IIabm/test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "85\n",
      "63\n",
      "51\n",
      "26\n",
      "30\n",
      "4\n",
      "7\n",
      "1\n",
      "17\n",
      "81\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2, 2])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.random.randint(0.5, 3.5)\n",
    "p_remove = 0.9\n",
    "np.random.choice([True, False], p=[p_remove, 1-p_remove])\n",
    "rng = np.random.default_rng(0)\n",
    "for _ in range(10):\n",
    "    print(rng.integers(0,100))\n",
    "np.random.choice([1, 2, 3], 2, p=[0.4, 0.4, 0.2])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "share = 0.8\n",
    "list_succ_firms = [1, 1]\n",
    "round(share * len(list_succ_firms)) if round(share * len(list_succ_firms)) > 0 else 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.17307692307692307, 0.19230769230769232, 0.20192307692307693, 0.21153846153846154, 0.22115384615384615]\n",
      "[0.14899116146026878, 0.1819782155490595, 0.20111703154812216, 0.22226869439668717, 0.24564489704586234]\n",
      "[0.10801741721030356, 0.16114305076975205, 0.19682056666851946, 0.2403971829915773, 0.29362178235984765]\n",
      "[0.07643198434626533, 0.13926815562848321, 0.18799234648997357, 0.25376312466637047, 0.34254438886890737]\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "size = [18,20,21,22,23]\n",
    "p = [s / sum(size) for s in size]\n",
    "print(p)\n",
    "for beta in [0.1, 0.2, 0.3]:\n",
    "    damp_size = [math.exp(beta*s) for s in size]\n",
    "    print([s / sum(damp_size) for s in damp_size])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.16666666666666666, 0.5, 0.6666666666666666, 0.8333333333333334, 1.0]\n",
      "[0.8359588020779368, 0.9330329915368074, 0.960264500792218, 0.9819330445619127, 1.0]\n",
      "[0.408248290463863, 0.7071067811865476, 0.816496580927726, 0.9128709291752769, 1.0]\n",
      "[0.23849484685087588, 0.5743491774985174, 0.7229811807984657, 0.8642810744472068, 1.0]\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "size = [18,20,21,22,23]\n",
    "p = [(s - min(size) + 1)/(max(size)-min(size)+1) for s in size]\n",
    "print(p)\n",
    "for beta in [0.1, 0.5, 0.8]:\n",
    "    p = [((s - min(size) + 1)/(max(size)-min(size)+1))**beta for s in size]\n",
    "    print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32\n"
     ]
    }
   ],
   "source": [
    "import multiprocess as mp\n",
    "\n",
    "print(mp.cpu_count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71\n"
     ]
    }
   ],
   "source": [
    "from orm import engine\n",
    "import pandas as pd\n",
    "import pickle\n",
    "str_sql = \"select e_id, count, max_max_ts, dct_lst_init_remove_firm_prod from iiabmdb.without_exp_experiment as a \" \\\n",
    "\"inner join \" \\\n",
    "\"(select e_id, count(id) as count, max(max_ts) as max_max_ts from iiabmdb.without_exp_sample as a \" \\\n",
    "\"inner join (select s_id, max(ts) as max_ts from iiabmdb.without_exp_result where ts > 0 group by s_id) as b \" \\\n",
    "\"on a.id = b.s_id \" \\\n",
    "\"group by e_id) as b \" \\\n",
    "\"on a.id = b.e_id \" \\\n",
    "\"order by count desc;\"\n",
    "result = pd.read_sql(sql=str_sql, con=engine)\n",
    "result['dct_lst_init_remove_firm_prod'] = result['dct_lst_init_remove_firm_prod'].apply(lambda x: pickle.loads(x))\n",
    "# print(result)\n",
    "list_dct = result.loc[result['count']>=9, 'dct_lst_init_remove_firm_prod'].to_list()\n",
    "print(len(list_dct))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "probabilities do not sum to 1",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[2], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m np\u001b[39m.\u001b[39;49mrandom\u001b[39m.\u001b[39;49mchoice([\u001b[39m1\u001b[39;49m], p\u001b[39m=\u001b[39;49m[\u001b[39m0.9\u001b[39;49m])\n",
      "File \u001b[1;32mmtrand.pyx:933\u001b[0m, in \u001b[0;36mnumpy.random.mtrand.RandomState.choice\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: probabilities do not sum to 1"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.random.choice([1], p=[0.9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.004495606232695251"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_remove = 0\n",
    "prob_remove = np.random.uniform(\n",
    "    prob_remove - 0.1, prob_remove + 0.1)\n",
    "prob_remove = 1 if prob_remove > 1 else prob_remove\n",
    "prob_remove = 0 if prob_remove < 0 else prob_remove\n",
    "prob_remove"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8]\n"
     ]
    }
   ],
   "source": [
    "nprandom = np.random.default_rng(0)\n",
    "lst_choose_firm = nprandom.choice(range(10),\n",
    "                                       1,\n",
    "                                       replace=False\n",
    "                                       )\n",
    "print(lst_choose_firm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Cannot take a larger sample than population when replace is False",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[9], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m nprandom \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mrandom\u001b[39m.\u001b[39mdefault_rng(\u001b[39m0\u001b[39m)\n\u001b[1;32m----> 2\u001b[0m lst_choose_firm \u001b[39m=\u001b[39m nprandom\u001b[39m.\u001b[39;49mchoice([\u001b[39m1\u001b[39;49m,\u001b[39m2\u001b[39;49m],\n\u001b[0;32m      3\u001b[0m                                   \u001b[39m3\u001b[39;49m,\n\u001b[0;32m      4\u001b[0m                                   replace\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m\n\u001b[0;32m      5\u001b[0m                                   )\n\u001b[0;32m      6\u001b[0m lst_choose_firm\n",
      "File \u001b[1;32m_generator.pyx:753\u001b[0m, in \u001b[0;36mnumpy.random._generator.Generator.choice\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Cannot take a larger sample than population when replace is False"
     ]
    }
   ],
   "source": [
    "nprandom = np.random.default_rng(0)\n",
    "lst_choose_firm = nprandom.choice([1,2],\n",
    "                                  3,\n",
    "                                  replace=False\n",
    "                                  )\n",
    "lst_choose_firm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0\n",
      "0 1\n",
      "0 2\n",
      "1 0\n",
      "1 1\n",
      "break\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for j in range(3):\n",
    "    for k in range(3):\n",
    "        print(j, k)\n",
    "        if j == k == 1:\n",
    "            print('break')\n",
    "            break\n",
    "    else:\n",
    "        continue\n",
    "    break\n",
    "\n"
   ]
  }
 ],
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