Initial commit: existing turf_saas codebase
Co-Authored-By: Paperclip <noreply@paperclip.ing>
This commit is contained in:
ML Engineer
207
compare_models.py
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207
compare_models.py
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#!/usr/bin/env python3
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"""
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Compare scoring models performance vs actual results
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"""
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import sqlite3
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import sys
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DB_PATH = "/home/h3r7/turf_scraper/turf.db"
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def get_results(date):
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"""Get actual race results"""
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conn = sqlite3.connect(DB_PATH)
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conn.row_factory = sqlite3.Row
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c = conn.cursor()
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c.execute("""
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SELECT race_name, horse_name, position
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FROM results
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WHERE date = ?
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ORDER BY race_name, position
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""", (date,))
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results = {}
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for row in c.fetchall():
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race = row['race_name']
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if race not in results:
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results[race] = []
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results[race].append({
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'horse': row['horse_name'],
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'position': row['position']
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})
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conn.close()
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return results
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def get_predictions_v1(date):
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"""Get scoring v1 predictions"""
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conn = sqlite3.connect(DB_PATH)
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conn.row_factory = sqlite3.Row
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c = conn.cursor()
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c.execute("""
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SELECT race_name, horse_name, score, rang_scoring
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FROM scoring
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WHERE date = ? AND scoring_version = 'v1'
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ORDER BY race_name, rang_scoring
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""", (date,))
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results = {}
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for row in c.fetchall():
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race = row['race_name']
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if race not in results:
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results[race] = []
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results[race].append({
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'horse': row['horse_name'],
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'score': row['score'],
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'rank': row['rang_scoring']
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})
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conn.close()
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return results
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def get_predictions_v2(date):
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"""Get scoring v2 predictions"""
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conn = sqlite3.connect(DB_PATH)
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conn.row_factory = sqlite3.Row
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c = conn.cursor()
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c.execute("""
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SELECT race_name, horse_name, score, rang_scoring
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FROM scoring
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WHERE date = ? AND scoring_version = 'v2'
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ORDER BY race_name, rang_scoring
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""", (date,))
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results = {}
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for row in c.fetchall():
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race = row['race_name']
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if race not in results:
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results[race] = []
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results[race].append({
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'horse': row['horse_name'],
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'score': row['score'],
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'rank': row['rang_scoring']
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})
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conn.close()
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return results
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def get_canalturf(date):
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"""Get CanalTurf predictions"""
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conn = sqlite3.connect(DB_PATH)
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conn.row_factory = sqlite3.Row
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c = conn.cursor()
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c.execute("""
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SELECT race_name, horse_name, prediction_rank
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FROM predictions
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WHERE date = ? AND source = 'canalturf_partants'
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ORDER BY race_name, prediction_rank
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""", (date,))
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results = {}
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for row in c.fetchall():
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race = row['race_name']
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if race not in results:
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results[race] = []
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results[race].append({
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'horse': row['horse_name'],
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'rank': row['prediction_rank']
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})
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conn.close()
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return results
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def calculate_hits(predictions, actual, top_n=3):
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"""Calculate hits for top N predictions"""
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hits = 0
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for race, actual_horses in actual.items():
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if race not in predictions:
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continue
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pred_horses = [h['horse'] for h in predictions[race][:top_n]]
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actual_top = [h['horse'] for h in actual_horses[:top_n]]
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for p in pred_horses:
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if p in actual_top:
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hits += 1
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return hits
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def compare_models(date):
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print(f"\n{'='*70}")
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print(f"COMPARAISON DES MODÈLES - {date}")
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print(f"{'='*70}\n")
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# Get data
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actual = get_results(date)
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v1 = get_predictions_v1(date)
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v2 = get_predictions_v2(date)
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canal = get_canalturf(date)
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if not actual:
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print("Aucun résultat trouvé pour cette date")
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return
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print(f"Courses avec résultats: {len(actual)}")
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# Calculate hits for each model
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print(f"\n{'MODÈLE':<20} | {'TOP 1':<8} | {'TOP 3':<8} | {'TOP 5':<8}")
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print("-" * 50)
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# Top 1
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v1_hits = calculate_hits(v1, actual, 1)
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v2_hits = calculate_hits(v2, actual, 1)
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canal_hits = calculate_hits(canal, actual, 1)
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total_races = len(actual)
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print(f"Scoring V1 | {v1_hits}/{total_races} ({v1_hits*100/total_races:.0f}%) | - | - ")
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print(f"Scoring V2 | {v2_hits}/{total_races} ({v2_hits*100/total_races:.0f}%) | - | - ")
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print(f"CanalTurf | {canal_hits}/{total_races} ({canal_hits*100/total_races:.0f}%) | - | - ")
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# Top 3
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v1_hits = calculate_hits(v1, actual, 3)
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v2_hits = calculate_hits(v2, actual, 3)
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canal_hits = calculate_hits(canal, actual, 3)
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print(f"Scoring V1 | - | {v1_hits}/{total_races*3} ({v1_hits*100/(total_races*3):.0f}%) | - ")
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print(f"Scoring V2 | - | {v2_hits}/{total_races*3} ({v2_hits*100/(total_races*3):.0f}%) | - ")
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print(f"CanalTurf | - | {canal_hits}/{total_races*3} ({canal_hits*100/(total_races*3):.0f}%) | - ")
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# Top 5
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v1_hits = calculate_hits(v1, actual, 5)
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v2_hits = calculate_hits(v2, actual, 5)
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canal_hits = calculate_hits(canal, actual, 5)
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print(f"Scoring V1 | - | - | {v1_hits}/{total_races*5} ({v1_hits*100/(total_races*5):.0f}%)")
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print(f"Scoring V2 | - | - | {v2_hits}/{total_races*5} ({v2_hits*100/(total_races*5):.0f}%)")
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print(f"CanalTurf | - | - | {canal_hits}/{total_races*5} ({canal_hits*100/(total_races*5):.0f}%)")
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# Detailed per race
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print(f"\n{'='*70}")
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print("DÉTAIL PAR COURSE")
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print(f"{'='*70}")
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for race, actual_horses in actual.items():
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print(f"\n🏇 {race}")
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print(f" Résultat: {' / '.join([h['horse'] for h in actual_horses[:5]])}")
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if race in v1:
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print(f" V1: {' / '.join([h['horse'] for h in v1[race][:3]])}")
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else:
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print(f" V1: Pas de prédictions")
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if race in v2:
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print(f" V2: {' / '.join([h['horse'] for h in v2[race][:3]])}")
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else:
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print(f" V2: Pas de prédictions")
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if race in canal:
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print(f" CT: {' / '.join([h['horse'] for h in canal[race][:3]])}")
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else:
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print(f" CT: Pas de prédictions")
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if __name__ == "__main__":
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date = sys.argv[1] if len(sys.argv) > 1 else "2026-04-06"
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compare_models(date)
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