docs: Add WaveML H2O-3 Algo example (#871)

py/examples/ml_h2o_algo.py

@@ -0,0 +1,68 @@
+# WaveML / H2O-3 / Algo
+# Configure a specific algo for Wave Models built using H2O-3 AutoML.
+# ---
+from h2o_wave import main, app, Q, ui, copy_expando
+from h2o_wave_ml import build_model, ModelType
+
+from sklearn.datasets import load_wine
+from sklearn.model_selection import train_test_split
+
+
+@app('/demo')
+async def serve(q: Q):
+    if q.args.train:
+        # train WaveML Model using H2O-3 AutoML
+        copy_expando(q.args, q.client)
+        q.client.wave_model = build_model(
+            train_df=q.client.train_df,
+            target_column='target',
+            model_type=ModelType.H2O3,
+            _h2o3_max_runtime_secs=30,
+            _h2o3_nfolds=2,
+            _h2o3_include_algos=[q.client.algo]
+        )
+        model_id = q.client.wave_model.model.model_id
+        accuracy = round(100 - q.client.wave_model.model.mean_per_class_error() * 100, 2)
+
+        # show training details and prediction option
+        q.page['example'].items[1].choice_group.value = q.client.algo
+        q.page['example'].items[2].buttons.items[1].button.disabled = False
+        q.page['example'].items[3].message_bar.type = 'success'
+        q.page['example'].items[3].message_bar.text = 'Training successfully completed!'
+        q.page['example'].items[4].text.content = f'''**H2O AutoML model id:** {model_id} <br />
+            **Accuracy:** {accuracy}%'''
+        q.page['example'].items[5].text.content = ''
+    elif q.args.predict:
+        # predict on test data
+        preds = q.client.wave_model.predict(test_df=q.client.test_df)
+
+        # show predictions
+        q.page['example'].items[3].message_bar.text = 'Prediction successfully completed!'
+        q.page['example'].items[5].text.content = f'''**Example predictions:** <br />
+            {preds[0]} <br /> {preds[1]} <br /> {preds[2]}'''
+    else:
+        # prepare sample train and test dataframes
+        data = load_wine(as_frame=True)['frame']
+        q.client.train_df, q.client.test_df = train_test_split(data, train_size=0.8)
+
+        # algos
+        algo_choices = [ui.choice(x, x) for x in ['DRF', 'GLM', 'XGBoost', 'GBM', 'DeepLearning']]
+
+        # display ui
+        q.page['example'] = ui.form_card(
+            box='1 1 -1 -1',
+            items=[
+                ui.text(content='''The sample dataset used is the
+                    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_wine.html" target="_blank">wine dataset</a>.'''),
+                ui.choice_group(name='algo', label='Select Algo', choices=algo_choices, value='DRF'),
+                ui.buttons(items=[
+                    ui.button(name='train', label='Train', primary=True),
+                    ui.button(name='predict', label='Predict', primary=True, disabled=True),
+                ]),
+                ui.message_bar(type='warning', text='Training will take a few seconds'),
+                ui.text(content=''),
+                ui.text(content='')
+            ]
+        )
+
+    await q.page.save()

py/examples/ml_h2o_categorical.py

@@ -46,7 +46,7 @@ async def serve(q: Q):
         q.client.train_df, q.client.test_df = train_test_split(data, train_size=0.8)
 
         # columns
-        column_choices = [ui.choice(x, x) for x in q.client.train_df.columns if x not in ['target', 'magnesium', 'proline']]
+        column_choices = [ui.choice(x, x) for x in q.client.train_df.columns if x != 'target']
 
         # display ui
         q.page['example'] = ui.form_card(

py/examples/tour.conf

@@ -195,6 +195,7 @@ ml_h2o.py
 ml_h2o_save.py
 ml_h2o_categorical.py
 ml_h2o_parameters.py
+ml_h2o_algo.py
 ml_h2o_shap.py
 ml_dai.py
 ml_dai_save.py