Lesson 69 - PySR.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pysr in c:\\miniconda3\\lib\\site-packages (0.6.13)\n",
      "Requirement already satisfied: numpy in c:\\miniconda3\\lib\\site-packages (from pysr) (1.20.2)\n",
      "Requirement already satisfied: sympy in c:\\miniconda3\\lib\\site-packages (from pysr) (1.9)\n",
      "Requirement already satisfied: pandas in c:\\miniconda3\\lib\\site-packages (from pysr) (1.2.4)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\miniconda3\\lib\\site-packages (from pandas->pysr) (2.8.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\miniconda3\\lib\\site-packages (from pandas->pysr) (2021.1)\n",
      "Requirement already satisfied: six>=1.5 in c:\\miniconda3\\lib\\site-packages (from python-dateutil>=2.7.3->pandas->pysr) (1.16.0)\n",
      "Requirement already satisfied: mpmath>=0.19 in c:\\miniconda3\\lib\\site-packages (from sympy->pysr) (1.2.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install pysr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fa094059",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f0dcf29f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pysr import pysr, best, best_callable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "67900f37",
   "metadata": {},
   "outputs": [],
   "source": [
    "x0 = np.random.random(100) * 2 * np.pi - np.pi\n",
    "x1 = np.random.random(100) * 2 * np.pi - np.pi\n",
    "y = np.sin(x0)\n",
    "df = pd.DataFrame(columns=[\"x0\",\"x1\",\"y\"],data=np.array([x0,x1,y]).transpose(),index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='x0', ylabel='Count'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x=\"x0\",data=df)\n",
    "sns.histplot(x=\"x1\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cec74b28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='x0', ylabel='y'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=\"x0\",y=\"y\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "72bf5421",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"y\"] += np.random.random(100) / 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "145c4683",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='x0', ylabel='y'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=\"x0\",y=\"y\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(df.drop(\"y\",axis=1))\n",
    "y = np.array(df[\"y\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2a59eab6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on julia -O3 C:\\Users\\GEORGE~1\\AppData\\Local\\Temp\\tmp2pnwens7\\runfile.jl\n",
      "Activating environment on workers.\n",
      "      From worker 5:\t  Activating environment at `C:\\miniconda3\\Lib\\site-packages\\Project.toml`\n",
      "      From worker 4:\t  Activating environment at `C:\\miniconda3\\Lib\\site-packages\\Project.toml`\n",
      "      From worker 3:\t  Activating environment at `C:\\miniconda3\\Lib\\site-packages\\Project.toml`\n",
      "      From worker 2:\t  Activating environment at `C:\\miniconda3\\Lib\\site-packages\\Project.toml`\n",
      "Importing installed module on workers...Finished!\n",
      "Copying definition of inv to workers...Finished!\n",
      "Testing module on workers...Finished!\n",
      "Testing entire pipeline on workers...Finished!\n",
      "Started!\n",
      "\n",
      "Cycles per second: 6.560e+02\n",
      "Head worker occupation: 2.9%\n",
      "Progress: 1 / 40 total iterations (2.500%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.0798819\n",
      "2           3.617e-03  5.071e+00  sin(x0)\n",
      "4           8.540e-04  7.218e-01  (0.052563727 + sin(x0))\n",
      "\n",
      "==============================\n",
      "\n",
      "Cycles per second: 3.320e+03\n",
      "Head worker occupation: 2.6%\n",
      "Progress: 2 / 40 total iterations (5.000%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.0798819\n",
      "2           3.617e-03  5.071e+00  sin(x0)\n",
      "4           8.540e-04  7.218e-01  (0.052563727 + sin(x0))\n",
      "\n",
      "==============================\n",
      "\n",
      "Cycles per second: 3.680e+03\n",
      "Head worker occupation: 2.3%\n",
      "Progress: 3 / 40 total iterations (7.500%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.0798819\n",
      "2           3.617e-03  5.071e+00  sin(x0)\n",
      "4           8.540e-04  7.218e-01  (0.052563727 + sin(x0))\n",
      "\n",
      "==============================\n",
      "\n",
      "Cycles per second: 2.990e+03\n",
      "Head worker occupation: 1.5%\n",
      "Progress: 4 / 40 total iterations (10.000%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.0798819\n",
      "2           3.617e-03  5.071e+00  sin(x0)\n",
      "4           8.540e-04  7.218e-01  (0.052563727 + sin(x0))\n",
      "\n",
      "==============================\n",
      "\n",
      "Cycles per second: 3.590e+03\n",
      "Head worker occupation: 1.5%\n",
      "Progress: 5 / 40 total iterations (12.500%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.0798819\n",
      "2           3.617e-03  5.071e+00  sin(x0)\n",
      "4           8.540e-04  7.218e-01  (0.052563727 + sin(x0))\n",
      "\n",
      "==============================\n",
      "\n",
      "Cycles per second: 3.530e+03\n",
      "Head worker occupation: 1.4%\n",
      "Progress: 6 / 40 total iterations (15.000%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.0798819\n",
      "2           3.617e-03  5.071e+00  sin(x0)\n",
      "4           8.540e-04  7.218e-01  (0.052563727 + sin(x0))\n",
      "\n",
      "==============================\n",
      "\n",
      "Cycles per second: 9.020e+03\n",
      "Head worker occupation: 1.5%\n",
      "Progress: 13 / 40 total iterations (32.500%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.0798819\n",
      "2           3.617e-03  5.071e+00  sin(x0)\n",
      "4           8.540e-04  7.218e-01  (0.052563727 + sin(x0))\n",
      "\n",
      "==============================\n",
      "\n",
      "Cycles per second: 8.640e+03\n",
      "Head worker occupation: 1.5%\n",
      "Progress: 14 / 40 total iterations (35.000%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.0798819\n",
      "2           3.617e-03  5.071e+00  sin(x0)\n",
      "4           8.540e-04  7.218e-01  (0.052563727 + sin(x0))\n",
      "\n",
      "==============================\n",
      "\n",
      "Cycles per second: 8.340e+03\n",
      "Head worker occupation: 1.5%\n",
      "Progress: 15 / 40 total iterations (37.500%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.0798819\n",
      "2           3.617e-03  5.071e+00  sin(x0)\n",
      "4           8.540e-04  7.218e-01  (0.052563727 + sin(x0))\n",
      "\n",
      "==============================\n",
      "\n",
      "Cycles per second: 7.830e+03\n",
      "Head worker occupation: 1.4%\n",
      "Progress: 16 / 40 total iterations (40.000%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.0798819\n",
      "2           3.617e-03  5.071e+00  sin(x0)\n",
      "4           8.540e-04  7.218e-01  (0.052563727 + sin(x0))\n",
      "\n",
      "==============================\n",
      "\n",
      "Cycles per second: 8.060e+03\n",
      "Head worker occupation: 1.3%\n",
      "Progress: 17 / 40 total iterations (42.500%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.0798819\n",
      "2           3.617e-03  5.071e+00  sin(x0)\n",
      "4           8.540e-04  7.218e-01  (0.052563727 + sin(x0))\n",
      "\n",
      "==============================\n",
      "\n",
      "Cycles per second: 1.050e+04\n",
      "Head worker occupation: 1.4%\n",
      "Progress: 22 / 40 total iterations (55.000%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.0798819\n",
      "2           3.617e-03  5.071e+00  sin(x0)\n",
      "4           8.540e-04  7.218e-01  (0.052563727 + sin(x0))\n",
      "\n",
      "==============================\n",
      "\n",
      "Cycles per second: 1.060e+04\n",
      "Head worker occupation: 1.3%\n",
      "Progress: 23 / 40 total iterations (57.500%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.0798819\n",
      "2           3.617e-03  5.071e+00  sin(x0)\n",
      "4           8.540e-04  7.218e-01  (0.052563727 + sin(x0))\n",
      "\n",
      "==============================\n",
      "\n",
      "Cycles per second: 1.350e+04\n",
      "Head worker occupation: 1.4%\n",
      "Progress: 28 / 40 total iterations (70.000%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.0798819\n",
      "2           3.617e-03  5.071e+00  sin(x0)\n",
      "4           8.540e-04  7.218e-01  (0.052563727 + sin(x0))\n",
      "\n",
      "==============================\n",
      "\n",
      "Cycles per second: 1.350e+04\n",
      "Head worker occupation: 1.4%\n",
      "Progress: 29 / 40 total iterations (72.500%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.0798819\n",
      "2           3.617e-03  5.071e+00  sin(x0)\n",
      "4           8.540e-04  7.218e-01  (0.052563727 + sin(x0))\n",
      "\n",
      "==============================\n",
      "\n",
      "Cycles per second: 1.340e+04\n",
      "Head worker occupation: 1.2%\n",
      "Progress: 30 / 40 total iterations (75.000%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.0798819\n",
      "2           3.617e-03  5.071e+00  sin(x0)\n",
      "4           8.540e-04  7.218e-01  (0.052563727 + sin(x0))\n",
      "\n",
      "==============================\n",
      "\n",
      "Cycles per second: 1.040e+04\n",
      "Head worker occupation: 1.2%\n",
      "Progress: 32 / 40 total iterations (80.000%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.0798819\n",
      "2           3.617e-03  5.071e+00  sin(x0)\n",
      "4           8.540e-04  7.218e-01  (0.052563727 + sin(x0))\n",
      "\n",
      "==============================\n",
      "\n",
      "Cycles per second: 1.220e+04\n",
      "Head worker occupation: 1.3%\n",
      "Progress: 36 / 40 total iterations (90.000%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.0798819\n",
      "2           3.617e-03  5.071e+00  sin(x0)\n",
      "4           8.540e-04  7.218e-01  (0.052563727 + sin(x0))\n",
      "\n",
      "==============================\n",
      "\n",
      "Cycles per second: 1.270e+04\n",
      "Head worker occupation: 1.3%\n",
      "Progress: 38 / 40 total iterations (95.000%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.0798819\n",
      "2           3.617e-03  5.071e+00  sin(x0)\n",
      "4           8.540e-04  7.218e-01  (0.052563727 + sin(x0))\n",
      "\n",
      "==============================\n",
      "\n",
      "Cycles per second: 1.280e+04\n",
      "Head worker occupation: 1.2%\n",
      "Progress: 39 / 40 total iterations (97.500%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.0798819\n",
      "2           3.617e-03  5.071e+00  sin(x0)\n",
      "4           8.540e-04  7.218e-01  (0.052563727 + sin(x0))\n",
      "\n",
      "==============================\n"
     ]
    }
   ],
   "source": [
    "equations = pysr(\n",
    "    X,\n",
    "    y,\n",
    "    niterations=2,\n",
    "    binary_operators=[\"+\", \"*\"],\n",
    "    unary_operators=[\n",
    "        \"cos\",\n",
    "        \"exp\",\n",
    "        \"sin\",\n",
    "        \"inv(x) = 1/x\",\n",
    "    ],\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cf9ea521",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sin(x0)\n"
     ]
    }
   ],
   "source": [
    "print(best(equations))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"r\"] = best_callable()(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='x0', ylabel='Y'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=\"x0\",y=\"Y\",hue=\"type\",data=df.melt(id_vars=[\"x0\"],value_vars=[\"y\",\"r\"],var_name=\"type\",value_name=\"Y\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on julia -O3 C:\\Users\\GEORGE~1\\AppData\\Local\\Temp\\tmpyrwd3f6_\\runfile.jl\n",
      "Activating environment on workers.\n",
      "      From worker 4:\t  Activating environment at `C:\\miniconda3\\Lib\\site-packages\\Project.toml`\n",
      "      From worker 3:\t  Activating environment at `C:\\miniconda3\\Lib\\site-packages\\Project.toml`\n",
      "      From worker 5:\t  Activating environment at `C:\\miniconda3\\Lib\\site-packages\\Project.toml`\n",
      "      From worker 2:\t  Activating environment at `C:\\miniconda3\\Lib\\site-packages\\Project.toml`\n",
      "Importing installed module on workers...Finished!\n",
      "Testing module on workers...Finished!\n",
      "Testing entire pipeline on workers...Finished!\n",
      "Started!\n",
      "\n",
      "Cycles per second: 4.330e+02\n",
      "Head worker occupation: 1.9%\n",
      "Progress: 1 / 100 total iterations (1.000%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.079908535\n",
      "3           1.576e-01  6.486e-01  (0.3497103 * x0)\n",
      "5           1.554e-01  6.884e-03  (0.04647951 + (0.34842828 * x0))\n",
      "10          2.782e-03  8.046e-01  (0.051279694 + ((0.87351865 * x0) + (-0.098690845 * cube(x0))))\n",
      "\n",
      "==============================\n",
      "\n",
      "Cycles per second: 9.180e+02\n",
      "Head worker occupation: 1.5%\n",
      "Progress: 2 / 100 total iterations (2.000%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.079908535\n",
      "3           1.576e-01  6.486e-01  (0.3497103 * x0)\n",
      "5           1.554e-01  6.884e-03  (0.04647951 + (0.34842828 * x0))\n",
      "10          2.782e-03  8.046e-01  (0.051279694 + ((0.87351865 * x0) + (-0.098690845 * cube(x0))))\n",
      "\n",
      "==============================\n",
      "\n",
      "Cycles per second: 4.600e+03\n",
      "Head worker occupation: 1.6%\n",
      "Progress: 4 / 100 total iterations (4.000%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.079908535\n",
      "3           1.576e-01  6.486e-01  (0.3497103 * x0)\n",
      "5           1.554e-01  6.884e-03  (0.04647951 + (0.34842828 * x0))\n",
      "8           5.405e-03  1.120e+00  ((0.874092 * x0) + (-0.09853267 * cube(x0)))\n",
      "10          2.782e-03  3.320e-01  (0.051279694 + ((0.87351865 * x0) + (-0.098690845 * cube(x0))))\n",
      "\n",
      "==============================\n",
      "\n",
      "Cycles per second: 4.950e+03\n",
      "Head worker occupation: 1.5%\n",
      "Progress: 5 / 100 total iterations (5.000%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.079908535\n",
      "3           1.576e-01  6.486e-01  (0.3497103 * x0)\n",
      "5           1.554e-01  6.884e-03  (0.04647951 + (0.34842828 * x0))\n",
      "8           5.405e-03  1.120e+00  ((0.874092 * x0) + (-0.09853267 * cube(x0)))\n",
      "10          2.782e-03  3.320e-01  (0.051279694 + ((0.87351865 * x0) + (-0.098690845 * cube(x0))))\n",
      "\n",
      "==============================\n",
      "\n",
      "Cycles per second: 5.160e+03\n",
      "Head worker occupation: 1.5%\n",
      "Progress: 6 / 100 total iterations (6.000%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.079908535\n",
      "3           1.576e-01  6.486e-01  (0.3497103 * x0)\n",
      "5           1.554e-01  6.884e-03  (0.04647951 + (0.34842828 * x0))\n",
      "6           6.054e-02  9.428e-01  (x0 + (-0.09853267 * cube(x0)))\n",
      "8           5.405e-03  1.208e+00  ((0.874092 * x0) + (-0.09853267 * cube(x0)))\n",
      "10          2.782e-03  3.320e-01  (0.051279694 + ((0.87351865 * x0) + (-0.098690845 * cube(x0))))\n",
      "\n",
      "==============================\n",
      "\n",
      "Cycles per second: 5.220e+03\n",
      "Head worker occupation: 1.5%\n",
      "Progress: 7 / 100 total iterations (7.000%)\n",
      "==============================\n",
      "Hall of Fame:\n",
      "-----------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           5.765e-01  1.192e-07  0.079908535\n",
      "3           1.576e-01  6.486e-01  (0.3497103 * x0)\n",
      "5           1.554e-01  6.884e-03  (0.04647951 + (0.34842828 * x0))\n",
      "6           1.297e-02  2.483e+00  (x0 + (-0.11894336 * cube(x0)))\n",
      "8           5.405e-03  4.378e-01  ((0.874092 * x0) + (-0.09853267 * cube(x0)))\n",
      "10          2.782e-03  3.320e-01  (0.051279694 + ((0.87351865 * x0) + (-0.098690845 * cube(x0))))\n",
      "\n",
      "==============================\n"
     ]
    }
   ],
   "source": [
    "equations = pysr(\n",
    "    X,\n",
    "    y,\n",
    "    niterations=5,\n",
    "    binary_operators=[\"+\", \"*\"],\n",
    "    unary_operators=[\n",
    "        \"square\",\n",
    "        \"cube\"\n",
    "    ],\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.11894336*x0**3 + x0\n"
     ]
    }
   ],
   "source": [
    "print(best(equations))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"t\"] = best_callable()(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='x0', ylabel='Y'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=\"x0\",y=\"Y\",hue=\"type\",data=df.melt(id_vars=[\"x0\"],value_vars=[\"y\",\"r\",\"t\"],var_name=\"type\",value_name=\"Y\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sin(x) = x - x^3 / 6 + ..."
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "10e1abf77ddef6b5a0d462bd0c1af8a6d69cacf984b7ff54f171d3af9bd75ca1"
  },
  "kernelspec": {
   "display_name": "Python 3.9.5 64-bit ('base': conda)",
   "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}