active_learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "precise-willow",
   "metadata": {},
   "source": [
    "## How fast does ROCES converge in active learning setting?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "future-aluminum",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_squarebox(pred_str: str):\n",
    "    ans = pred_str.split(\"boxed\")[-1]\n",
    "    if not ans:\n",
    "        return \"\"\n",
    "    if ans[0] == \"{\":\n",
    "        stack = 1\n",
    "        a = \"\"\n",
    "        for c in ans[1:]:\n",
    "            if c == \"{\":\n",
    "                stack += 1\n",
    "                a += c\n",
    "            elif c == \"}\":\n",
    "                stack -= 1\n",
    "                if stack == 0:\n",
    "                    break\n",
    "                a += c\n",
    "            else:\n",
    "                a += c\n",
    "    else:\n",
    "        a = ans.split(\"$\")[0].strip()\n",
    "    return a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "positive-authorization",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "chief-mother",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1', '10']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_squarebox('\\\\boxed{[1, 10]}').replace(\" \", \"\").replace(\"[\", \"\").replace(\"]\", \"\").split(',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "present-siemens",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "defined-auditor",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, random\n",
    "from utils.simple_solution import SimpleSolution\n",
    "from utils.evaluator import Evaluator\n",
    "from utils.data import Data\n",
    "from ontolearn.knowledge_base import KnowledgeBase\n",
    "from owlapy.render import DLSyntaxObjectRenderer\n",
    "from roces import BaseConceptSynthesis\n",
    "from roces.synthesizer import ConceptSynthesizer\n",
    "from owlapy.parser import DLSyntaxParser\n",
    "from utils.dataset import DatasetNoLabel\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.nn.utils.rnn import pad_sequence\n",
    "import torch.nn.functional as F\n",
    "from tqdm import tqdm\n",
    "import json\n",
    "import torch\n",
    "import numpy as np, time\n",
    "import re\n",
    "from argparse import Namespace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "blank-charles",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_roces_vocabulary(data_train, data_test, kb, args):\n",
    "    def add_data_values(path):\n",
    "        print(\"\\n*** Finding relevant data values ***\")\n",
    "        values = set()\n",
    "        for ce, lp in data_train+data_test:\n",
    "            if '[' in ce:\n",
    "                for val in re.findall(\"\\[(.*?)\\]\", ce):\n",
    "                    values.add(val.split(' ')[-1])\n",
    "        print(\"*** Done! ***\\n\")\n",
    "        print(\"Added values: \", values)\n",
    "        print()\n",
    "        return list(values)\n",
    "    renderer = DLSyntaxObjectRenderer()\n",
    "    individuals = [ind.get_iri().as_str().split(\"/\")[-1] for ind in kb.individuals()]\n",
    "    atomic_concepts = list(kb.ontology().classes_in_signature())\n",
    "    atomic_concept_names = [renderer.render(a) for a in atomic_concepts]\n",
    "    role_names = [rel.get_iri().get_remainder() for rel in kb.ontology().object_properties_in_signature()] + \\\n",
    "                 [rel.get_iri().get_remainder() for rel in kb.ontology().data_properties_in_signature()]\n",
    "    vocab = atomic_concept_names + role_names + ['⊔', '⊓', '∃', '∀', '¬', '⊤', '⊥', '.', ' ', '(', ')',\\\n",
    "                                                '⁻', '≤', '≥', 'True', 'False', '{', '}', ':', '[', ']',\n",
    "                                                'double', 'integer', 'date', 'xsd']\n",
    "    quantified_restriction_values = [str(i) for i in range(1,12)]\n",
    "    data_values = add_data_values(args.knowledge_base_path)\n",
    "    vocab = vocab + data_values + quantified_restriction_values\n",
    "    vocab = sorted(set(vocab)) + ['PAD']\n",
    "    print(\"Vocabulary size: \", len(vocab))\n",
    "    num_examples = min(args.num_examples, kb.individuals_count()//2)\n",
    "    return vocab, num_examples\n",
    "\n",
    "\n",
    "def before_pad(arg):\n",
    "    arg_temp = []\n",
    "    for atm in arg:\n",
    "        if atm == 'PAD':\n",
    "            break\n",
    "        arg_temp.append(atm)\n",
    "    return arg_temp\n",
    "\n",
    "num_examples = 1000\n",
    "def collate_batch(batch):\n",
    "    pos_emb_list = []\n",
    "    neg_emb_list = []\n",
    "    target_labels = []\n",
    "    for pos_emb, neg_emb in batch:\n",
    "        if pos_emb.ndim != 2:\n",
    "            pos_emb = pos_emb.reshape(1, -1)\n",
    "        if neg_emb.ndim != 2:\n",
    "            neg_emb = neg_emb.reshape(1, -1)\n",
    "        pos_emb_list.append(pos_emb)\n",
    "        neg_emb_list.append(neg_emb)\n",
    "    pos_emb_list[0] = F.pad(pos_emb_list[0], (0, 0, 0, num_examples - pos_emb_list[0].shape[0]), \"constant\", 0)\n",
    "    pos_emb_list = pad_sequence(pos_emb_list, batch_first=True, padding_value=0)\n",
    "    neg_emb_list[0] = F.pad(neg_emb_list[0], (0, 0, 0, num_examples - neg_emb_list[0].shape[0]), \"constant\", 0)\n",
    "    neg_emb_list = pad_sequence(neg_emb_list, batch_first=True, padding_value=0)\n",
    "    return pos_emb_list, neg_emb_list\n",
    "\n",
    "def predict(kb, positives, negatives, models, embedding_models, args):\n",
    "    args.path_to_triples = f\"datasets/{kb}/Triples/\"\n",
    "    global num_examples\n",
    "    num_examples = models[0].num_examples\n",
    "    vocab = models[0].vocab\n",
    "    inv_vocab = models[0].inv_vocab\n",
    "    kb_embedding_data = Data(args)\n",
    "    k = max(len(positives), len(negatives))\n",
    "    Scores = []\n",
    "    test_dataset = DatasetNoLabel([(\"dummy_key\", {\"positive examples\": positives, \"negative examples\": negatives})], kb_embedding_data, k) #data, triples_data, k\n",
    "    for i, (model, embedding_model) in enumerate(zip(models, embedding_models)):\n",
    "        model = model.eval()\n",
    "        scores = []\n",
    "        test_dataset.load_embeddings(embedding_model.eval())\n",
    "        test_dataloader = DataLoader(test_dataset, batch_size=args.batch_size, num_workers=args.num_workers, collate_fn=collate_batch, shuffle=False)\n",
    "        for x1, x2 in tqdm(test_dataloader):\n",
    "            _, sc = model(x1, x2)\n",
    "            scores.append(sc.detach()) \n",
    "        scores = torch.cat(scores, 0)\n",
    "        if i == 0:\n",
    "            cum_scores = scores\n",
    "        else:\n",
    "            cum_scores = cum_scores + scores\n",
    "    avg_scores = cum_scores / len(models)\n",
    "    pred_sequence = model.inv_vocab[avg_scores.argmax(1)]\n",
    "    return pred_sequence[0]\n",
    "\n",
    "\n",
    "def initialize_synthesizer(vocab, num_examples, num_inds, args):\n",
    "    args.num_inds = num_inds\n",
    "    roces = ConceptSynthesizer(vocab, num_examples, args)\n",
    "    roces.refresh()\n",
    "    return roces.model, roces.embedding_model\n",
    "\n",
    "def synthesize_class_expression(kb_name, vocab, positives, negatives, num_examples, num_inds, args):\n",
    "    args.knowledge_base_path = \"datasets/\" + f\"{kb_name}/{kb_name}.owl\"\n",
    "    embs = torch.load(f\"datasets/{kb_name}/Model_weights/SetTransformer_{args.kb_emb_model}_Emb_inducing_points32.pt\", map_location=torch.device(\"cpu\"))\n",
    "    setattr(args, 'num_entities', embs['emb_ent_real.weight'].shape[0])\n",
    "    setattr(args, 'num_relations', embs['emb_rel_real.weight'].shape[0])\n",
    "    models, embedding_models = [], []\n",
    "    for inds in num_inds:\n",
    "        model, embedding_model = initialize_synthesizer(vocab, num_examples, inds, args)\n",
    "        if args.sampling_strategy != 'uniform':\n",
    "            model.load_state_dict(torch.load(f\"datasets/{kb_name}/Model_weights/{args.kb_emb_model}_SetTransformer_inducing_points{inds}.pt\", map_location=torch.device(\"cpu\")))\n",
    "            embedding_model.load_state_dict(torch.load(f\"datasets/{kb_name}/Model_weights/SetTransformer_{args.kb_emb_model}_Emb_inducing_points{inds}.pt\", map_location=torch.device(\"cpu\")))\n",
    "        else:\n",
    "            model.load_state_dict(torch.load(f\"datasets/{kb_name}/Model_weights/{args.kb_emb_model}_SetTransformer_uniform_inducing_points{inds}.pt\", map_location=torch.device(\"cpu\")))\n",
    "            embedding_model.load_state_dict(torch.load(f\"datasets/{kb_name}/Model_weights/SetTransformer_{args.kb_emb_model}_Emb_uniform_inducing_points{inds}.pt\", map_location=torch.device(\"cpu\")))\n",
    "        models.append(model)\n",
    "        embedding_models.append(embedding_model)\n",
    "    return predict(kb_name, positives, negatives, models, embedding_models, args)\n",
    "\n",
    "def prepare_utilities_for_roces(kb_name, args):\n",
    "    kb = KnowledgeBase(path=f\"datasets/{kb_name}/{kb_name}.owl\")\n",
    "    with open(f\"datasets/{kb_name}/Test_data/Data.json\", \"r\") as file:\n",
    "        test_data = json.load(file)\n",
    "    with open(f\"datasets/{kb_name}/Train_data/Data.json\", \"r\") as file:\n",
    "        train_data = json.load(file)\n",
    "    vocab, num_examples = build_roces_vocabulary(train_data, test_data, kb, args)\n",
    "    namespace = list(kb.individuals())[0].get_iri().get_namespace()\n",
    "    print(\"KB namespace: \", namespace)\n",
    "    print()\n",
    "    simpleSolution = SimpleSolution(kb)\n",
    "    evaluator = Evaluator(kb)\n",
    "    dl_parser = DLSyntaxParser(namespace = namespace)\n",
    "    all_individuals = set(kb.individuals())\n",
    "    return kb, simpleSolution, evaluator, dl_parser, all_individuals, vocab\n",
    "\n",
    "def prepare_utilities_search_based(kb_name, args):\n",
    "    kb = KnowledgeBase(path=f\"datasets/{kb_name}/{kb_name}.owl\")\n",
    "    namespace = list(kb.individuals())[0].get_iri().get_namespace()\n",
    "    print(\"KB namespace: \", namespace)\n",
    "    print()\n",
    "    simpleSolution = SimpleSolution(kb)\n",
    "    evaluator = Evaluator(kb)\n",
    "    dl_parser = DLSyntaxParser(namespace = namespace)\n",
    "    all_individuals = set(kb.individuals())\n",
    "    return kb, simpleSolution, evaluator, dl_parser, all_individuals\n",
    "\n",
    "\n",
    "def predict_with_roces(kb_name, vocab, positives, negatives, dl_parser, simpleSolution, args):\n",
    "    ensemble_models = \"+\".join([\"SetTransformer_I32\", \"SetTransformer_I64\", \"SetTransformer_I128\"])\n",
    "    num_inds = [int(model_name.split(\"I\")[-1]) for model_name in ensemble_models.split(\"+\")]\n",
    "    pred = synthesize_class_expression(kb_name, vocab, positives, negatives, num_examples, num_inds, args)\n",
    "    prediction = None\n",
    "    try:\n",
    "        end_idx = np.where(pred == 'PAD')[0][0] # remove padding token\n",
    "    except IndexError:\n",
    "        end_idx = -1\n",
    "    pred = pred[:end_idx]\n",
    "    try:\n",
    "        prediction = dl_parser.parse(\"\".join(pred.tolist()))\n",
    "    except Exception as err:\n",
    "        try:\n",
    "            pred = simpleSolution.predict(pred.sum())\n",
    "            prediction = dl_parser.parse(pred)\n",
    "        except Exception:\n",
    "            print(f\"Could not understand expression {pred}\")\n",
    "    if prediction is None:\n",
    "        prediction = dl_parser.parse('⊤')\n",
    "    return prediction\n",
    "\n",
    "def query_oracle(prediction, oracle, kb, positives, negatives, all_individuals, pos_diff, neg_diff, k_max):\n",
    "    true_positive_examples = set([ind.get_iri().as_str().split(\"/\")[-1] for ind in kb.individuals(oracle)])\n",
    "    true_negative_examples = all_individuals-true_positive_examples\n",
    "    predicted_positives = set([ind.get_iri().as_str().split(\"/\")[-1] for ind in kb.individuals(prediction)])\n",
    "    covered_positives = predicted_positives.intersection(true_positive_examples)\n",
    "    candidate_negatives = (true_negative_examples.intersection(all_individuals.difference(predicted_positives))).difference(set(negatives))\n",
    "    candidate_positives = covered_positives.difference(set(positives))\n",
    "    new_positives = positives + random.sample(list(candidate_positives), min(k_max, len(candidate_positives)))\n",
    "    new_negatives = negatives + random.sample(list(candidate_negatives), min(k_max, len(candidate_negatives)))\n",
    "    if not candidate_positives:\n",
    "        random.shuffle(new_positives)\n",
    "        if len(new_positives) >= 2 and random.random() > 0.8:\n",
    "            new_positives = new_positives[:-1]\n",
    "        \n",
    "    if not candidate_negatives:\n",
    "        random.shuffle(new_negatives)\n",
    "        if len(new_negatives) >= 2 and random.random() > 0.8:\n",
    "            new_negatives = new_negatives[:-1]\n",
    "        \n",
    "    return new_positives, new_negatives\n",
    "    \n",
    "def evaluate_prediction(kb, prediction, oracle, evaluator, simpleSolution, all_individuals):\n",
    "    positive_examples = set(kb.individuals(oracle))\n",
    "    negative_examples = all_individuals-positive_examples\n",
    "    try:\n",
    "        _, f1 = evaluator.evaluate(prediction, positive_examples, negative_examples)\n",
    "    except Exception as err:\n",
    "        print(f\"Parsing error on \", prediction)\n",
    "        prediction = dl_parser.parse('⊤')\n",
    "        _, f1 = evaluator.evaluate(prediction, positive_examples, negative_examples)\n",
    "    try:\n",
    "        prediction_str = simpleSolution.renderer.render(prediction)\n",
    "    except:\n",
    "        prediction_str = \"Unknown\"\n",
    "    return prediction_str, f1\n",
    "\n",
    "def start_active_learning(kb_name, oracle, positives, negatives, args, max_iter=10, k_max=5, approach=\"roces\"):\n",
    "    ###### Improve here\n",
    "    i = 0\n",
    "    best_prediction = None\n",
    "    best_score = None\n",
    "    previous_score = None\n",
    "    all_predictions = []\n",
    "    F1 = []\n",
    "    if approach == \"roces\":\n",
    "        kb, simpleSolution, evaluator, dl_parser, all_individuals, vocab = prepare_utilities_for_roces(kb_name, args)\n",
    "    else:\n",
    "        kb, simpleSolution, evaluator, dl_parser, all_individuals = prepare_utilities_search_based(kb_name, args)\n",
    "    all_individuals_str = set([ind.get_iri().as_str().split(\"/\")[-1] for ind in all_individuals])\n",
    "    oracle = dl_parser.parse(oracle)\n",
    "    while i < max_iter:\n",
    "        if approach == \"roces\":\n",
    "            if i == 0:\n",
    "                prediction = predict_with_roces(kb_name, vocab, positives, negatives, dl_parser, simpleSolution, args)\n",
    "                prediction_str, f1 = evaluate_prediction(kb, prediction, oracle, evaluator, simpleSolution, all_individuals)\n",
    "                best_prediction = prediction_str\n",
    "                all_predictions.append(prediction_str)\n",
    "                F1.append(f1)\n",
    "                new_positives, new_negatives = positives, negatives\n",
    "                pos_diff = len(new_positives) - len(positives)\n",
    "                neg_diff = len(new_negatives) - len(negatives)\n",
    "                previous_score = f1\n",
    "                best_score = f1\n",
    "            else:\n",
    "                remove_wrong_examples = (previous_score > f1 or f1==0)\n",
    "                copy_pos = new_positives\n",
    "                copy_neg = new_negatives\n",
    "                new_positives, new_negatives = query_oracle(prediction, oracle, kb, new_positives, new_negatives, all_individuals_str, pos_diff, neg_diff, k_max)\n",
    "                pos_diff = len(new_positives) - len(copy_pos)\n",
    "                neg_diff = len(new_negatives) - len(copy_neg)\n",
    "                print(\"new positives\", len(new_positives))\n",
    "                print(\"new negatives\", len(new_negatives))\n",
    "                prediction = predict_with_roces(kb_name, vocab, new_positives, new_negatives, dl_parser, simpleSolution, args)\n",
    "                previous_score = f1\n",
    "                prediction_str, f1 = evaluate_prediction(kb, prediction, oracle, evaluator, simpleSolution, all_individuals)\n",
    "                all_predictions.append(prediction_str)\n",
    "                F1.append(f1)\n",
    "                if f1 > previous_score:\n",
    "                    best_prediction = prediction_str\n",
    "        else:\n",
    "            pass\n",
    "        if f1 > best_score:\n",
    "            print(\"improved performance\")\n",
    "            best_score = f1\n",
    "        i += 1\n",
    "        print()\n",
    "        if f1 == 100:\n",
    "            break\n",
    "    return best_prediction, all_predictions, F1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "nervous-modem",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"config.json\") as config:\n",
    "    nces_args = json.load(config)\n",
    "    nces_args = Namespace(**nces_args)\n",
    "nces_args.kb_emb_model = \"ConEx\"\n",
    "nces_args.sampling_strategy = \"original\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "optimum-liver",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CognitiveAgent ⊔ LandArea\n",
      "Agent ⊓ (Human ⊔ (¬God)) ⊓ (∀ spouseOf.Woman)\n",
      "Series ⊔ (∃ relativeOf.(Woman ⊓ (∀ visitedPlace.⊥)))\n",
      "(LandArea ⊓ (¬City)) ⊔ (∃ member.Agent)\n",
      "(Mountain ⊓ (∀ location.⊥)) ⊔ (∃ knows.(¬SonOfGod))\n"
     ]
    }
   ],
   "source": [
    "kb_name = \"semantic_bible\"\n",
    "with open(f\"datasets/{kb_name}/Test_data/Data.json\") as file:\n",
    "    test_data = json.load(file)\n",
    "    test_lps = [lp for lp,examples in test_data]\n",
    "    full_examples = [examples for lp,examples in test_data]\n",
    "    print(\"\\n\".join(test_lps[:5]))\n",
    "oracle = test_lps[0]\n",
    "full_positives, full_negatives = full_examples[0][\"positive examples\"], full_examples[0][\"negative examples\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "peripheral-limit",
   "metadata": {},
   "outputs": [],
   "source": [
    "positives = random.sample(full_positives, 3)\n",
    "negatives = random.sample(full_negatives, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "alert-mixture",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CognitiveAgent ⊔ LandArea'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oracle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "uniform-cradle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NTNames#GaiusTheBeloved',\n",
       " 'NTNames#AlexanderOfTheSanhedrin',\n",
       " 'NTNames#Bartholomew']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "romantic-carbon",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NTNames#Macedonians', 'NTNames#CosGeodata', 'NTNames#Elamites']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "important-journalist",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "* Owlready2 * Warning: optimized Cython parser module 'owlready2_optimized' is not available, defaulting to slower Python implementation\n",
      "\n",
      "Warning: SQLite3 version 3.40.0 and 3.41.2 have huge performance regressions; please install version 3.41.1 or 3.42!\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "*** Finding relevant data values ***\n",
      "*** Done! ***\n",
      "\n",
      "Added values:  {'44.42', '35.583', '41.013'}\n",
      "\n",
      "Vocabulary size:  126\n",
      "KB namespace:  http://semanticbible.org/ns/2006/NTNames#\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.06s/it]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "new positives 5\n",
      "new negatives 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "improved performance\n",
      "\n",
      "new positives 7\n",
      "new negatives 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "improved performance\n",
      "\n",
      "new positives 9\n",
      "new negatives 7\n"
     ]
    },
    {
     "name": "stderr",
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    },
    {
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      "\n",
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     ]
    },
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    {
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      "\n",
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     ]
    },
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    {
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      "\n",
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     ]
    },
    {
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    {
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      "\n",
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     ]
    },
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     ]
    },
    {
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      "\n",
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     ]
    },
    {
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    },
    {
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      "\n",
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     ]
    },
    {
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     ]
    },
    {
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     "text": [
      "\n",
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     ]
    },
    {
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     ]
    },
    {
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      "\n",
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      "new negatives 23\n"
     ]
    },
    {
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     ]
    },
    {
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     "text": [
      "\n",
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     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "new positives 29\n",
      "new negatives 27\n"
     ]
    },
    {
     "name": "stderr",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "new positives 31\n",
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     ]
    },
    {
     "name": "stderr",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "new positives 33\n",
      "new negatives 31\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "new positives 35\n",
      "new negatives 33\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.13s/it]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "new positives 37\n",
      "new negatives 35\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.08s/it]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "new positives 39\n",
      "new negatives 37\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.01s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00,  1.03it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "new positives 41\n",
      "new negatives 39\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.10s/it]\n",
      "100%|██████████| 1/1 [00:01<00:00,  1.15s/it]\n",
      "100%|██████████| 1/1 [00:01<00:00,  1.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "best_prediction, all_predictions, F1 = start_active_learning(kb_name, oracle, positives, negatives, nces_args, max_iter=20, k_max=2, approach=\"roces\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "religious-processing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[77.283,\n",
       " 83.974,\n",
       " 97.913,\n",
       " 97.913,\n",
       " 97.913,\n",
       " 97.913,\n",
       " 97.913,\n",
       " 97.913,\n",
       " 97.913,\n",
       " 97.913,\n",
       " 97.913,\n",
       " 97.913,\n",
       " 97.913,\n",
       " 97.913,\n",
       " 97.913,\n",
       " 97.913,\n",
       " 97.913,\n",
       " 97.913,\n",
       " 97.913,\n",
       " 97.913]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "antique-import",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Man',\n",
       " '⊤',\n",
       " 'Human ⊔ GeographicArea',\n",
       " 'Human ⊔ GeographicArea',\n",
       " 'Human ⊔ GeographicArea',\n",
       " 'Human ⊔ GeographicArea',\n",
       " 'Human ⊔ GeographicArea',\n",
       " 'Human ⊔ GeographicArea',\n",
       " 'Human ⊔ GeographicArea',\n",
       " 'Human ⊔ GeographicArea',\n",
       " 'Human ⊔ GeographicArea',\n",
       " 'Human ⊔ GeographicArea',\n",
       " 'Human ⊔ GeographicArea',\n",
       " 'Human ⊔ GeographicArea',\n",
       " 'Human ⊔ GeographicArea',\n",
       " 'Human ⊔ GeographicArea',\n",
       " 'Human ⊔ GeographicArea',\n",
       " 'Human ⊔ GeographicArea',\n",
       " 'Human ⊔ GeographicArea',\n",
       " 'Human ⊔ GeographicArea']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "focused-scheduling",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Human ⊔ GeographicArea'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "opened-bradley",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open(\"./datasets/carcinogenesis/Train_data/Data.json\") as f:\n",
    "    data = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "specified-birth",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "≤ 9 hasAtom.⊤\n",
      "≤ 9 hasAtom.⊤\n",
      "≤ 9 hasStructure.⊤\n",
      "≤ 9 hasStructure.⊤\n",
      "Compound ⊓ (≤ 9 hasBond.⊤)\n",
      "Compound ⊓ (≤ 9 hasBond.⊤)\n",
      "≤ 9 hasBond.⊤\n",
      "≤ 9 hasBond.⊤\n"
     ]
    }
   ],
   "source": [
    "for ce, example in data:\n",
    "    if \"≤\" in ce:\n",
    "        print(ce)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "coordinated-county",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "floppy-color",
   "metadata": {},
   "source": [
    "### View Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "liberal-privacy",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "corporate-field",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "monthly-silicon",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Results_active_learning.json') as f:\n",
    "    results = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "relative-transfer",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_results(results: dict, kb_name: str) -> np.ndarray:\n",
    "    kb_res = []\n",
    "    for k in [3,5,7,9,11,13]:\n",
    "        Res = []\n",
    "        for lp_num in range(100):\n",
    "            if len(results[str(k)][kb_name][\"f1\"][lp_num]) < 20:\n",
    "                res = results[str(k)][kb_name][\"f1\"][lp_num] + [results[str(k)][kb_name][\"f1\"][lp_num][-1]]*(20-len(results[str(k)][kb_name][\"f1\"][lp_num]))\n",
    "                Res.append(res)\n",
    "            else:\n",
    "                Res.append(results[str(k)][kb_name][\"f1\"][lp_num])\n",
    "        kb_res.append(Res)\n",
    "    return np.array(kb_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "acoustic-saudi",
   "metadata": {},
   "outputs": [],
   "source": [
    "carcino_res = get_results(results, kb_name=\"carcinogenesis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "passing-exposure",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 100, 20)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carcino_res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "brazilian-peeing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[64.47      , 64.47      , 64.47      , ..., 64.47      ,\n",
       "        64.47      , 64.47      ],\n",
       "       [96.09042857, 96.09042857, 96.09042857, ..., 96.09042857,\n",
       "        96.09042857, 96.09042857],\n",
       "       [30.89314286, 30.89314286, 30.89314286, ..., 30.89314286,\n",
       "        30.89314286, 30.89314286],\n",
       "       ...,\n",
       "       [66.27314286, 66.27314286, 66.27314286, ..., 66.27314286,\n",
       "        66.27314286, 66.27314286],\n",
       "       [96.63985714, 96.63985714, 96.63985714, ..., 96.63985714,\n",
       "        96.63985714, 96.63985714],\n",
       "       [71.42828571, 71.42828571, 71.42828571, ..., 71.42828571,\n",
       "        71.42828571, 71.42828571]])"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carcino_res.mean(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "several-glasgow",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "legal-discount",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "#sns.set(rc={\"figure.figsize\": (15, 10)})\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "# Sample data (replace with your actual data)\n",
    "\n",
    "def plot_results(results: dict, kb_name=\"carcinogenesis\") -> None:\n",
    "    data = get_results(results, kb_name)\n",
    "    kmax = [3,7,9,13]\n",
    "    data_ids = [0,2,3,5]\n",
    "    data = {f\"k = {kmax[i]}\": sorted(data[i].mean(1)) for i,j in zip(range(len(kmax)), data_ids)}\n",
    "    colors = [\"tab:blue\", \"tab:orange\", \"tab:green\", \"tab:red\", \"tab:purple\", \"tab:brown\"]\n",
    "    # Create the figure and axes\n",
    "    fig, axes = plt.subplots(2, 2, figsize=(15, 10), sharey=True)  # 2 rows, 3 columns\n",
    "\n",
    "    # Flatten the axes array for easier iteration\n",
    "    axes = axes.flatten()\n",
    "\n",
    "    # Plot the histograms\n",
    "    for i, col in enumerate(data.keys()):\n",
    "        sns.histplot(data[col], ax=axes[i], color=colors[i], kde=True, bins=10)  # kde=True adds a density curve\n",
    "        axes[i].set_title(f'${col}$',fontsize=25)\n",
    "        axes[i].set_xlabel(\"$F_1$\", fontsize=25)\n",
    "        axes[i].set_ylabel(\"$Counts$\", fontsize=25)\n",
    "        axes[i].tick_params(axis='both', which='major', labelsize=22)\n",
    "    rename_kb = \" \".join([name.capitalize() for name in kb_name.split(\"_\")])\n",
    "    fig.suptitle(rename_kb, fontsize=30, fontweight=\"bold\")\n",
    "    # Adjust layout\n",
    "    plt.tight_layout()\n",
    "    fig.savefig(f'active-learn-{kb_name}-roces.pdf', bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "negative-retro",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "static-benjamin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(results, kb_name=\"semantic_bible\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "martial-discovery",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(results, kb_name=\"mutagenesis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "painted-territory",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(results, kb_name=\"vicodi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "billion-arthritis",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "roces",
   "language": "python",
   "name": "roces"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}