This is an innovative machine learning project that utilizes patient reviews with many other attributes to analyze and evaluate the effectiveness of drugs.
[Performance]: Fix ineffective loops in Cython. Significant speedups (up to 3x) on dataset construction from data in C-order can be expected.
[Performance]: Make features data initialization from C-order numpy.ndarrays with float32 data type multithreaded. Significant speedups of 5x up to 10x (on CPUs with many cores) can be expected. #385, #2542
Save training metrics into the model metadata. So best_score_, evals_result_, best_iteration_ model attributes now work after model saving and loading. Can be removed by model metadata manipulation if needed. #1166
[Breaking change]. Support a separate boolean target type, now Class predictions for models that have been trained with boolean targets will also be boolean instead of True, False strings as before. Such models will be incompatible with the previous versions of CatBoost appliers. If you want the old behavior convert your target to False, True strings before training. #1954
Restrict jupyterlab version for setup to 3.x for now. Fixes #2530
utils.read_cd: Support CD files with non-increasing column indices.
Make log_cout, log_cerr specification consistent, avoid reset in recursive calls.
Late-initialize default values for log_cout, log_cerr. #2195
Support boolean target/labels type during training in Python and Spark (in the latter case only when using fit with Pool arguments) and Class prediction in Python. #1954
[Spark]: Support Spark 3.5.x.
[C/C++ applier]. Add functions for getting indices of features of different types to C and C++ API. #2568. Thanks to @nimusp.
[C/C++ applier]. Add staged prediction functions to C API. #2584. Thanks to @Mb-NextTime.
[JVM applier]. Add loading CatBoostModel from a byte array to API. #2539
[Linux] Support CgroupsV2 when computing default number of threads used in parallel computations. #2519. Thanks to @elukey.
Support printing Auxiliary columns by name in evaluation result output.
Save training metrics into the model metadata. Can be removed by model metadata manipulation if needed. #1166
Build & testing
[Windows]: Use clang-cl compiler and tools from Visual Studio 2022 for the build without CUDA (build with CUDA still uses standard Microsoft toolchain from Visual Studio 2019).
[macOS]: Pass os.version to conan host settings to ensure version consistency.
[Linux aarch64]: Set -mno-outline-atomics for modern versions of CLang and GCC to avoid unresolved symbols linking errors. #2527
Added missing CMakeLists for unit tests for util. #2525
Bugfixes
[Performance]: Fix performance regression that could slow down training on GPU by 50% on some datasets that had been introduced in release 1.2. Thanks to @JeanPaulShapo.
[Python-package]: Fix segfault on Pool(data=None). #2522
[Python-package]: Fix Python exception in Pool() when pairs_weight is a numpy array. #1913
[Python-package]: Fix segfault and other strange errors when specifying custom logger with __call__ method. #2277
[Python-package]: Fix returning complex params in hyperparameter search. #1741, #1833
[Python-package]: Fix ignored exceptions for missed metrics descriptions on startup. This has not been visible to users but has been making debugging more difficult.
[Python-package]: Fix misleading Targets are required for YetiRank loss function. error in Cross validation. #2083
[Python-package]: Fix Pool.get_label() returns constant True for boolean labels. #2133
[Spark]: Fix hangs at the end of the training. #2151
Precision metric default value in the absense of positive samples is changed to 0 and a warning is added
(similar to the behavior of scikit-learn implementation). #2422
Fix ignoring embedding features
Try to avoid hash collisions when computing group ids with datasets with a lot of groups (may occur in datasets with around a 10^9 samples).
Fix Multiclass models export to C++ and Python code. #2549
Fix dataset_statistics mode when no Target data is available.
Fix Error: can't proceed some features error on GPU. #1024
Fix allow_const_label=True for classification. #1933
Add checking of approx and target dimensions for SurvivalAft objective/metric.
[Performance]: Fix ineffective loops in Cython. Significant speedups (up to 3x) on dataset construction from data in C-order can be expected.
[Performance]: Make features data initialization from C-order numpy.ndarrays with float32 data type multithreaded. Significant speedups of 5x up to 10x (on CPUs with many cores) can be expected. #385, #2542
Save training metrics into the model metadata. So best_score_, evals_result_, best_iteration_ model attributes now work after model saving and loading. Can be removed by model metadata manipulation if needed. #1166
[Breaking change]. Support a separate boolean target type, now Class predictions for models that have been trained with boolean targets will also be boolean instead of True, False strings as before. Such models will be incompatible with the previous versions of CatBoost appliers. If you want the old behavior convert your target to False, True strings before training. #1954
Restrict jupyterlab version for setup to 3.x for now. Fixes #2530
utils.read_cd: Support CD files with non-increasing column indices.
Make log_cout, log_cerr specification consistent, avoid reset in recursive calls.
Late-initialize default values for log_cout, log_cerr. #2195
Support boolean target/labels type during training in Python and Spark (in the latter case only when using fit with Pool arguments) and Class prediction in Python. #1954
[Spark]: Support Spark 3.5.x.
[C/C++ applier]. Add functions for getting indices of features of different types to C and C++ API. #2568. Thanks to @nimusp.
[C/C++ applier]. Add staged prediction functions to C API. #2584. Thanks to @Mb-NextTime.
[JVM applier]. Add loading CatBoostModel from a byte array to API. #2539
[Linux] Support CgroupsV2 when computing default number of threads used in parallel computations. #2519. Thanks to @elukey.
Support printing Auxiliary columns by name in evaluation result output.
Save training metrics into the model metadata. Can be removed by model metadata manipulation if needed. #1166
Build & testing
[Windows]: Use clang-cl from Visual Studio 2022 for the build without CUDA (build with CUDA still uses standard Microsoft toolchain from Visual Studio 2019).
[macOS]: Pass os.version to conan host settings to ensure version consistency.
[Linux aarch64]: Set -mno-outline-atomics for modern versions of CLang and GCC to avoid unresolved symbols linking errors. #2527
Added missing CMakeLists for unit tests for util. #2525
Bugfixes
[Performance]: Fix performance regression that could slow down training on GPU by 50% on some datasets that had been introduced in release 1.2. Thanks to @JeanPaulShapo.
[Python-package]: Fix segfault on Pool(data=None). #2522
[Python-package]: Fix Python exception in Pool() when pairs_weight is a numpy array. #1913
[Python-package]: Fix segfault and other strange errors when specifying custom logger with __call__ method. #2277
[Python-package]: Fix returning complex params in hyperparameter search. #1741, #1833
[Python-package]: Fix ignored exceptions for missed metrics descriptions on startup. This has not been visible to users but has been making debugging more difficult.
[Python-package]: Fix misleading Targets are required for YetiRank loss function. error in Cross validation. #2083
[Python-package]: Fix Pool.get_label() returns constant True for boolean labels. #2133
[Spark]: Fix hangs at the end of the training. #2151
Precision metric default value in the absense of positive samples is changed to 0 and a warning is added
(similar to the behavior of scikit-learn implementation). #2422
Fix ignoring embedding features
Try to avoid hash collisions when computing group ids with datasets with a lot of groups (may occur in datasets with around a 10^9 samples).
Fix Multiclass models export to C++ and Python code. #2549
Fix dataset_statistics mode when no Target data is available.
Fix Error: can't proceed some features error on GPU. #1024
Fix allow_const_label=True for classification. #1933
Add checking of approx and target dimensions for SurvivalAft objective/metric.
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Bumps catboost from 1.2.2 to 1.2.3.
Release notes
Sourced from catboost's releases.
Changelog
Sourced from catboost's changelog.
Commits
fe0941bUse paths from CMAKE_*_DIR when running in open source to avoid issues on Win...cf282f7CatBoost release 1.2.3.ec263e7Update contrib/python/ipywidgets/py3 to 8.1.2704a5d8Intermediate changesa13b5baAdd loading CatBoostModel from a byte array to API.. Fix #253956a0b44Add Get*FeaturesIndices to C++ wrapper. #2323, #2568caed72bAdd GetEmbeddingFeaturesCount() to C++ wrapper4490314Add Spark 3.5 to pyspark_wrapper_generator.98c3667Support boolean target type in Spark (where possible).ad980daManually unroll the loopDependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
@dependabot rebase.Dependabot commands and options
You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot show