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Merged
YichengYang-Ethan merged 1 commit into from
Mar 15, 2026
+1,766 −17
Merged

Add real admission data pipeline and model calibration engine #1

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189 changes: 189 additions & 0 deletions cli/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,8 @@
from rich.table import Table
from rich.text import Text

from core.admission_data import load_admission_csv, load_all_admission_data, summarize_records
from core.calibrator import calibrate_all, generate_ranker_overrides
from core.data_loader import load_all_programs, load_profile
from core.gap_advisor import analyze_gaps
from core.interview_prep import (
Expand Down Expand Up @@ -610,6 +612,180 @@ def cmd_gaps(args: argparse.Namespace) -> None:
console.print()


def cmd_stats(args: argparse.Namespace) -> None:
"""Show statistics from real admission data."""
if args.file:
records = load_admission_csv(args.file)
else:
records = load_all_admission_data()

if not records:
console.print("[yellow]No admission data found.[/yellow]")
console.print("[dim]Add CSV files to data/admissions/ or use --file.[/dim]")
return

summary = summarize_records(records)

console.print()
console.print(
Panel(
f"[bold]{summary['total_records']}[/bold] records from "
f"[bold]{summary['unique_applicants']}[/bold] applicants | "
f"Sources: {', '.join(summary['sources'])} | "
f"Seasons: {', '.join(summary['seasons'])}",
title="Admission Data Statistics",
border_style="cyan",
)
)

# Per-program breakdown
table = Table(border_style="cyan", title="Per-Program Breakdown")
table.add_column("Program", style="bold")
table.add_column("Accepted", style="green", justify="right")
table.add_column("Rejected", style="red", justify="right")
table.add_column("Waitlisted", style="yellow", justify="right")
table.add_column("Total", justify="right")
table.add_column("Obs. Rate", justify="right")

for prog_id, counts in sorted(summary["programs"].items()):
acc = counts.get("accepted", 0)
rej = counts.get("rejected", 0)
wl = counts.get("waitlisted", 0)
total = acc + rej + wl
decided = acc + rej
rate = f"{acc / decided:.0%}" if decided > 0 else "N/A"
table.add_row(prog_id, str(acc), str(rej), str(wl), str(total), rate)

console.print(table)
console.print()

# GPA distribution
from core.admission_data import compute_all_program_stats

all_stats = compute_all_program_stats(records)
if all_stats:
console.print(Panel("Accepted vs Rejected GPA Comparison", border_style="cyan"))
gpa_table = Table(border_style="cyan")
gpa_table.add_column("Program", style="bold")
gpa_table.add_column("Avg GPA (Acc)", style="green", justify="right")
gpa_table.add_column("Avg GPA (Rej)", style="red", justify="right")
gpa_table.add_column("Gap", justify="right")
gpa_table.add_column("Top Feature", justify="right")

for pid, stats in sorted(all_stats.items()):
if stats.accepted == 0:
continue
gpa_acc = f"{stats.avg_gpa_accepted:.2f}" if stats.avg_gpa_accepted else "N/A"
gpa_rej = f"{stats.avg_gpa_rejected:.2f}" if stats.avg_gpa_rejected else "N/A"
gap = ""
if stats.avg_gpa_accepted and stats.avg_gpa_rejected:
diff = stats.avg_gpa_accepted - stats.avg_gpa_rejected
gap = f"+{diff:.2f}" if diff >= 0 else f"{diff:.2f}"

top_feat = ""
if stats.feature_importance:
top = max(stats.feature_importance, key=lambda k: abs(stats.feature_importance[k]))
top_feat = f"{top} ({stats.feature_importance[top]:.2f})"

gpa_table.add_row(pid, gpa_acc, gpa_rej, gap, top_feat)

console.print(gpa_table)
console.print()


def cmd_calibrate(args: argparse.Namespace) -> None:
"""Calibrate scoring model using real admission data."""
if args.file:
records = load_admission_csv(args.file)
else:
records = load_all_admission_data()

if not records:
console.print("[yellow]No admission data found.[/yellow]")
console.print("[dim]Add CSV files to data/admissions/ or use --file.[/dim]")
return

console.print()
console.print(Panel("Running Calibration...", border_style="cyan"))

result = calibrate_all(records)

# Thresholds table
table = Table(border_style="cyan", title="Calibrated Program Thresholds")
table.add_column("Program", style="bold")
table.add_column("GPA Floor", justify="right")
table.add_column("GPA Target", justify="right")
table.add_column("GPA Safe", justify="right")
table.add_column("Obs. Rate", justify="right")
table.add_column("Samples", justify="right")
table.add_column("Confidence")

for pid, threshold in sorted(result.program_thresholds.items()):
conf_color = {"high": "green", "medium": "yellow", "low": "red"}.get(
threshold.confidence, "white"
)
table.add_row(
pid,
f"{threshold.gpa_floor:.2f}",
f"{threshold.gpa_target:.2f}",
f"{threshold.gpa_safe:.2f}",
f"{threshold.observed_acceptance_rate:.0%}",
str(threshold.sample_size),
f"[{conf_color}]{threshold.confidence}[/{conf_color}]",
)

console.print(table)

# Global feature weights
if result.global_feature_weights:
console.print()
console.print(Panel("Global Feature Importance", border_style="cyan"))
fw_table = Table(border_style="cyan")
fw_table.add_column("Feature", style="bold")
fw_table.add_column("Weight", justify="right")
fw_table.add_column("Bar", width=20)

for feat, weight in result.global_feature_weights.items():
bar_len = round(weight * 40)
fw_table.add_row(feat, f"{weight:.1%}", "█" * bar_len)

console.print(fw_table)

# Accuracy report
acc = result.accuracy_report
if acc.get("total_predictions", 0) > 0:
console.print()
accuracy_pct = acc.get("accuracy", 0)
acc_color = "green" if accuracy_pct >= 0.7 else "yellow" if accuracy_pct >= 0.5 else "red"
console.print(
f" [bold]Model Accuracy:[/bold] [{acc_color}]{accuracy_pct:.0%}[/{acc_color}] "
f"({acc['correct']} correct / {acc['correct'] + acc['incorrect']} decided, "
f"{acc['borderline']} borderline)"
)

# Recommendations
if result.recommendations:
console.print()
console.print(Panel("Recommendations", border_style="yellow"))
for rec in result.recommendations:
console.print(f" - {rec}")

console.print()

# If --apply flag, show the overrides that would be applied
if args.apply:
overrides = generate_ranker_overrides(result)
if overrides:
console.print(Panel("Ranker Overrides (Applied)", border_style="green"))
for pid, ov in sorted(overrides.items()):
console.print(
f" {pid}: reach<{ov['reach_gpa_threshold']:.2f} "
f"safe>={ov['safety_gpa_threshold']:.2f} "
f"[dim](n={ov['sample_size']}, {ov['confidence']})[/dim]"
)
Comment on lines +779 to +785
console.print()


def main() -> None:
parser = argparse.ArgumentParser(
prog="quantpath",
Expand Down Expand Up @@ -675,6 +851,17 @@ def main() -> None:
p_gaps = subparsers.add_parser("gaps", help="Analyze profile gaps and suggest improvements")
p_gaps.add_argument("--profile", "-p", required=True, help="Path to profile YAML")

# stats (real data)
p_stats = subparsers.add_parser("stats", help="Show statistics from real admission data")
p_stats.add_argument("--file", "-f", help="Path to a specific CSV file (default: all)")

# calibrate (real data)
p_cal = subparsers.add_parser("calibrate", help="Calibrate model using real admission data")
p_cal.add_argument("--file", "-f", help="Path to a specific CSV file (default: all)")
p_cal.add_argument(
"--apply", action="store_true", help="Show ranker overrides that would be applied"
)

args = parser.parse_args()

if args.command is None:
Expand All @@ -690,6 +877,8 @@ def main() -> None:
"compare": cmd_compare,
"interview": cmd_interview,
"gaps": cmd_gaps,
"stats": cmd_stats,
"calibrate": cmd_calibrate,
}
commands[args.command](args)

Expand Down
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