python - 如何使用 sklearn 管道缩放 Keras 自动编码器模型的目标值？

Question

我正在使用 sklearn 管道来构建 Keras 自动编码器模型并使用 gridsearch 来查找最佳超参数。如果我使用多层感知器模型进行分类，这很好用；但是，在自动编码器中，我需要输出值与输入相同。换句话说，我使用的是 StandardScalar管道中的实例以缩放输入值，因此这引出了我的问题:我如何制作 StandardScalar管道内的实例同时处理输入数据和目标数据，以便它们最终相同？
我提供了一个代码片段作为示例。

from sklearn.datasets import make_classification
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV, KFold
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop, Adam
from tensorflow.keras.wrappers.scikit_learn import KerasRegressor

X, y = make_classification (n_features = 50, n_redundant = 0, random_state = 0,
                            scale = 100, n_clusters_per_class = 1)

# Define wrapper
def create_model (learn_rate = 0.01, input_shape, metrics = ['mse']):
  model = Sequential ()
  model.add (Dense (units = 64, activation = 'relu',
                   input_shape = (input_shape, )))
  model.add (Dense (32, activation = 'relu'))
  model.add (Dense (8,  activation = 'relu'))
  model.add (Dense (32, activation = 'relu'))
  model.add (Dense (input_shape, activation = None))
  model.compile (loss = 'mean_squared_error',
                 optimizer = Adam (lr = learn_rate),
                 metrics = metrics)
  return model

# Create scaler
my_scaler = StandardScaler ()
steps = list ()
steps.append (('scaler', my_scaler))
standard_scaler_transformer = Pipeline (steps)

# Create classifier
clf = KerasRegressor (build_fn = create_model, verbose = 2)

# Assemble pipeline
# How to scale input and output??
clf = Pipeline (steps = [('scaler', my_scaler),
                         ('classifier', clf)],
                verbose = True)

# Run grid search
param_grid = {'classifier__input_shape' : [X.shape [1]],
              'classifier__batch_size' : [50],
              'classifier__learn_rate' : [0.001],
              'classifier__epochs' : [5, 10]}
cv = KFold (n_splits = 5, shuffle = False)
grid = GridSearchCV (estimator = clf, param_grid = param_grid,
                     scoring = 'neg_mean_squared_error', verbose = 1, cv = cv)
grid_result = grid.fit (X, X)

print ('Best: %f using %s' % (grid_result.best_score_, grid_result.best_params_))

Answer 1

您可以使用 TransformedTargetRegressor 通过提供函数(即使用 y 参数)或转换器(即 func 参数)对目标值(即 transformer )应用任意变换。
在这种情况下(即拟合自动编码器模型)，因为您想应用相同的 StandardScalar目标值上的实例，您可以使用 transformer争论。它可以通过以下方式之一完成:

您可以将其用作管道步骤之一，包装回归量:

scaler = StandardScaler()
regressor = KerasRegressor(...)

pipe = Pipeline(steps=[
    ('scaler', scaler),
    ('ttregressor', TransformedTargetRegressor(regressor, transformer=scaler))
])

# Use `__regressor` to access the regressor hyperparameters
param_grid = {'ttregressor__regressor__hyperparam_name' : ...}

gridcv = GridSearchCV(estimator=pipe, param_grid=param_grid, ...)
gridcv.fit(X, X)

或者，您可以将它包裹在 GridSearchCV 周围。像这样:

 ttgridcv = TransformedTargetRegressor(GridSearchCV(...), transformer=scalar)
 ttgridcv.fit(X, X)

 # Use `regressor_` attribute to access the fitted regressor (i.e. `GridSearchCV` instance) 
 print(ttgridcv.regressor_.best_score_, ttgridcv.regressor_.best_params_))