Delete run_experiment.py

run_experiment.py

@@ -1,42 +0,0 @@
-import json
-from pathlib import Path
-
-from assignment5.chrf import calculate_chrf
-from assignment5.mbr import select_best_hypothesis
-
-# Load data
-data_dir = Path(__file__).parent / "mt_data"
-source_sentences = (data_dir / "source_sentences.txt").read_text().splitlines()
-reference_translations = (data_dir / "reference_translations.txt").read_text().splitlines()
-beam_search_translations = (data_dir / "beam_search_translations.txt").read_text().splitlines()
-
-with open(data_dir / "samples.jsonl") as f:
-    samples = [json.loads(line)["samples"] for line in f]
-
-
-
-# Step 1: Select the best hypothesis for each source sentence using MBR decoding
-mbr_translations = [select_best_hypothesis(sample_set) for sample_set in samples]
-
-# Step 2: Calculate ChrF scores for MBR translations
-mbr_chrf_scores = [
-    calculate_chrf(mbr_translation, reference)
-    for mbr_translation, reference in zip(mbr_translations, reference_translations)
-]
-average_mbr_chrf = sum(mbr_chrf_scores) / len(mbr_chrf_scores)
-
-# Step 3: Calculate ChrF scores for beam search translations
-beam_chrf_scores = [
-    calculate_chrf(beam_translation, reference)
-    for beam_translation, reference in zip(beam_search_translations, reference_translations)
-]
-average_beam_chrf = sum(beam_chrf_scores) / len(beam_chrf_scores)
-
-# Step 4: Print the results
-print(f"Average ChrF score for MBR decoding: {average_mbr_chrf:.2f}")
-print(f"Average ChrF score for beam search: {average_beam_chrf:.2f}")
-
-if average_mbr_chrf > average_beam_chrf:
-    print("MBR decoding produced better translations.")
-else:
-    print("Beam search produced better translations.")