504 KiB
504 KiB
In [2]:
import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import fetch_lfw_people
people=fetch_lfw_people(min_faces_per_person=20, resize=0.7)
image_shape=people.images[0].shape
fig, axes=plt.subplots(2,5, figsize=(15,8), subplot_kw={'xticks': (),'yticks': ()})
for target,image, ax in zip(people.target, people.images, axes.ravel()):
ax.imshow(image)
ax.set_title(people.target_names[target])
#plt.subtitle("some_faces")
plt.show()
print (people.images.shape)
print (len(people.target_names))
#count how often each target appears
counts=np.bincount(people.target)
#prints counts next to target names:
for i, (count,name) in enumerate(zip(counts, people.target_names)):
print("{0:25} {1:3}".format(name, count), end=' ')
if (i+i)%3==0:
print()
mask=np.zeros(people.target.shape, dtype=np.bool)
for target in np.unique(people.target):
mask[np.where(people.target==target)[0][:50]]=1
X_people=people.data[mask]
y_people=people.target[mask]
#scale the grey-scale values between 0-1 instead of 0 and 255 for numerical stability
X_people=X_people/255
#Use a kneighbor classifier
import mglearn
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.decomposition import PCA
#split data into training and test sets
X_train, X_test, y_train, y_test=train_test_split(X_people, y_people, stratify=y_people, random_state=0)
#build a KNeighborsClassifier with one neighbor
knn=KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)
print ("Knn score: ", knn.score(X_test, y_test))
mglearn.plots.plot_pca_whitening()
pca=PCA(n_components=100, whiten=True).fit(X_train)
X_train_pca=pca.transform(X_train)
X_test_pca=pca.transform(X_test)
print(X_train_pca.shape)
knn=KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train_pca, y_train)
print ("Knn score: ", knn.score(X_test_pca, y_test))
pca.components_.shape
fig, axes= plt.subplots(3,5, figsize=(15,12),subplot_kw={'xticks': (), 'yticks': ()})
for i, (component, ax) in enumerate(zip(pca.components_, axes.ravel())):
ax.imshow(component.reshape(image_shape), cmap='viridis')
ax.set_title("%d. component" % (i+i))
(3023, 87, 65) 62 Alejandro Toledo 39 Alvaro Uribe 35 Amelie Mauresmo 21 Andre Agassi 36 Angelina Jolie 20 Ariel Sharon 77 Arnold Schwarzenegger 42 Atal Bihari Vajpayee 24 Bill Clinton 29 Carlos Menem 21 Colin Powell 236 David Beckham 31 Donald Rumsfeld 121 George Robertson 22 George W Bush 530 Gerhard Schroeder 109 Gloria Macapagal Arroyo 44 Gray Davis 26 Guillermo Coria 30 Hamid Karzai 22 Hans Blix 39 Hugo Chavez 71 Igor Ivanov 20 Jack Straw 28 Jacques Chirac 52 Jean Chretien 55 Jennifer Aniston 21 Jennifer Capriati 42 Jennifer Lopez 21 Jeremy Greenstock 24 Jiang Zemin 20 John Ashcroft 53 John Negroponte 31 Jose Maria Aznar 23 Juan Carlos Ferrero 28 Junichiro Koizumi 60 Kofi Annan 32 Laura Bush 41 Lindsay Davenport 22 Lleyton Hewitt 41 Luiz Inacio Lula da Silva 48 Mahmoud Abbas 29 Megawati Sukarnoputri 33 Michael Bloomberg 20 Naomi Watts 22 Nestor Kirchner 37 Paul Bremer 20 Pete Sampras 22 Recep Tayyip Erdogan 30 Ricardo Lagos 27 Roh Moo-hyun 32 Rudolph Giuliani 26 Saddam Hussein 23 Serena Williams 52 Silvio Berlusconi 33 Tiger Woods 23 Tom Daschle 25 Tom Ridge 33 Tony Blair 144 Vicente Fox 32 Vladimir Putin 49 Winona Ryder 24 Knn score: 0.23255813953488372 (1547, 100) Knn score: 0.3003875968992248
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