Alien-XGBoost

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xgboost/R-package/R/callbacks.R  view on Meta::CPAN

}


#' Callback closure to activate the early stopping.
#' 
#' @param stopping_rounds The number of rounds with no improvement in 
#'        the evaluation metric in order to stop the training.
#' @param maximize whether to maximize the evaluation metric
#' @param metric_name the name of an evaluation column to use as a criteria for early
#'        stopping. If not set, the last column would be used.
#'        Let's say the test data in \code{watchlist} was labelled as \code{dtest}, 
#'        and one wants to use the AUC in test data for early stopping regardless of where 
#'        it is in the \code{watchlist}, then one of the following would need to be set:
#'        \code{metric_name='dtest-auc'} or \code{metric_name='dtest_auc'}.
#'        All dash '-' characters in metric names are considered equivalent to '_'.
#' @param verbose whether to print the early stopping information.
#' 
#' @details
#' This callback function determines the condition for early stopping 
#' by setting the \code{stop_condition = TRUE} flag in its calling frame.
#' 

xgboost/R-package/man/cb.early.stop.Rd  view on Meta::CPAN

  verbose = TRUE)
}
\arguments{
\item{stopping_rounds}{The number of rounds with no improvement in 
the evaluation metric in order to stop the training.}

\item{maximize}{whether to maximize the evaluation metric}

\item{metric_name}{the name of an evaluation column to use as a criteria for early
stopping. If not set, the last column would be used.
Let's say the test data in \code{watchlist} was labelled as \code{dtest}, 
and one wants to use the AUC in test data for early stopping regardless of where 
it is in the \code{watchlist}, then one of the following would need to be set:
\code{metric_name='dtest-auc'} or \code{metric_name='dtest_auc'}.
All dash '-' characters in metric names are considered equivalent to '_'.}

\item{verbose}{whether to print the early stopping information.}
}
\description{
Callback closure to activate the early stopping.
}

xgboost/demo/binary_classification/README.md  view on Meta::CPAN

xgboost also supports monitoring multiple metrics, suppose we also want to monitor average log-likelihood of each prediction during training, simply add ```eval_metric=logloss``` to configure. Run again, we can find the log file becomes
```
[0]     test-error:0.016139     test-negllik:0.029795   trainname-error:0.014433        trainname-negllik:0.027023
[1]     test-error:0.000000     test-negllik:0.000000   trainname-error:0.001228        trainname-negllik:0.002457
```
### Saving Progress Models
If you want to save model every two round, simply set save_period=2. You will find 0002.model in the current folder. If you want to change the output folder of models, add model_dir=foldername. By default xgboost saves the model of last round.

#### Continue from Existing Model
If you want to continue boosting from existing model, say 0002.model, use
```
../../xgboost mushroom.conf model_in=0002.model num_round=2 model_out=continue.model
```
xgboost will load from 0002.model continue boosting for 2 rounds, and save output to continue.model. However, beware that the training and evaluation data specified in mushroom.conf should not change when you use this function.
#### Use Multi-Threading
When you are working with a large dataset, you may want to take advantage of parallelism. If your compiler supports OpenMP, xgboost is naturally multi-threaded, to set number of parallel running add ```nthread``` parameter to you configuration.
Eg. ```nthread=10```

Set nthread to be the number of your real cpu (On Unix, this can be found using ```lscpu```)
Some systems will have ```Thread(s) per core = 2```, for example, a 4 core cpu with 8 threads, in such case set ```nthread=4``` and not 8.

xgboost/doc/how_to/contribute.md  view on Meta::CPAN

Git Workflow Howtos
-------------------
### How to resolve conflict with master
- First rebase to most recent master
```bash
# The first two steps can be skipped after you do it once.
git remote add upstream https://github.com/dmlc/xgboost
git fetch upstream
git rebase upstream/master
```
- The git may show some conflicts it cannot merge, say ```conflicted.py```.
  - Manually modify the file to resolve the conflict.
  - After you resolved the conflict, mark it as resolved by
```bash
git add conflicted.py
```
- Then you can continue rebase by
```bash
git rebase --continue
```
- Finally push to your fork, you may need to force push here.

xgboost/doc/how_to/param_tuning.md  view on Meta::CPAN

Handle Imbalanced Dataset
-------------------------
For common cases such as ads clickthrough log, the dataset is extremely imbalanced.
This can affect the training of xgboost model, and there are two ways to improve it.
* If you care only about the ranking order (AUC) of your prediction
  - Balance the positive and negative weights, via ```scale_pos_weight```
  - Use AUC for evaluation
* If you care about predicting the right probability
  - In such a case, you cannot re-balance the dataset
  - In such a case, set parameter ```max_delta_step``` to a finite number (say 1) will help convergence

xgboost/doc/input_format.md  view on Meta::CPAN

XGBoost supports providing each instance an weight to differentiate the importance of instances. For example, if we provide an instance weight file for the "train.txt" file in the example as below:

train.txt.weight
```
1
0.5
0.5
1
0.5
```
It means that XGBoost will emphasize more on the first and fourth instance， that is to say positive instances while training.
The configuration is similar to configuring the group information. If the instance file name is "xxx", XGBoost will check whether there is a file named "xxx.weight" in the same directory and if there is, will use the weights while training models. We...

### Initial Margin file
XGBoost supports providing each instance an initial margin prediction. For example, if we have a initial prediction using logistic regression for "train.txt" file, we can create the following file:

train.txt.base_margin
```
-0.4
1.0
3.4

xgboost/plugin/README.md  view on Meta::CPAN

XGBoost Plugins Modules
=======================
This folder contains plugin modules to xgboost that can be optionally installed.
The plugin system helps us to extend xgboost with additional features,
and add experimental features that may not yet be ready to be included in the
main project.

To include a certain plugin, say ```plugin_a```, you only need to add the following line to the config.mk.

```makefile
# Add plugin by including the plugin in config.mk
XGB_PLUGINS += plugin/plugin_a/plugin.mk
```

Then rebuild libxgboost by typing ```make```, you can get a new library with the plugin enabled.

Link Static XGBoost Library with Plugins
----------------------------------------