rm(list = ls())
###############################input data_1
###############################input data_1
dir_path <- "C:\\Users\\liyix\\OneDrive\\Desktop\\data_for_model\\"
dir_path_name <- dir(dir_path,pattern = ".*.csv",full.names = T)
#dir_path_name
###############################merge data
data_train <- read.csv(grep("training_data.csv",dir_path_name,value = T),header = T,stringsAsFactors = F)
dim(data_train) #[1] 2357 148
## [1] 2357 148
#View(data_train)
data_test <- read.csv(grep("test_data.csv",dir_path_name,value = T),header = T,stringsAsFactors = F)
#dim(data_test) #[1] 1009 150
#table(data_train$Freq) # 0 1 2057 300
#table(data_test$Freq) # 0 1 894 115
#View(head(data_test))
#setdiff(colnames(data_test),colnames(data_train)) #[1] "Drug" "Virus"
#setdiff(colnames(data_train),colnames(data_test)) #character(0)
#colnames(data_train)[ncol(data_train)] #[1] "Freq"
################################
#data_new <- data_test
#data_train <- unique(data_train)
###model
###################################model
library(xgboost)
library(ROCR)
library(pROC)
## Type 'citation("pROC")' for a citation.
##
## 载入程辑包：'pROC'
## The following objects are masked from 'package:stats':
##
## cov, smooth, var
library(Matrix)
library(ROSE)
## Loaded ROSE 0.0-4
library(DMwR)
## 载入需要的程辑包：lattice
## 载入需要的程辑包：grid
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
#colnames(data_train)[ncol(data_train)] #[1] "Freq"
colnames(data_train)[ncol(data_train)] <- "num"
data_train <- data.frame(sapply(data_train, as.numeric))
#str(data_train$num)
############################################################################test_cross_validation
#dim(data_train) #[1] 2357 148
#View(head(data_train))
#colnames(data_train)
#table(data_train$num) # 0 1 2057 300
#table(data_train[ncol(data_train)]) ## 0 1 2057 300
testdata_ori <- data_train[sample(nrow(data_train),size = floor(nrow(data_train)*0.3),replace = F),]
traindata_ori <- data_train[-match(row.names(testdata_ori),row.names(data_train)),]
#intersect(row.names(testdata_ori),row.names(traindata_ori)) #character(0)
traindata_ori <- ovun.sample(num ~ ., data = traindata_ori, method = "over")$data
#dim(testdata_ori);dim(traindata_ori) #[1] 707 148 [1] 1650 148
n_col_newdata <- ncol(data_train)
train_label_ori = traindata_ori[,n_col_newdata]
train_matrix_ori = as(as.matrix(traindata_ori[,-n_col_newdata]), "dgCMatrix")
class(train_matrix_ori);dim(train_matrix_ori) #[1][1] "dgCMatrix" attr(,"package") [1] "Matrix" #[1] 3487 50
## [1] "dgCMatrix"
## attr(,"package")
## [1] "Matrix"
## [1] 2873 147
val_label_ori = testdata_ori[,n_col_newdata]
val_matrix_ori = as(as.matrix(testdata_ori[,-n_col_newdata]), "dgCMatrix")
#parameters
xgb_params = list(objective = "binary:logistic",
eval_metric = "error",
max_depth = 3,
eta = 0.01,
gammma = 1,
colsample_bytree = 0.5,
min_child_weight = 1)
#model
#class(train_matrix_ori)
#class(train_label_ori)
bst_model_ori = xgboost(params = xgb_params, data = train_matrix_ori, label = train_label_ori,nrounds = 1000)
## [15:46:22] WARNING: amalgamation/../src/learner.cc:573:
## Parameters: { "gammma" } might not be used.
##
## This may not be accurate due to some parameters are only used in language bindings but
## passed down to XGBoost core. Or some parameters are not used but slip through this
## verification. Please open an issue if you find above cases.
##
##
## [1] train-error:0.336930
## [2] train-error:0.311869
## [3] train-error:0.285416
## [4] train-error:0.258267
## [5] train-error:0.265924
## [6] train-error:0.250957
## [7] train-error:0.249565
## [8] train-error:0.245388
## [9] train-error:0.250609
## [10] train-error:0.246084
## [11] train-error:0.247825
## [12] train-error:0.229029
## [13] train-error:0.222416
## [14] train-error:0.220327
## [15] train-error:0.202576
## [16] train-error:0.215454
## [17] train-error:0.206056
## [18] train-error:0.211277
## [19] train-error:0.206753
## [20] train-error:0.222068
## [21] train-error:0.221719
## [22] train-error:0.183084
## [23] train-error:0.185868
## [24] train-error:0.184128
## [25] train-error:0.191438
## [26] train-error:0.190741
## [27] train-error:0.215802
## [28] train-error:0.189697
## [29] train-error:0.210581
## [30] train-error:0.212670
## [31] train-error:0.209189
## [32] train-error:0.205012
## [33] train-error:0.208841
## [34] train-error:0.210233
## [35] train-error:0.214410
## [36] train-error:0.214062
## [37] train-error:0.214758
## [38] train-error:0.215454
## [39] train-error:0.217891
## [40] train-error:0.219631
## [41] train-error:0.221023
## [42] train-error:0.225200
## [43] train-error:0.221371
## [44] train-error:0.221023
## [45] train-error:0.223112
## [46] train-error:0.228333
## [47] train-error:0.225896
## [48] train-error:0.225896
## [49] train-error:0.222764
## [50] train-error:0.222764
## [51] train-error:0.223808
## [52] train-error:0.224156
## [53] train-error:0.226592
## [54] train-error:0.223460
## [55] train-error:0.226244
## [56] train-error:0.227985
## [57] train-error:0.227637
## [58] train-error:0.225548
## [59] train-error:0.224852
## [60] train-error:0.224156
## [61] train-error:0.225548
## [62] train-error:0.221371
## [63] train-error:0.221371
## [64] train-error:0.220675
## [65] train-error:0.221719
## [66] train-error:0.223112
## [67] train-error:0.222416
## [68] train-error:0.223112
## [69] train-error:0.223112
## [70] train-error:0.221371
## [71] train-error:0.219979
## [72] train-error:0.216846
## [73] train-error:0.221719
## [74] train-error:0.221371
## [75] train-error:0.215802
## [76] train-error:0.220327
## [77] train-error:0.215802
## [78] train-error:0.218587
## [79] train-error:0.216150
## [80] train-error:0.216846
## [81] train-error:0.217543
## [82] train-error:0.217543
## [83] train-error:0.219283
## [84] train-error:0.218587
## [85] train-error:0.217891
## [86] train-error:0.219283
## [87] train-error:0.220675
## [88] train-error:0.218587
## [89] train-error:0.214410
## [90] train-error:0.215454
## [91] train-error:0.215106
## [92] train-error:0.215106
## [93] train-error:0.214062
## [94] train-error:0.214062
## [95] train-error:0.211974
## [96] train-error:0.213366
## [97] train-error:0.212322
## [98] train-error:0.207797
## [99] train-error:0.208145
## [100] train-error:0.208145
## [101] train-error:0.208493
## [102] train-error:0.209189
## [103] train-error:0.209189
## [104] train-error:0.210233
## [105] train-error:0.210929
## [106] train-error:0.210929
## [107] train-error:0.210581
## [108] train-error:0.209537
## [109] train-error:0.210581
## [110] train-error:0.210581
## [111] train-error:0.211277
## [112] train-error:0.211625
## [113] train-error:0.211974
## [114] train-error:0.211625
## [115] train-error:0.209885
## [116] train-error:0.209885
## [117] train-error:0.209885
## [118] train-error:0.209537
## [119] train-error:0.209885
## [120] train-error:0.209537
## [121] train-error:0.208493
## [122] train-error:0.205360
## [123] train-error:0.206404
## [124] train-error:0.199095
## [125] train-error:0.199095
## [126] train-error:0.199095
## [127] train-error:0.199095
## [128] train-error:0.195614
## [129] train-error:0.195614
## [130] train-error:0.195266
## [131] train-error:0.195614
## [132] train-error:0.197007
## [133] train-error:0.199791
## [134] train-error:0.196659
## [135] train-error:0.194918
## [136] train-error:0.190393
## [137] train-error:0.189697
## [138] train-error:0.192134
## [139] train-error:0.189001
## [140] train-error:0.190045
## [141] train-error:0.190741
## [142] train-error:0.190045
## [143] train-error:0.190045
## [144] train-error:0.190045
## [145] train-error:0.190045
## [146] train-error:0.189349
## [147] train-error:0.188653
## [148] train-error:0.187957
## [149] train-error:0.189001
## [150] train-error:0.188653
## [151] train-error:0.188653
## [152] train-error:0.187957
## [153] train-error:0.187957
## [154] train-error:0.187957
## [155] train-error:0.187609
## [156] train-error:0.187609
## [157] train-error:0.185520
## [158] train-error:0.185172
## [159] train-error:0.185520
## [160] train-error:0.186565
## [161] train-error:0.186913
## [162] train-error:0.186565
## [163] train-error:0.187957
## [164] train-error:0.188305
## [165] train-error:0.187609
## [166] train-error:0.186913
## [167] train-error:0.187261
## [168] train-error:0.187261
## [169] train-error:0.187261
## [170] train-error:0.186913
## [171] train-error:0.186913
## [172] train-error:0.183780
## [173] train-error:0.184128
## [174] train-error:0.184476
## [175] train-error:0.184476
## [176] train-error:0.184476
## [177] train-error:0.184476
## [178] train-error:0.183780
## [179] train-error:0.183780
## [180] train-error:0.184824
## [181] train-error:0.183432
## [182] train-error:0.183084
## [183] train-error:0.184476
## [184] train-error:0.185520
## [185] train-error:0.185520
## [186] train-error:0.184824
## [187] train-error:0.185172
## [188] train-error:0.183780
## [189] train-error:0.185868
## [190] train-error:0.184128
## [191] train-error:0.183780
## [192] train-error:0.183780
## [193] train-error:0.183084
## [194] train-error:0.183432
## [195] train-error:0.183084
## [196] train-error:0.183084
## [197] train-error:0.182040
## [198] train-error:0.182040
## [199] train-error:0.180995
## [200] train-error:0.180647
## [201] train-error:0.180995
## [202] train-error:0.180299
## [203] train-error:0.179951
## [204] train-error:0.179603
## [205] train-error:0.179951
## [206] train-error:0.179255
## [207] train-error:0.179255
## [208] train-error:0.179603
## [209] train-error:0.179951
## [210] train-error:0.180647
## [211] train-error:0.180995
## [212] train-error:0.179951
## [213] train-error:0.180299
## [214] train-error:0.179951
## [215] train-error:0.180647
## [216] train-error:0.180995
## [217] train-error:0.180995
## [218] train-error:0.180995
## [219] train-error:0.180995
## [220] train-error:0.180647
## [221] train-error:0.181344
## [222] train-error:0.181344
## [223] train-error:0.181344
## [224] train-error:0.179951
## [225] train-error:0.179951
## [226] train-error:0.180299
## [227] train-error:0.180299
## [228] train-error:0.180647
## [229] train-error:0.180647
## [230] train-error:0.180995
## [231] train-error:0.181344
## [232] train-error:0.181344
## [233] train-error:0.181692
## [234] train-error:0.181344
## [235] train-error:0.179951
## [236] train-error:0.180647
## [237] train-error:0.180995
## [238] train-error:0.181344
## [239] train-error:0.181692
## [240] train-error:0.180995
## [241] train-error:0.183084
## [242] train-error:0.183084
## [243] train-error:0.183084
## [244] train-error:0.180995
## [245] train-error:0.183084
## [246] train-error:0.183432
## [247] train-error:0.183084
## [248] train-error:0.183432
## [249] train-error:0.183432
## [250] train-error:0.184476
## [251] train-error:0.184128
## [252] train-error:0.184476
## [253] train-error:0.184824
## [254] train-error:0.184824
## [255] train-error:0.182388
## [256] train-error:0.184824
## [257] train-error:0.185172
## [258] train-error:0.186217
## [259] train-error:0.183780
## [260] train-error:0.182736
## [261] train-error:0.183084
## [262] train-error:0.182736
## [263] train-error:0.182040
## [264] train-error:0.182736
## [265] train-error:0.182736
## [266] train-error:0.183084
## [267] train-error:0.183084
## [268] train-error:0.183780
## [269] train-error:0.183084
## [270] train-error:0.182736
## [271] train-error:0.183780
## [272] train-error:0.183084
## [273] train-error:0.183084
## [274] train-error:0.182736
## [275] train-error:0.182736
## [276] train-error:0.182736
## [277] train-error:0.182736
## [278] train-error:0.182736
## [279] train-error:0.182040
## [280] train-error:0.181692
## [281] train-error:0.182040
## [282] train-error:0.182388
## [283] train-error:0.180995
## [284] train-error:0.181692
## [285] train-error:0.182040
## [286] train-error:0.179603
## [287] train-error:0.179951
## [288] train-error:0.180995
## [289] train-error:0.181344
## [290] train-error:0.180647
## [291] train-error:0.180647
## [292] train-error:0.180647
## [293] train-error:0.180647
## [294] train-error:0.181692
## [295] train-error:0.181692
## [296] train-error:0.181692
## [297] train-error:0.182040
## [298] train-error:0.182040
## [299] train-error:0.180299
## [300] train-error:0.180299
## [301] train-error:0.180647
## [302] train-error:0.180647
## [303] train-error:0.180647
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## [306] train-error:0.180647
## [307] train-error:0.180995
## [308] train-error:0.180299
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## [310] train-error:0.177863
## [311] train-error:0.177515
## [312] train-error:0.178211
## [313] train-error:0.176819
## [314] train-error:0.176819
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## [317] train-error:0.177167
## [318] train-error:0.177167
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## [321] train-error:0.176819
## [322] train-error:0.175774
## [323] train-error:0.176819
## [324] train-error:0.176471
## [325] train-error:0.176819
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## [327] train-error:0.176123
## [328] train-error:0.176819
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## [330] train-error:0.175078
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## [332] train-error:0.176123
## [333] train-error:0.175774
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## [336] train-error:0.175426
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## [415] train-error:0.150017
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## [418] train-error:0.148973
## [419] train-error:0.149321
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## [441] train-error:0.146189
## [442] train-error:0.146885
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## [445] train-error:0.146537
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## [448] train-error:0.146189
## [449] train-error:0.147581
## [450] train-error:0.147929
## [451] train-error:0.146885
## [452] train-error:0.147233
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## [454] train-error:0.146189
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## [463] train-error:0.143056
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## [821] train-error:0.087017
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## [823] train-error:0.085277
## [824] train-error:0.085973
## [825] train-error:0.085625
## [826] train-error:0.084929
## [827] train-error:0.085625
## [828] train-error:0.085973
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## [833] train-error:0.085625
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## [835] train-error:0.085277
## [836] train-error:0.084929
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## [839] train-error:0.083188
## [840] train-error:0.083188
## [841] train-error:0.084233
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## [845] train-error:0.082492
## [846] train-error:0.083188
## [847] train-error:0.082144
## [848] train-error:0.082144
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## [855] train-error:0.083188
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## [865] train-error:0.081796
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## [870] train-error:0.081448
## [871] train-error:0.081100
## [872] train-error:0.081448
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## [875] train-error:0.081100
## [876] train-error:0.081100
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## [880] train-error:0.081100
## [881] train-error:0.081100
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## [884] train-error:0.081448
## [885] train-error:0.081448
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## [887] train-error:0.081100
## [888] train-error:0.080752
## [889] train-error:0.078315
## [890] train-error:0.078315
## [891] train-error:0.078315
## [892] train-error:0.077619
## [893] train-error:0.077619
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## [897] train-error:0.076575
## [898] train-error:0.076575
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## [902] train-error:0.076227
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## [908] train-error:0.075879
## [909] train-error:0.075531
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## [912] train-error:0.075879
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## [915] train-error:0.076227
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## [920] train-error:0.075531
## [921] train-error:0.076227
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## [923] train-error:0.075879
## [924] train-error:0.076575
## [925] train-error:0.075879
## [926] train-error:0.074835
## [927] train-error:0.074835
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## [947] train-error:0.072746
## [948] train-error:0.073094
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## [950] train-error:0.069614
## [951] train-error:0.069266
## [952] train-error:0.068918
## [953] train-error:0.068221
## [954] train-error:0.068221
## [955] train-error:0.068221
## [956] train-error:0.067873
## [957] train-error:0.067525
## [958] train-error:0.067525
## [959] train-error:0.067525
## [960] train-error:0.067873
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## [974] train-error:0.067525
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## [977] train-error:0.067177
## [978] train-error:0.067177
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## [980] train-error:0.067177
## [981] train-error:0.067177
## [982] train-error:0.068221
## [983] train-error:0.068221
## [984] train-error:0.068221
## [985] train-error:0.067873
## [986] train-error:0.067873
## [987] train-error:0.067873
## [988] train-error:0.067873
## [989] train-error:0.067177
## [990] train-error:0.067177
## [991] train-error:0.067177
## [992] train-error:0.066829
## [993] train-error:0.066829
## [994] train-error:0.066829
## [995] train-error:0.066829
## [996] train-error:0.066829
## [997] train-error:0.066133
## [998] train-error:0.066133
## [999] train-error:0.066133
## [1000] train-error:0.066481
pre_auc_xgboost_ori <- predict(bst_model_ori, newdata = val_matrix_ori, type = 'raw')
pred_auc_xgboost_ori <- prediction(pre_auc_xgboost_ori,testdata_ori[,n_col_newdata])
auc_xgboost_mean_ori <- performance(pred_auc_xgboost_ori,"auc")@y.values[[1]]
auc_xgboost_mean_ori #[1] 0.8821492
## [1] 0.9058855
modelroc_xgboost_ori <- pROC::roc(testdata_ori[,n_col_newdata],as.numeric(pre_auc_xgboost_ori))
## Setting levels: control = 0, case = 1
## Setting direction: controls < cases
cutoff_xgboost_ori <- pROC::coords(modelroc_xgboost_ori, "best", ret = "threshold") #[1] 0.07717859
predict.results <- ifelse(pre_auc_xgboost_ori > as.numeric(cutoff_xgboost_ori[1]),"1","0")
freq_default_xgboost_ori <- table(predict.results, testdata_ori[,n_col_newdata])
Sensitivity_xgboost_ori <- freq_default_xgboost_ori[1,1]/sum(freq_default_xgboost_ori[,1])
Specificity_xgboost_ori <- freq_default_xgboost_ori[2,2]/sum(freq_default_xgboost_ori[,2])
accuracy_xgboost_mean_ori <- mean(testdata_ori[,n_col_newdata] == predict.results)
Balanced_accuracy_xgboost_mean_ori <- (Sensitivity_xgboost_ori+Specificity_xgboost_ori)/2
Balanced_accuracy_xgboost_mean_ori #[1] 0.7862057
## [1] 0.8397844
library(caret)
## 载入需要的程辑包：ggplot2
#confusionMatrix(factor(predict.results),factor(testdata_ori[,n_col_newdata]))
#################################################################################################test
data_train <- ovun.sample(num ~ ., data = data_train, method = "over")$data
train_matrix = as(as.matrix(data_train[,-n_col_newdata]), "dgCMatrix")
class(train_matrix);dim(train_matrix) #[1] "dgCMatrix" attr(,"package") [1] "Matrix" #[1] 4103 147
## [1] "dgCMatrix"
## attr(,"package")
## [1] "Matrix"
## [1] 4072 147
n_col_newdata <- ncol(data_train) #[1] 148
#dim(data_train)
train_label = data_train[,n_col_newdata]
#length(train_label)
################################################probability
#parameters
xgb_params = list(objective = "binary:logistic",
eval_metric = "error",
max_depth = 3,
eta = 0.01,
gammma = 1,
colsample_bytree = 0.5,
min_child_weight = 1)
#model
bst_model = xgboost(params = xgb_params, data = train_matrix, label = train_label,
nrounds = 2000)
## [15:46:25] WARNING: amalgamation/../src/learner.cc:573:
## Parameters: { "gammma" } might not be used.
##
## This may not be accurate due to some parameters are only used in language bindings but
## passed down to XGBoost core. Or some parameters are not used but slip through this
## verification. Please open an issue if you find above cases.
##
##
## [1] train-error:0.342092
## [2] train-error:0.320727
## [3] train-error:0.318026
## [4] train-error:0.314587
## [5] train-error:0.300589
## [6] train-error:0.299607
## [7] train-error:0.295923
## [8] train-error:0.288556
## [9] train-error:0.286100
## [10] train-error:0.288310
## [11] train-error:0.289538
## [12] train-error:0.288065
## [13] train-error:0.284136
## [14] train-error:0.287819
## [15] train-error:0.288556
## [16] train-error:0.285609
## [17] train-error:0.280943
## [18] train-error:0.281434
## [19] train-error:0.279715
## [20] train-error:0.278978
## [21] train-error:0.279715
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## [23] train-error:0.279224
## [24] train-error:0.271120
## [25] train-error:0.272102
## [26] train-error:0.273821
## [27] train-error:0.275540
## [28] train-error:0.273330
## [29] train-error:0.274312
## [30] train-error:0.271120
## [31] train-error:0.270383
## [32] train-error:0.264489
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## [37] train-error:0.269646
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## [51] train-error:0.262033
## [52] train-error:0.255648
## [53] train-error:0.255894
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## [56] train-error:0.251228
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## [97] train-error:0.238703
## [98] train-error:0.237721
## [99] train-error:0.236984
## [100] train-error:0.236248
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## [133] train-error:0.219794
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## [169] train-error:0.214882
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## [173] train-error:0.214391
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## [182] train-error:0.211690
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## [205] train-error:0.209234
## [206] train-error:0.208251
## [207] train-error:0.208743
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## [211] train-error:0.205059
## [212] train-error:0.204813
## [213] train-error:0.205305
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## [221] train-error:0.200884
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## [1573] train-error:0.062623
## [1574] train-error:0.061886
## [1575] train-error:0.061886
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## [1579] train-error:0.062132
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## [1589] train-error:0.061886
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## [1597] train-error:0.061640
## [1598] train-error:0.061640
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## [1612] train-error:0.058939
## [1613] train-error:0.059185
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## [1618] train-error:0.056974
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## [1630] train-error:0.057466
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## [1635] train-error:0.057711
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## [1659] train-error:0.057466
## [1660] train-error:0.057466
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## [1922] train-error:0.052063
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## [1926] train-error:0.052063
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## [1930] train-error:0.052554
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## [1959] train-error:0.052308
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## [1969] train-error:0.051817
## [1970] train-error:0.051817
## [1971] train-error:0.051817
## [1972] train-error:0.051817
## [1973] train-error:0.051817
## [1974] train-error:0.051817
## [1975] train-error:0.051817
## [1976] train-error:0.051817
## [1977] train-error:0.051817
## [1978] train-error:0.051817
## [1979] train-error:0.051817
## [1980] train-error:0.051817
## [1981] train-error:0.051817
## [1982] train-error:0.051817
## [1983] train-error:0.051817
## [1984] train-error:0.051817
## [1985] train-error:0.051817
## [1986] train-error:0.051817
## [1987] train-error:0.051817
## [1988] train-error:0.051817
## [1989] train-error:0.051817
## [1990] train-error:0.051817
## [1991] train-error:0.051817
## [1992] train-error:0.051817
## [1993] train-error:0.051817
## [1994] train-error:0.051817
## [1995] train-error:0.051817
## [1996] train-error:0.051817
## [1997] train-error:0.051817
## [1998] train-error:0.051817
## [1999] train-error:0.051817
## [2000] train-error:0.051817
##################################################################################
val_matrix <- as(as.matrix(data_test[,-c(ncol(data_test),ncol(data_test)-1, ncol(data_test)-2)]), "dgCMatrix")
#dim(val_matrix) #[1] 2409 50
#class(val_matrix)
#View(head(as.matrix(data_test[,-n_col_newdata])))
#setdiff(colnames(as.matrix(data_train[,-n_col_newdata])),colnames(as.matrix(data_test[,-n_col_newdata])))
#setdiff(colnames(as.matrix(data_test[,-n_col_newdata])),colnames(as.matrix(data_train[,-n_col_newdata])))
#### auc_xgboost
pre_auc_xgboost <- predict(bst_model, newdata = val_matrix)
#class(pre_auc_xgboost)
data_pro <- data.frame(pre_auc_xgboost)
pre_probability <- data_pro
#head(pre_probability)
########################################
colnames(pre_probability) <- c("probability_1")
#dim(pre_probability) #[1] 1009 2
#colnames(data_test)
data_1 <- cbind(pre_probability, data_test[,c(ncol(data_test),ncol(data_test)-1, ncol(data_test)-2)])
#head(data_1)
#table(cut(pre_probability$probability_1,breaks = c(-0.1,.5,1)))
#head(pre_probability)
#write.table(pre_probability,paste0(dir_path,Sys.Date(),"-ecfp4_auc_rf_original.txt"),row.names = F,sep = "\t")
#write.csv(pre_probability,paste0(dir_path,Sys.Date(),"-ecfp4_auc_RF_original_0_52.csv"),row.names = F)
pred_auc <- prediction(data_1$probability_1,data_1$Freq)
auc_roc <- performance(pred_auc,"auc")@y.values[[1]]
auc_roc #[1] 0.967474
## [1] 0.9711555
write.csv(data_1,paste0(dir_path,Sys.Date(),"-xgboost_upsample_probability.csv"),row.names = F)
#######################plot
#View(data_1)
head(data_1)
## probability_1 Freq Virus Drug
## 1 0.376196653 0 HIV-2 arbidol
## 2 0.273858935 0 hCoV-19-Alpha propagermanium
## 3 0.009781853 0 hCoV-19-Delta telaprevir
## 4 0.040998276 0 HPV-11 tenofovir-alafenamide
## 5 0.067581207 0 HBV cobicistat
## 6 0.190207511 0 HCV-7 chloroquine
library(ggplot2)
data_1$Freq <- factor(data_1$Freq)
ggplot(data_1) +
stat_density(aes(x = probability_1, color = Freq),size = 1,
alpha=0.5, bw = 0.01, geom="line",position="identity") +
scale_y_discrete(expand = c(0.01, 0)) +
scale_x_continuous(expand = c(0, 0),limits = c(-0.05, 1.05),
breaks = seq(0,1,.2)) +
labs(colour="Antiviral drug",x = "Probability", y = "Density",title = "XGboost")+
geom_vline(aes(xintercept=0.5),
color="gray80", linetype="dashed", size=0.5)+
theme(panel.spacing = unit(0.1, "cm"),
legend.position= "top",
legend.key = element_rect(colour = NA, fill = NA),
legend.text=element_text(size=14),
legend.title = element_text(size=14),
axis.ticks = element_line(colour = "black",
size = 0.5, linetype = "solid"),
axis.line = element_line(colour = "black",
size = 0.5, linetype = "solid"),
axis.text =element_text(face="plain", color="black", family = "sans",
size=14,angle = 0),
panel.background = element_rect(fill = "white",
colour = "white",
size = 0.5, linetype = "solid"),
panel.grid.major = element_line(size = 1, linetype = 'dashed',
colour = "white"),
axis.title = element_text(color="black", size=14, face="plain",family="sans")) +
scale_color_manual(name = "Antiviral drug",values=c("#3b58a7","#90278e"))
##########################output
ggsave(filename = paste0(Sys.Date(),"-probability_plot_SVM.tif"), plot = last_plot(),
device = "tiff", path = dir_path,
scale = 1, width = 16, height = 12, units = "cm",
dpi = 300, limitsize = TRUE, compression = "lzw")
#View(data_1)
data_11 <- data_1[data_1$Freq == 1, ]
data_22 <- data_1[data_1$Freq != 1, ]
#dim(data_1) #[1] 115 5
table(cut(data_11$probability_1, breaks = c(0,0.5,1), include.lowest = T))
##
## [0,0.5] (0.5,1]
## 9 106
table(cut(data_22$probability_1, breaks = c(0,0.5,1), include.lowest = T))
##
## [0,0.5] (0.5,1]
## 841 53