def test_sparse_coef(): # Check that the sparse_coef property works clf = ElasticNet() clf.coef_ = [1, 2, 3] assert sp.isspmatrix(clf.sparse_coef_) assert clf.sparse_coef_.toarray().tolist()[0] == clf.coef_
def deserialize_elastic_regressor(model_dict): model = ElasticNet(model_dict["params"]) model.coef_ = np.array(model_dict["coef_"]) model.alpha = np.array(model_dict["alpha"]).astype(np.float) if isinstance(model_dict["n_iter_"], list): model.n_iter_ = np.array(model_dict["n_iter_"]) else: model.n_iter_ = int(model_dict["n_iter_"]) if isinstance(model_dict["intercept_"], list): model.intercept_ = np.array(model_dict["intercept_"]) else: model.intercept_ = float(model_dict["intercept_"]) return model
def deserialize_elastic_regressor(model_dict): model = ElasticNet(model_dict['params']) model.coef_ = np.array(model_dict['coef_']) model.alpha = np.array(model_dict['alpha']).astype(np.float) if isinstance(model_dict['n_iter_'], list): model.n_iter_ = np.array(model_dict['n_iter_']) else: model.n_iter_ = int(model_dict['n_iter_']) if isinstance(model_dict['intercept_'], list): model.intercept_ = np.array(model_dict['intercept_']) else: model.intercept_ = float(model_dict['intercept_']) return model