B ^{UZD@sdgZddlZddlmZmZmZddlmZer:ddl Z ddZ ddZ d d Z Gd d d e ZeeZd dZde_ddZddZdS)bsN) have_pandas no_picklingassert_no_pickling)stateful_transformcCs@yddlm}Wntk r,tdYnXttj|td}|jdksPt| t |}t|}|jdkr|j ddkr|dddf}|jdkstt |t |kst |t |krtdt||d}tj|j d|ftd}xBt|D]6}t|f}d||<|||||f|dd|f<qW|S)Nr)splevz#spline functionality requires scipy)Zdtypezksome data points fall outside the outermost knots, and I'm not sure how to handle them. (Patches accepted!))Zscipy.interpolater ImportErrornp atleast_1dasarrayfloatndimAssertionErrorsortintshapeminmaxNotImplementedErrorlenemptyrangezeros)xknotsdegreerZn_basesbasisiZcoefsr ,lib/python3.7/site-packages/patsy/splines.py_eval_bspline_basiss* ( "r"cs:t|}tfdd|jddD}|j|jddS)Ncsg|]}td|qS)d)r Z percentile).0prob)rr r! Asz&_R_compat_quantile..C)order)r r ZravelZreshaper)rZprobsZ quantilesr )rr!_R_compat_quantile>s r)cCs`dd}|ddgdd|ddgdd|ddgdd gdd g|ttddd gd d gdS) NcSstt|||stdS)N)r Zallcloser)r)rr%Zexpectedr r r!tFsz"test__R_compat_quantile..t g?g333333? gffffff?g@g333333@)listr)r*r r r!test__R_compat_quantileEs r1c@s8eZdZdZddZd ddZd d Zdd d ZeZ dS)BSa3 bs(x, df=None, knots=None, degree=3, include_intercept=False, lower_bound=None, upper_bound=None) Generates a B-spline basis for ``x``, allowing non-linear fits. The usual usage is something like:: y ~ 1 + bs(x, 4) to fit ``y`` as a smooth function of ``x``, with 4 degrees of freedom given to the smooth. :arg df: The number of degrees of freedom to use for this spline. The return value will have this many columns. You must specify at least one of ``df`` and ``knots``. :arg knots: The interior knots to use for the spline. If unspecified, then equally spaced quantiles of the input data are used. You must specify at least one of ``df`` and ``knots``. :arg degree: The degree of the spline to use. :arg include_intercept: If ``True``, then the resulting spline basis will span the intercept term (i.e., the constant function). If ``False`` (the default) then this will not be the case, which is useful for avoiding overspecification in models that include multiple spline terms and/or an intercept term. :arg lower_bound: The lower exterior knot location. :arg upper_bound: The upper exterior knot location. A spline with ``degree=0`` is piecewise constant with breakpoints at each knot, and the default knot positions are quantiles of the input. So if you find yourself in the situation of wanting to quantize a continuous variable into ``num_bins`` equal-sized bins with a constant effect across each bin, you can use ``bs(x, num_bins - 1, degree=0)``. (The ``- 1`` is because one degree of freedom will be taken by the intercept; alternatively, you could leave the intercept term out of your model and use ``bs(x, num_bins, degree=0, include_intercept=True)``. A spline with ``degree=1`` is piecewise linear with breakpoints at each knot. The default is ``degree=3``, which gives a cubic b-spline. This is a stateful transform (for details see :ref:`stateful-transforms`). If ``knots``, ``lower_bound``, or ``upper_bound`` are not specified, they will be calculated from the data and then the chosen values will be remembered and re-used for prediction from the fitted model. Using this function requires scipy be installed. .. note:: This function is very similar to the R function of the same name. In cases where both return output at all (e.g., R's ``bs`` will raise an error if ``degree=0``, while patsy's will not), they should produce identical output given identical input and parameter settings. .. warning:: I'm not sure on what the proper handling of points outside the lower/upper bounds is, so for now attempting to evaluate a spline basis at such points produces an error. Patches gratefully accepted. .. versionadded:: 0.2.0 cCsi|_d|_d|_dS)N)_tmp_degree _all_knots)selfr r r!__init__sz BS.__init__NFc Csx||||||d}||jd<t|}|jdkrN|jddkrN|dddf}|jdkr`td|jdg|dS)N)dfrrinclude_intercept lower_bound upper_boundargsr rrz1input to 'bs' must be 1-d, or a 2-d column vectorxs)r3r r rr ValueError setdefaultappend) r6rr9rrr:r;r<r=r r r!memorize_chunks   zBS.memorize_chunkc Csf|j}|d}|`|ddkr0td|dft|d|dkrTtd|jft|d}|ddkr|ddkrtd |dd }|ddk rR|d|}|d s|d 7}|dkrtd |d|d|d |d|f|ddk r.t|d|krRtd |d|d|t|dfn$tdd |dd d}t||}|ddk rh|d}|ddk r|d}n t |}|ddk r|d} n t |} || krtd|| ft |}|j d krtdt ||kr td|||k|ft || kr4td||| k| ft|| g||f} | |d|_| |_dS)Nr=rrz°ree must be greater than 0 (not %r)z"degree must be an integer (not %r)r>r9rzmust specify either df or knotsrr:zHdf=%r is too small for degree=%r and include_intercept=%r; must be >= %szAdf=%s with degree=%r implies %s knots, but %s knots were providedr r;r<z#lower_bound > upper_bound (%r > %r)zknots must be 1 dimensionalz1some knot values (%s) fall below lower bound (%r)z1some knot values (%s) fall above upper bound (%r))r3r?rr4r Z concatenaterlinspacer)rrr ranyrr5) r6Ztmpr=rr(Z n_inner_knotsZknot_quantilesZ inner_knotsr;r<Z all_knotsr r r!memorize_finishsp                    zBS.memorize_finishc CsTt||j|j}|s(|ddddf}trPt|tjtjfrPt|}|j|_|S)Nr) r"r5r4r isinstancepandasZSeriesZ DataFrameindex) r6rr9rrr:r;r<rr r r! transforms z BS.transform)NNr8FNN)NNr8FNN) __name__ __module__ __qualname____doc__r7rBrFrJr __getstate__r r r r!r2Ms: I r2cCsdddlm}ddlm}m}m}|d}d}|d}x||dksJP|d7}|d|}|||}i} x$|D]} | dd\} } | | | <qtWt| d t | d t | d d } | d dkrt | d \}}|| d<|| d<| ddk| d<t t | d}| d dk r,|j d| d ks,t |td||f| |d7}|d}qs ,.-