feat(eval): Correct table metrics evaluations (#2615)

This PR:
- replaces `-1.0` value in table metrics with `nan`s 
- corrected rows filtering basing on above

CHANGELOG.md

@@ -12,6 +12,7 @@
 * **Include warnings** about the potential risk of installing a version of `pandoc` which does not support RTF files + instructions that will help resolve that issue.
 * **Incorporate the `install-pandoc` Makefile recipe** into relevant stages of CI workflow, ensuring it is a version that supports RTF input files.
 * **Fix Google Drive source key** Allow passing string for source connector key.
+* **Fix table structure evaluations calculations** Replaced special value `-1.0` with `np.nan` and corrected rows filtering of files metrics basing on that.
 
 ## 0.12.5
 

unstructured/metrics/evaluate.py

@@ -327,20 +327,20 @@ def measure_table_structure_accuracy(
             ]
         ).transpose()
     else:
-        # filter out documents with no tables
+        element_metrics_results = {}
-        having_table_df = df[df["total_tables"] > 0]
+        for metric in [
-        # compute aggregated metrics for tables
+            "total_tables",
-        agg_df = having_table_df.agg(
+            "table_level_acc",
-            {
+            "element_col_level_index_acc",
-                "total_tables": [_mean, _stdev, _pstdev, "count"],
+            "element_row_level_index_acc",
-                "table_level_acc": [_mean, _stdev, _pstdev, "count"],
+            "element_col_level_content_acc",
-                "element_col_level_index_acc": [_mean, _stdev, _pstdev, "count"],
+            "element_row_level_content_acc",
-                "element_row_level_index_acc": [_mean, _stdev, _pstdev, "count"],
+        ]:
-                "element_col_level_content_acc": [_mean, _stdev, _pstdev, "count"],
+            metric_df = df[df[metric].notnull()]
-                "element_row_level_content_acc": [_mean, _stdev, _pstdev, "count"],
+            element_metrics_results[metric] = (
-            }
+                metric_df[metric].agg([_mean, _stdev, _pstdev, _count]).transpose()
-        ).transpose()
+            )
-        agg_df = agg_df.reset_index()
+        agg_df = pd.DataFrame(element_metrics_results).transpose().reset_index()
         agg_df.columns = agg_headers
 
     _write_to_file(export_dir, "all-docs-table-structure-accuracy.tsv", df)

unstructured/metrics/table/table_eval.py

@@ -143,10 +143,6 @@ class TableEvalProcessor:
         """
         total_predicted_tables = 0
         total_tables = 0
-        total_row_index_acc = []
-        total_col_index_acc = []
-        total_row_content_acc = []
-        total_col_content_acc = []
 
         predicted_table_data = extract_and_convert_tables_from_prediction(
             self.prediction,
@@ -168,29 +164,16 @@ class TableEvalProcessor:
             matched_indices,
             cutoff=self.cutoff,
         )
-        if metrics:
-            total_col_index_acc.append(metrics["col_index_acc"])
-            total_row_index_acc.append(metrics["row_index_acc"])
-            total_col_content_acc.append(metrics["col_content_acc"])
-            total_row_content_acc.append(metrics["row_content_acc"])
 
         return TableEvaluation(
             total_tables=total_tables,
             table_level_acc=(
-                round(total_predicted_tables / total_tables, 2) if total_tables else -1.0
+                round(total_predicted_tables / total_tables, 2) if total_tables else np.nan
-            ),
-            element_col_level_index_acc=(
-                round(np.mean(total_col_index_acc), 2) if len(total_col_index_acc) > 0 else -1.0
-            ),
-            element_row_level_index_acc=(
-                round(np.mean(total_row_index_acc), 2) if len(total_row_index_acc) > 0 else -1.0
-            ),
-            element_col_level_content_acc=(
-                round(np.mean(total_col_content_acc), 2) if len(total_col_content_acc) > 0 else -1.0
-            ),
-            element_row_level_content_acc=(
-                round(np.mean(total_row_content_acc), 2) if len(total_row_content_acc) > 0 else -1.0
             ),
+            element_col_level_index_acc=metrics.get("col_index_acc", np.nan),
+            element_row_level_index_acc=metrics.get("row_index_acc", np.nan),
+            element_col_level_content_acc=metrics.get("col_content_acc", np.nan),
+            element_row_level_content_acc=metrics.get("row_content_acc", np.nan),
         )