From a0a47512102cb85d8466c3dd3f024e9343aef9fd Mon Sep 17 00:00:00 2001 From: "Steve Nyemba, The Architect" Date: Thu, 18 Oct 2018 03:51:04 -0500 Subject: [PATCH] analysis of 500 runs --- notebooks/data-analysis.ipynb | 214 +++++++ src/out/risk_xoi.csv | 1002 ++++++++++++++++----------------- 2 files changed, 715 insertions(+), 501 deletions(-) create mode 100644 notebooks/data-analysis.ipynb diff --git a/notebooks/data-analysis.ipynb b/notebooks/data-analysis.ipynb new file mode 100644 index 0000000..d7c44c2 --- /dev/null +++ b/notebooks/data-analysis.ipynb @@ -0,0 +1,214 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "df = pd.read_csv('../src/out/risk_xoi.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Unnamed: 0 group_count row_count marketer prosecutor field_count\n", + "0 0 432512 79080802 0.005469 1 10\n", + "1 0 17824004 79080802 0.225390 1 28\n", + "2 0 43538084 79080802 0.550552 1 38\n", + "3 0 64042788 79080802 0.809840 1 46\n", + "4 0 6866070 79080802 0.086823 1 17" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "compiled = df.groupby('field_count')[['field_count','marketer','prosecutor']].mean()\n", + "figure = compiled[['marketer','prosecutor']].plot.line().get_figure()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "variables": { + " figure ": "" + } + }, + "source": [ + "# Dataset Used\n", + "---\n", + "\n", + "We performed joins against all the tables from all-of-us and truncated records while randomly selecting on record per every join. As a result we have roughly a dataset of about **80 million** records and about **5000** distinct patients.\n", + "\n", + "## Expriment Design\n", + "---\n", + "\n", + "We compute both marketer and prosecutor risk computation while randomly selecting the number of attributes out of **111**. This selection is between ***2*** and **111** attributes. The number of maximum number of attributes that can be computed at any time is **64** : limitations of Google's Big-query. We performed **500** runs.\n", + "\n", + "## Results\n", + "---\n", + "\n", + "The results show the prosecutor risk is unchanging perhaps as an artifact of the number of runs **500** or the dataset curation: The joins we performed. The prosecutor risk shows there is at least one record that vulnerable.\n", + "\n", + "The marketer risk seems to increase as the number of randomly selected attributes increases as a general trend. \n", + "\n", + "{{ figure }} \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + 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