def insert_test_data():
with status("Dropping all tables"):
drop_tables()
with status("Creating tables"):
create_tables()
for model in models.Base.metadata.tables:
print(' -', model)
USERNAME = '******'
with status(f"Creating dummy user: {USERNAME}... "):
u = models.User(username=USERNAME)
models.DBSession().add(u)
models.DBSession().commit()
for i in range(3):
with status("Inserting dummy project"):
p = models.Project(name=f'test project {i}', users=[u])
models.DBSession().add(p)
models.DBSession().commit()
print(f"\n{textwrap.indent(str(p), ' ')}\n")
with status("Assert that user has 3 projects"):
assert len(u.projects) == 3
with status("Inserting dummy dataset and time series... "):
files = [models.DatasetFile(uri=f'/dir/ts{i}.npz') for i in range(3)]
d = models.Dataset(name='test dataset', project=p, files=files)
models.DBSession().add_all(files + [d])
models.DBSession().commit()
with status("Inserting dummy featureset... "):
f = models.Featureset(project=p,
name='test featureset',
file_uri='/dir/fset.npz',
features_list=['amplitude'])
models.DBSession().add(f)
models.DBSession().commit()
with status("Inserting dummy model... "):
m = models.Model(project=p,
featureset=f,
name='test model',
params={'n_estimators': 10},
type='RFC',
file_uri='/tmp/blah.pkl')
models.DBSession().add(m)
models.DBSession().commit()
with status("Inserting dummy prediction... "):
pr = models.Prediction(project=p,
model=m,
file_uri='/tmp/blergh.pkl',
dataset=d)
models.DBSession().add(pr)
models.DBSession().commit()
def insert_test_data():
with status("Dropping all tables"):
drop_tables()
with status("Creating tables"):
create_tables()
for model in models.Base.metadata.tables:
print(' -', model)
USERNAME = '******'
with status(f"Creating dummy user: {USERNAME}... "):
u = models.User(username=USERNAME)
models.DBSession().add(u)
models.DBSession().commit()
for i in range(3):
with status("Inserting dummy project"):
p = models.Project(name=f'test project {i}', users=[u])
models.DBSession().add(p)
models.DBSession().commit()
print(f"\n{textwrap.indent(str(p), ' ')}\n")
with status("Assert that user has 3 projects"):
assert len(u.projects) == 3
with status("Inserting dummy dataset and time series... "):
files = [models.DatasetFile(uri=f'/dir/ts{i}.npz') for i in range(3)]
d = models.Dataset(name='test dataset', project=p, files=files)
models.DBSession().add_all(files + [d])
models.DBSession().commit()
with status("Inserting dummy featureset... "):
f = models.Featureset(project=p, name='test featureset',
file_uri='/dir/fset.npz',
features_list=['amplitude'])
models.DBSession().add(f)
models.DBSession().commit()
with status("Inserting dummy model... "):
m = models.Model(project=p, featureset=f, name='test model',
params={'n_estimators': 10}, type='RFC',
file_uri='/tmp/blah.pkl')
models.DBSession().add(m)
models.DBSession().commit()
with status("Inserting dummy prediction... "):
pr = models.Prediction(project=p, model=m, file_uri='/tmp/blergh.pkl', dataset=d)
models.DBSession().add(pr)
models.DBSession().commit()
f'Make sure that {data_source} is in the data_load section of '
'your config.yaml.'
)
if cfg['data_load'][data_source].get("file") is not None:
fname = str(topdir / cfg['data_load'][data_source]["file"])
src = yaml.load(open(fname, "r"), Loader=Loader)
else:
src = cfg['data_load'][data_source]
with status(f"Connecting to database {cfg['database']['database']}"):
init_db(**cfg["database"])
if src.get("drop_tables", False):
with status("Dropping all tables"):
drop_tables()
if src.get("create_tables", False):
with status("Creating tables"):
create_tables()
if src.get("print_tables", False):
for model in Base.metadata.tables:
print(" -", model)
if src.get("create_permissions", False):
with status(f"Creating permissions"):
setup_permissions()
if src.get("users") is not None:
with status(f"Creating users & sitewide public group"):
def setup_survey_db():
# if os.getcwd().endswith('survey_app'):
# os.chdir('./cesium_web')
env, cfg = load_env()
for data_dir in cfg['paths'].values():
if not os.path.exists(data_dir):
os.makedirs(data_dir)
db_session = init_db(**baselayer.app.config.load_config()['database'])
# Drop & create tables
with status('Dropping and re-creating tables'):
drop_tables()
create_tables()
# Add testuser
with status('Adding testuser'):
u = models.User(username='******')
models.DBSession().add(u)
models.DBSession().commit()
# Add project
with status('Adding project'):
p = models.Project(name='Survey Classifier', users=[u])
models.DBSession().add(p)
models.DBSession().commit()
# Add datasets
with status('Adding datasets'):
for dataset_name, ts_data_dir in [
['Survey Light Curve Data',
'survey_classifier_data/data/lightcurves'],
['ASAS',
'survey_classifier_data/data/ASAS_lcs'],
['Noisified to CoRoT',
'survey_classifier_data/data/noisified_CoRoT_lcs'],
['Noisified to HATNet',
'survey_classifier_data/data/noisified_HATNet_lcs'],
['Noisified to Hipparcos',
'survey_classifier_data/data/noisified_Hipparcos_lcs'],
['Noisified to KELT',
'survey_classifier_data/data/noisified_KELT_lcs'],
['Noisified to Kepler',
'survey_classifier_data/data/noisified_Kepler_lcs'],
['Noisified to LINEAR',
'survey_classifier_data/data/noisified_LINEAR_lcs'],
['Noisified to OGLE-III',
'survey_classifier_data/data/noisified_OGLE-III_lcs'],
['Noisified to SuperWASP',
'survey_classifier_data/data/noisified_SuperWASP_lcs'],
['Noisified to TrES',
'survey_classifier_data/data/noisified_TrES_lcs']]:
ts_paths = []
# As these are only ever accessed to determine meta features, only
# copy first ten (arbitrary) TS
for src in glob.glob(os.path.join(os.path.abspath(ts_data_dir),
'*.npz'))[:10]:
# Add the path to the copied file in cesium data directory
ts_paths.append(os.path.abspath(shutil.copy(
os.path.abspath(src), cfg['paths']['ts_data_folder'])))
try:
meta_features = list(load_ts(ts_paths[0])
.meta_features.keys())
except IndexError: # No TS data on disk
meta_features = None
files = [models.DatasetFile(uri=ts_path) for ts_path in ts_paths]
dataset = models.Dataset(name=dataset_name, project=p, files=files,
meta_features=meta_features)
models.DBSession().add_all(files + [dataset])
models.DBSession().commit()
print(f'Added dataset {dataset.id}')
# Add featuresets
fset_dict = {}
for fset_name, orig_fset_path, features_list in [
['Survey LC Cadence/Error Features',
'./survey_classifier_data/data/survey_lc_features.npz',
CADENCE_FEATS],
['ASAS',
'./survey_classifier_data/data/ASAS_features.npz',
GENERAL_FEATS + LOMB_SCARGLE_FEATS],
['CoRoT',
'./survey_classifier_data/data/noisified_CoRoT_features_100.npz',
GENERAL_FEATS + LOMB_SCARGLE_FEATS],
['HATNet',
'./survey_classifier_data/data/noisified_HATNet_features_100.npz',
GENERAL_FEATS + LOMB_SCARGLE_FEATS],
['Hipparcos',
'./survey_classifier_data/data/noisified_Hipparcos_features_100.npz',
GENERAL_FEATS + LOMB_SCARGLE_FEATS],
['KELT',
'./survey_classifier_data/data/noisified_KELT_features_100.npz',
GENERAL_FEATS + LOMB_SCARGLE_FEATS],
['Kepler',
'./survey_classifier_data/data/noisified_Kepler_features_100.npz',
GENERAL_FEATS + LOMB_SCARGLE_FEATS],
['LINEAR',
'./survey_classifier_data/data/noisified_LINEAR_features_100.npz',
GENERAL_FEATS + LOMB_SCARGLE_FEATS],
['OGLE-III',
'./survey_classifier_data/data/noisified_OGLE-III_features_100.npz',
GENERAL_FEATS + LOMB_SCARGLE_FEATS],
['SuperWASP',
'./survey_classifier_data/data/noisified_SuperWASP_features_100.npz',
GENERAL_FEATS + LOMB_SCARGLE_FEATS],
['TrES',
'./survey_classifier_data/data/noisified_TrES_features_100.npz',
GENERAL_FEATS + LOMB_SCARGLE_FEATS]]:
fset_path = os.path.abspath(
shutil.copy(os.path.abspath(orig_fset_path),
cfg['paths']['features_folder']))
fset = models.Featureset(name=fset_name, file_uri=fset_path,
project=p, features_list=features_list,
task_id=None, finished=datetime.datetime.now())
models.DBSession().add(fset)
models.DBSession().commit()
# fset.task_id = None
# fset.finished = datetime.datetime.now()
# fset.save()
fset_dict[fset_name] = fset
print(f'Added featureset {fset.id}')
# Add models
# TODO: Add actual model params
for model_name, orig_model_path, model_type, params, fset_name in [
['Survey LCs RFC',
os.path.abspath('./survey_classifier_data/data/survey_classifier.pkl'),
'RandomForestClassifier', {}, 'Survey LC Cadence/Error Features'],
['ASAS',
os.path.abspath('./survey_classifier_data/data/ASAS_model_compressed.pkl'),
'RandomForestClassifier', {}, 'ASAS'],
['CoRoT',
os.path.abspath('./survey_classifier_data/data/noisified_CoRoT_model_compressed.pkl'),
'RandomForestClassifier', {}, 'CoRoT'],
['HATNet',
os.path.abspath('./survey_classifier_data/data/noisified_HATNet_model_compressed.pkl'),
'RandomForestClassifier', {}, 'HATNet'],
['Hipparcos',
os.path.abspath('./survey_classifier_data/data/noisified_Hipparcos_model_compressed.pkl'),
'RandomForestClassifier', {}, 'Hipparcos'],
['KELT',
os.path.abspath('./survey_classifier_data/data/noisified_KELT_model_compressed.pkl'),
'RandomForestClassifier', {}, 'KELT'],
['Kepler',
os.path.abspath('./survey_classifier_data/data/noisified_Kepler_model_compressed.pkl'),
'RandomForestClassifier', {}, 'Kepler'],
['LINEAR',
os.path.abspath('./survey_classifier_data/data/noisified_LINEAR_model_compressed.pkl'),
'RandomForestClassifier', {}, 'LINEAR'],
['OGLE-III',
os.path.abspath('./survey_classifier_data/data/noisified_OGLE-III_model_compressed.pkl'),
'RandomForestClassifier', {}, 'OGLE-III'],
['SuperWASP',
os.path.abspath('./survey_classifier_data/data/noisified_SuperWASP_model_compressed.pkl'),
'RandomForestClassifier', {}, 'SuperWASP'],
['TrES',
os.path.abspath('./survey_classifier_data/data/noisified_TrES_model_compressed.pkl'),
'RandomForestClassifier', {}, 'TrES']]:
model_path = os.path.abspath(
shutil.copy(orig_model_path, cfg['paths']['models_folder']))
model = models.Model(name=model_name, file_uri=model_path,
featureset_id=fset_dict[fset_name].id, project=p,
project_id=p.id,
params=params, type=model_type, task_id=None,
finished=datetime.datetime.now())
models.DBSession().add(model)
models.DBSession().commit()
# model.task_id = None
# model.finished = datetime.datetime.now()
# model.save()
print(f'Added model {model.id}')
print(cfg)