数据分析：clustlasso基于聚类分析的特征选择分类构建

介绍

clustlasso是结合lasso和cluster-lasso策略的R包，并发表在Interpreting k-mer based signatures for antibiotic resistance prediction。更多知识分享请到 [https://zouhua.top/](https://zouhua.top/**)。

标准交叉验证lasso分类或回归流程如下：

1. 选择交叉验证数据集（数据分割）；
2. 选择最佳模型（训练参数）；
3. 测试集评估模型效能（确定最终模型）；

通过看源代码发现相比标准的lasso聚类或回归它多了一个cluster的过程，通过比较自变量之间的相关系数大小进行聚类分析。

加载R包和数据

上gitlab下载该包的tar.gz文件，然后本地安装软件（可适用于windows和Linux）。

install.packages("NMF")
install.packages("D:/Downloads/clustlasso-master.tar.gz", repos = NULL, type = "source")
suppressWarnings(suppressMessages(library(clustlasso)))

加载所需要数据

# specify / set random seed
seed = 42
set.seed(seed)
# load example dataset
input.file = system.file("data", "NG-dataset.Rdata", package = "clustlasso")
load(input.file)

以及80%和20%切割数据集合

# pick 20% for test
test.frac = 0.2
# stratify by origin / population structure
ind.by.struct = split(seq(nrow(meta)), meta$pop_structure)
ind.sample = sapply(ind.by.struct, function(x) {
sample(x, round(test.frac * length(x)))
})
ind.test = unlist(ind.sample)
# split
X.test = X[ind.test, ]
y.test = y[ind.test]
meta.test = meta[ind.test, ]
X.train = X[-ind.test, ]
y.train = y[-ind.test]
meta.train = meta[-ind.test, ]

标准交叉验证lasso过程

该过程没有使用cluster方法。

1. 选择交叉验证数据集（数据分割）；
2. 选择最佳模型（训练参数）；
3. 测试集评估模型效能（确定最终模型）；

Cross-validattion process

交叉验证的目的是训练模型参数，调参的对象是lasso模型的lambda参数。可以设置n.folds和n.repeat参数。

# specify cross-validation parameters
n.folds = 10
n.lambda = 100
n.repeat = 3
# run cross-validation process
cv.res.lasso = lasso_cv(X.train, y.train, subgroup = meta.train$pop_structure, 
      n.lambda = n.lambda, n.folds = n.folds, n.repeat = n.repeat, 
      seed = seed, verbose = FALSE)

最佳参数展示show_cv_overall（modsel.criterion+best.eps）。模型标准和最佳特征均展示出来。

par(mfcol = c(1, 3))
show_cv_overall(cv.res.lasso, modsel.criterion = "balanced.accuracy.best", best.eps = 1)

Selecting the best model

最佳模型根据modsel.criterion参数确定，该参数可根据auc和balanced.accuracy.best确定。

layout(matrix(c(1, 2, 3), nrow = 1, byrow = TRUE), width = c(0.3, 0.3, 0.4), height = c(1))
perf.best.lasso = show_cv_best(cv.res.lasso, modsel.criterion = "balanced.accuracy.best", best.eps = 1, method = "lasso")

# print cross-validation performance of best model
print(perf.best.lasso)

提取最佳模型extract_best_model.

best.model.lasso = extract_best_model(cv.res.lasso, modsel.criterion = "balanced.accuracy.best", best.eps = 1)

Making predictions and measuring performance

根据上一步选择的最佳模型应用于测试集，进而评估模型的效能。

# make predictions
preds.lasso = predict_clustlasso(X.test, best.model.lasso)
# compute performance
perf.lasso = compute_perf(preds.lasso$preds, preds.lasso$probs, y.test)
# print
print(t(perf.lasso$perf))

可视化结果

par(mfcol = c(1, 2))
plot(perf.lasso$roc.curves[[1]], lwd = 2, main = "lasso - test set ROC curve")
grid()
plot(perf.lasso$pr.curves[[1]], lwd = 2, main = "lasso - test set precision / recall curve")
grid()

总结：调参后选择最佳参数确定最终模型对分类器构建至关重要，这里选择balanced.accuracy.best而没有选择auc（大家可以试试auc的结果如何）。

Cluster-lasso过程

与上面标准lasso流程类似，但增加了cluster过程。

Cross-validattion process

该过程多增加了screen.thresh和clust.thresh，该参数用于cluster过程。

# specify cross-validation parameters
n.folds = 10
n.lambda = 100
n.repeat = 3
# specify screening and clustering thresholds
screen.thresh = 0.95
clust.thresh = 0.95
# run cross-validation process
cv.res.cluster = clusterlasso_cv(X.train, y.train, subgroup = meta.train$pop_structure,
        n.lambda = n.lambda, n.folds = n.folds, n.repeat = n.repeat,
        seed = seed, screen.thresh = screen.thresh, clust.thresh = clust.thresh,
verbose = FALSE)
par(mfcol = c(1, 3))
show_cv_overall(cv.res.cluster, modsel.criterion = "balanced.accuracy.best",
best.eps = 1)

Selecting the best model

layout(matrix(c(1, 2, 3, 4, 4, 4), nrow = 2, byrow = TRUE), width = c(0.3,0.3, 0.4), height = c(0.6, 0.4))
perf.best.cluster = show_cv_best(cv.res.cluster, modsel.criterion = "balanced.accuracy.best",
          best.eps = 1, method = "clusterlasso")

# print cross-validation performance of best model
print(perf.best.cluster)

best.model.cluster = extract_best_model(cv.res.cluster, modsel.criterion = "balanced.accuracy.best",
best.eps = 1, method = "clusterlasso")

Making predictions and measuring performance

# make predictions
preds.cluster = predict_clustlasso(X.test, best.model.cluster,
method = "clusterlasso")
# compute performance
perf.cluster = compute_perf(preds.cluster$preds, preds.cluster$probs, y.test)
# print
print(t(perf.cluster$perf))

比较两类方法的结果

比较standard lasso和cluster-lasso 方法在测试集上的预测效能以及特征的区别。

ROC曲线

plot(perf.lasso$roc.curves[[1]], lwd = 2, main = "test set ROC curves")
points(1 - (perf.lasso$perf$speci)/100, perf.lasso$perf$sensi/100, pch = 19, col = 1, cex = 1.25)
plot(perf.cluster$roc.curves[[1]], lwd = 2, col = 2, add = TRUE)
points(1 - (perf.cluster$perf$speci)/100, perf.cluster$perf$sensi/100,
pch = 17, col = 2, cex = 1.25)
grid()
abline(0, 1, lty = 2)
legend("bottomright", c("lasso", "cluster-lasso"), col = c(1, 2), lwd = 2)

特征

heatmap_correlation_signatures(X, best.model.lasso, best.model.cluster,
              clust.min = 5, plot.title = "features correlation matrix")

Note: 最上面橘色和蓝色分布表示lasso和cluster-lasso选择出来的特征，两者重叠部分较多。从热图聚类结果看，聚类效果和cluster-lasso分类结果类似。

Reference

1. clustlasso

参考文章如引起任何侵权问题，可以与我联系，谢谢。