From 7c16ad0cced6c63ac8cd154dd6b242fde362a689 Mon Sep 17 00:00:00 2001
From: xiaolongsun <95378566+xiaolong0728@users.noreply.github.com>
Date: Wed, 8 Jan 2025 11:54:07 +0100
Subject: [PATCH] Add annotation

---
 .../evaluation/metric_calculation.py          | 34 +++++++++++++++++++
 1 file changed, 34 insertions(+)

diff --git a/views_evaluation/evaluation/metric_calculation.py b/views_evaluation/evaluation/metric_calculation.py
index f67e23d..4302489 100644
--- a/views_evaluation/evaluation/metric_calculation.py
+++ b/views_evaluation/evaluation/metric_calculation.py
@@ -41,6 +41,17 @@ def __init__(self, metrics_list):
     def time_series_wise_evaluation(
             self, actual: pd.DataFrame, predictions: List[pd.DataFrame], target: str
             ) -> pd.DataFrame:
+        """
+        Evaluates the predictions time series-wise and calculates the specified metrics.
+
+        Args:
+            actual (pd.DataFrame): The actual values.
+            predictions (List[pd.DataFrame]): A list of DataFrames containing the predictions.
+            target (str): The target column in the actual DataFrame.
+
+        Returns:
+            pd.DataFrame: A DataFrame containing the evaluation metrics.
+        """
         evaluation_dict = EvaluationMetrics.make_time_series_wise_evaluation_dict(len(predictions))
         
         for metric in self.metrics_list:
@@ -58,6 +69,18 @@ def time_series_wise_evaluation(
     def step_wise_evaluation(
             self, actual: pd.DataFrame, predictions: List[pd.DataFrame], target: str, steps: List[int]
             ) -> pd.DataFrame:
+        """
+        Evaluates the predictions step-wise and calculates the specified metrics.
+
+        Args:
+            actual (pd.DataFrame): The actual values.
+            predictions (List[pd.DataFrame]): A list of DataFrames containing the predictions.
+            target (str): The target column in the actual DataFrame.
+            steps (List[int]): The steps to evaluate.
+
+        Returns:
+            pd.DataFrame: A DataFrame containing the evaluation metrics.
+        """ 
         evaluation_dict = EvaluationMetrics.make_step_wise_evaluation_dict(steps=max(steps))
         step_metrics = {}
 
@@ -88,6 +111,17 @@ def step_wise_evaluation(
     def month_wise_evaluation(
             self, actual: pd.DataFrame, predictions: List[pd.DataFrame], target: str
             ) -> pd.DataFrame:
+        """
+        Evaluates the predictions month-wise and calculates the specified metrics.
+
+        Args:
+            actual (pd.DataFrame): The actual values.
+            predictions (List[pd.DataFrame]): A list of DataFrames containing the predictions.
+            target (str): The target column in the actual DataFrame.
+
+        Returns:
+            pd.DataFrame: A DataFrame containing the evaluation metrics.
+        """
         pred_concat = pd.concat(predictions)
         pred_concat_target = pred_concat.columns[0]
         month_range = pred_concat.index.get_level_values(0).unique()