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"""Confidence calculation and visualization utilities.
Provides normalized softmax confidence and color-coded badges"""
from typing import Tuple, List
import numpy as np
import torch
import torch.nn.functional as F
def calculate_softmax_confidence(
logits: torch.Tensor,
) -> Tuple[np.ndarray, float, str, str]:
"""Calculate normalized confidence using softmax
Args:
logits: Raw model logits tensor
Returns:
Tuple of (probabilities, max_confidence, confidence_level, confidence_emoji)
"""
# ===Apply softmax to get probabilities===
probs_np = F.softmax(logits, dim=1).cpu().numpy().flatten()
# ===Get maximum probability as confidence===
max_confidence = float(np.max(probs_np))
# ===Determine confidence level and emoji===
if max_confidence >= 0.80:
confidence_level = "HIGH"
confidence_emoji = "🟢"
elif max_confidence >= 0.60:
confidence_level = "MEDIUM"
confidence_emoji = "🟡"
else:
confidence_level = "LOW"
confidence_emoji = "🔴"
return probs_np, max_confidence, confidence_level, confidence_emoji
def get_confidence_badge(confidence: float) -> Tuple[str, str]:
"""Get confidence badge emoji and level description
Args:
confidence: Confidence value (0-1)
Returns:
Tuple of (emoji, level)
"""
if confidence >= 0.80:
return "🟢", "HIGH"
elif confidence >= 0.60:
return "🟡", "MEDIUM"
else:
return "🔴", "LOW"
def format_confidence_display(confidence: float, level: str, emoji: str) -> str:
"""
Format confidence for display in UI
Args:
confidence: Confidence value (0-1)
level: Confidence level (HIGH/MEDIUM/LOW)
emoji: Confidence emoji
Returns:
Formatted confidence string
"""
return f"{emoji} **{level}** ({confidence:.1%})"
def calculate_legacy_confidence(logits_list: List[float]) -> Tuple[float, str, str]:
"""
Calculate confidence using legacy logit margin method for backward compatibility
Args:
logits_list: List of raw logits
Returns:
Tuple of (margin, confidence_level, confidence_emoji)
"""
if len(logits_list) < 2:
return 0.0, "LOW", "🔴"
logits_array = np.array(logits_list)
sorted_logits = np.sort(logits_array)[::-1] # Descending order
margin = sorted_logits[0] - sorted_logits[1]
# ===Define thresholds for margin-based confidence===
if margin >= 2.0:
confidence_level = "HIGH"
confidence_emoji = "🟢"
elif margin >= 1.0:
confidence_level = "MEDIUM"
confidence_emoji = "🟡"
else:
confidence_level = "LOW"
confidence_emoji = "🔴"
return margin, confidence_level, confidence_emoji
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