Predictor

nnero.predictor.check_values(vals, metadata: MetaData)

nnero.predictor.input_values(metadata: MetaData, default: str = {'ALPHA_ESC': 0.3, 'ALPHA_STAR': 0.5, 'FRAC_WDM': 0.0, 'F_ESC10': -1.0, 'F_STAR10': -1.5, 'INVERSE_M_WDM': 0.0, 'LOG10_PMF_SB': -5.0, 'L_X': 40.0, 'Ln_1010_As': 3.047, 'M_TURN': 8.7, 'M_WDM': '20.0', 'NEUTRINO_MASS_1': 0.0, 'NU_X_THRESH': 500, 'Ombh2': 0.02242, 'Omdmh2': 0.11933, 'PMF_NB': -2.0, 'POWER_INDEX': 0.9665, 'hlittle': 0.6736, 't_STAR': 0.5}, **kwargs)

nnero.predictor.predict_Xe(classifier: Classifier | None = None, regressor: Regressor | None = None, default: dict | None = None, **kwargs) → ndarray | bool

Prediction of the free electron fraction X_e, taking into account the selection from the classifier. See predict_Xe_numpy for a more advanced but faster method (working with numpy arrays).

Parameters:

• classifier (Classifier | None, optional) – Classifier object already trained. Default is None. If None, the default classifier DefaultClassifier is used.

• regressor (Regressor | None, optional) – Regressor object already trained. Default is None. If None, the default regressor DefaultRegressor is used.

• default (dict | None, optional) – Dictionnary of default values, by default None. The default dictionnaly should have keys corresponding to those of the trained dataset as accessible from regressor.info() or classifier.info().

• **kwargs – Any value for a parameter the classifier and regressor have been trained on. Keys should corresponds to regressor.parameters_name or classifier.parameters_name.

Returns:

Returns False if the classifier returns False. Returns the prediction from the regressor for the free electron fraction X_e otherwise.

Return type:

np.ndarray | bool

nnero.predictor.predict_Xe_numpy(theta: ndarray, classifier: Classifier | None = None, regressor: Regressor | None = None) → ndarray

Prediction of the free electron fraction X_e, taking into account the selection from the classifier. Same as predict_Xe but much faster as it work with numpy arrays.

Parameters:

• theta (np.ndarray) – Array of shape (n, p) of parameters, where p is the number of parameters. The p parameters values should be given in the order they were fed to the regressor for training. These parameters and their order are accesible calling regressor.info().

• classifier (Classifier | None, optional) – Classifier object already trained. Default is None. If None, the default classifier DefaultClassifier is used.

• regressor (Regressor | None, optional) – Regressor object already trained. Default is None. If None, the default regressor DefaultRegressor is used.

Returns:

2D array of shape (n, q) for the values for X_e with q the number for parameters. When the classifier outputs false, the array is filled with -1.

Return type:

np.ndarray

nnero.predictor.predict_classifier(classifier: Classifier | None = None, default: dict | None = None, **kwargs) → bool

Prediction of the classifier. See predict_classifier_numpy for a more advanced but faster method (working with numpy arrays).

Parameters:

• classifier (nnero.Classifier | None, optional) – Classifier object already trained. Default is None. If None, the default classifier DefaultClassifier is used.

• **kwargs – Any value for a parameter the classifier has been trained on. Keys should corresponds to classifier.parameters_name.

Returns:

True for an early reionization. False for a late reionization.

Return type:

bool

nnero.predictor.predict_classifier_numpy(theta: ndarray, classifier: Classifier | None = None) → ndarray

Prediction of the classifier. Same as predict_classifier but much faster as it work with numpy arrays.

Parameters:

• theta (np.ndarray) – Array of shape (n, p) of parameters, where p is the number of parameters. The p parameters values should be given in the order they were fed to the classifier for training. These parameters and their order are accesible calling classifier.info().

• classifier (nnero.Classifier | None, optional) – Classifier object already trained. Default is None. If None, the default classifier DefaultClassifier is used.

Returns:

Array of booleans. True for an early reionization. False for a late reionization.

Return type:

np.ndarray

nnero.predictor.predict_interpolator(interpolator: Interpolator | None = None, default: dict | None = None, parameter: str | None = None, **kwargs) → ndarray

Prediction of the interpolator. See predict_interpolator_numpy for a more advanced but faster method (working with numpy arrays).

Parameters:

• interpolator (nnero.Interpolator | None, optional) – Interpolator object already trained. Default is None. If None, the default interpolator DefaultInterpolator_<parameter> is used.

• default (dict | None, optional) – Dictionnary of default values, by default None. The default dictionnaly should have keys corresponding to those of the trained dataset as accessible from interpolator.info().

• parameter (str | None, optional) – If no interpolator given in input, must be specified to know which interpolator to load. Default is None.

• **kwargs – Any value for a parameter the interpolator has been trained on. Keys should corresponds to interpolator.parameters_name.

Returns:

Interpolated value.

Return type:

float

Raises:

ValueError – If no interpolator given, need to specify on which parameter we cant to interpolate in order to load the associated default interpolator.

nnero.predictor.predict_interpolator_numpy(theta: ndarray, interpolator: Regressor | None = None, parameter: str | None = None) → ndarray

Prediction of the interpolator. Same as predict_interpolator but much faster as it work with numpy arrays.

Parameters:

• theta (np.ndarray) – Array of shape (n, p) of parameters, where p is the number of parameters. The p parameters values should be given in the order they were fed to the regressor for training. These parameters and their order are accesible calling regressor.info().

• interpolator (nnero.Interpolator | None, optional) – Regressor object already trained. Default is None. If None, the default regressor DefaultRegressor is used.

• parameter (str | None, optional) – If no interpolator given in input, must be specified to know which interpolator to load. Default is None.

Returns:

1D array of shape n for the inteprolated values.

Return type:

np.ndarray

Raises:

ValueError – If no interpolator given, need to specify on which parameter we cant to interpolate in order to load the associated default interpolator.

nnero.predictor.predict_parameter(classifier: Classifier | None = None, interpolator: Interpolator | None = None, default: dict | None = None, parameter: str | None = None, **kwargs) → ndarray | bool

Prediction of the interpolated parameter, taking into account the selection from the classifier. See predict_parameter_numpy for a more advanced but faster method (working with numpy arrays).

Parameters:

• classifier (Classifier | None, optional) – Classifier object already trained. Default is None. If None, the default classifier DefaultClassifier is used.

• interpolator (Interpolator | None, optional) – Interpolator object already trained. Default is None. If None, the default interpolator DefaultInterpolator_<parameter> is used.

• default (dict | None, optional) – Dictionnary of default values, by default None. The default dictionnaly should have keys corresponding to those of the trained dataset as accessible from interpolator.info() or classifier.info().

• **kwargs – Any value for a parameter the classifier and interpolator have been trained on. Keys should corresponds to regressor.parameters_name or classifier.parameters_name.

Returns:

Returns False if the classifier returns False. Returns the prediction from the interpolator for the parameter otherwise.

Return type:

np.ndarray | bool

nnero.predictor.predict_parameter_numpy(theta: ndarray, classifier: Classifier | None = None, interpolator: Interpolator | None = None, parameter: str | None = None) → ndarray

Prediction of the interpolated parameter, taking into account the selection from the classifier. Same as predict_parameter but much faster as it work with numpy arrays.

Parameters:

• theta (np.ndarray) – Array of shape (n, p) of parameters, where p is the number of parameters. The p parameters values should be given in the order they were fed to the regressor for training. These parameters and their order are accesible calling regressor.info().

• classifier (Classifier | None, optional) – Classifier object already trained. Default is None. If None, the default classifier DefaultClassifier is used.

• interpolator (Interpolator | None, optional) – Interpolator object already trained. Default is None. If None, the default interpolator DefaultInterpolator is used.

Returns:

1D array of shape (n) for the value of the parameter When the classifier outputs false, the array is filled with -1.

Return type:

np.ndarray

nnero.predictor.predict_regressor(regressor: Regressor | None = None, default: dict | None = None, **kwargs) → ndarray

Prediction of the regressor. See predict_regressor_numpy for a more advanced but faster method (working with numpy arrays).

Parameters:

• regressor (nnero.Regressor | None, optional) – Regressor object already trained. Default is None. If None, the default regressor DefaultRegressor is used.

• default (dict | None, optional) – Dictionnary of default values, by default None. The default dictionnaly should have keys corresponding to those of the trained dataset as accessible from regressor.info().

• **kwargs – Any value for a parameter the regressor has been trained on. Keys should corresponds to regressor.parameters_name.

Returns:

Array of values for X_e.

Return type:

np.ndarray

nnero.predictor.predict_regressor_numpy(theta: ndarray, regressor: Regressor | None = None) → ndarray

Prediction of the regressor. Same as predict_regressor but much faster as it work with numpy arrays.

Parameters:

• theta (np.ndarray) – Array of shape (n, p) of parameters, where p is the number of parameters. The p parameters values should be given in the order they were fed to the regressor for training. These parameters and their order are accesible calling regressor.info().

• regressor (nnero.Regressor | None, optional) – Regressor object already trained. Default is None. If None, the default regressor DefaultRegressor is used.

Returns:

2D array of shape (n, q) for the values for X_e wit q the number of redshift bins.

Return type:

np.ndarray

nnero.predictor.predict_tau(classifier: Classifier | None = None, regressor: Regressor | None = None, default: dict | None = None, **kwargs) → float

Predict the optical depth to reionization from a trained classifier and regressor as well as parameters passed in kwargs or default. See predict_tau_numpy for a more advanced but faster method (working with numpy arrays).

Parameters:

• classifier (Classifier | None, optional) – Classifier object already trained. Default is None. If None, the default classifier DefaultClassifier is used.

• regressor (Regressor | None, optional) – Regressor object already trained. Default is None. If None, the default regressor DefaultRegressor is used.

• default (dict | None, optional) – Dictionnary of default values, by default None. The default dictionnaly should have keys corresponding to those of the trained dataset as accessible from regressor.info() or classifier.info().

• **kwargs – Any value for a parameter the classifier and regressor have been trained on. Keys should corresponds to regressor.parameters_name or classifier.parameters_name.

Return type:

float

nnero.predictor.predict_tau_from_Xe(xe: ndarray, metadata: MetaData, default: dict | None = None, **kwargs) → float

Predict the optical depth to reionization from an array of X_e. See predict_tau_from_Xe_numpy for a more advanced but faster method (working with numpy arrays).

Parameters:

• xe (np.ndarray) – Array for the free electron fraction X_e = xHII(1+db).

• metadata (MetaData) – Metadata object (attached to the networks) describing properties of the data the networks have been trained on.

• default (dict | None, optional) – Dictionnary of default values, by default None. The default dictionnaly should have keys corresponding to those of the trained dataset as accessible from metadata.parameters_name.

• **kwargs – Any value for a parameter in metadata. Keys should corresponds to metadata.parameters_name.

Return type:

float

nnero.predictor.predict_tau_from_Xe_numpy(xe: ndarray, theta: ndarray, metadata: MetaData) → ndarray

Predict the optical depth to reionization from an array of X_e. Same as predict_tau_from_Xe but much faster as it work with numpy arrays.

Parameters:

• xe (np.ndarray) – Array for the free electron fraction X_e = xHII(1+db).

• theta (np.ndarray) – Array of shape (n, p) of parameters, where p is the number of parameters. The p parameters values should be given in the order they were fed to the regressor for training. These parameters and their order are accesible calling metadata.parameters_name.

• metadata (MetaData) – Metadata object (attached to the networks) describing properties of the data the networks have been trained on.

Returns:

Array of values of the optical depth to reionization.

Return type:

np.ndarray

nnero.predictor.predict_tau_from_xHII(Xe, metadata: MetaData, default: dict | None = None, **kwargs) → float

Deprecated since version See: predict_tau_from_Xe instead.

nnero.predictor.predict_tau_from_xHII_numpy(Xe, theta: ndarray, metadata: MetaData) → ndarray

Deprecated since version See: predict_tau_from_Xe_numpy instead.

nnero.predictor.predict_tau_numpy(theta: ndarray, classifier: Classifier | None = None, regressor: Regressor | None = None) → ndarray

Predict the optical depth to reionization from a trained classifier and regressor as well as parameters passed in kwargs or default. Same as predict_tau but much faster as it work with numpy arrays.

Parameters:

• theta (np.ndarray) – Array of shape (n, p) of parameters, where p is the number of parameters. The p parameters values should be given in the order they were fed to the regressor for training. These parameters and their order are accesible calling regressor.info() or classifier.info().

• classifier (Classifier | None, optional) – Classifier object already trained. Default is None. If None, the default classifier DefaultClassifier is used.

• regressor (Regressor | None, optional) – Regressor object already trained. Default is None. If None, the default regressor DefaultRegressor is used.

Returns:

Array of values of the optical depth to reionization.

Return type:

np.ndarray

nnero.predictor.predict_xHII(classifier: Classifier | None = None, regressor: Regressor | None = None, default: dict | None = None, **kwargs) → ndarray

Deprecated since version See: predict_Xe instead.

nnero.predictor.predict_xHII_numpy(theta: ndarray, classifier: Classifier | None = None, regressor: Regressor | None = None) → ndarray

Deprecated since version See: predict_Xe_numpy instead.

nnero.predictor.uniform_input_array(theta: ndarray, metadata: MetaData)

nnero.predictor.uniform_input_values(metadata: MetaData, default: dict = {'ALPHA_ESC': 0.3, 'ALPHA_STAR': 0.5, 'FRAC_WDM': 0.0, 'F_ESC10': -1.0, 'F_STAR10': -1.5, 'INVERSE_M_WDM': 0.0, 'LOG10_PMF_SB': -5.0, 'L_X': 40.0, 'Ln_1010_As': 3.047, 'M_TURN': 8.7, 'M_WDM': '20.0', 'NEUTRINO_MASS_1': 0.0, 'NU_X_THRESH': 500, 'Ombh2': 0.02242, 'Omdmh2': 0.11933, 'PMF_NB': -2.0, 'POWER_INDEX': 0.9665, 'hlittle': 0.6736, 't_STAR': 0.5}, **kwargs)