handbook4.py

# Application: Exploring Handwritten Digits
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import load_digits
from sklearn.manifold import Isomap
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix

digits = load_digits()

#print(digits.images.shape)
"""
fig, axes = plt.subplots(10, 10, figsize=(8, 8),
                        subplot_kw={'xticks':[], 'yticks':[]},
                        gridspec_kw=dict(hspace=0.1, wspace=0.1))

for i, ax in enumerate(axes.flat):
    ax.imshow(digits.images[i], cmap='binary', interpolation='nearest')
    ax.text(0.05, 0.05, str(digits.target[i]),
            transform=ax.transAxes, color='green')
"""

X = digits.data
y = digits.target

iso = Isomap(n_components=2)
iso.fit(digits.data)
data_projected = iso.transform(digits.data)
#data_projected.shape

"""
plt.scatter(data_projected[:, 0], data_projected[:, 1], c=digits.target,
            edgecolor='none', alpha=0.5,
            cmap=plt.cm.get_cmap('Spectral', 10))
plt.colorbar(label='digit label', ticks=range(10))
plt.clim(-0.5, 9.5);

plt.show()
"""

Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, random_state=0)
model = GaussianNB()
model.fit(Xtrain, ytrain)
y_model = model.predict(Xtest)

print(accuracy_score(ytest, y_model))

mat = confusion_matrix(ytest, y_model)
sns.heatmap(mat, square=True, annot=True, cbar=False)
plt.xlabel('predicted value')
plt.ylabel('true value');
plt.show()