#####################################################################
## This file takes file intermediate_tanzania_for_R_1 and uses it to
## compare linear regression, LASSO, Ridge regressions and regresion trees.
## It compares the techniques for predicting consumption and poverty,
## using baseline covariates, both for the baseline year and future years
#####################################################################
library(magrittr)
library(foreign)
library(xlsx)
library(MLlibrary)
# Load data ---------------------------
load_data <- function() {
read.dta(DATA_PATH)
}
add_covariates <- function(output_df, ghana) {
feature_info <- read.xlsx(VARIABLE_TABLE_PATH, sheetName="Sheet1")
feature_info <- feature_info[!is.na(feature_info$var_name), ]
select_names_by_type <- function(type) {
is_desired_type <- feature_info$type == type
as.vector(feature_info$var_name[is_desired_type])
}
covariates_categorical <- select_names_by_type("Categorical")
covariates_cardinal <- select_names_by_type("Cardinal")
covariates_yesno <- select_names_by_type("Yes/No")
covariates <- c(covariates_categorical, covariates_cardinal, covariates_yesno)
# Add features to output
output_df[, c(covariates_categorical)] <- ghana[, c(covariates_categorical)]
output_df[, c(covariates_cardinal)] <- ghana[, c(covariates_cardinal)]
output_df[, c(covariates_yesno)] <- ghana[, c(covariates_yesno)]
# Make sure features are cast correctly
output_df[, c(covariates_categorical)] <- lapply(output_df[, c(covariates_categorical)], as.factor)
output_df[, c(covariates_cardinal)] <- lapply(output_df[, c(covariates_cardinal)], as.numeric)
# Note, may wish to recode some of the categoricals as ordered and the yes/no as categorical.
output_df[, c(covariates_yesno)] <- lapply(output_df[, c(covariates_yesno)], as.logical)
output_df
}
add_target <- function(output_df, panel_df) {
output_df$lnwelfare <- log(panel_df$lnwelfare)
output_df
}
remove_missing_data <- function(output_df) {
output_df[complete.cases(output_df), ]
}
create_dataset <- function(remove_missing=TRUE) {
ghana <- load_data()
df <-
matrix(nrow=nrow(ghana), ncol=0) %>%
data.frame() %>%
add_covariates(ghana) %>%
add_target(ghana)
if (remove_missing) remove_missing_data(df)
df
}
# Create first dataset ---------------------------
DATA_FNAME <- "model_a_vars1.dta"
VARIABLE_TABLE_FNAME <- "variable_table_ghana.xlsx"
DATA_PATH <- paste(TARGETING_DATA_IN, DATA_FNAME, sep="/")
VARIABLE_TABLE_PATH <- paste(TARGETING_DATA_IN, VARIABLE_TABLE_FNAME, sep="/")
NAME <- "ghana_pe"
gh_missing <- create_dataset(remove_missing=FALSE)
gh <- create_dataset()
gh <- standardize_predictors(gh, "lnwelfare")
save_dataset(NAME, gh)
x <- model.matrix(lnwelfare ~ ., gh)
y <- gh[rownames(x), "lnwelfare"]
x_ix <- model.matrix(lnwelfare ~ . + .:., gh)
y_ix <- gh[rownames(x_ix), "lnwelfare"]
# Create second dataset ---------------------------
DATA_FNAME <- "pre_feature_extraction.dta"
VARIABLE_TABLE_FNAME <- "variable_table_ghana_pre_extraction.xlsx"
DATA_PATH <- paste(TARGETING_DATA_IN, DATA_FNAME, sep="/")
VARIABLE_TABLE_PATH <- paste(TARGETING_DATA_IN, VARIABLE_TABLE_FNAME, sep="/")
NAME <- "ghana_pe"
gh_pe_missing <- create_dataset(remove_missing=FALSE)
gh_pe <- create_dataset()
gh_pe <- standardize_predictors(gh_pe, "lnwelfare")
save_dataset(NAME, gh_pe)
x_pe <- model.matrix(lnwelfare ~ ., gh_pe)
y_pe <- gh_pe[rownames(x), "lnwelfare"]
x_pe_ix <- model.matrix(lnwelfare ~ . + .:., gh_pe)
y_pe_ix <- gh[rownames(x_pe_ix), "lnwelfare"]
# Run analysis ---------------------------
k <- 5
print("Running lasso")
lasso <- kfold(k, Lasso(), y, x)
print("Running least squares")
least_squares <- kfold(k, LeastSquares(), y, x)
print("Running lasso PE")
lasso_pe <- kfold(k, Lasso(), y_pe, x_pe)
print("Running least squares PE")
least_squares_pe <- kfold(k, LeastSquares(), y_pe, x_pe)
print("Running lasso IX")
lasso_ix <- kfold(k, Lasso(), y_ix, x_ix)
print("Running least squares IX")
least_squares_ix <- kfold(k, LeastSquares(), y_ix, x_ix)
# print("Running lasso PE IX")
# lasso_pe_ix <- kfold(k, Lasso(), y_pe_ix, x_pe_ix)
# print("Running least squares PE IX")
# least_squares_pe_ix <- kfold(k, LeastSquares(), y_pe_ix, x_pe_ix)
#Add pe_ix in following if will run
save_models(NAME,
lasso=lasso,
least_squares=least_squares,
lasso_pe=lasso_pe,
least_squares_pe=least_squares_pe,
lasso_ix=lasso_ix,
least_squares_ix=least_squares_ix
)
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