类型错误：wrapper() 采用 1 个位置参数，但给出了 2 个

Question

我正在尝试使用 sci-kit 学习优化器，但我遇到了一些无法解决的问题。我正在尝试使用贝叶斯优化来调整我的 LSTM DNN 的超参数。 任何帮助都会在很大程度上受到重视。 这是我的代码。

from skopt.space import Integer, Categorical, Real
from skopt.utils import use_named_args
from skopt import gp_minimize
import tensorflow
import keras.backend as K
import GetPrediction
import Model

dim_learning_rate = Real(low=1e-4, high=1e-2, prior='log-uniform',
                         name='learning_rate')
dim_num_dense_layers = Integer(low=1, high=5, name='num_dense_layers')
dim_num_input_nodes = Integer(low=16, high=128, name='num_input_nodes')
dim_num_dense_nodes = Integer(low=8, high=64, name='num_dense_nodes')
dim_dropout = Real(low=0.01, high=2, name='dropout')
dim_activation = Categorical(categories=['relu', 'sigmoid'],
                             name='activation')
dim_batch_size = Integer(low=1, high=128, name='batch_size')
dim_adam_decay = Real(low=1e-6, high=1e-2, name="adam_decay")

dimensions = [dim_learning_rate,
              dim_num_dense_layers,
              dim_num_input_nodes,
              dim_num_dense_nodes,
              dim_dropout,
              dim_activation,
              dim_batch_size,
              dim_adam_decay
              ]
default_parameters = [1e-3, 1, 512, 13, 0.5, 'relu', 64, 1e-3]


class Optimize:
    def __init__(self, _STOCK, _INTERVAL, _TYPE):
        self.stock = _STOCK
        self.interval = _INTERVAL
        self._type = _TYPE

    @use_named_args(dimensions=dimensions)
    def fitness(self, learning_rate, num_dense_layers, num_input_nodes,
                num_dense_nodes, dropout, activation, batch_size, rms_decay):
        model = Model.Tuning_Model(learning_rate=learning_rate,
                                   num_dense_layers=num_dense_layers,
                                   num_input_nodes=num_input_nodes,
                                   num_dense_nodes=num_dense_nodes,
                                   dropout=dropout,
                                   activation=activation,
                                   rms_decay=rms_decay
                                   )

        Train_Closing, \
        Train_Volume, \
        Train_Labels, \
        Test_Closing, \
        Test_Volume, \
        Test_Labels, \
        ClosingData_scaled, \
        VolumeData_scaled = GetPrediction.Return_Data(self.stock, self.interval, self._type)

        # named blackbox becuase it represents the structure
        blackbox = model.fit(
            [
                Train_Closing,
                Train_Volume
            ],
            [
                Train_Labels
            ],
            validation_data=(
                [
                    Test_Closing,
                    Test_Volume
                ],
                [
                    Test_Labels
                ]
            ),
            epochs=250,
            batch_size=batch_size
        )
        # return the validation accuracy for the last epoch.
        accuracy = blackbox.history['val_mae'][-1]

        # Delete the Keras model with these hyper-parameters from memory.
        del model

        # Clear the Keras session, otherwise it will keep adding new
        # models to the same TensorFlow graph each time we create
        # a model with a different set of hyper-parameters.
        K.clear_session()
        tensorflow.reset_default_graph()

        # the optimizer aims for the lowest score, so we return our negative accuracy
        return -accuracy

    def Return_BestHyperParameters(self):
        gp_result = gp_minimize(func=self.fitness,
                                dimensions=dimensions,
                                n_calls=12)
        return gp_result


if __name__ == '__main__':
    MyClass = Optimize('DJI', '', 'Daily')
    print(MyClass.Return_BestHyperParameters())

大部分代码来自我最近阅读的一篇文章。 这是错误。

/home/martin/PycharmProjects/MarketPredictor/venv/lib/python3.6/site-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.
  warnings.warn(msg, category=FutureWarning)
/home/martin/PycharmProjects/MarketPredictor/venv/lib/python3.6/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.metrics.scorer module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.
  warnings.warn(message, FutureWarning)
Using TensorFlow backend.
Traceback (most recent call last):
  File "/home/martin/PycharmProjects/MarketPredictor/Optimizer.py", line 104, in <module>
    print(MyClass.Return_BestHyperParameters())
  File "/home/martin/PycharmProjects/MarketPredictor/Optimizer.py", line 98, in Return_BestHyperParameters
    n_calls=12)
  File "/home/martin/PycharmProjects/MarketPredictor/venv/lib/python3.6/site-packages/skopt/optimizer/gp.py", line 237, in gp_minimize
    n_jobs=n_jobs)
  File "/home/martin/PycharmProjects/MarketPredictor/venv/lib/python3.6/site-packages/skopt/optimizer/base.py", line 248, in base_minimize
    next_y = func(next_x)
TypeError: wrapper() takes 1 positional argument but 2 were given

Process finished with exit code 1

谢谢大家。

Answer 1

一个最小的可重复示例会很好。 但我认为问题是，必须最小化的函数是一种方法。 因此 scipy 中的 func 有两个参数（self 和 x）。 试试这个：

    def Return_BestHyperParameters(self):
        gp_result = gp_minimize(func=lambda x: self.fitness(x),
                                dimensions=dimensions,
                                n_calls=12)
        return gp_result

编辑以适合装饰器：

from skopt.space import Integer, Categorical, Real
from skopt.utils import use_named_args
from skopt import gp_minimize
import tensorflow
import keras.backend as K
import GetPrediction
import Model

dim_learning_rate = Real(low=1e-4, high=1e-2, prior='log-uniform',
                         name='learning_rate')
dim_num_dense_layers = Integer(low=1, high=5, name='num_dense_layers')
dim_num_input_nodes = Integer(low=16, high=128, name='num_input_nodes')
dim_num_dense_nodes = Integer(low=8, high=64, name='num_dense_nodes')
dim_dropout = Real(low=0.01, high=2, name='dropout')
dim_activation = Categorical(categories=['relu', 'sigmoid'],
                             name='activation')
dim_batch_size = Integer(low=1, high=128, name='batch_size')
dim_adam_decay = Real(low=1e-6, high=1e-2, name="adam_decay")

dimensions = [dim_learning_rate,
              dim_num_dense_layers,
              dim_num_input_nodes,
              dim_num_dense_nodes,
              dim_dropout,
              dim_activation,
              dim_batch_size,
              dim_adam_decay
              ]
default_parameters = [1e-3, 1, 512, 13, 0.5, 'relu', 64, 1e-3]


def fitness_wrapper(_STOCK, _INTERVALL, _TYPE):

    @use_named_args(dimensions=dimensions)
    def fitness(self, learning_rate, num_dense_layers, num_input_nodes,
                num_dense_nodes, dropout, activation, batch_size, rms_decay):
        model = Model.Tuning_Model(learning_rate=learning_rate,
                                   num_dense_layers=num_dense_layers,
                                   num_input_nodes=num_input_nodes,
                                   num_dense_nodes=num_dense_nodes,
                                   dropout=dropout,
                                   activation=activation,
                                   rms_decay=rms_decay
                                   )

        Train_Closing, \
        Train_Volume, \
        Train_Labels, \
        Test_Closing, \
        Test_Volume, \
        Test_Labels, \
        ClosingData_scaled, \
        VolumeData_scaled = GetPrediction.Return_Data(self.stock, self.interval, self._type)

        # named blackbox becuase it represents the structure
        blackbox = model.fit(
            [
                Train_Closing,
                Train_Volume
            ],
            [
                Train_Labels
            ],
            validation_data=(
                [
                    Test_Closing,
                    Test_Volume
                ],
                [
                    Test_Labels
                ]
            ),
            epochs=250,
            batch_size=batch_size
        )
        # return the validation accuracy for the last epoch.
        accuracy = blackbox.history['val_mae'][-1]

        # Delete the Keras model with these hyper-parameters from memory.
        del model

        # Clear the Keras session, otherwise it will keep adding new
        # models to the same TensorFlow graph each time we create
        # a model with a different set of hyper-parameters.
        K.clear_session()
        tensorflow.reset_default_graph()

        # the optimizer aims for the lowest score, so we return our negative accuracy
        return -accuracy

    return fitness


def Return_BestHyperParameters(_STOCK, _INTERVALL, _TYPE):
    gp_result = gp_minimize(func=fitness_wrapper(_STOCK, _INTERVALL, _TYPE),
                            dimensions=dimensions,
                            n_calls=12)
    return gp_result


if __name__ == '__main__':
    print(Return_BestHyperParameters(_STOCK="DJI", _INTERVALL="", _TYPE="Daily"))

Answer 2

您的问题似乎是在此处调用func时，它有效地将函数调用为func(func.__self__, next_x)因为您的函数是bound method 。 重现该问题的一个小例子是：

from functools import wraps

def decorate(func):
    @wraps(func)
    def wrapper(foo):
        func(foo)
    return wrapper

class Foo:
    @decorate
    def bar(self, foo):
        pass

foo = Foo()
foo.bar('foo')

您可以改用 lambda：

gp_result = gp_minimize(func=lambda params: self.fitness(params),
                        dimensions=dimensions,
                        n_calls=12)

或者重新组织您的代码，以便您传递静态方法。