Shorten CHANGELOG for v0.13.0 release

Summary:
This commit updates the CHANGELOG to be shorter before releasing 0.13.0.

Also shorten the welcome message.

Differential Revision: D88159391

fbshipit-source-id: c82301511c24be23171a7b149e26c348f5d22391

Author: talgalili

CHANGELOG.md

@@ -1,33 +1,32 @@
-# 0.12.x (2025-11-21)
+# 0.13.x (???)
 
-> TODO: update 0.12.x to 0.13.0 before release.
+> TODO: update to final version
+
+# 0.13.0 (2025-12-02)
 
 ## New Features
 
-- **Raking algorithm refactor**
-  - Removed `ipfn` dependency and replaced with a vectorized NumPy
-    implementation (`_run_ipf_numpy`) for iterative proportional fitting,
-    resulting in significant performance improvements and eliminating external
-    dependency ([#135](https://github.com/facebookresearch/balance/pull/135)).
-- **Propensity modeling flexibility**
-  - `ipw()` now accepts any sklearn classifier via the `model` argument and
-    deprecates the old `sklearn_model` alias, enabling the use of models like
-    random forests while preserving all existing trimming and diagnostic
-    workflows. Dense-only estimators and models without linear coefficients are
-    fully supported, and propensity probabilities are stabilized to avoid
-    numerical issues.
-  - Implemented logistic regression customization by passing a configured
+- **Propensity modeling beyond static logistic regression**
+  - `ipw()` now accepts any sklearn classifier via the `model` argument,
+    enabling the use of models like random forests and gradient boosting while
+    preserving all existing trimming and diagnostic features. Dense-only
+    estimators and models without linear coefficients are fully supported.
+    Propensity probabilities are stabilized to avoid numerical issues.
+  - Allow customization of logistic regression by passing a configured
     :class:`~sklearn.linear_model.LogisticRegression` instance through the
-    `model` argument; the CLI now accepts `--ipw_logistic_regression_kwargs`
-    JSON to build that estimator directly for command-line workflows.
+    `model` argument. Also, the CLI now accepts
+    `--ipw_logistic_regression_kwargs` JSON to build that estimator directly for
+    command-line workflows.
 - **Covariate diagnostics**
   - Added KL divergence calculations for covariate comparisons (numeric and
     one-hot categorical), exposed via `BalanceDF.kld()` alongside linked-sample
     aggregation support.
-- **Renamed Balance___DF to BalanceDF___**
-  - BalanceCovarsDF to BalanceDFCovars
-  - BalanceOutcomesDF to BalanceDFOutcomes
-  - BalanceWeightsDF to BalanceDFWeights
+- **Weighting Methods**
+  - `rake()` and `poststratify()` now honour `weight_trimming_mean_ratio` and
+    `weight_trimming_percentile`, trimming and renormalising weights through the
+    enhanced `trim_weights(..., target_sum_weights=...)` API so the documented
+    parameters work as expected
+    ([#147](https://github.com/facebookresearch/balance/pull/147)).
 
 ## Documentation
 
@@ -44,138 +43,67 @@
   ([#145](https://github.com/facebookresearch/balance/pull/145)).
 - Added IPW quickstart tutorial showcasing default logistic regression and
   custom sklearn classifier usage in (`balance_quickstart.ipynb`).
+- Shorten the welcome message (for when importing the package).
 
 ## Code Quality & Refactoring
 
+- **Raking algorithm refactor**
+  - Removed `ipfn` dependency and replaced with a vectorized NumPy
+    implementation (`_run_ipf_numpy`) for iterative proportional fitting,
+    resulting in significant performance improvements and eliminating external
+    dependency ([#135](https://github.com/facebookresearch/balance/pull/135)).
+
 - **IPW method refactoring**
   - Reduced Cyclomatic Complexity Number (CCN) by extracting repeated code
-    patterns into reusable helper functions:
-    - Added `_compute_deviance()` to consolidate `2 * log_loss(...)` pattern
-      (used 4+ times)
-    - Added `_compute_proportion_deviance()` to consolidate `1 - dev/null_dev`
-      pattern
-    - Added `_convert_to_dense_array()` to consolidate sparse-to-dense matrix
-      conversion pattern (CSC→CSR→dense)
-  - Improved code maintainability by eliminating duplication in deviance
-    calculations and matrix conversions
-  - Fixed TODO: Removed manual ASMD improvement calculation and now uses
-    existing `compute_asmd_improvement()` from `weighted_comparisons_stats.py`
+    patterns into reusable helper functions: `_compute_deviance()`,
+    `_compute_proportion_deviance()`, `_convert_to_dense_array()`.
+  - Removed manual ASMD improvement calculation and now uses existing
+    `compute_asmd_improvement()` from `weighted_comparisons_stats.py`
+
 - **Type safety improvements**
-  - **Pyre-strict migration**: Converted 32 Python files from `# pyre-unsafe` to
-    `# pyre-strict` mode, significantly improving type safety across the
-    codebase. Files converted include core modules (`__init__.py`,
-    `adjustment.py`, `balancedf_class.py`, `cli.py`, `sample_class.py`,
-    `util.py`, `typing.py`), statistics modules
-    (`stats_and_plots/general_stats.py`, `stats_and_plots/weighted_stats.py`,
-    `stats_and_plots/weighted_comparisons_plots.py`,
-    `stats_and_plots/weighted_comparisons_stats.py`,
-    `stats_and_plots/weights_stats.py`), weighting methods
-    (`weighting_methods/cbps.py`, `weighting_methods/ipw.py`,
-    `weighting_methods/poststratify.py`, `weighting_methods/rake.py`), datasets
-    module (`datasets/__init__.py`), and test files
-    (`parent_balance/tests/test_adjust_null.py`,
-    `parent_balance/tests/test_adjustment.py`,
-    `parent_balance/tests/test_cbps.py`, `parent_balance/tests/test_cli.py`,
-    `parent_balance/tests/test_datasets.py`, `parent_balance/tests/test_ipw.py`,
-    `parent_balance/tests/test_logging.py`,
-    `parent_balance/tests/test_poststratify.py`,
-    `parent_balance/tests/test_rake.py`, `parent_balance/tests/test_sample.py`,
-    `parent_balance/tests/test_stats_and_plots.py`,
-    `parent_balance/tests/test_testutil.py`,
-    `parent_balance/tests/test_util.py`)
-  - **Modernized type hints to PEP 604 syntax**: Updated all type annotations
-    across 11 files to use the newer PEP 604 union syntax (`X | Y` instead of
-    `Union[X, Y]` and `X | None` instead of `Optional[X]`), improving code
-    readability and aligning with Python 3.10+ typing conventions. Updated
-    `from __future__ import` statements to use `annotations` instead of the
-    older `absolute_import, division, print_function, unicode_literals`. Removed
-    unnecessary `Union` and `Optional` imports from `typing`. Files updated:
-    `__init__.py`, `adjustment.py`, `balancedf_class.py`, `cli.py`,
-    `datasets/__init__.py`, `sample_class.py`,
-    `stats_and_plots/weighted_comparisons_stats.py`,
-    `stats_and_plots/weighted_stats.py`, `stats_and_plots/weights_stats.py`,
-    `util.py`, `weighting_methods/ipw.py`.
-  - **Important compatibility note**: Type alias definitions in `typing.py`
-    retain `Union` syntax for Python 3.9 compatibility, as the `|` operator for
-    type aliases only works at runtime in Python 3.10+. Added comprehensive
-    inline documentation explaining this limitation and the distinction between
-    type annotations (which support `|` with
-    `from __future__ import annotations`) and type alias assignments (which
-    require `Union` for runtime evaluation in Python 3.9).
-  - **Enhanced type safety for plotting functions**: Replaced loose dictionary
-    type hints with structured `TypedDict` definition (`DataFrameWithWeight`)
-    for better type checking in `weighted_comparisons_plots.py`. Added
-    `SampleName` type alias to precisely specify valid sample name literals.
-    Removed numerous `# pyre-ignore` comments by properly handling type casts
-    and narrowing types. Added validation for plotly `dist_type` parameter to
-    raise clear errors when unsupported types are used.
-  - Fixed missing `Any` import in `weighted_comparisons_plots.py` to resolve
-    pyre-fixme[10] error
-  - Added comprehensive type annotations for previously untyped parameters and
-    return values throughout the codebase
-  - Fixed type casts and narrowed types where appropriate
-  - Initialized optional variables to handle pyre-fixme[61] issues
-  - Updated method signatures to match parent class interfaces
-  - **Replaced assert-based type narrowing with `_verify_value_type()` helper**:
-    Refactored code to use the `_verify_value_type()` utility function instead
-    of bare `assert x is not None` statements for type narrowing. This improves
-    code clarity, provides better error messages, and follows best practices for
-    pyre-strict mode. Enhanced `_verify_value_type()` in `testutil.py` with
-    optional type checking via `isinstance()` and improved overload signatures.
-    Changes applied to test files (`test_datasets.py`, `test_sample.py`,
-    `test_stats_and_plots.py`, `test_testutil.py`, `test_util.py`,
-    `test_weighted_comparisons_plots.py`) and production code (`ipw.py`).
+  - Migrated 32 Python files from `# pyre-unsafe` to `# pyre-strict` mode,
+    covering core modules, statistics, weighting methods, datasets, and test
+    files
+  - Modernized type hints to PEP 604 syntax (`X | Y` instead of `Union[X, Y]`)
+    across 11 files for improved readability and Python 3.10+ alignment
+  - Type alias definitions in `typing.py` retain `Union` syntax for Python 3.9
+    compatibility
+  - Enhanced plotting function type safety with `TypedDict` definitions and
+    proper type narrowing
+  - Replaced assert-based type narrowing with `_verify_value_type()` helper for
+    better error messages and pyre-strict compliance
+
+- **Renamed Balance**_DF to BalanceDF_\*\*\*\*
+  - BalanceCovarsDF to BalanceDFCovars
+  - BalanceOutcomesDF to BalanceDFOutcomes
+  - BalanceWeightsDF to BalanceDFWeights
 
 ## Bug Fixes
 
 - **Utility Functions**
-  - Improved `quantize` function: preserves column ordering and replaces
-    assertions with proper TypeError exceptions
-    ([#133](https://github.com/facebookresearch/balance/pull/133)).
+  - Fixed `quantize()` to preserve column ordering and use proper TypeError
+    exceptions ([#133](https://github.com/facebookresearch/balance/pull/133))
 - **Statistical Functions**
-  - **Fixed division by zero in `asmd_improvement()`**: Added safety check to
-    prevent RuntimeWarning when `asmd_mean_before` is zero or very close to zero
-    (< 1e-10). The function now returns `0.0` (representing 0% improvement) when
-    the sample was already perfectly matched to the target before adjustment,
-    which is the semantically correct result. This eliminates the "invalid value
-    encountered in scalar divide" warning that appeared in test runs.
-- **Weighting Methods**
-  - `rake()` and `poststratify()` now honour `weight_trimming_mean_ratio` and
-    `weight_trimming_percentile`, trimming and renormalising weights through the
-    enhanced `trim_weights(..., target_sum_weights=...)` API so the documented
-    parameters work as expected
-    ([#147](https://github.com/facebookresearch/balance/pull/147)).
+  - Fixed division by zero in `asmd_improvement()` when `asmd_mean_before` is
+    zero, now returns `0.0` for 0% improvement
 - **CLI & Infrastructure**
-  - Replaced deprecated argparse FileType with pathlib.Path, eliminating
-    PendingDeprecationWarning
-    ([#134](https://github.com/facebookresearch/balance/pull/134)).
+  - Replaced deprecated argparse FileType with pathlib.Path
+    ([#134](https://github.com/facebookresearch/balance/pull/134))
 - **Weight Trimming**
-  - Ensured both `weight_trimming_mean_ratio` and `weight_trimming_percentile`
-    paths in `trim_weights()` return `pd.Series` with `dtype=np.float64` and
-    preserve the original index.
-  - **Fixed edge case in percentile-based winsorization**: `_validate_limit()`
-    now automatically adjusts percentile limits upward by
-    `min(2/n_weights, limit/10)` (capped at 1.0) before passing them to
-    `scipy.stats.mstats.winsorize`. This prevents edge cases where discrete data
-    distributions or floating-point precision issues could prevent winsorization
-    at exact boundary percentiles, ensuring at least one value gets winsorized
-    when a non-zero limit is specified
-    ([#144](https://github.com/facebookresearch/balance/issues/144)).
-  - **Improved documentation**: Enhanced docstrings for `trim_weights()` and
-    `_validate_limit()` to clearly explain the automatic limit adjustment
-    mechanism, provide concrete examples of percentile behavior (e.g., how
-    single values vs. tuples work), and document the relationship between mean
-    ratio trimming and percentile-based winsorization.
+  - Fixed `trim_weights()` to consistently return `pd.Series` with
+    `dtype=np.float64` and preserve original index across both trimming methods
+  - Fixed percentile-based winsorization edge case: `_validate_limit()` now
+    automatically adjusts limits to prevent floating-point precision issues
+    ([#144](https://github.com/facebookresearch/balance/issues/144))
+  - Enhanced documentation for `trim_weights()` and `_validate_limit()` with
+    clearer examples and explanations
 
 ## Tests
 
-- Enhanced test coverage for weight trimming:
-  - Added `test_trim_weights_return_type_consistency` to validate that both
-    trimming methods return `pd.Series` with `dtype=np.float64` and preserve
-    indices.
-  - Added 11 comprehensive tests for `_validate_limit()` covering normal
-    operation, edge cases, error conditions, type handling, and boundary
-    conditions.
+- Enhanced test coverage for weight trimming with
+  `test_trim_weights_return_type_consistency` and 11 comprehensive tests for
+  `_validate_limit()` covering edge cases, error conditions, and boundary
+  conditions
 
 ## Contributors
 

balance/__init__.py

@@ -19,20 +19,18 @@
 from balance.util import TruncationFormatter  # noqa
 
 global __version__
-__version__ = "0.12.x"
+__version__ = "0.13.0"
 
 WELCOME_MESSAGE = f"""
-Welcome to balance (Version {__version__})!
-An open-source Python package for balancing biased data samples.
-
-📖 Documentation: https://import-balance.org/
-🛠️ Get Help / Report Issues: https://github.com/facebookresearch/balance/issues/
-📄 Citation:
-    Sarig, T., Galili, T., & Eilat, R. (2023).
-    balance - a Python package for balancing biased data samples.
-    https://arxiv.org/abs/2307.06024
-
-Tip: You can access this information at any time with balance.help()
+balance (Version {__version__}) loaded:
+    📖 Documentation: https://import-balance.org/
+    🛠️ Help / Issues: https://github.com/facebookresearch/balance/issues/
+    📄 Citation:
+        Sarig, T., Galili, T., & Eilat, R. (2023).
+        balance - a Python package for balancing biased data samples.
+        https://arxiv.org/abs/2307.06024
+
+    Tip: You can view this message anytime with balance.help()
 """