04_01_02_algoritmos_regresion_logistica.py

# -*- coding: utf-8 -*-

from sklearn.datasets import load_digits

# carga el data set
# en este caso son números pintados (imagen)
# 8x8 pixeles del tamaño, 64 en total
# la clasificación es adivinar el número pintado
digits = load_digits()

# Print to show there are 1797 images (8 by 8 images for a dimensionality of 64)
print("Image Data Shape", digits.data.shape)
# Print to show there are 1797 labels (integers from 0–9)
print("Label Data Shape", digits.target.shape)

import numpy as np
import matplotlib.pyplot as plt

plt.figure(figsize=(20, 4))
for index, (image, label) in enumerate(zip(digits.data[0:5], digits.target[0:5])):
    plt.subplot(1, 5, index + 1)
    plt.imshow(np.reshape(image, (8, 8)), cmap=plt.cm.gray)
    plt.title('Training: %i\n' % label, fontsize=20)
plt.show()

from sklearn.model_selection import train_test_split

x_train, x_test, y_train, y_test = train_test_split(
    digits.data, digits.target, test_size=0.25, random_state=0)

from sklearn.linear_model import LogisticRegression

# all parameters not specified are set to their defaults
logisticRegr = LogisticRegression(max_iter=10000)

logisticRegr.fit(x_train, y_train)

# Returns a NumPy Array
# Predict for One Observation (image)
logisticRegr.predict(x_test[0].reshape(1, -1))
logisticRegr.predict(x_test[0:10])

predictions = logisticRegr.predict(x_test)

# Use score method to get accuracy of model
score = logisticRegr.score(x_test, y_test)
print("Score:", score)

import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics

cm = metrics.confusion_matrix(y_test, predictions)
print(cm)

plt.figure(figsize=(9, 9))
sns.heatmap(cm, annot=True, fmt=".3f", linewidths=.5, square=True, cmap='Blues_r');
plt.ylabel('Actual label');
plt.xlabel('Predicted label');
all_sample_title = 'Accuracy Score: {0}'.format(score)
plt.title(all_sample_title, size=15)
plt.show()

plt.figure(figsize=(9, 9))
plt.imshow(cm, interpolation='nearest', cmap='Pastel1')
plt.title('Confusion matrix', size=15)
plt.colorbar()
tick_marks = np.arange(10)
plt.xticks(tick_marks, ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], rotation=45, size=10)
plt.yticks(tick_marks, ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], size=10)
plt.tight_layout()
plt.ylabel('Actual label', size=15)
plt.xlabel('Predicted label', size=15)
width, height = cm.shape
for x in range(width):
    for y in range(height):
        plt.annotate(str(cm[x][y]), xy=(y, x),
                     horizontalalignment='center',
                     verticalalignment='center')

plt.show()