Mercurial > hg > from-my-pen-to-your-ears-supplementary-material
view results/data/saves/.ipynb_checkpoints/Untitled-checkpoint.ipynb @ 1:eb3b846ae0ef tip
second commit
| author | Emmanouil Theofanis Chourdakis <e.t.chourdakis@qmul.ac.uk> |
|---|---|
| date | Wed, 16 May 2018 18:13:41 +0100 |
| parents | 4dad87badb0c |
| children |
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{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import glob\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "sns.set(style=\"ticks\", color_codes=True, font_scale=1.3)\n", "\n", "page1_df = pd.read_csv('ratings/page1-default-ratings.csv').set_index('file_keys')\n", "page2_df = pd.read_csv('ratings/page2-default-ratings.csv').set_index('file_keys')\n", "page3_df = pd.read_csv('ratings/page3-default-ratings.csv').set_index('file_keys')\n", "page4_df = pd.read_csv('ratings/page4-default-ratings.csv').set_index('file_keys')\n", "page5_df = pd.read_csv('ratings/page5-default-ratings.csv').set_index('file_keys')\n", "page6_df = pd.read_csv('ratings/page6-default-ratings.csv').set_index('file_keys')\n", "page7_df = pd.read_csv('ratings/page7-default-ratings.csv').set_index('file_keys')\n", "page8_df = pd.read_csv('ratings/page8-default-ratings.csv').set_index('file_keys')\n", "page9_df = 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<td>0.10</td>\n", " <td>0.29</td>\n", " <td>0.51</td>\n", " <td>0.95</td>\n", " <td>0.52</td>\n", " <td>0.87</td>\n", " <td>c</td>\n", " </tr>\n", " <tr>\n", " <th>KBKzd0PezF8bsGoEZTkVkpmj1o8OECIo</th>\n", " <td>0.12</td>\n", " <td>0.66</td>\n", " <td>0.28</td>\n", " <td>0.05</td>\n", " <td>0.54</td>\n", " <td>0.44</td>\n", " <td>c</td>\n", " </tr>\n", " <tr>\n", " <th>uoBPmOWdbNI4uowtBZRZK8BIEUVvUn1o</th>\n", " <td>0.12</td>\n", " <td>0.14</td>\n", " <td>0.77</td>\n", " <td>1.00</td>\n", " <td>0.95</td>\n", " <td>0.97</td>\n", " <td>c</td>\n", " </tr>\n", " <tr>\n", " <th>lIOWKvmCLdlUhGFYwE3lOfizSeqtxyNT</th>\n", " <td>0.24</td>\n", " <td>0.12</td>\n", " <td>0.89</td>\n", " <td>0.82</td>\n", " <td>0.69</td>\n", " <td>0.90</td>\n", " <td>c</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0000 0011 1011 1101 1110 1111 story\n", "file_keys \n", "IXHrDvdwYoDCO7bvwRMB7jD46hoWAjDi 0.06 0.13 0.68 0.71 0.65 0.65 a\n", "iWogwaa7GooHWHcp8C0ZjRrTMDgcae0t 0.00 0.04 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a\n", "lIOWKvmCLdlUhGFYwE3lOfizSeqtxyNT 0.11 0.24 0.69 1.00 0.66 0.83 a\n", "IXHrDvdwYoDCO7bvwRMB7jD46hoWAjDi 0.01 0.02 0.70 0.68 0.79 0.75 b\n", "iWogwaa7GooHWHcp8C0ZjRrTMDgcae0t 0.00 0.00 1.00 1.00 1.00 1.00 b\n", "bOyRMU1QCcMqMpcjcHNBuVMF45oksQMD 0.00 0.24 0.76 0.76 0.76 1.00 b\n", "U6mgdJX1DgAfYJ6DR7sKY0CL4YgcwZKq 0.00 0.00 0.96 0.91 0.92 0.92 b\n", "9UODgpqx7pTDhiiLm7ds39wh59aYBrHK 0.20 0.29 0.60 0.96 0.70 0.81 b\n", "VyX492RQzqQXRL84PByL9pLt8C5p4c50 0.00 0.00 1.00 1.00 1.00 0.81 b\n", "bKhIcFzfZ3YXPxNiw4E7kFyoTFAqeFTp 0.00 0.00 1.00 0.75 1.00 1.00 b\n", "ufohn9b31ZNI2zLGNqmmRYOMtIwiw6o1 0.13 0.08 0.86 0.95 0.74 0.76 b\n", "fZGbsBmuEtrB35G2IzQu0IxOCVoljWpz 0.00 0.00 0.97 0.99 1.00 0.61 b\n", "G2WO7k3tSjvBrlMG1Nqz4DsKBO8oaEcQ 0.00 0.00 0.49 1.00 0.65 0.82 b\n", "OK2yLWD54tr3klqiS631VXjQlroZfmD3 0.00 0.03 0.95 1.00 0.83 0.83 b\n", "t1Ybl1O1EAdpBEbxOLbaU4QlHtAcGCxa 0.00 0.15 0.48 0.76 0.74 0.87 b\n", "D0Fq5YbSHwHaHK9V5pIWlIg5T6Ji3fQH 0.00 0.00 0.98 0.74 0.92 0.91 b\n", "uoBPmOWdbNI4uowtBZRZK8BIEUVvUn1o 0.09 0.10 0.77 1.00 0.90 1.00 b\n", "lIOWKvmCLdlUhGFYwE3lOfizSeqtxyNT 0.12 0.11 0.77 0.92 1.00 0.90 b\n", "IXHrDvdwYoDCO7bvwRMB7jD46hoWAjDi 0.02 0.01 0.87 0.98 0.87 0.93 c\n", "iWogwaa7GooHWHcp8C0ZjRrTMDgcae0t 0.00 0.00 0.97 1.00 1.00 1.00 c\n", "bOyRMU1QCcMqMpcjcHNBuVMF45oksQMD 0.00 0.24 1.00 1.00 1.00 1.00 c\n", "U6mgdJX1DgAfYJ6DR7sKY0CL4YgcwZKq 0.00 0.00 0.72 0.85 0.88 0.66 c\n", "VyX492RQzqQXRL84PByL9pLt8C5p4c50 0.02 0.00 0.93 0.82 0.97 0.83 c\n", "bkHwhN78d7k2kIanOievgityZQD7gVOr 0.18 0.23 0.89 0.92 0.95 0.90 c\n", "ufohn9b31ZNI2zLGNqmmRYOMtIwiw6o1 0.07 0.11 0.77 0.93 0.87 0.93 c\n", "fZGbsBmuEtrB35G2IzQu0IxOCVoljWpz 0.01 0.00 1.00 1.00 0.97 0.75 c\n", "G2WO7k3tSjvBrlMG1Nqz4DsKBO8oaEcQ 0.20 0.20 0.60 1.00 1.00 0.75 c\n", "OK2yLWD54tr3klqiS631VXjQlroZfmD3 0.00 0.07 0.91 0.97 0.84 0.73 c\n", "D0Fq5YbSHwHaHK9V5pIWlIg5T6Ji3fQH 0.10 0.29 0.51 0.95 0.52 0.87 c\n", "KBKzd0PezF8bsGoEZTkVkpmj1o8OECIo 0.12 0.66 0.28 0.05 0.54 0.44 c\n", "uoBPmOWdbNI4uowtBZRZK8BIEUVvUn1o 0.12 0.14 0.77 1.00 0.95 0.97 c\n", "lIOWKvmCLdlUhGFYwE3lOfizSeqtxyNT 0.24 0.12 0.89 0.82 0.69 0.90 c" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "task1_df = page1.append(page2).append(page3)\n", "task2_df = page4.append(page5).append(page6)\n", "task3_df = page7.append(page8).append(page9)\n", "\n", "def transform_df(df):\n", " records = []\n", " for n in range(len(df)):\n", " for c in df.columns:\n", " if c not in ['file_keys', 'story']:\n", " records.append({\n", " 'stimulus': c,\n", " 'story': df['story'].iloc[n],\n", " 'preference': df[c].iloc[n]\n", " \n", " })\n", " \n", " return pd.DataFrame.from_records(records)\n", " \n", "task1_df" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "task1_transformed = transform_df(task1_df)\n", "task2_transformed = transform_df(task2_df)\n", "task3_transformed = transform_df(task3_df)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f8053549c18>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(nrows=4, ncols=4)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "from matplotlib import gridspec\n", "#sns.set(font_scale=1.3)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f80511c4208>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#sns.set(style=\"whitegrid\", color_codes=True)\n", "fig = plt.figure(figsize=(5,5))\n", "gs = gridspec.GridSpec(2,1,height_ratios=[2,1])\n", "plt.subplot(gs[0])\n", "g = sns.boxplot(data=task1_transformed, x='stimulus', y='preference', hue='story', width=0.8, palette={'a':'red','b':'yellow','c':'cyan'})\n", "g.set_ylabel('individual')\n", "g.set(xticklabels=[], xlabel='',)\n", "g.set_xticks([])\n", "plt.subplot(gs[1])\n", "g2 = sns.boxplot(data=task1_transformed, x='stimulus', y='preference', width=0.6, palette='gray')\n", "g2.set_ylabel('aggregate')\n", "for box in g2.artists:\n", " box.set_facecolor(\"magenta\")\n", "fig.tight_layout()\n", "plt.savefig('task1.pdf', dpi=300,bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f8050e212e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(5,5))\n", "gs = gridspec.GridSpec(2,1,height_ratios=[2,1])\n", "plt.subplot(gs[0])\n", "\n", "g = sns.boxplot(data=task2_transformed, x='stimulus', y='preference', hue='story', width=0.8, palette={'a':'red','b':'yellow','c':'cyan'})\n", "sns.set_style({'legend.frameon':False})\n", "g.set_ylabel('individual')\n", "g.set(xticklabels=[], xlabel='',)\n", "g.set_xticks([])\n", "g.set(yticklabels=[], ylabel='',)\n", "g.set_yticks([])\n", "\n", "plt.subplot(gs[1])\n", "g2 = sns.boxplot(data=task2_transformed, x='stimulus', y='preference', width=0.6, palette='gray')\n", "g2.set_ylabel('aggregate')\n", "g2.set(yticklabels=[], ylabel='',)\n", "g2.set_yticks([])\n", "for box in g2.artists:\n", " box.set_facecolor(\"magenta\")\n", "fig.tight_layout()\n", "plt.savefig('task2.pdf', dpi=300,bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f8050e93198>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#sns.set(style=\"whitegrid\", color_codes=True)\n", "fig = plt.figure(figsize=(5,5))\n", "gs = gridspec.GridSpec(2,1,height_ratios=[2,1])\n", "plt.subplot(gs[0])\n", "g = sns.boxplot(data=task3_transformed, x='stimulus', y='preference', hue='story', width=0.8, palette={'a':'red','b':'yellow','c':'cyan'})\n", "g.set_ylabel('individual')\n", "g.set(xticklabels=[], xlabel='',)\n", "g.set_xticks([])\n", "\n", "g.set(yticklabels=[], ylabel='',)\n", "g.set_yticks([])\n", "plt.subplot(gs[1])\n", "g2 = sns.boxplot(data=task3_transformed, x='stimulus', y='preference', width=0.6, palette='gray')\n", "g2.set_ylabel('aggregate')\n", "\n", "g2.set(yticklabels=[], ylabel='')\n", "g2.set_yticks([])\n", "g2.legend('')\n", "for box in g2.artists:\n", " box.set_facecolor(\"magenta\")\n", "fig.tight_layout()\n", "plt.savefig('task3.pdf', dpi=300, bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f80901d0080>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[(0.8941176470588235, 0.10196078431372557, 0.10980392156862737),\n", " (0.21568627450980393, 0.4941176470588236, 0.7215686274509804),\n", " (0.3019607843137256, 0.6862745098039216, 0.29019607843137263),\n", " (0.5960784313725492, 0.3058823529411765, 0.6392156862745098),\n", " (1.0, 0.4980392156862745, 0.0),\n", " (0.9999999999999998, 1.0, 0.19999999999999996),\n", " (0.6509803921568629, 0.33725490196078434, 0.1568627450980391),\n", " (0.9686274509803922, 0.5058823529411766, 0.7490196078431374),\n", " (0.6, 0.6, 0.6)]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sns.choose_colorbrewer_palette('qualitative')" ] } ], "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }