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[MRG] ENH validate sample_weight with _check_sample_weight in IsotonicRegression #16203

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[MRG] ENH validate sample_weight with _check_sample_weight in IsotonicRegression #16203

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de68f03
Replaced in-fuction checks with_check_sample_weight for IsotonicRegre…
marijavlajic Jan 25, 2020
2090462
Added import for check_sample_weight.
marijavlajic Jan 25, 2020
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16 changes: 5 additions & 11 deletions sklearn/isotonic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@
from scipy.stats import spearmanr
from .base import BaseEstimator, TransformerMixin, RegressorMixin
from .utils import check_array, check_consistent_length
from .utils.validation import _check_sample_weight
from ._isotonic import _inplace_contiguous_isotonic_regression, _make_unique
import warnings
import math
Expand Down Expand Up @@ -121,10 +122,7 @@ def isotonic_regression(y, sample_weight=None, y_min=None, y_max=None,
order = np.s_[:] if increasing else np.s_[::-1]
y = check_array(y, ensure_2d=False, dtype=[np.float64, np.float32])
y = np.array(y[order], dtype=y.dtype)
if sample_weight is None:
sample_weight = np.ones(len(y), dtype=y.dtype)
else:
sample_weight = np.array(sample_weight[order], dtype=y.dtype)
sample_weight = _check_sample_weight(sample_weight, y, dtype=y.dtype)
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Thanks!

You need to apply the order parameter, maybe as,

    if sample_weight is None:
        sample_weight = np.ones(len(y), dtype=y.dtype)
    else:
        sample_weight = _check_sample_weight(sample_weight, y, dtype=y.dtype)
        sample_weight = sample_weight[order]

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don't you think

sample_weight = _check_sample_weight(sample_weight, y, dtype=y.dtype)
sample_weight = sample_weight[order]

is enough ? It makes the code simpler and reordering ones has no effect and efficiency is not a problem there.

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It makes the code simpler and reordering ones has no effect and efficiency is not a problem there.

If you prefer. Slicing an array with int indexing does have some cost, but it should indeed be minimal.

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I thought the same :)


_inplace_contiguous_isotonic_regression(y, sample_weight)
if y_min is not None or y_max is not None:
Expand Down Expand Up @@ -261,13 +259,9 @@ def _build_y(self, X, y, sample_weight, trim_duplicates=True):

# If sample_weights is passed, removed zero-weight values and clean
# order
if sample_weight is not None:
sample_weight = check_array(sample_weight, ensure_2d=False,
dtype=X.dtype)
mask = sample_weight > 0
X, y, sample_weight = X[mask], y[mask], sample_weight[mask]
else:
sample_weight = np.ones(len(y), dtype=X.dtype)
sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)
mask = sample_weight > 0
X, y, sample_weight = X[mask], y[mask], sample_weight[mask]

order = np.lexsort((y, X))
X, y, sample_weight = [array[order] for array in [X, y, sample_weight]]
Expand Down