def test_inverse_lookup_table_deck_popularities_expire(redis, mock_time):
# 8am: observe a bunch of decks with 1h expiry
mock_time(datetime(2019, 1, 1, 8, 0))
ilt = RedisInverseLookupTable(
redis,
FormatType.FT_STANDARD,
CardClass.DRUID,
full_deck_size=4,
ilt_lookback_mins=240,
deck_popularity_lookback_mins=60,
)
ilt.observe({1: 1, 2: 1, 3: 2}, 1)
ilt.observe({1: 1, 2: 1, 3: 2}, 1)
# 10am: observe a single deck
mock_time(datetime(2019, 1, 1, 10, 0))
ilt.observe({1: 1, 3: 2, 4: 1}, 2)
# ensure that we have forgotten about popularity from the decks from 2hrs ago
remote_ilt = RedisInverseLookupTable(
redis,
FormatType.FT_STANDARD,
CardClass.DRUID,
min_cards_for_prediction=1,
ilt_lookback_mins=240,
deck_popularity_lookback_mins=60,
)
assert remote_ilt.predict({1: 1, 3: 2}) == 2
def test_inverse_lookup_table_ilts_expire_in_sliding_window(redis, mock_time):
# 8am: Observe a deck with 90m expiry twice
mock_time(datetime(2019, 1, 1, 8, 0))
ilt = RedisInverseLookupTable(
redis,
FormatType.FT_STANDARD,
CardClass.DRUID,
full_deck_size=4,
ilt_lookback_mins=90,
deck_popularity_lookback_mins=240,
)
# Let's observe this deck twice and keep the popularity lookback very high.
# This ensures that if the ILTs erroneously reference this deck, it will be the result
ilt.observe({1: 1, 2: 1, 3: 2}, 1)
ilt.observe({1: 1, 2: 1, 3: 2}, 1)
# 9am: Observe a similar deck to refresh the card keys' timestamp
mock_time(datetime(2019, 1, 1, 9))
ilt.observe({1: 1, 3: 2, 4: 1}, 2)
# 10am: Ensure we don't have any reference to the old deck
mock_time(datetime(2019, 1, 1, 10))
remote_ilt = RedisInverseLookupTable(
redis,
FormatType.FT_STANDARD,
CardClass.DRUID,
min_cards_for_prediction=1,
ilt_lookback_mins=90,
deck_popularity_lookback_mins=240,
)
assert remote_ilt.predict({1: 1, 3: 2}) == 2
def test_inverse_lookup_table_observe_enforces_dbf_ids(redis):
ilt = RedisInverseLookupTable(redis,
FormatType.FT_STANDARD,
CardClass.DRUID,
full_deck_size=4)
with pytest.raises(ValueError):
ilt.observe({"GVG_006": 2, "GVG_037": 2}, 1)
def test_inverse_lookup_table_does_not_predict_too_few_cards(redis):
ilt = RedisInverseLookupTable(redis,
FormatType.FT_STANDARD,
CardClass.DRUID,
min_cards_for_prediction=3,
full_deck_size=4)
ilt.observe({1: 1, 2: 1, 3: 2}, 1)
assert ilt.predict({1: 1}) is None
def test_inverse_lookup_table_isolates_player_class(redis):
ilt = RedisInverseLookupTable(redis,
FormatType.FT_STANDARD,
CardClass.DRUID,
full_deck_size=4)
ilt.observe({1: 1, 2: 1, 3: 2}, 1)
remote_ilt = RedisInverseLookupTable(redis,
FormatType.FT_STANDARD,
CardClass.SHAMAN,
min_cards_for_prediction=1)
assert remote_ilt.predict({1: 1, 3: 2}) is None
def test_inverse_lookup_table_predicts_fuzzy_deck(redis):
ilt = RedisInverseLookupTable(redis,
FormatType.FT_STANDARD,
CardClass.DRUID,
full_deck_size=4)
ilt.observe({1: 1, 2: 1, 3: 2}, 1)
remote_ilt = RedisInverseLookupTable(redis,
FormatType.FT_STANDARD,
CardClass.DRUID,
min_cards_for_prediction=1)
assert remote_ilt.predict({1: 1, 2: 1, 4: 1}) == 1
def test_inverse_lookup_table_predicts_single_deck(redis):
ilt = RedisInverseLookupTable(redis,
FormatType.FT_STANDARD,
CardClass.DRUID,
full_deck_size=4)
ilt.observe({1: 1, 2: 1, 3: 2}, 1)
# connect to Redis through a second ILT to ensure there is no local state inside the ILT
remote_ilt = RedisInverseLookupTable(redis,
FormatType.FT_STANDARD,
CardClass.DRUID,
min_cards_for_prediction=1)
assert remote_ilt.predict({1: 1, 3: 2}) == 1
def test_inverse_lookup_table_does_not_remove_required_card(redis):
ilt = RedisInverseLookupTable(redis,
FormatType.FT_STANDARD,
CardClass.DRUID,
full_deck_size=4)
ilt.observe({1: 1, 2: 1, 3: 2}, 1)
remote_ilt = RedisInverseLookupTable(
redis,
FormatType.FT_STANDARD,
CardClass.DRUID,
min_cards_for_prediction=1,
required_cards={4},
)
assert remote_ilt.predict({1: 1, 2: 1, 4: 1}) is None
def test_inverse_lookup_table_does_not_remove_too_many_fuzzy_cards(redis):
ilt = RedisInverseLookupTable(
redis,
FormatType.FT_STANDARD,
CardClass.DRUID,
full_deck_size=4,
)
ilt.observe({1: 1, 2: 1, 3: 2}, 1)
remote_ilt = RedisInverseLookupTable(redis,
FormatType.FT_STANDARD,
CardClass.DRUID,
min_cards_for_prediction=1,
max_fuzzy_cards_removed=0)
assert remote_ilt.predict({1: 1, 2: 1, 4: 1}) is None
def test_inverse_lookup_table_does_not_observe_incomplete_deck(redis):
ilt = RedisInverseLookupTable(redis, FormatType.FT_STANDARD, CardClass.DRUID)
with pytest.raises(ValueError):
ilt.observe({
1: 2,
2: 2,
3: 2,
4: 2,
5: 2,
6: 2,
7: 2,
8: 2,
9: 2,
10: 2,
11: 2,
12: 2,
13: 2,
14: 2,
15: 1,
}, 1)
def test_inverse_lookup_table_observes_complete_deck(redis):
ilt = RedisInverseLookupTable(redis, FormatType.FT_STANDARD, CardClass.DRUID)
ilt.observe({
1: 2,
2: 2,
3: 2,
4: 2,
5: 2,
6: 2,
7: 2,
8: 2,
9: 2,
10: 2,
11: 2,
12: 2,
13: 2,
14: 2,
15: 1,
16: 1,
}, 1)
def test_inverse_lookup_table_deck_popularities_expire_in_sliding_window(
redis, mock_time):
# 8am: observe deck #1 twice
mock_time(datetime(2019, 1, 1, 8, 0))
ilt = RedisInverseLookupTable(
redis,
FormatType.FT_STANDARD,
CardClass.DRUID,
full_deck_size=4,
ilt_lookback_mins=240,
deck_popularity_lookback_mins=90,
)
ilt.observe({1: 1, 2: 1, 3: 2}, 1)
ilt.observe({1: 1, 2: 1, 3: 2}, 1)
# 9am: refresh deck #1
mock_time(datetime(2019, 1, 1, 9, 0))
ilt.observe({1: 1, 2: 1, 3: 2}, 1)
# 10am: ensure that deck #1 has only 1 observation remaining and deck #2 is picked
mock_time(datetime(2019, 1, 1, 10, 0))
ilt.observe({1: 1, 3: 2, 4: 1}, 2)
ilt.observe({1: 1, 3: 2, 4: 1}, 2)
remote_ilt = RedisInverseLookupTable(
redis,
FormatType.FT_STANDARD,
CardClass.DRUID,
min_cards_for_prediction=1,
ilt_lookback_mins=240,
deck_popularity_lookback_mins=90,
)
assert remote_ilt.predict({1: 1, 3: 2}) == 2
def test_inverse_lookup_table_ilts_expire(redis, mock_time):
# 8am: Observe a deck with 1h expiry
mock_time(datetime(2019, 1, 1, 8, 0))
ilt = RedisInverseLookupTable(
redis,
FormatType.FT_STANDARD,
CardClass.DRUID,
full_deck_size=4,
ilt_lookback_mins=60,
)
ilt.observe({1: 1, 2: 1, 3: 2}, 1)
# 10am: Ensure we have forgotten about the deck
mock_time(datetime(2019, 1, 1, 10, 0))
remote_ilt = RedisInverseLookupTable(
redis,
FormatType.FT_STANDARD,
CardClass.DRUID,
min_cards_for_prediction=1,
ilt_lookback_mins=60,
)
assert remote_ilt.predict({1: 1, 3: 2}) is None