]jQ SSKrSSKJr SSKJr SSKrSSKJr SSK J r J r Sr \ "\ "\SSSS 9/\ "\SSSS 9/\ "\SSSS 9/S .S S 9SS .Sj5r \ "\ "\SSSS 9/\ "\SSSS 9/\ "\SSSS 9S/S.S S 9SS.Sj5rSSS.Sjrg)N)islice)Integral) get_config)Intervalvalidate_paramsc#R# [[X55nU(aUv OgM#7f)zvChunk generator, ``gen`` into lists of length ``chunksize``. The last chunk may have a length less than ``chunksize``.N)listr)gen chunksizechunks T/home/mande/repo/quber/.venv/lib/python3.13/site-packages/sklearn/utils/_chunking.pychunk_generatorrs) VC+, K  s%'left)closed)n batch_sizemin_batch_sizeT)prefer_skip_nested_validation)rc## Sn[[X-55H nX1-nXR-U:aM[X55v UnM" X0:a[X05v gg7f)aGenerator to create slices containing `batch_size` elements from 0 to `n`. The last slice may contain less than `batch_size` elements, when `batch_size` does not divide `n`. Parameters ---------- n : int Size of the sequence. batch_size : int Number of elements in each batch. min_batch_size : int, default=0 Minimum number of elements in each batch. Yields ------ slice of `batch_size` elements See Also -------- gen_even_slices: Generator to create n_packs slices going up to n. Examples -------- >>> from sklearn.utils import gen_batches >>> list(gen_batches(7, 3)) [slice(0, 3, None), slice(3, 6, None), slice(6, 7, None)] >>> list(gen_batches(6, 3)) [slice(0, 3, None), slice(3, 6, None)] >>> list(gen_batches(2, 3)) [slice(0, 2, None)] >>> list(gen_batches(7, 3, min_batch_size=0)) [slice(0, 3, None), slice(3, 6, None), slice(6, 7, None)] >>> list(gen_batches(7, 3, min_batch_size=2)) [slice(0, 3, None), slice(3, 7, None)] rN)rangeintslice)rrrstart_ends r gen_batchesrs_Z E 3q' (   ! # E )  yEosAA)rn_packs n_samples)rc## Sn[U5H>nX-nX@U-:aUS- nUS:dMX5-nUb [X&5n[X6S5v UnM@ g7f)a>Generator to create `n_packs` evenly spaced slices going up to `n`. If `n_packs` does not divide `n`, except for the first `n % n_packs` slices, remaining slices may contain fewer elements. Parameters ---------- n : int Size of the sequence. n_packs : int Number of slices to generate. n_samples : int, default=None Number of samples. Pass `n_samples` when the slices are to be used for sparse matrix indexing; slicing off-the-end raises an exception, while it works for NumPy arrays. Yields ------ `slice` representing a set of indices from 0 to n. See Also -------- gen_batches: Generator to create slices containing batch_size elements from 0 to n. Examples -------- >>> from sklearn.utils import gen_even_slices >>> list(gen_even_slices(10, 1)) [slice(0, 10, None)] >>> list(gen_even_slices(10, 10)) [slice(0, 1, None), slice(1, 2, None), ..., slice(9, 10, None)] >>> list(gen_even_slices(10, 5)) [slice(0, 2, None), slice(2, 4, None), ..., slice(8, 10, None)] >>> list(gen_even_slices(10, 3)) [slice(0, 4, None), slice(4, 7, None), slice(7, 10, None)] rrN)rminr)rrrrpack_numthis_nrs r gen_even_slicesr$Qse\ E'N 'k ! aKF A:.C$))D) )E#s &A'A) max_n_rowsworking_memorycUc [5Sn[US-U-5nUb [X15nUS:a4[R"SU[ R "US-54-5 SnU$)aCalculate how many rows can be processed within `working_memory`. Parameters ---------- row_bytes : int The expected number of bytes of memory that will be consumed during the processing of each row. max_n_rows : int, default=None The maximum return value. working_memory : int or float, default=None The number of rows to fit inside this number of MiB will be returned. When None (default), the value of ``sklearn.get_config()['working_memory']`` is used. Returns ------- int The number of rows which can be processed within `working_memory`. Warns ----- Issues a UserWarning if `row_bytes exceeds `working_memory` MiB. r&irzOCould not adhere to working_memory config. Currently %.0fMiB, %.0fMiB required.g>)rrr!warningswarnnpceil) row_bytesr%r& chunk_n_rowss r get_chunk_n_rowsr.s|2#&67~/9<=L<4 a  3rwwy6'9:; <  )r( itertoolsrnumbersrnumpyr*sklearn._configrsklearn.utils._param_validationrrrrr$r.r/r r6s&ExD8 9!T&AB#HafEF #' 23--`xD8 9Xq$v>?xD@$G #' .200f/34&r/