Adding start of reinforecement

choose_hyperparams.py

@@ -31,10 +31,11 @@ def k_folds_ml(x_train, y_train, model, random_state=0):
 def choose_hyperparams(ml_model, method):
     """Given a ml_model and a method, a file with the hyperparameters
     chosen by cross validation is created"""
-    this_dataset_file = find_dataset_filename('train', method=method)
+    this_dataset_file = find_dataset_filename('Train', method=method)
     with open(this_dataset_file, 'rb') as f:
-        x_train, y_train, _ = pickle.load(f)
-    hyperparams = k_folds_ml(x_train, y_train, model=ml_model)
+        dataset = pickle.load(f)
+    hyperparams = k_folds_ml(dataset['features'], dataset['labels'], model=ml_model)
+    print(hyperparams)
     hyperparams_filename = find_hyperparams_filename(method, ml_model)
     print(hyperparams_filename)
     write_yaml_to_file(hyperparams, hyperparams_filename)

config/hyperparameters_grid.py

@@ -2,10 +2,16 @@
 
 grid = dict()
 grid['RF'] = {
-    'n_estimators': [200, 300, 400, 500],
-    'max_features': ['sqrt', 'log2'],
-    'max_depth': [4, 5, 6, 7, 8],
-    'criterion': ['gini', 'entropy']
+    # 'n_estimators': [200, 300, 400, 500],
+    # 'max_features': ['sqrt', 'log2'],
+    # 'max_depth': [4, 5, 6, 7, 8],
+    # 'criterion': ['gini', 'entropy']
+    'n_estimators': [50, 100, 200],
+    'criterion': ['gini', 'entropy'],
+    'max_depth': [None, 10, 20, 30],
+    'min_samples_split': [2, 5, 10],
+    'min_samples_leaf': [1, 2, 4],
+    'class_weight': [None, 'balanced'],
 }
 grid['KNN'] = {
     'n_neighbors': [1,3,5,7,12],
@@ -35,9 +41,18 @@
 }
 
 grid['RFR'] = {
-    'criterion': ['squared_error', 'friedman_mse'],
-    "max_depth": [1,3,7],
-    "min_samples_leaf": [1,5,10],
+    # 'n_estimators': [200, 300, 400, 500],
+    # 'max_features': ['sqrt', 'log2'],
+    # 'max_depth': [4, 5, 6, 7, 8],
+    # 'criterion': ['squared_error', 'entropy']
+    # # 'criterion': ['squared_error', 'friedman_mse'],
+    # # "max_depth": [1,3,7],
+    # # "min_samples_leaf": [1,5,10],
+    'n_estimators': [50, 100, 200],
+    'criterion': ['mse', 'mae'],
+    'max_depth': [None, 10, 20, 30],
+    'min_samples_split': [2, 5, 10],
+    'min_samples_leaf': [1, 2, 4],
 }
 grid['KNNR'] = {
     'n_neighbors': [3, 5, 10],

config/hyperparams/augmented_RF.yaml

@@ -1,5 +1,6 @@
+class_weight: null
 criterion: entropy
-max_depth: 8
-max_features: sqrt
-n_estimators: 500
-random_state: 18
+max_depth: null
+min_samples_leaf: 1
+min_samples_split: 2
+n_estimators: 200

config/hyperparams/balanced_RF.yaml

@@ -1,5 +1,6 @@
-criterion: gini
-max_depth: 8
-max_features: sqrt
-n_estimators: 500
-random_state: 18
+class_weight: balanced
+criterion: entropy
+max_depth: 20
+min_samples_leaf: 2
+min_samples_split: 5
+n_estimators: 50