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3a8e9de | 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 | #!/bin/bash
# Script to precompute FNR thresholds for different alpha values
# Usage: ./precompute_fnr_thresholds.sh [OPTIONS]
# Get the directory where this script is located
SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
PROJECT_ROOT="$( cd "$SCRIPT_DIR/.." &> /dev/null && pwd )"
# Default parameters - can be modified as needed
MIN_ALPHA=0.01
MAX_ALPHA=0.2
NUM_ALPHA_VALUES=100
NUM_TRIALS=100
N_CALIB=100
OUTPUT_DIR="$PROJECT_ROOT/results"
TEMP_DIR="$SCRIPT_DIR/temp_fnr_results"
CSV_OUTPUT="$OUTPUT_DIR/fnr_thresholds.csv"
echo "Script directory: $SCRIPT_DIR"
echo "Project root: $PROJECT_ROOT"
echo "Output directory: $OUTPUT_DIR"
echo ""
mkdir -p "$OUTPUT_DIR"
mkdir -p "$TEMP_DIR"
# Initialize CSV file with header
echo "alpha,lambda_threshold,exact_fnr,partial_fnr" > "$CSV_OUTPUT"
# Generate alpha values using Python
ALPHA_VALUES=$(python -c "
import numpy as np
alphas = np.linspace($MIN_ALPHA, $MAX_ALPHA, $NUM_ALPHA_VALUES)
print(' '.join([str(a) for a in alphas]))
")
# Counter for progress tracking
counter=0
total=$NUM_ALPHA_VALUES
# Loop over alpha values
for alpha in $ALPHA_VALUES; do
counter=$((counter + 1))
# Run FNR generation for exact matches
python "$PROJECT_ROOT/pfam/generate_fnr.py" \
--alpha "$alpha" \
--num_trials "$NUM_TRIALS" \
--n_calib "$N_CALIB" \
--output "$TEMP_DIR/fnr_exact_$alpha"
# Run FNR generation for partial matches
echo " Running partial matches..."
python "$PROJECT_ROOT/pfam/generate_fnr.py" \
--alpha "$alpha" \
--partial \
--num_trials "$NUM_TRIALS" \
--n_calib "$N_CALIB" \
--output "$TEMP_DIR/fnr_partial_$alpha"
# Extract results and append to CSV using Python
python -c "
import numpy as np
import sys
import os
# Convert MINGW64 paths to Windows format
def mingw_to_windows_path(path):
if path.startswith('/c/'):
return 'C:/' + path[3:]
return path
try:
temp_dir = mingw_to_windows_path('$TEMP_DIR')
csv_output = mingw_to_windows_path('$CSV_OUTPUT')
# Load exact match results
exact_file = os.path.join(temp_dir, 'fnr_exact_$alpha.npy')
exact_data = np.load(exact_file, allow_pickle=True).item()
exact_lhat = np.mean(exact_data['lhats'])
exact_fnr = np.mean(exact_data['fnrs'])
# Load partial match results
partial_file = os.path.join(temp_dir, 'fnr_partial_$alpha.npy')
partial_data = np.load(partial_file, allow_pickle=True).item()
partial_fnr = np.mean(partial_data['fnrs'])
# Write to CSV
with open(csv_output, 'a') as f:
f.write(f'$alpha,{exact_lhat},{exact_fnr},{partial_fnr}\n')
print(f' Results: lambda={exact_lhat:.6f}, exact_fnr={exact_fnr:.6f}, partial_fnr={partial_fnr:.6f}')
except Exception as e:
print(f'Error processing alpha=$alpha: {e}', file=sys.stderr)
sys.exit(1)
"
done
# Clean up temporary files
rm -rf "$TEMP_DIR"
echo "Results saved to: $CSV_OUTPUT"
echo "Total alpha values processed: $total"
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