Evaluate the raw fitness of the program according to X, y. Parameters ---------- X : {array-like}, shape = [n_samples, n_features] Training vectors, where n_samples is the number of samples and n_features is the number of features. y : array-
(self, X, y, sample_weight)
| 439 | return self.get_all_indices()[0] |
| 440 | |
| 441 | def raw_fitness(self, X, y, sample_weight): |
| 442 | """Evaluate the raw fitness of the program according to X, y. |
| 443 | |
| 444 | Parameters |
| 445 | ---------- |
| 446 | X : {array-like}, shape = [n_samples, n_features] |
| 447 | Training vectors, where n_samples is the number of samples and |
| 448 | n_features is the number of features. |
| 449 | |
| 450 | y : array-like, shape = [n_samples] |
| 451 | Target values. |
| 452 | |
| 453 | sample_weight : array-like, shape = [n_samples] |
| 454 | Weights applied to individual samples. |
| 455 | |
| 456 | Returns |
| 457 | ------- |
| 458 | raw_fitness : float |
| 459 | The raw fitness of the program. |
| 460 | |
| 461 | """ |
| 462 | y_pred = self.execute(X) |
| 463 | if self.transformer: |
| 464 | y_pred = self.transformer(y_pred) |
| 465 | raw_fitness = self.metric(y, y_pred, sample_weight) |
| 466 | |
| 467 | return raw_fitness |
| 468 | |
| 469 | def fitness(self, parsimony_coefficient=None): |
| 470 | """Evaluate the penalized fitness of the program according to X, y. |