File size: 2,304 Bytes
fe8e241 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | args <- commandArgs(trailingOnly = FALSE)
script_arg <- grep("^--file=", args, value = TRUE)
if (length(script_arg) > 0) {
script_path <- normalizePath(sub("^--file=", "", script_arg[1]))
setwd(normalizePath(file.path(dirname(script_path), "..")))
}
library(reticulate)
use_python(Sys.getenv("RETICULATE_PYTHON"), required = TRUE)
tf <- import("tensorflow", convert = FALSE)
helper <- import_from_path("tf_savedmodel_helper", path = file.path(getwd(), "scripts"), convert = TRUE)
mcc_score <- function(pred, real) {
tp <- as.numeric(sum(pred == 1 & real == 1))
tn <- as.numeric(sum(pred == 0 & real == 0))
fp <- as.numeric(sum(pred == 1 & real == 0))
fn <- as.numeric(sum(pred == 0 & real == 1))
denom <- sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
if (is.na(denom) || denom == 0) return(NA_real_)
(tp * tn - fp * fn) / denom
}
get_input_name <- function(model_dir) {
model <- tf$saved_model$load(model_dir)
serving <- model$signatures$get("serving_default")
sig_text <- py_str(serving$structured_input_signature)
out_text <- py_str(serving$structured_outputs)
cat("\nModel:", model_dir, "\n")
cat("Input signature:", sig_text, "\n")
cat("Output signature:", out_text, "\n")
input_name <- sub(".*'([^']+)': TensorSpec.*", "\\1", sig_text)
cat("Input name:", input_name, "\n")
input_name
}
run_eval <- function(model_dir, x_file, y_file, label) {
x <- readRDS(x_file)
y <- readRDS(y_file)
input_name <- get_input_name(model_dir)
pred_prob <- helper$predict_saved_model(model_dir, input_name, x)
y_real <- max.col(y) - 1
y_pred <- max.col(pred_prob) - 1
acc <- mean(y_real == y_pred)
mcc <- mcc_score(y_pred, y_real)
cat("\n====", label, "====\n")
cat("n_test:", length(y_real), "\n")
cat("accuracy:", round(acc, 4), "\n")
cat("mcc:", round(mcc, 4), "\n")
print(table(real = y_real, pred = y_pred))
invisible(list(prob = pred_prob, real = y_real, pred = y_pred))
}
c1_res <- run_eval("weight/CNN/model_c1", "model/CNN/c1_test.RDS", "model/CNN/c1_test_y.RDS", "CTLA-4")
p1_res <- run_eval("weight/CNN/model_p1", "model/CNN/p1_test.RDS", "model/CNN/p1_test_y.RDS", "PD-1")
saveRDS(c1_res, "model/CNN/c1_tf218_inference_result.RDS")
saveRDS(p1_res, "model/CNN/p1_tf218_inference_result.RDS")
cat("\nCNN inference OK\n")
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