ISOVIS
/
FeatureEnVi
3
0
Fork 0
Code Issues Pull Requests Releases 1 Wiki Activity
FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches https://doi.org/10.1109/TVCG.2022.3141040
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
29 Commits
1 Branch
1 Tag
10 MiB
Tag: Branch: Tree: 44aabbbf6e
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from '44aabbbf6e'
${ noResults }
FeatureEnVi/cachedir/joblib/run/executeModel/func_code.py

16 lines
603 B
Raw Normal View History Unescape

fixed SVC
4 years ago
# first line: 457
@memory.cache
def executeModel():
create_global_function()
global estimator
params = {"C": (0.0001, 10000), "gamma": (0.0001, 10000)}
svc_bayesopt = BayesianOptimization(estimator, params)
fixed network
4 years ago
svc_bayesopt.maximize(init_points=10, n_iter=25, acq='ucb')
fixed SVC
4 years ago
bestParams = svc_bayesopt.max['params']
estimator = SVC(C=bestParams.get('C'), gamma=bestParams.get('gamma'), probability=True)
estimator.fit(XData, yData)
yPredict = estimator.predict(XData)
yPredictProb = cross_val_predict(estimator, XData, yData, cv=crossValidation, method='predict_proba')