{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jkWhN6XUu3J8"
      },
      "source": [
        "## Evaluating the `AnasAlokla/multilingual_go_emotions` model"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "this code is same eval_roberta_base_go_emotions code\n",
        "with some differant"
      ],
      "metadata": {
        "id": "KyTLRUO-LdyQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "9nyB_Qv9u3J-"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install datasets transformers pandas matplotlib tqdm --upgrade --quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "0R-5tfv6u3J-"
      },
      "outputs": [],
      "source": [
        "import datasets\n",
        "from transformers import pipeline\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm import tqdm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "43Ot9cU6u3J-"
      },
      "source": [
        "### Load the dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XNORs19Pu3J_"
      },
      "source": [
        "We just want the dataset test split here for evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "4gKwWqCEu3J_",
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        "colab": {
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          "height": 435,
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          ]
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/523 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "train_dataset.csv:   0%|          | 0.00/27.4M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "259e98067c9145029e2050dd972e0c64"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "validation_dataset.csv:   0%|          | 0.00/3.44M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ea08345314cd464f91f6f9159fae56a5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "test_dataset.csv:   0%|          | 0.00/3.42M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aaca193a5a4741c38d2b2abcd37fc7d2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/260460 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8776be210fb4472a9b51f55c3bd25632"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/32556 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e1c67900d83a422f96f90cfa927368c4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/32562 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "867491119fa049a8ab709bda5f63706e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': \"Haha ouais. Merci. C'est réglé\\xa0!\",\n",
              " 'labels': '[1, 15]',\n",
              " 'id': 'FRee1a6me',\n",
              " 'language': 'fr'}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "split_name = \"test\"\n",
        "\n",
        "dataset_name = \"AnasAlokla/multilingual_go_emotions\"\n",
        "dataset_dict = datasets.load_dataset(dataset_name)\n",
        "#dataset = load_dataset('AnasAlokla/multilingual_go_emotions')\n",
        "dataset_dict[split_name][0]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: update the labels in dataset_dict convert from string like '[1, 15]' to normal list [1,15]\n",
        "\n",
        "import ast\n",
        "\n",
        "def convert_labels(example):\n",
        "    example['labels'] = ast.literal_eval(example['labels'])\n",
        "    # Check if 'text' is None and remove the example if it is\n",
        "    return example if example['text'] is not None else None\n",
        "\n",
        "# Apply the function to convert labels and filter out examples with None text\n",
        "dataset_dict = dataset_dict.map(convert_labels, remove_columns=None)\n",
        "\n",
        "# Drop examples where the map function returned None\n",
        "for split_name in dataset_dict:\n",
        "    if hasattr(dataset_dict[split_name], 'filter'):\n",
        "        dataset_dict[split_name] = dataset_dict[split_name].filter(lambda x: x is not None)\n",
        "\n",
        "# Check a sample\n",
        "dataset_dict[split_name][0]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278,
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        "outputId": "e80ccba7-e204-4705-c54e-c4c723168542"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/260460 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "046c806a02ab47adaaa0bc9599a4c59b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/32556 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e89638e74259482ea047a239be0bb215"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/32562 [00:00<?, ? examples/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Filter:   0%|          | 0/260448 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "deb8c3dbe60f4047b2d53cd5e2f01319"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/32544 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8c1ba982e19842698d97680f687465ff"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/32556 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4bd69c62ca5d45dab04c340c7415c0b6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': \"Haha ouais. Merci. C'est réglé\\xa0!\",\n",
              " 'labels': [1, 15],\n",
              " 'id': 'FRee1a6me',\n",
              " 'language': 'fr'}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels_name=[\n",
        "          \"admiration\",\n",
        "          \"amusement\",\n",
        "          \"anger\",\n",
        "          \"annoyance\",\n",
        "          \"approval\",\n",
        "          \"caring\",\n",
        "          \"confusion\",\n",
        "          \"curiosity\",\n",
        "          \"desire\",\n",
        "          \"disappointment\",\n",
        "          \"disapproval\",\n",
        "          \"disgust\",\n",
        "          \"embarrassment\",\n",
        "          \"excitement\",\n",
        "          \"fear\",\n",
        "          \"gratitude\",\n",
        "          \"grief\",\n",
        "          \"joy\",\n",
        "          \"love\",\n",
        "          \"nervousness\",\n",
        "          \"optimism\",\n",
        "          \"pride\",\n",
        "          \"realization\",\n",
        "          \"relief\",\n",
        "          \"remorse\",\n",
        "          \"sadness\",\n",
        "          \"surprise\",\n",
        "          \"neutral\"\n",
        "        ]"
      ],
      "metadata": {
        "id": "01RFsEzP_9WU"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "kgpGMl13u3J_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1c3e7e53-423f-440e-dc05-cd313295eb7f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0: 'admiration', 1: 'amusement', 2: 'anger', 3: 'annoyance', 4: 'approval', 5: 'caring', 6: 'confusion', 7: 'curiosity', 8: 'desire', 9: 'disappointment', 10: 'disapproval', 11: 'disgust', 12: 'embarrassment', 13: 'excitement', 14: 'fear', 15: 'gratitude', 16: 'grief', 17: 'joy', 18: 'love', 19: 'nervousness', 20: 'optimism', 21: 'pride', 22: 'realization', 23: 'relief', 24: 'remorse', 25: 'sadness', 26: 'surprise', 27: 'neutral'}\n"
          ]
        }
      ],
      "source": [
        "labels = labels_name\n",
        "print({i: l for i, l in enumerate(labels)})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ld0eGzSPu3KA"
      },
      "source": [
        "Load into a `y_target` stucture of arrays by label (since we wish to evaluate per label for a multi-label, multi-class dataset like this)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "KImv6nGzu3KA",
        "outputId": "30a8ea47-d72f-437a-e5cb-1b146b35bc85",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            " [0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            " [1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n"
          ]
        }
      ],
      "source": [
        "num_items, num_labels = len(dataset_dict[split_name]), len(labels)\n",
        "y_targets_all = np.zeros((num_items, num_labels), dtype=int)\n",
        "for i, labels_indices in enumerate(dataset_dict[split_name][\"labels\"]):\n",
        "    for label_index in labels_indices:\n",
        "        y_targets_all[i, label_index] = 1\n",
        "\n",
        "print(y_targets_all[0:3])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "00a4E8pYu3KA"
      },
      "source": [
        "### Load the model and run it"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WicjyT2mu3KA"
      },
      "source": [
        "Loading in a multi-label, multi-class classifier model based on Roberta-base"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "ZVvjacRxu3KB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194,
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        "outputId": "ee9594cf-1c6a-4ef7-a26c-aafcb3a44f7b"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/2.03k [00:00<?, ?B/s]"
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              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/712M [00:00<?, ?B/s]"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/996k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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              "special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "79fc7fd1cf07427aa474203015191526"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Device set to use cuda:0\n"
          ]
        }
      ],
      "source": [
        "model_name='AnasAlokla/multilingual_go_emotions'\n",
        "classifier = pipeline(task=\"text-classification\", model=model_name, top_k=None, truncation=True, max_length=128)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LStbHXeSu3KB"
      },
      "source": [
        "Very simple to then run the pipeline on the dataset test split.\n",
        "- c.5k items so can be run on a decent CPU in a few minutes (E.g. 3.5 mins on a 11th gen i7 laptop)\n",
        "- (will take signifcantly longer on a free Colab instance's 2 core CPU)\n",
        "- or of course via a GPU in seconds."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "model_outputs = classifier(dataset_dict[split_name][\"text\"][0])\n",
        "\n",
        "print(dataset_dict[split_name][\"text\"][0])\n",
        "print(model_outputs[0])\n",
        "\n",
        "text1='انا سعيد جداً'\n",
        "model_outputs = classifier(text1)\n",
        "\n",
        "print(text1)\n",
        "print(model_outputs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SNLUbJuHExiw",
        "outputId": "8f5c61b8-070d-44d7-abc4-f7afd6419a41"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Haha ouais. Merci. C'est réglé !\n",
            "[{'label': 'gratitude', 'score': 0.948058009147644}, {'label': 'amusement', 'score': 0.39725184440612793}, {'label': 'joy', 'score': 0.02780122682452202}, {'label': 'approval', 'score': 0.01285602431744337}, {'label': 'neutral', 'score': 0.01262691617012024}, {'label': 'optimism', 'score': 0.011523596942424774}, {'label': 'admiration', 'score': 0.010046065784990788}, {'label': 'realization', 'score': 0.00984946545213461}, {'label': 'curiosity', 'score': 0.008166581392288208}, {'label': 'annoyance', 'score': 0.008126690983772278}, {'label': 'confusion', 'score': 0.00798523984849453}, {'label': 'disapproval', 'score': 0.007851085625588894}, {'label': 'relief', 'score': 0.007259519305080175}, {'label': 'anger', 'score': 0.006232301238924265}, {'label': 'sadness', 'score': 0.0061301616951823235}, {'label': 'caring', 'score': 0.0057256268337368965}, {'label': 'desire', 'score': 0.005361439660191536}, {'label': 'excitement', 'score': 0.004961717873811722}, {'label': 'remorse', 'score': 0.00481715751811862}, {'label': 'disappointment', 'score': 0.00478516286239028}, {'label': 'embarrassment', 'score': 0.004637743346393108}, {'label': 'surprise', 'score': 0.004140368662774563}, {'label': 'disgust', 'score': 0.0038111144676804543}, {'label': 'love', 'score': 0.0035116635262966156}, {'label': 'pride', 'score': 0.003216362092643976}, {'label': 'grief', 'score': 0.0020106961019337177}, {'label': 'fear', 'score': 0.002010415541008115}, {'label': 'nervousness', 'score': 0.001956311287358403}]\n",
            "انا سعيد جداً\n",
            "[[{'label': 'joy', 'score': 0.7635045647621155}, {'label': 'gratitude', 'score': 0.06735119223594666}, {'label': 'admiration', 'score': 0.0527753122150898}, {'label': 'excitement', 'score': 0.03825107216835022}, {'label': 'approval', 'score': 0.03391667455434799}, {'label': 'neutral', 'score': 0.018981069326400757}, {'label': 'amusement', 'score': 0.013305669650435448}, {'label': 'relief', 'score': 0.013039222918450832}, {'label': 'realization', 'score': 0.012697044759988785}, {'label': 'love', 'score': 0.012569001875817776}, {'label': 'caring', 'score': 0.012478037737309933}, {'label': 'curiosity', 'score': 0.008435865864157677}, {'label': 'optimism', 'score': 0.007864754647016525}, {'label': 'annoyance', 'score': 0.006654617842286825}, {'label': 'disappointment', 'score': 0.006281535606831312}, {'label': 'sadness', 'score': 0.005045545753091574}, {'label': 'pride', 'score': 0.004569108132272959}, {'label': 'disapproval', 'score': 0.004447621759027243}, {'label': 'confusion', 'score': 0.0038060415536165237}, {'label': 'fear', 'score': 0.0037364992313086987}, {'label': 'nervousness', 'score': 0.003684108844026923}, {'label': 'surprise', 'score': 0.0029752871487289667}, {'label': 'anger', 'score': 0.002593569690361619}, {'label': 'embarrassment', 'score': 0.002536698943004012}, {'label': 'remorse', 'score': 0.002517224056646228}, {'label': 'desire', 'score': 0.002486541634425521}, {'label': 'grief', 'score': 0.0020299775060266256}, {'label': 'disgust', 'score': 0.0017967477906495333}]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "6cR_67UXu3KB",
        "outputId": "a9b0797e-6ffd-418d-e2f6-038cc972076e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Haha ouais. Merci. C'est réglé !\n",
            "[{'label': 'gratitude', 'score': 0.948058009147644}, {'label': 'amusement', 'score': 0.39725184440612793}, {'label': 'joy', 'score': 0.02780122682452202}, {'label': 'approval', 'score': 0.01285602431744337}, {'label': 'neutral', 'score': 0.01262691617012024}, {'label': 'optimism', 'score': 0.011523596942424774}, {'label': 'admiration', 'score': 0.010046065784990788}, {'label': 'realization', 'score': 0.00984946545213461}, {'label': 'curiosity', 'score': 0.008166581392288208}, {'label': 'annoyance', 'score': 0.008126690983772278}, {'label': 'confusion', 'score': 0.00798523984849453}, {'label': 'disapproval', 'score': 0.007851085625588894}, {'label': 'relief', 'score': 0.007259519305080175}, {'label': 'anger', 'score': 0.006232301238924265}, {'label': 'sadness', 'score': 0.0061301616951823235}, {'label': 'caring', 'score': 0.0057256268337368965}, {'label': 'desire', 'score': 0.005361439660191536}, {'label': 'excitement', 'score': 0.004961717873811722}, {'label': 'remorse', 'score': 0.00481715751811862}, {'label': 'disappointment', 'score': 0.00478516286239028}, {'label': 'embarrassment', 'score': 0.004637743346393108}, {'label': 'surprise', 'score': 0.004140368662774563}, {'label': 'disgust', 'score': 0.0038111144676804543}, {'label': 'love', 'score': 0.0035116635262966156}, {'label': 'pride', 'score': 0.003216362092643976}, {'label': 'grief', 'score': 0.0020106961019337177}, {'label': 'fear', 'score': 0.002010415541008115}, {'label': 'nervousness', 'score': 0.001956311287358403}]\n"
          ]
        }
      ],
      "source": [
        "model_outputs = classifier(dataset_dict[split_name][\"text\"])\n",
        "\n",
        "print(dataset_dict[split_name][\"text\"][0])\n",
        "print(model_outputs[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "JsW7kmenEXZ3"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ma2WsWwvu3KB"
      },
      "source": [
        "The model gave us floating point outputs for each label for each item, as a list of dicts. We need to arrange these into an array to be able to easily compare them with the dataset values. These values are sometimes known as probabilities (or `probas` for short) - but note, although these values are quantitative and can be compared to an extent (as we will), they are not really probabilities in any real statistical sense.\n",
        "\n",
        "We still need to convert these to binary prediction (`preds`) to be able to compare them to the `y_target` values from the dataset, but since the best threshold value to use to convert them is not yet known, we'll keep the float values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "sqlKORsku3KB"
      },
      "outputs": [],
      "source": [
        "y_probas_all = np.zeros((num_items, num_labels), dtype=float)\n",
        "for i, item_probas in enumerate(model_outputs):\n",
        "    for item_proba in item_probas:\n",
        "        label, score = item_proba[\"label\"], item_proba[\"score\"]\n",
        "        label_index = labels.index(label)\n",
        "        y_probas_all[i, label_index] = score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "JRUGZuhVu3KB",
        "outputId": "f75784b3-15f0-4262-ba54-5409d2714d8c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'text': 'The internet never ceases to surprise', 'labels': [26], 'id': 'ENeddjy8h', 'language': 'en'}\n",
            "[0.027, 0.005, 0.01, 0.042, 0.05, 0.003, 0.004, 0.002, 0.007, 0.028, 0.049, 0.009, 0.007, 0.027, 0.005, 0.002, 0.002, 0.003, 0.001, 0.002, 0.015, 0.002, 0.04, 0.002, 0.001, 0.005, 0.122, 0.583]\n",
            "Top label proba is label number 27 (neutral): 0.5827971696853638\n"
          ]
        }
      ],
      "source": [
        "i = 3856\n",
        "print(dataset_dict[split_name][i])\n",
        "print(np.round(y_probas_all[i], 3).tolist())\n",
        "top = np.argmax(y_probas_all[i])\n",
        "print(f\"Top label proba is label number {top} ({labels[top]}): {y_probas_all[i][top]}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bM470YxJu3KB"
      },
      "source": [
        "### Evalutation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KfWkK9Ulu3KB"
      },
      "source": [
        "We'll use good old SKLearn for evaluation here because it's super well known and simple. You could use something else, such as Huggingface Evaluate if you prefer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "wifECQX7u3KC"
      },
      "outputs": [],
      "source": [
        "from sklearn import metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jbHv90iPu3KC"
      },
      "source": [
        "We can see the metrics at a threshold of 0.5 (which intuitively seems like the right one to use).\n",
        "\n",
        "Should ignore accuracy because it isn't really helpful when applied per item for a multi-label dataset E.g. if there are 3 positive labels for a particular item but the model only gets 2 of them, accuracy will treat that as a 0/fail, not a partial success."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "Vq9WJFPhu3KC",
        "outputId": "9dab7a30-2fcd-47e8-a60e-c1dba3845136",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overall (macro)\n",
            "===============\n",
            "Accuracy: 0.411\n",
            "Precision: 0.500\n",
            "Recall: 0.299\n",
            "F1: 0.349\n"
          ]
        }
      ],
      "source": [
        "threshold = 0.5\n",
        "y_preds_all = y_probas_all > threshold\n",
        "\n",
        "print(\"Overall (macro)\")\n",
        "print(\"===============\")\n",
        "print(f\"Accuracy: {metrics.accuracy_score(y_targets_all, y_preds_all):.3f}\")\n",
        "print(f\"Precision: {metrics.precision_score(y_targets_all, y_preds_all, average='macro', zero_division=0):.3f}\")\n",
        "print(f\"Recall: {metrics.recall_score(y_targets_all, y_preds_all, average='macro', zero_division=0):.3f}\")\n",
        "print(f\"F1: {metrics.f1_score(y_targets_all, y_preds_all, average='macro', zero_division=0):.3f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NdfORzk7u3KC"
      },
      "source": [
        "And now per label (which is more meaningful for a multi-label, multiclass dataset such as this)\n",
        "\n",
        "Note the support for certain labels is tiny - grief has 6 positives in the dataset test split (out of c5.4k items). So measurement of the test set for such labels is meaningless, and anyway (given there is a similarly tiny number in the training split) unsurprisingly the model fails to predict positives for such labels since it's been trained on overwhelmling negative examples.\n",
        "\n",
        "We really should prune any labels with less than (for example) 50 positive examples in the training split (which is significantly bigger than the test split we are looking at here), or we should augment the data synthetically for these tiny/scarce labels to allow the model to learn these signals.\n",
        "\n",
        "However, they have been left in here for visibility."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "TMTnW44Bu3KC"
      },
      "outputs": [],
      "source": [
        "def calc_label_metrics(label, y_targets, y_preds, threshold):\n",
        "    return {\n",
        "        \"label\": label,\n",
        "        \"accuracy\": metrics.accuracy_score(y_targets, y_preds),\n",
        "        \"precision\": metrics.precision_score(y_targets, y_preds, zero_division=0),\n",
        "        \"recall\": metrics.recall_score(y_targets, y_preds, zero_division=0),\n",
        "        \"f1\": metrics.f1_score(y_targets, y_preds, zero_division=0),\n",
        "        \"mcc\": metrics.matthews_corrcoef(y_targets, y_preds),\n",
        "        \"support\": y_targets.sum(),\n",
        "        \"threshold\": threshold,\n",
        "    }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "eG0D2qYQu3KC",
        "outputId": "64153a2e-e757-4700-c38e-12f4d454377c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                accuracy  precision  recall     f1    mcc  support  threshold\n",
              "admiration         0.946      0.700   0.637  0.667  0.638     2790        0.5\n",
              "amusement          0.973      0.763   0.757  0.760  0.745     1866        0.5\n",
              "anger              0.966      0.513   0.276  0.359  0.360     1128        0.5\n",
              "annoyance          0.948      0.535   0.013  0.026  0.079     1704        0.5\n",
              "approval           0.937      0.521   0.195  0.284  0.293     2094        0.5\n",
              "caring             0.974      0.468   0.181  0.261  0.281      816        0.5\n",
              "confusion          0.972      0.638   0.272  0.381  0.405     1020        0.5\n",
              "curiosity          0.948      0.514   0.365  0.427  0.407     1734        0.5\n",
              "desire             0.987      0.488   0.377  0.425  0.422      414        0.5\n",
              "disappointment     0.969      0.656   0.021  0.040  0.113     1014        0.5\n",
              "disapproval        0.954      0.430   0.234  0.303  0.295     1398        0.5\n",
              "disgust            0.983      0.603   0.157  0.249  0.301      600        0.5\n",
              "embarrassment      0.993      0.000   0.000  0.000  0.000      240        0.5\n",
              "excitement         0.981      0.491   0.131  0.207  0.247      624        0.5\n",
              "fear               0.988      0.683   0.420  0.520  0.530      498        0.5\n",
              "gratitude          0.989      0.934   0.889  0.911  0.906     2004        0.5\n",
              "grief              0.999      0.000   0.000  0.000  0.000       36        0.5\n",
              "joy                0.970      0.531   0.353  0.424  0.418     1032        0.5\n",
              "love               0.974      0.766   0.756  0.761  0.747     1812        0.5\n",
              "nervousness        0.996      0.000   0.000  0.000  0.000      120        0.5\n",
              "optimism           0.975      0.682   0.420  0.520  0.523     1062        0.5\n",
              "pride              0.997      0.000   0.000  0.000  0.000       84        0.5\n",
              "realization        0.976      0.583   0.053  0.097  0.171      792        0.5\n",
              "relief             0.996      0.000   0.000  0.000  0.000      138        0.5\n",
              "remorse            0.988      0.667   0.508  0.576  0.576      516        0.5\n",
              "sadness            0.972      0.607   0.371  0.461  0.461     1062        0.5\n",
              "surprise           0.977      0.560   0.463  0.507  0.497      828        0.5\n",
              "neutral            0.764      0.668   0.535  0.594  0.436    10524        0.5"
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>accuracy</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1</th>\n",
              "      <th>mcc</th>\n",
              "      <th>support</th>\n",
              "      <th>threshold</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>admiration</th>\n",
              "      <td>0.946</td>\n",
              "      <td>0.700</td>\n",
              "      <td>0.637</td>\n",
              "      <td>0.667</td>\n",
              "      <td>0.638</td>\n",
              "      <td>2790</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>amusement</th>\n",
              "      <td>0.973</td>\n",
              "      <td>0.763</td>\n",
              "      <td>0.757</td>\n",
              "      <td>0.760</td>\n",
              "      <td>0.745</td>\n",
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              "    <tr>\n",
              "      <th>anger</th>\n",
              "      <td>0.966</td>\n",
              "      <td>0.513</td>\n",
              "      <td>0.276</td>\n",
              "      <td>0.359</td>\n",
              "      <td>0.360</td>\n",
              "      <td>1128</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>annoyance</th>\n",
              "      <td>0.948</td>\n",
              "      <td>0.535</td>\n",
              "      <td>0.013</td>\n",
              "      <td>0.026</td>\n",
              "      <td>0.079</td>\n",
              "      <td>1704</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>approval</th>\n",
              "      <td>0.937</td>\n",
              "      <td>0.521</td>\n",
              "      <td>0.195</td>\n",
              "      <td>0.284</td>\n",
              "      <td>0.293</td>\n",
              "      <td>2094</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>caring</th>\n",
              "      <td>0.974</td>\n",
              "      <td>0.468</td>\n",
              "      <td>0.181</td>\n",
              "      <td>0.261</td>\n",
              "      <td>0.281</td>\n",
              "      <td>816</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>confusion</th>\n",
              "      <td>0.972</td>\n",
              "      <td>0.638</td>\n",
              "      <td>0.272</td>\n",
              "      <td>0.381</td>\n",
              "      <td>0.405</td>\n",
              "      <td>1020</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>curiosity</th>\n",
              "      <td>0.948</td>\n",
              "      <td>0.514</td>\n",
              "      <td>0.365</td>\n",
              "      <td>0.427</td>\n",
              "      <td>0.407</td>\n",
              "      <td>1734</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>desire</th>\n",
              "      <td>0.987</td>\n",
              "      <td>0.488</td>\n",
              "      <td>0.377</td>\n",
              "      <td>0.425</td>\n",
              "      <td>0.422</td>\n",
              "      <td>414</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>disappointment</th>\n",
              "      <td>0.969</td>\n",
              "      <td>0.656</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.040</td>\n",
              "      <td>0.113</td>\n",
              "      <td>1014</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>disapproval</th>\n",
              "      <td>0.954</td>\n",
              "      <td>0.430</td>\n",
              "      <td>0.234</td>\n",
              "      <td>0.303</td>\n",
              "      <td>0.295</td>\n",
              "      <td>1398</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>disgust</th>\n",
              "      <td>0.983</td>\n",
              "      <td>0.603</td>\n",
              "      <td>0.157</td>\n",
              "      <td>0.249</td>\n",
              "      <td>0.301</td>\n",
              "      <td>600</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>embarrassment</th>\n",
              "      <td>0.993</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>240</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>excitement</th>\n",
              "      <td>0.981</td>\n",
              "      <td>0.491</td>\n",
              "      <td>0.131</td>\n",
              "      <td>0.207</td>\n",
              "      <td>0.247</td>\n",
              "      <td>624</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fear</th>\n",
              "      <td>0.988</td>\n",
              "      <td>0.683</td>\n",
              "      <td>0.420</td>\n",
              "      <td>0.520</td>\n",
              "      <td>0.530</td>\n",
              "      <td>498</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gratitude</th>\n",
              "      <td>0.989</td>\n",
              "      <td>0.934</td>\n",
              "      <td>0.889</td>\n",
              "      <td>0.911</td>\n",
              "      <td>0.906</td>\n",
              "      <td>2004</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>grief</th>\n",
              "      <td>0.999</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>36</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>joy</th>\n",
              "      <td>0.970</td>\n",
              "      <td>0.531</td>\n",
              "      <td>0.353</td>\n",
              "      <td>0.424</td>\n",
              "      <td>0.418</td>\n",
              "      <td>1032</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>love</th>\n",
              "      <td>0.974</td>\n",
              "      <td>0.766</td>\n",
              "      <td>0.756</td>\n",
              "      <td>0.761</td>\n",
              "      <td>0.747</td>\n",
              "      <td>1812</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nervousness</th>\n",
              "      <td>0.996</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>120</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>optimism</th>\n",
              "      <td>0.975</td>\n",
              "      <td>0.682</td>\n",
              "      <td>0.420</td>\n",
              "      <td>0.520</td>\n",
              "      <td>0.523</td>\n",
              "      <td>1062</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pride</th>\n",
              "      <td>0.997</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>84</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>realization</th>\n",
              "      <td>0.976</td>\n",
              "      <td>0.583</td>\n",
              "      <td>0.053</td>\n",
              "      <td>0.097</td>\n",
              "      <td>0.171</td>\n",
              "      <td>792</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>relief</th>\n",
              "      <td>0.996</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>138</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>remorse</th>\n",
              "      <td>0.988</td>\n",
              "      <td>0.667</td>\n",
              "      <td>0.508</td>\n",
              "      <td>0.576</td>\n",
              "      <td>0.576</td>\n",
              "      <td>516</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sadness</th>\n",
              "      <td>0.972</td>\n",
              "      <td>0.607</td>\n",
              "      <td>0.371</td>\n",
              "      <td>0.461</td>\n",
              "      <td>0.461</td>\n",
              "      <td>1062</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>surprise</th>\n",
              "      <td>0.977</td>\n",
              "      <td>0.560</td>\n",
              "      <td>0.463</td>\n",
              "      <td>0.507</td>\n",
              "      <td>0.497</td>\n",
              "      <td>828</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>neutral</th>\n",
              "      <td>0.764</td>\n",
              "      <td>0.668</td>\n",
              "      <td>0.535</td>\n",
              "      <td>0.594</td>\n",
              "      <td>0.436</td>\n",
              "      <td>10524</td>\n",
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              "      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-afcfeb36-2e90-4e71-b8f4-6a127a7d2320 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(per_label_results\",\n  \"rows\": 28,\n  \"fields\": [\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04319415990311016,\n        \"min\": 0.764,\n        \"max\": 0.999,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          0.999,\n          0.954,\n          0.946\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.26006231192669205,\n        \"min\": 0.0,\n        \"max\": 0.934,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          0.488,\n          0.531,\n          0.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2582174548094183,\n        \"min\": 0.0,\n        \"max\": 0.889,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          0.889,\n          0.021,\n          0.637\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.26515885225882413,\n        \"min\": 0.0,\n        \"max\": 0.911,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          0.911,\n          0.04,\n          0.667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mcc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2490522856857391,\n        \"min\": 0.0,\n        \"max\": 0.906,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          0.422,\n          0.418,\n          0.638\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"support\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1927,\n        \"min\": 36,\n        \"max\": 10524,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          414,\n          624,\n          1014\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"threshold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.5,\n        \"max\": 0.5,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "threshold = 0.5\n",
        "y_preds_all = (y_probas_all > threshold).astype(int)\n",
        "\n",
        "results = []\n",
        "for label_index, label in enumerate(labels):\n",
        "    y_targets, y_preds = y_targets_all[:, label_index], y_preds_all[:, label_index]\n",
        "    results.append(calc_label_metrics(label, y_targets, y_preds, threshold))\n",
        "\n",
        "per_label_results = pd.DataFrame(results, index=labels)\n",
        "display(per_label_results.drop(columns=[\"label\"]).round(3))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vbimC5KWu3KD"
      },
      "source": [
        "Note, should probably ignore accuracy metric again, but in this case at the per-label level a multi-label dataset has a huge number of true negatives which make the accuracy figure pretty meaningless. E.g. in a situation where there are 10 positive items and 990 negative items, if a model simply predicts negative for everything, its accuracy figure still appears very high (0.99) even though its clearly not performing to a useful level."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "Y8Yd9cOEu3KD",
        "outputId": "3af87a8b-482b-4af7-8b6c-6e3427896709",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "per_label_results[\"label (support)\"] = (\n",
        "    per_label_results.index + \" (\" + per_label_results[\"support\"].astype(str) + \")\"\n",
        ")\n",
        "ax = per_label_results.sort_values(by=\"support\").plot.bar(\n",
        "    x=\"label (support)\",\n",
        "    y=[\"f1\"],\n",
        "    rot=90,\n",
        "    title=\"F1 (sorted by label support) @ 0.5 threshold\",\n",
        "    figsize=(11,3),\n",
        ")\n",
        "ax.tick_params(axis='x', which='major', labelsize=8)\n",
        "ax.axes.xaxis.label.set_text(f\"label (support, in {split_name} split)\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LjpIxBCau3KD"
      },
      "source": [
        "We can recreate dataset-wide metrics from per-label metrics, which will prove useful later."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "_AuMS95Ou3KD",
        "outputId": "daabecc7-7c9f-4beb-ccb5-c90d83e571fa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Simple mean of labels: {'precision': np.float64(0.5), 'recall': np.float64(0.299), 'f1': np.float64(0.349), 'mcc': np.float64(0.352)}\n",
            "Weighted average (using support): {'precision': np.float64(0.628), 'recall': np.float64(0.436), 'f1': np.float64(0.489), 'mcc': np.float64(0.449)}\n"
          ]
        }
      ],
      "source": [
        "def dataset_wide_metrics(df):\n",
        "    simple_mean = {\n",
        "        m: round(df[m].mean(), 3)\n",
        "        for m in [\"precision\", \"recall\", \"f1\", \"mcc\"]\n",
        "    }\n",
        "    print(\"Simple mean of labels:\", simple_mean)\n",
        "    weighted = {\n",
        "        m: round(sum(df[m] * df[\"support\"]) / df[\"support\"].sum(), 3)\n",
        "        for m in [\"precision\", \"recall\", \"f1\", \"mcc\"]\n",
        "    }\n",
        "    print(\"Weighted average (using support):\", weighted)\n",
        "    return simple_mean, weighted\n",
        "\n",
        "_ = dataset_wide_metrics(per_label_results)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FEccLmVyu3KD"
      },
      "source": [
        "So, now lets do a crude search for how the metrics vary by threshold, rather than naively setting it at 0.5 as above"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "1eJkZAdBu3KD",
        "outputId": "28ccff1d-15c0-4cc6-810e-395cc6747acb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 19/19 [00:08<00:00,  2.17it/s]\n"
          ]
        }
      ],
      "source": [
        "threshold_results = {}\n",
        "for t in tqdm(range(5, 100, 5)):\n",
        "    threshold = t / 100\n",
        "    y_preds_all = (y_probas_all > threshold).astype(int)\n",
        "    threshold_results[threshold] = []\n",
        "    for label_index, label in enumerate(labels):\n",
        "        y_targets, y_preds = y_targets_all[:, label_index], y_preds_all[:, label_index]\n",
        "        threshold_results[threshold].append(calc_label_metrics(label, y_targets, y_preds, threshold))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "tAYFHOAEu3KE",
        "outputId": "3156ec90-3baf-4e8a-c0ce-c5463513b649",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                accuracy  precision  recall     f1    mcc  support  threshold\n",
              "admiration         0.942      0.652   0.684  0.667  0.636     2790       0.40\n",
              "amusement          0.973      0.735   0.817  0.774  0.760     1866       0.35\n",
              "anger              0.960      0.411   0.364  0.386  0.366     1128       0.35\n",
              "annoyance          0.896      0.246   0.481  0.325  0.293     1704       0.15\n",
              "approval           0.910      0.329   0.383  0.354  0.307     2094       0.20\n",
              "caring             0.958      0.285   0.460  0.352  0.341      816       0.15\n",
              "confusion          0.965      0.444   0.401  0.421  0.404     1020       0.25\n",
              "curiosity          0.935      0.433   0.740  0.546  0.535     1734       0.25\n",
              "desire             0.984      0.404   0.534  0.460  0.457      414       0.25\n",
              "disappointment     0.942      0.224   0.345  0.272  0.249     1014       0.15\n",
              "disapproval        0.935      0.306   0.413  0.352  0.322     1398       0.25\n",
              "disgust            0.975      0.343   0.418  0.377  0.366      600       0.15\n",
              "embarrassment      0.990      0.280   0.242  0.260  0.255      240       0.10\n",
              "excitement         0.973      0.344   0.425  0.380  0.369      624       0.15\n",
              "fear               0.987      0.599   0.522  0.558  0.553      498       0.35\n",
              "gratitude          0.989      0.924   0.902  0.913  0.907     2004       0.40\n",
              "grief              0.999      0.000   0.000  0.000  0.000       36       0.05\n",
              "joy                0.965      0.454   0.532  0.490  0.474     1032       0.25\n",
              "love               0.973      0.731   0.829  0.777  0.765     1812       0.35\n",
              "nervousness        0.996      0.385   0.250  0.303  0.308      120       0.10\n",
              "optimism           0.973      0.588   0.525  0.555  0.542     1062       0.25\n",
              "pride              0.997      0.000   0.000  0.000  0.000       84       0.05\n",
              "realization        0.962      0.202   0.189  0.195  0.176      792       0.15\n",
              "relief             0.996      0.000   0.000  0.000  0.000      138       0.05\n",
              "remorse            0.988      0.597   0.808  0.687  0.689      516       0.15\n",
              "sadness            0.970      0.548   0.434  0.484  0.473     1062       0.40\n",
              "surprise           0.974      0.487   0.569  0.524  0.513      828       0.30\n",
              "neutral            0.726      0.551   0.818  0.658  0.468    10524       0.20"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4132d8e2-df06-4381-9c4b-62828895194e\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>accuracy</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1</th>\n",
              "      <th>mcc</th>\n",
              "      <th>support</th>\n",
              "      <th>threshold</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>admiration</th>\n",
              "      <td>0.942</td>\n",
              "      <td>0.652</td>\n",
              "      <td>0.684</td>\n",
              "      <td>0.667</td>\n",
              "      <td>0.636</td>\n",
              "      <td>2790</td>\n",
              "      <td>0.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>amusement</th>\n",
              "      <td>0.973</td>\n",
              "      <td>0.735</td>\n",
              "      <td>0.817</td>\n",
              "      <td>0.774</td>\n",
              "      <td>0.760</td>\n",
              "      <td>1866</td>\n",
              "      <td>0.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>anger</th>\n",
              "      <td>0.960</td>\n",
              "      <td>0.411</td>\n",
              "      <td>0.364</td>\n",
              "      <td>0.386</td>\n",
              "      <td>0.366</td>\n",
              "      <td>1128</td>\n",
              "      <td>0.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>annoyance</th>\n",
              "      <td>0.896</td>\n",
              "      <td>0.246</td>\n",
              "      <td>0.481</td>\n",
              "      <td>0.325</td>\n",
              "      <td>0.293</td>\n",
              "      <td>1704</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>approval</th>\n",
              "      <td>0.910</td>\n",
              "      <td>0.329</td>\n",
              "      <td>0.383</td>\n",
              "      <td>0.354</td>\n",
              "      <td>0.307</td>\n",
              "      <td>2094</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>caring</th>\n",
              "      <td>0.958</td>\n",
              "      <td>0.285</td>\n",
              "      <td>0.460</td>\n",
              "      <td>0.352</td>\n",
              "      <td>0.341</td>\n",
              "      <td>816</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>confusion</th>\n",
              "      <td>0.965</td>\n",
              "      <td>0.444</td>\n",
              "      <td>0.401</td>\n",
              "      <td>0.421</td>\n",
              "      <td>0.404</td>\n",
              "      <td>1020</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>curiosity</th>\n",
              "      <td>0.935</td>\n",
              "      <td>0.433</td>\n",
              "      <td>0.740</td>\n",
              "      <td>0.546</td>\n",
              "      <td>0.535</td>\n",
              "      <td>1734</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>desire</th>\n",
              "      <td>0.984</td>\n",
              "      <td>0.404</td>\n",
              "      <td>0.534</td>\n",
              "      <td>0.460</td>\n",
              "      <td>0.457</td>\n",
              "      <td>414</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>disappointment</th>\n",
              "      <td>0.942</td>\n",
              "      <td>0.224</td>\n",
              "      <td>0.345</td>\n",
              "      <td>0.272</td>\n",
              "      <td>0.249</td>\n",
              "      <td>1014</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>disapproval</th>\n",
              "      <td>0.935</td>\n",
              "      <td>0.306</td>\n",
              "      <td>0.413</td>\n",
              "      <td>0.352</td>\n",
              "      <td>0.322</td>\n",
              "      <td>1398</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>disgust</th>\n",
              "      <td>0.975</td>\n",
              "      <td>0.343</td>\n",
              "      <td>0.418</td>\n",
              "      <td>0.377</td>\n",
              "      <td>0.366</td>\n",
              "      <td>600</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>embarrassment</th>\n",
              "      <td>0.990</td>\n",
              "      <td>0.280</td>\n",
              "      <td>0.242</td>\n",
              "      <td>0.260</td>\n",
              "      <td>0.255</td>\n",
              "      <td>240</td>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>excitement</th>\n",
              "      <td>0.973</td>\n",
              "      <td>0.344</td>\n",
              "      <td>0.425</td>\n",
              "      <td>0.380</td>\n",
              "      <td>0.369</td>\n",
              "      <td>624</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fear</th>\n",
              "      <td>0.987</td>\n",
              "      <td>0.599</td>\n",
              "      <td>0.522</td>\n",
              "      <td>0.558</td>\n",
              "      <td>0.553</td>\n",
              "      <td>498</td>\n",
              "      <td>0.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gratitude</th>\n",
              "      <td>0.989</td>\n",
              "      <td>0.924</td>\n",
              "      <td>0.902</td>\n",
              "      <td>0.913</td>\n",
              "      <td>0.907</td>\n",
              "      <td>2004</td>\n",
              "      <td>0.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>grief</th>\n",
              "      <td>0.999</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>36</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>joy</th>\n",
              "      <td>0.965</td>\n",
              "      <td>0.454</td>\n",
              "      <td>0.532</td>\n",
              "      <td>0.490</td>\n",
              "      <td>0.474</td>\n",
              "      <td>1032</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>love</th>\n",
              "      <td>0.973</td>\n",
              "      <td>0.731</td>\n",
              "      <td>0.829</td>\n",
              "      <td>0.777</td>\n",
              "      <td>0.765</td>\n",
              "      <td>1812</td>\n",
              "      <td>0.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nervousness</th>\n",
              "      <td>0.996</td>\n",
              "      <td>0.385</td>\n",
              "      <td>0.250</td>\n",
              "      <td>0.303</td>\n",
              "      <td>0.308</td>\n",
              "      <td>120</td>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>optimism</th>\n",
              "      <td>0.973</td>\n",
              "      <td>0.588</td>\n",
              "      <td>0.525</td>\n",
              "      <td>0.555</td>\n",
              "      <td>0.542</td>\n",
              "      <td>1062</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pride</th>\n",
              "      <td>0.997</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>84</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>realization</th>\n",
              "      <td>0.962</td>\n",
              "      <td>0.202</td>\n",
              "      <td>0.189</td>\n",
              "      <td>0.195</td>\n",
              "      <td>0.176</td>\n",
              "      <td>792</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>relief</th>\n",
              "      <td>0.996</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>138</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>remorse</th>\n",
              "      <td>0.988</td>\n",
              "      <td>0.597</td>\n",
              "      <td>0.808</td>\n",
              "      <td>0.687</td>\n",
              "      <td>0.689</td>\n",
              "      <td>516</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sadness</th>\n",
              "      <td>0.970</td>\n",
              "      <td>0.548</td>\n",
              "      <td>0.434</td>\n",
              "      <td>0.484</td>\n",
              "      <td>0.473</td>\n",
              "      <td>1062</td>\n",
              "      <td>0.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>surprise</th>\n",
              "      <td>0.974</td>\n",
              "      <td>0.487</td>\n",
              "      <td>0.569</td>\n",
              "      <td>0.524</td>\n",
              "      <td>0.513</td>\n",
              "      <td>828</td>\n",
              "      <td>0.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>neutral</th>\n",
              "      <td>0.726</td>\n",
              "      <td>0.551</td>\n",
              "      <td>0.818</td>\n",
              "      <td>0.658</td>\n",
              "      <td>0.468</td>\n",
              "      <td>10524</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
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              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cb2a10dc-5896-4e2e-8f9d-d45a766ec9cb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(per_label_threshold_results\",\n  \"rows\": 28,\n  \"fields\": [\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.052259362848115595,\n        \"min\": 0.726,\n        \"max\": 0.999,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          0.942,\n          0.988,\n          0.997\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.22366664301090405,\n        \"min\": 0.0,\n        \"max\": 0.924,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          0.404,\n          0.0,\n          0.652\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.25121616095269333,\n        \"min\": 0.0,\n        \"max\": 0.902,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          0.534,\n          0.0,\n          0.684\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2296643168032388,\n        \"min\": 0.0,\n        \"max\": 0.913,\n        \"num_unique_values\": 25,\n        \"samples\": [\n          0.46,\n          0.49,\n          0.667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mcc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.22475597584324353,\n        \"min\": 0.0,\n        \"max\": 0.907,\n        \"num_unique_values\": 25,\n        \"samples\": [\n          0.457,\n          0.474,\n          0.636\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"support\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1927,\n        \"min\": 36,\n        \"max\": 10524,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          414,\n          624,\n          1014\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"threshold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11007452982594162,\n        \"min\": 0.05,\n        \"max\": 0.4,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.35,\n          0.1,\n          0.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "metric_name = \"f1\"\n",
        "best = {label: {metric_name: -1, \"result\": None} for label in labels}\n",
        "for threshold, results in threshold_results.items():\n",
        "    for result in results:\n",
        "        label = result[\"label\"]\n",
        "        if result[metric_name] > best[label][metric_name]:\n",
        "            best[label] = {metric_name: result[metric_name], \"result\": result}\n",
        "\n",
        "results = [b[\"result\"] for b in best.values()]\n",
        "per_label_threshold_results = pd.DataFrame(results, index=[result[\"label\"] for result in results])\n",
        "display(per_label_threshold_results.drop(columns=[\"label\"]).round(3))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rZWIMiaNu3KE"
      },
      "source": [
        "Charting it"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "rzO3-saeu3KE",
        "outputId": "ce481ac1-aee1-4ef3-d601-53fad6e9a840",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "per_label_threshold_results[\"label (support)\"] = (\n",
        "    per_label_threshold_results[\"label\"] + \" (\" + per_label_threshold_results[\"support\"].astype(str) + \")\"\n",
        ")\n",
        "ax = per_label_threshold_results.sort_values(by=\"support\").plot.bar(\n",
        "    x='label (support)',\n",
        "    y=[\"f1\", \"threshold\"],\n",
        "    rot=90,\n",
        "    title=\"F1 (sorted by label support) @ threshold for max f1\",\n",
        "    width=0.7,\n",
        "    figsize=(11,3),\n",
        ")\n",
        "ax.tick_params(axis='x', which='major', labelsize=8)\n",
        "ax.axes.xaxis.label.set_text(f\"label (support, in {split_name} split)\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PtNhfM6hu3KF"
      },
      "source": [
        "Note how the best performing threshold (when measured by maximizing f1) is always a lower threshold than the 0.5 assumed before. This is likely because there are so mny true negatives in the dataset (as is often the case with multi-label, multi-class datasets).\n",
        "\n",
        "As you'd expect precision takes a dip vs before as the model is predicting positive more often, but to a lesser degree than recall climbs, causing F1 to overall increase (since F1 is the harmonic mean of precision and recall).\n",
        "\n",
        "The most extreme examples being the labels with the smallest support (e.g. grief) where the smallest threshold tried (0.05) is the one that cajoles the model into finally giving some positives and hence getting valid precision and recall scores that can't do if they never predict positive.\n",
        "\n",
        "Various techniques could be used to increase the probas so these low thresholds were not the best, such as boosting/duplicating the true positives in the training set, data augmentation, or a linear layer to boost the proba output, but simply using a label-bespoke threshold here gives us a view of the perf of the model as it is."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "AMmCBcdbu3KF",
        "outputId": "7af4d4b7-3e32-4e5a-a3c0-6b4afe257306",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x300 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "_label_name = \"anger\"\n",
        "label_per_threshold_results = []\n",
        "for threshold, results in threshold_results.items():\n",
        "    for result in results:\n",
        "        if result[\"label\"] == _label_name:\n",
        "            label_per_threshold_results.append(result)\n",
        "\n",
        "label_per_threshold_results = pd.DataFrame(\n",
        "    label_per_threshold_results, index=[r[\"threshold\"] for r in label_per_threshold_results]\n",
        ")\n",
        "ax = label_per_threshold_results.plot.line(\n",
        "    x=\"threshold\", y=[\"precision\", \"recall\", \"f1\"], rot=90, title=_label_name, figsize=(7,3)\n",
        ")\n",
        "ax.axvline(0.5, color='gray', linestyle='--')\n",
        "ax.axhline(label_per_threshold_results[\"f1\"].max(), color=\"gray\", linestyle=\"--\")\n",
        "ax.xaxis.set_ticks(np.arange(0, 1, 0.05))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "KWu-HlY_u3KF",
        "outputId": "8d84f802-9e2c-4fc2-c029-d135023ae041",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "### Threshold set to arbitary 0.5\n",
            "Simple mean of labels: {'precision': np.float64(0.5), 'recall': np.float64(0.299), 'f1': np.float64(0.349), 'mcc': np.float64(0.352)}\n",
            "Weighted average (using support): {'precision': np.float64(0.628), 'recall': np.float64(0.436), 'f1': np.float64(0.489), 'mcc': np.float64(0.449)}\n",
            "\n",
            "### Threshold from per label search (for best F1 score)\n",
            "Simple mean of labels: {'precision': np.float64(0.411), 'recall': np.float64(0.467), 'f1': np.float64(0.431), 'mcc': np.float64(0.412)}\n",
            "Weighted average (using support): {'precision': np.float64(0.511), 'recall': np.float64(0.634), 'f1': np.float64(0.559), 'mcc': np.float64(0.493)}\n",
            "\n",
            "F1 (simple mean) improved by 23.5%\n",
            "F1 (weighted) improved by 20.1%\n"
          ]
        }
      ],
      "source": [
        "print(\"### Threshold set to arbitary 0.5\")\n",
        "_before = dataset_wide_metrics(per_label_results)\n",
        "print()\n",
        "print(\"### Threshold from per label search (for best F1 score)\")\n",
        "_after = dataset_wide_metrics(per_label_threshold_results)\n",
        "\n",
        "print()\n",
        "percentage = (_after[0][\"f1\"] - _before[0][\"f1\"]) / _before[0][\"f1\"] * 100\n",
        "print(f\"F1 (simple mean) improved by {round(percentage, 1)}%\")\n",
        "percentage = (_after[1][\"f1\"] - _before[1][\"f1\"]) / _before[0][\"f1\"] * 100\n",
        "print(f\"F1 (weighted) improved by {round(percentage, 1)}%\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rhSYhM0Xu3KJ"
      },
      "source": [
        "### Dataset constraints on the model performance"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SgFJzW89u3KJ"
      },
      "source": [
        "Some labels (E.g. gratitude) when considered independently perform very strongly with F1 around 0.9, whilst others (E.g. relief) perform very poorly.\n",
        "\n",
        "This is a challenging dataset. Labels such as relief do have much fewer examples in the training data (less than 100 out of the 40k+, and only 11 in the test split).\n",
        "\n",
        "But there is also some ambiguity and/or labelling errors visible in the training data of go_emotions that is suspected to constrain the performance. Data cleaning on the dataset to reduce some of the mistakes, ambiguity, conflicts and duplication in the labelling would produce a higher performing model."
      ]
    }
  ],
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