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aa0bed2 e2f8b29 aa0bed2 e2f8b29 aa0bed2 e2f8b29 aa0bed2 e2f8b29 0b9b77b e2f8b29 aa0bed2 e2f8b29 0b9b77b e2f8b29 aa0bed2 e2f8b29 0b9b77b e2f8b29 0b9b77b e2f8b29 0b9b77b e2f8b29 0b9b77b e2f8b29 0b9b77b e2f8b29 0b9b77b e2f8b29 0b9b77b e2f8b29 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 | """Training curve generation — real PyTorch mini-training with parametric fallback.
Primary: run_real_training() from pytorch_engine (20 real epochs, cached per task/seed).
Fallback: parametric torch.Tensor formulas for edge cases.
Zero numpy. Spec reference: Section 6.
"""
from __future__ import annotations
import torch
from ml_training_debugger.scenarios import ScenarioParams
EPOCHS = 20
def _get_real_curves(scenario: ScenarioParams) -> dict[str, list[float]] | None:
"""Try to get real training curves. Returns None on failure."""
try:
from ml_training_debugger.pytorch_engine import run_real_training
return run_real_training(scenario)
except Exception:
return None
def gen_loss_history(scenario: ScenarioParams) -> list[float]:
"""Generate training loss history (20 epochs).
Uses real mini-training (cached). Falls back to parametric on failure.
"""
real = _get_real_curves(scenario)
if real is not None:
return real["loss_history"]
# Parametric fallback
torch.manual_seed(scenario.seed)
t = torch.arange(EPOCHS, dtype=torch.float32)
root = scenario.root_cause.value
if root == "lr_too_high":
# Exponentially growing loss
lr_tensor = torch.tensor(scenario.learning_rate, dtype=torch.float32)
base = torch.exp(lr_tensor * t * 0.5)
loss = 2.3 * base
# Add NaN marker after epoch 12
loss_list = loss.tolist()
for i in range(12, EPOCHS):
loss_list[i] = float("inf")
return loss_list
if root == "vanishing_gradients":
# Flat loss — barely decreases
noise = torch.randn(EPOCHS) * 0.02
loss = 2.3 - t * 0.002 + noise
return loss.clamp(min=0.01).tolist()
if root == "data_leakage":
# Normal-looking training loss
loss = 2.3 * torch.exp(-0.15 * t) + 0.05
noise = torch.randn(EPOCHS) * 0.02
return (loss + noise).clamp(min=0.01).tolist()
if root == "overfitting":
# Steadily decreasing to near-zero
loss = 2.3 * torch.exp(-0.25 * t) + 0.01
noise = torch.randn(EPOCHS) * 0.01
return (loss + noise).clamp(min=0.001).tolist()
if root == "batchnorm_eval_mode":
# Roughly normal with higher variance
base = 2.3 * torch.exp(-0.1 * t) + 0.3
noise = torch.randn(EPOCHS) * 0.15
return (base + noise).clamp(min=0.1).tolist()
if root == "code_bug":
loss = 2.3 * torch.exp(-0.05 * t) + 0.5
noise = torch.randn(EPOCHS) * 0.1
return (loss + noise).clamp(min=0.1).tolist()
if root == "scheduler_misconfigured":
# Training starts well, then LR drops too aggressively causing stagnation
step_size = scenario.scheduler_step_size
gamma = scenario.scheduler_gamma
loss_list: list[float] = []
for i in range(EPOCHS):
if i < step_size:
val = 2.3 * (1.0 - 0.15 * i) # normal decrease
else:
steps_decayed = (i - step_size) // step_size + 1
effective_lr_ratio = gamma ** steps_decayed
val = 2.3 * (1.0 - 0.15 * step_size) + 0.05 * (i - step_size) * (1 - effective_lr_ratio)
loss_list.append(max(0.3, val + torch.randn(1).item() * 0.05))
return loss_list
# Fallback
return (2.3 * torch.exp(-0.1 * t)).tolist()
def gen_val_accuracy_history(scenario: ScenarioParams) -> list[float]:
"""Generate validation accuracy history (20 epochs).
Uses real mini-training (cached). Falls back to parametric on failure.
"""
real = _get_real_curves(scenario)
if real is not None:
return real["val_acc_history"]
# Parametric fallback
torch.manual_seed(scenario.seed + 1)
t = torch.arange(EPOCHS, dtype=torch.float32)
root = scenario.root_cause.value
if root == "lr_too_high":
# Collapses along with training loss
acc = torch.sigmoid(torch.linspace(0, -3, EPOCHS)) * 0.5
return acc.clamp(0.0, 1.0).tolist()
if root == "vanishing_gradients":
# Near random chance
noise = torch.randn(EPOCHS) * 0.02
acc = 0.10 + t * 0.001 + noise
return acc.clamp(0.0, 1.0).tolist()
if root == "data_leakage":
# Suspiciously high from epoch 1
leakage = torch.tensor(scenario.leakage_pct, dtype=torch.float32)
base = torch.sigmoid(torch.linspace(-3, 3, EPOCHS))
acc = base * (1.0 - leakage) + leakage * 0.95
# Inflate early epochs
acc = acc.clamp(0.0, 1.0)
# Ensure suspiciously high from epoch 1
acc_list = acc.tolist()
for i in range(EPOCHS):
acc_list[i] = max(acc_list[i], 0.82 * (1.0 + scenario.leakage_pct))
return [min(v, 0.99) for v in acc_list]
if root == "overfitting":
# Rises then falls — classic divergence
div = scenario.divergence_epoch
acc_list: list[float] = []
for i in range(EPOCHS):
if i < div:
val = 0.10 + (0.75 - 0.10) * (i / max(div, 1))
else:
decline = (i - div) * 0.02
val = 0.75 - decline
acc_list.append(max(0.0, min(1.0, val)))
return acc_list
if root == "batchnorm_eval_mode":
# Slow degradation ~1-2% per epoch
start = 0.76
noise = torch.randn(EPOCHS) * 0.01
acc = torch.tensor(
[start - 0.015 * i for i in range(EPOCHS)], dtype=torch.float32
)
acc = acc + noise
return acc.clamp(0.0, 1.0).tolist()
if root == "code_bug":
noise = torch.randn(EPOCHS) * 0.03
acc = 0.10 + t * 0.005 + noise
return acc.clamp(0.0, 1.0).tolist()
if root == "scheduler_misconfigured":
# Accuracy improves initially, then stagnates/degrades when scheduler kills LR
step_size = scenario.scheduler_step_size
acc_list: list[float] = []
for i in range(EPOCHS):
if i < step_size:
val = 0.10 + 0.08 * i
else:
val = 0.10 + 0.08 * step_size - 0.01 * (i - step_size)
acc_list.append(max(0.05, min(0.95, val + torch.randn(1).item() * 0.02)))
return acc_list
# Fallback
return (torch.sigmoid(torch.linspace(-3, 3, EPOCHS)) * 0.9).tolist()
def gen_val_loss_history(scenario: ScenarioParams) -> list[float]:
"""Generate validation loss history (20 epochs).
Uses real mini-training (cached). Falls back to parametric on failure.
"""
real = _get_real_curves(scenario)
if real is not None:
return real["val_loss_history"]
# Parametric fallback
torch.manual_seed(scenario.seed + 2)
t = torch.arange(EPOCHS, dtype=torch.float32)
root = scenario.root_cause.value
if root == "lr_too_high":
# Mirrors training loss divergence
lr_tensor = torch.tensor(scenario.learning_rate, dtype=torch.float32)
loss = 2.3 * torch.exp(lr_tensor * t * 0.5)
loss_list = loss.tolist()
for i in range(12, EPOCHS):
loss_list[i] = float("inf")
return loss_list
if root == "vanishing_gradients":
noise = torch.randn(EPOCHS) * 0.02
loss = 2.3 - t * 0.001 + noise
return loss.clamp(min=0.01).tolist()
if root == "data_leakage":
# Low val loss (because leaking train data into val)
base = 2.3 * torch.exp(-0.2 * t) + 0.03
noise = torch.randn(EPOCHS) * 0.02
return (base + noise).clamp(min=0.01).tolist()
if root == "overfitting":
# Initially decreases, then diverges upward
div = scenario.divergence_epoch
loss_list: list[float] = []
for i in range(EPOCHS):
if i < div:
val = 2.3 * (1.0 - 0.8 * i / max(div, 1))
else:
val = 0.46 + 0.1 * (i - div)
loss_list.append(max(0.01, val))
return loss_list
if root == "batchnorm_eval_mode":
# Slightly increasing
base = 1.5 + t * 0.03
noise = torch.randn(EPOCHS) * 0.1
return (base + noise).clamp(min=0.1).tolist()
if root == "code_bug":
loss = 2.3 * torch.exp(-0.03 * t) + 0.8
noise = torch.randn(EPOCHS) * 0.1
return (loss + noise).clamp(min=0.1).tolist()
if root == "scheduler_misconfigured":
step_size = scenario.scheduler_step_size
loss_list: list[float] = []
for i in range(EPOCHS):
if i < step_size:
val = 2.3 * (1.0 - 0.12 * i)
else:
val = 2.3 * (1.0 - 0.12 * step_size) + 0.03 * (i - step_size)
loss_list.append(max(0.1, val + torch.randn(1).item() * 0.05))
return loss_list
# Fallback
return (2.3 * torch.exp(-0.1 * t) + 0.1).tolist()
def _gen_confusion_matrix(scenario: ScenarioParams) -> list[list[float]]:
"""Generate a 10x10 confusion matrix based on the fault type."""
torch.manual_seed(scenario.seed + 10)
root = scenario.root_cause.value
n = 10
if root == "data_leakage":
# High diagonal but with leakage-induced off-diagonal noise
base = torch.eye(n) * 0.8
noise = torch.rand(n, n) * scenario.leakage_pct * 0.3
cm = base + noise
elif root == "overfitting":
# Near-perfect diagonal (memorized)
cm = torch.eye(n) * 0.95 + torch.rand(n, n) * 0.02
else:
# Normal confusion with moderate accuracy
cm = torch.eye(n) * 0.6 + torch.rand(n, n) * 0.08
# Normalize rows to sum to ~1.0
row_sums = cm.sum(dim=1, keepdim=True)
cm = cm / row_sums
return cm.tolist()
def gen_data_batch_stats(scenario: ScenarioParams) -> dict:
"""Generate data batch statistics for the scenario."""
torch.manual_seed(scenario.seed + 3)
root = scenario.root_cause.value
cm = _gen_confusion_matrix(scenario)
if root == "data_leakage":
overlap = 0.5 + scenario.leakage_pct * 1.5
overlap = min(overlap, 0.92)
return {
"label_distribution": {i: 0.1 for i in range(10)},
"feature_mean": 0.45 + torch.randn(1).item() * 0.05,
"feature_std": 0.22 + torch.randn(1).item() * 0.02,
"null_count": 0,
"class_overlap_score": overlap,
"batch_size": 64,
"duplicate_ratio": scenario.leakage_pct,
"confusion_matrix": cm,
}
if root == "overfitting":
return {
"label_distribution": {i: 0.1 for i in range(10)},
"feature_mean": 0.48 + torch.randn(1).item() * 0.03,
"feature_std": 0.25 + torch.randn(1).item() * 0.02,
"null_count": 0,
"class_overlap_score": 0.0,
"batch_size": 64,
"duplicate_ratio": 0.0,
"confusion_matrix": cm,
}
# Default: normal data
return {
"label_distribution": {i: 0.1 for i in range(10)},
"feature_mean": 0.47 + torch.randn(1).item() * 0.03,
"feature_std": 0.24 + torch.randn(1).item() * 0.02,
"null_count": 0,
"class_overlap_score": 0.0 + torch.randn(1).abs().item() * 0.05,
"batch_size": 64,
"duplicate_ratio": 0.0,
"confusion_matrix": cm,
}
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