comparison notebooks/test_hubness.ipynb @ 71:04fc6e809a42 branch-tests

notebooks
author mpanteli <m.x.panteli@gmail.com>
date Fri, 22 Sep 2017 18:03:41 +0100
parents b0e194bfb71d
children 930c35ab894c
comparison
equal deleted inserted replaced
65:9b10b688c2ac 71:04fc6e809a42
1 { 1 {
2 "cells": [ 2 "cells": [
3 { 3 {
4 "cell_type": "code", 4 "cell_type": "code",
5 "execution_count": 1, 5 "execution_count": 16,
6 "metadata": { 6 "metadata": {},
7 "collapsed": true 7 "outputs": [
8 }, 8 {
9 "outputs": [], 9 "name": "stdout",
10 "output_type": "stream",
11 "text": [
12 "The autoreload extension is already loaded. To reload it, use:\n",
13 " %reload_ext autoreload\n"
14 ]
15 }
16 ],
10 "source": [ 17 "source": [
11 "import numpy as np\n", 18 "import numpy as np\n",
12 "import pickle\n", 19 "import pickle\n",
13 "from scipy.stats import pearsonr\n", 20 "from scipy.stats import pearsonr\n",
14 "from scipy.stats import skew\n", 21 "from scipy.stats import skew\n",
26 "import scripts.utils as utils" 33 "import scripts.utils as utils"
27 ] 34 ]
28 }, 35 },
29 { 36 {
30 "cell_type": "code", 37 "cell_type": "code",
31 "execution_count": 3, 38 "execution_count": 17,
32 "metadata": { 39 "metadata": {
33 "collapsed": true 40 "collapsed": true
34 }, 41 },
35 "outputs": [], 42 "outputs": [],
36 "source": [ 43 "source": [
44 "#df_global, threshold, MD = outliers.get_outliers_df(X, Y, chi2thr=0.999)" 51 "#df_global, threshold, MD = outliers.get_outliers_df(X, Y, chi2thr=0.999)"
45 ] 52 ]
46 }, 53 },
47 { 54 {
48 "cell_type": "code", 55 "cell_type": "code",
49 "execution_count": 4, 56 "execution_count": 18,
50 "metadata": {}, 57 "metadata": {},
51 "outputs": [ 58 "outputs": [
52 { 59 {
53 "data": { 60 "data": {
54 "text/plain": [ 61 "text/plain": [
55 "(8200, 380)" 62 "(8200, 380)"
56 ] 63 ]
57 }, 64 },
58 "execution_count": 4, 65 "execution_count": 18,
59 "metadata": {}, 66 "metadata": {},
60 "output_type": "execute_result" 67 "output_type": "execute_result"
61 } 68 }
62 ], 69 ],
63 "source": [ 70 "source": [
77 "metadata": { 84 "metadata": {
78 "collapsed": true 85 "collapsed": true
79 }, 86 },
80 "outputs": [], 87 "outputs": [],
81 "source": [ 88 "source": [
82 "D = pairwise_distances(X, metric='mahalanobis')" 89 "D = pairwise_distances(X, metric='mahalanobis')\n",
83 ] 90 "np.savetxt('../data/D_mahal.csv', D)"
84 }, 91 ]
85 { 92 },
86 "cell_type": "code", 93 {
87 "execution_count": 5, 94 "cell_type": "code",
95 "execution_count": 19,
88 "metadata": {}, 96 "metadata": {},
89 "outputs": [ 97 "outputs": [
90 { 98 {
91 "data": { 99 "data": {
92 "text/plain": [ 100 "text/plain": [
93 "(8200, 8200)" 101 "(8200, 8200)"
94 ] 102 ]
95 }, 103 },
96 "execution_count": 5, 104 "execution_count": 19,
97 "metadata": {}, 105 "metadata": {},
98 "output_type": "execute_result" 106 "output_type": "execute_result"
99 } 107 }
100 ], 108 ],
101 "source": [ 109 "source": [
102 "np.savetxt('../data/D_mahal.csv', D)\n",
103 "D = np.loadtxt('../data/D_mahal.csv')\n", 110 "D = np.loadtxt('../data/D_mahal.csv')\n",
104 "D.shape" 111 "D.shape"
105 ] 112 ]
106 }, 113 },
107 { 114 {