Make lint

Author: C-BdB

examples/regression/1-quickstart/plot_ts-tutorial.py

@@ -210,7 +210,7 @@ class that block bootstraps the training set.
 mapie_enbpi = mapie_enbpi.fit(X_train, y_train)
 
 y_pred_enbpi_npfit, y_pis_enbpi_npfit = mapie_enbpi.predict(
-    X_test, alpha=alpha, ensemble=True, 
+    X_test, alpha=alpha, ensemble=True,
     allow_infinite_bounds=True
 )
 y_pis_enbpi_npfit = np.clip(y_pis_enbpi_npfit, 1, 10)
@@ -233,7 +233,7 @@ class that block bootstraps the training set.
 y_pred_aci_npfit = np.zeros(y_pred_enbpi_npfit.shape)
 y_pis_aci_npfit = np.zeros(y_pis_enbpi_npfit.shape)
 y_pred_aci_npfit[:gap], y_pis_aci_npfit[:gap, :, :] = mapie_aci.predict(
-    X_test.iloc[:gap, :], alpha=alpha, ensemble=True, 
+    X_test.iloc[:gap, :], alpha=alpha, ensemble=True,
     allow_infinite_bounds=True
 )
 for step in range(gap, len(X_test), gap):
@@ -249,7 +249,6 @@ class that block bootstraps the training set.
         X_test.iloc[step:(step + gap), :],
         alpha=alpha,
         ensemble=True,
-        
         allow_infinite_bounds=True
     )
     y_pis_aci_npfit[step:step + gap, :, :] = np.clip(
@@ -284,7 +283,7 @@ class that block bootstraps the training set.
 y_pred_enbpi_pfit = np.zeros(y_pred_enbpi_npfit.shape)
 y_pis_enbpi_pfit = np.zeros(y_pis_enbpi_npfit.shape)
 y_pred_enbpi_pfit[:gap], y_pis_enbpi_pfit[:gap, :, :] = mapie_enbpi.predict(
-    X_test.iloc[:gap, :], alpha=alpha, ensemble=True, 
+    X_test.iloc[:gap, :], alpha=alpha, ensemble=True,
     allow_infinite_bounds=True
 )
 
@@ -300,7 +299,6 @@ class that block bootstraps the training set.
         X_test.iloc[step:(step + gap), :],
         alpha=alpha,
         ensemble=True,
-        
         allow_infinite_bounds=True
     )
     y_pis_enbpi_pfit[step:step + gap, :, :] = np.clip(
@@ -332,7 +330,7 @@ class that block bootstraps the training set.
 y_pred_aci_pfit = np.zeros(y_pred_aci_npfit.shape)
 y_pis_aci_pfit = np.zeros(y_pis_aci_npfit.shape)
 y_pred_aci_pfit[:gap], y_pis_aci_pfit[:gap, :, :] = mapie_aci.predict(
-    X_test.iloc[:gap, :], alpha=alpha, ensemble=True, 
+    X_test.iloc[:gap, :], alpha=alpha, ensemble=True,
     allow_infinite_bounds=True
 )
 
@@ -353,7 +351,6 @@ class that block bootstraps the training set.
         X_test.iloc[step:(step + gap), :],
         alpha=alpha,
         ensemble=True,
-        
         allow_infinite_bounds=True
     )
     y_pis_aci_pfit[step:step + gap, :, :] = np.clip(

examples/regression/2-advanced-analysis/plot_timeseries_enbpi.py

@@ -152,7 +152,6 @@
         X_test.iloc[step:(step + step_size), :],
         alpha=alpha,
         ensemble=True,
-        
     )
 coverage_pfit_enbpi = regression_coverage_score(
     y_test, y_pis_pfit_enbpi[:, 0, 0], y_pis_pfit_enbpi[:, 1, 0]