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<div class="eyebrow">05 · python</div>
<h1>The same engine,<br>a Pythonic API.</h1>
<p class="lede"><code class="inl">pip install millwright</code> — a Pythonic pipeline over the same Rust engine, shipped on <a href="https://pypi.org/project/millwright/">PyPI</a> as an abi3 wheel built with maturin. Run it at Rust speed from a notebook.</p>
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<div class="eyebrow">Install & use</div>
<h2>A pipeline, from Python.</h2>
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<pre>pip install millwright</pre>
<pre><span class="k">import</span> millwright <span class="k">as</span> mw
train = mw.Frame.from_pandas(df) <span class="c"># or from_numpy / from_rows</span>
pipe = (mw.<span class="f">Pipeline</span>()
.step(<span class="s">"impute"</span>, mw.SimpleImputer.median())
.step(<span class="s">"scale"</span>, mw.StandardScaler())
.estimator(<span class="s">"rf"</span>, mw.RandomForest(n_trees=<span class="k">200</span>, max_depth=<span class="k">8</span>)))
pipe.fit(train, y_train)
preds = pipe.predict(test)
metrics = pipe.evaluate(test, y_test) <span class="c"># -> {"accuracy": …, "f1": …}</span></pre>
<p>The transformer / estimator objects (<code class="inl">StandardScaler</code>, <code class="inl">MinMaxScaler</code>, <code class="inl">SimpleImputer</code>, <code class="inl">OneHotEncoder</code>, <code class="inl">RandomForest</code>, <code class="inl">LinearRegression</code>, <code class="inl">Knn</code>, <code class="inl">Svc</code>, <code class="inl">NaiveBayes</code>) are the same engines as Rust. The older builder form — <code class="inl">pipe.standard_scaler()</code>, <code class="inl">pipe.random_forest()</code> — still works.</p>
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<div class="eyebrow">Ingest & EDA</div>
<h2>numpy, pandas, or a typed table.</h2>
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<pre><span class="c"># a Frame reads arrays and DataFrames directly</span>
train = mw.Frame.from_numpy(X) <span class="c"># or from_pandas(df) / from_rows(rows)</span>
<span class="c"># or the dtype-aware Table (strings, dates, nulls) + automated EDA</span>
data = mw.Table.from_csv(<span class="s">"churn.csv"</span>)
mw.Profile.of_with_target(data, <span class="s">"churned"</span>).to_html(<span class="s">"eda.html"</span>)
train = data.to_frame()</pre>
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<div class="eyebrow">Tune, explain, export</div>
<h2>The whole lifecycle.</h2>
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<pre><span class="c"># grid search + stratified CV over the pipeline</span>
best = (mw.GridSearch(pipe, {<span class="s">"rf__max_depth"</span>: [<span class="k">4</span>, <span class="k">8</span>, <span class="k">16</span>]})
.cv(mw.StratifiedKFold(<span class="k">5</span>)).scoring(<span class="s">"f1"</span>)
.fit(train, y_train))
best.best_score; best.best_params()
<span class="c"># SHAP importance, and one portable ONNX artifact</span>
pipe.fit(train, y_train)
pipe.explain(test) <span class="c"># [(feature, mean|shap|), …]</span>
pipe.export_onnx(<span class="s">"churn.onnx"</span>)
<span class="c"># consume an external sklearn / PyTorch model (exported to ONNX) as a step</span>
ext = mw.<span class="f">Pipeline</span>().estimator(<span class="s">"onnx"</span>, mw.OnnxModel(<span class="s">"model.onnx"</span>))</pre>
<div class="callout"><b>Note.</b> ONNX export folds affine preprocessing (scalers) into the graph; a non-affine step (impute, one-hot) raises, naming the step. Fit / predict / evaluate / explain work with any steps.</div>
<p class="tiny"><code class="inl">python</code> is deliberately not part of <code class="inl">full</code>: pyo3's <code class="inl">extension-module</code> defers libpython symbols, so a plain <code class="inl">cargo test</code> can't link it. It is built and tested the way it ships — as a wheel. To build from source, from a virtualenv: <code class="inl">maturin develop --features python</code>. The wheel bundles the EDA (polars), model-selection, explain, and ONNX engines.</p>
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