ó ‡ˆ\c@sJdZddlZddlZddlZddljZddlm Z ddl m Z ddl mZddlmZdd lmZdd lmZdd lmZdd lmZdd lmZddlmZddlm ZejejejfZ ej!deƒe e ƒdkZ"e#d„Z$e#d„Z%e&e&d„Z'd„Z(d„Z)d„Z*d„Z+d„Z,d„Z-d„Z.d„Z/e#e&dde#e&e&e#dde#dd„ Z1e#d„Z2e#e&dde#e&e&e#e#dde#e#dd„Z3e#d „Z4d!„Z5d"„Z6d#e&e#d$„Z7de8d%„Z9d&„Z:dS('sUtilities for input validationiÿÿÿÿN(t __version__(t LooseVersion(tComplexWarningi(tsixi(t signature(t get_config(tNonBLASDotWarning(tNotFittedError(tDataConversionWarning(tMemorytignores0.14.0cCsËtƒdrdStj|ƒ}|jjdk}|rPtj|jƒƒrPnw|rÇd}|rwtj|ƒjƒs”| rÇtj|ƒj ƒ rÇ|r dnd}t |j ||jƒƒ‚qÇndS(s-Like assert_all_finite, but only for ndarray.t assume_finiteNtfcs0Input contains {} or a value too large for {!r}.tinfinitys NaN, infinity( t _get_configtnpt asanyarraytdtypetkindtisfinitetsumtisinftanytallt ValueErrortformat(tXt allow_nantis_floattmsg_errttype_err((s7lib/python2.7/site-packages/sklearn/utils/validation.pyt_assert_all_finite(s cCs)ttj|ƒr|jn||ƒdS(sŠThrow a ValueError if X contains NaN or infinity. Parameters ---------- X : array or sparse matrix allow_nan : bool N(Rtsptissparsetdata(RR((s7lib/python2.7/site-packages/sklearn/utils/validation.pytassert_all_finite;s c Cs3t|tjƒs5t|tjƒ rftj|ƒ rft|dddgdtjd|d|dtƒStj|ƒr¤|j tj tjgkr¤|r |j ƒS|S|j tj tjgkré|rå|j |j drÞd nd ƒS|S|j j d kr|j jd krtj }n tj}|j|ƒSd S(sëConverts an array-like to an array of floats. The new dtype will be np.float32 or np.float64, depending on the original type. The function can create a copy or modify the argument depending on the argument copy. Parameters ---------- X : {array-like, sparse matrix} copy : bool, optional If True, a copy of X will be created. If False, a copy may still be returned if X's dtype is not a floating point type. force_all_finite : boolean or 'allow-nan', (default=True) Whether to raise an error on np.inf and np.nan in X. The possibilities are: - True: Force all values of X to be finite. - False: accept both np.inf and np.nan in X. - 'allow-nan': accept only np.nan values in X. Values cannot be infinite. .. versionadded:: 0.20 ``force_all_finite`` accepts the string ``'allow-nan'``. Returns ------- XT : {array, sparse matrix} An array of type np.float tcsrtcsctcooRtcopytforce_all_finitet ensure_2dt F_CONTIGUOUStFtCtuibiN(t isinstanceRtmatrixtndarrayR R!t check_arraytfloat64tFalseRtfloat32R'tflagsRtitemsizetastype(RR'R(t return_dtype((s7lib/python2.7/site-packages/sklearn/utils/validation.pytas_float_arrayGs % **$  cCs+t|dƒp*t|dƒp*t|dƒS(s'Returns whether the input is array-liket__len__tshapet __array__(thasattr(tx((s7lib/python2.7/site-packages/sklearn/utils/validation.pyt _is_arraylikexscCst|dƒr1t|jƒr1td|ƒ‚nt|dƒ r‹t|dƒ r‹t|dƒrrtj|ƒ}q‹tdt|ƒƒ‚nt|dƒrót|jƒdkrÂtd|ƒ‚nt |jdt j ƒræ|jdSt|ƒSn t|ƒSd S( s)Return number of samples in array-like x.tfits1Expected sequence or array-like, got estimator %sR:R;R<s'Expected sequence or array-like, got %sis;Singleton array %r cannot be considered a valid collection.N( R=tcallableR@t TypeErrorRtasarrayttypetlenR;R.tnumberstIntegral(R>((s7lib/python2.7/site-packages/sklearn/utils/validation.pyt _num_sampless      cCsVt|ƒdkrdSdjd„|Dƒƒ}t|ƒdkrN|d7}nd|S(ssReturn a platform independent representation of an array shape Under Python 2, the `long` type introduces an 'L' suffix when using the default %r format for tuples of integers (typically used to store the shape of an array). Under Windows 64 bit (and Python 2), the `long` type is used by default in numpy shapes even when the integer dimensions are well below 32 bit. The platform specific type causes string messages or doctests to change from one platform to another which is not desirable. Under Python 3, there is no more `long` type so the `L` suffix is never introduced in string representation. >>> _shape_repr((1, 2)) '(1, 2)' >>> one = 2 ** 64 / 2 ** 64 # force an upcast to `long` under Python 2 >>> _shape_repr((one, 2 * one)) '(1, 2)' >>> _shape_repr((1,)) '(1,)' >>> _shape_repr(()) '()' is()s, css|]}d|VqdS(s%dN((t.0te((s7lib/python2.7/site-packages/sklearn/utils/validation.pys ´sit,s(%s)(REtjoin(R;tjoined((s7lib/python2.7/site-packages/sklearn/utils/validation.pyt _shape_repr™s  cCs‹|dkst|tjƒr`ttƒdkrHtd|ddƒ}q‡td|ddƒ}n't|dƒs‡tdj |ƒƒ‚n|S( süCheck that ``memory`` is joblib.Memory-like. joblib.Memory-like means that ``memory`` can be converted into a joblib.Memory instance (typically a str denoting the ``location``) or has the same interface (has a ``cache`` method). Parameters ---------- memory : None, str or object with the joblib.Memory interface Returns ------- memory : object with the joblib.Memory interface Raises ------ ValueError If ``memory`` is not joblib.Memory-like. s0.12tcachedirtverboseitlocationtcachesg'memory' should be None, a string or have the same interface as joblib.Memory. Got memory='{}' instead.N( tNoneR.Rt string_typesRtjoblib_versionR R=RR(tmemory((s7lib/python2.7/site-packages/sklearn/utils/validation.pyt check_memory»s cGs|g|D]}|dk rt|ƒ^q}tj|ƒ}t|ƒdkrxtdg|D]}t|ƒ^qYƒ‚ndS(sCheck that all arrays have consistent first dimensions. Checks whether all objects in arrays have the same shape or length. Parameters ---------- *arrays : list or tuple of input objects. Objects that will be checked for consistent length. is>Found input variables with inconsistent numbers of samples: %rN(RSRHRtuniqueRERtint(tarraysRtlengthstuniquestl((s7lib/python2.7/site-packages/sklearn/utils/validation.pytcheck_consistent_lengthÜs +cGsªg}x“|D]‹}tj|ƒr8|j|jƒƒq t|dƒsVt|dƒrf|j|ƒq |dkr‚|j|ƒq |jtj|ƒƒq Wt|Œ|S(swMake arrays indexable for cross-validation. Checks consistent length, passes through None, and ensures that everything can be indexed by converting sparse matrices to csr and converting non-interable objects to arrays. Parameters ---------- *iterables : lists, dataframes, arrays, sparse matrices List of objects to ensure sliceability. t __getitem__tilocN( R R!tappendttocsrR=RSRtarrayR^(t iterablestresultR((s7lib/python2.7/site-packages/sklearn/utils/validation.pyt indexableîs    cCsu|d kr|j}nt}t|tjƒr<|g}nt||ƒ|tkrdtdƒ‚nˆt|tt fƒrÈt |ƒdkršt dƒ‚n|j |krì|j |dƒ}t}qìn$|tk rìt dj |ƒƒ‚n||jkr |j|ƒ}n|r)| r)|jƒ}n|rqt|dƒsUtjd|j ƒqqt|jd|dkƒn|S( s®Convert a sparse matrix to a given format. Checks the sparse format of spmatrix and converts if necessary. Parameters ---------- spmatrix : scipy sparse matrix Input to validate and convert. accept_sparse : string, boolean or list/tuple of strings String[s] representing allowed sparse matrix formats ('csc', 'csr', 'coo', 'dok', 'bsr', 'lil', 'dia'). If the input is sparse but not in the allowed format, it will be converted to the first listed format. True allows the input to be any format. False means that a sparse matrix input will raise an error. dtype : string, type or None Data type of result. If None, the dtype of the input is preserved. copy : boolean Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion. force_all_finite : boolean or 'allow-nan', (default=True) Whether to raise an error on np.inf and np.nan in X. The possibilities are: - True: Force all values of X to be finite. - False: accept both np.inf and np.nan in X. - 'allow-nan': accept only np.nan values in X. Values cannot be infinite. .. versionadded:: 0.20 ``force_all_finite`` accepts the string ``'allow-nan'``. Returns ------- spmatrix_converted : scipy sparse matrix. Matrix that is ensured to have an allowed type. sjA sparse matrix was passed, but dense data is required. Use X.toarray() to convert to a dense numpy array.is]When providing 'accept_sparse' as a tuple or list, it must contain at least one string value.sjParameter 'accept_sparse' should be a string, boolean or list of strings. You provided 'accept_sparse={}'.R"s,Can't check %s sparse matrix for nan or inf.Rs allow-nanN(RSRR3R.RRTt_check_large_sparseRBtlistttupleRERRtasformattTrueR7R'R=twarningstwarnRR"(tspmatrixt accept_sparseRR'R(taccept_large_sparsetchanged_format((s7lib/python2.7/site-packages/sklearn/utils/validation.pyt_ensure_sparse_formats8*           cCs^t|dƒrZ|jdk rZt|jdƒrZ|jjdkrZtdj|ƒƒ‚ndS(NRRtcsComplex data not supported {} (R=RRSRRR(Rc((s7lib/python2.7/site-packages/sklearn/utils/validation.pyt_ensure_no_complex_datacs$ tnumericc  Cs|dkr%tjdtƒt}n|} t|tjƒoF|dk}t|ddƒ}t |dƒssd}nd}t |dƒr¯t |j dƒr¯t j |j ƒ}n|rå|dk rÜ|j dkrÜt j}qåd}nt|ttfƒr(|dk r||krd}q(|d}n|ttd fkrUtd j|ƒƒ‚n| dk r‹t| tjƒr|| }q‘| jj}nd }| dk r§d |nd }tj|ƒrót|ƒt|d|d|d|d|d|ƒ}n{tjƒ]y/tjdtƒt j|d|d|ƒ}Wn&tk rWtdj|ƒƒ‚nXWdQXt|ƒ|r¿|jdkr•tdj|ƒƒ‚n|jdkr¿tdj|ƒƒ‚q¿n|rðt j|j t j!ƒrðtjdt"ƒn|r|j j dkr|j#t jƒ}n| rO|jdkrOtd|j|fƒ‚n|rnt$|d|d kƒnt%|j&ƒ}| dkrÃt'|ƒ}|| krÃtd||| |fƒ‚qÃn| dkr|jdkr|j&d}|| krtd||| |fƒ‚qn| rc|dk rc|j |krcd ||j |f}tj|t(ƒn|r™t j)|| ƒr™t j |d|d|ƒ}n| r|dk r|j ht*|ƒkrd!d"j+t,t-t.t*|ƒƒƒƒ|j |f}tj|t(d#dƒn|S($s«Input validation on an array, list, sparse matrix or similar. By default, the input is checked to be a non-empty 2D array containing only finite values. If the dtype of the array is object, attempt converting to float, raising on failure. Parameters ---------- array : object Input object to check / convert. accept_sparse : string, boolean or list/tuple of strings (default=False) String[s] representing allowed sparse matrix formats, such as 'csc', 'csr', etc. If the input is sparse but not in the allowed format, it will be converted to the first listed format. True allows the input to be any format. False means that a sparse matrix input will raise an error. .. deprecated:: 0.19 Passing 'None' to parameter ``accept_sparse`` in methods is deprecated in version 0.19 "and will be removed in 0.21. Use ``accept_sparse=False`` instead. accept_large_sparse : bool (default=True) If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by accept_sparse, accept_large_sparse=False will cause it to be accepted only if its indices are stored with a 32-bit dtype. .. versionadded:: 0.20 dtype : string, type, list of types or None (default="numeric") Data type of result. If None, the dtype of the input is preserved. If "numeric", dtype is preserved unless array.dtype is object. If dtype is a list of types, conversion on the first type is only performed if the dtype of the input is not in the list. order : 'F', 'C' or None (default=None) Whether an array will be forced to be fortran or c-style. When order is None (default), then if copy=False, nothing is ensured about the memory layout of the output array; otherwise (copy=True) the memory layout of the returned array is kept as close as possible to the original array. copy : boolean (default=False) Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion. force_all_finite : boolean or 'allow-nan', (default=True) Whether to raise an error on np.inf and np.nan in array. The possibilities are: - True: Force all values of array to be finite. - False: accept both np.inf and np.nan in array. - 'allow-nan': accept only np.nan values in array. Values cannot be infinite. .. versionadded:: 0.20 ``force_all_finite`` accepts the string ``'allow-nan'``. ensure_2d : boolean (default=True) Whether to raise a value error if array is not 2D. allow_nd : boolean (default=False) Whether to allow array.ndim > 2. ensure_min_samples : int (default=1) Make sure that the array has a minimum number of samples in its first axis (rows for a 2D array). Setting to 0 disables this check. ensure_min_features : int (default=1) Make sure that the 2D array has some minimum number of features (columns). The default value of 1 rejects empty datasets. This check is only enforced when the input data has effectively 2 dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0 disables this check. warn_on_dtype : boolean (default=False) Raise DataConversionWarning if the dtype of the input data structure does not match the requested dtype, causing a memory copy. estimator : str or estimator instance (default=None) If passed, include the name of the estimator in warning messages. Returns ------- array_converted : object The converted and validated array. s¯Passing 'None' to parameter 'accept_sparse' in methods check_array and check_X_y is deprecated in version 0.19 and will be removed in 0.21. Use 'accept_sparse=False' instead.RuRRtdtypesR<tOis allow-nansBforce_all_finite should be a bool or "allow-nan". Got {!r} insteadt Estimators by %stRoR'R(RpterrortordersComplex data not supported {} NsÄExpected 2D array, got scalar array instead: array={}. Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.isÀExpected 2D array, got 1D array instead: array={}. Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.s Beginning in version 0.22, arrays of bytes/strings will be converted to decimal numbers if dtype='numeric'. It is recommended that you convert the array to a float dtype before using it in scikit-learn, for example by using your_array = your_array.astype(np.float64).is*Found array with dim %d. %s expected <= 2.RsMFound array with %d sample(s) (shape=%s) while a minimum of %d is required%s.isNFound array with %d feature(s) (shape=%s) while a minimum of %d is required%s.s/Data with input dtype %s was converted to %s%s.s4Data with input dtype %s were all converted to %s%s.s, t stacklevel(/RSRlRmtDeprecationWarningR3R.RRTtgetattrR=RvRRcRR2RhRiRkRRt __class__t__name__R R!RtRrtcatch_warningst simplefilterRRCtndimt issubdtypeRtflexiblet FutureWarningR7RRNR;RHRtmay_share_memorytsetRLtmaptstrtsorted(RcRoRpRR{R'R(R)tallow_ndtensure_min_samplestensure_min_featurest warn_on_dtypet estimatort array_origt dtype_numerict dtype_origt dtypes_origtestimator_nametcontextt shape_reprt n_samplest n_featurestmsg((s7lib/python2.7/site-packages/sklearn/utils/validation.pyR1js¶_   !                       !' cCs½|o ts¹dg}|jƒdkr6ddg}n%|jƒd krWdd g}nd Sx[|D]P}t||ƒj}||krbtsŸtd tƒ‚ntd |ƒ‚qbqbWnd S(sLRaise a ValueError if X has 64bit indices and accept_large_sparse=False tint32R&tcoltrowR$R%tbsrtindicestindptrNs]Scipy version %s does not support large indices, please upgrade your scipy to 0.14.0 or abovesNOnly sparse matrices with 32-bit integer indices are accepted. Got %s indices.(R$R%Rž(tLARGE_SPARSE_SUPPORTEDt getformatR~RRt scipy_version(RRptsupported_indicest index_keystkeytindices_datatype((s7lib/python2.7/site-packages/sklearn/utils/validation.pyRges     cCsù|dkrtdƒ‚nt|d|d|d|d|d|d|d|d | d | d | d |d |ƒ }| r™t|ddtdtddƒ}nt|dtƒ}t|ƒ| râ|jjdkrâ|j t j ƒ}nt ||ƒ||fS(s”Input validation for standard estimators. Checks X and y for consistent length, enforces X to be 2D and y 1D. By default, X is checked to be non-empty and containing only finite values. Standard input checks are also applied to y, such as checking that y does not have np.nan or np.inf targets. For multi-label y, set multi_output=True to allow 2D and sparse y. If the dtype of X is object, attempt converting to float, raising on failure. Parameters ---------- X : nd-array, list or sparse matrix Input data. y : nd-array, list or sparse matrix Labels. accept_sparse : string, boolean or list of string (default=False) String[s] representing allowed sparse matrix formats, such as 'csc', 'csr', etc. If the input is sparse but not in the allowed format, it will be converted to the first listed format. True allows the input to be any format. False means that a sparse matrix input will raise an error. .. deprecated:: 0.19 Passing 'None' to parameter ``accept_sparse`` in methods is deprecated in version 0.19 "and will be removed in 0.21. Use ``accept_sparse=False`` instead. accept_large_sparse : bool (default=True) If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by accept_sparse, accept_large_sparse will cause it to be accepted only if its indices are stored with a 32-bit dtype. .. versionadded:: 0.20 dtype : string, type, list of types or None (default="numeric") Data type of result. If None, the dtype of the input is preserved. If "numeric", dtype is preserved unless array.dtype is object. If dtype is a list of types, conversion on the first type is only performed if the dtype of the input is not in the list. order : 'F', 'C' or None (default=None) Whether an array will be forced to be fortran or c-style. copy : boolean (default=False) Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion. force_all_finite : boolean or 'allow-nan', (default=True) Whether to raise an error on np.inf and np.nan in X. This parameter does not influence whether y can have np.inf or np.nan values. The possibilities are: - True: Force all values of X to be finite. - False: accept both np.inf and np.nan in X. - 'allow-nan': accept only np.nan values in X. Values cannot be infinite. .. versionadded:: 0.20 ``force_all_finite`` accepts the string ``'allow-nan'``. ensure_2d : boolean (default=True) Whether to raise a value error if X is not 2D. allow_nd : boolean (default=False) Whether to allow X.ndim > 2. multi_output : boolean (default=False) Whether to allow 2D y (array or sparse matrix). If false, y will be validated as a vector. y cannot have np.nan or np.inf values if multi_output=True. ensure_min_samples : int (default=1) Make sure that X has a minimum number of samples in its first axis (rows for a 2D array). ensure_min_features : int (default=1) Make sure that the 2D array has some minimum number of features (columns). The default value of 1 rejects empty datasets. This check is only enforced when X has effectively 2 dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0 disables this check. y_numeric : boolean (default=False) Whether to ensure that y has a numeric type. If dtype of y is object, it is converted to float64. Should only be used for regression algorithms. warn_on_dtype : boolean (default=False) Raise DataConversionWarning if the dtype of the input data structure does not match the requested dtype, causing a memory copy. estimator : str or estimator instance (default=None) If passed, include the name of the estimator in warning messages. Returns ------- X_converted : object The converted and validated X. y_converted : object The converted and validated y. sy cannot be NoneRoRpRR{R'R(R)RŒRRŽRRR$RmRwN( RSRR1RkR3t column_or_1dRRRR7RR2R^(RtyRoRpRR{R'R(R)RŒt multi_outputRRŽt y_numericRR((s7lib/python2.7/site-packages/sklearn/utils/validation.pyt check_X_y|s(m      cCs•tj|ƒ}t|ƒdkr.tj|ƒSt|ƒdkr||ddkr||rotjdtddƒntj|ƒStdj|ƒƒ‚dS(sÞ Ravel column or 1d numpy array, else raises an error Parameters ---------- y : array-like warn : boolean, default False To control display of warnings. Returns ------- y : array iis„A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().R|sbad input shape {0}N( RR;REtravelRlRmRRR(R©RmR;((s7lib/python2.7/site-packages/sklearn/utils/validation.pyR¨s "  cCs€|dks|tjkr(tjjjSt|tjtjfƒrStjj |ƒSt|tjj ƒrl|St d|ƒ‚dS(sTurn seed into a np.random.RandomState instance Parameters ---------- seed : None | int | instance of RandomState If seed is None, return the RandomState singleton used by np.random. If seed is an int, return a new RandomState instance seeded with seed. If seed is already a RandomState instance, return it. Otherwise raise ValueError. s=%r cannot be used to seed a numpy.random.RandomState instanceN( RSRtrandomtmtrandt_randR.RFRGtintegert RandomStateR(tseed((s7lib/python2.7/site-packages/sklearn/utils/validation.pytcheck_random_state s  cCs|t|jƒjkS(sêChecks whether the estimator's fit method supports the given parameter. Parameters ---------- estimator : object An estimator to inspect. parameter : str The searched parameter. Returns ------- is_parameter: bool Whether the parameter was found to be a named parameter of the estimator's fit method. Examples -------- >>> from sklearn.svm import SVC >>> has_fit_parameter(SVC(), "sample_weight") True (RR@t parameters(Rt parameter((s7lib/python2.7/site-packages/sklearn/utils/validation.pythas_fit_parameter5sg»½×Ùß|Û=cCs?|jdks)|jd|jdkrDtdj|jƒƒ‚ntj|ƒrŸ||j}|jd kr~|jƒ}ntj t |j ƒ|kƒ}ntj ||jd|ƒ}|s;|rÕtd ƒ‚n|rët jd ƒntj|ƒr'd |j}td ||j|ƒƒ}q;d ||j}n|S(sËMake sure that array is 2D, square and symmetric. If the array is not symmetric, then a symmetrized version is returned. Optionally, a warning or exception is raised if the matrix is not symmetric. Parameters ---------- array : nd-array or sparse matrix Input object to check / convert. Must be two-dimensional and square, otherwise a ValueError will be raised. tol : float Absolute tolerance for equivalence of arrays. Default = 1E-10. raise_warning : boolean (default=True) If True then raise a warning if conversion is required. raise_exception : boolean (default=False) If True then raise an exception if array is not symmetric. Returns ------- array_sym : ndarray or sparse matrix Symmetrized version of the input array, i.e. the average of array and array.transpose(). If sparse, then duplicate entries are first summed and zeros are eliminated. iiis3array must be 2-dimensional and square. shape = {0}R$R%R&tatolsArray must be symmetricsYArray is not symmetric, and will be converted to symmetric by average with its transpose.ttogà?(R$R%R&(RƒR;RRR R!tTRbRRtabsR"tallcloseRlRmR~(Rcttolt raise_warningtraise_exceptiontdifft symmetrict conversion((s7lib/python2.7/site-packages/sklearn/utils/validation.pytcheck_symmetricPs&)  !  cCs§|dkrd}nt|dƒs7td|ƒ‚nt|ttfƒsX|g}n|g|D]}t||ƒ^qbƒs£t|it|ƒjd6ƒ‚ndS(sLPerform is_fitted validation for estimator. Checks if the estimator is fitted by verifying the presence of "all_or_any" of the passed attributes and raises a NotFittedError with the given message. Parameters ---------- estimator : estimator instance. estimator instance for which the check is performed. attributes : attribute name(s) given as string or a list/tuple of strings Eg.: ``["coef_", "estimator_", ...], "coef_"`` msg : string The default error message is, "This %(name)s instance is not fitted yet. Call 'fit' with appropriate arguments before using this method." For custom messages if "%(name)s" is present in the message string, it is substituted for the estimator name. Eg. : "Estimator, %(name)s, must be fitted before sparsifying". all_or_any : callable, {all, any}, default all Specify whether all or any of the given attributes must exist. Returns ------- None Raises ------ NotFittedError If the attributes are not found. siThis %(name)s instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.R@s %s is not an estimator instance.tnameN( RSR=RBR.RhRiRRDR€(Rt attributesRšt all_or_anytattr((s7lib/python2.7/site-packages/sklearn/utils/validation.pytcheck_is_fitted‡s%   (cCsGtj|ƒr|jn|}|dkjƒrCtd|ƒ‚ndS(sÌ Check if there is any negative value in an array. Parameters ---------- X : array-like or sparse matrix Input data. whom : string Who passed X to this function. is$Negative values in data passed to %sN(R R!R"RR(Rtwhom((s7lib/python2.7/site-packages/sklearn/utils/validation.pytcheck_non_negativeºs (;t__doc__RlRFtnumpyRt scipy.sparsetsparseR tscipyRR£tdistutils.versionRtnumpy.core.numericRt externalsRtfixesRRyRRt exceptionsRRRt_joblibR RUR2R4tfloat16t FLOAT_DTYPESR‚R¡R3RR#RkR9R?RHRNRWR^RfRrRtRSR1RgR¬R¨R´R·RÃRRÈRÊ(((s7lib/python2.7/site-packages/sklearn/utils/validation.pyts\     1   " !   [   ø    ƒ    63