UserSyncUI / docs /api /tinytroupe /profiling.html
harvesthealth's picture
Upload folder using huggingface_hub
f6686e1 verified
<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.10.0" />
<title>tinytroupe.profiling API documentation</title>
<meta name="description" content="Provides mechanisms for creating understanding the characteristics of agent populations, such as
their age distribution, typical interests, and so on …" />
<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/sanitize.min.css" integrity="sha256-PK9q560IAAa6WVRRh76LtCaI8pjTJ2z11v0miyNNjrs=" crossorigin>
<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/typography.min.css" integrity="sha256-7l/o7C8jubJiy74VsKTidCy1yBkRtiUGbVkYBylBqUg=" crossorigin>
<link rel="stylesheet preload" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/styles/github.min.css" crossorigin>
<style>:root{--highlight-color:#fe9}.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}#sidebar > *:last-child{margin-bottom:2cm}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}h1:target,h2:target,h3:target,h4:target,h5:target,h6:target{background:var(--highlight-color);padding:.2em 0}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{margin-top:.6em;font-weight:bold}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}dt:target .name{background:var(--highlight-color)}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}td{padding:0 .5em}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/highlight.min.js" integrity="sha256-Uv3H6lx7dJmRfRvH8TH6kJD1TSK1aFcwgx+mdg3epi8=" crossorigin></script>
<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
</head>
<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>tinytroupe.profiling</code></h1>
</header>
<section id="section-intro">
<p>Provides mechanisms for creating understanding the characteristics of agent populations, such as
their age distribution, typical interests, and so on.</p>
<p>Guideline for plotting the methods: all plot methods should also return a Pandas dataframe with the data used for
plotting.</p>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">&#34;&#34;&#34;
Provides mechanisms for creating understanding the characteristics of agent populations, such as
their age distribution, typical interests, and so on.
Guideline for plotting the methods: all plot methods should also return a Pandas dataframe with the data used for
plotting.
&#34;&#34;&#34;
import pandas as pd
import matplotlib.pyplot as plt
from tinytroupe.agent import TinyPerson
import pandas as pd
import matplotlib.pyplot as plt
from typing import List
class Profiler:
def __init__(self, attributes: List[str]=[&#34;age&#34;, &#34;occupation.title&#34;, &#34;nationality&#34;]) -&gt; None:
self.attributes = attributes
self.attributes_distributions = {} # attribute -&gt; Dataframe
def profile(self, agents: List[dict], plot:bool=True) -&gt; dict:
&#34;&#34;&#34;
Profiles the given agents.
Args:
agents (List[dict]): The agents to be profiled.
&#34;&#34;&#34;
self.attributes_distributions = self._compute_attributes_distributions(agents)
if plot:
self.render()
return self.attributes_distributions
def render(self) -&gt; None:
&#34;&#34;&#34;
Renders the profile of the agents.
&#34;&#34;&#34;
return self._plot_attributes_distributions()
def _compute_attributes_distributions(self, agents:list) -&gt; dict:
&#34;&#34;&#34;
Computes the distributions of the attributes for the agents.
Args:
agents (list): The agents whose attributes distributions are to be computed.
Returns:
dict: The distributions of the attributes.
&#34;&#34;&#34;
distributions = {}
for attribute in self.attributes:
distributions[attribute] = self._compute_attribute_distribution(agents, attribute)
return distributions
def _compute_attribute_distribution(self, agents: list, attribute: str) -&gt; pd.DataFrame:
&#34;&#34;&#34;
Computes the distribution of a given attribute for the agents and plots it.
Args:
agents (list): The agents whose attribute distribution is to be plotted.
Returns:
pd.DataFrame: The data used for plotting.
&#34;&#34;&#34;
values = [agent.get(attribute) for agent in agents]
# corresponding dataframe of the value counts. Must be ordered by value, not counts
df = pd.DataFrame(values, columns=[attribute]).value_counts().sort_index()
return df
def _plot_attributes_distributions(self) -&gt; None:
&#34;&#34;&#34;
Plots the distributions of the attributes for the agents.
&#34;&#34;&#34;
for attribute in self.attributes:
self._plot_attribute_distribution(attribute)
def _plot_attribute_distribution(self, attribute: str) -&gt; pd.DataFrame:
&#34;&#34;&#34;
Plots the distribution of a given attribute for the agents.
Args:
attribute (str): The attribute whose distribution is to be plotted.
Returns:
pd.DataFrame: The data used for plotting.
&#34;&#34;&#34;
df = self.attributes_distributions[attribute]
df.plot(kind=&#39;bar&#39;, title=f&#34;{attribute.capitalize()} distribution&#34;)
plt.show()
</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="tinytroupe.profiling.Profiler"><code class="flex name class">
<span>class <span class="ident">Profiler</span></span>
<span>(</span><span>attributes: List[str] = ['age', 'occupation.title', 'nationality'])</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class Profiler:
def __init__(self, attributes: List[str]=[&#34;age&#34;, &#34;occupation.title&#34;, &#34;nationality&#34;]) -&gt; None:
self.attributes = attributes
self.attributes_distributions = {} # attribute -&gt; Dataframe
def profile(self, agents: List[dict], plot:bool=True) -&gt; dict:
&#34;&#34;&#34;
Profiles the given agents.
Args:
agents (List[dict]): The agents to be profiled.
&#34;&#34;&#34;
self.attributes_distributions = self._compute_attributes_distributions(agents)
if plot:
self.render()
return self.attributes_distributions
def render(self) -&gt; None:
&#34;&#34;&#34;
Renders the profile of the agents.
&#34;&#34;&#34;
return self._plot_attributes_distributions()
def _compute_attributes_distributions(self, agents:list) -&gt; dict:
&#34;&#34;&#34;
Computes the distributions of the attributes for the agents.
Args:
agents (list): The agents whose attributes distributions are to be computed.
Returns:
dict: The distributions of the attributes.
&#34;&#34;&#34;
distributions = {}
for attribute in self.attributes:
distributions[attribute] = self._compute_attribute_distribution(agents, attribute)
return distributions
def _compute_attribute_distribution(self, agents: list, attribute: str) -&gt; pd.DataFrame:
&#34;&#34;&#34;
Computes the distribution of a given attribute for the agents and plots it.
Args:
agents (list): The agents whose attribute distribution is to be plotted.
Returns:
pd.DataFrame: The data used for plotting.
&#34;&#34;&#34;
values = [agent.get(attribute) for agent in agents]
# corresponding dataframe of the value counts. Must be ordered by value, not counts
df = pd.DataFrame(values, columns=[attribute]).value_counts().sort_index()
return df
def _plot_attributes_distributions(self) -&gt; None:
&#34;&#34;&#34;
Plots the distributions of the attributes for the agents.
&#34;&#34;&#34;
for attribute in self.attributes:
self._plot_attribute_distribution(attribute)
def _plot_attribute_distribution(self, attribute: str) -&gt; pd.DataFrame:
&#34;&#34;&#34;
Plots the distribution of a given attribute for the agents.
Args:
attribute (str): The attribute whose distribution is to be plotted.
Returns:
pd.DataFrame: The data used for plotting.
&#34;&#34;&#34;
df = self.attributes_distributions[attribute]
df.plot(kind=&#39;bar&#39;, title=f&#34;{attribute.capitalize()} distribution&#34;)
plt.show()</code></pre>
</details>
<h3>Methods</h3>
<dl>
<dt id="tinytroupe.profiling.Profiler.profile"><code class="name flex">
<span>def <span class="ident">profile</span></span>(<span>self, agents: List[dict], plot: bool = True) ‑> dict</span>
</code></dt>
<dd>
<div class="desc"><p>Profiles the given agents.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>agents</code></strong> :&ensp;<code>List[dict]</code></dt>
<dd>The agents to be profiled.</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def profile(self, agents: List[dict], plot:bool=True) -&gt; dict:
&#34;&#34;&#34;
Profiles the given agents.
Args:
agents (List[dict]): The agents to be profiled.
&#34;&#34;&#34;
self.attributes_distributions = self._compute_attributes_distributions(agents)
if plot:
self.render()
return self.attributes_distributions</code></pre>
</details>
</dd>
<dt id="tinytroupe.profiling.Profiler.render"><code class="name flex">
<span>def <span class="ident">render</span></span>(<span>self) ‑> None</span>
</code></dt>
<dd>
<div class="desc"><p>Renders the profile of the agents.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def render(self) -&gt; None:
&#34;&#34;&#34;
Renders the profile of the agents.
&#34;&#34;&#34;
return self._plot_attributes_distributions()</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="tinytroupe" href="index.html">tinytroupe</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="tinytroupe.profiling.Profiler" href="#tinytroupe.profiling.Profiler">Profiler</a></code></h4>
<ul class="">
<li><code><a title="tinytroupe.profiling.Profiler.profile" href="#tinytroupe.profiling.Profiler.profile">profile</a></code></li>
<li><code><a title="tinytroupe.profiling.Profiler.render" href="#tinytroupe.profiling.Profiler.render">render</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
</nav>
</main>
<footer id="footer">
<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
</footer>
</body>
</html>