Add GPCCA::plot_tsi

Add class method to plot terminal state identification.

src/cellrank/estimators/terminal_states/_gpcca.py

@@ -2,6 +2,7 @@
 import enum
 import pathlib
 import types
+from pathlib import PosixPath
 from typing import Any, Dict, List, Literal, Mapping, Optional, Sequence, Tuple, Union
 
 import numpy as np
@@ -10,6 +11,7 @@
 from pandas.api.types import infer_dtype
 
 import matplotlib.pyplot as plt
+import seaborn as sns
 from matplotlib.axes import Axes
 from matplotlib.colorbar import ColorbarBase
 from matplotlib.colors import ListedColormap, Normalize
@@ -586,6 +588,96 @@ def get_tsi_score(self) -> float:
 
         return self.tsi["Identified terminal states"].sum() / optimal_score
 
+    def plot_tsi(
+        self,
+        tsi_df: pd.DataFrame,
+        fname: Optional[PosixPath] = None,
+        x_offset: Optional[Tuple[float, float]] = None,
+        y_offset: Optional[Tuple[float, float]] = None,
+        **kwargs: Any,
+    ):
+        """Plot terminal state identificiation (TSI).
+
+        Parameters
+        ----------
+        tsi_df
+            Pre-computed TSI DataFrame.
+        fname
+            File name under which the plot is saved. The plot is not saved if the argument is not specified.
+        x_offset
+            Offset of x-axis. Defaults to `[0.2, 0.2]` if not specified.
+        y_offset
+            Offset of y-axis. Defaults to `[0.1, 0.1]` if not specified.
+        kwargs
+            Keyword arguments for :meth:`~seaborn.lineplot`.
+
+        Returns
+        -------
+        Returns TSI as a Pandas DataFrame and adds the class attribute :attr:`tsi`.
+        """
+        if x_offset is None:
+            x_offset = [0.2, 0.2]
+
+        if y_offset is None:
+            y_offset = [0.1, 0.1]
+
+        optimal_identification = tsi_df[["Number of macrostates", "Optimal identification"]]
+        optimal_identification = optimal_identification.rename(
+            columns={"Optimal identification": "Identified terminal states"}
+        )
+        optimal_identification["Method"] = "Optimal identification"
+        optimal_identification["line_style"] = "--"
+
+        df = tsi_df[["Number of macrostates", "Identified terminal states"]]
+        df["Method"] = self.kernel.__class__.__name__
+        df["line_style"] = "-"
+
+        df = pd.concat([df, optimal_identification])
+
+        fig, ax = plt.subplots(figsize=(6, 4))
+        sns.lineplot(
+            data=df,
+            x="Number of macrostates",
+            y="Identified terminal states",
+            hue="Method",
+            style="line_style",
+            drawstyle="steps-post",
+            ax=ax,
+            **kwargs,
+        )
+
+        ax.set_xticks(df["Number of macrostates"].unique().astype(int))
+        for label_id, label in enumerate(ax.xaxis.get_ticklabels()):
+            if ((label_id + 1) % 5 != 0) and label_id != 0:
+                label.set_visible(False)
+        ax.set_yticks(df["Identified terminal states"].unique())
+
+        x_min = df["Number of macrostates"].min() - x_offset[0]
+        x_max = df["Number of macrostates"].max() + x_offset[1]
+        y_min = df["Identified terminal states"].min() - y_offset[0]
+        y_max = df["Identified terminal states"].max() + y_offset[1]
+        ax.set(xlim=[x_min, x_max], ylim=[y_min, y_max])
+
+        ax.get_legend().remove()
+
+        n_methods = len(df["Method"].unique())
+        handles, labels = ax.get_legend_handles_labels()
+        handles[n_methods].set_linestyle("--")
+        handles = handles[: (n_methods + 1)]
+        labels = labels[: (n_methods + 1)]
+        fig.legend(handles=handles, labels=labels, loc="lower center", ncol=(n_methods + 1), bbox_to_anchor=(0.5, -0.1))
+        plt.tight_layout()
+        plt.show()
+
+        if fname is not None:
+            format = fname.suffix[1:]
+            fig.savefig(
+                fname=fname,
+                format=format,
+                transparent=True,
+                bbox_inches="tight",
+            )
+
     @d.dedent
     def fit(
         self,