# -*- coding: utf-8 -*- """Untitled17.ipynb Automatically generated by Colab. Original file is located at https://colab.research.google.com/drive/1GwdSjrwh3f6QCzOa8KHr_XkWy0KmZvdV """ import pandas as pd import numpy as np from pathlib import Path from sklearn.preprocessing import StandardScaler from sklearn.metrics import roc_auc_score, average_precision_score, roc_curve, precision_recall_curve from sklearn.linear_model import LogisticRegression import matplotlib.pyplot as plt from tqdm import tqdm import pickle import torch import torch.nn as nn import warnings warnings.filterwarnings('ignore') PATHS = { 'features': BASE_PATH / 'features', 'embeddings': BASE_PATH / 'embeddings', 'models': BASE_PATH / 'models', 'results': BASE_PATH / 'results', 'figures': BASE_PATH / 'results' / 'figures', } df_features = pd.read_parquet(PATHS['features'] / 'features_classical_full.parquet') id_cols = ['mutation_idx', 'uniprot_acc', 'gene_symbol', 'position', 'wt_aa', 'mut_aa', 'label'] feature_cols = [c for c in df_features.columns if c not in id_cols] X_features = df_features[feature_cols].values.astype(np.float32) X_features = np.nan_to_num(X_features, nan=0.0, posinf=0.0, neginf=0.0) y = df_features['label'].values proteins = df_features['uniprot_acc'].values X_emb_combined = np.load(PATHS['embeddings'] / 'embeddings_combined_full.npy').astype(np.float32) X_emb_local = np.load(PATHS['embeddings'] / 'embeddings_local_full.npy').astype(np.float32) print(f" {X_features.shape}") print(f" {X_emb_combined.shape}") from sklearn.decomposition import PCA pca_combined = PCA(n_components=128, random_state=42) X_emb_pca = pca_combined.fit_transform(X_emb_combined).astype(np.float32) pca_local = PCA(n_components=64, random_state=42) X_emb_local_pca = pca_local.fit_transform(X_emb_local).astype(np.float32) configs = [ {'name': 'Features classiques', 'X': X_features}, {'name': 'Embeddings ESM-2', 'X': X_emb_pca}, {'name': 'Features + Embeddings', 'X': np.concatenate([X_features, X_emb_pca], axis=1)}, {'name': 'Features + Emb. Local', 'X': np.concatenate([X_features, X_emb_local_pca], axis=1)}, ] class SimpleMLP(nn.Module): def __init__(self, input_dim, hidden_dim=256): super().__init__() self.net = nn.Sequential( nn.Linear(input_dim, hidden_dim), nn.ReLU(), nn.Dropout(0.3), nn.Linear(hidden_dim, hidden_dim // 2), nn.ReLU(), nn.Dropout(0.2), nn.Linear(hidden_dim // 2, 1), nn.Sigmoid() ) def forward(self, x): return self.net(x).squeeze() def train_mlp(X_train, y_train, X_test, input_dim, device, epochs=50, lr=0.001): model = SimpleMLP(input_dim).to(device) optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=1e-4) criterion = nn.BCELoss() X_train_t = torch.FloatTensor(X_train).to(device) y_train_t = torch.FloatTensor(y_train).to(device) X_test_t = torch.FloatTensor(X_test).to(device) model.train() batch_size = 512 for epoch in range(epochs): perm = torch.randperm(len(X_train_t)) for i in range(0, len(X_train_t), batch_size): idx = perm[i:i+batch_size] optimizer.zero_grad() outputs = model(X_train_t[idx]) loss = criterion(outputs, y_train_t[idx]) loss.backward() optimizer.step() model.eval() with torch.no_grad(): y_pred = model(X_test_t).cpu().numpy() return y_pred def evaluate_lpocv_gpu(X, y, proteins, device, epochs=30): unique_proteins = np.unique(proteins) results = [] scaler = StandardScaler() X_scaled = scaler.fit_transform(X) for protein in tqdm(unique_proteins, desc="LPOCV GPU"): test_mask = proteins == protein train_mask = ~test_mask if test_mask.sum() < 2: continue X_train, y_train = X_scaled[train_mask], y[train_mask] X_test, y_test = X_scaled[test_mask], y[test_mask] y_pred = train_mlp(X_train, y_train, X_test, X.shape[1], device, epochs=epochs) for pred, true in zip(y_pred, y_test): results.append({'y_true': true, 'y_pred': float(pred)}) df_res = pd.DataFrame(results) if len(df_res) > 0 and len(df_res['y_true'].unique()) > 1: auc_roc = roc_auc_score(df_res['y_true'], df_res['y_pred']) auc_pr = average_precision_score(df_res['y_true'], df_res['y_pred']) else: auc_roc, auc_pr = 0, 0 return auc_roc, auc_pr, df_res results_all = {} for cfg in configs: print(f"\n 📊 {cfg['name']}...") auc_roc, auc_pr, df_res = evaluate_lpocv_gpu( cfg['X'], y, proteins, device, epochs=30 ) results_all[cfg['name']] = { 'auc_roc': auc_roc, 'auc_pr': auc_pr, 'predictions': df_res, 'n_features': cfg['X'].shape[1], } print(f" AUC-ROC: {auc_roc:.4f}") print(f" AUC-PR: {auc_pr:.4f}") def evaluate_lpocv_logreg(X, y, proteins): unique_proteins = np.unique(proteins) results = [] scaler = StandardScaler() X_scaled = scaler.fit_transform(X) for protein in tqdm(unique_proteins, desc="LPOCV LogReg", leave=False): test_mask = proteins == protein train_mask = ~test_mask if test_mask.sum() < 2: continue X_train, y_train = X_scaled[train_mask], y[train_mask] X_test, y_test = X_scaled[test_mask], y[test_mask] model = LogisticRegression(max_iter=500, C=0.1, random_state=42) model.fit(X_train, y_train) y_pred = model.predict_proba(X_test)[:, 1] for pred, true in zip(y_pred, y_test): results.append({'y_true': true, 'y_pred': pred}) df_res = pd.DataFrame(results) if len(df_res) > 0: auc_roc = roc_auc_score(df_res['y_true'], df_res['y_pred']) auc_pr = average_precision_score(df_res['y_true'], df_res['y_pred']) else: auc_roc, auc_pr = 0, 0 return auc_roc, auc_pr for cfg in configs: auc_roc, auc_pr = evaluate_lpocv_logreg(cfg['X'], y, proteins) results_all[cfg['name']]['auc_roc_logreg'] = auc_roc results_all[cfg['name']]['auc_pr_logreg'] = auc_pr print(f" {cfg['name']}: LogReg AUC-ROC = {auc_roc:.4f}") comparison_data = [] for name, res in results_all.items(): comparison_data.append({ 'Configuration': name, 'Features': res['n_features'], 'AUC-ROC (MLP)': res['auc_roc'], 'AUC-PR (MLP)': res['auc_pr'], 'AUC-ROC (LogReg)': res.get('auc_roc_logreg', 0), }) df_comparison = pd.DataFrame(comparison_data) df_comparison = df_comparison.sort_values('AUC-ROC (MLP)', ascending=False) print("\n" + df_comparison.to_string(index=False)) best_name = df_comparison.iloc[0]['Configuration'] best_auc = df_comparison.iloc[0]['AUC-ROC (MLP)'] print(f"\n 🏆 Meilleur: {best_name} (AUC-ROC = {best_auc:.4f})") df_comparison.to_csv(PATHS['results'] / 'comparison_final.csv', index=False)