{ "cells": [ { "cell_type": "code", "execution_count": 185, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "import json\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "date = '2019-12-20_21'\n", "root_dir = '/Users/Leo/Documents/github/GradProj/'\n", "N = 30" ] }, { "cell_type": "code", "execution_count": 186, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "end2\n" ] } ], "source": [ "stream_tag_path = root_dir + 'DataSet/result/' + date + '/stream_tag.txt'\n", "stream_tag_pd = pd.read_table(stream_tag_path, sep='\\s+', header=None)\n", "stream_tag_pd.iloc[:,3]=443\n", "stream_tag_map = {tuple(x[0:4]):x[4] for x in list(stream_tag_pd.values)}\n", "print('end2')" ] }, { "cell_type": "code", "execution_count": 187, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "end2\n" ] } ], "source": [ "stream_bursd_l = list()\n", "stream_length_l = list()\n", "stream_type_l = list()\n", "stream_label = list()\n", "#stream_bytes_l = list()\n", "stream_feature_path = root_dir + 'DataSet/result/' + date + '/stream_stat.txt'\n", "stream_feature_f = open(stream_feature_path, 'r')\n", "i = 0\n", "for line in stream_feature_f.readlines():\n", " stream_feature_json = json.loads(line)\n", " try:\n", " key = (stream_feature_json['sip'], stream_feature_json['sport'], stream_feature_json['dip'], stream_feature_json['dport'])\n", " stream_label.append(stream_tag_map[key])\n", " i += 1\n", " except Exception:\n", " continue\n", " packets = stream_feature_json['packets']\n", " tls_message_list = stream_feature_json['tls']['tls_message_list']\n", " stream_length = [tls_message['length'] for tls_message in tls_message_list]\n", " stream_type = [tls_message['type'] for tls_message in tls_message_list]\n", " #stream_length.append((stream_feature_json['sip'], stream_feature_json['sport'], stream_feature_json['dip'], stream_feature_json['dport']))\n", " stream_burst = list()\n", " burst_length = 0\n", "# stream_bytes = list()\n", " old_dir = 0\n", " for j,packet in enumerate(packets):\n", "# stream_bytes.append(packet['bytes'])\n", " if j == 0:\n", " burst_length = packet['bytes']\n", " old_dir = packet['dir']\n", " continue\n", " if old_dir == packet['dir']:\n", " burst_length += packet['bytes']\n", " continue\n", " if old_dir != packet['dir']:\n", " stream_burst.append(burst_length)\n", " old_dir = packet['dir']\n", " burst_length = packet['bytes']\n", "# stream_bytes_l.append(stream_bytes)\n", " stream_burst.append(burst_length)\n", " \n", " #取前10个，20个，30个\n", " stream_length = stream_length[0:N]\n", " stream_type = stream_type[0:N]\n", " stream_burst = stream_burst[0:N]\n", " \n", " stream_length_l.append(stream_length)\n", " stream_type_l.append(stream_type)\n", " stream_bursd_l.append(stream_burst)\n", "stream_length_array = np.array(stream_length_l)\n", "stream_type_array = np.array(stream_type_l)\n", "stream_burst_array = np.array(stream_bursd_l)\n", "#stream_bytes_array = np.array(stream_bytes_l)\n", "print('end2')" ] }, { "cell_type": "code", "execution_count": 188, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[517, 3624, 1647, 223, 31], [517, 3888, 910, 763, 133],\n", " [517, 3888, 1427, 388, 133], ...,\n", " [517, 3124, 82, 38, 6100, 32496, 5840, 7312, 1460, 33735, 1448, 7361, 25797, 7694, 2920, 86252, 1460, 12030, 4380, 50499, 4380, 2205, 2920, 53671, 2920, 5220, 10220, 18364, 4380, 3252],\n", " [517, 3211, 7260, 38, 2832, 881, 9661, 441, 4044, 882, 3790, 440, 3783, 440, 3782, 440, 3789, 441, 9709, 880, 7622, 881, 5848, 441, 1757, 441, 5617, 440, 4084, 441],\n", " [517, 3211, 1696, 340, 10007, 1087, 14520, 931, 5560, 930, 6222, 465, 5252, 465, 8553, 931, 6207, 466, 9992, 931, 13855, 931, 6222, 930, 10007, 932, 3806, 465, 16249, 1397]], dtype=object)" ] }, "execution_count": 188, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stream_burst_array" ] }, { "cell_type": "code", "execution_count": 189, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[512, 76, 2933, 333, 4, 202, 1, 40, 641, 996, 218, 26],\n", " [512, 122, 1, 3154, 529, 57, 1, 512, 53, 41, 38, 30, 200, 26, 353, 330, 34, 26, 34, 34, 19],\n", " [512, 122, 1, 3154, 529, 57, 1, 512, 53, 41, 38, 30, 145, 567, 26, 282, 26, 34, 26, 34, 34, 19],\n", " ...,\n", " [512, 74, 2415, 300, 4, 186, 1, 40, 64, 43, 29, 33, 1036, 900, 899, 890, 890, 878, 891, 5459, 33, 4743, 33, 7242, 33, 4019, 33, 3641, 33, 7134],\n", " [512, 70, 2473, 333, 4, 186, 1, 40, 64, 48, 45, 37, 5859, 4040, 33, 33, 397, 33, 398, 33, 4047, 5604, 398, 33, 4039, 398, 33, 398, 33, 3785],\n", " [512, 70, 2473, 333, 4, 186, 1, 40, 64, 48, 45, 37, 1157, 346, 33, 33, 37, 217, 33, 6196, 3801, 500, 33, 501, 33, 6231, 3813, 6214, 423, 33]], dtype=object)" ] }, "execution_count": 189, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stream_length_array" 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6428, 393, 829],\n", " [307, 6428, 393, 829],\n", " [307, 3771, 403, 616674, 409, 616708, 410, 616680],\n", " [307, 6428, 247, 2002],\n", " [307, 6428, 394, 796],\n", " [307, 6428, 390, 795],\n", " [307, 3771, 403, 616736],\n", " [307, 6428, 247, 1637],\n", " ...]}" ] }, "execution_count": 191, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stream_type_dict = dict()\n", "stream_length_dict = dict()\n", "stream_burst_dict = dict()\n", "for i in range(len(stream_type_l)):\n", " stream_type = stream_type_l[i]\n", " stream_length = stream_length_l[i]\n", " stream_burst = stream_bursd_l[i]\n", " if stream_label[i] not in stream_type_dict:\n", " stream_type_dict[stream_label[i]] = list()\n", " stream_type_dict[stream_label[i]].append(stream_type)\n", " stream_length_dict[stream_label[i]] = list()\n", " stream_length_dict[stream_label[i]].append(stream_length)\n", " stream_burst_dict[stream_label[i]] = list()\n", " stream_burst_dict[stream_label[i]].append(stream_burst)\n", " else :\n", " stream_type_dict[stream_label[i]].append(stream_type)\n", " stream_length_dict[stream_label[i]].append(stream_length)\n", " stream_burst_dict[stream_label[i]].append(stream_burst)\n", "pd.Series(stream_label).value_counts()\n", "stream_burst_dict" ] }, { "cell_type": "code", "execution_count": 192, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'douyin': [[517, 3124, 558, 1061, 1098, 2215],\n", " [517, 3448, 413, 4211, 319],\n", " [517, 3385, 1338, 14169, 1638, 1919, 520, 432, 388, 575],\n", " [188, 3030, 786, 1733],\n", " [188, 3030, 788, 1734],\n", " [517, 3448, 414, 5971, 287, 4299, 288, 4091, 288, 4629, 288, 4734, 31],\n", " [517, 3385, 1329, 756],\n", " [517, 3979, 862, 4800, 24, 24],\n", " [545, 3979, 923, 3720, 281, 4543, 281, 3577, 281, 3829, 280, 3482, 24],\n", " [517, 3979, 886, 24],\n", " [517, 3979, 862, 3639, 281, 3693, 24],\n", " [517,\n", " 3979,\n", " 862,\n", " 4793,\n", " 281,\n", " 4663,\n", " 281,\n", " 3065,\n", " 281,\n", " 3835,\n", " 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63],\n", " [307, 6428, 394, 829],\n", " [307, 6428, 414, 795],\n", " [307, 3771, 403, 616645],\n", " [307, 6428, 247, 2137],\n", " [307, 6428, 247, 2093],\n", " [307, 6428, 393, 796],\n", " [307,\n", " 3771,\n", " 403,\n", " 616602,\n", " 409,\n", " 616840,\n", " 410,\n", " 616609,\n", " 410,\n", " 616580,\n", " 410,\n", " 616638,\n", " 410,\n", " 616725,\n", " 410,\n", " 616783,\n", " 410,\n", " 616725],\n", " [307, 6428, 414, 828],\n", " [307, 3771, 403, 616795],\n", " [517, 4168, 1176, 340290, 123, 340252, 784, 340252, 123, 340252, 42],\n", " [307, 6428, 393, 795],\n", " [307, 6428, 379, 828],\n", " [307, 6428, 379, 829],\n", " [307, 6428, 393, 796],\n", " [307, 6428, 390, 828],\n", " [307, 6428, 379, 829],\n", " [307, 6428, 414, 828],\n", " ...]}" ] }, "execution_count": 192, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import random\n", "def divide_train(stream_dict):\n", " stream_train_dict = dict()\n", " for key, value in stream_dict.items():\n", " random.shuffle(value)\n", " stream_train_dict[key] = value[:int(len(value)*0.75)]\n", " return stream_train_dict\n", "def divide_test(stream_dict):\n", " stream_test_dict = dict()\n", " for key, value in stream_dict.items():\n", " random.shuffle(value)\n", " stream_test_dict[key] = value[int(len(value)*0.75):]\n", " return stream_test_dict\n", "stream_type_train_dict = divide_train(stream_type_dict)\n", "stream_length_train_dict = divide_train(stream_length_dict)\n", "stream_burst_train_dict = divide_train(stream_burst_dict)\n", "stream_type_test_dict = divide_test(stream_type_dict)\n", "stream_length_test_dict = divide_test(stream_length_dict)\n", "stream_burst_test_dict = divide_test(stream_burst_dict)\n", "len(stream_type_train_dict['weibo'])\n", "stream_burst_train_dict" ] }, { "cell_type": "code", "execution_count": 193, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "end2\n" ] } ], "source": [ "#type markov start and end state\n", "type_start_state = dict()\n", "type_end_state = dict()\n", "#transition_matrix = np.zero((27,27))\n", "for key,value in stream_type_train_dict.items():\n", " start_state = pd.Series([line[0] for line in value]).value_counts()\n", " end_state = pd.Series([line[-1] for line in value]).value_counts()\n", " type_start_state[key] = start_state/start_state.sum()\n", " type_end_state[key] = end_state/end_state.sum()\n", "\n", "print('end2')" ] }, { "cell_type": "code", "execution_count": 194, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "end2\n" ] } ], "source": [ "#type markov transition_matrix\n", "type_transition_matrix = {key : pd.DataFrame(np.zeros((27,27))) for key in stream_type_train_dict.keys()}\n", "for key,value in stream_type_test_dict.items():\n", " t_value = type_transition_matrix[key]\n", " for line in value:\n", " for i,i_value in enumerate(line):\n", " if i == len(line) - 1:\n", " break\n", " t_value.iloc[i_value,line[i+1]] += 1\n", " type_transition_matrix[key] = t_value.apply(lambda x:x/x.sum(),axis = 'columns').fillna(0)\n", "type_transition_matrix['weibo']\n", "print('end2')" ] }, { "cell_type": "code", "execution_count": 195, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "end2\n" ] } ], "source": [ "#length markov start and end state\n", "length_count = dict()\n", "for key,value in stream_length_train_dict.items():\n", " length_count[key] = dict()\n", " for line in value:\n", " for i in line:\n", " if i not in length_count[key]:\n", " length_count[key][i] = 1\n", " else :\n", " length_count[key][i] += 1\n", " length_count[key] = pd.Series(length_count[key]).sort_values(ascending=False)\n", "len(length_count['weibo'])\n", "print('end2')" ] }, { "cell_type": "code", "execution_count": 196, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "end2\n" ] } ], "source": [ "length_list = dict()\n", "for key,value in length_count.items():\n", " length_list[key] = dict()\n", " length_sum = 0\n", " pos = 0\n", " for i in range(len(value)):\n", " if length_sum >= value.sum()*0.8:# or value.iloc[i] <= 1:\n", " break\n", " length_sum += value.iloc[i]\n", " length_list[key][value.index[i]] = pos\n", " pos += 1\n", "len(length_list['weibo'])\n", "print('end2')" ] }, { "cell_type": "code", "execution_count": 197, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "end2\n" ] } ], "source": [ "stream_length_cdict = dict()\n", "for key,value in stream_length_train_dict.items():\n", " change_list = length_list[key].keys()\n", " for i,line in enumerate(value):\n", " change_line = [min(change_list, key=lambda x:abs(x-i)) for i in line]\n", " value[i] = change_line\n", " stream_length_cdict[key] = value\n", "print('end2')" ] }, { "cell_type": "code", "execution_count": 198, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "end2\n" ] } ], "source": [ "length_start_state = dict()\n", "length_end_state = dict()\n", "for key,value in stream_length_cdict.items():\n", " length_start_state[key] = pd.Series([length_list[key][x[0]] for x in value]).value_counts()\n", " length_end_state[key] = pd.Series([length_list[key][x[-1]] for x in value]).value_counts()\n", " length_start_state[key] = length_start_state[key]/length_start_state[key].sum()\n", " length_end_state[key] = length_end_state[key]/length_end_state[key].sum()\n", "print('end2')" ] }, { "cell_type": "code", "execution_count": 199, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "end2\n" ] } ], "source": [ "#length transition matrix\n", "length_transition_matrix = dict()\n", "\n", "for key,value in stream_length_cdict.items():\n", " shape = (len(length_list[key]),len(length_list[key]))\n", " transition = pd.DataFrame(np.zeros(shape))\n", " for line in value:\n", " for i,x in enumerate(line):\n", " if i >= len(line)-1:\n", " break\n", " transition.iloc[length_list[key][x], length_list[key][line[i+1]]] += 1\n", " length_transition_matrix[key] = transition.apply(lambda x:x/x.sum(),axis = 'columns').fillna(0)\n", "#print(length_transition_matrix['chrome'].iloc[5,24])\n", "print('end2')" ] }, { "cell_type": "code", "execution_count": 200, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "end2\n" ] } ], "source": [ "#burst markov start and end state\n", "burst_count = dict()\n", "for key,value in stream_burst_train_dict.items():\n", " burst_count[key] = dict()\n", " for line in value:\n", " for i in line:\n", " if i not in burst_count[key]:\n", " burst_count[key][i] = 1\n", " else :\n", " burst_count[key][i] += 1\n", " burst_count[key] = pd.Series(burst_count[key]).sort_values(ascending=False)\n", "print('end2')" ] }, { "cell_type": "code", "execution_count": 201, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "52" ] }, "execution_count": 201, "metadata": {}, "output_type": "execute_result" } ], "source": [ "burst_list = dict()\n", "for key,value in burst_count.items():\n", " burst_list[key] = dict()\n", " burst_sum = 0\n", " pos = 0\n", " for i in range(len(value)):\n", " if burst_sum >= value.sum()*0.6:# or value.iloc[i] <= 1:\n", " break\n", " burst_sum += value.iloc[i]\n", " burst_list[key][value.index[i]] = pos\n", " pos += 1\n", "len(burst_list['weibo'])" ] }, { "cell_type": "code", "execution_count": 202, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[190, 66097],\n", " [183, 3564, 886, 5407, 878, 903, 886, 5299],\n", " [183, 3564, 1588, 1548, 1588, 1548, 1588, 1552],\n", " [517, 3624, 1507, 223, 1507, 223, 31],\n", " [183, 3564, 948, 564],\n", " [151, 3564, 886, 934],\n", " [517, 3030],\n", " [183, 3564, 948, 3124],\n", " [183, 3564, 1740, 474, 1775, 505],\n", " [517, 5033, 146, 38, 229, 66096, 42, 5491, 102, 66096, 42, 5491, 102],\n", " [517, 3387, 518, 66097, 42, 66097, 31],\n", " [517, 3624, 1548, 223],\n", " [517, 3658, 948, 1440, 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392,\n", " 3886,\n", " 392,\n", " 5319,\n", " 392,\n", " 5491,\n", " 392,\n", " 5491,\n", " 392,\n", " 5491,\n", " 392,\n", " 5491,\n", " 392,\n", " 4356,\n", " 31],\n", " [517, 3658, 948, 1440],\n", " [517, 3624, 1548, 223, 1487, 223, 1487, 223, 31],\n", " [517, 3658, 3886, 505, 31],\n", " [183, 3564, 894, 4356],\n", " [183,\n", " 3564,\n", " 1773,\n", " 474,\n", " 1775,\n", " 474,\n", " 1773,\n", " 474,\n", " 1775,\n", " 474,\n", " 1738,\n", " 474,\n", " 3030,\n", " 474,\n", " 1738,\n", " 474,\n", " 1773,\n", " 505],\n", " [517, 3658, 573, 38, 38, 392],\n", " ...]" ] }, "execution_count": 202, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stream_burst_cdict = dict()\n", "for key,value in stream_burst_train_dict.items():\n", " change_list = burst_list[key].keys()\n", " for i,line in enumerate(value):\n", " change_line = [min(change_list, key=lambda x:abs(x-i)) for i in line]\n", " value[i] = change_line\n", " stream_burst_cdict[key] = value\n", "stream_burst_cdict['hupu']" ] }, { "cell_type": "code", "execution_count": 203, "metadata": {}, "outputs": [], "source": [ "burst_start_state = dict()\n", "burst_end_state = dict()\n", "#print(stream_burst_cdict['hupu'])\n", "for key,value in stream_burst_cdict.items():\n", " burst_start_state[key] = pd.Series([burst_list[key][x[0]] for x in value]).value_counts()\n", " burst_end_state[key] = pd.Series([burst_list[key][x[-1]] for x in value]).value_counts()\n", " burst_start_state[key] = burst_start_state[key]/burst_start_state[key].sum()\n", " burst_end_state[key] = burst_end_state[key]/burst_end_state[key].sum()\n", "#burst_start_state['weibo']" ] }, { "cell_type": "code", "execution_count": 204, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n", "51 0.003802 0.019011 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "\n", "[52 rows x 52 columns]" ] }, "execution_count": 204, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#burst transition matrix\n", "burst_transition_matrix = dict()\n", "#print(stream_burst_cdict['hupu'])\n", "#for stream in stream_burst_cdict['hupu']:\n", " # print(len(stream))\n", "for key,value in stream_burst_cdict.items():\n", " shape = (len(burst_list[key]),len(burst_list[key]))\n", " transition = pd.DataFrame(np.zeros(shape))\n", " for line in value:\n", " for i,x in enumerate(line):\n", " if i >= len(line)-1:\n", " break\n", " transition.iloc[burst_list[key][x], burst_list[key][line[i+1]]] += 1\n", " burst_transition_matrix[key] = transition.apply(lambda x:x/x.sum(),axis = 'columns').fillna(0)\n", "burst_transition_matrix['weibo']" ] }, { "cell_type": "code", "execution_count": 205, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.66968603203726429, 0.72735699019817579, 0.64216524965536059, 0.60480787940876213, 0.0]\n" ] } ], "source": [ "#type markov train\n", "def train_markov(line, start_state, end_state, transition_matrix):\n", " result = 0\n", " try:\n", " result = start_state[line[0]]\n", " #print('s' + str(result))\n", " except Exception:\n", " pass\n", " for i,x in enumerate(line):\n", " if i == len(line)-1:\n", " try:\n", " result *= end_state[line[-1]]\n", " #print('e' + str(result))\n", " except Exception:\n", " result *=0\n", " return pow(result, 1/len(line))\n", " result *= transition_matrix.iloc[x,line[i+1]]\n", " #print(str(x)+','+str(line[i+1])+':'+str(transition_matrix.iloc[x,line[i+1]]))\n", " #print(result)\n", "type_markov_train = dict()\n", "for key,value in stream_type_train_dict.items():\n", " train_data = list()\n", " for line in value:\n", " data = [train_markov(line, type_start_state[k], type_end_state[k], type_transition_matrix[k]) for k in type_start_state.keys()]\n", " train_data.append(data)\n", " type_markov_train[key] = train_data\n", "print(type_markov_train['weibo'][0])" ] }, { "cell_type": "code", "execution_count": 206, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.65870931080077844, 0.71800201786136475, 0.62966868927015829, 0.59026303379748613, 0.0]\n" ] } ], "source": [ "#type markov test\n", "type_markov_test = dict()\n", "for key,value in stream_type_test_dict.items():\n", " test_data = list()\n", " for line in value:\n", " data = [train_markov(line, type_start_state[k], type_end_state[k], type_transition_matrix[k]) for k in type_start_state.keys()]\n", " test_data.append(data)\n", " type_markov_test[key] = test_data\n", "print(type_markov_test['weibo'][0])" ] }, { "cell_type": "code", "execution_count": 207, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.0, 0.17106276153743247, 0.041009964603852089, 0.0, 0.0]" ] }, "execution_count": 207, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#length markov train\n", "def train_cmarkov(line, start_state, end_state, transition_matrix,key, trans_list):\n", " #print(key)\n", " line = [min(trans_list, key=lambda x:abs(x-i)) for i in line]\n", " #print(line)\n", " result = 0\n", " try:\n", " result = start_state[trans_list[line[0]]]\n", " #print('s' + str(result))\n", " except Exception:\n", " pass\n", " for i,x in enumerate(line):\n", " if i == len(line)-1:\n", " try:\n", " result *= end_state[trans_list[line[-1]]]\n", " #print('e' + ','+str(result))\n", " except Exception:\n", " #print('e')\n", " result *=0\n", " return pow(result, 1/len(line))\n", " result *= transition_matrix.iloc[trans_list[x],trans_list[line[i+1]]]\n", " #print(str(trans_list[x])+','+str(trans_list[line[i+1]])+':'+str(transition_matrix.iloc[trans_list[x],trans_list[line[i+1]]]))\n", " #print(result)\n", "length_markov_train = dict()\n", "for key,value in stream_length_train_dict.items():\n", " train_data = list()\n", " #print(key)\n", " for line in value:\n", " #print(line)\n", " #data_c = train_cmarkov(line, length_start_state[key], length_end_state[key], length_transition_matrix[key], key, length_list[key])\n", " data = [train_cmarkov(line, length_start_state[k], length_end_state[k], length_transition_matrix[k], k, length_list[k]) for k in length_start_state.keys()]\n", " train_data.append(data)\n", " length_markov_train[key] = train_data\n", "length_markov_train['weibo'][0]" ] }, { "cell_type": "code", "execution_count": 210, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.0, 0.27023615405704027, 0.0, 0.0, 0.18671676784061836]" ] }, "execution_count": 210, "metadata": {}, "output_type": "execute_result" } ], "source": [ "length_markov_test = dict()\n", "for key,value in stream_length_test_dict.items():\n", " test_data = list()\n", " #print(key)\n", " for line in value:\n", " #print(line)\n", " #data_c = test_cmarkov(line, lengthf_start_state[key], length_end_state[key], length_transition_matrix[key], key, length_list[key])\n", " data = [train_cmarkov(line, length_start_state[k], length_end_state[k], length_transition_matrix[k], k, length_list[k]) for k in length_start_state.keys()]\n", " test_data.append(data)\n", " length_markov_test[key] = test_data\n", "length_markov_test['weibo'][0]" ] }, { "cell_type": "code", "execution_count": 211, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.0, 0.23149164270900893, 0.040354188036192708, 0.0, 0.056403457596829326]\n" ] } ], "source": [ "burst_markov_train = dict()\n", "for key,value in stream_burst_train_dict.items():\n", " train_data = list()\n", " #print(key)\n", " for line in value:\n", " #print(line)\n", " #data_c = train_cmarkov(line, burst_start_state[key], burst_end_state[key], burst_transition_matrix[key], key, burst_list[key])\n", " data = [train_cmarkov(line, burst_start_state[k], burst_end_state[k], burst_transition_matrix[k], k, burst_list[k]) for k in burst_start_state.keys()]\n", " train_data.append(data)\n", " burst_markov_train[key] = train_data\n", "print(burst_markov_train['weibo'][0])" ] }, { "cell_type": "code", "execution_count": 212, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.0, 0.60695553459216889, 0.0, 0.0, 0.025895500011248445]\n", "end2\n" ] } ], "source": [ "burst_markov_test = dict()\n", "for key,value in stream_burst_test_dict.items():\n", " test_data = list()\n", " #print(key)\n", " for line in value:\n", " #print(line)\n", " #data_c = test_cmarkov(line, burst_start_state[key], burst_end_state[key], burst_transition_matrix[key], key, burst_list[key])\n", " data = [train_cmarkov(line, burst_start_state[k], burst_end_state[k], burst_transition_matrix[k], k, burst_list[k]) for k in burst_start_state.keys()]\n", " test_data.append(data)\n", " burst_markov_test[key] = test_data\n", "print(burst_markov_test['weibo'][0])\n", "print('end2')" ] }, { "cell_type": "code", "execution_count": 213, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "end2\n" ] } ], "source": [ "train_path = root_dir + 'Experiment/MarkovModel/CsvFile/' + date + '/train_' + str(N) + '.csv'\n", "train_data_dict = dict()\n", "for key,value in type_markov_train.items():\n", " data = pd.concat([pd.DataFrame(value),pd.DataFrame(length_markov_train[key]), pd.DataFrame(burst_markov_train[key])], axis=1)\n", " data['label'] = key\n", " train_data_dict[key] = data\n", "train_data_df = pd.concat([value for value in train_data_dict.values()],axis=0)\n", "train_data_df.to_csv(train_path)\n", "print('end2')" ] }, { "cell_type": "code", "execution_count": 214, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "end2\n" ] } ], "source": [ "test_path = root_dir + 'Experiment/MarkovModel/CsvFile/' + date + '/test_' + str(N) + '.csv'\n", "test_data_dict = dict()\n", "for key,value in type_markov_test.items():\n", " data = pd.concat([pd.DataFrame(value),pd.DataFrame(length_markov_test[key]), pd.DataFrame(burst_markov_test[key])], axis=1)\n", " data['label'] = key\n", " test_data_dict[key] = data\n", "test_data_df = pd.concat([value for value in test_data_dict.values()],axis=0)\n", "test_data_df.to_csv(test_path)\n", "print('end2')" ] }, { "cell_type": "code", "execution_count": 149, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.73704496788008567" ] }, "execution_count": 149, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#randomforest\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.svm import SVC\n", "x_train = train_data_df.iloc[:,0:5].values.copy()\n", "y_train = train_data_df.iloc[:,-1].values.copy()\n", "classifer = RandomForestClassifier()\n", "classifer.fit(x_train,y_train)\n", "x_test = test_data_df.iloc[:,0:5].values.copy()\n", "y_test = test_data_df.iloc[:,-1].values.copy()\n", "y_pred = classifer.predict(x_test)\n", "accuracy_score(y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }