refactor(frontend): simplify train_size and test_size derivation in t…

…est_train_split function

ivy/functional/frontends/sklearn/model_selection/_split.py

@@ -40,27 +40,15 @@ def train_test_split(*arrays, test_size=None, train_size=None, random_state=None
     # TODO: implement stratify
     if stratify is not None:
         raise NotImplementedError
-    n_arrays = len(arrays)
-    if n_arrays == 0:
+    if len(arrays) == 0:
         raise ValueError("At least one array required as input")
     if test_size is None and train_size is None:
         test_size = 0.25
     n_samples = arrays[0].shape[0]
-    test_size_type, train_size_type = type(test_size), type(train_size)
-    if "f" in str(test_size_type):
-        n_test = ivy.ceil(test_size * n_samples)
-    elif "i" in str(test_size_type):
-        n_test = float(test_size)
-    else:
-        n_test = 0
-
-    if "f" in str(train_size_type):
-        n_train = ivy.floor(train_size * n_samples)
-    elif "i" in str(train_size_type):
-        n_train = float(train_size)
-    else:
-        n_train = 0
-
+    n_train = ivy.floor(train_size * n_samples) if isinstance(train_size, float) \
+        else float(train_size) if isinstance(train_size, int) else None
+    n_test = ivy.ceil(test_size * n_samples) if isinstance(test_size, float) \
+        else float(test_size) if isinstance(test_size, int) else None
     if train_size is None:
         n_train = n_samples - n_test
     elif test_size is None: