1-9-Figure 9-MixtureTW performance for demographic groups and contexts.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from collections import defaultdict\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from src.config import FIG_DIR, DATA_DIR, OUTPUT_DIR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# demographic, day of week, meal\n",
    "figure_data_files = ['fig_9a_data.csv', 'fig_9b_data.csv', 'fig_9c_data.csv']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data_9a(figure_data_file=os.path.join(FIG_DIR, figure_data_files[0])):\n",
    "    if not os.path.exists(figure_data_file):\n",
    "        # load results\n",
    "        df = pd.read_csv(os.path.join(OUTPUT_DIR, 'model_user_results_mixture_decay.csv'))\n",
    "        # load profile data\n",
    "        file_path = os.path.join(DATA_DIR, 'profiles.csv')\n",
    "        pro_df = pd.read_csv(file_path)\n",
    "        data = pd.merge(df, pro_df, on='uid', how='left').fillna('NA')\n",
    "        data.to_csv(figure_data_file, index=False)\n",
    "    data = pd.read_csv(figure_data_file)\n",
    "    return (data)\n",
    "\n",
    "\n",
    "def get_data_9b(figure_data_file=os.path.join(FIG_DIR, figure_data_files[1])):\n",
    "    if not os.path.exists(figure_data_file):\n",
    "        from datetime import datetime\n",
    "        from collections import defaultdict\n",
    "        from src.util.io import load_pickle\n",
    "        from src.util.eval_measures import eval_lst\n",
    "        from src.recommendation.Mixture import train_mixture_model\n",
    "        from src.recommendation.all_evaluation import model_results\n",
    "        from src.config import DATA_DIR, MODEL_DIR, PARAM_DIR\n",
    "\n",
    "        data_dir = os.path.join(DATA_DIR, 'day')\n",
    "        results_dir = os.path.join(MODEL_DIR, 'day')\n",
    "        datasets = os.listdir(data_dir)\n",
    "\n",
    "        model_type = 'mixture_decay_model'\n",
    "        param = load_pickle(os.path.join(PARAM_DIR, model_type))\n",
    "        df = model_results(datasets, train_mixture_model, model_type, data_dir, results_dir, eval_lst,\n",
    "                           n_components=param, save_multinomials=False, overwrite=False)\n",
    "\n",
    "        df = df[['nDCG@5']]\n",
    "        df['Model'] = 'Mixture Decay'\n",
    "        df['days'] = df.index.astype(int) + 8\n",
    "        # initial date falls on which weekday\n",
    "        df_initial_date = '2014-09-14'\n",
    "        date_format = \"%Y-%m-%d\"\n",
    "        df_offset = datetime.strptime(df_initial_date, date_format).weekday() + 1\n",
    "        weekday_ref = {7: 'Sunday', 1: 'Monday', 2: 'Tuesday', 3: 'Wednesday', 4: 'Thursday', 5: 'Friday',\n",
    "                       6: 'Saturday'}\n",
    "        df['Day'] = df['days'].apply(lambda t: (t + df_offset - 1) % 7 + 1).apply(lambda s: weekday_ref[s])\n",
    "        ref = defaultdict(lambda: 'Weekdays')\n",
    "        ref.update({'Saturday': 'Weekends', 'Sunday': \"Weekends\"})\n",
    "        df['Weekday'] = df['Day'].apply(lambda s: ref[s])\n",
    "        df = df.reset_index(drop=True)\n",
    "        df.to_csv(figure_data_file, index=False)\n",
    "\n",
    "    data = pd.read_csv(figure_data_file)\n",
    "    return data\n",
    "\n",
    "\n",
    "def get_data_9c(figure_data_file=os.path.join(FIG_DIR, figure_data_files[2])):\n",
    "    data = pd.read_csv(figure_data_file)\n",
    "    return data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_fig9(df, factor, order, label=False, model='Mixture Decay', context='poster'):\n",
    "    # set background param\n",
    "    if context == 'paper':\n",
    "        plt.rc('pdf', fonttype=42)\n",
    "        sns.set(style=\"ticks\")\n",
    "        sns.set_context(context, font_scale=1.3)\n",
    "        figsize = (3, 3)\n",
    "        c1, c2 = 'darkgrey', 'dimgrey'\n",
    "    else:\n",
    "        plt.rc('pdf')\n",
    "        sns.set(style=\"whitegrid\")\n",
    "        sns.set_context(context, font_scale=0.8)\n",
    "        figsize = (6, 6)\n",
    "        c1, c2 = c1, c2 = sns.color_palette(\"Paired\")[:2]\n",
    "\n",
    "    fig, axs = plt.subplots(1, 1, figsize=figsize)\n",
    "\n",
    "    curr_df = df[df['Model'] == model]\n",
    "    lst = curr_df.groupby(factor)['nDCG@5'].mean().sort_values().index\n",
    "\n",
    "    my_colors = defaultdict(lambda: c1)\n",
    "    my_colors.update({[l for l in lst][-1]: c2})\n",
    "    ax = sns.barplot(x=factor, y='nDCG@5', data=curr_df, order=order,\n",
    "                     palette=my_colors, ax=axs)\n",
    "    ax.grid(ls=\"dotted\")\n",
    "\n",
    "    ax.set_ylabel(\"nDCG@5\")\n",
    "    ax.set_xlabel('')\n",
    "    if label:\n",
    "        ax.set(xticklabels=label)\n",
    "    ax.set(ylim=(0, 0.6))\n",
    "    sns.despine(offset=10, trim=True)\n",
    "\n",
    "    # save figure\n",
    "    fig.tight_layout()\n",
    "    if context == \"paper\":\n",
    "        figure_name = os.path.join(FIG_DIR, 'paper_fig_9_' + factor + '.png')\n",
    "    else:\n",
    "        figure_name = os.path.join(FIG_DIR, 'fig_9' + factor + '.png')\n",
    "    plt.savefig(figure_name, dpi=200, transparent=False, bbox_inches='tight', pad_inches=0.0)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = get_data_9a()\n",
    "df2 = get_data_9b()\n",
    "df3 = get_data_9c()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure a-c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factor = 'Gender'\n",
    "order = ['Male', 'Female']\n",
    "plot_fig9(df1, factor, order)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factor = 'Gender'\n",
    "order = ['Male', 'Female']\n",
    "plot_fig9(df1, factor, order, context='paper')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factor = 'Age Groups'\n",
    "order = ['18-44', '45+',]\n",
    "plot_fig9(df1, factor, order)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factor = 'Age Groups'\n",
    "order = ['18-44', '45+',]\n",
    "plot_fig9(df1, factor, order, context='paper')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factor = 'Age Groups'\n",
    "order = ['18-44', '45+',]\n",
    "plot_fig9(df1, factor, order)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factor =  'US Regions'\n",
    "order = ['Northeast', 'Midwest', 'South', 'West']\n",
    "label = [ 'NE', 'MW', 'S', 'W']\n",
    "plot_fig9(df1, factor, order, label, context='paper')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure d, e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factor = 'Weekday'\n",
    "order = ['Weekdays', 'Weekends']\n",
    "plot_fig9(df2, factor, order)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factor = 'Weekday'\n",
    "order = ['Weekdays', 'Weekends']\n",
    "plot_fig9(df2, factor, order, context='paper')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factor = 'Day'\n",
    "order = ['Monday', 'Tuesday', \"Wednesday\", 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "label = ['M', 'T', 'W', 'TH', 'F', 'SA', \"SU\"]\n",
    "plot_fig9(df2, factor, order, label, context='paper')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factor = 'Meal'\n",
    "order = ['breakfast', 'lunch', 'dinner', 'snack']\n",
    "label = ['B', 'L', 'D', 'S']\n",
    "plot_fig9(df3, factor, order, label, context='paper')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}