bulge_classifications.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>subject_id</th>\n",
       "      <th>user</th>\n",
       "      <th>created_at</th>\n",
       "      <th>lang</th>\n",
       "      <th>user_agent</th>\n",
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       "      <th>decals-8</th>\n",
       "      <th>decals-9</th>\n",
       "      <th>decals-10</th>\n",
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       "      <td>a-1</td>\n",
       "      <td>a-0</td>\n",
       "      <td>a-0</td>\n",
       "    </tr>\n",
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       "      <td>55e8b6b417662710f8004e0d</td>\n",
       "      <td>55a7d7a37a726119cd0005e3</td>\n",
       "      <td>KWillett</td>\n",
       "      <td>2015-09-03 21:08:05 UTC</td>\n",
       "      <td>en</td>\n",
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       "      <td>a-1</td>\n",
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      "text/plain": [
       "                         id                subject_id      user  \\\n",
       "0  55e8b5061766270dbc005058  55a7d7a37a726119cd0006a0  KWillett   \n",
       "1  55e8b5151766270fb100516a  55a7d7a27a726119cd000582  KWillett   \n",
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       "3  55e8b6b417662710f8004e0d  55a7d7a37a726119cd0005e3  KWillett   \n",
       "4  55e8b86e176627129b005008  55a7d7a27a726119cd00036c  KWillett   \n",
       "\n",
       "                created_at lang user_agent decals-0 decals-1 decals-2  \\\n",
       "0  2015-09-03 21:00:54 UTC   en    unknown      a-1      a-0      NaN   \n",
       "1  2015-09-03 21:01:09 UTC   en    unknown      a-1      a-0      NaN   \n",
       "2  2015-09-03 21:07:29 UTC   en    unknown      a-1      a-1      a-0   \n",
       "3  2015-09-03 21:08:05 UTC   en    unknown      a-1      a-1      a-0   \n",
       "4  2015-09-03 21:15:26 UTC   en    unknown      a-0      NaN      NaN   \n",
       "\n",
       "  decals-3 decals-4 decals-5 decals-6 decals-7 decals-8 decals-9 decals-10  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN       NaN   \n",
       "2      a-0      a-2      a-1      a-2      NaN      NaN      a-1       a-0   \n",
       "3      a-0      a-1      a-2      a-1      NaN      NaN      a-3       a-0   \n",
       "4      NaN      NaN      NaN      NaN      NaN      a-0      a-3       a-0   \n",
       "\n",
       "  decals-11  \n",
       "0       NaN  \n",
       "1       NaN  \n",
       "2       a-0  \n",
       "3       a-0  \n",
       "4       a-1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifications_loc = '/data/galaxy_zoo/weekly_dumps/2017-10-22_galaxy_zoo_decals_classifications.csv'\n",
    "df = pd.read_csv(classifications_loc)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a-1    221135\n",
       "a-2     68316\n",
       "a-0     54088\n",
       "Name: decals-4, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    }
   ],
   "source": [
    "bar_answers = df['decals-4']\n",
    "bar_answers.value_counts()\n",
    "bar_df = df[['subject_id', 'decals-4']]\n",
    "mapping = {\n",
    "    'a-0': 'no_bulge',\n",
    "    'a-1': 'obvious',\n",
    "    'a-2': 'dominant',\n",
    "    'NaN': np.nan,\n",
    "    'nan': np.nan\n",
    "}\n",
    "bar_df['bar_response'] = bar_df['decals-4'].apply(lambda x: mapping[str(x)])\n",
    "bar_df = bar_df.dropna(subset=['bar_response'])\n",
    "del bar_df['decals-4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>dominant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       subject_id bar_response\n",
       "384056   55a7d7ad7a726119cd0060c2     dominant\n",
       "1567670  55a7d7af7a726119cd00764a      obvious\n",
       "1132877  55a7d7a77a726119cd002ec5      obvious\n",
       "1050457  55a7d7a27a726119cd0004bb     dominant\n",
       "1626295  55a7d7af7a726119cd0071d7     no_bulge\n",
       "1317721  55a7d7a27a726119cd0004da      obvious\n",
       "592761   55a7d7b47a726119cd009e6b     no_bulge\n",
       "163330   55a7d7b07a726119cd007bb7      obvious\n",
       "796096   55a7d7b17a726119cd0081b0      obvious\n",
       "1199104  55a7d7a37a726119cd00066c     dominant"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bar_df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th colspan=\"3\" halign=\"left\">bar_response</th>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <th></th>\n",
       "      <th>count_if_no_bulge</th>\n",
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      "text/plain": [
       "                 subject_id      bar_response                   \\\n",
       "                            count_if_no_bulge count_if_obvious   \n",
       "0  55a7d7a27a726119cd00000b                 0               12   \n",
       "1  55a7d7a27a726119cd00000c                 0                1   \n",
       "2  55a7d7a27a726119cd00000d                 0                1   \n",
       "3  55a7d7a27a726119cd00000e                 4               11   \n",
       "4  55a7d7a27a726119cd00000f                 1                4   \n",
       "\n",
       "                     \n",
       "  count_if_dominant  \n",
       "0                 1  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def count_responses(series):\n",
    "    counts = {\n",
    "        'no_bulge': 0,\n",
    "        'obvious': 0,\n",
    "        'dominant': 0\n",
    "    }\n",
    "    for value in list(series):\n",
    "        print(value)\n",
    "        counts[value] = counts[value] + 1\n",
    "        print(counts[value])\n",
    "        raise Exception\n",
    "    return counts\n",
    "\n",
    "def count_if_no_bulge(series):\n",
    "    return len(series[series == 'no_bulge'])\n",
    "def count_if_obvious(series):\n",
    "    return len(series[series == 'obvious'])\n",
    "def count_if_dominant(series):\n",
    "    return len(series[series == 'dominant'])\n",
    "\n",
    "response_df = bar_df.groupby('subject_id').agg([count_if_no_bulge, count_if_obvious, count_if_dominant]).reset_index()\n",
    "display(response_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "response_df = response_df['bar_response']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10d14c668>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p73GpLwPNiFqdpH5isWoHsANgdna25ubmxr7pYDBg1LhbF7jsctV56Qcjy8OXhy7U41rT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\ne7HxpSUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b187f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "response_df['total_responses'] = response_df['count_if_no_bulge'] + response_df['count_if_obvious'] + response_df['count_if_dominant']\n",
    "response_df['total_responses'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "response_df['no_bulge_frac'] = response_df['count_if_no_bulge'] / response_df['total_responses']\n",
    "response_df['obvious_frac'] = response_df['count_if_obvious'] / response_df['total_responses']\n",
    "response_df['dominant_frac'] = response_df['count_if_dominant'] / response_df['total_responses']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "responses = response_df[response_df['total_responses'] > 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10d145860>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10b187fd0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10c8a7780>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10c8aea90>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10cc1fba8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10c8df400>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10d1b6748>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DccBpne29P3Bmmd6Zajh5pw7E/Q1gaZn+byVpxBBs8wWMf8DpgTz6gNOrhnTb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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d19d908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=3, figsize=(12, 4), sharex=True, sharey=True)\n",
    "responses['no_bulge_frac'].hist(ax=axes[0])\n",
    "axes[0].set_xlabel('no bulge vote fraction')\n",
    "responses['obvious_frac'].hist(ax=axes[1])\n",
    "axes[1].set_xlabel('obvious bulge vote fraction')\n",
    "responses['dominant_frac'].hist(ax=axes[2])\n",
    "axes[2].set_xlabel('dominant bulge vote fraction')\n",
    "axes[0].set_ylabel('num of galaxies')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def compare_remaining_votes(df, col_baseline, col_a, col_b, ax):\n",
    "    \n",
    "    color={\n",
    "        'count_if_no_bulge': 'b',\n",
    "        'count_if_obvious': 'r', \n",
    "        'count_if_dominant': 'g'\n",
    "    }\n",
    "    vote_frac_bins = np.linspace(start=0, stop=30, num=30)\n",
    "    mean_col_a = []\n",
    "    mean_col_b = []\n",
    "    df['bin_index'] = np.digitize(df[col_baseline], bins=vote_frac_bins)\n",
    "    for bin_index in range(len(vote_frac_bins)):\n",
    "        selected = df[df['bin_index'] == bin_index]\n",
    "        mean_col_a.append(selected[col_a].mean())\n",
    "        mean_col_b.append(selected[col_b].mean())\n",
    "    ax.plot(vote_frac_bins, mean_col_a, color[col_a])\n",
    "    ax.plot(vote_frac_bins, mean_col_b, color[col_b])\n",
    "    ax.set_xlabel('{} votes'.format(col_baseline.replace('count_if_', '')))\n",
    "    ax.set_ylabel('other votes')\n",
    "    ax.legend([col_a.replace('count_if_', ''), col_b.replace('count_if_', '')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "/data/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py:2909: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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u1EkCydImIkIKoPly/507B/dyapGR0K+frA8+a5akvLvHEs5U0N9+k3nw770n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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11419d828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_context('talk')\n",
    "fig, axes = plt.subplots(ncols=3, figsize=(14, 4))\n",
    "compare_remaining_votes(responses, 'count_if_no_bulge', 'count_if_obvious', 'count_if_dominant', axes[0])\n",
    "compare_remaining_votes(responses, 'count_if_obvious', 'count_if_no_bulge', 'count_if_dominant', axes[1])\n",
    "compare_remaining_votes(responses, 'count_if_dominant', 'count_if_no_bulge', 'count_if_obvious', axes[2])\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare_remaining_fractions(df, col_baseline, col_a, col_b, ax):\n",
    "    \n",
    "    # define line colors for plot\n",
    "    color={\n",
    "        'count_if_no_bulge': 'b',\n",
    "        'count_if_obvious': 'r', \n",
    "        'count_if_dominant': 'g'\n",
    "    }\n",
    "    \n",
    "    # 20 vote fraction bins from 0 to 1\n",
    "    vote_frac_bins = np.linspace(start=0., stop=.8, num=20)\n",
    "    baseline_vote_fraction = df[col_baseline] / (df[col_baseline] + df[col_a] + df[col_b])\n",
    "\n",
    "    # for every row, work out which vote fraction bin it should be in\n",
    "    bin_indices = np.digitize(baseline_vote_fraction, bins=vote_frac_bins)\n",
    "    bin_indices = bin_indices - 1 # should be bin 0 through bin 19, not bin 1 through 20\n",
    "\n",
    "    mean_col_a_votes = np.zeros_like(vote_frac_bins)\n",
    "    mean_col_b_votes = np.zeros_like(vote_frac_bins)\n",
    "    # for all the rows in each vote fraction bin, what's the mean number of votes?\n",
    "    for bin_index in range(20):\n",
    "        selected = df[bin_indices == bin_index]  # values with that baseline vote fraction\n",
    "        mean_col_a_votes[bin_index] = selected[col_a].mean() # mean num. of votes for col_a\n",
    "        mean_col_b_votes[bin_index] = selected[col_b].mean() # mean num. of votes for col b\n",
    "\n",
    "    total_other_votes = mean_col_a_votes + mean_col_b_votes\n",
    "    ax.plot(vote_frac_bins, mean_col_a_votes/total_other_votes, color[col_a])\n",
    "    ax.plot(vote_frac_bins, mean_col_b_votes/total_other_votes, color[col_b])\n",
    "    ax.set_xlabel('{} vote fraction'.format(col_baseline.replace('count_if_', '')))\n",
    "    ax.set_ylabel('fraction of other votes')\n",
    "    ax.legend([col_a.replace('count_if_', ''), col_b.replace('count_if_', '')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QwilY2sIdCBy5xWuW9qEu8JYbm7XaJCfDgAEQF2dm+5k+3XrXFkI4M2vEylnA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\ns2ZOR6aU8hR3C+4ngGbAHyJyxDWvHPAr8Lg3AvMnA5oO4NP1nzJ381w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F0hVZfPtijxTb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DOLW0liZzfQVlOwgZEtKuQ/FtAy8TMpteufJrOHMZv8ziSswNezPEuByzr6WXizjlhS3nlZqapn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e6d0908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_context('talk')\n",
    "fig, axes = plt.subplots(ncols=3, figsize=(14, 4))\n",
    "compare_remaining_fractions(responses, 'count_if_no_bulge', 'count_if_obvious', 'count_if_dominant', axes[0])\n",
    "compare_remaining_fractions(responses, 'count_if_obvious', 'count_if_no_bulge', 'count_if_dominant', axes[1])\n",
    "compare_remaining_fractions(responses, 'count_if_dominant', 'count_if_no_bulge', 'count_if_obvious', axes[2])\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(ncols=3, figsize=(12, 4), sharex=True)\n",
    "_ = axes[0].hist2d(responses['no_bulge_frac'], responses['obvious_frac'])\n",
    "_ = axes[1].hist2d(responses['no_bulge_frac'], responses['dominant_frac'])\n",
    "_ = axes[2].hist2d(responses['obvious_frac'], responses['dominant_frac'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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
}