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dropped bottleneck warning message #311

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dropped bottleneck warning message #311

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9dfe22f
dropped bottleneck warning message
ryanhausen Aug 8, 2024
91f6271
dropped unused import
ryanhausen Aug 8, 2024
68e8f9e
added warning back in and fixed issue with checking bottleneck import
ryanhausen Aug 14, 2024
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13 changes: 8 additions & 5 deletions treeple/stats/utils.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
import importlib.util
import os
import sys
import warnings
from typing import Optional, Tuple

Expand All @@ -20,20 +20,20 @@
from treeple._lib.sklearn.ensemble._forest import BaseForest, ForestClassifier

BOTTLENECK_AVAILABLE = False
if "bottleneck" in sys.modules:
if importlib.util.find_spec("bottleneck"):
import bottleneck as bn

BOTTLENECK_AVAILABLE = True

BOTTLENECK_WARNING = (
"Not using bottleneck for calculations involvings nans. Expect slower performance."
)
DISABLE_BN_ENV_VAR = "TREEPLE_NO_BOTTLENECK"

if BOTTLENECK_AVAILABLE and DISABLE_BN_ENV_VAR not in os.environ:
nanmean_f = bn.nanmean
anynan_f = lambda arr: bn.anynan(arr, axis=2)
else:
warnings.warn(
"Not using bottleneck for calculations involvings nans. Expect slower performance."
)
nanmean_f = np.nanmean
anynan_f = lambda arr: np.isnan(arr).any(axis=2)

Expand Down Expand Up @@ -261,6 +261,9 @@ def _compute_null_distribution_coleman(
metric_star_pi : ArrayLike of shape (n_samples,)
An array of the metrics computed on the other half of the trees.
"""
if not BOTTLENECK_AVAILABLE:
warnings.warn(BOTTLENECK_WARNING)

# sample two sets of equal number of trees from the combined forest these are the posteriors
# (n_estimators * 2, n_samples, n_outputs)
all_y_pred = np.concatenate((y_pred_proba_normal, y_pred_proba_perm), axis=0)
Expand Down
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