xau-ai-trading-bot/training_output.log at 4514b57de57e1986070e028007743bd51272dc93

Files

T

buckybonez c0976c4518 feat: implement Professor AI recommendations v0.2.2 (5 critical fixes)

Exit Strategy v6.6 "Professor AI Validated" - All recommendations implemented

FIX #1: Remove Misleading Debug Code
- Removed manual trajectory calculation (line 1262-1269)
- Trajectory predictor was CORRECT, debug comparison was WRONG
- Cleaned up false "bug found" warnings

FIX #2: Peak Detection Logic (CHECK 0A.4)
- Detects approaching peak (vel > 0, accel < 0)
- Holds position if peak within 30s and 15%+ profit ahead
- Suppresses fuzzy exits during peak approach
- Target: Peak capture 38% -> 70%+
- Added peak_hold_active field to PositionGuard

FIX #3: London False Breakout Filter
- London session + ATR ratio < 1.2 = whipsaw risk
- Requires ML confidence 70% (instead of 60%)
- Prevents false breakouts during low volatility
- Implemented in main_live.py before signal logic

FIX #4: Enhanced Kelly Partial Exit Strategy
- Active for all profits >= tp_min * 0.5 (not just >$8)
- Recommends partial exits for better peak capture
- Full exit when Kelly suggests >70% close
- Note: Actual partial close needs MT5 volume parameter (TODO)

FIX #5: Unicode Encoding Fixes
- Added UTF-8 encoding to file logger
- Replaced all emoji (⚠️ -> [WARNING]) and arrows (-> -> ->)
- No more UnicodeEncodeError on Windows console
- Fixed in 11 src/*.py files

Expected Performance:
- Peak Capture: 38% -> 70%+ (+84%)
- Avg Profit: $2.00 -> $4.50 (+125%)
- Risk/Reward: 0.49 -> 1.2+ (+145%)
- Win Rate: Maintain 76%

Files Modified:
- src/smart_risk_manager.py (peak detection, Kelly, unicode)
- src/trajectory_predictor.py (unicode arrows)
- main_live.py (London filter, UTF-8 encoding)
- src/*.py (unicode cleanup: 11 files)
- VERSION (0.2.1 -> 0.2.2)
- CHANGELOG.md (comprehensive v0.2.2 docs)

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

2026-02-11 18:16:34 +07:00

316 KiB

Raw Blame History

08:53:51 | INFO | ============================================================
08:53:51 | INFO | SMART TRADING BOT - MODEL TRAINING
08:53:51 | INFO | ============================================================
08:53:51 | INFO | Symbol: XAUUSD
08:53:51 | INFO | Capital: $4,944.68
08:53:51 | INFO | Mode: small
08:53:51 | INFO | Connecting to MT5...
08:53:53 | INFO | Connected to MT5: FinexBisnisSolusi-Demo (Account: 61045904)
08:53:54 | INFO | MT5 connected successfully!
08:53:54 | INFO | Account Balance: $5,469.11
08:53:54 | INFO | Account Equity: $5,469.11
08:53:54 | INFO | Fetching 15000 bars of XAUUSD M15 data...
08:53:54 | INFO | Received 15000 bars
08:53:54 | INFO | Date range: 2025-06-24 07:00:00 to 2026-02-11 03:45:00
08:53:54 | INFO | Applying feature engineering...
08:53:54 | INFO | Total features created: 77
08:53:54 | INFO | Training data saved to data/training_data.parquet
08:53:54 | INFO | ============================================================
08:53:54 | INFO | Training HMM Regime Model
08:53:54 | INFO | ============================================================
08:53:54 | INFO | Features scaled: 14888 samples, 8 features
Model is not converging. Current: -92742.19358737442 is not greater than -92742.19354307871. Delta is -4.429570981301367e-05
Model is not converging. Current: -92742.19358626119 is not greater than -92742.19354347987. Delta is -4.278132109902799e-05
Model is not converging. Current: -92742.19358626322 is not greater than -92742.1935434767. Delta is -4.278653068467975e-05
Model is not converging. Current: -92742.19358586664 is not greater than -92742.19354362821. Delta is -4.223843279760331e-05
Model is not converging. Current: -92742.19358624959 is not greater than -92742.19354351076. Delta is -4.273882950656116e-05
08:53:59 | INFO | HMM v3 fitted: 14888 samples, score=-92742.2, seed=93
08:53:59 | INFO | State distribution: {'medium_volatility': '41.5%', 'low_volatility': '48.8%', 'high_volatility': '9.8%'}
08:53:59 | INFO | Transmat diag: min=0.951, mean=0.968
08:53:59 | INFO | Stability: avg_duration=48.7 bars, transitions=305/14888 (20.5/1000)
08:53:59 | INFO | Smoothing: enabled=True, min_duration=5
08:53:59 | INFO | low_volatility: [0.015, 0.981, 0.004]
08:53:59 | INFO | medium_volatility: [0.971, 0.022, 0.007]
08:53:59 | INFO | high_volatility: [0.048, 0.001, 0.951]
08:53:59 | INFO | HMM model v3 saved to models\hmm_regime.pkl
08:53:59 | INFO | Regime Distribution:
08:53:59 | INFO | medium_volatility: 6197 bars
08:53:59 | INFO | high_volatility: 1394 bars
08:53:59 | INFO | low_volatility: 7297 bars
08:53:59 | INFO | Transition Matrix:
08:53:59 | INFO | low_volatility: ['0.01', '0.98', '0.00']
08:53:59 | INFO | medium_volatility: ['0.97', '0.02', '0.01']
08:53:59 | INFO | high_volatility: ['0.05', '0.00', '0.95']
08:53:59 | INFO | ============================================================
08:53:59 | INFO | Training XGBoost Model (Anti-Overfit Config)
08:53:59 | INFO | ============================================================
08:53:59 | INFO | Available features: 37/37
08:53:59 | INFO | Train/Test gap: 50 bars to prevent temporal leakage
08:53:59 | INFO | Training with 10420 samples, testing with 4417 samples
[0] train-auc:0.52182 eval-auc:0.51845
[6] train-auc:0.63970 eval-auc:0.61127
08:54:00 | INFO | Training complete: Train AUC=0.6397, Test AUC=0.6113
08:54:00 | INFO | Model saved to models\xgboost_model.pkl
08:54:00 | INFO | Top 10 Feature Importance:
08:54:00 | INFO | ob: 214.0011
08:54:00 | INFO | returns_1: 42.0991
08:54:00 | INFO | log_returns: 38.2524
08:54:00 | INFO | volatility_20: 17.0382
08:54:00 | INFO | hh_count_5: 11.5063
08:54:00 | INFO | rsi: 11.2418
08:54:00 | INFO | atr_percent: 10.4820
08:54:00 | INFO | price_position: 10.2246
08:54:00 | INFO | bb_percent_b: 8.7114
08:54:00 | INFO | returns_5: 7.8374
08:54:00 | INFO | Running walk-forward validation...
08:54:00 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:00 | INFO | Training with 388 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.64126 eval-auc:nan
[10] train-auc:0.73821 eval-auc:nan
[20] train-auc:0.75034 eval-auc:nan
[30] train-auc:0.75874 eval-auc:nan
[40] train-auc:0.77722 eval-auc:nan
[49] train-auc:0.78851 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:00 | INFO | Training complete: Train AUC=0.7885, Test AUC=nan
08:54:00 | INFO | Model saved to models\xgboost_model.pkl
08:54:00 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:00 | INFO | Training with 438 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60338 eval-auc:nan
[10] train-auc:0.73058 eval-auc:nan
[20] train-auc:0.74836 eval-auc:nan
[30] train-auc:0.75708 eval-auc:nan
[40] train-auc:0.78787 eval-auc:nan
[49] train-auc:0.79755 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:00 | INFO | Training complete: Train AUC=0.7975, Test AUC=nan
08:54:00 | INFO | Model saved to models\xgboost_model.pkl
08:54:00 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:00 | INFO | Training with 488 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59757 eval-auc:nan
[10] train-auc:0.72917 eval-auc:nan
[20] train-auc:0.73993 eval-auc:nan
[30] train-auc:0.74975 eval-auc:nan
[40] train-auc:0.77913 eval-auc:nan
[49] train-auc:0.79125 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:00 | INFO | Training complete: Train AUC=0.7913, Test AUC=nan
08:54:00 | INFO | Model saved to models\xgboost_model.pkl
08:54:00 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:00 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61530 eval-auc:nan
[10] train-auc:0.72250 eval-auc:nan
[20] train-auc:0.75768 eval-auc:nan
[30] train-auc:0.77540 eval-auc:nan
[40] train-auc:0.79125 eval-auc:nan
[49] train-auc:0.80170 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:00 | INFO | Training complete: Train AUC=0.8017, Test AUC=nan
08:54:00 | INFO | Model saved to models\xgboost_model.pkl
08:54:00 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:00 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59705 eval-auc:nan
[10] train-auc:0.73495 eval-auc:nan
[20] train-auc:0.75927 eval-auc:nan
[30] train-auc:0.78931 eval-auc:nan
[40] train-auc:0.81059 eval-auc:nan
[49] train-auc:0.81817 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:00 | INFO | Training complete: Train AUC=0.8182, Test AUC=nan
08:54:00 | INFO | Model saved to models\xgboost_model.pkl
08:54:00 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:00 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62345 eval-auc:nan
[10] train-auc:0.73010 eval-auc:nan
[20] train-auc:0.74792 eval-auc:nan
[30] train-auc:0.77591 eval-auc:nan
[40] train-auc:0.79690 eval-auc:nan
[49] train-auc:0.80857 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:00 | INFO | Training complete: Train AUC=0.8086, Test AUC=nan
08:54:00 | INFO | Model saved to models\xgboost_model.pkl
08:54:00 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:00 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60120 eval-auc:nan
[10] train-auc:0.72558 eval-auc:nan
[20] train-auc:0.76551 eval-auc:nan
[30] train-auc:0.78087 eval-auc:nan
[40] train-auc:0.80399 eval-auc:nan
[49] train-auc:0.81028 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:00 | INFO | Training complete: Train AUC=0.8103, Test AUC=nan
08:54:00 | INFO | Model saved to models\xgboost_model.pkl
08:54:00 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:00 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.57060 eval-auc:nan
[10] train-auc:0.71310 eval-auc:nan
[20] train-auc:0.74741 eval-auc:nan
[30] train-auc:0.78711 eval-auc:nan
[40] train-auc:0.79660 eval-auc:nan
[49] train-auc:0.80626 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:00 | INFO | Training complete: Train AUC=0.8063, Test AUC=nan
08:54:00 | INFO | Model saved to models\xgboost_model.pkl
08:54:00 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:00 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.57571 eval-auc:nan
[10] train-auc:0.71602 eval-auc:nan
[20] train-auc:0.74168 eval-auc:nan
[30] train-auc:0.76615 eval-auc:nan
[40] train-auc:0.79330 eval-auc:nan
[49] train-auc:0.80998 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:00 | INFO | Training complete: Train AUC=0.8100, Test AUC=nan
08:54:00 | INFO | Model saved to models\xgboost_model.pkl
08:54:00 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:00 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60960 eval-auc:nan
[10] train-auc:0.71699 eval-auc:nan
[20] train-auc:0.74091 eval-auc:nan
[30] train-auc:0.76603 eval-auc:nan
[40] train-auc:0.77557 eval-auc:nan
[49] train-auc:0.78631 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:00 | INFO | Training complete: Train AUC=0.7863, Test AUC=nan
08:54:00 | INFO | Model saved to models\xgboost_model.pkl
08:54:00 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:00 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58375 eval-auc:nan
[10] train-auc:0.70071 eval-auc:nan
[20] train-auc:0.74127 eval-auc:nan
[30] train-auc:0.76555 eval-auc:nan
[40] train-auc:0.78784 eval-auc:nan
[49] train-auc:0.78539 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:00 | INFO | Training complete: Train AUC=0.7854, Test AUC=nan
08:54:00 | INFO | Model saved to models\xgboost_model.pkl
08:54:00 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:00 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60365 eval-auc:nan
[10] train-auc:0.69943 eval-auc:nan
[20] train-auc:0.72336 eval-auc:nan
[30] train-auc:0.74610 eval-auc:nan
[40] train-auc:0.77103 eval-auc:nan
[49] train-auc:0.78290 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:00 | INFO | Training complete: Train AUC=0.7829, Test AUC=nan
08:54:00 | INFO | Model saved to models\xgboost_model.pkl
08:54:00 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:00 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61921 eval-auc:nan
[10] train-auc:0.72281 eval-auc:nan
[20] train-auc:0.72807 eval-auc:nan
[30] train-auc:0.75505 eval-auc:nan
[40] train-auc:0.77508 eval-auc:nan
[49] train-auc:0.79438 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:00 | INFO | Training complete: Train AUC=0.7944, Test AUC=nan
08:54:00 | INFO | Model saved to models\xgboost_model.pkl
08:54:00 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:00 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62075 eval-auc:nan
[10] train-auc:0.72208 eval-auc:nan
[20] train-auc:0.75516 eval-auc:nan
[30] train-auc:0.77255 eval-auc:nan
[40] train-auc:0.79316 eval-auc:nan
[49] train-auc:0.79919 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:00 | INFO | Training complete: Train AUC=0.7992, Test AUC=nan
08:54:00 | INFO | Model saved to models\xgboost_model.pkl
08:54:00 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:00 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.57626 eval-auc:nan
[10] train-auc:0.71985 eval-auc:nan
[20] train-auc:0.75614 eval-auc:nan
[30] train-auc:0.77632 eval-auc:nan
[40] train-auc:0.79104 eval-auc:nan
[49] train-auc:0.79922 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:00 | INFO | Training complete: Train AUC=0.7992, Test AUC=nan
08:54:00 | INFO | Model saved to models\xgboost_model.pkl
08:54:00 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:00 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60275 eval-auc:nan
[10] train-auc:0.76094 eval-auc:nan
[20] train-auc:0.78586 eval-auc:nan
[30] train-auc:0.79866 eval-auc:nan
[40] train-auc:0.80537 eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[49] train-auc:0.81279 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:01 | INFO | Training complete: Train AUC=0.8128, Test AUC=nan
08:54:01 | INFO | Model saved to models\xgboost_model.pkl
08:54:01 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:01 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59361 eval-auc:nan
[10] train-auc:0.72328 eval-auc:nan
[20] train-auc:0.75797 eval-auc:nan
[30] train-auc:0.78220 eval-auc:nan
[40] train-auc:0.80274 eval-auc:nan
[49] train-auc:0.81845 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:01 | INFO | Training complete: Train AUC=0.8185, Test AUC=nan
08:54:01 | INFO | Model saved to models\xgboost_model.pkl
08:54:01 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:01 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59133 eval-auc:nan
[10] train-auc:0.73540 eval-auc:nan
[20] train-auc:0.77679 eval-auc:nan
[30] train-auc:0.80375 eval-auc:nan
[40] train-auc:0.81171 eval-auc:nan
[49] train-auc:0.81905 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:01 | INFO | Training complete: Train AUC=0.8191, Test AUC=nan
08:54:01 | INFO | Model saved to models\xgboost_model.pkl
08:54:01 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:01 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.57464 eval-auc:nan
[10] train-auc:0.73134 eval-auc:nan
[20] train-auc:0.75108 eval-auc:nan
[30] train-auc:0.77162 eval-auc:nan
[40] train-auc:0.79213 eval-auc:nan
[49] train-auc:0.80659 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:01 | INFO | Training complete: Train AUC=0.8066, Test AUC=nan
08:54:01 | INFO | Model saved to models\xgboost_model.pkl
08:54:01 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:01 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.56984 eval-auc:nan
[10] train-auc:0.72960 eval-auc:nan
[20] train-auc:0.76316 eval-auc:nan
[30] train-auc:0.78911 eval-auc:nan
[40] train-auc:0.79571 eval-auc:nan
[49] train-auc:0.81194 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:01 | INFO | Training complete: Train AUC=0.8119, Test AUC=nan
08:54:01 | INFO | Model saved to models\xgboost_model.pkl
08:54:01 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:01 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.56926 eval-auc:nan
[10] train-auc:0.69272 eval-auc:nan
[20] train-auc:0.74312 eval-auc:nan
[30] train-auc:0.76152 eval-auc:nan
[40] train-auc:0.77448 eval-auc:nan
[49] train-auc:0.79754 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:01 | INFO | Training complete: Train AUC=0.7975, Test AUC=nan
08:54:01 | INFO | Model saved to models\xgboost_model.pkl
08:54:01 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:01 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.57209 eval-auc:nan
[10] train-auc:0.72939 eval-auc:nan
[20] train-auc:0.74585 eval-auc:nan
[30] train-auc:0.75876 eval-auc:nan
[40] train-auc:0.78106 eval-auc:nan
[49] train-auc:0.79939 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:01 | INFO | Training complete: Train AUC=0.7994, Test AUC=nan
08:54:01 | INFO | Model saved to models\xgboost_model.pkl
08:54:01 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:01 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60721 eval-auc:nan
[10] train-auc:0.75822 eval-auc:nan
[20] train-auc:0.79324 eval-auc:nan
[30] train-auc:0.80856 eval-auc:nan
[40] train-auc:0.81585 eval-auc:nan
[49] train-auc:0.81559 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:01 | INFO | Training complete: Train AUC=0.8156, Test AUC=nan
08:54:01 | INFO | Model saved to models\xgboost_model.pkl
08:54:01 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:01 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60504 eval-auc:nan
[10] train-auc:0.75491 eval-auc:nan
[20] train-auc:0.78560 eval-auc:nan
[30] train-auc:0.79117 eval-auc:nan
[40] train-auc:0.79069 eval-auc:nan
[49] train-auc:0.81230 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:01 | INFO | Training complete: Train AUC=0.8123, Test AUC=nan
08:54:01 | INFO | Model saved to models\xgboost_model.pkl
08:54:01 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:01 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60014 eval-auc:nan
[10] train-auc:0.74592 eval-auc:nan
[20] train-auc:0.76880 eval-auc:nan
[30] train-auc:0.78706 eval-auc:nan
[40] train-auc:0.79485 eval-auc:nan
[49] train-auc:0.81010 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:01 | INFO | Training complete: Train AUC=0.8101, Test AUC=nan
08:54:01 | INFO | Model saved to models\xgboost_model.pkl
08:54:01 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:01 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59102 eval-auc:nan
[10] train-auc:0.72950 eval-auc:nan
[20] train-auc:0.75210 eval-auc:nan
[30] train-auc:0.78330 eval-auc:nan
[40] train-auc:0.79940 eval-auc:nan
[49] train-auc:0.80852 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:01 | INFO | Training complete: Train AUC=0.8085, Test AUC=nan
08:54:01 | INFO | Model saved to models\xgboost_model.pkl
08:54:01 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:01 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.63856 eval-auc:nan
[10] train-auc:0.75910 eval-auc:nan
[20] train-auc:0.78522 eval-auc:nan
[30] train-auc:0.78930 eval-auc:nan
[40] train-auc:0.79506 eval-auc:nan
[49] train-auc:0.80404 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:01 | INFO | Training complete: Train AUC=0.8040, Test AUC=nan
08:54:01 | INFO | Model saved to models\xgboost_model.pkl
08:54:01 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:01 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62444 eval-auc:nan
[10] train-auc:0.73497 eval-auc:nan
[20] train-auc:0.77076 eval-auc:nan
[30] train-auc:0.78657 eval-auc:nan
[40] train-auc:0.79697 eval-auc:nan
[49] train-auc:0.80737 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:01 | INFO | Training complete: Train AUC=0.8074, Test AUC=nan
08:54:01 | INFO | Model saved to models\xgboost_model.pkl
08:54:01 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:01 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60739 eval-auc:nan
[10] train-auc:0.71578 eval-auc:nan
[20] train-auc:0.74139 eval-auc:nan
[30] train-auc:0.75398 eval-auc:nan
[40] train-auc:0.78559 eval-auc:nan
[49] train-auc:0.78924 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:01 | INFO | Training complete: Train AUC=0.7892, Test AUC=nan
08:54:01 | INFO | Model saved to models\xgboost_model.pkl
08:54:01 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:01 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60341 eval-auc:nan
[10] train-auc:0.71363 eval-auc:nan
[20] train-auc:0.72994 eval-auc:nan
[30] train-auc:0.74958 eval-auc:nan
[40] train-auc:0.76102 eval-auc:nan
[49] train-auc:0.77753 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:01 | INFO | Training complete: Train AUC=0.7775, Test AUC=nan
08:54:01 | INFO | Model saved to models\xgboost_model.pkl
08:54:01 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:01 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60600 eval-auc:nan
[10] train-auc:0.72069 eval-auc:nan
[20] train-auc:0.74494 eval-auc:nan
[30] train-auc:0.76473 eval-auc:nan
[40] train-auc:0.77431 eval-auc:nan
[49] train-auc:0.78567 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:01 | INFO | Training complete: Train AUC=0.7857, Test AUC=nan
08:54:01 | INFO | Model saved to models\xgboost_model.pkl
08:54:01 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:01 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59840 eval-auc:nan
[10] train-auc:0.70331 eval-auc:nan
[20] train-auc:0.73009 eval-auc:nan
[30] train-auc:0.74756 eval-auc:nan
[40] train-auc:0.76485 eval-auc:nan
[49] train-auc:0.77830 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:01 | INFO | Training complete: Train AUC=0.7783, Test AUC=nan
08:54:01 | INFO | Model saved to models\xgboost_model.pkl
08:54:01 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:01 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61583 eval-auc:nan
[10] train-auc:0.71498 eval-auc:nan
[20] train-auc:0.73542 eval-auc:nan
[30] train-auc:0.74804 eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[40] train-auc:0.76269 eval-auc:nan
[49] train-auc:0.77693 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:02 | INFO | Training complete: Train AUC=0.7769, Test AUC=nan
08:54:02 | INFO | Model saved to models\xgboost_model.pkl
08:54:02 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:02 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60078 eval-auc:nan
[10] train-auc:0.70454 eval-auc:nan
[20] train-auc:0.73727 eval-auc:nan
[30] train-auc:0.75579 eval-auc:nan
[40] train-auc:0.76773 eval-auc:nan
[49] train-auc:0.77698 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:02 | INFO | Training complete: Train AUC=0.7770, Test AUC=nan
08:54:02 | INFO | Model saved to models\xgboost_model.pkl
08:54:02 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:02 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59964 eval-auc:nan
[10] train-auc:0.72059 eval-auc:nan
[20] train-auc:0.74781 eval-auc:nan
[30] train-auc:0.76123 eval-auc:nan
[40] train-auc:0.77539 eval-auc:nan
[49] train-auc:0.78974 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:02 | INFO | Training complete: Train AUC=0.7897, Test AUC=nan
08:54:02 | INFO | Model saved to models\xgboost_model.pkl
08:54:02 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:02 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59701 eval-auc:nan
[10] train-auc:0.72820 eval-auc:nan
[20] train-auc:0.74491 eval-auc:nan
[30] train-auc:0.76568 eval-auc:nan
[40] train-auc:0.78072 eval-auc:nan
[49] train-auc:0.79011 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:02 | INFO | Training complete: Train AUC=0.7901, Test AUC=nan
08:54:02 | INFO | Model saved to models\xgboost_model.pkl
08:54:02 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:02 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60937 eval-auc:nan
[10] train-auc:0.70482 eval-auc:nan
[20] train-auc:0.74141 eval-auc:nan
[30] train-auc:0.74375 eval-auc:nan
[40] train-auc:0.77000 eval-auc:nan
[49] train-auc:0.78338 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:02 | INFO | Training complete: Train AUC=0.7834, Test AUC=nan
08:54:02 | INFO | Model saved to models\xgboost_model.pkl
08:54:02 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:02 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.63651 eval-auc:nan
[10] train-auc:0.72627 eval-auc:nan
[20] train-auc:0.74804 eval-auc:nan
[30] train-auc:0.76180 eval-auc:nan
[40] train-auc:0.78020 eval-auc:nan
[49] train-auc:0.78607 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:02 | INFO | Training complete: Train AUC=0.7861, Test AUC=nan
08:54:02 | INFO | Model saved to models\xgboost_model.pkl
08:54:02 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:02 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58865 eval-auc:nan
[10] train-auc:0.74434 eval-auc:nan
[20] train-auc:0.74639 eval-auc:nan
[30] train-auc:0.75210 eval-auc:nan
[40] train-auc:0.77199 eval-auc:nan
[49] train-auc:0.77825 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:02 | INFO | Training complete: Train AUC=0.7783, Test AUC=nan
08:54:02 | INFO | Model saved to models\xgboost_model.pkl
08:54:02 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:02 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61706 eval-auc:nan
[10] train-auc:0.74672 eval-auc:nan
[20] train-auc:0.76265 eval-auc:nan
[30] train-auc:0.77829 eval-auc:nan
[40] train-auc:0.78624 eval-auc:nan
[49] train-auc:0.79622 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:02 | INFO | Training complete: Train AUC=0.7962, Test AUC=nan
08:54:02 | INFO | Model saved to models\xgboost_model.pkl
08:54:02 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:02 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59151 eval-auc:nan
[10] train-auc:0.72977 eval-auc:nan
[20] train-auc:0.73870 eval-auc:nan
[30] train-auc:0.74612 eval-auc:nan
[40] train-auc:0.76738 eval-auc:nan
[49] train-auc:0.77810 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:02 | INFO | Training complete: Train AUC=0.7781, Test AUC=nan
08:54:02 | INFO | Model saved to models\xgboost_model.pkl
08:54:02 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:02 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59438 eval-auc:nan
[10] train-auc:0.70996 eval-auc:nan
[20] train-auc:0.73589 eval-auc:nan
[30] train-auc:0.75203 eval-auc:nan
[40] train-auc:0.77593 eval-auc:nan
[49] train-auc:0.78955 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:02 | INFO | Training complete: Train AUC=0.7896, Test AUC=nan
08:54:02 | INFO | Model saved to models\xgboost_model.pkl
08:54:02 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:02 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58407 eval-auc:nan
[10] train-auc:0.71992 eval-auc:nan
[20] train-auc:0.74625 eval-auc:nan
[30] train-auc:0.77028 eval-auc:nan
[40] train-auc:0.78464 eval-auc:nan
[49] train-auc:0.80249 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:02 | INFO | Training complete: Train AUC=0.8025, Test AUC=nan
08:54:02 | INFO | Model saved to models\xgboost_model.pkl
08:54:02 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:02 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61828 eval-auc:nan
[10] train-auc:0.73273 eval-auc:nan
[20] train-auc:0.77206 eval-auc:nan
[30] train-auc:0.79054 eval-auc:nan
[40] train-auc:0.80593 eval-auc:nan
[49] train-auc:0.81669 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:02 | INFO | Training complete: Train AUC=0.8167, Test AUC=nan
08:54:02 | INFO | Model saved to models\xgboost_model.pkl
08:54:02 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:02 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58019 eval-auc:nan
[10] train-auc:0.73870 eval-auc:nan
[20] train-auc:0.76539 eval-auc:nan
[30] train-auc:0.79055 eval-auc:nan
[40] train-auc:0.80435 eval-auc:nan
[49] train-auc:0.81436 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:02 | INFO | Training complete: Train AUC=0.8144, Test AUC=nan
08:54:02 | INFO | Model saved to models\xgboost_model.pkl
08:54:02 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:02 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59776 eval-auc:nan
[10] train-auc:0.72057 eval-auc:nan
[20] train-auc:0.75272 eval-auc:nan
[30] train-auc:0.77067 eval-auc:nan
[40] train-auc:0.78382 eval-auc:nan
[49] train-auc:0.80058 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:02 | INFO | Training complete: Train AUC=0.8006, Test AUC=nan
08:54:02 | INFO | Model saved to models\xgboost_model.pkl
08:54:02 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:02 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.55415 eval-auc:nan
[10] train-auc:0.72266 eval-auc:nan
[20] train-auc:0.73254 eval-auc:nan
[30] train-auc:0.76305 eval-auc:nan
[40] train-auc:0.77745 eval-auc:nan
[49] train-auc:0.80388 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:02 | INFO | Training complete: Train AUC=0.8039, Test AUC=nan
08:54:02 | INFO | Model saved to models\xgboost_model.pkl
08:54:02 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:02 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.56272 eval-auc:nan
[10] train-auc:0.72173 eval-auc:nan
[20] train-auc:0.76281 eval-auc:nan
[30] train-auc:0.77385 eval-auc:nan
[40] train-auc:0.79148 eval-auc:nan
[49] train-auc:0.79851 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:02 | INFO | Training complete: Train AUC=0.7985, Test AUC=nan
08:54:02 | INFO | Model saved to models\xgboost_model.pkl
08:54:02 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:02 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60108 eval-auc:nan
[10] train-auc:0.73143 eval-auc:nan
[20] train-auc:0.74728 eval-auc:nan
[30] train-auc:0.77209 eval-auc:nan
[40] train-auc:0.78259 eval-auc:nan
[49] train-auc:0.78725 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:02 | INFO | Training complete: Train AUC=0.7872, Test AUC=nan
08:54:02 | INFO | Model saved to models\xgboost_model.pkl
08:54:03 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:03 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59910 eval-auc:nan
[10] train-auc:0.71128 eval-auc:nan
[20] train-auc:0.74497 eval-auc:nan
[30] train-auc:0.76285 eval-auc:nan
[40] train-auc:0.78418 eval-auc:nan
[49] train-auc:0.78841 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:03 | INFO | Training complete: Train AUC=0.7884, Test AUC=nan
08:54:03 | INFO | Model saved to models\xgboost_model.pkl
08:54:03 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:03 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62327 eval-auc:nan
[10] train-auc:0.75221 eval-auc:nan
[20] train-auc:0.76776 eval-auc:nan
[30] train-auc:0.78295 eval-auc:nan
[40] train-auc:0.79403 eval-auc:nan
[49] train-auc:0.80951 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:03 | INFO | Training complete: Train AUC=0.8095, Test AUC=nan
08:54:03 | INFO | Model saved to models\xgboost_model.pkl
08:54:03 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:03 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61812 eval-auc:nan
[10] train-auc:0.74843 eval-auc:nan
[20] train-auc:0.76281 eval-auc:nan
[30] train-auc:0.78741 eval-auc:nan
[40] train-auc:0.79969 eval-auc:nan
[49] train-auc:0.80743 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:03 | INFO | Training complete: Train AUC=0.8074, Test AUC=nan
08:54:03 | INFO | Model saved to models\xgboost_model.pkl
08:54:03 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:03 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62527 eval-auc:nan
[10] train-auc:0.75578 eval-auc:nan
[20] train-auc:0.78034 eval-auc:nan
[30] train-auc:0.78667 eval-auc:nan
[40] train-auc:0.80003 eval-auc:nan
[49] train-auc:0.80397 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:03 | INFO | Training complete: Train AUC=0.8040, Test AUC=nan
08:54:03 | INFO | Model saved to models\xgboost_model.pkl
08:54:03 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:03 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62839 eval-auc:nan
[10] train-auc:0.75835 eval-auc:nan
[20] train-auc:0.77501 eval-auc:nan
[30] train-auc:0.79320 eval-auc:nan
[40] train-auc:0.80623 eval-auc:nan
[49] train-auc:0.81986 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:03 | INFO | Training complete: Train AUC=0.8199, Test AUC=nan
08:54:03 | INFO | Model saved to models\xgboost_model.pkl
08:54:03 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:03 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61540 eval-auc:nan
[10] train-auc:0.76817 eval-auc:nan
[20] train-auc:0.80050 eval-auc:nan
[30] train-auc:0.81930 eval-auc:nan
[40] train-auc:0.82866 eval-auc:nan
[49] train-auc:0.83679 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:03 | INFO | Training complete: Train AUC=0.8368, Test AUC=nan
08:54:03 | INFO | Model saved to models\xgboost_model.pkl
08:54:03 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:03 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62442 eval-auc:nan
[10] train-auc:0.73594 eval-auc:nan
[20] train-auc:0.77465 eval-auc:nan
[30] train-auc:0.78278 eval-auc:nan
[40] train-auc:0.79112 eval-auc:nan
[49] train-auc:0.81513 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:03 | INFO | Training complete: Train AUC=0.8151, Test AUC=nan
08:54:03 | INFO | Model saved to models\xgboost_model.pkl
08:54:03 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:03 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58608 eval-auc:nan
[10] train-auc:0.72097 eval-auc:nan
[20] train-auc:0.76308 eval-auc:nan
[30] train-auc:0.79239 eval-auc:nan
[40] train-auc:0.80960 eval-auc:nan
[49] train-auc:0.81744 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:03 | INFO | Training complete: Train AUC=0.8174, Test AUC=nan
08:54:03 | INFO | Model saved to models\xgboost_model.pkl
08:54:03 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:03 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60945 eval-auc:nan
[10] train-auc:0.71640 eval-auc:nan
[20] train-auc:0.74784 eval-auc:nan
[30] train-auc:0.76102 eval-auc:nan
[40] train-auc:0.77951 eval-auc:nan
[49] train-auc:0.79907 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:03 | INFO | Training complete: Train AUC=0.7991, Test AUC=nan
08:54:03 | INFO | Model saved to models\xgboost_model.pkl
08:54:03 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:03 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.64286 eval-auc:nan
[10] train-auc:0.74450 eval-auc:nan
[20] train-auc:0.76981 eval-auc:nan
[30] train-auc:0.79264 eval-auc:nan
[40] train-auc:0.80567 eval-auc:nan
[49] train-auc:0.81227 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:03 | INFO | Training complete: Train AUC=0.8123, Test AUC=nan
08:54:03 | INFO | Model saved to models\xgboost_model.pkl
08:54:03 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:03 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.63587 eval-auc:nan
[10] train-auc:0.74095 eval-auc:nan
[20] train-auc:0.77523 eval-auc:nan
[30] train-auc:0.78243 eval-auc:nan
[40] train-auc:0.80109 eval-auc:nan
[49] train-auc:0.81184 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:03 | INFO | Training complete: Train AUC=0.8118, Test AUC=nan
08:54:03 | INFO | Model saved to models\xgboost_model.pkl
08:54:03 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:03 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.57806 eval-auc:nan
[10] train-auc:0.71320 eval-auc:nan
[20] train-auc:0.75545 eval-auc:nan
[30] train-auc:0.77091 eval-auc:nan
[40] train-auc:0.78497 eval-auc:nan
[49] train-auc:0.79580 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:03 | INFO | Training complete: Train AUC=0.7958, Test AUC=nan
08:54:03 | INFO | Model saved to models\xgboost_model.pkl
08:54:03 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:03 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60323 eval-auc:nan
[10] train-auc:0.74192 eval-auc:nan
[20] train-auc:0.76749 eval-auc:nan
[30] train-auc:0.78072 eval-auc:nan
[40] train-auc:0.79201 eval-auc:nan
[49] train-auc:0.80477 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:03 | INFO | Training complete: Train AUC=0.8048, Test AUC=nan
08:54:03 | INFO | Model saved to models\xgboost_model.pkl
08:54:03 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:03 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59283 eval-auc:nan
[10] train-auc:0.71361 eval-auc:nan
[20] train-auc:0.75234 eval-auc:nan
[30] train-auc:0.75622 eval-auc:nan
[40] train-auc:0.76886 eval-auc:nan
[49] train-auc:0.78424 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:03 | INFO | Training complete: Train AUC=0.7842, Test AUC=nan
08:54:03 | INFO | Model saved to models\xgboost_model.pkl
08:54:03 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:03 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58564 eval-auc:nan
[10] train-auc:0.71039 eval-auc:nan
[20] train-auc:0.75089 eval-auc:nan
[30] train-auc:0.77866 eval-auc:nan
[40] train-auc:0.78561 eval-auc:nan
[49] train-auc:0.79469 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:03 | INFO | Training complete: Train AUC=0.7947, Test AUC=nan
08:54:03 | INFO | Model saved to models\xgboost_model.pkl
08:54:03 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:03 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58715 eval-auc:nan
[10] train-auc:0.70653 eval-auc:nan
[20] train-auc:0.73744 eval-auc:nan
[30] train-auc:0.76009 eval-auc:nan
[40] train-auc:0.77847 eval-auc:nan
[49] train-auc:0.79532 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:03 | INFO | Training complete: Train AUC=0.7953, Test AUC=nan
08:54:03 | INFO | Model saved to models\xgboost_model.pkl
08:54:04 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:04 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60148 eval-auc:nan
[10] train-auc:0.73586 eval-auc:nan
[20] train-auc:0.75517 eval-auc:nan
[30] train-auc:0.77235 eval-auc:nan
[40] train-auc:0.77564 eval-auc:nan
[49] train-auc:0.78747 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:04 | INFO | Training complete: Train AUC=0.7875, Test AUC=nan
08:54:04 | INFO | Model saved to models\xgboost_model.pkl
08:54:04 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:04 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59693 eval-auc:nan
[10] train-auc:0.71557 eval-auc:nan
[20] train-auc:0.75831 eval-auc:nan
[30] train-auc:0.78069 eval-auc:nan
[40] train-auc:0.78897 eval-auc:nan
[49] train-auc:0.80507 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:04 | INFO | Training complete: Train AUC=0.8051, Test AUC=nan
08:54:04 | INFO | Model saved to models\xgboost_model.pkl
08:54:04 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:04 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58496 eval-auc:nan
[10] train-auc:0.71445 eval-auc:nan
[20] train-auc:0.73869 eval-auc:nan
[30] train-auc:0.75962 eval-auc:nan
[40] train-auc:0.77052 eval-auc:nan
[49] train-auc:0.77559 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:04 | INFO | Training complete: Train AUC=0.7756, Test AUC=nan
08:54:04 | INFO | Model saved to models\xgboost_model.pkl
08:54:04 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:04 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61672 eval-auc:nan
[10] train-auc:0.72189 eval-auc:nan
[20] train-auc:0.74663 eval-auc:nan
[30] train-auc:0.75988 eval-auc:nan
[40] train-auc:0.76811 eval-auc:nan
[49] train-auc:0.77924 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:04 | INFO | Training complete: Train AUC=0.7792, Test AUC=nan
08:54:04 | INFO | Model saved to models\xgboost_model.pkl
08:54:04 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:04 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60677 eval-auc:nan
[10] train-auc:0.73373 eval-auc:nan
[20] train-auc:0.74586 eval-auc:nan
[30] train-auc:0.76032 eval-auc:nan
[40] train-auc:0.76883 eval-auc:nan
[49] train-auc:0.78380 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:04 | INFO | Training complete: Train AUC=0.7838, Test AUC=nan
08:54:04 | INFO | Model saved to models\xgboost_model.pkl
08:54:04 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:04 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61107 eval-auc:nan
[10] train-auc:0.73476 eval-auc:nan
[20] train-auc:0.75704 eval-auc:nan
[30] train-auc:0.78347 eval-auc:nan
[40] train-auc:0.79786 eval-auc:nan
[49] train-auc:0.80276 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:04 | INFO | Training complete: Train AUC=0.8028, Test AUC=nan
08:54:04 | INFO | Model saved to models\xgboost_model.pkl
08:54:04 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:04 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59196 eval-auc:nan
[10] train-auc:0.71890 eval-auc:nan
[20] train-auc:0.75012 eval-auc:nan
[30] train-auc:0.75933 eval-auc:nan
[40] train-auc:0.77703 eval-auc:nan
[49] train-auc:0.78454 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:04 | INFO | Training complete: Train AUC=0.7845, Test AUC=nan
08:54:04 | INFO | Model saved to models\xgboost_model.pkl
08:54:04 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:04 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59640 eval-auc:nan
[10] train-auc:0.72163 eval-auc:nan
[20] train-auc:0.73839 eval-auc:nan
[30] train-auc:0.76187 eval-auc:nan
[40] train-auc:0.77331 eval-auc:nan
[49] train-auc:0.78876 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:04 | INFO | Training complete: Train AUC=0.7888, Test AUC=nan
08:54:04 | INFO | Model saved to models\xgboost_model.pkl
08:54:04 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:04 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62603 eval-auc:nan
[10] train-auc:0.73800 eval-auc:nan
[20] train-auc:0.76229 eval-auc:nan
[30] train-auc:0.77816 eval-auc:nan
[40] train-auc:0.78826 eval-auc:nan
[49] train-auc:0.80562 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:04 | INFO | Training complete: Train AUC=0.8056, Test AUC=nan
08:54:04 | INFO | Model saved to models\xgboost_model.pkl
08:54:04 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:04 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60779 eval-auc:nan
[10] train-auc:0.73159 eval-auc:nan
[20] train-auc:0.77060 eval-auc:nan
[30] train-auc:0.77902 eval-auc:nan
[40] train-auc:0.79459 eval-auc:nan
[49] train-auc:0.80244 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:04 | INFO | Training complete: Train AUC=0.8024, Test AUC=nan
08:54:04 | INFO | Model saved to models\xgboost_model.pkl
08:54:04 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:04 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60662 eval-auc:nan
[10] train-auc:0.71568 eval-auc:nan
[20] train-auc:0.74645 eval-auc:nan
[30] train-auc:0.76677 eval-auc:nan
[40] train-auc:0.78159 eval-auc:nan
[49] train-auc:0.79704 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:04 | INFO | Training complete: Train AUC=0.7970, Test AUC=nan
08:54:04 | INFO | Model saved to models\xgboost_model.pkl
08:54:04 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:04 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59623 eval-auc:nan
[10] train-auc:0.71235 eval-auc:nan
[20] train-auc:0.74552 eval-auc:nan
[30] train-auc:0.77698 eval-auc:nan
[40] train-auc:0.79403 eval-auc:nan
[49] train-auc:0.80791 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:04 | INFO | Training complete: Train AUC=0.8079, Test AUC=nan
08:54:04 | INFO | Model saved to models\xgboost_model.pkl
08:54:04 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:04 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59888 eval-auc:nan
[10] train-auc:0.72695 eval-auc:nan
[20] train-auc:0.74232 eval-auc:nan
[30] train-auc:0.76668 eval-auc:nan
[40] train-auc:0.77413 eval-auc:nan
[49] train-auc:0.78510 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:04 | INFO | Training complete: Train AUC=0.7851, Test AUC=nan
08:54:04 | INFO | Model saved to models\xgboost_model.pkl
08:54:04 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:04 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.63122 eval-auc:nan
[10] train-auc:0.72776 eval-auc:nan
[20] train-auc:0.74767 eval-auc:nan
[30] train-auc:0.77189 eval-auc:nan
[40] train-auc:0.78595 eval-auc:nan
[49] train-auc:0.79027 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:04 | INFO | Training complete: Train AUC=0.7903, Test AUC=nan
08:54:04 | INFO | Model saved to models\xgboost_model.pkl
08:54:04 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:04 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59918 eval-auc:nan
[10] train-auc:0.70898 eval-auc:nan
[20] train-auc:0.74405 eval-auc:nan
[30] train-auc:0.76442 eval-auc:nan
[40] train-auc:0.78421 eval-auc:nan
[49] train-auc:0.79176 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:04 | INFO | Training complete: Train AUC=0.7918, Test AUC=nan
08:54:04 | INFO | Model saved to models\xgboost_model.pkl
08:54:04 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:04 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62748 eval-auc:nan
[10] train-auc:0.73512 eval-auc:nan
[20] train-auc:0.75265 eval-auc:nan
[30] train-auc:0.75979 eval-auc:nan
[40] train-auc:0.77616 eval-auc:nan
[49] train-auc:0.78833 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:04 | INFO | Training complete: Train AUC=0.7883, Test AUC=nan
08:54:04 | INFO | Model saved to models\xgboost_model.pkl
08:54:05 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:05 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61205 eval-auc:nan
[10] train-auc:0.72181 eval-auc:nan
[20] train-auc:0.74811 eval-auc:nan
[30] train-auc:0.75185 eval-auc:nan
[40] train-auc:0.76121 eval-auc:nan
[49] train-auc:0.77493 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:05 | INFO | Training complete: Train AUC=0.7749, Test AUC=nan
08:54:05 | INFO | Model saved to models\xgboost_model.pkl
08:54:05 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:05 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60534 eval-auc:nan
[10] train-auc:0.71542 eval-auc:nan
[20] train-auc:0.74795 eval-auc:nan
[30] train-auc:0.76755 eval-auc:nan
[40] train-auc:0.77324 eval-auc:nan
[49] train-auc:0.78779 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:05 | INFO | Training complete: Train AUC=0.7878, Test AUC=nan
08:54:05 | INFO | Model saved to models\xgboost_model.pkl
08:54:05 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:05 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62277 eval-auc:nan
[10] train-auc:0.75720 eval-auc:nan
[20] train-auc:0.76158 eval-auc:nan
[30] train-auc:0.77554 eval-auc:nan
[40] train-auc:0.78431 eval-auc:nan
[49] train-auc:0.79216 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:05 | INFO | Training complete: Train AUC=0.7922, Test AUC=nan
08:54:05 | INFO | Model saved to models\xgboost_model.pkl
08:54:05 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:05 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.63952 eval-auc:nan
[10] train-auc:0.73414 eval-auc:nan
[20] train-auc:0.75263 eval-auc:nan
[30] train-auc:0.77302 eval-auc:nan
[40] train-auc:0.78398 eval-auc:nan
[49] train-auc:0.79365 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:05 | INFO | Training complete: Train AUC=0.7937, Test AUC=nan
08:54:05 | INFO | Model saved to models\xgboost_model.pkl
08:54:05 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:05 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61150 eval-auc:nan
[10] train-auc:0.71968 eval-auc:nan
[20] train-auc:0.74658 eval-auc:nan
[30] train-auc:0.75580 eval-auc:nan
[40] train-auc:0.76780 eval-auc:nan
[49] train-auc:0.78313 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:05 | INFO | Training complete: Train AUC=0.7831, Test AUC=nan
08:54:05 | INFO | Model saved to models\xgboost_model.pkl
08:54:05 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:05 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59121 eval-auc:nan
[10] train-auc:0.72139 eval-auc:nan
[20] train-auc:0.74210 eval-auc:nan
[30] train-auc:0.76874 eval-auc:nan
[40] train-auc:0.78744 eval-auc:nan
[49] train-auc:0.80435 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:05 | INFO | Training complete: Train AUC=0.8044, Test AUC=nan
08:54:05 | INFO | Model saved to models\xgboost_model.pkl
08:54:05 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:05 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62495 eval-auc:nan
[10] train-auc:0.71286 eval-auc:nan
[20] train-auc:0.74365 eval-auc:nan
[30] train-auc:0.76206 eval-auc:nan
[40] train-auc:0.78000 eval-auc:nan
[49] train-auc:0.78689 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:05 | INFO | Training complete: Train AUC=0.7869, Test AUC=nan
08:54:05 | INFO | Model saved to models\xgboost_model.pkl
08:54:05 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:05 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61799 eval-auc:nan
[10] train-auc:0.73085 eval-auc:nan
[20] train-auc:0.75642 eval-auc:nan
[30] train-auc:0.77086 eval-auc:nan
[40] train-auc:0.78336 eval-auc:nan
[49] train-auc:0.80482 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:05 | INFO | Training complete: Train AUC=0.8048, Test AUC=nan
08:54:05 | INFO | Model saved to models\xgboost_model.pkl
08:54:05 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:05 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.63913 eval-auc:nan
[10] train-auc:0.73981 eval-auc:nan
[20] train-auc:0.76502 eval-auc:nan
[30] train-auc:0.77841 eval-auc:nan
[40] train-auc:0.78708 eval-auc:nan
[49] train-auc:0.79722 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:05 | INFO | Training complete: Train AUC=0.7972, Test AUC=nan
08:54:05 | INFO | Model saved to models\xgboost_model.pkl
08:54:05 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:05 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.56720 eval-auc:nan
[10] train-auc:0.74804 eval-auc:nan
[20] train-auc:0.77606 eval-auc:nan
[30] train-auc:0.78470 eval-auc:nan
[40] train-auc:0.79320 eval-auc:nan
[49] train-auc:0.79949 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:05 | INFO | Training complete: Train AUC=0.7995, Test AUC=nan
08:54:05 | INFO | Model saved to models\xgboost_model.pkl
08:54:05 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:05 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.63767 eval-auc:nan
[10] train-auc:0.73356 eval-auc:nan
[20] train-auc:0.76931 eval-auc:nan
[30] train-auc:0.77603 eval-auc:nan
[40] train-auc:0.78761 eval-auc:nan
[49] train-auc:0.81087 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:05 | INFO | Training complete: Train AUC=0.8109, Test AUC=nan
08:54:05 | INFO | Model saved to models\xgboost_model.pkl
08:54:05 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:05 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59227 eval-auc:nan
[10] train-auc:0.70443 eval-auc:nan
[20] train-auc:0.75264 eval-auc:nan
[30] train-auc:0.77043 eval-auc:nan
[40] train-auc:0.78209 eval-auc:nan
[49] train-auc:0.79030 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:05 | INFO | Training complete: Train AUC=0.7903, Test AUC=nan
08:54:05 | INFO | Model saved to models\xgboost_model.pkl
08:54:05 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:05 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62220 eval-auc:nan
[10] train-auc:0.71963 eval-auc:nan
[20] train-auc:0.74307 eval-auc:nan
[30] train-auc:0.76745 eval-auc:nan
[40] train-auc:0.78652 eval-auc:nan
[49] train-auc:0.79833 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:05 | INFO | Training complete: Train AUC=0.7983, Test AUC=nan
08:54:05 | INFO | Model saved to models\xgboost_model.pkl
08:54:05 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:05 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58955 eval-auc:nan
[10] train-auc:0.73387 eval-auc:nan
[20] train-auc:0.76492 eval-auc:nan
[30] train-auc:0.76914 eval-auc:nan
[40] train-auc:0.78498 eval-auc:nan
[49] train-auc:0.79957 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:06 | INFO | Training complete: Train AUC=0.7996, Test AUC=nan
08:54:06 | INFO | Model saved to models\xgboost_model.pkl
08:54:06 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:06 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:06] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60938 eval-auc:nan
[10] train-auc:0.71776 eval-auc:nan
[20] train-auc:0.74348 eval-auc:nan
[30] train-auc:0.76402 eval-auc:nan
[40] train-auc:0.77696 eval-auc:nan
[49] train-auc:0.79126 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:06 | INFO | Training complete: Train AUC=0.7913, Test AUC=nan
08:54:06 | INFO | Model saved to models\xgboost_model.pkl
08:54:06 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:06 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:06] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62323 eval-auc:nan
[10] train-auc:0.73901 eval-auc:nan
[20] train-auc:0.77618 eval-auc:nan
[30] train-auc:0.79067 eval-auc:nan
[40] train-auc:0.79789 eval-auc:nan
[49] train-auc:0.81138 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:06 | INFO | Training complete: Train AUC=0.8114, Test AUC=nan
08:54:06 | INFO | Model saved to models\xgboost_model.pkl
08:54:06 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:06 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:06] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62337 eval-auc:nan
[10] train-auc:0.73867 eval-auc:nan
[20] train-auc:0.76904 eval-auc:nan
[30] train-auc:0.78107 eval-auc:nan
[40] train-auc:0.79925 eval-auc:nan
[49] train-auc:0.81021 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:06 | INFO | Training complete: Train AUC=0.8102, Test AUC=nan
08:54:06 | INFO | Model saved to models\xgboost_model.pkl
08:54:06 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:06 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:06] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61166 eval-auc:nan
[10] train-auc:0.74134 eval-auc:nan
[20] train-auc:0.78185 eval-auc:nan
[30] train-auc:0.80042 eval-auc:nan
[40] train-auc:0.81374 eval-auc:nan
[49] train-auc:0.82431 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:06 | INFO | Training complete: Train AUC=0.8243, Test AUC=nan
08:54:06 | INFO | Model saved to models\xgboost_model.pkl
08:54:06 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:06 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:06] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61546 eval-auc:nan
[10] train-auc:0.77594 eval-auc:nan
[20] train-auc:0.79332 eval-auc:nan
[30] train-auc:0.80233 eval-auc:nan
[40] train-auc:0.80764 eval-auc:nan
[49] train-auc:0.82130 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:06 | INFO | Training complete: Train AUC=0.8213, Test AUC=nan
08:54:06 | INFO | Model saved to models\xgboost_model.pkl
08:54:06 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:06 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:06] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.65153 eval-auc:nan
[10] train-auc:0.74867 eval-auc:nan
[20] train-auc:0.77945 eval-auc:nan
[30] train-auc:0.79697 eval-auc:nan
[40] train-auc:0.81748 eval-auc:nan
[49] train-auc:0.83136 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:06 | INFO | Training complete: Train AUC=0.8314, Test AUC=nan
08:54:06 | INFO | Model saved to models\xgboost_model.pkl
08:54:06 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:06 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:06] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61052 eval-auc:nan
[10] train-auc:0.73060 eval-auc:nan
[20] train-auc:0.76308 eval-auc:nan
[30] train-auc:0.78958 eval-auc:nan
[40] train-auc:0.79512 eval-auc:nan
[49] train-auc:0.81402 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:06 | INFO | Training complete: Train AUC=0.8140, Test AUC=nan
08:54:06 | INFO | Model saved to models\xgboost_model.pkl
08:54:06 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:06 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:06] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60337 eval-auc:nan
[10] train-auc:0.73073 eval-auc:nan
[20] train-auc:0.75881 eval-auc:nan
[30] train-auc:0.77331 eval-auc:nan
[40] train-auc:0.79658 eval-auc:nan
[49] train-auc:0.81885 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:06 | INFO | Training complete: Train AUC=0.8188, Test AUC=nan
08:54:06 | INFO | Model saved to models\xgboost_model.pkl
08:54:06 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:06 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:06] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59897 eval-auc:nan
[10] train-auc:0.72551 eval-auc:nan
[20] train-auc:0.73547 eval-auc:nan
[30] train-auc:0.77519 eval-auc:nan
[40] train-auc:0.78258 eval-auc:nan
[49] train-auc:0.81252 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:06 | INFO | Training complete: Train AUC=0.8125, Test AUC=nan
08:54:06 | INFO | Model saved to models\xgboost_model.pkl
08:54:06 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:06 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:06] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62284 eval-auc:nan
[10] train-auc:0.71785 eval-auc:nan
[20] train-auc:0.75469 eval-auc:nan
[30] train-auc:0.77356 eval-auc:nan
[40] train-auc:0.79844 eval-auc:nan
[49] train-auc:0.81267 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:06 | INFO | Training complete: Train AUC=0.8127, Test AUC=nan
08:54:06 | INFO | Model saved to models\xgboost_model.pkl
08:54:06 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:06 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:06] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60058 eval-auc:nan
[10] train-auc:0.73796 eval-auc:nan
[20] train-auc:0.75724 eval-auc:nan
[30] train-auc:0.78310 eval-auc:nan
[40] train-auc:0.79742 eval-auc:nan
[49] train-auc:0.81018 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:06 | INFO | Training complete: Train AUC=0.8102, Test AUC=nan
08:54:07 | INFO | Model saved to models\xgboost_model.pkl
08:54:07 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:07 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:07] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59439 eval-auc:nan
[10] train-auc:0.74271 eval-auc:nan
[20] train-auc:0.76468 eval-auc:nan
[30] train-auc:0.77360 eval-auc:nan
[40] train-auc:0.79995 eval-auc:nan
[49] train-auc:0.81169 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:07 | INFO | Training complete: Train AUC=0.8117, Test AUC=nan
08:54:07 | INFO | Model saved to models\xgboost_model.pkl
08:54:07 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:07 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:07] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60259 eval-auc:nan
[10] train-auc:0.73422 eval-auc:nan
[20] train-auc:0.74413 eval-auc:nan
[30] train-auc:0.78035 eval-auc:nan
[40] train-auc:0.81061 eval-auc:nan
[49] train-auc:0.82145 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:07 | INFO | Training complete: Train AUC=0.8214, Test AUC=nan
08:54:07 | INFO | Model saved to models\xgboost_model.pkl
08:54:07 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:07 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:07] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60853 eval-auc:nan
[10] train-auc:0.71246 eval-auc:nan
[20] train-auc:0.76171 eval-auc:nan
[30] train-auc:0.78832 eval-auc:nan
[40] train-auc:0.79681 eval-auc:nan
[49] train-auc:0.80923 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:07 | INFO | Training complete: Train AUC=0.8092, Test AUC=nan
08:54:07 | INFO | Model saved to models\xgboost_model.pkl
08:54:07 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:07 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:07] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.63755 eval-auc:nan
[10] train-auc:0.72781 eval-auc:nan
[20] train-auc:0.75749 eval-auc:nan
[30] train-auc:0.78799 eval-auc:nan
[40] train-auc:0.79490 eval-auc:nan
[49] train-auc:0.81275 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:07 | INFO | Training complete: Train AUC=0.8127, Test AUC=nan
08:54:07 | INFO | Model saved to models\xgboost_model.pkl
08:54:07 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:07 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:07] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62377 eval-auc:nan
[10] train-auc:0.72534 eval-auc:nan
[20] train-auc:0.76922 eval-auc:nan
[30] train-auc:0.79245 eval-auc:nan
[40] train-auc:0.80396 eval-auc:nan
[49] train-auc:0.81762 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:07 | INFO | Training complete: Train AUC=0.8176, Test AUC=nan
08:54:07 | INFO | Model saved to models\xgboost_model.pkl
08:54:07 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:07 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:07] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59579 eval-auc:nan
[10] train-auc:0.72531 eval-auc:nan
[20] train-auc:0.76042 eval-auc:nan
[30] train-auc:0.77494 eval-auc:nan
[40] train-auc:0.78665 eval-auc:nan
[49] train-auc:0.80008 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:07 | INFO | Training complete: Train AUC=0.8001, Test AUC=nan
08:54:07 | INFO | Model saved to models\xgboost_model.pkl
08:54:07 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:07 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:07] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62551 eval-auc:nan
[10] train-auc:0.72821 eval-auc:nan
[20] train-auc:0.75326 eval-auc:nan
[30] train-auc:0.78709 eval-auc:nan
[40] train-auc:0.80578 eval-auc:nan
[49] train-auc:0.81529 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:07 | INFO | Training complete: Train AUC=0.8153, Test AUC=nan
08:54:07 | INFO | Model saved to models\xgboost_model.pkl
08:54:07 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:07 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:07] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59962 eval-auc:nan
[10] train-auc:0.73624 eval-auc:nan
[20] train-auc:0.77314 eval-auc:nan
[30] train-auc:0.79508 eval-auc:nan
[40] train-auc:0.80896 eval-auc:nan
[49] train-auc:0.81913 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:07 | INFO | Training complete: Train AUC=0.8191, Test AUC=nan
08:54:07 | INFO | Model saved to models\xgboost_model.pkl
08:54:07 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:07 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:07] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61935 eval-auc:nan
[10] train-auc:0.74558 eval-auc:nan
[20] train-auc:0.76006 eval-auc:nan
[30] train-auc:0.77639 eval-auc:nan
[40] train-auc:0.79222 eval-auc:nan
[49] train-auc:0.80714 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:07 | INFO | Training complete: Train AUC=0.8071, Test AUC=nan
08:54:07 | INFO | Model saved to models\xgboost_model.pkl
08:54:07 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:07 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:07] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58289 eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:08] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[10] train-auc:0.73097 eval-auc:nan
[20] train-auc:0.76715 eval-auc:nan
[30] train-auc:0.78989 eval-auc:nan
[40] train-auc:0.79088 eval-auc:nan
[49] train-auc:0.80586 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:08 | INFO | Training complete: Train AUC=0.8059, Test AUC=nan
08:54:08 | INFO | Model saved to models\xgboost_model.pkl
08:54:08 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:08 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:08] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60159 eval-auc:nan
[10] train-auc:0.72245 eval-auc:nan
[20] train-auc:0.75890 eval-auc:nan
[30] train-auc:0.79219 eval-auc:nan
[40] train-auc:0.80743 eval-auc:nan
[49] train-auc:0.81309 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:08 | INFO | Training complete: Train AUC=0.8131, Test AUC=nan
08:54:08 | INFO | Model saved to models\xgboost_model.pkl
08:54:08 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:08 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:08] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60832 eval-auc:nan
[10] train-auc:0.72768 eval-auc:nan
[20] train-auc:0.77328 eval-auc:nan
[30] train-auc:0.78311 eval-auc:nan
[40] train-auc:0.79987 eval-auc:nan
[49] train-auc:0.82509 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:08 | INFO | Training complete: Train AUC=0.8251, Test AUC=nan
08:54:08 | INFO | Model saved to models\xgboost_model.pkl
08:54:08 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:08 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:08] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58448 eval-auc:nan
[10] train-auc:0.71605 eval-auc:nan
[20] train-auc:0.74696 eval-auc:nan
[30] train-auc:0.78124 eval-auc:nan
[40] train-auc:0.80090 eval-auc:nan
[49] train-auc:0.81696 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:08 | INFO | Training complete: Train AUC=0.8170, Test AUC=nan
08:54:08 | INFO | Model saved to models\xgboost_model.pkl
08:54:08 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:08 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:08] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59727 eval-auc:nan
[10] train-auc:0.75823 eval-auc:nan
[20] train-auc:0.77682 eval-auc:nan
[30] train-auc:0.79460 eval-auc:nan
[40] train-auc:0.80149 eval-auc:nan
[49] train-auc:0.81574 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:08 | INFO | Training complete: Train AUC=0.8157, Test AUC=nan
08:54:08 | INFO | Model saved to models\xgboost_model.pkl
08:54:08 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:08 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:08] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58337 eval-auc:nan
[10] train-auc:0.70992 eval-auc:nan
[20] train-auc:0.76951 eval-auc:nan
[30] train-auc:0.78605 eval-auc:nan
[40] train-auc:0.78623 eval-auc:nan
[49] train-auc:0.78727 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:08 | INFO | Training complete: Train AUC=0.7873, Test AUC=nan
08:54:08 | INFO | Model saved to models\xgboost_model.pkl
08:54:08 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:08 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:08] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61816 eval-auc:nan
[10] train-auc:0.69998 eval-auc:nan
[20] train-auc:0.73782 eval-auc:nan
[30] train-auc:0.75099 eval-auc:nan
[40] train-auc:0.76887 eval-auc:nan
[49] train-auc:0.78246 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:08 | INFO | Training complete: Train AUC=0.7825, Test AUC=nan
08:54:08 | INFO | Model saved to models\xgboost_model.pkl
08:54:08 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:08 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:08] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59449 eval-auc:nan
[10] train-auc:0.70412 eval-auc:nan
[20] train-auc:0.75158 eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:09] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[30] train-auc:0.77511 eval-auc:nan
[40] train-auc:0.78799 eval-auc:nan
[49] train-auc:0.80667 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:09 | INFO | Training complete: Train AUC=0.8067, Test AUC=nan
08:54:09 | INFO | Model saved to models\xgboost_model.pkl
08:54:09 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:09 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:09] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60417 eval-auc:nan
[10] train-auc:0.69915 eval-auc:nan
[20] train-auc:0.72602 eval-auc:nan
[30] train-auc:0.75190 eval-auc:nan
[40] train-auc:0.77129 eval-auc:nan
[49] train-auc:0.78761 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:09 | INFO | Training complete: Train AUC=0.7876, Test AUC=nan
08:54:09 | INFO | Model saved to models\xgboost_model.pkl
08:54:09 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:09 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:09] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.57535 eval-auc:nan
[10] train-auc:0.69222 eval-auc:nan
[20] train-auc:0.74264 eval-auc:nan
[30] train-auc:0.77400 eval-auc:nan
[40] train-auc:0.77895 eval-auc:nan
[49] train-auc:0.79121 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:09 | INFO | Training complete: Train AUC=0.7912, Test AUC=nan
08:54:09 | INFO | Model saved to models\xgboost_model.pkl
08:54:09 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:09 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:09] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.56031 eval-auc:nan
[10] train-auc:0.73599 eval-auc:nan
[20] train-auc:0.77037 eval-auc:nan
[30] train-auc:0.78436 eval-auc:nan
[40] train-auc:0.79855 eval-auc:nan
[49] train-auc:0.80940 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:09 | INFO | Training complete: Train AUC=0.8094, Test AUC=nan
08:54:09 | INFO | Model saved to models\xgboost_model.pkl
08:54:09 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:09 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:09] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.57237 eval-auc:nan
[10] train-auc:0.70780 eval-auc:nan
[20] train-auc:0.73909 eval-auc:nan
[30] train-auc:0.76519 eval-auc:nan
[40] train-auc:0.78519 eval-auc:nan
[49] train-auc:0.80320 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:09 | INFO | Training complete: Train AUC=0.8032, Test AUC=nan
08:54:09 | INFO | Model saved to models\xgboost_model.pkl
08:54:09 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:09 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:09] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58321 eval-auc:nan
[10] train-auc:0.71285 eval-auc:nan
[20] train-auc:0.74984 eval-auc:nan
[30] train-auc:0.76907 eval-auc:nan
[40] train-auc:0.77337 eval-auc:nan
[49] train-auc:0.79265 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:09 | INFO | Training complete: Train AUC=0.7926, Test AUC=nan
08:54:09 | INFO | Model saved to models\xgboost_model.pkl
08:54:09 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:09 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:09] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60158 eval-auc:nan
[10] train-auc:0.71037 eval-auc:nan
[20] train-auc:0.73646 eval-auc:nan
[30] train-auc:0.76553 eval-auc:nan
[40] train-auc:0.77481 eval-auc:nan
[49] train-auc:0.78843 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:09 | INFO | Training complete: Train AUC=0.7884, Test AUC=nan
08:54:09 | INFO | Model saved to models\xgboost_model.pkl
08:54:09 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:09 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:09] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62264 eval-auc:nan
[10] train-auc:0.70782 eval-auc:nan
[20] train-auc:0.72733 eval-auc:nan
[30] train-auc:0.75854 eval-auc:nan
[40] train-auc:0.77389 eval-auc:nan
[49] train-auc:0.78593 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:09 | INFO | Training complete: Train AUC=0.7859, Test AUC=nan
08:54:09 | INFO | Model saved to models\xgboost_model.pkl
08:54:09 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:09 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:09] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61976 eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:10] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[10] train-auc:0.70945 eval-auc:nan
[20] train-auc:0.73834 eval-auc:nan
[30] train-auc:0.77960 eval-auc:nan
[40] train-auc:0.79266 eval-auc:nan
[49] train-auc:0.79501 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:10 | INFO | Training complete: Train AUC=0.7950, Test AUC=nan
08:54:10 | INFO | Model saved to models\xgboost_model.pkl
08:54:10 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:10 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:10] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59252 eval-auc:nan
[10] train-auc:0.70589 eval-auc:nan
[20] train-auc:0.74507 eval-auc:nan
[30] train-auc:0.76887 eval-auc:nan
[40] train-auc:0.78931 eval-auc:nan
[49] train-auc:0.80074 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:10 | INFO | Training complete: Train AUC=0.8007, Test AUC=nan
08:54:10 | INFO | Model saved to models\xgboost_model.pkl
08:54:10 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:10 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:10] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61071 eval-auc:nan
[10] train-auc:0.75049 eval-auc:nan
[20] train-auc:0.78163 eval-auc:nan
[30] train-auc:0.80391 eval-auc:nan
[40] train-auc:0.81564 eval-auc:nan
[49] train-auc:0.82443 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:10 | INFO | Training complete: Train AUC=0.8244, Test AUC=nan
08:54:10 | INFO | Model saved to models\xgboost_model.pkl
08:54:10 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:10 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:10] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59821 eval-auc:nan
[10] train-auc:0.74084 eval-auc:nan
[20] train-auc:0.78808 eval-auc:nan
[30] train-auc:0.80806 eval-auc:nan
[40] train-auc:0.82073 eval-auc:nan
[49] train-auc:0.83896 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:10 | INFO | Training complete: Train AUC=0.8390, Test AUC=nan
08:54:10 | INFO | Model saved to models\xgboost_model.pkl
08:54:10 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:10 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:10] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.57247 eval-auc:nan
[10] train-auc:0.73165 eval-auc:nan
[20] train-auc:0.77031 eval-auc:nan
[30] train-auc:0.78381 eval-auc:nan
[40] train-auc:0.79723 eval-auc:nan
[49] train-auc:0.81334 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:10 | INFO | Training complete: Train AUC=0.8133, Test AUC=nan
08:54:10 | INFO | Model saved to models\xgboost_model.pkl
08:54:10 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:10 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:10] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61086 eval-auc:nan
[10] train-auc:0.77570 eval-auc:nan
[20] train-auc:0.78849 eval-auc:nan
[30] train-auc:0.80995 eval-auc:nan
[40] train-auc:0.83325 eval-auc:nan
[49] train-auc:0.83580 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:10 | INFO | Training complete: Train AUC=0.8358, Test AUC=nan
08:54:10 | INFO | Model saved to models\xgboost_model.pkl
08:54:10 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:10 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:10] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.57315 eval-auc:nan
[10] train-auc:0.69806 eval-auc:nan
[20] train-auc:0.75288 eval-auc:nan
[30] train-auc:0.76632 eval-auc:nan
[40] train-auc:0.78813 eval-auc:nan
[49] train-auc:0.79826 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:10 | INFO | Training complete: Train AUC=0.7983, Test AUC=nan
08:54:10 | INFO | Model saved to models\xgboost_model.pkl
08:54:10 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:10 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:10] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59736 eval-auc:nan
[10] train-auc:0.71316 eval-auc:nan
[20] train-auc:0.73498 eval-auc:nan
[30] train-auc:0.74412 eval-auc:nan
[40] train-auc:0.76335 eval-auc:nan
[49] train-auc:0.77278 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:10 | INFO | Training complete: Train AUC=0.7728, Test AUC=nan
08:54:10 | INFO | Model saved to models\xgboost_model.pkl
08:54:10 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:10 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:10] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.63025 eval-auc:nan
[10] train-auc:0.73205 eval-auc:nan
[20] train-auc:0.76153 eval-auc:nan
[30] train-auc:0.78938 eval-auc:nan
[40] train-auc:0.79481 eval-auc:nan
[49] train-auc:0.80845 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:11 | INFO | Training complete: Train AUC=0.8084, Test AUC=nan
08:54:11 | INFO | Model saved to models\xgboost_model.pkl
08:54:11 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:11 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:11] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58652 eval-auc:nan
[10] train-auc:0.71165 eval-auc:nan
[20] train-auc:0.76374 eval-auc:nan
[30] train-auc:0.76661 eval-auc:nan
[40] train-auc:0.78437 eval-auc:nan
[49] train-auc:0.79387 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:11 | INFO | Training complete: Train AUC=0.7939, Test AUC=nan
08:54:11 | INFO | Model saved to models\xgboost_model.pkl
08:54:11 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:11 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:11] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.57896 eval-auc:nan
[10] train-auc:0.73647 eval-auc:nan
[20] train-auc:0.75505 eval-auc:nan
[30] train-auc:0.77726 eval-auc:nan
[40] train-auc:0.79303 eval-auc:nan
[49] train-auc:0.80600 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:11 | INFO | Training complete: Train AUC=0.8060, Test AUC=nan
08:54:11 | INFO | Model saved to models\xgboost_model.pkl
08:54:11 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:11 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:11] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62418 eval-auc:nan
[10] train-auc:0.73731 eval-auc:nan
[20] train-auc:0.75429 eval-auc:nan
[30] train-auc:0.78274 eval-auc:nan
[40] train-auc:0.78498 eval-auc:nan
[49] train-auc:0.79265 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:11 | INFO | Training complete: Train AUC=0.7926, Test AUC=nan
08:54:11 | INFO | Model saved to models\xgboost_model.pkl
08:54:11 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:11 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:11] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61034 eval-auc:nan
[10] train-auc:0.72273 eval-auc:nan
[20] train-auc:0.75488 eval-auc:nan
[30] train-auc:0.76962 eval-auc:nan
[40] train-auc:0.78570 eval-auc:nan
[49] train-auc:0.79578 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:11 | INFO | Training complete: Train AUC=0.7958, Test AUC=nan
08:54:11 | INFO | Model saved to models\xgboost_model.pkl
08:54:11 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:11 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:11] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60772 eval-auc:nan
[10] train-auc:0.72183 eval-auc:nan
[20] train-auc:0.74782 eval-auc:nan
[30] train-auc:0.76105 eval-auc:nan
[40] train-auc:0.77534 eval-auc:nan
[49] train-auc:0.78704 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:11 | INFO | Training complete: Train AUC=0.7870, Test AUC=nan
08:54:11 | INFO | Model saved to models\xgboost_model.pkl
08:54:11 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:11 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:11] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62688 eval-auc:nan
[10] train-auc:0.74607 eval-auc:nan
[20] train-auc:0.76822 eval-auc:nan
[30] train-auc:0.77687 eval-auc:nan
[40] train-auc:0.79932 eval-auc:nan
[49] train-auc:0.80608 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:11 | INFO | Training complete: Train AUC=0.8061, Test AUC=nan
08:54:11 | INFO | Model saved to models\xgboost_model.pkl
08:54:11 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:11 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:11] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61732 eval-auc:nan
[10] train-auc:0.74268 eval-auc:nan
[20] train-auc:0.77208 eval-auc:nan
[30] train-auc:0.77479 eval-auc:nan
[40] train-auc:0.78637 eval-auc:nan
[49] train-auc:0.79475 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:11 | INFO | Training complete: Train AUC=0.7947, Test AUC=nan
08:54:11 | INFO | Model saved to models\xgboost_model.pkl
08:54:11 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:11 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:11] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61071 eval-auc:nan
[10] train-auc:0.74389 eval-auc:nan
[20] train-auc:0.75840 eval-auc:nan
[30] train-auc:0.77398 eval-auc:nan
[40] train-auc:0.79303 eval-auc:nan
[49] train-auc:0.80196 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:11 | INFO | Training complete: Train AUC=0.8020, Test AUC=nan
08:54:11 | INFO | Model saved to models\xgboost_model.pkl
08:54:11 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:11 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:11] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58110 eval-auc:nan
[10] train-auc:0.74083 eval-auc:nan
[20] train-auc:0.75627 eval-auc:nan
[30] train-auc:0.76996 eval-auc:nan
[40] train-auc:0.77884 eval-auc:nan
[49] train-auc:0.79720 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:12 | INFO | Training complete: Train AUC=0.7972, Test AUC=nan
08:54:12 | INFO | Model saved to models\xgboost_model.pkl
08:54:12 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:12 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:12] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60591 eval-auc:nan
[10] train-auc:0.73640 eval-auc:nan
[20] train-auc:0.75642 eval-auc:nan
[30] train-auc:0.77684 eval-auc:nan
[40] train-auc:0.78954 eval-auc:nan
[49] train-auc:0.80910 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:12 | INFO | Training complete: Train AUC=0.8091, Test AUC=nan
08:54:12 | INFO | Model saved to models\xgboost_model.pkl
08:54:12 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:12 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:12] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58958 eval-auc:nan
[10] train-auc:0.72778 eval-auc:nan
[20] train-auc:0.77165 eval-auc:nan
[30] train-auc:0.78009 eval-auc:nan
[40] train-auc:0.79898 eval-auc:nan
[49] train-auc:0.81312 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:12 | INFO | Training complete: Train AUC=0.8131, Test AUC=nan
08:54:12 | INFO | Model saved to models\xgboost_model.pkl
08:54:12 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:12 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:12] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58496 eval-auc:nan
[10] train-auc:0.73422 eval-auc:nan
[20] train-auc:0.77071 eval-auc:nan
[30] train-auc:0.80120 eval-auc:nan
[40] train-auc:0.81193 eval-auc:nan
[49] train-auc:0.81838 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:12 | INFO | Training complete: Train AUC=0.8184, Test AUC=nan
08:54:12 | INFO | Model saved to models\xgboost_model.pkl
08:54:12 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:12 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:12] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60131 eval-auc:nan
[10] train-auc:0.68937 eval-auc:nan
[20] train-auc:0.73059 eval-auc:nan
[30] train-auc:0.75097 eval-auc:nan
[40] train-auc:0.78027 eval-auc:nan
[49] train-auc:0.79968 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:12 | INFO | Training complete: Train AUC=0.7997, Test AUC=nan
08:54:12 | INFO | Model saved to models\xgboost_model.pkl
08:54:12 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:12 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:12] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59077 eval-auc:nan
[10] train-auc:0.71064 eval-auc:nan
[20] train-auc:0.73499 eval-auc:nan
[30] train-auc:0.74932 eval-auc:nan
[40] train-auc:0.77136 eval-auc:nan
[49] train-auc:0.79431 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:12 | INFO | Training complete: Train AUC=0.7943, Test AUC=nan
08:54:12 | INFO | Model saved to models\xgboost_model.pkl
08:54:12 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:12 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:12] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58906 eval-auc:nan
[10] train-auc:0.69691 eval-auc:nan
[20] train-auc:0.74698 eval-auc:nan
[30] train-auc:0.76311 eval-auc:nan
[40] train-auc:0.77968 eval-auc:nan
[49] train-auc:0.79154 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:12 | INFO | Training complete: Train AUC=0.7915, Test AUC=nan
08:54:12 | INFO | Model saved to models\xgboost_model.pkl
08:54:12 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:12 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:12] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61274 eval-auc:nan
[10] train-auc:0.71801 eval-auc:nan
[20] train-auc:0.75490 eval-auc:nan
[30] train-auc:0.75974 eval-auc:nan
[40] train-auc:0.77631 eval-auc:nan
[49] train-auc:0.79649 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:12 | INFO | Training complete: Train AUC=0.7965, Test AUC=nan
08:54:12 | INFO | Model saved to models\xgboost_model.pkl
08:54:12 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:12 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:12] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62675 eval-auc:nan
[10] train-auc:0.73526 eval-auc:nan
[20] train-auc:0.76185 eval-auc:nan
[30] train-auc:0.78233 eval-auc:nan
[40] train-auc:0.78905 eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:13] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[49] train-auc:0.79576 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:13 | INFO | Training complete: Train AUC=0.7958, Test AUC=nan
08:54:13 | INFO | Model saved to models\xgboost_model.pkl
08:54:13 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:13 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:13] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58584 eval-auc:nan
[10] train-auc:0.73684 eval-auc:nan
[20] train-auc:0.76532 eval-auc:nan
[30] train-auc:0.79060 eval-auc:nan
[40] train-auc:0.79951 eval-auc:nan
[49] train-auc:0.81258 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:13 | INFO | Training complete: Train AUC=0.8126, Test AUC=nan
08:54:13 | INFO | Model saved to models\xgboost_model.pkl
08:54:13 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:13 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:13] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.56544 eval-auc:nan
[10] train-auc:0.72203 eval-auc:nan
[20] train-auc:0.74687 eval-auc:nan
[30] train-auc:0.77040 eval-auc:nan
[40] train-auc:0.79292 eval-auc:nan
[49] train-auc:0.80617 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:13 | INFO | Training complete: Train AUC=0.8062, Test AUC=nan
08:54:13 | INFO | Model saved to models\xgboost_model.pkl
08:54:13 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:13 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:13] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58184 eval-auc:nan
[10] train-auc:0.68947 eval-auc:nan
[20] train-auc:0.74093 eval-auc:nan
[30] train-auc:0.76676 eval-auc:nan
[40] train-auc:0.79377 eval-auc:nan
[49] train-auc:0.79824 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:13 | INFO | Training complete: Train AUC=0.7982, Test AUC=nan
08:54:13 | INFO | Model saved to models\xgboost_model.pkl
08:54:13 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:13 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:13] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59716 eval-auc:nan
[10] train-auc:0.73770 eval-auc:nan
[20] train-auc:0.76137 eval-auc:nan
[30] train-auc:0.78070 eval-auc:nan
[40] train-auc:0.79389 eval-auc:nan
[49] train-auc:0.80053 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:13 | INFO | Training complete: Train AUC=0.8005, Test AUC=nan
08:54:13 | INFO | Model saved to models\xgboost_model.pkl
08:54:13 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:13 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:13] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61680 eval-auc:nan
[10] train-auc:0.71606 eval-auc:nan
[20] train-auc:0.73973 eval-auc:nan
[30] train-auc:0.76190 eval-auc:nan
[40] train-auc:0.78364 eval-auc:nan
[49] train-auc:0.79952 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:13 | INFO | Training complete: Train AUC=0.7995, Test AUC=nan
08:54:13 | INFO | Model saved to models\xgboost_model.pkl
08:54:13 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:13 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:13] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58470 eval-auc:nan
[10] train-auc:0.73138 eval-auc:nan
[20] train-auc:0.76147 eval-auc:nan
[30] train-auc:0.78575 eval-auc:nan
[40] train-auc:0.80881 eval-auc:nan
[49] train-auc:0.81547 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:13 | INFO | Training complete: Train AUC=0.8155, Test AUC=nan
08:54:13 | INFO | Model saved to models\xgboost_model.pkl
08:54:13 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:13 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:13] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61794 eval-auc:nan
[10] train-auc:0.71862 eval-auc:nan
[20] train-auc:0.73959 eval-auc:nan
[30] train-auc:0.76630 eval-auc:nan
[40] train-auc:0.78060 eval-auc:nan
[49] train-auc:0.80141 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:13 | INFO | Training complete: Train AUC=0.8014, Test AUC=nan
08:54:13 | INFO | Model saved to models\xgboost_model.pkl
08:54:13 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:13 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:13] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62192 eval-auc:nan
[10] train-auc:0.70910 eval-auc:nan
[20] train-auc:0.73660 eval-auc:nan
[30] train-auc:0.75229 eval-auc:nan
[40] train-auc:0.77839 eval-auc:nan
[49] train-auc:0.79447 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:13 | INFO | Training complete: Train AUC=0.7945, Test AUC=nan
08:54:13 | INFO | Model saved to models\xgboost_model.pkl
08:54:13 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:13 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:13] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59790 eval-auc:nan
[10] train-auc:0.71110 eval-auc:nan
[20] train-auc:0.73864 eval-auc:nan
[30] train-auc:0.76435 eval-auc:nan
[40] train-auc:0.78813 eval-auc:nan
[49] train-auc:0.79573 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:13 | INFO | Training complete: Train AUC=0.7957, Test AUC=nan
08:54:13 | INFO | Model saved to models\xgboost_model.pkl
08:54:13 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:13 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:13] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58716 eval-auc:nan
[10] train-auc:0.70039 eval-auc:nan
[20] train-auc:0.73173 eval-auc:nan
[30] train-auc:0.75244 eval-auc:nan
[40] train-auc:0.76589 eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:14] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[49] train-auc:0.77832 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:14 | INFO | Training complete: Train AUC=0.7783, Test AUC=nan
08:54:14 | INFO | Model saved to models\xgboost_model.pkl
08:54:14 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:14 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:14] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60572 eval-auc:nan
[10] train-auc:0.70959 eval-auc:nan
[20] train-auc:0.73499 eval-auc:nan
[30] train-auc:0.74913 eval-auc:nan
[40] train-auc:0.76566 eval-auc:nan
[49] train-auc:0.77833 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:14 | INFO | Training complete: Train AUC=0.7783, Test AUC=nan
08:54:14 | INFO | Model saved to models\xgboost_model.pkl
08:54:14 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:14 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:14] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58467 eval-auc:nan
[10] train-auc:0.73488 eval-auc:nan
[20] train-auc:0.76174 eval-auc:nan
[30] train-auc:0.78199 eval-auc:nan
[40] train-auc:0.79030 eval-auc:nan
[49] train-auc:0.79666 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:14 | INFO | Training complete: Train AUC=0.7967, Test AUC=nan
08:54:14 | INFO | Model saved to models\xgboost_model.pkl
08:54:14 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:14 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:14] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60025 eval-auc:nan
[10] train-auc:0.74875 eval-auc:nan
[20] train-auc:0.78340 eval-auc:nan
[30] train-auc:0.79777 eval-auc:nan
[40] train-auc:0.81048 eval-auc:nan
[49] train-auc:0.82650 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:14 | INFO | Training complete: Train AUC=0.8265, Test AUC=nan
08:54:14 | INFO | Model saved to models\xgboost_model.pkl
08:54:14 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:14 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:14] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60908 eval-auc:nan
[10] train-auc:0.70826 eval-auc:nan
[20] train-auc:0.75811 eval-auc:nan
[30] train-auc:0.77492 eval-auc:nan
[40] train-auc:0.78340 eval-auc:nan
[49] train-auc:0.80489 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:14 | INFO | Training complete: Train AUC=0.8049, Test AUC=nan
08:54:14 | INFO | Model saved to models\xgboost_model.pkl
08:54:14 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:14 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:14] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62305 eval-auc:nan
[10] train-auc:0.73977 eval-auc:nan
[20] train-auc:0.77993 eval-auc:nan
[30] train-auc:0.79480 eval-auc:nan
[40] train-auc:0.80622 eval-auc:nan
[49] train-auc:0.81616 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:14 | INFO | Training complete: Train AUC=0.8162, Test AUC=nan
08:54:14 | INFO | Model saved to models\xgboost_model.pkl
08:54:14 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:14 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:14] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60336 eval-auc:nan
[10] train-auc:0.74016 eval-auc:nan
[20] train-auc:0.77037 eval-auc:nan
[30] train-auc:0.78700 eval-auc:nan
[40] train-auc:0.79611 eval-auc:nan
[49] train-auc:0.80730 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:14 | INFO | Training complete: Train AUC=0.8073, Test AUC=nan
08:54:14 | INFO | Model saved to models\xgboost_model.pkl
08:54:14 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:14 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:14] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62771 eval-auc:nan
[10] train-auc:0.72911 eval-auc:nan
[20] train-auc:0.76976 eval-auc:nan
[30] train-auc:0.77925 eval-auc:nan
[40] train-auc:0.79250 eval-auc:nan
[49] train-auc:0.81189 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:14 | INFO | Training complete: Train AUC=0.8119, Test AUC=nan
08:54:14 | INFO | Model saved to models\xgboost_model.pkl
08:54:14 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:14 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:14] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59090 eval-auc:nan
[10] train-auc:0.72648 eval-auc:nan
[20] train-auc:0.75725 eval-auc:nan
[30] train-auc:0.78042 eval-auc:nan
[40] train-auc:0.79005 eval-auc:nan
[49] train-auc:0.79928 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:14 | INFO | Training complete: Train AUC=0.7993, Test AUC=nan
08:54:14 | INFO | Model saved to models\xgboost_model.pkl
08:54:14 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:14 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:14] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58067 eval-auc:nan
[10] train-auc:0.72815 eval-auc:nan
[20] train-auc:0.76314 eval-auc:nan
[30] train-auc:0.77561 eval-auc:nan
[40] train-auc:0.78331 eval-auc:nan
[49] train-auc:0.79895 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:14 | INFO | Training complete: Train AUC=0.7990, Test AUC=nan
08:54:14 | INFO | Model saved to models\xgboost_model.pkl
08:54:14 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:14 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:14] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59361 eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:15] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[10] train-auc:0.72346 eval-auc:nan
[20] train-auc:0.75451 eval-auc:nan
[30] train-auc:0.76650 eval-auc:nan
[40] train-auc:0.77333 eval-auc:nan
[49] train-auc:0.78185 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:15 | INFO | Training complete: Train AUC=0.7819, Test AUC=nan
08:54:15 | INFO | Model saved to models\xgboost_model.pkl
08:54:15 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:15 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:15] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60225 eval-auc:nan
[10] train-auc:0.75589 eval-auc:nan
[20] train-auc:0.79933 eval-auc:nan
[30] train-auc:0.79717 eval-auc:nan
[40] train-auc:0.81155 eval-auc:nan
[49] train-auc:0.82061 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:15 | INFO | Training complete: Train AUC=0.8206, Test AUC=nan
08:54:15 | INFO | Model saved to models\xgboost_model.pkl
08:54:15 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:15 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:15] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61044 eval-auc:nan
[10] train-auc:0.73632 eval-auc:nan
[20] train-auc:0.77125 eval-auc:nan
[30] train-auc:0.79179 eval-auc:nan
[40] train-auc:0.80556 eval-auc:nan
[49] train-auc:0.81967 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:15 | INFO | Training complete: Train AUC=0.8197, Test AUC=nan
08:54:15 | INFO | Model saved to models\xgboost_model.pkl
08:54:15 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:15 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:15] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59773 eval-auc:nan
[10] train-auc:0.74045 eval-auc:nan
[20] train-auc:0.77537 eval-auc:nan
[30] train-auc:0.78821 eval-auc:nan
[40] train-auc:0.79300 eval-auc:nan
[49] train-auc:0.80560 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:15 | INFO | Training complete: Train AUC=0.8056, Test AUC=nan
08:54:15 | INFO | Model saved to models\xgboost_model.pkl
08:54:15 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:15 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:15] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60509 eval-auc:nan
[10] train-auc:0.73261 eval-auc:nan
[20] train-auc:0.74898 eval-auc:nan
[30] train-auc:0.77577 eval-auc:nan
[40] train-auc:0.80143 eval-auc:nan
[49] train-auc:0.80776 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:15 | INFO | Training complete: Train AUC=0.8078, Test AUC=nan
08:54:15 | INFO | Model saved to models\xgboost_model.pkl
08:54:15 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:15 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:15] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59273 eval-auc:nan
[10] train-auc:0.70327 eval-auc:nan
[20] train-auc:0.73105 eval-auc:nan
[30] train-auc:0.76408 eval-auc:nan
[40] train-auc:0.77841 eval-auc:nan
[49] train-auc:0.79826 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:15 | INFO | Training complete: Train AUC=0.7983, Test AUC=nan
08:54:15 | INFO | Model saved to models\xgboost_model.pkl
08:54:15 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:15 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:15] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61589 eval-auc:nan
[10] train-auc:0.72766 eval-auc:nan
[20] train-auc:0.76475 eval-auc:nan
[30] train-auc:0.79244 eval-auc:nan
[40] train-auc:0.79426 eval-auc:nan
[49] train-auc:0.80812 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:15 | INFO | Training complete: Train AUC=0.8081, Test AUC=nan
08:54:15 | INFO | Model saved to models\xgboost_model.pkl
08:54:15 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:15 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:15] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60277 eval-auc:nan
[10] train-auc:0.71655 eval-auc:nan
[20] train-auc:0.77139 eval-auc:nan
[30] train-auc:0.78916 eval-auc:nan
[40] train-auc:0.79352 eval-auc:nan
[49] train-auc:0.81575 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:15 | INFO | Training complete: Train AUC=0.8157, Test AUC=nan
08:54:15 | INFO | Model saved to models\xgboost_model.pkl
08:54:15 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:15 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:15] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59594 eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:16] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[10] train-auc:0.73544 eval-auc:nan
[20] train-auc:0.77444 eval-auc:nan
[30] train-auc:0.79599 eval-auc:nan
[40] train-auc:0.80112 eval-auc:nan
[49] train-auc:0.81206 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:16 | INFO | Training complete: Train AUC=0.8121, Test AUC=nan
08:54:16 | INFO | Model saved to models\xgboost_model.pkl
08:54:16 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:16 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:16] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59370 eval-auc:nan
[10] train-auc:0.72233 eval-auc:nan
[20] train-auc:0.76728 eval-auc:nan
[30] train-auc:0.78540 eval-auc:nan
[40] train-auc:0.81090 eval-auc:nan
[49] train-auc:0.82612 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:16 | INFO | Training complete: Train AUC=0.8261, Test AUC=nan
08:54:16 | INFO | Model saved to models\xgboost_model.pkl
08:54:16 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:16 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:16] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58058 eval-auc:nan
[10] train-auc:0.72036 eval-auc:nan
[20] train-auc:0.76034 eval-auc:nan
[30] train-auc:0.78914 eval-auc:nan
[40] train-auc:0.78862 eval-auc:nan
[49] train-auc:0.81527 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:16 | INFO | Training complete: Train AUC=0.8153, Test AUC=nan
08:54:16 | INFO | Model saved to models\xgboost_model.pkl
08:54:16 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:16 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:16] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.56349 eval-auc:nan
[10] train-auc:0.72454 eval-auc:nan
[20] train-auc:0.75251 eval-auc:nan
[30] train-auc:0.76662 eval-auc:nan
[40] train-auc:0.78404 eval-auc:nan
[49] train-auc:0.79939 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:16 | INFO | Training complete: Train AUC=0.7994, Test AUC=nan
08:54:16 | INFO | Model saved to models\xgboost_model.pkl
08:54:16 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:16 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:16] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62536 eval-auc:nan
[10] train-auc:0.78166 eval-auc:nan
[20] train-auc:0.80441 eval-auc:nan
[30] train-auc:0.81064 eval-auc:nan
[40] train-auc:0.81756 eval-auc:nan
[49] train-auc:0.82587 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:16 | INFO | Training complete: Train AUC=0.8259, Test AUC=nan
08:54:16 | INFO | Model saved to models\xgboost_model.pkl
08:54:16 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:16 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:16] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62035 eval-auc:nan
[10] train-auc:0.73565 eval-auc:nan
[20] train-auc:0.76004 eval-auc:nan
[30] train-auc:0.78144 eval-auc:nan
[40] train-auc:0.80278 eval-auc:nan
[49] train-auc:0.81366 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:16 | INFO | Training complete: Train AUC=0.8137, Test AUC=nan
08:54:16 | INFO | Model saved to models\xgboost_model.pkl
08:54:16 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:16 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:16] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60544 eval-auc:nan
[10] train-auc:0.74901 eval-auc:nan
[20] train-auc:0.78262 eval-auc:nan
[30] train-auc:0.80046 eval-auc:nan
[40] train-auc:0.80987 eval-auc:nan
[49] train-auc:0.81613 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:16 | INFO | Training complete: Train AUC=0.8161, Test AUC=nan
08:54:16 | INFO | Model saved to models\xgboost_model.pkl
08:54:16 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:16 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:16] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60875 eval-auc:nan
[10] train-auc:0.71353 eval-auc:nan
[20] train-auc:0.74911 eval-auc:nan
[30] train-auc:0.77565 eval-auc:nan
[40] train-auc:0.79604 eval-auc:nan
[49] train-auc:0.80603 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:16 | INFO | Training complete: Train AUC=0.8060, Test AUC=nan
08:54:16 | INFO | Model saved to models\xgboost_model.pkl
08:54:16 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:16 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:16] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60486 eval-auc:nan
[10] train-auc:0.74211 eval-auc:nan
[20] train-auc:0.76931 eval-auc:nan
[30] train-auc:0.78213 eval-auc:nan
[40] train-auc:0.80078 eval-auc:nan
[49] train-auc:0.81138 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:16 | INFO | Training complete: Train AUC=0.8114, Test AUC=nan
08:54:16 | INFO | Model saved to models\xgboost_model.pkl
08:54:16 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:16 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:16] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61124 eval-auc:nan
[10] train-auc:0.74830 eval-auc:nan
[20] train-auc:0.77556 eval-auc:nan
[30] train-auc:0.79560 eval-auc:nan
[40] train-auc:0.81400 eval-auc:nan
[49] train-auc:0.83058 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:16 | INFO | Training complete: Train AUC=0.8306, Test AUC=nan
08:54:16 | INFO | Model saved to models\xgboost_model.pkl
08:54:17 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:17 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:17] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61050 eval-auc:nan
[10] train-auc:0.73725 eval-auc:nan
[20] train-auc:0.78658 eval-auc:nan
[30] train-auc:0.80011 eval-auc:nan
[40] train-auc:0.81980 eval-auc:nan
[49] train-auc:0.82634 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:17 | INFO | Training complete: Train AUC=0.8263, Test AUC=nan
08:54:17 | INFO | Model saved to models\xgboost_model.pkl
08:54:17 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:17 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:17] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59007 eval-auc:nan
[10] train-auc:0.72025 eval-auc:nan
[20] train-auc:0.75405 eval-auc:nan
[30] train-auc:0.76530 eval-auc:nan
[40] train-auc:0.78143 eval-auc:nan
[49] train-auc:0.79535 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:17 | INFO | Training complete: Train AUC=0.7953, Test AUC=nan
08:54:17 | INFO | Model saved to models\xgboost_model.pkl
08:54:17 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:17 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:17] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61502 eval-auc:nan
[10] train-auc:0.72241 eval-auc:nan
[20] train-auc:0.74495 eval-auc:nan
[30] train-auc:0.76444 eval-auc:nan
[40] train-auc:0.78075 eval-auc:nan
[49] train-auc:0.79460 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:17 | INFO | Training complete: Train AUC=0.7946, Test AUC=nan
08:54:17 | INFO | Model saved to models\xgboost_model.pkl
08:54:17 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:17 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:17] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59667 eval-auc:nan
[10] train-auc:0.70052 eval-auc:nan
[20] train-auc:0.73642 eval-auc:nan
[30] train-auc:0.76280 eval-auc:nan
[40] train-auc:0.77787 eval-auc:nan
[49] train-auc:0.78930 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:17 | INFO | Training complete: Train AUC=0.7893, Test AUC=nan
08:54:17 | INFO | Model saved to models\xgboost_model.pkl
08:54:17 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:17 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:17] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60616 eval-auc:nan
[10] train-auc:0.72239 eval-auc:nan
[20] train-auc:0.75124 eval-auc:nan
[30] train-auc:0.76333 eval-auc:nan
[40] train-auc:0.77680 eval-auc:nan
[49] train-auc:0.78881 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:17 | INFO | Training complete: Train AUC=0.7888, Test AUC=nan
08:54:17 | INFO | Model saved to models\xgboost_model.pkl
08:54:17 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:17 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:17] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58260 eval-auc:nan
[10] train-auc:0.69158 eval-auc:nan
[20] train-auc:0.74179 eval-auc:nan
[30] train-auc:0.75748 eval-auc:nan
[40] train-auc:0.78480 eval-auc:nan
[49] train-auc:0.79951 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:17 | INFO | Training complete: Train AUC=0.7995, Test AUC=nan
08:54:17 | INFO | Model saved to models\xgboost_model.pkl
08:54:17 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:17 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:17] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60295 eval-auc:nan
[10] train-auc:0.71787 eval-auc:nan
[20] train-auc:0.74548 eval-auc:nan
[30] train-auc:0.76412 eval-auc:nan
[40] train-auc:0.78106 eval-auc:nan
[49] train-auc:0.79762 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:17 | INFO | Training complete: Train AUC=0.7976, Test AUC=nan
08:54:17 | INFO | Model saved to models\xgboost_model.pkl
08:54:17 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:17 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:17] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.63200 eval-auc:nan
[10] train-auc:0.73796 eval-auc:nan
[20] train-auc:0.75566 eval-auc:nan
[30] train-auc:0.77386 eval-auc:nan
[40] train-auc:0.78714 eval-auc:nan
[49] train-auc:0.79885 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:17 | INFO | Training complete: Train AUC=0.7989, Test AUC=nan
08:54:17 | INFO | Model saved to models\xgboost_model.pkl
08:54:17 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:17 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:17] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.55151 eval-auc:nan
[10] train-auc:0.71638 eval-auc:nan
[20] train-auc:0.76434 eval-auc:nan
[30] train-auc:0.78295 eval-auc:nan
[40] train-auc:0.79186 eval-auc:nan
[49] train-auc:0.81077 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:17 | INFO | Training complete: Train AUC=0.8108, Test AUC=nan
08:54:17 | INFO | Model saved to models\xgboost_model.pkl
08:54:17 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:17 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:17] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58289 eval-auc:nan
[10] train-auc:0.69458 eval-auc:nan
[20] train-auc:0.72122 eval-auc:nan
[30] train-auc:0.76020 eval-auc:nan
[40] train-auc:0.78361 eval-auc:nan
[49] train-auc:0.79281 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:17 | INFO | Training complete: Train AUC=0.7928, Test AUC=nan
08:54:17 | INFO | Model saved to models\xgboost_model.pkl
08:54:17 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:17 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:17] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58773 eval-auc:nan
[10] train-auc:0.70915 eval-auc:nan
[20] train-auc:0.73897 eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:18] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[30] train-auc:0.77324 eval-auc:nan
[40] train-auc:0.78043 eval-auc:nan
[49] train-auc:0.77908 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:18 | INFO | Training complete: Train AUC=0.7791, Test AUC=nan
08:54:18 | INFO | Model saved to models\xgboost_model.pkl
08:54:18 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:18 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:18] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59688 eval-auc:nan
[10] train-auc:0.70307 eval-auc:nan
[20] train-auc:0.73593 eval-auc:nan
[30] train-auc:0.75386 eval-auc:nan
[40] train-auc:0.77147 eval-auc:nan
[49] train-auc:0.77767 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:18 | INFO | Training complete: Train AUC=0.7777, Test AUC=nan
08:54:18 | INFO | Model saved to models\xgboost_model.pkl
08:54:18 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:18 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:18] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58860 eval-auc:nan
[10] train-auc:0.68382 eval-auc:nan
[20] train-auc:0.72234 eval-auc:nan
[30] train-auc:0.74722 eval-auc:nan
[40] train-auc:0.76107 eval-auc:nan
[49] train-auc:0.77164 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:18 | INFO | Training complete: Train AUC=0.7716, Test AUC=nan
08:54:18 | INFO | Model saved to models\xgboost_model.pkl
08:54:18 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:18 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:18] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60582 eval-auc:nan
[10] train-auc:0.72087 eval-auc:nan
[20] train-auc:0.75132 eval-auc:nan
[30] train-auc:0.75836 eval-auc:nan
[40] train-auc:0.76906 eval-auc:nan
[49] train-auc:0.77916 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:18 | INFO | Training complete: Train AUC=0.7792, Test AUC=nan
08:54:18 | INFO | Model saved to models\xgboost_model.pkl
08:54:18 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:18 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:18] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60521 eval-auc:nan
[10] train-auc:0.72012 eval-auc:nan
[20] train-auc:0.74708 eval-auc:nan
[30] train-auc:0.76421 eval-auc:nan
[40] train-auc:0.78048 eval-auc:nan
[49] train-auc:0.79736 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:18 | INFO | Training complete: Train AUC=0.7974, Test AUC=nan
08:54:18 | INFO | Model saved to models\xgboost_model.pkl
08:54:18 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:18 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:18] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60799 eval-auc:nan
[10] train-auc:0.71899 eval-auc:nan
[20] train-auc:0.75767 eval-auc:nan
[30] train-auc:0.76979 eval-auc:nan
[40] train-auc:0.79163 eval-auc:nan
[49] train-auc:0.80088 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:18 | INFO | Training complete: Train AUC=0.8009, Test AUC=nan
08:54:18 | INFO | Model saved to models\xgboost_model.pkl
08:54:18 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:18 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:18] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62377 eval-auc:nan
[10] train-auc:0.71175 eval-auc:nan
[20] train-auc:0.74277 eval-auc:nan
[30] train-auc:0.76226 eval-auc:nan
[40] train-auc:0.77391 eval-auc:nan
[49] train-auc:0.78154 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:18 | INFO | Training complete: Train AUC=0.7815, Test AUC=nan
08:54:18 | INFO | Model saved to models\xgboost_model.pkl
08:54:18 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:18 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:18] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60491 eval-auc:nan
[10] train-auc:0.71732 eval-auc:nan
[20] train-auc:0.73637 eval-auc:nan
[30] train-auc:0.76178 eval-auc:nan
[40] train-auc:0.77098 eval-auc:nan
[49] train-auc:0.79057 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:18 | INFO | Training complete: Train AUC=0.7906, Test AUC=nan
08:54:18 | INFO | Model saved to models\xgboost_model.pkl
08:54:18 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:18 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:18] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60088 eval-auc:nan
[10] train-auc:0.71712 eval-auc:nan
[20] train-auc:0.73691 eval-auc:nan
[30] train-auc:0.75740 eval-auc:nan
[40] train-auc:0.77895 eval-auc:nan
[49] train-auc:0.78947 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:18 | INFO | Training complete: Train AUC=0.7895, Test AUC=nan
08:54:18 | INFO | Model saved to models\xgboost_model.pkl
08:54:18 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:18 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:18] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58789 eval-auc:nan
[10] train-auc:0.72688 eval-auc:nan
[20] train-auc:0.75299 eval-auc:nan
[30] train-auc:0.78038 eval-auc:nan
[40] train-auc:0.79660 eval-auc:nan
[49] train-auc:0.80917 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:18 | INFO | Training complete: Train AUC=0.8092, Test AUC=nan
08:54:18 | INFO | Model saved to models\xgboost_model.pkl
08:54:18 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:18 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:18] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61598 eval-auc:nan
[10] train-auc:0.70822 eval-auc:nan
[20] train-auc:0.73139 eval-auc:nan
[30] train-auc:0.74528 eval-auc:nan
[40] train-auc:0.75819 eval-auc:nan
[49] train-auc:0.77398 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:19 | INFO | Training complete: Train AUC=0.7740, Test AUC=nan
08:54:19 | INFO | Model saved to models\xgboost_model.pkl
08:54:19 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:19 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:19] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59767 eval-auc:nan
[10] train-auc:0.71330 eval-auc:nan
[20] train-auc:0.73522 eval-auc:nan
[30] train-auc:0.76301 eval-auc:nan
[40] train-auc:0.77457 eval-auc:nan
[49] train-auc:0.79120 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:19 | INFO | Training complete: Train AUC=0.7912, Test AUC=nan
08:54:19 | INFO | Model saved to models\xgboost_model.pkl
08:54:19 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:19 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:19] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.57988 eval-auc:nan
[10] train-auc:0.70174 eval-auc:nan
[20] train-auc:0.73308 eval-auc:nan
[30] train-auc:0.76300 eval-auc:nan
[40] train-auc:0.77397 eval-auc:nan
[49] train-auc:0.79388 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:19 | INFO | Training complete: Train AUC=0.7939, Test AUC=nan
08:54:19 | INFO | Model saved to models\xgboost_model.pkl
08:54:19 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:19 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:19] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61081 eval-auc:nan
[10] train-auc:0.67913 eval-auc:nan
[20] train-auc:0.71390 eval-auc:nan
[30] train-auc:0.74361 eval-auc:nan
[40] train-auc:0.76993 eval-auc:nan
[49] train-auc:0.78383 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:19 | INFO | Training complete: Train AUC=0.7838, Test AUC=nan
08:54:19 | INFO | Model saved to models\xgboost_model.pkl
08:54:19 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:19 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:19] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.57255 eval-auc:nan
[10] train-auc:0.71228 eval-auc:nan
[20] train-auc:0.73502 eval-auc:nan
[30] train-auc:0.74854 eval-auc:nan
[40] train-auc:0.76543 eval-auc:nan
[49] train-auc:0.77475 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:19 | INFO | Training complete: Train AUC=0.7748, Test AUC=nan
08:54:19 | INFO | Model saved to models\xgboost_model.pkl
08:54:19 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:19 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:19] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60085 eval-auc:nan
[10] train-auc:0.69657 eval-auc:nan
[20] train-auc:0.71456 eval-auc:nan
[30] train-auc:0.74570 eval-auc:nan
[40] train-auc:0.76565 eval-auc:nan
[49] train-auc:0.77503 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:19 | INFO | Training complete: Train AUC=0.7750, Test AUC=nan
08:54:19 | INFO | Model saved to models\xgboost_model.pkl
08:54:19 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:19 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:19] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.57024 eval-auc:nan
[10] train-auc:0.70914 eval-auc:nan
[20] train-auc:0.73352 eval-auc:nan
[30] train-auc:0.74339 eval-auc:nan
[40] train-auc:0.77528 eval-auc:nan
[49] train-auc:0.78304 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:19 | INFO | Training complete: Train AUC=0.7830, Test AUC=nan
08:54:19 | INFO | Model saved to models\xgboost_model.pkl
08:54:19 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:19 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:19] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59540 eval-auc:nan
[10] train-auc:0.71927 eval-auc:nan
[20] train-auc:0.74406 eval-auc:nan
[30] train-auc:0.76638 eval-auc:nan
[40] train-auc:0.78232 eval-auc:nan
[49] train-auc:0.78659 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:19 | INFO | Training complete: Train AUC=0.7866, Test AUC=nan
08:54:19 | INFO | Model saved to models\xgboost_model.pkl
08:54:19 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:19 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:19] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60596 eval-auc:nan
[10] train-auc:0.73112 eval-auc:nan
[20] train-auc:0.75009 eval-auc:nan
[30] train-auc:0.76380 eval-auc:nan
[40] train-auc:0.78176 eval-auc:nan
[49] train-auc:0.78909 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:19 | INFO | Training complete: Train AUC=0.7891, Test AUC=nan
08:54:19 | INFO | Model saved to models\xgboost_model.pkl
08:54:19 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:19 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:19] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60522 eval-auc:nan
[10] train-auc:0.74166 eval-auc:nan
[20] train-auc:0.75949 eval-auc:nan
[30] train-auc:0.79360 eval-auc:nan
[40] train-auc:0.79524 eval-auc:nan
[49] train-auc:0.81023 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:19 | INFO | Training complete: Train AUC=0.8102, Test AUC=nan
08:54:19 | INFO | Model saved to models\xgboost_model.pkl
08:54:19 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:19 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:19] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59997 eval-auc:nan
[10] train-auc:0.73955 eval-auc:nan
[20] train-auc:0.75448 eval-auc:nan
[30] train-auc:0.77247 eval-auc:nan
[40] train-auc:0.78000 eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:20] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[49] train-auc:0.79077 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:20 | INFO | Training complete: Train AUC=0.7908, Test AUC=nan
08:54:20 | INFO | Model saved to models\xgboost_model.pkl
08:54:20 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:20 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:20] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58106 eval-auc:nan
[10] train-auc:0.74387 eval-auc:nan
[20] train-auc:0.74495 eval-auc:nan
[30] train-auc:0.76251 eval-auc:nan
[40] train-auc:0.77773 eval-auc:nan
[49] train-auc:0.79525 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:20 | INFO | Training complete: Train AUC=0.7952, Test AUC=nan
08:54:20 | INFO | Model saved to models\xgboost_model.pkl
08:54:20 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:20 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:20] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58621 eval-auc:nan
[10] train-auc:0.74838 eval-auc:nan
[20] train-auc:0.76948 eval-auc:nan
[30] train-auc:0.77170 eval-auc:nan
[40] train-auc:0.78669 eval-auc:nan
[49] train-auc:0.80217 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:20 | INFO | Training complete: Train AUC=0.8022, Test AUC=nan
08:54:20 | INFO | Model saved to models\xgboost_model.pkl
08:54:20 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:20 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:20] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59705 eval-auc:nan
[10] train-auc:0.73947 eval-auc:nan
[20] train-auc:0.76035 eval-auc:nan
[30] train-auc:0.78331 eval-auc:nan
[40] train-auc:0.79839 eval-auc:nan
[49] train-auc:0.80733 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:20 | INFO | Training complete: Train AUC=0.8073, Test AUC=nan
08:54:20 | INFO | Model saved to models\xgboost_model.pkl
08:54:20 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:20 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:20] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62888 eval-auc:nan
[10] train-auc:0.77790 eval-auc:nan
[20] train-auc:0.79112 eval-auc:nan
[30] train-auc:0.79420 eval-auc:nan
[40] train-auc:0.80377 eval-auc:nan
[49] train-auc:0.81198 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:20 | INFO | Training complete: Train AUC=0.8120, Test AUC=nan
08:54:20 | INFO | Model saved to models\xgboost_model.pkl
08:54:20 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:20 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:20] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58766 eval-auc:nan
[10] train-auc:0.73931 eval-auc:nan
[20] train-auc:0.78821 eval-auc:nan
[30] train-auc:0.80713 eval-auc:nan
[40] train-auc:0.80992 eval-auc:nan
[49] train-auc:0.82168 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:20 | INFO | Training complete: Train AUC=0.8217, Test AUC=nan
08:54:20 | INFO | Model saved to models\xgboost_model.pkl
08:54:20 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:20 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:20] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.57346 eval-auc:nan
[10] train-auc:0.76528 eval-auc:nan
[20] train-auc:0.77833 eval-auc:nan
[30] train-auc:0.81216 eval-auc:nan
[40] train-auc:0.82894 eval-auc:nan
[49] train-auc:0.83959 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:20 | INFO | Training complete: Train AUC=0.8396, Test AUC=nan
08:54:20 | INFO | Model saved to models\xgboost_model.pkl
08:54:20 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:20 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:20] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62849 eval-auc:nan
[10] train-auc:0.75871 eval-auc:nan
[20] train-auc:0.78298 eval-auc:nan
[30] train-auc:0.80330 eval-auc:nan
[40] train-auc:0.81261 eval-auc:nan
[49] train-auc:0.82718 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:20 | INFO | Training complete: Train AUC=0.8272, Test AUC=nan
08:54:20 | INFO | Model saved to models\xgboost_model.pkl
08:54:20 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:20 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:20] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60352 eval-auc:nan
[10] train-auc:0.75021 eval-auc:nan
[20] train-auc:0.78133 eval-auc:nan
[30] train-auc:0.80449 eval-auc:nan
[40] train-auc:0.81345 eval-auc:nan
[49] train-auc:0.82141 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:20 | INFO | Training complete: Train AUC=0.8214, Test AUC=nan
08:54:20 | INFO | Model saved to models\xgboost_model.pkl
08:54:20 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:20 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:20] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60313 eval-auc:nan
[10] train-auc:0.72661 eval-auc:nan
[20] train-auc:0.76145 eval-auc:nan
[30] train-auc:0.78157 eval-auc:nan
[40] train-auc:0.79408 eval-auc:nan
[49] train-auc:0.79936 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:20 | INFO | Training complete: Train AUC=0.7994, Test AUC=nan
08:54:20 | INFO | Model saved to models\xgboost_model.pkl
08:54:20 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:20 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:20] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62491 eval-auc:nan
[10] train-auc:0.73262 eval-auc:nan
[20] train-auc:0.75848 eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:21] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[30] train-auc:0.78662 eval-auc:nan
[40] train-auc:0.79843 eval-auc:nan
[49] train-auc:0.80254 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:21 | INFO | Training complete: Train AUC=0.8025, Test AUC=nan
08:54:21 | INFO | Model saved to models\xgboost_model.pkl
08:54:21 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:21 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:21] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60803 eval-auc:nan
[10] train-auc:0.75171 eval-auc:nan
[20] train-auc:0.76744 eval-auc:nan
[30] train-auc:0.78326 eval-auc:nan
[40] train-auc:0.79761 eval-auc:nan
[49] train-auc:0.80276 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:21 | INFO | Training complete: Train AUC=0.8028, Test AUC=nan
08:54:21 | INFO | Model saved to models\xgboost_model.pkl
08:54:21 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:21 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:21] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61595 eval-auc:nan
[10] train-auc:0.74799 eval-auc:nan
[20] train-auc:0.77954 eval-auc:nan
[30] train-auc:0.80286 eval-auc:nan
[40] train-auc:0.81213 eval-auc:nan
[49] train-auc:0.81713 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:21 | INFO | Training complete: Train AUC=0.8171, Test AUC=nan
08:54:21 | INFO | Model saved to models\xgboost_model.pkl
08:54:21 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:21 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:21] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59247 eval-auc:nan
[10] train-auc:0.74075 eval-auc:nan
[20] train-auc:0.76100 eval-auc:nan
[30] train-auc:0.78358 eval-auc:nan
[40] train-auc:0.79208 eval-auc:nan
[49] train-auc:0.81172 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:21 | INFO | Training complete: Train AUC=0.8117, Test AUC=nan
08:54:21 | INFO | Model saved to models\xgboost_model.pkl
08:54:21 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:21 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:21] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62501 eval-auc:nan
[10] train-auc:0.73900 eval-auc:nan
[20] train-auc:0.76904 eval-auc:nan
[30] train-auc:0.78823 eval-auc:nan
[40] train-auc:0.80905 eval-auc:nan
[49] train-auc:0.81956 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:21 | INFO | Training complete: Train AUC=0.8196, Test AUC=nan
08:54:21 | INFO | Model saved to models\xgboost_model.pkl
08:54:21 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:21 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:21] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.63394 eval-auc:nan
[10] train-auc:0.76201 eval-auc:nan
[20] train-auc:0.76502 eval-auc:nan
[30] train-auc:0.79011 eval-auc:nan
[40] train-auc:0.80381 eval-auc:nan
[49] train-auc:0.80967 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:21 | INFO | Training complete: Train AUC=0.8097, Test AUC=nan
08:54:21 | INFO | Model saved to models\xgboost_model.pkl
08:54:21 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:21 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:21] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60953 eval-auc:nan
[10] train-auc:0.74038 eval-auc:nan
[20] train-auc:0.77163 eval-auc:nan
[30] train-auc:0.79689 eval-auc:nan
[40] train-auc:0.81636 eval-auc:nan
[49] train-auc:0.83423 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:21 | INFO | Training complete: Train AUC=0.8342, Test AUC=nan
08:54:21 | INFO | Model saved to models\xgboost_model.pkl
08:54:21 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:21 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:21] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58307 eval-auc:nan
[10] train-auc:0.72438 eval-auc:nan
[20] train-auc:0.75433 eval-auc:nan
[30] train-auc:0.76545 eval-auc:nan
[40] train-auc:0.79888 eval-auc:nan
[49] train-auc:0.81112 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:21 | INFO | Training complete: Train AUC=0.8111, Test AUC=nan
08:54:21 | INFO | Model saved to models\xgboost_model.pkl
08:54:21 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:21 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:21] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60998 eval-auc:nan
[10] train-auc:0.72666 eval-auc:nan
[20] train-auc:0.75603 eval-auc:nan
[30] train-auc:0.78573 eval-auc:nan
[40] train-auc:0.81097 eval-auc:nan
[49] train-auc:0.82424 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:21 | INFO | Training complete: Train AUC=0.8242, Test AUC=nan
08:54:21 | INFO | Model saved to models\xgboost_model.pkl
08:54:21 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:21 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:21] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.58318 eval-auc:nan
[10] train-auc:0.72923 eval-auc:nan
[20] train-auc:0.77906 eval-auc:nan
[30] train-auc:0.79240 eval-auc:nan
[40] train-auc:0.80429 eval-auc:nan
[49] train-auc:0.81426 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:21 | INFO | Training complete: Train AUC=0.8143, Test AUC=nan
08:54:21 | INFO | Model saved to models\xgboost_model.pkl
08:54:21 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:21 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:21] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60979 eval-auc:nan
[10] train-auc:0.71432 eval-auc:nan
[20] train-auc:0.74102 eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:22] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[30] train-auc:0.76768 eval-auc:nan
[40] train-auc:0.78190 eval-auc:nan
[49] train-auc:0.79541 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:22 | INFO | Training complete: Train AUC=0.7954, Test AUC=nan
08:54:22 | INFO | Model saved to models\xgboost_model.pkl
08:54:22 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:22 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:22] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60319 eval-auc:nan
[10] train-auc:0.75208 eval-auc:nan
[20] train-auc:0.77528 eval-auc:nan
[30] train-auc:0.79264 eval-auc:nan
[40] train-auc:0.79898 eval-auc:nan
[49] train-auc:0.80820 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:22 | INFO | Training complete: Train AUC=0.8082, Test AUC=nan
08:54:22 | INFO | Model saved to models\xgboost_model.pkl
08:54:22 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:22 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:22] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60090 eval-auc:nan
[10] train-auc:0.71684 eval-auc:nan
[20] train-auc:0.73841 eval-auc:nan
[30] train-auc:0.75852 eval-auc:nan
[40] train-auc:0.77850 eval-auc:nan
[49] train-auc:0.78957 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:22 | INFO | Training complete: Train AUC=0.7896, Test AUC=nan
08:54:22 | INFO | Model saved to models\xgboost_model.pkl
08:54:22 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:22 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:22] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60470 eval-auc:nan
[10] train-auc:0.71534 eval-auc:nan
[20] train-auc:0.74616 eval-auc:nan
[30] train-auc:0.76268 eval-auc:nan
[40] train-auc:0.78869 eval-auc:nan
[49] train-auc:0.78944 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:22 | INFO | Training complete: Train AUC=0.7894, Test AUC=nan
08:54:22 | INFO | Model saved to models\xgboost_model.pkl
08:54:22 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:22 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:22] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61586 eval-auc:nan
[10] train-auc:0.72851 eval-auc:nan
[20] train-auc:0.74350 eval-auc:nan
[30] train-auc:0.74620 eval-auc:nan
[40] train-auc:0.75469 eval-auc:nan
[49] train-auc:0.77162 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:22 | INFO | Training complete: Train AUC=0.7716, Test AUC=nan
08:54:22 | INFO | Model saved to models\xgboost_model.pkl
08:54:22 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:22 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:22] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62284 eval-auc:nan
[10] train-auc:0.72407 eval-auc:nan
[20] train-auc:0.75050 eval-auc:nan
[30] train-auc:0.74938 eval-auc:nan
[40] train-auc:0.76005 eval-auc:nan
[49] train-auc:0.77428 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:22 | INFO | Training complete: Train AUC=0.7743, Test AUC=nan
08:54:22 | INFO | Model saved to models\xgboost_model.pkl
08:54:22 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:22 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:22] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62182 eval-auc:nan
[10] train-auc:0.73667 eval-auc:nan
[20] train-auc:0.75940 eval-auc:nan
[30] train-auc:0.77804 eval-auc:nan
[40] train-auc:0.78611 eval-auc:nan
[49] train-auc:0.79566 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:22 | INFO | Training complete: Train AUC=0.7957, Test AUC=nan
08:54:22 | INFO | Model saved to models\xgboost_model.pkl
08:54:22 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:22 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:22] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.63954 eval-auc:nan
[10] train-auc:0.73519 eval-auc:nan
[20] train-auc:0.77194 eval-auc:nan
[30] train-auc:0.78262 eval-auc:nan
[40] train-auc:0.79268 eval-auc:nan
[49] train-auc:0.79835 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:22 | INFO | Training complete: Train AUC=0.7983, Test AUC=nan
08:54:22 | INFO | Model saved to models\xgboost_model.pkl
08:54:22 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:22 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:22] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60190 eval-auc:nan
[10] train-auc:0.72704 eval-auc:nan
[20] train-auc:0.75398 eval-auc:nan
[30] train-auc:0.77765 eval-auc:nan
[40] train-auc:0.78947 eval-auc:nan
[49] train-auc:0.80493 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:22 | INFO | Training complete: Train AUC=0.8049, Test AUC=nan
08:54:22 | INFO | Model saved to models\xgboost_model.pkl
08:54:22 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:22 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:22] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62254 eval-auc:nan
[10] train-auc:0.75280 eval-auc:nan
[20] train-auc:0.77255 eval-auc:nan
[30] train-auc:0.79260 eval-auc:nan
[40] train-auc:0.79349 eval-auc:nan
[49] train-auc:0.80807 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:22 | INFO | Training complete: Train AUC=0.8081, Test AUC=nan
08:54:22 | INFO | Model saved to models\xgboost_model.pkl
08:54:22 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:22 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:22] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61847 eval-auc:nan
[10] train-auc:0.72788 eval-auc:nan
[20] train-auc:0.75636 eval-auc:nan
[30] train-auc:0.77244 eval-auc:nan
[40] train-auc:0.79711 eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:23] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[49] train-auc:0.80845 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:23 | INFO | Training complete: Train AUC=0.8085, Test AUC=nan
08:54:23 | INFO | Model saved to models\xgboost_model.pkl
08:54:23 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:23 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:23] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.63166 eval-auc:nan
[10] train-auc:0.72233 eval-auc:nan
[20] train-auc:0.73577 eval-auc:nan
[30] train-auc:0.75872 eval-auc:nan
[40] train-auc:0.77652 eval-auc:nan
[49] train-auc:0.78654 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:23 | INFO | Training complete: Train AUC=0.7865, Test AUC=nan
08:54:23 | INFO | Model saved to models\xgboost_model.pkl
08:54:23 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:23 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:23] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62698 eval-auc:nan
[10] train-auc:0.73889 eval-auc:nan
[20] train-auc:0.75839 eval-auc:nan
[30] train-auc:0.77451 eval-auc:nan
[40] train-auc:0.78084 eval-auc:nan
[49] train-auc:0.78975 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:23 | INFO | Training complete: Train AUC=0.7898, Test AUC=nan
08:54:23 | INFO | Model saved to models\xgboost_model.pkl
08:54:23 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:23 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:23] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.63191 eval-auc:nan
[10] train-auc:0.73347 eval-auc:nan
[20] train-auc:0.75524 eval-auc:nan
[30] train-auc:0.77779 eval-auc:nan
[40] train-auc:0.79315 eval-auc:nan
[49] train-auc:0.79780 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:23 | INFO | Training complete: Train AUC=0.7978, Test AUC=nan
08:54:23 | INFO | Model saved to models\xgboost_model.pkl
08:54:23 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:23 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:23] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61065 eval-auc:nan
[10] train-auc:0.74741 eval-auc:nan
[20] train-auc:0.75973 eval-auc:nan
[30] train-auc:0.77605 eval-auc:nan
[40] train-auc:0.78838 eval-auc:nan
[49] train-auc:0.79903 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:23 | INFO | Training complete: Train AUC=0.7990, Test AUC=nan
08:54:23 | INFO | Model saved to models\xgboost_model.pkl
08:54:23 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:23 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:23] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.57933 eval-auc:nan
[10] train-auc:0.71406 eval-auc:nan
[20] train-auc:0.75492 eval-auc:nan
[30] train-auc:0.76461 eval-auc:nan
[40] train-auc:0.78099 eval-auc:nan
[49] train-auc:0.79006 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:23 | INFO | Training complete: Train AUC=0.7901, Test AUC=nan
08:54:23 | INFO | Model saved to models\xgboost_model.pkl
08:54:23 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:23 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:23] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61780 eval-auc:nan
[10] train-auc:0.72935 eval-auc:nan
[20] train-auc:0.75414 eval-auc:nan
[30] train-auc:0.77374 eval-auc:nan
[40] train-auc:0.78245 eval-auc:nan
[49] train-auc:0.78918 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:23 | INFO | Training complete: Train AUC=0.7892, Test AUC=nan
08:54:23 | INFO | Model saved to models\xgboost_model.pkl
08:54:23 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:23 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:23] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.63270 eval-auc:nan
[10] train-auc:0.71846 eval-auc:nan
[20] train-auc:0.73000 eval-auc:nan
[30] train-auc:0.75459 eval-auc:nan
[40] train-auc:0.77510 eval-auc:nan
[49] train-auc:0.78933 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:23 | INFO | Training complete: Train AUC=0.7893, Test AUC=nan
08:54:23 | INFO | Model saved to models\xgboost_model.pkl
08:54:23 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:23 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:23] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60909 eval-auc:nan
[10] train-auc:0.72600 eval-auc:nan
[20] train-auc:0.74130 eval-auc:nan
[30] train-auc:0.75315 eval-auc:nan
[40] train-auc:0.76976 eval-auc:nan
[49] train-auc:0.78410 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:23 | INFO | Training complete: Train AUC=0.7841, Test AUC=nan
08:54:23 | INFO | Model saved to models\xgboost_model.pkl
08:54:23 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:23 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:23] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59953 eval-auc:nan
[10] train-auc:0.72494 eval-auc:nan
[20] train-auc:0.74669 eval-auc:nan
[30] train-auc:0.76223 eval-auc:nan
[40] train-auc:0.78575 eval-auc:nan
[49] train-auc:0.79317 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:23 | INFO | Training complete: Train AUC=0.7932, Test AUC=nan
08:54:23 | INFO | Model saved to models\xgboost_model.pkl
08:54:23 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:23 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:23] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.57937 eval-auc:nan
[10] train-auc:0.72038 eval-auc:nan
[20] train-auc:0.74880 eval-auc:nan
[30] train-auc:0.76822 eval-auc:nan
[40] train-auc:0.79570 eval-auc:nan
[49] train-auc:0.80228 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:24 | INFO | Training complete: Train AUC=0.8023, Test AUC=nan
08:54:24 | INFO | Model saved to models\xgboost_model.pkl
08:54:24 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:24 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:24] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60802 eval-auc:nan
[10] train-auc:0.71673 eval-auc:nan
[20] train-auc:0.72606 eval-auc:nan
[30] train-auc:0.75112 eval-auc:nan
[40] train-auc:0.77023 eval-auc:nan
[49] train-auc:0.78546 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:24 | INFO | Training complete: Train AUC=0.7855, Test AUC=nan
08:54:24 | INFO | Model saved to models\xgboost_model.pkl
08:54:24 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:24 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:24] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61530 eval-auc:nan
[10] train-auc:0.71262 eval-auc:nan
[20] train-auc:0.74913 eval-auc:nan
[30] train-auc:0.77033 eval-auc:nan
[40] train-auc:0.79397 eval-auc:nan
[49] train-auc:0.80447 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:24 | INFO | Training complete: Train AUC=0.8045, Test AUC=nan
08:54:24 | INFO | Model saved to models\xgboost_model.pkl
08:54:24 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:24 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:24] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59774 eval-auc:nan
[10] train-auc:0.71685 eval-auc:nan
[20] train-auc:0.75544 eval-auc:nan
[30] train-auc:0.78023 eval-auc:nan
[40] train-auc:0.78228 eval-auc:nan
[49] train-auc:0.80050 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:24 | INFO | Training complete: Train AUC=0.8005, Test AUC=nan
08:54:24 | INFO | Model saved to models\xgboost_model.pkl
08:54:24 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:24 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:24] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60768 eval-auc:nan
[10] train-auc:0.73430 eval-auc:nan
[20] train-auc:0.76393 eval-auc:nan
[30] train-auc:0.78908 eval-auc:nan
[40] train-auc:0.80738 eval-auc:nan
[49] train-auc:0.80557 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:24 | INFO | Training complete: Train AUC=0.8056, Test AUC=nan
08:54:24 | INFO | Model saved to models\xgboost_model.pkl
08:54:24 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:24 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:24] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62514 eval-auc:nan
[10] train-auc:0.74810 eval-auc:nan
[20] train-auc:0.77422 eval-auc:nan
[30] train-auc:0.77685 eval-auc:nan
[40] train-auc:0.79030 eval-auc:nan
[49] train-auc:0.79821 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:24 | INFO | Training complete: Train AUC=0.7982, Test AUC=nan
08:54:24 | INFO | Model saved to models\xgboost_model.pkl
08:54:24 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:24 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:24] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61793 eval-auc:nan
[10] train-auc:0.72798 eval-auc:nan
[20] train-auc:0.76128 eval-auc:nan
[30] train-auc:0.77182 eval-auc:nan
[40] train-auc:0.77739 eval-auc:nan
[49] train-auc:0.78859 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:24 | INFO | Training complete: Train AUC=0.7886, Test AUC=nan
08:54:24 | INFO | Model saved to models\xgboost_model.pkl
08:54:24 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:24 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:24] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.63207 eval-auc:nan
[10] train-auc:0.71108 eval-auc:nan
[20] train-auc:0.73769 eval-auc:nan
[30] train-auc:0.76183 eval-auc:nan
[40] train-auc:0.78927 eval-auc:nan
[49] train-auc:0.80148 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:24 | INFO | Training complete: Train AUC=0.8015, Test AUC=nan
08:54:24 | INFO | Model saved to models\xgboost_model.pkl
08:54:24 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:24 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:24] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61342 eval-auc:nan
[10] train-auc:0.71247 eval-auc:nan
[20] train-auc:0.74038 eval-auc:nan
[30] train-auc:0.75633 eval-auc:nan
[40] train-auc:0.76489 eval-auc:nan
[49] train-auc:0.76817 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:24 | INFO | Training complete: Train AUC=0.7682, Test AUC=nan
08:54:24 | INFO | Model saved to models\xgboost_model.pkl
08:54:24 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:24 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:24] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61614 eval-auc:nan
[10] train-auc:0.72650 eval-auc:nan
[20] train-auc:0.76164 eval-auc:nan
[30] train-auc:0.77206 eval-auc:nan
[40] train-auc:0.78308 eval-auc:nan
[49] train-auc:0.78877 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:24 | INFO | Training complete: Train AUC=0.7888, Test AUC=nan
08:54:24 | INFO | Model saved to models\xgboost_model.pkl
08:54:24 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:24 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:24] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60267 eval-auc:nan
[10] train-auc:0.73374 eval-auc:nan
[20] train-auc:0.76585 eval-auc:nan
[30] train-auc:0.78239 eval-auc:nan
[40] train-auc:0.78585 eval-auc:nan
[49] train-auc:0.79696 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:24 | INFO | Training complete: Train AUC=0.7970, Test AUC=nan
08:54:24 | INFO | Model saved to models\xgboost_model.pkl
08:54:24 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:24 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:25] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61319 eval-auc:nan
[10] train-auc:0.71857 eval-auc:nan
[20] train-auc:0.74940 eval-auc:nan
[30] train-auc:0.77293 eval-auc:nan
[40] train-auc:0.79024 eval-auc:nan
[49] train-auc:0.80264 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:25 | INFO | Training complete: Train AUC=0.8026, Test AUC=nan
08:54:25 | INFO | Model saved to models\xgboost_model.pkl
08:54:25 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:25 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:25] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60500 eval-auc:nan
[10] train-auc:0.70339 eval-auc:nan
[20] train-auc:0.73286 eval-auc:nan
[30] train-auc:0.75582 eval-auc:nan
[40] train-auc:0.78008 eval-auc:nan
[49] train-auc:0.79737 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:25 | INFO | Training complete: Train AUC=0.7974, Test AUC=nan
08:54:25 | INFO | Model saved to models\xgboost_model.pkl
08:54:25 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:25 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:25] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.60464 eval-auc:nan
[10] train-auc:0.73022 eval-auc:nan
[20] train-auc:0.76632 eval-auc:nan
[30] train-auc:0.78323 eval-auc:nan
[40] train-auc:0.79806 eval-auc:nan
[49] train-auc:0.80681 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:25 | INFO | Training complete: Train AUC=0.8068, Test AUC=nan
08:54:25 | INFO | Model saved to models\xgboost_model.pkl
08:54:25 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:25 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:25] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61222 eval-auc:nan
[10] train-auc:0.73832 eval-auc:nan
[20] train-auc:0.77334 eval-auc:nan
[30] train-auc:0.79421 eval-auc:nan
[40] train-auc:0.80396 eval-auc:nan
[49] train-auc:0.81019 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:25 | INFO | Training complete: Train AUC=0.8102, Test AUC=nan
08:54:25 | INFO | Model saved to models\xgboost_model.pkl
08:54:25 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:25 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:25] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.55041 eval-auc:nan
[10] train-auc:0.70677 eval-auc:nan
[20] train-auc:0.74107 eval-auc:nan
[30] train-auc:0.75340 eval-auc:nan
[40] train-auc:0.77782 eval-auc:nan
[49] train-auc:0.79610 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:25 | INFO | Training complete: Train AUC=0.7961, Test AUC=nan
08:54:25 | INFO | Model saved to models\xgboost_model.pkl
08:54:25 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:25 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:25] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62203 eval-auc:nan
[10] train-auc:0.70308 eval-auc:nan
[20] train-auc:0.74496 eval-auc:nan
[30] train-auc:0.76238 eval-auc:nan
[40] train-auc:0.78789 eval-auc:nan
[49] train-auc:0.79590 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:25 | INFO | Training complete: Train AUC=0.7959, Test AUC=nan
08:54:25 | INFO | Model saved to models\xgboost_model.pkl
08:54:25 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:25 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:25] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.63022 eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:26] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[10] train-auc:0.72671 eval-auc:nan
[20] train-auc:0.73606 eval-auc:nan
[30] train-auc:0.76981 eval-auc:nan
[40] train-auc:0.78587 eval-auc:nan
[49] train-auc:0.79961 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:26 | INFO | Training complete: Train AUC=0.7996, Test AUC=nan
08:54:26 | INFO | Model saved to models\xgboost_model.pkl
08:54:26 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:26 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:26] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.62639 eval-auc:nan
[10] train-auc:0.71306 eval-auc:nan
[20] train-auc:0.73356 eval-auc:nan
[30] train-auc:0.75206 eval-auc:nan
[40] train-auc:0.76996 eval-auc:nan
[49] train-auc:0.78848 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:26 | INFO | Training complete: Train AUC=0.7885, Test AUC=nan
08:54:26 | INFO | Model saved to models\xgboost_model.pkl
08:54:26 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:26 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:26] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.61308 eval-auc:nan
[10] train-auc:0.70639 eval-auc:nan
[20] train-auc:0.74028 eval-auc:nan
[30] train-auc:0.76069 eval-auc:nan
[40] train-auc:0.77010 eval-auc:nan
[49] train-auc:0.78029 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:26 | INFO | Training complete: Train AUC=0.7803, Test AUC=nan
08:54:26 | INFO | Model saved to models\xgboost_model.pkl
08:54:26 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:26 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:26] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59834 eval-auc:nan
[10] train-auc:0.71778 eval-auc:nan
[20] train-auc:0.73858 eval-auc:nan
[30] train-auc:0.76284 eval-auc:nan
[40] train-auc:0.77648 eval-auc:nan
[49] train-auc:0.78535 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:26 | INFO | Training complete: Train AUC=0.7854, Test AUC=nan
08:54:26 | INFO | Model saved to models\xgboost_model.pkl
08:54:26 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:26 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:26] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.57705 eval-auc:nan
[10] train-auc:0.74847 eval-auc:nan
[20] train-auc:0.78271 eval-auc:nan
[30] train-auc:0.79498 eval-auc:nan
[40] train-auc:0.80677 eval-auc:nan
[49] train-auc:0.82268 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:26 | INFO | Training complete: Train AUC=0.8227, Test AUC=nan
08:54:26 | INFO | Model saved to models\xgboost_model.pkl
08:54:26 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:26 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:26] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59035 eval-auc:nan
[10] train-auc:0.69604 eval-auc:nan
[20] train-auc:0.75628 eval-auc:nan
[30] train-auc:0.78456 eval-auc:nan
[40] train-auc:0.78833 eval-auc:nan
[49] train-auc:0.80046 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:26 | INFO | Training complete: Train AUC=0.8005, Test AUC=nan
08:54:26 | INFO | Model saved to models\xgboost_model.pkl
08:54:26 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:26 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:26] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.55826 eval-auc:nan
[10] train-auc:0.73195 eval-auc:nan
[20] train-auc:0.77602 eval-auc:nan
[30] train-auc:0.79505 eval-auc:nan
[40] train-auc:0.79973 eval-auc:nan
[49] train-auc:0.81236 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:26 | INFO | Training complete: Train AUC=0.8124, Test AUC=nan
08:54:26 | INFO | Model saved to models\xgboost_model.pkl
08:54:26 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:26 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:26] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.56635 eval-auc:nan
[10] train-auc:0.71472 eval-auc:nan
[20] train-auc:0.75192 eval-auc:nan
[30] train-auc:0.77732 eval-auc:nan
[40] train-auc:0.78860 eval-auc:nan
[49] train-auc:0.79153 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:26 | INFO | Training complete: Train AUC=0.7915, Test AUC=nan
08:54:26 | INFO | Model saved to models\xgboost_model.pkl
08:54:26 | INFO | Train/Test gap: -1 bars to prevent temporal leakage
08:54:26 | INFO | Training with 500 samples, testing with 1 samples
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:26] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0] train-auc:0.59997 eval-auc:nan
[10] train-auc:0.72610 eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:27] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[20] train-auc:0.76509 eval-auc:nan
[30] train-auc:0.78448 eval-auc:nan
[40] train-auc:0.80099 eval-auc:nan
[49] train-auc:0.82018 eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
warnings.warn(
08:54:27 | INFO | Training complete: Train AUC=0.8202, Test AUC=nan
08:54:27 | INFO | Model saved to models\xgboost_model.pkl
08:54:27 | INFO | Walk-forward: Avg Train AUC=0.8005, Avg Test AUC=0.5185
08:54:27 | INFO | Walk-forward Results:
08:54:27 | INFO | Avg Train AUC: 0.8005
08:54:27 | INFO | Avg Test AUC: 0.5185
08:54:27 | INFO | Overfitting ratio: 1.54
08:54:27 | INFO | ============================================================
08:54:27 | INFO | TRAINING COMPLETE
08:54:27 | INFO | ============================================================
08:54:27 | INFO | HMM Model: SAVED
08:54:27 | INFO | XGBoost Model: SAVED
08:54:27 | INFO | Models saved in: models/
08:54:27 | INFO | Training data saved in: data/
08:54:27 | INFO | Disconnected from MT5
08:54:27 | INFO | MT5 disconnected

316 KiB Raw Blame History

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Raw Blame History