examples/metrics_artifacts_zoo.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/logikon-ai/logikon/blob/main/examples/metrics_artifacts_zoo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "13hfBALnYtHL"
      },
      "source": [
        "# **Logikon `/\\/`** – Metrics and Artifacts Zoo\n",
        "\n",
        "*Analyzing and Evaluating LLM Reasoning Traces*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TUzKTbJtYtHM"
      },
      "source": [
        "This notebook showcases the reasoning **metrics** and **artifacts** that can be generated with the `logikon` python library.\n",
        "\n",
        "We're using 🤗's lightweight `zephyr-7b-beta` in this demo for its speed and small footprint. It tends to produce argumentative analyses that are creative and eloquent, rather than rigorous and exact -- and may hence not be the optimal expert model for your use cases."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "apPN-0GVYtHN"
      },
      "source": [
        "> ⚠️ **Important notice.**\n",
        ">\n",
        "> **Logikon `/\\/`** is in early development stage:\n",
        "> * the package is subject to change at any time;\n",
        "> * results can vary due to changes in methods, pipelines, or underlying models;\n",
        "> * current speed and quality of evaluation results are not representative of future product.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Table of contents**\n",
        "\n",
        "1. [Set-up](#set-up)\n",
        "2. [Collect metrics and artifacts](#collect)\n",
        "3. [Run analysis](#run)\n",
        "4. [Inspect results](#inspect)"
      ],
      "metadata": {
        "id": "PBHAr94lFgCD"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fMyE0lBQYtHN"
      },
      "source": [
        "## Set-up environment and examples <a name=\"set-up\"></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Please make sure that the following files from the `examples` folder\n",
        "# in the `logikon-ai/logikon` GitHub repo are available.\n",
        "!test -f \"requirements.txt\" || echo \"Missing requirements.txt\"\n",
        "!test -f \"setup.sh\" || echo \"Missing setup.sh\"\n",
        "!test -f \"scoring.py\" || echo \"Missing scoring.py\""
      ],
      "metadata": {
        "id": "VEz318IJCaw7"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Configure the environment\n",
        "import os\n",
        "\n",
        "os.environ[\"LGK_FRAMEWORK\"] = \"llamacpp\"\n",
        "os.environ[\"LGK_DEVICE\"] = \"gpu\"\n",
        "os.environ[\"LGK_HUBREPO\"] = \"TheBloke/zephyr-7B-beta-GGUF\"\n",
        "os.environ[\"LGK_MODELWEIGHTS\"] = \"zephyr-7b-beta.Q6_K.gguf\"\n",
        "os.environ[\"LGK_TOKENIZER\"] = \"HuggingFaceH4/zephyr-7b-beta\"\n",
        "os.environ[\"LGK_RELEASE\"] = \"main\""
      ],
      "metadata": {
        "id": "uDIpY9XuCwkm"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check gpu available\n",
        "! [ \"$LGK_DEVICE\" = \"gpu\" ] && nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "epMW5Zx6W9nr",
        "outputId": "66fb242f-ce21-42b9-8843-a73e22003268"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sat Nov 11 15:09:32 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   66C    P8    11W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!sh setup.sh"
      ],
      "metadata": {
        "id": "tSSiej4pY8-i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "if os.environ.get(\"LGK_MODELWEIGHTS\"):\n",
        "  abspath = os.path.join(\n",
        "    os.path.abspath(\"./models\"),\n",
        "    os.environ.get(\"LGK_MODELWEIGHTS\")\n",
        "  )\n",
        "else:\n",
        "  abspath = \"\"\n",
        "os.environ[\"LGK_ABSPATH_MODELWEIGHTS\"] = abspath\n",
        "os.environ[\"LGK_ABSPATH_MODELWEIGHTS\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "iFteYtUPJvDI",
        "outputId": "4776c715-eb0b-4e7f-d365-e6093d61212e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/models/zephyr-7b-beta.Q6_K.gguf'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check whether model weights file is available\n",
        "!test -f \"$LGK_ABSPATH_MODELWEIGHTS\" || echo \"Cannot find model weights\""
      ],
      "metadata": {
        "id": "BN9HH97iL9OF"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Example reasoning traces\n",
        "\n",
        "EXAMPLE = {\n",
        "  \"prompt\": \"\"\"Can you give me some advice on whether I should use Vim or Emacs?\"\"\",\n",
        "  \"completion\": \"\"\"\n",
        "The choice between Vim and Emacs is a classic debate in the world of text editors and integrated\n",
        "development environments (IDEs). Both Vim and Emacs are powerful and highly customizable, but they\n",
        "have different philosophies and approaches to text editing. Your choice should depend on your\n",
        "specific needs and preferences. Here's a careful analysis of both editors:\n",
        "\n",
        "Vim:\n",
        "\n",
        "Modal Editing: Vim uses a modal editing approach, which means it has different modes for different\n",
        "tasks (insert mode, normal mode, visual mode, etc.). This can be more efficient for many users once\n",
        "they become proficient, as it minimizes the need for extensive keyboard shortcuts.\n",
        "\n",
        "Lightweight: Vim is known for its minimalistic design and lightweight footprint. It can run\n",
        "efficiently on even older hardware and over SSH connections, making it an excellent choice for\n",
        "remote server editing.\n",
        "\n",
        "Highly Efficient for Editing: Once you've mastered Vim, it can be incredibly efficient for text\n",
        "editing and code navigation. Vim users often rave about its speed and precision.\n",
        "\n",
        "Emacs:\n",
        "\n",
        "Extensible: Emacs is known for its extensibility. It's not just a text editor; it's an extensible\n",
        "platform that can be turned into a full-fledged IDE or customized for any purpose. If you like the\n",
        "idea of a highly customized environment, Emacs is a strong contender.\n",
        "\n",
        "Moderate Learning Curve: Emacs has a learning curve, but it may be perceived as less steep than Vim's\n",
        "for some users. The keybindings are more traditional, and it offers a range of graphical menus that\n",
        "can help beginners.\n",
        "\n",
        "Resource Intensive: Emacs can be more resource-intensive compared to Vim. It may not be the best\n",
        "choice for extremely resource-constrained environments.\"\"\",\n",
        "}"
      ],
      "metadata": {
        "id": "4wDx93tsZulU"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df_traces = pd.DataFrame([EXAMPLE])"
      ],
      "metadata": {
        "id": "0gGYxzS9aoyI"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save reasoning traces to file\n",
        "df_traces.to_json(\"reasoning_traces.jsonl\", orient=\"records\", lines=True)"
      ],
      "metadata": {
        "id": "SKnqYjOca6De"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check data file available\n",
        "!head -2 \"reasoning_traces.jsonl\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "txykfjd-bX5C",
        "outputId": "de4a2c54-7288-4e21-d0ea-f1167ef9d182"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\"prompt\":\"Can you give me some advice on whether I should use Vim or Emacs?\",\"completion\":\"\\nThe choice between Vim and Emacs is a classic debate in the world of text editors and integrated\\ndevelopment environments (IDEs). Both Vim and Emacs are powerful and highly customizable, but they\\nhave different philosophies and approaches to text editing. Your choice should depend on your\\nspecific needs and preferences. Here's a careful analysis of both editors:\\n\\nVim:\\n\\nModal Editing: Vim uses a modal editing approach, which means it has different modes for different\\ntasks (insert mode, normal mode, visual mode, etc.). This can be more efficient for many users once\\nthey become proficient, as it minimizes the need for extensive keyboard shortcuts.\\n\\nLightweight: Vim is known for its minimalistic design and lightweight footprint. It can run\\nefficiently on even older hardware and over SSH connections, making it an excellent choice for\\nremote server editing.\\n\\nHighly Efficient for Editing: Once you've mastered Vim, it can be incredibly efficient for text\\nediting and code navigation. Vim users often rave about its speed and precision.\\n\\nEmacs:\\n\\nExtensible: Emacs is known for its extensibility. It's not just a text editor; it's an extensible\\nplatform that can be turned into a full-fledged IDE or customized for any purpose. If you like the\\nidea of a highly customized environment, Emacs is a strong contender.\\n\\nModerate Learning Curve: Emacs has a learning curve, but it may be perceived as less steep than Vim's\\nfor some users. The keybindings are more traditional, and it offers a range of graphical menus that\\ncan help beginners.\\n\\nResource Intensive: Emacs can be more resource-intensive compared to Vim. It may not be the best\\nchoice for extremely resource-constrained environments.\"}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Collect all available metrics and artifacts <a name=\"collect\"></a>"
      ],
      "metadata": {
        "id": "nSrgZcyMAdwP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Metrics and artifacts are created by analysts, which are centrally registered\n",
        "\n",
        "import logikon\n",
        "import pprint\n",
        "\n",
        "registry = logikon.analysts.registry.get_analyst_registry()"
      ],
      "metadata": {
        "id": "6Vpgpx0dvPHH"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# List all metrics\n",
        "\n",
        "mdl = []\n",
        "\n",
        "mdl.append(f\"## Logikon `/\\/` Metrics ({logikon.__version__})\")\n",
        "mdl.append(\"\")\n",
        "\n",
        "for key, analyst_classes in registry.items():\n",
        "  for analyst_class in analyst_classes:\n",
        "    if issubclass(analyst_class, logikon.analysts.base.AbstractScoreAnalyst):\n",
        "      mdl.append(f\"#### Metric `{key}`\")\n",
        "      mdl.append(\"\")\n",
        "      mdl.append(f\"* *Description*: {analyst_class.get_description()}\")\n",
        "      mdl.append(f\"* *Requirements*: {analyst_class.get_requirements()}\")\n",
        "    mdl.append(\"\")\n",
        "\n",
        "from IPython.core.display import Markdown\n",
        "display(Markdown(\"\\n\".join(mdl)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        },
        "id": "Sn1YTbr4zBJa",
        "outputId": "f7b3202b-7572-4d2b-90e5-09fb164c70fd"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "## Logikon `/\\/` Metrics (0.0.1-dev1)\n\n\n\n\n\n\n\n\n#### Metric `argmap_size`\n\n* *Description*: Measure the size of the argument map (number of nodes)\n* *Requirements*: [{'fuzzy_argmap_nx'}, {'networkx_graph'}]\n\n#### Metric `n_root_nodes`\n\n* *Description*: Cont the number of root nodes in argument map (out degree = 0)\n* *Requirements*: [{'fuzzy_argmap_nx'}, {'networkx_graph'}]\n\n#### Metric `argmap_avg_katz_centrality`\n\n* *Description*: Average Katz centrality of all nodes in the graph\n* *Requirements*: [{'fuzzy_argmap_nx'}, {'networkx_graph'}]\n\n#### Metric `argmap_attack_ratio`\n\n* *Description*: Ratio of attacking reasons (cons) in the informal argmap\n* *Requirements*: [{'fuzzy_argmap_nx'}, {'networkx_graph'}]\n\n#### Metric `mean_root_support`\n\n* *Description*: The mean root support in the argument map (global balance score)\n* *Requirements*: [{'fuzzy_argmap_nx'}, {'networkx_graph'}]\n\n#### Metric `mean_absolute_root_support`\n\n* *Description*: The mean absolute root support in the argument map (local balance score)\n* *Requirements*: [{'fuzzy_argmap_nx'}, {'networkx_graph'}]\n\n#### Metric `global_balance`\n\n* *Description*: The argument map's global balance (assumes mutually exclusive and collectively exhaustive root claims)\n* *Requirements*: [{'fuzzy_argmap_nx'}, {'networkx_graph'}]\n\n#### Metric `mean_reason_strength`\n\n* *Description*: Mean strength (absolute weight) of support and attack reasons (cons) in the fuzzy argmap\n* *Requirements*: [{'fuzzy_argmap_nx'}, {'networkx_graph'}]\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# List all artifacts\n",
        "\n",
        "mdl = []\n",
        "\n",
        "mdl.append(f\"## Logikon `/\\/` Artifacts ({logikon.__version__})\")\n",
        "mdl.append(\"\")\n",
        "\n",
        "for key, analyst_classes in registry.items():\n",
        "  for analyst_class in analyst_classes:\n",
        "    if issubclass(analyst_class, logikon.analysts.base.AbstractArtifactAnalyst):\n",
        "      mdl.append(f\"#### Artifact `{key}`\")\n",
        "      mdl.append(\"\")\n",
        "      mdl.append(f\"* *Description*: {analyst_class.get_description()}\")\n",
        "      mdl.append(f\"* *Requirements*: {analyst_class.get_requirements()}\")\n",
        "    mdl.append(\"\")\n",
        "\n",
        "\n",
        "from IPython.core.display import Markdown\n",
        "display(Markdown(\"\\n\".join(mdl)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 603
        },
        "id": "B5zwCR2h23L9",
        "outputId": "023b35ad-e38b-4f3c-ecb5-8dc513720687"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "## Logikon `/\\/` Artifacts (0.0.1-dev1)\n\n#### Artifact `issue`\n\n* *Description*: Issue or decision problem addressed in the deliberation\n* *Requirements*: []\n\n#### Artifact `proscons`\n\n* *Description*: Pros and cons list with multiple root claims\n* *Requirements*: ['issue']\n\n#### Artifact `relevance_network`\n\n* *Description*: Relevance network describing comprehensively the strengths of pairwise support and attack relations\n* *Requirements*: ['issue', 'proscons']\n\n#### Artifact `relevance_network_nx`\n\n* *Description*: Relevance network rendered as a networkx graph\n* *Requirements*: ['relevance_network']\n\n#### Artifact `fuzzy_argmap_nx`\n\n* *Description*: Informal argument map (nx graph) with weighted support and attack relations\n* *Requirements*: ['relevance_network_nx']\n\n#### Artifact `svg_argmap`\n\n* *Description*: Exports a networkx graph as a graphviz argument map (svg)\n* *Requirements*: [{'fuzzy_argmap_nx'}, {'networkx_graph'}]\n\n#### Artifact `html_sunburst`\n\n* *Description*: Exports a networkx graph as a HTML sunburst\n* *Requirements*: [{'fuzzy_argmap_nx', 'issue'}, {'issue', 'networkx_graph'}]\n\n\n\n\n\n\n\n\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Write config als yaml file\n",
        "\n",
        "config_dict = {\n",
        "    # all metrics\n",
        "    \"metrics\": [\n",
        "        k for k,v in registry.items()\n",
        "        if issubclass(v[0], logikon.analysts.base.AbstractScoreAnalyst)\n",
        "    ],\n",
        "    # artifacts to generate\n",
        "    \"artifacts\": [\n",
        "        k for k,v in registry.items()\n",
        "        if issubclass(v[0], logikon.analysts.base.AbstractArtifactAnalyst)\n",
        "    ],\n",
        "    \"global_kwargs\": {\n",
        "        \"expert_model\": os.environ[\"LGK_ABSPATH_MODELWEIGHTS\"],\n",
        "        \"expert_model_kwargs\": {\n",
        "            \"n_ctx\": 3072,\n",
        "            # comment out line below if no gpu\n",
        "            \"n_gpu_layers\": -1,\n",
        "            \"tokenizer\": os.environ[\"LGK_TOKENIZER\"],\n",
        "            # custom prompt template, make sure this fits the expert_model\n",
        "            \"prompt_template\": {\n",
        "                \"sys_start\": \"\\n<|system|>\\n\",\n",
        "                \"sys_end\": \"\",\n",
        "                \"user_start\": \"\\n<|user|>\\n\",\n",
        "                \"user_end\": \"\",\n",
        "                \"ass_start\": \"\\n<|assistant|>\\n\",\n",
        "                \"ass_end\": \"\",\n",
        "            },\n",
        "        },\n",
        "        \"generation_kwargs\": {\"max_len\": 3072},\n",
        "        \"llm_framework\": \"llama.cpp\",\n",
        "    },\n",
        "}\n",
        "\n",
        "import yaml\n",
        "with open('config.yml', 'w') as outfile:\n",
        "    yaml.dump(config_dict, outfile, default_flow_style=False)\n",
        "\n",
        "# Check config file available\n",
        "!head -10 \"config.yml\" && echo \"...\""
      ],
      "metadata": {
        "id": "sIGXP7YieNQg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Run analysis <a name=\"run\"></a>"
      ],
      "metadata": {
        "id": "Pwx5YGLtA5wW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 🫖 This will take some time ... you may monitor `scoring.log`\n",
        "!python scoring.py \\\n",
        "  --config-file config.yml \\\n",
        "  --reasoning-traces-file reasoning_traces.jsonl"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BL4ADqpSf_WB",
        "outputId": "75657460-f69f-4b3a-b951-09722a1b9ff5"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0it [00:00, ?it/s]The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n",
            "\n",
            "0it [00:00, ?it/s]\n",
            "/usr/bin/dot\n",
            "\n",
            "(…)-beta/resolve/main/tokenizer_config.json: 100% 1.43k/1.43k [00:00<00:00, 7.42MB/s]\n",
            "\n",
            "tokenizer.model: 100% 493k/493k [00:00<00:00, 9.96MB/s]\n",
            "\n",
            "(…)phyr-7b-beta/resolve/main/tokenizer.json: 100% 1.80M/1.80M [00:00<00:00, 21.9MB/s]\n",
            "\n",
            "(…)r-7b-beta/resolve/main/added_tokens.json: 100% 42.0/42.0 [00:00<00:00, 289kB/s]\n",
            "\n",
            "(…)eta/resolve/main/special_tokens_map.json: 100% 168/168 [00:00<00:00, 1.25MB/s]\n",
            "[Loading llama.cpp model from llama.cpp:/content/models/zephyr-7b-beta.Q6_K.gguf  with  {'n_ctx': 3072, 'n_gpu_layers': -1} ]\n",
            "2023-11-11 15:49:14.796972: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2023-11-11 15:49:14.797036: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2023-11-11 15:49:14.797078: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2023-11-11 15:49:17.133019: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "\n",
            "Reason-root edges:   0% 0/2 [00:00<?, ?it/s]\u001b[A\n",
            "Reason-root edges:  50% 1/2 [00:18<00:18, 18.17s/it]\u001b[A\n",
            "Reason-root edges: 100% 2/2 [00:34<00:00, 17.21s/it]\n",
            "\n",
            "Reason-reason edges:   0% 0/15 [00:00<?, ?it/s]\u001b[A\n",
            "Reason-reason edges:  13% 2/15 [00:31<03:23, 15.68s/it]\u001b[A\n",
            "Reason-reason edges:  20% 3/15 [01:00<04:14, 21.18s/it]\u001b[A\n",
            "Reason-reason edges:  27% 4/15 [01:35<04:50, 26.42s/it]\u001b[A\n",
            "Reason-reason edges:  33% 5/15 [02:05<04:36, 27.63s/it]\u001b[A\n",
            "Reason-reason edges:  40% 6/15 [02:32<04:08, 27.59s/it]\u001b[A\n",
            "Reason-reason edges:  47% 7/15 [03:03<03:48, 28.51s/it]\u001b[A\n",
            "Reason-reason edges:  53% 8/15 [03:33<03:23, 29.13s/it]\u001b[A\n",
            "Reason-reason edges:  67% 10/15 [04:04<01:52, 22.60s/it]\u001b[A\n",
            "Reason-reason edges:  73% 11/15 [04:35<01:38, 24.64s/it]\u001b[A\n",
            "Reason-reason edges:  80% 12/15 [05:08<01:20, 26.84s/it]\u001b[A\n",
            "Reason-reason edges:  87% 13/15 [05:43<00:58, 29.27s/it]\u001b[A\n",
            "Reason-reason edges:  93% 14/15 [06:20<00:31, 31.25s/it]\u001b[A\n",
            "Reason-reason edges: 100% 15/15 [06:52<00:00, 27.47s/it]\n",
            "1it [11:06, 666.40s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Inspect generated metrics and artifacts <a name=\"inspect\"></a>"
      ],
      "metadata": {
        "id": "7uaXFSRjA_Wu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_scores = pd.read_json(\"outputs/reasoning_traces-scores.jsonl\", lines=True)"
      ],
      "metadata": {
        "id": "cttOhhYfpyP6"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Metrics"
      ],
      "metadata": {
        "id": "69ZoV1CR4_dN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Display metric scores\n",
        "df_scores[config_dict.get(\"metrics\",[])].T.rename(columns={0: \"value\"})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "JCC66EW08MAx",
        "outputId": "cac02b36-e8d2-4ec6-ce1a-eedc7ef5616c"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                value\n",
              "argmap_size                 15.000000\n",
              "n_root_nodes                 2.000000\n",
              "argmap_avg_katz_centrality   0.256013\n",
              "argmap_attack_ratio          0.153846\n",
              "mean_root_support            0.699909\n",
              "mean_absolute_root_support   0.699909\n",
              "global_balance               0.253621\n",
              "mean_reason_strength         0.868988"
            ],
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              "      <th>value</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>argmap_size</th>\n",
              "      <td>15.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n_root_nodes</th>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>argmap_avg_katz_centrality</th>\n",
              "      <td>0.256013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>argmap_attack_ratio</th>\n",
              "      <td>0.153846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean_root_support</th>\n",
              "      <td>0.699909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean_absolute_root_support</th>\n",
              "      <td>0.699909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>global_balance</th>\n",
              "      <td>0.253621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean_reason_strength</th>\n",
              "      <td>0.868988</td>\n",
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              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-21c416c4-9f57-4bfd-8377-d4043bd54d6c 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-21c416c4-9f57-4bfd-8377-d4043bd54d6c');\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",
              "\n",
              "\n",
              "<div id=\"df-60b55320-86dc-4cea-9c52-955c47aea92e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-60b55320-86dc-4cea-9c52-955c47aea92e')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-60b55320-86dc-4cea-9c52-955c47aea92e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Artifacts"
      ],
      "metadata": {
        "id": "xcE02utN5MvQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pprint import pformat\n",
        "\n",
        "import networkx as nx\n",
        "\n",
        "from IPython.display import SVG, HTML\n",
        "\n",
        "from logikon.schemas.pros_cons import ProsConsList\n",
        "from logikon.schemas.argument_mapping import FuzzyArgMap\n",
        "\n",
        "def render_artifact(artifact_id, data):\n",
        "  \"\"\"helper function for rendering artifacts\"\"\"\n",
        "\n",
        "  if \"nx\" in artifact_id:\n",
        "    digraph = nx.node_link_graph(data)\n",
        "    return Markdown(str(digraph))\n",
        "\n",
        "  if \"svg\" in artifact_id:\n",
        "    style = \"<style>svg{width:100% !important;height:100% !important;</style>\"\n",
        "    return HTML(style), SVG(data=data)\n",
        "\n",
        "  if \"html\" in artifact_id:\n",
        "    return HTML(data)\n",
        "\n",
        "  if artifact_id == \"proscons\":\n",
        "    proscons = ProsConsList(**data)\n",
        "    return Markdown(f\"`{pformat(proscons)}`\")\n",
        "\n",
        "  if artifact_id == \"relevance_network\":\n",
        "    relnet = FuzzyArgMap(**data)\n",
        "    return Markdown(f\"`{pformat(relnet)[:800]} ...`\")\n",
        "\n",
        "  text = str(data)\n",
        "  text = text[:200] + \" ...\" if len(text)>200 else text\n",
        "  return Markdown(f\"`{text}`\")"
      ],
      "metadata": {
        "id": "saeFhPUzIN_k"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import display\n",
        "\n",
        "for artifact_id in config_dict.get(\"artifacts\",[]):\n",
        "  display(Markdown(f\"## {artifact_id}\"))\n",
        "  data = df_scores.iloc[0].get(artifact_id)\n",
        "  r = render_artifact(artifact_id, data)\n",
        "  if isinstance(r, tuple):\n",
        "    display(*r)\n",
        "  else:\n",
        "    display(r)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "tFo-h4Ba6zfO",
        "outputId": "566ec28f-1ca3-4799-a584-4b296756ab77"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "## issue"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "`Should one use Vim or Emacs for text editing and development?`"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "## proscons"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "`ProsConsList(roots=[RootClaim(text='Use Vim for text editing and development.', label='Use Vim', pros=[Claim(text='Vim uses modal editing for different tasks, minimizing keyboard shortcuts and promoting efficiency.', label='Modal Editing'), Claim(text='Vim is lightweight and can run efficiently on older hardware and over SSH connections.', label='Lightweight'), Claim(text='Vim is highly efficient for text editing and code navigation once mastered.', label='Highly Efficient for Editing')], cons=[]), RootClaim(text='Use Emacs for text editing and development.', label='Use Emacs', pros=[Claim(text='Emacs is extensible and can be turned into a full-fledged IDE or customized for any purpose.', label='Extensible'), Claim(text='Emacs may have a less steep learning curve compared to Vim due to traditional keybindings and graphical menus.', label='Moderate Learning Curve')], cons=[Claim(text='Emacs may be more resource-intensive compared to Vim and not the best choice for resource-constrained environments.', label='Resource Intensive')])], options=['Use Vim', 'Use Emacs', 'Use other text editors', \"Don't use any text editor (?)\"])`"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "## relevance_network"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "`FuzzyArgMap(nodelist=[ArgMapNode(id='77ed2989-29c4-4172-a4cd-504695b032d9', text='Use Vim for text editing and development.', label='Use Vim', annotations=[], node_type='central_claim'), ArgMapNode(id='cb2ef89b-421b-43bf-9c91-cacddd63d308', text='Vim is highly efficient for text editing and code navigation once mastered.', label='Highly Efficient for Editing', annotations=[], node_type='reason'), ArgMapNode(id='274578a7-75f2-4591-9f5b-7f07a8063712', text='Vim uses modal editing for different tasks.', label='Modal Editing', annotations=[], node_type='reason'), ArgMapNode(id='3996db4b-8850-47e2-90b2-abfa4626b9bf', text='Vim minimizes keyboard shortcuts.', label='Minimizing keyboard shortcuts', annotations=[], node_type='reason'), ArgMapNode(id='aa27986b-8346-4575-8a99-7f3c2ce7d6b6', text='Vi ...`"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "## relevance_network_nx"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "DiGraph with 15 nodes and 169 edges"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "## fuzzy_argmap_nx"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "DiGraph with 15 nodes and 13 edges"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "## svg_argmap"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<style>svg{width:100% !important;height:100% !important;</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
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            "text/markdown": "## html_sunburst"
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