139 lines
4.5 KiB
R
139 lines
4.5 KiB
R
# Project 6 for the University of Tulsa's CS-7863 Sci-Stat Course
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# Penalized Machine Learning
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# Professor: Dr. McKinney, Spring 2023
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# Noah L. Schrick - 1492657
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# 1. Penalized Regression and Classification
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## a. Modified Ridge classification for LASSO penalties
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### Add cross-validation to tune penalty param
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### Use npdro simulated data to test
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### Compare with Ridge
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### Compare with Random Forest
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if (!require("randomForest")) install.packages("randomForest")
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library(randomForest)
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if (!require("ranger")) install.packages("ranger")
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library(ranger)
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rf_comp <- function(train){
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rf<-randomForest(as.factor(train$class) ~ .,data=train, ntree=5000,
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importance=T)
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print(rf) # error
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rf_imp<-data.frame(rf_score=importance(rf, type=1))
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#dplyr::arrange(rf_imp,-MeanDecreaseAccuracy)
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dplyr::slice_max(rf_imp,order_by=MeanDecreaseAccuracy, n=20)
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rf2<-ranger(as.factor(train$class) ~ ., data=train, num.trees=5000,
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importance="permutation")
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print(rf2) # error
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rf2_imp<-data.frame(rf_score=rf2$variable.importance)
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#dplyr::arrange(rf_imp,-MeanDecreaseAccuracy)
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dplyr::slice_max(rf2_imp,order_by=rf_score, n=20)
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#rftest <- predict(rf, newdata=test, type="class")
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#confusionMatrix(table(rftest,test$class))
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}
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rf_comp(train)
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### Compare with glmnet
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if (!require("glmnet")) install.packages("glmnet")
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library(glmnet)
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glm_fcn <- function(train.X, train.y, alpha_p){
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glmnet.class.model<-cv.glmnet(as.matrix(train.X), train.y, alpha=alpha_p,
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family="binomial", type.measure="class")
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glmnet.class.model$lambda.1se
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glmnet.class.model$lambda.min
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plot(glmnet.class.model)
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glmnet.class.coeffs<-predict(glmnet.class.model,type="coefficients")
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#glmnet.cc.coeffs # maybe 3 is most important, Excess kurtosis
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model.class.terms <- colnames(train.X) # glmnet includes an intercept but we are going to ignore
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#nonzero.glmnet.qtrait.coeffs <- model.qtrait.terms[glmnet.qtrait.coeffs@i[which(glmnet.qtrait.coeffs@i!=0)]] # skip intercept if there, 0-based counting
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glmnet.df <- data.frame(as.matrix(glmnet.class.coeffs))
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glmnet.df$abs_scores <- abs(glmnet.df$lambda.1se)
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dplyr::slice_max(glmnet.df,order_by=abs_scores,n=21)
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}
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#### Alpha = 0
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glm_fcn(train.X, train.y, 0)
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#### Alpha = 1
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glm_fcn(train.X, train.y, 1)
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## b. Repeat comparison using a graph with clusters
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if (!require("igraph")) install.packages("igraph")
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library(igraph)
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if (!require("Matrix")) install.packages("Matrix")
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library(Matrix) # bdiag
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npc <-25 # nodes per cluster
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n_clust <- 4 # 4 clusters with 25 nodes each
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# no clusters
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g0 <- erdos.renyi.game(npc*n_clust, 0.2)
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plot(g0)
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matlist = list()
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for (i in 1:n_clust){
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matlist[[i]] = get.adjacency(erdos.renyi.game(npc, 0.2))
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}
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# merge clusters into one matrix
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mat_clust <- bdiag(matlist) # create block-diagonal matrix
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## the following two things might not be necessary
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# check for loner nodes, connected to nothing, and join them to something
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k <- rowSums(mat_clust)
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node_vector <- seq(1,npc*n_clust)
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for (i in node_vector){
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if (k[i]==0){ # if k=0, connect to something random
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j <- sample(node_vector[-i],1)
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mat_clust[i,j] <- 1
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mat_clust[j,i] <- 1
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}
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}
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node_colors <- c(rep("red",npc), rep("green",npc), rep("blue",npc), rep("orange",npc))
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g1 <- graph_from_adjacency_matrix(mat_clust, mode="undirected", diag=F)
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plot(g1, vertex.color=node_colors)
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## c. Use npdro and igraph to create knn
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my.k <- 3 # larger k, fewer clusters
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npdr.nbpairs.idx <- npdro::nearestNeighbors(t(train.X),
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# transpose does dist between predictors
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# without transpose does dist between samples
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#nbd.method="multisurf", k=0,
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nbd.method = "relieff",
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nbd.metric="manhattan",
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k=my.k)
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knn.graph <- graph_from_edgelist(as.matrix(npdr.nbpairs.idx),
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directed=F)
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knn.graph <- simplify(knn.graph)
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### Plot network
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plot.igraph(knn.graph,layout=layout_with_fr(knn.graph),
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vertex.color="red",
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vertex.size=3,vertex.label=NA,
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main="Manhattan, knn-graph")
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## d. Add Laplace graph penalty
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### Find resulting beta coeffs
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### Optimize or choose value for lambda2
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### Compare to a) and b)
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# 2. Gradient Descent
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## Write fn with learning param
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## Solve Rosenbrock function minimum
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## Add momentum term |