[MNT] support ListData in reasoning

abl/evaluation/semantics_metric.py

@@ -1,11 +1,11 @@
 from typing import Optional, Sequence
 
-from ..reasoning import BaseKB
+from ..reasoning import KBBase
 from .base_metric import BaseMetric
 
 
 class SemanticsMetric(BaseMetric):
-    def __init__(self, kb: BaseKB = None, prefix: Optional[str] = None) -> None:
+    def __init__(self, kb: KBBase = None, prefix: Optional[str] = None) -> None:
         super().__init__(prefix)
         self.kb = kb
 

abl/reasoning/kb.py

@@ -9,7 +9,7 @@ from functools import lru_cache
 import numpy as np
 import pyswip
 
-from abl.utils.utils import flatten, reform_idx, hamming_dist, to_hashable, restore_from_hashable
+from ..utils.utils import flatten, reform_idx, hamming_dist, to_hashable, restore_from_hashable
 
 
 class KBBase(ABC):

abl/reasoning/reasoner.py

@@ -1,6 +1,6 @@
 import numpy as np
 from zoopt import Dimension, Objective, Parameter, Opt
-from abl.utils.utils import (
+from ..utils.utils import (
     confidence_dist,
     flatten,
     reform_idx,
@@ -191,7 +191,7 @@ class ReasonerBase:
             return max_revision
     
     def abduce(
-        self, pred_prob, pred_pseudo_label, y, max_revision=-1, require_more_revision=0
+        self, data_sample, max_revision=-1, require_more_revision=0
     ):
         """
         Perform abductive reasoning on the given prediction data.
@@ -219,9 +219,13 @@ class ReasonerBase:
             A revised pseudo label through abductive reasoning, which is consistent with the
             knowledge base.
         """
-        symbol_num = len(flatten(pred_pseudo_label))
+        symbol_num = data_sample.elements_num("pred_pseudo_label")
         max_revision_num = self._get_max_revision_num(max_revision, symbol_num)
-
+        
+        pred_pseudo_label = data_sample.pred_pseudo_label[0]
+        pred_prob = data_sample.pred_prob[0]
+        y = data_sample.Y[0]
+        
         if self.use_zoopt:
             solution = self.zoopt_get_solution(
                 symbol_num, pred_pseudo_label, pred_prob, y, max_revision_num
@@ -237,20 +241,18 @@ class ReasonerBase:
         return candidate
 
     def batch_abduce(
-        self, pred_probs, pred_pseudo_labels, Ys, max_revision=-1, require_more_revision=0
+        self, data_samples, max_revision=-1, require_more_revision=0
     ):
         """
         Perform abductive reasoning on the given prediction data in batches.
         For detailed information, refer to `abduce`.
         """
-        return [
-            self.abduce(
-                pred_prob, pred_pseudo_label, Y, max_revision, require_more_revision
-            )
-            for pred_prob, pred_pseudo_label, Y in zip(
-                pred_probs, pred_pseudo_labels, Ys
-            )
+        abduced_pseudo_label = [
+            self.abduce(data_sample, max_revision, require_more_revision)
+                for data_sample in data_samples
         ]
+        data_samples.abduced_pseudo_label = abduced_pseudo_label
+        return abduced_pseudo_label
 
     # def _batch_abduce_helper(self, args):
     #     z, prob, y, max_revision, require_more_revision = args

abl/utils/utils.py

@@ -192,7 +192,7 @@ def to_hashable(x):
     return x
 
 
-def hashable_to_list(x):
+def restore_from_hashable(x):
     """
     Convert a nested tuple back to a nested list.
 
@@ -208,7 +208,7 @@ def hashable_to_list(x):
         otherwise the original input.
     """
     if isinstance(x, tuple):
-        return [hashable_to_list(item) for item in x]
+        return [restore_from_hashable(item) for item in x]
     return x
 
 

examples/mnist_add/mnist_add_example.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,16 +13,16 @@
     "\n",
     "from abl.learning import BasicNN, ABLModel\n",
     "from abl.bridge import SimpleBridge\n",
-    "from abl.evaluation import SymbolMetric, ABLMetric\n",
+    "from abl.evaluation import SymbolMetric\n",
     "from abl.utils import ABLLogger\n",
     "\n",
-    "from models.nn import LeNet5\n",
+    "from examples.models.nn import LeNet5\n",
     "from examples.mnist_add.datasets.get_mnist_add import get_mnist_add"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,19 +40,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Initialize knowledge base and abducer\n",
     "class add_KB(KBBase):\n",
-    "    def __init__(self, pseudo_label_list=list(range(10)), prebuild_GKB=False, GKB_len_list=[2], max_err=0, use_cache=True):\n",
-    "        super().__init__(pseudo_label_list, prebuild_GKB, GKB_len_list, max_err, use_cache)\n",
+    "    def __init__(self, pseudo_label_list=list(range(10)), max_err=0, use_cache=True):\n",
+    "        super().__init__(pseudo_label_list, max_err, use_cache)\n",
     "\n",
     "    def logic_forward(self, nums):\n",
     "        return sum(nums)\n",
     "\n",
-    "kb = add_KB(prebuild_GKB=True)\n",
+    "kb = add_KB()\n",
     "\n",
     "# kb = prolog_KB(pseudo_label_list=list(range(10)), pl_file='datasets/mnist_add/add.pl')\n",
     "abducer = ReasonerBase(kb, dist_func=\"confidence\")"
@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -81,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,7 +93,6 @@
     "    optimizer,\n",
     "    device,\n",
     "    save_interval=1,\n",
-    "    save_dir=logger.save_dir,\n",
     "    batch_size=32,\n",
     "    num_epochs=1,\n",
     ")"
@@ -109,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -129,12 +128,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Add metric\n",
-    "metric = [SymbolMetric(prefix=\"mnist_add\"), ABLMetric(prefix=\"mnist_add\")]"
+    "metric = [SymbolMetric(prefix=\"mnist_add\")]"
    ]
   },
   {
@@ -147,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -166,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -181,15 +180,47 @@
     "### Train and Test"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11/15 13:36:00 - abl - WARNING - Transform used in the training phase will be used in prediction.\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "Input must be of type list.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m/home/huwc/ABL-Package/examples/mnist_add/mnist_add_example.ipynb 单元格 17\u001b[0m line \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2B210.28.135.93/home/huwc/ABL-Package/examples/mnist_add/mnist_add_example.ipynb#X22sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m bridge\u001b[39m.\u001b[39;49mtrain(train_data, loops\u001b[39m=\u001b[39;49m\u001b[39m5\u001b[39;49m, segment_size\u001b[39m=\u001b[39;49m\u001b[39m10000\u001b[39;49m)\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2B210.28.135.93/home/huwc/ABL-Package/examples/mnist_add/mnist_add_example.ipynb#X22sdnNjb2RlLXJlbW90ZQ%3D%3D?line=1'>2</a>\u001b[0m bridge\u001b[39m.\u001b[39mtest(test_data)\n",
+      "File \u001b[0;32m~/ABL-Package/abl/bridge/simple_bridge.py:92\u001b[0m, in \u001b[0;36mSimpleBridge.train\u001b[0;34m(self, train_data, loops, segment_size, eval_interval, save_interval, save_dir)\u001b[0m\n\u001b[1;32m     90\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpredict(sub_data_samples)\n\u001b[1;32m     91\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39midx_to_pseudo_label(sub_data_samples)\n\u001b[0;32m---> 92\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mabduce_pseudo_label(sub_data_samples)\n\u001b[1;32m     93\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpseudo_label_to_idx(sub_data_samples)\n\u001b[1;32m     94\u001b[0m loss \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel\u001b[39m.\u001b[39mtrain(sub_data_samples)\n",
+      "File \u001b[0;32m~/ABL-Package/abl/bridge/simple_bridge.py:36\u001b[0m, in \u001b[0;36mSimpleBridge.abduce_pseudo_label\u001b[0;34m(self, data_samples, max_revision, require_more_revision)\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mabduce_pseudo_label\u001b[39m(\n\u001b[1;32m     31\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[1;32m     32\u001b[0m     data_samples: ListData,\n\u001b[1;32m     33\u001b[0m     max_revision: \u001b[39mint\u001b[39m \u001b[39m=\u001b[39m \u001b[39m-\u001b[39m\u001b[39m1\u001b[39m,\n\u001b[1;32m     34\u001b[0m     require_more_revision: \u001b[39mint\u001b[39m \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m,\n\u001b[1;32m     35\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m List[List[Any]]:\n\u001b[0;32m---> 36\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mabducer\u001b[39m.\u001b[39;49mbatch_abduce(data_samples, max_revision, require_more_revision)\n\u001b[1;32m     37\u001b[0m     \u001b[39mreturn\u001b[39;00m data_samples[\u001b[39m\"\u001b[39m\u001b[39mabduced_pseudo_label\u001b[39m\u001b[39m\"\u001b[39m]\n",
+      "File \u001b[0;32m~/ABL-Package/abl/reasoning/reasoner.py:246\u001b[0m, in \u001b[0;36mReasonerBase.batch_abduce\u001b[0;34m(self, data_samples, max_revision, require_more_revision)\u001b[0m\n\u001b[1;32m    239\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mbatch_abduce\u001b[39m(\n\u001b[1;32m    240\u001b[0m     \u001b[39mself\u001b[39m, data_samples, max_revision\u001b[39m=\u001b[39m\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m, require_more_revision\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m\n\u001b[1;32m    241\u001b[0m ):\n\u001b[1;32m    242\u001b[0m     \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    243\u001b[0m \u001b[39m    Perform abductive reasoning on the given prediction data in batches.\u001b[39;00m\n\u001b[1;32m    244\u001b[0m \u001b[39m    For detailed information, refer to `abduce`.\u001b[39;00m\n\u001b[1;32m    245\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 246\u001b[0m     \u001b[39mreturn\u001b[39;00m [\n\u001b[1;32m    247\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mabduce(data_sample, max_revision, require_more_revision)\n\u001b[1;32m    248\u001b[0m             \u001b[39mfor\u001b[39;00m data_sample \u001b[39min\u001b[39;00m data_samples\n\u001b[1;32m    249\u001b[0m     ]\n",
+      "File \u001b[0;32m~/ABL-Package/abl/reasoning/reasoner.py:247\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    239\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mbatch_abduce\u001b[39m(\n\u001b[1;32m    240\u001b[0m     \u001b[39mself\u001b[39m, data_samples, max_revision\u001b[39m=\u001b[39m\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m, require_more_revision\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m\n\u001b[1;32m    241\u001b[0m ):\n\u001b[1;32m    242\u001b[0m     \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    243\u001b[0m \u001b[39m    Perform abductive reasoning on the given prediction data in batches.\u001b[39;00m\n\u001b[1;32m    244\u001b[0m \u001b[39m    For detailed information, refer to `abduce`.\u001b[39;00m\n\u001b[1;32m    245\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m    246\u001b[0m     \u001b[39mreturn\u001b[39;00m [\n\u001b[0;32m--> 247\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mabduce(data_sample, max_revision, require_more_revision)\n\u001b[1;32m    248\u001b[0m             \u001b[39mfor\u001b[39;00m data_sample \u001b[39min\u001b[39;00m data_samples\n\u001b[1;32m    249\u001b[0m     ]\n",
+      "File \u001b[0;32m~/ABL-Package/abl/reasoning/reasoner.py:222\u001b[0m, in \u001b[0;36mReasonerBase.abduce\u001b[0;34m(self, pred_prob, pred_pseudo_label, y, max_revision, require_more_revision)\u001b[0m\n\u001b[1;32m    193\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mabduce\u001b[39m(\n\u001b[1;32m    194\u001b[0m     \u001b[39mself\u001b[39m, pred_prob, pred_pseudo_label, y, max_revision\u001b[39m=\u001b[39m\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m, require_more_revision\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m\n\u001b[1;32m    195\u001b[0m ):\n\u001b[1;32m    196\u001b[0m     \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    197\u001b[0m \u001b[39m    Perform abductive reasoning on the given prediction data.\u001b[39;00m\n\u001b[1;32m    198\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    220\u001b[0m \u001b[39m        knowledge base.\u001b[39;00m\n\u001b[1;32m    221\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 222\u001b[0m     symbol_num \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(flatten(pred_pseudo_label))\n\u001b[1;32m    223\u001b[0m     max_revision_num \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_max_revision_num(max_revision, symbol_num)\n\u001b[1;32m    225\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39muse_zoopt:\n",
+      "File \u001b[0;32m~/ABL-Package/abl/utils/utils.py:26\u001b[0m, in \u001b[0;36mflatten\u001b[0;34m(nested_list)\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m      8\u001b[0m \u001b[39mFlattens a nested list.\u001b[39;00m\n\u001b[1;32m      9\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[39m    If the input object is not a list.\u001b[39;00m\n\u001b[1;32m     24\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m     25\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(nested_list, \u001b[39mlist\u001b[39m):\n\u001b[0;32m---> 26\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mInput must be of type list.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m     28\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m nested_list \u001b[39mor\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(nested_list[\u001b[39m0\u001b[39m], (\u001b[39mlist\u001b[39m, \u001b[39mtuple\u001b[39m)):\n\u001b[1;32m     29\u001b[0m     \u001b[39mreturn\u001b[39;00m nested_list\n",
+      "\u001b[0;31mTypeError\u001b[0m: Input must be of type list."
+     ]
+    }
+   ],
+   "source": [
+    "bridge.train(train_data, loops=5, segment_size=10000)\n",
+    "bridge.test(test_data)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "bridge.train(train_data, epochs=5, batch_size=10000)\n",
-    "bridge.test(test_data)"
-   ]
+   "source": []
   }
  ],
  "metadata": {