Adding MOre scikit learn models which don't take weights

from sklearn.ensemble import RandomForestClassifier

from sklearn.ensemble import RandomForestRegressor

from sklearn.gaussian_process import GaussianProcessRegressor

from sklearn.linear_model import LogisticRegression

from sklearn.linear_model import LogisticRegression, BayesianRidge

from sklearn.linear_model import LinearRegression

from sklearn.linear_model import RidgeCV

from sklearn.linear_model import LassoCV

from deepchem.utils.save import load_from_disk

from deepchem.utils.save import save_to_disk

NON_WEIGHTED_MODELS = {LogisticRegression, PLSRegression, GaussianProcessRegressor, ElasticNetCV, LassoCV}

NON_WEIGHTED_MODELS = {

    LogisticRegression, PLSRegression, GaussianProcessRegressor, ElasticNetCV,

    LassoCV, BayesianRidge

}

class SklearnModel(Model):

  def reload(self):

    """Loads sklearn model from joblib file on disk."""

    self.model_instance = load_from_disk(Model.get_model_filename(self.model_dir))

    self.model_instance = load_from_disk(

        Model.get_model_filename(self.model_dir))

  def get_num_tasks(self):

    """Number of tasks for this model. Defaults to 1"""