parse generic table into pairs; search for labels when missing

src/relation_validator/utils/utils.py

@@ -22,6 +22,14 @@
     # LIMIT
 """
 
+QUERY_LABEL = """
+    VALUES (?subject) {{
+        {terms}
+    }}
+    ?subject rdfs:label ?object .
+    # LIMIT
+"""
+
 DEFAULT_STYLE = "obograph-style.json"
 
 
@@ -211,14 +219,25 @@ def to_set(term_pairs: Set[str]) -> Set[Tuple[str, str]]:
 
 def get_labels(data: DataFrame) -> Dict[str, str]:
     """
-    Get labels from terms in table
+    Get labels from terms in table or search for missing labels in Ubergraph
     """
     labels = {}
+    terms = set()
     for _, row in data.iterrows():
         if row["s"] not in labels:
-            labels[row["s"]] = row["slabel"]
+            if row["slabel"] != "":
+                labels[row["s"]] = row["slabel"]
+            else:
+                terms.add(f"({row['s']})")
         if row["o"] not in labels:
-            labels[row["o"]] = row["olabel"]
+            if row["olabel"] != "":
+                labels[row["o"]] = row["olabel"]
+            else:
+                terms.add(f"({row['o']})")
+
+    ont_labels = search_labels(terms)
+    for term, label in ont_labels:
+        labels[term] = label
 
     return labels
 
@@ -234,4 +253,60 @@ def tsv_or_csv(filename: Path) -> tuple[str, str]:
     if "tsv" in extension:
         sep = "\t"
 
-    return temp_filename, sep
+    return temp_filename, sep, extension
+
+
+def save_tsv_or_csv(data: DataFrame, filename: str):
+    """
+    Save DataFrame with file proprer TSV or CSV extension
+    """
+    if str(filename).endswith(".csv"):
+        sep = ','
+    elif str(filename).endswith(".tsv"):
+        sep = '\t'
+    else:
+        raise ValueError("Unsupported file extension. Please provide a .csv or .tsv file.")
+    data.to_csv(filename, sep=sep, index=False)
+
+
+def parse_table(data: DataFrame) -> DataFrame:
+    """
+    Parse generic tree table to pairs table
+    """
+    terms = {}
+    # Generate dict with terms and label
+    for _, row in data.iterrows():
+        for term_id, label in chunks(data.columns, 2):
+            terms[row.loc[term_id]] = row.loc[label]
+
+    table_parsed = []
+    for _, row in data.filter(regex=".*ID").iterrows():
+        for current, next in zip(row, row[1:]):
+            r = {}
+            r['s'] = next
+            r['slabel'] = ""
+            r['user_slabel'] = terms[next]
+            r['o'] = current
+            r['olabel'] = ""
+            r['user_olabel'] = terms[current]
+            table_parsed.append(r)
+
+    return DataFrame.from_records(table_parsed).drop_duplicates()
+
+
+def search_labels(terms: set) -> set:
+    """
+    Search label for terms in Ubergraph
+    """
+    result = set()
+    for chunk in chunks(list(terms), 90):
+        result = result.union(
+            extract_results(
+                query_ubergraph(
+                    QUERY_LABEL.format(
+                        terms=" ".join(chunk)
+                    )
+                )
+            )
+        )
+    return result

src/relation_validator/validator.py

@@ -7,8 +7,9 @@
 import pandas as pd
 
 from .utils.utils import (get_config, get_labels, get_obograph,
-                          get_ontologies_version, get_pairs, save_obograph,
-                          split_terms, to_set, tsv_or_csv, verify_relationship)
+                          get_ontologies_version, get_pairs, parse_table,
+                          save_obograph, save_tsv_or_csv, split_terms, to_set,
+                          tsv_or_csv, verify_relationship)
 
 
 def run_validation(
@@ -45,15 +46,17 @@ def validate(args):
         return exit
 
     filename = config["filename"]
-    temp_filename, sep = tsv_or_csv(filename)
+    temp_filename, sep, ext = tsv_or_csv(filename)
 
     data = pd.read_csv(filename, sep=sep)
+    if config["to_be_parsed"]:
+        data = parse_table(data)
+        save_tsv_or_csv(data, f"{temp_filename}_parsed{ext}")
 
     report, rel_terms = run_validation(data, config["relationships"])
 
     output_filename = args.output
-    _, sep = tsv_or_csv(output_filename)
-    report.to_csv(output_filename, sep=sep, index=False)
+    save_tsv_or_csv(report, output_filename)
 
     labels = get_labels(data)
     graph = get_obograph(rel_terms, labels, config["relationships"])