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‎Chapter5/machine_learning.ipynb

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"source": [
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"[Link to Lazy Predict](https://github.com/shankarpandala/lazypredict)."
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]
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},
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{
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"cell_type": "markdown",
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"id": "ee14002f",
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"metadata": {},
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"source": [
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"### AutoGluon: Fast and Accurate ML in 3 Lines of Code"
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]
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},
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{
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"cell_type": "markdown",
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"id": "35aec697",
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"metadata": {},
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"source": [
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"The traditional scikit-learn approach requires extensive manual work, including data preprocessing, model selection, and hyperparameter tuning.\n",
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"\n",
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"In contrast, AutoGluon automates these tasks, allowing you to train and deploy accurate models with minimal code."
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]
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},
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{
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"cell_type": "markdown",
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"id": "28784b94",
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"metadata": {},
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"source": [
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"```python\n",
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"from sklearn.impute import SimpleImputer\n",
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"from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
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"from sklearn.compose import ColumnTransformer\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.model_selection import GridSearchCV\n",
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"\n",
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"# Preprocessing Pipeline\n",
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"numeric_transformer = SimpleImputer(strategy='mean')\n",
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"categorical_transformer = OneHotEncoder(handle_unknown='ignore')\n",
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"\n",
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"preprocessor = ColumnTransformer(\n",
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" transformers=[\n",
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" ('num', numeric_transformer, numerical_columns),\n",
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" ('cat', categorical_transformer, categorical_columns)\n",
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" ])\n",
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"\n",
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"# Machine Learning Pipeline\n",
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"model = RandomForestClassifier()\n",
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"\n",
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"pipeline = Pipeline(steps=[\n",
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" ('preprocessor', preprocessor),\n",
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" ('scaler', StandardScaler()),\n",
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" ('model', model)\n",
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"])\n",
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"\n",
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"# Hyperparameter Tuning\n",
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"param_grid = {\n",
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" 'model__n_estimators': [100, 200, 300],\n",
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" 'model__max_depth': [5, 10, None],\n",
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" 'model__min_samples_split': [2, 5, 10]\n",
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"}\n",
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"\n",
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"grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring='accuracy')\n",
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"grid_search.fit(X_train, y_train)\n",
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"grid_search.predict(X_test)\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"id": "25742686",
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"metadata": {},
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"source": [
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"```python\n",
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"from autogluon.tabular import TabularPredictor\n",
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"\n",
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"predictor = TabularPredictor(label=\"class\").fit(train_data)\n",
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"predictions = predictor.predict(test_data)\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9e51ccf5",
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"metadata": {},
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"source": [
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"[Link to AutoGluon](https://bit.ly/45ljoOd)."
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]
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}
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],
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"metadata": {
@@ -2500,7 +2584,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.6"
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"version": "3.11.4"
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},
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"toc": {
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"base_numbering": 1,

‎docs/Chapter5/machine_learning.html

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<li class="toctree-l2"><a class="reference internal" href="../Chapter2/dataclasses.html">3.7. Data Classes</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../Chapter2/typing.html">3.8. Typing</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../Chapter2/pathlib.html">3.9. pathlib</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../Chapter2/pydantic.html">3.10. Pydantic</a></li>
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</ul>
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</li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../Chapter3/Chapter3.html">4. Pandas</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#mlem-capture-your-machine-learning-model-s-metadata">6.5.15. MLEM: Capture Your Machine Learning Model’s Metadata</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#distributed-machine-learning-with-mllib">6.5.16. Distributed Machine Learning with MLlib</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#rapid-prototyping-and-comparison-of-basic-models-with-lazy-predict">6.5.17. Rapid Prototyping and Comparison of Basic Models with Lazy Predict</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#autogluon-fast-and-accurate-ml-in-3-lines-of-code">6.5.18. AutoGluon: Fast and Accurate ML in 3 Lines of Code</a></li>
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</ul>
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</div>
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</div>
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<p><a class="reference external" href="https://github.com/shankarpandala/lazypredict">Link to Lazy Predict</a>.</p>
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</section>
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<section id="autogluon-fast-and-accurate-ml-in-3-lines-of-code">
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<h2><span class="section-number">6.5.18. </span>AutoGluon: Fast and Accurate ML in 3 Lines of Code<a class="headerlink" href="#autogluon-fast-and-accurate-ml-in-3-lines-of-code" title="Permalink to this heading">#</a></h2>
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<p>The traditional scikit-learn approach requires extensive manual work, including data preprocessing, model selection, and hyperparameter tuning.</p>
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<p>In contrast, AutoGluon automates these tasks, allowing you to train and deploy accurate models with minimal code.</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.impute</span> <span class="kn">import</span> <span class="n">SimpleImputer</span>
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<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">OneHotEncoder</span><span class="p">,</span> <span class="n">StandardScaler</span>
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<span class="kn">from</span> <span class="nn">sklearn.compose</span> <span class="kn">import</span> <span class="n">ColumnTransformer</span>
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<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
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<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>
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<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchCV</span>
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<span class="c1"># Preprocessing Pipeline</span>
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<span class="n">numeric_transformer</span> <span class="o">=</span> <span class="n">SimpleImputer</span><span class="p">(</span><span class="n">strategy</span><span class="o">=</span><span class="s1">&#39;mean&#39;</span><span class="p">)</span>
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<span class="n">categorical_transformer</span> <span class="o">=</span> <span class="n">OneHotEncoder</span><span class="p">(</span><span class="n">handle_unknown</span><span class="o">=</span><span class="s1">&#39;ignore&#39;</span><span class="p">)</span>
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<span class="n">preprocessor</span> <span class="o">=</span> <span class="n">ColumnTransformer</span><span class="p">(</span>
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<span class="n">transformers</span><span class="o">=</span><span class="p">[</span>
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<span class="p">(</span><span class="s1">&#39;num&#39;</span><span class="p">,</span> <span class="n">numeric_transformer</span><span class="p">,</span> <span class="n">numerical_columns</span><span class="p">),</span>
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<span class="p">(</span><span class="s1">&#39;cat&#39;</span><span class="p">,</span> <span class="n">categorical_transformer</span><span class="p">,</span> <span class="n">categorical_columns</span><span class="p">)</span>
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<span class="p">])</span>
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<span class="c1"># Machine Learning Pipeline</span>
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<span class="n">model</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">()</span>
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<span class="n">pipeline</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span><span class="n">steps</span><span class="o">=</span><span class="p">[</span>
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<span class="p">(</span><span class="s1">&#39;preprocessor&#39;</span><span class="p">,</span> <span class="n">preprocessor</span><span class="p">),</span>
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<span class="p">(</span><span class="s1">&#39;scaler&#39;</span><span class="p">,</span> <span class="n">StandardScaler</span><span class="p">()),</span>
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<span class="p">(</span><span class="s1">&#39;model&#39;</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
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<span class="p">])</span>
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<span class="c1"># Hyperparameter Tuning</span>
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<span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span>
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<span class="s1">&#39;model__n_estimators&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">300</span><span class="p">],</span>
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<span class="s1">&#39;model__max_depth&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span>
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<span class="s1">&#39;model__min_samples_split&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">]</span>
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<span class="p">}</span>
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<span class="n">grid_search</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">pipeline</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">)</span>
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<span class="n">grid_search</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
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<span class="n">grid_search</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
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</pre></div>
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</div>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">autogluon.tabular</span> <span class="kn">import</span> <span class="n">TabularPredictor</span>
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<span class="n">predictor</span> <span class="o">=</span> <span class="n">TabularPredictor</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;class&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_data</span><span class="p">)</span>
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<span class="n">predictions</span> <span class="o">=</span> <span class="n">predictor</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test_data</span><span class="p">)</span>
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</pre></div>
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</div>
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<p><a class="reference external" href="https://bit.ly/45ljoOd">Link to AutoGluon</a>.</p>
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</section>
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</section>
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<script type="text/x-thebe-config">
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#mlem-capture-your-machine-learning-model-s-metadata">6.5.15. MLEM: Capture Your Machine Learning Model’s Metadata</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#distributed-machine-learning-with-mllib">6.5.16. Distributed Machine Learning with MLlib</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#rapid-prototyping-and-comparison-of-basic-models-with-lazy-predict">6.5.17. Rapid Prototyping and Comparison of Basic Models with Lazy Predict</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#autogluon-fast-and-accurate-ml-in-3-lines-of-code">6.5.18. AutoGluon: Fast and Accurate ML in 3 Lines of Code</a></li>
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</ul>
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</nav></div>
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‎docs/_sources/Chapter5/machine_learning.ipynb

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"source": [
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"[Link to Lazy Predict](https://github.com/shankarpandala/lazypredict)."
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]
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},
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{
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"cell_type": "markdown",
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"id": "ee14002f",
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"metadata": {},
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"source": [
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"### AutoGluon: Fast and Accurate ML in 3 Lines of Code"
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]
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},
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{
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"cell_type": "markdown",
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"id": "35aec697",
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"metadata": {},
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"source": [
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"The traditional scikit-learn approach requires extensive manual work, including data preprocessing, model selection, and hyperparameter tuning.\n",
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"\n",
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"In contrast, AutoGluon automates these tasks, allowing you to train and deploy accurate models with minimal code."
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]
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},
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{
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"cell_type": "markdown",
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"id": "28784b94",
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"metadata": {},
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"source": [
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"```python\n",
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"from sklearn.impute import SimpleImputer\n",
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"from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
2510+
"from sklearn.compose import ColumnTransformer\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.model_selection import GridSearchCV\n",
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"\n",
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"# Preprocessing Pipeline\n",
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"numeric_transformer = SimpleImputer(strategy='mean')\n",
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"categorical_transformer = OneHotEncoder(handle_unknown='ignore')\n",
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"\n",
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"preprocessor = ColumnTransformer(\n",
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" transformers=[\n",
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" ('num', numeric_transformer, numerical_columns),\n",
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" ('cat', categorical_transformer, categorical_columns)\n",
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" ])\n",
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"\n",
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"# Machine Learning Pipeline\n",
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"model = RandomForestClassifier()\n",
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"\n",
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"pipeline = Pipeline(steps=[\n",
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" ('preprocessor', preprocessor),\n",
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" ('scaler', StandardScaler()),\n",
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" ('model', model)\n",
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"])\n",
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"\n",
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"# Hyperparameter Tuning\n",
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"param_grid = {\n",
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" 'model__n_estimators': [100, 200, 300],\n",
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" 'model__max_depth': [5, 10, None],\n",
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" 'model__min_samples_split': [2, 5, 10]\n",
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"}\n",
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"\n",
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"grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring='accuracy')\n",
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"grid_search.fit(X_train, y_train)\n",
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"grid_search.predict(X_test)\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"id": "25742686",
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"metadata": {},
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"source": [
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"```python\n",
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"from autogluon.tabular import TabularPredictor\n",
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"\n",
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"predictor = TabularPredictor(label=\"class\").fit(train_data)\n",
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"predictions = predictor.predict(test_data)\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9e51ccf5",
2563+
"metadata": {},
2564+
"source": [
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"[Link to AutoGluon](https://bit.ly/45ljoOd)."
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]
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}
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],
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"metadata": {
@@ -2500,7 +2584,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.6"
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"version": "3.11.4"
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},
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"toc": {
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"base_numbering": 1,

‎docs/searchindex.js

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