[scikit-learn] gridsearchCV able to handle list of input?

[Carlton Banks]

hmm.. guess I can give it a try.. i currently optimizing with for loops..
> Den 1. maj 2017 kl. 05.19 skrev Joel Nothman <joel.nothman at gmail.com>:
> 
> Unless I'm mistaken about what we're looking at, you could use something like:
> 
> class ToMultiInput(TransformerMixin, BaseEstimator):
>     def fit(self, shapes):
>         self.shapes = shapes
>     def transform(self, X):
>         return [X.]
> 
> tmi = ToMultiInput([single.shape for single in train_input])
> # this assumes that train_input is a sequence of ndarrays with the same first dimension:
> train_input = np.hstack([single.reshape(single.shape[0], -1)
>                          for single in train_input])
> 
> GridSearchCV(make_pipeline(tmi, my_predictor), ...)
> 
> 
> On 1 May 2017 at 11:45, Carlton Banks <noflaco at gmail.com <mailto:noflaco at gmail.com>> wrote:
> How …  batchsize could also be 1, I’ve just stored it like that.  
> 
> But how do reshape me data to be a matrix.. thats the big question.. is possible?
> 
>> Den 1. maj 2017 kl. 02.21 skrev Joel Nothman <joel.nothman at gmail.com <mailto:joel.nothman at gmail.com>>:
>> 
>> Do each of your 33 inputs have a batch of size 100? If you reshape your data so that it all fits in one matrix, and then split it back out into its 33 components as the first transformation in a Pipeline, there should be no problem.
>> 
>> On 1 May 2017 at 10:17, Joel Nothman <joel.nothman at gmail.com <mailto:joel.nothman at gmail.com>> wrote:
>> Sorry, I don't know enough about keras and its terminology.
>> 
>> Scikit-learn usually limits itself to datasets where features and targets are a rectangular matrix.
>> 
>> But grid search and other model selection tools should allow data of other shapes as long as they can be indexed on the first axis. You may be best off, however, getting support from the Keras folks.
>> 
>> On 30 April 2017 at 23:23, Carlton Banks <noflaco at gmail.com <mailto:noflaco at gmail.com>> wrote:
>> It seems like scikit-learn is not able to handle network with multiple inputs. 
>> Keras documentation states: 
>> 
>> You can use Sequential Keras models (single-input only) as part of your Scikit-Learn workflow via the wrappers found at keras.wrappers.scikit_learn.py <http://keras.wrappers.scikit_learn.py/>.
>> 
>> But besides what the wrapper can do.. can scikit-learn really not handle multiple inputs?.. 
>> 
>> 
>>> Den 30. apr. 2017 kl. 14.18 skrev Carlton Banks <noflaco at gmail.com <mailto:noflaco at gmail.com>>:
>>> 
>>> The shapes are
>>> 
>>> print len(train_input)
>>> print train_input[0].shape
>>> print train_output.shape
>>> 
>>> 33
>>> (100, 8, 45, 3)
>>> (100, 1, 145)
>>> 
>>> 100 is the batch-size..
>>>> Den 30. apr. 2017 kl. 12.57 skrev Joel Nothman <joel.nothman at gmail.com <mailto:joel.nothman at gmail.com>>:
>>>> 
>>>> Scikit-learn should accept a list as X to grid search and index it just fine. So I'm not sure that constraint applies to Grid Search
>>>> 
>>>> On 30 April 2017 at 20:11, Julio Antonio Soto de Vicente <julio at esbet.es <mailto:julio at esbet.es>> wrote:
>>>> Tbh I've never tried, but I would say that te current sklearn API does not support multi-input data...
>>>> 
>>>> El 30 abr 2017, a las 12:02, Joel Nothman <joel.nothman at gmail.com <mailto:joel.nothman at gmail.com>> escribió:
>>>> 
>>>>> What are the shapes of train_input and train_output?
>>>>> 
>>>>> On 30 April 2017 at 12:59, Carlton Banks <noflaco at gmail.com <mailto:noflaco at gmail.com>> wrote:
>>>>> I am currently trying to run some gridsearchCV on a keras model which has multiple inputs. 
>>>>> The inputs is stored in a list in which each entry in the list is a input for a specific channel. 
>>>>> 
>>>>> 
>>>>> Here is my model and how i use the gridsearch. 
>>>>> 
>>>>> https://pastebin.com/GMKH1L80 <https://pastebin.com/GMKH1L80>
>>>>> 
>>>>> The error i am getting is: 
>>>>> 
>>>>> https://pastebin.com/A3cB0rMv <https://pastebin.com/A3cB0rMv>
>>>>> 
>>>>> Any idea how i can resolve this?
>>>>> 
>>>>> 
>>>>> 
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