gistfile1.txt

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets

# Load Iris dataset
iris = load_penguins()

# Set up the figure size and subplots
fig, axarr = plt.subplots(4, 4, figsize=(12, 12))  # 4x4 grid for 4 features

# Loop through each grid cell
for x in range(4):
    for y in range(4):
        ax = axarr[x, y]
        if x == y:
            # Display feature name on the diagonal
            ax.text(0.5, 0.5, iris.feature_names[x], horizontalalignment='center', verticalalignment='center')
            ax.set_xticks([])
            ax.set_yticks([])
        else:
            # Scatter plot for feature combinations
            ax.scatter(iris.data[:, x], iris.data[:, y], c=iris.target, cmap=plt.cm.get_cmap('RdYlBu', 3))

        # Formatting the plots
        if y == 0:
            ax.set_ylabel(iris.feature_names[x])
        else:
            ax.set_yticks([])
        if x == 3:
            ax.set_xlabel(iris.feature_names[y])
        else:
            ax.set_xticks([])

# Add a color bar to the right of the plots
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
formatter = plt.FuncFormatter(lambda i, *args: iris.target_names[int(i)])
plt.colorbar(cm.ScalarMappable(norm=plt.Normalize(-0.5, 2.5), cmap=plt.cm.get_cmap('RdYlBu', 3)), cax=cbar_ax, ticks=[0, 1, 2], format=formatter)

plt.show()