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model firm product
2023-02-27 22:02:46 +08:00
{
"cells": [
capacity init network
2023-02-28 16:56:12 +08:00
{
"cell_type": "code",
accept order conditionally
2023-03-14 17:51:17 +08:00
"execution_count": 14,
capacity init network
2023-02-28 16:56:12 +08:00
"metadata": {},
"outputs": [
{
affect customer / remove customer up product conditionally
2023-03-14 12:53:49 +08:00
"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"
]
accept order conditionally
2023-03-14 17:51:17 +08:00
},
{
"data": {
"text/plain": [
"array([2, 2])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
capacity init network
2023-02-28 16:56:12 +08:00
}
],
"source": [
"import numpy as np\n",
"\n",
affect customer / remove customer up product conditionally
2023-03-14 12:53:49 +08:00
"np.random.randint(0.5, 3.5)\n",
accept order conditionally
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"p_remove = 0.9\n",
affect customer / remove customer up product conditionally
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"np.random.choice([True, False], p=[p_remove, 1-p_remove])\n",
"rng = np.random.default_rng(0)\n",
"for _ in range(10):\n",
accept order conditionally
2023-03-14 17:51:17 +08:00
" 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"
capacity init network
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]
analysis
2023-05-15 10:42:16 +08:00
},
{
"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())"
]
with_exp
2023-05-21 17:05:06 +08:00
},
{
"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))"
]
sample removal + seek connect first
2023-06-10 18:59:38 +08:00
},
{
"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"
]
graph inconsistant
2023-06-10 23:48:11 +08:00
},
{
"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)"
]
larger sample than population when replace is False
2023-06-11 11:49:15 +08:00
},
{
"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"
]
dct_prod_up_prod_stat + no need for dct_list_remove_firm_prod/dct_list_disrupt_firm_prod
2023-06-18 17:29:11 +08:00
},
{
"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"
]
revenue reduce
2023-06-18 20:27:23 +08:00
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.25\n",
"2.25\n"
]
}
],
"source": [
"print(27 / (4 * 3))\n",
"print(27 / 4 / 3)"
]
log status of every firm product in every time step + mysql
2023-07-02 20:44:06 +08:00
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"for i in range(1,1):\n",
" print(i)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n"
]
},
{
"data": {
"text/plain": [
"[6, 7, 8, 9, 10]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lst = list(range(1,11))\n",
"print(lst)\n",
"\n",
"lst[-5:]"
]
experiments'
2023-07-03 12:40:10 +08:00
},
{
"cell_type": "code",
"execution_count": 7,
"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>e_id</th>\n",
" <th>n_disrupt_sample</th>\n",
" <th>total_n_disrupt_firm_prod_experiment</th>\n",
" <th>dct_lst_init_disrupt_firm_prod</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>383</td>\n",
" <td>50</td>\n",
" <td>300.0</td>\n",
" <td>b'\\x80\\x05\\x95\\x17\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>227</td>\n",
" <td>50</td>\n",
" <td>250.0</td>\n",
" <td>b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>83</td>\n",
" <td>50</td>\n",
" <td>200.0</td>\n",
" <td>b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>135</td>\n",
" <td>50</td>\n",
" <td>200.0</td>\n",
" <td>b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>179</td>\n",
" <td>50</td>\n",
" <td>200.0</td>\n",
" <td>b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>76</td>\n",
" <td>24</td>\n",
" <td>56.0</td>\n",
" <td>b'\\x80\\x05\\x95\\x14\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>89</td>\n",
" <td>24</td>\n",
" <td>54.0</td>\n",
" <td>b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>90</td>\n",
" <td>24</td>\n",
" <td>54.0</td>\n",
" <td>b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>335</td>\n",
" <td>24</td>\n",
" <td>54.0</td>\n",
" <td>b'\\x80\\x05\\x95\\x17\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>449</td>\n",
" <td>24</td>\n",
" <td>53.0</td>\n",
" <td>b'\\x80\\x05\\x95\\x15\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>95 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" e_id n_disrupt_sample total_n_disrupt_firm_prod_experiment \\\n",
"0 383 50 300.0 \n",
"1 227 50 250.0 \n",
"2 83 50 200.0 \n",
"3 135 50 200.0 \n",
"4 179 50 200.0 \n",
".. ... ... ... \n",
"90 76 24 56.0 \n",
"91 89 24 54.0 \n",
"92 90 24 54.0 \n",
"93 335 24 54.0 \n",
"94 449 24 53.0 \n",
"\n",
" dct_lst_init_disrupt_firm_prod \n",
"0 b'\\x80\\x05\\x95\\x17\\x00\\x00\\x00\\x00\\x00\\x00\\x00... \n",
"1 b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00... \n",
"2 b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00... \n",
"3 b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00... \n",
"4 b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00... \n",
".. ... \n",
"90 b'\\x80\\x05\\x95\\x14\\x00\\x00\\x00\\x00\\x00\\x00\\x00... \n",
"91 b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00... \n",
"92 b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00... \n",
"93 b'\\x80\\x05\\x95\\x17\\x00\\x00\\x00\\x00\\x00\\x00\\x00... \n",
"94 b'\\x80\\x05\\x95\\x15\\x00\\x00\\x00\\x00\\x00\\x00\\x00... \n",
"\n",
"[95 rows x 4 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with open('SQL_export_high_risk_setting.sql', 'r') as f:\n",
" contents = f.read()\n",
"\n",
"import pandas as pd\n",
"from orm import engine\n",
"result = pd.read_sql(sql=contents, con=engine)\n",
"result"
]
model firm product
2023-02-27 22:02:46 +08:00
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"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",
analysis
2023-05-15 10:42:16 +08:00
"version": "3.8.8"
model firm product
2023-02-27 22:02:46 +08:00
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "bcdafc093860683ffb58d6956591562b7f8ed5d58147d17d71a5d4d6605a08df"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}