#!/usr/bin/python
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_lfw_people
import seaborn as sns; sns.set()
faces = fetch_lfw_people(min_faces_per_person=60)
#print(faces.head())
print(faces.target_names)
print(faces.images.shape)
fig, ax = plt.subplots(3, 5)
for i, axi in enumerate(ax.flat):
axi.imshow(faces.images[i], cmap='bone')
axi.set(xticks=, yticks=,
xlabel=faces.target_names[faces.target[i]])
plt.show()
from sklearn.svm import svc
#from sklearn.decomposition import randomizedpca
from sklearn.decomposition import pca
from sklearn.pipeline import make_pipeline
#利用pca技術將特徵降到150維
pca = pca(n_components=150, whiten=true, random_state=42)
svc = svc(kernel='rbf', class_weight='balanced')
model = make_pipeline(pca, svc)
print(model)
from sklearn.model_selection import train_test_split
xtrain, xtest, ytrain, ytest = train_test_split(faces.data, faces.target,
random_state=42)
#使用grid search cross-validation來選擇我們的引數
from sklearn.grid_search import gridsearchcv
param_grid =
grid = gridsearchcv(model, param_grid=param_grid,cv=5)
grid.fit(xtrain,ytrain)
print(grid.best_params_)
model = grid.best_estimator_
yfit = model.predict(xtest)
print(yfit.shape)
fig, ax = plt.subplots(4, 6)
for i, axi in enumerate(ax.flat):
axi.imshow(xtest[i].reshape(62, 47), cmap='bone')
axi.set(xticks=, yticks=)
axi.set_ylabel(faces.target_names[yfit[i]].split()[-1],
color='black' if yfit[i] == ytest[i] else 'red')
fig.suptitle('predicted names; incorrect labels in red', size=14)
plt.show()
from sklearn.metrics import classification_report
print(classification_report(ytest, yfit,
target_names=faces.target_names))
#精度(precision) = 正確**的個數(tp)/被**正確的個數(tp+fp)
#召回率(recall)=正確**的個數(tp)/**個數(tp+fn)
#f1 = 2精度召回率/(精度+召回率)
from sklearn.metrics import confusion_matrix
mat = confusion_matrix(ytest, yfit)
sns.heatmap(mat.t, square=true, annot=true, fmt='d', cbar=false,
xticklabels=faces.target_names,
yticklabels=faces.target_names)
plt.xlabel('true label')
plt.ylabel('predicted label')
plt.show()
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