fix test predictions depending on model output specification
This commit is contained in:
parent
8ac195ba6f
commit
787f43b328
10
main.py
10
main.py
@ -183,9 +183,17 @@ def main_test():
|
||||
domain_val, flow_val, client_val, server_val = load_or_generate_h5data(args.test_h5data, args.test_data,
|
||||
args.domain_length, args.window)
|
||||
clf = load_model(args.clf_model, custom_objects=models.get_metrics())
|
||||
c_pred, s_pred = clf.predict([domain_val, flow_val],
|
||||
pred = clf.predict([domain_val, flow_val],
|
||||
batch_size=args.batch_size,
|
||||
verbose=1)
|
||||
if args.model_output == "both":
|
||||
c_pred, s_pred = pred
|
||||
elif args.model_output == "client":
|
||||
c_pred = pred
|
||||
s_pred = np.array()
|
||||
else:
|
||||
c_pred = np.array()
|
||||
s_pred = pred
|
||||
dataset.save_predictions(args.future_prediction, c_pred, s_pred)
|
||||
|
||||
|
||||
|
26
run.sh
26
run.sh
@ -1,49 +1,51 @@
|
||||
python3 main.py --mode train \
|
||||
#!/usr/bin/env bash
|
||||
|
||||
python main.py --mode train \
|
||||
--train /tmp/rk/currentData.csv \
|
||||
--model /tmp/rk/results/simple_both \
|
||||
--epochs 25 \
|
||||
--hidden_char_dims 64 \
|
||||
--hidden_char_dims 128 \
|
||||
--domain_embd 32 \
|
||||
--batch 256 \
|
||||
--balanced_weights \
|
||||
--model_output both
|
||||
|
||||
python3 main.py --mode test --batch 512 --model /tmp/rk/results/simple_both --test /tmp/rk/futureData.csv
|
||||
python main.py --mode test --batch 512 --model /tmp/rk/results/simple_both --test /tmp/rk/futureData.csv --model_output both
|
||||
|
||||
python3 main.py --mode train \
|
||||
python main.py --mode train \
|
||||
--train /tmp/rk/currentData.csv \
|
||||
--model /tmp/rk/results/simple_client \
|
||||
--epochs 25 \
|
||||
--hidden_char_dims 64 \
|
||||
--hidden_char_dims 128 \
|
||||
--domain_embd 32 \
|
||||
--batch 256 \
|
||||
--balanced_weights \
|
||||
--model_output client
|
||||
|
||||
python3 main.py --mode test --batch 512 --model /tmp/rk/results/simple_client --test /tmp/rk/futureData.csv
|
||||
python main.py --mode test --batch 512 --model /tmp/rk/results/simple_client --test /tmp/rk/futureData.csv --model_output client
|
||||
|
||||
python3 main.py --mode train \
|
||||
python main.py --mode train \
|
||||
--train /tmp/rk/currentData.csv \
|
||||
--model /tmp/rk/results/simple_new_both \
|
||||
--epochs 25 \
|
||||
--hidden_char_dims 64 \
|
||||
--hidden_char_dims 128 \
|
||||
--domain_embd 32 \
|
||||
--batch 256 \
|
||||
--balanced_weights \
|
||||
--model_output both \
|
||||
--new_model
|
||||
|
||||
python3 main.py --mode test --batch 512 --model /tmp/rk/results/simple_new_both --test /tmp/rk/futureData.csv
|
||||
python main.py --mode test --batch 512 --model /tmp/rk/results/simple_new_both --test /tmp/rk/futureData.csv --model_output both
|
||||
|
||||
python3 main.py --mode train \
|
||||
python main.py --mode train \
|
||||
--train /tmp/rk/currentData.csv \
|
||||
--model /tmp/rk/results/simple_new_client \
|
||||
--epochs 25 \
|
||||
--hidden_char_dims 64 \
|
||||
--hidden_char_dims 128 \
|
||||
--domain_embd 32 \
|
||||
--batch 256 \
|
||||
--balanced_weights \
|
||||
--model_output client \
|
||||
--new_model
|
||||
|
||||
python3 main.py --mode test --batch 512 --model /tmp/rk/results/simple_new_client --test /tmp/rk/futureData.csv
|
||||
python main.py --mode test --batch 512 --model /tmp/rk/results/simple_new_client --test /tmp/rk/futureData.csv --model_output client
|
@ -1,17 +1,26 @@
|
||||
#!/usr/bin/python2
|
||||
|
||||
import sys
|
||||
|
||||
import joblib
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
df = joblib.load("/mnt/projekte/pmlcluster/cisco/trainData/multipleTaskLearning/currentData.joblib")
|
||||
fn = sys.argv[1]
|
||||
|
||||
df = joblib.load("/mnt/projekte/pmlcluster/cisco/trainData/multipleTaskLearning/{}.joblib".format(fn))
|
||||
df = pd.concat(df["data"])
|
||||
df.reset_index(inplace=True)
|
||||
df.dropna(axis=0, how="any", inplace=True)
|
||||
df[["duration", "bytes_down", "bytes_up"]] = df[["duration", "bytes_down", "bytes_up"]].astype(np.int)
|
||||
df[["domain", "server_ip"]] = df[["domain", "server_ip"]].astype(str)
|
||||
|
||||
df.serverLabel = pd.to_numeric(df.serverLabel, errors='coerce')
|
||||
df.duration = pd.to_numeric(df.duration, errors='coerce')
|
||||
df.bytes_down = pd.to_numeric(df.bytes_down, errors='coerce')
|
||||
df.bytes_up = pd.to_numeric(df.bytes_up, errors='coerce')
|
||||
|
||||
df.http_method = df.http_method.astype("category")
|
||||
df.serverLabel = df.serverLabel.astype(np.bool)
|
||||
df.virusTotalHits = df.virusTotalHits.astype(np.int8)
|
||||
df.trustedHits = df.trustedHits.astype(np.int8)
|
||||
|
||||
df.to_csv("/tmp/rk/full_future_dataset.csv.gz", compression="gzip")
|
||||
df.to_csv("/tmp/rk/{}.csv".format(fn), encoding="utf-8")
|
||||
|
Loading…
Reference in New Issue
Block a user