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     "text": [
      "Cloning into 'GitModel'...\n",
      "remote: Enumerating objects: 453, done.\u001b[K\n",
      "remote: Counting objects: 100% (258/258), done.\u001b[K\n",
      "remote: Compressing objects: 100% (219/219), done.\u001b[K\n",
      "remote: Total 453 (delta 123), reused 100 (delta 34), pack-reused 195\u001b[K\n",
      "Receiving objects: 100% (453/453), 1.62 MiB | 6.61 MiB/s, done.\n",
      "Resolving deltas: 100% (221/221), done.\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/danielpatrickhug/GitModel.git"
   ]
  },
  {
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   "source": [
    "%%capture\n",
    "%%bash\n",
    "pip install python-louvain"
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   "execution_count": 24,
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   ],
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   "outputs": []
  },
  {
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    "df = pd.read_csv(\"/content/gitmodel_topic_info_0.csv\")\n",
    "df"
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       "     Unnamed: 0  Topic  Count  \\\n",
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       "185  ['What is the difference between `BaselineMode...  \n",
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       "      <th>4</th>\n",
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       "      <td>3</td>\n",
       "      <td>392</td>\n",
       "      <td>3_Shortest path graph generation with Floyd-Wa...</td>\n",
       "      <td>['```class FloydWarshallSampler(Sampler):\\n  \"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>185</td>\n",
       "      <td>184</td>\n",
       "      <td>12</td>\n",
       "      <td>184_Understanding the difference and purpose o...</td>\n",
       "      <td>['What is the difference between `BaselineMode...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>186</td>\n",
       "      <td>185</td>\n",
       "      <td>11</td>\n",
       "      <td>185_MaxSubarraySampler class purpose and usage___</td>\n",
       "      <td>['What is the purpose of the `MaxSubarraySampl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>187</td>\n",
       "      <td>186</td>\n",
       "      <td>11</td>\n",
       "      <td>186_Improving Mobile App User Experience with ...</td>\n",
       "      <td>[\"This code also appears to be related to mach...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>188</td>\n",
       "      <td>187</td>\n",
       "      <td>11</td>\n",
       "      <td>187_Understanding the _expand_to function in n...</td>\n",
       "      <td>['What does the `_expand_to` function do and h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>189</td>\n",
       "      <td>188</td>\n",
       "      <td>11</td>\n",
       "      <td>188_Understanding the Purpose and Functionalit...</td>\n",
       "      <td>['What is the purpose of the `DfsSampler` clas...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190 rows × 5 columns</p>\n",
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     "metadata": {},
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  },
  {
   "cell_type": "code",
   "source": [
    "df = pd.read_csv(\"/content/gitmodeltopic_model_outputs_0.csv\")\n",
    "df"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "id": "r6HrnKBMN-gg",
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       "     Unnamed: 0                                              query  _id  \\\n",
       "0             0  What is the purpose of the `get_repo_contents`...    0   \n",
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
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       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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  },
  {
   "cell_type": "code",
   "source": [
    "data = pd.read_json(\"/content/gitmodel_self-instruction.jsonl\", lines=True)\n",
    "data"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "Ki3dXMFDOKDT",
    "outputId": "a48c83e7-e6b2-4700-8b3b-96a2cb56b676"
   },
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                         git_repo_path  \\\n",
       "0    /Users/danielhug/GitModelT/GitModel/work/gitmodel   \n",
       "1    /Users/danielhug/GitModelT/GitModel/work/gitmodel   \n",
       "2    /Users/danielhug/GitModelT/GitModel/work/gitmodel   \n",
       "3    /Users/danielhug/GitModelT/GitModel/work/gitmodel   \n",
       "4    /Users/danielhug/GitModelT/GitModel/work/gitmodel   \n",
       "..                                                 ...   \n",
       "231  /Users/danielhug/GitModelT/GitModel/work/gitmodel   \n",
       "232  /Users/danielhug/GitModelT/GitModel/work/gitmodel   \n",
       "233  /Users/danielhug/GitModelT/GitModel/work/gitmodel   \n",
       "234  /Users/danielhug/GitModelT/GitModel/work/gitmodel   \n",
       "235  /Users/danielhug/GitModelT/GitModel/work/gitmodel   \n",
       "\n",
       "                                             file_name code_type  \\\n",
       "0    /Users/danielhug/GitModelT/GitModel/work/gitmo...   classes   \n",
       "1    /Users/danielhug/GitModelT/GitModel/work/gitmo...   classes   \n",
       "2    /Users/danielhug/GitModelT/GitModel/work/gitmo...   classes   \n",
       "3    /Users/danielhug/GitModelT/GitModel/work/gitmo...   classes   \n",
       "4    /Users/danielhug/GitModelT/GitModel/work/gitmo...   classes   \n",
       "..                                                 ...       ...   \n",
       "231  /Users/danielhug/GitModelT/GitModel/work/gitmo...   classes   \n",
       "232  /Users/danielhug/GitModelT/GitModel/work/gitmo...   classes   \n",
       "233  /Users/danielhug/GitModelT/GitModel/work/gitmo...   classes   \n",
       "234  /Users/danielhug/GitModelT/GitModel/work/gitmo...   classes   \n",
       "235  /Users/danielhug/GitModelT/GitModel/work/gitmo...   classes   \n",
       "\n",
       "          system_task                                      system_prompt  \\\n",
       "0    self-instruction  \\n            GitHub repository: /Users/daniel...   \n",
       "1    self-instruction  \\n            GitHub repository: /Users/daniel...   \n",
       "2    self-instruction  \\n            GitHub repository: /Users/daniel...   \n",
       "3    self-instruction  \\n            GitHub repository: /Users/daniel...   \n",
       "4    self-instruction  \\n            GitHub repository: /Users/daniel...   \n",
       "..                ...                                                ...   \n",
       "231  self-instruction  \\n            GitHub repository: /Users/daniel...   \n",
       "232  self-instruction  \\n            GitHub repository: /Users/daniel...   \n",
       "233  self-instruction  \\n            GitHub repository: /Users/daniel...   \n",
       "234  self-instruction  \\n            GitHub repository: /Users/daniel...   \n",
       "235  self-instruction  \\n            GitHub repository: /Users/daniel...   \n",
       "\n",
       "                                  conversation_history  \\\n",
       "0    [{'role': 'system', 'content': '\n",
       "            G...   \n",
       "1    [{'role': 'system', 'content': '\n",
       "            G...   \n",
       "2    [{'role': 'system', 'content': '\n",
       "            G...   \n",
       "3    [{'role': 'system', 'content': '\n",
       "            G...   \n",
       "4    [{'role': 'system', 'content': '\n",
       "            G...   \n",
       "..                                                 ...   \n",
       "231  [{'role': 'system', 'content': '\n",
       "            G...   \n",
       "232  [{'role': 'system', 'content': '\n",
       "            G...   \n",
       "233  [{'role': 'system', 'content': '\n",
       "            G...   \n",
       "234  [{'role': 'system', 'content': '\n",
       "            G...   \n",
       "235  [{'role': 'system', 'content': '\n",
       "            G...   \n",
       "\n",
       "                                       assistant_reply  \n",
       "0    The purpose of this code is to define a `Confi...  \n",
       "1    The `Config` class takes four parameters in it...  \n",
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     "metadata": {},
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    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%cd GitModel"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jusbqr9NOT2g",
    "outputId": "4a31cb8c-2d62-460a-b1e0-70b99d978e11"
   },
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "/content/GitModel\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%capture\n",
    "%%bash\n",
    "pip install -r requirements.txt"
   ],
   "metadata": {
    "id": "pOlwrs2oPtYg"
   },
   "execution_count": 8,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "from sentence_transformers import SentenceTransformer\n",
    "import torch\n",
    "from torch import Tensor\n",
    "import pandas as pd\n",
    "import math\n",
    "import numpy as np\n",
    "import scipy\n",
    "from bertopic import BERTopic\n",
    "from bertopic.representation import OpenAI\n",
    "from bertopic.representation import MaximalMarginalRelevance\n",
    "from bertopic.vectorizers import ClassTfidfTransformer\n",
    "from sentence_transformers import SentenceTransformer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from torch import Tensor"
   ],
   "metadata": {
    "id": "bADyjV_QPz0e"
   },
   "execution_count": 9,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "def embed_data(data, key=\"query\", model_name=\"all-MiniLM-L6-v2\", cores=1, gpu=False, batch_size=128):\n",
    "    \"\"\"\n",
    "    Embed the sentences/text using the MiniLM language model (which uses mean pooling)\n",
    "    \"\"\"\n",
    "    print(\"Embedding data\")\n",
    "    model = SentenceTransformer(model_name)\n",
    "    print(\"Model loaded\")\n",
    "\n",
    "    sentences = data[key].tolist()\n",
    "    unique_sentences = data[key].unique()\n",
    "    print(\"Unique sentences\", len(unique_sentences))\n",
    "\n",
    "    if cores == 1:\n",
    "        embeddings = model.encode(unique_sentences, show_progress_bar=True, batch_size=batch_size)\n",
    "    else:\n",
    "        devices = [\"cpu\"] * cores\n",
    "        if gpu:\n",
    "            devices = None  # use all CUDA devices\n",
    "\n",
    "        # Start the multi-process pool on multiple devices\n",
    "        print(\"Multi-process pool starting\")\n",
    "        pool = model.start_multi_process_pool(devices)\n",
    "        print(\"Multi-process pool started\")\n",
    "\n",
    "        chunk_size = math.ceil(len(unique_sentences) / cores)\n",
    "\n",
    "        # Compute the embeddings using the multi-process pool\n",
    "        embeddings = model.encode_multi_process(unique_sentences, pool, batch_size=batch_size, chunk_size=chunk_size)\n",
    "        model.stop_multi_process_pool(pool)\n",
    "\n",
    "    print(\"Embeddings computed\")\n",
    "\n",
    "    mapping = {sentence: embedding for sentence, embedding in zip(unique_sentences, embeddings)}\n",
    "    embeddings = np.array([mapping[sentence] for sentence in sentences])\n",
    "\n",
    "    return embeddings"
   ],
   "metadata": {
    "id": "NP_Pgp0yQdOY"
   },
   "execution_count": 10,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# kernel\n",
    "def cos_sim(a: Tensor, b: Tensor):\n",
    "    \"\"\"\n",
    "    Computes the cosine similarity cos_sim(a[i], b[j]) for all i and j.\n",
    "    :return: Matrix with res[i][j]  = cos_sim(a[i], b[j])\n",
    "    \"\"\"\n",
    "    if not isinstance(a, torch.Tensor):\n",
    "        a = torch.tensor(np.array(a))\n",
    "\n",
    "    if not isinstance(b, torch.Tensor):\n",
    "        b = torch.tensor(np.array(b))\n",
    "\n",
    "    if len(a.shape) == 1:\n",
    "        a = a.unsqueeze(0)\n",
    "\n",
    "    if len(b.shape) == 1:\n",
    "        b = b.unsqueeze(0)\n",
    "\n",
    "    a_norm = torch.nn.functional.normalize(a, p=2, dim=1)\n",
    "    b_norm = torch.nn.functional.normalize(b, p=2, dim=1)\n",
    "    return torch.mm(a_norm, b_norm.transpose(0, 1))"
   ],
   "metadata": {
    "id": "Tw1f_i_3Qhd-"
   },
   "execution_count": 11,
   "outputs": []
  },
  {
   "cell_type": "code",
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    "MODEL_NAME = \"all-MiniLM-L6-v2\"  # \"allenai-specter\"#\n",
    "embs = embed_data(data, model_name=MODEL_NAME, key=\"assistant_reply\")"
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    "outputId": "ed064003-1af1-49b8-9401-bf34586b7bf3"
   },
   "execution_count": 12,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Embedding data\n"
     ]
    },
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     "output_type": "stream",
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     "text": [
      "Model loaded\n",
      "Unique sentences 198\n"
     ]
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    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Embeddings computed\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "threshold = 0.7"
   ],
   "metadata": {
    "id": "MTxpzMHcQppf"
   },
   "execution_count": 13,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "def compute_kernel_with_activation_fn(embs, activation_fn=F.relu, kernel_type=\"cosine\"):\n",
    "    # match case to kernel type\n",
    "    if kernel_type == \"cosine\":\n",
    "        A = cos_sim(embs, embs)\n",
    "    A = activation_fn(A)\n",
    "    adj_matrix = torch.zeros_like(A)\n",
    "    adj_matrix[A > threshold] = 1\n",
    "    adj_matrix[A <= threshold] = 0\n",
    "    adj_matrix = adj_matrix.numpy().astype(np.float32)\n",
    "    return adj_matrix\n",
    "\n",
    "\n",
    "def degree_matrix(A):\n",
    "    \"\"\"\n",
    "    compute degree matrix using adjacency distance matrix from pairwise distances\n",
    "    :A: nxn size matrix embedding minmaxed using mu sigma and pairwise distances\n",
    "    :return: degree matrix\n",
    "    \"\"\"\n",
    "    n = A.shape[0]\n",
    "    D = np.zeros((n, n))\n",
    "    for i in range(n):\n",
    "        D[i, i] = np.sum(A[i, :])\n",
    "    return D\n",
    "\n",
    "\n",
    "def graph_laplacian(A):\n",
    "    \"\"\"\n",
    "    compute graph laplacian using degree and adjacency matrix from pairwise distances\n",
    "    :A: nxn size matrix embedding minmaxed using mu sigma and pairwise distances\n",
    "    :return: graph laplacian, and degree matrix\n",
    "    \"\"\"\n",
    "    D = degree_matrix(A)\n",
    "    L = D - A\n",
    "    return L, D\n",
    "\n",
    "\n",
    "def compute_regularized_kernel(embs, threshold=0.65):\n",
    "    A = cos_sim(embs, embs)\n",
    "    adj_matrix = torch.zeros_like(A)\n",
    "    adj_matrix[A > threshold] = 1\n",
    "    adj_matrix[A <= threshold] = 0\n",
    "    adj_matrix = adj_matrix.numpy().astype(np.float32)\n",
    "    return adj_matrix"
   ],
   "metadata": {
    "id": "3Jwy-730Qnke"
   },
   "execution_count": 14,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "A = compute_regularized_kernel(embs)"
   ],
   "metadata": {
    "id": "7DLcKu7KRyPJ"
   },
   "execution_count": 15,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import scipy.sparse as sp\n",
    "from tqdm import tqdm\n",
    "\n",
    "\n",
    "def k_hop_message_passing(A, node_features, k):\n",
    "    \"\"\"\n",
    "    Compute the k-hop adjacency matrix and aggregated features using message passing.\n",
    "\n",
    "    Parameters:\n",
    "    A (numpy array): The adjacency matrix of the graph.\n",
    "    node_features (numpy array): The feature matrix of the nodes.\n",
    "    k (int): The number of hops for message passing.\n",
    "\n",
    "    Returns:\n",
    "    A_k (numpy array): The k-hop adjacency matrix.\n",
    "    agg_features (numpy array): The aggregated feature matrix for each node in the k-hop neighborhood.\n",
    "    \"\"\"\n",
    "\n",
    "    print(\"Compute the k-hop adjacency matrix\")\n",
    "    A_k = np.linalg.matrix_power(A, k)\n",
    "\n",
    "    print(\"Aggregate the messages from the k-hop neighborhood:\")\n",
    "    agg_features = node_features.copy()\n",
    "\n",
    "    for i in tqdm(range(k)):\n",
    "        agg_features += np.matmul(np.linalg.matrix_power(A, i + 1), node_features)\n",
    "\n",
    "    return A_k, agg_features"
   ],
   "metadata": {
    "id": "O2mby84URbEL"
   },
   "execution_count": 16,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "def dfs(v, visited, adj_matrix, component):\n",
    "    visited[v] = True\n",
    "    component.append(v)\n",
    "    for i, val in enumerate(adj_matrix[v]):\n",
    "        if val > 0 and not visited[i]:\n",
    "            dfs(i, visited, adj_matrix, component)\n",
    "\n",
    "\n",
    "def connected_components(adj_matrix):\n",
    "    visited = [False for _ in range(adj_matrix.shape[0])]\n",
    "    components = []\n",
    "\n",
    "    for v in range(adj_matrix.shape[0]):\n",
    "        if not visited[v]:\n",
    "            component = []\n",
    "            dfs(v, visited, adj_matrix, component)\n",
    "            components.append(component)\n",
    "\n",
    "    return components"
   ],
   "metadata": {
    "id": "sTcaZ6etSV2z"
   },
   "execution_count": 22,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "A_k, node_embs = k_hop_message_passing(A, embs, 2)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qgC55WNAR6Tm",
    "outputId": "b62811a4-b50c-47a8-bd15-8fc24c9ba0cd"
   },
   "execution_count": 18,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Compute the k-hop adjacency matrix\n",
      "Aggregate the messages from the k-hop neighborhood:\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|██████████| 2/2 [00:00<00:00, 257.25it/s]\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "A_k3, node_embs3 = k_hop_message_passing(A, embs, 3)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0uqKEYGYN9x5",
    "outputId": "487f5610-3639-4339-fb31-24aa36e08e8e"
   },
   "execution_count": 19,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Compute the k-hop adjacency matrix\n",
      "Aggregate the messages from the k-hop neighborhood:\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|██████████| 3/3 [00:00<00:00, 1457.71it/s]\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "A_k4, node_embs4 = k_hop_message_passing(A, embs, 4)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rRZjx3BJOwV9",
    "outputId": "68d5d437-6ef7-4187-a9ca-6ed154d7a03c"
   },
   "execution_count": 20,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Compute the k-hop adjacency matrix\n",
      "Aggregate the messages from the k-hop neighborhood:\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|██████████| 4/4 [00:00<00:00, 1823.61it/s]\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def laplacian_matrix(adj_matrix):\n",
    "    degree_matrix = np.diag(np.sum(adj_matrix, axis=1))\n",
    "    lap_matrix = degree_matrix - adj_matrix\n",
    "    return lap_matrix"
   ],
   "metadata": {
    "id": "W277y6hR5vL2"
   },
   "execution_count": 47,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "L = laplacian_matrix(A_k)\n",
    "L_k, lap_node_embs = k_hop_message_passing(L, node_embs, 2)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Hy_xckKjTVK6",
    "outputId": "121e5f15-a4fb-4dc5-b53f-7a3c4ab98363"
   },
   "execution_count": 83,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Compute the k-hop adjacency matrix\n",
      "Aggregate the messages from the k-hop neighborhood:\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|██████████| 2/2 [00:00<00:00, 1972.40it/s]\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import networkx as nx\n",
    "from networkx.algorithms import community\n",
    "\n",
    "# Convert the adjacency matrix to a NetworkX graph\n",
    "G = nx.from_numpy_array(L_k)\n",
    "\n",
    "# Perform Girvan-Newman community detection\n",
    "communities_generator = community.girvan_newman(G)\n",
    "\n",
    "# Get the first level of communities\n",
    "communities = next(communities_generator)\n",
    "\n",
    "# Sort communities by size (number of nodes)\n",
    "sorted_communities = sorted(communities, key=lambda x: len(x), reverse=True)\n",
    "\n",
    "# Print sorted communities\n",
    "for i, community in enumerate(sorted_communities):\n",
    "    print(f\"Community {i + 1}:\")\n",
    "    print(f\"Nodes: {list(community)}\")\n",
    "    component_files_structure = []\n",
    "    component_text_structure = []\n",
    "    component_history_structure = []\n",
    "    for row_key in list(community):\n",
    "        file_component = data.loc[row_key, \"file_name\"]\n",
    "        text_component = data.loc[row_key, \"assistant_reply\"]\n",
    "        history_component = data.loc[row_key, \"conversation_history\"]\n",
    "        component_files_structure.append(file_component)\n",
    "        component_text_structure.append(text_component)\n",
    "        component_history_structure.append(history_component)\n",
    "    print(component_files_structure)\n",
    "    for text, history in zip(component_text_structure, component_history_structure):\n",
    "        # print(history[-2]['content'])\n",
    "        print()\n",
    "        print(text)\n",
    "        print()\n",
    "    print()\n",
    "    break"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "91hC6ThZKT2V",
    "outputId": "10c51802-1c8b-49a5-95a8-d5e6cbbf8f40"
   },
   "execution_count": 85,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Community 1:\n",
      "Nodes: [0, 1, 2, 3, 128, 4, 5, 6, 7, 9, 138, 140, 141, 142, 143, 144, 19, 148, 21, 149, 23, 24, 25, 150, 151, 152, 30, 31, 32, 33, 34, 35, 193, 194, 195, 196, 197, 76, 77, 78, 231, 232, 234, 235, 114, 115, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]\n",
      "['/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/config.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/config.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/config.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/config.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/config.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/config.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/config.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/config.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/config.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/config.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/dynamic_import.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/dynamic_import.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/dynamic_import.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/dynamic_import.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/dynamic_import.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/dynamic_import.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/dynamic_import.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/pipeline.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/dynamic_import.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/pipeline.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/dynamic_import.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/dynamic_import.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/dynamic_import.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/pipeline.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/pipeline.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/pipeline.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/dynamic_import.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/pipeline.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/pipeline.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/pipeline.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/pipeline.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/pipeline.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/alembic/env.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/alembic/env.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/alembic/env.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/alembic/env.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/alembic/env.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/alembic/env.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/alembic/env.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/alembic/env.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/transformers/semantic_graph_context_generator.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/transformers/semantic_graph_context_generator.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/transformers/semantic_graph_context_generator.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/transformers/semantic_graph_context_generator.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/transformers/semantic_graph_context_generator.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/transformers/semantic_graph_context_generator.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/transformers/semantic_graph_context_generator.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/transformers/semantic_graph_context_generator.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/config.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/config.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/config.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/config.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/config.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/config.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/config.py', '/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/config.py']\n",
      "\n",
      "The purpose of this code is to define a `Config` class that can be used to store and retrieve configuration settings for a pipeline. The `__init__` method initializes the class with four parameters: `pipeline_settings`, `semantic_graph_context_generator`, `gnn_heads`, and `topic_model`. The `from_yaml` method is a class method that loads a YAML file and returns a new instance of the `Config` class with the settings specified in the YAML file.\n",
      "\n",
      "\n",
      "The `Config` class takes four parameters in its constructor:\n",
      "\n",
      "1. `pipeline_settings`: A dictionary containing settings for the pipeline.\n",
      "2. `semantic_graph_context_generator`: An object that generates context for a semantic graph.\n",
      "3. `gnn_heads`: A list of objects representing the heads of a graph neural network.\n",
      "4. `topic_model`: An object representing a topic model.\n",
      "\n",
      "\n",
      "The purpose of the `from_yaml` class method is to create a new instance of the `Config` class with settings specified in a YAML file. The method takes a single parameter, `yaml_path`, which is the path to the YAML file. The method opens the file, reads its contents, and loads them into a dictionary using the `yaml.safe_load` function. The dictionary is then passed as keyword arguments to the `Config` class constructor, and a new instance of the class is returned.\n",
      "\n",
      "\n",
      "The `yaml.safe_load` function is used to safely load a YAML file into a Python object. It takes a string or file-like object containing YAML data as input and returns a Python object representing the data. The function is \"safe\" in the sense that it only allows the loading of basic Python objects such as lists, dictionaries, strings, and numbers, and does not allow the loading of more complex objects such as functions or classes. This helps to prevent security vulnerabilities that could arise from loading untrusted YAML data.\n",
      "\n",
      "\n",
      "The purpose of the `__repr__` method is to provide a string representation of the `PipelineSettings` object. The `__repr__` method is a special method in Python that is used to provide a string representation of an object that can be used to recreate the object. In this case, the `__repr__` method returns a string that includes the string representation of the `Config` object stored as an attribute of the `PipelineSettings` object. This can be useful for debugging and logging purposes, as it allows developers to easily see the settings that were used to create a `PipelineSettings` object.\n",
      "\n",
      "\n",
      "The expected format of the YAML file that is being loaded is a dictionary with keys corresponding to the parameter names of the `Config` class constructor. The values of the dictionary should be valid Python objects that can be used to initialize the corresponding parameters of the `Config` class. For example, the YAML file might look like this:\n",
      "\n",
      "```\n",
      "pipeline_settings:\n",
      "  setting1: value1\n",
      "  setting2: value2\n",
      "semantic_graph_context_generator: <object>\n",
      "gnn_heads:\n",
      "  - <object1>\n",
      "  - <object2>\n",
      "topic_model: <object>\n",
      "```\n",
      "\n",
      "This YAML file would create a `Config` object with `pipeline_settings` set to a dictionary with two keys, `setting1` and `setting2`, `semantic_graph_context_generator` set to an object, `gnn_heads` set to a list containing two objects, and `topic_model` set to an object.\n",
      "\n",
      "\n",
      "The `**` syntax in the `return` statement of the `from_yaml` method is used to unpack the contents of a dictionary into keyword arguments for the `Config` class constructor. The `config_dict` dictionary contains key-value pairs that correspond to the parameter names and values of the `Config` class constructor. By using the `**` syntax, the dictionary can be unpacked into keyword arguments that are passed to the `Config` class constructor. This allows the `Config` object to be created with the settings specified in the YAML file.\n",
      "\n",
      "\n",
      "The purpose of this code is to define a `PipelineSettings` class that takes a `Config` object as a parameter and stores it as an attribute. The `__repr__` method is defined to provide a string representation of the `PipelineSettings` object that includes the `Config` object stored as an attribute. This can be useful for debugging and logging purposes.\n",
      "\n",
      "\n",
      "The `PipelineSettings` class takes a single parameter, `config`, in its constructor. The `config` parameter is an instance of the `Config` class, which contains settings for the pipeline. The `config` parameter is stored as an attribute of the `PipelineSettings` object.\n",
      "\n",
      "\n",
      "The purpose of the `__repr__` method is to provide a string representation of the `PipelineSettings` object. The `__repr__` method is a special method in Python that is used to define the string representation of an object. When an object is printed or displayed in the console, its `__repr__` method is called to generate a string representation of the object. In this case, the `__repr__` method returns a string that includes the `config` attribute of the `PipelineSettings` object. This can be useful for debugging and logging purposes, as it allows the contents of the `PipelineSettings` object to be easily inspected.\n",
      "\n",
      "\n",
      "The purpose of this code is to instantiate classes based on a configuration object and return a dictionary containing the instantiated objects. The configuration object is expected to have attributes for `pipeline_settings`, `semantic_graph_context_generator`, `gnn_heads`, and `topic_model`, each of which is a list of dictionaries containing the arguments to be passed to the corresponding class constructor. The `__impl__` key in each dictionary specifies the class to be instantiated. The code iterates over each list of arguments, instantiates the corresponding class, and appends the resulting object to the appropriate list in the `obj_map` dictionary. The `deepcopy` function is used to create a copy of the configuration object to avoid modifying the original object.\n",
      "\n",
      "\n",
      "The code processes the input by iterating over each of the four attributes of the `Config` object (`pipeline_settings`, `semantic_graph_context_generator`, `gnn_heads`, and `topic_model`) and for each attribute, iterating over the list of dictionaries contained in the attribute. For each dictionary, the code extracts the value of the `__impl__` key, which specifies the class to be instantiated, and removes the `__impl__` key from the dictionary. The code then instantiates the corresponding class with the remaining dictionary as arguments and appends the resulting object to the appropriate list in the `obj_map` dictionary. The `deepcopy` function is used to create a copy of the configuration object to avoid modifying the original object.\n",
      "\n",
      "\n",
      "The output of this code is a dictionary containing the instantiated objects. The keys of the dictionary are the names of the four attributes of the `Config` object (`pipeline_settings`, `semantic_graph_context_generator`, `gnn_heads`, and `topic_model`), and the values are lists of objects instantiated from the corresponding classes specified in the `__impl__` key of the dictionaries contained in the attribute lists.\n",
      "\n",
      "\n",
      "The purpose of the `deepcopy` function call is to create a deep copy of the `Config` object passed as input to the function. This is done to avoid modifying the original object, as the function modifies the object by removing the `__impl__` key from the dictionaries contained in the attribute lists. By creating a deep copy, the function can modify the copy of the object without affecting the original object.\n",
      "\n",
      "\n",
      "The purpose of the `obj_map` dictionary is to store the instantiated objects created by the function. The dictionary has four keys, corresponding to the four attributes of the `Config` object (`pipeline_settings`, `semantic_graph_context_generator`, `gnn_heads`, and `topic_model`). The values of the dictionary are lists of objects instantiated from the corresponding classes specified in the `__impl__` key of the dictionaries contained in the attribute lists. The `append` method is used to add the instantiated objects to the appropriate list in the `obj_map` dictionary.\n",
      "\n",
      "\n",
      "The possible values of `component_name` are `\"pipeline_settings\"`, `\"semantic_graph_context_generator\"`, `\"gnn_heads\"`, and `\"topic_model\"`. These are the names of the four attributes of the `Config` object that are expected to contain lists of dictionaries specifying the arguments to be passed to the corresponding class constructors.\n",
      "\n",
      "\n",
      "The purpose of this code is to instantiate classes based on a configuration object and return a dictionary containing the instantiated objects. The configuration object is expected to have attributes for `pipeline_settings`, `semantic_graph_context_generator`, `gnn_heads`, and `topic_model`, each of which is a list of dictionaries containing the arguments to be passed to the corresponding class constructor. The `__impl__` key in each dictionary specifies the class to be instantiated. The instantiated objects are stored in a dictionary with keys corresponding to the component names and values being lists of instantiated objects.\n",
      "\n",
      "\n",
      "The `Pipeline` class is a class that represents a pipeline for processing data. It takes a configuration object as input and uses it to instantiate various objects that are used in the pipeline. It also provides methods for finding files with a given substring, loading data from JSONL files, and running the pipeline on a given Git repository.\n",
      "\n",
      "\n",
      "The code processes the input by iterating over the `pipeline_settings`, `semantic_graph_context_generator`, `gnn_heads`, and `topic_model` attributes of the `Config` object. For each attribute, it iterates over the list of dictionaries contained in the attribute and extracts the `__impl__` key to determine which class to instantiate. It then instantiates the class with the remaining arguments in the dictionary and appends the instantiated object to a list stored in a dictionary with the corresponding component name as the key. The resulting dictionary contains lists of instantiated objects for each component.\n",
      "\n",
      "\n",
      "The input to the `Pipeline` class constructor is a `Config` object.\n",
      "\n",
      "\n",
      "The purpose of the `deepcopy` function call is to create a deep copy of the `Config` object passed as input. This is done to ensure that any modifications made to the object during the instantiation of classes do not affect the original object.\n",
      "\n",
      "\n",
      "The purpose of the `obj_map` dictionary is to store the instantiated objects for each component. The keys of the dictionary correspond to the component names, and the values are lists of instantiated objects for each component.\n",
      "\n",
      "\n",
      "The possible values for `component_name` are `\"pipeline_settings\"`, `\"semantic_graph_context_generator\"`, `\"gnn_heads\"`, and `\"topic_model\"`.\n",
      "\n",
      "\n",
      "The `_obj_map` attribute of the `Pipeline` class is a dictionary that maps object names to their corresponding instantiated objects. It is created by calling the `instantiate_class_from_config` function with the `config` object passed to the `Pipeline` constructor.\n",
      "\n",
      "\n",
      "The `pipeline_settings` attribute of the `Pipeline` class is a list containing a single object that represents the settings for the pipeline. It is obtained from the `_obj_map` dictionary by accessing the `\"pipeline_settings\"` key.\n",
      "\n",
      "\n",
      "The `semantic_graph_context_generator` attribute of the `Pipeline` class is a list containing objects that generate the semantic graph context for the pipeline. It is obtained from the `_obj_map` dictionary by accessing the `\"semantic_graph_context_generator\"` key.\n",
      "\n",
      "\n",
      "The purpose of the `obj_map[component_name].append(_cls(config=args))` line is to instantiate a class based on the arguments in the dictionary for a component and append the instantiated object to the list of instantiated objects for the component stored in the `obj_map` dictionary.\n",
      "\n",
      "\n",
      "The `Pipeline` class is a container for a set of methods that together form a pipeline for processing data related to a git repository. It takes a `Config` object as input and instantiates a set of objects based on the configuration. The `run` method is the main method of the pipeline, which takes a git repository and its name as input, and returns a tuple of numpy arrays and a dictionary.\n",
      "\n",
      "\n",
      "The input to the `Pipeline` class constructor is a `Config` object.\n",
      "\n",
      "\n",
      "The `_obj_map` attribute of the `Pipeline` class is a dictionary that maps object names to their corresponding instantiated objects. It is created by calling the `instantiate_class_from_config` function with the `config` object passed to the `Pipeline` constructor.\n",
      "\n",
      "\n",
      "The `pipeline_settings` attribute of the `Pipeline` class is a list containing a single object, which is the pipeline settings object. It is obtained from the `_obj_map` dictionary by indexing the `\"pipeline_settings\"` key.\n",
      "\n",
      "\n",
      "The `semantic_graph_context_generator` attribute of the `Pipeline` class is a list containing objects that generate semantic graph contexts. It is obtained from the `_obj_map` dictionary by indexing the `\"semantic_graph_context_generator\"` key. The objects are instantiated based on the configuration.\n",
      "\n",
      "\n",
      "The `connectable` variable is used to store the result of the `engine_from_config()` function call, which creates an Engine object based on the configuration settings in the `config` object.\n",
      "\n",
      "\n",
      "The `engine_from_config()` function is used to create an Engine object based on the configuration settings in the `config` object. It takes the configuration section to use as an argument, as well as other optional parameters such as the prefix for the configuration keys and the pool class to use.\n",
      "\n",
      "\n",
      "The `poolclass` parameter in the `engine_from_config()` function is used to specify the class to use for connection pooling. In this case, `pool.NullPool` is used to disable connection pooling.\n",
      "\n",
      "\n",
      "Inside the first `with` block, a connection is established to the database using the `connect()` method of the `connectable` object. The `context` object is then configured to use this connection and the target metadata.\n",
      "\n",
      "\n",
      "The `connection` variable is used to store the connection object returned by the `connect()` method of the `connectable` object. It is used to execute SQL statements and to associate the connection with the `context` object.\n",
      "\n",
      "\n",
      "`engine_from_config()` creates a new SQLAlchemy engine based on the configuration settings provided in the `config` object. It takes the configuration section to use as an argument, as well as any additional configuration options specified with the `prefix` argument. In this case, it also sets the `poolclass` to `pool.NullPool`, which disables connection pooling.\n",
      "\n",
      "\n",
      "`poolclass=pool.NullPool` in the `engine_from_config()` method sets the connection pool class to `NullPool`, which disables connection pooling. This means that a new connection will be created for each request, which can be useful in certain scenarios where connection pooling is not desirable.\n",
      "\n",
      "\n",
      "The `with connectable.connect() as connection:` block creates a new connection to the database using the `connectable` engine. The `connection` object is then used to configure the migration context and execute the migrations. The `with` statement ensures that the connection is properly closed when the block is exited.\n",
      "\n",
      "\n",
      "The `SemanticGraphContextGenerator` class is a Python class that contains methods for generating context for a given piece of code. It uses OpenAI's GPT-3 language model to generate responses to user prompts and can process code repositories to extract relevant code snippets.\n",
      "\n",
      "\n",
      "The `config` parameter in the `__init__` method is used to initialize the `config` attribute of the `SemanticGraphContextGenerator` class. This attribute is used in the `chat_gpt_inference` method to set the maximum number of tokens and the temperature for the OpenAI GPT-3 language model.\n",
      "\n",
      "\n",
      "The `chat_gpt_inference` method is used to generate responses to a list of messages using OpenAI's GPT-3 language model. It takes a list of messages as input and uses the `openai.ChatCompletion.create` method to generate a response. The `max_tokens` and `temperature` parameters for the language model are set using the `config` attribute of the `SemanticGraphContextGenerator` class. The method returns the response generated by the language model.\n",
      "\n",
      "\n",
      "The `create_prompt_message_template` method is used to create a message template for a given text input. It takes a `text` parameter and an optional `role` parameter, which defaults to `\"user\"`. The method returns a dictionary with the `role` and `content` keys set to the input parameters. This message template can be used as input to the `chat_gpt_inference` method to generate a response.\n",
      "\n",
      "\n",
      "The purpose of the `SemanticGraphContextGenerator` class is to provide methods for generating context for a given piece of code using OpenAI's GPT-3 language model. It includes methods for retrieving the contents of a GitHub repository, generating chat responses using GPT-3, and processing a transcript of user interactions with the system.\n",
      "\n",
      "\n",
      "The `config` parameter in the `__init__` method is used to initialize the `SemanticGraphContextGenerator` object with a configuration dictionary that contains parameters for the OpenAI GPT-3 language model, such as `max_tokens` and `temperature`. These parameters are used in the `chat_gpt_inference` method to generate chat responses.\n",
      "\n",
      "\n",
      "The `chat_gpt_inference` method is used to generate chat responses using OpenAI's GPT-3 language model. It takes a list of messages as input and uses the `max_tokens` and `temperature` parameters from the `config` dictionary to generate a response. The method returns the response as a dictionary containing information such as the generated text and the confidence score.\n",
      "\n",
      "\n",
      "The `create_prompt_message_template` method is used to create a message template for a given text input. It takes the `text` parameter as input and an optional `role` parameter, which defaults to `\"user\"`. The method returns a dictionary containing the `role` and `content` of the message. This method is used in the `compose_inference` method to create message templates for user and assistant messages.\n",
      "\n",
      "\n",
      "The purpose of this code is to define a `Config` class that can be used to store and retrieve configuration settings for a pipeline. The `__init__` method initializes the class with four parameters: `pipeline_settings`, `semantic_graph_context_generator`, `gnn_heads`, and `topic_model`. The `from_yaml` method is a class method that loads a YAML file and returns a new instance of the `Config` class with the settings specified in the YAML file.\n",
      "\n",
      "\n",
      "The `Config` class takes four parameters in its constructor:\n",
      "\n",
      "1. `pipeline_settings`: A dictionary containing settings for the pipeline.\n",
      "2. `semantic_graph_context_generator`: An object that generates context for a semantic graph.\n",
      "3. `gnn_heads`: A list of objects representing the heads of a graph neural network.\n",
      "4. `topic_model`: An object representing a topic model.\n",
      "\n",
      "\n",
      "The purpose of the `from_yaml` class method is to create a new instance of the `Config` class with settings specified in a YAML file. The method takes a single parameter, `yaml_path`, which is the path to the YAML file. The method opens the file, reads its contents, and loads them into a dictionary using the `yaml.safe_load` function. The dictionary is then passed as keyword arguments to the `Config` class constructor, and a new instance of the class is returned.\n",
      "\n",
      "\n",
      "The `yaml.safe_load` function is used to safely load a YAML file into a Python object. It takes a string or file-like object containing YAML data as input and returns a Python object representing the data. The function is \"safe\" in the sense that it only allows the loading of basic Python objects such as lists, dictionaries, strings, and numbers, and does not allow the loading of more complex objects such as functions or classes. This helps to prevent security vulnerabilities that could arise from loading untrusted YAML data.\n",
      "\n",
      "\n",
      "The expected format of the YAML file that is being loaded is a dictionary with keys corresponding to the parameter names of the `Config` class constructor. The values of the dictionary should be valid Python objects that can be used to initialize the corresponding parameters of the `Config` class. For example, the YAML file might look like this:\n",
      "\n",
      "```\n",
      "pipeline_settings:\n",
      "  max_iterations: 100\n",
      "  learning_rate: 0.01\n",
      "semantic_graph_context_generator: my_context_generator\n",
      "gnn_heads:\n",
      "  - my_gnn_head_1\n",
      "  - my_gnn_head_2\n",
      "topic_model: my_topic_model\n",
      "```\n",
      "\n",
      "This YAML file would create a new `Config` object with `pipeline_settings` set to a dictionary with `max_iterations` and `learning_rate` keys, `semantic_graph_context_generator` set to the string `\"my_context_generator\"`, `gnn_heads` set to a list containing the strings `\"my_gnn_head_1\"` and `\"my_gnn_head_2\"`, and `topic_model` set to the string `\"my_topic_model\"`.\n",
      "\n",
      "\n",
      "The `**` syntax in the `return` statement of the `from_yaml` method is used to unpack a dictionary into keyword arguments. In this case, the `config_dict` dictionary returned by `yaml.safe_load` contains key-value pairs that correspond to the parameters of the `Config` class constructor. The `**config_dict` syntax unpacks the dictionary into keyword arguments, so that each key in the dictionary becomes a keyword argument name and its corresponding value becomes the value of that argument. This allows the `Config` object to be created with the settings specified in the YAML file.\n",
      "\n",
      "\n",
      "The purpose of this code is to define a `PipelineSettings` class that takes a `Config` object as a parameter and stores it as an attribute. The `__repr__` method is defined to provide a string representation of the `PipelineSettings` object that includes the `Config` object stored as an attribute. This can be useful for debugging and logging purposes.\n",
      "\n",
      "\n",
      "The `PipelineSettings` class takes a single parameter, `config`, in its constructor. The `config` parameter is an instance of the `Config` class, which contains settings for the pipeline. The `config` parameter is stored as an attribute of the `PipelineSettings` object.\n",
      "\n",
      "\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "connected_comps = connected_components(L)\n",
    "file_components = defaultdict(list)\n",
    "components = defaultdict(list)\n",
    "for i, comp in enumerate(connected_comps):\n",
    "    print(f\"Component {i + 1}:\")\n",
    "    for idx in comp:\n",
    "        file_component = data.loc[idx, \"file_name\"]\n",
    "        file_components[file_component].append(idx)\n",
    "        components[idx].append(data.loc[idx, \"file_name\"])\n",
    "        print(f\"Index {idx}: {data.loc[idx, 'file_name']}\")\n",
    "    print()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cX_XssgkToud",
    "outputId": "cba48fcd-5012-4cf9-e7fb-08776f554c6c"
   },
   "execution_count": 74,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Component 1:\n",
      "Index 0: /Users/danielhug/GitModelT/GitModel/work/gitmodel/src/config.py\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Component 32:\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Component 37:\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Component 206:\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Component 215:\n",
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      "\n",
      "Component 216:\n",
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      "\n",
      "Component 217:\n",
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      "\n",
      "Component 218:\n",
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      "\n",
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      "\n",
      "Component 220:\n",
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      "\n",
      "Component 221:\n",
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      "\n",
      "Component 222:\n",
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      "\n",
      "Component 223:\n",
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      "\n",
      "Component 224:\n",
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      "\n",
      "Component 225:\n",
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      "\n",
      "Component 226:\n",
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      "\n",
      "Component 227:\n",
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      "\n",
      "Component 228:\n",
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      "\n",
      "Component 229:\n",
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      "\n",
      "Component 230:\n",
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      "\n",
      "Component 231:\n",
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      "\n",
      "Component 232:\n",
      "Index 231: /Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/transformers/semantic_graph_context_generator.py\n",
      "\n",
      "Component 233:\n",
      "Index 232: /Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/transformers/semantic_graph_context_generator.py\n",
      "\n",
      "Component 234:\n",
      "Index 233: /Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/transformers/semantic_graph_context_generator.py\n",
      "\n",
      "Component 235:\n",
      "Index 234: /Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/transformers/semantic_graph_context_generator.py\n",
      "\n",
      "Component 236:\n",
      "Index 235: /Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/transformers/semantic_graph_context_generator.py\n",
      "\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "for idz in file_components[\n",
    "    \"/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/graph_networks/gnn_head.py\"\n",
    "]:\n",
    "    sym_slice = components[idz]\n",
    "    print(sym_slice)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BIgXkzdYRphR",
    "outputId": "0b491a74-60a0-4ae4-b12c-cc9cc8e1dc9f"
   },
   "execution_count": 62,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "['/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/graph_networks/gnn_head.py']\n",
      "['/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/graph_networks/gnn_head.py']\n",
      "['/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/graph_networks/gnn_head.py']\n",
      "['/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/graph_networks/gnn_head.py']\n",
      "['/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/graph_networks/gnn_head.py']\n",
      "['/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/graph_networks/gnn_head.py']\n",
      "['/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/graph_networks/gnn_head.py']\n",
      "['/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/graph_networks/gnn_head.py']\n",
      "['/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/graph_networks/gnn_head.py']\n",
      "['/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/graph_networks/gnn_head.py']\n",
      "['/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/graph_networks/gnn_head.py']\n",
      "['/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/graph_networks/gnn_head.py']\n",
      "['/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/graph_networks/gnn_head.py']\n",
      "['/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/graph_networks/gnn_head.py']\n",
      "['/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/graph_networks/gnn_head.py']\n",
      "['/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/graph_networks/gnn_head.py']\n",
      "['/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/graph_networks/gnn_head.py']\n",
      "['/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/graph_networks/gnn_head.py']\n",
      "[]\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "components[213]"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "id": "wUPokWmVRxZD",
    "outputId": "6d45762a-a9dd-453d-b522-c936d05ece4f"
   },
   "execution_count": 33,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'/Users/danielhug/GitModelT/GitModel/work/gitmodel/src/ml_models/graph_networks/gnn_head.py'"
      ],
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      }
     },
     "metadata": {},
     "execution_count": 33
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "for a in [A, A_k, A_k3, A_k4, L]:\n",
    "    connected_comps = connected_components(a)\n",
    "    unique_files = []\n",
    "\n",
    "    for i, comp in enumerate(connected_comps):\n",
    "        unique_files_comp = set()\n",
    "        for idx in comp:\n",
    "            unique_files_comp.add(data.loc[idx, \"file_name\"])\n",
    "        unique_files.append(unique_files_comp)\n",
    "\n",
    "    # Plot the unique file names count in each connected component\n",
    "    component_indices = [i + 1 for i in range(len(unique_files))]\n",
    "    unique_files_count = [len(files) for files in unique_files]\n",
    "\n",
    "    # Set figure size\n",
    "    plt.figure(figsize=(10, 6))\n",
    "\n",
    "    # Set bar width and spacing\n",
    "    bar_width = 0.4\n",
    "    spacing = [i - bar_width / 2 for i in component_indices]\n",
    "\n",
    "    plt.bar(spacing, unique_files_count, width=bar_width)\n",
    "\n",
    "    plt.xlabel(\"Connected Component\", fontsize=14)\n",
    "    plt.ylabel(\"Unique File Names Count\", fontsize=14)\n",
    "    plt.title(\"Unique File Names in Each Connected Component\", fontsize=16)\n",
    "    plt.xticks(component_indices, fontsize=10)\n",
    "    plt.yticks(fontsize=12)\n",
    "\n",
    "    # Adjust the margins\n",
    "    plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1)\n",
    "\n",
    "    plt.show()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "iMKiw8evUgaV",
    "outputId": "0b04d431-6765-4190-c0ea-38de8b4966f8"
   },
   "execution_count": 75,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "\"\"\"\n",
    "unique_files = []\n",
    "for i, comp in enumerate(connected_comps):\n",
    "    unique_files_comp = set()\n",
    "    for idx in comp:\n",
    "        unique_files_comp.add(data.loc[idx, 'file_name'])\n",
    "    unique_files.append(unique_files_comp)\n",
    "    print(f\"Component {i + 1}:\")\n",
    "    print(f\"Unique file names: {unique_files_comp}\")\n",
    "    print()\n",
    "\"\"\""
   ],
   "metadata": {
    "id": "YCntXu-iUqiM"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "\"\"\"\n",
    "connected_comps = connected_components(L)\n",
    "for i, comp in enumerate(connected_comps):\n",
    "    print(f\"Component {i + 1}:\")\n",
    "    for idx in comp:\n",
    "        print(f\"Index {idx}: {data.loc[idx, 'assistant_reply']}\")\n",
    "    print()\n",
    "\"\"\""
   ],
   "metadata": {
    "id": "ZmDcNQWwQ6UZ"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Visualizing the node features before and after message passing\n",
    "fig, (ax1, ax2, ax3, ax4, lx5) = plt.subplots(1, 5, figsize=(25, 5))\n",
    "cax1 = ax1.matshow(A)\n",
    "ax1.set_title(\"Adj matrix before message passing\")\n",
    "cax2 = ax2.matshow(A_k)\n",
    "ax2.set_title(\"Adj matrix after message passing A^2\")\n",
    "cax3 = ax3.matshow(A_k3)\n",
    "ax3.set_title(\"Adj matrix after message passing A^3\")\n",
    "cax4 = ax4.matshow(A_k4)\n",
    "ax4.set_title(\"Adj matrix after message passing A^4\")\n",
    "clx5 = lx5.matshow(L)\n",
    "ax4.set_title(\"Laplacian matrix after message passing L\")\n",
    "fig.colorbar(cax1, ax=ax1)\n",
    "fig.colorbar(cax2, ax=ax2)\n",
    "fig.colorbar(cax3, ax=ax3)\n",
    "fig.colorbar(cax4, ax=ax4)\n",
    "fig.colorbar(clx5, ax=lx5)\n",
    "plt.show()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 313
    },
    "id": "4-zA83oHNivI",
    "outputId": "39d98a67-d9ae-4f18-a5a7-be2b76493fb9"
   },
   "execution_count": 80,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1800x360 with 10 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Visualizing the node features before and after message passing\n",
    "fig, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, figsize=(25, 5))\n",
    "cax1 = ax1.matshow(embs)\n",
    "ax1.set_title(\"Node features before message passing\")\n",
    "cax2 = ax2.matshow(node_embs)\n",
    "ax2.set_title(\"Node features after message passing A^2\")\n",
    "cax3 = ax3.matshow(node_embs3)\n",
    "ax3.set_title(\"Node features after message passing A^3\")\n",
    "cax4 = ax4.matshow(node_embs4)\n",
    "ax4.set_title(\"Node features after message passing A^4\")\n",
    "\n",
    "fig.colorbar(cax1, ax=ax1)\n",
    "fig.colorbar(cax2, ax=ax2)\n",
    "fig.colorbar(cax3, ax=ax3)\n",
    "fig.colorbar(cax4, ax=ax4)\n",
    "plt.show()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 305
    },
    "id": "X8zDR6DSPUl1",
    "outputId": "7946005e-0c73-4027-da6b-33172c9e19bd"
   },
   "execution_count": 81,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1800x360 with 8 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "A_k"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rEfomISFSFFk",
    "outputId": "6717c0e2-a4ec-4bf8-d160-34bde2da4c45"
   },
   "execution_count": 21,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[82.21375 , 82.21375 , 62.76475 , ..., 69.296394, 59.37149 ,\n",
       "        64.58403 ],\n",
       "       [82.21375 , 82.21375 , 62.76475 , ..., 69.296394, 59.37149 ,\n",
       "        64.58403 ],\n",
       "       [62.76475 , 62.76475 , 53.45026 , ..., 54.47696 , 47.4187  ,\n",
       "        50.76499 ],\n",
       "       ...,\n",
       "       [69.296394, 69.296394, 54.47696 , ..., 68.10137 , 57.442898,\n",
       "        58.174835],\n",
       "       [59.37149 , 59.37149 , 47.418697, ..., 57.442894, 55.590706,\n",
       "        50.268524],\n",
       "       [64.58403 , 64.58403 , 50.76499 , ..., 58.17484 , 50.268524,\n",
       "        56.670044]], dtype=float32)"
      ]
     },
     "metadata": {},
     "execution_count": 21
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def load_topic_model(diversity=0.1, min_topic_size=10, nr_topics=\"auto\"):\n",
    "    vectorizer_model = CountVectorizer(stop_words=\"english\")\n",
    "    ctfidf_model = ClassTfidfTransformer(reduce_frequent_words=True)\n",
    "    representation_model = OpenAI(model=\"gpt-3.5-turbo\", delay_in_seconds=1, chat=True)\n",
    "    model = SentenceTransformer(MODEL_NAME)\n",
    "    # representation_model = MaximalMarginalRelevance(diversity=diversity)\n",
    "    topic_model = BERTopic(\n",
    "        nr_topics=nr_topics,\n",
    "        min_topic_size=min_topic_size,\n",
    "        representation_model=representation_model,\n",
    "        vectorizer_model=vectorizer_model,\n",
    "        ctfidf_model=ctfidf_model,\n",
    "        embedding_model=model,\n",
    "    )\n",
    "    return topic_model\n",
    "\n",
    "\n",
    "def fit_topic_model(topic_model, data, embeddings, key=\"assistant_reply\"):\n",
    "    topics, probs = topic_model.fit_transform(data[key].to_list(), embeddings)\n",
    "    return topics, probs\n",
    "\n",
    "\n",
    "def get_topic_info(topic_model):\n",
    "    return topic_model.get_topic_info()\n",
    "\n",
    "\n",
    "def reduce_topics(topic_model, data, nr_topics, key=\"assistant_reply\"):\n",
    "    topic_model.reduce_topics(data[key].to_list(), nr_topics)\n",
    "    return topic_model\n",
    "\n",
    "\n",
    "def get_representative_docs(topic_model):\n",
    "    return topic_model.get_representative_docs()\n",
    "\n",
    "\n",
    "def reduce_outliers(topic_model, data, topics, probs, key=\"assistant_reply\", strategy=\"c-tf-idf\"):\n",
    "    vectorizer_model = CountVectorizer()  # stop_words=\"english\")\n",
    "    ctfidf_model = ClassTfidfTransformer(reduce_frequent_words=True)\n",
    "    representation_model = OpenAI(model=\"gpt-3.5-turbo\", delay_in_seconds=1, chat=True)\n",
    "    # representation_model = MaximalMarginalRelevance(diversity=diversity)\n",
    "    if strategy == \"c-tf-idf\":\n",
    "        new_topics = topic_model.reduce_outliers(data[key].to_list(), topics, strategy, threshold=0.1)\n",
    "    elif strategy == \"embeddings\":\n",
    "        new_topics = topic_model.reduce_outliers(data[key].to_list(), topics, strategy)\n",
    "    elif strategy == \"distributions\":\n",
    "        new_topics = topic_model.reduce_outliers(data[key].to_list(), topics, probabilities=probs, strategy=strategy)\n",
    "    else:\n",
    "        raise ValueError(\"Invalid strategy\")\n",
    "    topic_model.update_topics(\n",
    "        data[key].to_list(),\n",
    "        topics=new_topics,\n",
    "        representation_model=representation_model,\n",
    "        vectorizer_model=vectorizer_model,\n",
    "        ctfidf_model=ctfidf_model,\n",
    "    )\n",
    "    return topic_model, new_topics\n",
    "\n",
    "\n",
    "def compute_hierarchical_topic_tree(topic_model, data, key=\"assistant_reply\"):\n",
    "    hierarchical_topics = topic_model.hierarchical_topics(data[key].to_list())\n",
    "    tree = topic_model.get_topic_tree(hierarchical_topics)\n",
    "    return hierarchical_topics, tree"
   ],
   "metadata": {
    "id": "sT-U7tVkSGk6"
   },
   "execution_count": 25,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "from getpass import getpass\n",
    "import openai\n",
    "\n",
    "openai_secret = getpass(\"Enter the secret key: \")\n",
    "# Set up OpenAI API credentials\n",
    "openai.api_key = openai_secret"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WYxd6aI1Ti34",
    "outputId": "dd9369f7-dbd9-4ad0-ca7f-3dbe7c5f2fb7"
   },
   "execution_count": 27,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the secret key: ··········\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "topic_model = load_topic_model(nr_topics=\"auto\")\n",
    "topics, probs = fit_topic_model(topic_model, data, node_embs)\n",
    "freq = get_topic_info(topic_model)\n",
    "rep_docs = get_representative_docs(topic_model)\n",
    "freq"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "bqwD2UdBSrku",
    "outputId": "13aaf82f-3ecf-4510-9c4e-da8e291d4eda"
   },
   "execution_count": 28,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   Topic  Count                                               Name\n",
       "0     -1     57  -1_Pydantic model for reading and validating w...\n",
       "1      0    422  0_Pydantic model for worker process performing...\n",
       "2      1     95  1_Defining `DbRefreshToken` and `DbMessage` cl...\n",
       "3      2     65  2_Repeatedly checking inference server's healt...\n",
       "4      3     46  3_ChatUser class behavior and message sending ...\n",
       "5      4     30  4_Running Migrations in Transaction Mode with ...\n",
       "6      5     20      5_Handling API Key Authorization in Python___\n",
       "7      6     14  6_Input prompt truncation based on maximum len..."
      ],
      "text/html": [
       "\n",
       "  <div id=\"df-2c174e5e-16af-4977-8fd9-3d68b50f66f0\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Topic</th>\n",
       "      <th>Count</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1</td>\n",
       "      <td>57</td>\n",
       "      <td>-1_Pydantic model for reading and validating w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>422</td>\n",
       "      <td>0_Pydantic model for worker process performing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>95</td>\n",
       "      <td>1_Defining `DbRefreshToken` and `DbMessage` cl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>65</td>\n",
       "      <td>2_Repeatedly checking inference server's healt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>46</td>\n",
       "      <td>3_ChatUser class behavior and message sending ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>4_Running Migrations in Transaction Mode with ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>5_Handling API Key Authorization in Python___</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>6_Input prompt truncation based on maximum len...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2c174e5e-16af-4977-8fd9-3d68b50f66f0')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-2c174e5e-16af-4977-8fd9-3d68b50f66f0 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-2c174e5e-16af-4977-8fd9-3d68b50f66f0');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 28
    }
   ]
  }
 ]
}