dl_tutorial/examples/iris_classification.py at master · mint-lab/dl_tutorial · GitHub

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import numpy as np

import matplotlib.pyplot as plt

from sklearn import (datasets, svm)

from matplotlib.lines import Line2D # For the custom legend

# Load a dataset

iris = datasets.load_iris()

# Train a model

model = svm.SVC() # Accuracy: 0.973 (146/150)

model.fit(iris.data, iris.target) # Try 'iris.data[:,0:2]' (Accuracy: 0.820)

# Test the model

predict = model.predict(iris.data) # Try 'iris.data[:,0:2]' (Accuracy: 0.820)

n_correct = sum(predict == iris.target)

accuracy = n_correct / len(iris.data)

# Visualize testing results

cmap = np.array([(1, 0, 0), (0, 1, 0), (0, 0, 1)])

clabel = [Line2D([0], [0], marker='o', lw=0, label=iris.target_names[i], color=cmap[i]) for i in range(len(cmap))]

for (x, y) in [(0, 1), (2, 3)]:

plt.figure ()

plt.title(f'svm.SVC ({n_correct}/{len(iris.data)}={accuracy:.3f})')

plt.scatter(iris.data[:,x], iris.data[:,y], c=cmap[iris.target], edgecolors=cmap[predict])

plt.xlabel(iris.feature_names[x])

plt.ylabel(iris.feature_names[y])

plt.legend(handles=clabel, framealpha=0.5)

plt.show()

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