Resolve "Experiment pipeline"

Closes #12 (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.data.task import Task
 from bolsonaro.models.model_parameters import ModelParameters
+from bolsonaro.models.similarity_forest_regressor import SimilarityForestRegressor
+from bolsonaro.data.task import Task
 
+from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
 import os
 import pickle
 
@@ -11,22 +13,35 @@ class ModelFactory(object):
 
     @staticmethod
     def build(task, model_parameters):
+        if task not in [Task.BINARYCLASSIFICATION, Task.REGRESSION, Task.MULTICLASSIFICATION]:
+            raise ValueError("Unsupported task '{}'".format(task))
+
         if task == Task.BINARYCLASSIFICATION:
-            model_func = OmpForestBinaryClassifier
+            if model_parameters.extraction_strategy == 'omp':
+                return OmpForestBinaryClassifier(model_parameters)
+            elif model_parameters.extraction_strategy == 'random':
+                return RandomForestClassifier(n_estimators=model_parameters.extracted_forest_size,
+                    random_state=model_parameters.seed)
+            else:
+                return RandomForestClassifier(n_estimators=model_parameters.hyperparameters['n_estimators'],
+                    random_state=model_parameters.seed)
         elif task == Task.REGRESSION:
-            model_func = OmpForestRegressor
+            if model_parameters.extraction_strategy == 'omp':
+                return OmpForestRegressor(model_parameters)
+            elif model_parameters.extraction_strategy == 'random':
+                return RandomForestRegressor(n_estimators=model_parameters.extracted_forest_size,
+                    random_state=model_parameters.seed)
+            elif model_parameters.extraction_strategy == 'similarity':
+                return SimilarityForestRegressor(model_parameters)
+            else:
+                return RandomForestRegressor(n_estimators=model_parameters.hyperparameters['n_estimators'],
+                    random_state=model_parameters.seed)
         elif task == Task.MULTICLASSIFICATION:
-            model_func = OmpForestMulticlassClassifier
+            if model_parameters.extraction_strategy == 'omp':
+                return OmpForestMulticlassClassifier(model_parameters)
+            elif model_parameters.extraction_strategy == 'random':
+                return RandomForestClassifier(n_estimators=model_parameters.extracted_forest_size,
+                    random_state=model_parameters.seed)
             else:
-            raise ValueError("Unsupported task '{}'".format(task))
-        return model_func(model_parameters)
-
-    @staticmethod
-    def load(task, directory_path, experiment_id, model_raw_results):
-        raise NotImplementedError
-        model_parameters = ModelParameters.load(directory_path, experiment_id)
-        model = ModelFactory.build(task, model_parameters)
-        # todo faire ce qu'il faut ici pour rétablir correctement le modèle
-        model.set_forest(model_raw_results.model_object.forest)
-        model.set_weights(model_raw_results.model_object.weights)
-        return model
+                return RandomForestClassifier(n_estimators=model_parameters.hyperparameters['n_estimators'],
+                    random_state=model_parameters.seed)