0.6.0

README.md

@@ -59,7 +59,7 @@ model = AutoTS(
     forecast_length=21,
     frequency='infer',
     prediction_interval=0.9,
-    ensemble=None,
+    ensemble='auto',
     model_list="fast",  # "superfast", "default", "fast_parallel"
     transformer_list="fast",  # "superfast",
     drop_most_recent=1,

TODO.md

@@ -12,7 +12,7 @@
 * The most recent data will generally be the most important
 * Forecasts are desired for the future immediately following the most recent data.
 
-# 0.5.9 ♨️♨️♨️
+# 0.6.0 ♨️♨️♨️
 * import_best_model and fit_data
 * added canberra distance to SeasonalityMotif and MetricMotif
 * DatepartRegressionTransformer now handles NaN in input data

autots/__init__.py

@@ -26,7 +26,7 @@
 from autots.models.cassandra import Cassandra
 
 
-__version__ = '0.5.9'
+__version__ = '0.6.0'
 
 TransformTS = GeneralTransformer
 

autots/evaluator/anomaly_detector.py

@@ -319,7 +319,12 @@ def plot_anomaly(self, kwargs={}):
         self.anomaly_model.plot(**kwargs)
 
     def plot(
-        self, series_name=None, include_anomalies=True, title=None, plot_kwargs={}, series=None
+        self,
+        series_name=None,
+        include_anomalies=True,
+        title=None,
+        plot_kwargs={},
+        series=None,
     ):
         import matplotlib.pyplot as plt
 

autots/evaluator/auto_model.py

@@ -733,7 +733,9 @@ def fit(self, df, future_regressor=None):
                         f,
                     )
             except Exception as e:
-                error_msg = f"failed to write {self.current_model_file} with error {repr(e)}"
+                error_msg = (
+                    f"failed to write {self.current_model_file} with error {repr(e)}"
+                )
                 try:
                     with open(f'{self.current_model_file}_failure.json', 'w') as f:
                         f.write(error_msg)
@@ -749,7 +751,9 @@ def fit(self, df, future_regressor=None):
 
         self.transformation_runtime = datetime.datetime.now() - transformationStartTime
         # from autots.evaluator.auto_model import ModelMonster
-        self.model = self.model.fit(df_train_transformed, future_regressor=future_regressor)
+        self.model = self.model.fit(
+            df_train_transformed, future_regressor=future_regressor
+        )
         self._fit_complete = True
         return self
 
@@ -790,7 +794,9 @@ def predict(self, forecast_length=None, future_regressor=None):
         )
         # CHECK Forecasts are proper length!
         if df_forecast.forecast.shape[0] != self.forecast_length:
-            raise ValueError(f"Model {self.model_str} returned improper forecast_length")
+            raise ValueError(
+                f"Model {self.model_str} returned improper forecast_length"
+            )
 
         if df_forecast.forecast.shape[1] != self.df.shape[1]:
             raise ValueError("Model failed to return correct number of series.")
@@ -806,7 +812,9 @@ def predict(self, forecast_length=None, future_regressor=None):
         if self.constraint is not None:
             if isinstance(self.constraint, dict):
                 constraint_method = self.constraint.get("constraint_method", "quantile")
-                constraint_regularization = self.constraint.get("constraint_regularization", 1)
+                constraint_regularization = self.constraint.get(
+                    "constraint_regularization", 1
+                )
                 lower_constraint = self.constraint.get("lower_constraint", 0)
                 upper_constraint = self.constraint.get("upper_constraint", 1)
                 bounds = self.constraint.get("bounds", False)
@@ -850,13 +858,12 @@ def predict(self, forecast_length=None, future_regressor=None):
         sys.stdout.flush()
 
         return df_forecast
-    
+
     def fit_data(self, df, future_regressor=None):
         self.df = df
         self.model.fit_data(df, future_regressor)
 
 
-
 class TemplateEvalObject(object):
     """Object to contain all the failures!.
 
@@ -1279,7 +1286,9 @@ def model_forecast(
             model_count=model_count,
         )
         model = model.fit(df_train_low, future_regressor_train)
-        return model.predict(forecast_length, future_regressor=future_regressor_forecast)
+        return model.predict(
+            forecast_length, future_regressor=future_regressor_forecast
+        )
 
 
 def _ps_metric(per_series_metrics, metric, model_id):