问题描述
我一直在尝试通过RandomizedSearchCV调整LSTM的超参数.
I am stuck on the trying to tune hyperparameters for LSTM via RandomizedSearchCV.
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)
我一直出错:
Found array with dim 3. Estimator expected <= 2.
这很有意义，因为GridSearch和RandomizedSearch都需要[n_samples，n_features]类型的数组.是否有人对如何处理此限制有经验或建议?
which makes sense, as both GridSearch and RandomizedSearch need [n_samples, n_features] type of array. Does anyone have an experience or suggestion on how to deal with this limitation?
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.
推荐答案
此问题不是由于scikit-learn引起的. RandomizedSearchCV不检查输入的形状.那是各个变压器或估计器确定传递的输入具有正确形状的工作.从堆栈跟踪中可以看到，该错误是由imblearn创建的，因为SMOTE要求数据必须是二维的才能工作.
This problem is not due to scikit-learn. RandomizedSearchCV does not check the shape of input. That is the work of the individual Transformer or Estimator to establish that the passed input is of correct shape. As you can see from the stack trace, that error is created by imblearn because SMOTE requires data to be 2-D to work.
为避免这种情况，您可以在SMOTE之后且将其传递给LSTM之前手动调整数据的形状.有多种方法可以实现这一目标.
To avoid that, you can reshape the data manually after SMOTE and before passing it to the LSTM. There are multiple ways to achieve this.
1)您传递2-D数据(在下面的几行中没有像当前所做的那样显式重塑):
1) You pass 2-D data (without explicitly reshaping as you are doing currently in the following lines):
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]))
到管道，并在SMOTE步骤之后，在clf之前，将数据重塑为3-D，然后将其传递给clf.
to your pipeline and after the SMOTE step, before your clf, reshape the data into 3-D and then pass it to clf.
2)您将当前的3-D数据传递到管道，将其转换为2-D以供SMOTE使用. SMOTE然后将输出新的过采样的2-D数据，然后将其重新整形为3-D.
2) You pass your current 3-D data to the pipeline, transform it into 2-D to be used with SMOTE. SMOTE will then output new oversampled 2-D data which you then again reshape into 3-D.
我认为更好的选择是1.即使如此，您也可以:
I think the better option will be 1. Even in that, you can either:
-
使用您的自定义类将数据从2-D转换为3-D，如下所示:
use your custom class to transform the data from 2-D to 3-D like the following:
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)
])
或使用现有的 Reshape类.我正在使用Reshape.
所以修改器代码将是(请参见注释):
So the modifier code would be (See the comments):
# 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
这篇关于带有LSTM的GridSearchCV/RandomizedSearchCV的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！