[ENH] Move tab_data_to_tuple to utils

abl/utils/__init__.py

@@ -1,6 +1,6 @@
 from .cache import Cache, abl_cache
 from .logger import ABLLogger, print_log
-from .utils import confidence_dist, flatten, hamming_dist, reform_list, to_hashable
+from .utils import confidence_dist, flatten, hamming_dist, reform_list, to_hashable, tab_data_to_tuple
 
 __all__ = [
     "Cache",
@@ -12,4 +12,5 @@ __all__ = [
     "reform_list",
     "to_hashable",
     "abl_cache",
+    "tab_data_to_tuple",
 ]

abl/utils/utils.py

@@ -154,4 +154,10 @@ def restore_from_hashable(x):
         return [restore_from_hashable(item) for item in x]
     return x
 
-
+def tab_data_to_tuple(X, y, reasoning_result = 0):
+    '''
+    Convert a tabular data to a tuple by adding a dimension to each element of X and y. The tuple is a list of three elements: data, label, and reasoning result.
+    '''
+    if len(X) != len(y):
+        raise ValueError("The length of X and y should be the same, but got {} and {}.".format(len(X), len(y)))
+    return ([[x] for x in X], [[y_item] for y_item in y], [reasoning_result] * len(y))

examples/zoo/main.py

@@ -8,14 +8,12 @@ from abl.bridge import SimpleBridge
 from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
 from abl.learning import ABLModel
 from abl.reasoning import Reasoner
-from abl.utils import ABLLogger, confidence_dist, print_log
+from abl.utils import ABLLogger, confidence_dist, print_log, tab_data_to_tuple
 
 from get_dataset import load_and_preprocess_dataset, split_dataset
 from kb import ZooKB
 
 
-def transform_tab_data(X, y):
-    return ([[x] for x in X], [[y_item] for y_item in y], [0] * len(y))
 
 def consitency(data_example, candidates, candidate_idxs, reasoning_results):
     pred_prob = data_example.pred_prob
@@ -39,9 +37,9 @@ def main():
     
     X, y = load_and_preprocess_dataset(dataset_id=62)
     X_label, y_label, X_unlabel, y_unlabel, X_test, y_test = split_dataset(X, y, test_size=0.3)
-    label_data = transform_tab_data(X_label, y_label)
-    test_data = transform_tab_data(X_test, y_test)
-    train_data = transform_tab_data(X_unlabel, y_unlabel)
+    label_data = tab_data_to_tuple(X_label, y_label)
+    test_data = tab_data_to_tuple(X_test, y_test)
+    train_data = tab_data_to_tuple(X_unlabel, y_unlabel)
 
     ### Building the Learning Part
     print_log("Building the Learning Part.", logger="current")

examples/zoo/zoo.ipynb

@@ -27,7 +27,7 @@
     "from abl.data.evaluation import ReasoningMetric, SymbolAccuracy\n",
     "from abl.learning import ABLModel\n",
     "from abl.reasoning import Reasoner\n",
-    "from abl.utils import ABLLogger, confidence_dist, print_log\n",
+    "from abl.utils import ABLLogger, confidence_dist, print_log, tab_data_to_tuple\n",
     "\n",
     "from get_dataset import load_and_preprocess_dataset, split_dataset\n",
     "from kb import ZooKB"
@@ -106,11 +106,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def transform_tab_data(X, y):\n",
-    "    return ([[x] for x in X], [[y_item] for y_item in y], [0] * len(y))\n",
-    "label_data = transform_tab_data(X_label, y_label)\n",
-    "test_data = transform_tab_data(X_test, y_test)\n",
-    "train_data = transform_tab_data(X_unlabel, y_unlabel)"
+    "label_data = tab_data_to_tuple(X_label, y_label, reasoning_result = 0)\n",
+    "test_data = tab_data_to_tuple(X_test, y_test, reasoning_result = 0)\n",
+    "train_data = tab_data_to_tuple(X_unlabel, y_unlabel, reasoning_result = 0)"
    ]
   },
   {