567 lines
20 KiB
Plaintext
567 lines
20 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"id": "ab5cd7d1-1a57-46fc-8282-dae0a6cc2944",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import matplotlib.pyplot as plt\n",
|
|
"import numpy as np\n",
|
|
"import random\n",
|
|
"import pandas as pd"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"id": "3d1ad0b9-f6a8-4e98-84aa-6e02e4279954",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"DATABASE_OBJECT_COUNT = 100\n",
|
|
"CACHE_SIZE = DATABASE_OBJECT_COUNT/2\n",
|
|
"ZIPF_CONSTANT = 2\n",
|
|
"\n",
|
|
"CACHE_MISS_COST = 2\n",
|
|
"CACHE_REFRESH_COST = 1\n",
|
|
"\n",
|
|
"SEED = 42\n",
|
|
"np.random.seed(SEED)\n",
|
|
"random.seed(SEED)\n",
|
|
"\n",
|
|
"LAMBDA_VALUES = np.array([np.random.zipf(ZIPF_CONSTANT) for i in np.arange(1, DATABASE_OBJECT_COUNT + 1,1)])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"id": "9cc83cf6-5c78-4f0d-b7cb-08cdb80c362e",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# LAMBDA_VALUES = np.array([0.03, 0.04,0.05,0.06,0.07,1,1.1,1.2,1.3,1.4,1.5])\n",
|
|
"# DATABASE_OBJECT_COUNT = len(LAMBDA_VALUES)\n",
|
|
"# CACHE_SIZE = 4.4\n",
|
|
"# CACHE_MISS_COST = 7\n",
|
|
"# CACHE_REFRESH_COST = 1"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"id": "3dc07233-0b56-4fee-a93b-212836c18b42",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"db_object_count = DATABASE_OBJECT_COUNT\n",
|
|
"cache_sz = CACHE_SIZE\n",
|
|
"\n",
|
|
"lambda_vals = LAMBDA_VALUES\n",
|
|
"c_f = CACHE_MISS_COST\n",
|
|
"c_delta = CACHE_REFRESH_COST"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"id": "5a27d416-8f98-4814-af9e-6c6bef95f4ef",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def eta_star(db_object_count, c_f, cache_sz, c_delta, lambda_vals):\n",
|
|
" num = (db_object_count * c_f - cache_sz * c_delta)\n",
|
|
" denom = np.sum(1.0/lambda_vals)\n",
|
|
" return max(0, num/denom)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "6276a9ce-f839-4fe6-90f2-2195cf065fc8",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def h_i_star(c_f, eta, lambda_vals, c_delta):\n",
|
|
" optimized_hitrate = (c_f - (eta/lambda_vals)) / c_delta\n",
|
|
" return optimized_hitrate"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "dcd31a8c-6864-4b9a-8bb3-998f0c32baf6",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def get_index_of_furthest_hitrate_from_boundary(hitrates):\n",
|
|
" lower_bound_violation = hitrates[(hitrates < 0)]\n",
|
|
" upper_bound_violation = hitrates[(hitrates > 1)]\n",
|
|
" smallest_delta = np.abs(np.min(lower_bound_violation))\n",
|
|
" biggest_delta = np.max(upper_bound_violation) - 1\n",
|
|
" if smallest_delta > biggest_delta:\n",
|
|
" print(smallest_delta)\n",
|
|
" index = np.where(hitrates == np.min(local_hitrates))[0][0]\n",
|
|
" return index\n",
|
|
" else:\n",
|
|
" \n",
|
|
" index = np.where(hitrates == np.max(local_hitrates))[0][0]\n",
|
|
" return index"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "9d774304-ae68-43b3-a76a-e970c06c5236",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def get_index_of_furthest_hitrate_from_boundary(hitrates):\n",
|
|
" outside_bounds = (hitrates < 0) | (hitrates > 1)\n",
|
|
" distances = np.where(outside_bounds, np.maximum(np.abs(hitrates - 0), np.abs(hitrates - 1)), -np.inf)\n",
|
|
" index = np.argmax(distances)\n",
|
|
" return index"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "19678083-15e1-439b-be8c-42033d501644",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([ 1, 3, 1, 1, 2, 1, 5, 1, 1, 1, 2, 1, 1, 1, 2, 2, 1,\n",
|
|
" 1, 3, 1, 1, 1, 1, 2, 1, 1, 1, 5, 1, 1, 1, 4, 1, 4,\n",
|
|
" 1, 1, 1, 3, 8, 1, 4, 4, 2, 1, 1, 1, 10, 1, 1, 1, 5,\n",
|
|
" 9, 1, 1, 1, 1, 1, 17, 2, 1, 26, 1, 1, 2, 1, 10, 1, 69,\n",
|
|
" 1, 1, 2, 1, 1, 1, 3, 2, 2, 3, 15, 1, 1, 5, 2, 1, 1,\n",
|
|
" 2, 1, 2, 1, 1, 2, 2, 3, 1, 2, 1, 1, 37, 4, 2])"
|
|
]
|
|
},
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"lambda_vals"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "ccd4b95d-1cdd-4c99-a22e-4b31338993cf",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2.1159070575516945\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([-0.11590706, 1.29469765, -0.11590706, -0.11590706, 0.94204647,\n",
|
|
" -0.11590706, -0.11590706, -0.11590706, -0.11590706, 0.94204647,\n",
|
|
" -0.11590706, -0.11590706, -0.11590706, 0.94204647, 0.94204647,\n",
|
|
" -0.11590706, -0.11590706, 1.29469765, -0.11590706, -0.11590706,\n",
|
|
" -0.11590706, -0.11590706, 0.94204647, -0.11590706, -0.11590706,\n",
|
|
" -0.11590706, -0.11590706, -0.11590706, -0.11590706, 1.47102324,\n",
|
|
" -0.11590706, 1.47102324, -0.11590706, -0.11590706, -0.11590706,\n",
|
|
" 1.29469765, 1.73551162, -0.11590706, 1.47102324, 1.47102324,\n",
|
|
" 0.94204647, -0.11590706, -0.11590706, -0.11590706, 1.78840929,\n",
|
|
" -0.11590706, -0.11590706, -0.11590706, 1.76489922, -0.11590706,\n",
|
|
" -0.11590706, -0.11590706, -0.11590706, -0.11590706, 1.87553488,\n",
|
|
" 0.94204647, -0.11590706, 1.91861896, -0.11590706, -0.11590706,\n",
|
|
" 0.94204647, -0.11590706, 1.78840929, -0.11590706, 1.96933468,\n",
|
|
" -0.11590706, -0.11590706, 0.94204647, -0.11590706, -0.11590706,\n",
|
|
" -0.11590706, 1.29469765, 0.94204647, 0.94204647, 1.29469765,\n",
|
|
" 1.85893953, -0.11590706, -0.11590706, 0.94204647, -0.11590706,\n",
|
|
" -0.11590706, 0.94204647, -0.11590706, 0.94204647, -0.11590706,\n",
|
|
" -0.11590706, 0.94204647, 0.94204647, 1.29469765, -0.11590706,\n",
|
|
" 0.94204647, -0.11590706, -0.11590706, 1.94281332, 1.47102324,\n",
|
|
" 0.94204647])"
|
|
]
|
|
},
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"eta = eta_star(db_object_count, c_f, cache_sz, c_delta, lambda_vals[lambda_vals != lambda_vals[6]])\n",
|
|
"print(eta)\n",
|
|
"optimized_hitrates = (c_f - eta / lambda_vals[lambda_vals != lambda_vals[6]]) / c_delta\n",
|
|
"optimized_hitrates"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "05b17074-719f-4bca-8434-2aaee26094d0",
|
|
"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>0</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>count</th>\n",
|
|
" <td>96.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>mean</th>\n",
|
|
" <td>0.437500</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>std</th>\n",
|
|
" <td>0.726101</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>min</th>\n",
|
|
" <td>-0.115907</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>25%</th>\n",
|
|
" <td>-0.115907</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>50%</th>\n",
|
|
" <td>-0.115907</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>75%</th>\n",
|
|
" <td>0.942046</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>max</th>\n",
|
|
" <td>1.969335</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" 0\n",
|
|
"count 96.000000\n",
|
|
"mean 0.437500\n",
|
|
"std 0.726101\n",
|
|
"min -0.115907\n",
|
|
"25% -0.115907\n",
|
|
"50% -0.115907\n",
|
|
"75% 0.942046\n",
|
|
"max 1.969335"
|
|
]
|
|
},
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"pd.DataFrame(optimized_hitrates).describe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "0e21c26f-058a-4e56-a5ad-1c47bf28656c",
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Optimized: 67 1.97 // [ 1.79077042 -0.09229584 1. -0.09229584 -0.09229584]\n",
|
|
"Optimized: 97 1.94 // [-0.07876743 -0.07876743 1. 1.48030814 0.96061628]\n",
|
|
"Optimized: 60 1.92 // [ 0.96720258 -0.06559484 1. -0.06559484 -0.06559484]\n",
|
|
"Optimized: 57 1.88 // [-0.05274002 -0.05274002 1. 0.97362999 -0.05274002]\n",
|
|
"Optimized: 78 1.86 // [ 0.97977406 1.31984937 1. -0.04045188 -0.04045188]\n",
|
|
"Optimized: 46 1.80 // [-0.02836604 -0.02836604 1. -0.02836604 -0.02836604]\n",
|
|
"Optimized: 65 1.80 // [ 0.99140044 -0.01719911 1. -0.01719911 1. ]\n",
|
|
"Optimized: 51 1.78 // [-0.00600086 1.59879983 1. -0.00600086 -0.00600086]\n",
|
|
"Optimized: 38 1.75 // [0.00491746 1.33497249 1. 0.00491746 1.50122936]\n",
|
|
"Optimized: 6 1.60 // [1.00774103 0.01548205 1. 0.01548205 0.01548205]\n",
|
|
"Optimized: 27 1.60 // [0.02399435 0.02399435 1. 0.02399435 0.02399435]\n",
|
|
"Optimized: 50 1.61 // [0.03255485 0.03255485 1. 1. 0.03255485]\n",
|
|
"Optimized: 81 1.61 // [0.04116395 0.04116395 1. 1.02058197 0.04116395]\n",
|
|
"Optimized: 31 1.51 // [0.04982206 0.04982206 1. 0.04982206 1.51245552]\n",
|
|
"Optimized: 33 1.51 // [1. 0.05714286 1. 0.05714286 0.05714286]\n",
|
|
"Optimized: 40 1.52 // [1. 0.06451613 1. 1.51612903 1.03225806]\n",
|
|
"Optimized: 41 1.52 // [0.07194245 1. 1. 1.03597122 0.07194245]\n",
|
|
"Optimized: 98 1.52 // [0.07942238 1. 1. 1.03971119]\n",
|
|
"Optimized: 1 1.36 // []\n",
|
|
"Optimized: 18 1.36 // [0.09223301 0.09223301 1. 0.09223301 0.09223301]\n",
|
|
"Optimized: 37 1.37 // [0.09756098 0.09756098 1. 1. 0.09756098]\n",
|
|
"Optimized: 74 1.37 // [0.10294118 0.10294118 1. 1.05147059 1.05147059]\n",
|
|
"Optimized: 77 1.37 // [1.05418719 1.05418719 1. 1. 0.10837438]\n",
|
|
"Optimized: 92 1.37 // [1.05693069 1.05693069 1. 0.11386139 1.05693069]\n",
|
|
"Optimized: 4 1.06 // [0.11940299 0.11940299 1. 0.11940299 1. ]\n",
|
|
"Optimized: 10 1.06 // [0.12030075 0.12030075 1. 0.12030075 0.12030075]\n",
|
|
"Optimized: 14 1.06 // [0.12121212 0.12121212 1. 1.06060606 0.12121212]\n",
|
|
"Optimized: 15 1.06 // [0.1221374 1. 1. 0.1221374 0.1221374]\n",
|
|
"Optimized: 23 1.06 // [0.12307692 0.12307692 1. 0.12307692 0.12307692]\n",
|
|
"Optimized: 42 1.06 // [1. 1. 1. 0.12403101 0.12403101]\n",
|
|
"Optimized: 58 1.06 // [0.125 1. 1. 0.125 1. ]\n",
|
|
"Optimized: 63 1.06 // [0.12598425 0.12598425 1. 0.12598425 1. ]\n",
|
|
"Optimized: 70 1.06 // [0.12698413 0.12698413 1. 0.12698413 0.12698413]\n",
|
|
"Optimized: 75 1.06 // [0.128 1. 1. 1.064 1. ]\n",
|
|
"Optimized: 76 1.06 // [1. 1. 1. 1. 1.]\n",
|
|
"Optimized: 82 1.07 // [0.1300813 1. 1. 0.1300813 0.1300813]\n",
|
|
"Optimized: 85 1.07 // [0.13114754 0.13114754 1. 0.13114754 1.06557377]\n",
|
|
"Optimized: 87 1.07 // [1. 0.1322314 1. 0.1322314 0.1322314]\n",
|
|
"Optimized: 90 1.07 // [0.13333333 0.13333333 1. 1.06666667 1. ]\n",
|
|
"Optimized: 91 1.07 // [0.13445378 1. 1. 1. 0.13445378]\n",
|
|
"Optimized: 94 1.07 // [1. 0.13559322 1. 0.13559322 0.13559322]\n",
|
|
"Optimized: 99 1.07 // [1. 1. 1.]\n",
|
|
"All values optimized.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"\"\"\"\n",
|
|
"Perform theoretical optimization to compute optimal hit probabilities.\n",
|
|
"\n",
|
|
"Parameters:\n",
|
|
"- lambda_vals (numpy array): Request rates for each item.\n",
|
|
"- B (float): Total cache size.\n",
|
|
"- c_f (float): Fetching linear cost (cache miss cost).\n",
|
|
"- c_delta (float): Age linear cost.\n",
|
|
"\n",
|
|
"Returns:\n",
|
|
"- h_opt (numpy array): Optimal hit probabilities for each item.\n",
|
|
"\"\"\"\n",
|
|
"optimized_hitrates = np.zeros(DATABASE_OBJECT_COUNT)\n",
|
|
"current_db_object_count = DATABASE_OBJECT_COUNT\n",
|
|
"current_cache_size = CACHE_SIZE\n",
|
|
"\n",
|
|
"differenc_set = np.arange(DATABASE_OBJECT_COUNT)\n",
|
|
"fix_i = []\n",
|
|
"\n",
|
|
"while True:\n",
|
|
" if current_db_object_count == 0:\n",
|
|
" print(\"No objects left to optimize.\")\n",
|
|
" if current_cache_size > 0:\n",
|
|
" print(\"Add obj with optimized hitrate 0 and add them to optimization pool for re-optimization.\")\n",
|
|
" # Redistribute unused cache size among items with zero hit probability\n",
|
|
" differenc_set = np.where(optimized_hitrates == 0)[0]\n",
|
|
" fix_i = np.setdiff1d(np.arange(DATABASE_OBJECT_COUNT), differenc_set).tolist()\n",
|
|
" current_db_object_count = len(differenc_set)\n",
|
|
" continue\n",
|
|
" else:\n",
|
|
" \"Reset\"\n",
|
|
" optimized_hitrates[differenc_set] = 0\n",
|
|
" break\n",
|
|
" # Compute Lagrangian multiplier and optimal hit probabilities\n",
|
|
" eta = eta_star(current_db_object_count, c_f, current_cache_size, c_delta, lambda_vals[differenc_set])\n",
|
|
" optimized_hitrates[differenc_set] = (c_f - eta / lambda_vals[differenc_set]) / c_delta\n",
|
|
" if eta < 0:\n",
|
|
" print(\"eta was negative.\")\n",
|
|
" current_cache_size = current_db_object_count * c_f / c_delta # Adjust cache size for next iteration\n",
|
|
" continue\n",
|
|
" \n",
|
|
" if len((optimized_hitrates[differenc_set])[((optimized_hitrates[differenc_set]) < 0) | ((optimized_hitrates[differenc_set])> 1)]) == 0:\n",
|
|
" print(\"All values optimized.\")\n",
|
|
" break\n",
|
|
" \n",
|
|
" max_outbound_index = get_index_of_furthest_hitrate_from_boundary(optimized_hitrates)\n",
|
|
" fix_i.append(max_outbound_index)\n",
|
|
" differenc_set = np.setdiff1d(np.arange(DATABASE_OBJECT_COUNT), fix_i)\n",
|
|
"\n",
|
|
" old_hitrate = optimized_hitrates[max_outbound_index]\n",
|
|
" optimized_hitrates[max_outbound_index] = (1 if optimized_hitrates[max_outbound_index] > 1 else 0)\n",
|
|
" \n",
|
|
" print(f\"Optimized: {max_outbound_index} {old_hitrate:.2f} // {optimized_hitrates[max_outbound_index-2:max_outbound_index+3]}\")\n",
|
|
" \n",
|
|
" current_db_object_count -= 1\n",
|
|
" current_cache_size -= optimized_hitrates[max_outbound_index]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "f559ee7a-be2f-4076-b01c-f08950ad5a88",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([0.13793103, 1. , 0.13793103, 0.13793103, 1. ,\n",
|
|
" 0.13793103, 1. , 0.13793103, 0.13793103, 0.13793103,\n",
|
|
" 1. , 0.13793103, 0.13793103, 0.13793103, 1. ,\n",
|
|
" 1. , 0.13793103, 0.13793103, 1. , 0.13793103,\n",
|
|
" 0.13793103, 0.13793103, 0.13793103, 1. , 0.13793103,\n",
|
|
" 0.13793103, 0.13793103, 1. , 0.13793103, 0.13793103,\n",
|
|
" 0.13793103, 1. , 0.13793103, 1. , 0.13793103,\n",
|
|
" 0.13793103, 0.13793103, 1. , 1. , 0.13793103,\n",
|
|
" 1. , 1. , 1. , 0.13793103, 0.13793103,\n",
|
|
" 0.13793103, 1. , 0.13793103, 0.13793103, 0.13793103,\n",
|
|
" 1. , 1. , 0.13793103, 0.13793103, 0.13793103,\n",
|
|
" 0.13793103, 0.13793103, 1. , 1. , 0.13793103,\n",
|
|
" 1. , 0.13793103, 0.13793103, 1. , 0.13793103,\n",
|
|
" 1. , 0.13793103, 1. , 0.13793103, 0.13793103,\n",
|
|
" 1. , 0.13793103, 0.13793103, 0.13793103, 1. ,\n",
|
|
" 1. , 1. , 1. , 1. , 0.13793103,\n",
|
|
" 0.13793103, 1. , 1. , 0.13793103, 0.13793103,\n",
|
|
" 1. , 0.13793103, 1. , 0.13793103, 0.13793103,\n",
|
|
" 1. , 1. , 1. , 0.13793103, 1. ,\n",
|
|
" 0.13793103, 0.13793103, 1. , 1. , 1. ])"
|
|
]
|
|
},
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"optimized_hitrates"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "8b2d3cea-1cc0-476e-92bf-2ac4344a9b1b",
|
|
"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>0</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>count</th>\n",
|
|
" <td>100.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>mean</th>\n",
|
|
" <td>0.500000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>std</th>\n",
|
|
" <td>0.427625</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>min</th>\n",
|
|
" <td>0.137931</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>25%</th>\n",
|
|
" <td>0.137931</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>50%</th>\n",
|
|
" <td>0.137931</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>75%</th>\n",
|
|
" <td>1.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>max</th>\n",
|
|
" <td>1.000000</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" 0\n",
|
|
"count 100.000000\n",
|
|
"mean 0.500000\n",
|
|
"std 0.427625\n",
|
|
"min 0.137931\n",
|
|
"25% 0.137931\n",
|
|
"50% 0.137931\n",
|
|
"75% 1.000000\n",
|
|
"max 1.000000"
|
|
]
|
|
},
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"pd.DataFrame(optimized_hitrates).describe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "7a998837-72b8-4039-95a5-ca8d9c8e65ab",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "graphs",
|
|
"language": "python",
|
|
"name": "graphs"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.12.7"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|