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Rough out Python sections #5

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merged 11 commits into from May 11, 2022
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Rough out Python sections #5

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d5d0c45
Rough out Python sections
juliasilge May 2, 2022
fdae598
roughing out python sections
isabelizimm May 9, 2022
084f2f9
Successfully using `vetiver_pin_write` in Python! :tada:
juliasilge May 10, 2022
4ab4d28
mtcars, linting, and parity checklist
isabelizimm May 10, 2022
e9b5adc
Chunk comments
juliasilge May 11, 2022
965baf6
Remove the `model` column from mtcars before fitting
juliasilge May 11, 2022
8fb04bf
Render site
juliasilge May 11, 2022
390a2b2
Render again
juliasilge May 11, 2022
8df32a3
One more render
juliasilge May 11, 2022
4ddb544
update py lm, req prose, rerender
isabelizimm May 11, 2022
e0a5e06
Update a few things and render once more
juliasilge May 11, 2022
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2 changes: 2 additions & 0 deletions _quarto.yml
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Expand Up @@ -56,6 +56,8 @@ website:
collapse-level: 3
align: left
contents:
- text:
href: learn-more/parity-checklist.qmd
- text:
href: learn-more/model-card.qmd

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222 changes: 173 additions & 49 deletions docs/get-started/deploy.html
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42 changes: 26 additions & 16 deletions docs/get-started/index.html
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Expand Up @@ -316,21 +316,25 @@ <h2 class="anchored" data-anchor-id="create-a-vetiver-model">Create a vetiver mo
<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a>np.random.seed(<span class="dv">500</span>)</span>
<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a>raw <span class="op">=</span> pd.read_csv(<span class="st">'https://tidymodels.org/start/case-study/hotels.csv'</span>)</span>
<span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a>raw <span class="op">=</span> pd.read_csv(<span class="st">"https://tidymodels.org/start/case-study/hotels.csv"</span>)</span>
<span id="cb6-9"><a href="#cb6-9" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> pd.DataFrame(raw)</span>
<span id="cb6-10"><a href="#cb6-10" aria-hidden="true" tabindex="-1"></a>df[<span class="st">'arrival_date'</span>] <span class="op">=</span> pd.to_datetime(df[<span class="st">'arrival_date'</span>])</span>
<span id="cb6-11"><a href="#cb6-11" aria-hidden="true" tabindex="-1"></a>df[<span class="st">'arrival_month'</span>] <span class="op">=</span> df[<span class="st">'arrival_date'</span>].dt.month</span>
<span id="cb6-12"><a href="#cb6-12" aria-hidden="true" tabindex="-1"></a>df[<span class="st">'arrival_dow'</span>] <span class="op">=</span> df[<span class="st">'arrival_date'</span>].dt.dayofweek</span>
<span id="cb6-13"><a href="#cb6-13" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> df.drop(columns <span class="op">=</span> <span class="st">'arrival_date'</span>).dropna()</span>
<span id="cb6-14"><a href="#cb6-14" aria-hidden="true" tabindex="-1"></a>X, y <span class="op">=</span> df.drop(columns <span class="op">=</span> <span class="st">'children'</span>), df[<span class="st">'children'</span>]</span>
<span id="cb6-15"><a href="#cb6-15" aria-hidden="true" tabindex="-1"></a>X_train, X_test, y_train, y_test <span class="op">=</span> model_selection.train_test_split(X, y, test_size <span class="op">=</span> <span class="fl">0.25</span>)</span>
<span id="cb6-16"><a href="#cb6-16" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb6-17"><a href="#cb6-17" aria-hidden="true" tabindex="-1"></a><span class="co">## let's only use half of the training data for now</span></span>
<span id="cb6-18"><a href="#cb6-18" aria-hidden="true" tabindex="-1"></a>X_1, X_2, y_1, y_2 <span class="op">=</span> model_selection.train_test_split(X_train, y_train, test_size <span class="op">=</span> <span class="fl">0.5</span>)</span>
<span id="cb6-19"><a href="#cb6-19" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb6-20"><a href="#cb6-20" aria-hidden="true" tabindex="-1"></a>le <span class="op">=</span> preprocessing.OrdinalEncoder().fit(X_1)</span>
<span id="cb6-21"><a href="#cb6-21" aria-hidden="true" tabindex="-1"></a>rf <span class="op">=</span> RandomForestClassifier().fit(le.transform(X_1), y_1)</span>
<span id="cb6-22"><a href="#cb6-22" aria-hidden="true" tabindex="-1"></a>rf <span class="op">=</span> pipeline.Pipeline([(<span class="st">'label_encoder'</span>, le), (<span class="st">'random_forest'</span>, rf)])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<span id="cb6-10"><a href="#cb6-10" aria-hidden="true" tabindex="-1"></a>df[<span class="st">"arrival_date"</span>] <span class="op">=</span> pd.to_datetime(df[<span class="st">"arrival_date"</span>])</span>
<span id="cb6-11"><a href="#cb6-11" aria-hidden="true" tabindex="-1"></a>df[<span class="st">"arrival_month"</span>] <span class="op">=</span> df[<span class="st">"arrival_date"</span>].dt.month</span>
<span id="cb6-12"><a href="#cb6-12" aria-hidden="true" tabindex="-1"></a>df[<span class="st">"arrival_dow"</span>] <span class="op">=</span> df[<span class="st">"arrival_date"</span>].dt.dayofweek</span>
<span id="cb6-13"><a href="#cb6-13" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> df.drop(columns<span class="op">=</span><span class="st">"arrival_date"</span>).dropna()</span>
<span id="cb6-14"><a href="#cb6-14" aria-hidden="true" tabindex="-1"></a>X, y <span class="op">=</span> df.drop(columns<span class="op">=</span><span class="st">"children"</span>), df[<span class="st">"children"</span>]</span>
<span id="cb6-15"><a href="#cb6-15" aria-hidden="true" tabindex="-1"></a>X_train, X_test, y_train, y_test <span class="op">=</span> model_selection.train_test_split(</span>
<span id="cb6-16"><a href="#cb6-16" aria-hidden="true" tabindex="-1"></a> X, y, test_size<span class="op">=</span><span class="fl">0.25</span></span>
<span id="cb6-17"><a href="#cb6-17" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb6-18"><a href="#cb6-18" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb6-19"><a href="#cb6-19" aria-hidden="true" tabindex="-1"></a><span class="co">## let's only use half of the training data for now</span></span>
<span id="cb6-20"><a href="#cb6-20" aria-hidden="true" tabindex="-1"></a>X_part_1, X_part_2, y_part_1, y_part_2 <span class="op">=</span> model_selection.train_test_split(</span>
<span id="cb6-21"><a href="#cb6-21" aria-hidden="true" tabindex="-1"></a> X_train, y_train, test_size<span class="op">=</span><span class="fl">0.5</span></span>
<span id="cb6-22"><a href="#cb6-22" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb6-23"><a href="#cb6-23" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb6-24"><a href="#cb6-24" aria-hidden="true" tabindex="-1"></a>le <span class="op">=</span> preprocessing.OrdinalEncoder().fit(X_part_1)</span>
<span id="cb6-25"><a href="#cb6-25" aria-hidden="true" tabindex="-1"></a>rf <span class="op">=</span> RandomForestClassifier().fit(le.transform(X_part_1), y_part_1)</span>
<span id="cb6-26"><a href="#cb6-26" aria-hidden="true" tabindex="-1"></a>rf <span class="op">=</span> pipeline.Pipeline([(<span class="st">"label_encoder"</span>, le), (<span class="st">"random_forest"</span>, rf)])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</div>
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Expand All @@ -354,8 +358,14 @@ <h2 class="anchored" data-anchor-id="create-a-vetiver-model">Create a vetiver mo
<div id="tabset-3-2" class="tab-pane" role="tabpanel" aria-labelledby="tabset-3-2-tab">
<div class="cell">
<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> vetiver <span class="im">import</span> VetiverModel</span>
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>v <span class="op">=</span> VetiverModel(rf, save_ptype <span class="op">=</span> <span class="va">True</span>, ptype_data <span class="op">=</span> X_1, model_name <span class="op">=</span> <span class="st">"hotel_rf"</span>)</span>
<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>v.description</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>v <span class="op">=</span> VetiverModel(</span>
<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a> rf, model_name <span class="op">=</span> <span class="st">"hotel_rf"</span>, </span>
<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a> save_ptype <span class="op">=</span> <span class="va">True</span>, ptype_data <span class="op">=</span> X_part_1</span>
<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a>v.description</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>"Scikit-learn &lt;class 'sklearn.pipeline.Pipeline'&gt; model"</code></pre>
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</div>
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