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 管道构建 Keras 自动编码器模型，并使用网格搜索来查找最佳超参数。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，我在管道中使用StandardScalar 实例来缩放输入值，因此这导致了我的问题：如何使管道内的StandardScalar 实例同时处理输入数据以及目标数据，以便它们最终相同？

我提供一个代码 sn-p 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

您可以通过提供函数（即使用func 参数）或转换器（即transformer 参数）来使用TransformedTargetRegressor 对目标值（即y）应用任意转换。

在这种情况下（即拟合自动编码器模型），由于您也想在目标值上应用相同的 StandardScalar 实例，因此可以使用 transformer 参数。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，包装回归器：

  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，您可以将其包裹在 GridSearchCV 周围，如下所示：

   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_))