def test_predictor_needs_predictions(matrix_type, predict_setup_args):
"""Test that the logic that figures out if predictions are needed for a given model/matrix"""
(project_storage, db_engine, model_id) = predict_setup_args
# if not all of the predictions for the given model id and matrix are present in the db,
# needs_predictions should return true. else, false
predictor = Predictor(project_storage.model_storage_engine(), db_engine,
'worst')
metadata = matrix_metadata_creator(matrix_type=matrix_type)
matrix_store = get_matrix_store(project_storage, metadata=metadata)
train_matrix_columns = matrix_store.columns()
# we haven't done anything yet, this should definitely need predictions
assert predictor.needs_predictions(matrix_store, model_id)
predictor.predict(
model_id,
matrix_store,
misc_db_parameters=dict(),
train_matrix_columns=train_matrix_columns,
)
# now that predictions have been made, this should no longer need predictions
assert not predictor.needs_predictions(matrix_store, model_id)
def test_predictor_needs_predictions(matrix_type, predict_setup_args):
(project_storage, db_engine, model_id) = predict_setup_args
# if not all of the predictions for the given model id and matrix are present in the db,
# needs_predictions should return true. else, false
predictor = Predictor(project_storage.model_storage_engine(), db_engine)
matrix = matrix_creator(index="entity_id")
metadata = matrix_metadata_creator(end_time=AS_OF_DATE,
matrix_type=matrix_type,
indices=["entity_id"])
matrix_store = get_matrix_store(project_storage, matrix, metadata)
train_matrix_columns = matrix.columns[0:-1].tolist()
# we haven't done anything yet, this should definitely need predictions
assert predictor.needs_predictions(matrix_store, model_id)
predictor.predict(
model_id,
matrix_store,
misc_db_parameters=dict(),
train_matrix_columns=train_matrix_columns,
)
# now that predictions have been made, this should no longer need predictions
assert not predictor.needs_predictions(matrix_store, model_id)