From dd80f72b312cdc9b3c2aabb766f6e1073fb3dac2 Mon Sep 17 00:00:00 2001
From: kubapok <jakub.pokrywka@gmail.com>
Date: Sun, 4 Aug 2024 14:02:57 +0200
Subject: [PATCH] add 1

---
 1.py | 64 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 64 insertions(+)
 create mode 100644 1.py

diff --git a/1.py b/1.py
new file mode 100644
index 0000000..65aa3d8
--- /dev/null
+++ b/1.py
@@ -0,0 +1,64 @@
+import copy
+import sys
+from sacrebleu.metrics import BLEU, CHRF, TER
+import pandas as pd
+
+
+# pip install sacrebleu pandas
+# example usage one arg python 1.py model_cv_1_0_preds.csv
+# example usage mulitple args python 1.py model_cv_1_0_preds.csv model_cv_1_1_preds.csv model_cv_1_2_preds.csv
+
+PREDICTED_COLUMN_NAME = 'query_annot'
+LABEL_COLUMN_NAME = 'target_annot'
+PREDICTED_COLUMN_NAME = 'plm_names'
+LABEL_COLUMN_NAME = 'targets'
+COLUMN_SEPARATOR = ','
+
+
+predicted_all_splits = list()
+label_all_splits = list()
+
+bleu = BLEU(effective_order=True)
+chrf = CHRF()
+def get_statistics(r):
+    metrics = dict()
+    r['score_bleu'] = r.apply(
+        lambda row: round(bleu.sentence_score(row[PREDICTED_COLUMN_NAME], [row[LABEL_COLUMN_NAME]]).score, 2), axis=1)
+    r['score_chrf'] = r.apply(
+        lambda row: round(chrf.sentence_score(row[PREDICTED_COLUMN_NAME], [row[LABEL_COLUMN_NAME]]).score, 2), axis=1)
+    r['score_exact_match'] = r.apply(lambda row: 1 if row[PREDICTED_COLUMN_NAME] == row[LABEL_COLUMN_NAME] else 0,
+                                     axis=1)
+
+    hyps = r[PREDICTED_COLUMN_NAME].tolist()
+    references = [r[LABEL_COLUMN_NAME].tolist(), ]
+
+    metrics['bleu'] = round(bleu.corpus_score(hyps, references).score, 2)
+    metrics['chrf'] = round(chrf.corpus_score(hyps, references).score, 2)
+    metrics['exact'] = round(float(100 * r['score_exact_match'].mean()), 2)
+
+    return r, metrics
+
+
+for FILE_PATH in sys.argv[1:]:
+    r = pd.read_csv(FILE_PATH,sep = COLUMN_SEPARATOR)
+
+    print(FILE_PATH + ':')
+    report_with_metrics, metrics = get_statistics(r)
+
+    predicted_all_splits.extend(r[PREDICTED_COLUMN_NAME].to_list())
+    label_all_splits.extend(r[LABEL_COLUMN_NAME].to_list())
+
+    print(metrics)
+
+    report_with_metrics = report_with_metrics.sort_values(by='score_chrf', ascending=False)[
+        [LABEL_COLUMN_NAME, PREDICTED_COLUMN_NAME, 'score_bleu', 'score_chrf', 'score_exact_match']].drop_duplicates()
+    report_with_metrics.to_csv(FILE_PATH.replace('.', '_metrics.'), sep=COLUMN_SEPARATOR, index=False)
+
+if len(sys.argv) > 2:
+    print('ALL SPLITS:')
+    label_all_splits = [label_all_splits, ]
+    metrics = dict()
+    metrics['bleu'] = round(bleu.corpus_score(predicted_all_splits, label_all_splits).score, 2)
+    metrics['chrf'] = round(chrf.corpus_score(predicted_all_splits, label_all_splits).score, 2)
+    metrics['exact'] = round(float(100 * r['score_exact_match'].mean()), 2)
+    print(metrics)
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