docs: Fix examples

strongio · Oct 8, 2024 · 6f5a2dc · 6f5a2dc
1 parent 9b6e23e
commit 6f5a2dc
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Showing 2 changed files with 11 additions and 5 deletions.
diff --git a/docs/examples/air_quality.py b/docs/examples/air_quality.py
@@ -97,7 +97,7 @@ def inverse_transform(df):
 )
 
 df_forecast = inverse_transform(forecast.to_dataframe(dataset_pm_univariate, conf=None))
-print(forecast.plot(df_forecast, max_num_groups=3, split_dt=SPLIT_DT))
+print(forecast.plot(df_forecast, max_num_groups=3, split_dt=SPLIT_DT, time_colname='time', group_colname='group'))
 
 # %% [markdown]
 # #### Evaluating Performance: Expanding Window
@@ -185,7 +185,10 @@ def inverse_transform(df):
 # In our case this unfortunately won't work: we have log-transformed our measures. This seems like it was the right choice (i.e. our residuals look reasonably normal and i.i.d):
 
 # %%
-pred_4step.plot(pred_4step.to_dataframe(dataset_pm_multivariate, type='components').query("process=='residuals'"))
+pred_4step.plot(
+    pred_4step.to_dataframe(dataset_pm_multivariate, type='components').query("process=='residuals'"),
+    time_colname='time', group_colname='group'
+)
 
 
 # %% [markdown]
@@ -308,7 +311,7 @@ def mc_preds_to_dataframe(preds: Predictions,
     )
 pred_4step.plot(
     pred_4step.to_dataframe(dataset_pm_lm, type='components').query("process.str.contains('lm')"),
-    split_dt=SPLIT_DT
+    split_dt=SPLIT_DT, time_colname='time', group_colname='group'
 )
 
 # %% [markdown]

diff --git a/docs/quick_start.py b/docs/quick_start.py
@@ -127,10 +127,13 @@
 # The `Predictions` class comes with a `plot` classmethod for getting simple plots of forecasted vs. actual:
 
 # %%
-print(pred.plot(df_pred.query("group=='Changping'"), split_dt=SPLIT_DT))
+print(pred.plot(df_pred.query("group=='Changping'"), split_dt=SPLIT_DT, time_colname='time', group_colname='group'))
 
 # %% [markdown]
 # Finally you can produce dataframes that decompose the predictions into the underlying `processes` that produced them:
 
 # %%
-pred.plot(pred.to_dataframe(dataset_all, type='components').query("group=='Changping'"), split_dt=SPLIT_DT)
+pred.plot(
+    pred.to_dataframe(dataset_all, type='components').query("group=='Changping'"), split_dt=SPLIT_DT,
+    time_colname='time', group_colname='group'
+)