docs: Add WaveML DAI examples (#870)

* added base WaveML DAI example

* WaveML DAI on Cloud example

* add O

* refactor to more readable

* refactor for better readability

* added all DAI examples

* added to tour conf

py/examples/ml_dai.py

@@ -0,0 +1,178 @@
+# WaveML / DAI
+# Build Wave Models for training and prediction of classification or regression using Driverless AI.
+# ---
+import os
+
+from h2o_wave import main, app, Q, copy_expando, ui
+from h2o_wave_ml import build_model, ModelType
+from h2o_wave_ml.utils import list_dai_instances
+
+from sklearn.datasets import load_wine
+from sklearn.model_selection import train_test_split
+
+STEAM_URL = os.environ.get('STEAM_URL')
+MLOPS_URL = os.environ.get('MLOPS_URL')
+
+DATASET_TEXT = '''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>.'''
+STEAM_TEXT = f'''No Driverless AI instances available. You may create one in 
+    <a href="{STEAM_URL}/#/driverless/instances" target="_blank">AI Engines</a> and refresh the page.'''
+
+
+def dai_experiment_url(instance_id: str, instance_name: str):
+    # URL link to Driverless AI experiment
+    return f'''**Driverless AI Experiment:**
+        <a href="{STEAM_URL}/oidc-login-start?forward=/proxy/driverless/{instance_id}/openid/callback" target="_blank">{instance_name}</a>'''
+
+
+def mlops_deployment_url(project_id: str):
+    # URL link to MLOps deployment
+    return f'**MLOps Deployment:** <a href="{MLOPS_URL}/projects/{project_id}" target="_blank">{project_id}'
+
+
+def form_unsupported():
+    # display when app is not running on cloud
+    return [
+        ui.text('''This example requires access to Driverless AI running on
+            <a href="https://h2oai.github.io/h2o-ai-cloud" target="_blank">H2O AI Hybrid Cloud</a> 
+            and does not support standalone app instances.'''),
+        ui.text('''Sign up at <a href="https://h2o.ai/free" target="_blank">https://h2o.ai/free</a>
+            to run apps on cloud.''')
+    ]
+
+
+def form_default(q: Q):
+    # display when app is initialized
+    return [
+        ui.text(content=DATASET_TEXT),
+        ui.dropdown(name='dai_instance_id', label='Select Driverless AI instance', value=q.client.dai_instance_id,
+                    choices=q.client.choices_dai_instances, required=True),
+        ui.text(content=STEAM_TEXT, visible=q.client.disable_training),
+        ui.buttons(items=[
+            ui.button(name='train', label='Train', primary=True, disabled=q.client.disable_training),
+            ui.button(name='predict', label='Predict', primary=True, disabled=True),
+        ])
+    ]
+
+
+def form_training_progress(q: Q):
+    # display when model training is in progress
+    return [
+        ui.text(content=DATASET_TEXT),
+        ui.dropdown(name='dai_instance_id', label='Select Driverless AI instance', value=q.client.dai_instance_id,
+                    choices=q.client.choices_dai_instances, required=True),
+        ui.buttons(items=[
+            ui.button(name='train', label='Train', primary=True, disabled=True),
+            ui.button(name='predict', label='Predict', primary=True, disabled=True)
+        ]),
+        ui.progress(label='Training in progress...', caption='This can take a few minutes...'),
+        ui.text(content=q.client.model_details)
+    ]
+
+
+def form_training_completed(q: Q):
+    # display when model training is completed
+    return [
+        ui.text(content=DATASET_TEXT),
+        ui.dropdown(name='dai_instance_id', label='Select Driverless AI instance', value=q.client.dai_instance_id,
+                    choices=q.client.choices_dai_instances, required=True),
+        ui.buttons(items=[
+            ui.button(name='train', label='Train', primary=True),
+            ui.button(name='predict', label='Predict', primary=True)
+        ]),
+        ui.message_bar(type='success', text='Training successfully completed!'),
+        ui.text(content=q.client.model_details)
+    ]
+
+
+def form_prediction_completed(q: Q):
+    # display when model prediction is completed
+    return [
+        ui.text(content=DATASET_TEXT),
+        ui.dropdown(name='dai_instance_id', label='Select Driverless AI instance', value=q.client.dai_instance_id,
+                    choices=q.client.choices_dai_instances, required=True),
+        ui.buttons(items=[
+            ui.button(name='train', label='Train', primary=True),
+            ui.button(name='predict', label='Predict', primary=True)
+        ]),
+        ui.message_bar(type='success', text='Prediction successfully completed!'),
+        ui.text(content=q.client.model_details),
+        ui.text(content=f'''**Example predictions:** <br />
+            {q.client.preds[0]} <br /> {q.client.preds[1]} <br /> {q.client.preds[2]}''')
+    ]
+
+
+@app('/demo')
+async def serve(q: Q):
+    if 'H2O_CLOUD_ENVIRONMENT' not in os.environ:
+        # show appropriate message if app is not running on cloud
+        q.page['example'] = ui.form_card(
+            box='1 1 -1 -1',
+            items=form_unsupported()
+        )
+    elif q.args.train:
+        # get DAI instance name
+        copy_expando(q.args, q.client)
+
+        for dai_instance in q.client.dai_instances:
+            if dai_instance['id'] == int(q.client.dai_instance_id):
+                q.client.dai_instance_name = dai_instance['name']
+
+        # set DAI model details
+        q.client.model_details = dai_experiment_url(q.client.dai_instance_id, q.client.dai_instance_name)
+
+        # show training progress and details
+        q.page['example'].items = form_training_progress(q)
+        await q.page.save()
+
+        # train WaveML Model using Driverless AI
+        q.client.wave_model = await q.run(
+            func=build_model,
+            train_df=q.client.train_df,
+            target_column='target',
+            model_type=ModelType.DAI,
+            refresh_token=q.auth.refresh_token,
+            _steam_dai_instance_name=q.client.dai_instance_name,
+            _dai_accuracy=1,
+            _dai_time=1,
+            _dai_interpretability=10
+        )
+
+        # update DAI model details
+        q.client.project_id = q.client.wave_model.project_id
+        q.client.model_details += f'<br />{mlops_deployment_url(q.client.project_id)}'
+
+        # show prediction option
+        q.page['example'].items = form_training_completed(q)
+    elif q.args.predict:
+        # predict on test data
+        q.client.preds = q.client.wave_model.predict(test_df=q.client.test_df)
+
+        # show predictions
+        q.page['example'].items = form_prediction_completed(q)
+    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)
+
+        # DAI instances
+        q.client.dai_instances = list_dai_instances(refresh_token=q.auth.refresh_token)
+        q.client.choices_dai_instances = [
+            ui.choice(
+                name=str(x['id']),
+                label=f'{x["name"]} ({x["status"].capitalize()})',
+                disabled=x['status'] != 'running'
+            ) for x in q.client.dai_instances
+        ]
+
+        running_dai_instances = [x['id'] for x in q.client.dai_instances if x['status'] == 'running']
+        q.client.disable_training = False if running_dai_instances else True
+        q.client.dai_instance_id = str(running_dai_instances[0]) if running_dai_instances else ''
+
+        # display ui
+        q.page['example'] = ui.form_card(
+            box='1 1 -1 -1',
+            items=form_default(q)
+        )
+
+    await q.page.save()

py/examples/ml_dai_autodoc.py

@@ -0,0 +1,156 @@
+# WaveML / DAI / AutoDoc
+# Download AutoDoc for Wave Models built using Driverless AI.
+# ---
+import os
+
+from h2o_wave import main, app, Q, copy_expando, ui
+from h2o_wave_ml import build_model, ModelType
+from h2o_wave_ml.utils import list_dai_instances, save_autodoc
+
+from sklearn.datasets import load_wine
+from sklearn.model_selection import train_test_split
+
+STEAM_URL = os.environ.get('STEAM_URL')
+MLOPS_URL = os.environ.get('MLOPS_URL')
+
+DATASET_TEXT = '''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>.'''
+STEAM_TEXT = f'''No Driverless AI instances available. You may create one in 
+    <a href="{STEAM_URL}/#/driverless/instances" target="_blank">AI Engines</a> and refresh the page.'''
+
+
+def dai_experiment_url(instance_id: str, instance_name: str):
+    # URL link to Driverless AI experiment
+    return f'''**Driverless AI Experiment:**
+        <a href="{STEAM_URL}/oidc-login-start?forward=/proxy/driverless/{instance_id}/openid/callback" target="_blank">{instance_name}</a>'''
+
+
+def mlops_deployment_url(project_id: str):
+    # URL link to MLOps deployment
+    return f'**MLOps Deployment:** <a href="{MLOPS_URL}/projects/{project_id}" target="_blank">{project_id}'
+
+
+def form_unsupported():
+    # display when app is not running on cloud
+    return [
+        ui.text('''This example requires access to Driverless AI running on
+            <a href="https://h2oai.github.io/h2o-ai-cloud" target="_blank">H2O AI Hybrid Cloud</a> 
+            and does not support standalone app instances.'''),
+        ui.text('''Sign up at <a href="https://h2o.ai/free" target="_blank">https://h2o.ai/free</a>
+            to run apps on cloud.''')
+    ]
+
+
+def form_default(q: Q):
+    # display when app is initialized
+    return [
+        ui.text(content=DATASET_TEXT),
+        ui.dropdown(name='dai_instance_id', label='Select Driverless AI instance', value=q.client.dai_instance_id,
+                    choices=q.client.choices_dai_instances, required=True),
+        ui.text(content=STEAM_TEXT, visible=q.client.disable_training),
+        ui.button(name='train', label='Train', primary=True, disabled=q.client.disable_training)
+    ]
+
+
+def form_training_progress(q: Q):
+    # display when model training is in progress
+    return [
+        ui.text(content=DATASET_TEXT),
+        ui.dropdown(name='dai_instance_id', label='Select Driverless AI instance', value=q.client.dai_instance_id,
+                    choices=q.client.choices_dai_instances, required=True),
+        ui.button(name='train', label='Train', primary=True, disabled=q.client.disable_training),
+        ui.progress(label='Training in progress...', caption='This can take a few minutes...'),
+        ui.text(content=q.client.model_details)
+    ]
+
+
+def form_training_completed(q: Q):
+    # display when model training is completed
+    return [
+        ui.text(content=DATASET_TEXT),
+        ui.dropdown(name='dai_instance_id', label='Select Driverless AI instance', value=q.client.dai_instance_id,
+                    choices=q.client.choices_dai_instances, required=True),
+        ui.button(name='train', label='Train', primary=True, disabled=q.client.disable_training),
+        ui.message_bar(type='success', text='Training successfully completed!'),
+        ui.text(content=q.client.model_details),
+        ui.text(content=f'**Download:** <a href="{q.client.path_autodoc}">AutoDoc</a>')
+    ]
+
+
+@app('/demo')
+async def serve(q: Q):
+    if 'H2O_CLOUD_ENVIRONMENT' not in os.environ:
+        # show appropriate message if app is not running on cloud
+        q.page['example'] = ui.form_card(
+            box='1 1 -1 -1',
+            items=form_unsupported()
+        )
+    elif q.args.train:
+        # get DAI instance name
+        copy_expando(q.args, q.client)
+
+        for dai_instance in q.client.dai_instances:
+            if dai_instance['id'] == int(q.client.dai_instance_id):
+                q.client.dai_instance_name = dai_instance['name']
+
+        # set DAI model details
+        q.client.model_details = dai_experiment_url(q.client.dai_instance_id, q.client.dai_instance_name)
+
+        # show training progress and details
+        q.page['example'].items = form_training_progress(q)
+        await q.page.save()
+
+        # train WaveML Model using Driverless AI
+        q.client.wave_model = await q.run(
+            func=build_model,
+            train_df=q.client.train_df,
+            target_column='target',
+            model_type=ModelType.DAI,
+            refresh_token=q.auth.refresh_token,
+            _steam_dai_instance_name=q.client.dai_instance_name,
+            _dai_accuracy=1,
+            _dai_time=1,
+            _dai_interpretability=10
+        )
+
+        # update DAI model details
+        q.client.project_id = q.client.wave_model.project_id
+        q.client.model_details += f'<br />{mlops_deployment_url(q.client.project_id)}'
+
+        # download AutoDoc
+        path_autodoc = save_autodoc(
+            project_id=q.client.project_id,
+            output_dir_path='.',
+            refresh_token=q.auth.refresh_token
+        )
+
+        q.client.path_autodoc, *_ = await q.site.upload([path_autodoc])
+
+        # show model outputs
+        q.page['example'].items = form_training_completed(q)
+    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)
+
+        # DAI instances
+        q.client.dai_instances = list_dai_instances(refresh_token=q.auth.refresh_token)
+        q.client.choices_dai_instances = [
+            ui.choice(
+                name=str(x['id']),
+                label=f'{x["name"]} ({x["status"].capitalize()})',
+                disabled=x['status'] != 'running'
+            ) for x in q.client.dai_instances
+        ]
+
+        running_dai_instances = [x['id'] for x in q.client.dai_instances if x['status'] == 'running']
+        q.client.disable_training = False if running_dai_instances else True
+        q.client.dai_instance_id = str(running_dai_instances[0]) if running_dai_instances else ''
+
+        # display ui
+        q.page['example'] = ui.form_card(
+            box='1 1 -1 -1',
+            items=form_default(q)
+        )
+
+    await q.page.save()