{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# TriScale Demo\n", "\n", "This notebook contains a short demonstration of _TriScale_ API, how the different functions are meant to be used, and the visualizations they produce. \n", "\n", "For more details about _TriScale,_ you may refer to [the paper](https://doi.org/10.5281/zenodo.3464273).\n", "\n", "- [List of Imports](#List-of-Imports)\n", "- [Experiment Sizing](#Experiment-Sizing)\n", "- [Runs and Metrics](#Runs-and-Metrics)\n", "- [Series and KPIs](#Series-and-KPIs)\n", "- [Sequels and Variability Scores](#Sequels-and-Variability-Scores)\n", "- [Network Profiling](#Network-Profiling)\n", "\n", "## List of Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "from pathlib import Path\n", "\n", "import pandas as pd\n", "import numpy as np\n", "\n", "import triscale" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Experiment Sizing\n", "\n", "[Back to top](#TriScale-Demo)\n", "\n", "During the design phase of an experiment, one important question to answer is \"how many time should the experiments be performed?\" This question directly relates to the definition of _TriScale_ KPIs and variability scores. \n", "\n", "_TriScale_ implements a statistical method that allows to estimate, based on a data sample, any percentile of the underlying distribution with any level of confidence. Importantly, the estimation does not rely on any assumption on the nature of the underlying distribution (eg normal, or Poisson). The estimate is valid as long as the sample is independent and identically distributed (or _iid_ ).\n", "\n", "Intuitively, it is \"easier\" to estimate the median (50th percentile) than the 99th percentile; the more extreme the percentile, the more samples are required to provide an estimate for a given level of confidence. More precisely, the minimal number of sample $N$ required to estimate a percentile $0
\n", "\n", "
\n", " | 0.1 | \n", "1.0 | \n", "5.0 | \n", "10.0 | \n", "25.0 | \n", "50.0 | \n", "75.0 | \n", "90.0 | \n", "95.0 | \n", "99.0 | \n", "99.9 | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|
Confidence level | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
75.00 | \n", "1386 | \n", "138 | \n", "28 | \n", "14 | \n", "5 | \n", "2 | \n", "5 | \n", "14 | \n", "28 | \n", "138 | \n", "1386 | \n", "
90.00 | \n", "2302 | \n", "230 | \n", "45 | \n", "22 | \n", "9 | \n", "4 | \n", "9 | \n", "22 | \n", "45 | \n", "230 | \n", "2302 | \n", "
95.00 | \n", "2995 | \n", "299 | \n", "59 | \n", "29 | \n", "11 | \n", "5 | \n", "11 | \n", "29 | \n", "59 | \n", "299 | \n", "2995 | \n", "
99.00 | \n", "4603 | \n", "459 | \n", "90 | \n", "44 | \n", "17 | \n", "7 | \n", "17 | \n", "44 | \n", "90 | \n", "459 | \n", "4603 | \n", "
99.90 | \n", "6905 | \n", "688 | \n", "135 | \n", "66 | \n", "25 | \n", "10 | \n", "25 | \n", "66 | \n", "135 | \n", "688 | \n", "6905 | \n", "
99.99 | \n", "9206 | \n", "917 | \n", "180 | \n", "88 | \n", "33 | \n", "14 | \n", "33 | \n", "88 | \n", "180 | \n", "917 | \n", "9206 | \n", "
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