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train_results.txt
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(szm) lindo@minuteman:~/develop/smart-zoneminder/face-det-rec$ python3 ./train.py
Encoding labels...
Finding best svm estimator...
Fitting 5 folds for each of 42 candidates, totalling 210 fits
[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.
[Parallel(n_jobs=4)]: Done 42 tasks | elapsed: 2.2min
[Parallel(n_jobs=4)]: Done 192 tasks | elapsed: 12.0min
[Parallel(n_jobs=4)]: Done 210 out of 210 | elapsed: 13.0min finished
Best estimator:
SVC(C=100, break_ties=False, cache_size=200, class_weight='balanced', coef0=0.0,
decision_function_shape='ovr', degree=3, gamma=10, kernel='rbf',
max_iter=-1, probability=True, random_state=1234, shrinking=True, tol=0.001,
verbose=False)
Best score for 5-fold search:
0.9016863551033761
Best hyperparameters:
{'C': 100, 'gamma': 10, 'kernel': 'rbf'}
Evaluating svm model...
Confusion matrix:
[[249 7 15 5 6]
[ 7 110 1 0 0]
[ 14 1 236 3 1]
[ 6 0 1 139 1]
[ 8 1 1 4 176]]
Classification matrix:
precision recall f1-score support
Unknown 0.88 0.88 0.88 282
eva_st_angel 0.92 0.93 0.93 118
lindo_st_angel 0.93 0.93 0.93 255
nico_st_angel 0.92 0.95 0.93 147
nikki_st_angel 0.96 0.93 0.94 190
accuracy 0.92 992
macro avg 0.92 0.92 0.92 992
weighted avg 0.92 0.92 0.92 992
Saving svm model...
Finding best XGBoost estimator...
Fitting 5 folds for each of 20 candidates, totalling 100 fits
[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.
[Parallel(n_jobs=4)]: Done 42 tasks | elapsed: 14.2min
[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed: 31.4min finished
Best estimator:
XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=0.6, gamma=0.5,
learning_rate=0.02, max_delta_step=0, max_depth=5,
min_child_weight=5, missing=None, n_estimators=600, n_jobs=1,
nthread=None, objective='multi:softprob', random_state=1234,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
silent=None, subsample=0.8, verbose=1, verbosity=1)
Best score for 5-fold search with 20 parameter combinations:
0.8694254830522743
Best hyperparameters:
{'subsample': 0.8, 'min_child_weight': 5, 'max_depth': 5, 'gamma': 0.5, 'colsample_bytree': 0.6}
Evaluating xgb model...
Confusion matrix:
[[232 10 25 7 8]
[ 8 108 1 1 0]
[ 11 1 238 1 4]
[ 9 2 3 131 2]
[ 4 1 2 6 177]]
Classification matrix:
precision recall f1-score support
Unknown 0.88 0.82 0.85 282
eva_st_angel 0.89 0.92 0.90 118
lindo_st_angel 0.88 0.93 0.91 255
nico_st_angel 0.90 0.89 0.89 147
nikki_st_angel 0.93 0.93 0.93 190
accuracy 0.89 992
macro avg 0.89 0.90 0.90 992
weighted avg 0.89 0.89 0.89 992
Saving xgb model...
Saving label encoder...
(szm) lindo@minuteman:~/develop/smart-zoneminder/face-det-rec$