Spaces:
Sleeping
Sleeping
File size: 13,555 Bytes
49fb892 090e487 49fb892 0c51058 49fb892 0010624 49fb892 0c51058 49fb892 0010624 49fb892 0c51058 49fb892 0c51058 49fb892 0010624 49fb892 0c51058 49fb892 0010624 49fb892 0c51058 090e487 49fb892 0c51058 49fb892 0c51058 7ac6efd 0c51058 49fb892 0010624 49fb892 090e487 49fb892 090e487 2885bcc 7ac6efd 49fb892 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 | from __future__ import annotations
import os
import sys
import unittest
import pandas as pd
sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
from analytics.props_view_model import (
build_best_on_slate_df,
build_best_on_slate_summary,
build_featured_hr_props_df,
build_game_player_props_map,
build_games_summary_df,
build_hr_props_view_model,
build_player_prop_detail_map,
select_best_lines_per_prop,
)
class TestPropsViewModel(unittest.TestCase):
def _sample_mapped_df(self) -> pd.DataFrame:
return pd.DataFrame(
[
{
"event_id": "evt-1",
"away_team": "Yankees",
"home_team": "Giants",
"commence_time": "2026-03-25T00:05:00Z",
"player_name": "aaron judge",
"player_name_raw": "Aaron Judge",
"sportsbook": "Caesars",
"market": "hr",
"market_family": "hr",
"threshold": 1,
"display_label": "1+ HR",
"is_modeled": True,
"has_model_probability": True,
"odds_american": 245,
"model_hr_prob": 0.19,
"bet_ev": 0.041,
"confidence_score": 84.0,
"edge": 0.035,
"final_recommendation_score": 0.91,
"player_event_market_key": "evt-1|aaron judge|hr|1",
},
{
"event_id": "evt-1",
"away_team": "Yankees",
"home_team": "Giants",
"commence_time": "2026-03-25T00:05:00Z",
"player_name": "aaron judge",
"player_name_raw": "Aaron Judge",
"sportsbook": "FanDuel",
"market": "hr",
"market_family": "hr",
"threshold": 1,
"display_label": "1+ HR",
"is_modeled": True,
"has_model_probability": True,
"odds_american": 230,
"model_hr_prob": 0.19,
"bet_ev": 0.036,
"confidence_score": 80.0,
"edge": 0.029,
"final_recommendation_score": 0.83,
"player_event_market_key": "evt-1|aaron judge|hr|1",
},
{
"event_id": "evt-1",
"away_team": "Yankees",
"home_team": "Giants",
"commence_time": "2026-03-25T00:05:00Z",
"player_name": "aaron judge",
"player_name_raw": "Aaron Judge",
"sportsbook": "Caesars",
"market": "hr",
"market_family": "hr",
"threshold": 2,
"display_label": "2+ HR",
"is_modeled": False,
"has_model_probability": False,
"odds_american": 1750,
"model_hr_prob": None,
"edge": None,
"player_event_market_key": "evt-1|aaron judge|hr|2",
},
{
"event_id": "evt-1",
"away_team": "Yankees",
"home_team": "Giants",
"commence_time": "2026-03-25T00:05:00Z",
"player_name": "giancarlo stanton",
"player_name_raw": "Giancarlo Stanton",
"sportsbook": "Caesars",
"market": "hr",
"market_family": "hr",
"threshold": 1,
"display_label": "1+ HR",
"is_modeled": True,
"has_model_probability": True,
"odds_american": 390,
"model_hr_prob": 0.16,
"bet_ev": 0.068,
"confidence_score": 77.0,
"edge": 0.041,
"final_recommendation_score": 0.95,
"player_event_market_key": "evt-1|giancarlo stanton|hr|1",
},
{
"event_id": "evt-2",
"away_team": "Mets",
"home_team": "Cubs",
"commence_time": "2026-03-25T01:10:00Z",
"player_name": "pete alonso",
"player_name_raw": "Pete Alonso",
"sportsbook": "Caesars",
"market": "hr",
"market_family": "hr",
"threshold": 1,
"display_label": "1+ HR",
"is_modeled": True,
"has_model_probability": True,
"odds_american": 310,
"model_hr_prob": 0.15,
"bet_ev": 0.022,
"confidence_score": 73.0,
"edge": 0.024,
"final_recommendation_score": 0.78,
"player_event_market_key": "evt-2|pete alonso|hr|1",
},
]
)
def _sample_mapped_df_with_missing_primary_prob(self) -> pd.DataFrame:
df = self._sample_mapped_df()
mask = df["player_name"] == "pete alonso"
df.loc[mask, "has_model_probability"] = False
df.loc[mask, "model_hr_prob"] = None
df.loc[mask, "edge"] = None
df.loc[mask, "bet_ev"] = None
df.loc[mask, "model_probability_status"] = "missing_baseline"
return df
def _sample_cross_market_df(self) -> pd.DataFrame:
base = self._sample_mapped_df()
strikeout_rows = pd.DataFrame(
[
{
"event_id": "evt-3",
"away_team": "Pirates",
"home_team": "Mets",
"commence_time": "2026-03-25T17:15:00Z",
"player_name": "paul skenes",
"player_name_raw": "Paul Skenes",
"sportsbook": "DraftKings",
"market": "k",
"market_family": "k",
"display_label": "Over 7.5 Ks",
"selection_side": "over",
"line": 7.5,
"is_modeled": True,
"odds_american": 110,
"fair_prob": 0.58,
"bet_ev": 0.081,
"confidence_score": 76.0,
"edge": 0.034,
"final_recommendation_score": 0.88,
"player_event_market_key": "evt-3|paul skenes|k|7.5|over",
},
{
"event_id": "evt-3",
"away_team": "Pirates",
"home_team": "Mets",
"commence_time": "2026-03-25T17:15:00Z",
"player_name": "paul skenes",
"player_name_raw": "Paul Skenes",
"sportsbook": "FanDuel",
"market": "k",
"market_family": "k",
"display_label": "Over 7.5 Ks",
"selection_side": "over",
"line": 7.5,
"is_modeled": True,
"odds_american": 102,
"fair_prob": 0.58,
"bet_ev": 0.063,
"confidence_score": 74.0,
"edge": 0.027,
"final_recommendation_score": 0.80,
"player_event_market_key": "evt-3|paul skenes|k|7.5|over",
},
]
)
return pd.concat([base, strikeout_rows], ignore_index=True, sort=False)
def test_featured_props_only_use_modeled_primary_rows(self) -> None:
featured = build_featured_hr_props_df(self._sample_mapped_df(), limit=5)
self.assertEqual(len(featured), 3)
self.assertEqual(featured.iloc[0]["player_name"], "giancarlo stanton")
self.assertTrue((featured["threshold"] == 1).all())
self.assertTrue(featured["is_modeled"].all())
self.assertNotIn("2+ HR", featured["display_label"].tolist())
judge_row = featured[featured["player_name"] == "aaron judge"].iloc[0]
self.assertEqual(judge_row["sportsbook"], "Caesars")
self.assertEqual(int(judge_row["odds_american"]), 245)
def test_featured_props_excludes_primary_rows_missing_probability(self) -> None:
featured = build_featured_hr_props_df(self._sample_mapped_df_with_missing_primary_prob(), limit=5)
self.assertNotIn("pete alonso", featured["player_name"].tolist())
def test_games_summary_tracks_modeled_props_and_top_edge(self) -> None:
summary = build_games_summary_df(self._sample_mapped_df())
self.assertEqual(len(summary), 2)
first_game = summary.iloc[0]
self.assertEqual(first_game["event_id"], "evt-1")
self.assertEqual(int(first_game["modeled_props_count"]), 2)
self.assertEqual(first_game["top_player_name"], "giancarlo stanton")
self.assertAlmostEqual(float(first_game["best_edge"]), 0.041, places=6)
def test_games_summary_excludes_primary_rows_without_probability(self) -> None:
summary = build_games_summary_df(self._sample_mapped_df_with_missing_primary_prob())
self.assertNotIn("evt-2", summary["event_id"].tolist())
def test_player_detail_map_separates_primary_and_alt_rows(self) -> None:
detail_map = build_player_prop_detail_map(self._sample_mapped_df())
judge = detail_map["evt-1|aaron judge"]
self.assertTrue(judge["has_modeled_row"])
self.assertTrue(judge["has_alt_ladders"])
self.assertEqual(len(judge["primary_rows"]), 2)
self.assertEqual(len(judge["alt_rows"]), 1)
self.assertEqual(judge["best_primary_row"]["display_label"], "1+ HR")
self.assertEqual(judge["best_book"], "Caesars")
self.assertAlmostEqual(float(judge["best_bet_ev"]), 0.041, places=6)
def test_game_player_map_groups_players_under_each_game(self) -> None:
game_map = build_game_player_props_map(self._sample_mapped_df())
self.assertEqual(set(game_map.keys()), {"evt-1", "evt-2"})
yankees_giants = game_map["evt-1"]
self.assertEqual(yankees_giants["top_player_name"], "giancarlo stanton")
self.assertEqual(len(yankees_giants["players"]), 2)
first_player = yankees_giants["players"][0]
self.assertEqual(first_player["player_name"], "giancarlo stanton")
self.assertFalse(first_player["has_alt_ladders"])
def test_full_view_model_returns_all_sections(self) -> None:
vm = build_hr_props_view_model(self._sample_mapped_df(), featured_limit=2)
self.assertEqual(set(vm.keys()), {
"featured_props_df",
"best_on_slate_df",
"best_on_slate_summary",
"games_summary_df",
"game_player_props_map",
"player_prop_detail_map",
})
self.assertEqual(len(vm["featured_props_df"]), 2)
self.assertEqual(len(vm["games_summary_df"]), 2)
self.assertIn("evt-1", vm["game_player_props_map"])
def test_select_best_lines_per_prop_is_threshold_aware(self) -> None:
best = select_best_lines_per_prop(self._sample_mapped_df())
self.assertEqual(len(best), 4)
judge_primary = best[(best["player_name"] == "aaron judge") & (best["threshold"] == 1)].iloc[0]
judge_alt = best[(best["player_name"] == "aaron judge") & (best["threshold"] == 2)].iloc[0]
self.assertEqual(judge_primary["sportsbook"], "Caesars")
self.assertEqual(int(judge_primary["odds_american"]), 245)
self.assertEqual(judge_alt["sportsbook"], "Caesars")
self.assertEqual(int(judge_alt["odds_american"]), 1750)
def test_best_on_slate_includes_hr_and_strikeouts_after_best_line_reduction(self) -> None:
best = build_best_on_slate_df(self._sample_cross_market_df(), limit=10)
self.assertEqual(set(best["market_family"].tolist()), {"hr", "k"})
skenes = best[best["player_name"] == "paul skenes"].iloc[0]
self.assertEqual(skenes["sportsbook"], "DraftKings")
self.assertEqual(float(best.iloc[0]["bet_ev"]), 0.081)
def test_best_on_slate_summary_counts_modes_and_books(self) -> None:
summary = build_best_on_slate_summary(self._sample_cross_market_df())
self.assertEqual(int(summary["modeled_props_count"]), 4)
self.assertEqual(int(summary["sportsbooks_count"]), 2)
self.assertEqual(int(summary["markets_count"]), 2)
self.assertAlmostEqual(float(summary["best_ev"]), 0.081, places=6)
def test_full_view_model_preserves_full_slate_summary_when_featured_is_limited(self) -> None:
vm = build_hr_props_view_model(self._sample_cross_market_df(), featured_limit=2)
self.assertEqual(len(vm["best_on_slate_df"]), 2)
self.assertEqual(int(vm["best_on_slate_summary"]["modeled_props_count"]), 4)
self.assertEqual(int(vm["best_on_slate_summary"]["markets_count"]), 2)
def test_game_player_map_excludes_games_with_zero_modeled_primary_props(self) -> None:
game_map = build_game_player_props_map(self._sample_mapped_df_with_missing_primary_prob())
self.assertNotIn("evt-2", game_map)
if __name__ == "__main__":
unittest.main()
|