Does PAA by any chance change the number of samples?
The error is:
ValueError: Found array with dim 37. Expected 19
Interestingly that happens only in the scoring.
Does it work without the grid-search?
On 04/27/2015 07:14 AM, Jitesh Khandelwal wrote:
Hi all,
I am trying to use grid search to evaluate some decomposition
techniques of my own. I have implemented some custom transformers such
as PAA, DFT, DWT as shown in the code below.
I am getting a strange "ValueError" when run the below code and I am
unable to figure out the origin of the problem.
I have pasted the code below and attached the error log file.
Any suggestions on how can I move forward from here would be helpful.
Thanks.
Code:
=======================================================
from sklearn.pipeline import Pipeline
from sklearn.grid_search import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from time_series.decomposition import PAA, DFT, DWT, ShapeX
from prepare_data import combine_train_test_dataset
knn = KNeighborsClassifier()
paa = PAA()
pipe = Pipeline([
('paa', paa),
('knn', knn)
])
n_components = [1,2,4,5,10,20,40]
n_neighbors = range(1,11)
metrics = ['euclidean']
datadir = "../keogh_datasets/Coffee"
X,y = combine_train_test_dataset(datadir)
model_tunning = GridSearchCV(pipe, {
'paa__n_components': n_components,
'knn__n_neighbors': n_neighbors,
'knn__metric': metrics,
},
n_jobs=-1)
model_tunning.fit(X,y)
print model_tunning.best_score_
print model_tunning.best_params_
=======================================================
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Widest out-of-the-box monitoring support with 50+ applications
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