uqregressors.conformal.k_fold_cqr
This class implements conformal quantile regression in a K-fold manner to obtain uncertainty estimates which are often conservative, but use the entire dataset available. This can result in large improvements over split conformal quantile regression, particularly in cases where the dataset is sparse.
Tip
The quantiles of the underlying quantile regressor can be tuned with the parameters tau_lo and tau_hi as in the paper. This can often result in more efficient intervals.
Note
K-fold Conformal Quantile Regression can be overly conservative in prediction intervals, particularly in sparse data settings or when the underlying estimator has high variance.
K-Fold-CQR
This module implements conformal quantile regression in a K-fold manner for regression of a one dimensional output.
Key features are
- Customizable neural network architecture
- Tunable quantiles of the underyling regressors
- Prediction intervals without distributional assumptions
- Parallel training of ensemble models with Joblib
- Customizable optimizer and loss function
- Optional Input/Output Normalization
KFoldCQR
Bases: BaseEstimator
, RegressorMixin
K-Fold Conformalized Quantile Regressor for uncertainty estimation in regression tasks.
This class trains an ensemble of quantile neural networks using K-Fold cross-validation, and applies conformal prediction to calibrate prediction intervals.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
Name of the model. |
'K_Fold_CQR_Regressor'
|
n_estimators
|
int
|
Number of K-Fold models to train. |
5
|
hidden_sizes
|
list
|
Sizes of the hidden layers for each quantile regressor. |
[64, 64]
|
dropout
|
float or None
|
Dropout rate for the neural network layers. |
None
|
alpha
|
float
|
Miscoverage rate (1 - confidence level). |
0.1
|
requires_grad
|
bool
|
Whether inputs should require gradient. |
False
|
tau_lo
|
float
|
Lower quantile, defaults to alpha/2. |
None
|
tau_hi
|
float
|
Upper quantile, defaults to 1 - alpha/2. |
None
|
n_jobs
|
int
|
Number of parallel jobs for training. |
1
|
activation_str
|
str
|
String identifier of the activation function. |
'ReLU'
|
learning_rate
|
float
|
Learning rate for training. |
0.001
|
epochs
|
int
|
Number of training epochs. |
200
|
batch_size
|
int
|
Batch size for training. |
32
|
optimizer_cls
|
type
|
Optimizer class. |
Adam
|
optimizer_kwargs
|
dict
|
Keyword arguments for optimizer. |
None
|
scheduler_cls
|
type or None
|
Learning rate scheduler class. |
None
|
scheduler_kwargs
|
dict
|
Keyword arguments for scheduler. |
None
|
loss_fn
|
callable or None
|
Loss function, defaults to quantile loss. |
None
|
device
|
str
|
Device to use for training and inference. |
'cpu'
|
use_wandb
|
bool
|
Whether to log training with Weights & Biases. |
False
|
wandb_project
|
str or None
|
wandb project name. |
None
|
wandb_run_name
|
str or None
|
wandb run name. |
None
|
scale_data
|
bool
|
Whether to normalize input/output data. |
True
|
input_scaler
|
TorchStandardScaler
|
Scaler for input features. |
None
|
output_scaler
|
TorchStandardScaler
|
Scaler for target outputs. |
None
|
random_seed
|
int or None
|
Random seed for reproducibility. |
None
|
tuning_loggers
|
list
|
Optional list of loggers for tuning. |
[]
|
Attributes:
Name | Type | Description |
---|---|---|
quantiles |
Tensor
|
The lower and upper quantiles for prediction. |
models |
list[QuantNN]
|
A list of the models in the ensemble. |
residuals |
Tensor
|
The combined residuals on the calibration sets. |
conformal_width |
Tensor
|
The width needed to conformalize the quantile regressor, q. |
_loggers |
list[Logger]
|
Training loggers for each ensemble member. |
Source code in uqregressors\conformal\k_fold_cqr.py
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 |
|
fit(X, y)
Fit the ensemble on training data.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
X
|
array - like or Tensor
|
Training inputs. |
required |
y
|
array - like or Tensor
|
Training targets. |
required |
Returns:
Type | Description |
---|---|
KFoldCQR
|
Fitted estimator. |
Source code in uqregressors\conformal\k_fold_cqr.py
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 |
|
load(path, device='cpu', load_logs=False)
classmethod
Load a saved KFoldCQR model from disk.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path
|
str or Path
|
Directory containing saved model files. |
required |
device
|
str
|
Device to load the model on ("cpu" or "cuda"). |
'cpu'
|
load_logs
|
bool
|
Whether to also load training logs. |
False
|
Returns:
Type | Description |
---|---|
KFoldCQR
|
The loaded model instance. |
Source code in uqregressors\conformal\k_fold_cqr.py
381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 |
|
predict(X)
Predicts the target values with uncertainty estimates.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
X
|
ndarray
|
Feature matrix of shape (n_samples, n_features). |
required |
Returns:
Type | Description |
---|---|
Union[Tuple[ndarray, ndarray, ndarray], Tuple[Tensor, Tensor, Tensor]]
|
Tuple containing: mean predictions, lower bound of the prediction interval, upper bound of the prediction interval. |
Note
If requires_grad
is False, all returned arrays are NumPy arrays.
Otherwise, they are PyTorch tensors with gradients.
Source code in uqregressors\conformal\k_fold_cqr.py
274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 |
|
quantile_loss(preds, y)
Quantile loss used for training the quantile regressors.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
preds
|
Tensor
|
Predicted quantiles, shape (batch_size, 2). |
required |
y
|
Tensor
|
True target values, shape (batch_size,). |
required |
Returns:
Type | Description |
---|---|
Tensor
|
Scalar loss. |
Source code in uqregressors\conformal\k_fold_cqr.py
172 173 174 175 176 177 178 179 180 181 182 183 184 |
|
save(path)
Save the trained model and associated configuration to disk.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path
|
str or Path
|
Directory to save model files. |
required |
Source code in uqregressors\conformal\k_fold_cqr.py
333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 |
|
QuantNN
Bases: Module
A simple quantile neural network that estimates the lower and upper quantile when trained with a pinball loss function.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
input_dim
|
int
|
Number of input features |
required |
hidden_sizes
|
list of int
|
List of hidden layer sizes |
required |
dropout
|
None or float
|
The dropout probability - None if no dropout |
required |
activation
|
Module
|
Activation function class (e.g., nn.ReLU). |
required |
Source code in uqregressors\conformal\k_fold_cqr.py
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 |
|