Mercurial > hg > from-my-pen-to-your-ears-supplementary-material

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initial commit
author Emmanouil Theofanis Chourdakis <e.t.chourdakis@qmul.ac.uk>
date Wed, 16 May 2018 17:56:10 +0100
parents
children
rev   line source
e@0 1 {
e@0 2 "cells": [
e@0 3 {
e@0 4 "cell_type": "code",
e@0 5 "execution_count": 1,
e@0 6 "metadata": {},
e@0 7 "outputs": [],
e@0 8 "source": [
e@0 9 "import glob\n",
e@0 10 "import pandas as pd\n",
e@0 11 "import matplotlib.pyplot as plt\n",
e@0 12 "import seaborn as sns\n",
e@0 13 "sns.set(style=\"ticks\", color_codes=True, font_scale=1.3)\n",
e@0 14 "\n",
e@0 15 "page1_df = pd.read_csv('ratings/page1-default-ratings.csv').set_index('file_keys')\n",
e@0 16 "page2_df = pd.read_csv('ratings/page2-default-ratings.csv').set_index('file_keys')\n",
e@0 17 "page3_df = pd.read_csv('ratings/page3-default-ratings.csv').set_index('file_keys')\n",
e@0 18 "page4_df = pd.read_csv('ratings/page4-default-ratings.csv').set_index('file_keys')\n",
e@0 19 "page5_df = pd.read_csv('ratings/page5-default-ratings.csv').set_index('file_keys')\n",
e@0 20 "page6_df = pd.read_csv('ratings/page6-default-ratings.csv').set_index('file_keys')\n",
e@0 21 "page7_df = pd.read_csv('ratings/page7-default-ratings.csv').set_index('file_keys')\n",
e@0 22 "page8_df = pd.read_csv('ratings/page8-default-ratings.csv').set_index('file_keys')\n",
e@0 23 "page9_df = pd.read_csv('ratings/page9-default-ratings.csv').set_index('file_keys')"
e@0 24 ]
e@0 25 },
e@0 26 {
e@0 27 "cell_type": "code",
e@0 28 "execution_count": 2,
e@0 29 "metadata": {},
e@0 30 "outputs": [],
e@0 31 "source": [
e@0 32 "renamedict = {k: k[1:] for k in page1_df.columns}\n",
e@0 33 "page1 = page1_df.rename(columns=renamedict)\n",
e@0 34 "page1['story'] = 'a'\n",
e@0 35 "renamedict = {k: k[1:] for k in page2_df.columns}\n",
e@0 36 "page2 = page2_df.rename(columns=renamedict)\n",
e@0 37 "page2['story'] = 'b'\n",
e@0 38 "renamedict = {k: k[1:] for k in page3_df.columns}\n",
e@0 39 "page3 = page3_df.rename(columns=renamedict)\n",
e@0 40 "page3['story'] = 'c'\n",
e@0 41 "renamedict = {k: k[1:] for k in page4_df.columns}\n",
e@0 42 "page4 = page4_df.rename(columns=renamedict)\n",
e@0 43 "page4['story'] = 'a'\n",
e@0 44 "renamedict = {k: k[1:] for k in page5_df.columns}\n",
e@0 45 "page5 = page5_df.rename(columns=renamedict)\n",
e@0 46 "page5['story'] = 'b'\n",
e@0 47 "renamedict = {k: k[1:] for k in page6_df.columns}\n",
e@0 48 "page6 = page6_df.rename(columns=renamedict)\n",
e@0 49 "page6['story'] = 'c'\n",
e@0 50 "renamedict = {k: k[1:] for k in page7_df.columns}\n",
e@0 51 "page7 = page7_df.rename(columns=renamedict)\n",
e@0 52 "page7['story'] = 'a'\n",
e@0 53 "renamedict = {k: k[1:] for k in page8_df.columns}\n",
e@0 54 "page8 = page8_df.rename(columns=renamedict)\n",
e@0 55 "page8['story'] = 'b'\n",
e@0 56 "renamedict = {k: k[1:] for k in page9_df.columns}\n",
e@0 57 "page9 = page9_df.rename(columns=renamedict)\n",
e@0 58 "page9['story'] = 'c'\n"
e@0 59 ]
e@0 60 },
e@0 61 {
e@0 62 "cell_type": "code",
e@0 63 "execution_count": 3,
e@0 64 "metadata": {},
e@0 65 "outputs": [
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e@0 434 " <td>0.93</td>\n",
e@0 435 " <td>c</td>\n",
e@0 436 " </tr>\n",
e@0 437 " <tr>\n",
e@0 438 " <th>iWogwaa7GooHWHcp8C0ZjRrTMDgcae0t</th>\n",
e@0 439 " <td>0.00</td>\n",
e@0 440 " <td>0.00</td>\n",
e@0 441 " <td>0.97</td>\n",
e@0 442 " <td>1.00</td>\n",
e@0 443 " <td>1.00</td>\n",
e@0 444 " <td>1.00</td>\n",
e@0 445 " <td>c</td>\n",
e@0 446 " </tr>\n",
e@0 447 " <tr>\n",
e@0 448 " <th>bOyRMU1QCcMqMpcjcHNBuVMF45oksQMD</th>\n",
e@0 449 " <td>0.00</td>\n",
e@0 450 " <td>0.24</td>\n",
e@0 451 " <td>1.00</td>\n",
e@0 452 " <td>1.00</td>\n",
e@0 453 " <td>1.00</td>\n",
e@0 454 " <td>1.00</td>\n",
e@0 455 " <td>c</td>\n",
e@0 456 " </tr>\n",
e@0 457 " <tr>\n",
e@0 458 " <th>U6mgdJX1DgAfYJ6DR7sKY0CL4YgcwZKq</th>\n",
e@0 459 " <td>0.00</td>\n",
e@0 460 " <td>0.00</td>\n",
e@0 461 " <td>0.72</td>\n",
e@0 462 " <td>0.85</td>\n",
e@0 463 " <td>0.88</td>\n",
e@0 464 " <td>0.66</td>\n",
e@0 465 " <td>c</td>\n",
e@0 466 " </tr>\n",
e@0 467 " <tr>\n",
e@0 468 " <th>VyX492RQzqQXRL84PByL9pLt8C5p4c50</th>\n",
e@0 469 " <td>0.02</td>\n",
e@0 470 " <td>0.00</td>\n",
e@0 471 " <td>0.93</td>\n",
e@0 472 " <td>0.82</td>\n",
e@0 473 " <td>0.97</td>\n",
e@0 474 " <td>0.83</td>\n",
e@0 475 " <td>c</td>\n",
e@0 476 " </tr>\n",
e@0 477 " <tr>\n",
e@0 478 " <th>bkHwhN78d7k2kIanOievgityZQD7gVOr</th>\n",
e@0 479 " <td>0.18</td>\n",
e@0 480 " <td>0.23</td>\n",
e@0 481 " <td>0.89</td>\n",
e@0 482 " <td>0.92</td>\n",
e@0 483 " <td>0.95</td>\n",
e@0 484 " <td>0.90</td>\n",
e@0 485 " <td>c</td>\n",
e@0 486 " </tr>\n",
e@0 487 " <tr>\n",
e@0 488 " <th>ufohn9b31ZNI2zLGNqmmRYOMtIwiw6o1</th>\n",
e@0 489 " <td>0.07</td>\n",
e@0 490 " <td>0.11</td>\n",
e@0 491 " <td>0.77</td>\n",
e@0 492 " <td>0.93</td>\n",
e@0 493 " <td>0.87</td>\n",
e@0 494 " <td>0.93</td>\n",
e@0 495 " <td>c</td>\n",
e@0 496 " </tr>\n",
e@0 497 " <tr>\n",
e@0 498 " <th>fZGbsBmuEtrB35G2IzQu0IxOCVoljWpz</th>\n",
e@0 499 " <td>0.01</td>\n",
e@0 500 " <td>0.00</td>\n",
e@0 501 " <td>1.00</td>\n",
e@0 502 " <td>1.00</td>\n",
e@0 503 " <td>0.97</td>\n",
e@0 504 " <td>0.75</td>\n",
e@0 505 " <td>c</td>\n",
e@0 506 " </tr>\n",
e@0 507 " <tr>\n",
e@0 508 " <th>G2WO7k3tSjvBrlMG1Nqz4DsKBO8oaEcQ</th>\n",
e@0 509 " <td>0.20</td>\n",
e@0 510 " <td>0.20</td>\n",
e@0 511 " <td>0.60</td>\n",
e@0 512 " <td>1.00</td>\n",
e@0 513 " <td>1.00</td>\n",
e@0 514 " <td>0.75</td>\n",
e@0 515 " <td>c</td>\n",
e@0 516 " </tr>\n",
e@0 517 " <tr>\n",
e@0 518 " <th>OK2yLWD54tr3klqiS631VXjQlroZfmD3</th>\n",
e@0 519 " <td>0.00</td>\n",
e@0 520 " <td>0.07</td>\n",
e@0 521 " <td>0.91</td>\n",
e@0 522 " <td>0.97</td>\n",
e@0 523 " <td>0.84</td>\n",
e@0 524 " <td>0.73</td>\n",
e@0 525 " <td>c</td>\n",
e@0 526 " </tr>\n",
e@0 527 " <tr>\n",
e@0 528 " <th>D0Fq5YbSHwHaHK9V5pIWlIg5T6Ji3fQH</th>\n",
e@0 529 " <td>0.10</td>\n",
e@0 530 " <td>0.29</td>\n",
e@0 531 " <td>0.51</td>\n",
e@0 532 " <td>0.95</td>\n",
e@0 533 " <td>0.52</td>\n",
e@0 534 " <td>0.87</td>\n",
e@0 535 " <td>c</td>\n",
e@0 536 " </tr>\n",
e@0 537 " <tr>\n",
e@0 538 " <th>KBKzd0PezF8bsGoEZTkVkpmj1o8OECIo</th>\n",
e@0 539 " <td>0.12</td>\n",
e@0 540 " <td>0.66</td>\n",
e@0 541 " <td>0.28</td>\n",
e@0 542 " <td>0.05</td>\n",
e@0 543 " <td>0.54</td>\n",
e@0 544 " <td>0.44</td>\n",
e@0 545 " <td>c</td>\n",
e@0 546 " </tr>\n",
e@0 547 " <tr>\n",
e@0 548 " <th>uoBPmOWdbNI4uowtBZRZK8BIEUVvUn1o</th>\n",
e@0 549 " <td>0.12</td>\n",
e@0 550 " <td>0.14</td>\n",
e@0 551 " <td>0.77</td>\n",
e@0 552 " <td>1.00</td>\n",
e@0 553 " <td>0.95</td>\n",
e@0 554 " <td>0.97</td>\n",
e@0 555 " <td>c</td>\n",
e@0 556 " </tr>\n",
e@0 557 " <tr>\n",
e@0 558 " <th>lIOWKvmCLdlUhGFYwE3lOfizSeqtxyNT</th>\n",
e@0 559 " <td>0.24</td>\n",
e@0 560 " <td>0.12</td>\n",
e@0 561 " <td>0.89</td>\n",
e@0 562 " <td>0.82</td>\n",
e@0 563 " <td>0.69</td>\n",
e@0 564 " <td>0.90</td>\n",
e@0 565 " <td>c</td>\n",
e@0 566 " </tr>\n",
e@0 567 " </tbody>\n",
e@0 568 "</table>\n",
e@0 569 "</div>"
e@0 570 ],
e@0 571 "text/plain": [
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e@0 620 ]
e@0 621 },
e@0 622 "execution_count": 3,
e@0 623 "metadata": {},
e@0 624 "output_type": "execute_result"
e@0 625 }
e@0 626 ],
e@0 627 "source": [
e@0 628 "task1_df = page1.append(page2).append(page3)\n",
e@0 629 "task2_df = page4.append(page5).append(page6)\n",
e@0 630 "task3_df = page7.append(page8).append(page9)\n",
e@0 631 "\n",
e@0 632 "def transform_df(df):\n",
e@0 633 " records = []\n",
e@0 634 " for n in range(len(df)):\n",
e@0 635 " for c in df.columns:\n",
e@0 636 " if c not in ['file_keys', 'story']:\n",
e@0 637 " records.append({\n",
e@0 638 " 'stimulus': c,\n",
e@0 639 " 'story': df['story'].iloc[n],\n",
e@0 640 " 'preference': df[c].iloc[n]\n",
e@0 641 " \n",
e@0 642 " })\n",
e@0 643 " \n",
e@0 644 " return pd.DataFrame.from_records(records)\n",
e@0 645 " \n",
e@0 646 "task1_df"
e@0 647 ]
e@0 648 },
e@0 649 {
e@0 650 "cell_type": "code",
e@0 651 "execution_count": 4,
e@0 652 "metadata": {},
e@0 653 "outputs": [],
e@0 654 "source": [
e@0 655 "task1_transformed = transform_df(task1_df)\n",
e@0 656 "task2_transformed = transform_df(task2_df)\n",
e@0 657 "task3_transformed = transform_df(task3_df)"
e@0 658 ]
e@0 659 },
e@0 660 {
e@0 661 "cell_type": "code",
e@0 662 "execution_count": 5,
e@0 663 "metadata": {},
e@0 664 "outputs": [
e@0 665 {
e@0 666 "data": {
e@0 667 "image/png": 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\n",
e@0 668 "text/plain": [
e@0 669 "<matplotlib.figure.Figure at 0x7fbd38ebb128>"
e@0 670 ]
e@0 671 },
e@0 672 "metadata": {},
e@0 673 "output_type": "display_data"
e@0 674 }
e@0 675 ],
e@0 676 "source": [
e@0 677 "fig, axes = plt.subplots(nrows=4, ncols=4)"
e@0 678 ]
e@0 679 },
e@0 680 {
e@0 681 "cell_type": "code",
e@0 682 "execution_count": 6,
e@0 683 "metadata": {},
e@0 684 "outputs": [],
e@0 685 "source": [
e@0 686 "from matplotlib import gridspec\n",
e@0 687 "#sns.set(font_scale=1.3)"
e@0 688 ]
e@0 689 },
e@0 690 {
e@0 691 "cell_type": "code",
e@0 692 "execution_count": 7,
e@0 693 "metadata": {},
e@0 694 "outputs": [
e@0 695 {
e@0 696 "data": {
e@0 697 "image/png": 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\n",
e@0 698 "text/plain": [
e@0 699 "<matplotlib.figure.Figure at 0x7fbcfc9585f8>"
e@0 700 ]
e@0 701 },
e@0 702 "metadata": {},
e@0 703 "output_type": "display_data"
e@0 704 }
e@0 705 ],
e@0 706 "source": [
e@0 707 "#sns.set(style=\"whitegrid\", color_codes=True)\n",
e@0 708 "fig = plt.figure(figsize=(5,5))\n",
e@0 709 "gs = gridspec.GridSpec(2,1,height_ratios=[2,1])\n",
e@0 710 "plt.subplot(gs[0])\n",
e@0 711 "g = sns.boxplot(data=task1_transformed, x='stimulus', y='preference', hue='story', width=0.8, palette={'a':'red','b':'yellow','c':'cyan'})\n",
e@0 712 "g.set_ylabel('individual')\n",
e@0 713 "g.set(xticklabels=[], xlabel='',)\n",
e@0 714 "g.set_xticks([])\n",
e@0 715 "plt.subplot(gs[1])\n",
e@0 716 "g2 = sns.boxplot(data=task1_transformed, x='stimulus', y='preference', width=0.6, palette='gray')\n",
e@0 717 "g2.set_ylabel('aggregate')\n",
e@0 718 "for box in g2.artists:\n",
e@0 719 " box.set_facecolor(\"magenta\")\n",
e@0 720 "fig.tight_layout()\n",
e@0 721 "plt.savefig('task1.pdf', dpi=300,bbox_inches='tight')"
e@0 722 ]
e@0 723 },
e@0 724 {
e@0 725 "cell_type": "code",
e@0 726 "execution_count": 8,
e@0 727 "metadata": {},
e@0 728 "outputs": [
e@0 729 {
e@0 730 "data": {
e@0 731 "image/png": 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\n",
e@0 732 "text/plain": [
e@0 733 "<matplotlib.figure.Figure at 0x7fbcfa0c80b8>"
e@0 734 ]
e@0 735 },
e@0 736 "metadata": {},
e@0 737 "output_type": "display_data"
e@0 738 }
e@0 739 ],
e@0 740 "source": [
e@0 741 "fig = plt.figure(figsize=(5,5))\n",
e@0 742 "gs = gridspec.GridSpec(2,1,height_ratios=[2,1])\n",
e@0 743 "plt.subplot(gs[0])\n",
e@0 744 "\n",
e@0 745 "g = sns.boxplot(data=task2_transformed, x='stimulus', y='preference', hue='story', width=0.8, palette={'a':'red','b':'yellow','c':'cyan'})\n",
e@0 746 "sns.set_style({'legend.frameon':False})\n",
e@0 747 "g.set_ylabel('individual')\n",
e@0 748 "g.set(xticklabels=[], xlabel='',)\n",
e@0 749 "g.set_xticks([])\n",
e@0 750 "g.set(yticklabels=[], ylabel='',)\n",
e@0 751 "g.set_yticks([])\n",
e@0 752 "\n",
e@0 753 "plt.subplot(gs[1])\n",
e@0 754 "g2 = sns.boxplot(data=task2_transformed, x='stimulus', y='preference', width=0.6, palette='gray')\n",
e@0 755 "g2.set_ylabel('aggregate')\n",
e@0 756 "g2.set(yticklabels=[], ylabel='',)\n",
e@0 757 "g2.set_yticks([])\n",
e@0 758 "for box in g2.artists:\n",
e@0 759 " box.set_facecolor(\"magenta\")\n",
e@0 760 "fig.tight_layout()\n",
e@0 761 "plt.savefig('task2.pdf', dpi=300,bbox_inches='tight')"
e@0 762 ]
e@0 763 },
e@0 764 {
e@0 765 "cell_type": "code",
e@0 766 "execution_count": 9,
e@0 767 "metadata": {},
e@0 768 "outputs": [
e@0 769 {
e@0 770 "data": {
e@0 771 "image/png": 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\n",
e@0 772 "text/plain": [
e@0 773 "<matplotlib.figure.Figure at 0x7fbcfa3d32b0>"
e@0 774 ]
e@0 775 },
e@0 776 "metadata": {},
e@0 777 "output_type": "display_data"
e@0 778 }
e@0 779 ],
e@0 780 "source": [
e@0 781 "#sns.set(style=\"whitegrid\", color_codes=True)\n",
e@0 782 "fig = plt.figure(figsize=(5,5))\n",
e@0 783 "gs = gridspec.GridSpec(2,1,height_ratios=[2,1])\n",
e@0 784 "plt.subplot(gs[0])\n",
e@0 785 "g = sns.boxplot(data=task3_transformed, x='stimulus', y='preference', hue='story', width=0.8, palette={'a':'red','b':'yellow','c':'cyan'})\n",
e@0 786 "g.set_ylabel('individual')\n",
e@0 787 "g.set(xticklabels=[], xlabel='',)\n",
e@0 788 "g.set_xticks([])\n",
e@0 789 "\n",
e@0 790 "g.set(yticklabels=[], ylabel='',)\n",
e@0 791 "g.set_yticks([])\n",
e@0 792 "plt.subplot(gs[1])\n",
e@0 793 "g2 = sns.boxplot(data=task3_transformed, x='stimulus', y='preference', width=0.6, palette='gray')\n",
e@0 794 "g2.set_ylabel('aggregate')\n",
e@0 795 "\n",
e@0 796 "g2.set(yticklabels=[], ylabel='')\n",
e@0 797 "g2.set_yticks([])\n",
e@0 798 "g2.legend('')\n",
e@0 799 "for box in g2.artists:\n",
e@0 800 " box.set_facecolor(\"magenta\")\n",
e@0 801 "fig.tight_layout()\n",
e@0 802 "plt.savefig('task3.pdf', dpi=300, bbox_inches='tight')"
e@0 803 ]
e@0 804 },
e@0 805 {
e@0 806 "cell_type": "code",
e@0 807 "execution_count": 10,
e@0 808 "metadata": {},
e@0 809 "outputs": [
e@0 810 {
e@0 811 "data": {
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e@0 814 "<matplotlib.figure.Figure at 0x7fbcfa3ca240>"
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e@0 834 "execution_count": 10,
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