New ELO building stuff finished up I think

pdelfin/eval/buildelo.py

@@ -3,6 +3,8 @@
 import dataclasses
 import random
 import re
+from concurrent.futures import ProcessPoolExecutor, as_completed
+import functools
 
 from tqdm import tqdm
 from itertools import combinations
@@ -13,18 +15,13 @@
 
 from pdelfin.eval.evalhtml import create_review_html
 
-s3_client = boto3.client('s3')
-
 @dataclasses.dataclass
 class Comparison:
     pdf_path: str
-
     comparison_a_path: str
     comparison_b_path: str
-
     comparison_a_str: str
     comparison_b_str: str
-
     alignment: float
 
     @property
@@ -35,7 +32,48 @@ def comparison_a_method(self):
     def comparison_b_method(self):
         return re.search(r'page[0-9]+_(\w+)\.md$', self.comparison_b_path).group(1)
 
-def build_review_page(args, comparisons):
+def process_single_pdf(pdf_path, all_mds, comparisons, segmenter_name="spacy"):
+    """Process a single PDF and return its comparisons."""
+    # Create resources inside the worker process
+    s3_client = boto3.client('s3')
+    segmenter = SpacySegmenter(segmenter_name)
+    aligner = HirschbergAligner(match_score=1,
+                               mismatch_score=-1,
+                               indel_score=-1)
+    comparer = DocumentEditSimilarity(segmenter=segmenter, aligner=aligner)
+
+    pdf_comps = []
+    result_comps = []
+
+    # Get all comparison files for this PDF
+    for comp in comparisons:
+        comp_path = pdf_path.replace(".pdf", f"_{comp}.md")
+        if comp_path in all_mds:
+            pdf_comps.append(comp_path)
+
+    # Generate all possible combinations
+    for (compa, compb) in combinations(pdf_comps, 2):
+        if random.choice([True, False]):
+            compa, compb = compb, compa
+
+        # Get the text content
+        text_a = get_s3_bytes(s3_client, compa).decode("utf-8")
+        text_b = get_s3_bytes(s3_client, compb).decode("utf-8")
+
+        result_comps.append(
+            Comparison(
+                pdf_path=pdf_path,
+                comparison_a_path=compa,
+                comparison_b_path=compb,
+                comparison_a_str=text_a,
+                comparison_b_str=text_b,
+                alignment=comparer.compute(text_a, text_b)
+            )
+        )
+
+    return result_comps
+
+def build_review_page(args, comparisons, index=0):
     page_data = []
 
     for comp in comparisons:
@@ -50,7 +88,8 @@ def build_review_page(args, comparisons):
                 "alignment": comp.alignment
             })
 
-    create_review_html(page_data, args.name + ".html")
+    report_name = f"{args.name}{f'_{index}' if args.num_copies > 1 else ''}.html"
+    create_review_html(page_data, report_name)
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
@@ -67,11 +106,23 @@ def build_review_page(args, comparisons):
         type=int,
         help="Number of entries to show on the generated review page",
     )
+    parser.add_argument(
+        '--max_workers',
+        type=int,
+        default=None,
+        help="Maximum number of worker processes to use for parallel processing",
+    )
     parser.add_argument(
         '--comparisons',
-        default=["pdelf", "gotocr", "gotocr_format", "mineru"],
+        default=["pdelf", "marker", "gotocr_format", "mineru"],
         help="Different variants to compare against"
     )
+    parser.add_argument(
+        '--num_copies',
+        default=1,
+        type=int,
+        help="Number of reports to generate, labeled _0, _1, etc. if greater than 1",
+    )
     parser.add_argument(
         's3_path',
         type=str,
@@ -80,47 +131,50 @@ def build_review_page(args, comparisons):
 
     args = parser.parse_args()
 
-    segmenter = SpacySegmenter("spacy")
-    aligner = HirschbergAligner(match_score=1,
-                                mismatch_score=-1,
-                                indel_score=-1)
-    comparer = DocumentEditSimilarity(segmenter=segmenter, aligner=aligner)
-
-    all_comps = []
+    # Create S3 client only for initial file listing
+    s3_client = boto3.client('s3')
+
+    # Get all PDFs and MD files
     all_pdfs = set(expand_s3_glob(s3_client, args.s3_path + "/*.pdf"))
     all_mds = set(expand_s3_glob(s3_client, args.s3_path + "/*.md"))
 
-    for pdf_path in tqdm(all_pdfs):
-        pdf_comps = []
-        for comp in args.comparisons:
-            comp_path = pdf_path.replace(".pdf", f"_{comp}.md")
-            if comp_path in all_mds:
-                pdf_comps.append(comp_path)
-
-        for (compa, compb) in combinations(pdf_comps, 2):
-            if random.choice([True, False]):
-                compa, compb = compb, compa
-
-            text_a = get_s3_bytes(s3_client, compa).decode("utf-8")
-            text_b = get_s3_bytes(s3_client, compb).decode("utf-8")
-
-            all_comps.append(
-                Comparison(pdf_path=pdf_path,
-                comparison_a_path=compa,
-                comparison_b_path=compb,
-                comparison_a_str=text_a,
-                comparison_b_str=text_b,
-                alignment=comparer.compute(text_a, text_b)
-                )
-            )
-
-        # if len(all_comps) > 1000:
-        #     break
-
-    # Sorting by alignment score is problemetic, because it only returns completely pathological parses
-    # And we miss cases where the parse is similar, but one thing hallucinated a word or two, etc.
-    #all_comps.sort(key=lambda c: c.alignment)
+    all_comps = []
 
+    # Create a partial function with all the common arguments
+    process_pdf = functools.partial(
+        process_single_pdf,
+        all_mds=all_mds,
+        comparisons=args.comparisons
+    )
+
+    # Use ProcessPoolExecutor for parallel processing
+    with ProcessPoolExecutor(max_workers=args.max_workers) as executor:
+        # Submit all PDF processing tasks
+        future_to_pdf = {
+            executor.submit(process_pdf, pdf_path): pdf_path 
+            for pdf_path in all_pdfs
+        }
+
+        # Process results as they complete using tqdm for progress
+        for future in tqdm(as_completed(future_to_pdf), total=len(all_pdfs)):
+            pdf_path = future_to_pdf[future]
+            try:
+                pdf_results = future.result()
+                all_comps.extend(pdf_results)
+            except Exception as e:
+                print(f"Error processing {pdf_path}: {str(e)}")
+
+    # Shuffle the results
     random.shuffle(all_comps)
 
-    result = build_review_page(args, all_comps[0:args.review_size])
+    # Generate the specified number of copies of the report
+    for i in range(args.num_copies):
+        start_index = i * args.review_size
+        end_index = start_index + args.review_size
+
+        # Check if there is enough data for the next report
+        if start_index >= len(all_comps):
+            print(f"Not enough data to generate report {i}. Stopping early.")
+            break
+
+        build_review_page(args, all_comps[start_index:end_index], index=i)