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CLN Move gradient and hessian closer to for loop in hist GBDT #15686

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Merged
NicolasHug merged 1 commit into from
Nov 20, 2019
Merged

CLN Move gradient and hessian closer to for loop in hist GBDT #15686

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20 changes: 7 additions & 13 deletions sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -191,13 +191,6 @@ def fit(self, X, y):
)
raw_predictions += self._baseline_prediction

# initialize gradients and hessians (empty arrays).
# shape = (n_trees_per_iteration, n_samples).
gradients, hessians = self.loss_.init_gradients_and_hessians(
n_samples=n_samples,
prediction_dim=self.n_trees_per_iteration_
)

# predictors is a matrix (list of lists) of TreePredictor objects
# with shape (n_iter_, n_trees_per_iteration)
self._predictors = predictors = []
Expand Down Expand Up @@ -278,17 +271,18 @@ def fit(self, X, y):
X_binned_small_train, y_small_train = self._get_small_trainset(
X_binned_train, y_train, self._random_seed)

# Initialize the gradients and hessians
gradients, hessians = self.loss_.init_gradients_and_hessians(
n_samples=n_samples,
prediction_dim=self.n_trees_per_iteration_
)

# Get the predictors from the previous fit
predictors = self._predictors

begin_at_stage = self.n_iter_

# initialize gradients and hessians (empty arrays).
# shape = (n_trees_per_iteration, n_samples).
gradients, hessians = self.loss_.init_gradients_and_hessians(
n_samples=n_samples,
prediction_dim=self.n_trees_per_iteration_
)

for iteration in range(begin_at_stage, self.max_iter):

if self.verbose:
Expand Down