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Merge pull request #265 from yzliao/master

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add doc for Python wrapper
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Tianqi Chen 2015-04-26 22:14:05 -07:00
commit abcc09286c
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doc/python.md
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@ -14,7 +14,7 @@ A [walk through python example](https://github.com/tqchen/xgboost/blob/master/de
=
#### Install
To install XGBoost, you need to run `make` in the root directory of the project and then in the `wrappers` directory run
To install XGBoost, you need to run `make` in the root directory of the project and then in the `wrappers` directory run
```shell
python setup.py install
@ -26,7 +26,7 @@ import xgboost as xgb
=
#### Data Interface
XGBoost python module is able to loading from libsvm txt format file, Numpy 2D array and xgboost binary buffer file. The data will be store in ```DMatrix``` object.
XGBoost python module is able to loading from libsvm txt format file, Numpy 2D array and xgboost binary buffer file. The data will be store in ```DMatrix``` object.
* To load libsvm text format file and XGBoost binary file into ```DMatrix```, the usage is like
```python
@ -48,11 +48,11 @@ dtrain = xgb.DMatrix( csr )
```python
dtrain = xgb.DMatrix('train.svm.txt')
dtrain.save_binary("train.buffer")
```
```
* To handle missing value in ```DMatrix```, you can initialize the ```DMatrix``` like:
```python
dtrain = xgb.DMatrix( data, label=label, missing = -999.0)
```
```
* Weight can be set when needed, like
```python
w = np.random.rand(5,1)
@ -63,13 +63,13 @@ dtrain = xgb.DMatrix( data, label=label, missing = -999.0, weight=w)
=
#### Setting Parameters
XGBoost use list of pair to save [parameters](parameter.md). Eg
* Booster parameters
* Booster parameters
```python
param = {'bst:max_depth':2, 'bst:eta':1, 'silent':1, 'objective':'binary:logistic' }
param['nthread'] = 4
plst = param.items()
plst += [('eval_metric', 'auc')] # Multiple evals can be handled in this way
plst += [('eval_metric', 'ams@0')]
plst += [('eval_metric', 'ams@0')]
```
* Specify validations set to watch performance
```python
@ -78,8 +78,8 @@ evallist = [(dtest,'eval'), (dtrain,'train')]
=
#### Training Model
With parameter list and data, you are able to train a model.
* Training
With parameter list and data, you are able to train a model.
* Training
```python
num_round = 10
bst = xgb.train( plst, dtrain, num_round, evallist )
@ -110,7 +110,7 @@ If you have a validation set, you can use early stopping to find the optimal num
`train(..., evals=evals, early_stopping_rounds=10)`
The model will train until the validation score stops improving. Validation error needs to decrease at least every `early_stopping_rounds` to continue training.
The model will train until the validation score stops improving. Validation error needs to decrease at least every `early_stopping_rounds` to continue training.
If early stopping occurs, the model will have two additional fields: `bst.best_score` and `bst.best_iteration`. Note that `train()` will return a model from the last iteration, not the best one.
@ -124,3 +124,8 @@ data = np.random.rand(7,10) # 7 entities, each contains 10 features
dtest = xgb.DMatrix( data, missing = -999.0 )
ypred = bst.predict( xgmat )
```
If early stopping is enabled during training, you can predict with the best iteration.
```python
ypred = bst.predict(xgmat,ntree_limit=bst.best_iteration)
```