Resolve "non negative omp"

Closes #24 (closed)

code/bolsonaro/models/model_factory.py

 from bolsonaro.models.omp_forest_classifier import OmpForestBinaryClassifier, OmpForestMulticlassClassifier
 from bolsonaro.models.omp_forest_regressor import OmpForestRegressor
+from bolsonaro.models.nn_omp_forest_regressor import NonNegativeOmpForestRegressor
+from bolsonaro.models.nn_omp_forest_classifier import NonNegativeOmpForestBinaryClassifier
 from bolsonaro.models.model_parameters import ModelParameters
 from bolsonaro.models.similarity_forest_regressor import SimilarityForestRegressor, SimilarityForestClassifier
 from bolsonaro.models.kmeans_forest_regressor import KMeansForestRegressor, KMeansForestClassifier
@@ -19,8 +21,10 @@ class ModelFactory(object):
             raise ValueError("Unsupported task '{}'".format(task))
 
         if task == Task.BINARYCLASSIFICATION:
-            if model_parameters.extraction_strategy in ['omp', 'omp_nn', 'omp_distillation']:
+            if model_parameters.extraction_strategy in ['omp', 'omp_distillation']:
                 return OmpForestBinaryClassifier(model_parameters)
+            elif model_parameters.extraction_strategy == 'omp_nn':
+                return NonNegativeOmpForestBinaryClassifier(model_parameters)
             elif model_parameters.extraction_strategy == 'random':
                 return RandomForestClassifier(**model_parameters.hyperparameters,
                     random_state=model_parameters.seed)
@@ -36,8 +40,10 @@ class ModelFactory(object):
             else:
                 raise ValueError('Invalid extraction strategy')
         elif task == Task.REGRESSION:
-            if model_parameters.extraction_strategy in ['omp', 'omp_nn', 'omp_distillation']:
+            if model_parameters.extraction_strategy in ['omp', 'omp_distillation']:
                 return OmpForestRegressor(model_parameters)
+            elif model_parameters.extraction_strategy == 'omp_nn':
+                return NonNegativeOmpForestRegressor(model_parameters)
             elif model_parameters.extraction_strategy == 'random':
                 return RandomForestRegressor(**model_parameters.hyperparameters,
                     random_state=model_parameters.seed)
@@ -53,8 +59,10 @@ class ModelFactory(object):
             else:
                 raise ValueError('Invalid extraction strategy')
         elif task == Task.MULTICLASSIFICATION:
-            if model_parameters.extraction_strategy in ['omp', 'omp_nn', 'omp_distillation']:
+            if model_parameters.extraction_strategy in ['omp', 'omp_distillation']:
                 return OmpForestMulticlassClassifier(model_parameters)
+            elif model_parameters.extraction_strategy == 'omp_nn':
+                raise ValueError('omp_nn is unsuported for multi classification')
             elif model_parameters.extraction_strategy == 'random':
                 return RandomForestClassifier(**model_parameters.hyperparameters,
                     random_state=model_parameters.seed)