Framework for data regression tests

benchmarks/README.md

@@ -1 +1,5 @@
 # a suite of a bit longer (regression) tests
+
+By running `data_regression_experiment.sh`, you can conduct a stand-alone experiment that demonstrates the rankings' capability of approximating the scores obtained by using the full data set.
+
+![comparison](./comparison.png)

benchmarks/analyse_rankings.py

@@ -0,0 +1,71 @@
+from __future__ import annotations
+
+import glob
+import os
+import sys
+
+import matplotlib.pyplot as plt
+
+def extract_just_ranking(dfile):
+    """Extract ranking from an output file."""
+    ranks = []
+    with open(dfile) as df:
+        next(df)  # Skip header line
+        for line in df:
+            parts = line.strip().split('\t')
+            ranks.append(parts[1])
+    return ranks
+
+def calculate_mismatch_scores(all_folders, mismatches):
+    """Calculate mismatch scores based on ranking files."""
+    all_counts = [int(folder.split('_').pop()) for folder in all_folders if 'ranking' in folder]
+
+    ranking_out_struct = {}
+    for count in all_counts:
+        rpath = os.path.join(dfolder, f'ranking_{count}', 'feature_singles.tsv')
+        ranking_out_struct[count] = extract_just_ranking(rpath)
+
+    pivot_score_key = max(all_counts)
+    reference_ranking = ranking_out_struct[pivot_score_key]
+
+    out_results = {}
+    for ranking_id, ranking in ranking_out_struct.items():
+        mismatches_counter = 0
+        for el in ranking[:mismatches]:
+            if el not in reference_ranking[:mismatches]:
+                mismatches_counter += 1
+        out_results[ranking_id] = 100 * (1 - mismatches_counter / mismatches)
+
+    return dict(sorted(out_results.items(), key=lambda x: x[0]))
+
+def plot_precision_curve(results, pivot_score_key, mismatches, axs, c1, c2):
+    """Plot the precision curve based on mismatch results."""
+    instances = [100 * (k / pivot_score_key) for k in results.keys()]
+    values = list(results.values())
+
+    axs[c1,c2].plot(instances, values, marker='o', linestyle='-', color='black')
+    axs[c1,c2].invert_xaxis()
+    axs[c1,c2].set(xlabel='Proportion of data used (%)', ylabel=f'hits@{mismatches} (%)', title=f'Approximation, top {mismatches} Features')
+    axs[c1,c2].grid(True)
+
+if __name__ == '__main__':
+    if len(sys.argv) < 2:
+        print('Usage: python script.py <directory>')
+        sys.exit(1)
+
+    dfolder = sys.argv[1]
+    mismatch_range = [1, 5, 10, 20]
+    fig, axs = plt.subplots(2, 2)
+    fig.set_figheight(10)
+    fig.set_figwidth(10)
+    row = -1
+    for enx, mismatches in enumerate(mismatch_range):
+        if enx % 2 == 0:
+            row += 1
+        col = enx % 2
+        all_folders = list(glob.glob(os.path.join(dfolder, '*')))
+        out_results = calculate_mismatch_scores(all_folders, mismatches)
+        pivot_score_key = max(out_results)
+        plot_precision_curve(out_results, pivot_score_key, mismatches, axs, row, col)
+    plt.tight_layout()
+    plt.savefig('comparison.png', dpi=300)

benchmarks/data_regression_experiment.sh

@@ -0,0 +1,84 @@
+#!/bin/bash
+
+set -euo pipefail  # Enable strict mode for safety
+
+# Configurable variables
+NUM_ROWS=1000000
+NUM_FEATURES=100
+INPUT_FILE="test_data_synthetic/data.csv"
+SIZES=('50000' '100000' '200000' '500000' '600000' '700000' '800000' '900000' '1000000')
+
+# Function to remove a directory safely
+remove_directory_safely() {
+    directory_to_remove=$1
+    if [ -d "$directory_to_remove" ]; then
+        echo "Removing directory: $directory_to_remove"
+        rm -rvf "$directory_to_remove"
+    else
+        echo "Directory does not exist, skipping: $directory_to_remove"
+    fi
+}
+
+# Function to generate random data
+generate_data() {
+    echo "Generating random data files with $NUM_ROWS rows and $NUM_FEATURES features..."
+    outrank --task data_generator --num_synthetic_rows $NUM_ROWS --num_synthetic_features $NUM_FEATURES
+    echo "Random data generation complete."
+}
+
+# Function to create subspaces from the data
+sample_subspaces() {
+    for i in "${SIZES[@]}"
+    do
+        dataset="test_data_synthetic/dataset_$i"
+        outfile="$dataset/data.csv"
+        mkdir -p "$dataset"
+
+        if [ -f "$INPUT_FILE" ]; then
+            echo "Sampling $i rows into $outfile..."
+            head -n $i "$INPUT_FILE" > "$outfile"
+            echo "Sampling for $outfile done."
+        else
+            echo "Input file $INPUT_FILE not found. Skipping sampling for $i rows."
+        fi
+    done
+}
+
+# Function to perform feature ranking
+feature_ranking() {
+    for i in "${SIZES[@]}"
+    do
+        dataset="test_data_synthetic/dataset_$i"
+        output_folder="./test_data_synthetic/ranking_$i"
+
+        if [ ! -d "$dataset" ]; then
+            echo "Dataset directory $dataset does not exist. Skipping ranking for $i rows."
+            continue
+        fi
+
+        echo "Proceeding with feature ranking for $i rows..."
+        outrank --task ranking --data_path "$dataset" --data_source csv-raw \
+                --combination_number_upper_bound 60 --output_folder "$output_folder" \
+                --disable_tqdm True
+
+        echo "Feature ranking summary for $i rows."
+        outrank --task ranking_summary --output_folder "$output_folder" --data_path "$dataset"
+        echo "Ranking for $i done."
+    done
+}
+
+# Function to analyze the rankings
+analyse_rankings() {
+    echo "Analyzing the rankings..."
+    python analyse_rankings.py test_data_synthetic
+    echo "Analysis complete."
+}
+
+# Main script execution
+remove_directory_safely test_data_synthetic/
+generate_data
+sample_subspaces
+feature_ranking
+analyse_rankings
+
+echo "Script execution finished."