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359 changes: 359 additions & 0 deletions examples/daily_physical_activity_adaptive_learning.py
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"""
Your Name: Ritwik Biswas
Your NetId: rbiswas2
Paper Title: Daily Physical Activity Monitoring: Adaptive Learning from Multi-source Motion Sensor Data
Paper Link: https://proceedings.mlr.press/v248/zhang24a.html
Description: A reproduction of the IPD-guided adaptive transfer learning framework on the UCI DSA dataset.
Includes independent implementations in PyTorch and Keras to isolate framework-specific artifacts.
Note: This reproduction observes negative transfer (56.01% vs 70.04% baseline).
"""

import os
import numpy as np
import copy
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
from sklearn.preprocessing import MinMaxScaler
# Note: You may need to install fastdtw: pip install fastdtw
from fastdtw import fastdtw
import requests
import zipfile
import io
from pathlib import Path

# PyHealth imports
from pyhealth.datasets import BaseDataset
from pyhealth.models import BaseModel

# Global Config
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {DEVICE}")

# ---------------------------------------------------------
# 1. Dataset Implementation (UCI DSA)
# ---------------------------------------------------------
class UCIDSADataset(BaseDataset):
"""
Dataset loader for the UCI Daily and Sports Activity (DSA) dataset.
Handles downloading, extraction, and subject-aware parsing.
"""
def __init__(self, root="data", **kwargs):
self.root = root
# Automatically download and process on init
self.download_and_extract_dsa()
self.data = self.load_dsa_dataset_by_subject()

# PyHealth BaseDataset requires a 'samples' attribute, but since
# your pipeline uses a custom subject-split loader, we store the
# dictionary structure in self.data for downstream access.
self.samples = []

def download_and_extract_dsa(self):
"""Downloads the UCI DSA dataset if not present."""
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/00256/data.zip"
if Path(self.root).exists():
print("Dataset already downloaded and extracted.")
return

print("Downloading DSA dataset...")
try:
r = requests.get(url)
r.raise_for_status()
z = zipfile.ZipFile(io.BytesIO(r.content))
print("Extracting dataset...")
z.extractall() # Extracts to 'data' folder by default in zip
print("Done.")
except Exception as e:
print(f"Error downloading dataset: {e}")

def load_dsa_dataset_by_subject(self):
"""Parses the raw text files into a Subject-Sensor dictionary."""
dataset = {}
activities = range(1, 20)
subjects = range(1, 9)
segments = range(1, 61)

sensor_map = {
"T": "torso", "RA": "right_arm", "LA": "left_arm",
"RL": "right_leg", "LL": "left_leg"
}

print("Parsing data files (Subject-Aware)...")
for activity_id in activities:
for subject_id in subjects:
dir_path = os.path.join(self.root, f"a{activity_id:02}", f"p{subject_id}")
if not os.path.exists(dir_path): continue

activity_label = activity_id - 1

for segment_id in segments:
file_path = os.path.join(dir_path, f"s{segment_id:02}.txt")
if not os.path.exists(file_path): continue

try:
raw_data = np.loadtxt(file_path, delimiter=",")
except:
continue

# Split raw 45 columns into 5 sensors (9 cols each)
for i, (prefix, name) in enumerate(sensor_map.items()):
if name not in dataset: dataset[name] = {}
if subject_id not in dataset[name]: dataset[name][subject_id] = []

start_col = i * 9
sensor_data = raw_data[:, start_col : start_col + 9]
dataset[name][subject_id].append((sensor_data, activity_label))
return dataset

# ---------------------------------------------------------
# 2. Model Implementation (IPD-Guided)
# ---------------------------------------------------------
class AdaptiveLSTM(BaseModel):
"""
LSTM classifier with Dropout and Softmax head.
Formula: f(x) = Softmax(Wd · Dropout(LSTM(x)))
"""
def __init__(self, input_dim, hidden_dim, num_classes, **kwargs):
super(AdaptiveLSTM, self).__init__(**kwargs)
# Note: PyTorch LSTM default num_layers is 1
self.lstm = nn.LSTM(input_dim, hidden_dim, batch_first=True)
self.dropout = nn.Dropout(0.2)
self.fc = nn.Linear(hidden_dim, num_classes)

def forward(self, x):
# x shape: [Batch, Time, Feat]
out, _ = self.lstm(x)
out = out[:, -1, :] # Take last timestep
out = self.dropout(out)
out = self.fc(out)
return out

def init_weights(model):
"""
Custom initialization to match Keras 'glorot_uniform' and 'orthogonal' defaults.
Verified necessary for convergence in PyTorch vs Keras reproduction.
"""
for name, param in model.named_parameters():
if 'weight_ih' in name: # Input-Hidden weights (LSTM)
nn.init.xavier_uniform_(param.data)
elif 'weight_hh' in name: # Hidden-Hidden weights (LSTM)
nn.init.orthogonal_(param.data)
elif 'bias' in name: # Biases
nn.init.zeros_(param.data)
elif 'fc.weight' in name: # Dense layer weights
nn.init.xavier_uniform_(param.data)
elif 'fc.bias' in name: # Dense layer bias
nn.init.zeros_(param.data)

# ---------------------------------------------------------
# 3. Helper Functions (Data Processing & IPD)
# ---------------------------------------------------------
def prepare_loaders_subject_split(domain_data, train_subjects, test_subjects, batch_size=16):
"""
Splits data by subject and applies Per-Sample MinMax Scaling (-1, 1).
Batch size 16 used for better generalization (Regularization noise).
"""
X_train, y_train = [], []
X_test, y_test = [], []

# Aggregate Train Data
for subj in train_subjects:
if subj in domain_data:
X_train.extend([item[0] for item in domain_data[subj]])
y_train.extend([item[1] for item in domain_data[subj]])

# Aggregate Test Data
for subj in test_subjects:
if subj in domain_data:
X_test.extend([item[0] for item in domain_data[subj]])
y_test.extend([item[1] for item in domain_data[subj]])

X_train = np.array(X_train, dtype=np.float32)
y_train = np.array(y_train, dtype=np.int64)
X_test = np.array(X_test, dtype=np.float32)
y_test = np.array(y_test, dtype=np.int64)

# Normalization (Per-Sample MinMax)
N, T, F = X_train.shape
scaler = MinMaxScaler(feature_range=(-1, 1))

# Reshape to (N*T, F) for scaling, then back
X_train_reshaped = X_train.reshape(-1, F)
X_train_scaled = scaler.fit_transform(X_train_reshaped).reshape(N, T, F)

N_test, _, _ = X_test.shape
X_test_reshaped = X_test.reshape(-1, F)
X_test_scaled = scaler.transform(X_test_reshaped).reshape(N_test, T, F)

# Create TensorDatasets
train_ds = TensorDataset(torch.from_numpy(X_train_scaled), torch.from_numpy(y_train))
test_ds = TensorDataset(torch.from_numpy(X_test_scaled), torch.from_numpy(y_test))

train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_ds, batch_size=batch_size, shuffle=False)

return train_loader, test_loader, F, 19 # 19 Classes

def calculate_ipd_proxy(target_data, source_data, subjects, max_pairs=100):
"""
Calculates Inter-domain Pairwise Distance (IPD) using Dynamic Time Warping (DTW).
Used to rank source domains by kinematic similarity.
"""
rng = np.random.default_rng(42)
t_pool, s_pool = [], []

for subj in subjects:
if subj in target_data: t_pool.extend([x[0] for x in target_data[subj]])
if subj in source_data: s_pool.extend([x[0] for x in source_data[subj]])

num_samples = min(len(t_pool), len(s_pool))
use_pairs = min(max_pairs, num_samples)

if use_pairs == 0: return np.inf

indices = rng.choice(num_samples, size=use_pairs, replace=False)
distances = []

for idx in indices:
# DTW distance using Euclidean norm
dist, _ = fastdtw(t_pool[idx], s_pool[idx], dist=lambda x, y: np.linalg.norm(x - y))
distances.append(dist)

return np.mean(distances) if distances else np.inf

def eval_model(model, loader, criterion):
model.eval()
total_loss, correct, total = 0, 0, 0
with torch.no_grad():
for X, y in loader:
X, y = X.to(DEVICE), y.to(DEVICE)
out = model(X)
loss = criterion(out, y)
total_loss += loss.item() * X.size(0)
pred = torch.max(out, 1)[1]
correct += (pred == y).sum().item()
total += y.size(0)
return total_loss / total, correct / total

# ---------------------------------------------------------
# 3. Main Execution
# ---------------------------------------------------------
if __name__ == "__main__":
print("Starting reproduction of Zhang et al. (2024)...")

# --- Config ---
TARGET_DOMAIN = "right_arm"
SOURCE_DOMAINS = ["torso", "left_arm", "right_leg", "left_leg"]
TRAIN_SUBJECTS = [1, 2, 3, 4, 5, 6]
VAL_SUBJECTS = [7, 8]

# 1. Load Data
dsa_dataset = UCIDSADataset(root="data")
dsa_data = dsa_dataset.data # Access the internal dict

# 2. Calculate IPD
ipd_scores = {}
print(f"Calculating IPD against '{TARGET_DOMAIN}'...")
for source_name in SOURCE_DOMAINS:
ipd = calculate_ipd_proxy(dsa_data[TARGET_DOMAIN], dsa_data[source_name], TRAIN_SUBJECTS)
ipd_scores[source_name] = ipd
print(f" IPD({source_name}): {ipd:.4f}")

# Prepare Domain Weights
valid_ipds = {k: v for k, v in ipd_scores.items() if not np.isinf(v)}
total_ipd = sum(valid_ipds.values())
domain_weights = {k: v / total_ipd for k, v in valid_ipds.items()}

# Sort Descending (Dissimilar -> Similar for pre-training)
sorted_sources = sorted(valid_ipds.keys(), key=lambda k: valid_ipds[k], reverse=True)
print(f"\nTraining Order (Dissimilar -> Similar): {sorted_sources}")

# 3. Setup Model
# Get dims from target domain loader
target_train_l, target_test_l, F_dim, C_classes = prepare_loaders_subject_split(
dsa_data[TARGET_DOMAIN], TRAIN_SUBJECTS, VAL_SUBJECTS
)

model = AdaptiveLSTM(input_dim=F_dim, hidden_dim=64, num_classes=C_classes).to(DEVICE)
model.apply(init_weights) # Apply Keras Init
criterion = nn.CrossEntropyLoss()

# --- Phase 1: Source Pre-training ---
print("\n--- Phase 1: Source Pre-training ---")
current_lr = 0.005 # High initial LR per repo

for domain in sorted_sources:
print(f"\nTraining on {domain} (LR: {current_lr:.5f})")
train_l, val_l, _, _ = prepare_loaders_subject_split(
dsa_data[domain], TRAIN_SUBJECTS, VAL_SUBJECTS
)

# Reset Optimizer (Clear Momentum between domains)
optimizer = optim.Adam(model.parameters(), lr=current_lr)

for ep in range(30): # 30 Epochs per domain
model.train()
total_loss, correct, total = 0, 0, 0
for X, y in train_l:
X, y = X.to(DEVICE), y.to(DEVICE)
optimizer.zero_grad()
out = model(X)
loss = criterion(out, y)
loss.backward()
# STABILIZATION: Gradient Clipping
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()

total_loss += loss.item() * X.size(0)
pred = torch.max(out, 1)[1]
correct += (pred == y).sum().item()
total += y.size(0)

t_acc = correct / total
v_loss, v_acc = eval_model(model, val_l, criterion)

if (ep+1) % 10 == 0:
print(f" Ep {ep+1}: Train Acc {t_acc:.3f} | Val Acc {v_acc:.3f}")

# Adaptive Decay based on IPD
alpha = domain_weights[domain]
current_lr = current_lr * (1 - alpha)

# --- Phase 2: Target Fine-tuning ---
print("\n--- Phase 2: Target Fine-tuning ---")
ft_lr = 0.001
optimizer = optim.Adam(model.parameters(), lr=ft_lr)

best_acc = 0.0
best_model_wts = copy.deepcopy(model.state_dict())

for ep in range(40):
model.train()
total_loss, correct, total = 0, 0, 0
for X, y in target_train_l:
X, y = X.to(DEVICE), y.to(DEVICE)
optimizer.zero_grad()
out = model(X)
loss = criterion(out, y)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()

total_loss += loss.item() * X.size(0)
pred = torch.max(out, 1)[1]
correct += (pred == y).sum().item()
total += y.size(0)

t_acc = correct / total
v_loss, v_acc = eval_model(model, target_test_l, criterion)

if v_acc > best_acc:
best_acc = v_acc
best_model_wts = copy.deepcopy(model.state_dict())

if (ep+1) % 5 == 0:
print(f" Ep {ep+1}: Train Acc {t_acc:.3f} | Val Acc {v_acc:.3f}")

print(f"Final Transfer Accuracy (Best): {best_acc:.4f}")