Loading deepchem/models/models.py +1 −1 Original line number Diff line number Diff line Loading @@ -25,7 +25,7 @@ class Model(BaseEstimator): """ def __init__(self, model_instance: Optional[Any] = None, model_instance=None, model_dir: Optional[str] = None, **kwargs) -> None: """Abstract class for all models. Loading deepchem/models/sklearn_models/sklearn_model.py +2 −4 Original line number Diff line number Diff line Loading @@ -82,11 +82,9 @@ class SklearnModel(Model): w = np.squeeze(dataset.w) # Some scikit-learn models don't use weights. if self.use_weights: # FIXME: BaseEstimator doesn't guarantee the class has `fit` method. self.model_instance.fit(X, y, w) # type: ignore self.model_instance.fit(X, y, w) return # FIXME: BaseEstimator doesn't guarantee the class has `fit` method. self.model_instance.fit(X, y) # type: ignore self.model_instance.fit(X, y) def predict_on_batch(self, X: np.ndarray) -> np.ndarray: """Makes predictions on batch of data. Loading deepchem/models/xgboost_models/xgboost_model.py +2 −1 Original line number Diff line number Diff line Loading @@ -52,7 +52,8 @@ class XGBoostModel(SklearnModel): else: self.early_stopping_rounds = 50 def fit(self, dataset: Dataset, **kwargs) -> None: # FIXME: Return type "None" of "fit" incompatible with return type "float" in supertype "Model" def fit(self, dataset: Dataset, **kwargs) -> None: # type: ignore[override] """Fits XGBoost model to data. dataset: Dataset Loading Loading
deepchem/models/models.py +1 −1 Original line number Diff line number Diff line Loading @@ -25,7 +25,7 @@ class Model(BaseEstimator): """ def __init__(self, model_instance: Optional[Any] = None, model_instance=None, model_dir: Optional[str] = None, **kwargs) -> None: """Abstract class for all models. Loading
deepchem/models/sklearn_models/sklearn_model.py +2 −4 Original line number Diff line number Diff line Loading @@ -82,11 +82,9 @@ class SklearnModel(Model): w = np.squeeze(dataset.w) # Some scikit-learn models don't use weights. if self.use_weights: # FIXME: BaseEstimator doesn't guarantee the class has `fit` method. self.model_instance.fit(X, y, w) # type: ignore self.model_instance.fit(X, y, w) return # FIXME: BaseEstimator doesn't guarantee the class has `fit` method. self.model_instance.fit(X, y) # type: ignore self.model_instance.fit(X, y) def predict_on_batch(self, X: np.ndarray) -> np.ndarray: """Makes predictions on batch of data. Loading
deepchem/models/xgboost_models/xgboost_model.py +2 −1 Original line number Diff line number Diff line Loading @@ -52,7 +52,8 @@ class XGBoostModel(SklearnModel): else: self.early_stopping_rounds = 50 def fit(self, dataset: Dataset, **kwargs) -> None: # FIXME: Return type "None" of "fit" incompatible with return type "float" in supertype "Model" def fit(self, dataset: Dataset, **kwargs) -> None: # type: ignore[override] """Fits XGBoost model to data. dataset: Dataset Loading
