#####################################################################
## 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(dplyr)
library(MLlibrary)
NAME <- "ghana_tuned"
# Load data ---------------------------
load_data <- function(data_path) {
read.dta(data_path)
}
add_covariates <- function(output_df, ghana, var_table_path) {
feature_info <- read.xlsx(var_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$var_name[is_desired_type])
}
covariates_categorical <- select_names_by_type("Categorical")
covariates_cardinal <- select_names_by_type("Cardinal")
covariates_yesno <- select_names_by_type("Yes/No")
covariates <- c(covariates_categorical, covariates_cardinal, covariates_yesno)
# Add features to output
output_df[, covariates_categorical] <- ghana[, covariates_categorical]
output_df[, covariates_cardinal] <- ghana[, covariates_cardinal]
output_df[, covariates_yesno] <- ghana[, covariates_yesno]
# Make sure features are cast correctly
output_df[, covariates_categorical] <- lapply(output_df[, covariates_categorical], as.factor)
output_df[, covariates_cardinal] <- lapply(output_df[, covariates_cardinal], as.numeric)
# Note, may wish to recode some of the categoricals as ordered and the yes/no as categorical.
output_df[, covariates_yesno] <- lapply(output_df[, covariates_yesno], as.factor)
output_df
}
add_target <- function(output_df, panel_df) {
output_df[, TARGET_VARIABLE] <- log(panel_df$lnwelfare)
output_df
}
remove_missing_data <- function(output_df) {
output_df[complete.cases(output_df), ]
}
create_dataset <- function(data_path, var_table_path, remove_missing=TRUE) {
ghana <- load_data(data_path)
df <-
matrix(nrow=nrow(ghana), ncol=0) %>%
as.data.frame() %>%
add_covariates(ghana, var_table_path) %>%
add_target(ghana)
if (remove_missing) df <- remove_missing_data(df)
df
}
# Run analysis ---------------------------
PE_DATA_FNAME <- "pre_feature_extraction.dta"
PE_VARIABLE_TABLE_FNAME <- "variable_table_ghana_pre_extraction.xlsx"
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="/")
pe_data_path <- paste(TARGETING_DATA_IN, PE_DATA_FNAME, sep="/")
pe_variable_table_path <- paste(TARGETING_DATA_IN, PE_VARIABLE_TABLE_FNAME, sep="/")
gh <- create_dataset(data_path, variable_table_path, remove_missing=FALSE)
gh <- standardize_predictors(gh, TARGET_VARIABLE)
gh <- na_indicator(gh)
save_dataset(NAME, gh)
clear_config(NAME)
output <- test_all_named(NAME, gh, test_fraction=0.2)
save_validation_models_(NAME, output)
# gh <- create_dataset(pe_data_path, pe_variable_table_path, remove_missing=FALSE)
# gh <- filter(gh, s7dq11 != 'generator') # only one observation, causes issues in cross validation
# gh <- na_indicator(gh)
# gh <- standardize_predictors(gh, TARGET_VARIABLE)
# pe_name <- paste(NAME, 'pe', sep='_')
# save_dataset(pe_name, gh)
# clear_config(pe_name)
# output <- test_all_named(pe_name, gh, test_fraction=0.2)
# save_validation_models_(pe_name, output)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.