M/Ph;<dZddlZddlZddlZddlmZddl m Z dZ ddZ ddZ dd Zd d ZdZd!dZdZdZdZd"dZdZdZdZGddeZd#dZd$dZdS)%z Utility functions models code N)_is_using_pandas) array_likect|tr|St|tr|dSt|S)Nlatin1) isinstancestrbytesdecode)ss W/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/statsmodels/tools/tools.pyasstr2r sH!S Au  xx!!!1vv cDi}t|D] \}}||||z<|S)zd Helper function to create a dictionary mapping a column number to the name in tmp_arr. ) enumerate)tmp_arroffsetcol_mapicol_names r _make_dictnamesrs: G ))'' 8&F  Nrctj|}|jdkr |dddf}|tj|}|jdkr |dddf}tjtj||tj||}||||fStj||}||S)a Returns views on the arrays Y and X where missing observations are dropped. Y : array_like X : array_like, optional axis : int Axis along which to look for missing observations. Default is 1, ie., observations in rows. Returns ------- Y : ndarray All Y where the X : ndarray Notes ----- If either Y or X is 1d, it is reshaped to be 2d. rN)npasarrayndimarray logical_andisnanany)YXaxiskeepidxs r drop_missingr$ s( 1 Av{{ aaagJ} HQKK 6Q;;!!!T' A."(1++//$"7"7!7"$(1++//$"7"7!799z1W:%%8A;;??4(((zrFc td)ax Construct a dummy matrix from categorical variables .. deprecated:: 0.12 Use pandas.get_dummies instead. Parameters ---------- data : array_like An array, Series or DataFrame. This can be either a 1d vector of the categorical variable or a 2d array with the column specifying the categorical variable specified by the col argument. col : {str, int, None} If data is a DataFrame col must in a column of data. If data is a Series, col must be either the name of the Series or None. For arrays, `col` can be an int that is the (zero-based) column index number. `col` can only be None for a 1d array. The default is None. dictnames : bool, optional If True, a dictionary mapping the column number to the categorical name is returned. Used to have information about plain arrays. drop : bool Whether or not keep the categorical variable in the returned matrix. Returns ------- dummy_matrix : array_like A matrix of dummy (indicator/binary) float variables for the categorical data. dictnames : dict[int, str], optional Mapping between column numbers and categorical names. Notes ----- This returns a dummy variable for *each* distinct variable. If a a DaataFrame is provided, the names for the new variable is the old variable name - underscore - category name. So if the a variable 'vote' had answers as 'yes' or 'no' then the returned array would have to new variables-- 'vote_yes' and 'vote_no'. There is currently no name checking. Examples -------- >>> import numpy as np >>> import statsmodels.api as sm Univariate examples >>> import string >>> string_var = [string.ascii_lowercase[0:5], ... string.ascii_lowercase[5:10], ... string.ascii_lowercase[10:15], ... string.ascii_lowercase[15:20], ... string.ascii_lowercase[20:25]] >>> string_var *= 5 >>> string_var = np.asarray(sorted(string_var)) >>> design = sm.tools.categorical(string_var, drop=True) Or for a numerical categorical variable >>> instr = np.floor(np.arange(10,60, step=2)/10) >>> design = sm.tools.categorical(instr, drop=True) With a structured array >>> num = np.random.randn(25,2) >>> struct_ar = np.zeros((25,1), ... dtype=[('var1', 'f4'),('var2', 'f4'), ... ('instrument','f4'),('str_instr','a5')]) >>> struct_ar['var1'] = num[:,0][:,None] >>> struct_ar['var2'] = num[:,1][:,None] >>> struct_ar['instrument'] = instr[:,None] >>> struct_ar['str_instr'] = string_var[:,None] >>> design = sm.tools.categorical(struct_ar, col='instrument', drop=True) Or >>> design2 = sm.tools.categorical(struct_ar, col='str_instr', drop=True) zcategorical has been removed)NotImplementedError)datacol dictnamesdrops r categoricalr+Gs` < = ==rTskipct|drddlm}||d||Stj|}|j}|dkr |dddf}n|jdkrt dtj|d dk}|tj|d kd z}| rz|d kr|S|d krl|dkrt d tj |j d}d d||D}t d|dtj |j d|g}|r|n |ddd}tj|S)aq Add a column of ones to an array. Parameters ---------- data : array_like A column-ordered design matrix. prepend : bool If true, the constant is in the first column. Else the constant is appended (last column). has_constant : str {'raise', 'add', 'skip'} Behavior if ``data`` already has a constant. The default will return data without adding another constant. If 'raise', will raise an error if any column has a constant value. Using 'add' will add a column of 1s if a constant column is present. Returns ------- array_like The original values with a constant (column of ones) as the first or last column. Returned value type depends on input type. Notes ----- When the input is a pandas Series or DataFrame, the added column's name is 'const'. Nr) add_trendc)trendprepend has_constantrz)Only implemented for 2-dimensional arraysr"r,raisezdata is constant.,c,g|]}t|S)r).0r/s r z add_constant..s K K KAQ K K Krz Column(s) z are constant.)rstatsmodels.tsa.tsatoolsr.rrr ValueErrorptpallrarangeshapejoinones column_stack) r'r1r2r.xris_nonzero_constcolumnscolss r add_constantrJs8d##V666666yS' UUUU 4A 6D qyy aaagJ !DEEEvaa(((A-qCxa0000 D 6 ! !H W $ $qyy !4555)AGAJ//xx K K9I1J K K KLL !Bd!B!B!BCCC   a A !!DDbD'A ?1  rct|dd}t|dd}|jdkr |dddfn|}|jd|jdkrtd|jdzt j||g}tj|tj|krd Sd S) a6 True if (Q, P) contrast `c` is estimable for (N, P) design `d`. From an Q x P contrast matrix `C` and an N x P design matrix `D`, checks if the contrast `C` is estimable by looking at the rank of ``vstack([C,D])`` and verifying it is the same as the rank of `D`. Parameters ---------- c : array_like A contrast matrix with shape (Q, P). If 1 dimensional assume shape is (1, P). d : array_like The design matrix, (N, P). Returns ------- bool True if the contrast `c` is estimable on design `d`. Examples -------- >>> d = np.array([[1, 1, 1, 0, 0, 0], ... [0, 0, 0, 1, 1, 1], ... [1, 1, 1, 1, 1, 1]]).T >>> isestimable([1, 0, 0], d) False >>> isestimable([1, -1, 0], d) True r/r3)maxdimdrrNzContrast should have %d columnsFT)rrrBr>rvstacklinalg matrix_rank)r/rMnews r isestimablerSs> 1c!$$$A1c"""Afkk$' qAwqzQWQZ:QWQZGHHH )QF  C yS!!RY%:%:1%=%===u 4rV瞯.Vs% 8 8 8A6!!!Q$< 8 8 8r)raddr[ flatnonzerorr_)r}colsumvals` r clean0rGs[V]]619a ( (F 8 8 8 8!7!7 8 8 8C 8BL%% & &&rc|tj|}tj|d\}}}tj|}|ddd}g}t |D]'}||dd||f(tjtj| tj S)a Return an array whose column span is the same as x. Parameters ---------- x : ndarray The array to adjust, 2d. r : int, optional The rank of x. If not provided, determined by `np.linalg.matrix_rank`. Returns ------- ndarray The array adjusted to have full rank. Notes ----- If the rank of x is known it can be specified as r -- no check is made to ensure that this really is the rank of x. NF) full_matricesr<) rrPrQrXargsortr\appendrr_astyperp)rFrvrMrcordervaluers r fullrankrZs* y I ! !! $ $immAUm33GAq! JqMME $$B$KE E 1XX%% Qqqq%({^$$$$ :bl5)) * * 1 1"* = ==rcTt|}d||<||S)aW Unsqueeze a collapsed array. Parameters ---------- data : ndarray The data to unsqueeze. axis : int The axis to unsqueeze. oldshape : tuple[int] The original shape before the squeeze or reduce operation. Returns ------- ndarray The unsqueezed array. Examples -------- >>> from numpy import mean >>> from numpy.random import standard_normal >>> x = standard_normal((3,4,5)) >>> m = mean(x, axis=1) >>> m.shape (3, 5) >>> m = unsqueeze(m, 1, x.shape) >>> m.shape (3, 1, 5) >>> r)listreshape)r'r"oldshapenewshapes r unsqueezer{s*>H~~HHTN << ! !!rcLtjtj||dk}tj|dktj|}||z}tjtj|tj|}tj||<|S)z Returns np.dot(left_matrix, right_matrix) with the convention that nan * 0 = 0 and nan * x = nan if x != 0. Parameters ---------- A, B : ndarray r)rr^r nan_to_numrr)ABshould_be_nan_1should_be_nan_2 should_be_nanCs r nan_dotrsfRXa[[1633Ofa1frx{{33O#o5M r}Qq!1!122AvAm Hrc$t|d|S)z Gets raw results back from wrapped results. Can be used in plotting functions or other post-estimation type routines. _results)getattr)resultss r maybe_unwrap_resultsrs 7J 0 00rc"eZdZdZfdZxZS)Buncha Returns a dict-like object with keys accessible via attribute lookup. Parameters ---------- *args Arguments passed to dict constructor, tuples (key, value). **kwargs Keyword agument passed to dict constructor, key=value. cHtj|i|||_dSr|)super__init____dict__)selfargskwargs __class__s r rzBunch.__init__s*$)&))) r)__name__ __module__ __qualname____doc__r __classcell__)rs@r rrsB  rrc||St|d}|jdkr|r ||jfS|dfS|jdkrtd|r|jnd}|r t j|dddf|fStj||fS)a Parameters ---------- x : ndarray, Series, DataFrame or None Input to verify dimensions, and to transform as necesary ndarray : bool Flag indicating whether to always return a NumPy array. Setting False will return an pandas DataFrame when the input is a Series or a DataFrame. Returns ------- out : ndarray, DataFrame or None array or DataFrame with 2 dimensiona. One dimensional arrays are returned as nobs by 1. None is returned if x is None. names : list of str or None list containing variables names when the input is a pandas datatype. Returns None if the input is an ndarray. Notes ----- Accepts None for simplicity Nr3zx mst be 1 or 2-dimensional.) rrrHr>namerrpd DataFrame)rFndarray is_pandasrs r _ensure_2drs2 y D))Iv{{  ai< d7N !7888 (166DD%z!}}QQQW%t++|A$$rqrct|dd}|dkr}|ddtj|dkdf}|tj|dzdz }|j|z}tj||d S|d krtj |d \}tj tj |}|5|d|j d ztj tjz}t!||kStj||S)a( Matrix rank calculation using QR or SVD Parameters ---------- m : array_like A 2-d array-like object to test tol : float, optional The tolerance to use when testing the matrix rank. If not provided an appropriate value is selected. method : {"ip", "qr", "svd"} The method used. "ip" uses the inner-product of a normalized version of m and then computes the rank using NumPy's matrix_rank. "qr" uses a QR decomposition and is the default. "svd" defers to NumPy's matrix_rank. Returns ------- int The rank of m. Notes ----- When using a QR factorization, the rank is determined by the number of elements on the leading diagonal of the R matrix that are above tol in absolute value. rfr3rNipNrr4T)tol hermitianrr)moder)r)rrrsqrtsumTrPrQscipyrabsdiagrBfinfofloatepsint)rfrmethodrabs_diags r rQrQs,8 1c"""A ~~ aaaQQ'''' ( a Q(( ( C!Gy$$QC4$@@@ 4 \__QS_ ) )6"'!**%% ;1+ *RXe__-@@CHsN''))***y$$QC$000r)r)Nr)NFF)Tr,)rTr|)F)Nr)rnumpyrpandasr scipy.linalgrstatsmodels.tools.datarstatsmodels.tools.validationrr rr$r+rJrSrjrvryrrrrrdictrrrQr9rr rs333333333333    NP>P>P>P>h7777t'''T000'''&>>>>B!"!"!"H   .111     D    (%(%(%(%V)1)1)1)1)1)1r