address PR comments

docs/source/notebooks/ogb_biokg_demo.ipynb

@@ -31,7 +31,7 @@
    "source": [
     "import sys\n",
     "!{sys.executable} -m pip uninstall -y kg_topology_toolbox\n",
-    "!pip install -q git+https://github.com/graphcore-research/kg-topology-toolbox.git@refactor_kgtt --no-cache-dir\n",
+    "!pip install -q git+https://github.com/graphcore-research/kg-topology-toolbox.git --no-cache-dir\n",
     "!pip install -q jupyter ipywidgets ogb seaborn"
    ]
   },

src/kg_topology_toolbox/topology_toolbox.py

@@ -5,16 +5,15 @@
 Topology toolbox main functionalities
 """
 
-import warnings
 from functools import cache
 
 import numpy as np
 import pandas as pd
-from pandas.api.types import is_integer_dtype
 from scipy.sparse import coo_array
 
 from kg_topology_toolbox.utils import (
     aggregate_by_relation,
+    check_kg_df_structure,
     composition_count,
     jaccard_similarity,
     node_degrees_and_rels,
@@ -49,22 +48,11 @@ def __init__(
             The name of the column with the IDs of tail entities. Default: "t".
 
         """
-        for col_name in [head_column, relation_column, tail_column]:
-            if col_name in kg_df.columns:
-                if not is_integer_dtype(kg_df[col_name]):
-                    raise TypeError(
-                        f"Column {col_name} needs to be of an integer dtype"
-                    )
-            else:
-                raise ValueError(f"DataFrame {kg_df} has no column named {col_name}")
+        check_kg_df_structure(kg_df, head_column, relation_column, tail_column)
+
         self.df = kg_df[[head_column, relation_column, tail_column]].rename(
             columns={head_column: "h", relation_column: "r", tail_column: "t"}
         )
-        if self.df.duplicated().any():
-            warnings.warn(
-                "The Knowledge Graph contains duplicated edges"
-                " -- some functionalities may produce incorrect results"
-            )
         self.n_entity = self.df[["h", "t"]].max().max() + 1
         self.n_rel = self.df.r.max() + 1
 

src/kg_topology_toolbox/utils.py

@@ -4,15 +4,47 @@
 Utility functions
 """
 
+import warnings
 from collections.abc import Iterable
 from multiprocessing import Pool
 
 import numpy as np
 import pandas as pd
 from numpy.typing import NDArray
+from pandas.api.types import is_integer_dtype
 from scipy.sparse import coo_array, csc_array, csr_array
 
 
+def check_kg_df_structure(kg_df: pd.DataFrame, h: str, r: str, t: str) -> None:
+    """
+    Utility to perform sanity checks on the structure of the provided DataFrame,
+    to ensure that it encodes a Knowledge Graph in a compatible way.
+
+    :param kg_df:
+        The Knowledge Graph DataFrame.
+    :param h:
+        The name of the column with the IDs of head entities.
+    :param r:
+        The name of the column with the IDs of relation types.
+    :param t:
+        The name of the column with the IDs of tail entities.
+
+    """
+    # check h,r,t columns are present and of an integer type
+    for col_name in [h, r, t]:
+        if col_name in kg_df.columns:
+            if not is_integer_dtype(kg_df[col_name]):
+                raise TypeError(f"Column {col_name} needs to be of an integer dtype")
+        else:
+            raise ValueError(f"DataFrame {kg_df} has no column named {col_name}")
+    # check there are no duplicated (h,r,t) triples
+    if kg_df[[h, r, t]].duplicated().any():
+        warnings.warn(
+            "The Knowledge Graph contains duplicated edges"
+            " -- some functionalities may produce incorrect results"
+        )
+
+
 def node_degrees_and_rels(
     df: pd.DataFrame, column: str, n_entity: int, return_relation_list: bool
 ) -> pd.DataFrame: