J/PhC5gdZddlZddlmZddlmZmZddlm Z ddl m Z ddl m Z mZddlmZmZmZerddlZd Zdd Zdd Zd Zidd dfdZidd dfdZdS))dmatrix dmatrices incr_dbuilderincr_dbuildersN) PatsyError) DesignMatrix DesignInfo)EvalEnvironment) ModelDesc)design_matrix_buildersbuild_design_matrices) have_pandasasarray_or_pandasatleast_2d_column_defaultct|trtgg|||d|fSt|trKt |dkr8t|dtrt|dtr|St |dr=||}t|tstd|dt|trtj |}t|tr5t|tsJt|j |j g|||SdS)Nr__patsy_get_model_desc__zbad value from z.__patsy_get_model_desc__) isinstancer r tuplelenhasattrrr rstr from_formular lhs_termlist rhs_termlist) formula_likedata_iter_makereval_env NA_actions O/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/patsy/highlevel.py_try_incr_buildersr"sf, ++ "B4(I N Nq Q   <''    " " |A 3 3 # |A 3 3 #|788#<.data_iter_makersTF||r#)r return_typerz$don't know what to do with a length z matrices tupleT)subokcDtj||}tr.t|tjtjfr|j}nd}dkr._regularize_matrixs&q*?@@B "z!fmV=M-NOO "W ! k))-aFFF$Q''O " :&$Q++Z88r#xr)dtypeyrAz#shape mismatch: outcome matrix has z rows, predictor matrix has z rowsz?index mismatch: outcome and predictor have incompatible indexes)rrr"r rrrrnpzerosshapefloatgetattrr equalsr?) rr6rr r7rr.lhsrhsrFrhs_orig_indexlhs_orig_indexs ` ` r!_do_highlevel_designrTsk!!+! L   111j)4 7   &oxL$ $)    lE * * M<  A%% j(+L(9(9(9(9<&JS## 1,d K K K#S 9 9 9 9 910c::^ ;(CIaL!,E:::C00c::^'#}d;;ZHHHHH'#}d;;ZHHHHH 9Q<39Q< ' '*25)A,,, ! N   %.*D!((88  X + % %)n.DI %.*DI Szr#r3ctj|d}t|||||\}}|jddkrt d|S)a Construct a single design matrix given a formula_like and data. :arg formula_like: An object that can be used to construct a design matrix. See below. :arg data: A dict-like object that can be used to look up variables referenced in `formula_like`. :arg eval_env: Either a :class:`EvalEnvironment` which will be used to look up any variables referenced in `formula_like` that cannot be found in `data`, or else a depth represented as an integer which will be passed to :meth:`EvalEnvironment.capture`. ``eval_env=0`` means to use the context of the function calling :func:`dmatrix` for lookups. If calling this function from a library, you probably want ``eval_env=1``, which means that variables should be resolved in *your* caller's namespace. :arg NA_action: What to do with rows that contain missing values. You can ``"drop"`` them, ``"raise"`` an error, or for customization, pass an :class:`NAAction` object. See :class:`NAAction` for details on what values count as 'missing' (and how to alter this). :arg return_type: Either ``"matrix"`` or ``"dataframe"``. See below. The `formula_like` can take a variety of forms. You can use any of the following: * (The most common option) A formula string like ``"x1 + x2"`` (for :func:`dmatrix`) or ``"y ~ x1 + x2"`` (for :func:`dmatrices`). For details see :ref:`formulas`. * A :class:`ModelDesc`, which is a Python object representation of a formula. See :ref:`formulas` and :ref:`expert-model-specification` for details. * A :class:`DesignInfo`. * An object that has a method called :meth:`__patsy_get_model_desc__`. For details see :ref:`expert-model-specification`. * A numpy array_like (for :func:`dmatrix`) or a tuple (array_like, array_like) (for :func:`dmatrices`). These will have metadata added, representation normalized, and then be returned directly. In this case `data` and `eval_env` are ignored. There is special handling for two cases: * :class:`DesignMatrix` objects will have their :class:`DesignInfo` preserved. This allows you to set up custom column names and term information even if you aren't using the rest of the patsy machinery. * :class:`pandas.DataFrame` or :class:`pandas.Series` objects will have their (row) indexes checked. If two are passed in, their indexes must be aligned. If ``return_type="dataframe"``, then their indexes will be preserved on the output. Regardless of the input, the return type is always either: * A :class:`DesignMatrix`, if ``return_type="matrix"`` (the default) * A :class:`pandas.DataFrame`, if ``return_type="dataframe"``. The actual contents of the design matrix is identical in both cases, and in both cases a :class:`DesignInfo` object will be available in a ``.design_info`` attribute on the return value. However, for ``return_type="dataframe"``, any pandas indexes on the input (either in `data` or directly passed through `formula_like`) will be preserved, which may be useful for e.g. time-series models. .. versionadded:: 0.2.0 The ``NA_action`` argument. rr&rr)r r+rTrLrrr6rr r7rPrQs r!rrsg~&x1===H%dHiJS# y|q T    Jr#ctj|d}t|||||\}}|jddkrt d||fS)aWConstruct two design matrices given a formula_like and data. This function is identical to :func:`dmatrix`, except that it requires (and returns) two matrices instead of one. By convention, the first matrix is the "outcome" or "y" data, and the second is the "predictor" or "x" data. See :func:`dmatrix` for details. rr&rr0rVrWs r!rr2sc&x1===H%dHiJS# y|qFGGG :r#)rr$)__all__numpyrJpatsyrpatsy.design_inforr patsy.evalr patsy.descr patsy.buildr r patsy.utilrrrr<r"rrrTrrr#r!rbsP F E E66666666&&&&&& EEEEEEEEPPPPPPPPPPMMM   F....bFOOOd "AXGGGGVAXr#