| | try: |
| | import sklearn |
| | from sklearn.base import BaseEstimator |
| | from sklearn.base import TransformerMixin |
| | except ImportError: |
| | sklearn = None |
| |
|
| | class BaseEstimator: |
| | pass |
| |
|
| | class TransformerMixin: |
| | pass |
| |
|
| |
|
| | def assert_sklearn_installed(symbol_name): |
| | if sklearn is None: |
| | raise ImportError( |
| | f"{symbol_name} requires `scikit-learn` to be installed. " |
| | "Run `pip install scikit-learn` to install it." |
| | ) |
| |
|
| |
|
| | def _check_model(model): |
| | """Check whether the model need sto be compiled.""" |
| | |
| | if not model.compiled or not model.loss or not model.optimizer: |
| | raise RuntimeError( |
| | "Given model needs to be compiled, and have a loss and an " |
| | "optimizer." |
| | ) |
| |
|
| |
|
| | class TargetReshaper(TransformerMixin, BaseEstimator): |
| | """Convert 1D targets to 2D and back. |
| | |
| | For use in pipelines with transformers that only accept |
| | 2D inputs, like OneHotEncoder and OrdinalEncoder. |
| | |
| | Attributes: |
| | ndim_ : int |
| | Dimensions of y that the transformer was trained on. |
| | """ |
| |
|
| | def fit(self, y): |
| | """Fit the transformer to a target y. |
| | |
| | Returns: |
| | TargetReshaper |
| | A reference to the current instance of TargetReshaper. |
| | """ |
| | self.ndim_ = y.ndim |
| | return self |
| |
|
| | def transform(self, y): |
| | """Makes 1D y 2D. |
| | |
| | Args: |
| | y : np.ndarray |
| | Target y to be transformed. |
| | |
| | Returns: |
| | np.ndarray |
| | A numpy array, of dimension at least 2. |
| | """ |
| | if y.ndim == 1: |
| | return y.reshape(-1, 1) |
| | return y |
| |
|
| | def inverse_transform(self, y): |
| | """Revert the transformation of transform. |
| | |
| | Args: |
| | y: np.ndarray |
| | Transformed numpy array. |
| | |
| | Returns: |
| | np.ndarray |
| | If the transformer was fit to a 1D numpy array, |
| | and a 2D numpy array with a singleton second dimension |
| | is passed, it will be squeezed back to 1D. Otherwise, it |
| | will eb left untouched. |
| | """ |
| | sklearn.base.check_is_fitted(self) |
| | xp, _ = sklearn.utils._array_api.get_namespace(y) |
| | if self.ndim_ == 1 and y.ndim == 2: |
| | return xp.squeeze(y, axis=1) |
| | return y |
| |
|
