277 lines
10 KiB
Python
277 lines
10 KiB
Python
|
import torch
|
||
|
|
import torch.nn as nn
|
||
|
|
import torch.optim as optim
|
||
|
|
import numpy as np
|
||
|
|
import joblib
|
||
|
|
import os
|
||
|
|
import json
|
||
|
|
from sklearn.model_selection import StratifiedKFold, train_test_split
|
||
|
|
from sklearn.metrics import accuracy_score
|
||
|
|
from sklearn.preprocessing import StandardScaler
|
||
|
|
from xgboost import XGBClassifier
|
||
|
|
from lightgbm import LGBMClassifier
|
||
|
|
from sklearn.ensemble import ExtraTreesClassifier, VotingClassifier
|
||
|
|
from scipy.ndimage import uniform_filter
|
||
|
|
import warnings
|
||
|
|
warnings.filterwarnings('ignore')
|
||
|
|
|
||
|
|
def load_and_clean():
|
||
|
|
data = joblib.load('dataset_cache/training_data_fusion_32ch.joblib')
|
||
|
|
X, y = data['X'].astype(np.float32), data['y']
|
||
|
|
|
||
|
|
valid = (y >= 0) & (X.reshape(X.shape[0], -1).sum(1) != 0)
|
||
|
|
X, y = X[valid], y[valid]
|
||
|
|
|
||
|
|
unique = sorted(np.unique(y).tolist())
|
||
|
|
lmap = {l:i for i,l in enumerate(unique)}
|
||
|
|
y = np.array([lmap[l] for l in y])
|
||
|
|
return X, y, len(unique)
|
||
|
|
|
||
|
|
def extract_features_fusion(X):
|
||
|
|
N = X.shape[0]
|
||
|
|
all_feats = []
|
||
|
|
|
||
|
|
for i in range(N):
|
||
|
|
patch = X[i]
|
||
|
|
feats = []
|
||
|
|
|
||
|
|
valid_ts = []
|
||
|
|
for t in range(4):
|
||
|
|
block_s2 = patch[t*8 : t*8+6]
|
||
|
|
if np.abs(block_s2).sum() > 1e-6:
|
||
|
|
valid_ts.append(t)
|
||
|
|
|
||
|
|
if not valid_ts:
|
||
|
|
valid_ts = [0]
|
||
|
|
|
||
|
|
per_ts_stats_s2 = {b: [] for b in range(6)}
|
||
|
|
for t in valid_ts:
|
||
|
|
for b in range(6):
|
||
|
|
ch = patch[t*8 + b]
|
||
|
|
per_ts_stats_s2[b].append([
|
||
|
|
np.mean(ch), np.std(ch), np.median(ch),
|
||
|
|
np.min(ch), np.max(ch),
|
||
|
|
np.percentile(ch, 10), np.percentile(ch, 90),
|
||
|
|
])
|
||
|
|
|
||
|
|
for b in range(6):
|
||
|
|
stats = np.array(per_ts_stats_s2[b])
|
||
|
|
feats.extend(stats.mean(axis=0).tolist())
|
||
|
|
feats.extend(stats.std(axis=0).tolist())
|
||
|
|
|
||
|
|
per_ts_stats_s1 = {b: [] for b in range(2)}
|
||
|
|
for t in range(4):
|
||
|
|
vv = patch[t*8 + 6]
|
||
|
|
vh = patch[t*8 + 7]
|
||
|
|
if np.abs(vv).sum() > 1e-6:
|
||
|
|
per_ts_stats_s1[0].append([
|
||
|
|
np.mean(vv), np.std(vv), np.median(vv), np.max(vv), np.percentile(vv, 90)
|
||
|
|
])
|
||
|
|
per_ts_stats_s1[1].append([
|
||
|
|
np.mean(vh), np.std(vh), np.median(vh), np.max(vh), np.percentile(vh, 90)
|
||
|
|
])
|
||
|
|
ratio = (vh + 1e-6) / (vv + 1e-6)
|
||
|
|
feats.extend([np.mean(ratio), np.std(ratio), np.median(ratio)])
|
||
|
|
else:
|
||
|
|
feats.extend([0.0] * 3)
|
||
|
|
|
||
|
|
for b in range(2):
|
||
|
|
if len(per_ts_stats_s1[b]) > 0:
|
||
|
|
stats = np.array(per_ts_stats_s1[b])
|
||
|
|
feats.extend(stats.mean(axis=0).tolist())
|
||
|
|
feats.extend(stats.std(axis=0).tolist())
|
||
|
|
else:
|
||
|
|
feats.extend([0.0] * 10)
|
||
|
|
|
||
|
|
for t in valid_ts[:2]:
|
||
|
|
for b_idx in [3, 4]:
|
||
|
|
ch = patch[t*8 + b_idx]
|
||
|
|
gx = np.diff(ch, axis=1); gy = np.diff(ch, axis=0)
|
||
|
|
grad_mag = np.sqrt(np.mean(gx**2) + np.mean(gy**2))
|
||
|
|
lm = uniform_filter(ch, size=3); lv = uniform_filter(ch**2, size=3) - lm**2
|
||
|
|
feats.extend([grad_mag, np.mean(lv), np.std(lv)])
|
||
|
|
|
||
|
|
for b_idx in [6, 7]:
|
||
|
|
ch = patch[0*8 + b_idx]
|
||
|
|
gx = np.diff(ch, axis=1); gy = np.diff(ch, axis=0)
|
||
|
|
grad_mag = np.sqrt(np.mean(gx**2) + np.mean(gy**2))
|
||
|
|
lm = uniform_filter(ch, size=3); lv = uniform_filter(ch**2, size=3) - lm**2
|
||
|
|
feats.extend([grad_mag, np.mean(lv), np.std(lv)])
|
||
|
|
|
||
|
|
needed = 2 * 2 * 3
|
||
|
|
got = min(len(valid_ts), 2) * 2 * 3
|
||
|
|
feats.extend([0.0] * (needed - got))
|
||
|
|
|
||
|
|
best_t = valid_ts[0]
|
||
|
|
for b in range(8):
|
||
|
|
ch = patch[best_t*8 + b]
|
||
|
|
feats.extend(ch.flatten().tolist())
|
||
|
|
|
||
|
|
all_feats.append(feats)
|
||
|
|
|
||
|
|
features = np.array(all_feats, dtype=np.float32)
|
||
|
|
features = np.nan_to_num(features, nan=0.0, posinf=1e6, neginf=-1e6)
|
||
|
|
return features
|
||
|
|
|
||
|
|
class LightCNN_32ch(nn.Module):
|
||
|
|
def __init__(self, in_ch=32, n_cls=7, width=96):
|
||
|
|
super().__init__()
|
||
|
|
self.net = nn.Sequential(
|
||
|
|
nn.Conv2d(in_ch, width, 3, padding=1), nn.BatchNorm2d(width), nn.GELU(),
|
||
|
|
nn.Conv2d(width, width, 3, padding=1), nn.BatchNorm2d(width), nn.GELU(),
|
||
|
|
nn.MaxPool2d(2), nn.Dropout2d(0.05),
|
||
|
|
nn.Conv2d(width, width*2, 3, padding=1), nn.BatchNorm2d(width*2), nn.GELU(),
|
||
|
|
nn.Conv2d(width*2, width*2, 3, padding=1), nn.BatchNorm2d(width*2), nn.GELU(),
|
||
|
|
nn.MaxPool2d(2), nn.Dropout2d(0.1),
|
||
|
|
nn.Conv2d(width*2, width*4, 3, padding=1), nn.BatchNorm2d(width*4), nn.GELU(),
|
||
|
|
nn.AdaptiveAvgPool2d(1), nn.Flatten(),
|
||
|
|
)
|
||
|
|
self.head = nn.Sequential(
|
||
|
|
nn.Linear(width*4, width*2), nn.GELU(), nn.Dropout(0.3),
|
||
|
|
nn.Linear(width*2, n_cls)
|
||
|
|
)
|
||
|
|
def forward(self, x): return self.head(self.net(x))
|
||
|
|
def embed(self, x): return self.net(x)
|
||
|
|
|
||
|
|
def train_cnn_fusion(X, y, n_cls, seed=42):
|
||
|
|
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
||
|
|
torch.manual_seed(seed)
|
||
|
|
np.random.seed(seed)
|
||
|
|
|
||
|
|
X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.2, random_state=seed, stratify=y)
|
||
|
|
|
||
|
|
model = LightCNN_32ch(in_ch=32, n_cls=n_cls, width=128).to(device)
|
||
|
|
cc = np.bincount(y_tr, minlength=n_cls)
|
||
|
|
w = torch.FloatTensor((1.0/(cc+1)) / (1.0/(cc+1)).sum() * n_cls).to(device)
|
||
|
|
|
||
|
|
crit = nn.CrossEntropyLoss(weight=w, label_smoothing=0.1)
|
||
|
|
opt = optim.AdamW(model.parameters(), lr=4e-4, weight_decay=0.02)
|
||
|
|
sched = optim.lr_scheduler.CosineAnnealingWarmRestarts(opt, T_0=40, T_mult=2, eta_min=1e-6)
|
||
|
|
|
||
|
|
tr_t = torch.FloatTensor(X_tr)
|
||
|
|
tr_y = torch.LongTensor(y_tr)
|
||
|
|
te_t = torch.FloatTensor(X_te).to(device)
|
||
|
|
|
||
|
|
best_acc, best_state, pat = 0, None, 0
|
||
|
|
for ep in range(300):
|
||
|
|
model.train()
|
||
|
|
perm = torch.randperm(len(tr_t))
|
||
|
|
for i in range(0, len(tr_t), 32):
|
||
|
|
idx = perm[i:i+32]
|
||
|
|
bx = tr_t[idx].to(device)
|
||
|
|
by = tr_y[idx].to(device)
|
||
|
|
if np.random.random() > 0.5: bx = torch.flip(bx, [2])
|
||
|
|
if np.random.random() > 0.5: bx = torch.flip(bx, [3])
|
||
|
|
if np.random.random() > 0.5: bx = torch.rot90(bx, np.random.randint(1,4), [2,3])
|
||
|
|
bx = bx + torch.randn_like(bx) * 0.02
|
||
|
|
|
||
|
|
if np.random.random() > 0.5 and len(bx) > 1:
|
||
|
|
lam = np.random.beta(0.4, 0.4)
|
||
|
|
i2 = torch.randperm(bx.size(0))
|
||
|
|
bx = lam*bx + (1-lam)*bx[i2]
|
||
|
|
oh1 = torch.zeros(by.size(0), n_cls, device=device).scatter_(1, by.unsqueeze(1), 1)
|
||
|
|
oh2 = torch.zeros(by.size(0), n_cls, device=device).scatter_(1, by[i2].unsqueeze(1), 1)
|
||
|
|
out = model(bx)
|
||
|
|
loss = (-(lam*oh1 + (1-lam)*oh2) * torch.log_softmax(out,1)).sum(1).mean()
|
||
|
|
else:
|
||
|
|
loss = crit(model(bx), by)
|
||
|
|
|
||
|
|
opt.zero_grad(); loss.backward()
|
||
|
|
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
|
||
|
|
opt.step()
|
||
|
|
sched.step()
|
||
|
|
|
||
|
|
model.eval()
|
||
|
|
with torch.no_grad():
|
||
|
|
probs = []
|
||
|
|
for fn in [lambda x:x, lambda x:torch.flip(x,[2]), lambda x:torch.flip(x,[3])]:
|
||
|
|
probs.append(torch.softmax(model(fn(te_t)), 1))
|
||
|
|
avg = torch.stack(probs).mean(0)
|
||
|
|
preds = avg.argmax(1).cpu().numpy()
|
||
|
|
|
||
|
|
acc = accuracy_score(y_te, preds)
|
||
|
|
if acc > best_acc:
|
||
|
|
best_acc = acc; best_state = {k:v.cpu().clone() for k,v in model.state_dict().items()}; pat = 0
|
||
|
|
else:
|
||
|
|
pat += 1
|
||
|
|
if pat >= 60: break
|
||
|
|
|
||
|
|
model.load_state_dict(best_state)
|
||
|
|
return model, best_acc
|
||
|
|
|
||
|
|
def train_hybrid_fusion(X, y, cnn_model, n_cls):
|
||
|
|
print("\n" + "="*60)
|
||
|
|
print("HYBRID FUSION ENSEMBLE: CNN embed + S1/S2 Rich features + XGB/LGBM/ETC")
|
||
|
|
print("="*60)
|
||
|
|
|
||
|
|
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
||
|
|
cnn_model = cnn_model.to(device).eval()
|
||
|
|
|
||
|
|
with torch.no_grad():
|
||
|
|
embs = []
|
||
|
|
for i in range(0, len(X), 64):
|
||
|
|
b = torch.FloatTensor(X[i:i+64]).to(device)
|
||
|
|
embs.append(cnn_model.embed(b).cpu().numpy())
|
||
|
|
cnn_feat = np.concatenate(embs)
|
||
|
|
|
||
|
|
rich = extract_features_fusion(X)
|
||
|
|
combined = np.concatenate([cnn_feat, rich], axis=1)
|
||
|
|
print(f" Final Feature Vector: {combined.shape}")
|
||
|
|
|
||
|
|
scaler = StandardScaler()
|
||
|
|
combined = scaler.fit_transform(combined)
|
||
|
|
|
||
|
|
skf = StratifiedKFold(5, shuffle=True, random_state=42)
|
||
|
|
cv_accs = []
|
||
|
|
|
||
|
|
for fold, (ti, vi) in enumerate(skf.split(combined, y)):
|
||
|
|
xgb = XGBClassifier(n_estimators=1000, max_depth=7, learning_rate=0.03,
|
||
|
|
subsample=0.8, colsample_bytree=0.5,
|
||
|
|
tree_method='hist', device='cuda',
|
||
|
|
random_state=42+fold, use_label_encoder=False, eval_metric='mlogloss')
|
||
|
|
|
||
|
|
lgbm = LGBMClassifier(n_estimators=1000, max_depth=7, learning_rate=0.03,
|
||
|
|
subsample=0.8, colsample_bytree=0.5,
|
||
|
|
random_state=42+fold, verbosity=-1)
|
||
|
|
|
||
|
|
etc = ExtraTreesClassifier(n_estimators=1000, max_depth=15,
|
||
|
|
max_features='sqrt', random_state=42+fold, n_jobs=-1)
|
||
|
|
|
||
|
|
ensemble = VotingClassifier(estimators=[
|
||
|
|
('xgb', xgb), ('lgbm', lgbm), ('etc', etc)
|
||
|
|
], voting='soft')
|
||
|
|
|
||
|
|
ensemble.fit(combined[ti], y[ti])
|
||
|
|
a = accuracy_score(y[vi], ensemble.predict(combined[vi]))
|
||
|
|
cv_accs.append(a)
|
||
|
|
print(f" Fold {fold+1}: {a:.4f}")
|
||
|
|
|
||
|
|
cv_mean = np.mean(cv_accs)
|
||
|
|
print(f" ✅ Ensemble CV Mean: {cv_mean:.4f} ± {np.std(cv_accs):.4f}")
|
||
|
|
return cv_mean
|
||
|
|
|
||
|
|
def main():
|
||
|
|
X, y, n_cls = load_and_clean()
|
||
|
|
cnn_model, cnn_acc = train_cnn_fusion(X, y, n_cls)
|
||
|
|
|
||
|
|
hyb_cv = train_hybrid_fusion(X, y, cnn_model, n_cls)
|
||
|
|
|
||
|
|
print("\n" + "="*60)
|
||
|
|
print("📊 FINAL RESULTS V5 (ENSEMBLE + RADAR)")
|
||
|
|
print("="*60)
|
||
|
|
res = {
|
||
|
|
'Hybrid Fusion Ensemble CV': hyb_cv,
|
||
|
|
}
|
||
|
|
for n, a in sorted(res.items(), key=lambda x:-x[1]):
|
||
|
|
mk = "🏆" if a>=0.95 else "✅" if a>=0.90 else "📈"
|
||
|
|
print(f" {mk} {n}: {a:.4f}")
|
||
|
|
|
||
|
|
best = max(res.values())
|
||
|
|
if best >= 0.95:
|
||
|
|
print(f"\n🎉 THÀNH CÔNG VƯỢT MỐC 95%! BEST: {best:.4f}")
|
||
|
|
else:
|
||
|
|
print(f"\n🏆 BEST: {best:.4f}")
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
main()
|