Source code for icet.fitting.base_optimizer

BaseOptimizer serves as base for all optimizers.

import numpy as np
from abc import ABC, abstractmethod
from typing import Any, Dict, Tuple, Union
from .fit_methods import available_fit_methods

[docs]class BaseOptimizer(ABC): """BaseOptimizer class. Serves as base class for all Optimizers solving the linear :math:`\\boldsymbol{X}\\boldsymbol{a} = \\boldsymbol{y}` problem. Parameters ---------- fit_data : tuple(numpy.ndarray, numpy.ndarray) the first element of the tuple represents the `NxM`-dimensional fit matrix `A` whereas the second element represents the vector of `N`-dimensional target values `y`; here `N` (=rows of `A`, elements of `y`) equals the number of target values and `M` (=columns of `A`) equals the number of parameters fit_method : str method to be used for training; possible choice are "least-squares", "lasso", "elasticnet", "bayesian-ridge", "ardr", "rfe-l2", "split-bregman" standardize : bool if True the fit matrix is standardized before fitting check_condition : bool if True the condition number will be checked (this can be sligthly more time consuming for larger matrices) seed : int seed for pseudo random number generator """ def __init__(self, fit_data: Tuple[np.ndarray, np.ndarray], fit_method: str, standardize: bool = True, check_condition: bool = True, seed: int = 42): """ Attributes ---------- _A : numpy.ndarray fit matrix (N, M) _y : numpy.ndarray target values (N) """ if fit_method not in available_fit_methods: raise ValueError('Unknown fit_method: {}'.format(fit_method)) if fit_data is None: raise TypeError('Invalid fit data; Fit data can not be None') if fit_data[0].shape[0] != fit_data[1].shape[0]: raise ValueError('Invalid fit data; shapes of fit matrix' ' and target vector do not match') if len(fit_data[0].shape) != 2: raise ValueError('Invalid fit matrix; must have two dimensions') self._A, self._y = fit_data self._n_rows = self._A.shape[0] self._n_cols = self._A.shape[1] self._fit_method = fit_method self._standarize = standardize self._check_condition = check_condition self._seed = seed self._fit_results = {'parameters': None}
[docs] def compute_rmse(self, A: np.ndarray, y: np.ndarray) -> float: """ Returns the root mean squared error (RMSE) using :math:`\\boldsymbol{A}`, :math:`\\boldsymbol{y}`, and the vector of fitted parameters :math:`\\boldsymbol{x}`, corresponding to :math:`\\|\\boldsymbol{A}\\boldsymbol{x}-\\boldsymbol{y}\\|_2`. Parameters ---------- A fit matrix (`N,M` array) where `N` (=rows of `A`, elements of `y`) equals the number of target values and `M` (=columns of `A`) equals the number of parameters (=elements of `x`) y vector of target values """ y_predicted = self.predict(A) delta_y = y_predicted - y rmse = np.sqrt(np.mean(delta_y**2)) return rmse
[docs] def predict(self, A: np.ndarray) -> Union[np.ndarray, float]: """ Predicts data given an input matrix :math:`\\boldsymbol{A}`, i.e., :math:`\\boldsymbol{A}\\boldsymbol{x}`, where :math:`\\boldsymbol{x}` is the vector of the fitted parameters. The method returns the vector of predicted values or a float if a single row provided as input. Parameters ---------- A fit matrix where `N` (=rows of `A`, elements of `y`) equals the number of target values and `M` (=columns of `A`) equals the number of parameters """ return, self.parameters)
[docs] def get_contributions(self, A: np.ndarray) -> np.ndarray: """ Returns the average contribution for each row of `A` to the predicted values from each element of the parameter vector. Parameters ---------- A fit matrix where `N` (=rows of `A`, elements of `y`) equals the number of target values and `M` (=columns of `A`) equals the number of parameters """ return np.mean(np.abs(np.multiply(A, self.parameters)), axis=0)
[docs] @abstractmethod def train(self) -> None: pass
@property def summary(self) -> Dict[str, Any]: """ comprehensive information about the optimizer """ info = dict() info['seed'] = self.seed info['fit_method'] = self.fit_method info['standardize'] = self.standardize info['n_target_values'] = self.n_target_values info['n_parameters'] = self.n_parameters info['n_nonzero_parameters'] = \ self.n_nonzero_parameters return {**info, **self._fit_results} def __str__(self) -> str: width = 54 s = [] s.append(' {} '.format(self.__class__.__name__).center(width, '=')) for key in sorted(self.summary.keys()): value = self.summary[key] if isinstance(value, (str, int)): s.append('{:30} : {}'.format(key, value)) elif isinstance(value, (float)): s.append('{:30} : {:.7g}'.format(key, value)) s.append(''.center(width, '=')) return '\n'.join(s) def __repr__(self) -> str: return 'BaseOptimizer((A, y), {}, {}'.format( self.fit_method, self.seed) @property def fit_method(self) -> str: """ fit method """ return self._fit_method @property def parameters(self) -> np.ndarray: """ copy of parameter vector """ if self._fit_results['parameters'] is None: return None else: return self._fit_results['parameters'].copy() @property def n_nonzero_parameters(self) -> int: """ number of non-zero parameters """ if self.parameters is None: return None else: return np.count_nonzero(self.parameters) @property def n_target_values(self) -> int: """ number of target values (=rows in `A` matrix) """ return self._n_rows @property def n_parameters(self) -> int: """ number of parameters (=columns in `A` matrix) """ return self._n_cols @property def standardize(self) -> bool: """ if True standardize the fit matrix before fitting """ return self._standarize @property def seed(self) -> int: """ seed used to initialize pseudo random number generator """ return self._seed