Change "multivariate_plot" parameter name to "component_wise"

darts/ad/anomaly_model/anomaly_model.py

@@ -247,7 +247,7 @@ def show_anomalies(
         names_of_scorers: Union[str, Sequence[str]] = None,
         title: str = None,
         metric: Optional[Literal["AUC_ROC", "AUC_PR"]] = None,
-        multivariate_plot: bool = False,
+        component_wise: bool = False,
         **score_kwargs,
     ):
         """Plot the results of the anomaly model.
@@ -284,7 +284,7 @@ def show_anomalies(
             Default: "AUC_ROC".
         score_kwargs
             parameters for the `score()` method.
-        multivariate_plot
+        component_wise
             If True, it will separately plot each component in multivariate series.
         """
         series = _check_input(series, name="series", num_series_expected=1)[0]
@@ -313,7 +313,7 @@ def show_anomalies(
             names_of_scorers=names_of_scorers,
             title=title,
             metric=metric,
-            multivariate_plot=multivariate_plot,
+            component_wise=component_wise,
         )
 
     @property

darts/ad/anomaly_model/forecasting_am.py

@@ -440,7 +440,7 @@ def show_anomalies(
         names_of_scorers: Union[str, Sequence[str]] = None,
         title: str = None,
         metric: Optional[Literal["AUC_ROC", "AUC_PR"]] = None,
-        multivariate_plot: bool = False,
+        component_wise: bool = False,
         **score_kwargs,
     ):
         """Plot the results of the anomaly model.
@@ -507,7 +507,7 @@ def show_anomalies(
             Optionally, the name of the metric function to use. Must be one of "AUC_ROC" (Area Under the
             Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores).
             Default: "AUC_ROC".
-        multivariate_plot
+        component_wise
             If True, it will separately plot each component in multivariate series.
         score_kwargs
             parameters for the `score()` method.
@@ -530,7 +530,7 @@ def show_anomalies(
             names_of_scorers=names_of_scorers,
             title=title,
             metric=metric,
-            multivariate_plot=multivariate_plot,
+            component_wise=component_wise,
             **score_kwargs,
         )
 

darts/ad/scorers/scorers.py

@@ -176,7 +176,7 @@ def show_anomalies_from_prediction(
         anomalies: TimeSeries = None,
         title: str = None,
         metric: Optional[Literal["AUC_ROC", "AUC_PR"]] = None,
-        multivariate_plot: bool = False,
+        component_wise: bool = False,
     ):
         """Plot the results of the scorer.
 
@@ -209,7 +209,7 @@ def show_anomalies_from_prediction(
             Optionally, the name of the metric function to use. Must be one of "AUC_ROC" (Area Under the
             Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores).
             Default: "AUC_ROC".
-        multivariate_plot
+        component_wise
             If True, it will separately plot each component in multivariate series.
         """
         series = _check_input(series, name="series", num_series_expected=1)[0]
@@ -233,7 +233,7 @@ def show_anomalies_from_prediction(
             names_of_scorers=scorer_name,
             title=title,
             metric=metric,
-            multivariate_plot=multivariate_plot,
+            component_wise=component_wise,
         )
 
     @property
@@ -584,7 +584,7 @@ def show_anomalies(
         scorer_name: str = None,
         title: str = None,
         metric: Optional[Literal["AUC_ROC", "AUC_PR"]] = None,
-        multivariate_plot: bool = False,
+        component_wise: bool = False,
     ):
         """Plot the results of the scorer.
 
@@ -615,7 +615,7 @@ def show_anomalies(
             Optionally, the name of the metric function to use. Must be one of "AUC_ROC" (Area Under the
             Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores).
             Default: "AUC_ROC".
-        multivariate_plot
+        component_wise
             If True, it will separately plot each component in multivariate series.
         """
         series = _check_input(series, name="series", num_series_expected=1)[0]
@@ -640,7 +640,7 @@ def show_anomalies(
             names_of_scorers=scorer_name,
             title=title,
             metric=metric,
-            multivariate_plot=multivariate_plot,
+            component_wise=component_wise,
         )
 
     @property

darts/ad/utils.py

@@ -311,7 +311,7 @@ def show_anomalies_from_scores(
     names_of_scorers: Union[str, Sequence[str]] = None,
     title: str = None,
     metric: Optional[Literal["AUC_ROC", "AUC_PR"]] = None,
-    multivariate_plot: bool = False,
+    component_wise: bool = False,
 ):
     """Plot the results generated by an anomaly model.
 
@@ -353,14 +353,14 @@ def show_anomalies_from_scores(
         Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores).
         Only effective when `pred_scores` is not `None`.
         Default: "AUC_ROC".
-    multivariate_plot
+    component_wise
         If True, it will separately plot each component in multivariate series.
     """
     series = _check_input(
         series,
         name="series",
         num_series_expected=1,
-        check_multivariate=multivariate_plot,
+        check_multivariate=component_wise,
     )[0]
 
     if title is None and pred_scores is not None:
@@ -433,30 +433,30 @@ def show_anomalies_from_scores(
             name="pred_series",
             width_expected=series_width,
             num_series_expected=1,
-            check_multivariate=multivariate_plot,
+            check_multivariate=component_wise,
         )[0]
 
-    if anomalies is not None and multivariate_plot:
+    if anomalies is not None and component_wise:
         anomalies = _check_input(
             anomalies,
             name="anomalies",
             width_expected=series_width,
             num_series_expected=1,
             check_binary=True,
-            check_multivariate=multivariate_plot,
+            check_multivariate=component_wise,
         )[0]
 
-    if pred_scores is not None and multivariate_plot:
+    if pred_scores is not None and component_wise:
         for pred_score in pred_scores:
             _ = _check_input(
                 pred_score,
                 name="pred_score",
                 width_expected=series_width,
                 num_series_expected=1,
-                check_multivariate=multivariate_plot,
+                check_multivariate=component_wise,
             )[0]
 
-    plots_per_ts = nbr_plots * series_width if multivariate_plot else nbr_plots
+    plots_per_ts = nbr_plots * series_width if component_wise else nbr_plots
     fig, axs = plt.subplots(
         plots_per_ts,
         figsize=(8, 4 * (plots_per_ts // nbr_plots) + 2 * (nbr_plots - 1)),
@@ -468,8 +468,8 @@ def show_anomalies_from_scores(
         layout="constrained",
     )
 
-    for i in range(series_width if multivariate_plot else 1):
-        if multivariate_plot:
+    for i in range(series_width if component_wise else 1):
+        if component_wise:
             series_ = series[series.components[i]]
             anomalies_ = (
                 anomalies[anomalies.components[i]] if anomalies is not None else None