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Changes
=======
.. currentmodule:: patsy
v0.5.0 (not yet released)
------
* Dropped support for Python 2.6 and 3.3.
v0.4.1
------
.. image:: https://zenodo.org/badge/doi/10.5281/zenodo.33471.svg
:target: http://dx.doi.org/10.5281/zenodo.33471
New features:
* On Python 2, accept ``unicode`` strings containing only ASCII
characters as valid formula descriptions in
the high-level formula API (:func:`dmatrix` and friends). This is
intended as a convenience for people using Python 2 with ``from
__future__ import unicode_literals``. (See :ref:`py2-versus-py3`.)
Bug fixes:
* Accept ``long`` as a valid integer type in the new
:class:`DesignInfo` classes. In particular this fixes errors that
arise on 64-bit Windows builds (where ``ndarray.shape`` contains
``long`` objects), like ``ValueError: For numerical factors,
num_columns must be an int.``
* Fix deprecation warnings encountered with numpy 1.10
v0.4.0
------
.. image:: https://zenodo.org/badge/doi/10.5281/zenodo.20981.svg
:target: http://dx.doi.org/10.5281/zenodo.20981
Incompatible changes:
* :class:`EvalFactor` and :meth:`ModelDesc.from_formula` no longer
take an ``eval_env`` argument.
* The :func:`design_matrix_builders` function and the
:meth:`factor_protocol.memorize_passes_needed` method now require an
``eval_env`` as an additional argument.
* The :class:`DesignInfo` constructor's arguments have totally
changed. In addition to the changes needed to support the new
features below, we no longer support "shim" DesignInfo objects that
have non-trivial term specifications. This was only included in the
first place to provide a compatibility hook for competing formula
libraries; four years later, no such libraries have shown up. If one
does, we can re-add it, but I'm not going to bother maintaining it
in the mean time...
* Dropped support for Python 3.2.
Other changes:
* Patsy now supports Pandas's new (version 0.15 or later) categorical
objects.
* Formulas (or more precisely, :class:`EvalFactor` objects) now only
keep a reference to the variables required from their environment
instead of the whole environment where the formula was
defined. (Thanks to Christian Hudon.)
* :class:`DesignInfo` has new attributes
:attr:`DesignInfo.factor_infos` and :attr:`DesignInfo.term_codings`
which provide detailed metadata about how each factor and term is
encoded.
* As a result of the above changes, the split between
:class:`DesignInfo` and :class:`DesignMatrixBuilder` is no longer
necessary; :class:`DesignMatrixBuiler` has been eliminated. So for
example, :func:`design_matrix_builders` now returns a list of
:class:`DesignInfo` objects, and you can now pass
:class:`DesignInfo` objects directly to any function for building
design matrices. For compatibility, :class:`DesignInfo` continues to
provide ``.builder`` and ``.design_info`` attributes, so that old
code should continue to work; however, these attributes are
deprecated.
* Ensured that attempting to pickle most Patsy objects raises an
error. This has never been supported, and the interesting cases
failed in any case, but now we're taking a more systematic
approach. (Soon we will add real, supported pickling support.)
* Fixed a bug when running under ``python -OO``.
v0.3.0
------
.. image:: https://zenodo.org/badge/doi/10.5281/zenodo.11444.svg
:target: http://dx.doi.org/10.5281/zenodo.11444
* New stateful transforms for computing natural and cylic cubic
splines with constraints, and tensor spline bases with
constraints. (Thanks to `@broessli <https://github.com/broessli>`_
and GDF Suez for contributing this code.)
* Dropped support for Python 2.5 and earlier.
* Switched to using a single source tree for both Python 2 and Python
3.
* Added a fast-path to skip NA detection for inputs with boolean
dtypes (thanks to Matt Davis for patch).
* Incompatible change: Sometimes when building a design matrix for a
formula that does not depend on the data in any way, like ``"1 ~
1"``, we have no way to determine how many rows the resulting matrix
should have. In previous versions of patsy, when this occurred we
simply returned a matrix with 1 row. In 0.3.0+, we instead refuse to
guess, and raise an error.
Note that because of the next change listed, this situation occurs
less frequently in 0.3.0 than in previous versions.
* If the ``data`` argument to :func:`build_design_matrices` (or
derived functions like :func:`dmatrix`, :func:`dmatrices`) is a
:class:`pandas.DataFrame`, then we now check its number of rows and
index, and insist that the output design matrices match. This also
means that if ``data`` is a DataFrame, then the error described in
the first bullet above cannot occur -- we will simply return a
column of 1s that is the same size as the input dataframe.
* Worked around some more limitations in py2exe/py2app and friends.
v0.2.1
------
.. image:: https://zenodo.org/badge/doi/10.5281/zenodo.11447.png
:target: http://dx.doi.org/10.5281/zenodo.11447
* Fixed a nasty bug in missing value handling where, if missing values
were present, ``dmatrix(..., result_type="dataframe")`` would always
crash, and ``dmatrices("y ~ 1")`` would produce left- and right-hand
side matrices that had different numbers of rows. (As far as I can
tell, this bug could not possibly cause incorrect results, only
crashes, since it always involved the creation of matrices with
incommensurate shapes. Therefore there is no need to worry about the
accuracy of any analyses that were successfully performed with
v0.2.0.)
* Modified ``patsy/__init__.py`` to work around limitations in
py2exe/py2app/etc.
v0.2.0
------
.. image:: https://zenodo.org/badge/doi/10.5281/zenodo.11448.png
:target: http://dx.doi.org/10.5281/zenodo.11448
Warnings:
* The lowest officially supported Python version is now 2.5. So far as
I know everything still works with Python 2.4, but as everyone else
has continued to drop support for 2.4, testing on 2.4 has become so
much trouble that I've given up.
New features:
* New support for automatically detecting and (optionally) removing
missing values (see :class:`NAAction`).
* New stateful transform for B-spline regression:
:func:`bs`. (Requires scipy.)
* Added a core API to make it possible to run predictions on only a
subset of model terms. (This is particularly useful for
e.g. plotting the isolated effect of a single fitted spline term.)
See :meth:`DesignMatrixBuilder.subset`.
* :class:`LookupFactor` now allows users to mark variables as
categorical directly.
* :class:`pandas.Categorical` objects are now recognized as
representing categorical data and handled appropriately.
* Better error reporting for exceptions raised by user code inside
formulas. We now, whenever possible, tag the generated exception
with information about which factor's code raised it, and use this
information to give better error reporting.
* :meth:`EvalEnvironment.capture` now takes a `reference` argument,
to make it easier to implement new :func:`dmatrix`-like functions.
Other: miscellaneous doc improvements and bug fixes.
v0.1.0
------
.. image:: https://zenodo.org/badge/doi/10.5281/zenodo.11449.png
:target: http://dx.doi.org/10.5281/zenodo.11449
First public release.
|
