fix: 🐛 add safeguard if there is a single column of a specific dtype, c…

…loses #31

src/arfs/association.py

@@ -194,7 +194,7 @@ def theils_u_matrix(X, sample_weight=None, n_jobs=-1, handle_na="drop"):
     dtypes_dic = create_dtype_dict(X, dic_keys="dtypes")
     cat_cols = dtypes_dic["cat"]
 
-    if cat_cols:
+    if cat_cols and (len(cat_cols) >= 2):
         # explicitely store the unique 2-permutation of column names
         # permutations and not combinations because U is asymmetric
         comb_list = [comb for comb in permutations(cat_cols, 2)]
@@ -353,7 +353,7 @@ def cramer_v_matrix(X, sample_weight=None, n_jobs=-1, handle_na="drop"):
     # in GLM supposed to be all the columns
     cat_cols = dtypes_dic["cat"]
 
-    if cat_cols:
+    if cat_cols and (len(cat_cols) >= 2):
         # explicitely store the unique 2-combinations of column names
         comb_list = [comb for comb in combinations(cat_cols, 2)]
         # define the number of cores
@@ -1212,51 +1212,66 @@ def association_matrix(
     """
     # sanity checks
     X, sample_weight = _check_association_input(X, sample_weight, handle_na)
+    dtypes_dic = create_dtype_dict(X, dic_keys="dtypes")
+
+    # Cramer's V only for categorical columns
+    # in GLM supposed to be all the columns
+    n_cat_cols = len(dtypes_dic["cat"])
+    n_num_cols = len(dtypes_dic["num"])
+
+    df_to_concat = []
 
     # num-num, NaNs already checked above, not repeating the process
-    if callable(num_num_assoc):
-        w_num_num = _callable_association_matrix_fn(
-            assoc_fn=num_num_assoc,
-            cols_comb=num_num_comb,
-            kind="num-num",
-            X=X,
-            sample_weight=sample_weight,
-            n_jobs=n_jobs,
-        )
-    else:
-        w_num_num = wcorr_matrix(
-            X, sample_weight, n_jobs, handle_na=None, method=num_num_assoc
-        )
+    if n_num_cols >= 2:
+        if callable(num_num_assoc):
+            w_num_num = _callable_association_matrix_fn(
+                assoc_fn=num_num_assoc,
+                cols_comb=num_num_comb,
+                kind="num-num",
+                X=X,
+                sample_weight=sample_weight,
+                n_jobs=n_jobs,
+            )
+        else:
+            w_num_num = wcorr_matrix(
+                X, sample_weight, n_jobs, handle_na=None, method=num_num_assoc
+            )
+        df_to_concat.append(w_num_num) 
 
     # nom-num
-    if callable(nom_num_assoc):
-        w_nom_num = _callable_association_matrix_fn(
-            assoc_fn=nom_num_assoc,
-            cols_comb=nom_num_comb,
-            kind="nom-num",
-            X=X,
-            sample_weight=sample_weight,
-            n_jobs=n_jobs,
-        )
-    else:
-        w_nom_num = correlation_ratio_matrix(X, sample_weight, n_jobs, handle_na=None)
+    if (n_num_cols >= 1) and (n_cat_cols >= 1):
+        if callable(nom_num_assoc):
+            w_nom_num = _callable_association_matrix_fn(
+                assoc_fn=nom_num_assoc,
+                cols_comb=nom_num_comb,
+                kind="nom-num",
+                X=X,
+                sample_weight=sample_weight,
+                n_jobs=n_jobs,
+            )
+        else:
+            w_nom_num = correlation_ratio_matrix(X, sample_weight, n_jobs, handle_na=None)
+        df_to_concat.append(w_nom_num) 
 
     # nom-nom
-    if callable(nom_nom_assoc):
-        w_nom_nom = _callable_association_matrix_fn(
-            assoc_fn=nom_nom_assoc,
-            cols_comb=nom_nom_comb,
-            kind="nom-nom",
-            X=X,
-            sample_weight=sample_weight,
-            n_jobs=n_jobs,
-        )
-    elif nom_nom_assoc == "cramer":
-        w_nom_nom = cramer_v_matrix(X, sample_weight, n_jobs, handle_na=None)
-    else:
-        w_nom_nom = theils_u_matrix(X, sample_weight, n_jobs, handle_na=None)
+    if n_cat_cols >= 2:
+        if callable(nom_nom_assoc):
+            w_nom_nom = _callable_association_matrix_fn(
+                assoc_fn=nom_nom_assoc,
+                cols_comb=nom_nom_comb,
+                kind="nom-nom",
+                X=X,
+                sample_weight=sample_weight,
+                n_jobs=n_jobs,
+            )
+        elif nom_nom_assoc == "cramer":
+            w_nom_nom = cramer_v_matrix(X, sample_weight, n_jobs, handle_na=None)
+        else:
+            w_nom_nom = theils_u_matrix(X, sample_weight, n_jobs, handle_na=None)
+        df_to_concat.append(w_nom_nom) 
+
 
-    return pd.concat([w_num_num, w_nom_num, w_nom_nom], ignore_index=True)
+    return pd.concat(df_to_concat, ignore_index=True)
 
 
 def _callable_association_series_fn(