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python package refactor into python-package

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This commit is contained in:
tqchen
2015-07-30 22:04:45 -07:00
parent f6fed76e7e
commit c2fec29bfa
13 changed files with 87 additions and 677 deletions
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3
demo/.gitignore vendored
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@@ -1 +1,2 @@
*.libsvm
*.libsvm
*.pkl

18
demo/README.md
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@@ -1,14 +1,14 @@
XGBoost Examples
====
This folder contains all the code examples using xgboost.
This folder contains all the code examples using xgboost.
* Contribution of examples, benchmarks is more than welcome!
* If you like to share how you use xgboost to solve your problem, send a pull request:)
Features Walkthrough
====
This is a list of short codes introducing different functionalities of xgboost and its wrapper.
* Basic walkthrough of wrappers
This is a list of short codes introducing different functionalities of xgboost packages.
* Basic walkthrough of packages
[python](guide-python/basic_walkthrough.py)
[R](../R-package/demo/basic_walkthrough.R)
[Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/basic_walkthrough.jl)
@@ -20,18 +20,18 @@ This is a list of short codes introducing different functionalities of xgboost a
[python](guide-python/boost_from_prediction.py)
[R](../R-package/demo/boost_from_prediction.R)
[Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/boost_from_prediction.jl)
* Predicting using first n trees
* Predicting using first n trees
[python](guide-python/predict_first_ntree.py)
[R](../R-package/demo/boost_from_prediction.R)
[Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/boost_from_prediction.jl)
[Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/boost_from_prediction.jl)
* Generalized Linear Model
[python](guide-python/generalized_linear_model.py)
[R](../R-package/demo/generalized_linear_model.R)
[Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/generalized_linear_model.jl)
[Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/generalized_linear_model.jl)
* Cross validation
[python](guide-python/cross_validation.py)
[R](../R-package/demo/cross_validation.R)
[Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/cross_validation.jl)
[Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/cross_validation.jl)
* Predicting leaf indices
[python](guide-python/predict_leaf_indices.py)
[R](../R-package/demo/predict_leaf_indices.R)
@@ -48,5 +48,5 @@ However, the parameter settings can be applied to all versions
Benchmarks
====
* [Starter script for Kaggle Higgs Boson](kaggle-higgs)
* [Kaggle Tradeshift winning solution by daxiongshu](https://github.com/daxiongshu/kaggle-tradeshift-winning-solution)
* [Kaggle Tradeshift winning solution by daxiongshu](https://github.com/daxiongshu/kaggle-tradeshift-winning-solution)

10
demo/guide-python/sklearn_examples.py
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@@ -75,13 +75,3 @@ clf = xgb.XGBClassifier()
clf.fit(X_train, y_train, early_stopping_rounds=10, eval_metric="auc",
eval_set=[(X_test, y_test)])
# Custom evaluation function
from sklearn.metrics import log_loss
def log_loss_eval(y_pred, y_true):
return "log-loss", log_loss(y_true.get_label(), y_pred)
clf.fit(X_train, y_train, early_stopping_rounds=10, eval_metric=log_loss_eval,
eval_set=[(X_test, y_test)])