feat: add plotting func for non-grid search 2 parameter CV

src/invert4geom/plotting.py

@@ -132,6 +132,117 @@ def plot_2_parameter_cv_scores(
     plt.tight_layout()
 
 
+def plot_2_parameter_cv_scores_uneven(
+    study: optuna.study.Study,
+    param_names: tuple[str, str],
+    plot_param_names: tuple[str, str] = ("Hyperparameter 1", "Hyperparameter 2"),
+    figsize: tuple[float, float] = (5, 3.5),
+    cmap: str | None = None,
+) -> 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
+    ----------
+    study : optuna.study.Study
+    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)
+
+    if cmap is None:
+        cmap = sns.color_palette("mako", as_cmap=True)
+
+    df0 = study.trials_dataframe().sort_values(by="value")
+
+    df0 = df0[[param_names[0], param_names[1], "value"]]
+
+    best = df0.iloc[0]
+
+    df = df0.set_index([param_names[0], param_names[1]])
+    df = df[~df.index.duplicated()]
+
+    df1 = df.reset_index()[["value", param_names[0], param_names[1]]]
+
+    plt.figure(figsize=figsize)
+    plt.title("Two parameter cross-validation")
+
+    x_min = df1[param_names[0]].min()
+    x_max = df1[param_names[0]].max()
+    y_min = df1[param_names[1]].min()
+    y_max = df1[param_names[1]].max()
+    x_buffer = (x_max - x_min) / 10
+    y_buffer = (y_max - y_min) / 10
+
+    dampings = list(np.logspace(-10, -2, num=9))
+    dampings.append(None)
+
+    # temporarily set Python's logging level to not get information about the
+    # inversion's progress
+    logging.disable(level=logging.INFO)
+
+    spline = utils.best_spline_cv(
+        coordinates=(df1[param_names[0]], df1[param_names[1]]),
+        data=df1.value,
+        dampings=dampings,
+    )
+
+    # reset logging level
+    logging.disable(level=logging.NOTSET)
+
+    region = vd.pad_region(
+        vd.get_region((df1[param_names[0]], df1[param_names[1]])),
+        (y_buffer, x_buffer),
+    )
+    grid = spline.grid(
+        shape=(100, 100),
+        region=region,
+    ).scalars
+
+    grid.plot(
+        cmap=cmap,
+    )
+
+    plt.scatter(
+        df1[param_names[0]],  # pylint: disable=unsubscriptable-object
+        df1[param_names[1]],  # pylint: disable=unsubscriptable-object
+        marker=".",
+        color="gray",
+        label="Trials",
+    )
+
+    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",
+    )
+    plt.xlim([x_min - x_buffer, x_max + x_buffer])
+    plt.ylim([y_min - y_buffer, y_max + y_buffer])
+    plt.xlabel(plot_param_names[0])
+    plt.ylabel(plot_param_names[1])
+
+    plt.tight_layout()
+
+
 def plot_cv_scores(
     scores: list[float],
     parameters: list[float],