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ec2e76e | 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 | #!/usr/bin/env python
"""
Test that precomputed probability lookup gives same results as computing from scratch.
"""
import numpy as np
import pandas as pd
import sys
sys.path.insert(0, '.')
from protein_conformal.util import simplifed_venn_abers_prediction, get_sims_labels
print("=" * 60)
print("Precomputed Probability Verification")
print("=" * 60)
print()
# Load calibration data
print("Loading calibration data...")
cal_data = np.load('data/pfam_new_proteins.npy', allow_pickle=True)
np.random.seed(42)
np.random.shuffle(cal_data)
cal_subset = cal_data[:100]
X_cal, y_cal = get_sims_labels(cal_subset, partial=False)
X_cal = X_cal.flatten()
y_cal = y_cal.flatten()
print(f" Calibration pairs: {len(X_cal)}")
print(f" Similarity range: [{X_cal.min():.6f}, {X_cal.max():.6f}]")
print()
# Create precomputed lookup table
print("Creating precomputed lookup table (100 bins)...")
min_sim, max_sim = X_cal.min(), X_cal.max()
bins = np.linspace(min_sim, max_sim, 100)
lookup = []
for sim in bins:
p0, p1 = simplifed_venn_abers_prediction(X_cal, y_cal, sim)
lookup.append({'similarity': sim, 'p0': p0, 'p1': p1, 'prob': (p0+p1)/2})
lookup_df = pd.DataFrame(lookup)
print(f" Lookup table: {len(lookup_df)} entries")
print()
# Test on random similarity values
print("Testing lookup vs direct computation on 20 random values...")
test_sims = np.random.uniform(min_sim, max_sim, 20)
print(f"{'Similarity':>12} | {'Direct':>8} | {'Lookup':>8} | {'Diff':>8}")
print("-" * 50)
max_diff = 0
for sim in test_sims:
# Direct computation
p0, p1 = simplifed_venn_abers_prediction(X_cal, y_cal, sim)
prob_direct = (p0 + p1) / 2
# Lookup with interpolation
lower = lookup_df[lookup_df['similarity'] <= sim].iloc[-1] if len(lookup_df[lookup_df['similarity'] <= sim]) > 0 else lookup_df.iloc[0]
upper = lookup_df[lookup_df['similarity'] >= sim].iloc[0] if len(lookup_df[lookup_df['similarity'] >= sim]) > 0 else lookup_df.iloc[-1]
prob_lookup = (lower['prob'] + upper['prob']) / 2
diff = abs(prob_direct - prob_lookup)
max_diff = max(max_diff, diff)
print(f"{sim:12.8f} | {prob_direct:8.4f} | {prob_lookup:8.4f} | {diff:8.4f}")
print()
print("=" * 60)
if max_diff < 0.01:
print(f"✓ VERIFICATION PASSED (max diff: {max_diff:.4f})")
print(" Precomputed lookup matches direct computation")
else:
print(f"⚠ VERIFICATION WARNING (max diff: {max_diff:.4f})")
print(" Consider using more bins for better accuracy")
print("=" * 60)
# Save the lookup table
output_path = 'data/sim2prob_lookup.csv'
lookup_df.to_csv(output_path, index=False)
print(f"\nSaved lookup table to: {output_path}")
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