80 lines
2.7 KiB
R
80 lines
2.7 KiB
R
# Install necessary packages
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if(!require(tidyverse)) install.packages("tidyverse")
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if(!require(caret)) install.packages("caret")
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# Load Libraries
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library(tidyverse)
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library(caret)
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# Download data
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url <- "https://archive.ics.uci.edu/ml/machine-learning-databases/00526/data.zip"
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dest_file <- "data/data.zip"
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if(!dir.exists("data"))dir.create("data")
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if(!file.exists(dest_file))download.file(url, destfile = dest_file)
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# Unzip
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if(!file.exists("data/BitcoinHeistData.csv"))unzip(dest_file, "BitcoinHeistData.csv", exdir="data")
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# Import data from CSV
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ransomware <- read_csv("data/BitcoinHeistData.csv")
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# Turn labels into factors, grey is a binary factor for ransomware/non-ransomware
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ransomware <- ransomware %>% mutate(label=as.factor(label),
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grey=as.factor(ifelse(label=="white", "white", "black")),
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address=as.factor(address))
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# Sample every other row (keeping it simple for now)
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#ransomware <- ransomware[seq(1, nrow(ransomware), 2), ]
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# Validation set made from 50% of BitcoinHeist data, reduce later if possible. Binary outcomes (grey)
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test_index <- createDataPartition(y = ransomware$grey, times = 1, p = .5, list = FALSE)
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# Validation set made from 50% of BitcoinHeist data, reduce later if possible. Categorical outcomes
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#test_index <- createDataPartition(y = ransomware$label, times = 1, p = .5, list = FALSE)
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workset <- ransomware[-test_index,]
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temp <- ransomware[test_index,]
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# Make sure addresses in validation set are also in working set...
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# validation <- temp %>%
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# semi_join(workset, by = "address")
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# Add rows removed from validation set back into working set...
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#removed <- anti_join(temp, validation)
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#workset <- rbind(workset, removed)
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# ... Or not
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validation <- temp
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# Split the working set into a training set and a test set @ 50%, reduce later if possible. Binary outcomes (grey)
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test_index <- createDataPartition(y = workset$grey, times = 1, p = .5, list = FALSE)
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# Split the working set into a training set and a test set @ 50%, reduce later if possible. Categorical outcomes
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#test_index <- createDataPartition(y = workset$label, times = 1, p = .5, list = FALSE)
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train_set <- workset[-test_index,]
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temp <- workset[test_index,]
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# Make sure addresses in validation set are also in working set....
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#test_set <- temp %>%
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# semi_join(train_set, by = "address")
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# Add rows removed from validation set back into working set....
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#removed <- anti_join(temp, test_set)
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#train_set <- rbind(train_set, removed)
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# ....Or not
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test_set <- temp
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# Clean up environment
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rm(dest_file, url, temp)
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# Inspect data frames
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#test_set %>% str()
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#test_set %>% head()
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#train_set %>% str()
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#train_set %>% head()
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## Data preparation is done, now focusing on Self Organizing Maps as our method
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