Gplearn

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- Require keyword only arguments for all public methods and functions to comply
with ``scikit-learn`` SLEP009.
- Replace ``n_features_`` attribute with ``n_features_in_`` to comply with
``scikit-learn`` SLEP010.
- Update test suite to ensure compatibility with ``scikit-learn``.
``scikit-learn`` 1.0.2 or newer will be required due to recent changes in
their testing requirements. Also requiring ``joblib`` to 1.0.0 or newer to
align with next release of scikit-learn.
- Added the `class_weight` parameter to :class:`genetic.SymbolicClassifier`
allowing users to easily compensate for imbalanced datasets.

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- Fixed a bug with multi-processing and custom functions, allowing pickling of
models with custom functions, fitness metrics or classifier transformers.
``joblib`` 0.13.0 or newer required in order to take advantage of this
release in order to wrap functions for pickling saved models.

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- Added the :class:`genetic.SymbolicClassifier` to use symbolic regression to
solve binary classification problems. This passes the outputs of a program
through a sigmoid function in order to translate the result into a
probability of either class.
- Allow users to express feature names as strings rather than X0, X1, etc.
Graphviz and ``print()`` output can now be customized by setting
``feature_names=[...]`` in :class:`genetic.SymbolicRegressor` or
:class:`genetic.SymbolicTransformer`.
- Allow users to exclude constants from their programs by setting
``const_range=None`` in :class:`genetic.SymbolicRegressor` or
:class:`genetic.SymbolicTransformer`.
- Record details (similar to the verbose output) of the evolution in the
estimator attribute ``run_details_`` dict in
:class:`genetic.SymbolicRegressor` and :class:`genetic.SymbolicTransformer`.
- Pearson and Spearman correlation coefficients added as first-class metrics to
:class:`genetic.SymbolicRegressor`. These metrics allow for evolution of
value-added features for second-stage estimators.
- Added a `low_memory` parameter in :class:`genetic.SymbolicRegressor` and
:class:`genetic.SymbolicTransformer` which can reduce memory use for cases
where there are large populations or many generations by removing early
generation program information. By
`Bartol Karuza <https://github.com/bartolkaruza>`_ and
`wulfihm <https://github.com/wulfihm>`_.
- Drop support for Python 2.7 and Python 3.4 to ensure compatibility with
``scikit-learn``. ``scikit-learn`` 0.20.0 or newer will also be required due
to recent changes in their testing suite. Additionally joblib 0.11 or newer
will be required due to scikit-learn devendoring it.

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- Fixed two bugs in :class:`genetic.SymbolicTransformer` where the final
solution selection logic was incorrect and suboptimal. This fix will change
the solutions from all previous versions of `gplearn`. Thanks to
`iblasi <https://github.com/iblasi>`_ for diagnosing the problem and helping
craft the solution.
- Fixed bug in :class:`genetic.SymbolicRegressor` where a custom fitness
measure was defined in :func:`fitness.make_fitness()` with the parameter
`greater_is_better=True`. This was ignored during final solution selection.
This change will alter the results from previous releases where
`greater_is_better=True` was set in a custom fitness measure. By
`sun ao <https://github.com/eggachecat>`_.
- Increase minimum required version of ``scikit-learn`` to 0.18.1. This allows
streamlining the test suite and removal of many utilities to reduce future
technical debt. **Please note that due to this change, previous versions
may have different results** due to a change in random sampling noted
`here <http://scikit-learn.org/stable/whats_new.html#version-0-18-1>`_.
- Drop support for Python 2.6 and add support for Python 3.5 and 3.6 in order
to support the latest release of ``scikit-learn`` 0.19 and avoid future test
failures. By `hugovk <https://github.com/hugovk>`_.

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- Allow more generations to be evolved on top of those already trained using a
previous call to fit. The :class:`genetic.SymbolicRegressor` and
:class:`genetic.SymbolicTransformer` classes now support the ``warm_start``
parameter which, when set to ``True``, reuse the solution of the previous
call to fit and add more generations to the evolution.
- Allow users to define their own fitness measures. Supported by the
:func:`fitness.make_fitness()` factory function. Using this a user may define
any metric by which to measure the fitness of a program to optimize any
problem. This also required modifying the API slightly with the deprecation
of the ``'rmsle'`` error measure for the :class:`genetic.SymbolicRegressor`.
- Allow users to define their own functions for use in genetic programs.
Supported by the :func:`functions.make_function()` factory function. Using
this a user may define any mathematical relationship with any number of
arguments and grow totally customized programs. This also required modifying
the API with the deprecation of the ``'comparison'``, ``'transformer'`` and
``'trigonometric'`` arguments to the :class:`genetic.SymbolicRegressor` and
:class:`genetic.SymbolicTransformer` classes in favor of the new
``function_set`` where any combination of preset and user-defined functions
can be supplied. To restore previous behavior initialize the estimator with
``function_set=['add2', 'sub2', 'mul2', 'div2', 'sqrt1', 'log1', 'abs1',
'neg1', 'inv1', 'max2', 'min2']``.
- Reduce memory consumption for large datasets, large populations or many
generations. Indices for in-sample/out-of-sample fitness calculations are now
generated on demand rather than being stored in the program objects which
reduces the size significantly for large datasets. Additionally "irrelevant"
programs from earlier generations are removed if they did not contribute to
the current population through genetic operations. This reduces the number of
programs stored in the estimator which helps for large populations, high
number of generations, as well as for runs with significant bloat.