def test_load_labels_data():
""" Test the load_labels_data function by checking whether the query
produces the correct labels
"""
# set up labeling config variables
dates = [
datetime.datetime(2016, 1, 1, 0, 0),
datetime.datetime(2016, 2, 1, 0, 0)
]
# make a dataframe of labels to test against
labels_df = pd.DataFrame(labels,
columns=[
'entity_id', 'as_of_date', 'label_timespan',
'label_name', 'label_type', 'label'
])
labels_df['as_of_date'] = convert_string_column_to_date(
labels_df['as_of_date'])
labels_df.set_index(['entity_id', 'as_of_date'])
# create an engine and generate a table with fake feature data
with testing.postgresql.Postgresql() as postgresql:
engine = create_engine(postgresql.url())
create_schemas(engine, features_tables, labels, states)
with get_matrix_storage_engine() as matrix_storage_engine:
builder = MatrixBuilder(
db_config=db_config,
matrix_storage_engine=matrix_storage_engine,
engine=engine,
)
# make the entity-date table
entity_date_table_name = builder.make_entity_date_table(
as_of_times=dates,
label_type='binary',
label_name='booking',
state='state_one AND state_two',
matrix_type='train',
matrix_uuid='my_uuid',
label_timespan='1 month')
result = builder.load_labels_data(
label_name=label_name,
label_type=label_type,
label_timespan='1 month',
matrix_uuid='my_uuid',
entity_date_table_name=entity_date_table_name,
)
df = pd.DataFrame.from_dict({
'entity_id': [2, 3, 4, 4],
'as_of_date': [dates[1], dates[1], dates[0], dates[1]],
'booking': [0, 0, 1, 0],
}).set_index(['entity_id', 'as_of_date'])
test = (result == df)
assert (test.all().all())
def test_load_labels_data_include_missing_labels_as_false():
""" Test the load_labels_data function by checking whether the query
produces the correct labels
"""
# set up labeling config variables
dates = [
datetime.datetime(2016, 1, 1, 0, 0),
datetime.datetime(2016, 2, 1, 0, 0),
datetime.datetime(2016, 6, 1, 0, 0),
]
# same as the other load_labels_data test, except we include an extra date, 2016-06-01
# this date does have entity 0 included via the states table, but no labels
# make a dataframe of labels to test against
labels_df = pd.DataFrame(
labels,
columns=[
"entity_id",
"as_of_date",
"label_timespan",
"label_name",
"label_type",
"label",
],
)
labels_df["as_of_date"] = convert_string_column_to_date(labels_df["as_of_date"])
labels_df.set_index(["entity_id", "as_of_date"])
# create an engine and generate a table with fake feature data
with testing.postgresql.Postgresql() as postgresql:
engine = create_engine(postgresql.url())
create_schemas(engine, features_tables, labels, states)
with get_matrix_storage_engine() as matrix_storage_engine:
builder = MatrixBuilder(
db_config=db_config,
matrix_storage_engine=matrix_storage_engine,
experiment_hash=experiment_hash,
engine=engine,
include_missing_labels_in_train_as=False,
)
# make the entity-date table
entity_date_table_name = builder.make_entity_date_table(
as_of_times=dates,
label_type="binary",
label_name="booking",
state="active",
matrix_type="train",
matrix_uuid="my_uuid",
label_timespan="1 month",
)
result = builder.load_labels_data(
label_name=label_name,
label_type=label_type,
label_timespan="1 month",
matrix_uuid="my_uuid",
entity_date_table_name=entity_date_table_name,
)
df = pd.DataFrame.from_dict(
{
"entity_id": [0, 2, 3, 4, 4],
"as_of_date": [dates[2], dates[1], dates[1], dates[0], dates[1]],
"booking": [0, 0, 0, 1, 0],
}
).set_index(["entity_id", "as_of_date"])
# the first row would not be here if we had not configured the Builder
# to include missing labels as false
test = result == df
assert test.all().all()
def test_load_labels_data():
""" Test the load_labels_data function by checking whether the query
produces the correct labels
"""
# set up labeling config variables
dates = [datetime.datetime(2016, 1, 1, 0, 0), datetime.datetime(2016, 2, 1, 0, 0)]
# make a dataframe of labels to test against
labels_df = pd.DataFrame(
labels,
columns=[
"entity_id",
"as_of_date",
"label_timespan",
"label_name",
"label_type",
"label",
],
)
labels_df["as_of_date"] = convert_string_column_to_date(labels_df["as_of_date"])
labels_df.set_index(["entity_id", "as_of_date"])
# create an engine and generate a table with fake feature data
with testing.postgresql.Postgresql() as postgresql:
engine = create_engine(postgresql.url())
create_schemas(engine, features_tables, labels, states)
with get_matrix_storage_engine() as matrix_storage_engine:
builder = MatrixBuilder(
db_config=db_config,
matrix_storage_engine=matrix_storage_engine,
experiment_hash=experiment_hash,
engine=engine,
)
# make the entity-date table
entity_date_table_name = builder.make_entity_date_table(
as_of_times=dates,
label_type="binary",
label_name="booking",
state="active",
matrix_type="train",
matrix_uuid="my_uuid",
label_timespan="1 month",
)
result = builder.load_labels_data(
label_name=label_name,
label_type=label_type,
label_timespan="1 month",
matrix_uuid="my_uuid",
entity_date_table_name=entity_date_table_name,
)
df = pd.DataFrame.from_dict(
{
"entity_id": [2, 3, 4, 4],
"as_of_date": [dates[1], dates[1], dates[0], dates[1]],
"booking": [0, 0, 1, 0],
}
).set_index(["entity_id", "as_of_date"])
test = result == df
assert test.all().all()
