diff --git a/Experiment/ExpertFeature/tls_cert_length.ipynb b/Experiment/ExpertFeature/tls_cert_length.ipynb index 4abb0e6..e25e45f 100644 --- a/Experiment/ExpertFeature/tls_cert_length.ipynb +++ b/Experiment/ExpertFeature/tls_cert_length.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 13, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -16,7 +16,7 @@ "Name: 4, dtype: int64" ] }, - "execution_count": 13, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 39, "metadata": { "collapsed": true }, @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 40, "metadata": { "collapsed": true }, @@ -181,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 41, "metadata": { "collapsed": true }, @@ -227,8 +227,10 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, + "execution_count": 42, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "#TODO: 加入cipher suites，extensions特征\n", @@ -248,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 43, "metadata": { "collapsed": true }, @@ -359,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 44, "metadata": { "collapsed": true }, @@ -374,7 +376,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 82, "metadata": {}, "outputs": [ { @@ -382,19 +384,23 @@ "output_type": "stream", "text": [ "164\n", - " precision recall f1\n", - "LogisticRegression 0.886296 0.886296 0.886296\n", - "SVM 0.897751 0.897751 0.897751\n", - "GaussianNB 0.694103 0.694103 0.694103\n", - "tree 0.911328 0.911328 0.911328\n", - "RandomForest 0.905388 0.905388 0.905388\n" + "[2357, 5]\n", + "0.999400838826\n", + "0.999400838826\n", + "0.999400838826\n", + " precision recall f1\n", + "LogisticRegression 0.00000 0.00000 0.00000\n", + "SVM 0.00000 0.00000 0.00000\n", + "GaussianNB 0.00000 0.00000 0.00000\n", + "tree 0.00000 0.00000 0.00000\n", + "RandomForest 0.89563 0.89563 0.89563\n" ] }, { "data": { - "image/png": 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Udmm8BkRbWPuhqN0yvyCZy/xEktkQxwBzI+KHmRZmVUHS3wA7F+mOZVuXpEkb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\nh7uZWQ453M3Mcuj/Azvm9WexyMnIAAAAAElFTkSuQmCC\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -413,6 +419,30 @@ "print(len(examples[0]))\n", "score_df = pd.DataFrame(np.zeros((5,3)),index = ['LogisticRegression', 'SVM', 'GaussianNB', 'tree', 'RandomForest'], \\\n", " columns = ['precision', 'recall', 'f1'])\n", + "\n", + "\n", + "def my_pred(y_pred, y_test, proba):\n", + " y_pred1 = list()\n", + " y_test1 = list()\n", + " [rows, clos] = proba.shape\n", + " print([rows, clos])\n", + " right_count = 0\n", + " wrong_count = 0\n", + " for i in range(rows):\n", + " temp = max(proba[i])\n", + " if temp < 0.95:\n", + " continue\n", + " y_pred1.append(y_pred[i])\n", + " y_test1.append(y_test[i])\n", + " f1 = f1_score(y_test1, y_pred1, average='micro')\n", + " recall = recall_score(y_test1, y_pred1, average='micro')\n", + " precision = precision_score(y_test1, y_pred1, average='micro')\n", + " print(precision)\n", + " print(recall)\n", + " print(f1)\n", + " \n", + "\n", + "'''\n", "#def a():\n", "f1_score_list = list()\n", "recall_score_list = list()\n", @@ -493,12 +523,13 @@ " precision_score_list.append(precision_score(y_test, y_pred, average='micro'))\n", "scores = [np.mean(precision_score_list), np.mean(recall_score_list), np.mean(f1_score_list)]\n", "score_df.loc['tree'] = scores\n", + "'''\n", "\n", "f1_score_list = list()\n", "recall_score_list = list()\n", "precision_score_list = list()\n", "for i in range(1):\n", - " #np.random.shuffle(examples)\n", + " np.random.shuffle(examples)\n", " examples_train = examples[:int(len(examples)*0.75)]\n", " examples_test = examples[int(len(examples)*0.75):]\n", " x_train = examples_train[:,0:-1]\n", @@ -510,16 +541,26 @@ " y_pred = classifer.predict(x_test)\n", " f1_score_list.append(f1_score(y_test, y_pred, average='micro'))\n", " recall_score_list.append(recall_score(y_test, y_pred, average='micro'))\n", - " precision_score_list.append(precision_score(y_test, y_pred, average='micro'))\n", - " \n", + " precision_score_list.append(precision_score(y_test, y_pred, average='micro')) \n", + " proba = classifer.predict_proba(x_test)\n", + " my_pred(y_pred, y_test, proba)\n", "scores = [np.mean(precision_score_list), np.mean(recall_score_list), np.mean(f1_score_list)]\n", "score_df.loc['RandomForest'] = scores\n", "print(score_df)\n", "ax = score_df.plot.bar(title='ssl-fingerprint')\n", "fig = ax.get_figure()\n", - "#fig.savefig('../figure/ssl.svg')" + "#fig.savefig('../figure/ssl.svg')\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -535,6 +576,18 @@ "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, diff --git a/Experiment/MarkovModel/.ipynb_checkpoints/markov-checkpoint.ipynb b/Experiment/MarkovModel/.ipynb_checkpoints/markov-checkpoint.ipynb index 4c4ac34..f0bfe46 100644 --- a/Experiment/MarkovModel/.ipynb_checkpoints/markov-checkpoint.ipynb +++ b/Experiment/MarkovModel/.ipynb_checkpoints/markov-checkpoint.ipynb @@ -12,1047 +12,52 @@ "import json\n", "import numpy as np\n", "import pandas as pd\n", - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n", + "date = '2019-12-20_21'\n", + "root_dir = '/Users/Leo/Documents/github/GradProj/'" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "{('192.168.50.150', 61251, '119.57.174.36', 443): 'douyin',\n", - " ('192.168.50.150', 61253, '119.57.174.36', 443): 'douyin',\n", - " ('192.168.50.150', 61254, '122.14.43.165', 443): 'douyin',\n", - " ('192.168.50.150', 61258, '119.57.174.38', 443): 'douyin',\n", - " ('192.168.50.150', 61259, 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"execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "end2\n" + ] } ], "source": [ - "stream_tag_path = '../datasets/2019-12-21/stream_tag.txt'\n", + "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", - "stream_tag_map" + "print('end2')" ] }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 3, + "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 = '../datasets/2019-12-21/stream_stat.txt'\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", @@ -1087,32 +92,39 @@ " 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", + " n = 10\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)" + "#stream_bytes_array = np.array(stream_bytes_l)\n", + "print('end2')" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": 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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()" + "pd.Series(stream_label).value_counts()\n", + "print('end2')" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "4115" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "end2\n" + ] } ], "source": [ @@ -1254,28 +254,21 @@ "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'])" + "len(stream_type_train_dict['weibo'])\n", + "print('end2')" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "{'hupu': 1 1.0\n", - " dtype: float64, 'weibo': 1 1.0\n", - " dtype: float64, 'douyin': 1 1.0\n", - " dtype: float64, 'toutiao': 1 1.0\n", - " dtype: float64, 'zhihu': 1 1.0\n", - " dtype: float64}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "end2\n" + ] } ], "source": [ @@ -1288,806 +281,21 @@ " 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", - "type_start_state" + "\n", + "print('end2')" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
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"11695" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "end2\n" + ] } ], "source": [ @@ -2132,23 +338,21 @@ " 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'])" + "len(length_count['weibo'])\n", + "print('end2')" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 9, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "429" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "end2\n" + ] } ], "source": [ @@ -2163,33414 +367,21 @@ " length_sum += value.iloc[i]\n", " length_list[key][value.index[i]] = pos\n", " pos += 1\n", - "len(length_list['weibo'])" + "len(length_list['weibo'])\n", + "print('end2')" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 10, "metadata": {}, "outputs": [ { - 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" 4847,\n", - " 339,\n", - " 26],\n", - " [512, 80, 3823, 300, 4, 186, 1, 40, 70, 1, 40, 339, 1187, 10958, 26],\n", - " [512, 80, 3823, 300, 4, 186, 1, 40, 70, 1, 40, 408, 7472, 26],\n", - " [512, 80, 3823, 300, 4, 186, 1, 40, 70, 1, 40, 339, 3004, 13957, 26],\n", - " [512,\n", - " 122,\n", - " 1,\n", - " 2944,\n", - " 529,\n", - " 57,\n", - " 1,\n", - " 512,\n", - " 53,\n", - " 41,\n", - " 38,\n", - " 30,\n", - " 156,\n", - " 582,\n", - " 26,\n", - " 26,\n", - " 282,\n", - " 335,\n", - " 202,\n", - " 34,\n", - " 34],\n", - " ...]" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "end2\n" + ] } ], "source": [ @@ -35581,34 +392,20 @@ " 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", - "stream_length_dict['weibo']" + "print('end2')" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 11, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "11 0.738275\n", - "27 0.129283\n", - "60 0.027461\n", - "4 0.022114\n", - "93 0.019927\n", - " ... \n", - "217 0.000243\n", - "193 0.000243\n", - "141 0.000243\n", - "113 0.000243\n", - "415 0.000243\n", - "Length: 82, dtype: float64" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "end2\n" + ] } ], "source": [ @@ -35619,16 +416,22 @@ " 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", - "length_end_state['weibo']" + "print('end2')" ] }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "end2\n" + ] + } + ], "source": [ "#length transition matrix\n", "length_transition_matrix = dict()\n", @@ -35642,23 +445,21 @@ " 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])" + "#print(length_transition_matrix['chrome'].iloc[5,24])\n", + "print('end2')" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 13, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "14831" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "end2\n" + ] } ], "source": [ @@ -35673,12 +474,12 @@ " else :\n", " burst_count[key][i] += 1\n", " burst_count[key] = pd.Series(burst_count[key]).sort_values(ascending=False)\n", - "len(burst_count['weibo'])" + "print('end2')" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -35687,7 +488,7 @@ "427" ] }, - "execution_count": 50, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -35709,7 +510,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -47234,7 +12035,7 @@ " ...]" ] }, - "execution_count": 33, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -47252,7 +12053,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -47261,14 +12062,14 @@ "1 0.951154\n", "28 0.020413\n", "37 0.019198\n", - "104 0.003888\n", - "106 0.003645\n", + "101 0.003888\n", + "108 0.003645\n", "105 0.001215\n", "102 0.000486\n", "dtype: float64" ] }, - "execution_count": 34, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -47286,25 +12087,25 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "\n", " \n", @@ -47339,7 +12140,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -47347,16 +12148,16 @@ " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -47379,15 +12180,15 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -47395,28 +12196,28 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -47426,9 +12227,9 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -47451,8 +12252,608 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -47479,12 +12880,420 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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0.48990142122777269, 0.43437524038861786]\n" ] } ], @@ -47737,16 +13888,16 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[0.0, 0.07760346742471443, 0.0, 0.0, 0.0]" + "[0.0, 0.077603467424714426, 0.0, 0.0, 0.0]" ] }, - "execution_count": 38, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -47790,16 +13941,16 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[0.20244355853008852, 0.07938612362983426, 0.0, 0.0, 0.12180917473163998]" + "[0.20244355853008852, 0.079641859087047087, 0.0, 0.0, 0.12180917473163998]" ] }, - "execution_count": 39, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -47820,14 +13971,14 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[0.0, 0.014372615059609054, 0.0, 0.0, 0.024239736243920267]\n" + "[0.0, 0.013140346002108845, 0.0, 0.0, 0.024239736243920267]\n" ] } ], @@ -47847,14 +13998,15 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[0.0, 0.005410506902821782, 0.018755715257705297, 0.0, 0.04036079872179655]\n" + "[0.0, 0.0039926679087497682, 0.018755715257705297, 0.0, 0.040360798721796549]\n", + "end2\n" ] } ], @@ -47869,305 +14021,25 @@ " 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])" + "print(burst_markov_test['weibo'][0])\n", + "print('end2')" ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 29, "metadata": {}, "outputs": [ { - 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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", @@ -48481,29 +14072,21 @@ " 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", - "test_data_df" + "print('end2')" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 28, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\python38\\lib\\site-packages\\sklearn\\ensemble\\forest.py:244: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n", - " warn(\"The default value of n_estimators will change from \"\n" - ] - }, { "data": { "text/plain": [ - "0.8014989293361884" + "0.80449678800856528" ] }, - "execution_count": 47, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } diff --git a/Experiment/MarkovModel/CsvFile/2019-12-20_21/test.csv b/Experiment/MarkovModel/CsvFile/2019-12-20_21/test.csv index 85f097c..f1d1466 100644 --- a/Experiment/MarkovModel/CsvFile/2019-12-20_21/test.csv +++ b/Experiment/MarkovModel/CsvFile/2019-12-20_21/test.csv @@ -1,4671 +1,4671 @@ ,0,1,2,3,4,0,1,2,3,4,0,1,2,3,4,label -0,0.7150311728326214,0.6087642619984183,0.6311235222995351,0.6502359344516286,0.512069761587831,0.27707078416014774,0.0,0.0,0.0,0.0,0.12594921437417658,0.0,0.00257183056424476,0.0,0.0,hupu -1,0.6622523756225499,0.5413353090181341,0.570617386355288,0.584464779977801,0.4336088092718866,0.45182140886565364,0.0,0.0,0.0,0.0,0.10029665508080002,0.0,0.0,0.0,0.0,hupu -2,0.8943042165464922,0.8611961752869071,0.8245615955563472,0.8701903438121712,0.850369930468415,0.4140117148200356,0.0,0.0,0.0,0.0,0.06810765624494897,0.0,0.0,0.0,0.03385655375762407,hupu 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"metadata": {}, "outputs": [ { @@ -40,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 187, "metadata": {}, "outputs": [ { @@ -92,6 +93,12 @@ " 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", @@ -104,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 188, "metadata": {}, "outputs": [ { @@ -112,12 +119,12 @@ "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, 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"output_type": "execute_result" } @@ -180,15 +187,19730 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 191, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "end2\n" - ] + "data": { + "text/plain": [ + "{'douyin': [[128, 4875],\n", + " [128, 4875],\n", + " [128, 4875],\n", + " [128, 4875],\n", + " [517, 9271, 6045, 472, 31],\n", + " [517, 3448, 414, 4500, 288, 5105, 319, 4525],\n", + " [517, 3979, 862, 4306, 281, 5617, 309, 3279, 24],\n", + " [205, 3097, 1931, 637],\n", + " [517, 9270, 2386, 472, 31],\n", + " [517, 3124, 1669, 870, 2272, 738],\n", + " [517, 3979, 861, 3340, 281, 3502, 281, 3699, 280, 4071, 24],\n", + " [517, 3979, 862, 3650, 281, 3368, 24],\n", + " [517, 3448, 414, 3972, 287, 3821, 287, 3738, 31],\n", + " [517, 3448, 414, 4521, 272, 1858, 287, 5306, 288, 3976, 31],\n", + " [517, 3448, 414, 4939, 31],\n", + " [517, 3077, 1397, 1379],\n", + " [517, 3097, 1931, 637],\n", + " [517, 3979, 861, 4200, 281, 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191, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -211,20 +19933,19238 @@ " 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", - "print('end2')" + "stream_burst_dict" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 192, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "end2\n" - ] + "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, 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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": [ @@ -232,13 +39172,13 @@ "def divide_train(stream_dict):\n", " stream_train_dict = dict()\n", " for key, value in stream_dict.items():\n", - " #random.shuffle(value)\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", + " 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", @@ -248,12 +39188,12 @@ "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", - "print('end2')" + "stream_burst_train_dict" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 193, "metadata": {}, "outputs": [ { @@ -280,7 +39220,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 194, "metadata": {}, "outputs": [ { @@ -308,7 +39248,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 195, "metadata": {}, "outputs": [ { @@ -337,7 +39277,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 196, "metadata": {}, "outputs": [ { @@ -366,7 +39306,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 197, "metadata": {}, "outputs": [ { @@ -390,7 +39330,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 198, "metadata": {}, "outputs": [ { @@ -414,7 +39354,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 199, "metadata": {}, "outputs": [ { @@ -444,7 +39384,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 200, "metadata": {}, "outputs": [ { @@ -472,16 +39412,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 201, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "427" + "52" ] }, - "execution_count": 14, + "execution_count": 201, "metadata": {}, "output_type": "execute_result" } @@ -503,11532 +39443,4223 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 202, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[[517, 3888, 910, 763, 133],\n", - " [517, 3888, 1427, 388, 133],\n", - " [517, 4053, 1051, 640],\n", - " [517, 5867, 349, 4762],\n", - " [517, 4053, 789, 796, 922, 11249],\n", - " [517, 5144, 390, 71854, 31],\n", - " [517, 4469, 478, 7261, 31],\n", - " [517, 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value\n", - "stream_burst_dict['weibo']" + "stream_burst_cdict['hupu']" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 203, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1 0.951154\n", - "28 0.020413\n", - "37 0.019198\n", - "101 0.003888\n", - "108 0.003645\n", - "105 0.001215\n", - "102 0.000486\n", - "dtype: float64" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "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']" + "#burst_start_state['weibo']" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 204, "metadata": {}, "outputs": [ { @@ -12115,110 +43729,110 @@ " 8\n", " 9\n", " ...\n", - " 417\n", - " 418\n", - " 419\n", - " 420\n", - " 421\n", - " 422\n", - " 423\n", - " 424\n", - " 425\n", - " 426\n", + " 42\n", + " 43\n", + " 44\n", + " 45\n", + " 46\n", + " 47\n", + " 48\n", + " 49\n", + " 50\n", + " 51\n", " \n", " \n", " \n", " \n", " 0\n", " 0.000000\n", - " 0.0\n", " 0.000000\n", - " 0.000367\n", + " 0.002374\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", - " 0.001742\n", + " 0.000000\n", + " 0.001826\n", " ...\n", - " 0.001284\n", - " 0.033933\n", - " 0.001284\n", - " 0.001926\n", - " 0.001009\n", - " 0.001651\n", - " 0.001559\n", - " 0.004585\n", - " 0.000734\n", - " 0.001192\n", + " 0.000000\n", + " 0.001279\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.002192\n", + " 0.000000\n", + " 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0.000000 0.000000 0.086957 0.000000 0.000000 0.152174 0.152174 \n", + "47 0.016598 0.029046 0.033195 0.004149 0.000000 0.008299 0.012448 \n", + "48 0.000000 0.000000 0.055556 0.000000 0.000000 0.000000 0.000000 \n", + "49 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "50 0.000000 0.038462 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "51 0.003802 0.019011 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "\n", - "[427 rows x 427 columns]" + "[52 rows x 52 columns]" ] }, - "execution_count": 17, + "execution_count": 204, "metadata": {}, "output_type": "execute_result" } @@ -13798,7 +45169,9 @@ "source": [ "#burst transition matrix\n", "burst_transition_matrix = dict()\n", - "\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", @@ -13813,14 +45186,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 205, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[0.24955573409390039, 0.44103735973916453, 0.58472149431908549, 0.0, 0.35796481391625612]\n" + "[0.66968603203726429, 0.72735699019817579, 0.64216524965536059, 0.60480787940876213, 0.0]\n" ] } ], @@ -13856,14 +45229,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 206, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[0.62848062854950826, 0.51129272045120233, 0.46302666351886079, 0.48990142122777269, 0.43437524038861786]\n" + "[0.65870931080077844, 0.71800201786136475, 0.62966868927015829, 0.59026303379748613, 0.0]\n" ] } ], @@ -13881,16 +45254,16 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 207, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[0.0, 0.077603467424714426, 0.0, 0.0, 0.0]" + "[0.0, 0.17106276153743247, 0.041009964603852089, 0.0, 0.0]" ] }, - "execution_count": 20, + "execution_count": 207, "metadata": {}, "output_type": "execute_result" } @@ -13934,16 +45307,16 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 210, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[0.20244355853008852, 0.079641859087047087, 0.0, 0.0, 0.12180917473163998]" + "[0.0, 0.27023615405704027, 0.0, 0.0, 0.18671676784061836]" ] }, - "execution_count": 21, + "execution_count": 210, "metadata": {}, "output_type": "execute_result" } @@ -13964,14 +45337,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 211, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[0.0, 0.013140346002108845, 0.0, 0.0, 0.024239736243920267]\n" + "[0.0, 0.23149164270900893, 0.040354188036192708, 0.0, 0.056403457596829326]\n" ] } ], @@ -13991,14 +45364,14 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 212, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[0.0, 0.0039926679087497682, 0.018755715257705297, 0.0, 0.040360798721796549]\n", + "[0.0, 0.60695553459216889, 0.0, 0.0, 0.025895500011248445]\n", "end2\n" ] } @@ -14020,7 +45393,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 213, "metadata": {}, "outputs": [ { @@ -14032,7 +45405,7 @@ } ], "source": [ - "train_path = root_dir + 'Experiment/MarkovModel/CsvFile/' + date + '/train.csv'\n", + "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", @@ -14045,7 +45418,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 214, "metadata": {}, "outputs": [ { @@ -14057,7 +45430,7 @@ } ], "source": [ - "test_path = root_dir + 'Experiment/MarkovModel/CsvFile/' + date + '/test.csv'\n", + "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", @@ -14070,16 +45443,16 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 149, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.80449678800856528" + "0.73704496788008567" ] }, - "execution_count": 28, + "execution_count": 149, "metadata": {}, "output_type": "execute_result" } diff --git a/Experiment/MarkovModel/markov_tofig.ipynb b/Experiment/MarkovModel/markov_tofig.ipynb index 84ab001..b2d8d27 100644 --- a/Experiment/MarkovModel/markov_tofig.ipynb +++ b/Experiment/MarkovModel/markov_tofig.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -16,6 +16,7 @@ "source": [ "import pandas as pd\n", "import numpy as np\n", + "N = 20\n", "date = '2019-12-20_21'\n", "root_dir = '/Users/Leo/Documents/github/GradProj/'\n", "train_path = root_dir + 'Experiment/MarkovModel/CsvFile/' + date + '/train.csv'\n", @@ -27,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 36, "metadata": { "collapsed": true }, @@ -49,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -57,18 +58,18 @@ "output_type": "stream", "text": [ " precision recall f1\n", - "LogisticRegression 0.712848 0.712848 0.712848\n", - "SVM 0.733191 0.733191 0.733191\n", - "GaussianNB 0.685011 0.685011 0.685011\n", - "tree 0.805996 0.805996 0.805996\n", - "RandomForest 0.800000 0.800000 0.800000\n" + "LogisticRegression 0.682227 0.682227 0.682227\n", + "SVM 0.706852 0.706852 0.706852\n", + "GaussianNB 0.680086 0.680086 0.680086\n", + "tree 0.736188 0.736188 0.736188\n", + "RandomForest 0.736831 0.736831 0.736831\n" ] }, { "data": { - "image/png": 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PZW7bVJ07d6Z79+506NCh2b6HtX1Z/rZ2AxZUbNcCe6yl/dHA7z9KUVZstbW1dO3alV69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\n", 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icpB+itmb5H2oH8Kd016ehylbzx0ASV8nuUjyBsnMkHsAoyPi3lwLaz0fJpn9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\nAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -131,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -139,18 +140,18 @@ "output_type": "stream", "text": [ " precision recall f1\n", - "LogisticRegression 0.974732 0.974732 0.974732\n", - "SVM 0.959529 0.959529 0.959529\n", - "GaussianNB 0.934904 0.934904 0.934904\n", - "tree 0.994433 0.994433 0.994433\n", - "RandomForest 0.995717 0.995717 0.995717\n" + "LogisticRegression 0.833619 0.833619 0.833619\n", + "SVM 0.817773 0.817773 0.817773\n", + "GaussianNB 0.738116 0.738116 0.738116\n", + "tree 0.967452 0.967452 0.967452\n", + "RandomForest 0.967452 0.967452 0.967452\n" ] }, { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -213,30 +214,29 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " precision recall f1\n", - "LogisticRegression 0.983298 0.983298 0.983298\n", - "SVM 0.964668 0.964668 0.964668\n", - "GaussianNB 0.966595 0.966595 0.966595\n", - "tree 0.997002 0.997002 0.997002\n", - "RandomForest 0.998929 0.998929 0.998929\n" + "[4670, 5]\n", + "0.32762312633832974\n", + "0.998692810458\n", + "0.998692810458\n", + "0.998692810458\n" ] }, { "data": { - "image/png": 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9yW0/kr+PByPihYxLyoSkiRExoE4X1eSI6Jt1bY0p1AlVSV+IiH9KOrTOpq0k\nERF3Z1JYRiQNIOmi+QbJSaORJLeZF8kAku6HM0mGHyi9EzMoWNdMeqPO1IjoA7yYdT0twML0stCa\nLqq9gHezLak8hQp34HPAP4Gv1bMtgEKEu6T/BxwGvENy084+EVGdbVXZ8JVDtUXECklTJPWIiFez\nrqcFOJ1kjuitJD0OdKWVDD9Q2G6ZIpM0Hrg4Ih5Jl48mab3PAc4tcFdEN1YdKfSR7CrKhqR/knyj\n+TewsGZ9RAzOrKgMpN9i9iJ5H2q6cGe0lvthitZyB0DSqSQnSd4nGRlyd2BERDyQaWHN59Mkox8i\naSDJlSHfJ7lZYyStpGVSSZIuITmJOJ2V/e4BFC7cSQbMKh0NUyTjDhVK+i3mNxGxNzAt63qaqpDh\nDhwXEVdK2h/YDDiWJOyLEu5tSlrnhwMj00sB75I0OcO6snQIyZjlhT6xnlovHTjrY+lAYkX0QHov\nxN3Ryro5ihruNSfNDgJGRcSUgg1pul7JjRj7ASeWbsuopqzNIrlrubDhLukk4GRgyzoTunQGHs+m\nqsydTjLW/3JJi1k5RMdG2ZbVuKL+IU+S9ADQGzhHUmeKdZPGbSTDmc4nmajjUQBJW9NKrgRYBxYB\nkyU9SO1hoH+QXUnNbjTwV+AiYETJ+veLeh4mIjo3vlfLVMgTqumJkr7ArIh4R9IngaqIaGj6udxJ\nL+nanGSCjoXpum1Jxqt+JtPiMtDQ6JBFHRXSVpI0mJJBBlvLdINFDfd9gMkRsVDSUSQnVK+sGZTf\nzAxA0sUkVw7dmq4aQjLk74iGf6tlKGq4TwV2BXYBbgGuBw4tmWLNCiadBPoikgkqOtas9/DHxZZm\nRd+IWJEutyUZfqDFj+de1IHDlqVnvg8mabFfSe15Eq14RpEM+7wM+DzJtIO3ZFqRtRSblDzeOLMq\nmqioJ1Tfl3QOyTjun00/jdtlXJNla/2IeFCS0u65cyU9SjLPrBXXRcCzkh4iuVJmIHBOtiWVp6jh\nfjjwbZLr3f+bzjTT4udEtHXqw/RE+0uSTgFeI7kHwgosIm6TNIGk313AjyLiv9lWVZ5C9rkDpDOq\nbBMR/5C0AcnEDO9nXZdlIx1E7QWSr+Dnk3z9/lVEPJlpYZYJSadExG/TxztFRKu7Q7WQ4S5pGMmN\nO5+MiK3Sk2nXRsR+GZdmZi1AzWxUdR+3JkXtlhkO7AE8BRARL0nyV/ACknRFRJwmaRz1zKVatMGy\nrF6t8u5QYpmjAAAIzUlEQVT1oob7RxGxpGbEgXR2leJ9hTFYeUXMrzOtwlqaTSR9neSKwo3qzgHR\nGuZ+KGq3zK9IxjI/mmQ0xJOB6RHxk0wLsxZB0ieA7kW6Y9lqkzRqNZsjIo5rtmLWUFHDvQ1wPPBl\nkq9c9wN/aG2jvlnlpFdEDCb5NjsZmAc8HBGnZ1mX2ZoqXLin17TfFBFHZV2LtRw1c2RKOoGk1f4L\nSVNbw52Itu5I2oTkG34vak/i0uIHlCtcn3tELJfUVVL7iFiSdT3WYqwnaXOS6QfdPWc1xgNPAs/R\nykaOLVy4p2YDj0saS+1pxC7LrCLL2nkk3XOPRcRESVsCL2Vck2WvY2vtmitctwyApHpvKY+IXzZ3\nLWbWckn6IfABcB+1x/lv8ePbFzLczepKr6C6gGTykr+RjBp6WkT8MdPCLFOShgMXklxdVxOW0RpG\nCy1kuDdww8q7wNPA7yPiw+avyrIkaXJE9E2vbT4E+CHwUETsmnFpliFJLwN7RsT8rGtpqqIO+TuL\n5KvWdenPe8CbwLbpshVPzaigBwG3tYav3dYsppFMwdjqFPWE6m4RMbBkeZykRyJioKRWN0CQVcQ4\nSS+SdMucLKkr4G9wtpxkbt2HaGVz6xY13LtK6hERrwKkQ/52Sbf58sgCiogRki4B3ksvl11IMpmL\nFdu96U+rU9RwPwN4LO1PE9CbpLW2IeAJkQtI0tElj0s33dz81VhLERE3SWpP0mULMCMilmZZU7kK\neUIVQFIHYHuScH/RJ1GLTdJVJYsdgf2AZyLimxmVZC2ApEEkDb7ZJFnRHRgaEY9kWFZZChnu6eQc\npwM9I2JYOp77dhFxX8alWQshaWPgFg/5W2ySJgHfjogZ6fK2JCfc+2VbWeOKerXMKJK+9b3T5WqS\na5zNaiwCtsm6CMtcu5pgB4iI/9BK5lsuap/7VhFxuKQhABGxWHU6Wq1Y6tz70AbYERiTXUXWQjwt\n6XpWjvt/JDApw3rKVtRwXyJpfdI/ZklbUXKZkxVS6WQdy4A5EVGdVTHWYpxEMnPbD0j63B8Brsm0\nojIVtc/9S8BPSVpnDwD7AMdExIQs6zIzq5RChjuApE2BvUg+jZ9sjbcXW+VI2gu4CtgBaA+0BRZG\nxEaZFmaZkPQcq5l6szWM81/UbhkiYgHwFwBJ20m6KCKGZVyWZee3wBHAnUB/kgkats60IsvSV9P/\nDk//W9rn3iqGIyjU1TKSdpH0gKTnJV0g6VOS7gIeBKZnXZ9lKyJmAm0jYnlEjAI+n3VNlo2ImBMR\nc4B9IuLsiHgu/RkB7J91feUoVLiTDAo2GvgGyRyZz5AMIrZ1RFyeZWGWuUXpnYhTJP0qHcd7w6yL\nssxtKGnfmgVJn6GV/LsoVJ97zbCuJctzgV4RsTzDsqwFkNSTZGTQ9iTD/W4E/C5tzVtBSeoH3ABs\nnK56BzguIp7JrqryFK3PvaOk3UhOokIy7O8uNde4t4b/YVZZkg4GqiLi6nT5YWAzkpNpTwAO9wKL\niEnArpI2ImkMv5t1TeUqWsv9odVsjoj4QrMVYy2CpMeBIyJibro8GfgC0AkYFRH7ZVmfZSsdg+ob\nQC9KGsMRcV5WNZWrUC33iPAJMqurfU2wpx5LJ+r4XzpKqBXbn0lmaZtEK7vRsVAt9xrpvIi3RsQ7\n6fIngCER0SruPLPKkTQzIuq95FHSyxGxVXPXZC2HpOcjok/WdayJol0tU2NYTbADRMTbgK9xL6an\nJK3y/17Sd4F/Z1CPtSz/krRz1kWsiaK23KcCu0b64iW1BaZGxE7ZVmbNTdJmJDPtfERyaSxAP6AD\ncEhEvJlVbZY9SdNJbmZ7heTfiEjOz7X4O1SLGu6XkpwguZbkqojvAXMj4ows67LsSPoCUPPhPi0i\n/pllPdYypJfIriK9walFK2q4twG+SzLbjkgGD/uDr3c3s/qk3/A61izXzL/ckhUy3M3MyiFpMPAb\nYAvgLaAn8EJr6MIt1KWQksZExGENjfjWGvrRzKxZnU8yeuw/ImI3SZ8HhmRcU1kKFe7Aqel/v7ra\nvczMEksjYoGkNpLaRMRDki7JuqhyFOpSyIh4I314cs2obyWjv52cZW1m1iK9I6kTyQxMt0q6kmSm\nrhavkH3ukp6JiN3rrJvqbhkzK5XepbyYpCF8JMkAYrem80G0aIUKd0knkbTQt6L2gFCdgccj4qhM\nCjOzViG9J+aIiLg161oaU7Rw3xj4BHARMKJk0/vpeCJmZqSjQA4HugFjgb+ny2cBkyPi4AzLK0uh\nwr2GpK2A6oj4SNIgYBfg5tIhCcysuCT9GXibZNjn/Ugahe2BUyNicpa1lauo4T6ZZJ7MXsD9JJ/M\n20XEQVnWZWYtg6TnImLn9HFbYD7QIyLez7ay8hXqapkSKyJiGXAocEVE/BDYPOOazKzlWFrzIL1z\n/ZXWFOxQvOvcayyVNIRkhvuvpevaZViPmbUsu0p6L30sYP10uWbgsI2yK608RQ33Y0kGC7swIl6R\n1Bv4Y8Y1mVkLERFts65hbRWyz93MLO8K1XL32DJmVhSFarlL2jwi3mjNYzSbmZWjUOFuZlYUheqW\nqSHpfVbtlnkXeBo4IyJmNX9VZmaVU8hwBy4DXgdGk1zadATwaWAGcAMwKLPKzMwqoJDdMpKeiog9\n66x7MiL2kjQlInbNqjYzs0oo7B2qkg6rGYBf0mEl24r3aWdmuVPUlvuWwJXA3umqJ4AfAq8B/SLi\nsaxqMzOrhEKGu5lZ3hWyW0ZSlaR7JL0l6U1Jd0mqyrouM7NKKWS4A6NIhvndgmQw/nHpOjOzXChk\nt4ykyRHRt7F1ZmatVVFb7vMlHSWpbfpzFNDiJ7w1MytXUVvuPYDfklwtE8C/gB9ExKuZFmZmViGF\nDPf6SDotIq7Iug4zs0pwuKckvRoRPbKuw8ysEora514fZV2AmVmlONxX8lcYM8uNQo0K2cBQv5BO\ngNvM5ZiZrTPuczczyyF3y5iZ5ZDD3cwshxzuZmY55HA3M8shh7uZWQ79f9q2RS5ZewARAAAAAElF\nTkSuQmCC\n", "text/plain": [ - "" + "\"\\nrf_precision = precision_score(y_test, y_pred, average='micro')\\nrf_recall = recall_score(y_test, y_pred, average='micro')\\nrf_f1 = f1_score(y_test, y_pred, average='micro')\\nscore_df = pd.DataFrame(np.zeros((5,3)),index = ['LogisticRegression', 'SVM', 'GaussianNB', 'tree', 'RandomForest'], columns = ['precision', 'recall', 'f1'])\\nscore_df.loc['LogisticRegression'] = [lr_precision, lr_recall, lr_f1]\\nscore_df.loc['SVM'] = [svm_precision, svm_recall, svm_f1]\\nscore_df.loc['GaussianNB'] = [gn_precision, gn_recall, gn_f1]\\nscore_df.loc['tree'] = [tr_precision, tr_recall, tr_f1]\\nscore_df.loc['RandomForest'] = [rf_precision, rf_recall, rf_f1]\\nax = score_df.plot.bar(title='type+length+burst markov')\\nfig = ax.get_figure()\\nprint(score_df)\\n#fig.savefig('../figure/type_length_burst.svg')\\n\"" ] }, + "execution_count": 37, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -245,6 +245,27 @@ "y_train = train_df['label'].values.copy()\n", "x_test = test_df.iloc[:,0:15].values.copy()\n", "y_test = test_df['label'].values.copy()\n", + "\n", + "def my_pred(y_pred, y_test, proba):\n", + " y_pred1 = list()\n", + " y_test1 = list()\n", + " [rows, clos] = proba.shape\n", + " print([rows, clos])\n", + " for i in range(rows):\n", + " temp = max(proba[i])\n", + " if temp < 0.95:\n", + " continue\n", + " y_pred1.append(y_pred[i])\n", + " y_test1.append(y_test[i])\n", + " f1 = f1_score(y_test1, y_pred1, average='micro')\n", + " recall = recall_score(y_test1, y_pred1, average='micro')\n", + " precision = precision_score(y_test1, y_pred1, average='micro')\n", + " print(len(y_test1) / len(y_test))\n", + " print(precision)\n", + " print(recall)\n", + " print(f1)\n", + "\n", + "'''\n", "lr_classifer = LogisticRegression()\n", "lr_classifer.fit(x_train, y_train)\n", "y_pred = lr_classifer.predict(x_test)\n", @@ -272,14 +293,19 @@ "tr_precision = precision_score(y_test, y_pred, average='micro')\n", "tr_recall = recall_score(y_test, y_pred, average='micro')\n", "tr_f1 = f1_score(y_test, y_pred, average='micro')\n", + "'''\n", "\n", "rf_classifer = RandomForestClassifier()\n", "rf_classifer.fit(x_train, y_train)\n", "y_pred = rf_classifer.predict(x_test)\n", + "proba = rf_classifer.predict_proba(x_test)\n", + "my_pred(y_pred, y_test, proba)\n", + "\n", + "\n", + "'''\n", "rf_precision = precision_score(y_test, y_pred, average='micro')\n", "rf_recall = recall_score(y_test, y_pred, average='micro')\n", "rf_f1 = f1_score(y_test, y_pred, average='micro')\n", - "\n", "score_df = pd.DataFrame(np.zeros((5,3)),index = ['LogisticRegression', 'SVM', 'GaussianNB', 'tree', 'RandomForest'], \\\n", " columns = ['precision', 'recall', 'f1'])\n", "score_df.loc['LogisticRegression'] = [lr_precision, lr_recall, lr_f1]\n", @@ -290,9 +316,19 @@ "ax = score_df.plot.bar(title='type+length+burst markov')\n", "fig = ax.get_figure()\n", "print(score_df)\n", - "#fig.savefig('../figure/type_length_burst.svg')" + "#fig.savefig('../figure/type_length_burst.svg')\n", + "'''" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, diff --git a/Experiment/statFeature/StatFeature.ipynb b/Experiment/statFeature/StatFeature.ipynb index 5da283f..b0573cd 100644 --- a/Experiment/statFeature/StatFeature.ipynb +++ b/Experiment/statFeature/StatFeature.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 31, "metadata": { "collapsed": true }, @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 32, "metadata": { "collapsed": true }, @@ -151,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 33, "metadata": { "collapsed": true }, @@ -197,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 36, "metadata": { "collapsed": true }, @@ -336,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 37, "metadata": { "collapsed": true }, @@ -351,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -393,7 +393,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -407,7 +407,7 @@ "Name: label, dtype: int64" ] }, - "execution_count": 7, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, @@ -415,7 +415,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -431,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -440,18 +440,18 @@ "text": [ "183\n", " precision recall f1\n", - "LogisticRegression 0.773019 0.773019 0.773019\n", - "SVM 0.841328 0.841328 0.841328\n", - "GaussianNB 0.730193 0.730193 0.730193\n", - "tree 0.984154 0.984154 0.984154\n", - "RandomForest 0.988651 0.988651 0.988651\n" + "LogisticRegression 0.775161 0.775161 0.775161\n", + "SVM 0.831906 0.831906 0.831906\n", + "GaussianNB 0.729122 0.729122 0.729122\n", + "tree 0.984582 0.984582 0.984582\n", + "RandomForest 0.989507 0.989507 0.989507\n" ] }, { "data": { - "image/png": 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65vtYkiP2saw9T6IVz/Ukwz6vBL5IMu3gzZlWZNVi+5L722VWRTMV9YTqYknf\nJxnH/aD023jzjGuybG0REX+UpLR57iJJfyKZZ9aK66fAs5KmkPSUORj4frYllaeo4T4M+AZJf/c3\n05lmqn5ORNuklqUn2v8iaTTwN5JrIKzAImKCpEdJ2t0F/FtEvJltVeUpZJs7QDqjSveIeFjSliQT\nMyzOui7LRjqI2hySn+A/Jvn5/fOIeDrTwiwTkkZHxBXp/b0jotVdoVrIcJd0GsmFO5+NiN3Tk2lX\nR8ThGZdmZlWgfjaqhvdbk6I2y5wF7A88AxARf5Hkn+AFJOnXEXGupPtoZC7Vog2WZY1qlVevFzXc\nP46I5fUjDqSzqxTvJ4zBmh4xv8y0Cqs220v6CkmPwm0bzgHRGuZ+KGqzzM9JxjI/iWQ0xFHA7Ii4\nMNPCrCpI+gywS5GuWLa1Sbp+A5sjIka2WDEbqajh3gY4BfgHkp9cDwK/bW2jvlnlpD0ihpL8mp0J\nLAAei4jzs6zLbGMVLtzTPu03RsSIrGux6lE/R6akU0mO2n8k6bnWcCWibTqStif5hd+VtSdxqfoB\n5QrX5h4RqyR1lNQ2IpZnXY9Vjc0k7Uwy/aCb56zeJOBp4Hla2cixhQv3VC3wpKSJrD2N2KWZVWRZ\nu5ikee6JiJgqaTfgLxnXZNlr31qb5grXLAMgqdFLyiPiP1q6FjOrXpLOA5YA97P2OP9VP759IcPd\nrKG0B9V/kkxe8geSUUPPjYjfZVqYZUrSWcBPSHrX1YdltIbRQgsZ7uu5YOUDYBrwm4hY1vJVWZYk\nzYyIPmnf5uOA84ApEdE749IsQ5JeAQZGxDtZ19JcRR3ydz7JT61r09si4C1gj3TZiqd+VNCjgQmt\n4We3tYgXSaZgbHWKekK1b0QcXLJ8n6THI+JgSa1ugCCriPskvUTSLDNKUkfAv+BsFcnculNoZXPr\nFjXcO0raNSJeA0iH/O2QbnP3yAKKiDGSfgYsSrvLfkgymYsV2z3prdUparhfADyRtqcJ6EZytLYV\n4AmRC0jSSSX3Szfd1PLVWLWIiBsltSVpsgWYGxErsqypXIU8oQogqR2wF0m4v+STqMUm6b9LFtsD\nhwMzIuJrGZVkVUDSoSQHfLUkWbEL8O2IeDzDsspSyHBPJ+c4H+gSEael47nvGRH3Z1yaVQlJ2wE3\ne8jfYpM0HfhGRMxNl/cgOeG+X7aVNa2ovWWuJ2lbH5wu15H0cTartxTonnURlrnN64MdICJeppXM\nt1zUNvfLZEJOAAAGFklEQVTdI2KYpOEAEfGRGjS0WrE0uPahDdATuCO7iqxKTJM0njXj/n8TmJ5h\nPWUrargvl7QF6R+zpN0p6eZkhVQ6WcdK4NWIqMuqGKsaZ5LM3HY2SZv748BVmVZUpqK2uR8B/IDk\n6GwyMAQ4OSIezbIuM7NKKWS4A0jaARhE8m38dGu8vNgqR9Ig4L+BHkBboAb4MCK2zbQwy4Sk59nA\n1JutYZz/ojbLEBELgd8DSNpT0k8j4rSMy7LsXAGcCNwJ9CeZoOELmVZkWTom/e9Z6X9L29xbxXAE\nheotI6mXpMmSXpD0n5J2knQX8Edgdtb1WbYiYh5QExGrIuJ64ItZ12TZiIhXI+JVYEhE/GtEPJ/e\nxgBfzrq+chQq3EkGBbsVOJ5kjswZJIOIfSEiLsuyMMvc0vRKxFmSfp6O471V1kVZ5raSdGD9gqQD\naCX/LgrV5l4/rGvJ8utA14hYlWFZVgUkdSEZGbQtyXC/2wLj0qN5KyhJ+wHXAdulq94HRkbEjOyq\nKk/R2tzbS+pLchIVkmF/e9X3cW8N/8OssiQdC3SOiCvT5ceAHUlOpv0ZcLgXWERMB3pL2pbkYPiD\nrGsqV9GO3KdsYHNExGEtVoxVBUlPAidGxOvp8kzgMGBr4PqIODzL+ixb6RhUxwNdKTkYjoiLs6qp\nXIU6co8InyCzhtrWB3vqiXSijnfTUUKt2O4lmaVtOq3sQsdCHbnXS+dFvCUi3k+XPwMMj4hWceWZ\nVY6keRHRaJdHSa9ExO4tXZNVD0kvRMQ+WdexMYrWW6beafXBDhAR7wHu415Mz0ha5/+9pO8C/5dB\nPVZdnpK0b9ZFbIyiHrk/B/SO9M1LqgGei4i9s63MWpqkHUlm2vmYpGsswH5AO+C4iHgrq9ose5Jm\nk1zM9leSfyMiOT9X9VeoFjXcf0FyguRqkl4RZwCvR8QFWdZl2ZF0GFD/5f5iRDySZT1WHdIusutI\nL3CqakUN9zbAd0lm2xHJ4GG/dX93M2tM+guvff1y/fzL1ayQ4W5mVg5JQ4FfAZ8H3ga6AHNaQxNu\nobpCSrojIk5Y34hvraEdzcxa1I9JRo99OCL6SvoiMDzjmspSqHAHzkn/e8wG9zIzS6yIiIWS2khq\nExFTJP0s66LKUaiukBHx9/TuqPpR30pGfxuVZW1mVpXel7Q1yQxMt0gaSzJTV9UrZJu7pBkR0a/B\nuufcLGNmpdKrlD8iORD+JskAYrek80FUtUKFu6QzSY7Qd2ftAaG2AZ6MiBGZFGZmrUJ6TcyJEXFL\n1rU0pWjhvh3wGeCnwJiSTYvT8UTMzEhHgTwL6ARMBB5Kl/8FmBkRx2ZYXlkKFe71JO0O1EXEx5IO\nBXoBN5UOSWBmxSXpXuA9kmGfDyc5KGwLnBMRM7OsrVxFDfeZJPNkdgUeJPlm3jMijs6yLjOrDpKe\nj4h90/s1wDvArhGxONvKyleo3jIlPomIlcBXgV9HxHnAzhnXZGbVY0X9nfTK9b+2pmCH4vVzr7dC\n0nCSGe7/KV23eYb1mFl16S1pUXpfwBbpcv3AYdtmV1p5ihru3yEZLOwnEfFXSd2A32Vck5lViYio\nybqGT6uQbe5mZnlXqCN3jy1jZkVRqCN3STtHxN9b8xjNZmblKFS4m5kVRaGaZepJWsy6zTIfANOA\nCyJifstXZWZWOYUMd+BS4A3gVpKuTScCnwPmAtcBh2ZWmZlZBRSyWUbSMxExsMG6pyNikKRZEdE7\nq9rMzCqhsFeoSjqhfgB+SSeUbCvet52Z5U5Rj9x3A8YCg9NVfwbOA/4G7BcRT2RVm5lZJRQy3M3M\n8q6QzTKSOku6W9Lbkt6SdJekzlnXZWZWKYUMd+B6kmF+P08yGP996Tozs1woZLOMpJkR0aepdWZm\nrVVRj9zfkTRCUk16GwFU/YS3ZmblKuqR+67AFSS9ZQJ4Cjg7Il7LtDAzswopZLg3RtK5EfHrrOsw\nM6sEh3tK0msRsWvWdZiZVUJR29wbo6wLMDOrFIf7Gv4JY2a5UahRIdcz1C+kE+C2cDlmZpuM29zN\nzHLIzTJmZjnkcDczyyGHu5lZDjnczcxyyOFuZpZD/x8yWD3RytIGRQAAAABJRU5ErkJggg==\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -475,6 +475,7 @@ " columns = ['precision', 'recall', 'f1'])\n", "#def a():\n", "\n", + "\n", "f1_score_list = list()\n", "recall_score_list = list()\n", "precision_score_list = list()\n", @@ -499,7 +500,7 @@ "recall_score_list = list()\n", "precision_score_list = list()\n", "for i in range(1):\n", - " #np.random.shuffle(examples)\n", + " np.random.shuffle(examples)\n", " examples_train = examples[:int(len(examples)*0.75)]\n", " examples_test = examples[int(len(examples)*0.75):]\n", " x_train = examples_train[:,0:-1]\n", @@ -559,7 +560,7 @@ "recall_score_list = list()\n", "precision_score_list = list()\n", "for i in range(1):\n", - " #np.random.shuffle(examples)\n", + " np.random.shuffle(examples)\n", " examples_train = examples[:int(len(examples)*0.75)]\n", " examples_test = examples[int(len(examples)*0.75):]\n", " x_train = examples_train[:,0:-1]\n", diff --git a/Presentation/Mid-term/信工所硕士-2017级-中期报告-崔一鸣.pptx 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