Display 5 worst predictions at the end of evaluation

Based on !339 (merged)

Closes #245 (closed)

dan/ocr/evaluate.py

@@ -6,12 +6,16 @@ Evaluate a trained DAN model.
 import logging
 import random
 from argparse import ArgumentTypeError
+from itertools import chain
+from operator import attrgetter
 from pathlib import Path
 from typing import Dict, List
 
 import numpy as np
 import torch
 import torch.multiprocessing as mp
+from edlib import align, getNiceAlignment
+from prettytable import MARKDOWN, PrettyTable
 
 from dan.bio import convert
 from dan.ocr.manager.metrics import Inference
@@ -25,6 +29,7 @@ from nerval.utils import print_results
 logger = logging.getLogger(__name__)
 
 NERVAL_THRESHOLD = 0.30
+NB_WORST_PREDICTIONS = 5
 
 
 def parse_threshold(value: str) -> float:
@@ -66,6 +71,38 @@ def add_evaluate_parser(subcommands) -> None:
     parser.set_defaults(func=run)
 
 
+def print_worst_predictions(all_inferences: Dict[str, List[Inference]]):
+    table = PrettyTable(
+        field_names=[
+            "Image name",
+            "WER",
+            "Alignment between ground truth - prediction",
+        ]
+    )
+    table.set_style(MARKDOWN)
+
+    worst_inferences = sorted(
+        chain.from_iterable(all_inferences.values()),
+        key=attrgetter("wer"),
+        reverse=True,
+    )[:NB_WORST_PREDICTIONS]
+    for inference in worst_inferences:
+        alignment = getNiceAlignment(
+            align(
+                inference.ground_truth,
+                inference.prediction,
+                task="path",
+            ),
+            inference.ground_truth,
+            inference.prediction,
+        )
+        alignment_str = f'{alignment["query_aligned"]}\n{alignment["matched_aligned"]}\n{alignment["target_aligned"]}'
+        table.add_row([inference.image, round(inference.wer * 100, 2), alignment_str])
+
+    print(f"\n#### {NB_WORST_PREDICTIONS} worst prediction(s)\n")
+    print(table)
+
+
 def eval_nerval(
     all_inferences: Dict[str, List[Inference]],
     tokens: Path,
@@ -159,6 +196,8 @@ def eval(rank, config: dict, nerval_threshold: float, mlflow_logging: bool):
             threshold=nerval_threshold,
         )
 
+    print_worst_predictions(all_inferences)
+
 
 def run(config: dict, nerval_threshold: float):
     update_config(config)