Python 中的数据隐私与匿名化
Rebeca Gonzalez
Data engineer
from diffprivlib.models import KMeans # 使用 DP 模型计算聚类 model = KMeans(epsilon=1, n_clusters=3) # 运行模型并获得聚类 clusters = model.fit_predict(X)
from diffprivlib.models import KMeans
# 使用 DP 模型计算聚类 model = KMeans(epsilon=1, n_clusters=3)
# 运行模型并获得聚类 clusters = model.fit_predict(X)
StandardScaler
diffprivlib
sklearn
from sklearn.decomposition import PCA # 初始化 PCA pca = PCA() # 用 PCA 拟合并转换数据 X = pca.fit_transform(X) # 使用 DP 模型计算聚类 model = dp_Kmeans(epsilon=1, n_clusters=3) # 运行模型并获得聚类 clusters = model.fit_predict(X)
from sklearn.decomposition import PCA
# 初始化 PCA pca = PCA()
# 用 PCA 拟合并转换数据 X = pca.fit_transform(X)
# 使用 DP 模型计算聚类 model = dp_Kmeans(epsilon=1, n_clusters=3)
from diffprivlib.models import KMeans as model # 使用 DP 模型计算聚类 model = dp_Kmeans(epsilon=0.2, n_clusters=3) # 运行模型并获得聚类 clusters = model.fit_predict(X)
from diffprivlib.models import KMeans as model
# 使用 DP 模型计算聚类 model = dp_Kmeans(epsilon=0.2, n_clusters=3)
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