Fix electricity forecasting tutorial

docs/source/index.rst

@@ -18,7 +18,6 @@ Lightning-Sandbox documentation
    :caption: Start here
    :glob:
 
-   notebooks/*
    notebooks/**/*
 
 .. raw:: html

flash_tutorials/electricity_forecasting/.meta.yml

@@ -1,12 +1,13 @@
 title: Electricity Price Forecasting with N-BEATS
 author: Ethan Harris (ethan@pytorchlightning.ai)
 created: 2021-11-23
-updated: 2021-11-23
+updated: 2021-12-16
 license: CC BY-SA
 build: 3
 tags:
   - Tabular
   - Forecasting
+  - Timeseries
 description: |
   This tutorial covers using Lightning Flash and it's integration with PyTorch Forecasting to train an autoregressive
   model (N-BEATS) on hourly electricity pricing data. We show how the built-in interpretability tools from PyTorch
@@ -15,7 +16,7 @@ description: |
   bonus, we show hat we can resample daily observations from the data to discover weekly trends instead.
 requirements:
   - pandas==1.1.5
-  - lightning-flash[tabular]>=0.5.2
+  - lightning-flash[tabular]>=0.6.0
 accelerator:
   - GPU
   - CPU

flash_tutorials/electricity_forecasting/electricity_forecasting.py

@@ -11,6 +11,7 @@
 # %%
 
 import os
+from typing import Any, Dict
 
 import flash
 import matplotlib.pyplot as plt
@@ -196,9 +197,15 @@ def preprocess(df: pd.DataFrame, frequency: str = "1H") -> pd.DataFrame:
 # %%
 
 
-def plot_interpretation(model_path: str, predict_df: pd.DataFrame):
+def plot_interpretation(model_path: str, predict_df: pd.DataFrame, parameters: Dict[str, Any]):
     model = TabularForecaster.load_from_checkpoint(model_path)
-    predictions = model.predict(predict_df)
+    datamodule = TabularForecastingData.from_data_frame(
+        parameters=parameters,
+        predict_data_frame=predict_df,
+        batch_size=256,
+    )
+    trainer = flash.Trainer(gpus=int(torch.cuda.is_available()))
+    predictions = trainer.predict(model, datamodule=datamodule)
     predictions, inputs = convert_predictions(predictions)
     model.pytorch_forecasting_model.plot_interpretation(inputs, predictions, idx=0)
     plt.show()
@@ -208,7 +215,7 @@ def plot_interpretation(model_path: str, predict_df: pd.DataFrame):
 # And now we run the function to plot the trend and seasonality curves:
 
 # %%
-plot_interpretation(trainer.checkpoint_callback.best_model_path, df_energy_hourly)
+plot_interpretation(trainer.checkpoint_callback.best_model_path, df_energy_hourly, datamodule.parameters)
 
 # %% [markdown]
 # It worked! The plot shows that the `TabularForecaster` does a reasonable job of modelling the time series and also
@@ -281,7 +288,7 @@ def plot_interpretation(model_path: str, predict_df: pd.DataFrame):
 # Now let's look at what it learned:
 
 # %%
-plot_interpretation(trainer.checkpoint_callback.best_model_path, df_energy_daily)
+plot_interpretation(trainer.checkpoint_callback.best_model_path, df_energy_daily, datamodule.parameters)
 
 # %% [markdown]
 # Success! We can now also see weekly trends / seasonality uncovered by our new model.