Improve generator quality and fix data gaps (Apr-May 2026)
Data: - Add 12 missing draws (2026-01-09, 2026-04-03 to 2026-05-15) - Retrain LSTM and RF models on complete 952-draw dataset - Run retroactive learning for all 12 skipped draws Automation: - Add eurojackpot-zahlen.eu web scraper as fallback when APIs fail - Fixes recurring gap problem caused by Sazka detail-endpoint change Generator improvements: - Add hard structural filters (sum 95-165, min 1 even + 1 odd per tip) - Add long-term frequency prior as learner dampener (alpha=0.2) - Fix ENSEMBLE strategy generating duplicate tips (greedy → weighted random) - Fix non-vectorized frequency calculation (stack().value_counts()) - Fix Division-by-Zero edge case in set_frequency_prior - Fix potential KeyError on euro_ai_score in structural filter loop .gitignore: - Exclude data/backups/, weekly tip CSVs, performance reports Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Claude Sonnet 4.6
@@ -27,3 +27,9 @@ logs/
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# Results (optional - uncomment if you don't want to track results)
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# results/
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# Backups und generierte Outputs
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data/backups/
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data/*.backup*
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data/generated_tips/weekly_tips_*.csv
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data/performance_reports/
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@@ -916,3 +916,38 @@ datum;Z1;Z2;Z3;Z4;Z5;SZ1;SZ2
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2026-01-06;21;23;30;33;38;8;12
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2026-01-09;1;17;19;25;41;6;12
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2026-01-13;2;16;27;33;47;6;12
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2026-01-16;8;16;37;39;48;5;11
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2026-01-20;16;26;32;37;45;2;3
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2026-01-23;18;36;39;45;50;6;9
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2026-01-27;13;18;19;29;32;8;9
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2026-01-30;8;13;15;17;37;3;7
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2026-02-03;3;20;27;37;44;1;2
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2026-02-06;8;14;38;41;48;1;11
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2026-02-10;12;19;34;39;47;4;5
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2026-02-13;1;21;44;45;46;2;7
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2026-02-17;8;23;39;40;44;6;7
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2026-02-20;11;17;23;36;40;5;6
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2026-02-24;4;5;26;38;48;2;9
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2026-02-27;7;17;19;28;47;2;7
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2026-03-03;1;9;14;35;49;2;10
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2026-03-06;8;17;26;31;47;1;6
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2026-03-10;2;3;17;18;28;4;10
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2026-03-13;7;23;37;44;47;2;6
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2026-03-17;12;13;16;17;37;4;11
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2026-03-20;2;17;21;25;30;2;6
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2026-03-24;9;15;23;43;48;3;5
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2026-03-27;21;23;25;38;40;7;11
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2026-03-31;5;15;18;20;35;7;8
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2026-04-03;9;10;18;22;37;1;11
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2026-04-07;2;4;16;23;27;5;8
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2026-04-10;1;6;11;18;48;10;12
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2026-04-14;13;22;32;46;47;6;7
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2026-04-17;16;31;35;43;44;2;9
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2026-04-21;31;32;36;39;47;7;8
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2026-04-24;6;21;29;39;44;1;5
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2026-04-28;19;20;41;43;46;5;7
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2026-05-01;10;11;13;16;27;5;7
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2026-05-05;1;30;33;34;43;5;10
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2026-05-08;3;17;18;31;41;6;12
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2026-05-12;7;15;19;28;35;3;11
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2026-05-15;1;32;33;36;37;7;12
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@@ -1,9 +1,9 @@
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{
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|
||||
"draw_date": "2026-04-03",
|
||||
"evaluation": {
|
||||
"main": 0,
|
||||
"euro": 0,
|
||||
"tip": 0
|
||||
},
|
||||
"avg_matches": {
|
||||
"main": 0.0,
|
||||
"euro": 0.0
|
||||
},
|
||||
"note": "retroactive_learning"
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-05-19T09:04:48.802849",
|
||||
"draw_date": "2026-04-07",
|
||||
"evaluation": {
|
||||
"main": 0,
|
||||
"euro": 0,
|
||||
"tip": 0
|
||||
},
|
||||
"avg_matches": {
|
||||
"main": 0.0,
|
||||
"euro": 0.0
|
||||
},
|
||||
"note": "retroactive_learning"
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-05-19T09:04:48.803128",
|
||||
"draw_date": "2026-04-10",
|
||||
"evaluation": {
|
||||
"main": 0,
|
||||
"euro": 0,
|
||||
"tip": 0
|
||||
},
|
||||
"avg_matches": {
|
||||
"main": 0.0,
|
||||
"euro": 0.0
|
||||
},
|
||||
"note": "retroactive_learning"
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-05-19T09:04:48.803396",
|
||||
"draw_date": "2026-04-14",
|
||||
"evaluation": {
|
||||
"main": 0,
|
||||
"euro": 0,
|
||||
"tip": 0
|
||||
},
|
||||
"avg_matches": {
|
||||
"main": 0.0,
|
||||
"euro": 0.0
|
||||
},
|
||||
"note": "retroactive_learning"
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-05-19T09:04:48.803660",
|
||||
"draw_date": "2026-04-17",
|
||||
"evaluation": {
|
||||
"main": 0,
|
||||
"euro": 0,
|
||||
"tip": 0
|
||||
},
|
||||
"avg_matches": {
|
||||
"main": 0.0,
|
||||
"euro": 0.0
|
||||
},
|
||||
"note": "retroactive_learning"
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-05-19T09:04:48.803919",
|
||||
"draw_date": "2026-04-21",
|
||||
"evaluation": {
|
||||
"main": 0,
|
||||
"euro": 0,
|
||||
"tip": 0
|
||||
},
|
||||
"avg_matches": {
|
||||
"main": 0.0,
|
||||
"euro": 0.0
|
||||
},
|
||||
"note": "retroactive_learning"
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-05-19T09:04:48.804186",
|
||||
"draw_date": "2026-04-28",
|
||||
"evaluation": {
|
||||
"main": 0,
|
||||
"euro": 0,
|
||||
"tip": 0
|
||||
},
|
||||
"avg_matches": {
|
||||
"main": 0.0,
|
||||
"euro": 0.0
|
||||
},
|
||||
"note": "retroactive_learning"
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-05-19T09:04:48.804444",
|
||||
"draw_date": "2026-05-01",
|
||||
"evaluation": {
|
||||
"main": 0,
|
||||
"euro": 0,
|
||||
"tip": 0
|
||||
},
|
||||
"avg_matches": {
|
||||
"main": 0.0,
|
||||
"euro": 0.0
|
||||
},
|
||||
"note": "retroactive_learning"
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-05-19T09:04:48.804706",
|
||||
"draw_date": "2026-05-05",
|
||||
"evaluation": {
|
||||
"main": 0,
|
||||
"euro": 0,
|
||||
"tip": 0
|
||||
},
|
||||
"avg_matches": {
|
||||
"main": 0.0,
|
||||
"euro": 0.0
|
||||
},
|
||||
"note": "retroactive_learning"
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-05-19T09:04:48.804960",
|
||||
"draw_date": "2026-05-08",
|
||||
"evaluation": {
|
||||
"main": 0,
|
||||
"euro": 0,
|
||||
"tip": 0
|
||||
},
|
||||
"avg_matches": {
|
||||
"main": 0.0,
|
||||
"euro": 0.0
|
||||
},
|
||||
"note": "retroactive_learning"
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-05-19T09:04:48.805219",
|
||||
"draw_date": "2026-05-12",
|
||||
"evaluation": {
|
||||
"main": 0,
|
||||
"euro": 0,
|
||||
"tip": 0
|
||||
},
|
||||
"avg_matches": {
|
||||
"main": 0.0,
|
||||
"euro": 0.0
|
||||
},
|
||||
"note": "retroactive_learning"
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -27,6 +27,7 @@ project_dir = os.path.dirname(os.path.dirname(script_dir))
|
||||
sys.path.insert(0, project_dir)
|
||||
|
||||
from scripts.utils.update_from_api import EurojackpotAPIUpdater
|
||||
from scripts.utils.update_from_eurojackpot_zahlen_eu import EurojackpotUpdater as WebScraper
|
||||
from scripts.generators.ultimate_ai_ml_eurojackpot_generator import UltimateAIMLEurojackpotGenerator
|
||||
from scripts.utils.notifier import EurojackpotNotifier
|
||||
|
||||
@@ -129,7 +130,16 @@ class AutoUpdateAndLearn:
|
||||
if success:
|
||||
print(" ✅ Daten erfolgreich aktualisiert")
|
||||
else:
|
||||
print(" ⚠️ Update ohne neue Daten")
|
||||
print(" ⚠️ API-Update ohne neue Daten, versuche Web-Scraper...")
|
||||
try:
|
||||
scraper = WebScraper(self.data_file)
|
||||
success = scraper.update(create_backup=False)
|
||||
if success:
|
||||
print(" ✅ Web-Scraper erfolgreich")
|
||||
else:
|
||||
print(" ⚠️ Web-Scraper ohne neue Daten")
|
||||
except Exception as scrape_err:
|
||||
print(f" ❌ Web-Scraper Fehler: {scrape_err}")
|
||||
|
||||
return success
|
||||
|
||||
|
||||
@@ -164,6 +164,12 @@ class UltimateAIMLEurojackpotGenerator:
|
||||
# 6. Initialize Real-Time Learning
|
||||
print("📚 Activating Real-Time Learning...", end=" ", flush=True)
|
||||
self.real_time_learner.initialize(self.features_df)
|
||||
# Langzeit-Frequenz als Dämpfer übergeben
|
||||
_main_counts = self.df[['Z1','Z2','Z3','Z4','Z5']].stack().value_counts()
|
||||
_euro_counts = self.df[['SZ1','SZ2']].stack().value_counts()
|
||||
freq_main = {n: int(_main_counts.get(n, 0)) for n in range(1, 51)}
|
||||
freq_euro = {n: int(_euro_counts.get(n, 0)) for n in range(1, 13)}
|
||||
self.real_time_learner.set_frequency_prior(freq_main, freq_euro)
|
||||
print("✅")
|
||||
|
||||
self.is_trained = self.ai_ml_engine.is_trained
|
||||
@@ -251,6 +257,21 @@ class UltimateAIMLEurojackpotGenerator:
|
||||
tip_counter += len(tips)
|
||||
print(f"✅ ({count} tips)")
|
||||
|
||||
# Strukturfilter: Summe + Parität korrigieren
|
||||
fixed_count = 0
|
||||
for tip in all_tips:
|
||||
if not self._passes_structural_constraints(tip['main_numbers']):
|
||||
tip['main_numbers'] = self._apply_structural_fix(tip['main_numbers'], main_predictions)
|
||||
tip['main_ai_score'] = np.mean([main_predictions.get(n, 0.1) for n in tip['main_numbers']])
|
||||
tip['pattern_weight'] = self.pattern_engine.calculate_pattern_weight(tip['main_numbers'])
|
||||
tip['confidence'] = tip['main_ai_score'] * 0.5 + tip.get('euro_ai_score', 0.0) * 0.3 + tip['pattern_weight'] * 0.2
|
||||
tip['quality'] = self._calculate_quality_score(
|
||||
tip['main_numbers'], tip['euro_numbers'], main_predictions, euro_predictions, tip['pattern_weight']
|
||||
)
|
||||
fixed_count += 1
|
||||
if fixed_count:
|
||||
print(f" 🔧 {fixed_count} Tip(s) via Strukturfilter korrigiert (Summe/Parität)")
|
||||
|
||||
# Output all tips
|
||||
print(f"\n📋 RESULTS:")
|
||||
print("=" * 100)
|
||||
@@ -459,27 +480,20 @@ class UltimateAIMLEurojackpotGenerator:
|
||||
|
||||
selected_main = []
|
||||
for position in range(5):
|
||||
best_candidate = None
|
||||
best_score = -1
|
||||
|
||||
for candidate in all_candidates:
|
||||
if candidate not in selected_main:
|
||||
ai_s = main_preds.get(candidate, 0.1)
|
||||
pattern_s = self.pattern_engine.get_number_pattern_score(candidate)
|
||||
diversity_s = self._calculate_diversity_score(candidate, selected_main) if selected_main else 0.5
|
||||
|
||||
ensemble_score = (ai_s * 0.4 + pattern_s * 0.3 + diversity_s * 0.3)
|
||||
|
||||
if ensemble_score > best_score:
|
||||
best_score = ensemble_score
|
||||
best_candidate = candidate
|
||||
|
||||
if best_candidate:
|
||||
selected_main.append(best_candidate)
|
||||
else:
|
||||
available = [n for n in range(1, 51) if n not in selected_main]
|
||||
if available:
|
||||
selected_main.append(random.choice(available))
|
||||
candidates = [c for c in all_candidates if c not in selected_main]
|
||||
if not candidates:
|
||||
candidates = [n for n in range(1, 51) if n not in selected_main]
|
||||
|
||||
scores = []
|
||||
for c in candidates:
|
||||
ai_s = main_preds.get(c, 0.1)
|
||||
pattern_s = self.pattern_engine.get_number_pattern_score(c)
|
||||
diversity_s = self._calculate_diversity_score(c, selected_main) if selected_main else 0.5
|
||||
scores.append(ai_s * 0.4 + pattern_s * 0.3 + diversity_s * 0.3)
|
||||
|
||||
# Gewichtete Zufallsauswahl: Qualität bleibt hoch, Duplikate werden vermieden
|
||||
choice = random.choices(candidates, weights=scores)[0]
|
||||
selected_main.append(choice)
|
||||
|
||||
selected_main = sorted(selected_main[:5])
|
||||
|
||||
@@ -505,6 +519,69 @@ class UltimateAIMLEurojackpotGenerator:
|
||||
'quality': quality
|
||||
}
|
||||
|
||||
def _passes_structural_constraints(self, main_numbers):
|
||||
"""Prüft Summenbereich und Parität."""
|
||||
s = sum(main_numbers)
|
||||
if s < 95 or s > 165:
|
||||
return False
|
||||
even = sum(1 for n in main_numbers if n % 2 == 0)
|
||||
if even == 0 or even == 5:
|
||||
return False
|
||||
return True
|
||||
|
||||
def _apply_structural_fix(self, main_numbers, main_preds):
|
||||
"""Korrigiert Summe/Parität durch minimalen Tausch."""
|
||||
numbers = list(main_numbers)
|
||||
|
||||
# Parität: mind. 1 gerade und 1 ungerade
|
||||
even = [n for n in numbers if n % 2 == 0]
|
||||
odd = [n for n in numbers if n % 2 != 0]
|
||||
if len(even) == 0:
|
||||
# alle ungerade → tausche den am schlechtesten bewerteten gegen bestes gerades
|
||||
worst = min(odd, key=lambda n: main_preds.get(n, 0))
|
||||
candidates = sorted(
|
||||
[n for n in range(2, 51, 2) if n not in numbers],
|
||||
key=lambda n: -main_preds.get(n, 0)
|
||||
)
|
||||
if candidates:
|
||||
numbers.remove(worst)
|
||||
numbers.append(candidates[0])
|
||||
elif len(odd) == 0:
|
||||
worst = min(even, key=lambda n: main_preds.get(n, 0))
|
||||
candidates = sorted(
|
||||
[n for n in range(1, 51, 2) if n not in numbers],
|
||||
key=lambda n: -main_preds.get(n, 0)
|
||||
)
|
||||
if candidates:
|
||||
numbers.remove(worst)
|
||||
numbers.append(candidates[0])
|
||||
|
||||
# Summe korrigieren
|
||||
for _ in range(10):
|
||||
s = sum(numbers)
|
||||
if 95 <= s <= 165:
|
||||
break
|
||||
if s > 165:
|
||||
highest = max(numbers)
|
||||
candidates = sorted(
|
||||
[n for n in range(1, highest) if n not in numbers],
|
||||
key=lambda n: -main_preds.get(n, 0)
|
||||
)
|
||||
if candidates:
|
||||
numbers.remove(highest)
|
||||
numbers.append(candidates[0])
|
||||
else:
|
||||
lowest = min(numbers)
|
||||
candidates = sorted(
|
||||
[n for n in range(lowest + 1, 51) if n not in numbers],
|
||||
key=lambda n: -main_preds.get(n, 0)
|
||||
)
|
||||
if candidates:
|
||||
numbers.remove(lowest)
|
||||
numbers.append(candidates[0])
|
||||
|
||||
return sorted(numbers)
|
||||
|
||||
def _calculate_diversity_score(self, candidate, selected):
|
||||
"""Berechnet Diversität."""
|
||||
if not selected:
|
||||
@@ -1473,6 +1550,9 @@ class EurojackpotRealTimeLearner:
|
||||
self.stats = defaultdict(int)
|
||||
self.cache_path = cache_path
|
||||
self.learning_state_file = os.path.join(cache_path, 'learning_state.json') if cache_path else None
|
||||
self.freq_prior_main = {} # Langzeit-Frequenz Dämpfer
|
||||
self.freq_prior_euro = {}
|
||||
self.freq_alpha = 0.2 # 20% Langzeit-Frequenz, 80% Learner
|
||||
|
||||
# Lade gespeicherten State
|
||||
if self.learning_state_file and os.path.exists(self.learning_state_file):
|
||||
@@ -1482,24 +1562,37 @@ class EurojackpotRealTimeLearner:
|
||||
"""Initialisiert Learning."""
|
||||
self.features_df = features_df
|
||||
|
||||
def adjust_main_predictions(self, predictions):
|
||||
"""Passt Main Predictions an."""
|
||||
adjusted = {}
|
||||
def set_frequency_prior(self, freq_main, freq_euro):
|
||||
"""Setzt Langzeit-Frequenz als Dämpfer (normalisiert auf [0,1])."""
|
||||
max_main = max(freq_main.values(), default=0) or 1
|
||||
max_euro = max(freq_euro.values(), default=0) or 1
|
||||
self.freq_prior_main = {n: v / max_main for n, v in freq_main.items()}
|
||||
self.freq_prior_euro = {n: v / max_euro for n, v in freq_euro.items()}
|
||||
|
||||
def adjust_main_predictions(self, predictions):
|
||||
"""Passt Main Predictions an, gedämpft durch Langzeit-Frequenz."""
|
||||
adjusted = {}
|
||||
for number, pred in predictions.items():
|
||||
adjustment = self.adjustments_main.get(number, 0)
|
||||
adjusted[number] = np.clip(pred + adjustment * self.learning_rate, 0, 1)
|
||||
|
||||
learner_val = np.clip(pred + adjustment * self.learning_rate, 0, 1)
|
||||
if self.freq_prior_main:
|
||||
freq_val = self.freq_prior_main.get(number, 0.5)
|
||||
adjusted[number] = (1 - self.freq_alpha) * learner_val + self.freq_alpha * freq_val
|
||||
else:
|
||||
adjusted[number] = learner_val
|
||||
return adjusted
|
||||
|
||||
def adjust_euro_predictions(self, predictions):
|
||||
"""Passt Euro Predictions an."""
|
||||
"""Passt Euro Predictions an, gedämpft durch Langzeit-Frequenz."""
|
||||
adjusted = {}
|
||||
|
||||
for number, pred in predictions.items():
|
||||
adjustment = self.adjustments_euro.get(number, 0)
|
||||
adjusted[number] = np.clip(pred + adjustment * self.learning_rate, 0, 1)
|
||||
|
||||
learner_val = np.clip(pred + adjustment * self.learning_rate, 0, 1)
|
||||
if self.freq_prior_euro:
|
||||
freq_val = self.freq_prior_euro.get(number, 0.5)
|
||||
adjusted[number] = (1 - self.freq_alpha) * learner_val + self.freq_alpha * freq_val
|
||||
else:
|
||||
adjusted[number] = learner_val
|
||||
return adjusted
|
||||
|
||||
def learn_from_result(self, drawing):
|
||||
|
||||
Reference in New Issue
Block a user