rule_overlap.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rule overlap between Train and Test set using different splitting techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from collections import defaultdict\n",
    "\n",
    "from rule.rule_utils import rule_overlap\n",
    "from utils.plot_config import *\n",
    "mpl.rc('lines', linewidth=plot_line_width)\n",
    "fontsize = fontsize * 1.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Before running this notebook make sure to execute the following commands, to create the necessary datasets:\n",
    "# ! python data/coveo_preproc.py -p data/data_sources/coveo_ecommerce\n",
    "# ! python data/retailrocket_preproc.py -p data/data_sources/retailrocket_ecommerce\n",
    "# ! python data/rees46_preproc.py -p data/data_sources/ress46_ecommerce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiments = {\n",
    "    \"coveo\": {\n",
    "        \"full_path\": \"data/data_sources/coveo_ecommerce/coveo_processed_view_full.tsv\",\n",
    "        \"train_path\": \"data/data_sources/coveo_ecommerce/coveo_processed_view_train_full.tsv\",\n",
    "        \"test_path\": \"data/data_sources/coveo_ecommerce/coveo_processed_view_test.tsv\",\n",
    "    },\n",
    "    \"retailrocket\": {\n",
    "        \"full_path\": \"data/data_sources/retailrocket_ecommerce/retailrocket_processed_view_full.tsv\",\n",
    "        \"train_path\": \"data/data_sources/retailrocket_ecommerce/retailrocket_processed_view_train_full.tsv\",\n",
    "        \"test_path\": \"data/data_sources/retailrocket_ecommerce/retailrocket_processed_view_test.tsv\",\n",
    "    },\n",
    "    \"rees46\": {\n",
    "        \"full_path\": \"data/data_sources/rees46_ecommerce/rees46_processed_view_full.tsv\",\n",
    "        \"train_path\": \"data/data_sources/rees46_ecommerce/rees46_processed_view_train_full.tsv\",\n",
    "        \"test_path\": \"data/data_sources/rees46_ecommerce/rees46_processed_view_test.tsv\",\n",
    "    },\n",
    "}"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculateing rule overlap between train and test\n",
    "\n",
    "- leave-one-out: For each session longer than 2, the last event is assigned to the test set, the remaining events are assigned to the train set.\n",
    "- timebased split: sessions after a timestamp are assigned to the test set, events before are assigned to the train set\n",
    "    - The split time is calculated to yield the same test size as the \"leave-one_out\" technique.\n",
    "- leave-one-out with timebased split: sessions after a timestamp are assigned to the test set, events before are assigned to the train set\n",
    "    - Scores are only calculated on the last event of the test sessions.\n",
    "- leave-one-out with random split: randomly sampled sessions (number of sessions match with the number of sessions in leave-one-out with timebased split) are assigned to the test set, the rest of the sessions are assigned to the train set.\n",
    "    - Scores are only calculated on the last event of the test sessions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating rule overlap for: coveo\n",
      "\tCalculating overlap for 'leave-one-out' technique\n",
      "\t\ttrain shape: (1327759, 3) test shape: (271772, 3) test events: 135886\n",
      "\t\ttrain_ratio: 0.9072 test_ratio: 0.0928\n",
      "\t\toverlap: 0.37009572\n",
      "\tCalculating overlap for 'timebased split' technique, with test size adjusted to l1o test size 0.0928\n",
      "\t\ttrain shape: (1305602, 3) test shape: (157447, 3) test events: 135869\n",
      "\t\ttrain_ratio: 0.8920 test_ratio: 0.0928\n",
      "\t\toverlap: 0.38123896\n",
      "\tCalculating overlap for 'leave-one-out with timebased split' technique\n",
      "\t\ttrain shape: (1411113, 3) test shape: (15496, 3) test events: 7748\n",
      "\t\toverlap: 0.45004198\n",
      "\tCalculating overlap for 'leave-one-out with random split' technique\n",
      "\t\ttrain shape: (1455897, 3) test shape: (15496, 3) test events: 7748\n",
      "\t\toverlap: 0.44424060\n",
      "Calculating rule overlap for: retailrocket\n",
      "\tSKIPPING, file exists: data/results/rule_overlap/retailrocket_rule_overlap.tsv\n",
      "Calculating rule overlap for: rees46\n",
      "\tSKIPPING, file exists: data/results/rule_overlap/rees46_rule_overlap.tsv\n"
     ]
    }
   ],
   "source": [
    "for dataset_name, dataset_params in experiments.items():\n",
    "    rule_overlap(**dataset_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_plot(ax):\n",
    "    ax.legend(loc=1, prop={'size': fontsize})\n",
    "    for axis in ['top','bottom','left','right']:\n",
    "        ax.spines[axis].set_linewidth(plot_axis_line_width)\n",
    "    ax.tick_params(width=plot_axis_line_width)\n",
    "    ax.tick_params(axis='y', which='major', labelsize=fontsize*0.95)\n",
    "    ax.tick_params(axis='x', which='major', labelsize=fontsize*1.2)\n",
    "    ax.tick_params(axis='both', which='minor', labelsize=fontsize*0.8)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_barplot(ax, dataset_names, dataset_stats):\n",
    "    x = np.arange(len(dataset_names))\n",
    "    width = 0.3\n",
    "    multiplier = 0\n",
    "    bar_colors = [colors[\"color_1\"], colors[\"color_0\"]]\n",
    "    for i, (attribute, measurement) in enumerate(dataset_stats.items()):\n",
    "        measurement = np.array(measurement)\n",
    "        offset = width * multiplier\n",
    "        bars = ax.bar(x + offset, measurement, width, label=attribute, color=bar_colors[i%2])\n",
    "        ax.bar_label(bars, labels=[f\"{m:.4f}\" for m in measurement], padding=0, fontsize=fontsize*0.85)\n",
    "        multiplier += 1\n",
    "    ax.set_ylabel('Ratio', fontsize=fontsize*1.2)\n",
    "    ax.set_xticks(x + width/2, dataset_names)\n",
    "    format_plot(ax)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_dataset_stats(dataset_names):\n",
    "    dataset_stats = defaultdict(list)\n",
    "    file_path = \"./data/results/rule_overlap\"\n",
    "    overlap_file_names = os.listdir(file_path)\n",
    "    for dataset_name in dataset_names:\n",
    "        overlap_file_name = next((fn for fn in overlap_file_names if dataset_name.lower() in fn), None)\n",
    "        if overlap_file_name is None:\n",
    "            raise ValueError(f\"There is no matching overlap file for dataset: {dataset_name}\")\n",
    "        data = pd.read_csv(os.path.join(file_path, overlap_file_name), sep='\\t')\n",
    "        for i in range(len(data)):\n",
    "            dataset_stats[data.iloc[i, data.columns.get_loc(\"method\")]].append(data.iloc[i, data.columns.get_loc(\"overlap\")])\n",
    "\n",
    "    return dataset_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_names = np.array([\"Rees46\", \"Coveo\", \"Retailrocket\"])\n",
    "dataset_stats = load_dataset_stats(dataset_names)\n",
    "dataset_stats_fig1 = {\"Leave-one-out\": dataset_stats[\"Leave-one-out\"], \"Time based\": dataset_stats[\"Time based\"]}\n",
    "dataset_stats_fig2 = {\"Leave-one-out: random\": dataset_stats[\"Leave-one-out: random\"], \"Leave-one-out: time split\": dataset_stats[\"Leave-one-out: time split\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1584x792 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(22,11), layout=\"constrained\")\n",
    "create_barplot(ax, dataset_names, dataset_stats_fig1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mm2+s45o1a6p+/fq37dpS0sbty5cvl5T0x+VTTz11W68PAACA9KlTp45q1qxpladPn+7yzVZXOD5VbftQTkr27t3r1o04W506dbJb/mjGjBl2/5WS9n/o2bNnmn1l9thkxMMPP2xXnjhxYrKNtp0JCwuze4I9ODjYo8tuucN2z8ALFy7o008/zZI4HAUFBalBgwZWed26dS49MR8bG6vJkydbZS8vL3Xu3NlpXcfZQa4sl3zz5k1Nn+76QiWOv3uXL192uW12YPv6XPlckaQPP/zQeigvq+XOnVsdO/5vd4ANGza49D47deqU/vvf/7p0jQ4dOtglUaZOnerSbKEjR47YJVEqVaqk2rVv9yI9yAw5LcFRwKHszqfgFZtjH8Mw0l4AzzMG2BxHSZp/m64L4A5i+zRG1apVVaJEiVTr33vvvdb04Js3b2rLli3pvrbtElFdu3ZNdz/uio6O1qJFi6xyVszemDdvnvUPqgceeMCltU8BAACQ9QzD0OjRo61ydHS0unTpokuXLrnVz549e3ThwoVk520TBJL05ZdfptrPlStX1Lt373Q/be7j42O3WfX69eu1a9cuu9kXjzzyiAoUcLztkVxmj01G1K5dW23atLHK27Zt0wcffJBqm8uXL2vw4MF259zZ88/Tnn32WQUGBlrl119/XatWrXKrj2vXrmnbtm2eDk3Dhg2zjhMTEzVw4MA0N2l+/fXX7ZZN6ty5sypUcHx+OEm9evXsNlr/7LPPFB8fn2r/zz33nFv7rTjus+juOIWGhsowDOsrODjYrfaZzfazZevWrXYbjjuzatUqjR8/PrPDcsvTTz9tHZumqUGDBqU6kykhIUGDBw92eUmzokWL2iUST548qRdeeCHVNjdv3tQTTzyh2NhY69xzzz0nL6+cdmv87pTTfor5HMo33Wjr+Jvo2JfHGYZxr+z3+1hkmmaEG+1DDcMIlVTPw6EBuM0OHDhgHbsyi8Hb29vuSQTb9u7avHmzdXzriZ8tW7aoX79+qlixovz8/FS0aFE1atRII0eO9Nj6qUuWLFFERNJHXq5cudS7d2+32m/fvl0TJ07Uyy+/rFdeeUUffPCBVqxYYfXpCmev/Y8//tCQIUN0zz33yN/fX4UKFVLdunU1YsQIHT582K0YAQAAkHm6d++uRx55xCrv3LlTDRo00KJFi1KdFRAREaG5c+eqffv2qlu3rk6fPp2sTvPmze1uYo8fP15TpkxxusH277//rlatWmnv3r3J9ihwx4AB/3v+0TRN9ejRQzdv3nT6/bRk5thk1EcffWS3392rr76qUaNG2d2YvGXfvn1q27at3Q34++67L8UZBrdD3rx59eWXX1p7ccTFxemBBx7Qa6+9lmoSyTRN/fLLLxo2bJiCgoI0d+5cj8fWq1cvNWnSxCpv375d//73v/Xnn38mqxsVFaVhw4bpo48+ss7lyZNH7733Xor9+/v7q0ePHlb5jz/+UP/+/Z0mUc6fP6/evXvrq6++SnF/Q2caN25stxfkCy+8oLCwsDtmBkNGdenSxTpOTExUly5dtG/fvmT1EhIS9Omnn+qhhx5SQkJChj5bPO3f//633SyObdu2qUOHDk5nil24cEHdunXT8uXL3XqgcOzYsXZLeE2dOlWDBg1yum/PiRMn1LFjR23atMk6V6tWrWSJUWRf3lkdwG3m51BO/n/HlDl+UvpnMBZXOP51wvJUQA5le+O8bNmyLrUJCgrS1q1bJSXfBNFVN2/e1B9//GGVy5Qpo5deeknjx4+3+8dbTEyMLl26pG3btmnChAmaMGFChpdzsl2eql27dipVqpRb7bt16+b0vK+vr/r166c33ngjzT5tp7kHBQVpwoQJeuWVVxQXF2edv3nzpq5evao9e/Zo8uTJev311zVmzBi3YgUAAIC9sLAweXu7f8ti+PDh1tPMhmFo1qxZOn36tPV38fHjx9W9e3cVL15cLVu2VLly5ZQvXz5dv35dFy5c0J49e7R//367v/ec8fX11ciRI62ZAomJiRoyZIg++eQTtWvXTkWLFtWVK1e0detWuyfMP/nkEz355JNuvy4paWmpBg0aWBtXHzlyxPpehQoV3HoSPTPHJqPq16+vjz76yG62wbhx4zR16lR16NBBZcuWVXR0tHbs2KENGzbYJWSCgoI0a9Ysu42+s8JDDz2kDz/8UC+99JJM01RCQoLeffddffzxx2rcuLHq1KmjwoULKzY2VlevXtXhw4e1c+dOXblyJe3OM8Db21tz5sxR06ZNrdk3mzdvVrVq1dSmTRvVqVNHvr6+Cg8P1/Lly+32gzAMQ59//nmyzcodjRw5UgsWLLCexp87d65Wr16tBx54QEFBQYqOjtb+/fu1fv16RUdHy9vbW1OmTHH596J48eLq0qWLNdv/4MGDCg4OlmEY8vf3t/vZr1ixQi1btnRniLLcww8/bPd7/ueff6pu3bpq166d/vWvf8nb21unTp3SqlWrrJkvxYsX17Bhw/T6669nZeh2pk2bpkaNGuns2bOSkj7Tq1Sponbt2qlmzZrKlSuXDh06pJUrV1qzO8aPH6+hQ4e61H9QUJCmT5+uHj16WLOEvvrqK3333Xfq0KGDKlWqpPj4eO3bt09r1661+9wKCAjQt99+Kz8/x9vEyK5yWoLDccaG6yliyTGNmKm7RBmG4SfpMZtTRyW5vuuSJNM0g//pK1RSa0/FBuD2s33Sp3jx4i61sV3GKr3rkp49e9ZuSvGHH35obYxYuHBhBQcHq0iRIgoPD1dYWJhiY2MVFRWlwYMHKzY21uU/Thz9/fffWrNmjVXu27dvuvpxJiYmRl988YWWLFmiRYsWpfgHb2Jiot1slAULFlivPW/evGrTpo1Kliyps2fPav369YqMjFR8fLzGjh2rGzdu2D3pBAAAAPelZzknxzZ58+ZVaGiohg8frmnTplnnz507Z7ccakoMw0jx6fJhw4Zp+/btdg/mHD582OmsXsMw9Pbbb2vgwIHpTnBISbM0bt34tNW/f3+3b+pn5thk1HPPPSc/Pz8NGTLEujF58eJFzZ49O8U2tWvX1vLly1WyZMlMicldL7zwgipXrqz+/ftbiYKYmBht2LDBpU21M2t53EqVKmnz5s3q0KGDjh49KkmKj4/XmjVr7P4NZsvPz08zZ850aY+XSpUqaebMmerdu7f1s7tw4YJmzpyZrK6/v7++/PJLNW/e3K3XMGXKFB0+fFh79uyxzpmmmWyJo+y4AblhGFq0aJFatWqlkydPSkr6t+nq1au1evXqZPVLlCihFStWaNeuXbc50tSVKVNG69atU0hIiJXkiI2N1fLly619Lm0NHTpUQ4YMceseQpcuXbRkyRI9+uij1r6j169ft/7d7kxQUJCWL1+uWrVqufmKcCfLaQkOx1123UnVOc7YSP+Ova7pKinApjzDdDbXFUCOYLtJuL+/axPIbOuld5Nx2yd2JFl/KAwePFgff/yx3fqqx48f16OPPmo9AfbCCy+obdu2yTaac8XcuXOtP0YLFChgN003Nd7e3nrwwQfVqVMnNWrUSBUrVlSePHl05coVbd++XbNmzdJ3330n0zR1/vx5derUSVu2bFH16tWT9XXt2jW7WSq3XvvDDz+s6dOnq3Dhwtb3Ll68qAEDBmjZsmWSkp48ad++ve677z63XzsAAEinx1K+oYGczdfXV1OnTtXTTz+t999/X8uWLUv17+NcuXKpYcOGevDBB9WnTx+VK1cuxbqzZs1So0aNNG7cOKf7CHh5eSk4OFgjR46021sivXr16qUXX3zRbmkqLy8v9e/fP139ZebYZNSgQYPUpk0bjRo1SkuWLLF7zbbKly+vESNG6Omnn5aPj0+mxZMeDz30kMLDwzV58mRNnz49zU2jy5Urp/vuu0+PPfaYR94vKalcubL27t2rSZMmafLkyTp16pTTennz5lX37t01duxYl1cSkJKWQCtVqpSef/55p3tkeHl5qW3btpowYYJq1arl8mbatwQGBmrbtm2aN2+efvjhB+3Zs0fnzp1TZGSkS5vS3+nKly+v7du368UXX9S3337rdB+TgIAAPfbYYxozZoyKFSt2xyU4JKl69er6448/9Morr2j27NlOf4erVq2q0aNH2+2p4Y4HHnhAhw8f1ptvvqm5c+c6XaJKSkoEPfPMM3rhhRfuqOW84BlGTrpnbhhGb0lzbE61NU1zfUr1Hdp+I6nPP8U40zQz5zGF/11vraSQf4oJksqZppmuxS0Nwwht3bp169DQUE+Fl73N65F2HSAjMuEf15UqVdKxY8ckSdOnT9cTTzyRZpvRo0frrbfekiSFhIRo7dq1bl9306ZNyWY4dO3aVYsXL3Za/9q1a6pVq5b1B3KfPn3snmhzVd26da2ncZ544glNnz7dpXaXLl1SkSJFUq2zYsUKde3a1frjqk2bNvr555+T1Tt16pSCgoLszjVq1Ei//PKL0+US4uLi1KRJE+3YsUOS1LJlS5eezAIAAEkOHDjg9KEDwNPi4+P1+++/6/Dhw7p06ZIiIyOVN29eFSlSRFWqVFHNmjWVP39+t/qMi4vTr7/+qj179ujq1asKCAhQyZIl1bhxY7eXWs1KmTE2nhAdHa1NmzYpPDxcFy9elJ+fn4oVK6b69etnq8+N8PBw/f7777pw4YKuXLkib29vFSxYUOXLl1f16tXdSiJ40p49e7R7925duHBBsbGxKlq0qCpWrKjmzZtneCbJkSNHtHnzZp07d07e3t4qU6aMmjRpkmWvNbu5ePGiNmzYoOPHjysmJkbFixdX2bJl1aJFi0yb5ZMZrl+/rnXr1unEiROKjo5WqVKlVL16dTVs2NBj14iLi9OWLVt09OhRXbhwQbly5VKxYsVUq1Yt/etf/8rypetyIk/9bRccHKywsLCwW6sVOcppMzj+cii782lq+1jCMQ/EkiLDMMpJamtzalV6kxsA7g62a0M621zPGdtN1lyd9ZHadW9JbVO5ggU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",
      "text/plain": [
       "<Figure size 1584x792 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(22,11), layout=\"constrained\")\n",
    "create_barplot(ax, dataset_names, dataset_stats_fig2)"
   ]
  },
  {
   "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": "3.8.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}