The dataset used in this example is a preprocessed excerpt of the "Labeled Faces in the Wild", aka LFW_:
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%matplotlib inline
from time import time
import logging
import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import fetch_lfw_people
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.decomposition import PCA
from sklearn.svm import SVC
from sklearn import manifold
print(__doc__)
# Display progress logs on stdout
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')
lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)
# introspect the images arrays to find the shapes (for plotting)
n_samples, h, w = lfw_people.images.shape
# for machine learning we use the 2 data directly (as relative pixel
# positions info is ignored by this model)
X = lfw_people.data
n_features = X.shape[1]
# the label to predict is the id of the person
y = lfw_people.target
target_names = lfw_people.target_names
n_classes = target_names.shape[0]
print("Total dataset size:")
print("n_samples: %d" % n_samples)
print("n_features: %d" % n_features)
print("n_classes: %d" % n_classes)
# split into a training and testing set
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.25, random_state=42)
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methods = ['standard', 'ltsa', 'hessian', 'modified']
accuracies = []
components = []
neighbors = []
n_components = 26
n_neighbors = 27
lle = manifold.LocallyLinearEmbedding(n_neighbors, n_components,eigen_solver='auto',method=methods[0])
X_train_changed = lle.fit_transform(X_train)
X_test_changed = lle.fit_transform(X_test)
param_grid = {'C': [1,1e1,1e2,5e2,1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)
clf = clf.fit(X_train_changed, y_train)
y_pred = clf.predict(X_test_changed)
accuracies.append(float(np.sum(y_test==y_pred))/len(y_pred))
components.append(n_components)
neighbors.append(n_neighbors)
print('For '+str(n_components)+' components '+str(n_neighbors)+' neighbors'+', accuracy is '+str(float(np.sum(y_test==y_pred))/len(y_pred))+' confusion matrix is: ')
print(confusion_matrix(y_test, y_pred, labels=range(n_classes)))
print(classification_report(y_test, y_pred, target_names=target_names))
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