make title fit better

CHANGELOG.md

@@ -12,7 +12,7 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 **Improved**
 
 - New model: `StatsForecastAutoTBATS`. This model offers the [AutoTBATS](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autotbats) model from Nixtla's `statsforecasts` library. [#2611](https://github.com/unit8co/darts/pull/2611) by [He Weilin](https://github.com/cnhwl).
-- Added `multivariate_plot` parameter in `show_anomalies()` to separately plot each component in multivariate series. [#2544](https://github.com/unit8co/darts/pull/2544) by [He Weilin](https://github.com/cnhwl).
+- Added parameter `component_wise` to `show_anomalies()` to separately plot each component in multivariate series. [#2544](https://github.com/unit8co/darts/pull/2544) by [He Weilin](https://github.com/cnhwl).
 
 **Fixed**
 - Fixed a bug when performing optimized historical forecasts with `stride=1` using a `RegressionModel` with `output_chunk_shift>=1` and `output_chunk_length=1`, where the forecast time index was not properly shifted. [#2634](https://github.com/unit8co/darts/pull/2634) by [Mattias De Charleroy](https://github.com/MattiasDC).

darts/ad/anomaly_model/anomaly_model.py

@@ -285,7 +285,7 @@ def show_anomalies(
         score_kwargs
             parameters for the `score()` method.
         component_wise
-            If True, it will separately plot each component in multivariate series.
+            If True, will separately plot each component in case of multivariate anomaly detection.
         """
         series = _check_input(series, name="series", num_series_expected=1)[0]
         predict_kwargs = predict_kwargs if predict_kwargs is not None else {}

darts/ad/anomaly_model/forecasting_am.py

@@ -508,7 +508,7 @@ def show_anomalies(
             Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores).
             Default: "AUC_ROC".
         component_wise
-            If True, it will separately plot each component in multivariate series.
+            If True, will separately plot each component in case of multivariate anomaly detection.
         score_kwargs
             parameters for the `score()` method.
         """

darts/ad/scorers/scorers.py

@@ -210,7 +210,7 @@ def show_anomalies_from_prediction(
             Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores).
             Default: "AUC_ROC".
         component_wise
-            If True, it will separately plot each component in multivariate series.
+            If True, will separately plot each component in case of multivariate anomaly detection.
         """
         series = _check_input(series, name="series", num_series_expected=1)[0]
         pred_series = _check_input(
@@ -616,7 +616,7 @@ def show_anomalies(
             Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores).
             Default: "AUC_ROC".
         component_wise
-            If True, it will separately plot each component in multivariate series.
+            If True, will separately plot each component in case of multivariate anomaly detection.
         """
         series = _check_input(series, name="series", num_series_expected=1)[0]
         pred_scores = self.score(series)

darts/ad/utils.py

@@ -354,7 +354,7 @@ def show_anomalies_from_scores(
         Only effective when `pred_scores` is not `None`.
         Default: "AUC_ROC".
     component_wise
-        If True, it will separately plot each component in multivariate series.
+        If True, will separately plot each component in case of multivariate anomaly detection.
     """
     series = _check_input(
         series,
@@ -457,15 +457,13 @@ def show_anomalies_from_scores(
             )[0]
 
     plots_per_ts = nbr_plots * series_width if component_wise else nbr_plots
+    height_ratios = ([2] + [1] * (nbr_plots - 1)) * (plots_per_ts // nbr_plots)
+    height_total = 2 * sum(height_ratios)
     fig, axs = plt.subplots(
-        plots_per_ts,
-        figsize=(8, 4 * (plots_per_ts // nbr_plots) + 2 * (nbr_plots - 1)),
+        nrows=plots_per_ts,
+        figsize=(8, height_total),
         sharex=True,
-        gridspec_kw={
-            "height_ratios": ([2] + [1] * (nbr_plots - 1)) * (plots_per_ts // nbr_plots)
-        },
-        squeeze=False,
-        layout="constrained",
+        gridspec_kw={"height_ratios": height_ratios},
     )
 
     for i in range(series_width if component_wise else 1):
@@ -500,9 +498,13 @@ def show_anomalies_from_scores(
             metric=metric,
             axs=axs,
             index_ax=i * nbr_plots,
-            nbr_plots=nbr_plots,
         )
-    fig.suptitle(title)
+    # make title fit nicely on plot
+    title_height = 0.1
+    title_y = 1 - title_height / height_total
+
+    fig.suptitle(title, y=title_y)
+    fig.tight_layout()
 
 
 def _assert_binary(series: TimeSeries, name: str):
@@ -774,7 +776,6 @@ def _plot_series_and_anomalies(
     metric: str,
     axs: plt.Axes,
     index_ax: int,
-    nbr_plots: int,
 ):
     """Helper function to plot series and anomalies.
 
@@ -798,25 +799,23 @@ def _plot_series_and_anomalies(
         The axes to plot on.
     index_ax
         The index of the current axis.
-    nbr_plots
-        The number of plots.
     """
-    _plot_series(series=series, ax_id=axs[index_ax][0], linewidth=0.5, label_name="")
+    _plot_series(series=series, ax_id=axs[index_ax], linewidth=0.5, label_name="")
 
     if pred_series is not None:
         _plot_series(
             series=pred_series,
-            ax_id=axs[index_ax][0],
+            ax_id=axs[index_ax],
             linewidth=0.5,
             label_name="model output",
         )
 
-    axs[index_ax][0].set_title("")
+    axs[index_ax].set_title("")
 
     if anomalies is not None or pred_scores is not None:
-        axs[index_ax][0].set_xlabel("")
+        axs[index_ax].set_xlabel("")
 
-    axs[index_ax][0].legend(loc="upper center", bbox_to_anchor=(0.5, 1.1), ncol=2)
+    axs[index_ax].legend(loc="upper center", bbox_to_anchor=(0.5, 1.1), ncol=2)
 
     if pred_scores is not None:
         dict_input = {}
@@ -858,33 +857,29 @@ def _plot_series_and_anomalies(
 
             _plot_series(
                 series=elem[1]["series_score"],
-                ax_id=axs[index_ax][0],
+                ax_id=axs[index_ax],
                 linewidth=0.5,
                 label_name=label,
             )
 
-            axs[index_ax][0].legend(
-                loc="upper center", bbox_to_anchor=(0.5, 1.19), ncol=2
-            )
-            axs[index_ax][0].set_title(f"Window: {str(w)}", loc="left")
-            axs[index_ax][0].set_title("")
-            axs[index_ax][0].set_xlabel("")
+            axs[index_ax].legend(loc="upper center", bbox_to_anchor=(0.5, 1.19), ncol=2)
+            axs[index_ax].set_title(f"Window: {str(w)}", loc="left")
+            axs[index_ax].set_title("")
+            axs[index_ax].set_xlabel("")
 
     if anomalies is not None:
         _plot_series(
             series=anomalies,
-            ax_id=axs[index_ax + 1][0],
+            ax_id=axs[index_ax + 1],
             linewidth=1,
             label_name="anomalies",
             color="red",
         )
 
-        axs[index_ax + 1][0].set_title("")
-        axs[index_ax + 1][0].set_ylim([-0.1, 1.1])
-        axs[index_ax + 1][0].set_yticks([0, 1])
-        axs[index_ax + 1][0].set_yticklabels(["no", "yes"])
-        axs[index_ax + 1][0].legend(
-            loc="upper center", bbox_to_anchor=(0.5, 1.2), ncol=2
-        )
+        axs[index_ax + 1].set_title("")
+        axs[index_ax + 1].set_ylim([-0.1, 1.1])
+        axs[index_ax + 1].set_yticks([0, 1])
+        axs[index_ax + 1].set_yticklabels(["no", "yes"])
+        axs[index_ax + 1].legend(loc="upper center", bbox_to_anchor=(0.5, 1.2), ncol=2)
     else:
-        axs[index_ax][0].set_xlabel("timestamp")
+        axs[index_ax].set_xlabel("timestamp")