Comments (7)
Hi!
Thanks for your bug report!
The example in the tutorial seems to be a little unintuitive since I load two datasets (synthetic_control and two_lead_ecg) and define x_train etc two times. The actual output is from the not where the training and testing data is split from the full data (i.e., from synthetic_control) (which contains the classes 1-6, thus 6 is a possible outcome). In the code you submit as part of this issue the two_lead_ecg dataset is used which only contains the classes 1 and 2 so the output of your example actually make sense.
If i run your example the resulting model has an accuracy of 90% and if I change the metric to "scaled_euclidean" the accuracy the expected 96% accuracy.
As for the other examples the accuracy i get using the following snippet:
def test_dataset(dataset):
x_train, x_test, y_train, y_test = load_dataset(dataset, merge_train_test=False)
clf =ShapeletForestClassifier(metric="scaled_euclidean", random_state=123, n_jobs=-1)
clf.fit(x_train, y_train)
return clf.score(x_test, y_test)
These are the results I get:
>>> test_dataset("GunPoint")
0.9866666666666667
>>> test_dataset("CricketX")
0.764102564102564
>>> test_dataset("CBF")
0.9722222222222222
To fully resolve the issue could you tell me your:
- Operating system
- Wildboar version
- Python version
Thanks again your bug report!
from wildboar.
Hello,
Thanks for your response. I've attached a screenshot of what I get when I run your code above - tl;dr its still giving pretty poor accuracy (49.3%).
My Operating system is Windows 10 20H2, Wildboar version is 1.0.9 and Python version is 3.7.10. Incidentally I'm working in a miniconda environment. However I tried this on my native/non-anaconda Python environment (also Python 3.7.9) and had the same problem.
from wildboar.
Thanks for your report!
I will try to debug the issue once I get hold of a windows computer.
Could you try with at 64bit installation of miniconda?
from wildboar.
Hello,
Thanks for your prompt response!
Sadly, no - I've only the one computer. But, the other Python installation I told you about (i.e the non-Anaconda one I also have) is 64-bit, and shows the same issue..
from wildboar.
The issue seems to affect only Windows. Pre-build binaries and code compiled from source.
from wildboar.
Fixed in version 1.0.10.
from wildboar.
Confirmed fixed, thanks! Got 1.0.10 from pip. Thanks again :)
from wildboar.
Related Issues (20)
- ShapeletForestEmbedding returns a matrix of all ones HOT 1
- RocketClassifier with n_jobs > 1 while using cross_validation does not work
- RocketClassifier predict_proba is not working
- RocketClassifier does not have a classes_-property
- 'IsolationShapeletForest' object has no attribute 'n_dims_'
- Bug when using dict or list scoring in importances
- Transform of SAX and PAA does not check if fitted
- ShapeletForestClassifier does not support single dimension 3D input (i.e. shape (10, 1, 20)) HOT 2
- Load multiple datasets with merge_train_test=False
- Support pairwise_distance(2d-array, 3d-array) HOT 2
- will it work for windows ? HOT 3
- you request scikit-learn>=1.2rc1,<1.3a0 but pip install -U wildboar installs very old scikit-learn-1.1.3 HOT 4
- Assigned leaf node in decision_path() HOT 2
- Unecessary splits in shapele trees
- CI builds fail on Windows.
- Raises ModuleNotFoundError when importing the datasets module
- Update What's New for Version 1.2
- _check_estimator_name no longer included in sklearn.utils.validation HOT 12
- Timeout too low
- Unstable sort results in failing test with Numpy 2.0 HOT 1
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