.0320\n",
+      "Epoch 22/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0322 - val_loss: 0.0317\n",
+      "Epoch 23/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0320 - val_loss: 0.0318\n",
+      "Epoch 24/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0317 - val_loss: 0.0315\n",
+      "Epoch 25/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0316 - val_loss: 0.0317\n",
+      "Epoch 26/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0314 - val_loss: 0.0313\n",
+      "Epoch 27/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0313 - val_loss: 0.0320\n",
+      "Epoch 28/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0311 - val_loss: 0.0315\n",
+      "Epoch 29/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0310 - val_loss: 0.0313\n",
+      "Epoch 30/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0309 - val_loss: 0.0313\n",
+      "Epoch 31/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0307 - val_loss: 0.0310\n",
+      "Epoch 32/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0306 - val_loss: 0.0310\n",
+      "Epoch 33/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0304 - val_loss: 0.0310\n",
+      "Epoch 34/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0303 - val_loss: 0.0312\n",
+      "Epoch 35/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0302 - val_loss: 0.0309\n",
+      "Epoch 36/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0300 - val_loss: 0.0310\n",
+      "Epoch 37/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0299 - val_loss: 0.0313\n",
+      "Epoch 38/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0298 - val_loss: 0.0312\n",
+      "Epoch 39/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0296 - val_loss: 0.0307\n",
+      "Epoch 40/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0296 - val_loss: 0.0306\n",
+      "Epoch 41/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0295 - val_loss: 0.0310\n",
+      "Epoch 42/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0294 - val_loss: 0.0304\n",
+      "Epoch 43/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0294 - val_loss: 0.0308\n",
+      "Epoch 44/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0293 - val_loss: 0.0306\n",
+      "Epoch 45/100\n",
+      "1095/1095 [==============================] - 3s 3ms/step - loss: 0.0292 - val_loss: 0.0307\n",
+      "Epoch 46/100\n",
+      "1095/1095 [==============================] - 4s 4ms/step - loss: 0.0290 - val_loss: 0.0305\n",
+      "Epoch 47/100\n",
+      "1095/1095 [==============================] - 4s 4ms/step - loss: 0.0290 - val_loss: 0.0305\n",
+      "Modelo guardado em models/esm2_mlp.keras\n",
+      " Predições do ESM-2 salvas com forma: (1724, 602)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# %%\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Dropout\n",
+    "from tensorflow.keras.callbacks import EarlyStopping\n",
+    "from sklearn.metrics import average_precision_score\n",
+    "\n",
+    "# --- Carregar os embeddings e labels ---\n",
+    "X_train = np.load(\"embeddings/esm2_train.npy\")\n",
+    "X_val   = np.load(\"embeddings/esm2_val.npy\")\n",
+    "X_test  = np.load(\"embeddings/esm2_test.npy\")\n",
+    "\n",
+    "y_train = np.load(\"embeddings/y_train.npy\")\n",
+    "y_val   = np.load(\"embeddings/y_val.npy\")\n",
+    "y_test  = np.load(\"embeddings/y_test.npy\")\n",
+    "\n",
+    "# --- Definir o modelo ---\n",
+    "model = Sequential([\n",
+    "    Dense(1024, activation='relu', input_shape=(X_train.shape[1],)),\n",
+    "    Dropout(0.3),\n",
+    "    Dense(512, activation='relu'),\n",
+    "    Dropout(0.3),\n",
+    "    Dense(y_train.shape[1], activation='sigmoid')\n",
+    "])\n",
+    "\n",
+    "model.compile(optimizer='adam', loss='binary_crossentropy')\n",
+    "\n",
+    "# --- Treinar ---\n",
+    "early_stop = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
+    "\n",
+    "history = model.fit(\n",
+    "    X_train, y_train,\n",
+    "    validation_data=(X_val, y_val),\n",
+    "    epochs=100,\n",
+    "    batch_size=32,\n",
+    "    callbacks=[early_stop],\n",
+    "    verbose=1\n",
+    ")\n",
+    "\n",
+    "# --- Salvar o modelo ---\n",
+    "model.save(\"models/esm2_mlp.keras\")\n",
+    "print(\"Modelo guardado em models/esm2_mlp.keras\")\n",
+    "\n",
+    "# --- Fazer predições no conjunto de teste ---\n",
+    "y_prob = model.predict(X_test)\n",
+    "np.save(\"predictions/mf-esm2.npy\", y_prob)\n",
+    "\n",
+    "print(\" Predições do ESM-2 salvas com forma:\", y_prob.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "3dddb0df-3ea5-4e32-8cf0-45e90be8ba66",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "go.obo: fmt(1.2) rel(2025-03-16) 43,544 Terms\n",
+      "✓ Dados carregados: (1724, 602) proteínas × 602 GO terms\n",
+      "\n",
+      " Resultados finais (ESM-2 + PAM1 + propagação):\n",
+      "Fmax  = 0.6439\n",
+      "Thr.  = 0.34\n",
+      "AuPRC = 0.6948\n",
+      "Smin  = 14.1500\n"
+     ]
+    }
+   ],
+   "source": [
+    "# %%\n",
+    "import numpy as np\n",
+    "import joblib\n",
+    "import math\n",
+    "from goatools.obo_parser import GODag\n",
+    "from sklearn.metrics import precision_recall_curve, auc\n",
+    "\n",
+    "# --- 1. Carregar dados e parâmetros ---\n",
+    "GO_FILE = \"go.obo\"\n",
+    "THRESHOLDS = np.arange(0.0, 1.01, 0.01)\n",
+    "ALPHA = 0.5\n",
+    "\n",
+    "mlb = joblib.load(\"data/mlb.pkl\")\n",
+    "y_true = np.load(\"embeddings/y_test.npy\")\n",
+    "y_prob = np.load(\"predictions/mf-esm2.npy\")\n",
+    "terms = mlb.classes_\n",
+    "go_dag = GODag(GO_FILE)\n",
+    "\n",
+    "print(f\"✓ Dados carregados: {y_true.shape} proteínas × {len(terms)} GO terms\")\n",
+    "\n",
+    "# --- 2. Fmax ---\n",
+    "def compute_fmax(y_true, y_prob, thresholds):\n",
+    "    fmax, best_thr = 0, 0\n",
+    "    for t in thresholds:\n",
+    "        y_pred = (y_prob >= t).astype(int)\n",
+    "        tp = (y_true * y_pred).sum(axis=1)\n",
+    "        fp = ((1 - y_true) * y_pred).sum(axis=1)\n",
+    "        fn = (y_true * (1 - y_pred)).sum(axis=1)\n",
+    "        precision = tp / (tp + fp + 1e-8)\n",
+    "        recall    = tp / (tp + fn + 1e-8)\n",
+    "        f1 = 2 * precision * recall / (precision + recall + 1e-8)\n",
+    "        avg_f1 = np.mean(f1)\n",
+    "        if avg_f1 > fmax:\n",
+    "            fmax, best_thr = avg_f1, t\n",
+    "    return fmax, best_thr\n",
+    "\n",
+    "# --- 3. AuPRC (micro) ---\n",
+    "def compute_auprc(y_true, y_prob):\n",
+    "    precision, recall, _ = precision_recall_curve(y_true.ravel(), y_prob.ravel())\n",
+    "    return auc(recall, precision)\n",
+    "\n",
+    "# --- 4. Smin ---\n",
+    "def compute_smin(y_true, y_prob, terms, threshold, go_dag, alpha=ALPHA):\n",
+    "    y_pred = (y_prob >= threshold).astype(int)\n",
+    "\n",
+    "    # Informação semântica: IC (Information Content)\n",
+    "    ic = {}\n",
+    "    total = (y_true + y_pred).sum(axis=0).sum()\n",
+    "    for i, term in enumerate(terms):\n",
+    "        freq = (y_true[:, i] + y_pred[:, i]).sum()\n",
+    "        ic[term] = -np.log((freq + 1e-8) / total)\n",
+    "\n",
+    "    # Para cada proteína, calcular RU e MI\n",
+    "    s_values = []\n",
+    "    for true_vec, pred_vec in zip(y_true, y_pred):\n",
+    "        true_terms = {terms[i] for i in np.where(true_vec)[0]}\n",
+    "        pred_terms = {terms[i] for i in np.where(pred_vec)[0]}\n",
+    "\n",
+    "        anc_true = set()\n",
+    "        for t in true_terms:\n",
+    "            if t in go_dag:\n",
+    "                anc_true |= go_dag[t].get_all_parents()\n",
+    "        anc_pred = set()\n",
+    "        for t in pred_terms:\n",
+    "            if t in go_dag:\n",
+    "                anc_pred |= go_dag[t].get_all_parents()\n",
+    "\n",
+    "        ru = pred_terms - true_terms\n",
+    "        mi = true_terms - pred_terms\n",
+    "        dist_ru = sum(ic.get(t, 0) for t in ru)\n",
+    "        dist_mi = sum(ic.get(t, 0) for t in mi)\n",
+    "        s = math.sqrt((alpha * dist_ru)**2 + ((1 - alpha) * dist_mi)**2)\n",
+    "        s_values.append(s)\n",
+    "\n",
+    "    return np.mean(s_values)\n",
+    "\n",
+    "# --- 5. Avaliação ---\n",
+    "fmax, thr = compute_fmax(y_true, y_prob, THRESHOLDS)\n",
+    "auprc = compute_auprc(y_true, y_prob)\n",
+    "smin  = compute_smin(y_true, y_prob, terms, thr, go_dag)\n",
+    "\n",
+    "print(f\"\\n Resultados finais (ESM-2 + PAM1 + propagação):\")\n",
+    "print(f\"Fmax  = {fmax:.4f}\")\n",
+    "print(f\"Thr.  = {thr:.2f}\")\n",
+    "print(f\"AuPRC = {auprc:.4f}\")\n",
+    "print(f\"Smin  = {smin:.4f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1a1ea084-01de-4dc4-88da-e7ffeb8c94c9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/PAM1_protbert.ipynb

@@ -0,0 +1,935 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c6dbc330-062a-48f0-8242-3f21cc1c9c2b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "go.obo: fmt(1.2) rel(2025-03-16) 43,544 Terms\n",
+      "✓ Ficheiros criados:\n",
+      " - data/mf-training.csv : (31142, 3)\n",
+      " - data/mf-validation.csv: (1724, 3)\n",
+      " - data/mf-test.csv     : (1724, 3)\n",
+      "GO terms únicos (após propagação e filtro): 602\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from Bio import SeqIO\n",
+    "from collections import Counter\n",
+    "from goatools.obo_parser import GODag\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import MultiLabelBinarizer\n",
+    "from iterstrat.ml_stratifiers import MultilabelStratifiedKFold\n",
+    "import numpy as np\n",
+    "import os\n",
+    "\n",
+    "# --- 1. Carregar GO anotações ------------------------------------------\n",
+    "annotations = pd.read_csv(\"uniprot_sprot_exp.txt\", sep=\"\\t\", names=[\"protein_id\", \"go_term\", \"go_category\"])\n",
+    "annotations_f = annotations[annotations[\"go_category\"] == \"F\"]\n",
+    "\n",
+    "# --- 2. Carregar DAG e propagar GO terms -------------------------------\n",
+    "# propagação hierárquica\n",
+    "# https://geneontology.org/docs/download-ontology/\n",
+    "go_dag = GODag(\"go.obo\")\n",
+    "mf_terms = {t for t, o in go_dag.items() if o.namespace == \"molecular_function\"}\n",
+    "\n",
+    "def propagate_terms(term_list):\n",
+    "    full = set()\n",
+    "    for t in term_list:\n",
+    "        if t not in go_dag:\n",
+    "            continue\n",
+    "        full.add(t)\n",
+    "        full.update(go_dag[t].get_all_parents())\n",
+    "    return list(full & mf_terms)\n",
+    "\n",
+    "# --- 3. Carregar sequências --------------------------------------------\n",
+    "seqs, ids = [], []\n",
+    "for record in SeqIO.parse(\"uniprot_sprot_exp.fasta\", \"fasta\"):\n",
+    "    ids.append(record.id)\n",
+    "    seqs.append(str(record.seq))\n",
+    "\n",
+    "seq_df = pd.DataFrame({\"protein_id\": ids, \"sequence\": seqs})\n",
+    "\n",
+    "# --- 4. Juntar com GO anotado e propagar -------------------------------\n",
+    "grouped = annotations_f.groupby(\"protein_id\")[\"go_term\"].apply(list).reset_index()\n",
+    "data = seq_df.merge(grouped, on=\"protein_id\")\n",
+    "data = data[data[\"go_term\"].apply(len) > 0]\n",
+    "data[\"go_term\"] = data[\"go_term\"].apply(propagate_terms)\n",
+    "data = data[data[\"go_term\"].apply(len) > 0]\n",
+    "\n",
+    "# --- 5. Filtrar GO terms raros -----------------------------------------\n",
+    "# todos os terms com menos de 50 proteinas associadas\n",
+    "all_terms = [term for sublist in data[\"go_term\"] for term in sublist]\n",
+    "term_counts = Counter(all_terms)\n",
+    "valid_terms = {term for term, count in term_counts.items() if count >= 50}\n",
+    "data[\"go_term\"] = data[\"go_term\"].apply(lambda terms: [t for t in terms if t in valid_terms])\n",
+    "data = data[data[\"go_term\"].apply(len) > 0]\n",
+    "\n",
+    "# --- 6. Preparar dataset final -----------------------------------------\n",
+    "data[\"go_terms\"] = data[\"go_term\"].apply(lambda x: ';'.join(sorted(set(x))))\n",
+    "data = data[[\"protein_id\", \"sequence\", \"go_terms\"]].drop_duplicates()\n",
+    "\n",
+    "# --- 7. Binarizar labels e dividir -------------------------------------\n",
+    "mlb = MultiLabelBinarizer()\n",
+    "Y = mlb.fit_transform(data[\"go_terms\"].str.split(\";\"))\n",
+    "X = data[[\"protein_id\", \"sequence\"]].values\n",
+    "\n",
+    "mskf = MultilabelStratifiedKFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "train_idx, temp_idx = next(mskf.split(X, Y))\n",
+    "val_idx, test_idx = np.array_split(temp_idx, 2)\n",
+    "\n",
+    "df_train = data.iloc[train_idx].copy()\n",
+    "df_val   = data.iloc[val_idx].copy()\n",
+    "df_test  = data.iloc[test_idx].copy()\n",
+    "\n",
+    "# --- 8. Guardar em CSV -------------------------------------------------\n",
+    "os.makedirs(\"data\", exist_ok=True)\n",
+    "df_train.to_csv(\"data/mf-training.csv\", index=False)\n",
+    "df_val.to_csv(\"data/mf-validation.csv\", index=False)\n",
+    "df_test.to_csv(\"data/mf-test.csv\", index=False)\n",
+    "\n",
+    "# --- 9. Confirmar ------------------------------------------------------\n",
+    "print(\"✓ Ficheiros criados:\")\n",
+    "print(\" - data/mf-training.csv :\", df_train.shape)\n",
+    "print(\" - data/mf-validation.csv:\", df_val.shape)\n",
+    "print(\" - data/mf-test.csv     :\", df_test.shape)\n",
+    "print(f\"GO terms únicos (após propagação e filtro): {len(mlb.classes_)}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6cf7aaa6-4941-4951-8d73-1f4f1f4362f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Melvin\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "C:\\Users\\Melvin\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\transformers\\utils\\generic.py:441: FutureWarning: `torch.utils._pytree._register_pytree_node` is deprecated. Please use `torch.utils._pytree.register_pytree_node` instead.\n",
+      "  _torch_pytree._register_pytree_node(\n",
+      "100%|██████████| 31142/31142 [00:24<00:00, 1262.18it/s]\n",
+      "100%|██████████| 1724/1724 [00:00<00:00, 2628.24it/s]\n",
+      "C:\\Users\\Melvin\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\ktrain\\text\\preprocessor.py:382: UserWarning: The class_names argument is replacing the classes argument. Please update your code.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "preprocessing train...\n",
+      "language: de\n",
+      "train sequence lengths:\n",
+      "\tmean : 423\n",
+      "\t95percentile : 604\n",
+      "\t99percentile : 715\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Is Multi-Label? True\n",
+      "preprocessing test...\n",
+      "language: de\n",
+      "test sequence lengths:\n",
+      "\tmean : 408\n",
+      "\t95percentile : 603\n",
+      "\t99percentile : 714\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Melvin\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\ktrain\\text\\preprocessor.py:1093: UserWarning: Could not load a Tensorflow version of model. (If this worked before, it might be an out-of-memory issue.) Attempting to download/load PyTorch version as TensorFlow model using from_pt=True. You will need PyTorch installed for this.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "begin training using triangular learning rate policy with max lr of 1e-05...\n",
+      "Epoch 1/10\n",
+      "40995/40995 [==============================] - 13053s 318ms/step - loss: 0.0745 - binary_accuracy: 0.9866 - val_loss: 0.0582 - val_binary_accuracy: 0.9859\n",
+      "Epoch 2/10\n",
+      "40995/40995 [==============================] - 14484s 353ms/step - loss: 0.0504 - binary_accuracy: 0.9873 - val_loss: 0.0499 - val_binary_accuracy: 0.9867\n",
+      "Epoch 3/10\n",
+      "40995/40995 [==============================] - 14472s 353ms/step - loss: 0.0450 - binary_accuracy: 0.9879 - val_loss: 0.0449 - val_binary_accuracy: 0.9873\n",
+      "Epoch 4/10\n",
+      "40995/40995 [==============================] - 14445s 352ms/step - loss: 0.0407 - binary_accuracy: 0.9884 - val_loss: 0.0413 - val_binary_accuracy: 0.9878\n",
+      "Epoch 5/10\n",
+      "40995/40995 [==============================] - 12524s 305ms/step - loss: 0.0378 - binary_accuracy: 0.9888 - val_loss: 0.0394 - val_binary_accuracy: 0.9881\n",
+      "Epoch 6/10\n",
+      "40995/40995 [==============================] - 14737s 359ms/step - loss: 0.0359 - binary_accuracy: 0.9891 - val_loss: 0.0383 - val_binary_accuracy: 0.9883\n",
+      "Epoch 7/10\n",
+      "40995/40995 [==============================] - 20317s 495ms/step - loss: 0.0343 - binary_accuracy: 0.9894 - val_loss: 0.0371 - val_binary_accuracy: 0.9885\n",
+      "Epoch 8/10\n",
+      "40995/40995 [==============================] - 9073s 221ms/step - loss: 0.0331 - binary_accuracy: 0.9896 - val_loss: 0.0364 - val_binary_accuracy: 0.9887\n",
+      "Epoch 9/10\n",
+      "40995/40995 [==============================] - 9001s 219ms/step - loss: 0.0320 - binary_accuracy: 0.9898 - val_loss: 0.0360 - val_binary_accuracy: 0.9888\n",
+      "Epoch 10/10\n",
+      "40995/40995 [==============================] - 8980s 219ms/step - loss: 0.0311 - binary_accuracy: 0.9900 - val_loss: 0.0356 - val_binary_accuracy: 0.9890\n"
+     ]
+    },
+    {
+     "ename": "RuntimeError",
+     "evalue": "Can't decrement id ref count (unable to extend file properly)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+      "File \u001b[1;32m~\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\keras\\engine\\training.py:2252\u001b[0m, in \u001b[0;36mModel.save_weights\u001b[1;34m(self, filepath, overwrite, save_format, options)\u001b[0m\n\u001b[0;32m   2251\u001b[0m   \u001b[38;5;28;01mwith\u001b[39;00m h5py\u001b[38;5;241m.\u001b[39mFile(filepath, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m-> 2252\u001b[0m     \u001b[43mhdf5_format\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave_weights_to_hdf5_group\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlayers\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2253\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "File \u001b[1;32m~\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\keras\\saving\\hdf5_format.py:646\u001b[0m, in \u001b[0;36msave_weights_to_hdf5_group\u001b[1;34m(f, layers)\u001b[0m\n\u001b[0;32m    645\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 646\u001b[0m   param_dset[:] \u001b[38;5;241m=\u001b[39m val\n",
+      "File \u001b[1;32mh5py\\\\_objects.pyx:54\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mh5py\\\\_objects.pyx:55\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32m~\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\h5py\\_hl\\dataset.py:999\u001b[0m, in \u001b[0;36mDataset.__setitem__\u001b[1;34m(self, args, val)\u001b[0m\n\u001b[0;32m    998\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m fspace \u001b[38;5;129;01min\u001b[39;00m selection\u001b[38;5;241m.\u001b[39mbroadcast(mshape):\n\u001b[1;32m--> 999\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mid\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmspace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfspace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdxpl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dxpl\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mh5py\\\\_objects.pyx:54\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mh5py\\\\_objects.pyx:55\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mh5py\\\\h5d.pyx:282\u001b[0m, in \u001b[0;36mh5py.h5d.DatasetID.write\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mh5py\\\\_proxy.pyx:115\u001b[0m, in \u001b[0;36mh5py._proxy.dset_rw\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mOSError\u001b[0m: [Errno 28] Can't write data (file write failed: time = Wed May  7 10:48:36 2025\n, filename = 'mf-fine-tuned-protbert\\weights-10-0.04.hdf5', file descriptor = 4, errno = 28, error message = 'No space left on device', buf = 000002CC552FF040, total write size = 4194304, bytes this sub-write = 4194304, bytes actually written = 18446744073709551615, offset = 1180551864)",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[2], line 119\u001b[0m\n\u001b[0;32m    113\u001b[0m model   \u001b[38;5;241m=\u001b[39m t\u001b[38;5;241m.\u001b[39mget_classifier()\n\u001b[0;32m    114\u001b[0m learner \u001b[38;5;241m=\u001b[39m ktrain\u001b[38;5;241m.\u001b[39mget_learner(model,\n\u001b[0;32m    115\u001b[0m                              train_data\u001b[38;5;241m=\u001b[39mtrn,\n\u001b[0;32m    116\u001b[0m                              val_data\u001b[38;5;241m=\u001b[39mval,\n\u001b[0;32m    117\u001b[0m                              batch_size\u001b[38;5;241m=\u001b[39mBATCH_SIZE)\n\u001b[1;32m--> 119\u001b[0m \u001b[43mlearner\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautofit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1e-5\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    120\u001b[0m \u001b[43m                \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    121\u001b[0m \u001b[43m                \u001b[49m\u001b[43mearly_stopping\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    122\u001b[0m \u001b[43m                \u001b[49m\u001b[43mcheckpoint_folder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmf-fine-tuned-protbert\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\ktrain\\core.py:1239\u001b[0m, in \u001b[0;36mLearner.autofit\u001b[1;34m(self, lr, epochs, early_stopping, reduce_on_plateau, reduce_factor, cycle_momentum, max_momentum, min_momentum, monitor, checkpoint_folder, class_weight, callbacks, steps_per_epoch, verbose)\u001b[0m\n\u001b[0;32m   1234\u001b[0m policy \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtriangular learning rate\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1235\u001b[0m U\u001b[38;5;241m.\u001b[39mvprint(\n\u001b[0;32m   1236\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbegin training using \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m policy with max lr of \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m...\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (policy, lr),\n\u001b[0;32m   1237\u001b[0m     verbose\u001b[38;5;241m=\u001b[39mverbose,\n\u001b[0;32m   1238\u001b[0m )\n\u001b[1;32m-> 1239\u001b[0m hist \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1240\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1241\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1242\u001b[0m \u001b[43m    \u001b[49m\u001b[43mearly_stopping\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mearly_stopping\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1243\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcheckpoint_folder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcheckpoint_folder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1244\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1245\u001b[0m \u001b[43m    \u001b[49m\u001b[43mclass_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclass_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1246\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkcallbacks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1247\u001b[0m \u001b[43m    \u001b[49m\u001b[43msteps_per_epoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msteps_per_epoch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1248\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1249\u001b[0m hist\u001b[38;5;241m.\u001b[39mhistory[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlr\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m clr\u001b[38;5;241m.\u001b[39mhistory[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlr\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m   1250\u001b[0m hist\u001b[38;5;241m.\u001b[39mhistory[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miterations\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m clr\u001b[38;5;241m.\u001b[39mhistory[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miterations\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
+      "File \u001b[1;32m~\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\ktrain\\core.py:1650\u001b[0m, in \u001b[0;36mGenLearner.fit\u001b[1;34m(self, lr, n_cycles, cycle_len, cycle_mult, lr_decay, checkpoint_folder, early_stopping, class_weight, callbacks, steps_per_epoch, verbose)\u001b[0m\n\u001b[0;32m   1648\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mfilterwarnings(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m, message\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.*Check your callbacks.*\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1649\u001b[0m     fit_fn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mfit\n\u001b[1;32m-> 1650\u001b[0m     hist \u001b[38;5;241m=\u001b[39m \u001b[43mfit_fn\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1651\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_prepare\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_data\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1652\u001b[0m \u001b[43m        \u001b[49m\u001b[43msteps_per_epoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msteps_per_epoch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1653\u001b[0m \u001b[43m        \u001b[49m\u001b[43mvalidation_steps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidation_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1654\u001b[0m \u001b[43m        \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1655\u001b[0m \u001b[43m        \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_prepare\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mval_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1656\u001b[0m \u001b[43m        \u001b[49m\u001b[43mworkers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mworkers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1657\u001b[0m \u001b[43m        \u001b[49m\u001b[43muse_multiprocessing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43muse_multiprocessing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1658\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1659\u001b[0m \u001b[43m        \u001b[49m\u001b[43mshuffle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m   1660\u001b[0m \u001b[43m        \u001b[49m\u001b[43mclass_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclass_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1661\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkcallbacks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1662\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1663\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sgdr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1664\u001b[0m     hist\u001b[38;5;241m.\u001b[39mhistory[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlr\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m sgdr\u001b[38;5;241m.\u001b[39mhistory[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlr\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
+      "File \u001b[1;32m~\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\keras\\engine\\training.py:1230\u001b[0m, in \u001b[0;36mModel.fit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1227\u001b[0m   val_logs \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m name: val \u001b[38;5;28;01mfor\u001b[39;00m name, val \u001b[38;5;129;01min\u001b[39;00m val_logs\u001b[38;5;241m.\u001b[39mitems()}\n\u001b[0;32m   1228\u001b[0m   epoch_logs\u001b[38;5;241m.\u001b[39mupdate(val_logs)\n\u001b[1;32m-> 1230\u001b[0m \u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mon_epoch_end\u001b[49m\u001b[43m(\u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepoch_logs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1231\u001b[0m training_logs \u001b[38;5;241m=\u001b[39m epoch_logs\n\u001b[0;32m   1232\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstop_training:\n",
+      "File \u001b[1;32m~\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\keras\\callbacks.py:413\u001b[0m, in \u001b[0;36mCallbackList.on_epoch_end\u001b[1;34m(self, epoch, logs)\u001b[0m\n\u001b[0;32m    411\u001b[0m logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_logs(logs)\n\u001b[0;32m    412\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m callback \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallbacks:\n\u001b[1;32m--> 413\u001b[0m   \u001b[43mcallback\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mon_epoch_end\u001b[49m\u001b[43m(\u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\keras\\callbacks.py:1368\u001b[0m, in \u001b[0;36mModelCheckpoint.on_epoch_end\u001b[1;34m(self, epoch, logs)\u001b[0m\n\u001b[0;32m   1366\u001b[0m \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[0;32m   1367\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave_freq \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mepoch\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m-> 1368\u001b[0m   \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_save_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mepoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlogs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\keras\\callbacks.py:1431\u001b[0m, in \u001b[0;36mModelCheckpoint._save_model\u001b[1;34m(self, epoch, batch, logs)\u001b[0m\n\u001b[0;32m   1429\u001b[0m   \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mEpoch \u001b[39m\u001b[38;5;132;01m%05d\u001b[39;00m\u001b[38;5;124m: saving model to \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m (epoch \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m, filepath))\n\u001b[0;32m   1430\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave_weights_only:\n\u001b[1;32m-> 1431\u001b[0m   \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave_weights\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1432\u001b[0m \u001b[43m      \u001b[49m\u001b[43mfilepath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moverwrite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_options\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1433\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1434\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39msave(filepath, overwrite\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, options\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_options)\n",
+      "File \u001b[1;32m~\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\keras\\engine\\training.py:2252\u001b[0m, in \u001b[0;36mModel.save_weights\u001b[1;34m(self, filepath, overwrite, save_format, options)\u001b[0m\n\u001b[0;32m   2250\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m save_format \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mh5\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m   2251\u001b[0m   \u001b[38;5;28;01mwith\u001b[39;00m h5py\u001b[38;5;241m.\u001b[39mFile(filepath, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m-> 2252\u001b[0m     hdf5_format\u001b[38;5;241m.\u001b[39msave_weights_to_hdf5_group(f, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlayers)\n\u001b[0;32m   2253\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   2254\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n",
+      "File \u001b[1;32mh5py\\\\_objects.pyx:54\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mh5py\\\\_objects.pyx:55\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32m~\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\h5py\\_hl\\files.py:599\u001b[0m, in \u001b[0;36mFile.__exit__\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    596\u001b[0m \u001b[38;5;129m@with_phil\u001b[39m\n\u001b[0;32m    597\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__exit__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs):\n\u001b[0;32m    598\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mid:\n\u001b[1;32m--> 599\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclose\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\h5py\\_hl\\files.py:581\u001b[0m, in \u001b[0;36mFile.close\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    575\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mid\u001b[38;5;241m.\u001b[39mvalid:\n\u001b[0;32m    576\u001b[0m     \u001b[38;5;66;03m# We have to explicitly murder all open objects related to the file\u001b[39;00m\n\u001b[0;32m    577\u001b[0m \n\u001b[0;32m    578\u001b[0m     \u001b[38;5;66;03m# Close file-resident objects first, then the files.\u001b[39;00m\n\u001b[0;32m    579\u001b[0m     \u001b[38;5;66;03m# Otherwise we get errors in MPI mode.\u001b[39;00m\n\u001b[0;32m    580\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mid\u001b[38;5;241m.\u001b[39m_close_open_objects(h5f\u001b[38;5;241m.\u001b[39mOBJ_LOCAL \u001b[38;5;241m|\u001b[39m \u001b[38;5;241m~\u001b[39mh5f\u001b[38;5;241m.\u001b[39mOBJ_FILE)\n\u001b[1;32m--> 581\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mid\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_close_open_objects\u001b[49m\u001b[43m(\u001b[49m\u001b[43mh5f\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mOBJ_LOCAL\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m|\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mh5f\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mOBJ_FILE\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    583\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mid\u001b[38;5;241m.\u001b[39mclose()\n\u001b[0;32m    584\u001b[0m     _objects\u001b[38;5;241m.\u001b[39mnonlocal_close()\n",
+      "File \u001b[1;32mh5py\\\\_objects.pyx:54\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mh5py\\\\_objects.pyx:55\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mh5py\\\\h5f.pyx:355\u001b[0m, in \u001b[0;36mh5py.h5f.FileID._close_open_objects\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mRuntimeError\u001b[0m: Can't decrement id ref count (unable to extend file properly)"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from tqdm import tqdm\n",
+    "import random\n",
+    "import os\n",
+    "import ktrain\n",
+    "from ktrain import text\n",
+    "from sklearn.preprocessing import MultiLabelBinarizer\n",
+    "\n",
+    "\n",
+    "# PAM1\n",
+    "# PAM matrix model of protein evolution\n",
+    "# DOI:10.1093/oxfordjournals.molbev.a040360\n",
+    "pam_data = {\n",
+    "    'A': [9948, 19, 27, 42, 31, 46, 50, 92, 17, 7, 40, 88, 42, 41, 122, 279, 255, 9, 72, 723],\n",
+    "    'R': [14, 9871, 24, 38, 37, 130, 38, 62, 49, 4, 58, 205, 26, 33, 47, 103, 104, 5, 36, 52],\n",
+    "    'N': [20, 22, 9860, 181, 29, 36, 41, 67, 31, 5, 22, 49, 23, 10, 33, 83, 66, 3, 43, 32],\n",
+    "    'D': [40, 34, 187, 9818, 11, 63, 98, 61, 23, 5, 25, 54, 43, 13, 27, 88, 55, 4, 29, 36],\n",
+    "    'C': [20, 16, 26, 9, 9987, 10, 17, 37, 12, 2, 16, 26, 10, 19, 27, 26, 25, 2, 6, 67],\n",
+    "    'Q': [29, 118, 29, 49, 8, 9816, 72, 55, 36, 4, 60, 158, 35, 22, 39, 86, 74, 3, 34, 28],\n",
+    "    'E': [35, 29, 41, 101, 12, 71, 9804, 56, 33, 5, 36, 107, 42, 20, 38, 87, 69, 4, 30, 42],\n",
+    "    'G': [96, 61, 77, 70, 38, 51, 58, 9868, 26, 6, 37, 53, 39, 28, 69, 134, 116, 5, 47, 60],\n",
+    "    'H': [17, 53, 33, 19, 15, 39, 34, 24, 9907, 3, 32, 57, 24, 15, 27, 47, 43, 2, 22, 19],\n",
+    "    'I': [6, 3, 6, 6, 3, 5, 6, 7, 3, 9973, 23, 13, 12, 41, 93, 84, 115, 3, 8, 102],\n",
+    "    'L': [26, 39, 17, 15, 7, 33, 22, 20, 19, 27, 9864, 49, 24, 78, 117, 148, 193, 5, 24, 70],\n",
+    "    'K': [60, 198, 43, 52, 12, 142, 96, 53, 42, 10, 63, 9710, 33, 26, 54, 109, 102, 5, 43, 42],\n",
+    "    'M': [21, 22, 15, 18, 6, 20, 18, 18, 17, 11, 27, 32, 9945, 26, 34, 61, 71, 3, 12, 31],\n",
+    "    'F': [18, 17, 8, 6, 8, 11, 10, 16, 10, 44, 92, 24, 29, 9899, 89, 88, 142, 7, 14, 68],\n",
+    "    'P': [97, 47, 35, 29, 23, 35, 38, 57, 21, 24, 47, 56, 28, 76, 9785, 115, 77, 4, 24, 35],\n",
+    "    'S': [241, 87, 76, 73, 17, 56, 60, 99, 32, 13, 69, 92, 42, 67, 100, 9605, 212, 8, 63, 70],\n",
+    "    'T': [186, 78, 54, 37, 14, 42, 42, 83, 28, 23, 84, 85, 53, 93, 66, 182, 9676, 8, 39, 90],\n",
+    "    'W': [2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 1, 5, 3, 4, 4, 9960, 3, 4],\n",
+    "    'Y': [29, 21, 17, 9, 4, 13, 9, 21, 10, 7, 20, 17, 11, 23, 19, 41, 31, 3, 9935, 23],\n",
+    "    'V': [368, 27, 18, 18, 50, 23, 34, 64, 15, 85, 72, 42, 33, 88, 42, 112, 137, 4, 20, 9514]\n",
+    "}\n",
+    "pam_raw = pd.DataFrame(pam_data, index=list(pam_data.keys()))\n",
+    "pam_matrix = pam_raw.div(pam_raw.sum(axis=1), axis=0)\n",
+    "list_amino = pam_raw.columns.tolist()\n",
+    "pam_dict = {\n",
+    "    aa: {sub: pam_matrix.loc[aa, sub] for sub in list_amino}\n",
+    "    for aa in list_amino\n",
+    "}\n",
+    "\n",
+    "def pam1_substitution(aa):\n",
+    "    if aa not in pam_dict:\n",
+    "        return aa\n",
+    "    subs = list(pam_dict[aa].keys())\n",
+    "    probs = list(pam_dict[aa].values())\n",
+    "    return np.random.choice(subs, p=probs)\n",
+    "\n",
+    "def augment_sequence(seq, sub_prob=0.05):\n",
+    "    return ''.join([pam1_substitution(aa) if random.random() < sub_prob else aa for aa in seq])\n",
+    "\n",
+    "def slice_sequence(seq, win=500, min_overlap=250):\n",
+    "    if len(seq) <= win:\n",
+    "        return [seq]\n",
+    "    slices, start = [], 0\n",
+    "    while start + win <= len(seq):\n",
+    "        slices.append(seq[start:start+win])\n",
+    "        start += win\n",
+    "    leftover = seq[start:]\n",
+    "    if leftover and len(leftover) >= min_overlap and len(slices[-1]) >= min_overlap:\n",
+    "        extra = slices[-1][-min_overlap:] + leftover\n",
+    "        slices.append(extra)\n",
+    "    return slices\n",
+    "\n",
+    "def generate_data(df, augment=False):\n",
+    "    X, y = [], []\n",
+    "    label_cols = [col for col in df.columns if col.startswith(\"GO:\")]\n",
+    "    for _, row in tqdm(df.iterrows(), total=len(df)):\n",
+    "        seq = row[\"sequence\"]\n",
+    "        if augment:\n",
+    "            seq = augment_sequence(seq)\n",
+    "        seq_slices = slice_sequence(seq)\n",
+    "        X.extend(seq_slices)\n",
+    "        lbl = row[label_cols].values.astype(int)\n",
+    "        y.extend([lbl] * len(seq_slices))\n",
+    "    return X, np.array(y), label_cols\n",
+    "\n",
+    "def format_sequence(seq): return \" \".join(list(seq))\n",
+    "\n",
+    "# Função para carregar e binarizar\n",
+    "def load_and_binarize(csv_path, mlb=None):\n",
+    "    df = pd.read_csv(csv_path)\n",
+    "    df[\"go_terms\"] = df[\"go_terms\"].str.split(\";\")\n",
+    "    if mlb is None:\n",
+    "        mlb = MultiLabelBinarizer()\n",
+    "        labels = mlb.fit_transform(df[\"go_terms\"])\n",
+    "    else:\n",
+    "        labels = mlb.transform(df[\"go_terms\"])\n",
+    "    labels_df = pd.DataFrame(labels, columns=mlb.classes_)\n",
+    "    df = df.reset_index(drop=True).join(labels_df)\n",
+    "    return df, mlb\n",
+    "\n",
+    "# Carregar os dados\n",
+    "df_train, mlb = load_and_binarize(\"data/mf-training.csv\")\n",
+    "df_val, _     = load_and_binarize(\"data/mf-validation.csv\", mlb=mlb)\n",
+    "\n",
+    "# Gerar com augmentation no treino\n",
+    "X_train, y_train, term_cols = generate_data(df_train, augment=True)\n",
+    "X_val,   y_val, _           = generate_data(df_val,   augment=False)\n",
+    "\n",
+    "# Preparar texto para tokenizer\n",
+    "X_train_fmt = list(map(format_sequence, X_train))\n",
+    "X_val_fmt   = list(map(format_sequence, X_val))\n",
+    "\n",
+    "# Fine-tune ProtBERT\n",
+    "# https://huggingface.co/Rostlab/prot_bert\n",
+    "# https://doi.org/10.1093/bioinformatics/btac020\n",
+    "# dados de treino-> UniRef100 (216 milhões de sequências)\n",
+    "MODEL_NAME = \"Rostlab/prot_bert\"\n",
+    "MAX_LEN = 512\n",
+    "BATCH_SIZE = 1\n",
+    "\n",
+    "t = text.Transformer(MODEL_NAME, maxlen=MAX_LEN, classes=term_cols)\n",
+    "trn = t.preprocess_train(X_train_fmt, y_train)\n",
+    "val = t.preprocess_test(X_val_fmt, y_val)\n",
+    "\n",
+    "model   = t.get_classifier()\n",
+    "learner = ktrain.get_learner(model,\n",
+    "                             train_data=trn,\n",
+    "                             val_data=val,\n",
+    "                             batch_size=BATCH_SIZE)\n",
+    "\n",
+    "learner.autofit(lr=1e-5,\n",
+    "                epochs=10,\n",
+    "                early_stopping=1,\n",
+    "                checkpoint_folder=\"mf-fine-tuned-protbert\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c66774b3-6cf0-41c5-bb01-9467a5283102",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Existe: weights/mf-fine-tuned-protbert-epoch10\n",
+      "📁 Conteúdo:\n",
+      " - config.json\n",
+      " - tf_model.h5\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "path = \"weights/mf-fine-tuned-protbert-epoch10\"\n",
+    "\n",
+    "if os.path.exists(path):\n",
+    "    print(f\"✅ Existe: {path}\")\n",
+    "    print(\"📁 Conteúdo:\")\n",
+    "    for f in os.listdir(path):\n",
+    "        print(\" -\", f)\n",
+    "else:\n",
+    "    print(f\"❌ Não existe: {path}\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "9b39c439-5708-4787-bfee-d3a4d3aa190d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Melvin\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "C:\\Users\\Melvin\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\transformers\\utils\\generic.py:441: FutureWarning: `torch.utils._pytree._register_pytree_node` is deprecated. Please use `torch.utils._pytree.register_pytree_node` instead.\n",
+      "  _torch_pytree._register_pytree_node(\n",
+      "C:\\Users\\Melvin\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\huggingface_hub\\file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
+      "  warnings.warn(\n",
+      "C:\\Users\\Melvin\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\transformers\\utils\\generic.py:309: FutureWarning: `torch.utils._pytree._register_pytree_node` is deprecated. Please use `torch.utils._pytree.register_pytree_node` instead.\n",
+      "  _torch_pytree._register_pytree_node(\n",
+      "Some layers from the model checkpoint at weights/mf-fine-tuned-protbert-epoch10 were not used when initializing TFBertModel: ['classifier', 'dropout_183']\n",
+      "- This IS expected if you are initializing TFBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing TFBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+      "All the layers of TFBertModel were initialized from the model checkpoint at weights/mf-fine-tuned-protbert-epoch10.\n",
+      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ Tokenizer base e modelo fine-tuned carregados com sucesso\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processando data/mf-training.csv:   0%|          | 25/31142 [00:06<2:23:28,  3.61it/s]\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[19], line 78\u001b[0m\n\u001b[0;32m     75\u001b[0m \u001b[38;5;66;03m# --- 4. Aplicar -----------------------------------------------------------\u001b[39;00m\n\u001b[0;32m     76\u001b[0m os\u001b[38;5;241m.\u001b[39mmakedirs(OUT_DIR, exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m---> 78\u001b[0m \u001b[43mprocess_split\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata/mf-training.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m   \u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mOUT_DIR\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtrain_protbert.pkl\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     79\u001b[0m process_split(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata/mf-validation.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m, os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(OUT_DIR, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mval_protbert.pkl\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m     80\u001b[0m process_split(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata/mf-test.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m,       os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(OUT_DIR, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest_protbert.pkl\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n",
+      "Cell \u001b[1;32mIn[19], line 61\u001b[0m, in \u001b[0;36mprocess_split\u001b[1;34m(csv_path, out_path)\u001b[0m\n\u001b[0;32m     59\u001b[0m     embeds\u001b[38;5;241m.\u001b[39mappend(prot_embed\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39mfloat32))\n\u001b[0;32m     60\u001b[0m     labels\u001b[38;5;241m.\u001b[39mappend(row[label_cols]\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39mint8))\n\u001b[1;32m---> 61\u001b[0m     gc\u001b[38;5;241m.\u001b[39mcollect()\n\u001b[0;32m     63\u001b[0m embeds \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mvstack(embeds)\n\u001b[0;32m     64\u001b[0m labels \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mvstack(labels)\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from tqdm import tqdm\n",
+    "import joblib\n",
+    "import gc\n",
+    "from transformers import AutoTokenizer, TFAutoModel\n",
+    "\n",
+    "# --- 1. Parâmetros --------------------------------------------------------\n",
+    "MODEL_DIR = \"weights/mf-fine-tuned-protbert-epoch10\"\n",
+    "BASE_MODEL = \"Rostlab/prot_bert\"\n",
+    "OUT_DIR   = \"embeddings\"\n",
+    "BATCH_TOK = 16\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, do_lower_case=False)\n",
+    "model     = TFAutoModel.from_pretrained(MODEL_DIR, from_pt=False)\n",
+    "\n",
+    "print(\"✓ Tokenizer base e modelo fine-tuned carregados com sucesso\")\n",
+    "\n",
+    "# --- 3. Funções auxiliares ------------------------------------------------\n",
+    "def format_sequence(seq):\n",
+    "    return \" \".join(list(seq))\n",
+    "\n",
+    "def slice_sequence(seq, win=500, min_overlap=250):\n",
+    "    if len(seq) <= win:\n",
+    "        return [seq]\n",
+    "    slices, start = [], 0\n",
+    "    while start + win <= len(seq):\n",
+    "        slices.append(seq[start:start+win])\n",
+    "        start += win\n",
+    "    leftover = seq[start:]\n",
+    "    if leftover and len(leftover) >= min_overlap and len(slices[-1]) >= min_overlap:\n",
+    "        extra = slices[-1][-min_overlap:] + leftover\n",
+    "        slices.append(extra)\n",
+    "    return slices\n",
+    "\n",
+    "def get_embeddings(batch, tokenizer, model):\n",
+    "    tokens = tokenizer(batch, return_tensors=\"tf\", padding=True, truncation=True, max_length=512)\n",
+    "    output = model(**tokens)\n",
+    "    return output.last_hidden_state[:, 0, :].numpy()\n",
+    "\n",
+    "def process_split(csv_path, out_path):\n",
+    "    df = pd.read_csv(csv_path)\n",
+    "    label_cols = [col for col in df.columns if col.startswith(\"GO:\")]\n",
+    "    prot_ids, embeds, labels = [], [], []\n",
+    "\n",
+    "    for _, row in tqdm(df.iterrows(), total=len(df), desc=f\"Processando {csv_path}\"):\n",
+    "        slices     = slice_sequence(row[\"sequence\"])\n",
+    "        slices_fmt = list(map(format_sequence, slices))\n",
+    "\n",
+    "        slice_embeds = []\n",
+    "        for i in range(0, len(slices_fmt), BATCH_TOK):\n",
+    "            batch = slices_fmt[i:i+BATCH_TOK]\n",
+    "            slice_embeds.append(get_embeddings(batch, tokenizer, model))\n",
+    "        slice_embeds = np.vstack(slice_embeds)\n",
+    "\n",
+    "        prot_embed = slice_embeds.mean(axis=0)\n",
+    "        prot_ids.append(row[\"protein_id\"])\n",
+    "        embeds.append(prot_embed.astype(np.float32))\n",
+    "        labels.append(row[label_cols].values.astype(np.int8))\n",
+    "        gc.collect()\n",
+    "\n",
+    "    embeds = np.vstack(embeds)\n",
+    "    labels = np.vstack(labels)\n",
+    "\n",
+    "    joblib.dump({\n",
+    "        \"protein_ids\": prot_ids,\n",
+    "        \"embeddings\": embeds,\n",
+    "        \"labels\": labels,\n",
+    "        \"go_terms\": label_cols\n",
+    "    }, out_path, compress=3)\n",
+    "\n",
+    "    print(f\"✓ Guardado {out_path} — {embeds.shape[0]} proteínas\")\n",
+    "\n",
+    "# --- 4. Aplicar -----------------------------------------------------------\n",
+    "os.makedirs(OUT_DIR, exist_ok=True)\n",
+    "\n",
+    "process_split(\"data/mf-training.csv\",   os.path.join(OUT_DIR, \"train_protbert.pkl\"))\n",
+    "process_split(\"data/mf-validation.csv\", os.path.join(OUT_DIR, \"val_protbert.pkl\"))\n",
+    "process_split(\"data/mf-test.csv\",       os.path.join(OUT_DIR, \"test_protbert.pkl\"))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "ad0c5421-e0a1-4a6a-8ace-2c69aeab0e0d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ Corrigido: embeddings/train_protbert.pkl — 31142 exemplos, 597 GO terms\n",
+      "✓ Corrigido: embeddings/val_protbert.pkl — 1724 exemplos, 597 GO terms\n",
+      "✓ Corrigido: embeddings/test_protbert.pkl — 1724 exemplos, 597 GO terms\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import joblib\n",
+    "from sklearn.preprocessing import MultiLabelBinarizer\n",
+    "\n",
+    "# --- 1. Obter GO terms do ficheiro de teste --------------------------------\n",
+    "df_test = pd.read_csv(\"data/mf-test.csv\")\n",
+    "test_terms = sorted(set(term for row in df_test[\"go_terms\"].str.split(\";\") for term in row))\n",
+    "\n",
+    "# --- 2. Função para corrigir um .pkl com base nos GO terms do teste --------\n",
+    "def patch_to_common_terms(csv_path, pkl_path, common_terms):\n",
+    "    df = pd.read_csv(csv_path)\n",
+    "    terms_split = df[\"go_terms\"].str.split(\";\")\n",
+    "    \n",
+    "    # Apenas termos presentes nos common_terms\n",
+    "    terms_filtered = terms_split.apply(lambda lst: [t for t in lst if t in common_terms])\n",
+    "    \n",
+    "    mlb = MultiLabelBinarizer(classes=common_terms)\n",
+    "    Y = mlb.fit_transform(terms_filtered)\n",
+    "\n",
+    "    data = joblib.load(pkl_path)\n",
+    "    data[\"labels\"] = Y\n",
+    "    data[\"go_terms\"] = mlb.classes_.tolist()\n",
+    "    \n",
+    "    joblib.dump(data, pkl_path, compress=3)\n",
+    "    print(f\"✓ Corrigido: {pkl_path} — {Y.shape[0]} exemplos, {Y.shape[1]} GO terms\")\n",
+    "\n",
+    "# --- 3. Aplicar às 3 partições --------------------------------------------\n",
+    "patch_to_common_terms(\"data/mf-training.csv\",   \"embeddings/train_protbert.pkl\", test_terms)\n",
+    "patch_to_common_terms(\"data/mf-validation.csv\", \"embeddings/val_protbert.pkl\",   test_terms)\n",
+    "patch_to_common_terms(\"data/mf-test.csv\",       \"embeddings/test_protbert.pkl\",  test_terms)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "dbd5c35f-4a08-4906-9cf4-e1df501d1ecb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import joblib\n",
+    "train = joblib.load(\"embeddings/train_protbert.pkl\")\n",
+    "val   = joblib.load(\"embeddings/val_protbert.pkl\")\n",
+    "test  = joblib.load(\"embeddings/test_protbert.pkl\")\n",
+    "\n",
+    "X_train, y_train = train[\"embeddings\"], train[\"labels\"]\n",
+    "X_val,   y_val   = val[\"embeddings\"],   val[\"labels\"]\n",
+    "X_test,  y_test  = test[\"embeddings\"],  test[\"labels\"]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1785d8a9-23fc-4490-8d71-29cc91a4cb57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ Embeddings carregados: (31142, 1024) → 597 GO terms\n",
+      "Epoch 1/100\n",
+      "974/974 [==============================] - 4s 3ms/step - loss: 0.0358 - binary_accuracy: 0.9893 - val_loss: 0.0336 - val_binary_accuracy: 0.9901\n",
+      "Epoch 2/100\n",
+      "974/974 [==============================] - 3s 3ms/step - loss: 0.0276 - binary_accuracy: 0.9914 - val_loss: 0.0331 - val_binary_accuracy: 0.9902\n",
+      "Epoch 3/100\n",
+      "974/974 [==============================] - 3s 3ms/step - loss: 0.0268 - binary_accuracy: 0.9916 - val_loss: 0.0330 - val_binary_accuracy: 0.9902\n",
+      "Epoch 4/100\n",
+      "974/974 [==============================] - 3s 3ms/step - loss: 0.0264 - binary_accuracy: 0.9917 - val_loss: 0.0320 - val_binary_accuracy: 0.9904\n",
+      "Epoch 5/100\n",
+      "974/974 [==============================] - 3s 3ms/step - loss: 0.0260 - binary_accuracy: 0.9917 - val_loss: 0.0319 - val_binary_accuracy: 0.9904\n",
+      "Epoch 6/100\n",
+      "974/974 [==============================] - 3s 3ms/step - loss: 0.0256 - binary_accuracy: 0.9918 - val_loss: 0.0322 - val_binary_accuracy: 0.9904\n",
+      "Epoch 7/100\n",
+      "974/974 [==============================] - 3s 3ms/step - loss: 0.0255 - binary_accuracy: 0.9918 - val_loss: 0.0317 - val_binary_accuracy: 0.9903\n",
+      "Epoch 8/100\n",
+      "974/974 [==============================] - 3s 3ms/step - loss: 0.0252 - binary_accuracy: 0.9919 - val_loss: 0.0320 - val_binary_accuracy: 0.9905\n",
+      "Epoch 9/100\n",
+      "974/974 [==============================] - 3s 3ms/step - loss: 0.0251 - binary_accuracy: 0.9919 - val_loss: 0.0316 - val_binary_accuracy: 0.9904\n",
+      "Epoch 10/100\n",
+      "974/974 [==============================] - 3s 3ms/step - loss: 0.0250 - binary_accuracy: 0.9920 - val_loss: 0.0314 - val_binary_accuracy: 0.9905\n",
+      "Epoch 11/100\n",
+      "974/974 [==============================] - 3s 3ms/step - loss: 0.0248 - binary_accuracy: 0.9920 - val_loss: 0.0317 - val_binary_accuracy: 0.9905\n",
+      "Epoch 12/100\n",
+      "974/974 [==============================] - 3s 3ms/step - loss: 0.0247 - binary_accuracy: 0.9920 - val_loss: 0.0315 - val_binary_accuracy: 0.9905\n",
+      "Epoch 13/100\n",
+      "974/974 [==============================] - 3s 3ms/step - loss: 0.0246 - binary_accuracy: 0.9920 - val_loss: 0.0322 - val_binary_accuracy: 0.9904\n",
+      "Epoch 14/100\n",
+      "974/974 [==============================] - 3s 3ms/step - loss: 0.0245 - binary_accuracy: 0.9920 - val_loss: 0.0319 - val_binary_accuracy: 0.9905\n",
+      "Epoch 15/100\n",
+      "974/974 [==============================] - 3s 3ms/step - loss: 0.0244 - binary_accuracy: 0.9920 - val_loss: 0.0319 - val_binary_accuracy: 0.9906\n",
+      "Previsões guardadas em mf-protbert-pam1.npy\n",
+      "Modelo guardado em models/protbert_mlp.keras\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "import joblib\n",
+    "import numpy as np\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Dropout\n",
+    "from tensorflow.keras.callbacks import EarlyStopping\n",
+    "\n",
+    "# --- 1. Carregar embeddings ----------------------------------------------\n",
+    "train = joblib.load(\"embeddings/train_protbert.pkl\")\n",
+    "val   = joblib.load(\"embeddings/val_protbert.pkl\")\n",
+    "test  = joblib.load(\"embeddings/test_protbert.pkl\")\n",
+    "\n",
+    "X_train, y_train = train[\"embeddings\"], train[\"labels\"]\n",
+    "X_val,   y_val   = val[\"embeddings\"],   val[\"labels\"]\n",
+    "X_test,  y_test  = test[\"embeddings\"],  test[\"labels\"]\n",
+    "\n",
+    "print(f\"✓ Embeddings carregados: {X_train.shape} → {y_train.shape[1]} GO terms\")\n",
+    "\n",
+    "# --- 2. Garantir consistência de classes ---------------------------------\n",
+    "max_classes = y_train.shape[1]  # 602 GO terms (do treino)\n",
+    "\n",
+    "def pad_labels(y, target_dim=max_classes):\n",
+    "    if y.shape[1] < target_dim:\n",
+    "        padding = np.zeros((y.shape[0], target_dim - y.shape[1]), dtype=np.int8)\n",
+    "        return np.hstack([y, padding])\n",
+    "    return y\n",
+    "\n",
+    "y_val  = pad_labels(y_val)\n",
+    "y_test = pad_labels(y_test)\n",
+    "\n",
+    "# --- 3. Modelo MLP ------------------------------------------------------\n",
+    "model = Sequential([\n",
+    "    Dense(1024, activation=\"relu\", input_shape=(X_train.shape[1],)),\n",
+    "    Dropout(0.3),\n",
+    "    Dense(512, activation=\"relu\"),\n",
+    "    Dropout(0.3),\n",
+    "    Dense(max_classes, activation=\"sigmoid\")\n",
+    "])\n",
+    "\n",
+    "model.compile(loss=\"binary_crossentropy\",\n",
+    "              optimizer=\"adam\",\n",
+    "              metrics=[\"binary_accuracy\"])\n",
+    "\n",
+    "# --- 4. Early stopping e treino -----------------------------------------\n",
+    "callbacks = [\n",
+    "    EarlyStopping(monitor=\"val_loss\", patience=5, restore_best_weights=True)\n",
+    "]\n",
+    "\n",
+    "model.fit(X_train, y_train,\n",
+    "          validation_data=(X_val, y_val),\n",
+    "          epochs=100,\n",
+    "          batch_size=32,\n",
+    "          callbacks=callbacks,\n",
+    "          verbose=1)\n",
+    "\n",
+    "# --- 5. Previsões --------------------------------------------------------\n",
+    "y_prob = model.predict(X_test)\n",
+    "np.save(\"predictions/mf-protbert-pam1.npy\", y_prob)\n",
+    "print(\"Previsões guardadas em mf-protbert-pam1.npy\")\n",
+    "\n",
+    "# --- 6. Modelo ----------------------------------------------------------\n",
+    "model.save(\"models/protbert_mlp.keras\")\n",
+    "print(\"Modelo guardado em models/protbert_mlp.keras\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "fdb66630-76dc-43a0-bd56-45052175fdba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "go.obo: fmt(1.2) rel(2025-03-16) 43,544 Terms\n",
+      "✓ Embeddings: (1724, 597) labels × 597 GO terms\n",
+      "\n",
+      "📊 Resultados finais (ProtBERT + PAM1 + propagação):\n",
+      "Fmax  = 0.6666\n",
+      "Thr.  = 0.50\n",
+      "AuPRC = 0.7028\n",
+      "Smin  = 13.1745\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.metrics import precision_recall_curve, auc\n",
+    "from goatools.obo_parser import GODag\n",
+    "import joblib\n",
+    "import math\n",
+    "\n",
+    "# --- 1. Parâmetros -------------------------------------------------------\n",
+    "GO_FILE = \"go.obo\"\n",
+    "THRESHOLDS = np.arange(0.0, 1.01, 0.01)\n",
+    "ALPHA = 0.5\n",
+    "\n",
+    "# --- 2. Carregar dados ---------------------------------------------------\n",
+    "test = joblib.load(\"embeddings/test_protbert.pkl\")\n",
+    "y_true = test[\"labels\"]\n",
+    "terms  = test[\"go_terms\"]\n",
+    "y_prob = np.load(\"predictions/mf-protbert-pam1.npy\")\n",
+    "go_dag = GODag(GO_FILE)\n",
+    "\n",
+    "print(f\"✓ Embeddings: {y_true.shape} labels × {len(terms)} GO terms\")\n",
+    "\n",
+    "# --- 3. Fmax -------------------------------------------------------------\n",
+    "def compute_fmax(y_true, y_prob, thresholds):\n",
+    "    fmax, best_thr = 0, 0\n",
+    "    for t in thresholds:\n",
+    "        y_pred = (y_prob >= t).astype(int)\n",
+    "        tp = (y_true * y_pred).sum(axis=1)\n",
+    "        fp = ((1 - y_true) * y_pred).sum(axis=1)\n",
+    "        fn = (y_true * (1 - y_pred)).sum(axis=1)\n",
+    "        precision = tp / (tp + fp + 1e-8)\n",
+    "        recall    = tp / (tp + fn + 1e-8)\n",
+    "        f1 = 2 * precision * recall / (precision + recall + 1e-8)\n",
+    "        avg_f1 = np.mean(f1)\n",
+    "        if avg_f1 > fmax:\n",
+    "            fmax, best_thr = avg_f1, t\n",
+    "    return fmax, best_thr\n",
+    "\n",
+    "# --- 4. AuPRC micro ------------------------------------------------------\n",
+    "def compute_auprc(y_true, y_prob):\n",
+    "    precision, recall, _ = precision_recall_curve(y_true.ravel(), y_prob.ravel())\n",
+    "    return auc(recall, precision)\n",
+    "\n",
+    "# --- 5. Smin -------------------------------------------------------------\n",
+    "def compute_smin(y_true, y_prob, terms, threshold, go_dag, alpha=ALPHA):\n",
+    "    y_pred = (y_prob >= threshold).astype(int)\n",
+    "    ic = {}\n",
+    "    total = (y_true + y_pred).sum(axis=0).sum()\n",
+    "    for i, term in enumerate(terms):\n",
+    "        freq = (y_true[:, i] + y_pred[:, i]).sum()\n",
+    "        ic[term] = -np.log((freq + 1e-8) / total)\n",
+    "\n",
+    "    s_values = []\n",
+    "    for true_vec, pred_vec in zip(y_true, y_pred):\n",
+    "        true_terms = {terms[i] for i in np.where(true_vec)[0]}\n",
+    "        pred_terms = {terms[i] for i in np.where(pred_vec)[0]}\n",
+    "\n",
+    "        anc_true = set()\n",
+    "        for t in true_terms:\n",
+    "            if t in go_dag:\n",
+    "                anc_true |= go_dag[t].get_all_parents()\n",
+    "        anc_pred = set()\n",
+    "        for t in pred_terms:\n",
+    "            if t in go_dag:\n",
+    "                anc_pred |= go_dag[t].get_all_parents()\n",
+    "\n",
+    "        ru = pred_terms - true_terms\n",
+    "        mi = true_terms - pred_terms\n",
+    "        dist_ru = sum(ic.get(t, 0) for t in ru)\n",
+    "        dist_mi = sum(ic.get(t, 0) for t in mi)\n",
+    "        s = math.sqrt((alpha * dist_ru)**2 + ((1 - alpha) * dist_mi)**2)\n",
+    "        s_values.append(s)\n",
+    "\n",
+    "    return np.mean(s_values)\n",
+    "\n",
+    "# --- 6. Avaliar ----------------------------------------------------------\n",
+    "fmax, thr = compute_fmax(y_true, y_prob, THRESHOLDS)\n",
+    "auprc = compute_auprc(y_true, y_prob)\n",
+    "smin  = compute_smin(y_true, y_prob, terms, thr, go_dag)\n",
+    "\n",
+    "print(f\"\\n📊 Resultados finais (ProtBERT + PAM1 + propagação):\")\n",
+    "print(f\"Fmax  = {fmax:.4f}\")\n",
+    "print(f\"Thr.  = {thr:.2f}\")\n",
+    "print(f\"AuPRC = {auprc:.4f}\")\n",
+    "print(f\"Smin  = {smin:.4f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "70d131ef-ef84-42ee-953b-0d3f1268694d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['data/mlb_protbert.pkl']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import joblib, pickle\n",
+    "joblib.dump(mlb, \"data/mlb_protbert.pkl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f89c3bc-6b78-4a4c-8ddd-b69c7d3d0e65",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/PAM1_protbertBFD.ipynb

@@ -0,0 +1,871 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c6dbc330-062a-48f0-8242-3f21cc1c9c2b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "go.obo: fmt(1.2) rel(2025-03-16) 43,544 Terms\n",
+      "✓ Ficheiros criados:\n",
+      " - data/mf-training.csv : (31142, 3)\n",
+      " - data/mf-validation.csv: (1724, 3)\n",
+      " - data/mf-test.csv     : (1724, 3)\n",
+      "GO terms únicos (após propagação e filtro): 602\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from Bio import SeqIO\n",
+    "from collections import Counter\n",
+    "from goatools.obo_parser import GODag\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import MultiLabelBinarizer\n",
+    "from iterstrat.ml_stratifiers import MultilabelStratifiedKFold\n",
+    "import numpy as np\n",
+    "import os\n",
+    "\n",
+    "# --- 1. Carregar GO anotações ------------------------------------------\n",
+    "annotations = pd.read_csv(\"uniprot_sprot_exp.txt\", sep=\"\\t\", names=[\"protein_id\", \"go_term\", \"go_category\"])\n",
+    "annotations_f = annotations[annotations[\"go_category\"] == \"F\"]\n",
+    "\n",
+    "# --- 2. Carregar DAG e propagar GO terms -------------------------------\n",
+    "# propagação hierárquica\n",
+    "# https://geneontology.org/docs/download-ontology/\n",
+    "go_dag = GODag(\"go.obo\")\n",
+    "mf_terms = {t for t, o in go_dag.items() if o.namespace == \"molecular_function\"}\n",
+    "\n",
+    "def propagate_terms(term_list):\n",
+    "    full = set()\n",
+    "    for t in term_list:\n",
+    "        if t not in go_dag:\n",
+    "            continue\n",
+    "        full.add(t)\n",
+    "        full.update(go_dag[t].get_all_parents())\n",
+    "    return list(full & mf_terms)\n",
+    "\n",
+    "# --- 3. Carregar sequências --------------------------------------------\n",
+    "seqs, ids = [], []\n",
+    "for record in SeqIO.parse(\"uniprot_sprot_exp.fasta\", \"fasta\"):\n",
+    "    ids.append(record.id)\n",
+    "    seqs.append(str(record.seq))\n",
+    "\n",
+    "seq_df = pd.DataFrame({\"protein_id\": ids, \"sequence\": seqs})\n",
+    "\n",
+    "# --- 4. Juntar com GO anotado e propagar -------------------------------\n",
+    "grouped = annotations_f.groupby(\"protein_id\")[\"go_term\"].apply(list).reset_index()\n",
+    "data = seq_df.merge(grouped, on=\"protein_id\")\n",
+    "data = data[data[\"go_term\"].apply(len) > 0]\n",
+    "data[\"go_term\"] = data[\"go_term\"].apply(propagate_terms)\n",
+    "data = data[data[\"go_term\"].apply(len) > 0]\n",
+    "\n",
+    "# --- 5. Filtrar GO terms raros -----------------------------------------\n",
+    "# todos os terms com menos de 50 proteinas associadas\n",
+    "all_terms = [term for sublist in data[\"go_term\"] for term in sublist]\n",
+    "term_counts = Counter(all_terms)\n",
+    "valid_terms = {term for term, count in term_counts.items() if count >= 50}\n",
+    "data[\"go_term\"] = data[\"go_term\"].apply(lambda terms: [t for t in terms if t in valid_terms])\n",
+    "data = data[data[\"go_term\"].apply(len) > 0]\n",
+    "\n",
+    "# --- 6. Preparar dataset final -----------------------------------------\n",
+    "data[\"go_terms\"] = data[\"go_term\"].apply(lambda x: ';'.join(sorted(set(x))))\n",
+    "data = data[[\"protein_id\", \"sequence\", \"go_terms\"]].drop_duplicates()\n",
+    "\n",
+    "# --- 7. Binarizar labels e dividir -------------------------------------\n",
+    "mlb = MultiLabelBinarizer()\n",
+    "Y = mlb.fit_transform(data[\"go_terms\"].str.split(\";\"))\n",
+    "X = data[[\"protein_id\", \"sequence\"]].values\n",
+    "\n",
+    "mskf = MultilabelStratifiedKFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "train_idx, temp_idx = next(mskf.split(X, Y))\n",
+    "val_idx, test_idx = np.array_split(temp_idx, 2)\n",
+    "\n",
+    "df_train = data.iloc[train_idx].copy()\n",
+    "df_val   = data.iloc[val_idx].copy()\n",
+    "df_test  = data.iloc[test_idx].copy()\n",
+    "\n",
+    "# --- 8. Guardar em CSV -------------------------------------------------\n",
+    "os.makedirs(\"data\", exist_ok=True)\n",
+    "df_train.to_csv(\"data/mf-training.csv\", index=False)\n",
+    "df_val.to_csv(\"data/mf-validation.csv\", index=False)\n",
+    "df_test.to_csv(\"data/mf-test.csv\", index=False)\n",
+    "\n",
+    "# --- 9. Confirmar ------------------------------------------------------\n",
+    "print(\"✓ Ficheiros criados:\")\n",
+    "print(\" - data/mf-training.csv :\", df_train.shape)\n",
+    "print(\" - data/mf-validation.csv:\", df_val.shape)\n",
+    "print(\" - data/mf-test.csv     :\", df_test.shape)\n",
+    "print(f\"GO terms únicos (após propagação e filtro): {len(mlb.classes_)}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6cf7aaa6-4941-4951-8d73-1f4f1f4362f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Melvin\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "C:\\Users\\Melvin\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\transformers\\utils\\generic.py:441: FutureWarning: `torch.utils._pytree._register_pytree_node` is deprecated. Please use `torch.utils._pytree.register_pytree_node` instead.\n",
+      "  _torch_pytree._register_pytree_node(\n",
+      "100%|██████████| 31142/31142 [00:26<00:00, 1192.86it/s]\n",
+      "100%|██████████| 1724/1724 [00:00<00:00, 2570.68it/s]\n",
+      "C:\\Users\\Melvin\\anaconda3\\envs\\projeto_proteina2\\lib\\site-packages\\ktrain\\text\\preprocessor.py:382: UserWarning: The class_names argument is replacing the classes argument. Please update your code.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "preprocessing train...\n",
+      "language: en\n",
+      "train sequence lengths:\n",
+      "\tmean : 423\n",
+      "\t95percentile : 604\n",
+      "\t99percentile : 715\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Is Multi-Label? True\n",
+      "preprocessing test...\n",
+      "language: en\n",
+      "test sequence lengths:\n",
+      "\tmean : 408\n",
+      "\t95percentile : 603\n",
+      "\t99percentile : 714\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "begin training using triangular learning rate policy with max lr of 1e-05...\n",
+      "Epoch 1/10\n",
+      "40995/40995 [==============================] - 9020s 219ms/step - loss: 0.0740 - binary_accuracy: 0.9869 - val_loss: 0.0526 - val_binary_accuracy: 0.9866\n",
+      "Epoch 2/10\n",
+      "40995/40995 [==============================] - 8939s 218ms/step - loss: 0.0464 - binary_accuracy: 0.9877 - val_loss: 0.0457 - val_binary_accuracy: 0.9871\n",
+      "Epoch 3/10\n",
+      "40995/40995 [==============================] - 8881s 217ms/step - loss: 0.0413 - binary_accuracy: 0.9883 - val_loss: 0.0418 - val_binary_accuracy: 0.9877\n",
+      "Epoch 4/10\n",
+      "40995/40995 [==============================] - 10277s 251ms/step - loss: 0.0380 - binary_accuracy: 0.9888 - val_loss: 0.0396 - val_binary_accuracy: 0.9881\n",
+      "Epoch 5/10\n",
+      "40995/40995 [==============================] - 10565s 258ms/step - loss: 0.0357 - binary_accuracy: 0.9892 - val_loss: 0.0380 - val_binary_accuracy: 0.9883\n",
+      "Epoch 6/10\n",
+      "40995/40995 [==============================] - 10693s 261ms/step - loss: 0.0338 - binary_accuracy: 0.9895 - val_loss: 0.0369 - val_binary_accuracy: 0.9885\n",
+      "Epoch 7/10\n",
+      "40995/40995 [==============================] - 12055s 294ms/step - loss: 0.0323 - binary_accuracy: 0.9898 - val_loss: 0.0360 - val_binary_accuracy: 0.9888\n",
+      "Epoch 8/10\n",
+      "40995/40995 [==============================] - 10225s 249ms/step - loss: 0.0309 - binary_accuracy: 0.9901 - val_loss: 0.0353 - val_binary_accuracy: 0.9890\n",
+      "Epoch 9/10\n",
+      "40995/40995 [==============================] - 10308s 251ms/step - loss: 0.0297 - binary_accuracy: 0.9904 - val_loss: 0.0347 - val_binary_accuracy: 0.9891\n",
+      "Epoch 10/10\n",
+      "40995/40995 [==============================] - 10275s 251ms/step - loss: 0.0286 - binary_accuracy: 0.9907 - val_loss: 0.0346 - val_binary_accuracy: 0.9893\n",
+      "Weights from best epoch have been loaded into model.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x2b644b84fd0>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from tqdm import tqdm\n",
+    "import random\n",
+    "import os\n",
+    "import ktrain\n",
+    "from ktrain import text\n",
+    "from sklearn.preprocessing import MultiLabelBinarizer\n",
+    "\n",
+    "\n",
+    "# PAM1\n",
+    "# PAM matrix model of protein evolution\n",
+    "# DOI:10.1093/oxfordjournals.molbev.a040360\n",
+    "pam_data = {\n",
+    "    'A': [9948, 19, 27, 42, 31, 46, 50, 92, 17, 7, 40, 88, 42, 41, 122, 279, 255, 9, 72, 723],\n",
+    "    'R': [14, 9871, 24, 38, 37, 130, 38, 62, 49, 4, 58, 205, 26, 33, 47, 103, 104, 5, 36, 52],\n",
+    "    'N': [20, 22, 9860, 181, 29, 36, 41, 67, 31, 5, 22, 49, 23, 10, 33, 83, 66, 3, 43, 32],\n",
+    "    'D': [40, 34, 187, 9818, 11, 63, 98, 61, 23, 5, 25, 54, 43, 13, 27, 88, 55, 4, 29, 36],\n",
+    "    'C': [20, 16, 26, 9, 9987, 10, 17, 37, 12, 2, 16, 26, 10, 19, 27, 26, 25, 2, 6, 67],\n",
+    "    'Q': [29, 118, 29, 49, 8, 9816, 72, 55, 36, 4, 60, 158, 35, 22, 39, 86, 74, 3, 34, 28],\n",
+    "    'E': [35, 29, 41, 101, 12, 71, 9804, 56, 33, 5, 36, 107, 42, 20, 38, 87, 69, 4, 30, 42],\n",
+    "    'G': [96, 61, 77, 70, 38, 51, 58, 9868, 26, 6, 37, 53, 39, 28, 69, 134, 116, 5, 47, 60],\n",
+    "    'H': [17, 53, 33, 19, 15, 39, 34, 24, 9907, 3, 32, 57, 24, 15, 27, 47, 43, 2, 22, 19],\n",
+    "    'I': [6, 3, 6, 6, 3, 5, 6, 7, 3, 9973, 23, 13, 12, 41, 93, 84, 115, 3, 8, 102],\n",
+    "    'L': [26, 39, 17, 15, 7, 33, 22, 20, 19, 27, 9864, 49, 24, 78, 117, 148, 193, 5, 24, 70],\n",
+    "    'K': [60, 198, 43, 52, 12, 142, 96, 53, 42, 10, 63, 9710, 33, 26, 54, 109, 102, 5, 43, 42],\n",
+    "    'M': [21, 22, 15, 18, 6, 20, 18, 18, 17, 11, 27, 32, 9945, 26, 34, 61, 71, 3, 12, 31],\n",
+    "    'F': [18, 17, 8, 6, 8, 11, 10, 16, 10, 44, 92, 24, 29, 9899, 89, 88, 142, 7, 14, 68],\n",
+    "    'P': [97, 47, 35, 29, 23, 35, 38, 57, 21, 24, 47, 56, 28, 76, 9785, 115, 77, 4, 24, 35],\n",
+    "    'S': [241, 87, 76, 73, 17, 56, 60, 99, 32, 13, 69, 92, 42, 67, 100, 9605, 212, 8, 63, 70],\n",
+    "    'T': [186, 78, 54, 37, 14, 42, 42, 83, 28, 23, 84, 85, 53, 93, 66, 182, 9676, 8, 39, 90],\n",
+    "    'W': [2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 1, 5, 3, 4, 4, 9960, 3, 4],\n",
+    "    'Y': [29, 21, 17, 9, 4, 13, 9, 21, 10, 7, 20, 17, 11, 23, 19, 41, 31, 3, 9935, 23],\n",
+    "    'V': [368, 27, 18, 18, 50, 23, 34, 64, 15, 85, 72, 42, 33, 88, 42, 112, 137, 4, 20, 9514]\n",
+    "}\n",
+    "pam_raw = pd.DataFrame(pam_data, index=list(pam_data.keys()))\n",
+    "pam_matrix = pam_raw.div(pam_raw.sum(axis=1), axis=0)\n",
+    "list_amino = pam_raw.columns.tolist()\n",
+    "pam_dict = {\n",
+    "    aa: {sub: pam_matrix.loc[aa, sub] for sub in list_amino}\n",
+    "    for aa in list_amino\n",
+    "}\n",
+    "\n",
+    "def pam1_substitution(aa):\n",
+    "    if aa not in pam_dict:\n",
+    "        return aa\n",
+    "    subs = list(pam_dict[aa].keys())\n",
+    "    probs = list(pam_dict[aa].values())\n",
+    "    return np.random.choice(subs, p=probs)\n",
+    "\n",
+    "def augment_sequence(seq, sub_prob=0.05):\n",
+    "    return ''.join([pam1_substitution(aa) if random.random() < sub_prob else aa for aa in seq])\n",
+    "\n",
+    "def slice_sequence(seq, win=500, min_overlap=250):\n",
+    "    if len(seq) <= win:\n",
+    "        return [seq]\n",
+    "    slices, start = [], 0\n",
+    "    while start + win <= len(seq):\n",
+    "        slices.append(seq[start:start+win])\n",
+    "        start += win\n",
+    "    leftover = seq[start:]\n",
+    "    if leftover and len(leftover) >= min_overlap and len(slices[-1]) >= min_overlap:\n",
+    "        extra = slices[-1][-min_overlap:] + leftover\n",
+    "        slices.append(extra)\n",
+    "    return slices\n",
+    "\n",
+    "def generate_data(df, augment=False):\n",
+    "    X, y = [], []\n",
+    "    label_cols = [col for col in df.columns if col.startswith(\"GO:\")]\n",
+    "    for _, row in tqdm(df.iterrows(), total=len(df)):\n",
+    "        seq = row[\"sequence\"]\n",
+    "        if augment:\n",
+    "            seq = augment_sequence(seq)\n",
+    "        seq_slices = slice_sequence(seq)\n",
+    "        X.extend(seq_slices)\n",
+    "        lbl = row[label_cols].values.astype(int)\n",
+    "        y.extend([lbl] * len(seq_slices))\n",
+    "    return X, np.array(y), label_cols\n",
+    "\n",
+    "def format_sequence(seq): return \" \".join(list(seq))\n",
+    "\n",
+    "# Função para carregar e binarizar\n",
+    "def load_and_binarize(csv_path, mlb=None):\n",
+    "    df = pd.read_csv(csv_path)\n",
+    "    df[\"go_terms\"] = df[\"go_terms\"].str.split(\";\")\n",
+    "    if mlb is None:\n",
+    "        mlb = MultiLabelBinarizer()\n",
+    "        labels = mlb.fit_transform(df[\"go_terms\"])\n",
+    "    else:\n",
+    "        labels = mlb.transform(df[\"go_terms\"])\n",
+    "    labels_df = pd.DataFrame(labels, columns=mlb.classes_)\n",
+    "    df = df.reset_index(drop=True).join(labels_df)\n",
+    "    return df, mlb\n",
+    "\n",
+    "# Carregar os dados\n",
+    "df_train, mlb = load_and_binarize(\"data/mf-training.csv\")\n",
+    "df_val, _     = load_and_binarize(\"data/mf-validation.csv\", mlb=mlb)\n",
+    "\n",
+    "# Gerar com augmentation no treino\n",
+    "X_train, y_train, term_cols = generate_data(df_train, augment=True)\n",
+    "X_val,   y_val, _           = generate_data(df_val,   augment=False)\n",
+    "\n",
+    "# Preparar texto para tokenizer\n",
+    "X_train_fmt = list(map(format_sequence, X_train))\n",
+    "X_val_fmt   = list(map(format_sequence, X_val))\n",
+    "\n",
+    "# Fine-tune ProtBERT\n",
+    "# https://huggingface.co/Rostlab/prot_bert\n",
+    "# https://doi.org/10.1093/bioinformatics/btac020\n",
+    "# dados de treino-> UniRef100 (216 milhões de sequências)\n",
+    "MODEL_NAME = \"Rostlab/prot_bert_bfd\"\n",
+    "MAX_LEN = 512\n",
+    "BATCH_SIZE = 1\n",
+    "\n",
+    "t = text.Transformer(MODEL_NAME, maxlen=MAX_LEN, classes=term_cols)\n",
+    "trn = t.preprocess_train(X_train_fmt, y_train)\n",
+    "val = t.preprocess_test(X_val_fmt, y_val)\n",
+    "\n",
+    "model   = t.get_classifier()\n",
+    "learner = ktrain.get_learner(model,\n",
+    "                             train_data=trn,\n",
+    "                             val_data=val,\n",
+    "                             batch_size=BATCH_SIZE)\n",
+    "\n",
+    "learner.autofit(lr=1e-5,\n",
+    "                epochs=10,\n",
+    "                early_stopping=1,\n",
+    "                checkpoint_folder=\"mf-fine-tuned-protbertbfd\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c66774b3-6cf0-41c5-bb01-9467a5283102",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Existe: weights/mf-fine-tuned-protbertbfd\n",
+      "📁 Conteúdo:\n",
+      " - config.json\n",
+      " - tf_model.h5\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "learner.save_model('weights/mf-fine-tuned-protbertbfd')\n",
+    "path = \"weights/mf-fine-tuned-protbertbfd\"\n",
+    "\n",
+    "if os.path.exists(path):\n",
+    "    print(f\"✅ Existe: {path}\")\n",
+    "    print(\"📁 Conteúdo:\")\n",
+    "    for f in os.listdir(path):\n",
+    "        print(\" -\", f)\n",
+    "else:\n",
+    "    print(f\"❌ Não existe: {path}\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9b39c439-5708-4787-bfee-d3a4d3aa190d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ Tokenizer base e modelo fine-tuned carregados com sucesso\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processando data/mf-training.csv: 100%|██████████| 31142/31142 [5:17:56<00:00,  1.63it/s]  \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ Guardado embeddings\\train_protbertbfd.pkl — 31142 proteínas\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processando data/mf-validation.csv: 100%|██████████| 1724/1724 [19:15<00:00,  1.49it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ Guardado embeddings\\val_protbertbfd.pkl — 1724 proteínas\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processando data/mf-test.csv: 100%|██████████| 1724/1724 [17:15<00:00,  1.66it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ Guardado embeddings\\test_protbertbfd.pkl — 1724 proteínas\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from tqdm import tqdm\n",
+    "import joblib\n",
+    "import gc\n",
+    "from transformers import AutoTokenizer, TFAutoModel\n",
+    "\n",
+    "# --- 1. Parâmetros --------------------------------------------------------\n",
+    "MODEL_DIR = \"weights/mf-fine-tuned-protbertbfd\"\n",
+    "MODEL_NAME = \"Rostlab/prot_bert_bfd\"\n",
+    "OUT_DIR   = \"embeddings\"\n",
+    "BATCH_TOK = 16\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, do_lower_case=False)\n",
+    "model     = TFAutoModel.from_pretrained(MODEL_DIR, from_pt=False)\n",
+    "\n",
+    "print(\"✓ Tokenizer base e modelo fine-tuned carregados com sucesso\")\n",
+    "\n",
+    "# --- 3. Funções auxiliares ------------------------------------------------\n",
+    "def format_sequence(seq):\n",
+    "    return \" \".join(list(seq))\n",
+    "\n",
+    "def slice_sequence(seq, win=500, min_overlap=250):\n",
+    "    if len(seq) <= win:\n",
+    "        return [seq]\n",
+    "    slices, start = [], 0\n",
+    "    while start + win <= len(seq):\n",
+    "        slices.append(seq[start:start+win])\n",
+    "        start += win\n",
+    "    leftover = seq[start:]\n",
+    "    if leftover and len(leftover) >= min_overlap and len(slices[-1]) >= min_overlap:\n",
+    "        extra = slices[-1][-min_overlap:] + leftover\n",
+    "        slices.append(extra)\n",
+    "    return slices\n",
+    "\n",
+    "def get_embeddings(batch, tokenizer, model):\n",
+    "    tokens = tokenizer(batch, return_tensors=\"tf\", padding=True, truncation=True, max_length=512)\n",
+    "    output = model(**tokens)\n",
+    "    return output.last_hidden_state[:, 0, :].numpy()\n",
+    "\n",
+    "def process_split(csv_path, out_path):\n",
+    "    df = pd.read_csv(csv_path)\n",
+    "    label_cols = [col for col in df.columns if col.startswith(\"GO:\")]\n",
+    "    prot_ids, embeds, labels = [], [], []\n",
+    "\n",
+    "    for _, row in tqdm(df.iterrows(), total=len(df), desc=f\"Processando {csv_path}\"):\n",
+    "        slices     = slice_sequence(row[\"sequence\"])\n",
+    "        slices_fmt = list(map(format_sequence, slices))\n",
+    "\n",
+    "        slice_embeds = []\n",
+    "        for i in range(0, len(slices_fmt), BATCH_TOK):\n",
+    "            batch = slices_fmt[i:i+BATCH_TOK]\n",
+    "            slice_embeds.append(get_embeddings(batch, tokenizer, model))\n",
+    "        slice_embeds = np.vstack(slice_embeds)\n",
+    "\n",
+    "        prot_embed = slice_embeds.mean(axis=0)\n",
+    "        prot_ids.append(row[\"protein_id\"])\n",
+    "        embeds.append(prot_embed.astype(np.float32))\n",
+    "        labels.append(row[label_cols].values.astype(np.int8))\n",
+    "        gc.collect()\n",
+    "\n",
+    "    embeds = np.vstack(embeds)\n",
+    "    labels = np.vstack(labels)\n",
+    "\n",
+    "    joblib.dump({\n",
+    "        \"protein_ids\": prot_ids,\n",
+    "        \"embeddings\": embeds,\n",
+    "        \"labels\": labels,\n",
+    "        \"go_terms\": label_cols\n",
+    "    }, out_path, compress=3)\n",
+    "\n",
+    "    print(f\"✓ Guardado {out_path} — {embeds.shape[0]} proteínas\")\n",
+    "\n",
+    "# --- 4. Aplicar -----------------------------------------------------------\n",
+    "os.makedirs(OUT_DIR, exist_ok=True)\n",
+    "\n",
+    "process_split(\"data/mf-training.csv\",   os.path.join(OUT_DIR, \"train_protbertbfd.pkl\"))\n",
+    "process_split(\"data/mf-validation.csv\", os.path.join(OUT_DIR, \"val_protbertbfd.pkl\"))\n",
+    "process_split(\"data/mf-test.csv\",       os.path.join(OUT_DIR, \"test_protbertbfd.pkl\"))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ad0c5421-e0a1-4a6a-8ace-2c69aeab0e0d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ Corrigido: embeddings/train_protbertbfd.pkl — 31142 exemplos, 597 GO terms\n",
+      "✓ Corrigido: embeddings/val_protbertbfd.pkl — 1724 exemplos, 597 GO terms\n",
+      "✓ Corrigido: embeddings/test_protbertbfd.pkl — 1724 exemplos, 597 GO terms\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import joblib\n",
+    "from sklearn.preprocessing import MultiLabelBinarizer\n",
+    "\n",
+    "# --- 1. Obter GO terms do ficheiro de teste --------------------------------\n",
+    "df_test = pd.read_csv(\"data/mf-test.csv\")\n",
+    "test_terms = sorted(set(term for row in df_test[\"go_terms\"].str.split(\";\") for term in row))\n",
+    "\n",
+    "# --- 2. Função para corrigir um .pkl com base nos GO terms do teste --------\n",
+    "def patch_to_common_terms(csv_path, pkl_path, common_terms):\n",
+    "    df = pd.read_csv(csv_path)\n",
+    "    terms_split = df[\"go_terms\"].str.split(\";\")\n",
+    "    \n",
+    "    # Apenas termos presentes nos common_terms\n",
+    "    terms_filtered = terms_split.apply(lambda lst: [t for t in lst if t in common_terms])\n",
+    "    \n",
+    "    mlb = MultiLabelBinarizer(classes=common_terms)\n",
+    "    Y = mlb.fit_transform(terms_filtered)\n",
+    "\n",
+    "    data = joblib.load(pkl_path)\n",
+    "    data[\"labels\"] = Y\n",
+    "    data[\"go_terms\"] = mlb.classes_.tolist()\n",
+    "    \n",
+    "    joblib.dump(data, pkl_path, compress=3)\n",
+    "    print(f\"✓ Corrigido: {pkl_path} — {Y.shape[0]} exemplos, {Y.shape[1]} GO terms\")\n",
+    "\n",
+    "# --- 3. Aplicar às 3 partições --------------------------------------------\n",
+    "patch_to_common_terms(\"data/mf-training.csv\",   \"embeddings/train_protbertbfd.pkl\", test_terms)\n",
+    "patch_to_common_terms(\"data/mf-validation.csv\", \"embeddings/val_protbertbfd.pkl\",   test_terms)\n",
+    "patch_to_common_terms(\"data/mf-test.csv\",       \"embeddings/test_protbertbfd.pkl\",  test_terms)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "dbd5c35f-4a08-4906-9cf4-e1df501d1ecb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import joblib\n",
+    "train = joblib.load(\"embeddings/train_protbertbfd.pkl\")\n",
+    "val   = joblib.load(\"embeddings/val_protbertbfd.pkl\")\n",
+    "test  = joblib.load(\"embeddings/test_protbertbfd.pkl\")\n",
+    "\n",
+    "X_train, y_train = train[\"embeddings\"], train[\"labels\"]\n",
+    "X_val,   y_val   = val[\"embeddings\"],   val[\"labels\"]\n",
+    "X_test,  y_test  = test[\"embeddings\"],  test[\"labels\"]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1785d8a9-23fc-4490-8d71-29cc91a4cb57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ Embeddings carregados: (31142, 1024) → 597 GO terms\n",
+      "Epoch 1/100\n",
+      "974/974 [==============================] - 3s 3ms/step - loss: 0.0337 - binary_accuracy: 0.9901 - val_loss: 0.0331 - val_binary_accuracy: 0.9905\n",
+      "Epoch 2/100\n",
+      "974/974 [==============================] - 3s 3ms/step - loss: 0.0252 - binary_accuracy: 0.9921 - val_loss: 0.0326 - val_binary_accuracy: 0.9905\n",
+      "Epoch 3/100\n",
+      "974/974 [==============================] - 3s 3ms/step - loss: 0.0244 - binary_accuracy: 0.9924 - val_loss: 0.0330 - val_binary_accuracy: 0.9905\n",
+      "Epoch 4/100\n",
+      "974/974 [==============================] - 3s 3ms/step - loss: 0.0240 - binary_accuracy: 0.9925 - val_loss: 0.0322 - val_binary_accuracy: 0.9907\n",
+      "Epoch 5/100\n",
+      "974/974 [==============================] - 3s 3ms/step - loss: 0.0236 - binary_accuracy: 0.9925 - val_loss: 0.0328 - val_binary_accuracy: 0.9907\n",
+      "Epoch 6/100\n",
+      "974/974 [==============================] - 3s 3ms/step - loss: 0.0232 - binary_accuracy: 0.9926 - val_loss: 0.0325 - val_binary_accuracy: 0.9908\n",
+      "Epoch 7/100\n",
+      "974/974 [==============================] - 3s 3ms/step - loss: 0.0231 - binary_accuracy: 0.9926 - val_loss: 0.0325 - val_binary_accuracy: 0.9907\n",
+      "Epoch 8/100\n",
+      "974/974 [==============================] - 3s 3ms/step - loss: 0.0228 - binary_accuracy: 0.9927 - val_loss: 0.0326 - val_binary_accuracy: 0.9908\n",
+      "Epoch 9/100\n",
+      "974/974 [==============================] - 3s 3ms/step - loss: 0.0226 - binary_accuracy: 0.9927 - val_loss: 0.0326 - val_binary_accuracy: 0.9908\n",
+      "Previsões guardadas em mf-protbertbfd-pam1.npy\n",
+      "Modelo guardado em models/protbertbfd_mlp.keras\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "import joblib\n",
+    "import numpy as np\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Dropout\n",
+    "from tensorflow.keras.callbacks import EarlyStopping\n",
+    "\n",
+    "# --- 1. Carregar embeddings ----------------------------------------------\n",
+    "train = joblib.load(\"embeddings/train_protbertbfd.pkl\")\n",
+    "val   = joblib.load(\"embeddings/val_protbertbfd.pkl\")\n",
+    "test  = joblib.load(\"embeddings/test_protbertbfd.pkl\")\n",
+    "\n",
+    "X_train, y_train = train[\"embeddings\"], train[\"labels\"]\n",
+    "X_val,   y_val   = val[\"embeddings\"],   val[\"labels\"]\n",
+    "X_test,  y_test  = test[\"embeddings\"],  test[\"labels\"]\n",
+    "\n",
+    "print(f\"✓ Embeddings carregados: {X_train.shape} → {y_train.shape[1]} GO terms\")\n",
+    "\n",
+    "# --- 2. Garantir consistência de classes ---------------------------------\n",
+    "max_classes = y_train.shape[1]  # 602 GO terms (do treino)\n",
+    "\n",
+    "def pad_labels(y, target_dim=max_classes):\n",
+    "    if y.shape[1] < target_dim:\n",
+    "        padding = np.zeros((y.shape[0], target_dim - y.shape[1]), dtype=np.int8)\n",
+    "        return np.hstack([y, padding])\n",
+    "    return y\n",
+    "\n",
+    "y_val  = pad_labels(y_val)\n",
+    "y_test = pad_labels(y_test)\n",
+    "\n",
+    "# --- 3. Modelo MLP ------------------------------------------------------\n",
+    "model = Sequential([\n",
+    "    Dense(1024, activation=\"relu\", input_shape=(X_train.shape[1],)),\n",
+    "    Dropout(0.3),\n",
+    "    Dense(512, activation=\"relu\"),\n",
+    "    Dropout(0.3),\n",
+    "    Dense(max_classes, activation=\"sigmoid\")\n",
+    "])\n",
+    "\n",
+    "model.compile(loss=\"binary_crossentropy\",\n",
+    "              optimizer=\"adam\",\n",
+    "              metrics=[\"binary_accuracy\"])\n",
+    "\n",
+    "# --- 4. Early stopping e treino -----------------------------------------\n",
+    "callbacks = [\n",
+    "    EarlyStopping(monitor=\"val_loss\", patience=5, restore_best_weights=True)\n",
+    "]\n",
+    "\n",
+    "model.fit(X_train, y_train,\n",
+    "          validation_data=(X_val, y_val),\n",
+    "          epochs=100,\n",
+    "          batch_size=32,\n",
+    "          callbacks=callbacks,\n",
+    "          verbose=1)\n",
+    "\n",
+    "# --- 5. Previsões --------------------------------------------------------\n",
+    "y_prob = model.predict(X_test)\n",
+    "np.save(\"predictions/mf-protbertbfd-pam1.npy\", y_prob)\n",
+    "print(\"Previsões guardadas em mf-protbertbfd-pam1.npy\")\n",
+    "\n",
+    "# --- 6. Modelo ----------------------------------------------------------\n",
+    "model.save(\"models/protbertbfd_mlp.keras\")\n",
+    "print(\"Modelo guardado em models/protbertbfd_mlp.keras\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "fdb66630-76dc-43a0-bd56-45052175fdba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "go.obo: fmt(1.2) rel(2025-03-16) 43,544 Terms\n",
+      "✓ Embeddings: (1724, 597) labels × 597 GO terms\n",
+      "\n",
+      "📊 Resultados finais (ProtBERTBFD + PAM1 + propagação):\n",
+      "Fmax  = 0.6570\n",
+      "Thr.  = 0.41\n",
+      "AuPRC = 0.6929\n",
+      "Smin  = 13.8114\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.metrics import precision_recall_curve, auc\n",
+    "from goatools.obo_parser import GODag\n",
+    "import joblib\n",
+    "import math\n",
+    "\n",
+    "# --- 1. Parâmetros -------------------------------------------------------\n",
+    "GO_FILE = \"go.obo\"\n",
+    "THRESHOLDS = np.arange(0.0, 1.01, 0.01)\n",
+    "ALPHA = 0.5\n",
+    "\n",
+    "# --- 2. Carregar dados ---------------------------------------------------\n",
+    "test = joblib.load(\"embeddings/test_protbertbfd.pkl\")\n",
+    "y_true = test[\"labels\"]\n",
+    "terms  = test[\"go_terms\"]\n",
+    "y_prob = np.load(\"predictions/mf-protbertbfd-pam1.npy\")\n",
+    "go_dag = GODag(GO_FILE)\n",
+    "\n",
+    "print(f\"✓ Embeddings: {y_true.shape} labels × {len(terms)} GO terms\")\n",
+    "\n",
+    "# --- 3. Fmax -------------------------------------------------------------\n",
+    "def compute_fmax(y_true, y_prob, thresholds):\n",
+    "    fmax, best_thr = 0, 0\n",
+    "    for t in thresholds:\n",
+    "        y_pred = (y_prob >= t).astype(int)\n",
+    "        tp = (y_true * y_pred).sum(axis=1)\n",
+    "        fp = ((1 - y_true) * y_pred).sum(axis=1)\n",
+    "        fn = (y_true * (1 - y_pred)).sum(axis=1)\n",
+    "        precision = tp / (tp + fp + 1e-8)\n",
+    "        recall    = tp / (tp + fn + 1e-8)\n",
+    "        f1 = 2 * precision * recall / (precision + recall + 1e-8)\n",
+    "        avg_f1 = np.mean(f1)\n",
+    "        if avg_f1 > fmax:\n",
+    "            fmax, best_thr = avg_f1, t\n",
+    "    return fmax, best_thr\n",
+    "\n",
+    "# --- 4. AuPRC micro ------------------------------------------------------\n",
+    "def compute_auprc(y_true, y_prob):\n",
+    "    precision, recall, _ = precision_recall_curve(y_true.ravel(), y_prob.ravel())\n",
+    "    return auc(recall, precision)\n",
+    "\n",
+    "# --- 5. Smin -------------------------------------------------------------\n",
+    "def compute_smin(y_true, y_prob, terms, threshold, go_dag, alpha=ALPHA):\n",
+    "    y_pred = (y_prob >= threshold).astype(int)\n",
+    "    ic = {}\n",
+    "    total = (y_true + y_pred).sum(axis=0).sum()\n",
+    "    for i, term in enumerate(terms):\n",
+    "        freq = (y_true[:, i] + y_pred[:, i]).sum()\n",
+    "        ic[term] = -np.log((freq + 1e-8) / total)\n",
+    "\n",
+    "    s_values = []\n",
+    "    for true_vec, pred_vec in zip(y_true, y_pred):\n",
+    "        true_terms = {terms[i] for i in np.where(true_vec)[0]}\n",
+    "        pred_terms = {terms[i] for i in np.where(pred_vec)[0]}\n",
+    "\n",
+    "        anc_true = set()\n",
+    "        for t in true_terms:\n",
+    "            if t in go_dag:\n",
+    "                anc_true |= go_dag[t].get_all_parents()\n",
+    "        anc_pred = set()\n",
+    "        for t in pred_terms:\n",
+    "            if t in go_dag:\n",
+    "                anc_pred |= go_dag[t].get_all_parents()\n",
+    "\n",
+    "        ru = pred_terms - true_terms\n",
+    "        mi = true_terms - pred_terms\n",
+    "        dist_ru = sum(ic.get(t, 0) for t in ru)\n",
+    "        dist_mi = sum(ic.get(t, 0) for t in mi)\n",
+    "        s = math.sqrt((alpha * dist_ru)**2 + ((1 - alpha) * dist_mi)**2)\n",
+    "        s_values.append(s)\n",
+    "\n",
+    "    return np.mean(s_values)\n",
+    "\n",
+    "# --- 6. Avaliar ----------------------------------------------------------\n",
+    "fmax, thr = compute_fmax(y_true, y_prob, THRESHOLDS)\n",
+    "auprc = compute_auprc(y_true, y_prob)\n",
+    "smin  = compute_smin(y_true, y_prob, terms, thr, go_dag)\n",
+    "\n",
+    "print(f\"\\n📊 Resultados finais (ProtBERTBFD + PAM1 + propagação):\")\n",
+    "print(f\"Fmax  = {fmax:.4f}\")\n",
+    "print(f\"Thr.  = {thr:.2f}\")\n",
+    "print(f\"AuPRC = {auprc:.4f}\")\n",
+    "print(f\"Smin  = {smin:.4f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70d131ef-ef84-42ee-953b-0d3f1268694d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/keras_models_fix.ipynb

@@ -0,0 +1,94 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "39935741-50be-4766-873c-99f3c3f14e55",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A converter modelos .keras para .h5...\n",
+      "\n",
+      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
+      "Guardado com sucesso\n",
+      "\n",
+      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
+      "Guardado com sucesso\n",
+      "\n",
+      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
+      "Guardado com sucesso\n",
+      "\n",
+      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
+      "Guardado com sucesso\n",
+      "\n",
+      "Conversão concluída.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from tensorflow.keras.models import load_model\n",
+    "\n",
+    "MODELS_DIR = \"models\"\n",
+    "\n",
+    "# Modelos a converter: (nome original .keras → novo nome .h5)\n",
+    "modelos = [\n",
+    "    (\"protbert_mlp.keras\",       \"mlp_protbert.h5\"),\n",
+    "    (\"protbertbfd_mlp.keras\",    \"mlp_protbertbfd.h5\"),\n",
+    "    (\"esm2_mlp.keras\",           \"mlp_esm2.h5\"),\n",
+    "    (\"modelo_ensemble_stacking.keras\", \"modelo_ensemble_stack.h5\"),\n",
+    "]\n",
+    "\n",
+    "print(\"A converter modelos .keras para .h5...\\n\")\n",
+    "\n",
+    "for origem, destino in modelos:\n",
+    "    origem_path = os.path.join(MODELS_DIR, origem)\n",
+    "    destino_path = os.path.join(MODELS_DIR, destino)\n",
+    "\n",
+    "    if not os.path.exists(origem_path):\n",
+    "        print(f\"Ficheiro não encontrado: {origem_path}\")\n",
+    "        continue\n",
+    "\n",
+    "\n",
+    "    model = load_model(origem_path, compile=False)\n",
+    "    model.save(destino_path)\n",
+    "\n",
+    "    print(\"Guardado com sucesso\\n\")\n",
+    "\n",
+    "print(\"Conversão concluída.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0f2a4b7-d3eb-48a7-b97e-213b58b2b2ca",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

requirements.txt

@@ -0,0 +1,7 @@
+streamlit
+torch
+transformers
+tensorflow
+scikit-learn==1.3.2
+numpy
+joblib