机器学习-特征工程算法

# 代码 3-7 from sklearn.datasets import load_iris from sklearn.feature_selection import SelectKBest from sklearn.feature_selection import chi2 iris = load_iris() x, y = iris.data, iris.target print('特征选择前数据集形状为：', x.shape) x_new = SelectKBest(chi2, k=2).fit_transform(x, y) print('特征选择后数据集形状为：', x_new.shape) # 代码 3-8 from sklearn.feature_selection import RFE from sklearn.linear_model import LinearRegression names = iris.feature_names lr = LinearRegression() rfe = RFE(lr, n_features_to_select=1) # 选择剔除1个 rfe.fit(x, y) print('剔除排名为：\n', sorted(zip(map(lambda x: round(x, 5), rfe.ranking_), names))) # 代码 3-9 from sklearn.svm import LinearSVC from sklearn.feature_selection import SelectFromModel print('L1正则化前数据集形状为：', x.shape) lsvc = LinearSVC(C=0.01, penalty='l1', dual=False).fit(x, y) model = SelectFromModel(lsvc, prefit=True) x_new = model.transform(x) print('L1正则化后数据集形状为：', x_new.shape) # 代码 3-10 import matplotlib.pyplot as plt import numpy as np from scipy import misc from sklearn.decomposition import MiniBatchDictionaryLearning from sklearn.feature_extraction.image import extract_patches_2d # 导入数据 face = misc.face(gray=True) # 对数据进行映射和采样 face = face / 255.0 face = face[::2, ::2] + face[1::2, ::2] + face[::2, 1::2] + face[1::2, 1::2] face = face / 4.0 height, width = face.shape print('数据行数：',height) print('数据特征数：',width) # 对照片的右半部分加上噪声 distorted = face.copy() distorted[:, width // 2:] += 0.075 * np.random.randn(height, width // 2) # 对图片的左半部分提取patch patch_size = (7, 7) # patch大小 data = extract_patches_2d(distorted[:, :width // 2], patch_size) data = data.reshape(data.shape[0], -1) # zscore标准化 data -= np.mean(data, axis=0) data /= np.std(data, axis=0) # 创建MiniBatchDictionaryLearning模型 dico = MiniBatchDictionaryLearning(n_components=100, alpha=1, n_iter=500).fit(data) print('字典学习：\n',dico) # 调用components_属性返回获取的字典 V = dico.components_ # 画出V中的字典 plt.figure(figsize=(4.2, 4)) # 定义图片大小，4.2英寸宽，4英寸高 # 循环画出100个字典 for i, comp in enumerate(V[:100]): plt.subplot(10, 10, i + 1) plt.imshow(comp.reshape(patch_size), cmap=plt.cm.gray_r, interpolation='nearest') plt.xticks(()) plt.yticks(()) plt.subtitle('Dictionary learned from face patches',fontsize=16) plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23) #left, right, bottom, top, wspace, hspace. plt.show() # 代码 3-11 from sklearn.feature_extraction.image import reconstruct_from_patches_2d # 定义函数用于对比字典学习前后的效果 def show_with_diff(image, reference, title): plt.figure(figsize=(5, 3.3)) plt.subplot(1, 2, 1) plt.title('Image') plt.imshow(image, vmin=0, vmax=1, cmap=plt.cm.gray, interpolation='nearest') plt.xticks(()) plt.yticks(()) plt.subplot(1, 2, 2) difference = image - reference plt.title('Difference (norm: %.2f)' % np.sqrt(np.sum(difference ** 2))) plt.imshow(difference, vmin=-0.5, vmax=0.5, cmap=plt.cm.PuOr, interpolation='nearest') plt.xticks(()) plt.yticks(()) plt.suptitle(title, size=16) plt.subplots_adjust(0.02, 0.02, 0.98, 0.79, 0.02, 0.2) # 画出图像及噪声 show_with_diff(distorted, face, 'Distorted image') plt.show() # 提取图像含有噪声的右半部进行字典学习。 data = extract_patches_2d(distorted[:, width // 2:], patch_size) data = data.reshape(data.shape[0], -1) intercept = np.mean(data, axis=0) data -= intercept # 四种不同的字典表示策略 transform_algorithms = [ ('Orthogonal Matching Pursuit\n1 atom', 'omp', {'transform_n_nonzero_coefs': 1}), ('Orthogonal Matching Pursuit\n2 atoms', 'omp', {'transform_n_nonzero_coefs': 2}), ('Least-angle regression\n5 atoms', 'lars', {'transform_n_nonzero_coefs': 5}), ('Thresholding\n alpha=0.1', 'threshold', {'transform_alpha': .1})] # 循环绘出4种字典表示策略的结果 reconstructions = {} for title, transform_algorithm, kwargs in transform_algorithms: print(title + '...') reconstructions[title] = face.copy() # 通过set_params方法对参数进行设置 dico.set_params(transform_algorithm=transform_algorithm, **kwargs) # 通过transform方法对数据进行字典表示 code = dico.transform(data) # code矩阵乘V得到复原后的矩阵patches patches = np.dot(code, V) patches += intercept patches = patches.reshape(len(data), *patch_size) if transform_algorithm == 'threshold': patches -= patches.min() patches /= patches.max() # 通过reconstruct_from_patches_2d函数将patches重新拼接回图片 reconstructions[title][:, width // 2:] = reconstruct_from_patches_2d( patches, (height, width // 2)) show_with_diff(reconstructions[title], face,title ) plt.show()