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Implement predict & predict_proba optimization

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reduces time in two orders of magnitude in creditcard dataset
This commit is contained in:
Ricardo Montañana Gómez
2020-05-15 23:35:33 +02:00
parent e56b955b92
commit 80b5cf8e72
6 changed files with 129 additions and 59 deletions
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6
trees/Snode.py
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@@ -18,8 +18,8 @@ class Snode:
self._interceptor = 0. if clf is None else clf.intercept_
self._title = title
self._belief = 0. # belief of the prediction in a leaf node based on samples
self._X = X if os.environ.get(
'TESTING', 'Not Set') != 'Not Set' else None
# Only store dataset in Testing
self._X = X if os.environ.get('TESTING', 'NS') != 'NS' else None
self._y = y
self._down = None
self._up = None
@@ -64,6 +64,6 @@ class Snode:
def __str__(self) -> str:
if self.is_leaf():
return f"Leaf class={self._class} belief={self._belief:.6f} counts={np.unique(self._y, return_counts=True)}\n"
return f"{self._title} - Leaf class={self._class} belief={self._belief:.6f} counts={np.unique(self._y, return_counts=True)}\n"
else:
return f"{self._title}\n"