8000 [MRG+1] Convert y in GradientBoosting to float64 instead of float32 by adrinjalali · Pull Request #13524 · scikit-learn/scikit-learn · GitHub
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[MRG+1] Convert y in GradientBoosting to float64 instead of float32 #13524

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Merged
merged 6 commits into from
Apr 9, 2019
Merged

[MRG+1] Convert y in GradientBoosting to float64 instead of float32 #13524

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acbe1f6
fix y validation
adrinjalali Mar 26, 2019
1b14b89
Merge remote-tracking branch 'upstream/master' into gbc/float32
adrinjalali Mar 26, 2019
9fa4b30
fix y validation
adrinjalali Mar 26, 2019
97304d9
fix the validation again
adrinjalali Mar 26, 2019
f95d6b1
Merge remote-tracking branch 'upstream/master' into gbc/float32
adrinjalali Mar 27, 2019
c6dfc8b
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adrinjalali Mar 27, 2019
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10 changes: 7 additions & 3 deletions sklearn/ensemble/gradient_boosting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,7 +44,7 @@
from time import time
from ..model_selection import train_test_split
from ..tree.tree import DecisionTreeRegressor
from ..tree._tree import DTYPE
from ..tree._tree import DTYPE, DOUBLE
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It is interesting how this is our naming convention for the dtypes of X and y. In the future we may consider X_DTYPE and Y_DTYPE.

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Yeah I know. You're the second +1 here now @thomasjpfan, wanna push the green button? ;)

from ..tree._tree import TREE_LEAF
from . import _gb_losses

Expand Down Expand Up @@ -1432,7 +1432,9 @@ def fit(self, X, y, sample_weight=None, monitor=None):
self._clear_state()

# Check input
X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'], dtype=DTYPE)
# Since check_array converts both X and y to the same dtype, but the
# trees use different types for X and y, checking them separately.
X = check_array(X, accept_sparse=['csr', 'csc', 'coo'], dtype=DTYPE)
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It's worth commenting why check_X_y is not used.

n_samples, self.n_features_ = X.shape

sample_weight_is_none = sample_weight is None
Expand All @@ -1444,6 +1446,8 @@ def fit(self, X, y, sample_weight=None, monitor=None):

check_consistent_length(X, y, sample_weight)

y = check_array(y, accept_sparse='csc', ensure_2d=False, dtype=None)
y = column_or_1d(y, warn=True)
y = self._validate_y(y, sample_weight)

if self.n_iter_no_change is not None:
Expand Down Expand Up @@ -1715,7 +1719,7 @@ def _validate_y(self, y, sample_weight):
# consistency with similar method _validate_y of GBC
self.n_classes_ = 1
if y.dtype.kind == 'O':
y = y.astype(np.float64)
y = y.astype(DOUBLE)
# Default implementation
return y

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