LSTM (test-all)

import pandas as pd
import os, sys
from pathlib import Path
import torch
from datetime import datetime
import logging
import mlflow
import warnings

# Set up paths and logging
root_folder_path = Path('/home/ytli/research')
experiment_folder_path = Path('/home/ytli/research/lstm')
sys.path.append(str(root_folder_path))
sys.path.append(str(experiment_folder_path))

from modules.study_multivar import RealDataTimeSeriesAnalysis, calculate_mse_by_subject_feature
from modules.plot import plot_subject_feature_html_interactive

study_name = "study5-realdata"
folder_path = "fisher"
method_name = "lstm"
datafile_path = "data/fisher_all.csv"

# Initialize the analysis
analysis = RealDataTimeSeriesAnalysis(study_name, folder_path, datafile_path)

# Load and prepare data
# feature_cols = ['energetic','enthusiastic','content','irritable','restless','worried','guilty','afraid','anhedonia','angry','hopeless','down','positive','fatigue','tension','concentrate','ruminate','avoid_act','reassure','procrast','avoid_people']
feature_cols =  [
    'down', 'positive', 'content', 'enthusiastic', 'energetic',
    'hopeless', 'angry', 'irritable', 'reassure'
]

df = analysis.load_data()
sliding_windows_dict = analysis.create_sliding_windows(df, feature_cols, window_size=5, stride=1)
train_loader, val_loader, test_loader = analysis.prepare_datasets(sliding_windows_dict)

# Train model
print("\033[1m\033[95mTraining model...\033[0m")
best_model, best_model_checkpoint_metrics, test_results = analysis.train_model(train_loader, val_loader, test_loader)
print(f"\033[1m\033[96mBEST MODEL VAL LOSS:\033[0m {best_model_checkpoint_metrics['val_loss']:.5f}")
print(f"\033[1m\033[96mTEST RESULTS:\033[0m loss = {test_results[0]['test_loss']:.5f}")

# Evaluate and visualize
print("\033[1m\033[95mEvaluating model on test data...\033[0m")
test_eval_data = analysis.evaluate_model(best_model, test_loader)
print("="*80)

2025/05/22 10:47:05 WARNING mlflow.utils.autologging_utils: MLflow pytorch autologging is known to be compatible with 1.9.0 <= torch <= 2.6.0, but the installed version is 2.6.0+cu124. If you encounter errors during autologging, try upgrading / downgrading torch to a compatible version, or try upgrading MLflow.
INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True
INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores
INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]

Training model...

2025/05/22 10:47:24 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.

Downloading artifacts:   0%|          | 0/1 [00:00<?, ?it/s]

Downloading artifacts:   0%|          | 0/1 [00:00<?, ?it/s]

LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]

🏃 View run fisher at: http://localhost:8093/#/experiments/92/runs/5d9db9f36acf40978c1508ea3458daf0
🧪 View experiment at: http://localhost:8093/#/experiments/92
BEST MODEL VAL LOSS: 0.74562
TEST RESULTS: loss = 1.24797
Evaluating model on test data...
================================================================================

plot_subject_feature_html_interactive(test_eval_data, feature_cols)

mse_df = calculate_mse_by_subject_feature(test_eval_data, feature_cols)
mse_df.to_csv(f'mse.csv', index=False)
mse_df.head(20)

	subject_id	feature_name	mse
0	p111	down	0.180548
1	p111	positive	0.117444
2	p111	content	0.503352
3	p111	enthusiastic	0.282189
4	p111	energetic	0.375488
5	p111	hopeless	0.400014
6	p111	angry	0.666034
7	p111	irritable	0.580033
8	p111	reassure	0.696139
9	p025	down	0.662425
10	p025	positive	2.178204
11	p025	content	2.336298
12	p025	enthusiastic	1.888744
13	p025	energetic	1.006653
14	p025	hopeless	1.221417
15	p025	angry	0.869682
16	p025	irritable	0.819950
17	p025	reassure	1.556690
18	p023	down	0.245138
19	p023	positive	0.570401