fixed docstring in plot function

Former-commit-id: 70d8a9e657

fclib/fclib/common/plot.py

@@ -31,29 +31,29 @@ def plot_predictions_with_history(
     """Plot prediction results with historical values
 
     Args:
-        predictions (Dataframe): Prediction results with a time step column (e.g., week_index), a 
-        forecasted value column (e.g., forecasted sales of each store-brand), and two columns that 
-        identify each individual time series (e.g., store_id and brand_id) 
-        history (Dataframe): A dataframe containing historical values of the prediction target, a 
-        time step column, and two columns that specify each time series
-        grain1_unique_vals (List): Unique values of the 1st column indicating the granularity of
-        the time series data (e.g, store_list)
-        grain2_unique_vals (List): Unique values of the 2nd column indicating the granularity of
-        the time series data (e.g., brand_list)
-        time_col_name (String): Name of the time step column (e.g., week_index)
-        target_col_name (String): Name of the forecast target column (e.g., unit_sales)
-        grain1_name (String): Name of the 1st column indicating the time series graunularity
-        grain2_name (String): Name of the 2nd column indicating the time series graunularity
-        min_timestep (Integer): Minimum time steps in the plots
-        num_samples (Integer): Number of samples from all the time series (each combination of 
-        grain1 column and grain2 column gives an individual time series)
-        predict_at_timestep (Integer): Time step at which the forecasts are made
-        line_at_predict_time (Boolean): Whether to add a vertical line indicating the time step 
-        when the forecasts are made
-        title (String): Overall title of the plots 
-        x_label (String): Label of the x-axis of the plots
-        y_label (String): Label of the y-axis of the plots
-        random_seed (Integer): Random seed used for sampling the time series
+        predictions (pd.DataFrame): Prediction results with a time step column (e.g., week_index), a 
+            forecasted value column (e.g., forecasted sales of each store-brand), and two columns that 
+            identify each individual time series (e.g., store_id and brand_id) 
+        history (pd.Dataframe): A dataframe containing historical values of the prediction target, a 
+            time step column, and two columns that specify each time series
+        grain1_unique_vals (list): Unique values of the 1st column indicating the granularity of
+            the time series data (e.g, store_list)
+        grain2_unique_vals (list): Unique values of the 2nd column indicating the granularity of
+            the time series data (e.g., brand_list)
+        time_col_name (str): Name of the time step column (e.g., week_index)
+        target_col_name (str): Name of the forecast target column (e.g., unit_sales)
+        grain1_name (str): Name of the 1st column indicating the time series graunularity
+        grain2_name (str): Name of the 2nd column indicating the time series graunularity
+        min_timestep (int): Minimum time steps in the plots
+        num_samples (int): Number of samples from all the time series (each combination of 
+            grain1 column and grain2 column gives an individual time series)
+        predict_at_timestep (int): Time step at which the forecasts are made
+        line_at_predict_time (bool): Whether to add a vertical line indicating the time step 
+            when the forecasts are made
+        title (str): Overall title of the plots 
+        x_label (str): Label of the x-axis of the plots
+        y_label (str): Label of the y-axis of the plots
+        random_seed (int): Random seed used for sampling the time series
     """
 
     random.seed(random_seed)