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<h1 id="add-new-features">Add New Features<a class="headerlink" href="#add-new-features" title="Permanent link">&para;</a></h1>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<p>We recommend reading the <a href="../feature-introduction/">Behavioral Features Introduction</a> before reading this page</p>
</div>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<p>You won&rsquo;t have to deal with time zones, dates, times, data cleaning or preprocessing. The data that RAPIDS pipes to your feature extraction code is ready to process.</p>
</div>
<h2 id="new-features-for-existing-sensors">New Features for Existing Sensors<a class="headerlink" href="#new-features-for-existing-sensors" title="Permanent link">&para;</a></h2>
<p>You can add new features to any existing sensors (see list below) by adding a new provider in three steps:</p>
<ol>
<li><a href="#modify-the-configyaml-file">Modify</a> the <code>config.yaml</code> file </li>
<li><a href="#create-a-provider-folder-script-and-function">Create</a> a provider folder, script and function</li>
<li><a href="#implement-your-feature-extraction-code">Implement</a> your features extraction code</li>
</ol>
<p>As a tutorial, we will add a new provider for <code>PHONE_ACCELEROMETER</code> called <code>VEGA</code> that extracts <code>feature1</code>, <code>feature2</code>, <code>feature3</code> in Python and that it requires a parameter from the user called <code>MY_PARAMETER</code>.</p>
<details class="info"><summary>Existing Sensors</summary><p>An existing sensor is any of the phone or Fitbit sensors with a configuration entry in <code>config.yaml</code>:</p>
<ul>
<li>Phone Accelerometer</li>
<li>Phone Activity Recognition</li>
<li>Phone Applications Foreground</li>
<li>Phone Battery</li>
<li>Phone Bluetooth</li>
<li>Phone Calls</li>
<li>Phone Conversation</li>
<li>Phone Data Yield</li>
<li>Phone Light</li>
<li>Phone Locations</li>
<li>Phone Messages</li>
<li>Phone Screen</li>
<li>Phone WiFI Connected</li>
<li>Phone WiFI Visible</li>
<li>Fitbit Heart Rate Summary</li>
<li>Fitbit Heart Rate Intraday</li>
<li>Fitbit Sleep Summary</li>
<li>Fitbit Steps Summary</li>
<li>Fitbit Steps Intraday</li>
</ul>
</details>
<h3 id="modify-the-configyaml-file">Modify the <code>config.yaml</code> file<a class="headerlink" href="#modify-the-configyaml-file" title="Permanent link">&para;</a></h3>
<p>In this step you need to add your provider configuration section under the relevant sensor in <code>config.yaml</code>. See our example for our tutorial&rsquo;s <code>VEGA</code> provider for <code>PHONE_ACCELEROMETER</code>:</p>
<details class="example"><summary>Example configuration for a new accelerometer provider <code>VEGA</code></summary><div class="highlight"><pre><span></span><code><span class="nt">PHONE_ACCELEROMETER</span><span class="p">:</span>
<span class="nt">TABLE</span><span class="p">:</span> <span class="l l-Scalar l-Scalar-Plain">accelerometer</span>
<span class="nt">PROVIDERS</span><span class="p">:</span>
<span class="nt">RAPIDS</span><span class="p">:</span>
<span class="nt">COMPUTE</span><span class="p">:</span> <span class="l l-Scalar l-Scalar-Plain">False</span>
<span class="l l-Scalar l-Scalar-Plain">...</span>
<span class="nt">PANDA</span><span class="p">:</span>
<span class="nt">COMPUTE</span><span class="p">:</span> <span class="l l-Scalar l-Scalar-Plain">False</span>
<span class="l l-Scalar l-Scalar-Plain">...</span>
<span class="nt">VEGA</span><span class="p">:</span>
<span class="nt">COMPUTE</span><span class="p">:</span> <span class="l l-Scalar l-Scalar-Plain">False</span>
<span class="nt">FEATURES</span><span class="p">:</span> <span class="p p-Indicator">[</span><span class="s">&quot;feature1&quot;</span><span class="p p-Indicator">,</span> <span class="s">&quot;feature2&quot;</span><span class="p p-Indicator">,</span> <span class="s">&quot;feature3&quot;</span><span class="p p-Indicator">]</span>
<span class="nt">MY_PARAMTER</span><span class="p">:</span> <span class="l l-Scalar l-Scalar-Plain">a_string</span>
<span class="nt">SRC_FOLDER</span><span class="p">:</span> <span class="s">&quot;vega&quot;</span>
<span class="nt">SRC_LANGUAGE</span><span class="p">:</span> <span class="s">&quot;python&quot;</span>
</code></pre></div>
</details>
<table>
<thead>
<tr>
<th>Key&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>[COMPUTE]</code></td>
<td>Flag to activate/deactivate your provider</td>
</tr>
<tr>
<td><code>[FEATURES]</code></td>
<td>List of features your provider supports. Your provider code should only return the features on this list</td>
</tr>
<tr>
<td><code>[MY_PARAMTER]</code></td>
<td>An arbitrary parameter that our example provider <code>VEGA</code> needs. This can be a boolean, integer, float, string or an array of any of such types.</td>
</tr>
<tr>
<td><code>[SRC_LANGUAGE]</code></td>
<td>The programming language of your provider script, it can be <code>python</code> or <code>r</code>, in our example <code>python</code></td>
</tr>
<tr>
<td><code>[SRC_FOLDER]</code></td>
<td>The name of your provider in lower case, in our example <code>vega</code> (this will be the name of your folder in the next step)</td>
</tr>
</tbody>
</table>
<h3 id="create-a-provider-folder-script-and-function">Create a provider folder, script and function<a class="headerlink" href="#create-a-provider-folder-script-and-function" title="Permanent link">&para;</a></h3>
<p>In this step you need to add a folder, script and function for your provider.</p>
<ol>
<li>Create your provider <strong>folder</strong> under <code>src/feature/DEVICE_SENSOR/YOUR_PROVIDER</code>, in our example <code>src/feature/phone_accelerometer/vega</code> (same as <code>[SRC_FOLDER]</code> in the step above).</li>
<li>Create your provider <strong>script</strong> inside your provider folder, it can be a Python file called <code>main.py</code> or an R file called <code>main.R</code>.</li>
<li>
<p>Add your provider <strong>function</strong> in your provider script. The name of such function should be <code>[providername]_features</code>, in our example <code>vega_features</code></p>
<div class="admonition info">
<p class="admonition-title">Python function</p>
<div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="p">[</span><span class="n">providername</span><span class="p">]</span><span class="n">_features</span><span class="p">(</span><span class="n">sensor_data_files</span><span class="p">,</span> <span class="n">time_segment</span><span class="p">,</span> <span class="n">provider</span><span class="p">,</span> <span class="n">filter_data_by_segment</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
</code></pre></div>
</div>
<div class="admonition info">
<p class="admonition-title">R function</p>
<div class="highlight"><pre><span></span><code><span class="p">[</span><span class="n">providername</span><span class="p">]</span>_<span class="n">features</span> <span class="o">&lt;-</span> <span class="nf">function</span><span class="p">(</span><span class="n">sensor_data</span><span class="p">,</span> <span class="n">time_segment</span><span class="p">,</span> <span class="n">provider</span><span class="p">)</span>
</code></pre></div>
</div>
</li>
</ol>
<h3 id="implement-your-feature-extraction-code">Implement your feature extraction code<a class="headerlink" href="#implement-your-feature-extraction-code" title="Permanent link">&para;</a></h3>
<p>The provider function that you created in the step above will receive the following parameters:</p>
<table>
<thead>
<tr>
<th>Parameter&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>sensor_data_files</code></td>
<td>Path to the CSV file containing the data of a single participant. This data has been cleaned and preprocessed. Your function will be automatically called for each participant in your study (in the <code>[PIDS]</code> array in <code>config.yaml</code>)</td>
</tr>
<tr>
<td><code>time_segment</code></td>
<td>The label of the time segment that should be processed.</td>
</tr>
<tr>
<td><code>provider</code></td>
<td>The parameters you configured for your provider in <code>config.yaml</code> will be available in this variable as a dictionary in Python or a list in R. In our example this dictionary contains <code>{MY_PARAMETER:"a_string"}</code></td>
</tr>
<tr>
<td><code>filter_data_by_segment</code></td>
<td>Python only. A function that you will use to filter your data. In R this function is already available in the environment.</td>
</tr>
<tr>
<td><code>*args</code></td>
<td>Python only. Not used for now</td>
</tr>
<tr>
<td><code>**kwargs</code></td>
<td>Python only. Not used for now</td>
</tr>
</tbody>
</table>
<p>The code to extract your behavioral features should be implemented in your provider function and in general terms it will have three stages:</p>
<details class="info"><summary>1. Read a participant&rsquo;s data by loading the CSV data stored in the file pointed by <code>sensor_data_files</code></summary><div class="highlight"><pre><span></span><code><span class="n">acc_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">sensor_data_files</span><span class="p">[</span><span class="s2">&quot;sensor_data&quot;</span><span class="p">])</span>
</code></pre></div>
<p>Note that phone&rsquo;s battery, screen, and activity recognition data is given as episodes instead of event rows (for example, start and end timestamps of the periods the phone screen was on)</p>
</details>
<details class="info"><summary>2. Filter your data to process only those rows that belong to <code>time_segment</code></summary><p>This step is only one line of code, but to undersand why we need it, keep reading.
<div class="highlight"><pre><span></span><code><span class="n">acc_data</span> <span class="o">=</span> <span class="n">filter_data_by_segment</span><span class="p">(</span><span class="n">acc_data</span><span class="p">,</span> <span class="n">time_segment</span><span class="p">)</span>
</code></pre></div></p>
<p>You should use the <code>filter_data_by_segment()</code> function to process and group those rows that belong to each of the <a href="../../setup/configuration/#time-segments">time segments RAPIDS could be configured with</a>.</p>
<p>Let&rsquo;s understand the <code>filter_data_by_segment()</code> function with an example. A RAPIDS user can extract features on any arbitrary <a href="../../setup/configuration/#time-segments">time segment</a>. A time segment is a period of time that has a label and one or more instances. For example, the user (or you) could have requested features on a daily, weekly, and week-end basis for <code>p01</code>. The labels are arbritrary and the instances depend on the days a participant was monitored for: </p>
<ul>
<li>the daily segment could be named <code>my_days</code> and if <code>p01</code> was monitored for 14 days, it would have 14 instances</li>
<li>the weekly segment could be named <code>my_weeks</code> and if <code>p01</code> was monitored for 14 days, it would have 2 instances.</li>
<li>the weekend segment could be named <code>my_weekends</code> and if <code>p01</code> was monitored for 14 days, it would have 2 instances.</li>
</ul>
<p>For this example, RAPIDS will call your provider function three times for <code>p01</code>, once where <code>time_segment</code> is <code>my_days</code>, once where <code>time_segment</code> is <code>my_weeks</code> and once where <code>time_segment</code> is <code>my_weekends</code>. In this example not every row in <code>p01</code>&lsquo;s data needs to take part in the feature computation for either segment <strong>and</strong> the rows need to be grouped differently. </p>
<p>Thus <code>filter_data_by_segment()</code> comes in handy, it will return a data frame that contains the rows that were logged during a time segment plus an extra column called <code>local_segment</code>. This new column will have as many unique values as time segment instances exist (14, 2, and 2 for our <code>p01</code>&lsquo;s <code>my_days</code>, <code>my_weeks</code>, and <code>my_weekends</code> examples). After filtering, <strong>you should group the data frame by this column and compute any desired features</strong>, for example:</p>
<div class="highlight"><pre><span></span><code><span class="n">acc_features</span><span class="p">[</span><span class="s2">&quot;maxmagnitude&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">acc_data</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s2">&quot;local_segment&quot;</span><span class="p">])[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
</code></pre></div>
<p>The reason RAPIDS does not filter the participant&rsquo;s data set for you is because your code might need to compute something based on a participant&rsquo;s complete dataset before computing their features. For example, you might want to identify the number that called a participant the most throughout the study before computing a feature with the number of calls the participant received from this number.</p>
</details>
<details class="info"><summary>3. Return a data frame with your features</summary><p>After filtering, grouping your data, and computing your features, your provider function should return a data frame that has:</p>
<ul>
<li>One row per time segment instance (e.g. 14 our <code>p01</code>&lsquo;s <code>my_days</code> example)</li>
<li>The <code>local_segment</code> column added by <code>filter_data_by_segment()</code></li>
<li>One column per feature. By convention the name of your features should only contain letters or numbers (<code>feature1</code>). RAPIDS will automatically add the right sensor and provider prefix (<code>phone_accelerometr_vega_</code>)</li>
</ul>
</details>
<details class="example"><summary><code>PHONE_ACCELEROMETER</code> Provider Example</summary><p>For your reference, this a short example of our own provider (<code>RAPIDS</code>) for <code>PHONE_ACCELEROMETER</code> that computes five acceleration features</p>
<div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">rapids_features</span><span class="p">(</span><span class="n">sensor_data_files</span><span class="p">,</span> <span class="n">time_segment</span><span class="p">,</span> <span class="n">provider</span><span class="p">,</span> <span class="n">filter_data_by_segment</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">acc_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">sensor_data_files</span><span class="p">[</span><span class="s2">&quot;sensor_data&quot;</span><span class="p">])</span>
<span class="n">requested_features</span> <span class="o">=</span> <span class="n">provider</span><span class="p">[</span><span class="s2">&quot;FEATURES&quot;</span><span class="p">]</span>
<span class="c1"># name of the features this function can compute</span>
<span class="n">base_features_names</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;maxmagnitude&quot;</span><span class="p">,</span> <span class="s2">&quot;minmagnitude&quot;</span><span class="p">,</span> <span class="s2">&quot;avgmagnitude&quot;</span><span class="p">,</span> <span class="s2">&quot;medianmagnitude&quot;</span><span class="p">,</span> <span class="s2">&quot;stdmagnitude&quot;</span><span class="p">]</span>
<span class="c1"># the subset of requested features this function can compute</span>
<span class="n">features_to_compute</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">requested_features</span><span class="p">)</span> <span class="o">&amp;</span> <span class="nb">set</span><span class="p">(</span><span class="n">base_features_names</span><span class="p">))</span>
<span class="n">acc_features</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;local_segment&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">features_to_compute</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">acc_data</span><span class="o">.</span><span class="n">empty</span><span class="p">:</span>
<span class="n">acc_data</span> <span class="o">=</span> <span class="n">filter_data_by_segment</span><span class="p">(</span><span class="n">acc_data</span><span class="p">,</span> <span class="n">time_segment</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">acc_data</span><span class="o">.</span><span class="n">empty</span><span class="p">:</span>
<span class="n">acc_features</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">()</span>
<span class="c1"># get magnitude related features: magnitude = sqrt(x^2+y^2+z^2)</span>
<span class="n">magnitude</span> <span class="o">=</span> <span class="n">acc_data</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">row</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s2">&quot;double_values_0&quot;</span><span class="p">]</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="n">row</span><span class="p">[</span><span class="s2">&quot;double_values_1&quot;</span><span class="p">]</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="n">row</span><span class="p">[</span><span class="s2">&quot;double_values_2&quot;</span><span class="p">]</span> <span class="o">**</span> <span class="mi">2</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">acc_data</span> <span class="o">=</span> <span class="n">acc_data</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">magnitude</span> <span class="o">=</span> <span class="n">magnitude</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
<span class="k">if</span> <span class="s2">&quot;maxmagnitude&quot;</span> <span class="ow">in</span> <span class="n">features_to_compute</span><span class="p">:</span>
<span class="n">acc_features</span><span class="p">[</span><span class="s2">&quot;maxmagnitude&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">acc_data</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s2">&quot;local_segment&quot;</span><span class="p">])[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
<span class="k">if</span> <span class="s2">&quot;minmagnitude&quot;</span> <span class="ow">in</span> <span class="n">features_to_compute</span><span class="p">:</span>
<span class="n">acc_features</span><span class="p">[</span><span class="s2">&quot;minmagnitude&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">acc_data</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s2">&quot;local_segment&quot;</span><span class="p">])[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
<span class="k">if</span> <span class="s2">&quot;avgmagnitude&quot;</span> <span class="ow">in</span> <span class="n">features_to_compute</span><span class="p">:</span>
<span class="n">acc_features</span><span class="p">[</span><span class="s2">&quot;avgmagnitude&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">acc_data</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s2">&quot;local_segment&quot;</span><span class="p">])[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="k">if</span> <span class="s2">&quot;medianmagnitude&quot;</span> <span class="ow">in</span> <span class="n">features_to_compute</span><span class="p">:</span>
<span class="n">acc_features</span><span class="p">[</span><span class="s2">&quot;medianmagnitude&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">acc_data</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s2">&quot;local_segment&quot;</span><span class="p">])[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">median</span><span class="p">()</span>
<span class="k">if</span> <span class="s2">&quot;stdmagnitude&quot;</span> <span class="ow">in</span> <span class="n">features_to_compute</span><span class="p">:</span>
<span class="n">acc_features</span><span class="p">[</span><span class="s2">&quot;stdmagnitude&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">acc_data</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s2">&quot;local_segment&quot;</span><span class="p">])[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">std</span><span class="p">()</span>
<span class="n">acc_features</span> <span class="o">=</span> <span class="n">acc_features</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
<span class="k">return</span> <span class="n">acc_features</span>
</code></pre></div>
</details>
<h2 id="new-features-for-non-existing-sensors">New Features for Non-Existing Sensors<a class="headerlink" href="#new-features-for-non-existing-sensors" title="Permanent link">&para;</a></h2>
<p>If you want to add features for a device or a sensor that we do not support at the moment (those that do not appear in the <code>"Existing Sensors"</code> list above), <a href="../../team">contact us</a> or request it on <a href="http://awareframework.com:3000/">Slack</a> and we can add the necessary code so you can follow the instructions above.</p>
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