Update catboost requirement

[dependabot]

Updates the requirements on catboost to permit the latest version.

Release notes

Sourced from catboost's releases.

1.2.3

Python package

• Support Python 3.12. #2510
• [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
• Add missing generated metrics: Cox, PairLogitPairwise, UserPerObjMetric, SurvivalAft.

New features

• 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.
• [CLI] Support printing Auxiliary columns by name in evaluation result output. #1659
• 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
• [Python-package]: Copying models does not lose best_score_, evals_result_, best_iteration_ attributes values anymore. #1793
• [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.
• Fix Focal loss derivatives sign. #2563

Changelog

Sourced from catboost's changelog.

Release 1.2.3

Python package

• Support Python 3.12. #2510
• [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
• Add missing generated metrics: Cox, PairLogitPairwise, UserPerObjMetric, SurvivalAft.

New features

• 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.
• [CLI] Support printing Auxiliary columns by name in evaluation result output. #1659
• 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
• [Python-package]: Copying models does not lose best_score_, evals_result_, best_iteration_ attributes values anymore. #1793
• [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.

... (truncated)

Commits

• fe0941b Use paths from CMAKE_*_DIR when running in open source to avoid issues on Win...
• cf282f7 CatBoost release 1.2.3.
• ec263e7 Update contrib/python/ipywidgets/py3 to 8.1.2
• 704a5d8 Intermediate changes
• a13b5ba Add loading CatBoostModel from a byte array to API.. Fix #2539
• 56a0b44 Add Get*FeaturesIndices to C++ wrapper. #2323, #2568
• caed72b Add GetEmbeddingFeaturesCount() to C++ wrapper
• 4490314 Add Spark 3.5 to pyspark_wrapper_generator.
• 98c3667 Support boolean target type in Spark (where possible).
• ad980da Manually unroll the loop
• Additional commits viewable in compare view

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[adri0]

Looks good to me

[adri0]

Indeed this is extra dependency option with all packages seems not very useful. But in the future it could be nice having one optional dependency for each algorithm. If the user is just interested in using probatus with lightgbm without having to install the others sounds like a fair use case.