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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import numpy as np
import pandas as pd
import json
import os
import random
# --- CONFIGURATION (COMPUTE MATCHED BASELINE) ---
# We match the ~15.6 steps from SCI v10
FIXED_K = 16
SEEDS = [42, 100, 2024]
BATCH_SIZE = 64
EPOCHS = 10
TEMPERATURE = 0.5
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
OUT_DIR = "experiments/mitbih_fixed_k"
# --- UTILS ---
def set_seed(seed):
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
def compute_sp(probs):
probs = torch.clamp(probs, min=1e-9)
entropy = -torch.sum(probs * torch.log(probs), dim=1)
max_entropy = np.log(2)
sp = 1.0 - (entropy / max_entropy)
return sp
# --- DATASET ---
class RealMITBIH(Dataset):
def __init__(self, csv_file, limit=None):
df = pd.read_csv(csv_file, header=None)
df.iloc[:, 187] = df.iloc[:, 187].apply(lambda x: 0 if x == 0 else 1)
if limit:
df = df.sample(n=limit, random_state=42).reset_index(drop=True)
self.y = df.iloc[:, 187].values.astype(int)
self.X = df.iloc[:, :187].values.astype(np.float32)
self.X = np.expand_dims(self.X, axis=1)
num_neg = (self.y == 0).sum()
num_pos = (self.y == 1).sum()
self.pos_weight = num_neg / (num_pos + 1e-6)
def __len__(self):
return len(self.y)
def __getitem__(self, idx):
return torch.tensor(self.X[idx]), torch.tensor(self.y[idx])
# --- MODEL ---
class ECGCNN(nn.Module):
def __init__(self):
super(ECGCNN, self).__init__()
self.conv1 = nn.Conv1d(1, 32, 5)
self.conv2 = nn.Conv1d(32, 64, 5)
self.dropout1 = nn.Dropout(0.3)
self.dropout2 = nn.Dropout(0.5)
self.pool = nn.MaxPool1d(2)
self.global_pool = nn.AdaptiveAvgPool1d(1)
self.fc1 = nn.Linear(64, 64)
self.fc2 = nn.Linear(64, 2)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = self.dropout1(x)
x = self.global_pool(x)
x = x.view(x.size(0), -1)
x = F.relu(self.fc1(x))
x = self.dropout2(x)
x = self.fc2(x)
return x
# --- RUNNER ---
def run_experiment(seed):
print(f"\n>>> Running Fixed-K Baseline (K={FIXED_K}), Seed {seed}...")
set_seed(seed)
train_ds = RealMITBIH("mitbih_train.csv", limit=12000)
test_ds = RealMITBIH("mitbih_test.csv", limit=2000)
train_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True)
test_loader = DataLoader(test_ds, batch_size=1, shuffle=False)
model = ECGCNN().to(DEVICE)
optimizer = optim.Adam(model.parameters(), lr=0.001)
weight = torch.tensor([1.0, train_ds.pos_weight], dtype=torch.float32).to(DEVICE)
criterion = nn.CrossEntropyLoss(weight=weight)
model.train()
for epoch in range(EPOCHS):
for data, target in train_loader:
data, target = data.to(DEVICE), target.to(DEVICE)
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
per_example = []
with torch.no_grad():
for i, (data, target) in enumerate(test_loader):
data, target = data.to(DEVICE), target.to(DEVICE)
# FIXED K ENSEMBLE
accum_logits = model(data)
# Already did 1, do K-1 more
for _ in range(FIXED_K - 1):
accum_logits += model(data)
final_mean_logits = accum_logits / FIXED_K
probs = F.softmax(final_mean_logits / TEMPERATURE, dim=1)
sp = compute_sp(probs).item()
pred = probs.argmax(dim=1).item()
correct = (pred == target.item())
per_example.append({
"seed": seed,
"y_true": target.item(),
"correct": bool(correct),
"sp": sp,
"steps": FIXED_K
})
# Basic stats for print
acc = np.mean([1 if x['correct'] else 0 for x in per_example])
return {"acc": acc}, per_example
def main():
if not os.path.exists(OUT_DIR):
os.makedirs(OUT_DIR)
all_metrics = []
all_examples = []
for seed in SEEDS:
m, ex = run_experiment(seed)
all_metrics.append(m)
all_examples.extend(ex)
print(f"Seed {seed} Fixed-K Accuracy: {m['acc']:.4f}")
with open(f"{OUT_DIR}/per_example.jsonl", "w") as f:
for e in all_examples:
f.write(json.dumps(e) + "\n")
print(f"\nDone. Logs saved to {OUT_DIR}")
if __name__ == "__main__":
main() |