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pasted changes from my rework in Acoustics_V2 #19

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109 changes: 50 additions & 59 deletions src/nn/nn_train.py
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Original file line number Diff line number Diff line change
@@ -1,8 +1,3 @@
"""

"""


import argparse
import os
import random
Expand Down Expand Up @@ -41,6 +36,7 @@ class HydrophoneDataset(Dataset):
def __init__(
self,
csv_path: str,
features_list: list[str],
dtype: torch.dtype = torch.float32,
):
"""
Expand Down Expand Up @@ -69,25 +65,21 @@ def __init__(
if not rows:
raise ValueError("CSV is empty.")
header = [h.strip() for h in rows[0]]

try:
idx1 = header.index("Envelope H1")
idx2 = header.index("Envelope H2")
idx3 = header.index("Envelope H3")
idxy = header.index("Truth")
except ValueError as e:
raise ValueError(
"CSV must include header columns: Envelope H1, Envelope H2, Envelope H3, Truth"
) from e
col_idx = {name: i for i, name in enumerate(header)}
missing = [name for name in features_list if name not in col_idx]

if missing:
raise ValueError(f"CSV missing requested feature columns: {missing}")

idxy = col_idx["Truth"]

feats, labels = [], []
feature_indices = [col_idx[name] for name in features_list] # e.g. [idx1, idx3]

for r in rows[1:]:
if not r or all(c.strip() == "" for c in r):
continue

feats.append([float(r[idx2])])
# feats.append([float(r[idx2]), float(r[idx3])])
# feats.append([float(r[idx1]), float(r[idx2]), float(r[idx3])])
feats.append([float(r[i]) for i in feature_indices])
labels.append(int(float(r[idxy])))

X = torch.tensor(feats, dtype=dtype)
Expand Down Expand Up @@ -163,8 +155,7 @@ class using a Softmax activation at the output layer.
model : nn.Sequential
A sequential container implementing the layer stack.
"""
# def __init__(self, in_dim=1, num_classes=4, p_drop=0.2):
# def __init__(self, in_dim=2, num_classes=4, p_drop=0.2):

def __init__(self, in_dim=3, num_classes=4, p_drop=0.2):

super().__init__()
Expand Down Expand Up @@ -233,14 +224,19 @@ def main():
parser.add_argument("--batch", type=int, default=32)
parser.add_argument("--dropout", type=float, default=0.2)
parser.add_argument("--save_dir", type=str, default="artifacts")
parser.add_argument("--feature_cols", type=str, default="Envelope H1,Envelope H2,Envelope H3")
parser.add_argument("--conf_thresh", type=str, default="0.5,0.8")
args = parser.parse_args()

set_seed(13)
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")
feature_cols = [c.strip() for c in args.feature_cols.split(",")]
thresholds = [float(t.strip()) for t in args.conf_thresh.split(",") if t.strip()]


# Load dataset
ds = HydrophoneDataset(args.csv)
ds = HydrophoneDataset(csv_path=args.csv, features_list=feature_cols)
n = len(ds)
n_train = int(0.8 * n)
n_val = n - n_train
Expand All @@ -250,10 +246,7 @@ def main():
val_loader = DataLoader(val_ds, batch_size=args.batch)

# Build model
model = MLPProb(in_dim=1, num_classes=4, p_drop=args.dropout).to(device)
# model = MLPProb(in_dim=2, num_classes=4, p_drop=args.dropout).to(device)
# model = MLPProb(in_dim=3, num_classes=4, p_drop=args.dropout).to(device)

model = MLPProb(in_dim=len(feature_cols),num_classes=4, p_drop=args.dropout).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
loss_fn = nn.NLLLoss() # using log(probs)

Expand Down Expand Up @@ -285,8 +278,9 @@ def main():
# Validation
model.eval()
val_loss, val_acc = 0, 0
val_conf_acc_50, val_coverage_50 = 0, 0
val_conf_acc_80, val_coverage_80 = 0, 0
val_conf_acc = {t: 0.0 for t in thresholds}
val_coverage = {t: 0.0 for t in thresholds}


with torch.no_grad():
for X, y in val_loader:
Expand All @@ -298,30 +292,29 @@ def main():
val_acc += accuracy(probs, y) * X.size(0)

# Confidence-aware metrics
acc_50, cov_50 = accuracy_with_pass(probs, y, confidence_threshold=0.5)
acc_80, cov_80 = accuracy_with_pass(probs, y, confidence_threshold=0.8)
val_conf_acc_50 += acc_50 * X.size(0)
val_coverage_50 += cov_50 * X.size(0)
val_conf_acc_80 += acc_80 * X.size(0)
val_coverage_80 += cov_80 * X.size(0)
for t in thresholds:
acc_t, cov_t = accuracy_with_pass(probs, y, confidence_threshold=t)
val_conf_acc[t] += acc_t * X.size(0)
val_coverage[t] += cov_t * X.size(0)

val_loss /= len(val_loader.dataset)
val_acc /= len(val_loader.dataset)
val_conf_acc_50 /= len(val_loader.dataset)
val_coverage_50 /= len(val_loader.dataset)
val_conf_acc_80 /= len(val_loader.dataset)
val_coverage_80 /= len(val_loader.dataset)
for t in thresholds:
val_conf_acc[t] /= len(val_loader.dataset)
val_coverage[t] /= len(val_loader.dataset)

if val_loss < best_val_loss:
best_val_loss = val_loss
best_acc = val_acc
best_state = {k: v.cpu() for k, v in model.state_dict().items()}

conf_parts = " | ".join(
[f"{int(t*100)}%: acc={val_conf_acc[t]:.3f} cov={val_coverage[t]:.3f}" for t in thresholds]
)
print(f"Epoch {epoch+1:03d}: "
f"train_loss={train_loss:.4f} acc={train_acc:.3f} | "
f"val_loss={val_loss:.4f} acc={val_acc:.3f} | "
f"50%: acc={val_conf_acc_50:.3f} cov={val_coverage_50:.3f} | "
f"80%: acc={val_conf_acc_80:.3f} cov={val_coverage_80:.3f}")
f"train_loss={train_loss:.4f} acc={train_acc:.3f} | "
f"val_loss={val_loss:.4f} acc={val_acc:.3f} | "
f"{conf_parts}")

# Save model + normalization stats
os.makedirs(args.save_dir, exist_ok=True)
Expand All @@ -330,28 +323,26 @@ def main():
# Calculate final confidence metrics for best model
model.load_state_dict(best_state)
model.eval()
final_conf_acc_50, final_coverage_50 = 0, 0
final_conf_acc_80, final_coverage_80 = 0, 0
final_conf_acc = {t: 0.0 for t in thresholds}
final_coverage = {t: 0.0 for t in thresholds}

with torch.no_grad():
for X, y in val_loader:
X, y = X.to(device), y.to(device)
probs = model(X)
acc_50, cov_50 = accuracy_with_pass(probs, y, confidence_threshold=0.5)
acc_80, cov_80 = accuracy_with_pass(probs, y, confidence_threshold=0.8)
final_conf_acc_50 += acc_50 * X.size(0)
final_coverage_50 += cov_50 * X.size(0)
final_conf_acc_80 += acc_80 * X.size(0)
final_coverage_80 += cov_80 * X.size(0)

final_conf_acc_50 /= len(val_loader.dataset)
final_coverage_50 /= len(val_loader.dataset)
final_conf_acc_80 /= len(val_loader.dataset)
final_coverage_80 /= len(val_loader.dataset)

print(f"BEST: val_loss={best_val_loss:.4f} acc={best_acc:.3f} | "
f"50%: acc={final_conf_acc_50:.3f} cov={final_coverage_50:.3f} | "
f"80%: acc={final_conf_acc_80:.3f} cov={final_coverage_80:.3f}")
for t in thresholds:
acc_t, cov_t = accuracy_with_pass(probs, y, confidence_threshold=t)
final_conf_acc[t] += acc_t * X.size(0)
final_coverage[t] += cov_t * X.size(0)

for t in thresholds:
final_conf_acc[t] /= len(val_loader.dataset)
final_coverage[t] /= len(val_loader.dataset)

final_parts = " | ".join(
[f"{int(t*100)}%: acc={final_conf_acc[t]:.3f} cov={final_coverage[t]:.3f}" for t in thresholds]
)
print(f"BEST: val_loss={best_val_loss:.4f} acc={best_acc:.3f} | {final_parts}")

torch.save({
"model_state_dict": best_state,
Expand Down