8000 Add 2D2PCA ( Two-directional two-dimensional PCA) by yedtoss · Pull Request #1503 · scikit-learn/scikit-learn · GitHub
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Add 2D2PCA ( Two-directional two-dimensional PCA) #1503

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0c26bd2
Added 2D PCA with tests and applications
yedtoss Dec 31, 2012
02cc220
minor corrections in 2DPCA module
yedtoss Dec 31, 2012
8e06561
corrections in 2DPCA module
yedtoss Jan 1, 2013
697fd4e
corrections in 2DPCA module
yedtoss Jan 1, 2013
3c5efff
corrections in 2DPCA module
yedtoss Jan 2, 2013
c9217e0
corrections in 2DPCA module
yedtoss Jan 2, 2013
493d783
corrections to 2D PCA module
yedtoss Jan 2, 2013
66862ad
style correction in PCA2D
yedtoss Jan 3, 2013
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172 changes: 172 additions & 0 deletions examples/applications/face_recognition_pca_2d.py
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"""
===================================================
Faces recognition example using 2DPCA and Random Forests
===================================================

This dataset contains a set of face images taken between April 1992 and
April 1994 at AT&T Laboratories Cambridge. It is the Olivetti faces :

http://cs.nyu.edu/~roweis/data/olivettifaces.mat (4MB)

.. Olivetti: http://www.cs.nyu.edu/~roweis/

Expected results for the 40 people in the dataset::

precision recall f1-score support

Face 0 1.00 0.40 0.57 5
Face 1 1.00 1.00 1.00 5
Face 2 0.80 0.80 0.80 5
Face 3 1.00 1.00 1.00 5
Face 4 1.00 0.80 0.89 5
Face 5 0.83 1.00 0.91 5
Face 6 0.71 1.00 0.83 5
Face 7 0.80 0.80 0.80 5
Face 8 1.00 1.00 1.00 5
Face 9 1.00 0.80 0.89 5
Face 10 1.00 1.00 1.00 5
Face 11 0.67 0.80 0.73 5
Face 12 1.00 0.80 0.89 5
Face 13 1.00 1.00 1.00 5
Face 14 0.83 1.00 0.91 5
Face 15 1.00 1.00 1.00 5
Face 16 1.00 1.00 1.00 5
Face 17 0.83 1.00 0.91 5
Face 18 1.00 1.00 1.00 5
Face 19 1.00 0.80 0.89 5
Face 20 0.71 1.00 0.83 5
Face 21 1.00 0.80 0.89 5
Face 22 1.00 1.00 1.00 5
Face 23 0.83 1.00 0.91 5
Face 24 0.83 1.00 0.91 5
Face 25 0.80 0.80 0.80 5
Face 26 1.00 1.00 1.00 5
Face 27 1.00 1.00 1.00 5
Face 28 1.00 1.00 1.00 5
Face 29 1.00 1.00 1.00 5
Face 30 1.00 1.00 1.00 5
Face 31 1.00 0.60 0.75 5
Face 32 1.00 1.00 1.00 5
Face 33 1.00 1.00 1.00 5
Face 34 1.00 0.80 0.89 5
Face 35 1.00 0.80 0.89 5
Face 36 0.83 1.00 0.91 5
Face 37 1.00 1.00 1.00 5
Face 38 0.83 1.00 0.91 5
Face 39 0.80 0.80 0.80 5

avg / total 0.93 0.92 0.91 200



"""
print __doc__

from time import time
import numpy as np

from sklearn.grid_search import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import fetch_olivetti_faces
from sklearn.cross_validation import StratifiedShuffleSplit
from sklearn.decomposition import PCA2D


##############################################################################
# Download the data, if not already on disk and load it as numpy arrays

olivetti_faces = fetch_olivetti_faces()

# introspect the images arrays to find the shapes
n_samples, h, w = olivetti_faces.images.shape

# we use the 2d data directly
X = olivetti_faces.images

# the label to predict is the id of the person
y = olivetti_faces.target

target_names = np.array(["Face %d" % x for x in range(0,
np.size(olivetti_faces.target), 1)])
n_classes = target_names.shape[0]

print "Total dataset size:"
print "n_samples: %d" % n_samples
print "features: %d X %d" % (h, w)
print "n_classes: %d" % n_classes


##############################################################################
# Split into a training set and a test set using a stratified shuffle split

# split into a training and testing set
sss = StratifiedShuffleSplit(y, 1, test_size=0.5, random_state=0)
for train_index, test_index in sss:
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]


##############################################################################
# Compute a 2DPCA (eigenfaces) on the face dataset (treated as unlabeled
# dataset): unsupervised feature extraction / dimensionality reduction

n_row_components = 0.9
n_column_components = 0.9

print("Reducing images by keeping %d %% of the variance on the row"
% (100 * n_row_components))
print "and %d %% of the variance on the column from %d faces" % (
100 * n_column_components, X_train.shape[0])
t0 = time()
pca = PCA2D(n_row_components=n_row_components, n_column_components=
n_column_components, row_whiten=True,
column_whiten=True, epsilon=0).fit(X_train)
print "done in %0.3fs" % (time() - t0)


print "Projecting the input data on the eigenfaces orthonormal basis"
t0 = time()
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print "done in %0.3fs" % (time() - t0)

# Converting the images to 1D for classification
X_train_pca = X_train_pca.reshape(X_train_pca.shape[0], X_train_pca.shape[1] *
X_train_pca.shape[2])

X_test_pca = X_test_pca.reshape(X_test_pca.shape[0], X_test_pca.shape[1] *
X_test_pca.shape[2])

X_test = X_test.reshape(X_test.shape[0], h * w)
X_train = X_train.reshape(X_train.shape[0], h * w)


##############################################################################
# Train a Random Forests classification model

print "Fitting the classifier to the training set"
t0 = time()
param_grid = {
'n_estimators': [10, 50, 90, 300, 500, 700, 1000, 1500, 2000],
}
clf = GridSearchCV(RandomForestClassifier(), param_grid)
clf = clf.fit(X_train_pca, y_train)
print "done in %0.3fs" % (time() - t0)
print "Best estimator found by grid search:"
print clf.best_estimator_


##############################################################################
# Quantitative evaluation of the model quality on the test set

print "Predicting the people names on the testing set"
t0 = time()
y_pred = clf.predict(X_test_pca)
print "done in %0.3fs" % (time() - t0)

print classification_report(y_test, y_pred, target_names=target_names)
print confusion_matrix(y_test, y_pred, labels=range(n_classes))
print "Size of the components retained %d X %d" % (pca.n_row_components_,
pca.n_column_components_)
4 changes: 3 additions & 1 deletion sklearn/decomposition/__init__.py
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Expand Up @@ -13,6 +13,7 @@
DictionaryLearning, MiniBatchDictionaryLearning,\
SparseCoder
from .factor_analysis import FactorAnalysis
from .pca_2d import PCA2D

__all__ = ['DictionaryLearning',
'FastICA',
Expand All @@ -30,4 +31,5 @@
'dict_learning_online',
'fastica',
'sparse_encode',
'FactorAnalysis']
'FactorAnalysis',
'PCA2D']
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