/dports/misc/py-xgboost/xgboost-1.5.1/R-package/man/ |
H A D | xgb.create.features.Rd | 2 % Please edit documentation in R/xgb.create.features.R 3 \name{xgb.create.features} 4 \alias{xgb.create.features} 7 xgb.create.features(model, data, ...) 62 dtrain <- with(agaricus.train, xgb.DMatrix(data, label = label)) 63 dtest <- with(agaricus.test, xgb.DMatrix(data, label = label)) 68 bst = xgb.train(params = param, data = dtrain, nrounds = nrounds, nthread = 2) 75 new.features.train <- xgb.create.features(model = bst, agaricus.train$data) 76 new.features.test <- xgb.create.features(model = bst, agaricus.test$data) 79 new.dtrain <- xgb.DMatrix(data = new.features.train, label = agaricus.train$label) [all …]
|
H A D | xgb.dump.Rd | 2 % Please edit documentation in R/xgb.dump.R 3 \name{xgb.dump} 4 \alias{xgb.dump} 7 xgb.dump( 52 # save the model in file 'xgb.model.dump' 54 xgb.dump(bst, dump_path, with_stats = TRUE) 57 print(xgb.dump(bst, with_stats = TRUE)) 60 cat(xgb.dump(bst, with_stats = TRUE, dump_format='json'))
|
/dports/misc/xgboost/xgboost-1.5.1/R-package/man/ |
H A D | xgb.create.features.Rd | 2 % Please edit documentation in R/xgb.create.features.R 3 \name{xgb.create.features} 4 \alias{xgb.create.features} 7 xgb.create.features(model, data, ...) 62 dtrain <- with(agaricus.train, xgb.DMatrix(data, label = label)) 63 dtest <- with(agaricus.test, xgb.DMatrix(data, label = label)) 68 bst = xgb.train(params = param, data = dtrain, nrounds = nrounds, nthread = 2) 75 new.features.train <- xgb.create.features(model = bst, agaricus.train$data) 76 new.features.test <- xgb.create.features(model = bst, agaricus.test$data) 79 new.dtrain <- xgb.DMatrix(data = new.features.train, label = agaricus.train$label) [all …]
|
H A D | xgb.importance.Rd | 2 % Please edit documentation in R/xgb.importance.R 3 \name{xgb.importance} 4 \alias{xgb.importance} 7 xgb.importance( 21 \item{model}{object of class \code{xgb.Booster}.} 75 xgb.importance(model = bst) 80 xgb.importance(model = bst) 89 xgb.importance(model = mbst) 91 xgb.importance(model = mbst, trees = seq(from=0, by=nclass, length.out=nrounds)) 92 xgb.importance(model = mbst, trees = seq(from=1, by=nclass, length.out=nrounds)) [all …]
|
H A D | xgb.dump.Rd | 2 % Please edit documentation in R/xgb.dump.R 3 \name{xgb.dump} 4 \alias{xgb.dump} 7 xgb.dump( 52 # save the model in file 'xgb.model.dump' 54 xgb.dump(bst, dump_path, with_stats = TRUE) 57 print(xgb.dump(bst, with_stats = TRUE)) 60 cat(xgb.dump(bst, with_stats = TRUE, dump_format='json'))
|
/dports/misc/py-xgboost/xgboost-1.5.1/tests/python-gpu/ |
H A D | test_gpu_updaters.py | 5 import xgboost as xgb namespace 29 xgb.train(param, dmat, num_rounds, [(dmat, 'train')], verbose_eval=False, 53 m = xgb.DMatrix(onehot, label, enable_categorical=False) 54 xgb.train( 62 m = xgb.DMatrix(cat, label, enable_categorical=True) 63 xgb.train( 133 dtrain = xgb.DMatrix(X, y) 135 bst = xgb.train({'verbosity': 2, 146 dtest = xgb.DMatrix(X)
|
H A D | test_gpu_basic_models.py | 4 import xgboost as xgb namespace 18 cls = xgb.XGBClassifier(tree_method='gpu_hist', 24 cls = xgb.XGBClassifier(tree_method='gpu_hist', 62 cls1 = xgb.XGBClassifier(tree_method='gpu_hist', gpu_id=9999) 65 cls2 = xgb.XGBClassifier(tree_method='gpu_hist', gpu_id=9999, fail_on_invalid_gpu_id=True) 69 except xgb.core.XGBoostError as err:
|
/dports/misc/xgboost/xgboost-1.5.1/tests/python-gpu/ |
H A D | test_gpu_updaters.py | 5 import xgboost as xgb namespace 29 xgb.train(param, dmat, num_rounds, [(dmat, 'train')], verbose_eval=False, 53 m = xgb.DMatrix(onehot, label, enable_categorical=False) 54 xgb.train( 62 m = xgb.DMatrix(cat, label, enable_categorical=True) 63 xgb.train( 133 dtrain = xgb.DMatrix(X, y) 135 bst = xgb.train({'verbosity': 2, 146 dtest = xgb.DMatrix(X)
|
H A D | test_gpu_basic_models.py | 4 import xgboost as xgb namespace 18 cls = xgb.XGBClassifier(tree_method='gpu_hist', 24 cls = xgb.XGBClassifier(tree_method='gpu_hist', 62 cls1 = xgb.XGBClassifier(tree_method='gpu_hist', gpu_id=9999) 65 cls2 = xgb.XGBClassifier(tree_method='gpu_hist', gpu_id=9999, fail_on_invalid_gpu_id=True) 69 except xgb.core.XGBoostError as err:
|
/dports/misc/py-xgboost/xgboost-1.5.1/tests/python/ |
H A D | test_survival.py | 4 import xgboost as xgb namespace 18 dmat = xgb.DMatrix(X) 32 bst = xgb.train(params, dmat, 15, [(dmat, 'train')], evals_result=evals_result, 59 dtrain = xgb.DMatrix(X) 61 bst = xgb.train({'objective': 'survival:aft', 'tree_method': 'hist'}, 74 dtrain = xgb.DMatrix(X) 93 bst = xgb.train(params, dtrain, num_boost_round=500, evals=[(dtrain, 'train')],
|
H A D | test_dt.py | 6 import xgboost as xgb namespace 26 dm = xgb.DMatrix(dtable, label=labels) 35 dm = xgb.DMatrix(dtable, label=pd.Series([1, 2]), 46 xgb.DMatrix(dtable) 50 dm = xgb.DMatrix(dtable)
|
/dports/misc/xgboost/xgboost-1.5.1/tests/python/ |
H A D | test_survival.py | 4 import xgboost as xgb namespace 18 dmat = xgb.DMatrix(X) 32 bst = xgb.train(params, dmat, 15, [(dmat, 'train')], evals_result=evals_result, 59 dtrain = xgb.DMatrix(X) 61 bst = xgb.train({'objective': 'survival:aft', 'tree_method': 'hist'}, 74 dtrain = xgb.DMatrix(X) 93 bst = xgb.train(params, dtrain, num_boost_round=500, evals=[(dtrain, 'train')],
|
H A D | test_dt.py | 6 import xgboost as xgb namespace 26 dm = xgb.DMatrix(dtable, label=labels) 35 dm = xgb.DMatrix(dtable, label=pd.Series([1, 2]), 46 xgb.DMatrix(dtable) 50 dm = xgb.DMatrix(dtable)
|
/dports/misc/py-xgboost/xgboost-1.5.1/demo/guide-python/ |
H A D | custom_objective.py | 6 import xgboost as xgb namespace 11 dtrain = xgb.DMatrix(os.path.join(CURRENT_DIR, '../data/agaricus.txt.train')) 12 dtest = xgb.DMatrix(os.path.join(CURRENT_DIR, '../data/agaricus.txt.test')) 49 py_logreg = xgb.train(py_params, dtrain, num_round, watchlist, obj=logregobj, 55 logreg = xgb.train(params, dtrain, num_boost_round=num_round, evals=watchlist,
|
H A D | predict_first_ntree.py | 3 import xgboost as xgb namespace 13 dtrain = xgb.DMatrix(train) 14 dtest = xgb.DMatrix(test) 18 bst = xgb.train(param, dtrain, num_round, watchlist) 34 clf = xgb.XGBClassifier(n_estimators=3, max_depth=2, eta=1, use_label_encoder=False)
|
H A D | gamma_regression.py | 1 import xgboost as xgb namespace 9 dtrain = xgb.DMatrix(data[0:4741, 0:34], data[0:4741, 34]) 10 dtest = xgb.DMatrix(data[4741:6773, 0:34], data[4741:6773, 34]) 21 bst = xgb.train(param, dtrain, num_round, watchlist)
|
/dports/misc/py-xgboost/xgboost-1.5.1/demo/multiclass_classification/ |
H A D | train.py | 6 import xgboost as xgb namespace 22 xg_train = xgb.DMatrix(train_X, label=train_Y) 23 xg_test = xgb.DMatrix(test_X, label=test_Y) 36 bst = xgb.train(param, xg_train, num_round, watchlist) 44 bst = xgb.train(param, xg_train, num_round, watchlist)
|
/dports/misc/xgboost/xgboost-1.5.1/demo/multiclass_classification/ |
H A D | train.py | 6 import xgboost as xgb namespace 22 xg_train = xgb.DMatrix(train_X, label=train_Y) 23 xg_test = xgb.DMatrix(test_X, label=test_Y) 36 bst = xgb.train(param, xg_train, num_round, watchlist) 44 bst = xgb.train(param, xg_train, num_round, watchlist)
|
/dports/misc/xgboost/xgboost-1.5.1/demo/guide-python/ |
H A D | custom_objective.py | 6 import xgboost as xgb namespace 11 dtrain = xgb.DMatrix(os.path.join(CURRENT_DIR, '../data/agaricus.txt.train')) 12 dtest = xgb.DMatrix(os.path.join(CURRENT_DIR, '../data/agaricus.txt.test')) 49 py_logreg = xgb.train(py_params, dtrain, num_round, watchlist, obj=logregobj, 55 logreg = xgb.train(params, dtrain, num_boost_round=num_round, evals=watchlist,
|
H A D | predict_first_ntree.py | 3 import xgboost as xgb namespace 13 dtrain = xgb.DMatrix(train) 14 dtest = xgb.DMatrix(test) 18 bst = xgb.train(param, dtrain, num_round, watchlist) 34 clf = xgb.XGBClassifier(n_estimators=3, max_depth=2, eta=1, use_label_encoder=False)
|
H A D | gamma_regression.py | 1 import xgboost as xgb namespace 9 dtrain = xgb.DMatrix(data[0:4741, 0:34], data[0:4741, 34]) 10 dtest = xgb.DMatrix(data[4741:6773, 0:34], data[4741:6773, 34]) 21 bst = xgb.train(param, dtrain, num_round, watchlist)
|
/dports/misc/py-xgboost/xgboost-1.5.1/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/ |
H A D | XGBoostRegressorSuite.scala | 155 val xgb = new XGBoostRegressor(paramMap) constant 156 val model = xgb.fit(training) 169 val xgb = new XGBoostRegressor(paramMap) constant 170 val model = xgb.fit(training) 183 val xgb = new XGBoostRegressor(paramMap) constant 184 val model = xgb.fit(training) 197 val xgb = new XGBoostRegressor(paramMap) constant 198 val model = xgb.fit(training) 211 val xgb = new XGBoostRegressor(paramMap) constant 212 val model = xgb.fit(training)
|
/dports/misc/xgboost/xgboost-1.5.1/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/ |
H A D | XGBoostRegressorSuite.scala | 155 val xgb = new XGBoostRegressor(paramMap) constant 156 val model = xgb.fit(training) 169 val xgb = new XGBoostRegressor(paramMap) constant 170 val model = xgb.fit(training) 183 val xgb = new XGBoostRegressor(paramMap) constant 184 val model = xgb.fit(training) 197 val xgb = new XGBoostRegressor(paramMap) constant 198 val model = xgb.fit(training) 211 val xgb = new XGBoostRegressor(paramMap) constant 212 val model = xgb.fit(training)
|
/dports/cad/jspice3/jspice3-2.5/src/lib/dev/mos/ |
H A D | mosacld.c | 24 double xgb; local 44 xgb = *(ckt->CKTstate0 + here->MOScapgb) + 50 xgb *= omega; 59 *(here->MOSGgPtr +1) += xgd + xgs + xgb; 60 *(here->MOSBbPtr +1) += xgb + xbd + xbs; 63 *(here->MOSGbPtr +1) -= xgb; 66 *(here->MOSBgPtr +1) -= xgb;
|
/dports/misc/py-xgboost/xgboost-1.5.1/tests/benchmark/ |
H A D | benchmark_tree.py | 8 import xgboost as xgb namespace 16 dtest = xgb.DMatrix('dtest.dm') 17 dtrain = xgb.DMatrix('dtrain.dm') 46 dtrain = xgb.DMatrix(X_train, y_train, nthread=-1) 47 dtest = xgb.DMatrix(X_test, y_test, nthread=-1) 61 xgb.train(param, dtrain, args.iterations, evals=[(dtest, "test")])
|