evaluate.py - cmikke97/Automatic-Malware-Signature-Generation GitHub Wiki
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import configparser- implements a basic configuration language for Python programs - configparser documentation -
import importlib- provides the implementation of the import statement in Python source code - importlib documentation -
import json- json encoder and decoder - json documentation -
import os- provides a portable way of using operating system dependent functionality - os documentation -
import tempfile- used to create temporary files and directories - tempfile documentation
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import baker- easy, powerful access to Python functions from the command line - baker documentation -
import mlflow- open source platform for managing the end-to-end machine learning lifecycle - mlflow documentation -
import pandas as pd- pandas is a flexible and easy to use open source data analysis and manipulation tool - pandas documentation -
import torch- tensor library like NumPy, with strong GPU support - pytorch documentation -
from logzero import logger- robust and effective logging for Python - logzero documentation -
from tqdm import tqdm- instantly makes loops show a smart progress meter - tqdm documentation
import_modules(net_type, gen_type) (function) - Dynamically import network, dataset and generator modules depending on the provided arguments.
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net_type(arg) - Network type (possible values: mtje, mtje_cosine, mtje_pairwise_distance, aloha) -
gen_type(arg) - Generator type (possible values: base, alt1, alt2, alt3)
evaluate_network(ds_path, checkpoint_file, net_type, gen_type, batch_size, test_n_samples, evaluate_malware, evaluate_count, evaluate_tags, feature_dimension) (function, baker command) - Take a trained feedforward neural network model and output evaluation results to a csv file.
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ds_path(arg) - Path of the directory where to find the pre-processed dataset (containing .dat files) -
checkpoint_file(arg) - The checkpoint file containing the weights to evaluate -
net_type(arg) - Network to use between 'mtje', 'mtje_cosine', 'mtje_pairwise_distance' and 'aloha' (default: 'mtje') -
gen_type(arg) - Generator (and dataset) class to use between 'base', 'alt1', 'alt2', 'alt3'. (default: 'base') -
batch_size(arg) - How many samples per batch to load (default: 8192) -
test_n_samples(arg) - Number of test samples to consider (used to access the right files) (default: 0 -> all) -
evaluate_malware(arg) - Whether (1/0) to record malware labels and predictions (default: 1) -
evaluate_count(arg) - Whether (1/0) to record count labels and predictions (default: 1) -
evaluate_tags(arg) - Whether (1/0) to use SMART tags as additional targets (default: 1) -
feature_dimension(arg) - The input dimension of the model (default: 2381)
__main__ (main) - Start baker in order to make it possible to run the script and use function names and parameters as the command line interface, using optparse-style options