uqregressors.conformal.cqr
This class implements split conformal quantile regression as described by Romano et al. 2018
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.
Conformalized Quantile Regression (CQR)
This module implements CQR in a split conformal context for regression on a one dimensional output
Key features are
- Customizable neural network architecture
- Tunable quantiles of the underyling quantile regressor
- Prediction intervals without distributional assumptions
- Customizable optimizer and loss function
- Optional Input/Output Normalization
ConformalQuantileRegressor
Bases: BaseEstimator
, RegressorMixin
Conformalized Quantile Regressor for uncertainty estimation in regression tasks.
This class trains one quantile neural network and conformalizes it with split conformal prediction
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name
|
str
|
Name of the model. |
'Conformal_Quantile_Regressor'
|
hidden_sizes
|
list
|
Sizes of the hidden layers for each quantile regressor. |
[64, 64]
|
cal_size
|
float
|
Proportion of training samples to use for calibration, between 0 and 1. |
0.2
|
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
|
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. |
residuals |
Tensor
|
The 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\cqr.py
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|
fit(X, y)
Fit the conformal quantile regressor model on training data.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
X
|
array - like
|
Training features of shape (n_samples, n_features). |
required |
y
|
array - like
|
Target values of shape (n_samples,). |
required |
Source code in uqregressors\conformal\cqr.py
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|
load(path, device='cpu', load_logs=False)
classmethod
Load a saved MC dropout regressor from disk.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path
|
str or Path
|
Directory path to load the model from. |
required |
device
|
str or device
|
Device to load the model onto. |
'cpu'
|
load_logs
|
bool
|
Whether to load training and tuning logs. |
False
|
Returns:
Type | Description |
---|---|
ConformalQuantileRegressor
|
Loaded model instance. |
Source code in uqregressors\conformal\cqr.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\cqr.py
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quantile_loss(preds, y)
Quantile loss used for training the quantile regressor.
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\cqr.py
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|
save(path)
Save model weights, config, and scalers to disk.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path
|
str or Path
|
Directory to save model components. |
required |
Source code in uqregressors\conformal\cqr.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 or None
|
Dropout rate (applied after each activation). |
required |
activation
|
callable
|
Activation function (e.g., nn.ReLU). |
required |
Source code in uqregressors\conformal\cqr.py
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