0Ph> |ddlZddlmZddlmZddlZddlmZddl m Z m Z dZ e e eddd ge eddd ge eddd gd d dddZ e e eddd ge eddd ge eddd dgdd dddZddddZdS)N)islice)Integral) get_config)Intervalvalidate_paramsc#VK tt||}|r|VndS')zzChunk generator, ``gen`` into lists of length ``chunksize``. The last chunk may have a length less than ``chunksize``.TN)listr)gen chunksizechunks W/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/sklearn/utils/_chunking.pychunk_generatorrs?VC++,,  KKKK F left)closed)n batch_sizemin_batch_sizeT)prefer_skip_nested_validation)rc#Kd}tt||zD]%}||z}||z|krt||V|}&||krt||VdSdS)a,Generator 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_batchesrsZ E 3qJ'' ( (j   ! # # E3 qyyE1ooyr)rn_packs n_samples)r!c#Kd}t|D]G}||z}|||zkr|dz }|dkr,||z}|t||}t||dV|}HdS)aGenerator 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)rr r!rpack_numthis_nrs rgen_even_slicesr&Qs\ E'NN  g a'k ! ! aKF A::&.C$)S))sD)) ) ) )E  r) max_n_rowsworking_memoryc|td}t|dz|z}|t||}|dkr0tjd|t j|dzfzd}|S)aCalculate how many rows can be processed within `working_memory`. Parameters ---------- row_bytes : int The expected number of bytes of memory that will be consumed during the processing of each row. max_n_rows : int, default=None The maximum return value. working_memory : int or float, default=None The number of rows to fit inside this number of MiB will be returned. When None (default), the value of ``sklearn.get_config()['working_memory']`` is used. Returns ------- int The number of rows which can be processed within `working_memory`. Warns ----- Issues a UserWarning if `row_bytes exceeds `working_memory` MiB. Nr(irzOCould not adhere to working_memory config. Currently %.0fMiB, %.0fMiB required.g>)rrr#warningswarnnpceil) row_bytesr'r( chunk_n_rowss rget_chunk_n_rowsr0s2#&67~/9<==L<44 a  3rwy6'9::; <    r)r* itertoolsrnumbersrnumpyr,_configr_param_validationrr rrr&r0rrr7s 88888888hxD888 9x!T&AAAB#8HafEEEF #' 23-----`hxD888 9HXq$v>>>?hxD@@@$G #' .200000f/34&&&&&&&r