Merge pull request #61 from Nixtla/ImprovedDocumentation2

Fixed missing documentation plots, working README example

README.md

@@ -76,36 +76,42 @@ import pandas as pd
 
 #obtain hierarchical dataset
 from datasetsforecast.hierarchical import HierarchicalData
+
+# compute base forecast no coherent
+from statsforecast.core import StatsForecast
+from statsforecast.models import AutoARIMA, Naive
+
 #obtain hierarchical reconciliation methods and evaluation
 from hierarchicalforecast.core import HierarchicalReconciliation
 from hierarchicalforecast.evaluation import HierarchicalEvaluation
 from hierarchicalforecast.methods import BottomUp, TopDown, MiddleOut
-# compute base forecast no coherent
-from statsforecast.core import StatsForecast
-from statsforecast.models import AutoARIMA, Naive
+
 
 # Load TourismSmall dataset
 Y_df, S, tags = HierarchicalData.load('./data', 'TourismSmall')
 Y_df['ds'] = pd.to_datetime(Y_df['ds'])
 
 #split train/test sets
-Y_df_test = Y_df.groupby('unique_id').tail(12)
-Y_df_train = Y_df.drop(Y_df_test.index)
-Y_df_test = Y_df_test.set_index('unique_id')
-Y_df_train = Y_df_train.set_index('unique_id')
+Y_test_df  = Y_df.groupby('unique_id').tail(12)
+Y_train_df = Y_df.drop(Y_test_df.index)
+Y_test_df  = Y_test_df.set_index('unique_id')
+Y_train_df = Y_train_df.set_index('unique_id')
 
 # Compute base auto-ARIMA predictions
-fcst = StatsForecast(df=Y_df_train, models=[AutoARIMA(season_length=12), Naive()], freq='M', n_jobs=-1)
+fcst = StatsForecast(df=Y_train_df, 
+                     models=[AutoARIMA(season_length=12), Naive()], 
+                     freq='M', n_jobs=-1)
 Y_hat_df = fcst.forecast(h=12)
 
 # Reconcile the base predictions
 reconcilers = [
     BottomUp(),
     TopDown(method='forecast_proportions'),
-    MiddleOut(level='Country/Purpose/State', top_down_method='forecast_proportions')
+    MiddleOut(middle_level='Country/Purpose/State',
+              top_down_method='forecast_proportions')
 ]
 hrec = HierarchicalReconciliation(reconcilers=reconcilers)
-Y_rec_df = hrec.reconcile(Y_hat_df, Y_df_train, S, tags)
+Y_rec_df = hrec.reconcile(Y_hat_df, Y_train_df, S, tags)
 ```
 
 ### Evaluation
@@ -115,7 +121,7 @@ def mse(y, y_hat):
     return np.mean((y-y_hat)**2)
 
 evaluator = HierarchicalEvaluation(evaluators=[mse])
-evaluator.evaluate(Y_h=Y_rec_df, Y_test=Y_df_test, 
+evaluator.evaluate(Y_h=Y_rec_df, Y_test=Y_test_df, 
                    tags=tags, benchmark='Naive')
 ```