For a better jump link #355
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A space is needed between `#` and the headers content according to Github's markdown format [guideline](https://guides.github.com/features/mastering-markdown/) After adding the spaces, we can jump to the exact position in page by click the link.
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wxchan
reviewed
Mar 24, 2017
docs/Python-API.md
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| ####__init__(params=None, train_set=None, model_file=None, silent=False) | ||
| #### __init__(params=None, train_set=None, model_file=None, silent=False) |
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change it to\_\_init\_\_ too
wxchan
reviewed
Mar 24, 2017
docs/Python-API.md
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| ####__init__(boosting_type="gbdt", num_leaves=31, max_depth=-1, learning_rate=0.1, n_estimators=10, max_bin=255, subsample_for_bin=50000, objective="regression", min_split_gain=0, min_child_weight=5, min_child_samples=10, subsample=1, subsample_freq=1, colsample_bytree=1, reg_alpha=0, reg_lambda=0, scale_pos_weight=1, is_unbalance=False, seed=0, nthread=-1, silent=True, sigmoid=1.0, huber_delta=1.0, gaussian_eta=1.0, fair_c=1.0, poisson_max_delta_step=0.7, max_position=20, label_gain=None, drop_rate=0.1, skip_drop=0.5, max_drop=50, uniform_drop=False, xgboost_dart_mode=False) | ||
| #### __init__(boosting_type="gbdt", num_leaves=31, max_depth=-1, learning_rate=0.1, n_estimators=10, max_bin=255, subsample_for_bin=50000, objective="regression", min_split_gain=0, min_child_weight=5, min_child_samples=10, subsample=1, subsample_freq=1, colsample_bytree=1, reg_alpha=0, reg_lambda=0, scale_pos_weight=1, is_unbalance=False, seed=0, nthread=-1, silent=True, sigmoid=1.0, huber_delta=1.0, gaussian_eta=1.0, fair_c=1.0, poisson_max_delta_step=0.7, max_position=20, label_gain=None, drop_rate=0.1, skip_drop=0.5, max_drop=50, uniform_drop=False, xgboost_dart_mode=False) |
huanzhang12
pushed a commit
to huanzhang12/LightGBM
that referenced
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Mar 27, 2017
* Update Python-API.md * for a better jump in page A space is needed between `#` and the headers content according to Github's markdown format [guideline](https://guides.github.com/features/mastering-markdown/) After adding the spaces, we can jump to the exact position in page by click the link. * fixed something mentioned by @wxchan * Update Python-API.md
guolinke
pushed a commit
that referenced
this pull request
Apr 9, 2017
* add dummy gpu solver code * initial GPU code * fix crash bug * first working version * use asynchronous copy * use a better kernel for root * parallel read histogram * sparse features now works, but no acceleration, compute on CPU * compute sparse feature on CPU simultaneously * fix big bug; add gpu selection; add kernel selection * better debugging * clean up * add feature scatter * Add sparse_threshold control * fix a bug in feature scatter * clean up debug * temporarily add OpenCL kernels for k=64,256 * fix up CMakeList and definition USE_GPU * add OpenCL kernels as string literals * Add boost.compute as a submodule * add boost dependency into CMakeList * fix opencl pragma * use pinned memory for histogram * use pinned buffer for gradients and hessians * better debugging message * add double precision support on GPU * fix boost version in CMakeList * Add a README * reconstruct GPU initialization code for ResetTrainingData * move data to GPU in parallel * fix a bug during feature copy * update gpu kernels * update gpu code * initial port to LightGBM v2 * speedup GPU data loading process * Add 4-bit bin support to GPU * re-add sparse_threshold parameter * remove kMaxNumWorkgroups and allows an unlimited number of features * add feature mask support for skipping unused features * enable kernel cache * use GPU kernels withoug feature masks when all features are used * REAdme. * REAdme. * update README * fix typos (#349) * change compile to gcc on Apple as default * clean vscode related file * refine api of constructing from sampling data. * fix bug in the last commit. * more efficient algorithm to sample k from n. * fix bug in filter bin * change to boost from average output. * fix tests. * only stop training when all classes are finshed in multi-class. * limit the max tree output. change hessian in multi-class objective. * robust tree model loading. * fix test. * convert the probabilities to raw score in boost_from_average of classification. * fix the average label for binary classification. * Add boost_from_average to docs (#354) * don't use "ConvertToRawScore" for self-defined objective function. * boost_from_average seems doesn't work well in binary classification. remove it. * For a better jump link (#355) * Update Python-API.md * for a better jump in page A space is needed between `#` and the headers content according to Github's markdown format [guideline](https://guides.github.com/features/mastering-markdown/) After adding the spaces, we can jump to the exact position in page by click the link. * fixed something mentioned by @wxchan * Update Python-API.md * add FitByExistingTree. * adapt GPU tree learner for FitByExistingTree * avoid NaN output. * update boost.compute * fix typos (#361) * fix broken links (#359) * update README * disable GPU acceleration by default * fix image url * cleanup debug macro * remove old README * do not save sparse_threshold_ in FeatureGroup * add details for new GPU settings * ignore submodule when doing pep8 check * allocate workspace for at least one thread during builing Feature4 * move sparse_threshold to class Dataset * remove duplicated code in GPUTreeLearner::Split * Remove duplicated code in FindBestThresholds and BeforeFindBestSplit * do not rebuild ordered gradients and hessians for sparse features * support feature groups in GPUTreeLearner * Initial parallel learners with GPU support * add option device, cleanup code * clean up FindBestThresholds; add some omp parallel * constant hessian optimization for GPU * Fix GPUTreeLearner crash when there is zero feature * use np.testing.assert_almost_equal() to compare lists of floats in tests * travis for GPU
guolinke
pushed a commit
that referenced
this pull request
Apr 12, 2017
* add dummy gpu solver code * initial GPU code * fix crash bug * first working version * use asynchronous copy * use a better kernel for root * parallel read histogram * sparse features now works, but no acceleration, compute on CPU * compute sparse feature on CPU simultaneously * fix big bug; add gpu selection; add kernel selection * better debugging * clean up * add feature scatter * Add sparse_threshold control * fix a bug in feature scatter * clean up debug * temporarily add OpenCL kernels for k=64,256 * fix up CMakeList and definition USE_GPU * add OpenCL kernels as string literals * Add boost.compute as a submodule * add boost dependency into CMakeList * fix opencl pragma * use pinned memory for histogram * use pinned buffer for gradients and hessians * better debugging message * add double precision support on GPU * fix boost version in CMakeList * Add a README * reconstruct GPU initialization code for ResetTrainingData * move data to GPU in parallel * fix a bug during feature copy * update gpu kernels * update gpu code * initial port to LightGBM v2 * speedup GPU data loading process * Add 4-bit bin support to GPU * re-add sparse_threshold parameter * remove kMaxNumWorkgroups and allows an unlimited number of features * add feature mask support for skipping unused features * enable kernel cache * use GPU kernels withoug feature masks when all features are used * REAdme. * REAdme. * update README * fix typos (#349) * change compile to gcc on Apple as default * clean vscode related file * refine api of constructing from sampling data. * fix bug in the last commit. * more efficient algorithm to sample k from n. * fix bug in filter bin * change to boost from average output. * fix tests. * only stop training when all classes are finshed in multi-class. * limit the max tree output. change hessian in multi-class objective. * robust tree model loading. * fix test. * convert the probabilities to raw score in boost_from_average of classification. * fix the average label for binary classification. * Add boost_from_average to docs (#354) * don't use "ConvertToRawScore" for self-defined objective function. * boost_from_average seems doesn't work well in binary classification. remove it. * For a better jump link (#355) * Update Python-API.md * for a better jump in page A space is needed between `#` and the headers content according to Github's markdown format [guideline](https://guides.github.com/features/mastering-markdown/) After adding the spaces, we can jump to the exact position in page by click the link. * fixed something mentioned by @wxchan * Update Python-API.md * add FitByExistingTree. * adapt GPU tree learner for FitByExistingTree * avoid NaN output. * update boost.compute * fix typos (#361) * fix broken links (#359) * update README * disable GPU acceleration by default * fix image url * cleanup debug macro * remove old README * do not save sparse_threshold_ in FeatureGroup * add details for new GPU settings * ignore submodule when doing pep8 check * allocate workspace for at least one thread during builing Feature4 * move sparse_threshold to class Dataset * remove duplicated code in GPUTreeLearner::Split * Remove duplicated code in FindBestThresholds and BeforeFindBestSplit * do not rebuild ordered gradients and hessians for sparse features * support feature groups in GPUTreeLearner * Initial parallel learners with GPU support * add option device, cleanup code * clean up FindBestThresholds; add some omp parallel * constant hessian optimization for GPU * Fix GPUTreeLearner crash when there is zero feature * use np.testing.assert_almost_equal() to compare lists of floats in tests * travis for GPU * add tutorial and more GPU docs
eisber
pushed a commit
to eisber/LightGBM
that referenced
this pull request
Mar 15, 2019
…ixed autogenerated code for model name (microsoft#355)
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A space is needed between
#and the headers content according to Github's Markdown format guideline (https://guides.github.com/features/mastering-markdown/).After adding the spaces, we can jump to the exact position in page by click the link.