can add predictor backends

demo_prophet.py

@@ -4,6 +4,7 @@ import matplotlib.pyplot as plt
 from prophet import Prophet
 import holidays
 import logging
+from sklearn.metrics import mean_absolute_error
 
 
 # kW
@@ -14,12 +15,8 @@ hungarian_holidays = holidays.Hungary(years=range(2019, 2031))
 HOLIDAY_DF = pd.DataFrame(list(hungarian_holidays.items()), columns=['ds', 'holiday'])
 
 
-def prediction_task(df, split_date, forecast_horizon):
-    # Split the data into training (past) and evaluation (future) sets
-    train_data = df[df['ds'] <= split_date]
-    eval_data = df[df['ds'] > split_date]
-
 
+def prophet_backend(train_data, forecast_horizon):
     # Initialize and train the Prophet model using the training data
     model = Prophet(seasonality_mode='multiplicative', growth='flat',
         yearly_seasonality=False, weekly_seasonality=True, daily_seasonality=True,
@@ -32,19 +29,33 @@ def prediction_task(df, split_date, forecast_horizon):
 
     # Make predictions for the evaluation period
     forecast = model.predict(future)
+    assert len(forecast) == forecast_horizon
 
-    # Calculate evaluation metrics (e.g., MAE, MSE, RMSE) by comparing eval_predictions with eval_data['y']
-
-    # For example, you can calculate MAE as follows:
-    from sklearn.metrics import mean_absolute_error
-
-    eval_data = eval_data[eval_data['ds'] <= forecast['ds'].max()]
     for key in ('yhat', 'yhat_lower', 'yhat_upper'):
         forecast[key] = np.maximum(forecast[key], PREDICTION_LOWER_BOUND)
 
-    mae = mean_absolute_error(eval_data['y'], forecast['yhat'])
+    return forecast
+
+
+def sklearn_backend(train_data, forecast_horizon):
+    dc = train_data[['y']]
+    # inserting new column with yesterday's consumption values
+    for i in range(1, 4 * 24 + 1):
+        dc.loc[:, 'd%02d' % i] = dc.loc[:,'y'].shift(4 * 24 + i) # t-2days to t-1day
+        dc.loc[:, 'w%02d' % i] = dc.loc[:,'y'].shift(7 * 4 * 24 + i) # t-7days to t-8days
+    dc.info()
+    exit()
 
-    # Print or store the evaluation metrics
+
+def prediction_task(backend, df, split_date, forecast_horizon):
+    # Split the data into training (past) and evaluation (future) sets
+    train_data = df[df['ds'] <= split_date]
+    eval_data = df[df['ds'] > split_date]
+    eval_data = eval_data.head(forecast_horizon)
+
+    forecast = backend(train_data, forecast_horizon)
+
+    mae = mean_absolute_error(eval_data['y'], forecast['yhat'])
 
     do_vis = False
     if do_vis:
@@ -66,6 +77,7 @@ def prediction_task(df, split_date, forecast_horizon):
 
         fig2 = model.plot_components(forecast)
         plt.show()
+
     return mae, eval_data['y'].mean()
 
 
@@ -100,7 +112,7 @@ weekly_date_range = pd.date_range(start=start_date, end=end_date, freq='8d')
 maes = []
 mean_values = []
 for split_date in weekly_date_range:
-    mae, mean_value = prediction_task(df, split_date, forecast_horizon)
+    mae, mean_value = prediction_task(prophet_backend, df, split_date, forecast_horizon)
     maes.append(mae)
     mean_values.append(mean_value)
     print(split_date, "Mean Absolute Error", mae, "MAE/true mean", mae / mean_value)