From 8b3a6c49adbed86d8caf6132cc78fe2eeb3ca0d4 Mon Sep 17 00:00:00 2001
From: Charly LAMOTHE <lamothe.c@intlocal.univ-amu.fr>
Date: Fri, 8 Nov 2019 11:15:37 +0100
Subject: [PATCH] Use OMP to compute the prediction instead of doing it
manually (same results)
---
code/bolsonaro/models/omp_forest_regressor.py | 12 +++++-------
1 file changed, 5 insertions(+), 7 deletions(-)
diff --git a/code/bolsonaro/models/omp_forest_regressor.py b/code/bolsonaro/models/omp_forest_regressor.py
index 3e95d12..b4896cd 100644
--- a/code/bolsonaro/models/omp_forest_regressor.py
+++ b/code/bolsonaro/models/omp_forest_regressor.py
@@ -38,7 +38,8 @@ class OmpForestRegressor(BaseEstimator):
def fit(self, X_forest, y_forest, X_omp, y_omp):
self._forest = self._train_forest(X_forest, y_forest)
- self._weights = self._extract_subforest(X_omp, y_omp)
+ self._omp = self._extract_subforest(X_omp, y_omp)
+ self._weights = self._omp.coef_
return self
def score_regressor(self, X, y):
@@ -56,8 +57,8 @@ class OmpForestRegressor(BaseEstimator):
if self._models_parameters.normalize_D:
D /= self._forest_norms
- # TODO: use self._models_parameters.normalize_weights here
- predictions = D @ self._weights
+ # TODO: use self._models_parameters.normalize_weights here?
+ predictions = self._omp.predict(D)
return predictions
@@ -109,10 +110,7 @@ class OmpForestRegressor(BaseEstimator):
fit_intercept=False, normalize=False)
self._logger.debug("Apply orthogonal maching pursuit on forest for {} extracted trees."
.format(self._models_parameters.extracted_forest_size))
- omp.fit(D, y)
- weights = omp.coef_
- # question: why not to use directly the omp estimator instead of bypassing it using the coefs?
- return weights
+ return omp.fit(D, y)
def _forest_prediction(self, X):
return np.array([tree.predict(X) for tree in self._forest]).T
--
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