plot

plot_mo(target_names, combinations, pareto, y_rf=None, pareto_front=False, y_best=None, y_add=None, y_add2=None, y_add_color='blue', y_add2_color='green', title='', y_orig=None, pareto_front_orig=False, pareto_label=False, y_rf_color='blue', y_best_color='red', x_axis_transformation='id', y_axis_transformation='id', y_best_label='Best', y_add_label='Add', y_add2_label='Add2', filename=None)

Generates scatter plots for each combination of two targets from a multi-output prediction while highlighting Pareto optimal points.

Parameters:

Name	Type	Description	Default
y_rf	ndarray	The predicted target values with shape (n_samples, n_targets).	None
target_names	list	A list of target names corresponding to the columns of y_rf.	required
combinations	list	A list of tuples, where each tuple contains the indices of the target combinations to plot.	required
pareto	str	Specifies whether to compute Pareto front based on ‘min’ or ‘max’ criterion.	required
pareto_front	bool	If True, connect Pareto optimal points with a red line for y_rf.	False
y_best	ndarray	A NumPy array representing the best point to highlight in red. Defaults to None.	None
y_add	ndarray	A NumPy array representing the additional points to highlight in blue. Defaults to None.	None
y_add2	ndarray	A NumPy array representing the additional points to highlight in green. Defaults to None.	None
y_add_color	str	The color of the additional points. Defaults to “blue”.	'blue'
y_add2_color	str	The color of the additional points. Defaults to “green”.	'green'
y_best_label	str	The label for the best point. Defaults to “Best”.	'Best'
y_add_label	str	The label for the additional points. Defaults to “Add”.	'Add'
y_add2_label	str	The label for the additional points. Defaults to “Add2”.	'Add2'
title	str	The title of the plot. Defaults to “” (empty string).	''
y_orig	ndarray	The original target values with shape (n_samples, n_targets). Defaults to None.	None
pareto_front_orig	bool	If True, connect Pareto optimal points with a light blue line for y_orig. Defaults to False.	False
pareto_label	bool	If True, label Pareto points with their index. Defaults to False.	False
y_rf_color	str	The color of the predicted points. Defaults to “blue”.	'blue'
y_best_color	str	The color of the best point. Defaults to “red”.	'red'
x_axis_transformation	str	Transformation for the x-axis. Options are “id” (linear), “log” (logarithmic), and “loglog” (log-log). Defaults to “id”.	'id'
y_axis_transformation	str	Transformation for the y-axis. Options are “id” (linear), “log” (logarithmic), and “loglog” (log-log). Defaults to “id”.	'id'
filename	str	If provided, saves the plot to the specified file. Supports “pdf” and “png” formats. Defaults to None.	None

Returns:

Name	Type	Description
None	None	Displays or saves the plot.

Examples:

>>> from spotpython.mo.plot import plot_mo
>>> import numpy as np
>>> target_names = ["Target 1", "Target 2"]
>>> combinations = [(0, 1)]
>>> pareto = "min"
>>> y_rf = np.random.rand(100, 2)
>>> y_orig = np.random.rand(100, 2)
>>> plot_mo(target_names, combinations, pareto, y_rf=y_rf, y_orig=y_orig, filename="plot.png")

Source code in spotpython/mo/plot.py

def plot_mo(
    target_names: list,
    combinations: list,
    pareto: str,
    y_rf: np.ndarray = None,
    pareto_front: bool = False,
    y_best: np.ndarray = None,
    y_add: np.ndarray = None,
    y_add2: np.ndarray = None,
    y_add_color="blue",
    y_add2_color="green",
    title: str = "",
    y_orig: np.ndarray = None,
    pareto_front_orig: bool = False,
    pareto_label: bool = False,
    y_rf_color="blue",
    y_best_color="red",
    x_axis_transformation: str = "id",
    y_axis_transformation: str = "id",
    y_best_label="Best",
    y_add_label="Add",
    y_add2_label="Add2",
    filename: str = None,
) -> None:
    """
    Generates scatter plots for each combination of two targets from a multi-output prediction while highlighting Pareto optimal points.

    Args:
        y_rf (np.ndarray): The predicted target values with shape (n_samples, n_targets).
        target_names (list): A list of target names corresponding to the columns of y_rf.
        combinations (list): A list of tuples, where each tuple contains the indices of the target combinations to plot.
        pareto (str): Specifies whether to compute Pareto front based on 'min' or 'max' criterion.
        pareto_front (bool): If True, connect Pareto optimal points with a red line for y_rf.
        y_best (np.ndarray, optional): A NumPy array representing the best point to highlight in red. Defaults to None.
        y_add (np.ndarray, optional): A NumPy array representing the additional points to highlight in blue. Defaults to None.
        y_add2 (np.ndarray, optional): A NumPy array representing the additional points to highlight in green. Defaults to None.
        y_add_color (str): The color of the additional points. Defaults to "blue".
        y_add2_color (str): The color of the additional points. Defaults to "green".
        y_best_label (str): The label for the best point. Defaults to "Best".
        y_add_label (str): The label for the additional points. Defaults to "Add".
        y_add2_label (str): The label for the additional points. Defaults to "Add2".
        title (str): The title of the plot. Defaults to "" (empty string).
        y_orig (np.ndarray, optional): The original target values with shape (n_samples, n_targets). Defaults to None.
        pareto_front_orig (bool): If True, connect Pareto optimal points with a light blue line for y_orig. Defaults to False.
        pareto_label (bool): If True, label Pareto points with their index. Defaults to False.
        y_rf_color (str): The color of the predicted points. Defaults to "blue".
        y_best_color (str): The color of the best point. Defaults to "red".
        x_axis_transformation (str): Transformation for the x-axis. Options are "id" (linear), "log" (logarithmic), and "loglog" (log-log). Defaults to "id".
        y_axis_transformation (str): Transformation for the y-axis. Options are "id" (linear), "log" (logarithmic), and "loglog" (log-log). Defaults to "id".
        filename (str, optional):
            If provided, saves the plot to the specified file.  Supports "pdf" and "png" formats. Defaults to None.

    Returns:
        None: Displays or saves the plot.

    Examples:
        >>> from spotpython.mo.plot import plot_mo
        >>> import numpy as np
        >>> target_names = ["Target 1", "Target 2"]
        >>> combinations = [(0, 1)]
        >>> pareto = "min"
        >>> y_rf = np.random.rand(100, 2)
        >>> y_orig = np.random.rand(100, 2)
        >>> plot_mo(target_names, combinations, pareto, y_rf=y_rf, y_orig=y_orig, filename="plot.png")
    """
    # Convert y_rf to numpy array if it's a pandas DataFrame
    if isinstance(y_rf, pd.DataFrame):
        y_rf = y_rf.values

    # Convert y_orig to numpy array if it's a pandas DataFrame
    if isinstance(y_orig, pd.DataFrame):
        y_orig = y_orig.values

    for i, j in combinations:
        plt.figure()
        s = 50  # Base size for points
        pareto_size = s  # Size for Pareto points
        if pareto_label:
            pareto_size = s * 4  # Increase the size for Pareto points
        a = 0.4

        # Plot original data if provided
        if y_orig is not None:
            minimize = pareto == "min"
            pareto_mask_orig = is_pareto_efficient(y_orig[:, [i, j]], minimize)
            plt.scatter(y_orig[:, i], y_orig[:, j], edgecolor="w", c="gray", s=s, marker="o", alpha=a, label="Non-Pareto Points")
            plt.scatter(y_orig[pareto_mask_orig, i], y_orig[pareto_mask_orig, j], edgecolor="k", c="gray", s=pareto_size, marker="o", alpha=a, label="Pareto Points")
            if pareto_label:
                for idx in np.where(pareto_mask_orig)[0]:
                    plt.text(y_orig[idx, i], y_orig[idx, j], str(idx), color="black", fontsize=8, ha="center", va="center")
            if pareto_front_orig:
                sorted_indices_orig = np.argsort(y_orig[pareto_mask_orig, i])
                plt.plot(y_orig[pareto_mask_orig, i][sorted_indices_orig], y_orig[pareto_mask_orig, j][sorted_indices_orig], "k-", alpha=a, label="Pareto Front")

        if y_rf is not None:
            minimize = pareto == "min"
            pareto_mask = is_pareto_efficient(y_rf[:, [i, j]], minimize)
            plt.scatter(y_rf[:, i], y_rf[:, j], edgecolor="w", c=y_rf_color, s=s, marker="^", alpha=a, label="Predicted Points")
            plt.scatter(y_rf[pareto_mask, i], y_rf[pareto_mask, j], edgecolor="k", c=y_rf_color, s=pareto_size, marker="s", alpha=a, label="Predicted Pareto")
            if pareto_label:
                for idx in np.where(pareto_mask)[0]:
                    plt.text(y_rf[idx, i], y_rf[idx, j], str(idx), color="black", fontsize=8, ha="center", va="center")
            if pareto_front:
                sorted_indices = np.argsort(y_rf[pareto_mask, i])
                plt.plot(
                    y_rf[pareto_mask, i][sorted_indices],
                    y_rf[pareto_mask, j][sorted_indices],
                    linestyle="-",
                    color=y_rf_color,
                    alpha=a,
                    label="Predicted Pareto Front",
                )

        if y_best is not None:
            plt.scatter(y_best[:, i], y_best[:, j], edgecolor="k", c=y_best_color, s=s, marker="D", alpha=1, label=y_best_label)
        if y_add is not None:
            plt.scatter(y_add[:, i], y_add[:, j], edgecolor="k", c=y_add_color, s=s, marker="D", alpha=1, label=y_add_label)
        if y_add2 is not None:
            plt.scatter(y_add2[:, i], y_add2[:, j], edgecolor="k", c=y_add2_color, s=s, marker="D", alpha=1, label=y_add2_label)

        # Apply axis transformations
        if x_axis_transformation == "log":
            plt.xscale("log")
        if y_axis_transformation == "log":
            plt.yscale("log")
        if x_axis_transformation == "loglog" or y_axis_transformation == "loglog":
            plt.xscale("log")
            plt.yscale("log")

        plt.xlabel(target_names[i])
        plt.ylabel(target_names[j])
        plt.grid()
        plt.title(title)
        # Move the legend outside the plot
        plt.legend(loc="center left", bbox_to_anchor=(1, 0.5))
        # Save or show the plot
        if filename:
            if filename.endswith(".pdf") or filename.endswith(".png"):
                plt.savefig(filename, format=filename.split(".")[-1], bbox_inches="tight")
            else:
                raise ValueError("Filename must have a valid suffix: '.pdf' or '.png'.")
        else:
            plt.show()