def deserialize_svr(model_dict):
model = SVR(**model_dict['params'])
model.shape_fit_ = model_dict['shape_fit_']
model._gamma = model_dict['_gamma']
model.class_weight_ = np.array(model_dict['class_weight_']).astype(
np.float64)
model.support_ = np.array(model_dict['support_']).astype(np.int32)
model.n_support_ = np.array(model_dict['n_support_']).astype(np.int32)
model.intercept_ = np.array(model_dict['intercept_']).astype(np.float64)
model.probA_ = np.array(model_dict['probA_']).astype(np.float64)
model.probB_ = np.array(model_dict['probB_']).astype(np.float64)
model._intercept_ = np.array(model_dict['_intercept_']).astype(np.float64)
if 'meta' in model_dict['support_vectors_'] and model_dict[
'support_vectors_']['meta'] == 'csr':
model.support_vectors_ = csr.deserialize_csr_matrix(
model_dict['support_vectors_'])
model._sparse = True
else:
model.support_vectors_ = np.array(
model_dict['support_vectors_']).astype(np.float64)
model._sparse = False
if 'meta' in model_dict['dual_coef_'] and model_dict['dual_coef_'][
'meta'] == 'csr':
model.dual_coef_ = csr.deserialize_csr_matrix(model_dict['dual_coef_'])
else:
model.dual_coef_ = np.array(model_dict['dual_coef_']).astype(
np.float64)
if 'meta' in model_dict['_dual_coef_'] and model_dict['_dual_coef_'][
'meta'] == 'csr':
model._dual_coef_ = csr.deserialize_csr_matrix(
model_dict['_dual_coef_'])
else:
model._dual_coef_ = np.array(model_dict['_dual_coef_']).astype(
np.float64)
return model
