https://www.youtube.com/watch?v=Q4gTV4r0zRs
- Python 3
- scikit-learn
- tqdm
pip install .
MIT
from onesan import onesan
# prepare the training and validation dataset
X = feature matrix # numpy.array
Y = target vector # numpy.array
# create onesan
robot = onesan(X, Y,
train_size=0.9, # divide to x0.9 for training, x0.1 for validation
n_onesan=8 # number of parallel processes, 1 by default
) # if classifier was not specified, onesan will use linear-SVM as a classifier by default
# Good luck! Onesan!!!!
result = onesan.run()
print(result)
'''
returns list of list
[
[1, '0000...01', accuracy_1],
[2, '0000...10', accuracy_2],
...,
[2^d - 1, '1111...11', accuracy]
]
'''
__init__(self, X, Y, train_size=0.8, n_onesan=1, classifier=None, classifier_param=None)
n_onesan specifies a number of onesans. If n_onesan == 1, Onesan would run alone.
If n_onesan >= 2, Onesan would fission into child processes and runs almost n_onesan times faster.
We can specify the classifier onesan uses.
The classifier must have fit and predict method to training and validation
the model.
classifier shold inherit sklearn.base.BaseEstimator.
Aiga SUZUKI [email protected]