def deserialize_random_forest(model_dict): model = RandomForestClassifier(**model_dict['params']) estimators = [deserialize_decision_tree(decision_tree) for decision_tree in model_dict['estimators_']] model.estimators_ = np.array(estimators) model.classes_ = np.array(model_dict['classes_']) model.n_features_ = model_dict['n_features_'] model.n_outputs_ = model_dict['n_outputs_'] model.max_depth = model_dict['max_depth'] model.min_samples_split = model_dict['min_samples_split'] model.min_samples_leaf = model_dict['min_samples_leaf'] model.min_weight_fraction_leaf = model_dict['min_weight_fraction_leaf'] model.max_features = model_dict['max_features'] model.max_leaf_nodes = model_dict['max_leaf_nodes'] model.min_impurity_decrease = model_dict['min_impurity_decrease'] model.min_impurity_split = model_dict['min_impurity_split'] if 'oob_score_' in model_dict: model.oob_score_ = model_dict['oob_score_'] if 'oob_decision_function_' in model_dict: model.oob_decision_function_ = model_dict['oob_decision_function_'] if isinstance(model_dict['n_classes_'], list): model.n_classes_ = np.array(model_dict['n_classes_']) else: model.n_classes_ = model_dict['n_classes_'] return model