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<title>Creating Custom Step Functions</title>



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<h1 class="title toc-ignore">Creating Custom Step Functions</h1>


<div id="TOC">
<ul>
<li><a href="#a-new-step-definition">A new step definition</a></li>
<li><a href="#create-the-initial-function.">Create the initial function.</a></li>
<li><a href="#initialization-of-new-objects">Initialization of new objects</a></li>
<li><a href="#define-the-estimation-procedure">Define the estimation procedure</a></li>
<li><a href="#create-the-bake-method">Create the <code>bake</code> method</a></li>
<li><a href="#running-the-example">Running the example</a></li>
</ul>
</div>

<p><code>recipes</code> contains a number of different steps included in the package:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(recipes)
steps &lt;-<span class="st"> </span><span class="kw">apropos</span>(<span class="st">&quot;^step_&quot;</span>)
steps[<span class="op">!</span><span class="kw">grepl</span>(<span class="st">&quot;new$&quot;</span>, steps)]
<span class="co">#&gt;  [1] &quot;step_BoxCox&quot;       &quot;step_YeoJohnson&quot;   &quot;step_bagimpute&quot;   </span>
<span class="co">#&gt;  [4] &quot;step_bin2factor&quot;   &quot;step_center&quot;       &quot;step_classdist&quot;   </span>
<span class="co">#&gt;  [7] &quot;step_corr&quot;         &quot;step_date&quot;         &quot;step_depth&quot;       </span>
<span class="co">#&gt; [10] &quot;step_discretize&quot;   &quot;step_dummy&quot;        &quot;step_holiday&quot;     </span>
<span class="co">#&gt; [13] &quot;step_hyperbolic&quot;   &quot;step_ica&quot;          &quot;step_interact&quot;    </span>
<span class="co">#&gt; [16] &quot;step_intercept&quot;    &quot;step_invlogit&quot;     &quot;step_isomap&quot;      </span>
<span class="co">#&gt; [19] &quot;step_knnimpute&quot;    &quot;step_kpca&quot;         &quot;step_lincomb&quot;     </span>
<span class="co">#&gt; [22] &quot;step_log&quot;          &quot;step_logit&quot;        &quot;step_meanimpute&quot;  </span>
<span class="co">#&gt; [25] &quot;step_modeimpute&quot;   &quot;step_ns&quot;           &quot;step_nzv&quot;         </span>
<span class="co">#&gt; [28] &quot;step_ordinalscore&quot; &quot;step_other&quot;        &quot;step_pca&quot;         </span>
<span class="co">#&gt; [31] &quot;step_poly&quot;         &quot;step_range&quot;        &quot;step_ratio&quot;       </span>
<span class="co">#&gt; [34] &quot;step_regex&quot;        &quot;step_rm&quot;           &quot;step_scale&quot;       </span>
<span class="co">#&gt; [37] &quot;step_shuffle&quot;      &quot;step_spatialsign&quot;  &quot;step_sqrt&quot;        </span>
<span class="co">#&gt; [40] &quot;step_window&quot;</span></code></pre></div>
<p>You might want to make your own and this page describes how to do that. If you are looking for good examples of existing steps, I would suggest looking at the code for <a href="https://github.com/topepo/recipes/blob/master/R/center.R">centering</a> or <a href="https://github.com/topepo/recipes/blob/master/R/pca.R">PCA</a> to start.</p>
<div id="a-new-step-definition" class="section level1">
<h1>A new step definition</h1>
<p>At an example, let’s create a step that replaces the value of a variable with its percentile from the training set. The date that I’ll use is from the <code>recipes</code> package:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">data</span>(biomass)
<span class="kw">str</span>(biomass)
<span class="co">#&gt; 'data.frame':    536 obs. of  8 variables:</span>
<span class="co">#&gt;  $ sample  : chr  &quot;Akhrot Shell&quot; &quot;Alabama Oak Wood Waste&quot; &quot;Alder&quot; &quot;Alfalfa&quot; ...</span>
<span class="co">#&gt;  $ dataset : chr  &quot;Training&quot; &quot;Training&quot; &quot;Training&quot; &quot;Training&quot; ...</span>
<span class="co">#&gt;  $ carbon  : num  49.8 49.5 47.8 45.1 46.8 ...</span>
<span class="co">#&gt;  $ hydrogen: num  5.64 5.7 5.8 4.97 5.4 5.75 5.99 5.7 5.5 5.9 ...</span>
<span class="co">#&gt;  $ oxygen  : num  42.9 41.3 46.2 35.6 40.7 ...</span>
<span class="co">#&gt;  $ nitrogen: num  0.41 0.2 0.11 3.3 1 2.04 2.68 1.7 0.8 1.2 ...</span>
<span class="co">#&gt;  $ sulfur  : num  0 0 0.02 0.16 0.02 0.1 0.2 0.2 0 0.1 ...</span>
<span class="co">#&gt;  $ HHV     : num  20 19.2 18.3 18.2 18.4 ...</span>

biomass_tr &lt;-<span class="st"> </span>biomass[biomass<span class="op">$</span>dataset <span class="op">==</span><span class="st"> &quot;Training&quot;</span>,]
biomass_te &lt;-<span class="st"> </span>biomass[biomass<span class="op">$</span>dataset <span class="op">==</span><span class="st"> &quot;Testing&quot;</span>,]</code></pre></div>
<p>To illustrate the transformation with the <code>carbon</code> variable, the training set distribution of that variables is shown below with a vertical line for the first value of the test set.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(ggplot2)
<span class="kw">theme_set</span>(<span class="kw">theme_bw</span>())
<span class="kw">ggplot</span>(biomass_tr, <span class="kw">aes</span>(<span class="dt">x =</span> carbon)) <span class="op">+</span><span class="st"> </span>
<span class="st">  </span><span class="kw">geom_histogram</span>(<span class="dt">binwidth =</span> <span class="dv">5</span>, <span class="dt">col =</span> <span class="st">&quot;blue&quot;</span>, <span class="dt">fill =</span> <span class="st">&quot;blue&quot;</span>, <span class="dt">alpha =</span> .<span class="dv">5</span>) <span class="op">+</span><span class="st"> </span>
<span class="st">  </span><span class="kw">geom_vline</span>(<span class="dt">xintercept =</span> biomass_te<span class="op">$</span>carbon[<span class="dv">1</span>], <span class="dt">lty =</span> <span class="dv">2</span>)</code></pre></div>
<p><img 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" width="672" /></p>
<p>Based on the training set, 42.1% of the data are less than a value of 46.35. There are some applications where it might be advantageous to represent the predictor values are percentiles rather than their original values.</p>
<p>Our new step will do this computation for any numeric variables of interest. We will call this <code>step_percentile</code>. The code below is designed for illustration and not speed or best practices. I’ve left out a lot of error trapping that we would want in a real implementation.</p>
</div>
<div id="create-the-initial-function." class="section level1">
<h1>Create the initial function.</h1>
<p>The user-exposed function <code>step_percentile</code> is just a simple wrapper around an internal function called <code>add_step</code>. This function takes the same arguments as your function and simply adds it to a new recipe. The <code>...</code> signfies the variable selectors that can be used.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">step_percentile &lt;-<span class="st"> </span><span class="cf">function</span>(recipe, ..., <span class="dt">role =</span> <span class="ot">NA</span>, 
                            <span class="dt">trained =</span> <span class="ot">FALSE</span>, <span class="dt">ref_dist =</span> <span class="ot">NULL</span>,
                            <span class="dt">approx =</span> <span class="ot">FALSE</span>, 
                            <span class="dt">options =</span> <span class="kw">list</span>(<span class="dt">probs =</span> (<span class="dv">0</span><span class="op">:</span><span class="dv">100</span>)<span class="op">/</span><span class="dv">100</span>, <span class="dt">names =</span> <span class="ot">TRUE</span>)) {
## bake but do not evaluate the variable selectors with
## the `quos` function in `rlang`
  terms &lt;-<span class="st"> </span>rlang<span class="op">::</span><span class="kw">quos</span>(...) 
  <span class="cf">if</span>(<span class="kw">length</span>(terms) <span class="op">==</span><span class="st"> </span><span class="dv">0</span>)
    <span class="kw">stop</span>(<span class="st">&quot;Please supply at least one variable specification. See ?selections.&quot;</span>)
  <span class="kw">add_step</span>(
    recipe, 
    <span class="kw">step_percentile_new</span>(
      <span class="dt">terms =</span> terms, 
      <span class="dt">trained =</span> trained,
      <span class="dt">role =</span> role, 
      <span class="dt">ref_dist =</span> ref_dist,
      <span class="dt">approx =</span> approx,
      <span class="dt">options =</span> options))
}</code></pre></div>
<p>You should always keep the first four arguments (<code>recipe</code> though <code>trained</code>) the same as listed above. Some notes:</p>
<ul>
<li>the <code>role</code> argument is used when you either 1) create new variables and want their role to be pre-set or 2) replace the existing variables with new values. The latter is what we will be doing and using <code>role = NA</code> will leave the existing role intact.</li>
<li><code>trained</code> is set by the package when the estimation step has been run. You should default your function definition’s argument to <code>FALSE</code>.</li>
</ul>
<p>I’ve added extra arguments specific to this step. In order to calculate the percentile, the training data for the relevant columns will need to be saved. This data will be saved in the <code>ref_dist</code> object. However, this might be problematic if the data set is large. <code>approx</code> would be used when you want to save a grid of pre-computed percentiles from the training set and use these to estimate the percentile for a new data point. If <code>approx = TRUE</code>, the argument <code>ref_dist</code> will contain the grid for each variable.</p>
<p>We will use the <code>stats::quantile</code> to compute the grid. However, we might also want to have control over the granularity of this grid, so the <code>options</code> argument will be used to define how that calculations is done. We could just use the ellipses (aka <code>...</code>) so that any options passed to <code>step_percentile</code> that are not one of its arguments will then be passed to <code>stats::quantile</code>. We recommend making a seperate list object with the options and use these inside the function.</p>
</div>
<div id="initialization-of-new-objects" class="section level1">
<h1>Initialization of new objects</h1>
<p>Next, you can utilize the internal function <code>step</code> that sets the class of new objects. Using <code>subclass = &quot;percentile&quot;</code> will set the class of new objects to `“step_percentile”.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">step_percentile_new &lt;-<span class="st"> </span><span class="cf">function</span>(<span class="dt">terms =</span> <span class="ot">NULL</span>, <span class="dt">role =</span> <span class="ot">NA</span>, <span class="dt">trained =</span> <span class="ot">FALSE</span>, 
                                <span class="dt">ref_dist =</span> <span class="ot">NULL</span>, <span class="dt">approx =</span> <span class="ot">NULL</span>, <span class="dt">options =</span> <span class="ot">NULL</span>) {
  <span class="kw">step</span>(
    <span class="dt">subclass =</span> <span class="st">&quot;percentile&quot;</span>, 
    <span class="dt">terms =</span> terms,
    <span class="dt">role =</span> role,
    <span class="dt">trained =</span> trained,
    <span class="dt">ref_dist =</span> ref_dist,
    <span class="dt">approx =</span> approx,
    <span class="dt">options =</span> options
  )
}</code></pre></div>
</div>
<div id="define-the-estimation-procedure" class="section level1">
<h1>Define the estimation procedure</h1>
<p>You will need to create a new <code>prep</code> method for your step’s class. To do this, three arguments that the method should have:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="cf">function</span>(x, training, <span class="dt">info =</span> <span class="ot">NULL</span>)</code></pre></div>
<p>where</p>
<ul>
<li><code>x</code> will be the <code>step_percentile</code> object</li>
<li><code>training</code> will be a <em>tibble</em> that has the training set data</li>
<li><code>info</code> will also be a tibble that has information on the current set of data available. This information is updated as each step is evaluated by its specific <code>prep</code> method so it may not have the variables from the original data. The columns in this tibble are <code>variable</code> (the variable name), <code>type</code> (currently either “numeric” or “nominal”), <code>role</code> (defining the variable’s role), and <code>source</code> (either “original” or “derived” depending on where it originated).</li>
</ul>
<p>You can define other options.</p>
<p>The first thing that you might want to do in the <code>prep</code> function is to translate the specification listed in the <code>terms</code> argument to column names in the current data. There is an internal function called <code>terms_select</code> that can be used to obtain this.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">prep.step_percentile &lt;-<span class="st"> </span><span class="cf">function</span>(x, training, <span class="dt">info =</span> <span class="ot">NULL</span>, ...) {
  col_names &lt;-<span class="st"> </span><span class="kw">terms_select</span>(<span class="dt">terms =</span> x<span class="op">$</span>terms, <span class="dt">info =</span> info) 
}</code></pre></div>
<p>Once we have this, we can either save the original data columns or estimate the approximation grid. For the grid, we will use a helper function that enables us to run <code>do.call</code> on a list of arguments that include the <code>options</code> list.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">get_pctl &lt;-<span class="st"> </span><span class="cf">function</span>(x, args) {
  args<span class="op">$</span>x &lt;-<span class="st"> </span>x
  <span class="kw">do.call</span>(<span class="st">&quot;quantile&quot;</span>, args)
}

prep.step_percentile &lt;-<span class="st"> </span><span class="cf">function</span>(x, training, <span class="dt">info =</span> <span class="ot">NULL</span>, ...) {
  col_names &lt;-<span class="st"> </span><span class="kw">terms_select</span>(<span class="dt">terms =</span> x<span class="op">$</span>terms, <span class="dt">info =</span> info) 
  ## You can add error trapping for non-numeric data here and so on.
  ## We'll use the names later so
  <span class="cf">if</span>(x<span class="op">$</span>options<span class="op">$</span>names <span class="op">==</span><span class="st"> </span><span class="ot">FALSE</span>)
    <span class="kw">stop</span>(<span class="st">&quot;`names` should be set to TRUE&quot;</span>)
  
  <span class="cf">if</span>(<span class="op">!</span>x<span class="op">$</span>approx) {
    x<span class="op">$</span>ref_dist &lt;-<span class="st"> </span>training[, col_names]
  } <span class="cf">else</span> {
    pctl &lt;-<span class="st"> </span><span class="kw">lapply</span>(
      training[, col_names],  
      get_pctl, 
      <span class="dt">args =</span> x<span class="op">$</span>options
    )
    x<span class="op">$</span>ref_dist &lt;-<span class="st"> </span>pctl
  }
  ## Always return the updated step
  x
}</code></pre></div>
</div>
<div id="create-the-bake-method" class="section level1">
<h1>Create the <code>bake</code> method</h1>
<p>Remember that the <code>prep</code> function does not <em>apply</em> the step to the data; it only estimates any required values such as <code>ref_dist</code>. We will need to create a new method for our <code>step_percentile</code> class. The minimum arguments for this are</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="cf">function</span>(object, newdata, ...)</code></pre></div>
<p>where <code>object</code> is the updated step function that has been through the corresponding <code>prep</code> code and <code>newdata</code> is a tibble of data to be preprocessingcessed.</p>
<p>Here is the code to convert the new data to percentiles. Two initial helper functions handle the two cases (approximation or not). We always return a tibble as the output.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## Two helper functions
pctl_by_mean &lt;-<span class="st"> </span><span class="cf">function</span>(x, ref) <span class="kw">mean</span>(ref <span class="op">&lt;=</span><span class="st"> </span>x)

pctl_by_approx &lt;-<span class="st"> </span><span class="cf">function</span>(x, ref) {
  ## go from 1 column tibble to vector
  x &lt;-<span class="st"> </span><span class="kw">getElement</span>(x, <span class="kw">names</span>(x))
  ## get the percentiles values from the names (e.g. &quot;10%&quot;)
  p_grid &lt;-<span class="st"> </span><span class="kw">as.numeric</span>(<span class="kw">gsub</span>(<span class="st">&quot;%$&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="kw">names</span>(ref))) 
  <span class="kw">approx</span>(<span class="dt">x =</span> ref, <span class="dt">y =</span> p_grid, <span class="dt">xout =</span> x)<span class="op">$</span>y<span class="op">/</span><span class="dv">100</span>
}

bake.step_percentile &lt;-<span class="st"> </span><span class="cf">function</span>(object, newdata, ...) {
  <span class="kw">require</span>(tibble)
  ## For illustration (and not speed), we will loop through the affected variables
  ## and do the computations
  vars &lt;-<span class="st"> </span><span class="kw">names</span>(object<span class="op">$</span>ref_dist)
  
  <span class="cf">for</span>(i <span class="cf">in</span> vars) {
    <span class="cf">if</span>(<span class="op">!</span>object<span class="op">$</span>approx) {
      ## We can use `apply` since tibbles do not drop dimensions:
      newdata[, i] &lt;-<span class="st"> </span><span class="kw">apply</span>(newdata[, i], <span class="dv">1</span>, pctl_by_mean, 
                            <span class="dt">ref =</span> object<span class="op">$</span>ref_dist[, i])
    } <span class="cf">else</span> 
      newdata[, i] &lt;-<span class="st"> </span><span class="kw">pctl_by_approx</span>(newdata[, i], object<span class="op">$</span>ref_dist[[i]])
  }
  ## Always convert to tibbles on the way out
  <span class="kw">as_tibble</span>(newdata)
}</code></pre></div>
</div>
<div id="running-the-example" class="section level1">
<h1>Running the example</h1>
<p>Let’s use the example data to make sure that it works:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">rec_obj &lt;-<span class="st"> </span><span class="kw">recipe</span>(HHV <span class="op">~</span><span class="st"> </span>., <span class="dt">data =</span> biomass_tr[, <span class="op">-</span>(<span class="dv">1</span><span class="op">:</span><span class="dv">2</span>)])
rec_obj &lt;-<span class="st"> </span>rec_obj <span class="op">%&gt;%</span>
<span class="st">  </span><span class="kw">step_percentile</span>(<span class="kw">all_predictors</span>(), <span class="dt">approx =</span> <span class="ot">TRUE</span>) 

rec_obj &lt;-<span class="st"> </span><span class="kw">prep</span>(rec_obj, <span class="dt">training =</span> biomass_tr)
<span class="co">#&gt; step 1 percentile training</span>

percentiles &lt;-<span class="st"> </span><span class="kw">bake</span>(rec_obj, biomass_te)
percentiles
<span class="co">#&gt; # A tibble: 80 x 5</span>
<span class="co">#&gt;    carbon hydrogen oxygen nitrogen sulfur</span>
<span class="co">#&gt;     &lt;dbl&gt;    &lt;dbl&gt;  &lt;dbl&gt;    &lt;dbl&gt;  &lt;dbl&gt;</span>
<span class="co">#&gt;  1 0.4209   0.4500 0.9026    0.215  0.735</span>
<span class="co">#&gt;  2 0.1800   0.3850 0.9217    0.928  0.839</span>
<span class="co">#&gt;  3 0.1561   0.3850 0.9447    0.900  0.805</span>
<span class="co">#&gt;  4 0.4233   0.7750 0.2800    0.845  0.902</span>
<span class="co">#&gt;  5 0.6662   0.8667 0.6314    0.155  0.090</span>
<span class="co">#&gt;  6 0.2175   0.3850 0.5363    0.495  0.700</span>
<span class="co">#&gt;  7 0.0803   0.2713 0.9859    0.695  0.903</span>
<span class="co">#&gt;  8 0.1395   0.1260 0.1604    0.606  0.700</span>
<span class="co">#&gt;  9 0.0226   0.1035 0.1312    0.126  0.996</span>
<span class="co">#&gt; 10 0.0178   0.0821 0.0987    0.972  0.974</span>
<span class="co">#&gt; # ... with 70 more rows</span></code></pre></div>
<p>The plot below shows how the original data line up with the percentiles for each split of the data for one of the predictors:</p>
<p><img src="data:image/png;base64,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r-cran-recipes 0.1.0-1 / usr / lib / R / site-library / recipes / doc / Custom_Steps.html
