ad1a527709
* Enable loading model from <1.0.0 trained with objective='binary:logitraw' * Add binary:logitraw in model compatibility testing suite * Feedback from @trivialfis: Override ProbToMargin() for LogisticRaw Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
151 lines
4.9 KiB
Python
151 lines
4.9 KiB
Python
import xgboost
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import numpy as np
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import os
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kRounds = 2
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kRows = 1000
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kCols = 4
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kForests = 2
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kMaxDepth = 2
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kClasses = 3
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X = np.random.randn(kRows, kCols)
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w = np.random.uniform(size=kRows)
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version = xgboost.__version__
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np.random.seed(1994)
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target_dir = 'models'
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def booster_bin(model):
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return os.path.join(target_dir,
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'xgboost-' + version + '.' + model + '.bin')
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def booster_json(model):
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return os.path.join(target_dir,
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'xgboost-' + version + '.' + model + '.json')
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def skl_bin(model):
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return os.path.join(target_dir,
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'xgboost_scikit-' + version + '.' + model + '.bin')
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def skl_json(model):
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return os.path.join(target_dir,
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'xgboost_scikit-' + version + '.' + model + '.json')
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def generate_regression_model():
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print('Regression')
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y = np.random.randn(kRows)
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data = xgboost.DMatrix(X, label=y, weight=w)
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booster = xgboost.train({'tree_method': 'hist',
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'num_parallel_tree': kForests,
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'max_depth': kMaxDepth},
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num_boost_round=kRounds, dtrain=data)
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booster.save_model(booster_bin('reg'))
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booster.save_model(booster_json('reg'))
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reg = xgboost.XGBRegressor(tree_method='hist',
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num_parallel_tree=kForests,
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max_depth=kMaxDepth,
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n_estimators=kRounds)
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reg.fit(X, y, w)
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reg.save_model(skl_bin('reg'))
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reg.save_model(skl_json('reg'))
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def generate_logistic_model():
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print('Logistic')
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y = np.random.randint(0, 2, size=kRows)
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assert y.max() == 1 and y.min() == 0
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for objective, name in [('binary:logistic', 'logit'), ('binary:logitraw', 'logitraw')]:
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data = xgboost.DMatrix(X, label=y, weight=w)
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booster = xgboost.train({'tree_method': 'hist',
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'num_parallel_tree': kForests,
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'max_depth': kMaxDepth,
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'objective': objective},
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num_boost_round=kRounds, dtrain=data)
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booster.save_model(booster_bin(name))
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booster.save_model(booster_json(name))
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reg = xgboost.XGBClassifier(tree_method='hist',
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num_parallel_tree=kForests,
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max_depth=kMaxDepth,
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n_estimators=kRounds,
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objective=objective)
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reg.fit(X, y, w)
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reg.save_model(skl_bin(name))
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reg.save_model(skl_json(name))
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def generate_classification_model():
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print('Classification')
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y = np.random.randint(0, kClasses, size=kRows)
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data = xgboost.DMatrix(X, label=y, weight=w)
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booster = xgboost.train({'num_class': kClasses,
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'tree_method': 'hist',
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'num_parallel_tree': kForests,
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'max_depth': kMaxDepth},
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num_boost_round=kRounds, dtrain=data)
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booster.save_model(booster_bin('cls'))
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booster.save_model(booster_json('cls'))
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cls = xgboost.XGBClassifier(tree_method='hist',
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num_parallel_tree=kForests,
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max_depth=kMaxDepth,
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n_estimators=kRounds)
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cls.fit(X, y, w)
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cls.save_model(skl_bin('cls'))
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cls.save_model(skl_json('cls'))
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def generate_ranking_model():
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print('Learning to Rank')
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y = np.random.randint(5, size=kRows)
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w = np.random.uniform(size=20)
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g = np.repeat(50, 20)
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data = xgboost.DMatrix(X, y, weight=w)
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data.set_group(g)
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booster = xgboost.train({'objective': 'rank:ndcg',
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'num_parallel_tree': kForests,
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'tree_method': 'hist',
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'max_depth': kMaxDepth},
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num_boost_round=kRounds,
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dtrain=data)
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booster.save_model(booster_bin('ltr'))
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booster.save_model(booster_json('ltr'))
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ranker = xgboost.sklearn.XGBRanker(n_estimators=kRounds,
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tree_method='hist',
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objective='rank:ndcg',
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max_depth=kMaxDepth,
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num_parallel_tree=kForests)
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ranker.fit(X, y, g, sample_weight=w)
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ranker.save_model(skl_bin('ltr'))
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ranker.save_model(skl_json('ltr'))
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def write_versions():
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versions = {'numpy': np.__version__,
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'xgboost': version}
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with open(os.path.join(target_dir, 'version'), 'w') as fd:
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fd.write(str(versions))
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if __name__ == '__main__':
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if not os.path.exists(target_dir):
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os.mkdir(target_dir)
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generate_regression_model()
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generate_logistic_model()
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generate_classification_model()
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generate_ranking_model()
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write_versions()
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