Fits self.estimator to the training data and labels provided to it so far. Args: bootstrap: If True, the method trains the model on a set bootstrapped from the known training instances. **fit_kwargs: Keyword arguments to be passed to the fit method of the pr
(self, bootstrap: bool = False, **fit_kwargs)
| 112 | 'agree with the training data and labels provided so far') |
| 113 | |
| 114 | def _fit_to_known(self, bootstrap: bool = False, **fit_kwargs) -> 'BaseLearner': |
| 115 | """ |
| 116 | Fits self.estimator to the training data and labels provided to it so far. |
| 117 | |
| 118 | Args: |
| 119 | bootstrap: If True, the method trains the model on a set bootstrapped from the known training instances. |
| 120 | **fit_kwargs: Keyword arguments to be passed to the fit method of the predictor. |
| 121 | |
| 122 | Returns: |
| 123 | self |
| 124 | """ |
| 125 | if not bootstrap: |
| 126 | self.estimator.fit(self.X_training, self.y_training, **fit_kwargs) |
| 127 | else: |
| 128 | n_instances = self.X_training.shape[0] |
| 129 | bootstrap_idx = np.random.choice( |
| 130 | range(n_instances), n_instances, replace=True) |
| 131 | self.estimator.fit( |
| 132 | self.X_training[bootstrap_idx], self.y_training[bootstrap_idx], **fit_kwargs) |
| 133 | |
| 134 | return self |
| 135 | |
| 136 | def fit(self, X: modALinput, y: modALinput, bootstrap: bool = False, **fit_kwargs) -> 'BaseLearner': |
| 137 | """ |