J/PhLBdgZddlZddlmZmZmZddlmZerddl Z dZ dZ dZ Gdd e ZeeZd Zd e_d Zd ZdS)bsN) have_pandas no_picklingassert_no_pickling)stateful_transformc` ddlm}n#t$rtdwxYwtjtj|t }|jdksJ|t|}tj|}|jdkr|j ddkr |dddf}|jdksJtj |tj |ks*tj |tj |krtdt||dzz }tj|j d|ft }t!|D]2}tj|f}d||<|||||f|dd|f<3|S)Nr)splevz#spline functionality requires scipy)dtypezksome data points fall outside the outermost knots, and I'm not sure how to handle them. (Patches accepted!))scipy.interpolater ImportErrornp atleast_1dasarrayfloatndimsortintshapeminmaxNotImplementedErrorlenemptyrangezeros)xknotsdegreer n_basesbasisicoefss M/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/patsy/splines.py_eval_bspline_basisr&sA+++++++ AAA?@@@A M"*U%888 9 9E :???? JJLLL [[F aAv{{qwqzQ aaadG 6Q;;;;  vayy26%==  BF1IIu $=$=! 2   "%jjFQJ'G Hagaj'*% 8 8 8E 7^^77'$$aeAuf566aaad Ls #ctj|}tjfd|dD}||jdS)Nc@g|]}tjd|zS)d)r percentile).0probrs r% z&_R_compat_quantile..Fs)III$q#* % %IIIC)order)rrravelreshaper)rprobs quantiless` r%_R_compat_quantiler5Bse Ju  E IIII%++C+2H2HIIII   U[  4 44r.cd}|ddgdd|ddgdd|ddgddgdd g|ttdddgd d gdS) NcPtjt|||sJdSN)rallcloser5)rr,expecteds r%tz"test__R_compat_quantile..tLs+{-a66AAAAAAAr. g?g333333? gffffff?g@g333333@)listr)r;s r%test__R_compat_quantilerBKsBBBAr2hRAr2hRAr2hc RH%%%Ad599ooSzC:.....r.cJeZdZdZdZ d dZdZ d dZeZ 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 c0i|_d|_d|_dSr8)_tmp_degree _all_knots)selfs r%__init__z BS.__init__s  r.NFc4||||||d}||jd<tj|}|jdkr|jddkr |dddf}|jdkrt d|jdg|dS)N)dfrr include_intercept lower_bound upper_boundargsr r rz1input to 'bs' must be 1-d, or a 2-d column vectorxs)rFrrrr ValueError setdefaultappend) rIrrMrr rNrOrPrQs r%memorize_chunkzBS.memorize_chunks!2&&   ! & M!   6Q;;171:??!!!Q$A 6A::STT T T2&&--a00000r.c |j}|d}|`|ddkrtd|ddt|d|dkrtd|jdt j|d}|d|d td |dd z}|d|d|z }|d s|d z }|dkr6td |dd|dd|d d|d|z |d Ut |d |kr;td |dd|dd|dt |d d n1t jdd |dzd d}t||}|d |d }|d |d}nt j |}|d |d} nt j |} || krtd|d| dt j |}|j d krtdt j ||kr td|||kd|dt j || kr td||| kd| dt j|| g|z|f} | |d|_| |_dS)NrQr rz#degree must be greater than 0 (not )zdegree must be an integer (not rRrMrzmust specify either df or knotsr rNzdf=z is too small for degree=z and include_intercept=z ; must be >= z with degree=z implies z knots, but z knots were providedr rOrPzlower_bound > upper_bound (z > zknots must be 1 dimensionalzsome knot values (z) fall below lower bound (z) fall above upper bound ()rFrSrrGr concatenaterlinspacer5rrrranyrrH) rItmprQrr0 n_inner_knotsknot_quantiles inner_knotsrOrP all_knotss r%memorize_finishzBS.memorize_finishsi6{ I >A  *<@NNNL  tH~  $x. 0 0*T\\\STT T N3t9 % % : $w-"7>?? ?X" : ! J.M+, #" q   jT X0111T ]22    G}(tG}%%66$*!JJJ NNN)MMW ....    7"$Q=13D!E!Ead!K0NCC = $w-K   *}-KK&))K   *}-KK&))K  $ $*9DkkkR j--  a  :;; ; 6+ + , , *%kK&?@@@+++O  6+ + , , *%kK&?@@@+++O N[+$>$F #TUU H~ #r.ct||j|j}|s|ddddf}trFt |t jt jfr t j|}|j|_|S)Nr ) r&rHrGr isinstancepandasSeries DataFrameindex) rIrrMrr rNrOrPr"s r% transformz BS.transformsx$At EE  !!!!QRR%LE  &!fmV-=>?? &(//g  r.)NNrKFNN) __name__ __module__ __qualname____doc__rJrVrbrir __getstate__r.r%rDrDUs99v 1111:R$R$R$n &LLLr.rDcddlm}ddlm}m}m}|d}d}|d} ||dksn+|dz }|d|}|||}i} |D] } | d d\} } | | | <!t| d t| d t| d d } | ddkr"t| d\}}|| d<|| d<| ddk| d<tj t| d}| d |j d| d ksJ|td||fi| |dz }|dz}9||ksJdS)Nr)check_stateful) R_bs_test_xR_bs_test_dataR_bs_num_tests z--BEGIN TEST CASE--Tr z--END TEST CASE--=r rMr)r rMrzBoundary.knotsNonerOrP interceptTRUErNoutputF)patsy.test_staterqpatsy.test_splines_bs_datarrrsrtsplitrhrevalrrrrD)rqrrrsrtlines tests_ran start_idxstop_idxblock test_datalinekeyvaluekwargslowerupperrzs r%test_bs_compatr s//////VVVVVVVVVV   & &EI 122I"!Y#888 Q ;;2I>>i()  # #DC++JC"IcNN)H-..y'')G,--    % && 0 0 *: ;<&A, 3 3333"J%&JQ1q5!" >&A, 3 3333"JJqAv >&A, 3 3333 > 999QqcTBBB Q!Q!Q    AaVAFFFJqAqEJJJM >*QQQU+] ; ;;;;;;r.c "ddl}tjddd}|tt |dd|tt |dd|t t |t |dddgd z t |dd dgd z t |dddgd zdt |dd dgdzd|t t |dddgdz |t t |dd dgd z |t t |dddgdzd|t t |dd dgd zd|t t |dddgd z |t t |dd dgdz |t t |dddgdzd|t t |dd dgd zd|t t |dd|t t |dd|t t |dd|t t |dd|t t |ddd|t t tj||fdtjt |ddgt |ddgsJ|t t |dgdgg|t t |ddg|t t |ddgd|t t |ddg|t t |ddgddS)Nrir<r=rK)rO)rPF)rMrNrT r )rMrNrr )rMr rYg?)rOrPr)r)rrPi)rrO) pytestrr[raisesrrrS column_stackr)rrs r%test_bs_errorsrmsMMM CR  A MM%r1aQM??? 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