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/dports/misc/py-xgboost/xgboost-1.5.1/R-package/man/
H A Dxgb.create.features.Rd2 % 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$label)
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H A Dxgb.dump.Rd2 % Please edit documentation in R/xgb.dump.R
3 \name{xgb.dump}
4 \alias{xgb.dump}
7 xgb.dump(
52 # save the model in file 'xgb.model.dump'
54 xgb.dump(bst, dump_path, with_stats = TRUE)
57 print(xgb.dump(bst, with_stats = TRUE))
60 cat(xgb.dump(bst, with_stats = TRUE, dump_format='json'))
/dports/misc/xgboost/xgboost-1.5.1/R-package/man/
H A Dxgb.create.features.Rd2 % Please edit documentation in R/xgb.create.features.R
3 \name{xgb.create.features}
4 \alias{xgb.create.features}
7 xgb.create.features(model, data, ...)
62 dtrain <- with(agaricus.train, xgb.DMatrix(data, label = label))
63 dtest <- with(agaricus.test, xgb.DMatrix(data, label = label))
68 bst = xgb.train(params = param, data = dtrain, nrounds = nrounds, nthread = 2)
75 new.features.train <- xgb.create.features(model = bst, agaricus.train$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 Dxgb.importance.Rd2 % 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))
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H A Dxgb.dump.Rd2 % 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 Dtest_gpu_updaters.py5 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 Dtest_gpu_basic_models.py4 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 Dtest_gpu_updaters.py5 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 Dtest_gpu_basic_models.py4 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 Dtest_survival.py4 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 Dtest_dt.py6 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 Dtest_survival.py4 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 Dtest_dt.py6 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 Dcustom_objective.py6 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 Dpredict_first_ntree.py3 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 Dgamma_regression.py1 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 Dtrain.py6 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 Dtrain.py6 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 Dcustom_objective.py6 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 Dpredict_first_ntree.py3 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 Dgamma_regression.py1 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 DXGBoostRegressorSuite.scala155 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 DXGBoostRegressorSuite.scala155 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 Dmosacld.c24 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 Dbenchmark_tree.py8 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")])

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