Diversity_analysis.ipynb

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        "id": "moarPsjQ0K2w",
        "outputId": "ccc3a8d8-caaf-4e4d-bd63-c8d4d17d2aff"
      },
      "outputs": [],
      "source": [
        "!pip install tree-sitter-python tree-sitter autopep8 sentence_transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IohMcmp3-Aaw"
      },
      "source": [
        "# Diversity analysis"
      ]
    },
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        "outputId": "f3b7a19f-0eaa-41d5-9638-c5df67252f7d"
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      "outputs": [],
      "source": [
        "from transformers import AutoModel, AutoTokenizer\n",
        "import torch\n",
        "import numpy as np\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from scipy.cluster.hierarchy import linkage, fcluster\n",
        "from scipy.spatial.distance import squareform, euclidean\n",
        "from scipy.sparse.csgraph import minimum_spanning_tree\n",
        "from math import log\n",
        "\n",
        "# Define a function to get embeddings\n",
        "def get_embedding(code, model, tokenizer, device):\n",
        "    inputs = tokenizer.encode(code, return_tensors=\"pt\").to(device)\n",
        "    with torch.no_grad():\n",
        "        embedding = model(inputs)[0].cpu().numpy()\n",
        "    return embedding.reshape(1, -1)\n",
        "\n",
        "\n",
        "def compute_cosine_similarity(embeddings):\n",
        "    return cosine_similarity(embeddings)\n",
        "\n",
        "\n",
        "def cluster_nodes(similarity_matrix, threshold=0.95):\n",
        "    # Convert similarity matrix to distance matrix\n",
        "    distance_matrix = 1 - similarity_matrix\n",
        "    distance_matrix = np.clip(distance_matrix, 0, None)\n",
        "\n",
        "    # Use hierarchical clustering\n",
        "    condensed_distance_matrix = squareform(distance_matrix)\n",
        "    Z = linkage(condensed_distance_matrix, method='complete')\n",
        "\n",
        "    clusters = fcluster(Z, t=1-threshold, criterion='distance')\n",
        "\n",
        "    # Collect nodes into clusters\n",
        "    node_clusters = {}\n",
        "    for node, cluster_id in enumerate(clusters):\n",
        "        if cluster_id not in node_clusters:\n",
        "            node_clusters[cluster_id] = []\n",
        "        node_clusters[cluster_id].append(node)\n",
        "\n",
        "    return list(node_clusters.values())\n",
        "\n",
        "\n",
        "def calculate_shannon_diversity(clusters, total_nodes):\n",
        "    proportions = [len(cluster) / total_nodes for cluster in clusters]\n",
        "    shannon_index = -sum(p * log(p) for p in proportions if p > 0)\n",
        "    return shannon_index\n",
        "\n",
        "\n",
        "def shannon_diversity(embeddings, threshold):\n",
        "    embeddings_2d = np.vstack(embeddings)\n",
        "\n",
        "    # Compute similarity matrix\n",
        "    similarity_matrix = compute_cosine_similarity(embeddings_2d)\n",
        "    np.fill_diagonal(similarity_matrix, 1)\n",
        "    #print(similarity_matrix)\n",
        "\n",
        "    # Cluster nodes with a similarity threshold\n",
        "    clusters = cluster_nodes(similarity_matrix, threshold)\n",
        "\n",
        "    # Calculate Shannon Diversity Index\n",
        "    total_nodes = len(embeddings)\n",
        "    shannon_diversity_index = calculate_shannon_diversity(clusters, total_nodes)\n",
        "\n",
        "    print(\"Clusters:\", clusters)\n",
        "    print(\"Shannon Diversity Index:\", shannon_diversity_index)\n",
        "    return shannon_diversity_index\n",
        "\n",
        "\n",
        "def total_diversity(embeddings):\n",
        "    embeddings_1d = np.vstack([emb.flatten() for emb in embeddings])  # Ensure 1D embeddings\n",
        "\n",
        "    # Compute the distance matrix\n",
        "    n = len(embeddings_1d)\n",
        "    distance_matrix = np.zeros((n, n))\n",
        "    for i in range(n):\n",
        "        for j in range(i + 1, n):\n",
        "            distance = euclidean(embeddings_1d[i], embeddings_1d[j])\n",
        "            distance_matrix[i, j] = distance\n",
        "            distance_matrix[j, i] = distance\n",
        "\n",
        "    # Find the Minimum Spanning Tree (MST)\n",
        "    mst = minimum_spanning_tree(distance_matrix).toarray()\n",
        "\n",
        "    # Calculate the diversity index\n",
        "    mst_distances = mst[mst != 0]\n",
        "    total_distance = np.sum(mst_distances)\n",
        "\n",
        "    proportions = mst_distances / total_distance\n",
        "    diversity_index = -np.sum(proportions * np.log(proportions))\n",
        "\n",
        "    print(f\"CDI: {diversity_index}\")\n",
        "    return diversity_index\n",
        "\n",
        "# Model and tokenizer setup\n",
        "checkpoint = \"Salesforce/codet5p-110m-embedding\"\n",
        "device = \"cpu\"  # Use \"cuda\" if you have a GPU\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True)\n",
        "model = AutoModel.from_pretrained(checkpoint, trust_remote_code=True).to(device)\n",
        "\n",
        "def get_static(code_snippets, threshold):\n",
        "    # Get embeddings for all code snippets\n",
        "    embeddings = [get_embedding(code, model, tokenizer, device) for code in code_snippets]\n",
        "\n",
        "    shannon_diversity_index = shannon_diversity(embeddings, threshold=threshold)\n",
        "    CID = total_diversity(embeddings)\n",
        "    return shannon_diversity_index, CID\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GsnE_Jx3-FSf"
      },
      "source": [
        "# Pre-process Log Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nZB0QFJi-ni4"
      },
      "source": [
        "##  Population encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ViGPBBnv-QG_"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import autopep8\n",
        "import tempfile\n",
        "import json\n",
        "import re\n",
        "\n",
        "import tree_sitter_python as tspython\n",
        "from tree_sitter import Language, Parser\n",
        "\n",
        "PY_LANGUAGE = Language(tspython.language())\n",
        "parser = Parser(PY_LANGUAGE)\n",
        "\n",
        "def format_python_code(code_str):\n",
        "    # Write the code string to a temporary file\n",
        "    with tempfile.NamedTemporaryFile('w', delete=False, suffix='.py') as temp_file:\n",
        "        temp_file.write(code_str)\n",
        "        temp_filename = temp_file.name\n",
        "\n",
        "    # Format the file using autopep8\n",
        "    autopep8_args = ['--in-place', '--aggressive', '--aggressive', temp_filename]\n",
        "    autopep8.fix_file(temp_filename, options=autopep8.parse_args(autopep8_args))\n",
        "\n",
        "    # Read the formatted code from the file\n",
        "    with open(temp_filename, 'r') as temp_file:\n",
        "        formatted_code = temp_file.read()\n",
        "\n",
        "    return formatted_code\n",
        "\n",
        "def remove_comments_and_docstrings(code):\n",
        "    tree = parser.parse(bytes(code, \"utf8\"))\n",
        "    root_node = tree.root_node\n",
        "\n",
        "    lines = code.split('\\n')\n",
        "    to_remove = []\n",
        "\n",
        "    def is_comment(node):\n",
        "        return node.type == 'comment'\n",
        "\n",
        "    def is_docstring(node):\n",
        "        return (node.type == 'string' and\n",
        "                (node.parent.type in ['expression_statement', 'module']))\n",
        "\n",
        "    def traverse(node):\n",
        "        if is_comment(node):\n",
        "            to_remove.append((node.start_point, node.end_point))\n",
        "        elif is_docstring(node):\n",
        "            to_remove.append((node.start_point, node.end_point))\n",
        "        for child in node.children:\n",
        "            traverse(child)\n",
        "\n",
        "    traverse(root_node)\n",
        "\n",
        "    for start, end in reversed(to_remove):\n",
        "        start_row, start_col = start\n",
        "        end_row, end_col = end\n",
        "        if start_row == end_row:\n",
        "            lines[start_row] = lines[start_row][:start_col] + lines[start_row][end_col:]\n",
        "        else:\n",
        "            lines[start_row] = lines[start_row][:start_col]\n",
        "            for row in range(start_row + 1, end_row):\n",
        "                lines[row] = ''\n",
        "            lines[end_row] = lines[end_row][end_col:]\n",
        "\n",
        "    cleaned_lines = [line for line in lines if line.strip() != '']\n",
        "\n",
        "    return '\\n'.join(cleaned_lines)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fU7ZbFVl-1iv"
      },
      "source": [
        "## Utils function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XIwK98oj-mUo"
      },
      "outputs": [],
      "source": [
        "def read_all_files_in_folder(folder_path, framework_name, parsed_data):\n",
        "    if 'funsearch' in framework_name:\n",
        "        folder_path = os.path.join(folder_path, 'samples')\n",
        "    files = os.listdir(folder_path)\n",
        "    lstcode = dict()\n",
        "\n",
        "    for file_name in files:\n",
        "        file_path = os.path.join(folder_path, file_name)\n",
        "        file_name_without_ext = os.path.splitext(file_name)[0]\n",
        "\n",
        "        if ('hsevo' in framework_name or 'reevo' in framework_name) and ('_response' in file_name_without_ext or '_hs' in file_name_without_ext) and '_prompt' not in file_name_without_ext and 'objs_log_' not in file_name_without_ext:\n",
        "            try:\n",
        "                with open(file_path, 'r', encoding='utf-8') as file:\n",
        "                    content = file.read()\n",
        "                    if '.txt_stdout' not in file_name_without_ext:\n",
        "                        lstcode[file_name_without_ext] = content.replace('```python', '').replace('```', '')\n",
        "                    elif '.txt_stdout' in file_name_without_ext and \"[*] Average:\" not in content:\n",
        "                        file_name_without_ext = os.path.splitext(file_name_without_ext)[0]\n",
        "                        lstcode.pop(file_name_without_ext)\n",
        "                    #print(f'Content of {file_name}:\\n{content}\\n')\n",
        "            except Exception as e:\n",
        "                print(f'Could not read {file_name}: {e}')\n",
        "        if 'funsearch' in framework_name:\n",
        "            id_sample = file_name_without_ext.split('_')[1]\n",
        "\n",
        "            if(int(id_sample) <= 330):\n",
        "                try:\n",
        "                    with open(file_path, 'r', encoding='utf-8') as file:\n",
        "                        content = file.read()\n",
        "                        content = json.loads(content)\n",
        "                        if content['score']:\n",
        "                            lstcode[file_name_without_ext] = content['function']\n",
        "                            excess = (-content['score'] - l1_bound) / l1_bound * 100\n",
        "                            parsed_data[int(id_sample)] = parsed_data[int(id_sample)] + (excess,)\n",
        "                except Exception as e:\n",
        "                    print(f'Could not read {file_name}: {e}')\n",
        "\n",
        "    return lstcode\n",
        "\n",
        "def parse_log(log_string):\n",
        "  best_obj_pattern = re.compile(r\"Best obj: (-?[\\d\\.]+)\")\n",
        "  tokens_pattern = re.compile(r\"prompt_tokens = (\\d+), completion_tokens = (\\d+)\")\n",
        "\n",
        "\n",
        "  best_objs = best_obj_pattern.findall(log_string)\n",
        "  tokens_matches = tokens_pattern.findall(log_string)\n",
        "\n",
        "  results = []\n",
        "  for i in range(max(len(best_objs), len(tokens_matches))):\n",
        "      prompt_tokens = int(tokens_matches[i][0])\n",
        "      completion_tokens = int(tokens_matches[i][1])\n",
        "      total_tokens = prompt_tokens + completion_tokens\n",
        "      try:\n",
        "          best_obj = float(best_objs[i])\n",
        "      except:\n",
        "          best_obj = None\n",
        "\n",
        "      if best_obj is not None:\n",
        "        results.append((total_tokens, best_obj))\n",
        "      else:\n",
        "        results.append((total_tokens, ))\n",
        "\n",
        "  return results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lici0ReluONk"
      },
      "outputs": [],
      "source": [
        "def create_sets_from_data(data, group_size=10):\n",
        "    result = []\n",
        "    min_second = float('inf')\n",
        "    for i in range(0, len(data), group_size):\n",
        "        group = data[i:i+group_size]\n",
        "        max_first = max(item[0] for item in group)\n",
        "\n",
        "        # Update the minimum value for the second position, considering all elements from the start to the current group\n",
        "        for item in data[:i+len(group)]:\n",
        "            if len(item) > 1:  # Check if there's a second position\n",
        "                min_second = min(min_second, item[1])\n",
        "        result.append((max_first, min_second))\n",
        "\n",
        "    return result\n",
        "\n",
        "def create_sets_from_data_eoh(parsed_data, num_init, obj_eoh):\n",
        "    num_gen = len(obj_eoh)\n",
        "    # Step 1: Group the first `num_init` elements and get the max value of this group\n",
        "    first_group = max(parsed_data[:num_init])[0]\n",
        "\n",
        "    # Step 2: Calculate the number of elements in each subsequent group\n",
        "    remaining_elements = len(parsed_data) - num_init\n",
        "    group_size = remaining_elements // num_gen\n",
        "\n",
        "    # Step 3: Initialize the list to store the result\n",
        "    result = [(first_group, 0)]\n",
        "\n",
        "    # Step 4: Group the remaining elements and get the max value of each group\n",
        "    for i in range(num_gen):\n",
        "        start_index = num_init + i * group_size\n",
        "        end_index = start_index + group_size\n",
        "        group = parsed_data[start_index:end_index]\n",
        "        max_value = max(group)[0]\n",
        "        result.append((max_value, obj_eoh[i]*100))\n",
        "\n",
        "    return result\n",
        "\n",
        "def get_max_obj_eoh(folder_path):\n",
        "    lst_obj_eoh = []\n",
        "    folder_path += \"/pops_best\"\n",
        "    files = os.listdir(folder_path)\n",
        "\n",
        "    for file_name in files:\n",
        "        file_path = os.path.join(folder_path, file_name)\n",
        "        try:\n",
        "            with open(file_path, 'r', encoding='utf-8') as file:\n",
        "                content = file.read()\n",
        "                content = json.loads(content)\n",
        "                lst_obj_eoh.append(content['objective'])\n",
        "        except Exception as e:\n",
        "            print(f'Could not read {file_name}: {e}')\n",
        "    return lst_obj_eoh\n",
        "\n",
        "\n",
        "def get_init_eoh(folder_path):\n",
        "    lstcode = dict()\n",
        "    init_logs = folder_path + \"/pops/population_generation_0.json\"\n",
        "    with open(init_logs, 'r', encoding='utf-8') as file:\n",
        "        content = file.read()\n",
        "        content = json.loads(content)\n",
        "        for idx, data in enumerate(content):\n",
        "            if data['objective']:\n",
        "                name_idv = \"pop_0_off\" + str(idx) + \"_i0\"\n",
        "                lstcode[name_idv] = data['code']\n",
        "    return lstcode\n",
        "\n",
        "def eoh_preprocess(folder_path, parsed_data):\n",
        "    # List all files in the folder\n",
        "    lstcode = get_init_eoh(folder_path)\n",
        "\n",
        "    folder_path += \"/history\"\n",
        "    files = os.listdir(folder_path)\n",
        "\n",
        "    for file_name in files:\n",
        "        file_path = os.path.join(folder_path, file_name)\n",
        "        file_name_without_ext = os.path.splitext(file_name)[0]\n",
        "\n",
        "        try:\n",
        "            with open(file_path, 'r', encoding='utf-8') as file:\n",
        "                content = file.read()\n",
        "                content = json.loads(content)\n",
        "                if content['offspring']['objective']:\n",
        "                    lstcode[file_name_without_ext] = content['offspring']['code']\n",
        "        except Exception as e:\n",
        "            print(f'Could not read {file_name}: {e}')\n",
        "\n",
        "    return lstcode"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XISFykGnCgRQ"
      },
      "source": [
        "# Diversity calculator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_G2c8Hsxzz9Z"
      },
      "outputs": [],
      "source": [
        "log_run = {\n",
        "        \"name\": \"hsevo_1\",\n",
        "        \"path\": \"<path>\"\n",
        "}\n",
        "\n",
        "\n",
        "f = open(log_run[\"path\"] + '/main.log', \"r\")\n",
        "data = f.read().split('\\n')\n",
        "f.close()\n",
        "raw_string = ''\n",
        "for idl in range(len(data)):\n",
        "  if \"hsevo\" in log_run[\"name\"] or \"reevo\" in log_run[\"name\"]:\n",
        "    if 'Best obj:' in data[idl]:\n",
        "      raw_string += data[idl] + '\\n' + data[idl+1] + '\\n'\n",
        "  elif \"funsearch\" in log_run[\"name\"] or \"eoh\" in log_run[\"name\"]:\n",
        "    match = re.search(r\"l1_bound:\\s*([\\d.]+)\", data[0])\n",
        "    if match:\n",
        "      l1_bound = float(match.group(1))\n",
        "      raw_string = '\\n'.join(data[1:])\n",
        "    else:\n",
        "      raw_string = '\\n'.join(data)\n",
        "\n",
        "\n",
        "\n",
        "if \"eoh\" in log_run[\"name\"]:\n",
        "    lstcode = eoh_preprocess(log_run[\"path\"], parsed_data)\n",
        "    lst_init_eoh = get_init_eoh(log_run[\"path\"])\n",
        "    obj_eoh = get_max_obj_eoh(log_run[\"path\"])\n",
        "    parsed_data = create_sets_from_data_eoh(parsed_data, len(lst_init_eoh), obj_eoh)\n",
        "elif \"funsearch\" in log_run[\"name\"]:\n",
        "    parsed_data = create_sets_from_data(parsed_data, 20)\n",
        "else:\n",
        "    parsed_data = parse_log(raw_string)\n",
        "\n",
        "lstcode = read_all_files_in_folder(log_run[\"path\"], log_run[\"name\"], parsed_data)\n",
        "for name, code in lstcode.items():\n",
        "    # 1. Format code following PEP8\n",
        "    formatted_code = format_python_code(code)\n",
        "\n",
        "    # 2. Remove comments and docstrings in code\n",
        "    cleaned_code = remove_comments_and_docstrings(formatted_code)\n",
        "    lstcode[name] = cleaned_code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "-xfHrljO0llR",
        "outputId": "b18218b5-f715-4946-a6c1-f64490d3b9c9"
      },
      "outputs": [],
      "source": [
        "from collections import defaultdict\n",
        "\n",
        "def group_names(lstcode, framework_name, base = 20):\n",
        "    grouped_lists = defaultdict(list)\n",
        "\n",
        "    # Regular expression patterns\n",
        "    pattern_hsevo = re.compile(r'problem_iter(\\d+)')\n",
        "    pattern_reevo = re.compile(r'problem_iter(\\d+)')\n",
        "    pattern_funsearch = re.compile(r'samples_(\\d+)')\n",
        "    pattern_eoh = re.compile(r'pop_(\\d+)')\n",
        "\n",
        "    for name in lstcode.keys():\n",
        "        log_run = lstcode[name]\n",
        "\n",
        "        if \"hsevo\" in framework_name:\n",
        "            match = pattern_reevo.match(name)\n",
        "            if match:\n",
        "                i = int(match.group(1))\n",
        "                grouped_lists[i].append(name)\n",
        "        elif \"reevo\" in framework_name:\n",
        "            match = pattern_reevo.match(name)\n",
        "            if match:\n",
        "                i = int(match.group(1))\n",
        "                grouped_lists[i].append(name)\n",
        "        elif \"funsearch\" in framework_name:\n",
        "            match = pattern_funsearch.match(name)\n",
        "            if match:\n",
        "                i = int(match.group(1)) - 1\n",
        "                group_key = i // base\n",
        "                grouped_lists[group_key].append(name)\n",
        "        elif \"eoh\" in framework_name:\n",
        "            match = pattern_eoh.match(name)\n",
        "            if match:\n",
        "                i = int(match.group(1))\n",
        "                grouped_lists[i].append(name)\n",
        "\n",
        "    for key in sorted(grouped_lists.keys()):\n",
        "        print(f\"List for group {key}:\", grouped_lists[key])\n",
        "    print(\"---------------\\n\\n\")\n",
        "\n",
        "    return grouped_lists\n",
        "\n",
        "if \"eoh\" in log_run[\"name\"]:\n",
        "    grouped_lists = group_names(lstcode, \"eoh\")\n",
        "elif \"funsearch\" in log_run[\"name\"]:\n",
        "    grouped_lists = group_names(lstcode, \"funsearch\", 20)\n",
        "elif \"hsevo\" in log_run[\"name\"]:\n",
        "    grouped_lists = group_names(lstcode, \"hsevo\")\n",
        "elif \"reevo\" in log_run[\"name\"]:\n",
        "    grouped_lists = group_names(lstcode, \"reevo\")\n",
        "else:\n",
        "    print(\"Group list error!\")\n",
        "\n",
        "SWDI_lst = []\n",
        "CDI_lst = []\n",
        "\n",
        "lst_pop = []\n",
        "\n",
        "for key in sorted(grouped_lists.keys()):\n",
        "    print(f\"List for i group {key}:\", grouped_lists[key])\n",
        "    code_snippets = [lstcode[name] for name in grouped_lists[key]]\n",
        "    lst_pop.extend(code_snippets)\n",
        "    a,b = get_static(lst_pop, 0.95)\n",
        "    print(key)\n",
        "    print(parsed_data)\n",
        "    parsed_data[key] = parsed_data[key] + (a, b,)\n",
        "    SWDI_lst.append(a)\n",
        "    CDI_lst.append(b)\n",
        "    print(\"---------------\\n\")\n",
        "\n",
        "print(\"SWDI:\", SWDI_lst)\n",
        "print(\"CDI:\", CDI_lst)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XayzJvHxCLFK"
      },
      "outputs": [],
      "source": [
        "parsed_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KBJy5wqGGLGI"
      },
      "outputs": [],
      "source": [
        "def find_nearest_best_obj(results, step=25000):\n",
        "    results.sort(key=lambda x: x[0])  # Sort by total_tokens\n",
        "    step_tokens = 0\n",
        "    nearest_objs = []\n",
        "\n",
        "    while step_tokens <= results[-1][0]:  # Iterate until the maximum total_tokens\n",
        "        nearest = min(results, key=lambda x: abs(x[0] - step_tokens))\n",
        "        # Extract the last 3 elements from the nearest tuple\n",
        "        nearest_objs.append((step_tokens, abs(nearest[-3]), nearest[-2], nearest[-1]))\n",
        "        step_tokens += step\n",
        "\n",
        "    return nearest_objs\n",
        "\n",
        "def fill_missing_elements(data):\n",
        "    if data[0][0] == 0:\n",
        "        data[0] = (data[0][0], data[0][1], 0, 0)\n",
        "    for i in range(1, len(data)):\n",
        "        if len(data[i]) == 2:\n",
        "            data[i] = (\n",
        "                data[i][0],\n",
        "                data[i][1],\n",
        "                data[i-1][2],\n",
        "                data[i-1][3]\n",
        "            )\n",
        "    return data\n",
        "\n",
        "parsed_data = fill_missing_elements(parsed_data)\n",
        "nearest_objs = find_nearest_best_obj(parsed_data, 25000)\n",
        "nearest_objs"
      ]
    }
  ],
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