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Updated XGBoost to 3.0.0
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6 files changed

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‎CHANGELOG.md‎

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## 0.10.0 (unreleased)
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- Updated XGBoost to 3.0.0
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## 0.9.0 (2024-10-17)
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- Updated XGBoost to 2.1.1

‎test/classifier_test.rb‎

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expected = [4.673004150390625e-05, 0.9999532699584961]
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assert_elements_in_delta expected, y_pred_proba.first
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expected = [0.17202478647232056, 0.26760509610176086, 0.4572588801383972, 0.10311124473810196]
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expected = [0.13950465619564056, 0.25203850865364075, 0.5016216039657593, 0.1068352460861206]
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assert_elements_in_delta expected, model.feature_importances
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model.save_model(tempfile)

‎test/cv_test.rb‎

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def test_binary
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eval_hist = XGBoost.cv(binary_params, binary_train, shuffle: false)
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assert_in_delta 0.306345, eval_hist["train-logloss-mean"].first
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assert_in_delta 0.071130, eval_hist["train-logloss-mean"].last
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assert_in_delta 0.030928, eval_hist["train-logloss-std"].first
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assert_in_delta 0.004158, eval_hist["train-logloss-std"].last
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assert_in_delta 0.345761, eval_hist["test-logloss-mean"].first
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assert_in_delta 0.166319, eval_hist["test-logloss-mean"].last
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assert_in_delta 0.046161, eval_hist["test-logloss-std"].first
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assert_in_delta 0.037050, eval_hist["test-logloss-std"].last
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assert_in_delta 0.290831, eval_hist["train-logloss-mean"].first
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assert_in_delta 0.069389, eval_hist["train-logloss-mean"].last
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assert_in_delta 0.029254, eval_hist["train-logloss-std"].first
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assert_in_delta 0.004453, eval_hist["train-logloss-std"].last
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assert_in_delta 0.324324, eval_hist["test-logloss-mean"].first
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assert_in_delta 0.151608, eval_hist["test-logloss-mean"].last
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assert_in_delta 0.049756, eval_hist["test-logloss-std"].first
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assert_in_delta 0.039735, eval_hist["test-logloss-std"].last
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end
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def test_multiclass

‎test/support/classifier.py‎

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print()
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print('test_early_stopping')
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model.fit(X_train, ym_train, eval_set=[(X_test, ym_test)], early_stopping_rounds=5, verbose=True)
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model = xgb.XGBClassifier(early_stopping_rounds=5)
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model.fit(X_train, ym_train, eval_set=[(X_test, ym_test)])
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print(model.get_booster().best_iteration)
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print()

‎test/train_test.rb‎

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def test_binary
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model = XGBoost.train(binary_params, binary_train)
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y_pred = model.predict(binary_test)
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assert_in_delta 0.9866828, y_pred.first
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assert_in_delta 0.9892826, y_pred.first
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assert_equal 200, y_pred.size
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model.save_model(tempfile)

‎vendor.yml‎

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platforms:
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x86_64-linux:
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url: https://github.com/ankane/ml-builds/releases/download/xgboost-2.1.1/xgboost-2.1.1-x86_64-linux.zip
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sha256: 38ee0df132257e0017290a2508a84e7bf78e47240ee46e01e9c29e9ed1e8bcf5
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url: https://github.com/ankane/ml-builds/releases/download/xgboost-3.0.0/xgboost-3.0.0-x86_64-linux.zip
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sha256: b051e20f4ae42969686bba2f10865eed0ed2f3ed0e2a3fa9cb0aaf052595157a
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x86_64-linux-musl:
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url: https://github.com/ankane/ml-builds/releases/download/xgboost-2.1.1/xgboost-2.1.1-x86_64-linux-musl.zip
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sha256: 8a0d6d0593fb3326e36c62ba5657cc0116b91a9532b0fb0976a24fe8e080c967
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url: https://github.com/ankane/ml-builds/releases/download/xgboost-3.0.0/xgboost-3.0.0-x86_64-linux-musl.zip
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sha256: 24fa1e505d32355e2f84f236594fac45233185de11c216c88672e2a7e4242c65
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aarch64-linux:
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url: https://github.com/ankane/ml-builds/releases/download/xgboost-2.1.1/xgboost-2.1.1-aarch64-linux.zip
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sha256: da5197335c29c66a627e0305623802e4c2eb3c3c56c1ccf44f40a38339eac8e5
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url: https://github.com/ankane/ml-builds/releases/download/xgboost-3.0.0/xgboost-3.0.0-aarch64-linux.zip
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sha256: 9a235503311edb79ce271cb7ae09f2c176b3e0312c1ae447ee6cda75b1cd96c7
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x86_64-darwin:
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url: https://github.com/ankane/ml-builds/releases/download/xgboost-2.1.1/xgboost-2.1.1-x86_64-darwin.zip
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sha256: 33ab7bfd8267c8f74ecf1f2a19b9006ff1b36c551aad665e97d811a3230a0cc2
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url: https://github.com/ankane/ml-builds/releases/download/xgboost-3.0.0/xgboost-3.0.0-x86_64-darwin.zip
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sha256: f1ca04e306162edf5e07446cbb950c216d6516ae37aaf8b2644de00e60df0ab6
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arm64-darwin:
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url: https://github.com/ankane/ml-builds/releases/download/xgboost-2.1.1/xgboost-2.1.1-aarch64-darwin.zip
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sha256: 770a58b721364daa24ac00d02bb80034f6b29d90fbc70f45766a15743a7e6b0b
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url: https://github.com/ankane/ml-builds/releases/download/xgboost-3.0.0/xgboost-3.0.0-aarch64-darwin.zip
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sha256: cc29659f43ca6bbee3db8c93ecc102ead1cb4a039edb6ea398f08de5ae0b5771
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x64-mingw:
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url: https://github.com/ankane/ml-builds/releases/download/xgboost-2.1.1/xgboost-2.1.1-x86_64-windows.zip
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sha256: bc968410de5654a992e70523bcc582c5517c506d92df691f3566fa46e365c6b0
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url: https://github.com/ankane/ml-builds/releases/download/xgboost-3.0.0/xgboost-3.0.0-x86_64-windows.zip
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sha256: 4d7b87db5972e2770e7a78d2a1285d0f6dfc4ea564b674badff58f810c5a000b

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