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run_mlp_scale.R
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library(dplyr)
library(ggplot2)
library(keras)
install_keras()
## read in the input data files
c(sel_multi, pdp_multi, bws_multi, gpp_multi, lcm_multi) %<-% lapply(stations, function(stn) {
read.csv(paste0("inputs/", stn_name[[stn]], "_multi.csv"), stringsAsFactors = FALSE)
})
in_dfs <- list(sel = sel_multi, pdp = pdp_multi, bws = bws_multi, gpp= gpp_multi, lcm = lcm_multi)
in_shape <- list(6, 9, 13, 14)
ff <- data.frame(site = c("sel", "pdp", "bws", "gpp", "lcm"),
shp6 = c(NA, NA, NA, NA, NA),
shp9 = c(NA, NA, NA, NA, NA),
shp13 = c(NA, NA, NA, NA, NA),
shp14 = c(NA, NA, NA, NA, NA))
set.seed(1973)
ns <- length(in_dfs)
for (s in 1:ns) {
df <- in_dfs[[s]]
df <- df[sample(nrow(df)),]
dfi <- df %>% select(dtime, neph, reff, pm25_0, pm25_1, pm25_2, pm25_3, pm25_4, pm25_5,
pm25_6, hour, wday, month, temp, pr, ws, wd, wds, wdc) %>%
filter(complete.cases(pm25_1, pm25_2, pm25_3, pm25_4, pm25_5,
pm25_6, hour, wday, month, temp, pr, ws, wd, wds, wdc))
dft_out <- dfi %>% filter(year(dtime) < 2015)
dfv_out <- dfi %>% filter(year(dtime) == 2015)
nvals <- ncol(dfi)
sc.fac <- data.frame(avg = sapply(dfi[, 5:nvals], function(x) mean(x, na.rm = TRUE)),
sd = sapply(dfi[, 5:nvals], function(x) sd(x, na.rm = TRUE)))
scdf <- sapply(5:nvals, function(n) { (dfi[, n] - sc.fac$avg[n - 4])/sc.fac$sd[n - 4] })
scdf <- cbind(dfi[, c("dtime", "neph", "reff", "pm25_0")], scdf)
colnames(scdf) <- colnames(dfi)
scdf <- as.data.frame(scdf)
for (shp in in_shape ) {
if (shp == 6) {
dft <- scdf %>% filter(year(dtime) < 2015) %>% select(pm25_1, pm25_2, pm25_3, pm25_4, pm25_5, pm25_6)
dfv <- scdf %>% filter(year(dtime) == 2015) %>% select(pm25_1, pm25_2, pm25_3, pm25_4, pm25_5, pm25_6)
}
if (shp == 9) {
dft <- scdf %>% filter(year(dtime) < 2015) %>% select(pm25_1, pm25_2, pm25_3, pm25_4, pm25_5,
pm25_6, hour, wday, month)
dfv <- scdf %>% filter(year(dtime) == 2015) %>% select(pm25_1, pm25_2, pm25_3, pm25_4, pm25_5,
pm25_6, hour, wday, month)
}
if (shp == 13) {
dft <- scdf %>% filter(year(dtime) < 2015) %>% select(pm25_1, pm25_2, pm25_3, pm25_4, pm25_5,
pm25_6, hour, wday, month, temp, pr, ws, wd)
dfv <- scdf %>% filter(year(dtime) == 2015) %>% select(pm25_1, pm25_2, pm25_3, pm25_4, pm25_5,
pm25_6, hour, wday, month, temp, pr, ws, wd)
}
if (shp == 14) {
dft <- scdf %>% filter(year(dtime) < 2015) %>% select(pm25_1, pm25_2, pm25_3, pm25_4, pm25_5,
pm25_6, hour, wday, month, temp, pr, ws, wds, wdc)
dfv <- scdf %>% filter(year(dtime) == 2015) %>% select(pm25_1, pm25_2, pm25_3, pm25_4, pm25_5,
pm25_6, hour, wday, month, temp, pr, ws, wds, wdc)
}
tr <- as.matrix(dft)
val <- as.matrix(dfv)
dimnames(tr) <- NULL
dimnames(val) <- NULL
tr_lab <- scdf %>% filter(year(dtime) < 2015) %>% select(pm25_0)
val_lab <- scdf %>% filter(year(dtime) == 2015) %>% select(pm25_0)
tr_lab <- as.matrix(tr_lab)
val_lab <- as.matrix(val_lab)
dimnames(tr_lab) <- NULL
dimnames(val_lab) <- NULL
if (exists("mlp")) rm(mlp)
mlp <- keras_model_sequential()
mlp %>% layer_dense(units = 48, activation = 'linear', input_shape = c(shp)) %>%
layer_dropout(rate = 0.4) %>%
layer_dense(units = 32, activation = 'elu') %>%
layer_dropout(rate = 0.4) %>%
layer_dense(units = 32, activation = 'elu') %>%
layer_dropout(rate = 0.3) %>%
# layer_dense(units = 16, activation = 'linear') %>%
# layer_dropout(rate = 0.3) %>%
layer_dense(units = 1)
mlp %>% compile( loss = 'mse', optimizer = optimizer_nadam(), metrics = 'mae')
hist <- mlp %>% fit(tr, tr_lab, epochs = 30, batch_size = 200, validation_split = 0.4)
pred <- mlp %>% predict(tr)
dft_out <- cbind(dft_out, mlp_pred = pred)
print("***")
print("***")
print("***")
print(summary(lm(data = dft_out, mlp_pred ~ neph)))
suff <- ncol(dft_out)
colnames(dft_out)[ncol(dft_out)] <- paste0("mlp", as.character(shp))
dfv_out <- cbind(dfv_out, mlp_pred = predict(mlp, val))
print(paste0(names(in_dfs)[[s]], "...................shape: ", as.character(shp)))
res <- summary(lm(data = dfv_out, mlp_pred ~ pm25_0))
r2 <- round(res$adj.r.squared, 3)
print(paste0("Adj. R2 : ", as.character(r2)))
colnames(dfv_out)[ncol(dfv_out)] <- paste0("mlp", as.character(shp))
print("***")
print("***")
print("***")
if (shp == 6) { ff[s,2] <- r2 }
if (shp == 9) { ff[s,3] <- r2 }
if (shp == 14) { ff[s,4] <- r2 }
if (shp == 14) { ff[s,5] <- r2 }
}
write.csv(dft_out, paste0("models/", names(in_dfs)[[s]], "_tr_mlp.csv" ), row.names = FALSE)
write.csv(dfv_out, paste0("models/", names(in_dfs)[[s]], "_val_mlp.csv" ), row.names = FALSE)
print(ff)
}
set.seed(23)
if (exists("mlp")) rm(mlp)
mlp <- keras_model_sequential()
mlp %>% layer_dense(units = 48, activation = 'elu', input_shape = c(shp)) %>%
layer_dropout(rate = 0.4) %>%
layer_dense(units = 32, activation = 'linear') %>%
layer_dropout(rate = 0.4) %>%
layer_dense(units = 32, activation = 'elu') %>%
layer_dropout(rate = 0.4) %>%
# layer_dense(units = 16, activation = 'linear') %>%
# layer_dropout(rate = 0.3) %>%
layer_dense(units = 1)
mlp %>% compile( loss = 'mse', optimizer = optimizer_nadam(), metrics = 'mae')
hist <- mlp %>% fit(tr, tr_lab, epochs = 20, batch_size = 100, validation_split = 0.2)
pred <- mlp %>% predict(tr)
dft_out <- cbind(dft_out, mlp_pred = pred)
summary(mlp)
print(paste0(names(in_dfs)[[s]], "...................shape: ", as.character(shp)))
print(summary(lm(data = dft_out, mlp_pred ~ neph)))
suff <- ncol(dft_out)
colnames(dft_out)[ncol(dft_out)] <- paste0("mlp", as.character(shp), "_", as.character(suff))
dfv_out <- cbind(dfv_out, mlp_pred = predict(mlp, val))
dfv_out$diff13 <- dfv_out$mlp_pred - dfv_out$neph
res <- summary(lm(data = dfv_out, mlp_pred ~ neph))
print(res)
colnames(dfv_out)[which(colnames(dfv_out) == "mlp_pred")] <- paste0("mlp", as.character(shp),"_", as.character(suff))
plot(data = dfv_out, diff13 ~ neph)
range(dfv_out$diff13, na.rm = TRUE)
dfv_out <- dfv_out %>% select(-diff13)
print("************************************************************")
pdx_tr <- pdx_multi[,13:18]
pdx_lab <- pdx_multi[, "pm25_0"]
pdx_tr <- as.matrix(pdx_tr)
dimnames(pdx_tr) <- NULL
dimnames(pdx_lab) <- NULL
#dimnames(pdx_val) <- NULL
set.seed(126)
rm(mlp1_pdx)
mlp1_pdx <- keras_model_sequential()
mlp1_pdx %>% layer_dense(units = 32, activation = 'linear', input_shape = c(6)) %>%
# layer_dense(units = 32, activation = 'sigmoid') %>%
# layer_dense(units = 16, activation = 'sigmoid') %>%
layer_dense(units = 1)
mlp1_pdx %>% compile( loss = 'mse', optimizer = optimizer_nadam(), metrics = 'mae')
hist1 <- mlp1_pdx %>% fit(pdx_tr, pdx_lab, epochs = 20, batch_size = 100, validation_split = 0.2)
pred <- mlp1_pdx %>% predict(pdx_tr)
res <- lm(pred ~ pdx_lab)
summary(res)
pred_val <- mlp1_pdx %>% predict(pdx_val[, 1:6])
res_val <- lm(pred_val ~ pdx16[, 3])
summary(res_val)
plot(hist1$metrics$loss, main = 'Model Loss', xlab = 'epoch', ylab = 'MSE', col = "blue", , type = 1)
lines(hist1$metrics$val_loss, col = "green")
pdx_tr <- pdx_multi[,c(4:6,13:18)]
pdx_lab <- pdx_multi[, "pm25_0"]
pdx_tr <- as.matrix(pdx_tr)
dimnames(pdx_tr) <- NULL
dimnames(pdx_lab) <- NULL
dimnames(pdx_val) <- NULL
set.seed(126)
rm(mlp2_pdx)
mlp2_pdx <- keras_model_sequential()
mlp2_pdx %>% layer_dense(units = 32, activation = 'linear', input_shape = c(9)) %>%
# layer_dense(units = 32, activation = 'sigmoid') %>%
# layer_dense(units = 16, activation = 'sigmoid') %>%
layer_dense(units = 1)
mlp2_pdx %>% compile( loss = 'mse', optimizer = optimizer_nadam(), metrics = 'mae')
hist2 <- mlp2_pdx %>% fit(pdx_tr, pdx_lab, epochs = 20, batch_size = 100, validation_split = 0.2)
pred <- mlp2_pdx %>% predict(pdx_tr)
res <- lm(pred ~ pdx_lab)
summary(res)
#pdx_tr <- pdx_multi[,c(4:10,13:18)]
pdx_tr <- pdx_multi[complete.cases(pdx_multi[,c(4:10, 13:18)]), c(4:10, 13:18)]
pdx_lab <- pdx_multi[complete.cases(pdx_multi[,c(4:10, 13:18)]), 12]
pdx_tr <- as.matrix(pdx_tr)
dimnames(pdx_tr) <- NULL
dimnames(pdx_lab) <- NULL
dimnames(pdx_val) <- NULL
set.seed(39)
rm(mlp3_pdx)
mlp3_pdx <- keras_model_sequential()
mlp3_pdx %>% layer_dense(units = 32, activation = 'linear', input_shape = c(13)) %>%
# layer_dense(units = 32, activation = 'sigmoid') %>%
# layer_dense(units = 16, activation = 'sigmoid') %>%
layer_dense(units = 1)
mlp3_pdx %>% compile( loss = 'mse', optimizer = optimizer_nadam(), metrics = 'mae')
hist3 <- mlp3_pdx %>% fit(pdx_tr, pdx_lab, epochs = 50, batch_size = 100, validation_split = 0.2)
pred <- mlp3_pdx %>% predict(pdx_tr)
res <- lm(pred ~ pdx_lab)
summary(res)