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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
2023-03-14 17:51:17 +08:00
"p_remove = 0.9\n",
affect customer / remove customer up product conditionally
2023-03-14 12:53:49 +08:00
"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
2023-02-28 16:56:12 +08:00
]
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"
]
connected firm no accept probability
2023-07-27 12:50:52 +08:00
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import networkx as nx\n",
"\n",
"G = nx.MultiDiGraph()\n",
"G.add_edge(1, 2)\n",
"nx.draw(G)\n",
"G.has_edge(1, 2, 1)"
]
},
debug
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{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n"
]
}
],
"source": [
"s = set()\n",
"s.add(1)\n",
"s.add(2)\n",
"s.add(1)\n",
"len(s)\n",
"if s:\n",
" print(1)"
]
},
connected firm no accept probability
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
model firm product
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}
],
"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
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"version": "3.8.8"
model firm product
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},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "bcdafc093860683ffb58d6956591562b7f8ed5d58147d17d71a5d4d6605a08df"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}