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Reorganize Tests #1303

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merged 8 commits into from
Jan 12, 2021
Merged

Reorganize Tests #1303

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0ccba66
update test_demo_data
Jan 7, 2021
69b4421
update test_demo_data
Jan 7, 2021
8dc46f3
update primitive utils tests
Jan 8, 2021
1a7b77e
update entityset tests
Jan 12, 2021
72de566
update computational backends tests
Jan 12, 2021
7460c23
Merge branch 'main' into reorg-tests
thehomebrewnerd Jan 12, 2021
0e698f2
update release notes
Jan 12, 2021
1b614ac
rename tests
Jan 12, 2021
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1 change: 1 addition & 0 deletions docs/source/release_notes.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,7 @@ Release Notes
* Testing Changes
* Fix non-deterministic Dask test (:pr:`1294`)
* Unpin python-graphviz package on Windows (:pr:`1296`)
* Reorganize tests into proper files/directories (:pr:`1303`)

Thanks to the following people for contributing to this release:
:user:`rwedge`, :user:`thehomebrewnerd`
Expand Down
42 changes: 20 additions & 22 deletions featuretools/tests/computational_backend/test_calculate_feature_matrix.py
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Expand Up @@ -195,7 +195,7 @@ def test_cfm_dask_compose(dask_es, lt):
assert (feature_matrix[property_feature.get_name()] == feature_matrix['label_func']).values.all()


# tests approximate, skip for dask
# tests approximate, skip for dask/koalas
def test_cfm_approximate_correct_ordering():
trips = {
'trip_id': [i for i in range(1000)],
Expand All @@ -219,9 +219,7 @@ def test_cfm_approximate_correct_ordering():
isinstance(feature.base_features[0],
AggregationFeature)]
property_feature = IdentityFeature(es['trips']['trip_id'])
# direct_agg_feat = DirectFeature(Sum(es['trips']['trip_duration'],
# es['flights']),
# es['trips'])

cutoff_time = pd.DataFrame.from_dict({'instance_id': df['trip_id'],
'time': df['flight_time']})
time_feature = IdentityFeature(es['trips']['flight_time'])
Expand All @@ -243,7 +241,7 @@ def test_cfm_approximate_correct_ordering():
assert ((pd.isnull(x) and pd.isnull(y)) or (x == y))


# uses approximate, skip for dask entitysets
# uses approximate, skip for dask/koalas entitysets
def test_cfm_no_cutoff_time_index(pd_es):
agg_feat = ft.Feature(pd_es['log']['id'], parent_entity=pd_es['sessions'], primitive=Count)
agg_feat4 = ft.Feature(agg_feat, parent_entity=pd_es['customers'], primitive=Sum)
Expand Down Expand Up @@ -316,7 +314,7 @@ def test_saveprogress(es, tmpdir):
save_progress=save_progress)
_, _, files = next(os.walk(save_progress))
files = [os.path.join(save_progress, file) for file in files]
# there is 17 datetime files created above
# there are 17 datetime files created above
assert len(files) == 17
list_df = []
for file_ in files:
Expand Down Expand Up @@ -523,7 +521,7 @@ def test_training_window_overlap(pd_es):
}).astype({'time': 'datetime64[ns]'})

# Case1. include_cutoff_time = True
actual = ft.calculate_feature_matrix(
actual = calculate_feature_matrix(
features=[count_log],
entityset=pd_es,
cutoff_time=cutoff_time,
Expand All @@ -534,7 +532,7 @@ def test_training_window_overlap(pd_es):
np.testing.assert_array_equal(actual.values, [1, 9])

# Case2. include_cutoff_time = False
actual = ft.calculate_feature_matrix(
actual = calculate_feature_matrix(
features=[count_log],
entityset=pd_es,
cutoff_time=cutoff_time,
Expand All @@ -560,7 +558,7 @@ def test_include_cutoff_time_without_training_window(es):
}).astype({'time': 'datetime64[ns]'})

# Case1. include_cutoff_time = True
actual = ft.calculate_feature_matrix(
actual = calculate_feature_matrix(
features=[count_log],
entityset=es,
cutoff_time=cutoff_time,
Expand All @@ -570,7 +568,7 @@ def test_include_cutoff_time_without_training_window(es):
np.testing.assert_array_equal(actual.values, [1, 6])

# Case2. include_cutoff_time = False
actual = ft.calculate_feature_matrix(
actual = calculate_feature_matrix(
features=[count_log],
entityset=es,
cutoff_time=cutoff_time,
Expand All @@ -580,7 +578,7 @@ def test_include_cutoff_time_without_training_window(es):
np.testing.assert_array_equal(actual.values, [0, 5])

# Case3. include_cutoff_time = True with single cutoff time value
actual = ft.calculate_feature_matrix(
actual = calculate_feature_matrix(
features=[count_log],
entityset=es,
cutoff_time=pd.to_datetime("2011-04-09 10:31:00"),
Expand All @@ -591,7 +589,7 @@ def test_include_cutoff_time_without_training_window(es):
np.testing.assert_array_equal(actual.values, [6])

# Case4. include_cutoff_time = False with single cutoff time value
actual = ft.calculate_feature_matrix(
actual = calculate_feature_matrix(
features=[count_log],
entityset=es,
cutoff_time=pd.to_datetime("2011-04-09 10:31:00"),
Expand Down Expand Up @@ -1544,7 +1542,7 @@ def test_no_data_for_cutoff_time(mock_customer):
trans_per_customer = ft.Feature(es["transactions"]["transaction_id"], parent_entity=es["customers"], primitive=Count)
features = [trans_per_customer, ft.Feature(trans_per_session, parent_entity=es["customers"], primitive=Max)]

fm = ft.calculate_feature_matrix(features, entityset=es, cutoff_time=cutoff_times)
fm = calculate_feature_matrix(features, entityset=es, cutoff_time=cutoff_times)

# due to default values for each primitive
# count will be 0, but max will nan
Expand Down Expand Up @@ -1619,9 +1617,9 @@ def test_some_instances_not_in_data(pd_es):
def test_missing_instances_with_categorical_index(pd_es):
instance_ids = [0, 1, 3, 2]
features = ft.dfs(entityset=pd_es, target_entity='customers', features_only=True)
fm = ft.calculate_feature_matrix(entityset=pd_es,
features=features,
instance_ids=instance_ids)
fm = calculate_feature_matrix(entityset=pd_es,
features=features,
instance_ids=instance_ids)
assert all(fm.index.values == instance_ids)
assert isinstance(fm.index, pd.CategoricalIndex)

Expand Down Expand Up @@ -1691,7 +1689,7 @@ def __call__(self, update, progress_percent, time_elapsed):
trans_per_session = ft.Feature(es["transactions"]["transaction_id"], parent_entity=es["sessions"], primitive=Count)
trans_per_customer = ft.Feature(es["transactions"]["transaction_id"], parent_entity=es["customers"], primitive=Count)
features = [trans_per_session, ft.Feature(trans_per_customer, entity=es["sessions"])]
ft.calculate_feature_matrix(features, entityset=es, progress_callback=mock_progress_callback)
calculate_feature_matrix(features, entityset=es, progress_callback=mock_progress_callback)

# second to last entry is the last update from feature calculation
assert np.isclose(mock_progress_callback.progress_history[-2], FEATURE_CALCULATION_PERCENTAGE * 100)
Expand All @@ -1705,7 +1703,7 @@ def __call__(self, update, progress_percent, time_elapsed):
pd.to_datetime("2014-01-01 02:00:00"),
pd.to_datetime("2014-01-01 03:00:00")]})

ft.calculate_feature_matrix(features, entityset=es, cutoff_time=cutoff_time, progress_callback=mock_progress_callback)
calculate_feature_matrix(features, entityset=es, cutoff_time=cutoff_time, progress_callback=mock_progress_callback)
assert np.isclose(mock_progress_callback.progress_history[-2], FEATURE_CALCULATION_PERCENTAGE * 100)
assert np.isclose(mock_progress_callback.total_update, 100.0)
assert np.isclose(mock_progress_callback.total_progress_percent, 100.0)
Expand All @@ -1731,10 +1729,10 @@ def __call__(self, update, progress_percent, time_elapsed):

with cluster() as (scheduler, [a, b]):
dkwargs = {'cluster': scheduler['address']}
ft.calculate_feature_matrix(features,
entityset=pd_mock_customer,
progress_callback=mock_progress_callback,
dask_kwargs=dkwargs)
calculate_feature_matrix(features,
entityset=pd_mock_customer,
progress_callback=mock_progress_callback,
dask_kwargs=dkwargs)

assert np.isclose(mock_progress_callback.total_update, 100.0)
assert np.isclose(mock_progress_callback.total_progress_percent, 100.0)
Expand Down
22 changes: 15 additions & 7 deletions featuretools/tests/demo_tests/test_demo_data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,8 +2,7 @@

import pytest

from featuretools.demo import load_flight, load_retail
from featuretools.synthesis import dfs
from featuretools.demo import load_flight, load_mock_customer, load_retail


@pytest.fixture(autouse=True)
Expand All @@ -22,11 +21,20 @@ def test_load_retail_diff():
assert es_second['order_products'].df.shape[0] == nrows_second


def test_mock_customer(mock_customer):
es = mock_customer
fm, fl = dfs(entityset=es, target_entity="customers", max_depth=3)
for feature in fl:
assert feature.get_name() in fm.columns
def test_mock_customer():
n_customers = 4
n_products = 3
n_sessions = 30
n_transactions = 400
es = load_mock_customer(n_customers=n_customers, n_products=n_products, n_sessions=n_sessions,
n_transactions=n_transactions, random_seed=0, return_entityset=True)
entity_names = [entity.id for entity in es.entities]
expected_names = ['transactions', 'products', 'sessions', 'customers']
assert set(expected_names) == set(entity_names)
assert len(es['customers'].df) == 4
assert len(es['products'].df) == 3
assert len(es['sessions'].df) == 30
assert len(es['transactions'].df) == 400


def test_load_flight():
Expand Down
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