sklearn RandomizedSearchCV与流水线KerasClassifier

Question

我正在Keras模型上执行超参数调优优化任务与sklearn。我想一个管道内优化KerasClassifiers ... 代码如下：

import numpy as np 
import pandas as pd 
from keras.models import Sequential 
from keras.layers import Dense 
from keras.wrappers.scikit_learn import KerasClassifier 
from sklearn.model_selection import cross_val_score, StratifiedKFold,RandomizedSearchCV 
from sklearn.preprocessing import LabelEncoder, StandardScaler 
from sklearn.pipeline import Pipeline 

my_seed=7 

dataframe = pd.read_csv("z:/sonar.all-data.txt", header=None) 

dataset = dataframe.values 
# split into input and output variables 
X = dataset[:,:60].astype(float) 
Y = dataset[:,60] 

encoder = LabelEncoder() 
Y_encoded=encoder.fit_transform(Y) 
myScaler = StandardScaler() 
X_scaled = myScaler.fit_transform(X) 

def create_keras_model(hidden=60): 
    model = Sequential() 
    model.add(Dense(units=hidden, input_dim=60, kernel_initializer="normal", activation="relu")) 
    model.add(Dense(1, kernel_initializer="normal", activation="sigmoid")) 
    #compile model 
    model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"]) 
    return model 

def create_pipeline(hidden=60): 
    steps = [] 
    steps.append(('scaler', StandardScaler())) 
    steps.append(('dl', KerasClassifier(build_fn=create_keras_model,hidden=hidden, verbose=0))) 
    pipeline = Pipeline(steps) 
    return pipeline 

my_neurons = [15, 30, 60] 
my_epochs= [50, 100, 150] 
my_batch_size = [5,10] 
my_param_grid = dict(hidden=my_neurons, epochs=my_epochs, batch_size=my_batch_size) 

model2Tune = KerasClassifier(build_fn=create_keras_model, verbose=0) 
model2Tune2 = create_pipeline() 

griglia = RandomizedSearchCV(estimator=model2Tune, param_distributions = my_param_grid, n_iter=8) 
griglia.fit(X_scaled, Y_encoded) #this works 

griglia2 = RandomizedSearchCV(estimator=create_pipeline, param_distributions = my_param_grid, n_iter=8) 
griglia2.fit(X, Y_encoded) #this does not 

我们看到，RandomizedSearchCV作品与griglia，虽然它不与griglia2工作，返回

"TypeError: estimator should be an estimator implementing 'fit' method, was passed".

是否有可能修改代码以使其在Pipeline对象下运行？

在此先感谢

Answer 1

estimator参数需要一个对象，而不是指针。目前您正在传递一个指向生成管道对象的方法的指针。尝试添加()给它解决这个问题：

griglia2 = RandomizedSearchCV(estimator=create_pipeline(), param_distributions = my_param_grid, n_iter=8)

现在对于关于该无效参数错误的第二注释。您需要将创建管道时定义的名称追加到实际参数中，以便它们可以成功传递。

查看Pipeline usage here的描述。

使用此：

my_param_grid = dict(dl__hidden=my_neurons, dl__epochs=my_epochs, 
        dl__batch_size=my_batch_size) 

通知的dl__（两个下划线）。当您想调整管道内多个对象的参数时，这非常有用。

例如，让我们与上述参数一起说，您还想调整或指定StandardScaler的参数。

那么你的参数网格变为：

my_param_grid = dict(dl__hidden=my_neurons, dl__epochs=my_epochs, 
        dl__batch_size=my_batch_size, 
        scaler__with_mean=False) 

希望这将清除的东西。