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newline at end of file diff --git a/cachedir/joblib/run/estimator/f4424e7e031a773cea1548224ffc9bc5/output.pkl b/cachedir/joblib/run/estimator/f4424e7e031a773cea1548224ffc9bc5/output.pkl new file mode 100644 index 0000000..716aa75 Binary files /dev/null and b/cachedir/joblib/run/estimator/f4424e7e031a773cea1548224ffc9bc5/output.pkl differ diff --git a/cachedir/joblib/run/estimator/f636511a768ea642182097d47d727cb3/metadata.json b/cachedir/joblib/run/estimator/f636511a768ea642182097d47d727cb3/metadata.json index 6a95e1f..aa24a74 100644 --- a/cachedir/joblib/run/estimator/f636511a768ea642182097d47d727cb3/metadata.json +++ b/cachedir/joblib/run/estimator/f636511a768ea642182097d47d727cb3/metadata.json @@ -1 +1 @@ -{"duration": 1.8177359104156494, "input_args": {"n_estimators": "122.21742728992572", "eta": "0.06452090304204987", "max_depth": "11.197056874649611", "subsample": "0.941614515559209", "colsample_bytree": "0.8311989040672406"}} \ No newline at end of file +{"duration": 9.109298944473267, "input_args": {"n_estimators": "122.21742728992572", "eta": "0.06452090304204987", "max_depth": "11.197056874649611", "subsample": "0.941614515559209", "colsample_bytree": "0.8311989040672406"}} \ No newline at end of file diff --git a/cachedir/joblib/run/estimator/f636511a768ea642182097d47d727cb3/output.pkl b/cachedir/joblib/run/estimator/f636511a768ea642182097d47d727cb3/output.pkl index e6a3ace..c69c0dc 100644 Binary files a/cachedir/joblib/run/estimator/f636511a768ea642182097d47d727cb3/output.pkl and b/cachedir/joblib/run/estimator/f636511a768ea642182097d47d727cb3/output.pkl differ diff --git a/cachedir/joblib/run/estimator/func_code.py b/cachedir/joblib/run/estimator/func_code.py index f2adce1..a416c83 100644 --- a/cachedir/joblib/run/estimator/func_code.py +++ b/cachedir/joblib/run/estimator/func_code.py @@ -2,7 +2,7 @@ @memory.cache def estimator(n_estimators, eta, max_depth, subsample, colsample_bytree): # initialize model - print('loopingIris') + print('loopingVehicle') n_estimators = int(n_estimators) max_depth = int(max_depth) model = XGBClassifier(n_estimators=n_estimators, eta=eta, max_depth=max_depth, subsample=subsample, colsample_bytree=colsample_bytree, n_jobs=-1, random_state=RANDOM_SEED, silent=True, verbosity = 0, use_label_encoder=False)