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0caf2ba c047289 0caf2ba c047289 0caf2ba c047289 8d1932d b3cffb8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | from pathlib import Path
from dota2tuned.recommend import DraftRecommender
from dota2tuned.schemas import DraftInput, Recommendation
from dota2tuned.storage import write_parquet
def test_recommendation_schema_and_exclusions(tmp_path: Path):
write_parquet(
tmp_path / "dim_hero.parquet",
[
{
"hero_id": 1,
"hero_name": "Anti-Mage",
"roles": "Carry,Escape,Nuker",
"pro_pick": 1000,
"pro_win": 520,
"pro_win_rate": 0.52,
},
{
"hero_id": 2,
"hero_name": "Axe",
"roles": "Initiator,Durable,Disabler",
"pro_pick": 200,
"pro_win": 90,
"pro_win_rate": 0.45,
},
],
)
write_parquet(tmp_path / "fact_hero_pair_stats.parquet", [])
recommender = DraftRecommender(tmp_path)
recs = recommender.recommend(DraftInput(banned_heroes=[1]), limit=5)
assert len(recs) == 1
assert isinstance(recs[0], Recommendation)
assert recs[0].hero_id == 2
def test_mid_recommendations_filter_support_first_heroes(tmp_path: Path):
write_parquet(
tmp_path / "dim_hero.parquet",
[
{
"hero_id": 13,
"hero_name": "Puck",
"roles": "Initiator,Disabler,Escape,Nuker",
"pro_pick": 12,
"pro_win": 4,
"pro_win_rate": 0.3333,
},
{
"hero_id": 91,
"hero_name": "Io",
"roles": "Support,Escape,Nuker",
"pro_pick": 4,
"pro_win": 4,
"pro_win_rate": 1.0,
},
],
)
write_parquet(tmp_path / "fact_hero_pair_stats.parquet", [])
recs = DraftRecommender(tmp_path).recommend(DraftInput(role="mid"), limit=5)
assert [rec.hero_name for rec in recs] == ["Puck"]
def test_low_sample_win_rates_are_shrunk(tmp_path: Path):
write_parquet(
tmp_path / "dim_hero.parquet",
[
{
"hero_id": 1,
"hero_name": "Tiny Sample",
"roles": "Carry",
"pro_pick": 1,
"pro_win": 1,
"pro_win_rate": 1.0,
},
{
"hero_id": 2,
"hero_name": "Large Sample",
"roles": "Carry",
"pro_pick": 200,
"pro_win": 120,
"pro_win_rate": 0.6,
},
],
)
write_parquet(tmp_path / "fact_hero_pair_stats.parquet", [])
recs = DraftRecommender(tmp_path).recommend(DraftInput(role="carry"), limit=2)
assert recs[0].hero_name == "Large Sample"
def test_recommendations_use_player_match_samples_for_confidence(tmp_path: Path):
write_parquet(
tmp_path / "dim_hero.parquet",
[
{
"hero_id": 1,
"hero_name": "Bigger Sample Hero",
"roles": "Carry",
"pro_pick": 3,
"pro_win": 3,
"pro_win_rate": 1.0,
}
],
)
write_parquet(tmp_path / "fact_hero_pair_stats.parquet", [])
write_parquet(
tmp_path / "fact_player_match.parquet",
[
{
"match_id": match_id,
"hero_id": 1,
"is_radiant": True,
"win": 1 if match_id <= 120 else 0,
}
for match_id in range(1, 151)
],
)
recs = DraftRecommender(tmp_path).recommend(DraftInput(role="carry"), limit=1)
assert recs[0].sample_size == 150
assert recs[0].confidence == "medium"
assert "normalized player matches" in recs[0].sources
def test_pair_lift_matches_filter_aggregate_semantics(tmp_path: Path):
write_parquet(
tmp_path / "dim_hero.parquet",
[{"hero_id": 1, "hero_name": "Anti-Mage", "roles": "Carry", "pro_pick": 10, "pro_win": 6}],
)
write_parquet(
tmp_path / "fact_hero_pair_stats.parquet",
[
{"hero_id": 1, "other_hero_id": 9, "relation": "enemy", "win_rate": 0.6, "games": 100},
{"hero_id": 1, "other_hero_id": 8, "relation": "enemy", "win_rate": 0.4, "games": 50},
{"hero_id": 1, "other_hero_id": 7, "relation": "ally", "win_rate": 0.7, "games": 30},
],
)
rec = DraftRecommender(tmp_path)
# mean(0.6, 0.4) = 0.5 -> (0.5 - 0.5) * 0.25 = 0.0 ; games summed across matches
assert rec._pair_lift(1, [9, 8], "enemy") == (0.0, 150)
# single match: (0.6 - 0.5) * 0.25 = 0.025
lift, games = rec._pair_lift(1, [9], "enemy")
assert games == 100
assert round(lift, 6) == 0.025
# ally relation is indexed separately from enemy
lift, games = rec._pair_lift(1, [7], "ally")
assert games == 30
assert round(lift, 6) == 0.05
# duplicates behave like is_in membership, not double counting
assert rec._pair_lift(1, [9, 9], "enemy") == rec._pair_lift(1, [9], "enemy")
# misses, empty input, and wrong relation all yield (0.0, 0)
assert rec._pair_lift(1, [999], "enemy") == (0.0, 0)
assert rec._pair_lift(1, [], "enemy") == (0.0, 0)
assert rec._pair_lift(1, [7], "enemy") == (0.0, 0)
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