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- backend/ → lotto/ 디렉토리 이동 - docker-compose: lotto-backend→lotto, lotto-frontend→frontend - deploy scripts, nginx, agent-office config 네이밍 일괄 반영 - lotto/app/db.py에서 todos·blog_posts CREATE TABLE 제거 (personal로 이관 완료) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
73 lines
1.9 KiB
Python
73 lines
1.9 KiB
Python
import sys, os
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "app"))
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import math
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import pytest
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def test_calc_draw_score_basic():
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"""세트별 결과 → draw_score 계산"""
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from strategy_evolver import calc_draw_score
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results = [
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{"correct": 3, "rank": 5}, # 3/6 + 0.1 = 0.6
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{"correct": 1, "rank": 0}, # 1/6 + 0 = 0.167
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]
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score = calc_draw_score(results)
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expected = ((3/6 + 0.1) + (1/6)) / 2
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assert abs(score - expected) < 0.01
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def test_calc_draw_score_empty():
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"""빈 결과 → 0"""
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from strategy_evolver import calc_draw_score
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assert calc_draw_score([]) == 0.0
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def test_recalculate_weights_softmax():
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"""EMA → Softmax 가중치 변환"""
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from strategy_evolver import _softmax_weights
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ema_scores = {
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"combined": 0.30,
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"simulation": 0.25,
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"heatmap": 0.15,
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"manual": 0.10,
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"custom": 0.05,
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}
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weights = _softmax_weights(ema_scores)
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assert abs(sum(weights.values()) - 1.0) < 0.001
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assert weights["combined"] > weights["simulation"]
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assert weights["simulation"] > weights["heatmap"]
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assert all(w >= 0.049 for w in weights.values())
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def test_recalculate_weights_min_weight():
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"""한 전략의 EMA가 매우 낮아도 최소 5% 보장"""
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from strategy_evolver import _softmax_weights
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ema_scores = {
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"combined": 0.50,
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"simulation": 0.01,
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"heatmap": 0.01,
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"manual": 0.01,
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"custom": 0.01,
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}
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weights = _softmax_weights(ema_scores)
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assert weights["simulation"] >= 0.049
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assert weights["custom"] >= 0.049
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assert abs(sum(weights.values()) - 1.0) < 0.001
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def test_update_ema():
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"""EMA 갱신 공식 검증"""
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from strategy_evolver import ALPHA
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old_ema = 0.15
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draw_score = 0.40
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new_ema = ALPHA * draw_score + (1 - ALPHA) * old_ema
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expected = 0.3 * 0.40 + 0.7 * 0.15 # = 0.225
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assert abs(new_ema - expected) < 0.001
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