Mpj>KSrSSKrSSKrSSKJr SSKJr SSKJr SSK r SSK r SSK Jr SSKJr SSKJrJr SS KJr SS KJr SS KJr S \S \S\4SjrS \S \S\ R64SjrSrSSjrSSjrSSjr g)z9Implementation of ARFF parsers: via LIAC-ARFF and pandas.N) OrderedDict) Generator)List)_arff)ArffSparseDataType)chunk_generatorget_chunk_n_rows)check_pandas_support)_align_api_if_sparse) pd_fillna arff_datainclude_columnsreturncT[5[5[54n[U5VVs0sHup4XC_M nnn[USUSUS5HKupgnX;dM USRU5 USRU5 USRXX5 MM U$s snnf)aYObtains several columns from sparse ARFF representation. Additionally, the column indices are re-labelled, given the columns that are not included. (e.g., when including [1, 2, 3], the columns will be relabelled to [0, 1, 2]). Parameters ---------- arff_data : tuple A tuple of three lists of equal size; first list indicating the value, second the x coordinate and the third the y coordinate. include_columns : list A list of columns to include. Returns ------- arff_data_new : tuple Subset of arff data with only the include columns indicated by the include_columns argument. r)list enumeratezipappend) r r arff_data_new array_idx column_idxreindexed_columnsvalrow_idxcol_idxs Y/var/www/html/pdf-tiff/venv/lib/python3.13/site-packages/sklearn/datasets/_arff_parser.py_split_sparse_columnsrs.*.(@M;D_;U;U"7) ;U"%Yq\9Q<1!Ng  % !  # #C ( !  # #G , !  # #$5$> ? "O sB$c0[US5S-nU[U54n[U5VVs0sHupEXT_M nnn[R"U[R S9n[ USUSUS5Hupn X;dM XXU 4'M U$s snnf)Nrdtyperr)maxlenrnpemptyfloat64r) r rnum_obsy_shaperrryrrrs r_sparse_data_to_arrayr+:s )A,!#GO,-G;D_;U;U"7) ;U  +A!$Yq\9Q<1!Ng  %58g11 2"O HsBcrXn[U5S:aXnX44$[U5S:Xa XSnX44$SnX44$)aPost process a dataframe to select the desired columns in `X` and `y`. Parameters ---------- frame : dataframe The dataframe to split into `X` and `y`. feature_names : list of str The list of feature names to populate `X`. target_names : list of str The list of target names to populate `y`. Returns ------- X : dataframe The dataframe containing the features. y : {series, dataframe} or None The series or dataframe containing the target. rrrN)r$)frame feature_names target_namesXr*s r_post_process_framer1Ls\, A <A   4K \ a  q/ " 4K  4Kc SnU"U5nUS:Xa[RO[RnUS:X+n [R"XxU S9n X4-n U SV V s0sH%up[ U [ 5(dMX;dM#X_M' nn n US:XGa[ S5n[U S5n[ UR55n[U S5nURU/USS 9nURS S 9R5n[U5nUVs/sH nUU ;dM UPM nnUU/n[U SU5H&nURURUUSS 9U5 M( [!U5S :a$US R#USR$5US 'UR'US S9n[)UU5nAA0nUR*HRn X,SnUR-5S:XaSUU 'M%UR-5S:XaSUU 'M@UR$U UU 'MT UR#U5n[/UX45unnGOU SnUV s/sHn [1UU S5PM n!n UV s/sHn [1UU S5PM n"n [ U[25(azUc [5S5eUS S:XaSn#O US US-n#[6R8"[:R<R?U5SU#S9nUR@"U6nUSS2U!4nUSS2U"4nO[ U[B5(a[EUU!5n$[GUS5S-n%U%[!U!54n&[HRJRMU$S U$SU$S 44U&[6RNS9n[QURS55n[UUU"5nO[5S[WU535eUV s1sHn U U;iM n'n U'(dO[YU'5(a[6RZ"[]U5V(V s/sH]un(n [6R^"[6R`"URcU 5SS9USS2U(U(S-24R#[0SS95PM_ sn n(5nOU'5(a [5S 5eURfSS:XaURAS!5nOURfSS :XaSnUS:XaUUWS4$UUSU4$s sn n fs snfs sn fs sn fs sn fs sn n(f)"aARFF parser using the LIAC-ARFF library coded purely in Python. This parser is quite slow but consumes a generator. Currently it is needed to parse sparse datasets. For dense datasets, it is recommended to instead use the pandas-based parser, although it does not always handles the dtypes exactly the same. Parameters ---------- gzip_file : GzipFile instance The file compressed to be read. output_arrays_type : {"numpy", "sparse", "pandas"} The type of the arrays that will be returned. The possibilities ara: - `"numpy"`: both `X` and `y` will be NumPy arrays; - `"sparse"`: `X` will be sparse matrix and `y` will be a NumPy array; - `"pandas"`: `X` will be a pandas DataFrame and `y` will be either a pandas Series or DataFrame. columns_info : dict The information provided by OpenML regarding the columns of the ARFF file. feature_names_to_select : list of str A list of the feature names to be selected. target_names_to_select : list of str A list of the target names to be selected. Returns ------- X : {ndarray, sparse matrix, dataframe} The data matrix. y : {ndarray, dataframe, series} The target. frame : dataframe or None A dataframe containing both `X` and `y`. `None` if `output_array_type != "pandas"`. categories : list of str or None The names of the features that are categorical. `None` if `output_array_type == "pandas"`. c3D# UHnURS5v M g7f)Nutf-8)decode) gzip_filelines r_io_to_generator+_liac_arff_parser.._io_to_generatorsD++g& &s sparsepandas) return_typeencode_nominal attributeszfetch_openml with as_frame=TruedataF)columnscopyT)deeprrr) ignore_index data_typeintegerInt64nominalcategoryindexNz6shape must be provided when arr['data'] is a Generatorr')r"count)shaper"z-Unexpected type for data obtained from arff: Or!)rBzAMix of nominal and non-nominal targets is not currently supported)rK)4rCOO DENSE_GENload isinstancerr rkeysnext DataFrame memory_usagesumr rrr$astypedtypesconcatr rAlowerr1intr ValueErrorr%fromiter itertoolschain from_iterablereshapetuplerr#spr; coo_arrayr'r tocsrr+typeallhstackrtakeasarraypopanyrM))r7output_arrays_typeopenml_columns_infofeature_names_to_selecttarget_names_to_selectrMr9streamr=r>arff_containercolumns_to_selectnamecat categoriespd columns_info column_names first_rowfirst_df row_bytes chunksizecolcolumns_to_keepdfsr@r-rY column_dtyper0r*r col_namefeature_indices_to_selecttarget_indices_to_selectrL arff_data_Xr(X_shapeis_classificationis) r_liac_arff_parserrlssn'i (F 2X=%))5??K-9NZZN0H( 55ID c4  %)%>  5 X% !"C D">,#?@ L--/0 /0 << \<N))t)488: $Y/ +7S,3#AR:R3,S()#N6$:IFD JJ T?G ##Q+a.+a.!ABjj$A %QWWY/A%i1IJA?Y?PQ  4J 3IxH "3I  !  " # # (11G'H  (I 8 GG :>>(#;3G!QQY,..s.?(I A" # #S  771:? % A WWQZ1_AX%!UD  az !!E&TL% $ N s7S$8S$?S$ S*S*S/#S4 S9A$S> c R^SSKnUH8nURS5R5RS5(dM8 O 0nUH>n X)Sn U R5S:XaSX'M$U R5S:XdM:S X'M@ [ U5V V s0sHupX;dM XU _M n n n SS S /S S S SSU S. n 0U EU=(d 0EnUR "U40UD6nUV s/sHoPM sn UlX4-nUR Vs/sH nUU;dM UPM nnUUn[R"S5mU4SjnURR5V Vs/sH%un n[UUR5(dM#U PM' nn nUH$nUUR R#U5UU'M& [%XU5unnUS:XaUUUS4$UR'5UR'5nnURR5V Vs0sH>un n[UUR5(dM#U UR(R+5_M@ nn nUUSU4$s sn n fs sn f![a!nURRS5UeSnAff=fs snfs snn fs snn f)aARFF parser using `pandas.read_csv`. This parser uses the metadata fetched directly from OpenML and skips the metadata headers of ARFF file itself. The data is loaded as a CSV file. Parameters ---------- gzip_file : GzipFile instance The GZip compressed file with the ARFF formatted payload. output_arrays_type : {"numpy", "sparse", "pandas"} The type of the arrays that will be returned. The possibilities are: - `"numpy"`: both `X` and `y` will be NumPy arrays; - `"sparse"`: `X` will be sparse matrix and `y` will be a NumPy array; - `"pandas"`: `X` will be a pandas DataFrame and `y` will be either a pandas Series or DataFrame. openml_columns_info : dict The information provided by OpenML regarding the columns of the ARFF file. feature_names_to_select : list of str A list of the feature names to be selected to build `X`. target_names_to_select : list of str A list of the target names to be selected to build `y`. read_csv_kwargs : dict, default=None Keyword arguments to pass to `pandas.read_csv`. It allows to overwrite the default options. Returns ------- X : {ndarray, sparse matrix, dataframe} The data matrix. y : {ndarray, dataframe, series} The target. frame : dataframe or None A dataframe containing both `X` and `y`. `None` if `output_array_type != "pandas"`. categories : list of str or None The names of the features that are categorical. `None` if `output_array_type == "pandas"`. rNr5z@datarErFrGrHrIF?%"T\) header index_col na_valueskeep_default_nacomment quotecharskipinitialspace escapecharr"zwThe number of columns provided by OpenML does not match the number of columns inferred by pandas when reading the file.z^'(?P.*)'$c^>[R"TU5nUcU$URS5$)Ncontents)researchgroup) input_stringmatchsingle_quote_patterns rstrip_single_quotes0_pandas_arff_parser..strip_single_quotess. . = = {{:&&r2r<)r<r6r[ startswithrread_csvrAr]errors ParserErrorrcompilerYitemsrRCategoricalDtypervrename_categoriesr1to_numpyrwtolist)r7rnrorprqread_csv_kwargsrxr8rYrurrdtypes_positionaldefault_read_csv_kwargsr-excrtrrrr"categorical_columnsr0r*rwrs @r_pandas_arff_parserr8sp ;;w  % % ' 2 27 ; ; F#*0=    9 ,#FL    !Y .%FL$'':;;MG > ;U  " M0L_5JLO KK 5_ 5E  +>>*=$*=> 0H&+mmPmss>O7OsmOP / "E::&=>'!<<--//KD% eR00 1 / #3Z^^556IJc # u?U VDAqX%!UD  zz|QZZ\1!<<--//KD% eR00 1 (e%%''/ az !!W0? ii## @  Q.sZ I' I#I*' I%2I* JJ "JJ"J#7J#%I** J4JJcxUS:Xa[UUUUUU5$US:Xa[UUUUUU5$[SUS35e)aLoad a compressed ARFF file using a given parser. Parameters ---------- gzip_file : GzipFile instance The file compressed to be read. parser : {"pandas", "liac-arff"} The parser used to parse the ARFF file. "pandas" is recommended but only supports loading dense datasets. output_type : {"numpy", "sparse", "pandas"} The type of the arrays that will be returned. The possibilities ara: - `"numpy"`: both `X` and `y` will be NumPy arrays; - `"sparse"`: `X` will be sparse matrix and `y` will be a NumPy array; - `"pandas"`: `X` will be a pandas DataFrame and `y` will be either a pandas Series or DataFrame. openml_columns_info : dict The information provided by OpenML regarding the columns of the ARFF file. feature_names_to_select : list of str A list of the feature names to be selected. target_names_to_select : list of str A list of the target names to be selected. read_csv_kwargs : dict, default=None Keyword arguments to pass to `pandas.read_csv`. It allows to overwrite the default options. Returns ------- X : {ndarray, sparse matrix, dataframe} The data matrix. y : {ndarray, dataframe, series} The target. frame : dataframe or None A dataframe containing both `X` and `y`. `None` if `output_array_type != "pandas"`. categories : list of str or None The names of the features that are categorical. `None` if `output_array_type == "pandas"`. z liac-arffr<zUnknown parser: 'z%'. Should be 'liac-arff' or 'pandas'.)rrr])r7parser output_typerorprqrMrs rload_arff_from_gzip_filersqv    # "     8 "    # "    x'L M  r2)N)NN)!__doc__r_r collectionsrcollections.abcrtypingrnumpyr%scipyrdsklearn.externalsrsklearn.externals._arffrsklearn.utils._chunkingrr $sklearn.utils._optional_dependenciesr sklearn.utils._sparser sklearn.utils.fixesr rndarrayr+r1rrrr2rrs?  #%#6EE6) ! 48  F ! 48 ZZ $L  I"d U"~ P r2