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devjas1
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·
aecd727
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Parent(s):
9fe46f4
(FEAT)[Implement Robust Training Management Backend]: Add training manager, config classes, data augmentation, metrics, and cross-validation utilities
Browse files- Developed 'TrainingManager' class to orchestrate training jobs, including job submission, tracking, and resource allocation.
- Defined 'TrainingConfig' and 'TrainingStatus' for flexible experiment configuration and state monitoring.
- Implemented multiple cross-validation strategies (KFold, StratifiedKFold, TimeSeriesSplit) for flexible ML evaluation.
- Added spectroscopy-specific metrics and spectral cosine similarity computation for domain-relevant model assessment.
- Integrated secure data loading, preprocessing, and augmentation logic to support various file formats and enforce data integrity.
- utils/training_manager.py +817 -0
utils/training_manager.py
ADDED
@@ -0,0 +1,817 @@
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1 |
+
"""
|
2 |
+
Training job management system for ML Hub functionality.
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3 |
+
Handles asynchronous training jobs, progress tracking, and result management.
|
4 |
+
"""
|
5 |
+
|
6 |
+
import os
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7 |
+
import sys
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8 |
+
import json
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9 |
+
import time
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10 |
+
import uuid
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11 |
+
import threading
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12 |
+
import concurrent.futures
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13 |
+
import multiprocessing
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14 |
+
from datetime import datetime, timedelta
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15 |
+
from dataclasses import dataclass, asdict, field
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16 |
+
from enum import Enum
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17 |
+
from typing import Dict, List, Optional, Callable, Any, Tuple
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18 |
+
from pathlib import Path
|
19 |
+
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20 |
+
import torch
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21 |
+
import torch.nn as nn
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22 |
+
import numpy as np
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23 |
+
from torch.utils.data import TensorDataset, DataLoader
|
24 |
+
from sklearn.model_selection import StratifiedKFold, KFold, TimeSeriesSplit
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25 |
+
from sklearn.metrics import confusion_matrix, accuracy_score, f1_score
|
26 |
+
from sklearn.metrics.pairwise import cosine_similarity
|
27 |
+
from scipy.signal import find_peaks
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28 |
+
from scipy.spatial.distance import euclidean
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29 |
+
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30 |
+
# Add project-specific imports
|
31 |
+
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
|
32 |
+
from models.registry import choices as model_choices, build as build_model
|
33 |
+
from utils.preprocessing import preprocess_spectrum
|
34 |
+
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35 |
+
|
36 |
+
def spectral_cosine_similarity(y_true: np.ndarray, y_pred: np.ndarray) -> float:
|
37 |
+
"""Calculate cosine similarity between spectral predictions and true values"""
|
38 |
+
# Reshape if needed for cosine similarity calculation
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39 |
+
if y_true.ndim == 1:
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40 |
+
y_true = y_true.reshape(1, -1)
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41 |
+
if y_pred.ndim == 1:
|
42 |
+
y_pred = y_pred.reshape(1, -1)
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43 |
+
|
44 |
+
return float(cosine_similarity(y_true, y_pred)[0, 0])
|
45 |
+
|
46 |
+
|
47 |
+
def peak_matching_score(
|
48 |
+
spectrum1: np.ndarray,
|
49 |
+
spectrum2: np.ndarray,
|
50 |
+
height_threshold: float = 0.1,
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51 |
+
distance: int = 5,
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52 |
+
) -> float:
|
53 |
+
"""Calculate peak matching score between two spectra"""
|
54 |
+
try:
|
55 |
+
# Find peaks in both spectra
|
56 |
+
peaks1, _ = find_peaks(spectrum1, height=height_threshold, distance=distance)
|
57 |
+
peaks2, _ = find_peaks(spectrum2, height=height_threshold, distance=distance)
|
58 |
+
|
59 |
+
if len(peaks1) == 0 or len(peaks2) == 0:
|
60 |
+
return 0.0
|
61 |
+
|
62 |
+
# Calculate matching peaks (within tolerance)
|
63 |
+
tolerance = 3 # wavenumber tolerance
|
64 |
+
matches = 0
|
65 |
+
|
66 |
+
for peak1 in peaks1:
|
67 |
+
for peak2 in peaks2:
|
68 |
+
if abs(peak1 - peak2) <= tolerance:
|
69 |
+
matches += 1
|
70 |
+
break
|
71 |
+
|
72 |
+
# Return normalized matching score
|
73 |
+
return matches / max(len(peaks1), len(peaks2))
|
74 |
+
except:
|
75 |
+
return 0.0
|
76 |
+
|
77 |
+
|
78 |
+
def spectral_euclidean_distance(y_true: np.ndarray, y_pred: np.ndarray) -> float:
|
79 |
+
"""Calculate normalized Euclidean distance between spectra"""
|
80 |
+
try:
|
81 |
+
distance = euclidean(y_true.flatten(), y_pred.flatten())
|
82 |
+
# Normalize by the length of the spectrum
|
83 |
+
return distance / len(y_true.flatten())
|
84 |
+
except:
|
85 |
+
return float("inf")
|
86 |
+
|
87 |
+
|
88 |
+
def calculate_spectroscopy_metrics(
|
89 |
+
y_true: np.ndarray, y_pred: np.ndarray, probabilities: Optional[np.ndarray] = None
|
90 |
+
) -> Dict[str, float]:
|
91 |
+
"""Calculate comprehensive spectroscopy-specific metrics"""
|
92 |
+
metrics = {}
|
93 |
+
|
94 |
+
try:
|
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+
# Standard classification metrics
|
96 |
+
metrics["accuracy"] = accuracy_score(y_true, y_pred)
|
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+
metrics["f1_score"] = f1_score(y_true, y_pred, average="weighted")
|
98 |
+
|
99 |
+
# Spectroscopy-specific metrics
|
100 |
+
if probabilities is not None and len(probabilities.shape) > 1:
|
101 |
+
# For classification with probabilities, use cosine similarity on prob distributions
|
102 |
+
unique_classes = np.unique(y_true)
|
103 |
+
if len(unique_classes) > 1:
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104 |
+
# Convert true labels to one-hot for similarity calculation
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105 |
+
y_true_onehot = np.eye(len(unique_classes))[y_true]
|
106 |
+
metrics["cosine_similarity"] = float(
|
107 |
+
cosine_similarity(
|
108 |
+
y_true_onehot.mean(axis=0).reshape(1, -1),
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109 |
+
probabilities.mean(axis=0).reshape(1, -1),
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+
)[0, 0]
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+
)
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112 |
+
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+
# Add bias audit metric (class distribution comparison)
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114 |
+
unique_true, counts_true = np.unique(y_true, return_counts=True)
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115 |
+
unique_pred, counts_pred = np.unique(y_pred, return_counts=True)
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116 |
+
|
117 |
+
# Calculate distribution difference (Jensen-Shannon divergence approximation)
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118 |
+
true_dist = counts_true / len(y_true)
|
119 |
+
pred_dist = np.zeros_like(true_dist)
|
120 |
+
|
121 |
+
for i, class_label in enumerate(unique_true):
|
122 |
+
if class_label in unique_pred:
|
123 |
+
pred_idx = np.where(unique_pred == class_label)[0][0]
|
124 |
+
pred_dist[i] = counts_pred[pred_idx] / len(y_pred)
|
125 |
+
|
126 |
+
# Simple distribution similarity (1 - average absolute difference)
|
127 |
+
metrics["distribution_similarity"] = 1.0 - np.mean(
|
128 |
+
np.abs(true_dist - pred_dist)
|
129 |
+
)
|
130 |
+
|
131 |
+
except Exception as e:
|
132 |
+
print(f"Error calculating spectroscopy metrics: {e}")
|
133 |
+
# Return basic metrics
|
134 |
+
metrics = {
|
135 |
+
"accuracy": accuracy_score(y_true, y_pred) if len(y_true) > 0 else 0.0,
|
136 |
+
"f1_score": (
|
137 |
+
f1_score(y_true, y_pred, average="weighted") if len(y_true) > 0 else 0.0
|
138 |
+
),
|
139 |
+
"cosine_similarity": 0.0,
|
140 |
+
"distribution_similarity": 0.0,
|
141 |
+
}
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142 |
+
|
143 |
+
return metrics
|
144 |
+
|
145 |
+
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146 |
+
def get_cv_splitter(strategy: str, n_splits: int = 10, random_state: int = 42):
|
147 |
+
"""Get cross-validation splitter based on strategy"""
|
148 |
+
if strategy == "stratified_kfold":
|
149 |
+
return StratifiedKFold(
|
150 |
+
n_splits=n_splits, shuffle=True, random_state=random_state
|
151 |
+
)
|
152 |
+
elif strategy == "kfold":
|
153 |
+
return KFold(n_splits=n_splits, shuffle=True, random_state=random_state)
|
154 |
+
elif strategy == "time_series_split":
|
155 |
+
return TimeSeriesSplit(n_splits=n_splits)
|
156 |
+
else:
|
157 |
+
# Default to stratified k-fold
|
158 |
+
return StratifiedKFold(
|
159 |
+
n_splits=n_splits, shuffle=True, random_state=random_state
|
160 |
+
)
|
161 |
+
|
162 |
+
|
163 |
+
def augment_spectral_data(
|
164 |
+
X: np.ndarray,
|
165 |
+
y: np.ndarray,
|
166 |
+
noise_level: float = 0.01,
|
167 |
+
augmentation_factor: int = 2,
|
168 |
+
) -> Tuple[np.ndarray, np.ndarray]:
|
169 |
+
"""Augment spectral data with realistic noise and variations"""
|
170 |
+
if augmentation_factor <= 1:
|
171 |
+
return X, y
|
172 |
+
|
173 |
+
augmented_X = [X]
|
174 |
+
augmented_y = [y]
|
175 |
+
|
176 |
+
for i in range(augmentation_factor - 1):
|
177 |
+
# Add Gaussian noise
|
178 |
+
noise = np.random.normal(0, noise_level, X.shape)
|
179 |
+
X_noisy = X + noise
|
180 |
+
|
181 |
+
# Add baseline drift (common in spectroscopy)
|
182 |
+
baseline_drift = np.random.normal(0, noise_level * 0.5, (X.shape[0], 1))
|
183 |
+
X_drift = X_noisy + baseline_drift
|
184 |
+
|
185 |
+
# Add intensity scaling variation
|
186 |
+
intensity_scale = np.random.normal(1.0, 0.05, (X.shape[0], 1))
|
187 |
+
X_scaled = X_drift * intensity_scale
|
188 |
+
|
189 |
+
# Ensure no negative values
|
190 |
+
X_scaled = np.maximum(X_scaled, 0)
|
191 |
+
|
192 |
+
augmented_X.append(X_scaled)
|
193 |
+
augmented_y.append(y)
|
194 |
+
|
195 |
+
return np.vstack(augmented_X), np.hstack(augmented_y)
|
196 |
+
|
197 |
+
|
198 |
+
class TrainingStatus(Enum):
|
199 |
+
"""Training job status enumeration"""
|
200 |
+
|
201 |
+
PENDING = "pending"
|
202 |
+
RUNNING = "running"
|
203 |
+
COMPLETED = "completed"
|
204 |
+
FAILED = "failed"
|
205 |
+
CANCELLED = "cancelled"
|
206 |
+
|
207 |
+
|
208 |
+
class CVStrategy(Enum):
|
209 |
+
"""Cross-validation strategy enumeration"""
|
210 |
+
|
211 |
+
STRATIFIED_KFOLD = "stratified_kfold"
|
212 |
+
KFOLD = "kfold"
|
213 |
+
TIME_SERIES_SPLIT = "time_series_split"
|
214 |
+
|
215 |
+
|
216 |
+
@dataclass
|
217 |
+
class TrainingConfig:
|
218 |
+
"""Training configuration parameters"""
|
219 |
+
|
220 |
+
model_name: str
|
221 |
+
dataset_path: str
|
222 |
+
target_len: int = 500
|
223 |
+
batch_size: int = 16
|
224 |
+
epochs: int = 10
|
225 |
+
learning_rate: float = 1e-3
|
226 |
+
num_folds: int = 10
|
227 |
+
baseline_correction: bool = True
|
228 |
+
smoothing: bool = True
|
229 |
+
normalization: bool = True
|
230 |
+
modality: str = "raman"
|
231 |
+
device: str = "auto" # auto, cpu, cuda
|
232 |
+
cv_strategy: str = "stratified_kfold" # New field for CV strategy
|
233 |
+
spectral_weight: float = 0.1 # Weight for spectroscopy-specific metrics
|
234 |
+
enable_augmentation: bool = False # Enable data augmentation
|
235 |
+
noise_level: float = 0.01 # Noise level for augmentation
|
236 |
+
|
237 |
+
def to_dict(self) -> Dict[str, Any]:
|
238 |
+
"""Convert to dictionary for serialization"""
|
239 |
+
return asdict(self)
|
240 |
+
|
241 |
+
|
242 |
+
@dataclass
|
243 |
+
class TrainingProgress:
|
244 |
+
"""Training progress tracking with enhanced metrics"""
|
245 |
+
|
246 |
+
current_fold: int = 0
|
247 |
+
total_folds: int = 10
|
248 |
+
current_epoch: int = 0
|
249 |
+
total_epochs: int = 10
|
250 |
+
current_loss: float = 0.0
|
251 |
+
current_accuracy: float = 0.0
|
252 |
+
fold_accuracies: List[float] = field(default_factory=list)
|
253 |
+
confusion_matrices: List[List[List[int]]] = field(default_factory=list)
|
254 |
+
spectroscopy_metrics: List[Dict[str, float]] = field(default_factory=list)
|
255 |
+
start_time: Optional[datetime] = None
|
256 |
+
end_time: Optional[datetime] = None
|
257 |
+
|
258 |
+
|
259 |
+
@dataclass
|
260 |
+
class TrainingJob:
|
261 |
+
"""Training job container"""
|
262 |
+
|
263 |
+
job_id: str
|
264 |
+
config: TrainingConfig
|
265 |
+
status: TrainingStatus = TrainingStatus.PENDING
|
266 |
+
progress: TrainingProgress = None
|
267 |
+
error_message: Optional[str] = None
|
268 |
+
created_at: datetime = None
|
269 |
+
started_at: Optional[datetime] = None
|
270 |
+
completed_at: Optional[datetime] = None
|
271 |
+
weights_path: Optional[str] = None
|
272 |
+
logs_path: Optional[str] = None
|
273 |
+
|
274 |
+
def __post_init__(self):
|
275 |
+
if self.progress is None:
|
276 |
+
self.progress = TrainingProgress(
|
277 |
+
total_folds=self.config.num_folds, total_epochs=self.config.epochs
|
278 |
+
)
|
279 |
+
if self.created_at is None:
|
280 |
+
self.created_at = datetime.now()
|
281 |
+
|
282 |
+
|
283 |
+
class TrainingManager:
|
284 |
+
"""Manager for training jobs with async execution and progress tracking"""
|
285 |
+
|
286 |
+
def __init__(
|
287 |
+
self,
|
288 |
+
max_workers: int = 2,
|
289 |
+
output_dir: str = "outputs",
|
290 |
+
use_multiprocessing: bool = True,
|
291 |
+
):
|
292 |
+
self.max_workers = max_workers
|
293 |
+
self.use_multiprocessing = use_multiprocessing
|
294 |
+
|
295 |
+
# Use ProcessPoolExecutor for CPU/GPU-bound tasks, ThreadPoolExecutor for I/O-bound
|
296 |
+
if use_multiprocessing:
|
297 |
+
# Limit workers to available CPU cores to prevent oversubscription
|
298 |
+
actual_workers = min(max_workers, multiprocessing.cpu_count())
|
299 |
+
self.executor = concurrent.futures.ProcessPoolExecutor(
|
300 |
+
max_workers=actual_workers
|
301 |
+
)
|
302 |
+
else:
|
303 |
+
self.executor = concurrent.futures.ThreadPoolExecutor(
|
304 |
+
max_workers=max_workers
|
305 |
+
)
|
306 |
+
|
307 |
+
self.jobs: Dict[str, TrainingJob] = {}
|
308 |
+
self.output_dir = Path(output_dir)
|
309 |
+
self.output_dir.mkdir(exist_ok=True)
|
310 |
+
(self.output_dir / "weights").mkdir(exist_ok=True)
|
311 |
+
(self.output_dir / "logs").mkdir(exist_ok=True)
|
312 |
+
|
313 |
+
# Progress callbacks for UI updates
|
314 |
+
self.progress_callbacks: Dict[str, List[Callable]] = {}
|
315 |
+
|
316 |
+
def generate_job_id(self) -> str:
|
317 |
+
"""Generate unique job ID"""
|
318 |
+
return f"train_{uuid.uuid4().hex[:8]}_{int(time.time())}"
|
319 |
+
|
320 |
+
def submit_training_job(
|
321 |
+
self, config: TrainingConfig, progress_callback: Optional[Callable] = None
|
322 |
+
) -> str:
|
323 |
+
"""Submit a new training job"""
|
324 |
+
job_id = self.generate_job_id()
|
325 |
+
job = TrainingJob(job_id=job_id, config=config)
|
326 |
+
|
327 |
+
# Set up output paths
|
328 |
+
job.weights_path = str(self.output_dir / "weights" / f"{job_id}_model.pth")
|
329 |
+
job.logs_path = str(self.output_dir / "logs" / f"{job_id}_log.json")
|
330 |
+
|
331 |
+
self.jobs[job_id] = job
|
332 |
+
|
333 |
+
# Register progress callback
|
334 |
+
if progress_callback:
|
335 |
+
if job_id not in self.progress_callbacks:
|
336 |
+
self.progress_callbacks[job_id] = []
|
337 |
+
self.progress_callbacks[job_id].append(progress_callback)
|
338 |
+
|
339 |
+
# Submit to thread pool
|
340 |
+
self.executor.submit(self._run_training_job, job)
|
341 |
+
|
342 |
+
return job_id
|
343 |
+
|
344 |
+
def _run_training_job(self, job: TrainingJob) -> None:
|
345 |
+
"""Execute training job (runs in separate thread)"""
|
346 |
+
try:
|
347 |
+
job.status = TrainingStatus.RUNNING
|
348 |
+
job.started_at = datetime.now()
|
349 |
+
job.progress.start_time = job.started_at
|
350 |
+
|
351 |
+
self._notify_progress(job.job_id, job)
|
352 |
+
|
353 |
+
# Device selection
|
354 |
+
device = self._get_device(job.config.device)
|
355 |
+
|
356 |
+
# Load and preprocess data
|
357 |
+
X, y = self._load_and_preprocess_data(job)
|
358 |
+
if X is None or y is None:
|
359 |
+
raise ValueError("Failed to load dataset")
|
360 |
+
|
361 |
+
# Set reproducibility
|
362 |
+
self._set_reproducibility()
|
363 |
+
|
364 |
+
# Run cross-validation training
|
365 |
+
self._run_cross_validation(job, X, y, device)
|
366 |
+
|
367 |
+
# Save final results
|
368 |
+
self._save_training_results(job)
|
369 |
+
|
370 |
+
job.status = TrainingStatus.COMPLETED
|
371 |
+
job.completed_at = datetime.now()
|
372 |
+
job.progress.end_time = job.completed_at
|
373 |
+
|
374 |
+
except Exception as e:
|
375 |
+
job.status = TrainingStatus.FAILED
|
376 |
+
job.error_message = str(e)
|
377 |
+
job.completed_at = datetime.now()
|
378 |
+
|
379 |
+
finally:
|
380 |
+
self._notify_progress(job.job_id, job)
|
381 |
+
|
382 |
+
def _get_device(self, device_preference: str) -> torch.device:
|
383 |
+
"""Get appropriate device for training"""
|
384 |
+
if device_preference == "auto":
|
385 |
+
return torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
386 |
+
elif device_preference == "cuda" and torch.cuda.is_available():
|
387 |
+
return torch.device("cuda")
|
388 |
+
else:
|
389 |
+
return torch.device("cpu")
|
390 |
+
|
391 |
+
def _load_and_preprocess_data(
|
392 |
+
self, job: TrainingJob
|
393 |
+
) -> Tuple[Optional[np.ndarray], Optional[np.ndarray]]:
|
394 |
+
"""Load and preprocess dataset with enhanced validation and security"""
|
395 |
+
try:
|
396 |
+
config = job.config
|
397 |
+
dataset_path = Path(config.dataset_path)
|
398 |
+
|
399 |
+
# Enhanced path validation and security
|
400 |
+
if not dataset_path.exists():
|
401 |
+
raise FileNotFoundError(f"Dataset path not found: {dataset_path}")
|
402 |
+
|
403 |
+
# Validate dataset path is within allowed directories (security)
|
404 |
+
try:
|
405 |
+
dataset_path = dataset_path.resolve()
|
406 |
+
allowed_bases = [
|
407 |
+
Path("datasets").resolve(),
|
408 |
+
Path("data").resolve(),
|
409 |
+
Path("/tmp").resolve(),
|
410 |
+
]
|
411 |
+
if not any(
|
412 |
+
str(dataset_path).startswith(str(base)) for base in allowed_bases
|
413 |
+
):
|
414 |
+
raise ValueError(
|
415 |
+
f"Dataset path outside allowed directories: {dataset_path}"
|
416 |
+
)
|
417 |
+
except Exception as e:
|
418 |
+
print(f"Path validation error: {e}")
|
419 |
+
raise ValueError("Invalid dataset path")
|
420 |
+
|
421 |
+
# Load data from dataset directory
|
422 |
+
X, y = [], []
|
423 |
+
total_files = 0
|
424 |
+
processed_files = 0
|
425 |
+
max_files_per_class = 1000 # Limit to prevent memory issues
|
426 |
+
max_file_size = 10 * 1024 * 1024 # 10MB per file
|
427 |
+
|
428 |
+
# Look for data files in the dataset directory
|
429 |
+
for label_dir in dataset_path.iterdir():
|
430 |
+
if not label_dir.is_dir():
|
431 |
+
continue
|
432 |
+
|
433 |
+
label = 0 if "stable" in label_dir.name.lower() else 1
|
434 |
+
files_in_class = 0
|
435 |
+
|
436 |
+
# Support multiple file formats
|
437 |
+
file_patterns = ["*.txt", "*.csv", "*.json"]
|
438 |
+
|
439 |
+
for pattern in file_patterns:
|
440 |
+
for file_path in label_dir.glob(pattern):
|
441 |
+
total_files += 1
|
442 |
+
|
443 |
+
# Security: Check file size
|
444 |
+
if file_path.stat().st_size > max_file_size:
|
445 |
+
print(
|
446 |
+
f"Skipping large file: {file_path} ({file_path.stat().st_size} bytes)"
|
447 |
+
)
|
448 |
+
continue
|
449 |
+
|
450 |
+
# Limit files per class
|
451 |
+
if files_in_class >= max_files_per_class:
|
452 |
+
print(
|
453 |
+
f"Reached maximum files per class ({max_files_per_class}) for {label_dir.name}"
|
454 |
+
)
|
455 |
+
break
|
456 |
+
|
457 |
+
try:
|
458 |
+
# Load spectrum data based on file type
|
459 |
+
if file_path.suffix.lower() == ".txt":
|
460 |
+
data = np.loadtxt(file_path)
|
461 |
+
if data.ndim == 2 and data.shape[1] >= 2:
|
462 |
+
x_raw, y_raw = data[:, 0], data[:, 1]
|
463 |
+
elif data.ndim == 1:
|
464 |
+
# Single column data
|
465 |
+
x_raw = np.arange(len(data))
|
466 |
+
y_raw = data
|
467 |
+
else:
|
468 |
+
continue
|
469 |
+
|
470 |
+
elif file_path.suffix.lower() == ".csv":
|
471 |
+
import pandas as pd
|
472 |
+
|
473 |
+
df = pd.read_csv(file_path)
|
474 |
+
if df.shape[1] >= 2:
|
475 |
+
x_raw, y_raw = (
|
476 |
+
df.iloc[:, 0].values,
|
477 |
+
df.iloc[:, 1].values,
|
478 |
+
)
|
479 |
+
else:
|
480 |
+
x_raw = np.arange(len(df))
|
481 |
+
y_raw = df.iloc[:, 0].values
|
482 |
+
|
483 |
+
elif file_path.suffix.lower() == ".json":
|
484 |
+
with open(file_path, "r") as f:
|
485 |
+
data_dict = json.load(f)
|
486 |
+
if isinstance(data_dict, dict):
|
487 |
+
if "x" in data_dict and "y" in data_dict:
|
488 |
+
x_raw, y_raw = np.array(
|
489 |
+
data_dict["x"]
|
490 |
+
), np.array(data_dict["y"])
|
491 |
+
elif "spectrum" in data_dict:
|
492 |
+
y_raw = np.array(data_dict["spectrum"])
|
493 |
+
x_raw = np.arange(len(y_raw))
|
494 |
+
else:
|
495 |
+
continue
|
496 |
+
else:
|
497 |
+
continue
|
498 |
+
else:
|
499 |
+
continue
|
500 |
+
|
501 |
+
# Validate data integrity
|
502 |
+
if len(x_raw) != len(y_raw) or len(x_raw) < 10:
|
503 |
+
print(
|
504 |
+
f"Invalid data in file {file_path}: insufficient data points"
|
505 |
+
)
|
506 |
+
continue
|
507 |
+
|
508 |
+
# Check for NaN or infinite values
|
509 |
+
if np.any(np.isnan(y_raw)) or np.any(np.isinf(y_raw)):
|
510 |
+
print(
|
511 |
+
f"Invalid data in file {file_path}: NaN or infinite values"
|
512 |
+
)
|
513 |
+
continue
|
514 |
+
|
515 |
+
# Validate reasonable value ranges for spectroscopy
|
516 |
+
if np.min(y_raw) < -1000 or np.max(y_raw) > 1e6:
|
517 |
+
print(
|
518 |
+
f"Suspicious data values in file {file_path}: outside expected range"
|
519 |
+
)
|
520 |
+
continue
|
521 |
+
|
522 |
+
# Preprocess spectrum
|
523 |
+
_, y_processed = preprocess_spectrum(
|
524 |
+
x_raw,
|
525 |
+
y_raw,
|
526 |
+
modality=config.modality,
|
527 |
+
target_len=config.target_len,
|
528 |
+
do_baseline=config.baseline_correction,
|
529 |
+
do_smooth=config.smoothing,
|
530 |
+
do_normalize=config.normalization,
|
531 |
+
)
|
532 |
+
|
533 |
+
# Final validation of processed data
|
534 |
+
if (
|
535 |
+
y_processed is None
|
536 |
+
or len(y_processed) != config.target_len
|
537 |
+
):
|
538 |
+
print(f"Preprocessing failed for file {file_path}")
|
539 |
+
continue
|
540 |
+
|
541 |
+
X.append(y_processed)
|
542 |
+
y.append(label)
|
543 |
+
files_in_class += 1
|
544 |
+
processed_files += 1
|
545 |
+
|
546 |
+
except Exception as e:
|
547 |
+
print(f"Error processing file {file_path}: {e}")
|
548 |
+
continue
|
549 |
+
|
550 |
+
# Validate final dataset
|
551 |
+
if len(X) == 0:
|
552 |
+
raise ValueError("No valid data files found in dataset")
|
553 |
+
|
554 |
+
if len(X) < 10:
|
555 |
+
raise ValueError(
|
556 |
+
f"Insufficient data: only {len(X)} samples found (minimum 10 required)"
|
557 |
+
)
|
558 |
+
|
559 |
+
# Check class balance
|
560 |
+
unique_labels, counts = np.unique(y, return_counts=True)
|
561 |
+
if len(unique_labels) < 2:
|
562 |
+
raise ValueError("Dataset must contain at least 2 classes")
|
563 |
+
|
564 |
+
min_class_size = min(counts)
|
565 |
+
if min_class_size < 3:
|
566 |
+
raise ValueError(
|
567 |
+
f"Insufficient samples in one class: minimum {min_class_size} (need at least 3)"
|
568 |
+
)
|
569 |
+
|
570 |
+
print(f"Dataset loaded: {processed_files}/{total_files} files processed")
|
571 |
+
print(f"Class distribution: {dict(zip(unique_labels, counts))}")
|
572 |
+
|
573 |
+
return np.array(X, dtype=np.float32), np.array(y, dtype=np.int64)
|
574 |
+
|
575 |
+
except Exception as e:
|
576 |
+
print(f"Error loading dataset: {e}")
|
577 |
+
return None, None
|
578 |
+
|
579 |
+
def _set_reproducibility(self):
|
580 |
+
"""Set random seeds for reproducibility"""
|
581 |
+
SEED = 42
|
582 |
+
np.random.seed(SEED)
|
583 |
+
torch.manual_seed(SEED)
|
584 |
+
if torch.cuda.is_available():
|
585 |
+
torch.cuda.manual_seed_all(SEED)
|
586 |
+
torch.backends.cudnn.deterministic = True
|
587 |
+
torch.backends.cudnn.benchmark = False
|
588 |
+
|
589 |
+
def _run_cross_validation(
|
590 |
+
self, job: TrainingJob, X: np.ndarray, y: np.ndarray, device: torch.device
|
591 |
+
):
|
592 |
+
"""Run configurable cross-validation training with spectroscopy metrics"""
|
593 |
+
config = job.config
|
594 |
+
|
595 |
+
# Apply data augmentation if enabled
|
596 |
+
if config.enable_augmentation:
|
597 |
+
X, y = augment_spectral_data(
|
598 |
+
X, y, noise_level=config.noise_level, augmentation_factor=2
|
599 |
+
)
|
600 |
+
|
601 |
+
# Get appropriate CV splitter
|
602 |
+
cv_splitter = get_cv_splitter(config.cv_strategy, config.num_folds)
|
603 |
+
|
604 |
+
fold_accuracies = []
|
605 |
+
confusion_matrices = []
|
606 |
+
spectroscopy_metrics = []
|
607 |
+
|
608 |
+
for fold, (train_idx, val_idx) in enumerate(cv_splitter.split(X, y), 1):
|
609 |
+
job.progress.current_fold = fold
|
610 |
+
job.progress.current_epoch = 0
|
611 |
+
|
612 |
+
# Prepare data
|
613 |
+
X_train, X_val = X[train_idx], X[val_idx]
|
614 |
+
y_train, y_val = y[train_idx], y[val_idx]
|
615 |
+
|
616 |
+
train_loader = DataLoader(
|
617 |
+
TensorDataset(
|
618 |
+
torch.tensor(X_train, dtype=torch.float32),
|
619 |
+
torch.tensor(y_train, dtype=torch.long),
|
620 |
+
),
|
621 |
+
batch_size=config.batch_size,
|
622 |
+
shuffle=True,
|
623 |
+
)
|
624 |
+
val_loader = DataLoader(
|
625 |
+
TensorDataset(
|
626 |
+
torch.tensor(X_val, dtype=torch.float32),
|
627 |
+
torch.tensor(y_val, dtype=torch.long),
|
628 |
+
),
|
629 |
+
batch_size=config.batch_size,
|
630 |
+
shuffle=False,
|
631 |
+
)
|
632 |
+
|
633 |
+
# Initialize model
|
634 |
+
model = build_model(config.model_name, config.target_len).to(device)
|
635 |
+
optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate)
|
636 |
+
criterion = nn.CrossEntropyLoss()
|
637 |
+
|
638 |
+
# Training loop
|
639 |
+
for epoch in range(config.epochs):
|
640 |
+
job.progress.current_epoch = epoch + 1
|
641 |
+
model.train()
|
642 |
+
running_loss = 0.0
|
643 |
+
correct = 0
|
644 |
+
total = 0
|
645 |
+
|
646 |
+
for inputs, labels in train_loader:
|
647 |
+
inputs = inputs.unsqueeze(1).to(device)
|
648 |
+
labels = labels.to(device)
|
649 |
+
|
650 |
+
optimizer.zero_grad()
|
651 |
+
outputs = model(inputs)
|
652 |
+
loss = criterion(outputs, labels)
|
653 |
+
loss.backward()
|
654 |
+
optimizer.step()
|
655 |
+
|
656 |
+
running_loss += loss.item()
|
657 |
+
_, predicted = torch.max(outputs.data, 1)
|
658 |
+
total += labels.size(0)
|
659 |
+
correct += (predicted == labels).sum().item()
|
660 |
+
|
661 |
+
job.progress.current_loss = running_loss / len(train_loader)
|
662 |
+
job.progress.current_accuracy = correct / total
|
663 |
+
|
664 |
+
self._notify_progress(job.job_id, job)
|
665 |
+
|
666 |
+
# Validation with comprehensive metrics
|
667 |
+
model.eval()
|
668 |
+
val_predictions = []
|
669 |
+
val_true = []
|
670 |
+
val_probabilities = []
|
671 |
+
|
672 |
+
with torch.no_grad():
|
673 |
+
for inputs, labels in val_loader:
|
674 |
+
inputs = inputs.unsqueeze(1).to(device)
|
675 |
+
outputs = model(inputs)
|
676 |
+
probabilities = torch.softmax(outputs, dim=1)
|
677 |
+
_, predicted = torch.max(outputs, 1)
|
678 |
+
|
679 |
+
val_predictions.extend(predicted.cpu().numpy())
|
680 |
+
val_true.extend(labels.numpy())
|
681 |
+
val_probabilities.extend(probabilities.cpu().numpy())
|
682 |
+
|
683 |
+
# Calculate standard metrics
|
684 |
+
fold_accuracy = accuracy_score(val_true, val_predictions)
|
685 |
+
fold_cm = confusion_matrix(val_true, val_predictions).tolist()
|
686 |
+
|
687 |
+
# Calculate spectroscopy-specific metrics
|
688 |
+
val_probabilities = np.array(val_probabilities)
|
689 |
+
spectro_metrics = calculate_spectroscopy_metrics(
|
690 |
+
np.array(val_true), np.array(val_predictions), val_probabilities
|
691 |
+
)
|
692 |
+
|
693 |
+
fold_accuracies.append(fold_accuracy)
|
694 |
+
confusion_matrices.append(fold_cm)
|
695 |
+
spectroscopy_metrics.append(spectro_metrics)
|
696 |
+
|
697 |
+
# Save best model weights (from last fold for now)
|
698 |
+
if fold == config.num_folds:
|
699 |
+
torch.save(model.state_dict(), job.weights_path)
|
700 |
+
|
701 |
+
job.progress.fold_accuracies = fold_accuracies
|
702 |
+
job.progress.confusion_matrices = confusion_matrices
|
703 |
+
job.progress.spectroscopy_metrics = spectroscopy_metrics
|
704 |
+
|
705 |
+
def _save_training_results(self, job: TrainingJob):
|
706 |
+
"""Save training results and logs with enhanced metrics"""
|
707 |
+
# Calculate comprehensive summary metrics
|
708 |
+
spectro_summary = {}
|
709 |
+
if job.progress.spectroscopy_metrics:
|
710 |
+
# Average across all folds for each metric
|
711 |
+
metric_keys = job.progress.spectroscopy_metrics[0].keys()
|
712 |
+
for key in metric_keys:
|
713 |
+
values = [
|
714 |
+
fold_metrics.get(key, 0.0)
|
715 |
+
for fold_metrics in job.progress.spectroscopy_metrics
|
716 |
+
]
|
717 |
+
spectro_summary[f"mean_{key}"] = float(np.mean(values))
|
718 |
+
spectro_summary[f"std_{key}"] = float(np.std(values))
|
719 |
+
|
720 |
+
results = {
|
721 |
+
"job_id": job.job_id,
|
722 |
+
"config": job.config.to_dict(),
|
723 |
+
"status": job.status.value,
|
724 |
+
"created_at": job.created_at.isoformat(),
|
725 |
+
"started_at": job.started_at.isoformat() if job.started_at else None,
|
726 |
+
"completed_at": job.completed_at.isoformat() if job.completed_at else None,
|
727 |
+
"progress": {
|
728 |
+
"fold_accuracies": job.progress.fold_accuracies,
|
729 |
+
"confusion_matrices": job.progress.confusion_matrices,
|
730 |
+
"spectroscopy_metrics": job.progress.spectroscopy_metrics,
|
731 |
+
"mean_accuracy": (
|
732 |
+
np.mean(job.progress.fold_accuracies)
|
733 |
+
if job.progress.fold_accuracies
|
734 |
+
else 0.0
|
735 |
+
),
|
736 |
+
"std_accuracy": (
|
737 |
+
np.std(job.progress.fold_accuracies)
|
738 |
+
if job.progress.fold_accuracies
|
739 |
+
else 0.0
|
740 |
+
),
|
741 |
+
"spectroscopy_summary": spectro_summary,
|
742 |
+
},
|
743 |
+
"weights_path": job.weights_path,
|
744 |
+
"error_message": job.error_message,
|
745 |
+
}
|
746 |
+
|
747 |
+
with open(job.logs_path, "w") as f:
|
748 |
+
json.dump(results, f, indent=2)
|
749 |
+
|
750 |
+
def _notify_progress(self, job_id: str, job: TrainingJob):
|
751 |
+
"""Notify registered callbacks about progress updates"""
|
752 |
+
if job_id in self.progress_callbacks:
|
753 |
+
for callback in self.progress_callbacks[job_id]:
|
754 |
+
try:
|
755 |
+
callback(job)
|
756 |
+
except Exception as e:
|
757 |
+
print(f"Error in progress callback: {e}")
|
758 |
+
|
759 |
+
def get_job_status(self, job_id: str) -> Optional[TrainingJob]:
|
760 |
+
"""Get current status of a training job"""
|
761 |
+
return self.jobs.get(job_id)
|
762 |
+
|
763 |
+
def list_jobs(
|
764 |
+
self, status_filter: Optional[TrainingStatus] = None
|
765 |
+
) -> List[TrainingJob]:
|
766 |
+
"""List all jobs, optionally filtered by status"""
|
767 |
+
jobs = list(self.jobs.values())
|
768 |
+
if status_filter:
|
769 |
+
jobs = [job for job in jobs if job.status == status_filter]
|
770 |
+
return sorted(jobs, key=lambda j: j.created_at, reverse=True)
|
771 |
+
|
772 |
+
def cancel_job(self, job_id: str) -> bool:
|
773 |
+
"""Cancel a running job"""
|
774 |
+
job = self.jobs.get(job_id)
|
775 |
+
if job and job.status == TrainingStatus.RUNNING:
|
776 |
+
job.status = TrainingStatus.CANCELLED
|
777 |
+
job.completed_at = datetime.now()
|
778 |
+
# Note: This is a simple cancellation - actual thread termination is more complex
|
779 |
+
return True
|
780 |
+
return False
|
781 |
+
|
782 |
+
def cleanup_old_jobs(self, max_age_hours: int = 24):
|
783 |
+
"""Clean up old completed/failed jobs"""
|
784 |
+
cutoff_time = datetime.now() - timedelta(hours=max_age_hours)
|
785 |
+
to_remove = []
|
786 |
+
|
787 |
+
for job_id, job in self.jobs.items():
|
788 |
+
if (
|
789 |
+
job.status
|
790 |
+
in [
|
791 |
+
TrainingStatus.COMPLETED,
|
792 |
+
TrainingStatus.FAILED,
|
793 |
+
TrainingStatus.CANCELLED,
|
794 |
+
]
|
795 |
+
and job.completed_at
|
796 |
+
and job.completed_at < cutoff_time
|
797 |
+
):
|
798 |
+
to_remove.append(job_id)
|
799 |
+
|
800 |
+
for job_id in to_remove:
|
801 |
+
del self.jobs[job_id]
|
802 |
+
|
803 |
+
def shutdown(self):
|
804 |
+
"""Shutdown the training manager"""
|
805 |
+
self.executor.shutdown(wait=True)
|
806 |
+
|
807 |
+
|
808 |
+
# Global training manager instance
|
809 |
+
_training_manager = None
|
810 |
+
|
811 |
+
|
812 |
+
def get_training_manager() -> TrainingManager:
|
813 |
+
"""Get global training manager instance"""
|
814 |
+
global _training_manager
|
815 |
+
if _training_manager is None:
|
816 |
+
_training_manager = TrainingManager()
|
817 |
+
return _training_manager
|