File size: 8,835 Bytes
35717ca | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 | #!/usr/bin/env python3
"""
Evaluate the ProtocolSegmentClassifier against labeled mixed dictation examples.
Measures:
1. Word-level accuracy (is each word correctly classified?)
2. Segment boundary accuracy (does the classifier find the right start/end?)
3. False positive rate on pure natural text
4. False negative rate on protocol segments
"""
import json
import numpy as np
from pathlib import Path
import importlib
_mod = importlib.import_module("train-segment-classifier")
extract_features = _mod.extract_features
is_protocol_word = _mod.is_protocol_word
is_strong_protocol = _mod.is_strong_protocol
STRONG_PROTOCOL = _mod.STRONG_PROTOCOL
WEAK_PROTOCOL = _mod.WEAK_PROTOCOL
EXPANDED_SYMBOLS = _mod.EXPANDED_SYMBOLS
def load_model(path: str) -> tuple[np.ndarray, float]:
"""Load trained model from JSON."""
with open(path) as f:
model = json.load(f)
return np.array(model["weights"]), model["bias"]
def classify_words(words: list[str], weights: np.ndarray, bias: float, threshold: float = 0.5) -> list[tuple[str, float, bool]]:
"""Classify each word and return (word, probability, is_protocol)."""
results = []
n = len(words)
for i, word in enumerate(words):
ctx_start = max(0, i - 2)
ctx_end = min(n, i + 3)
context = words[ctx_start:ctx_end]
features = extract_features(word, context, i, n)
logit = np.dot(features, weights) + bias
prob = 1.0 / (1.0 + np.exp(-logit))
results.append((word, prob, prob >= threshold))
return results
def expand_anchors(is_anchor: list[bool], n: int, radius: int = 2) -> list[bool]:
"""Expand anchor words ±radius to capture adjacent command tokens."""
expanded = [False] * n
for i in range(n):
if is_anchor[i]:
for j in range(max(0, i - radius), min(n, i + radius + 1)):
expanded[j] = True
return expanded
def build_segments(words: list[str], in_protocol: list[bool]) -> list[tuple[str, str]]:
"""Build (kind, text) segments from word-level classifications."""
segments = []
current_kind = "protocol" if in_protocol[0] else "natural"
current_words = [words[0]]
for i in range(1, len(words)):
kind = "protocol" if in_protocol[i] else "natural"
if kind == current_kind:
current_words.append(words[i])
else:
segments.append((current_kind, " ".join(current_words)))
current_kind = kind
current_words = [words[i]]
segments.append((current_kind, " ".join(current_words)))
return segments
def label_words_from_example(example: dict) -> list[int]:
"""
Label each word in the text as natural (0) or protocol (1)
based on the 'natural' and 'protocol' fields.
The protocol field may contain ||| to indicate multiple protocol segments.
The natural field may contain ||| to indicate natural segments between protocol segments.
"""
text = example["text"]
words = text.split()
natural_parts = [p.strip() for p in example["natural"].split("|||") if p.strip()]
protocol_parts = [p.strip() for p in example["protocol"].split("|||") if p.strip()]
labels = [0] * len(words)
# Mark protocol words
for proto_part in protocol_parts:
proto_words = proto_part.split()
if not proto_words:
continue
# Find this sequence in the text
plen = len(proto_words)
for start in range(len(words) - plen + 1):
if words[start:start + plen] == proto_words:
for j in range(start, start + plen):
labels[j] = 1
break
return labels
def evaluate(eval_path: str, model_path: str):
"""Run full evaluation."""
with open(eval_path) as f:
examples = json.load(f)
weights, bias = load_model(model_path)
print("=" * 70)
print("PROTOCOL SEGMENT CLASSIFIER EVALUATION")
print(f" Model: {model_path}")
print(f" Eval set: {len(examples)} examples")
print("=" * 70)
# Aggregate metrics
total_words = 0
correct_words = 0
tp = fp = fn = tn = 0
# Per-category tracking
pure_natural_count = 0
pure_natural_correct = 0
mixed_count = 0
mixed_correct = 0
# Detailed results
errors = []
for ex in examples:
text = ex["text"]
words = text.split()
n = len(words)
is_pure_natural = not ex["protocol"].strip()
# Get ground truth labels
gt_labels = label_words_from_example(ex)
# Classify
results = classify_words(words, weights, bias)
anchors = [r[2] for r in results]
predicted = expand_anchors(anchors, n)
# Word-level metrics
for i in range(n):
pred = 1 if predicted[i] else 0
gt = gt_labels[i]
total_words += 1
if pred == gt:
correct_words += 1
if pred == 1 and gt == 1:
tp += 1
elif pred == 1 and gt == 0:
fp += 1
elif pred == 0 and gt == 1:
fn += 1
else:
tn += 1
# Example-level tracking
example_correct = all(
(1 if predicted[i] else 0) == gt_labels[i]
for i in range(n)
)
if is_pure_natural:
pure_natural_count += 1
if example_correct:
pure_natural_correct += 1
else:
mixed_count += 1
if example_correct:
mixed_correct += 1
# Collect errors for display
if not example_correct:
pred_segments = build_segments(words, predicted)
gt_segments = build_segments(words, [bool(l) for l in gt_labels])
errors.append({
"id": ex["id"],
"note": ex.get("note", ""),
"words": words,
"gt": gt_labels,
"pred": [1 if p else 0 for p in predicted],
"pred_segments": pred_segments,
"gt_segments": gt_segments,
})
# Print results
word_acc = correct_words / total_words if total_words else 0
precision = tp / (tp + fp) if (tp + fp) > 0 else 0
recall = tp / (tp + fn) if (tp + fn) > 0 else 0
f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0
print(f"\n{'WORD-LEVEL METRICS':─^70}")
print(f" Accuracy: {word_acc:.1%} ({correct_words}/{total_words})")
print(f" Precision: {precision:.1%} (protocol words correctly identified)")
print(f" Recall: {recall:.1%} (protocol words found)")
print(f" F1: {f1:.3f}")
print(f" Confusion: TP={tp} FP={fp} FN={fn} TN={tn}")
print(f"\n{'EXAMPLE-LEVEL METRICS':─^70}")
print(f" Pure natural (no protocol): {pure_natural_correct}/{pure_natural_count} perfect")
print(f" Mixed (natural + protocol): {mixed_correct}/{mixed_count} perfect")
total_ex = pure_natural_count + mixed_count
total_correct = pure_natural_correct + mixed_correct
print(f" Overall: {total_correct}/{total_ex} perfect ({100*total_correct/total_ex:.0f}%)")
# Show errors
if errors:
print(f"\n{'ERRORS':─^70}")
for err in errors:
print(f"\n #{err['id']}: {err['note']}")
# Show words with color coding
parts = []
for i, word in enumerate(err["words"]):
gt = err["gt"][i]
pred = err["pred"][i]
if gt == pred:
if gt == 1:
parts.append(f"\033[92m{word}\033[0m") # green = correct protocol
else:
parts.append(word) # plain = correct natural
elif pred == 1 and gt == 0:
parts.append(f"\033[91m[FP:{word}]\033[0m") # red = false positive
else:
parts.append(f"\033[93m[FN:{word}]\033[0m") # yellow = false negative
print(f" {' '.join(parts)}")
# Summary
print(f"\n{'SUMMARY':─^70}")
fp_rate = fp / (fp + tn) if (fp + tn) > 0 else 0
fn_rate = fn / (fn + tp) if (fn + tp) > 0 else 0
print(f" False positive rate: {fp_rate:.1%} (natural words misclassified as protocol)")
print(f" False negative rate: {fn_rate:.1%} (protocol words missed)")
print(f" Key concern: FP on natural text → model hallucinates on speech")
print(f" Secondary: FN on protocol → falls through as raw dictation")
return word_acc, precision, recall, f1
if __name__ == "__main__":
eval_path = Path(__file__).parent / "eval-segment-classifier.json"
model_path = Path(__file__).parent / "segment-classifier-model.json"
evaluate(str(eval_path), str(model_path))
|