Refactor ARIMA model params.

hyperts/framework/search_space/macro_search_space.py

@@ -248,7 +248,7 @@ def default_arima_init_kwargs(self):
             'seasonal_order': Choice([(1, 0, 0), (1, 0, 1), (1, 0, 1),
                                       (1, 0, 2), (0, 2, 1), (0, 1, 2),
                                       (2, 0, 1), (2, 0, 2), (0, 1, 1)]),
-            'period_offset': Choice([0, 0, 0, 0, 0, 0, 1, -1, 2, -2]),
+            # 'period_offset': Choice([0, 0, 0, 0, 0, 0, 1, -1, 2, -2]),
             'y_scale': Choice(['min_max']*8 + ['max_abs']*1 + ['z_scale']*1)
         }
 

hyperts/framework/wrappers/stats_wrappers.py

@@ -78,21 +78,23 @@ def fit(self, X, y=None, **kwargs):
         q = self.init_kwargs.pop('q', 1)
         trend = self.init_kwargs.pop('trend', 'c')
         seasonal_order = self.init_kwargs.pop('seasonal_order', (1, 1, 1))
-        period_offset = self.init_kwargs.pop('period_offset', 0)
-        if period != 2 and period+period_offset > 1:
-            period = min(period+period_offset, 30)
-        else:
-            period = 3
+        # period_offset = self.init_kwargs.pop('period_offset', 0)
+        # if period != 2 and period+period_offset > 1:
+        #     period = min(period+period_offset, 30)
+        # else:
+        #     period = 3
         period = kwargs.get('period', period)
-        seasonal_order = seasonal_order + (period,)
+        if period > 2 and period <= 12:
+            seasonal_order = seasonal_order + (period,)
+        else:
+            seasonal_order = (0, 0, 0, 0)
 
         try:
             model = ARIMA(endog=y, order=(p, d, q), trend=trend, freq=freq,
                           seasonal_order=seasonal_order, dates=X[self.timestamp])
-            self.model = model.fit(**self.init_kwargs)
         except:
             model = ARIMA(endog=y, order=(p, d, q), trend=trend, freq=freq, dates=X[self.timestamp])
-            self.model = model.fit(**self.init_kwargs)
+        self.model = model.fit(**self.init_kwargs)
 
     def predict(self, X, **kwargs):
         last_date = X[self.timestamp].tail(1).to_list()[0].to_pydatetime()

hyperts/utils/metrics.py

@@ -389,7 +389,7 @@ def metric_to_scorer(metric, task, pos_label=None, **options):
        (callable(metric) and metric.__name__ in const.POSLABEL_REQUIRED):
         average = _task_to_average(task)
         scorer._kwargs['average'] = average
-        if average is 'binary':
+        if average == 'binary':
             scorer._kwargs['pos_label'] = pos_label
         logger.info(f"pos_label is {pos_label}.")