@@ -86,8 +86,8 @@ def _build_model(self) -> pd.DataFrame:
8686 model = pm .auto_arima (y = y , X = X_in , ** self .spec .model_kwargs )
8787
8888 fitted_values [target ] = model .predict_in_sample (X = X_in )
89- print (f"y: { y .head (10 ), y .tail (10 )} " )
9089 actual_values [target ] = y
90+ actual_values [target ].index = pd .to_datetime (y .index )
9191
9292 # Build future dataframe
9393 start_date = y .index .values [- 1 ]
@@ -108,7 +108,7 @@ def _build_model(self) -> pd.DataFrame:
108108 )
109109 yhat_clean = pd .DataFrame (yhat , index = yhat .index , columns = ["yhat" ])
110110
111- dt_columns [target ] = ( df_encoded [self .spec .datetime_column .name ],)
111+ dt_columns [target ] = df_encoded [self .spec .datetime_column .name ]
112112 conf_int_clean = pd .DataFrame (
113113 conf_int , index = yhat .index , columns = ["yhat_lower" , "yhat_upper" ]
114114 )
@@ -133,16 +133,12 @@ def _build_model(self) -> pd.DataFrame:
133133 yhat_upper_name = ForecastOutputColumns .UPPER_BOUND
134134 yhat_lower_name = ForecastOutputColumns .LOWER_BOUND
135135 for cat in self .categories :
136- print (f"cat: { cat } " )
137136 output_i = pd .DataFrame ()
138137 output_i ["Date" ] = dt_columns [f"{ col } _{ cat } " ]
139- output_i = output_i .set_index ("Date" )
140138 output_i ["Series" ] = cat
141- print (f"output_i: { output_i } " )
142- print (f"actual_values: { actual_values [f'{ col } _{ cat } ' ]} " )
139+ output_i = output_i .set_index ("Date" )
143140
144141 output_i ["input_value" ] = actual_values [f"{ col } _{ cat } " ]
145-
146142 output_i ["fitted_value" ] = fitted_values [f"{ col } _{ cat } " ]
147143 output_i ["forecast_value" ] = outputs [f"{ col } _{ cat } " ]["yhat" ]
148144 output_i [yhat_upper_name ] = outputs [f"{ col } _{ cat } " ]["yhat_upper" ]
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