Add few stage5 results, code fixes and begins to caught warnings

code/bolsonaro/models/ensemble_selection_forest_regressor.py

@@ -52,7 +52,7 @@ class EnsembleSelectionForestRegressor(BaseEstimator, metaclass=ABCMeta):
             del class_list[candidate_index]
 
     def score(self, X, y):
-        predictions = self._predict_base_estimator(X)
+        predictions = self.predict_base_estimator(X)
         return self._score_metric(predictions, y)
 
     def predict_base_estimator(self, X):

code/bolsonaro/models/omp_forest.py

@@ -5,6 +5,7 @@ from abc import abstractmethod, ABCMeta
 import numpy as np
 from sklearn.linear_model import OrthogonalMatchingPursuit
 from sklearn.base import BaseEstimator
+import warnings
 
 
 class OmpForest(BaseEstimator, metaclass=ABCMeta):
@@ -109,7 +110,17 @@ class SingleOmpForest(OmpForest):
         super().__init__(models_parameters, base_forest_estimator)
 
     def fit_omp(self, atoms, objective):
-        self._omp.fit(atoms, objective)
+        with warnings.catch_warnings(record=True) as caught_warnings:
+            # Cause all warnings to always be triggered.
+            warnings.simplefilter("always")
+
+            self._omp.fit(atoms, objective)
+
+            # ignore any non-custom warnings that may be in the list
+            caught_warnings = list(filter(lambda i: i.message != RuntimeWarning(omp_premature_warning), caught_warnings))
+
+            if len(caught_warnings) > 0:
+                logger.error(f'number of linear dependences in the dictionary: {len(caught_warnings)}. model parameters: {str(self._models_parameters.__dict__)}')
 
     def predict(self, X):
         """

code/bolsonaro/models/omp_forest_classifier.py

 from bolsonaro.models.omp_forest import OmpForest, SingleOmpForest
-from bolsonaro.utils import binarize_class_data
+from bolsonaro.utils import binarize_class_data, omp_premature_warning
 
 import numpy as np
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.linear_model import OrthogonalMatchingPursuit
+import warnings
 
 
 class OmpForestBinaryClassifier(SingleOmpForest):
@@ -92,7 +93,19 @@ class OmpForestMulticlassClassifier(OmpForest):
             omp_class = OrthogonalMatchingPursuit(
                 n_nonzero_coefs=self.models_parameters.extracted_forest_size,
                 fit_intercept=True, normalize=False)
-            omp_class.fit(atoms_binary, objective_binary)
+
+            with warnings.catch_warnings(record=True) as caught_warnings:
+                # Cause all warnings to always be triggered.
+                warnings.simplefilter("always")
+
+                omp_class.fit(atoms_binary, objective_binary)
+
+                # ignore any non-custom warnings that may be in the list
+                caught_warnings = list(filter(lambda i: i.message != RuntimeWarning(omp_premature_warning), caught_warnings))
+
+                if len(caught_warnings) > 0:
+                    logger.error(f'number of linear dependences in the dictionary: {len(caught_warnings)}. model parameters: {str(self._models_parameters.__dict__)}')
+
             self._dct_class_omp[class_label] = omp_class
         return self._dct_class_omp
 

code/bolsonaro/utils.py

@@ -124,3 +124,7 @@ def is_float(value):
         return True
     except ValueError:
         return False
+
+omp_premature_warning = """ Orthogonal matching pursuit ended prematurely due to linear
+    dependence in the dictionary. The requested precision might not have been met.
+    """