add more info on output and reformat in md

• format the output in md
• add more info on output

nerval/evaluate.py

@@ -10,6 +10,7 @@ import re
 
 import editdistance
 import edlib
+import termtables as tt
 
 THRESHOLD = 0.30
 NOT_ENTITY_TAG = "O"
@@ -22,7 +23,7 @@ def parse_bio(path: str) -> dict:
     Output format : { "words": str, "tags": list; "entity_count" : { tag : int} }
     """
 
-    assert os.path.exists(path)
+    assert os.path.exists(path), f"Error: Input file {path} does not exist"
 
     words = []
     tags = []
@@ -269,9 +270,12 @@ def compute_scores(
             else 2 * (prec * rec) / (prec + rec)
         )
 
+        scores[tag]["predicted"] = nb_predict
+        scores[tag]["matched"] = nb_match
         scores[tag]["P"] = prec
         scores[tag]["R"] = rec
         scores[tag]["F1"] = f1
+        scores[tag]["Support"] = nb_annot
 
     return scores
 
@@ -281,18 +285,25 @@ def print_results(scores: dict):
 
     None values are kept to indicate the absence of a certain tag in either annotation or prediction.
     """
-    logging.info("-- Results --")
-
+    header = ["tag", "predicted", "matched", "Precision", "Recall", "F1", "Support"]
+    results = []
     for tag in sorted(scores.keys())[::-1]:
+        prec = None if scores[tag]["P"] is None else round(scores[tag]["P"], 3)
+        rec = None if scores[tag]["R"] is None else round(scores[tag]["R"], 3)
+        f1 = None if scores[tag]["F1"] is None else round(scores[tag]["F1"], 3)
 
-        prec = None if scores[tag]["P"] is None else round(scores[tag]["P"], 2)
-        rec = None if scores[tag]["R"] is None else round(scores[tag]["R"], 2)
-        f1 = None if scores[tag]["F1"] is None else round(scores[tag]["F1"], 2)
-
-        logging.info(f"{tag} :")
-        logging.info(f"P = {prec}")
-        logging.info(f"R = {rec}")
-        logging.info(f"F1 = {f1}")
+        results.append(
+            [
+                tag,
+                scores[tag]["predicted"],
+                scores[tag]["matched"],
+                prec,
+                rec,
+                f1,
+                scores[tag]["Support"],
+            ]
+        )
+    tt.print(results, header, style=tt.styles.markdown)
 
 
 def run(annotation: str, prediction: str) -> dict: