From 8a371a0739042267e07b632ff0f50c934f6d003d Mon Sep 17 00:00:00 2001
From: shawk masboob <masboob.shawk@gmail.com>
Date: Thu, 13 Feb 2020 23:54:16 -0500
Subject: [PATCH] add html file
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+</head>
+<body>
+<main>
+<article id="content">
+<header>
+<h1 class="title">Module <code>0214-PROJECT_Auto_Documentation</code></h1>
+</header>
+<section id="section-intro">
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python"># importing toy & real world datasets from the scikit-learn library
+
+from sklearn import datasets
+
+def dataload():
+ """
+ upload toy datasets from scikit-learn
+ """
+ data = None
+ return data
+
+def datafetch(file_name):
+ """
+ upload real world datasets from scikit-learn
+ """
+ data = None
+ print("reading data from:", file_name)
+ return data
+
+# standard descriptive statistic analysis of data
+
+def descriptive_statistic(df):
+ """
+ Provides brief descriptive statistics on dataset.
+ Takes dataframe as input.
+ """
+ print("Type : ", None, "\n\n")
+ print("Shape : ", None)
+ print("Head -- \n", None)
+ print("\n\n Tail -- \n", None)
+ print("Describe : ", None)
+
+
+# model selection
+
+def model_selection(df):
+ """
+ Takes dateframe as input. Performs foward/backward stepwise
+ regression. Returns best model for both methods.
+ """
+ null_fit = None
+ foward_step = None
+ backward_step = None
+ return foward_step, backward_step
+
+# model accuracy
+
+def MSE_fit(fit):
+ """
+ Takes in a fitted model as the input.
+ Calculates the MSU of the fitted model.
+ Outputs the model's MSE.
+ """
+ MSE = None
+ return MSE
+
+def accuracy_metrics(fit, MSE):
+ """
+ This function is used for model validation. It returns a dictionary
+ of several regression model accuracy metrics. Its inputs are a fitted model
+ and the MSE of the fitted model.
+ """
+ d = dict()
+ sumObj = None
+ SSE = None
+ n = None
+ p = None
+ pr = None
+ d['R2'] = None
+ d['R2ad'] = None
+ d['AIC'] = None
+ d['BIC'] = None
+ d['PRESS'] = None
+ d['Cp']= None
+ return d
+
+# test code
+
+file_name = 'data.csv'
+
+a = datafetch(file_name)
+print(a)
+
+b = descriptive_statistic(a)
+print(b)
+
+c = model_selection(a)
+print(c)
+
+d = MSE_fit(c)
+print(d)
+
+print(accuracy_metrics(c, d))</code></pre>
+</details>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+<h2 class="section-title" id="header-functions">Functions</h2>
+<dl>
+<dt id="0214-PROJECT_Auto_Documentation.MSE_fit"><code class="name flex">
+<span>def <span class="ident">MSE_fit</span></span>(<span>fit)</span>
+</code></dt>
+<dd>
+<section class="desc"><p>Takes in a fitted model as the input.
+Calculates the MSU of the fitted model.
+Outputs the model's MSE.</p></section>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def MSE_fit(fit):
+ """
+ Takes in a fitted model as the input.
+ Calculates the MSU of the fitted model.
+ Outputs the model's MSE.
+ """
+ MSE = None
+ return MSE</code></pre>
+</details>
+</dd>
+<dt id="0214-PROJECT_Auto_Documentation.accuracy_metrics"><code class="name flex">
+<span>def <span class="ident">accuracy_metrics</span></span>(<span>fit, MSE)</span>
+</code></dt>
+<dd>
+<section class="desc"><p>This function is used for model validation. It returns a dictionary
+of several regression model accuracy metrics. Its inputs are a fitted model
+and the MSE of the fitted model.</p></section>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def accuracy_metrics(fit, MSE):
+ """
+ This function is used for model validation. It returns a dictionary
+ of several regression model accuracy metrics. Its inputs are a fitted model
+ and the MSE of the fitted model.
+ """
+ d = dict()
+ sumObj = None
+ SSE = None
+ n = None
+ p = None
+ pr = None
+ d['R2'] = None
+ d['R2ad'] = None
+ d['AIC'] = None
+ d['BIC'] = None
+ d['PRESS'] = None
+ d['Cp']= None
+ return d</code></pre>
+</details>
+</dd>
+<dt id="0214-PROJECT_Auto_Documentation.datafetch"><code class="name flex">
+<span>def <span class="ident">datafetch</span></span>(<span>file_name)</span>
+</code></dt>
+<dd>
+<section class="desc"><p>upload real world datasets from scikit-learn</p></section>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def datafetch(file_name):
+ """
+ upload real world datasets from scikit-learn
+ """
+ data = None
+ print("reading data from:", file_name)
+ return data</code></pre>
+</details>
+</dd>
+<dt id="0214-PROJECT_Auto_Documentation.dataload"><code class="name flex">
+<span>def <span class="ident">dataload</span></span>(<span>)</span>
+</code></dt>
+<dd>
+<section class="desc"><p>upload toy datasets from scikit-learn</p></section>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def dataload():
+ """
+ upload toy datasets from scikit-learn
+ """
+ data = None
+ return data</code></pre>
+</details>
+</dd>
+<dt id="0214-PROJECT_Auto_Documentation.descriptive_statistic"><code class="name flex">
+<span>def <span class="ident">descriptive_statistic</span></span>(<span>df)</span>
+</code></dt>
+<dd>
+<section class="desc"><p>Provides brief descriptive statistics on dataset.
+Takes dataframe as input.</p></section>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def descriptive_statistic(df):
+ """
+ Provides brief descriptive statistics on dataset.
+ Takes dataframe as input.
+ """
+ print("Type : ", None, "\n\n")
+ print("Shape : ", None)
+ print("Head -- \n", None)
+ print("\n\n Tail -- \n", None)
+ print("Describe : ", None)</code></pre>
+</details>
+</dd>
+<dt id="0214-PROJECT_Auto_Documentation.model_selection"><code class="name flex">
+<span>def <span class="ident">model_selection</span></span>(<span>df)</span>
+</code></dt>
+<dd>
+<section class="desc"><p>Takes dateframe as input. Performs foward/backward stepwise
+regression. Returns best model for both methods.</p></section>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def model_selection(df):
+ """
+ Takes dateframe as input. Performs foward/backward stepwise
+ regression. Returns best model for both methods.
+ """
+ null_fit = None
+ foward_step = None
+ backward_step = None
+ return foward_step, backward_step</code></pre>
+</details>
+</dd>
+</dl>
+</section>
+<section>
+</section>
+</article>
+<nav id="sidebar">
+<h1>Index</h1>
+<div class="toc">
+<ul></ul>
+</div>
+<ul id="index">
+<li><h3><a href="#header-functions">Functions</a></h3>
+<ul class="">
+<li><code><a title="0214-PROJECT_Auto_Documentation.MSE_fit" href="#0214-PROJECT_Auto_Documentation.MSE_fit">MSE_fit</a></code></li>
+<li><code><a title="0214-PROJECT_Auto_Documentation.accuracy_metrics" href="#0214-PROJECT_Auto_Documentation.accuracy_metrics">accuracy_metrics</a></code></li>
+<li><code><a title="0214-PROJECT_Auto_Documentation.datafetch" href="#0214-PROJECT_Auto_Documentation.datafetch">datafetch</a></code></li>
+<li><code><a title="0214-PROJECT_Auto_Documentation.dataload" href="#0214-PROJECT_Auto_Documentation.dataload">dataload</a></code></li>
+<li><code><a title="0214-PROJECT_Auto_Documentation.descriptive_statistic" href="#0214-PROJECT_Auto_Documentation.descriptive_statistic">descriptive_statistic</a></code></li>
+<li><code><a title="0214-PROJECT_Auto_Documentation.model_selection" href="#0214-PROJECT_Auto_Documentation.model_selection">model_selection</a></code></li>
+</ul>
+</li>
+</ul>
+</nav>
+</main>
+<footer id="footer">
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