EPDE/epde/operators/common/coeff_calculation.py at 7e33c8e4dd63674678ac63fd2623ad4438a610fa · Gromwud/EPDE · GitHub

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 17 13:58:18 2021

@author: mike_ubuntu
"""

import numpy as np
from sklearn.linear_model import LinearRegression, Ridge

import epde.globals as global_var
from epde.operators.utils.template import CompoundOperator
from epde.structure.main_structures import Equation

class LinRegBasedCoeffsEquation(CompoundOperator):
    '''

    The operatror, dedicated to the calculation of the weights of the equation (for the free coefficient and
    each of its terms except the target one).

    Attributes:
        _tags (`set`):
        g_fun_vals (`numpy.ndarray`):

    Methods:
        apply(equation)
            Calculate the coefficients of the equation, using the linear regression. The result is stored in the
            equation.weights_final attribute
    '''
    key = 'LinRegCoeffCalc'

    def apply(self, objective : Equation, arguments : dict = None):
        """
        Calculate the coefficients of the equation, using the linear regression.The result is stored in the
        objective.weights_final attribute

        Args:
            objective (`Equation`): the equation object, to that the fitness function is obtained.
            arguments (`dict`):

        Returns:
            None
        """
        # self_args, subop_args = self.parse_suboperator_args(arguments = arguments)

        assert objective.weights_internal_evald, 'Trying to calculate final weights before evaluating intermeidate ones (no sparsity).'
        # target = objective.structure[objective.target_idx]
        #
        # target_vals = target.evaluate(False)
        # features_vals = []
        # nonzero_features_indexes = []
        # for i in range(len(objective.structure)):
        #     if i == objective.target_idx:
        #         continue
        #     idx = i if i < objective.target_idx else i-1
        #     if objective.weights_internal[idx] != 0:
        #         features_vals.append(objective.structure[i].evaluate(False))
        #         nonzero_features_indexes.append(idx)
        #
        # if len(features_vals) == 0:
        #     objective.weights_final = np.zeros(len(objective.structure))
        # else:
        #     features = features_vals[0]
        #     if len(features_vals) > 1:
        #         for i in range(1, len(features_vals)):
        #             features = np.vstack([features, features_vals[i]])
        #     features = np.vstack([features, np.ones(features_vals[0].shape)]) # Добавляем константную фичу
        #     features = np.transpose(features)
        #     estimator = LinearRegression(copy_X=True, fit_intercept=False, n_jobs=-1,
        #                       positive=False, tol=0.0001)
        #     # estimator = LinearRegression(fit_intercept=False)
        #     if features.ndim == 1:
        #         features = features.reshape(-1, 1)
        #     try:
        #         self.g_fun_vals = global_var.grid_cache.g_func[global_var.grid_cache.g_func != 0]
        #     except AttributeError:
        #         self.g_fun_vals = None
        #     estimator.fit(features, target_vals, sample_weight = self.g_fun_vals)
        #
        #     valuable_weights = estimator.coef_
        #     weights = np.zeros(len(objective.structure))
        #     for weight_idx in range(len(weights)-1):
        #         if weight_idx in nonzero_features_indexes:
        #             weights[weight_idx] = valuable_weights[nonzero_features_indexes.index(weight_idx)]
        #     weights[-1] = valuable_weights[-1]
        #     objective.weights_final = weights
        # _, target, features = objective.evaluate(normalize=False, return_val=False)
        # self.g_fun_vals = global_var.grid_cache.g_func[global_var.grid_cache.g_func != 0]
        # estimator = LinearRegression(copy_X=True, fit_intercept=True, n_jobs=-1, positive=False, tol=0.0001)
        # estimator.fit(features, target, sample_weight=self.g_fun_vals)
        # valuable_weights = estimator.coef_
        # objective.weights_final = np.append(valuable_weights, estimator.intercept_)
        # objective.weights_final_evald = True

    def use_default_tags(self):
        self._tags = {'coefficient calculation', 'gene level', 'no suboperators', 'inplace'}