update_tests

skforecast/ForecasterAutoreg/tests/test_predict.py

@@ -32,8 +32,8 @@ def test_predict_NotFittedError_when_fitted_is_False():
     forecaster = ForecasterAutoreg(LinearRegression(), lags=3)
 
     err_msg = re.escape(
-                ('This Forecaster instance is not fitted yet. Call `fit` with '
-                 'appropriate arguments before using predict.')
+                ("This Forecaster instance is not fitted yet. Call `fit` with "
+                 "appropriate arguments before using predict.")
               )
     with pytest.raises(NotFittedError, match = err_msg):
         forecaster.predict(steps=5)

skforecast/ForecasterAutoreg/tests/test_predict_bootstrapping.py

@@ -4,6 +4,7 @@
 import pytest
 import numpy as np
 import pandas as pd
+from sklearn.exceptions import NotFittedError
 from skforecast.ForecasterAutoreg import ForecasterAutoreg
 from skforecast.preprocessing import TimeSeriesDifferentiator
 from sklearn.linear_model import LinearRegression
@@ -16,6 +17,20 @@
 from .fixtures_ForecasterAutoreg import data # to test results when using differentiation
 
 
+def test_predict_bootstrapping_NotFittedError_when_fitted_is_False():
+    """
+    Test NotFittedError is raised when fitted is False.
+    """
+    forecaster = ForecasterAutoreg(LinearRegression(), lags=3)
+
+    err_msg = re.escape(
+                ("This Forecaster instance is not fitted yet. Call `fit` with "
+                 "appropriate arguments before using predict.")
+              )
+    with pytest.raises(NotFittedError, match = err_msg):
+        forecaster.predict_bootstrapping(steps=1)
+
+
 def test_predict_bootstrapping_ValueError_when_out_sample_residuals_is_None():
     """
     Test ValueError is raised when in_sample_residuals=False and

skforecast/ForecasterAutoregCustom/tests/test_predict.py

@@ -32,7 +32,7 @@ def create_predictors(y): # pragma: no cover
 
     lags = y[-1:-6:-1]
 
-    return lags 
+    return lags
 
 
 def test_predict_NotFittedError_when_fitted_is_False():
@@ -46,8 +46,8 @@ def test_predict_NotFittedError_when_fitted_is_False():
                  )
 
     err_msg = re.escape(
-                ('This Forecaster instance is not fitted yet. Call `fit` with '
-                 'appropriate arguments before using predict.')
+                ("This Forecaster instance is not fitted yet. Call `fit` with "
+                 "appropriate arguments before using predict.")
               )
     with pytest.raises(NotFittedError, match = err_msg):
         forecaster.predict(steps=5)

skforecast/ForecasterAutoregCustom/tests/test_predict_bootstrapping.py

@@ -4,6 +4,7 @@
 import pytest
 import numpy as np
 import pandas as pd
+from sklearn.exceptions import NotFittedError
 from skforecast.ForecasterAutoregCustom import ForecasterAutoregCustom
 from sklearn.linear_model import LinearRegression
 from sklearn.preprocessing import StandardScaler
@@ -21,7 +22,25 @@ def create_predictors(y): # pragma: no cover
 
     lags = y[-1:-4:-1]
 
-    return lags 
+    return lags
+
+
+def test_predict_bootstrapping_NotFittedError_when_fitted_is_False():
+    """
+    Test NotFittedError is raised when fitted is False.
+    """
+    forecaster = ForecasterAutoregCustom(
+                     regressor      = LinearRegression(),
+                     fun_predictors = create_predictors,
+                     window_size    = 5
+                 )
+
+    err_msg = re.escape(
+                ("This Forecaster instance is not fitted yet. Call `fit` with "
+                 "appropriate arguments before using predict.")
+              )
+    with pytest.raises(NotFittedError, match = err_msg):
+        forecaster.predict_bootstrapping(steps=1)
 
 
 def test_predict_interval_exception_when_out_sample_residuals_is_None():

skforecast/ForecasterAutoregMultiSeries/tests/test_predict.py

@@ -35,8 +35,8 @@ def test_predict_NotFittedError_when_fitted_is_False():
     forecaster = ForecasterAutoregMultiSeries(LinearRegression(), lags=5)
 
     err_msg = re.escape(
-                ('This Forecaster instance is not fitted yet. Call `fit` with '
-                 'appropriate arguments before using predict.')
+                ("This Forecaster instance is not fitted yet. Call `fit` with "
+                 "appropriate arguments before using predict.")
               )
     with pytest.raises(NotFittedError, match = err_msg):
         forecaster.predict(steps=5)

skforecast/ForecasterAutoregMultiSeries/tests/test_predict_bootstrapping.py

@@ -2,9 +2,9 @@
 # ==============================================================================
 import re
 import pytest
-from pytest import approx
 import numpy as np
 import pandas as pd
+from sklearn.exceptions import NotFittedError
 from skforecast.ForecasterAutoregMultiSeries import ForecasterAutoregMultiSeries
 from sklearn.compose import ColumnTransformer
 from sklearn.preprocessing import StandardScaler
@@ -24,6 +24,20 @@
                    )
 
 
+def test_predict_bootstrapping_NotFittedError_when_fitted_is_False():
+    """
+    Test NotFittedError is raised when fitted is False.
+    """
+    forecaster = ForecasterAutoregMultiSeries(LinearRegression(), lags=5)
+
+    err_msg = re.escape(
+                ("This Forecaster instance is not fitted yet. Call `fit` with "
+                 "appropriate arguments before using predict.")
+              )
+    with pytest.raises(NotFittedError, match = err_msg):
+        forecaster.predict_bootstrapping(steps=1, levels=None)
+
+
 def test_predict_bootstrapping_ValueError_when_not_in_sample_residuals_for_any_level():
     """
     Test ValueError is raised when in_sample_residuals=True but there is no

skforecast/ForecasterAutoregMultiSeriesCustom/tests/test_predict.py

@@ -47,8 +47,8 @@ def test_predict_NotFittedError_when_fitted_is_False():
                  )
 
     err_msg = re.escape(
-                ('This Forecaster instance is not fitted yet. Call `fit` with '
-                 'appropriate arguments before using predict.')
+                ("This Forecaster instance is not fitted yet. Call `fit` with "
+                 "appropriate arguments before using predict.")
               )
     with pytest.raises(NotFittedError, match = err_msg):
         forecaster.predict(steps=5)

skforecast/ForecasterAutoregMultiSeriesCustom/tests/test_predict_bootstrapping.py

@@ -2,9 +2,9 @@
 # ==============================================================================
 import re
 import pytest
-from pytest import approx
 import numpy as np
 import pandas as pd
+from sklearn.exceptions import NotFittedError
 from skforecast.ForecasterAutoregMultiSeriesCustom import ForecasterAutoregMultiSeriesCustom
 from sklearn.compose import ColumnTransformer
 from sklearn.preprocessing import StandardScaler
@@ -32,6 +32,24 @@ def create_predictors(y): # pragma: no cover
     return lags
 
 
+def test_predict_bootstrapping_NotFittedError_when_fitted_is_False():
+    """
+    Test NotFittedError is raised when fitted is False.
+    """
+    forecaster = ForecasterAutoregMultiSeriesCustom(
+                     regressor      = LinearRegression(),
+                     fun_predictors = create_predictors,
+                     window_size    = 5
+                 )
+
+    err_msg = re.escape(
+                ("This Forecaster instance is not fitted yet. Call `fit` with "
+                 "appropriate arguments before using predict.")
+              )
+    with pytest.raises(NotFittedError, match = err_msg):
+        forecaster.predict(steps=5)
+
+
 def test_predict_bootstrapping_ValueError_when_not_in_sample_residuals_for_any_level():
     """
     Test ValueError is raised when in_sample_residuals=True but there is no

skforecast/datasets/tests/test_fetch_dataset.py

@@ -20,7 +20,7 @@ def test_fetch_dataset():
     assert df.index[-1].strftime('%Y-%m-%d') == '2008-06-01'
 
     # Test fetching the 'items_sales' dataset
-    df = fetch_dataset('items_sales', version='latest', raw=False, verbose=False)
+    df = fetch_dataset('items_sales', version='latest', raw=False, verbose=True)
     assert isinstance(df, pd.DataFrame)
     assert df.shape == (1097, 3)
     assert df.index.freq == 'D'

skforecast/datasets/tests/test_load_dataset.py

@@ -9,6 +9,7 @@ def test_load_demo_dataset():
     Test load_demo_dataset function.
     """
     df = load_demo_dataset()
+
     assert isinstance(df, pd.Series)
     assert df.index.freq == 'MS'
     assert df.index.is_monotonic_increasing