53 lines
2.3 KiB
Python
53 lines
2.3 KiB
Python
# ## Example: Face Recognition
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#
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# As an example of support vector machines in action, let's take a look at the facial recognition problem.
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# We will use the Labeled Faces in the Wild dataset, which consists of several thousand collated photos of various public figures.
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#读取数据集
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy import stats
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import seaborn as sns; sns.set()
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from sklearn.datasets import fetch_lfw_people
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faces = fetch_lfw_people(min_faces_per_person=60)
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#看一下数据的规模
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print(faces.target_names)
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print(faces.images.shape)
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# Let's plot a few of these faces to see what we're working with:
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# * 每个图的大小是 [62×47]
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# * 在这里我们就把每一个像素点当成了一个特征，但是这样特征太多了，用PCA降维一下吧！
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from sklearn.svm import SVC
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from sklearn.decomposition import PCA
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from sklearn.pipeline import make_pipeline
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#降维到150维
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pca = PCA(n_components=150, whiten=True, random_state=42)
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svc = SVC(kernel='rbf', class_weight='balanced')
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#先降维然后再SVM
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model = make_pipeline(pca, svc)
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from sklearn.model_selection import train_test_split
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Xtrain, Xtest, ytrain, ytest = train_test_split(faces.data, faces.target,
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random_state=40)
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# 使用grid search cross-validation来选择我们的参数
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from sklearn.model_selection import GridSearchCV
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param_grid = {'svc__C': [1, 5, 10],
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'svc__gamma': [0.0001, 0.0005, 0.001]}
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grid = GridSearchCV(model, param_grid)
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# get_ipython().run_line_magic('time', 'grid.fit(Xtrain, ytrain)')
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# print(grid.best_params_)
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model = grid.best_estimator_
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yfit = model.predict(Xtest)
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yfit.shape
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from sklearn.metrics import classification_report
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print(classification_report(ytest, yfit,
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target_names=faces.target_names))
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# * 精度(precision) = 正确预测的个数(TP)/被预测正确的个数(TP+FP)
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# * 召回率(recall)=正确预测的个数(TP)/预测个数(TP+FN)
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# * F1 = 完整例子*精度*召回率/(精度+召回率)
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from sklearn.metrics import confusion_matrix
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mat = confusion_matrix(ytest, yfit)
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sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,
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xticklabels=faces.target_names,
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yticklabels=faces.target_names)
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plt.xlabel('true label')
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plt.ylabel('predicted label')
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plt.show()
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# * 这样显示出来能帮助我们查看哪些人更容易弄混 |