X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/predictops.git/blobdiff_plain/964c1b87a6996c828c150a2b06a827350a4c2b10..a2faba3f0797b7be72d0c8fa9cb9db67456136d6:/predictops/learn/learning.py?ds=sidebyside

diff --git a/predictops/learn/learning.py b/predictops/learn/learning.py
index 4164500..9a5860a 100644
--- a/predictops/learn/learning.py
+++ b/predictops/learn/learning.py
@@ -15,8 +15,6 @@ class Learning:
         df = X
         df['cible'] = y
 
-        print(df.head())
-
         train_val_set, test_set = train_test_split(df, test_size = 0.2, random_state = 42)
         train_set, val_set = train_test_split(train_val_set, test_size = 0.2, random_state = 42)
 
@@ -30,12 +28,13 @@ class Learning:
 
 
         if self._config['MODEL']['method'] == 'xgboost':
-            xgb_reg = xgboost.XGBRegressor(learning_rate = 0.01,
-                                                   max_depth = 10,
-                                                   random_state=42,
-                                                   n_estimators = 173,
-                                                   n_jobs=-1,
-                                                   objective = 'count:poisson')
+
+            xgb_reg = xgboost.XGBRegressor(learning_rate = self._config['HYPERPARAMETERS'].getfloat('learning_rate'),
+                                           max_depth     = self._config['HYPERPARAMETERS'].getint('max_depth'),
+                                           random_state  = self._config['HYPERPARAMETERS'].getint('random_state'),
+                                           n_estimators  = self._config['HYPERPARAMETERS'].getint('n_estimators'),
+                                           n_jobs        = self._config['HYPERPARAMETERS'].getint('n_jobs'),
+                                           objective     = 'count:poisson')
 
             xgb_reg.fit(X_train, y_train,
                         eval_set=[(X_val, y_val)],