Version 1 hotfix #11 for merge

app.py

@@ -180,18 +180,19 @@ def sales_growth(dataframe, fittedValues):
     return sales_growth
 
 @st.cache_data
-def merge_forecast_data(historical, test, future):
-    historical = historical.to_frame()
-    historical.rename(columns={historical.columns[0]: "Actual Sales"}, inplace=True)
-    test = test.to_frame()
-    test.rename(columns={test.columns[0]: "Predicted Sales"}, inplace=True)
+def merge_forecast_data(actual, predicted, future):
+    actual = actual.to_frame()
+    actual.rename(columns={actual.columns[0]: "Actual Sales"}, inplace=True)
+
+    predicted = predicted.to_frame()
+    predicted.rename(columns={predicted.columns[0]: "Predicted Sales"}, inplace=True)
+
     future = future.to_frame()
+    future = future.rename_axis('Date')
     future.rename(columns={future.columns[0]: "Future Forecasted Sales"}, inplace=True)
-    historical.head()
-    test.head()
-    future.head()
-    merged_dataframe = pd.merge_ordered(historical, pd.merge_ordered(test, future, fill_method="ffill", left_by="Predicted Sales"), fill_method="ffill", left_by="Actual Sales")
 
+    merged_dataframe = pd.merge_ordered(actual, predicted, on='Date', suffixes=('_Actual', '_Predicted'))
+    merged_dataframe = pd.merge_ordered(merged_dataframe, future, on='Date')
     return merged_dataframe
 
 # TAPAS Model
@@ -300,6 +301,7 @@ if (st.session_state.uploaded):
     train = train_test(df)
     training_y, test_y, test_y_series, training_X, test_X, future_X = train
     train_test_model = test_fitting(df, training_X, training_y)
+    
     n_periods = round(len(df) * 0.2)
     future_n_periods = forecast_period + n_periods
     fitted, confint = train_test_model.predict(X=test_X, n_periods=n_periods, return_conf_int=True)
@@ -341,19 +343,21 @@ if (st.session_state.uploaded):
     future_sales_growth = sales_growth(df, future_fitted_series)
     future_sales_growth = future_sales_growth.iloc[n_periods:]
     df = dates_df(future_sales_growth)
+
+    test_y, predictions = np.array(test_y), np.array(fitted)
+    acc = forecast_accuracy(predictions, test_y)
     
     col = st.columns(2)
     with col[0]:
        col[0].header("Sales Forecast")
        merged_data = merge_forecast_data(df['Sales'], fitted_series, future_fitted_series)
        col[0].line_chart(merged_data)
+       col[0].write(f"MAPE score: {acc['mape']} (lower is better)")
     with col[1]:
        col[1].subheader(f"Forecasted sales in the next {period} days")
        col[1].write(df)
     st.session_state.forecasted = True
 
-    test_y, predictions = np.array(test_y), np.array(fitted)
-    forecast_accuracy(predictions, test_y)
 
     with st.form("question_form"):
         question = st.text_input('Ask a Question about the Forecasted Data', placeholder="What is the total sales in the month of December?")