Merge pull request #15 from UBC-MDS/score_df_docstring

adding scoring_df docstring

CONTRIBUTING.md

@@ -46,13 +46,15 @@ Ready to contribute? Here's how to set up `compare_classifiers` for local develo
 2. Install `compare_classifiers` using `poetry`:
 
     ```console
-    $ poetry install
+    $ poetry install compare_classifiers
+
     ```
 
 3. Use `git` (or similar) to create a branch for local development and make your changes:
 
     ```console
     $ git checkout -b name-of-your-bugfix-or-feature
+
     ```
 
 4. When you're done making changes, check that your changes conform to any code formatting requirements and pass any tests.

src/compare_classifiers/compare_classifiers/compare_f1.py

@@ -0,0 +1,30 @@
+def compare_f1(estimators, X, y):
+    """
+    Show cross validation results, including fit time and f1 scores for each estimator.
+
+    Parameters
+    ----------
+    estimators : list of tuples
+        A list of (name, estimator) tuples, consisting of individual estimators to be processed through the voting or stacking classifying ensemble. Each tuple contains a string: name/label of estimator, and a model: the estimator, which implements
+        the scikit-learn API (`fit`, `predict`, etc.).
+    
+    X_train : Pandas data frame
+        Data frame containing training data along with n features.
+        
+    y_train : Pandas series
+        Target class labels for data in X_train.
+
+    Returns:
+    --------
+    Pandas data frame
+        A data frame showing cross validation results on training data, with 3 columns: fit_time, test_score, train_score and 1 rows for each estimator.
+
+    Example:
+    -------- 
+    >>> estimators = [
+    ...     ('rf', RandomForestClassifier(n_estimators=10, random_state=42)),
+    ...     ('svm', make_pipeline(StandardScaler(), LinearSVC(random_state=42)))
+    ... ]
+    >>> compare_f1(estimators, X, y)
+    """
+    pass

src/compare_classifiers/compare_classifiers/ensemble_compare_f1.py

@@ -25,7 +25,7 @@ def ensemble_compare_f1(estimators, X_train, y_train):
     ...     ('rf', RandomForestClassifier(n_estimators=10, random_state=42)),
     ...     ('svm', make_pipeline(StandardScaler(), LinearSVC(random_state=42)))
     ... ]
-    ensemble_compare_f1(estimators, X, y)
+    >>> ensemble_compare_f1(estimators, X, y)
     """
     # ...existing code...