带有 LSTM 的 GridSearchCV/RandomizedSearchCV

我一直在尝试通过 RandomizedSearchCV 调整 LSTM 的超参数。

我的代码如下：

X_train = X_train.reshape((X_train.shape[0], 1, X_train.shape[1]))
X_test = X_test.reshape((X_test.shape[0], 1, X_test.shape[1]))

print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)

from imblearn.pipeline import Pipeline
from keras.initializers import RandomNormal

def create_model(activation_1='relu', activation_2='relu',
                 neurons_input = 1, neurons_hidden_1=1,
                 optimizer='Adam' , 
                 #input_shape = (X_train.shape[1], X_train.shape[2])
                 #input_shape=(X_train.shape[0],X_train.shape[1]) #input shape should be timesteps, features
                                                                 ):

  model = Sequential()
  model.add(LSTM(neurons_input, activation=activation_1, input_shape=(X_train.shape[1], X_train.shape[2]),
                  kernel_initializer=RandomNormal(mean=0.0, stddev=0.05, seed=42), 
                  bias_initializer=RandomNormal(mean=0.0, stddev=0.05, seed=42)))

  model.add(Dense(2, activation='sigmoid'))

  model.compile (loss = 'sparse_categorical_crossentropy', optimizer=optimizer)
  return model


clf=KerasClassifier(build_fn=create_model, epochs=10, verbose=0)

param_grid = {
    'clf__neurons_input':   [20, 25, 30, 35],
    'clf__batch_size': [40,60,80,100], 
    'clf__optimizer': ['Adam', 'Adadelta']}



pipe = Pipeline([
    ('oversample', SMOTE(random_state=12)),
    ('clf', clf)
    ])

my_cv = TimeSeriesSplit(n_splits=5).split(X_train)

rs_keras = RandomizedSearchCV(pipe, param_grid, cv=my_cv, scoring='f1_macro', 
                              refit='f1_macro', verbose=3,n_jobs=1, random_state=42)
rs_keras.fit(X_train, y_train)

我一直有一个错误：

Found array with dim 3. Estimator expected <= 2.

这是有道理的，因为网格搜索和随机搜索都需要 [n_samples, n_features] 类型的数组。有谁对如何处理此限制有经验或建议？

谢谢。

这是错误的完整回溯：

Traceback (most recent call last):

  File "<ipython-input-2-b0be4634c98a>", line 1, in <module>
    runfile('Scratch/prediction_lstm.py', wdir='/Simulations/2017-2018/Scratch')

  File "\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 786, in runfile
    execfile(filename, namespace)

  File "\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 110, in execfile
    exec(compile(f.read(), filename, 'exec'), namespace)

  File "Scratch/prediction_lstm.py", line 204, in <module>
    rs_keras.fit(X_train, y_train)

  File "Anaconda3\lib\site-packages\sklearn\model_selection\_search.py", line 722, in fit
    self._run_search(evaluate_candidates)

  File "\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py", line 1515, in _run_search
    random_state=self.random_state))

  File "\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py", line 711, in evaluate_candidates
    cv.split(X, y, groups)))

  File "\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 917, in __call__
    if self.dispatch_one_batch(iterator):

  File "\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 759, in dispatch_one_batch
    self._dispatch(tasks)

  File "\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 716, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)

  File "\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 182, in apply_async
    result = ImmediateResult(func)

  File "\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 549, in __init__
    self.results = batch()

  File "\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 225, in __call__
    for func, args, kwargs in self.items]

  File "\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 225, in <listcomp>
    for func, args, kwargs in self.items]

  File "\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 528, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)

  File "\Anaconda3\lib\site-packages\imblearn\pipeline.py", line 237, in fit
    Xt, yt, fit_params = self._fit(X, y, **fit_params)

  File "\Anaconda3\lib\site-packages\imblearn\pipeline.py", line 200, in _fit
    cloned_transformer, Xt, yt, **fit_params_steps[name])

  File "\Anaconda3\lib\site-packages\sklearn\externals\joblib\memory.py", line 342, in __call__
    return self.func(*args, **kwargs)

  File "\Anaconda3\lib\site-packages\imblearn\pipeline.py", line 576, in _fit_resample_one
    X_res, y_res = sampler.fit_resample(X, y, **fit_params)

  File "\Anaconda3\lib\site-packages\imblearn\base.py", line 80, in fit_resample
    X, y, binarize_y = self._check_X_y(X, y)

  File "\Anaconda3\lib\site-packages\imblearn\base.py", line 138, in _check_X_y
    X, y = check_X_y(X, y, accept_sparse=['csr', 'csc'])

  File "\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 756, in check_X_y
    estimator=estimator)

  File "\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 570, in check_array
    % (array.ndim, estimator_name))

ValueError: Found array with dim 3. Estimator expected <= 2.

这个问题不是由于scikit-learn. RandomizedSearchCV不检查输入的形状。这是各个 Transformer 或 Estimator 的工作，以确定传递的输入具有正确的形状。正如您从堆栈跟踪中看到的，该错误是由imblearn因为SMOTE要求数据是二维的才能工作。

为了避免这种情况，您可以在之后手动重新调整数据SMOTE在将其传递给之前LSTM。有多种方法可以实现这一目标。

1）您传递二维数据（没有像您当前在以下几行中所做的那样显式重塑）：

X_train = X_train.reshape((X_train.shape[0], 1, X_train.shape[1]))
X_test = X_test.reshape((X_test.shape[0], 1, X_test.shape[1]))

到您的管道并之后SMOTE步骤，在你之前clf，将数据重塑为 3-D，然后将其传递给clf.

2) 将当前的 3-D 数据传递到管道，将其转换为 2-D 以供使用SMOTE. SMOTE然后将输出新的过采样 2-D 数据，然后您再次将其重塑为 3-D。

我认为更好的选择是 1。即使如此，您也可以：

• 使用自定义类将数据从 2-D 转换为 3-D，如下所示：

  pipe = Pipeline([
      ('oversample', SMOTE(random_state=12)),
  
      # Check out custom scikit-learn transformers
      # You need to impletent your reshape logic in "transform()" method
      ('reshaper', CustomReshaper(),
      ('clf', clf)
  ])
  

• 或使用已有的Reshape class https://keras.io/layers/core/。我在用Reshape.

所以修改器代码将是（请参阅注释）：

# Remove the following two lines, so the data is 2-D while going to "RandomizedSearchCV". 

#    X_train = X_train.reshape((X_train.shape[0], 1, X_train.shape[1]))
#    X_test = X_test.reshape((X_test.shape[0], 1, X_test.shape[1]))


from keras.layers import Reshape

def create_model(activation_1='relu', activation_2='relu',
                 neurons_input = 1, neurons_hidden_1=1,
                 optimizer='Adam' ,):

  model = Sequential()

  # Add this before LSTM. The tuple denotes the last two dimensions of input
  model.add(Reshape((1, X_train.shape[1])))
  model.add(LSTM(neurons_input, 
                 activation=activation_1, 

                 # Since the data is 2-D, the following needs to be changed from "X_train.shape[1], X_train.shape[2]"
                 input_shape=(1, X_train.shape[1]),
                 kernel_initializer=RandomNormal(mean=0.0, stddev=0.05, seed=42), 
                 bias_initializer=RandomNormal(mean=0.0, stddev=0.05, seed=42)))

  model.add(Dense(2, activation='sigmoid'))

  model.compile (loss = 'sparse_categorical_crossentropy', optimizer=optimizer)
  return model