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March 27, 2024 23:12
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| 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() |
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