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
