a j @sddlZddlmZddlmZddlZddlmZddl m Z m Z dd Z e e eddd d ge eddd d ge eddd d gd d dddddZ e e eddd d ge eddd d ge eddd d dgdd dddddZdddddZdS)N)islice)Integral) get_config)Intervalvalidate_paramsccs$tt||}|r|VqdSqdS)zzChunk generator, ``gen`` into lists of length ``chunksize``. The last chunk may have a length less than ``chunksize``.N)listr)gen chunksizechunkr =E:\soccer\ml_env\Lib\site-packages\sklearn/utils/_chunking.pychunk_generatorsrleft)closed)n batch_sizemin_batch_sizeT)Zprefer_skip_nested_validation)rccsXd}tt||D]*}||}|||kr.qt||V|}q||krTt||VdS)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_endr r r gen_batchess-  r)rn_packs n_samples)rccsfd}t|D]T}||}|||kr,|d7}|dkr ||}|durNt||}t||dV|}q dS)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)rrrrZpack_numZthis_nrr r rgen_even_slicesQs.   r) max_n_rowsworking_memorycCs`|durtd}t|d|}|dur4t||}|dkr\td|t|dfd}|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>)rrrwarningswarnnpceil)Z row_bytesr r!Z chunk_n_rowsr r rget_chunk_n_rowss  r&)r" itertoolsrnumbersrnumpyr$_configrZ_param_validationrrrrrr&r r r rs.    03