Loading deepchem/models/sklearn_models/__init__.py +2 −4 Original line number Diff line number Diff line Loading @@ -18,16 +18,14 @@ class SklearnModel(Model): """ Abstract base class for different ML models. """ def __init__(self, task_types, model_params, fit_transformers, model_instance=None, initialize_raw_model=True): def __init__(self, task_types, model_params, fit_transformers=None, model_instance=None, initialize_raw_model=True): super(SklearnModel, self).__init__( task_types, model_params, fit_transformers=fit_transformers, initialize_raw_model=initialize_raw_model) self.task_types = task_types self.model_params = model_params self.raw_model = model_instance self.fit_transformers = fit_transformers # TODO(rbharath): This does not work with very large datasets! sklearn does # support partial_fit, but only for some models. Might make sense to make Loading Loading
deepchem/models/sklearn_models/__init__.py +2 −4 Original line number Diff line number Diff line Loading @@ -18,16 +18,14 @@ class SklearnModel(Model): """ Abstract base class for different ML models. """ def __init__(self, task_types, model_params, fit_transformers, model_instance=None, initialize_raw_model=True): def __init__(self, task_types, model_params, fit_transformers=None, model_instance=None, initialize_raw_model=True): super(SklearnModel, self).__init__( task_types, model_params, fit_transformers=fit_transformers, initialize_raw_model=initialize_raw_model) self.task_types = task_types self.model_params = model_params self.raw_model = model_instance self.fit_transformers = fit_transformers # TODO(rbharath): This does not work with very large datasets! sklearn does # support partial_fit, but only for some models. Might make sense to make Loading
