multi_discrete.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total rows: 1344154\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "preset_id\n",
       "0      33121\n",
       "1       2078\n",
       "2      29829\n",
       "3       6738\n",
       "4      10223\n",
       "5      18425\n",
       "6       9682\n",
       "7      11648\n",
       "8     259460\n",
       "9     159355\n",
       "10     71061\n",
       "11     83998\n",
       "12      2398\n",
       "13      2796\n",
       "14      1361\n",
       "15      9443\n",
       "16     73158\n",
       "17    132062\n",
       "18    181740\n",
       "19       959\n",
       "20     29726\n",
       "21     54765\n",
       "22     30371\n",
       "23     35260\n",
       "24      1920\n",
       "25     48056\n",
       "26      1806\n",
       "27     38415\n",
       "28      3242\n",
       "29      1058\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from itertools import accumulate\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import minimize\n",
    "\n",
    "df_revlogs = pd.read_parquet(\"./data/revlogs\")\n",
    "df_cards = pd.read_parquet(\"./data/cards\")\n",
    "df_decks = pd.read_parquet(\"./data/decks\")\n",
    "df_joined = df_revlogs.merge(df_cards, on=\"card_id\", how=\"left\").merge(\n",
    "    df_decks, on=\"deck_id\", how=\"left\"\n",
    ")\n",
    "print(f\"total rows: {len(df_joined)}\")\n",
    "df_joined[\"review_th\"] = range(1, len(df_joined) + 1)\n",
    "df_joined.sort_values(by=[\"card_id\", \"review_th\"], inplace=True)\n",
    "\n",
    "\n",
    "def cum_concat(x):\n",
    "    return list(accumulate(x))\n",
    "\n",
    "\n",
    "t_history_list = df_joined.groupby(\"card_id\", group_keys=False)[\n",
    "    \"elapsed_seconds\"\n",
    "].apply(lambda x: cum_concat([[max(0, i)] for i in x]))\n",
    "r_history_list = df_joined.groupby(\"card_id\", group_keys=False)[\"rating\"].apply(\n",
    "    lambda x: cum_concat([[i] for i in x])\n",
    ")\n",
    "df_joined[\"r_history\"] = [\n",
    "    \",\".join(map(str, item[:-1])) for sublist in r_history_list for item in sublist\n",
    "]\n",
    "df_joined[\"t_history\"] = [\n",
    "    \",\".join(map(str, item[:-1])) for sublist in t_history_list for item in sublist\n",
    "]\n",
    "df_joined[\"y\"] = df_joined[\"rating\"].map(lambda x: 1 if x > 1 else 0)\n",
    "df_joined.groupby(\"preset_id\").size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DECAY = -0.5\n",
    "\n",
    "\n",
    "def power_forgetting_curve(t, s, decay=DECAY):\n",
    "    factor = 0.9 ** (1 / decay) - 1\n",
    "    return (1 + factor * t / s) ** decay\n",
    "\n",
    "\n",
    "def fit_stability(delta_t, retention, size, decay=DECAY):\n",
    "\n",
    "    def loss(stability):\n",
    "        y_pred = power_forgetting_curve(delta_t, stability, decay).clip(\n",
    "            1e-10, 1 - 1e-10\n",
    "        )\n",
    "        loss = sum(\n",
    "            -(retention * np.log(y_pred) + (1 - retention) * np.log(1 - y_pred)) * size\n",
    "        )\n",
    "        return loss\n",
    "\n",
    "    res = minimize(loss, x0=1, bounds=[(0.1, None)])\n",
    "    return res.x[0]\n",
    "\n",
    "\n",
    "def format_time(x, pos=None):\n",
    "    if x < 60:\n",
    "        return f\"{x:.0f}s\"\n",
    "    elif x < 3600:\n",
    "        return f\"{x/60:.2f}m\"\n",
    "    elif x < 86400:\n",
    "        return f\"{x/3600:.2f}h\"\n",
    "    else:\n",
    "        return f\"{x/86400:.2f}d\"\n",
    "\n",
    "\n",
    "def filter_data(data):\n",
    "    Q1 = data[\"elapsed_seconds\"].quantile(0.25)\n",
    "    Q3 = data[\"elapsed_seconds\"].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    lower_bound = Q1 - 1.5 * IQR\n",
    "    upper_bound = Q3 + 1.5 * IQR\n",
    "    data = data[\n",
    "        (data[\"elapsed_seconds\"] >= lower_bound)\n",
    "        & (data[\"elapsed_seconds\"] <= upper_bound)\n",
    "    ]\n",
    "    return data\n",
    "\n",
    "\n",
    "r_history_list = [\"1\", \"1,3\", \"1,3,3\", \"1,3,3,3\", \"1,3,3,3,3\", \"1,3,3,3,3,3\"]\n",
    "# r_history_list = [\"3\", \"3,3\", \"3,3,3\", \"3,3,3,3\", \"3,3,3,3,3\", \"3,3,3,3,3,3\"]\n",
    "r_history_delta_t_bounds = {}\n",
    "N = 5  # Can be adjusted to any number\n",
    "\n",
    "df = df_joined[\n",
    "    (df_joined[\"r_history\"].isin(r_history_list)) & (df_joined[\"preset_id\"] == 11)\n",
    "]\n",
    "df = (\n",
    "    df.groupby(\"r_history\", as_index=True)\n",
    "    .apply(filter_data, include_groups=False)\n",
    "    .reset_index(level=\"r_history\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3764, 15)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split into groups and assign group numbers\n",
    "n_splits = 5\n",
    "data_subset = df[df[\"r_history\"] == \"1\"].sort_values(by=\"elapsed_seconds\").copy()\n",
    "print(data_subset.shape)\n",
    "split_indices = np.array_split(np.arange(len(data_subset)), n_splits)\n",
    "\n",
    "# Add group column\n",
    "data_subset[\"group\"] = -1\n",
    "for i, indices in enumerate(split_indices):\n",
    "    data_subset.iloc[indices, data_subset.columns.get_loc(\"group\")] = i\n",
    "\n",
    "# Update original dataframe\n",
    "df.loc[data_subset.index, \"group\"] = data_subset[\"group\"]\n",
    "\n",
    "# Rest of the plotting code remains the same\n",
    "means = []\n",
    "for i in range(n_splits):\n",
    "    group_data = data_subset[data_subset[\"group\"] == i]\n",
    "    x_mean = group_data[\"elapsed_seconds\"].mean()\n",
    "    y_mean = group_data[\"y\"].mean()\n",
    "    means.append((x_mean, y_mean))\n",
    "\n",
    "# Convert to numpy array for easier plotting\n",
    "means = np.array(means)\n",
    "\n",
    "# Plot the means\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(means[:, 0], means[:, 1], color=\"red\", s=100, label=\"Group means\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Retention\")\n",
    "plt.legend()\n",
    "plt.title(\"Retention vs Time (5 groups)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(411, 16)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split into groups and assign group numbers\n",
    "n_splits = 5\n",
    "data_subset = (\n",
    "    df[\n",
    "        (df[\"r_history\"] == \"1,3\")\n",
    "        & (df[\"card_id\"].isin(df[df[\"group\"] == 3][\"card_id\"]))\n",
    "    ]\n",
    "    .sort_values(by=\"elapsed_seconds\")\n",
    "    .copy()\n",
    ")\n",
    "print(data_subset.shape)\n",
    "split_indices = np.array_split(np.arange(len(data_subset)), n_splits)\n",
    "\n",
    "# Add group column\n",
    "data_subset[\"group\"] = -1\n",
    "for i, indices in enumerate(split_indices):\n",
    "    data_subset.iloc[indices, data_subset.columns.get_loc(\"group\")] = i\n",
    "\n",
    "# Update original dataframe\n",
    "df.loc[data_subset.index, \"group\"] = data_subset[\"group\"]\n",
    "\n",
    "# Rest of the plotting code remains the same\n",
    "means = []\n",
    "for i in range(n_splits):\n",
    "    group_data = data_subset[data_subset[\"group\"] == i]\n",
    "    x_mean = group_data[\"elapsed_seconds\"].mean()\n",
    "    y_mean = group_data[\"y\"].mean()\n",
    "    means.append((x_mean, y_mean))\n",
    "\n",
    "# Convert to numpy array for easier plotting\n",
    "means = np.array(means)\n",
    "\n",
    "# Plot the means\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(means[:, 0], means[:, 1], color=\"red\", s=100, label=\"Group means\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Retention\")\n",
    "plt.legend()\n",
    "plt.title(\"Retention vs Time (5 groups)\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fsrs4anki",
   "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": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}