{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# CS579: Lecture 17 \n", "\n", "**Text Regression II** \n", "*[Dr. Aron Culotta](http://cs.iit.edu/~culotta)* \n", "*[Illinois Institute of Technology](http://iit.edu)*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Today, we'll look more closely at the flu-tracking example." ] }, { "cell_type": "code", "execution_count": 197, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "nice exclamationpoint looking forward to seeing the piece on you twittelator dude exclamationpoint congratulations exclamationpoint \r\n" ] } ], "source": [ "RAW_DATA='tweets' # I haven't placed this directory in GitHub, since it's so large.\n", "# Data looks like this. (first 10 lines of second file)\n", "! head `ls $RAW_DATA/* | head -1 `\n", "# This is already tokenized." ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "reading tweets/2009-08-20\n", "read 15 tokens for 13 words\n", "reading tweets/2009-09-01\n", "read 112851 tokens for 14763 words\n", "reading tweets/2009-09-02\n", "read 113951 tokens for 15052 words\n", "reading tweets/2009-09-03\n", "read 114283 tokens for 14908 words\n", "reading tweets/2009-09-04\n", "read 112864 tokens for 14953 words\n", "reading tweets/2009-09-05\n", "read 110453 tokens for 15044 words\n", "reading tweets/2009-09-06\n", "read 109191 tokens for 14659 words\n", "reading tweets/2009-09-07\n", "read 112170 tokens for 14899 words\n", "reading tweets/2009-09-08\n", "read 107959 tokens for 14384 words\n", "reading tweets/2009-09-09\n", "read 112892 tokens for 14759 words\n", "reading tweets/2009-09-10\n", "read 111702 tokens for 14846 words\n", "reading tweets/2009-09-11\n", "read 110466 tokens for 14655 words\n", "reading tweets/2009-09-12\n", "read 109467 tokens for 14993 words\n", "reading tweets/2009-09-13\n", "read 110850 tokens for 14541 words\n", "reading tweets/2009-09-14\n", "read 105590 tokens for 13606 words\n", "reading tweets/2009-09-15\n", "read 111750 tokens for 14504 words\n", "reading tweets/2009-09-16\n", "read 111442 tokens for 15051 words\n", "reading tweets/2009-09-17\n", "read 113606 tokens for 14909 words\n", "reading tweets/2009-09-18\n", "read 110347 tokens for 14972 words\n", "reading tweets/2009-09-19\n", "read 109105 tokens for 15182 words\n", "reading tweets/2009-09-20\n", "read 109844 tokens for 15312 words\n", "reading tweets/2009-09-21\n", "read 110194 tokens for 14288 words\n", "reading tweets/2009-09-22\n", "read 110845 tokens for 14686 words\n", "reading tweets/2009-09-23\n", "read 110829 tokens for 14957 words\n", "reading tweets/2009-09-24\n", "read 112000 tokens for 15084 words\n", "reading tweets/2009-09-25\n", "read 111658 tokens for 15142 words\n", "reading tweets/2009-09-26\n", "read 109578 tokens for 15130 words\n", "reading tweets/2009-09-27\n", "read 109567 tokens for 14847 words\n", "reading tweets/2009-09-28\n", "read 108502 tokens for 14792 words\n", "reading tweets/2009-09-29\n", "read 109033 tokens for 14688 words\n", "reading tweets/2009-09-30\n", "read 110650 tokens for 14927 words\n", "reading tweets/2009-10-01\n", "read 112405 tokens for 15257 words\n", "reading tweets/2009-10-02\n", "read 106972 tokens for 14938 words\n", "reading tweets/2009-10-03\n", "read 109021 tokens for 15192 words\n", "reading tweets/2009-10-04\n", "read 109088 tokens for 15046 words\n", "reading tweets/2009-10-05\n", "read 109948 tokens for 14630 words\n", "reading tweets/2009-10-06\n", "read 108938 tokens for 15085 words\n", "reading tweets/2009-10-07\n", "read 110704 tokens for 15026 words\n", "reading tweets/2009-10-08\n", "read 111030 tokens for 14569 words\n", "reading tweets/2009-10-09\n", "read 110342 tokens for 15003 words\n", "reading tweets/2009-10-10\n", "read 108434 tokens for 15240 words\n", "reading tweets/2009-10-11\n", "read 107951 tokens for 14404 words\n", "reading tweets/2009-10-12\n", "read 110014 tokens for 14460 words\n", "reading tweets/2009-10-13\n", "read 110369 tokens for 14810 words\n", "reading tweets/2009-10-14\n", "read 109465 tokens for 14697 words\n", "reading tweets/2009-10-15\n", "read 110649 tokens for 15208 words\n", "reading tweets/2009-10-16\n", "read 110487 tokens for 14885 words\n", "reading tweets/2009-10-17\n", "read 108903 tokens for 14985 words\n", "reading tweets/2009-10-18\n", "read 107276 tokens for 14622 words\n", "reading tweets/2009-10-19\n", "read 108847 tokens for 14393 words\n", "reading tweets/2009-10-20\n", "read 111058 tokens for 14902 words\n", "reading tweets/2009-10-21\n", "read 110852 tokens for 15182 words\n", "reading tweets/2009-10-22\n", "read 111766 tokens for 15212 words\n", "reading tweets/2009-10-23\n", "read 109688 tokens for 15035 words\n", "reading tweets/2009-10-24\n", "read 107894 tokens for 15024 words\n", "reading tweets/2009-10-25\n", "read 107384 tokens for 14907 words\n", "reading tweets/2009-10-26\n", "read 109615 tokens for 14556 words\n", "reading tweets/2009-10-27\n", "read 110153 tokens for 15033 words\n", "reading tweets/2009-10-28\n", "read 108262 tokens for 14597 words\n", "reading tweets/2009-10-29\n", "read 111013 tokens for 14915 words\n", "reading tweets/2009-10-30\n", "read 108500 tokens for 14935 words\n", "reading tweets/2009-10-31\n", "read 108171 tokens for 14945 words\n", "reading tweets/2009-11-01\n", "read 107784 tokens for 14427 words\n", "reading tweets/2009-11-02\n", "read 110849 tokens for 14896 words\n", "reading tweets/2009-11-03\n", "read 113113 tokens for 15444 words\n", "reading tweets/2009-11-04\n", "read 112538 tokens for 15397 words\n", "reading tweets/2009-11-05\n", "read 112720 tokens for 15653 words\n", "reading tweets/2009-11-06\n", "read 112489 tokens for 15500 words\n", "reading tweets/2009-11-07\n", "read 110574 tokens for 15486 words\n", "reading tweets/2009-11-08\n", "read 110931 tokens for 15102 words\n", "reading tweets/2009-11-09\n", "read 108961 tokens for 14805 words\n", "reading tweets/2009-11-10\n", "read 109584 tokens for 15228 words\n", "reading tweets/2009-11-11\n", "read 112315 tokens for 15360 words\n", "reading tweets/2009-11-12\n", "read 108823 tokens for 15185 words\n", "reading tweets/2009-11-13\n", "read 111545 tokens for 15220 words\n", "reading tweets/2009-11-14\n", "read 108765 tokens for 15118 words\n", "reading tweets/2009-11-15\n", "read 108385 tokens for 14508 words\n", "reading tweets/2009-11-16\n", "read 109166 tokens for 15028 words\n", "reading tweets/2009-11-17\n", "read 109148 tokens for 15219 words\n", "reading tweets/2009-11-18\n", "read 111023 tokens for 15224 words\n", "reading tweets/2009-11-19\n", "read 111777 tokens for 15455 words\n", "reading tweets/2009-11-20\n", "read 112980 tokens for 15041 words\n", "reading tweets/2009-11-21\n", "read 109907 tokens for 15290 words\n", "reading tweets/2009-11-22\n", "read 111542 tokens for 15400 words\n", "reading tweets/2009-11-23\n", "read 114745 tokens for 15708 words\n", "reading tweets/2009-11-24\n", "read 109427 tokens for 15371 words\n", "reading tweets/2009-11-25\n", "read 110655 tokens for 15125 words\n", "reading tweets/2009-11-26\n", "read 108549 tokens for 15261 words\n", "reading tweets/2009-11-27\n", "read 105793 tokens for 15308 words\n", "reading tweets/2009-11-28\n", "read 108411 tokens for 14984 words\n", "reading tweets/2009-11-29\n", "read 107720 tokens for 14769 words\n", "reading tweets/2009-11-30\n", "read 113674 tokens for 13752 words\n", "reading tweets/2009-12-01\n", "read 110829 tokens for 14878 words\n", "reading tweets/2009-12-02\n", "read 110234 tokens for 14965 words\n", "reading tweets/2009-12-03\n", "read 111591 tokens for 15426 words\n", "reading tweets/2009-12-04\n", "read 111643 tokens for 15606 words\n", "reading tweets/2009-12-05\n", "read 110992 tokens for 15261 words\n", "reading tweets/2009-12-06\n", "read 107936 tokens for 14714 words\n", "reading tweets/2009-12-07\n", "read 108016 tokens for 14710 words\n", "reading tweets/2009-12-08\n", "read 111097 tokens for 15161 words\n", "reading tweets/2009-12-09\n", "read 111591 tokens for 15421 words\n", "reading tweets/2009-12-10\n", "read 113669 tokens for 15528 words\n", "reading tweets/2009-12-11\n", "read 111335 tokens for 15556 words\n", "reading tweets/2009-12-12\n", "read 110497 tokens for 15314 words\n", "reading tweets/2009-12-13\n", "read 107376 tokens for 14779 words\n", "reading tweets/2009-12-14\n", "read 107704 tokens for 14675 words\n", "reading tweets/2009-12-15\n", "read 111002 tokens for 15353 words\n", "reading tweets/2009-12-16\n", "read 110423 tokens for 15217 words\n", "reading tweets/2009-12-17\n", "read 110182 tokens for 15463 words\n", "reading tweets/2009-12-18\n", "read 108764 tokens for 15160 words\n", "reading tweets/2009-12-19\n", "read 111658 tokens for 14978 words\n", "reading tweets/2009-12-21\n", "read 110391 tokens for 14970 words\n", "reading tweets/2009-12-22\n", "read 110550 tokens for 15227 words\n", "reading tweets/2009-12-23\n", "read 110270 tokens for 15510 words\n", "reading tweets/2009-12-24\n", "read 109092 tokens for 15080 words\n", "reading tweets/2009-12-25\n", "read 103392 tokens for 13208 words\n", "reading tweets/2009-12-26\n", "read 106601 tokens for 13880 words\n", "reading tweets/2009-12-27\n", "read 108230 tokens for 14697 words\n", "reading tweets/2009-12-28\n", "read 108223 tokens for 14695 words\n", "reading tweets/2009-12-29\n", "read 109394 tokens for 15230 words\n", "reading tweets/2009-12-30\n", "read 109112 tokens for 15206 words\n", "reading tweets/2009-12-31\n", "read 110775 tokens for 15241 words\n", "reading tweets/2010-01-01\n", "read 105552 tokens for 12742 words\n", "reading tweets/2010-01-02\n", "read 107839 tokens for 14489 words\n", "reading tweets/2010-01-03\n", "read 107679 tokens for 14454 words\n", "reading tweets/2010-01-04\n", "read 110156 tokens for 14767 words\n", "reading tweets/2010-01-05\n", "read 112415 tokens for 15393 words\n", "reading tweets/2010-01-06\n", "read 112478 tokens for 15169 words\n", "reading tweets/2010-01-07\n", "read 108945 tokens for 15068 words\n", "reading tweets/2010-01-08\n", "read 113086 tokens for 15065 words\n", "reading tweets/2010-01-09\n", "read 107570 tokens for 15224 words\n", "reading tweets/2010-01-10\n", "read 106221 tokens for 14715 words\n", "reading tweets/2010-01-11\n", "read 108903 tokens for 14755 words\n", "reading tweets/2010-01-12\n", "read 111074 tokens for 15686 words\n", "reading tweets/2010-01-13\n", "read 110037 tokens for 15250 words\n", "reading tweets/2010-01-14\n", "read 112219 tokens for 15605 words\n", "reading tweets/2010-01-15\n", "read 111010 tokens for 15431 words\n", "reading tweets/2010-01-16\n", "read 108836 tokens for 15246 words\n", "reading tweets/2010-01-17\n", "read 107107 tokens for 14891 words\n", "reading tweets/2010-01-18\n", "read 107102 tokens for 14582 words\n", "reading tweets/2010-01-19\n", "read 111386 tokens for 15483 words\n", "reading tweets/2010-01-20\n", "read 110250 tokens for 15395 words\n", "reading tweets/2010-01-21\n", "read 111415 tokens for 15167 words\n", "reading tweets/2010-01-22\n", "read 109265 tokens for 15506 words\n", "reading tweets/2010-01-23\n", "read 109175 tokens for 15430 words\n", "reading tweets/2010-01-24\n", "read 106465 tokens for 15237 words\n", "reading tweets/2010-01-25\n", "read 103069 tokens for 13985 words\n", "reading tweets/2010-01-26\n", "read 108982 tokens for 15225 words\n", "reading tweets/2010-01-27\n", "read 110316 tokens for 15479 words\n", "reading tweets/2010-01-28\n", "read 110848 tokens for 15546 words\n", "reading tweets/2010-01-29\n", "read 109563 tokens for 15522 words\n", "reading tweets/2010-01-30\n", "read 107295 tokens for 15301 words\n", "reading tweets/2010-01-31\n", "read 106842 tokens for 15009 words\n", "reading tweets/2010-02-01\n", "read 105056 tokens for 14454 words\n", "reading tweets/2010-02-02\n", "read 109074 tokens for 15296 words\n", "reading tweets/2010-02-03\n", "read 108897 tokens for 15543 words\n", "reading tweets/2010-02-04\n", "read 114662 tokens for 14484 words\n", "reading tweets/2010-02-05\n", "read 108489 tokens for 15573 words\n", "reading tweets/2010-02-06\n", "read 107277 tokens for 15353 words\n", "reading tweets/2010-02-07\n", "read 106984 tokens for 15233 words\n", "reading tweets/2010-02-08\n", "read 95307 tokens for 12138 words\n", "reading tweets/2010-02-09\n", "read 108689 tokens for 15435 words\n", "reading tweets/2010-02-10\n", "read 110216 tokens for 15337 words\n", "reading tweets/2010-02-11\n", "read 110492 tokens for 15466 words\n", "reading tweets/2010-02-12\n", "read 110314 tokens for 15300 words\n", "reading tweets/2010-02-13\n", "read 107124 tokens for 15083 words\n", "reading tweets/2010-02-14\n", "read 106555 tokens for 15021 words\n", "reading tweets/2010-02-15\n", "read 106150 tokens for 14705 words\n", "reading tweets/2010-02-16\n", "read 109076 tokens for 15242 words\n", "reading tweets/2010-02-17\n", "read 111034 tokens for 15072 words\n", "reading tweets/2010-02-18\n", "read 107711 tokens for 15611 words\n", "reading tweets/2010-02-19\n", "read 108507 tokens for 15698 words\n", "reading tweets/2010-02-20\n", "read 106115 tokens for 15525 words\n", "reading tweets/2010-02-21\n", "read 106177 tokens for 15525 words\n", "reading tweets/2010-02-22\n", "read 104792 tokens for 14805 words\n", "reading tweets/2010-02-23\n", "read 107749 tokens for 15288 words\n", "reading tweets/2010-02-24\n", "read 107716 tokens for 15650 words\n", "reading tweets/2010-02-25\n", "read 109616 tokens for 15314 words\n", "reading tweets/2010-03-02\n", "read 112267 tokens for 16106 words\n", "reading tweets/2010-03-03\n", "read 108426 tokens for 15716 words\n", "reading tweets/2010-03-04\n", "read 108858 tokens for 15910 words\n", "reading tweets/2010-03-05\n", "read 107379 tokens for 15781 words\n", "reading tweets/2010-03-06\n", "read 104583 tokens for 15457 words\n", "reading tweets/2010-03-07\n", "read 104829 tokens for 15535 words\n", "reading tweets/2010-03-08\n", "read 106083 tokens for 14918 words\n", "reading tweets/2010-03-09\n", "read 108228 tokens for 15684 words\n", "reading tweets/2010-03-11\n", "read 109565 tokens for 15747 words\n", "reading tweets/2010-03-12\n", "read 108226 tokens for 15522 words\n", "reading tweets/2010-03-13\n", "read 106277 tokens for 15620 words\n", "reading tweets/2010-03-14\n", "read 105333 tokens for 15189 words\n", "reading tweets/2010-03-15\n", "read 106531 tokens for 14902 words\n", "reading tweets/2010-03-16\n", "read 105982 tokens for 15450 words\n", "reading tweets/2010-03-17\n", "read 109162 tokens for 15527 words\n", "reading tweets/2010-03-18\n", "read 109265 tokens for 15504 words\n", "reading tweets/2010-03-19\n", "read 106696 tokens for 15659 words\n", "reading tweets/2010-03-20\n", "read 106340 tokens for 15700 words\n", "reading tweets/2010-03-21\n", "read 108759 tokens for 15059 words\n", "reading tweets/2010-03-22\n", "read 107921 tokens for 15188 words\n", "reading tweets/2010-03-23\n", "read 105984 tokens for 15124 words\n", "reading tweets/2010-03-24\n", "read 107987 tokens for 15361 words\n", "reading tweets/2010-03-25\n", "read 106502 tokens for 15628 words\n", "reading tweets/2010-03-26\n", "read 105383 tokens for 15431 words\n", "reading tweets/2010-03-27\n", "read 104627 tokens for 15382 words\n", "reading tweets/2010-03-28\n", "read 103414 tokens for 14971 words\n", "reading tweets/2010-03-29\n", "read 106617 tokens for 14944 words\n", "reading tweets/2010-03-30\n", "read 106517 tokens for 15128 words\n", "reading tweets/2010-03-31\n", "read 107741 tokens for 15214 words\n", "reading tweets/2010-04-01\n", "read 105481 tokens for 15197 words\n", "reading tweets/2010-04-02\n", "read 106309 tokens for 15209 words\n", "reading tweets/2010-04-03\n", "read 104793 tokens for 15360 words\n", "reading tweets/2010-04-04\n", "read 104770 tokens for 15170 words\n", "reading tweets/2010-04-05\n", "read 105540 tokens for 14773 words\n", "reading tweets/2010-04-06\n", "read 106829 tokens for 15027 words\n", "reading tweets/2010-04-07\n", "read 106723 tokens for 15452 words\n", "reading tweets/2010-04-08\n", "read 107946 tokens for 15754 words\n", "reading tweets/2010-04-09\n", "read 107657 tokens for 15335 words\n", "reading tweets/2010-04-10\n", "read 103465 tokens for 15589 words\n", "reading tweets/2010-04-11\n", "read 104499 tokens for 15463 words\n", "reading tweets/2010-04-12\n", "read 103600 tokens for 14603 words\n", "reading tweets/2010-04-13\n", "read 107547 tokens for 15618 words\n", "reading tweets/2010-04-14\n", "read 107136 tokens for 15539 words\n", "reading tweets/2010-04-15\n", "read 107014 tokens for 15359 words\n", "reading tweets/2010-04-16\n", "read 104346 tokens for 15293 words\n", "reading tweets/2010-04-17\n", "read 103672 tokens for 15308 words\n", "reading tweets/2010-04-18\n", "read 105339 tokens for 15222 words\n", "reading tweets/2010-04-19\n", "read 103085 tokens for 14678 words\n", "reading tweets/2010-04-20\n", "read 104852 tokens for 15291 words\n", "reading tweets/2010-04-21\n", "read 106207 tokens for 15486 words\n", "reading tweets/2010-04-22\n", "read 105254 tokens for 15170 words\n", "reading tweets/2010-04-23\n", "read 107802 tokens for 14876 words\n", "reading tweets/2010-04-24\n", "read 104366 tokens for 15686 words\n", "reading tweets/2010-04-25\n", "read 103182 tokens for 15230 words\n", "reading tweets/2010-04-26\n", "read 103105 tokens for 14824 words\n", "reading tweets/2010-04-27\n", "read 106444 tokens for 15230 words\n", "reading tweets/2010-04-28\n", "read 103120 tokens for 15256 words\n", "reading tweets/2010-04-29\n", "read 104974 tokens for 15397 words\n", "reading tweets/2010-04-30\n", "read 106525 tokens for 15585 words\n", "reading tweets/2010-05-01\n", "read 105375 tokens for 15288 words\n", "reading tweets/2010-05-02\n", "read 106362 tokens for 15147 words\n", "reading tweets/2010-05-03\n", "read 105517 tokens for 14865 words\n", "reading tweets/2010-05-04\n", "read 104905 tokens for 15254 words\n", "reading tweets/2010-05-05\n", "read 105700 tokens for 15480 words\n", "reading tweets/2010-05-06\n", "read 105047 tokens for 15206 words\n" ] } ], "source": [ "# Read tweets. One file per day, one tweet per line.\n", "# Store a list of Counter objects, tracking the frequency of terms for each day.\n", "from collections import Counter\n", "import glob\n", "import io\n", "import os\n", "import subprocess\n", "import sys\n", "\n", "def read_data():\n", " counts = []\n", " dates = []\n", " lines = []\n", " # For demonstration purposes, we'll only read the first 10K lines of a file.\n", " max_lines = 10000\n", " for filename in glob.glob(RAW_DATA + '/*'):\n", " print 'reading', filename\n", " lines.append(file_length(filename))\n", " dates.append(os.path.basename(filename))\n", " this_counter = Counter()\n", " line_ct = 0\n", " for line in io.open(filename, 'rt', encoding='utf8'):\n", " this_counter.update(line.split())\n", " line_ct += 1\n", " if line_ct > max_lines:\n", " break\n", " print 'read %d tokens for %d words' % (sum(this_counter.values()), \n", " len(this_counter.keys()))\n", " counts.append(this_counter)\n", " return counts, dates, lines\n", " \n", "\n", "def file_length(filename):\n", " p = subprocess.Popen(['wc', '-l', filename], stdout=subprocess.PIPE, \n", " stderr=subprocess.PIPE)\n", " result, err = p.communicate()\n", " if p.returncode != 0:\n", " raise IOError(err)\n", " return int(result.strip().split()[0])\n", "\n", "counts, dates, lines = read_data()" ] }, { "cell_type": "code", "execution_count": 200, "metadata": {}, "outputs": [], "source": [ "# Convert to np arrays.\n", "import numpy as np\n", "\n", "counts = np.array(counts)\n", "dates = np.array(dates)\n", "lines = np.array(lines)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Pickle everything\n", "import pickle\n", "pickle.dump((counts, dates, lines), open('data.pkl', 'wb'))\n", "# ~73Mb" ] }, { "cell_type": "code", "execution_count": 204, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "243 days of tweets\n" ] } ], "source": [ "print '%d days of tweets' % len(dates)" ] }, { "cell_type": "code", "execution_count": 205, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "26302164 total tokens\n", "97329 total unique words\n" ] } ], "source": [ "print sum(sum(c.values()) for c in counts), 'total tokens'\n", "words = set()\n", "for c in counts:\n", " words.update(c.keys())\n", "print len(words), 'total unique words'" ] }, { "cell_type": "code", "execution_count": 206, "metadata": {}, "outputs": [ { "data": { "image/png": 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CO1+/3snwgpgpGTbAwrxzZ36nIwAsXVq6YHtl2OPjzj4VRaltyjqXSH8/cOaZjmj4kXbB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\nomcBPEpEA0R0aQxttGN1E9GPiegAgH0AniWirUS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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot occurrences of 'flu'\n", "import matplotlib.pyplot as plt\n", "\n", "def get_trend(counts, term):\n", " return np.array([c[term] for c in counts])\n", "\n", "def plot_trend(x, y, title):\n", " plt.figure()\n", " plt.plot(y)\n", " plt.xticks(range(len(x))[::20], x[::20], rotation='90')\n", " plt.title(title)\n", " plt.show()\n", " \n", "flu_trend = get_trend(counts, 'flu')\n", "plot_trend(dates, flu_trend, 'flu')\n" ] }, { "cell_type": "code", "execution_count": 208, "metadata": {}, "outputs": [ { "data": { "image/png": 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c+Mxn4iuIWNFC1NPDLu6AA4CjjmKR+eY3eftVq4ClSzkb0n78aJRDkrGYec2d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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Normalize by number of tweets per day.\n", "norm_flu_trend = 1. * flu_trend / lines\n", "\n", "def plot_trends(x, y1, y2, label1, label2):\n", " # Plot two trends with different y-scales\n", " fig, ax1 = plt.subplots()\n", " ax1.plot(y1, 'b.-', label=label1)\n", " ax1.set_ylabel(label1, color='b')\n", " plt.xticks(range(len(x))[::20], x[::20], rotation='90')\n", " ax2 = ax1.twinx()\n", " ax2.plot(y2, 'g.-', label=label2)\n", " ax2.set_ylabel(label2, color='g')\n", " plt.show()\n", "\n", "plot_trends(dates, flu_trend, norm_flu_trend, 'flu', 'norm_flu')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Need to smooth! **\n", "\n", "We'll group by week rather than day to deal with the high variance of counts." ] }, { "cell_type": "code", "execution_count": 209, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2009-08-29,2009-09-05,0.03475\r\n", "2009-09-05,2009-09-12,0.04060\r\n", "2009-09-12,2009-09-19,0.04248\r\n", "2009-09-19,2009-09-26,0.04189\r\n", "2009-09-26,2009-10-03,0.04869\r\n", "2009-10-03,2009-10-10,0.06076\r\n", "2009-10-10,2009-10-17,0.07028\r\n", "2009-10-17,2009-10-24,0.07688\r\n", "2009-10-24,2009-10-31,0.07514\r\n", "2009-11-01,2009-11-07,0.06615\r\n" ] } ], "source": [ "# Read CDC's ILI data, which looks like this:\n", "!head ili.csv" ] }, { "cell_type": "code", "execution_count": 210, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['2009-08-29', '2009-09-05', '0.03475']\n" ] } ], "source": [ "ili_dates = [l.strip().split(',') for l in open('ili.csv')]\n", "print ili_dates[0]" ] }, { "cell_type": "code", "execution_count": 211, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "got 5 dates from 2009-08-29 to 2009-09-05\n", "got 7 dates from 2009-09-05 to 2009-09-12\n", "got 7 dates from 2009-09-12 to 2009-09-19\n", "got 7 dates from 2009-09-19 to 2009-09-26\n", "got 7 dates from 2009-09-26 to 2009-10-03\n", "got 7 dates from 2009-10-03 to 2009-10-10\n", "got 7 dates from 2009-10-10 to 2009-10-17\n", "got 7 dates from 2009-10-17 to 2009-10-24\n", "got 7 dates from 2009-10-24 to 2009-10-31\n", "got 6 dates from 2009-11-01 to 2009-11-07\n", "got 7 dates from 2009-11-07 to 2009-11-14\n", "got 7 dates from 2009-11-14 to 2009-11-21\n", "got 7 dates from 2009-11-21 to 2009-11-28\n", "got 7 dates from 2009-11-28 to 2009-12-05\n", "got 7 dates from 2009-12-05 to 2009-12-12\n", "got 7 dates from 2009-12-12 to 2009-12-19\n", "got 6 dates from 2009-12-19 to 2009-12-26\n", "got 7 dates from 2009-12-26 to 2010-01-02\n", "got 7 dates from 2010-01-02 to 2010-01-09\n", "got 7 dates from 2010-01-09 to 2010-01-16\n", "got 7 dates from 2010-01-16 to 2010-01-23\n", "got 7 dates from 2010-01-23 to 2010-01-30\n", "got 7 dates from 2010-01-30 to 2010-02-06\n", "got 7 dates from 2010-02-06 to 2010-02-13\n", "got 7 dates from 2010-02-13 to 2010-02-20\n", "got 5 dates from 2010-02-20 to 2010-02-27\n", "got 5 dates from 2010-02-27 to 2010-03-06\n", "got 6 dates from 2010-03-06 to 2010-03-13\n", "got 7 dates from 2010-03-13 to 2010-03-20\n", "got 7 dates from 2010-03-20 to 2010-03-27\n", "got 7 dates from 2010-03-27 to 2010-04-03\n", "got 7 dates from 2010-04-03 to 2010-04-10\n", "got 7 dates from 2010-04-10 to 2010-04-17\n", "got 7 dates from 2010-04-17 to 2010-04-24\n", "got 7 dates from 2010-04-24 to 2010-05-01\n", "got 5 dates from 2010-05-01 to 2010-05-08\n" ] } ], "source": [ "# Sum together tweet data for each week.\n", "\n", "def get_week_counts(counts, dates, ili_dates, lines):\n", " week_counts = []\n", " week_dates = []\n", " week_lines = []\n", " for start, end, value in ili_dates:\n", " week_dates.append(end)\n", " this_counts, this_lines = get_counts_in_range(counts, lines, dates, start, end)\n", " week_counts.append(this_counts)\n", " week_lines.append(this_lines)\n", " return np.array(week_counts), np.array(week_dates), np.array(week_lines)\n", "\n", "def get_counts_in_range(counts, lines, dates, start, end):\n", " indices = [i for i, v in enumerate(dates) \n", " if dates[i] > start and dates[i] <= end]\n", " print 'got %d dates from %s to %s' % (len(indices), start, end)\n", " sumc = Counter()\n", " for cts in counts[indices]:\n", " sumc.update(cts)\n", " return sumc, sum(lines[indices])\n", " \n", "week_counts, week_dates, week_lines = get_week_counts(counts, dates, ili_dates, lines)" ] }, { "cell_type": "code", "execution_count": 212, "metadata": {}, "outputs": [ { "data": { "image/png": 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ob76BHXYIJbjMF/b8+fDyyyGpDxsGHTuGL/yjj4a994YmTRodOlB7IsfdG/wD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\nAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "flu_trend = get_trend(week_counts, 'flu')\n", "plot_trend(week_dates, flu_trend, 'flu')" ] }, { "cell_type": "code", "execution_count": 213, "metadata": {}, "outputs": [ { "data": { "image/png": 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ySf/lP/1UNeqaezX5f08U6b7/2/A/7fZGN1VVbfZ8M92ctblI5UvDiswV2vrl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\nZSgim1T18sDzGsAyVb1aRBoBy1X1Mm8jNCZ6rAKNMe7z6wS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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "norm_flu_trend = 1. * flu_trend / week_lines\n", "plot_trends(week_dates, flu_trend, norm_flu_trend, 'flu', 'norm_flu')" ] }, { "cell_type": "code", "execution_count": 214, "metadata": {}, "outputs": [ { "data": { "image/png": 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kXJXUHs2vx5o9a8qdf9YsuPZa57hvX/j66xBX0BhTo1hgDIO6deHRR+Evf/Ev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\neASoB1ZExJ+l7WOAR4H3AruAeRHxSoUYXRjN7IT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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Compare norm_flu_trend and ILI data\n", "ilis = [float(v[2]) for v in ili_dates]\n", "plot_trends(dates, norm_flu_trend, ilis, 'flu', 'ILI')" ] }, { "cell_type": "code", "execution_count": 215, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0.92186448815003175, 1.4721218653313829e-15)\n" ] } ], "source": [ "# What is correlation?\n", "from scipy.stats.stats import pearsonr\n", "print pearsonr(norm_flu_trend, ilis)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "r = \\frac{\\sum_{i=1}^n (x_i - \\bar{x})(y_i - \\bar{y})}{\\left(\\sum_{i=1}^n(x_i - \\bar{x})^2\\right)^.5 \\left(\\sum_{i=1}^n(y_i - \\bar{y})^2\\right)^.5}\n", "$$" ] }, { "cell_type": "code", "execution_count": 216, "metadata": {}, "outputs": [ { "data": { "image/png": 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ESllaFl5rDPQTQqS77h89ejTD3hrGNQXXsG5ZeU0Rw+jYEWIzQmtO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\nT/V4ytRiUW+ezqpVqqJD+/bW5nC++DyTN0zmhv/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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "correlation for zebra (0.11135875119317874, 0.51789856630330067)\n" ] }, { "data": { "image/png": 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VY7hP5ZuLXA30Ax4JGG6vqv2BXwCPi0iU0bljiTUJCFIT05y2ehoD2g6gUd1GCe8V7XGT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\npxw/eZz6teqXqN5tcSQkuHujrVuX7P7oW2/BwoUwY0bFTZp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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "correlation for cough (0.73992319956590169, 2.5123341693956533e-07)\n" ] }, { "data": { "image/png": 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nfLyqdlDVTpiq+H91Ey9fVTs5jwCqEA4cgBUroG9f//OCTT2ZPt3slZoa6J0D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\n/r77IDPTFWH4+mvXirziPJPndOniWsBFFTaoyrVnLWmWgSV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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "correlation for headache (0.81506787372637202, 1.4331660744340132e-09)\n" ] }, { "data": { "image/png": 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P5mz7LLnGCTJ9ulsVT5wIgwcXy5CGcTjRAPgl6P0qryzeOgp8KiIzReQ632aJ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\nNIWFsGSJ8yT9y1+gd28X9/SRR5xIPvt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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "correlation for fever (0.69213469769648095, 2.9244777232518385e-06)\n" ] }, { "data": { "image/png": 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y08e6dXM1uJtvdvMC8/L8827AzBtvuBpfTnr3hnnz4Lnn4NFH3SCZDHFxrmY5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\nOZQZBwwCjgHDVXV5XteKSC3gY6AJsBW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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "correlation for sick (0.85208866376956682, 4.3486363046679577e-11)\n" ] } ], "source": [ "# Try some other terms.\n", "def evaluate_terms(terms, week_counts, week_lines, week_dates, ilis):\n", " for term in terms:\n", " trend = 1. * get_trend(week_counts, term) / week_lines\n", " plot_trends(week_dates, trend, ilis, term, 'ILI')\n", " print 'correlation for', term, pearsonr(trend, ilis)\n", "\n", "evaluate_terms(['zebra', 'cough', 'headache', 'fever', 'sick'], week_counts, week_lines, week_dates, ilis)" ] }, { "cell_type": "code", "execution_count": 217, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "slope= [ 4875.46999025] intercept= 0.0111974820114\n" ] }, { "data": { "image/png": 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oNarUOOPc/PnQq1fxCQucbWA2bnS6amvV8mOwxphA8nqGYHGKm9h9HfAUUBdI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\nOicw/abpjPlyDO+ufDfgMeQ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "correlation: (0.88896023873430152, 1.3017011234610459e-09)\n", "mse= 0.00536794341945\n" ] } ], "source": [ "# Fit a model on first N weeks, then predict on remaining.\n", "from sklearn.linear_model import LinearRegression\n", "import math\n", "\n", "def train_test(trend, ilis, dates, train_size):\n", " reg = LinearRegression()\n", " x = [[i] for i in trend]\n", " reg.fit(x[:train_size], ilis[:train_size])\n", " print 'slope=', reg.coef_, 'intercept=', reg.intercept_\n", " train_preds = reg.predict(x[:train_size])\n", " test_preds = reg.predict(x[train_size:])\n", " all_preds = np.concatenate((train_preds, test_preds))\n", " plot_trends(dates, all_preds, ilis, 'flu', 'ILI')\n", " print 'correlation:', pearsonr(test_preds, \n", " ilis[train_size:])\n", " print 'mse=', math.sqrt(np.mean((test_preds -\n", " ilis[train_size:])**2))\n", " \n", "train_test(norm_flu_trend, ilis, week_dates, 10)" ] }, { "cell_type": "code", "execution_count": 218, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "coef= [ 5251.82254491 -84.69640503 -1916.69957167 875.47184323] intercept= -0.0061411779179\n" ] }, { "data": { "image/png": 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0rtmYmEYxTlfFMX2a9mHBlgV06WJ3PNi27cz3Pe16zKFBTSmVF50oUgZ6fNSD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\nsuMXRswaQWZ2Jm8PeJseUT0KveZE1gl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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "correlation: 0.717677334484\n" ] } ], "source": [ "def train_test_multi(trends, ilis, dates, train_size):\n", " reg = LinearRegression()\n", " x = []\n", " for i in range(len(trends[0])):\n", " x.append([trends[j][i] for j in range(len(trends))])\n", " reg.fit(x[:train_size], ilis[:train_size])\n", " print 'coef=', reg.coef_, 'intercept=', reg.intercept_\n", " train_preds = reg.predict(x[:train_size])\n", " test_preds = reg.predict(x[train_size:])\n", " all_preds = np.concatenate((train_preds, test_preds))\n", " plot_trends(dates, all_preds, ilis, 'flu', 'ILI')\n", " corr = pearsonr(test_preds, ilis[train_size:])\n", " print 'correlation:', corr[0]\n", " return corr[0]\n", " \n", "fever = 1. * get_trend(week_counts, 'fever') / week_lines\n", "cough = 1. * get_trend(week_counts, 'cough') / week_lines\n", "sick = 1. * get_trend(week_counts, 'sick') / week_lines\n", "\n", "corr = train_test_multi([norm_flu_trend, fever, cough, sick],\n", " ilis, week_dates, 15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Searching for term combinations\n", "\n", "Given a list of $N$ potential terms to track, we want to search for the subset that results in the most accurate model.\n", "\n", "Assume we have a training set $D_t$ and a validation set $D_v$ of tweets and CDC values.\n", "\n", "A common iterative feature selection strategy is as follows:\n", "\n", "Let $F = \\{\\}$ be the set of terms used in the regression model.\n", "\n", "Let $T=\\{t_1 \\ldots t_N\\}$ be the set of possible terms to use.\n", "\n", "1. For $t \\in T$:\n", " 1. Let $F = F \\cup \\{t\\}$ (i.e., add $t$ to $F$)\n", " 2. Fit the model on $D_t$\n", " 3. Evaluate the model on $D_v$\n", "2. Pick the term that resulted in the best score on $D_v$ and add it to $F$.\n", "3. Repeat as long as the model's accuracy improves.\n" ] } ], "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 1 }