reasoning-gym/notebooks/check-collisions-in-reasoning-gym-dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e81636e0-fa3c-462d-ae2e-2bb35cafd544",
   "metadata": {},
   "source": [
    "## Investigating collisions in reasoning-gym datasets\n",
    "\n",
    "This notebook helps to investigate collisions in training and validation datasets generated with different seeds as intended to be used for RL training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "42323371-404e-4e86-b8b8-a420b4c79303",
   "metadata": {},
   "outputs": [],
   "source": [
    "import reasoning_gym"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f06e7932-6c77-4609-8a33-7c4d815841d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of data:  15\n"
     ]
    }
   ],
   "source": [
    "with open(\"data.txt\") as f:\n",
    "    data_names = f.readlines()\n",
    "    data_names = [name.strip() for name in data_names]\n",
    "    print(\"Total number of data: \", len(data_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d7a5a5bf-7428-46f5-a7f5-a46238df2543",
   "metadata": {},
   "outputs": [],
   "source": [
    "TOTAL = 10000\n",
    "collisions = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7138aced-d61a-4e2a-9935-a9b251e6d554",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "for name in data_names:\n",
    "    data_1 = reasoning_gym.create_dataset(name, size=TOTAL, seed=1)\n",
    "    data_2 = reasoning_gym.create_dataset(name, size=TOTAL, seed=2)\n",
    "    count = 0\n",
    "    for item_1, item_2 in zip(data_1, data_2):\n",
    "        if item_1[\"question\"] == item_2[\"question\"]:\n",
    "            count += 1\n",
    "\n",
    "    # Add name, count to collisions.txt\n",
    "    with open('collisions_1.txt', 'a') as file:\n",
    "        file.write(f\"{name}, {count}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73297817-0eda-4bb4-9850-1375b2fcfa4d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "86d9535a-73fd-4930-a2df-393efd73cb75",
   "metadata": {},
   "source": [
    "# Report on collisions data generated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d41efd7a-3d23-4433-98d5-617b5aa66f07",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e732e125-e232-4e1a-bc66-1ee37405d6ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('collisions.txt', 'r') as file:\n",
    "    data = [line.strip().split(\",\") for line in file.readlines()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "77a90340-f760-41d4-ab2a-0b14b6dbfc08",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean data\n",
    "data = [(name, collision.strip()) for name, collision in data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "36054684-470c-4ea4-8a60-25efb8218926",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame([[name, collision] for name, collision in data], columns=[\"name\", \"collisions\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0d2e6752-8d04-46c9-8057-3e71a39819f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change collision to int\n",
    "df[\"collisions\"] = df[\"collisions\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a2b05dc7-4319-4709-ae57-b3a6637bc66a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>collisions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>complex_arithmetic</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>intermediate_integration</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>polynomial_equations</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>polynomial_multiplication</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>simple_equations</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name  collisions\n",
       "0         complex_arithmetic           0\n",
       "1   intermediate_integration          12\n",
       "2       polynomial_equations           0\n",
       "3  polynomial_multiplication           0\n",
       "4           simple_equations           0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f817273d-3875-4b7f-9f7e-a8ea2cb0b455",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of data:  86\n"
     ]
    }
   ],
   "source": [
    "print(\"Total number of data: \", len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5c056fd2-2f05-443e-8ccc-17811d810852",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter out non-zero collision entries\n",
    "df_zero_collision = df[df[\"collisions\"] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6c8df99f-9d20-4d06-852f-98e4576e1a1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of data with zero collision:  66\n",
      "Percentage of data with zero collision:  0.7674418604651163\n"
     ]
    }
   ],
   "source": [
    "print(\"The number of data with zero collision: \", len(df_zero_collision))\n",
    "print(\"Percentage of data with zero collision: \", len(df_zero_collision) / len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f09f6190-0436-4a76-9acf-0f775d0b8c2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter out zero collision entries\n",
    "df_non_zero_collision = df[df[\"collisions\"] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9a260c44-e492-43e5-8e55-77b78457e142",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of data with non-zero collision:  20\n",
      "Percentage of data with non-zero collision:  0.23255813953488372\n"
     ]
    }
   ],
   "source": [
    "print(\"The number of data with non-zero collision: \", len(df_non_zero_collision))\n",
    "print(\"Percentage of data with non-zero collision: \", len(df_non_zero_collision) / len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a59e38b-5914-4dbd-b6fc-4be425f28d4b",
   "metadata": {},
   "source": [
    "## Visualize datasets with collisions data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1906a0a9-264c-4b45-8def-e94ed69a7c44",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sort values for better visualization\n",
    "df_non_zero_collision = df_non_zero_collision.sort_values(by=\"collisions\", ascending=False)\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(12, 6))\n",
    "ax = sns.barplot(\n",
    "    y=\"name\", \n",
    "    x=\"collisions\", \n",
    "    hue=\"name\",  # Assign hue to the y-variable\n",
    "    data=df_non_zero_collision, \n",
    "    palette=\"viridis\", \n",
    "    legend=False  # Hide legend since hue is just for color mapping\n",
    ")\n",
    "\n",
    "# Annotate bars with their values\n",
    "for index, value in enumerate(df_non_zero_collision[\"collisions\"]):\n",
    "    ax.text(value + 0.5, index, str(value), va='center', fontsize=10, color='black')\n",
    "\n",
    "# Labels and title\n",
    "plt.xlabel(\"Collisions\")\n",
    "plt.ylabel(\"Dataset Name\")\n",
    "plt.title(\"Number of Collisions Per Reasoning-gym dataset\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f3d1b1f6-bcf6-4f70-9168-33b3179ae171",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataset not yet done, \n",
    "# futoshiki\n",
    "# knight_swap\n",
    "# mahjong_puzzle\n",
    "# maze\n",
    "# mini_sudoku\n",
    "# n_queens\n",
    "# puzzle24\n",
    "# rush_hour\n",
    "# sokoban\n",
    "# sudoku\n",
    "# tower_of_hanoi\n",
    "# tsumego\n",
    "# zebra_puzzles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3c11508-0a4a-4578-82f5-3908c5a45604",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51263e94-0867-4b2a-b205-ebafb316f811",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}