0cf88d036f
* add qid for https://github.com/dmlc/xgboost/issues/2748 * change names * change spaces * change qid to bst_uint type * change qid type to size_t * change qid first to SIZE_MAX * change qid type from size_t to uint64_t * update dmlc-core * fix qids name error * fix group_ptr_ error * Style fix * Add qid handling logic to SparsePage * New MetaInfo format + backward compatibility fix Old MetaInfo format (1.0) doesn't contain qid field. We still want to be able to read from MetaInfo files saved in old format. Also, define a new format (2.0) that contains the qid field. This way, we can distinguish files that contain qid and those that do not. * Update MetaInfo test * Simply group assignment logic * Explicitly set qid=nullptr in NativeDataIter NativeDataIter's callback does not support qid field. Users of NativeDataIter will need to call setGroup() function separately to set group information. * Save qids_ in SaveBinary() * Upgrade dmlc-core submodule * Add a test for reading qid * Add contributor * Check the size of qids_ * Document qid format
81 lines
2.3 KiB
Markdown
81 lines
2.3 KiB
Markdown
# Get Started with XGBoost
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This is a quick start tutorial showing snippets for you to quickly try out xgboost
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on the demo dataset on a binary classification task.
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## Links to Helpful Other Resources
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- See [Installation Guide](../build.md) on how to install xgboost.
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- See [Text Input Format](../input_format.md) on using text format for specifying training/testing data.
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- See [How to pages](../how_to/index.md) on various tips on using xgboost.
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- See [Tutorials](../tutorials/index.md) on tutorials on specific tasks.
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- See [Learning to use XGBoost by Examples](../../demo) for more code examples.
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## Python
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```python
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import xgboost as xgb
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# read in data
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dtrain = xgb.DMatrix('demo/data/agaricus.txt.train')
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dtest = xgb.DMatrix('demo/data/agaricus.txt.test')
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# specify parameters via map
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param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
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num_round = 2
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bst = xgb.train(param, dtrain, num_round)
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# make prediction
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preds = bst.predict(dtest)
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```
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## R
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```r
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# load data
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data(agaricus.train, package='xgboost')
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data(agaricus.test, package='xgboost')
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train <- agaricus.train
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test <- agaricus.test
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# fit model
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bst <- xgboost(data = train$data, label = train$label, max.depth = 2, eta = 1, nround = 2,
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nthread = 2, objective = "binary:logistic")
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# predict
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pred <- predict(bst, test$data)
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```
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## Julia
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```julia
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using XGBoost
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# read data
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train_X, train_Y = readlibsvm("demo/data/agaricus.txt.train", (6513, 126))
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test_X, test_Y = readlibsvm("demo/data/agaricus.txt.test", (1611, 126))
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# fit model
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num_round = 2
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bst = xgboost(train_X, num_round, label=train_Y, eta=1, max_depth=2)
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# predict
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pred = predict(bst, test_X)
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```
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## Scala
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```scala
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import ml.dmlc.xgboost4j.scala.DMatrix
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import ml.dmlc.xgboost4j.scala.XGBoost
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object XGBoostScalaExample {
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def main(args: Array[String]) {
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// read trainining data, available at xgboost/demo/data
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val trainData =
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new DMatrix("/path/to/agaricus.txt.train")
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// define parameters
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val paramMap = List(
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"eta" -> 0.1,
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"max_depth" -> 2,
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"objective" -> "binary:logistic").toMap
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// number of iterations
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val round = 2
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// train the model
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val model = XGBoost.train(trainData, paramMap, round)
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// run prediction
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val predTrain = model.predict(trainData)
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// save model to the file.
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model.saveModel("/local/path/to/model")
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}
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}
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```
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