зеркало из https://github.com/microsoft/LightGBM.git
solve conflicts; add README for python examples
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@ -17,7 +17,7 @@ For more details, please refer to [Features](https://github.com/Microsoft/LightG
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News
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----
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12/02/2012 : Release [python-package](https://github.com/Microsoft/LightGBM/tree/master/python-package) beta version, welcome to have a try and provide issues and feedback.
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12/02/2016 : Release [python-package](https://github.com/Microsoft/LightGBM/tree/master/python-package) beta version, welcome to have a try and provide issues and feedback.
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Get Started
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------------
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@ -0,0 +1,18 @@
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Python Package Example
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=====================
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Here is an example for LightGBM to use python package.
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***You should install lightgbm (both c++ and python verion) first.***
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For the installation, check the wiki [here](https://github.com/Microsoft/LightGBM/wiki/Installation-Guide).
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You also need scikit-learn and pandas to run the examples, but they are not required for the package itself. You can install them with pip:
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```
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pip install -U scikit-learn
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pip install -U pandas
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```
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Now you can run examples in this folder, for example:
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```
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python simple_example.py
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```
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@ -62,4 +62,3 @@ model_json = gbm.dump_model()
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with open('model.json', 'w+') as f:
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json.dump(model_json, f, indent=4)
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@ -21,3 +21,4 @@ __version__ = 0.1
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__all__ = ['Dataset', 'Booster',
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'train', 'cv',
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'LGBMModel', 'LGBMRegressor', 'LGBMClassifier', 'LGBMRanker']
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@ -245,13 +245,14 @@ class CVBooster(object):
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return self.booster.eval_valid(feval)
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try:
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try:
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from sklearn.model_selection import StratifiedKFold
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except ImportError:
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from sklearn.cross_validation import StratifiedKFold
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from sklearn.model_selection import StratifiedKFold
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SKLEARN_StratifiedKFold = True
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except ImportError:
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SKLEARN_StratifiedKFold = False
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try:
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from sklearn.cross_validation import StratifiedKFold
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SKLEARN_StratifiedKFold = True
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except ImportError:
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SKLEARN_StratifiedKFold = False
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def _make_n_folds(full_data, nfold, param, seed, fpreproc=None, stratified=False):
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"""
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@ -416,17 +416,7 @@ std::string GBDT::DumpModel() const {
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ss << models_[i]->ToJSON();
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ss << "}";
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}
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ss << "]," << std::endl;
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std::vector<std::pair<size_t, std::string>> pairs = FeatureImportance();
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ss << "\"feature_importances\":{" << std::endl;
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for (size_t i = 0; i < pairs.size(); ++i) {
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if (i > 0) {
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ss << ",";
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}
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ss << "\"" << pairs[i].second << "\":" << pairs[i].first;
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}
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ss << "}" << std::endl;
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ss << "]" << std::endl;
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ss << "}" << std::endl;
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@ -3,8 +3,8 @@ import numpy as np
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from sklearn import datasets, metrics, model_selection
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import lightgbm as lgb
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X, Y = datasets.make_classification(n_samples=100000, n_features=100, random_state=42)
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x_train, x_test, y_train, y_test = model_selection.train_test_split(X, Y, test_size=0.1, random_state=42)
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X, Y = datasets.make_classification(n_samples=100000, n_features=100)
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x_train, x_test, y_train, y_test = model_selection.train_test_split(X, Y, test_size=0.1)
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train_data = lgb.Dataset(x_train, max_bin=255, label=y_train)
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valid_data = train_data.create_valid(x_test, label=y_test)
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