baseline_val = self.baseline if self.baseline is not None else 0.0
baseline_str = f"{baseline_val:.4f}" if self.baseline is not None else "N/A"
target_threshold_str = (
    f"{baseline_val * (1 - self.tuning_config.tolerable_loss):.4f}" if self.baseline is not None else "N/A"
)

# Calculate relative loss if baseline is available
relative_loss_val = 0.0
relative_loss_str = "N/A"
if self.baseline is not None:
    relative_loss_val = (baseline_val - trial_result) / baseline_val
    relative_loss_str = f"{relative_loss_val*100:.2f}%"

# Get best result so far
best_result = max(record.trial_result for record in self.tuning_history)

# Status indicator with emoji
if self.baseline is not None and trial_result >= (baseline_val * (1 - self.tuning_config.tolerable_loss)):
    status = "✅ PASSED"
else:
    status = "❌ FAILED"

# Prepare data for Statistics table with combined fields
field_names = ["📊 Metric", "📈 Value"]
output_data = [
    ["Trial / Progress", f"{len(self.tuning_history)}/{self.tuning_config.max_trials}"],
    ["Baseline / Target", f"{baseline_str} / {target_threshold_str}"],
    ["Current / Status", f"{trial_result:.4f} | {status}"],
    ["Best / Relative Loss", f"{best_result:.4f} / {relative_loss_str}"],
]

# Use Statistics class to print the table
Statistics(
    output_data, header=f"🎯 Auto-Tune Trial #{trial_index} Results", field_names=field_names
).print_stat()

    self._print_trial_results_table(trial_index, trial_result)

def _print_trial_results_table(self, trial_index: int, trial_result: Union[int, float]) -> None:
    """Print trial results in a formatted table using Statistics class."""
    baseline_val = self.baseline if self.baseline is not None else 0.0
    baseline_str = f"{baseline_val:.4f}" if self.baseline is not None else "N/A"
    target_threshold_str = (
        f"{baseline_val * (1 - self.tuning_config.tolerable_loss):.4f}" if self.baseline is not None else "N/A"
    )

    # Calculate relative loss if baseline is available
    relative_loss_val = 0.0
    relative_loss_str = "N/A"
    if self.baseline is not None:
        relative_loss_val = (baseline_val - trial_result) / baseline_val
        relative_loss_str = f"{relative_loss_val*100:.2f}%"

    # Get best result so far
    best_result = max(record.trial_result for record in self.tuning_history)

    # Status indicator with emoji
    if self.baseline is not None and trial_result >= (baseline_val * (1 - self.tuning_config.tolerable_loss)):
        status = "✅ PASSED"
    else:
        status = "❌ FAILED"

    # Prepare data for Statistics table with combined fields
    field_names = ["📊 Metric", "📈 Value"]
    output_data = [
        ["Trial / Progress", f"{len(self.tuning_history)}/{self.tuning_config.max_trials}"],
        ["Baseline / Target", f"{baseline_str} / {target_threshold_str}"],
        ["Current / Status", f"{trial_result:.4f} | {status}"],
        ["Best / Relative Loss", f"{best_result:.4f} / {relative_loss_str}"],
    ]

    # Use Statistics class to print the table
    Statistics(
        output_data, header=f"🎯 Auto-Tune Trial #{trial_index} Results", field_names=field_names
    ).print_stat()