Regressor

class nnero.regressor.Regressor(*, n_input: int = 16, n_output: int = 50, n_hidden_features: int = 80, n_hidden_layers: int = 5, alpha_tau: float = 0.5, model=None, name: str | None = None, use_pca: bool = True, pca_precision: float = 0.001, dataset: DataSet | None = None)[source]

Bases: NeuralNetwork

Daughter class of NeuralNetwork specialised for regressors.

Parameters:

  • model (torch.nn.Module | None) – If not None, the model that will be used for classifier. Otherwise, a new model is constructed from n_input, n_hidden_features and n_hidden_layers. Default is None.

  • n_input (int, optional) – Number of input on the neural network (corresponds to the number of parameters). Default is 16.

  • n_input – Number of output on the neural network (corresponds to the number of redshift bins). Default is 50. Overriden if dataset is specified.

  • n_hidden_features (int, optional) – Number of hidden features per layer. Default is 80.

  • n_hidden_layers (int, optional) – Number of layers. Default is 5.

  • name (str | None) – Name of the neural network. If None, automatically set to DefaultClassifier. Default is None.

  • dataset (Dataset | None) – Dataset on which the model will be trained. If provided, gets n_input and n_output from the data and overrides the user input value.

  • use_pca (bool, optional) – If True, decompose the interpolated function on the principal component eigenbasis. Default is True.

  • pca_precision (float, optional) – If use_pca is True sets how many eigenvalues needs to be considered. Only consider eigenvectors with eigenvalues > precision^2 * max(eigenvalues). Default is 1e-3.

  • alpha_tau (float, optioal) – Weighting of the relative error on X_e and optical depth in the cost function. Default is 0.5

- name

the name of the model

Type:

str

forward(x)[source]

Forward evaluation of the model.

Parameters:

x (torch.Tensor) – input features

classmethod load(path: str | None = None)[source]

Loads a regressor.

Parameters:

path (str | None) – Path to the saved files containing the regressor data. If None automatically fetch the DefaultRegressor.

Return type:

Regressor

loss_tau(tau_pred, target)[source]
loss_xHII(output, target)[source]
tau_ion(x, y)[source]

Optical depth to reionization.

Parameters:

  • x (torch.Tensor) – Input features.

  • y (torch.Tensor) – Output of the Regressor. Corresponds to X_e(z).

test_tau(dataset: DataSet) → ndarray[source]

Test the efficiency of the regressor to reconstruct the optical depth to reionization.

Parameters:

dataset (DataSet) – DataSet containing the training partition and the test partition.

Returns:

Distribution of relative error between the predicted and true optical depth. Array with the size of the test dataset.

Return type:

np.ndarray

test_xHII(dataset: DataSet) → tuple[ndarray, ndarray][source]

Test the efficiency of the regressor to reconstruct the free electron fraction X_e.

Parameters:

dataset (DataSet) – DataSet containing the training partition and the test partition.

Returns:

Prediction for X_e and true values.

Return type:

tuple(np.ndarray, np.ndarray)

nnero.regressor.train_regressor(model: Regressor, dataset: DataSet, optimizer: Optimizer, *, epochs=50, learning_rate=0.001, verbose=True, batch_size=64, **kwargs)[source]

Trains a given regressor.

Parameters:

  • model (Regressor) – Regressor model to train.

  • dataset (DataSet) – Dataset on which to train the regressor.

  • optimizer (torch.optim.Optimizer) – Optimizer used for training.

  • epochs (int, optional) – Number of epochs, by default 50.

  • learning_rate (float, optional) – Learning rate for training, by default 1e-3.

  • verbose (bool, optional) – If true, outputs a summary of the losses at each epoch, by default True.

  • batch_size (int, optional) – Size of the training batches, by default 64.