machine-learning-scikit-learn/utils.py at main · MahbubAlam231/machine-learning-scikit-learn · GitHub

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"""
#!/usr/bin/python3
Author      : Mahbub Alam
File        : utils.py
Created     : 2025-04-04 (Apr, Fri) 15:59:27 CEST
Description : Helper function(s) for Computer_Vision_in_Agriculture # {{{

Based on an educational example from the *Applied Machine Learning with Python* course (Coursera).

# }}}
"""

import numpy
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import neighbors
import matplotlib.patches as mpatches
import matplotlib.patches as mpatches

def plot_fruit_knn(X, y, n_neighbors, weights):
    X_mat = X[['height', 'width']].values
    y_mat = y.values

    # Create color maps
    cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF','#AFAFAF'])
    cmap_bold  = ListedColormap(['#FF0000', '#00FF00', '#0000FF','#AFAFAF'])

    clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
    clf.fit(X_mat, y_mat)

    # Plot the decision boundary by assigning a color in the color map
    # to each mesh point.

    mesh_step_size = .01  # step size in the mesh
    plot_symbol_size = 50

    x_min, x_max = X_mat[:, 0].min() - 1, X_mat[:, 0].max() + 1
    y_min, y_max = X_mat[:, 1].min() - 1, X_mat[:, 1].max() + 1
    xx, yy = numpy.meshgrid(numpy.arange(x_min, x_max, mesh_step_size),
                         numpy.arange(y_min, y_max, mesh_step_size))
    Z = clf.predict(numpy.c_[xx.ravel(), yy.ravel()])

    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    plt.figure()
    plt.pcolormesh(xx, yy, Z, cmap=cmap_light)

    # Plot training points
    plt.scatter(X_mat[:, 0], X_mat[:, 1], s=plot_symbol_size, c=y, cmap=cmap_bold, edgecolor = 'black')
    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())

    patch0 = mpatches.Patch(color='#FF0000', label='apple')
    patch1 = mpatches.Patch(color='#00FF00', label='mandarin')
    patch2 = mpatches.Patch(color='#0000FF', label='orange')
    patch3 = mpatches.Patch(color='#AFAFAF', label='lemon')
    plt.legend(handles=[patch0, patch1, patch2, patch3])


    plt.xlabel('height (cm)')
    plt.ylabel('width (cm)')

    plt.savefig('decision_boundaries.jpg')
    plt.show()