8000 MAINT Parameters validation for utils.gen_batches (#25864) · jeremiedbb/scikit-learn@f4fbb28 · GitHub
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pooja-subramaniampooja-subramaniam
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MAINT Parameters validation for utils.gen_batches (scikit-learn#25864)
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‎sklearn/tests/test_public_functions.py

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@@ -189,6 +189,7 @@ def _check_function_param_validation(
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"sklearn.model_selection.train_test_split",
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"sklearn.random_projection.johnson_lindenstrauss_min_dim",
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"sklearn.svm.l1_min_c",
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"sklearn.utils.gen_batches",
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]
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‎sklearn/utils/__init__.py

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@@ -39,6 +39,7 @@
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)
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from .. import get_config
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from ._bunch import Bunch
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from ._param_validation import validate_params, Interval
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# Do not deprecate parallel_backend and register_parallel_backend as they are
@@ -725,6 +726,13 @@ def _chunk_generator(gen, chunksize):
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return
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@validate_params(
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{
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"n": [Interval(numbers.Integral, 1, None, closed="left")],
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"batch_size": [Interval(numbers.Integral, 1, None, closed="left")],
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"min_batch_size": [Interval(numbers.Integral, 0, None, closed="left")],
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}
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)
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def gen_batches(n, batch_size, *, min_batch_size=0):
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"""Generator to create slices containing `batch_size` elements from 0 to `n`.
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@@ -762,12 +770,6 @@ def gen_batches(n, batch_size, *, min_batch_size=0):
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>>> list(gen_batches(7, 3, min_batch_size=2))
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[slice(0, 3, None), slice(3, 7, None)]
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"""
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if not isinstance(batch_size, numbers.Integral):
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raise TypeError(
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"gen_batches got batch_size=%s, must be an integer" % batch_size
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)
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if batch_size <= 0:
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raise ValueError("gen_batches got batch_size=%s, must be positive" % batch_size)
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start = 0
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for _ in range(int(n // batch_size)):
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end = start + batch_size

‎sklearn/utils/tests/test_utils.py< EBE2 /h3>

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@@ -17,7 +17,6 @@
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from sklearn.utils import check_random_state
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from sklearn.utils import _determine_key_type
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from sklearn.utils import deprecated
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from sklearn.utils import gen_batches
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from sklearn.utils import _get_column_indices
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from sklearn.utils import resample
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from sklearn.utils import safe_mask
@@ -56,19 +55,6 @@ def test_make_rng():
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check_random_state("some invalid seed")
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def test_gen_batches():
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# Make sure gen_batches errors on invalid batch_size
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assert_array_equal(list(gen_batches(4, 2)), [slice(0, 2, None), slice(2, 4, None)])
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msg_zero = "gen_batches got batch_size=0, must be positive"
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with pytest.raises(ValueError, match=msg_zero):
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next(gen_batches(4, 0))
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msg_float = "gen_batches got batch_size=0.5, must be an integer"
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with pytest.raises(TypeError, match=msg_float):
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next(gen_batches(4, 0.5))
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def test_deprecated():
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# Test whether the deprecated decorator issues appropriate warnings
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# Copied almost verbatim from https://docs.python.org/library/warnings.html

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