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coding style update (#2752)

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* coding style update

Current coding style varies(for example: the mixed use of single quote and double quote), and it will be confusing, especially for new users.
This PR will try to follow proposal of PEP8, make the documents more readable.

* minor fix
This commit is contained in:
Icyblade Dai 2017-10-01 05:42:15 -07:00 committed by Yuan (Terry) Tang
parent 4cb2f7598b
commit 6e378452f2
1 changed files with 13 additions and 13 deletions
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26
doc/python/python_intro.md
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@ -37,9 +37,9 @@ dtest = xgb.DMatrix('test.svm.buffer')
``` ```
* To load a numpy array into ```DMatrix```: * To load a numpy array into ```DMatrix```:
```python ```python
data = np.random.rand(5,10) # 5 entities, each contains 10 features data = np.random.rand(5, 10) # 5 entities, each contains 10 features
label = np.random.randint(2, size=5) # binary target label = np.random.randint(2, size=5) # binary target
dtrain = xgb.DMatrix( data, label=label) dtrain = xgb.DMatrix(data, label=label)
``` ```
* To load a scpiy.sparse array into ```DMatrix```: * To load a scpiy.sparse array into ```DMatrix```:
```python ```python
@ -49,16 +49,16 @@ dtrain = xgb.DMatrix(csr)
* Saving ```DMatrix``` into a XGBoost binary file will make loading faster: * Saving ```DMatrix``` into a XGBoost binary file will make loading faster:
```python ```python
dtrain = xgb.DMatrix('train.svm.txt') dtrain = xgb.DMatrix('train.svm.txt')
dtrain.save_binary("train.buffer") dtrain.save_binary('train.buffer')
``` ```
* Missing values can be replaced by a default value in the ```DMatrix``` constructor: * Missing values can be replaced by a default value in the ```DMatrix``` constructor:
```python ```python
dtrain = xgb.DMatrix(data, label=label, missing = -999.0) dtrain = xgb.DMatrix(data, label=label, missing=-999.0)
``` ```
* Weights can be set when needed: * Weights can be set when needed:
```python ```python
w = np.random.rand(5, 1) w = np.random.rand(5, 1)
dtrain = xgb.DMatrix(data, label=label, missing = -999.0, weight=w) dtrain = xgb.DMatrix(data, label=label, missing=-999.0, weight=w)
``` ```
Setting Parameters Setting Parameters
@ -66,7 +66,7 @@ Setting Parameters
XGBoost can use either a list of pairs or a dictionary to set [parameters](../parameter.md). For instance: XGBoost can use either a list of pairs or a dictionary to set [parameters](../parameter.md). For instance:
* Booster parameters * Booster parameters
```python ```python
param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' } param = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}
param['nthread'] = 4 param['nthread'] = 4
param['eval_metric'] = 'auc' param['eval_metric'] = 'auc'
``` ```
@ -81,7 +81,7 @@ param['eval_metric'] = ['auc', 'ams@0']
* Specify validations set to watch performance * Specify validations set to watch performance
```python ```python
evallist = [(dtest,'eval'), (dtrain,'train')] evallist = [(dtest, 'eval'), (dtrain, 'train')]
``` ```
Training Training
@ -90,7 +90,7 @@ Training
Training a model requires a parameter list and data set. Training a model requires a parameter list and data set.
```python ```python
num_round = 10 num_round = 10
bst = xgb.train( plst, dtrain, num_round, evallist ) bst = xgb.train(plst, dtrain, num_round, evallist)
``` ```
After training, the model can be saved. After training, the model can be saved.
```python ```python
@ -101,12 +101,12 @@ The model and its feature map can also be dumped to a text file.
# dump model # dump model
bst.dump_model('dump.raw.txt') bst.dump_model('dump.raw.txt')
# dump model with feature map # dump model with feature map
bst.dump_model('dump.raw.txt','featmap.txt') bst.dump_model('dump.raw.txt', 'featmap.txt')
``` ```
A saved model can be loaded as follows: A saved model can be loaded as follows:
```python ```python
bst = xgb.Booster({'nthread':4}) #init model bst = xgb.Booster({'nthread': 4}) # init model
bst.load_model("model.bin") # load data bst.load_model('model.bin') # load data
``` ```
Early Stopping Early Stopping
@ -134,7 +134,7 @@ ypred = bst.predict(dtest)
If early stopping is enabled during training, you can get predictions from the best iteration with `bst.best_ntree_limit`: If early stopping is enabled during training, you can get predictions from the best iteration with `bst.best_ntree_limit`:
```python ```python
ypred = bst.predict(dtest,ntree_limit=bst.best_ntree_limit) ypred = bst.predict(dtest, ntree_limit=bst.best_ntree_limit)
``` ```
Plotting Plotting