bump v0.1.1

thierrymoudiki · Aug 1, 2024 · 9bdecba · 9bdecba
1 parent 20aeab7
commit 9bdecba
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Showing 5 changed files with 17 additions and 15 deletions.
diff --git a/examples/classification.py b/examples/classification.py
@@ -12,23 +12,23 @@
 
 # Initialize the unified regressor (example with XGBoost)
 regressor1 = ub.GBDTClassifier(model_type='xgboost')
-regressor2 = ub.GBDTClassifier(model_type='catboost')
+#regressor2 = ub.GBDTClassifier(model_type='catboost')
 regressor3 = ub.GBDTClassifier(model_type='lightgbm')
 
 # Fit the model
 regressor1.fit(X_train, y_train)
-regressor2.fit(X_train, y_train)
+#regressor2.fit(X_train, y_train)
 regressor3.fit(X_train, y_train)
 
 # Predict on the test set
 y_pred1 = regressor1.predict(X_test)
-y_pred2 = regressor2.predict(X_test)
+#y_pred2 = regressor2.predict(X_test)
 y_pred3 = regressor3.predict(X_test)
 
 # Evaluate the model
 accuracy1 = accuracy_score(y_test, y_pred1)
-accuracy2 = accuracy_score(y_test, y_pred2)
+#accuracy2 = accuracy_score(y_test, y_pred2)
 accuracy3 = accuracy_score(y_test, y_pred3)
 print(f"Classification Accuracy xgboost: {accuracy1:.2f}")
-print(f"Classification Accuracy catboost: {accuracy2:.2f}")
+#print(f"Classification Accuracy catboost: {accuracy2:.2f}")
 print(f"Classification Accuracy lightgbm: {accuracy3:.2f}")
diff --git a/examples/regression.py b/examples/regression.py
@@ -12,23 +12,23 @@
 
 # Initialize the unified regressor (example with XGBoost)
 regressor1 = ub.GBDTRegressor(model_type='xgboost')
-regressor2 = ub.GBDTRegressor(model_type='catboost')
+#regressor2 = ub.GBDTRegressor(model_type='catboost')
 regressor3 = ub.GBDTRegressor(model_type='lightgbm')
 
 # Fit the model
 regressor1.fit(X_train, y_train)
-regressor2.fit(X_train, y_train)
+#regressor2.fit(X_train, y_train)
 regressor3.fit(X_train, y_train)
 
 # Predict on the test set
 y_pred1 = regressor1.predict(X_test)
-y_pred2 = regressor2.predict(X_test)
+#y_pred2 = regressor2.predict(X_test)
 y_pred3 = regressor3.predict(X_test)
 
 # Evaluate the model
 mse1 = mean_squared_error(y_test, y_pred1)
-mse2 = mean_squared_error(y_test, y_pred2)
+#mse2 = mean_squared_error(y_test, y_pred2)
 mse3 = mean_squared_error(y_test, y_pred3)
 print(f"Regression Mean Squared Error xgboost: {mse1:.2f}")
-print(f"Regression Mean Squared Error catboost: {mse2:.2f}")
+#print(f"Regression Mean Squared Error catboost: {mse2:.2f}")
 print(f"Regression Mean Squared Error lightgbm: {mse3:.2f}")
diff --git a/setup.py b/setup.py
@@ -10,7 +10,7 @@
 
 subprocess.check_call(['pip', 'install', 'Cython'])
 
-__version__ = "0.1.0"
+__version__ = "0.1.1"
 
 here = path.abspath(path.dirname(__file__))
 

diff --git a/unifiedbooster/gbdt_classification.py b/unifiedbooster/gbdt_classification.py
@@ -43,12 +43,13 @@ def __init__(self, model_type='xgboost',
                 **kwargs
             }
         elif self.model_type == "lightgbm":
+             verbose = self.verbosity - 1 if self.verbosity==0 else self.verbosity
              self.params = {
                 'num_iterations': self.n_estimators,
                 'learning_rate': self.learning_rate,
                 'bagging_fraction': self.subsample,
                 'max_depth': self.max_depth,
-                'verbose': self.verbosity - 1 if self.verbosity==0 else self.verbosity
+                'verbose': verbose,
                 **kwargs
             }
         elif self.model_type == "catboost":
@@ -57,7 +58,7 @@ def __init__(self, model_type='xgboost',
                 'learning_rate': self.learning_rate,
                 'rsm': self.subsample,
                 'depth': self.max_depth,
-                'verbose': self.verbosity
+                'verbose': self.verbosity,
                 **kwargs
             }           
 

diff --git a/unifiedbooster/gbdt_regression.py b/unifiedbooster/gbdt_regression.py
@@ -43,12 +43,13 @@ def __init__(self, model_type='xgboost',
                 **kwargs
             }
         elif self.model_type == "lightgbm":
+             verbose = self.verbosity - 1 if self.verbosity==0 else self.verbosity
              self.params = {
                 'num_iterations': self.n_estimators,
                 'learning_rate': self.learning_rate,
                 'bagging_fraction': self.subsample,
                 'max_depth': self.max_depth,
-                'verbose': self.verbosity - 1 if self.verbosity==0 else self.verbosity
+                'verbose': verbose,
                 **kwargs
             }
         elif self.model_type == "catboost":
@@ -57,7 +58,7 @@ def __init__(self, model_type='xgboost',
                 'learning_rate': self.learning_rate,
                 'rsm': self.subsample,
                 'depth': self.max_depth,
-                'verbose': self.verbosity
+                'verbose': self.verbosity,
                 **kwargs
             }