bump:stable v0.2
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#!/usr/bin/env python
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"""
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Read traffic_csv
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"""
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import os
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import argparse
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import csv
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import glob
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import re
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FLAGS = None
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INPUT = "../raw_csvs/classes/browsing/reg/CICNTTor_browsing.raw.csv"#"../dataset/iscxNTVPN2016/CompletePCAPs" # ""
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INPUT_DIR = "../raw_csvs/classes/chat/vpn/"
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CLASSES_DIR = "../raw_csvs/classes/**/**/"
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# LABEL_IND = 1
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TPS = 60 # TimePerSession in secs
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DELTA_T = 60 # Delta T between splitted sessions
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MIN_TPS = 50
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# def insert_dataset(dataset, labels, session, label_ind=LABEL_IND):
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# dataset.append(session)
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# labels.append(label_ind)
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# def export_dataset(dataset, labels):
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# print "Start export dataset"
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# np.savez(INPUT.split(".")[0] + ".npz", X=dataset, Y=labels)
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# print dataset.shape, labels.shape
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#
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# def import_dataset():
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# print "Import dataset"
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# dataset = np.load(INPUT.split(".")[0] + ".npz")
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# print dataset["X"].shape, dataset["Y"].shape
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import matplotlib.pyplot as plt
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import numpy as np
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MTU = 1500
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def session_spectogram(ts, sizes, name=None):
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plt.scatter(ts, sizes, marker='.')
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plt.ylim(0, MTU)
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plt.xlim(ts[0], ts[-1])
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# plt.yticks(np.arange(0, MTU, 10))
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# plt.xticks(np.arange(int(ts[0]), int(ts[-1]), 10))
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plt.title(name + " Session Spectogram")
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plt.ylabel('Size [B]')
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plt.xlabel('Time [sec]')
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plt.grid(True)
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plt.show()
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def session_atricle_spectogram(ts, sizes, fpath=None, show=True, tps=None):
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if tps is None:
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max_delta_time = ts[-1] - ts[0]
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else:
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max_delta_time = tps
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ts_norm = ((np.array(ts) - ts[0]) / max_delta_time) * MTU
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plt.figure()
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plt.scatter(ts_norm, sizes, marker=',', c='k', s=5)
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plt.ylim(0, MTU)
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plt.xlim(0, MTU)
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plt.ylabel('Packet Size [B]')
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plt.xlabel('Normalized Arrival Time')
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plt.set_cmap('binary')
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plt.axes().set_aspect('equal')
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plt.grid(False)
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if fpath is not None:
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# plt.savefig(OUTPUT_DIR + fname, bbox_inches='tight', pad_inches=1)
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plt.savefig(fpath, bbox_inches='tight')
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if show:
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plt.show()
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plt.close()
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def session_histogram(sizes, plot=False):
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hist, bin_edges = np.histogram(sizes, bins=range(0, MTU + 1, 1))
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if plot:
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plt.bar(bin_edges[:-1], hist, width=1)
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plt.xlim(min(bin_edges), max(bin_edges)+100)
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plt.show()
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return hist.astype(np.uint16)
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def session_2d_histogram(ts, sizes, plot=False, tps=None):
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if tps is None:
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max_delta_time = ts[-1] - ts[0]
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else:
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max_delta_time = tps
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# ts_norm = map(int, ((np.array(ts) - ts[0]) / max_delta_time) * MTU)
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ts_norm = ((np.array(ts) - ts[0]) / max_delta_time) * MTU
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H, xedges, yedges = np.histogram2d(sizes, ts_norm, bins=(range(0, MTU + 1, 1), range(0, MTU + 1, 1)))
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if plot:
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plt.pcolormesh(xedges, yedges, H)
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plt.colorbar()
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plt.xlim(0, MTU)
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plt.ylim(0, MTU)
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plt.set_cmap('binary')
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plt.show()
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return H.astype(np.uint16)
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def export_dataset(dataset):
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print("Start export dataset")
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np.save(os.path.splitext(INPUT)[0], dataset)
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print(dataset.shape)
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def export_class_dataset(dataset, class_dir):
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print("Start export dataset")
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np.save(class_dir + "/" + "_".join(re.findall(r"[\w']+", class_dir)[-2:]), dataset)
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print(dataset.shape)
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def import_dataset():
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print("Import dataset")
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dataset = np.load(os.path.splitext(INPUT)[0] + ".npy")
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print(dataset.shape)
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return dataset
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def traffic_csv_converter(file_path):
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print("Running on " + file_path)
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dataset = []
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# labels = []
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counter = 0
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with open(file_path, 'r') as csv_file:
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reader = csv.reader(csv_file)
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for i, row in enumerate(reader):
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# print row[0], row[7]
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session_tuple_key = tuple(row[:8])
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length = int(row[7])
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ts = np.array(row[8:8+length], dtype=float)
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sizes = np.array(row[9+length:], dtype=int)
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# if (sizes > MTU).any():
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# a = [(sizes[i], i) for i in range(len(sizes)) if (np.array(sizes) > MTU)[i]]
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# print len(a), session_tuple_key
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if length > 10:
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# print ts[0], ts[-1]
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# h = session_2d_histogram(ts, sizes)
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# session_spectogram(ts, sizes, session_tuple_key[0])
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# dataset.append([h])
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# counter += 1
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# if counter % 100 == 0:
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# print counter
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for t in range(int(ts[-1]/DELTA_T - TPS/DELTA_T) + 1):
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mask = ((ts >= t * DELTA_T) & (ts <= (t * DELTA_T + TPS)))
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# print t * DELTA_T, t * DELTA_T + TPS, ts[-1]
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ts_mask = ts[mask]
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sizes_mask = sizes[mask]
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if len(ts_mask) > 10 and ts_mask[-1] - ts_mask[0] > MIN_TPS:
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# if "facebook" in session_tuple_key[0]:
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# session_spectogram(ts[mask], sizes[mask], session_tuple_key[0])
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# # session_2d_histogram(ts[mask], sizes[mask], True)
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# session_histogram(sizes[mask], True)
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# exit()
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# else:
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# continue
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h = session_2d_histogram(ts_mask, sizes_mask)
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# session_spectogram(ts_mask, sizes_mask, session_tuple_key[0])
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dataset.append([h])
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counter += 1
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if counter % 100 == 0:
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print(counter)
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return np.asarray(dataset) #, np.asarray(labels)
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def traffic_csv_converter_splitted(file_path):
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def split_converter(ts, sizes, dataset, counter):
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if ts[-1] - ts[0] > MIN_TPS and len(ts) > 20:
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# print ts[0], ts[-1]
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h = session_2d_histogram(ts-ts[0], sizes)
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# session_spectogram(ts, sizes, session_tuple_key[0])
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dataset.append([h])
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counter += 1
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# if counter % 100 == 0:
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# print counter
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total_time = ts[-1] - ts[0]
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if total_time > TPS:
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for ts_split, sizes_split in zip(np.split(ts, [len(ts)/2]), np.split(sizes, [len(sizes)/2])):
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split_converter(ts_split, sizes_split, dataset, counter)
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print("Running on " + file_path)
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dataset = []
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# labels = []
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counter = 0
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with open(file_path, 'r') as csv_file:
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reader = csv.reader(csv_file)
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for i, row in enumerate(reader):
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# print row[0], row[7]
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session_tuple_key = tuple(row[:8])
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length = int(row[7])
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ts = np.array(row[8:8+length], dtype=float)
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sizes = np.array(row[9+length:], dtype=int)
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# if (sizes > MTU).any():
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# a = [(sizes[i], i) for i in range(len(sizes)) if (np.array(sizes) > MTU)[i]]
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# print len(a), session_tuple_key
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if length > 10:
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split_converter(ts, sizes, dataset, counter)
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return np.asarray(dataset)
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def traffic_class_converter(dir_path):
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dataset_tuple = ()
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for file_path in [os.path.join(dir_path, fn) for fn in next(os.walk(dir_path))[2] if (".csv" in os.path.splitext(fn)[-1])]:
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dataset_tuple += (traffic_csv_converter(file_path),) ################
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return np.concatenate(dataset_tuple, axis=0)
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def iterate_all_classes():
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for class_dir in glob.glob(CLASSES_DIR):
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if "other" not in class_dir: #"browsing" not in class_dir and
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print("working on " + class_dir)
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dataset = traffic_class_converter(class_dir)
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print(dataset.shape)
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export_class_dataset(dataset, class_dir)
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def random_sampling_dataset(input_array, size=2000):
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print("Import dataset " + input_array)
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dataset = np.load(input_array)
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print(dataset.shape)
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p = size*1.0/len(dataset)
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print(p)
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if p >= 1:
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raise Exception
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mask = np.random.choice([True, False], len(dataset), p=[p, 1-p])
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dataset = dataset[mask]
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print("Start export dataset")
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np.save(os.path.splitext(input_array)[0] + "_samp", dataset)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--input', type=str, default=INPUT, help='Path to csv file')
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FLAGS = parser.parse_args()
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##
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# iterate_all_classes()
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# dataset = traffic_class_converter(INPUT_DIR)
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# dataset = traffic_csv_converter(INPUT)
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input_array = "./_dataset/FlowPic/browsing_reg.npy"
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random_sampling_dataset(input_array)
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# export_class_dataset(dataset)
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# import_dataset()
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