async def serve(self, q: Q) -> None:
copy_expando(q.args, q.client)
if not q.client.client_initialized:
await self._initialize_client(q)
q.client.client_initialized = True
elif q.args.input_model:
await self._load_model(q)
await self._process_text(q)
await self._update_all_cards(q)
elif q.args.analyze_text:
await self._process_text(q)
await self._update_all_cards(q)
elif q.args.select_ents:
await self._update_entity_cards(q)
elif any([
q.args.split_sentences, q.args.fine_grained, q.args.add_lemma,
q.args.collapse_punct, q.args.collapse_phrases, q.args.compact,
q.args.color, q.args.bg, q.args.font, q.args.offset_x,
q.args.arrow_stroke, q.args.arrow_width, q.args.arrow_spacing,
q.args.word_spacing, q.args.word_distance
]):
await self._update_dependency_cards(q)
elif q.args.compare_texts:
await self._process_text(q)
await self._update_similarity_card(q)
async def serve(q: Q):
await init(q)
if q.args.generate_text:
copy_expando(q.args, q.app)
await show_results(q)
else:
await get_inputs(q)
await q.page.save()
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()
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()
def test_expando_copy():
e = copy_expando(Expando(dict(answer=42)), Expando())
assert e.answer == 42
assert e['answer'] == 42
assert 'answer' in e
