uqregressors.conformal.conformal_ens
This method employs normalized conformal prediction as described in Tibshirani, 2023. The difficulty measure, , used for normalization is taken to be the standard deviation of the predictions of all models in an ensemble, while the ensemble mean is returned as the mean prediction.
Normalized Conformal Ensemble
This module implements normalized conformal ensemble prediction in a split conformal context for regression on a one dimensional output
Key features are
- Customizable neural network architecture
- Customizable dropout to increase ensemble diversity
- Prediction intervals without distributional assumptions
- Customizable optimizer and loss function
- Optional Input/Output Normalization
ConformalEnsRegressor
Bases: BaseEstimator
, RegressorMixin
Conformal Ensemble Regressor for uncertainty estimation in regression tasks.
This class trains an ensemble of MLP models, and applies normalized conformal prediction on a split calibration set to calibrate prediction intervals.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
Name of the model. |
'Conformal_Ens_Regressor'
|
n_estimators
|
int
|
Number of models to train. |
5
|
hidden_sizes
|
list
|
sizes of the hidden layers for each quantile regressor. |
[64, 64]
|
alpha
|
float
|
Miscoverage rate (1 - confidence level). |
0.1
|
requires_grad
|
bool
|
Whether inputs should require gradient, determines output type. |
False
|
dropout
|
float or None
|
Dropout rate for the neural network layers. |
None
|
pred_with_dropout
|
bool
|
Whether dropout should be applied at test time, dropout must be non-Null |
False
|
activation_str
|
str
|
String identifier of the activation function. |
'ReLU'
|
cal_size
|
float
|
Proportion of training samples to use for calibration, between 0 and 1. |
0.2
|
gamma
|
float
|
Stability constant added to difficulty score . |
0
|
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. |
mse_loss
|
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
|
n_jobs
|
float
|
Number of parallel jobs for training. |
1
|
random_seed
|
int or None
|
Random seed for reproducibility. |
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
|
tuning_loggers
|
list
|
Optional list of loggers for tuning. |
[]
|
Attributes:
Name | Type | Description |
---|---|---|
models |
list[QuantNN]
|
A list of the models in the ensemble. |
residuals |
Tensor
|
The combined residuals on the calibration set. |
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\conformal_ens.py
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|
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 |
---|---|
ConformalEnsRegressor
|
Fitted estimator. |
Source code in uqregressors\conformal\conformal_ens.py
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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 |
---|---|
ConformalEnsRegressor
|
The loaded model instance. |
Source code in uqregressors\conformal\conformal_ens.py
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|
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\conformal_ens.py
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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\conformal_ens.py
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MLP
Bases: Module
A simple feedforward neural network with dropout for regression.
This MLP supports customizable hidden layer sizes, activation functions, and dropout. It outputs a single scalar per input — the predictive mean.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
input_dim
|
int
|
Number of input features. |
required |
hidden_sizes
|
list of int
|
Sizes of the hidden layers. |
required |
dropout
|
float
|
Dropout rate (applied after each activation). |
required |
activation
|
callable
|
Activation function (e.g., nn.ReLU). |
required |
Source code in uqregressors\conformal\conformal_ens.py
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