sklearn和n_作业的超参数优化>1：酸洗

Question

我有麻烦了。下面是我的代码结构：

• 一个类似于抽象类的基类
• 可以实例化的子类
  • 设置参数并使用n_jobs=-1调用RandomizedSearchCV或{}的方法。
    • 一个局部函数，create_model，创建神经网络模型（参见this教程），由KerasClassifier或KerasRegressor调用

我得到一个错误，说本地对象不能被pickle。如果我改变n_jobs=1，那么没有问题。所以我怀疑问题出在局部函数和并行处理上。有解决办法吗？在google上搜索一下之后，似乎序列化程序dill可以在这里工作（我甚至找到了一个名为multiprocessing_on_dill）的包。但我目前依赖的是sklearn的包。在

Answer 1

我可以在jupyter笔记本电脑/ipython（在Unix上没有问题）的Windows下，在kerasClassifier模型上运行sklearn的网格搜索时，可以确认同样的问题。在

我通过将导致pickle问题的create_model函数放入模块中并导入模块而不是在环境中定义函数来解决这个问题。在

要为Python创建一个简单的模块

• 在运行主代码的同一个文件夹中创建一个文本文件，并将其另存为my_模块.py在
• 将create_model函数的定义放入文件中
• 与其在代码中定义create_model，不如用import my_module导入模块，然后用my_module.create_model()从模块中调用函数

Answer 2

我找到了解决问题的办法。我真的很困惑，为什么示例here与n_jobs=-1一起工作，但我的代码却不起作用。似乎问题出在子类的方法中的本地函数{}。如果我把局部函数作为子类的一个方法，我就可以设置n_jobs > 1。在

总而言之，我的代码结构如下：

• 一个类似于抽象类的基类
• 可以实例化的子类
  • 设置参数并用n_jobs=-1调用RandomizedSearchCV或{}的方法。在
  • 一种方法，create_model，创建由KerasClassifier或{}调用的神经网络模型

准则的总体思路：

from abc import ABCMeta
import numpy as np
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV

class MLAlgorithms(metaclass=ABCMeta):

    def __init__(self, X_train, y_train, X_test, y_test=None):
        """
        Constructor with train and test data.
        :param X_train: Train descriptor data
        :param y_train: Train observed data
        :param X_test: Test descriptor data
        :param y_test: Test observed data
        """
        ...

    @abstractmethod
    def setmlalg(self, mlalg):
        """
        Sets a machine learning algorithm.
        :param mlalg: Dictionary of the machine learning algorithm.
        """
        pass

    @abstractmethod
    def fitmlalg(self, mlalg, rid=None):
        """
        Fits a machine learning algorithm.
        :param mlalg: Machine learning algorithm
        """
        pass


class MLClassification(MLAlgorithms):
    """
    Main class for classification machine learning algorithms.
    """

    def setmlalg(self, mlalg):
        """
        Sets a classification machine learning algorithm.
        :param mlalg: Dictionary of the classification machine learning algorithm.
        """
        ...

    def fitmlalg(self, mlalg):
        """
        Fits a classification machine learning algorithm.
        :param mlalg: Classification machine learning algorithm
        """
        ...

    # Function to create model, required for KerasClassifier
    def create_model(self, n_layers=1, units=10, input_dim=10, output_dim=1,
                     optimizer="rmsprop", loss="binary_crossentropy",
                     kernel_initializer="glorot_uniform", activation="sigmoid",
                     kernel_regularizer="l2", kernel_regularizer_weight=0.01,
                     lr=0.01, momentum=0.0, decay=0.0, nesterov=False, rho=0.9, epsilon=1E-8,
                     beta_1=0.9, beta_2=0.999, schedule_decay=0.004):
        from keras.models import Sequential
        from keras.layers import Dense
        from keras import regularizers, optimizers

        # Create model
        if kernel_regularizer.lower() == "l1":
            kernel_regularizer = regularizers.l1(l=kernel_regularizer_weight)
        elif kernel_regularizer.lower() == "l2":
            kernel_regularizer = regularizers.l2(l=kernel_regularizer_weight)
        elif kernel_regularizer.lower() == "l1_l2":
            kernel_regularizer = regularizers.l1_l2(l1=kernel_regularizer_weight, l2=kernel_regularizer_weight)
        else:
            print("Warning: Kernel regularizer {0} not supported. Using default 'l2' regularizer.".format(
                kernel_regularizer))
            kernel_regularizer = regularizers.l2(l=kernel_regularizer_weight)

        if optimizer.lower() == "sgd":
            optimizer = optimizers.sgd(lr=lr, momentum=momentum, decay=decay, nesterov=nesterov)
        elif optimizer.lower() == "rmsprop":
            optimizer = optimizers.rmsprop(lr=lr, rho=rho, epsilon=epsilon, decay=decay)
        elif optimizer.lower() == "adagrad":
            optimizer = optimizers.adagrad(lr=lr, epsilon=epsilon, decay=decay)
        elif optimizer.lower() == "adadelta":
            optimizer = optimizers.adadelta(lr=lr, rho=rho, epsilon=epsilon, decay=decay)
        elif optimizer.lower() == "adam":
            optimizer = optimizers.adam(lr=lr, beta_1=beta_1, beta_2=beta_2, epsilon=epsilon, decay=decay)
        elif optimizer.lower() == "adamax":
            optimizer = optimizers.adamax(lr=lr, beta_1=beta_1, beta_2=beta_2, epsilon=epsilon, decay=decay)
        elif optimizer.lower() == "nadam":
            optimizer = optimizers.nadam(lr=lr, beta_1=beta_1, beta_2=beta_2, epsilon=epsilon,
                                         schedule_decay=schedule_decay)
        else:
            print("Warning: Optimizer {0} not supported. Using default 'sgd' optimizer.".format(optimizer))
            optimizer = "sgd"

        model = Sequential()
        model.add(
            Dense(units=units, input_dim=input_dim,
                  kernel_initializer=kernel_initializer, activation=activation,
                  kernel_regularizer=kernel_regularizer))
        for layer_count in range(n_layers - 1):
            model.add(
                Dense(units=units, kernel_initializer=kernel_initializer, activation=activation,
                      kernel_regularizer=kernel_regularizer))
        model.add(Dense(units=output_dim,
                        kernel_initializer=kernel_initializer, activation=activation,
                        kernel_regularizer=kernel_regularizer))

        # Compile model
        model.compile(loss=loss, optimizer=optimizer, metrics=['accuracy'])
        return model


class MLRegression(MLAlgorithms):
    """
    Main class for regression machine learning algorithms.
    """
    ...