feat: add 2 parameter cross validation plotting

src/invert4geom/plotting.py

@@ -28,6 +28,101 @@
 from invert4geom import utils
 
 
+def plot_2_parameter_cv_scores(
+    scores: list[float],
+    parameter_pairs: list[tuple[float, float]],
+    logx: bool = False,
+    logy: bool = False,
+    param_names: tuple[str, str] = ("Hyperparameter 1", "Hyperparameter 2"),
+    figsize: tuple[float, float] = (5, 3.5),
+    cmap: str = "viridis",
+) -> None:
+    """
+    plot a scatter plot graph with x axis equal to parameter 1, y axis equal to
+    parameter 2, and points colored by cross-validation scores.
+
+    Parameters
+    ----------
+    scores : list[float]
+        score values
+    parameter_pairs : list[float]
+        parameter values
+    logx, logy : bool, optional
+        make the x or y axes log scale, by default False
+    param_names : tuple[str, str], optional
+        name to give for the parameters, by default "Hyperparameter"
+    figsize : tuple[float, float], optional
+        size of the figure, by default (5, 3.5)
+    cmap : str, optional
+        matplotlib colormap for scores, by default "viridis"
+    """
+    # Check if seaborn is installed
+    if sns is None:
+        msg = "Missing optional dependency 'seaborn' required for plotting."
+        raise ImportError(msg)
+    sns.set_theme()
+    # Check if matplotlib is installed
+    if plt is None:
+        msg = "Missing optional dependency 'matplotlib' required for plotting."
+        raise ImportError(msg)
+
+    df = pd.DataFrame(
+        {
+            "scores": scores,
+            param_names[0]: [
+                parameter_pairs[i][0] for i in list(range(len(parameter_pairs)))
+            ],
+            param_names[1]: [
+                parameter_pairs[i][1] for i in list(range(len(parameter_pairs)))
+            ],
+        }
+    )
+    df = df.sort_values(by="scores")
+
+    best = df.iloc[0]
+
+    plt.figure(figsize=figsize)
+    plt.title("Two parameter cross-validation")
+
+    grid = df.set_index([param_names[1], param_names[0]]).to_xarray().scores
+    grid.plot(cmap=cmap)
+    # plt.contourf(
+    #     df[param_names[0]],
+    #     df[param_names[1]],
+    #     Z = grid,
+    #     cmap = cmap,
+    # )
+    plt.scatter(
+        df[param_names[0]],  # pylint: disable=unsubscriptable-object
+        df[param_names[1]],  # pylint: disable=unsubscriptable-object
+        # c = df.scores,
+        # cmap = cmap,
+        # marker="o",
+        marker=".",
+        color="black",
+    )
+    plt.plot(
+        best[param_names[0]],
+        best[param_names[1]],
+        "s",
+        markersize=10,
+        color=sns.color_palette()[3],
+        label="Minimum",
+    )
+    plt.legend(
+        loc="upper right",
+    )
+
+    if logx:
+        plt.xscale("log")
+    if logy:
+        plt.yscale("log")
+    plt.xlabel(param_names[0])
+    plt.ylabel(param_names[1])
+    # plt.colorbar()
+    plt.tight_layout()
+
+
 def plot_cv_scores(
     scores: list[float],
     parameters: list[float],