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Mar 10, 2025
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Quick notebooks #17

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d47a06c
Quick assurance computation notebook
smjoshiatglobus Mar 9, 2025
93f7ab5
Fixed error message
smjoshiatglobus Mar 9, 2025
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2 changes: 2 additions & 0 deletions Pipfile
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Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,8 @@ matplotlib = "*"
pexpect = "*"
seaborn = "*"
tabulate = "*"
numpy = "*"
asttokens = "*"

[dev-packages]
black = "*"
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204 changes: 204 additions & 0 deletions notebooks/assurance.ipynb
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Original file line number Diff line number Diff line change
@@ -0,0 +1,204 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reliable Statistics: Assurance\n",
"\n",
"Assurance provides optimal reliability and confidence levels given number of\n",
"samples and number of failures.\n",
"\n",
"### Setup\n",
"\n",
"- Local use: see instructions in `README.md` in the project's root directory.\n",
"- [Google colab](https://colab.research.google.com/github/sanjaymjoshi/relistats/blob/main/notebooks/assurance.ipynb) use: Execute the code block below for set up."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%pip install relistats\n",
"%pip install tabulate"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Assurance 75.2% at 10 samples with 1 failures\n"
]
}
],
"source": [
"#@title Assurance {vertical-output: true}\n",
"from relistats.binomial import assurance\n",
"\n",
"num_samples = 10 #@param {type:\"integer\"}\n",
"num_failures = 1 #@param {type:\"integer\"}\n",
"assurance_value = assurance(num_samples, num_failures)\n",
"\n",
"print(f\"Assurance {assurance_value*100:.1f}% at {num_samples} samples with {num_failures} failures\")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Assurance below desired level at 0 failures: 96.7%\n"
]
},
{
"ename": "AssertionError",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[19], line 12\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m aa0 \u001b[38;5;241m<\u001b[39m assurance_pct \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m100\u001b[39m:\n\u001b[0;32m 11\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAssurance below desired level at 0 failures: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00maa0\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.1f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 12\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 14\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m assurance(num_samples, num_failures) \u001b[38;5;241m>\u001b[39m assurance_pct \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m100\u001b[39m:\n\u001b[0;32m 15\u001b[0m num_failures \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
"\u001b[1;31mAssertionError\u001b[0m: "
]
}
],
"source": [
"# @title Maximum Tolerable Failures {vertical-output: true}\n",
"from relistats.binomial import assurance\n",
"\n",
"assurance_pct = 99 # @param {type:\"integer\"}\n",
"num_samples = 100 # @param {type:\"integer\"}\n",
"\n",
"# Start with 0 failures and increase until we reach the desired assurance\n",
"num_failures = 0\n",
"aa0 = assurance(num_samples, 0)\n",
"if aa0 < assurance_pct / 100:\n",
" print(f\"Assurance below desired level at 0 failures: {aa0*100:.1f}%\")\n",
" assert False\n",
"\n",
"while assurance(num_samples, num_failures) > assurance_pct / 100:\n",
" num_failures += 1\n",
"aa1 = assurance(num_samples, num_failures-1)\n",
"print(\n",
" f\"{num_failures} failures out of {num_samples} yield assurance {aa1*100:.1f}% >= {assurance_pct}%\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Minimum 152 samples needed to tolerate 10 failures for assurance 90.1% >= 90%\n"
]
}
],
"source": [
"# @title Minimum Samples Needed {vertical-output: true}\n",
"from relistats.binomial import assurance\n",
"\n",
"assurance_pct = 90 # @param {type:\"integer\"}\n",
"num_failures = 10 # @param {type:\"integer\"}\n",
"\n",
"# Start with 0 failures and increase until we reach the desired assurance\n",
"num_samples = num_failures + 1\n",
"\n",
"while assurance(num_samples, num_failures) < assurance_pct / 100:\n",
" num_samples += 1\n",
"\n",
"aa1 = assurance(num_samples, num_failures)\n",
"print(\n",
" f\"Minimum {num_samples} samples needed to tolerate {num_failures} failures for assurance {aa1*100:.1f}% >= {assurance_pct}%\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Assurance levels with 2 failure(s).\n",
"Starting number of samples for each row is in first column.\n",
" n % % % % %\n",
"--- ---- ---- ---- ---- ----\n",
" 10 67.7 69.6 71.3 72.9 74.2\n",
" 15 75.4 76.4 77.4 78.3 79.1\n",
" 20 79.8 80.6 81.2 81.8 82.3\n",
" 25 82.8 83.3 83.8 84.2 84.6\n",
" 30 85.0 85.3 85.6 86.0 86.3\n",
" 35 86.6 86.9 87.2 87.4 87.6\n",
" 40 87.9 88.1 88.3 88.5 88.7\n",
" 45 88.9 89.1 89.3 89.4 89.6\n"
]
}
],
"source": [
"#@title Assurance Table {vertical-output: true}\n",
"from relistats.binomial import assurance\n",
"from tabulate import tabulate\n",
"table_format = \"simple\" # for notebook\n",
"\n",
"n_start = 10 #@param {type:\"integer\"}\n",
"n_end = 50 #@param {type:\"integer\"}\n",
"n_step = 1 #@param {type:\"integer\"}\n",
"n_per_row = 5 #@param {type:\"integer\"}\n",
"\n",
"num_failures = 2 #@param {type:\"integer\"}\n",
"\n",
"all_a = []\n",
"all_n_row_starts = range(n_start, n_end, n_per_row)\n",
"for n_row_start in all_n_row_starts:\n",
" nn = range(n_row_start, n_row_start+n_step*n_per_row, n_step)\n",
" # assurance may return None, so provide default of 0\n",
" aa = [(assurance(n, num_failures) or 0)*100 for n in nn]\n",
" all_a.append(aa)\n",
"\n",
"nh = range(n_start, n_start+n_step*n_per_row, n_step)\n",
"headers = [\"n\"] + [\"%\" for _ in nh]\n",
"print(f\"Assurance levels with {num_failures} failure(s).\")\n",
"print(\"Starting number of samples for each row is in first column.\")\n",
"print(tabulate(all_a, tablefmt=table_format, headers=headers, floatfmt=\".1f\", showindex=all_n_row_starts))\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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",
"version": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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