def test_main_content_disabled_no_project(driver):
Project.query.delete()
DBSession.commit()
driver.refresh()
proj_select = Select(driver.find_element_by_css_selector('[name=project]'))
try:
proj_select.first_selected_option
except NoSuchElementException:
pytest.raises(WebDriverException, driver.find_element_by_id('react-tabs-2').click)
def test_main_content_disabled_no_project(driver):
Project.query.delete()
DBSession.commit()
driver.refresh()
proj_select = Select(driver.find_element_by_css_selector('[name=project]'))
try:
proj_select.first_selected_option
except NoSuchElementException:
pytest.raises(WebDriverException,
driver.find_element_by_id('react-tabs-2').click)
def add_file(prediction, create, value, *args, **kwargs):
train_featureset = prediction.model.featureset
fset_data, data = featurize.load_featureset(train_featureset.file_uri)
if 'class' in prediction.dataset.name or 'regr' in prediction.dataset.name:
labels = data['labels']
else:
labels = []
model_data = joblib.load(prediction.model.file_uri)
if hasattr(model_data, 'best_estimator_'):
model_data = model_data.best_estimator_
preds = model_data.predict(fset_data)
pred_probs = (pd.DataFrame(model_data.predict_proba(fset_data),
index=fset_data.index.astype(str),
columns=model_data.classes_) if hasattr(
model_data, 'predict_proba') else [])
all_classes = model_data.classes_ if hasattr(model_data,
'classes_') else []
pred_path = pjoin(TMP_DIR, '{}.npz'.format(str(uuid.uuid4())))
featurize.save_featureset(fset_data,
pred_path,
labels=labels,
preds=preds,
pred_probs=pred_probs)
prediction.file_uri = pred_path
DBSession().commit()
def add_file(model, create, value, *args, **kwargs):
model_params = {
"RandomForestClassifier": {
"bootstrap": True,
"criterion": "gini",
"oob_score": False,
"max_features": "auto",
"n_estimators": 10,
"random_state": 0
},
"RandomForestRegressor": {
"bootstrap": True,
"criterion": "mse",
"oob_score": False,
"max_features": "auto",
"n_estimators": 10
},
"LinearSGDClassifier": {
"loss": "hinge"
},
"LinearRegressor": {
"fit_intercept": True
}
}
fset_data, data = featurize.load_featureset(model.featureset.file_uri)
model_data = MODELS_TYPE_DICT[model.type](**model_params[model.type])
model_data.fit(fset_data, data['labels'])
model.file_uri = pjoin('/tmp/', '{}.pkl'.format(str(uuid.uuid4())))
joblib.dump(model_data, model.file_uri)
DBSession().commit()
def add_file(featureset, create, value, *args, **kwargs):
if not create:
return
if 'class' in featureset.name:
labels = ['Mira', 'Classical_Cepheid']
elif 'regr' in featureset.name:
labels = [2.2, 3.4, 4.4, 2.2, 3.1]
else:
labels = []
fset_data, fset_labels = sample_featureset(5, 1,
featureset.features_list,
labels)
fset_path = pjoin(TMP_DIR, '{}.npz'.format(str(uuid.uuid4())))
featurize.save_featureset(fset_data, fset_path, labels=fset_labels)
featureset.file_uri = fset_path
DBSession().commit()
def add_files(dataset, create, value, *args, **kwargs):
if not create:
return
if 'class' in dataset.name:
header = pjoin(os.path.dirname(__file__), 'data',
'asas_training_subset_classes.dat')
elif 'regr' in dataset.name:
header = pjoin(os.path.dirname(__file__), 'data',
'asas_training_subset_targets.dat')
else:
header = None
tarball = pjoin(os.path.dirname(__file__), 'data',
'asas_training_subset.tar.gz')
header = shutil.copy2(header, TMP_DIR) if header else None
tarball = shutil.copy2(tarball, TMP_DIR)
ts_paths = data_management.parse_and_store_ts_data(
tarball, TMP_DIR, header)
dataset.files = [DatasetFile(uri=uri) for uri in ts_paths]
DBSession().commit()
class Meta:
sqlalchemy_session = DBSession()
sqlalchemy_session_persistence = 'commit'
model = Featureset
def set_user(project, create, extracted, **kwargs):
if not create:
return
project.users = User.query.filter(
User.username == '*****@*****.**').all()
DBSession().commit()
class Meta:
sqlalchemy_session = DBSession()
sqlalchemy_session_persistence = 'commit'
model = Project