Refactor forecast stats wrappers.

DataCanvasIO · Mar 11, 2022 · 9033414 · 9033414
1 parent 35c2290
commit 9033414
Show file tree

Hide file tree

Showing 2 changed files with 9 additions and 3 deletions.
diff --git a/hyperts/framework/wrappers/stats_wrappers.py b/hyperts/framework/wrappers/stats_wrappers.py
@@ -83,6 +83,9 @@ def fit(self, X, y=None, **kwargs):
 
     def predict(self, X, **kwargs):
         last_date = X[self.timestamp].tail(1).to_list()[0].to_pydatetime()
+        if last_date == self._end_date:
+            raise ValueError('The end date of the valid set must be '
+                             'less than the end date of the test set.')
         steps = int((last_date - self._end_date).total_seconds() / self._freq)
         predict_result = self.model.forecast(steps=steps).values
 
@@ -120,6 +123,9 @@ def fit(self, X, y=None, **kwargs):
 
     def predict(self, X, **kwargs):
         last_date = X[self.timestamp].tail(1).to_list()[0].to_pydatetime()
+        if last_date == self._end_date:
+            raise ValueError('The end date of the valid set must be '
+                             'less than the end date of the test set.')
         steps = int((last_date - self._end_date).total_seconds() / self._freq)
         predict_result = self.model.forecast(self.model.y, steps=steps)
 

diff --git a/hyperts/tests/experiment/task_test.py b/hyperts/tests/experiment/task_test.py
@@ -29,7 +29,7 @@ def test_univariate_forecast(self):
                             optimize_direction=OptimizeDirection.Minimize)
         hyper_model = HyperTS(rs, task='univariate-forecast', reward_metric='rmse', callbacks=[SummaryCallback()])
 
-        exp = TSCompeteExperiment(hyper_model, X_train, y_train, X_eval=X_test, y_eval=y_test,
+        exp = TSCompeteExperiment(hyper_model, X_train, y_train,
                                   timestamp_col='ds',
                                   covariate_cols=[['id'], cs.covariables_],
                                   covariate_cleaner=cs)
@@ -46,7 +46,7 @@ def test_multivariate_forecast(self):
                             optimize_direction=OptimizeDirection.Minimize)
         hyper_model = HyperTS(rs, task='multivariate-forecast', reward_metric='rmse', callbacks=[SummaryCallback()])
 
-        exp = TSCompeteExperiment(hyper_model, X_train, y_train, X_eval=X_test, y_eval=y_test, timestamp_col='ds')
+        exp = TSCompeteExperiment(hyper_model, X_train, y_train, timestamp_col='ds')
         pipeline_model = exp.run(max_trials=3)
 
         y_pred = pipeline_model.predict(X_test)
@@ -60,7 +60,7 @@ def test_univariate_classification(self):
                             optimize_direction=OptimizeDirection.Maximize)
         hyper_model = HyperTS(rs, task='univariate-multiclass', reward_metric='accuracy', callbacks=[SummaryCallback()])
 
-        exp = TSCompeteExperiment(hyper_model, X_train, y_train, X_eval=X_test, y_eval=y_test)
+        exp = TSCompeteExperiment(hyper_model, X_train, y_train)
         pipeline_model = exp.run(max_trials=3)
 
         y_pred = pipeline_model.predict(X_test)