python - tensorflow keras RandomForestModel get_config() 为空

Question

我希望能够查看传递给 keras 的 RandomForestModel 的超参数。我认为这应该可以通过 model.get_config() 实现。但是，在创建和训练模型后，get_config() 总是返回一个空字典。

这是在我的 RandomForestWrapper 类中创建模型的函数:

def add_new_model(self, model_name, params):

    self.train_test_split()

        model = tfdf.keras.RandomForestModel(
            random_seed=params["random_seed"],
            num_trees=params["num_trees"],
            categorical_algorithm=params["categorical_algorithm"],
            compute_oob_performances=params["compute_oob_performances"],
            growing_strategy=params["growing_strategy"],
            honest=params["honest"],
            max_depth=params["max_depth"],
            max_num_nodes=params["max_num_nodes"]
           )

    print(model.get_config())
    self.models.update({model_name: model})
    print(f"{model_name} added")

示例参数:

params_v2 = {
    "random_seed": 123456,
    "num_trees": 1000,
    "categorical_algorithm": "CART",
    "compute_oob_performances": True,
    "growing_strategy": "LOCAL",
    "honest": True,
    "max_depth": 8,
    "max_num_nodes": None
}

然后我实例化类并训练模型:

rf_models = RF(data, obs_col="obs", class_col="cell_type")
rf_models.add_new_model("model_2", params_v2)
rf_models.train_model("model_2", verbose=False, metrics=["Accuracy"])

model = rf_models.models["model_2"]
model.get_config()

##
{}

在模型摘要中，我可以看到参数已被接受。

Answer 1

关于 get_config()，请注意 docs 状态:

Returns the config of the Model.

Config is a Python dictionary (serializable) containing theconfiguration of an object, which in this case is a Model. This allowsthe Model to be be reinstantiated later (without its trained weights)from this configuration.

Note that get_config() does not guarantee to return a fresh copy ofdict every time it is called. The callers should make a copy of thereturned dict if they want to modify it.

Developers of subclassed Model are advised to override this method,and continue to update the dict from super(MyModel, self).get_config()to provide the proper configuration of this Model. The default configis an empty dict. Optionally, raise NotImplementedError to allow Kerasto attempt a default serialization.

我认为你可以做的就是调用 model.learner_params 来获得你想要的细节:

import tensorflow_decision_forests as tfdf
import pprint

params_v2 = {
    "random_seed": 123456,
    "num_trees": 1000,
    "categorical_algorithm": "CART",
    "compute_oob_performances": True,
    "growing_strategy": "LOCAL",
    "honest": True,
    "max_depth": 8,
    "max_num_nodes": None
}

model = tfdf.keras.RandomForestModel().from_config(params_v2)
pprint.pprint(model.learner_params)

{'adapt_bootstrap_size_ratio_for_maximum_training_duration': False,
 'allow_na_conditions': False,
 'bootstrap_size_ratio': 1.0,
 'bootstrap_training_dataset': True,
 'categorical_algorithm': 'CART',
 'categorical_set_split_greedy_sampling': 0.1,
 'categorical_set_split_max_num_items': -1,
 'categorical_set_split_min_item_frequency': 1,
 'compute_oob_performances': True,
 'compute_oob_variable_importances': False,
 'growing_strategy': 'LOCAL',
 'honest': True,
 'honest_fixed_separation': False,
 'honest_ratio_leaf_examples': 0.5,
 'in_split_min_examples_check': True,
 'keep_non_leaf_label_distribution': True,
 'max_depth': 8,
 'max_num_nodes': None,
 'maximum_model_size_in_memory_in_bytes': -1.0,
 'maximum_training_duration_seconds': -1.0,
 'min_examples': 5,
 'missing_value_policy': 'GLOBAL_IMPUTATION',
 'num_candidate_attributes': 0,
 'num_candidate_attributes_ratio': -1.0,
 'num_oob_variable_importances_permutations': 1,
 'num_trees': 1000,
 'pure_serving_model': False,
 'random_seed': 123456,
 'sampling_with_replacement': True,
 'sorting_strategy': 'PRESORT',
 'sparse_oblique_normalization': None,
 'sparse_oblique_num_projections_exponent': None,
 'sparse_oblique_projection_density_factor': None,
 'sparse_oblique_weights': None,
 'split_axis': 'AXIS_ALIGNED',
 'uplift_min_examples_in_treatment': 5,
 'uplift_split_score': 'KULLBACK_LEIBLER',
 'winner_take_all': True}