使用类权重的网格搜索和 KerasClassifier答案

【问题标题】：Grid search and KerasClassifier using class weights使用类权重的网格搜索和 KerasClassifier
【发布时间】：2019-06-06 04:47:57
【问题描述】：

我正在尝试使用 scikit-learn RandomizedSearchCV 函数和 Keras KerasClassifier 包装器进行网格搜索，以解决我的不平衡多类分类问题。但是，当我尝试将class_weight 作为输入时，fit 方法给了我以下错误：

RuntimeError: Cannot clone object <keras.wrappers.scikit_learn.KerasClassifier object at 0x000002AA3C676710>, as the constructor either does not set or modifies parameter class_weight

以下是我用来构建KerasClassifier 的函数和RandomizedSearchCV 的脚本：

build_fn：

import keras as k

def build_keras_model(loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'], optimiser = 'adam', 
                  learning_rate = 0.001, n_neurons = 30, n_layers = 1, n_classes = 3,
                  l1_reg = 0.001, l2_reg = 0.001, batch_norm = False, dropout = None, 
                  input_shape = (8,)):

model = k.models.Sequential()

model.add(k.layers.Dense(n_neurons, 
                         input_shape = input_shape,
                         kernel_regularizer = k.regularizers.l1_l2(l1 = l1_reg, l2 = l2_reg),
                         activation = 'relu'))
if batch_norm is True:
    model.add(k.layers.BatchNormalization())
if dropout is not None:
    model.add(k.layers.Dropout(dropout))

i = 1   
while i < n_layers:
    model.add(k.layers.Dense(n_neurons,
                             kernel_regularizer = k.regularizers.l1_l2(l1 = l1_reg, l2 = l2_reg),
                             activation = 'relu'))
    if batch_norm is True:
        model.add(k.layers.BatchNormalization())
    if dropout is not None:
        model.add(k.layers.Dropout(dropout))
    i += 1
del i

model.add(k.layers.Dense(n_classes, activation = 'softmax'))

if optimiser == 'adam':
    koptimiser = k.optimizers.Adam(lr = learning_rate)
elif optimiser == 'adamax':
    koptimiser = k.optimizers.Adamax(lr = learning_rate)
elif optimiser == 'nadam':
    koptimiser = k.optimizers.Nadam(lr = learning_rate)
else:
    print('Unknown optimiser type')

model.compile(optimizer = koptimiser, loss = loss, metrics = metrics)

model.summary()

return model

脚本：

import scipy as sp
from sklearn.utils.class_weight import compute_class_weight
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import RandomizedSearchCV

parameters =    {
            'optimiser': ['adam', 'adamax', 'nadam'],
            'learning_rate': sp.stats.uniform(0.0005, 0.0015),
            'epochs': sp.stats.randint(500, 1501),
            'n_neurons': sp.stats.randint(20, 61),
            'n_layers': sp.stats.randint(1, 3),
            'n_classes': [3],
            'batch_size': sp.stats.randint(1, 11),
            'l1_reg': sp.stats.reciprocal(1e-3, 1e1),
            'l2_reg': sp.stats.reciprocal(1e-3, 1e1),
            'batch_norm': [False],
            'dropout': [None],
            'metrics': [['accuracy']],
            'loss': ['sparse_categorical_crossentropy'],
            'input_shape': [(training_features.shape[1],)]
            }

class_weights = compute_class_weight('balanced', np.unique(training_targets), 
                                     training_targets[target_label[0]])
class_weights = dict(enumerate(class_weights))

keras_model = KerasClassifier(build_fn = build_keras_model, verbose = 0, class_weight = class_weights)

clf = RandomizedSearchCV(keras_model, parameters, n_iter = 1, scoring = 'f1_micro', 
                         n_jobs = 1, cv = 5, random_state = random_state)


clf.fit(training_features, training_targets.values[:, 0])

model = clf.best_estimator_

【问题讨论】：

啊，你有没有试过在 fit 方法中传递 class_weights：grid_result = clf.fit(training_features, training_targets.values[:, 0], clf__class_weight=class_weights)
当我尝试这样做时，出现以下错误：TypeError: Unrecognized keyword arguments: {'clf__class_weight': {0: 1.76, 1: 0.6285714285714286, 2: 1.1891891891891893}}
并且没有 clf__ 前缀？
是的，我现在试过了，它奏效了。谢谢！
好的，我将它作为答案发布，只是为了完成问题

标签： scikit-learn keras grid-search

【解决方案1】：

要在这种情况下使用KerasClassifier 传递class_weights，应该在fit 方法中传递class_weights，然后将其转发给keras 模型。

grid_result = clf.fit(training_features, training_targets.values[:, 0], class_weight=class_weights)

在旧版本中，必须使用 clf__ 前缀来传递它们：

grid_result = clf.fit(training_features, training_targets.values[:, 0], clf__class_weight=class_weights)

【讨论】：

【解决方案2】：

当使用 KerasClassifier 时，要使用类权重，即使对于 GridSearch，使用 fit_params 功能添加多个参数，因为 build_fn 调用模型函数，不接受参数。

classifier = KerasClassifier(build_fn = build_classifier, epochs=20, batch_size = 128)
    
accuracies = cross_val_score(estimator=classifier, X = X_train, y = y_train, cv = 3, 
                             n_jobs = -1, verbose=0, 
                             fit_params = {'callbacks': [EarlyStopping()],
                             class_weight:class_weights})

【讨论】：