def test_baseline_deep_dive_integration(notebooks, output_notebook,
kernel_name, size, expected_values):
notebook_path = notebooks["baseline_deep_dive"]
pm.execute_notebook(
notebook_path,
output_notebook,
kernel_name=kernel_name,
parameters=dict(TOP_K=10, MOVIELENS_DATA_SIZE=size),
)
results = sb.read_notebook(output_notebook).scraps.dataframe.set_index(
"name")["data"]
for key, value in expected_values.items():
assert results[key] == pytest.approx(value, rel=TOL, abs=ABS_TOL)
def test_01_notebook_run(detection_notebooks):
epochs = 3
notebook_path = detection_notebooks["01"]
pm.execute_notebook(
notebook_path,
OUTPUT_NOTEBOOK,
parameters=dict(PM_VERSION=pm.__version__, EPOCHS=epochs),
kernel_name=KERNEL_NAME,
)
nb_output = sb.read_notebook(OUTPUT_NOTEBOOK)
assert len(nb_output.scraps["training_losses"].data) == epochs
assert nb_output.scraps["training_losses"].data[-1] < 0.5
assert nb_output.scraps["training_average_precision"].data[-1] > 0.5
def test_01_notebook_run(classification_notebooks):
if linux_with_gpu():
notebook_path = classification_notebooks["01_training_introduction"]
pm.execute_notebook(
notebook_path,
OUTPUT_NOTEBOOK,
parameters=dict(PM_VERSION=pm.__version__),
kernel_name=KERNEL_NAME,
)
nb_output = sb.read_notebook(OUTPUT_NOTEBOOK)
assert len(nb_output.scraps["training_accuracies"].data) == 10
assert nb_output.scraps["training_accuracies"].data[-1] > 0.70
assert nb_output.scraps["validation_accuracy"].data > 0.70
def test_10_notebook_run(classification_notebooks, tiny_ic_data_path):
notebook_path = classification_notebooks["10_image_annotation"]
pm.execute_notebook(
notebook_path,
OUTPUT_NOTEBOOK,
parameters=dict(
PM_VERSION=pm.__version__,
IM_DIR=os.path.join(tiny_ic_data_path, "can"),
),
kernel_name=KERNEL_NAME,
)
nb_output = sb.read_notebook(OUTPUT_NOTEBOOK)
assert nb_output.scraps["num_images"].data == 6
async def generate_report(athlete, activity_id):
input_path = Path(NOTEBOOK_TEMPLATES_PATH,
f'{NOTEBOOK_TEMPLATE_NAME}.ipynb')
output_dir = Path(REPORT_OUTPUT_DIR, str(athlete.id))
output_dir.mkdir(parents=True, exist_ok=True)
notebook_filename = f'{activity_id}.ipynb'
html_filename = f'{activity_id}.html'
notebook_path = Path(output_dir, notebook_filename)
html_path = Path(output_dir, html_filename)
try:
papermill.execute_notebook(input_path=input_path.as_posix(),
output_path=notebook_path.as_posix(),
parameters=dict(
access_token=athlete.access_token,
activity_id=activity_id))
except papermill.exceptions.PapermillExecutionError:
pass
nb = sb.read_notebook(notebook_path.as_posix())
start_date_local = nb.scraps['activity_detail'].data['start_date_local']
dt = datetime.fromisoformat(start_date_local)
title = nb.scraps['activity_detail'].data['name']
await Report.objects.create(activity_id=activity_id,
strava_athlete=athlete,
title=title,
datetime=dt,
notebook_filename=notebook_filename,
html_filename=html_filename)
with notebook_path.open('r') as f:
notebook = nbformat.reads(f.read(), as_version=4)
c = Config()
c.TagRemovePreprocessor.enabled = True
c.TagRemovePreprocessor.remove_cell_tags = ("remove_cell", "parameters",
"injected-parameters")
c.TagRemovePreprocessor.remove_all_outputs_tags = ('remove_output', )
c.TagRemovePreprocessor.remove_input_tags = ('remove_input', )
c.preprocessors = ["TagRemovePreprocessor"]
html_exporter = HTMLExporter(config=c)
html_exporter.template_file = 'full'
body, _ = html_exporter.from_notebook_node(notebook)
with html_path.open('w') as f:
f.write(body)
def test_ncf_smoke(notebooks, output_notebook, kernel_name):
notebook_path = notebooks["ncf"]
pm.execute_notebook(
notebook_path,
output_notebook,
kernel_name=kernel_name,
parameters=dict(TOP_K=10, MOVIELENS_DATA_SIZE="100k", EPOCHS=1, BATCH_SIZE=256),
)
results = sb.read_notebook(output_notebook).scraps.dataframe.set_index("name")[
"data"
]
assert results["map"] == pytest.approx(0.0409234, rel=TOL, abs=ABS_TOL)
assert results["ndcg"] == pytest.approx(0.1773, rel=TOL, abs=ABS_TOL)
assert results["precision"] == pytest.approx(0.160127, rel=TOL, abs=ABS_TOL)
assert results["recall"] == pytest.approx(0.0879193, rel=TOL, abs=ABS_TOL)
def test_cornac_bivae_smoke(notebooks, output_notebook, kernel_name):
notebook_path = notebooks["cornac_bivae_deep_dive"]
pm.execute_notebook(
notebook_path,
output_notebook,
kernel_name=kernel_name,
parameters=dict(MOVIELENS_DATA_SIZE="100k"),
)
results = sb.read_notebook(output_notebook).scraps.dataframe.set_index("name")[
"data"
]
assert results["map"] == pytest.approx(0.146552, rel=TOL, abs=ABS_TOL)
assert results["ndcg"] == pytest.approx(0.474124, rel=TOL, abs=ABS_TOL)
assert results["precision"] == pytest.approx(0.412527, rel=TOL, abs=ABS_TOL)
assert results["recall"] == pytest.approx(0.225064, rel=TOL, abs=ABS_TOL)
def test_lstur_smoke(notebooks, output_notebook, kernel_name):
notebook_path = notebooks["lstur_quickstart"]
pm.execute_notebook(
notebook_path,
output_notebook,
kernel_name=kernel_name,
parameters=dict(epochs=1, seed=40, MIND_type="demo"),
)
results = sb.read_notebook(output_notebook).scraps.dataframe.set_index("name")[
"data"
]
assert results["res_syn"]["group_auc"] == pytest.approx(
0.5977, rel=TOL, abs=ABS_TOL
)
assert results["res_syn"]["mean_mrr"] == pytest.approx(0.2618, rel=TOL, abs=ABS_TOL)