analyze_svdfeature_srcvecs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.stats import f_oneway"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('/home/michael/software/svdfeature-1.2.2/demo/basicMF/src_vecs_16.csv', header=None)\n",
    "pal = data[data[1]=='Palestinian']\n",
    "isr = data[data[1]=='Israeli']\n",
    "print(len(pal))\n",
    "print(len(isr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 F-val: 0.224803, p-val: 0.640540\n",
      "2 F-val: 5.028718, p-val: 0.036422\n",
      "3 F-val: 0.024472, p-val: 0.877259\n",
      "4 F-val: 0.468095, p-val: 0.501716\n",
      "5 F-val: 0.076860, p-val: 0.784444\n",
      "6 F-val: 0.208788, p-val: 0.652643\n",
      "7 F-val: 3.395093, p-val: 0.080266\n",
      "8 F-val: 0.139339, p-val: 0.712866\n",
      "9 F-val: 2.353333, p-val: 0.140683\n",
      "10 F-val: 2.990939, p-val: 0.099137\n",
      "11 F-val: 0.011438, p-val: 0.915897\n",
      "12 F-val: 0.015633, p-val: 0.901746\n",
      "13 F-val: 0.292638, p-val: 0.594510\n",
      "14 F-val: 0.004167, p-val: 0.949169\n",
      "15 F-val: 0.029553, p-val: 0.865236\n",
      "16 F-val: 2.643421, p-val: 0.119634\n"
     ]
    }
   ],
   "source": [
    "# Do ANOVA for each factor\n",
    "for i in range(2, 18):\n",
    "    print(i-1, \"F-val: {:f}, p-val: {:f}\".format(*f_oneway(pal[i], isr[i])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9mNGefZd7j30t6VgZnLgXoISyxqguJ+hxSj7ol3OZqES5Lk+XSjLQlxLEOG5nXkoplUxr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\nME7cZmaFceI2MyuME7eZWWH+H/fWXVcC6lihAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8cff8eda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.title('Palestinian factors')\n",
    "plt.boxplot(pal.loc[:, range(2, 18)].values)\n",
    "plt.savefig('/home/michael/software/svdfeature-1.2.2/demo/basicMF/pal_16.png', dpi=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8cff8e630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.title(\"Israeli factors\")\n",
    "plt.boxplot(isr.loc[:, range(2, 18)].values)\n",
    "plt.savefig('/home/michael/software/svdfeature-1.2.2/demo/basicMF/isr_16.png', dpi=400)"
   ]
  }
 ],
 "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": "3.4.3+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}