kevin-ensemble_compare_f1.py

README.md

@@ -16,7 +16,7 @@ as follows:
 ```python
 from ensemble_best_method.confusion_matrices import confusion_matrices
 from ensemble_best_method.compare_f1 import compare_f1
-from ensemble_best_method.ensumble_compare_f1 import ensemble_compare_f1
+from ensemble_best_method.ensemble_compare_f1 import ensemble_compare_f1
 
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.svm import LinearSVC
@@ -58,4 +58,4 @@ Interested in contributing? Check out the contributing guidelines. Please note t
 
 ## Credits
 
-`ensemble_best_method` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).
+`ensemble_best_method` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).

src/ensemble_best_method/ensemble_compare_f1.py

@@ -0,0 +1,21 @@
+def ensemble_compare_f1(estimators, X_train, y_train, method='stacking'):
+    """
+    Show cross validation fit time and f1 scores of a classifier by stacking or voting the estimators.
+
+    Parameters:
+    estimators (list): List of (str, estimator) tuples.
+    X_train (array-like): Feature matrix for training.
+    y_train (array-like): Target vector for training.
+    method (str): Ensemble method, 'stacking' or 'voting'. Default is 'stacking'.
+
+    Returns:
+    dict: Dictionary containing fit times and f1 scores.
+    """
+    # ...existing code...
+
+# Example usage:
+# estimators = [('lr', LogisticRegression()), ('rf', RandomForestClassifier())]
+# X_train = ... # feature matrix for training
+# y_train = ... # target vector for training
+# result = ensemble_compare_f1(estimators, X_train, y_train, method='stacking')
+# print(result)