

In [2]:

    
from __future__ import print_function

print(__doc__)

# Authors: Yann N. Dauphin, Vlad Niculae, Gabriel Synnaeve
# License: BSD

import numpy as np
import matplotlib.pyplot as plt

from scipy.ndimage import convolve
from sklearn import linear_model, datasets, metrics
from sklearn.cross_validation import train_test_split
from sklearn.neural_network import BernoulliRBM
from sklearn.pipeline import Pipeline


###############################################################################
# Setting up

def nudge_dataset(X, Y):
    """
    This produces a dataset 5 times bigger than the original one,
    by moving the 8x8 images in X around by 1px to left, right, down, up
    """
    direction_vectors = [
        [[0, 1, 0],
         [0, 0, 0],
         [0, 0, 0]],

        [[0, 0, 0],
         [1, 0, 0],
         [0, 0, 0]],

        [[0, 0, 0],
         [0, 0, 1],
         [0, 0, 0]],

        [[0, 0, 0],
         [0, 0, 0],
         [0, 1, 0]]]

    shift = lambda x, w: convolve(x.reshape((8, 8)), mode='constant',
                                  weights=w).ravel()
    X = np.concatenate([X] +
                       [np.apply_along_axis(shift, 1, X, vector)
                        for vector in direction_vectors])
    Y = np.concatenate([Y for _ in range(5)], axis=0)
    return X, Y

# Load Data
digits = datasets.load_digits()
X = np.asarray(digits.data, 'float32')
X, Y = nudge_dataset(X, digits.target)
X = (X - np.min(X, 0)) / (np.max(X, 0) + 0.0001)  # 0-1 scaling

X_train, X_test, Y_train, Y_test = train_test_split(X, Y,
                                                    test_size=0.2,
                                                    random_state=0)

# Models we will use
logistic = linear_model.LogisticRegression()
rbm = BernoulliRBM(random_state=0, verbose=True)

classifier = Pipeline(steps=[('rbm', rbm), ('logistic', logistic)])

###############################################################################
# Training

# Hyper-parameters. These were set by cross-validation,
# using a GridSearchCV. Here we are not performing cross-validation to
# save time.
rbm.learning_rate = 0.06
rbm.n_iter = 20
# More components tend to give better prediction performance, but larger
# fitting time
rbm.n_components = 100
logistic.C = 6000.0

# Training RBM-Logistic Pipeline
classifier.fit(X_train, Y_train)

# Training Logistic regression
logistic_classifier = linear_model.LogisticRegression(C=100.0)
logistic_classifier.fit(X_train, Y_train)

###############################################################################
# Evaluation

print()
print("Logistic regression using RBM features:\n%s\n" % (
    metrics.classification_report(
        Y_test,
        classifier.predict(X_test))))

print("Logistic regression using raw pixel features:\n%s\n" % (
    metrics.classification_report(
        Y_test,
        logistic_classifier.predict(X_test))))

###############################################################################
# Plotting

plt.figure(figsize=(4.2, 4))
for i, comp in enumerate(rbm.components_):
    plt.subplot(10, 10, i + 1)
    plt.imshow(comp.reshape((8, 8)), cmap=plt.cm.gray_r,
               interpolation='nearest')
    plt.xticks(())
    plt.yticks(())
plt.suptitle('100 components extracted by RBM', fontsize=16)
plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23)

plt.show()









    



Automatically created module for IPython interactive environment
Iteration 0, pseudo-likelihood = -28.84, time = 0.96s
Iteration 1, pseudo-likelihood = -25.92, time = 0.97s
Iteration 2, pseudo-likelihood = -24.82, time = 0.97s
Iteration 3, pseudo-likelihood = -23.71, time = 0.97s
Iteration 4, pseudo-likelihood = -23.03, time = 0.97s
Iteration 5, pseudo-likelihood = -22.44, time = 0.97s
Iteration 6, pseudo-likelihood = -21.91, time = 0.97s
Iteration 7, pseudo-likelihood = -21.66, time = 0.97s
Iteration 8, pseudo-likelihood = -21.39, time = 0.97s
Iteration 9, pseudo-likelihood = -21.07, time = 0.97s
Iteration 10, pseudo-likelihood = -20.85, time = 0.97s
Iteration 11, pseudo-likelihood = -20.74, time = 0.97s
Iteration 12, pseudo-likelihood = -20.57, time = 0.97s
Iteration 13, pseudo-likelihood = -20.44, time = 0.96s
Iteration 14, pseudo-likelihood = -20.29, time = 0.97s
Iteration 15, pseudo-likelihood = -20.20, time = 0.96s
Iteration 16, pseudo-likelihood = -19.98, time = 0.96s
Iteration 17, pseudo-likelihood = -19.75, time = 0.96s
Iteration 18, pseudo-likelihood = -19.78, time = 0.96s
Iteration 19, pseudo-likelihood = -19.67, time = 0.96s

Logistic regression using RBM features:
             precision    recall  f1-score   support

          0       0.99      0.99      0.99       174
          1       0.92      0.95      0.93       184
          2       0.95      0.98      0.97       166
          3       0.97      0.91      0.94       194
          4       0.97      0.95      0.96       186
          5       0.93      0.93      0.93       181
          6       0.98      0.97      0.97       207
          7       0.95      1.00      0.97       154
          8       0.90      0.88      0.89       182
          9       0.91      0.93      0.92       169

avg / total       0.95      0.95      0.95      1797


Logistic regression using raw pixel features:
             precision    recall  f1-score   support

          0       0.85      0.94      0.89       174
          1       0.57      0.55      0.56       184
          2       0.72      0.85      0.78       166
          3       0.76      0.74      0.75       194
          4       0.85      0.82      0.84       186
          5       0.74      0.75      0.75       181
          6       0.93      0.88      0.91       207
          7       0.86      0.90      0.88       154
          8       0.68      0.55      0.61       182
          9       0.71      0.74      0.72       169

avg / total       0.77      0.77      0.77      1797