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2f74c56 | 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 | # Portions of this file were developed with assistance from OpenAI ChatGPT/Codex and reviewed/modified by the author.
"""Evaluation and error analysis for Campus Triage models.
Portions of this file were developed with assistance from OpenAI ChatGPT/Codex and reviewed/modified by the author.
"""
from __future__ import annotations
from pathlib import Path
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, f1_score
from campus_triage.config import (
BASELINE_MODEL_PATH,
CATEGORY_LABELS,
CLASSICAL_MODEL_PATH,
OUTPUTS_DIR,
TEST_PATH,
TRAIN_PATH,
URGENCY_LABELS,
)
from campus_triage.data import create_and_save_dataset
from campus_triage.models import (
DualClassifier,
load_dual_classifier,
load_transformer_dual_classifier,
transformer_model_available,
)
from campus_triage.train import train_all
def ensure_models_and_data() -> None:
"""Create data and default non-transformer models when they do not exist yet."""
if not TRAIN_PATH.exists() or not TEST_PATH.exists():
create_and_save_dataset()
if not BASELINE_MODEL_PATH.exists() or not CLASSICAL_MODEL_PATH.exists():
train_all(include_transformer=False)
def load_evaluation_models() -> list[DualClassifier]:
"""Load every trained model available for evaluation."""
models = [
load_dual_classifier(str(BASELINE_MODEL_PATH)),
load_dual_classifier(str(CLASSICAL_MODEL_PATH)),
]
if transformer_model_available():
models.append(load_transformer_dual_classifier())
return models
def label_metrics(y_true: pd.Series, y_pred: pd.Series, prefix: str) -> dict[str, float]:
"""Compute high-level metrics for one label task."""
return {
f"{prefix}_accuracy": accuracy_score(y_true, y_pred),
f"{prefix}_macro_f1": f1_score(y_true, y_pred, average="macro", zero_division=0),
f"{prefix}_weighted_f1": f1_score(y_true, y_pred, average="weighted", zero_division=0),
}
def evaluate_dual_classifier(model: DualClassifier, test_dataframe: pd.DataFrame) -> dict[str, float | str]:
"""Evaluate category and urgency predictions for a model."""
category_predictions, urgency_predictions = model.predict(test_dataframe)
results: dict[str, float | str] = {"model": model.model_name}
results.update(label_metrics(test_dataframe["category"], category_predictions, "category"))
results.update(label_metrics(test_dataframe["urgency"], urgency_predictions, "urgency"))
return results
def save_confusion_matrix(y_true: pd.Series, y_pred: pd.Series, labels: list[str], title: str, output_path: Path) -> None:
"""Save a confusion matrix plot."""
matrix = confusion_matrix(y_true, y_pred, labels=labels)
plt.figure(figsize=(9, 6))
plt.imshow(matrix, interpolation="nearest", cmap="Blues")
plt.colorbar()
plt.title(title)
tick_marks = range(len(labels))
plt.xticks(tick_marks, labels, rotation=35, ha="right")
plt.yticks(tick_marks, labels)
threshold = matrix.max() / 2 if matrix.size else 0
for row_index in range(matrix.shape[0]):
for column_index in range(matrix.shape[1]):
value = matrix[row_index, column_index]
color = "white" if value > threshold else "black"
plt.text(column_index, row_index, str(value), ha="center", va="center", color=color)
plt.xlabel("Predicted")
plt.ylabel("True")
plt.tight_layout()
plt.savefig(output_path, dpi=160)
plt.close()
def build_reports(models: list[DualClassifier], test_dataframe: pd.DataFrame) -> str:
"""Build plain-text per-class classification reports."""
report_sections = []
for model in models:
category_predictions, urgency_predictions = model.predict(test_dataframe)
report_sections.append(f"Model: {model.model_name}\n")
report_sections.append("Category report\n")
report_sections.append(
classification_report(test_dataframe["category"], category_predictions, labels=CATEGORY_LABELS, zero_division=0)
)
report_sections.append("\nUrgency report\n")
report_sections.append(
classification_report(test_dataframe["urgency"], urgency_predictions, labels=URGENCY_LABELS, zero_division=0)
)
report_sections.append("\n" + "=" * 80 + "\n")
return "\n".join(report_sections)
def create_error_analysis(best_model: DualClassifier, test_dataframe: pd.DataFrame, output_path: Path) -> pd.DataFrame:
"""Save five concrete mispredictions with root-cause and mitigation notes."""
category_predictions, urgency_predictions = best_model.predict(test_dataframe)
analysis_dataframe = test_dataframe.copy()
analysis_dataframe["predicted_category"] = category_predictions
analysis_dataframe["predicted_urgency"] = urgency_predictions
mistakes = analysis_dataframe[
(analysis_dataframe["category"] != analysis_dataframe["predicted_category"])
| (analysis_dataframe["urgency"] != analysis_dataframe["predicted_urgency"])
].head(5)
rows = []
for _, row in mistakes.iterrows():
root_cause = infer_root_cause(row["message_text"], row["category"], row["predicted_category"])
rows.append(
{
"message_text": row["message_text"],
"true_category": row["category"],
"predicted_category": row["predicted_category"],
"true_urgency": row["urgency"],
"predicted_urgency": row["predicted_urgency"],
"likely_root_cause": root_cause,
"concrete_mitigation_strategy": mitigation_for_root_cause(root_cause),
}
)
error_dataframe = pd.DataFrame(rows)
error_dataframe.to_csv(output_path, index=False)
return error_dataframe
def infer_root_cause(message_text: str, true_category: str, predicted_category: str) -> str:
"""Infer a simple root cause for a misprediction."""
text = str(message_text)
if len(text.split()) <= 4:
return "Very short message lacks enough context for reliable routing."
if true_category != predicted_category and any(word in text.lower() for word in ["help", "question", "portal"]):
return "Ambiguous shared vocabulary appears in multiple support categories."
if any(symbol in text for symbol in ["???", "!!", ":/"]):
return "Informal punctuation and noisy phrasing may distort text features."
return "The message contains limited category-specific keywords."
def mitigation_for_root_cause(root_cause: str) -> str:
"""Map a root cause to an actionable mitigation."""
if "short" in root_cause:
return "Ask for one clarifying detail before auto-routing short requests."
if "Ambiguous" in root_cause:
return "Add a human-review threshold for low-confidence cross-category cases."
if "punctuation" in root_cause:
return "Expand training augmentation with noisy chat-style messages."
return "Collect more labeled examples with advisor-reviewed category keywords."
def run_evaluation() -> pd.DataFrame:
"""Evaluate all trained strategies and save required outputs."""
ensure_models_and_data()
OUTPUTS_DIR.mkdir(parents=True, exist_ok=True)
test_dataframe = pd.read_csv(TEST_PATH)
models = load_evaluation_models()
comparison = pd.DataFrame([evaluate_dual_classifier(model, test_dataframe) for model in models])
comparison.to_csv(OUTPUTS_DIR / "model_comparison.csv", index=False)
best_model_name = comparison.sort_values(["category_macro_f1", "urgency_macro_f1"], ascending=False).iloc[0]["model"]
best_model = next(model for model in models if model.model_name == best_model_name)
category_predictions, urgency_predictions = best_model.predict(test_dataframe)
save_confusion_matrix(
test_dataframe["category"],
category_predictions,
CATEGORY_LABELS,
"Category Confusion Matrix",
OUTPUTS_DIR / "category_confusion_matrix.png",
)
save_confusion_matrix(
test_dataframe["urgency"],
urgency_predictions,
URGENCY_LABELS,
"Urgency Confusion Matrix",
OUTPUTS_DIR / "urgency_confusion_matrix.png",
)
(OUTPUTS_DIR / "classification_reports.txt").write_text(build_reports(models, test_dataframe), encoding="utf-8")
create_error_analysis(best_model, test_dataframe, OUTPUTS_DIR / "error_analysis.csv")
return comparison
def main() -> None:
"""Run evaluation from the command line."""
comparison = run_evaluation()
print(comparison.to_string(index=False))
if __name__ == "__main__":
main()
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