> > given a list of of features - e.g. dataDescrs[0] = (140.0, 2, 0.5 -
and a
> > list of experimental observations - e.g. data_activities[0] = 0 - how
do I
> > transform these lists to the scikit-learn nomenclature?
>
> Depends on what these things represent, but if all tuples in
> dataDescrs have the same length and data_activities contains what you
> want to predict, then the simplest possible "transformation" would be
>
> X = dataDescrs
> y = data_activities
I was trying to do a train/test set split:
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(dataDescrs,
data_activities, test_size=.4)
However, I found it strange that "X_train.shape" gives (373, 177) -
shouldn't be the second bit be the number of classes, i.e. 2?
I also tried this
"
dataDescrs_array = np.array(dataDescrs)
print dataDescrs_array.shape
"
which gives (622,177).
177 corresponds, BTW, to the number of features..
622 corresponds to the number of samples in my dataset.
Cheers & thanks so far,
Paul
>
> i.e., you should be able to feed your data to an estimator as-is. If
> that doesn't work, or gives very surprising results, then please
> report back with some more details of what your data looks like.
>
> (You might have to call np.array on both; if you do and it works, also
> please report back, as it would likely mean there's an input
> validation bug in one of our estimators.)
>
> --
> Lars Buitinck
> Scientific programmer, ILPS
> University of Amsterdam
>
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