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construct_ngram_model.py
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construct_ngram_model.py
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import os
import numpy as np
import pandas as pd
import ujson
import cPickle as pickle
import config
import sys
NUM_CLASSES = 9
def get_all_keys(folder, env):
total_features = 7
sets = {}
for i in range(total_features):
sets[i] = set()
counter = 0
for root, dirs, files in os.walk(folder):
for file_name in files:
key = file_name.split('/')[-1]
key = key.split('.')[0]
file_name = '%s/%s' % (root, file_name)
data = np.load(file_name)
for fn in range(total_features):
feature_set = data.items()[0][1][fn]
sets[fn].update(list(feature_set[:, 0]))
counter += 1
if counter % 100 == 0:
all_featues = sets.items()
all_featues.sort(key=lambda x: x[0])
print counter, [len(v) for k, v in all_featues]
all_featues = sets.items()
all_featues.sort(key=lambda x: x[0])
print 'Results:', [len(v) for k, v in all_featues]
sets = [to_array(v) for k, v in all_featues]
fname = config.locate_file(env, 'all_featuresets_keys')
np.savez_compressed(fname, sets)
print 'saved to', fname
def count_n_fset_stats(env, n, folder):
print 'Counting featureset stats', n
labels = pd.read_csv(config.conf[env]['labels'])
labels.set_index('Id', inplace=True)
sum_per_class = {}
count_per_class = {}
counter = 0
for root, dirs, files in os.walk(folder):
for file_name in files:
key = file_name.split('/')[-1]
key = key.split('.')[0]
cls = int(labels.ix[key].Class) - 1
file_name = '%s/%s' % (root, file_name)
data = np.load(file_name)
fset = dict(data.items()[0][1][n - 1])
sum_d = sum_per_class.get(cls, {})
count_d = count_per_class.get(cls, {})
for k, v in fset.iteritems():
if k in sum_d:
sum_d[k] = sum_d[k] + v
count_d[k] = count_d[k] + 1
else:
sum_d[k] = v
count_d[k] = 1
sum_per_class[cls] = sum_d
count_per_class[cls] = count_d
counter += 1
if counter % 100 == 0:
print 'processed', counter
total = {'sum': sum_per_class, 'count': count_per_class}
fname = config.locate_file(env, 'featureset_stats_%s.pkl' % n)
with open(fname, 'w') as f:
pickle.dump(total, f)
def to_array(st):
return np.array(list(set(list(st))))
def constuct_n_model(env, n, folder, all_sets, force_str, postfix,
known_vals=None):
print 'Constructing featureset', n
all_keys = list(all_sets[n - 1])
if isinstance(all_keys[0], np.ndarray):
all_keys = [tuple(k) for k in all_keys]
all_keys.sort()
model = construct_n_model_using_keys(env, n, folder, all_sets, all_keys,
force_str, known_vals)
model = pd.DataFrame(model)
fname = config.locate_file(env, 'opcodes_%s_model_%s.pd' % (n, postfix))
model.to_csv(fname)
print 'saved to', fname
def top_by_freq(env, n, top_n):
keys = []
fname = config.locate_file(env, 'featureset_stats_%s.pkl' % n)
with open(fname) as f:
stats = pickle.load(f)
freqs = compute_freq(stats)
for i in range(NUM_CLASSES):
if i in freqs:
class_freqs = list(freqs[i])
class_freqs.sort(key=lambda x: x[1], reverse=True)
keys.extend(class_freqs[: top_n])
keys = list(set(keys))
keys.sort()
return keys
def top_unique(env, n, top_n):
# keys = []
fname = config.locate_file(env, 'featureset_stats_%s.pkl' % n)
with open(fname) as f:
stats = pickle.load(f)
all_keys = {}
for i in range(NUM_CLASSES):
for k in stats['count'][i].iterkeys():
if k not in all_keys:
all_keys[k] = 1
else:
all_keys[k] = all_keys[k] + 1
unique = set([k for k, v in all_keys.iteritems() if v == 1])
columns = []
for i in range(NUM_CLASSES):
counts = [(k, v) for k, v in stats['count'][i].iteritems() if k in unique]
counts.sort(key=lambda x: x[1], reverse=True)
columns.extend([k for k, v in counts[:top_n]])
return columns
def constuct_n_model_top_n(env, n, folder, all_sets, top_n_fn, postfix):
print 'Constructing top n model', n, postfix
all_keys = top_n_fn(n)
print 'Max key number', len(all_keys)
model = construct_n_model_using_keys(env, n, folder, all_sets, all_keys)
model = pd.DataFrame(model)
fname = config.locate_file(env, 'opcodes_%s_model_%s.pd' % (n, postfix))
model.to_csv(fname)
print 'saved to file', fname
def compute_freq(stats):
sums = stats['sum']
counts = stats['count']
freqs = {}
for i in range(NUM_CLASSES):
current_freq = {}
for i in sums:
sum_vals = sums[i]
count_vals = counts[i]
for key, val in sum_vals.iteritems():
cval = count_vals[key]
current_freq[key] = val / float(cval)
freqs[i] = current_freq
return freqs
def construct_n_model_using_keys(env, n, folder, all_sets, all_keys,
force_str=False, known_vals=None):
model = []
for root, dirs, files in os.walk(folder):
for file_name in files:
key = file_name.split('/')[-1]
key = key.split('.')[0]
file_name = '%s/%s' % (root, file_name)
data = np.load(file_name)
fsets = data.items()[0][1][n - 1]
if fsets.shape == ():
fsets = np.array(fsets.item(0).items())
fsets = dict(fsets)
if force_str:
fsets = {str(k): v for k, v in fsets.iteritems()}
# NOTE: this is new and not tested stuff
std = np.std([int(v) for v in fsets.itervalues()])
total_count = len(fsets)
total_sum = sum([int(v) for v in fsets.itervalues()])
known_counter = []
unknown_counter = []
if known_vals:
kv = [k for k in fsets.iterkeys() if k in known_vals]
known_counter = [len(kv)]
ukv = [k for k in fsets.iterkeys() if k not in known_vals]
unknown_counter = [len(ukv)]
fsets = np.array([key] +
[int(fsets.get(k, 0)) for k in all_keys] +
[std, total_count, total_sum] + known_counter +
unknown_counter,
dtype=np.object)
model.append(fsets)
if len(model) % 100 == 0:
print 'Processed', len(model)
return model
# def extract_known_keys(all_sets):
# print 'Extracting known keys'
# keys = {k: 1 for k in all_sets[0]}
# save_json('train_set_mappings.txt', keys)
# def extract_feature_mappings(all_sets):
# groups = [
# ('sections', 6, None),
# ('ngrams_1', 1, None),
# ('ngrams_2', 2, lambda n: top_by_freq(n, 1000)),
# ('ngrams_3', 2, lambda n: top_by_freq(n, 1000)),
# ('ngrams_4', 2, lambda n: top_by_freq(n, 1000)),
# ('ngrams_5', 2, lambda n: top_by_freq(n, 1000))
# ]
# mappings = {}
# for name, n, fn in groups:
# if fn:
# keys = fn(n)
# else:
# keys = list(all_sets[n - 1])
# mappings[name] = [(k, i) for i, k in enumerate(keys)]
# save_json('all_mappings.txt', mappings)
def save_json(fname, object_):
with open(fname, 'w') as f:
f.write(ujson.dumps(object_))
print 'Saved to', fname
if __name__ == '__main__':
env = sys.argv[1]
folder = config.locate_file(env, config.conf[env]['asm_folder'])
if config.conf[env]['calc_stats']:
get_all_keys(folder, env)
fname = config.locate_file(env, 'all_featuresets_keys.npz')
all_sets = np.load(fname).items()[0][1]
constuct_n_model(env, 1, folder, all_sets, False, env)
if config.conf[env]['calc_stats']:
count_n_fset_stats(env, 2, folder)
count_n_fset_stats(env, 3, folder)
count_n_fset_stats(env, 4, folder)
count_n_fset_stats(env, 5, folder)
constuct_n_model_top_n(env, 2, folder, all_sets,
lambda n: top_by_freq(env, n, 1000),
'2gram_top_1000_%s' % env)
constuct_n_model_top_n(env, 3, folder, all_sets,
lambda n: top_by_freq(env, n, 1000),
'3gram_top_1000_%s' % env)
constuct_n_model_top_n(env, 4, folder, all_sets,
lambda n: top_by_freq(env, n, 1000),
'4gram_top_1000_%s' % env)
constuct_n_model_top_n(env, 5, folder, all_sets,
lambda n: top_by_freq(env, n, 1000),
'5gram_top_1000_%s' % env)
constuct_n_model(env, 6, folder, all_sets, False, 'sections_%s' % env)
constuct_n_model(env, 7, folder, all_sets, False, 'misc_%s' % env)