bert_gender_stress_bias_2B.ipynb

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    "colab": {
      "name": "bert_gender_stress_bias_2B.ipynb",
      "provenance": [],
      "collapsed_sections": [
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        "d0w-3TWNGH_W",
        "-JHDfUB_GURY",
        "7E9QSqPjBU-Z",
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      "machine_shape": "hm"
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    "kernelspec": {
      "name": "python3",
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    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard",
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Git repo: https://github.com/lleviraz/bert-gender-bias\n",
        "\n",
        "This is the second part of an Advanced ML Course Final Project, Reichman University, 2022.\n",
        "\n",
        "\n",
        "Part 2.B :Training the probe to detect gender bias in a model's latent space.\n",
        "\n",
        "\n",
        "---\n",
        "By: Eli Terris-Assa and Liad Levi-Raz\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "py_lVxz1XNyh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This notebook is comprised of the steps taken to create and train a custom bert model made out of a previous bert model's layers up to and including the encoder and a simple probe tasked in predicting a different task from the original bert model's task\n",
        " \n",
        " \n",
        "Running this will **not use any preset weights for the probe layers, and will train on the given data**. This task takes about **1~2.5** hours depending on GPU capacity, number of epochs and patience. \n",
        "\n"
      ],
      "metadata": {
        "id": "4INa-KLmX9x9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Setup"
      ],
      "metadata": {
        "id": "bU6NDPfSF-V6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import clear_output"
      ],
      "metadata": {
        "id": "TqiCk7BYfuFP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# in case of saving in google drive through colab\n",
        "#from google.colab import drive\n",
        "#drive.mount('/content/')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZUntglT30FE6",
        "outputId": "b0ecf2dd-23a9-4e58-ef0e-401c666bfadd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# where to save logs and df\n",
        "env = '/content/'"
      ],
      "metadata": {
        "id": "16K3YlSI2cHI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers\n",
        "clear_output()"
      ],
      "metadata": {
        "id": "z07GSoXOuqmg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install gdown\n",
        "!pip install  simpletransformers gensim transformers-interpret -q\n",
        "clear_output()"
      ],
      "metadata": {
        "id": "4UjQXpmToqv_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nBcSlPdHewjQ"
      },
      "outputs": [],
      "source": [
        "import pandas as pd \n",
        "import torch\n",
        "import transformers\n",
        "import numpy as np\n",
        "from transformers import AutoModel,BertForSequenceClassification \n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import gdown \n",
        "from simpletransformers.classification import ClassificationModel, ClassificationArgs\n"
      ],
      "metadata": {
        "id": "oELCwAApZ3Nr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def download_from_gdrive(file_id,dest_path):\n",
        "  if(not os.path.exists(dest_path)):\n",
        "    url = 'https://drive.google.com/uc?id='+file_id\n",
        "    gdown.download(url, dest_path, quiet=False)\n",
        "  else:\n",
        "    print('{} already exists'.format(dest_path))"
      ],
      "metadata": {
        "id": "q0fvT_-13mkG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# loading data original data"
      ],
      "metadata": {
        "id": "d0w-3TWNGH_W"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9ZcM4Tc_26HX"
      },
      "outputs": [],
      "source": [
        "VOCAB_FILE='dreaddit_vocab.txt'\n",
        "DATASET_FILE='dreaddit_data.zip'"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "download_from_gdrive('1q6wpGrdvgNihjoLwlooQiFcUSVmNCsTS',DATASET_FILE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hk62rBo-U4Ca",
        "outputId": "5ba0e6c0-04ec-4253-b17f-50d5ccd952d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1q6wpGrdvgNihjoLwlooQiFcUSVmNCsTS\n",
            "To: /content/dreaddit_data.zip\n",
            "100%|██████████| 1.35M/1.35M [00:00<00:00, 164MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "download_from_gdrive('1Q2Fpg3K0ayRtXsjRQAdXHnUp0rYoCgjj',VOCAB_FILE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "usn6zSOUUcgM",
        "outputId": "9524334d-594b-4ed0-836b-181ecaa92606"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1Q2Fpg3K0ayRtXsjRQAdXHnUp0rYoCgjj\n",
            "To: /content/dreaddit_vocab.txt\n",
            "100%|██████████| 44.3M/44.3M [00:00<00:00, 177MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DFsGFYgWQMb2",
        "outputId": "8229dcc0-e335-4bb9-8891-344125c0ef6a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  dreaddit_data.zip\n",
            "  inflating: data/dreaddit-train.csv  \n",
            "  inflating: data/dreaddit-test.csv  \n"
          ]
        }
      ],
      "source": [
        "!mkdir -p data \n",
        "!unzip -n dreaddit_data.zip -d data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = pd.read_csv(\"data/dreaddit-train.csv\")\n",
        "df_test = pd.read_csv(\"data/dreaddit-test.csv\")\n"
      ],
      "metadata": {
        "id": "oXApnWCMBCUV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = df_train[['text','label','id']]\n",
        "df_test = df_test[['text','label','id']]"
      ],
      "metadata": {
        "id": "hRrgspcoKXsT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.head(8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "W_eWlcmgrrxZ",
        "outputId": "25ed2bc5-bdc4-4625-8697-17a9fbd970e5"
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      "execution_count": null,
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              "4  October is Domestic Violence Awareness Month a...      1   1421\n",
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              "7    It cleared up and I was okay but. On Monday ...      1  33053"
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    {
      "cell_type": "code",
      "source": [
        "len(df_train),len(df_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mDZpQETvJhIv",
        "outputId": "bcff65b8-f3e7-4a6c-8755-c601745bb901"
      },
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2838, 715)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# loading models and creating labels"
      ],
      "metadata": {
        "id": "-JHDfUB_GURY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "text_length = 128\n"
      ],
      "metadata": {
        "id": "51k2NHbpIhla"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#w2v_tok = transformers.BertTokenizer(env+'dreaddit_vocab.txt') VOCAB_FILE\n",
        "w2v_tok = transformers.BertTokenizer(VOCAB_FILE)"
      ],
      "metadata": {
        "id": "duMNlbeUfCVM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "P2_TRAINED_MODEL='bert_dreaddit_gender_model_part2.zip'\n",
        "download_from_gdrive('1uim3vGMQ_oUhpM6l7tsQ9nYypQaYsf3O',P2_TRAINED_MODEL)\n",
        "P2_INFERENCE_MODE = os.path.isfile(P2_TRAINED_MODEL)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EZvmGBfpyuFK",
        "outputId": "7476196f-1f47-443d-ffe5-c0bc778da2a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1uim3vGMQ_oUhpM6l7tsQ9nYypQaYsf3O\n",
            "To: /content/bert_dreaddit_gender_model_part2.zip\n",
            "100%|██████████| 402M/402M [00:03<00:00, 119MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Inference mode, loading best model...')\n",
        "!unzip -q -n {P2_TRAINED_MODEL}\n",
        "# Create a ClassificationModel\n",
        "model2 = ClassificationModel(\n",
        "    \"bert\", '/content/part2_outputs',\n",
        "    tokenizer_type=w2v_tok,\n",
        "    use_cuda=torch.cuda.is_available()\n",
        ")\n",
        "print('Best model loaded.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NX_mVjOJ3RtX",
        "outputId": "1d12130a-8994-4255-ef79-535e15d7922a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Inference mode, loading best model...\n",
            "Best model loaded.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#model2.predict(list(df_test['text'][:1]))"
      ],
      "metadata": {
        "id": "FHPHWvFD4U2-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model2.model.base_model.embeddings"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2jfY4lI8eNqK",
        "outputId": "f4657b04-1bfd-4bce-916b-60ed5d558121"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertEmbeddings(\n",
              "  (word_embeddings): Embedding(28996, 768, padding_idx=0)\n",
              "  (position_embeddings): Embedding(512, 768)\n",
              "  (token_type_embeddings): Embedding(2, 768)\n",
              "  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#model_gender = BertForSequenceClassification.from_pretrained(\"Cameron/BERT-rtgender-opgender-annotations\").to(device)\n",
        "#model_gender = BertForSequenceClassification.from_pretrained(env+\"outputs/gender_classifier\").to(device)\n",
        "#model_gender.resize_token_embeddings(3000002)\n",
        "\n",
        "def prepare_data(data, batch_size = 32):\n",
        "  data = data.copy()\n",
        "  \n",
        "  \n",
        "  data_sequences = []\n",
        "  batches = np.ceil(len(data)/batch_size)\n",
        "  for batch in range(int(batches)):\n",
        "    start_idx = batch * batch_size\n",
        "    end_idx = start_idx + batch_size\n",
        "    if end_idx > len(data):\n",
        "        end_idx = len(data)\n",
        "    \n",
        "\n",
        "    texts = list(data['text'].iloc[start_idx:end_idx])\n",
        "    labels = list(data['label'].iloc[start_idx:end_idx])\n",
        "    inputs = w2v_tok(texts, return_tensors=\"pt\", truncation = True, padding = True, max_length=text_length) # add things\n",
        "    with torch.no_grad():\n",
        "        logits = model2.predict(texts)[1]\n",
        "        #data['logits_0'].iloc[start_idx:end_idx] = logits[:,0].tolist()\n",
        "        #data['logits_1'].iloc[start_idx:end_idx] = logits[:,1].tolist()\n",
        "        #logits = model_stress_encoder(model_stress_embeddings.forward(inputs['input_ids']),inputs['attention_mask'])\n",
        "        #print(logits)\n",
        "        datum = (inputs,labels,logits)\n",
        "        data_sequences.append(datum)\n",
        "    \n",
        "  return data_sequences\n",
        "\n",
        "\n",
        "train = prepare_data(df_train)\n",
        "test = prepare_data(df_test)\n",
        "\n",
        "#model_gender.resize_token_embeddings(30002)\n",
        "#del model_gender; \n",
        "torch.cuda.empty_cache()\n",
        "clear_output()"
      ],
      "metadata": {
        "id": "s_4ecu2dx3kQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#train[0]"
      ],
      "metadata": {
        "id": "YgVW6g_wZ3-P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TMVri_j7Z37j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#model_stress = BertForSequenceClassification.from_pretrained(\"/content/drive/MyDrive/Studies/Adv Machine Learning/Final project/outputs/best_model\").to(device)\n",
        "#model_stress.resize_token_embeddings(3000002)\n"
      ],
      "metadata": {
        "id": "Z_31Ra6AZuwp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "81MBXmaHZ34r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# create the bert and probe custom model"
      ],
      "metadata": {
        "id": "7E9QSqPjBU-Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_parameters(params, freeze_from = 0, freeze_last_until=0,verbose = False):\n",
        "    output = []\n",
        "    \n",
        "    \n",
        "    temp = list(params)\n",
        "    count = 0\n",
        "    for name, param in temp[:freeze_from]: \n",
        "        if verbose:\n",
        "          print('I won\\'t be frozen: {}'.format(name)) \n",
        "        param.requires_grad = True\n",
        "        output.append(param)\n",
        "        count+=len(param)\n",
        "\n",
        "    for name, param in temp[freeze_from:(-1)*freeze_last_until]: \n",
        "        if verbose:\n",
        "          print('I will be frozen: {}'.format(name)) \n",
        "        param.requires_grad = False\n",
        "    \n",
        "    for name, param in temp[(-1)*freeze_last_until:]: \n",
        "        if verbose:\n",
        "          print('I won\\'t be frozen: {}'.format(name)) \n",
        "        param.requires_grad = True\n",
        "        output.append(param)\n",
        "        count+=len(param)\n",
        "\n",
        "    print(f'Number of params = {count}')\n",
        "    return output"
      ],
      "metadata": {
        "id": "VM3LMDHPrF2G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n"
      ],
      "metadata": {
        "id": "QIsouu2vDspW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "TRAINED_MODEL='bert_dreaddit_stress_model_part2.zip'\n",
        "download_from_gdrive('1a4Qym6w7z-tmsjoZ7WJ3-Ft1doZ-gHTW',TRAINED_MODEL)\n",
        "INFERENCE_MODE = os.path.isfile(TRAINED_MODEL)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YLNVczHYYNnu",
        "outputId": "eafac536-68ba-4f22-e73c-9120f04c70d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1a4Qym6w7z-tmsjoZ7WJ3-Ft1doZ-gHTW\n",
            "To: /content/bert_dreaddit_stress_model_part2.zip\n",
            "100%|██████████| 1.14G/1.14G [00:09<00:00, 120MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Inference mode, loading best model...')\n",
        "!unzip -q -n {TRAINED_MODEL}\n",
        "# Create a ClassificationModel\n",
        "bert = ClassificationModel(\n",
        "    \"bert\", \"outputs/best_model\",\n",
        "    tokenizer_type=w2v_tok,\n",
        "    use_cuda=torch.cuda.is_available()\n",
        ")\n",
        "print('Best model loaded.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6byUthkgYRaV",
        "outputId": "1d901cd9-7d08-4fa5-faac-e8f3b4f422aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Inference mode, loading best model...\n",
            "Best model loaded.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "class CustomBERTModel(torch.nn.Module):\n",
        "    def __init__(self,model):\n",
        "          super(CustomBERTModel, self).__init__()          \n",
        "          model.resize_token_embeddings(3000002)\n",
        "          self.model_stress_embeddings = model.base_model.embeddings\n",
        "          self.model_stress_encoder = model.base_model.encoder\n",
        "\n",
        "          # add the probe\n",
        "          self.linear1 = torch.nn.Linear(768*text_length, 768*2)  \n",
        "          self.batchnorm = torch.nn.BatchNorm1d(768*2)\n",
        "          self.linear2 = torch.nn.Linear(768*2, 2)\n",
        "\n",
        "          \n",
        "    def forward(self, input_ids, attention_mask):\n",
        "          embedding_output = self.model_stress_embeddings(input_ids=input_ids)\n",
        "          encoder_output = self.model_stress_encoder(embedding_output).last_hidden_state\n",
        "          logits = self.linear1(encoder_output.view(-1,768*text_length))\n",
        "          logits = self.batchnorm(logits)\n",
        "          logits = self.linear2(logits)\n",
        "          \n",
        "          return logits"
      ],
      "metadata": {
        "id": "S-TlJd8MtnME"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bert_and_probe = CustomBERTModel(bert.model).to(device)\n",
        "optimizer = torch.optim.AdamW(get_parameters(bert_and_probe.named_parameters(),0,6,verbose = True), lr=0.000001)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ie7F78MnfbV3",
        "outputId": "22cf7e53-7451-4465-bf06-fac064f9f31b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "I will be frozen: model_stress_embeddings.word_embeddings.weight\n",
            "I will be frozen: model_stress_embeddings.position_embeddings.weight\n",
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            "I will be frozen: model_stress_encoder.layer.10.attention.output.LayerNorm.weight\n",
            "I will be frozen: model_stress_encoder.layer.10.attention.output.LayerNorm.bias\n",
            "I will be frozen: model_stress_encoder.layer.10.intermediate.dense.weight\n",
            "I will be frozen: model_stress_encoder.layer.10.intermediate.dense.bias\n",
            "I will be frozen: model_stress_encoder.layer.10.output.dense.weight\n",
            "I will be frozen: model_stress_encoder.layer.10.output.dense.bias\n",
            "I will be frozen: model_stress_encoder.layer.10.output.LayerNorm.weight\n",
            "I will be frozen: model_stress_encoder.layer.10.output.LayerNorm.bias\n",
            "I will be frozen: model_stress_encoder.layer.11.attention.self.query.weight\n",
            "I will be frozen: model_stress_encoder.layer.11.attention.self.query.bias\n",
            "I will be frozen: model_stress_encoder.layer.11.attention.self.key.weight\n",
            "I will be frozen: model_stress_encoder.layer.11.attention.self.key.bias\n",
            "I will be frozen: model_stress_encoder.layer.11.attention.self.value.weight\n",
            "I will be frozen: model_stress_encoder.layer.11.attention.self.value.bias\n",
            "I will be frozen: model_stress_encoder.layer.11.attention.output.dense.weight\n",
            "I will be frozen: model_stress_encoder.layer.11.attention.output.dense.bias\n",
            "I will be frozen: model_stress_encoder.layer.11.attention.output.LayerNorm.weight\n",
            "I will be frozen: model_stress_encoder.layer.11.attention.output.LayerNorm.bias\n",
            "I will be frozen: model_stress_encoder.layer.11.intermediate.dense.weight\n",
            "I will be frozen: model_stress_encoder.layer.11.intermediate.dense.bias\n",
            "I will be frozen: model_stress_encoder.layer.11.output.dense.weight\n",
            "I will be frozen: model_stress_encoder.layer.11.output.dense.bias\n",
            "I will be frozen: model_stress_encoder.layer.11.output.LayerNorm.weight\n",
            "I will be frozen: model_stress_encoder.layer.11.output.LayerNorm.bias\n",
            "I won't be frozen: linear1.weight\n",
            "I won't be frozen: linear1.bias\n",
            "I won't be frozen: batchnorm.weight\n",
            "I won't be frozen: batchnorm.bias\n",
            "I won't be frozen: linear2.weight\n",
            "I won't be frozen: linear2.bias\n",
            "Number of params = 6148\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#from torchviz import make_dot\n",
        "\n",
        "#make_dot(yhat, params=dict(list(bert_and_probe.named_parameters()))).render(\"bert_torchviz\", format=\"png\")"
      ],
      "metadata": {
        "id": "XQQdF52O7gxC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# train the probe"
      ],
      "metadata": {
        "id": "75iuiz1_BZdd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "def train_loop_probe(model, n_epochs, train_data, dev_data):\n",
        "  # Loss function\n",
        "  criterion1 = torch.nn.MSELoss() #other options:  HuberLoss  L1Loss\n",
        "  criterion2 = torch.nn.MSELoss()\n",
        "  best_loss = np.inf\n",
        "  patience = 10\n",
        "  triggertimes = 0\n",
        "  hist_loss_train = []\n",
        "  hist_loss_val = []\n",
        "\n",
        "  hist_tp_train = []\n",
        "  hist_tn_train = []\n",
        "  hist_fp_train = []\n",
        "  hist_fn_train = []\n",
        "\n",
        "  hist_tp_val = []\n",
        "  hist_tn_val = []\n",
        "  hist_fp_val = []\n",
        "  hist_fn_val = []\n",
        "  for e in range(1, n_epochs + 1):\n",
        "      #TRAIN\n",
        "      model.train()\n",
        "      train_loss_total = 0\n",
        "      y_hat_train = []\n",
        "      y_train = []\n",
        "      for batch in train_data:\n",
        "          inputs,labels,logits = batch\n",
        "          logits = torch.tensor(logits, dtype = torch.float).to(device)\n",
        "\n",
        "          input_ids = inputs['input_ids']\n",
        "          attention_mask = inputs['attention_mask']\n",
        "          model.zero_grad()\n",
        "\n",
        "          \n",
        "          outputs = model(input_ids=input_ids.to(device),\n",
        "                          attention_mask=attention_mask.to(device))\n",
        "          \n",
        "          y_hat_train.extend(np.argmax(outputs.cpu().detach().numpy(),axis =1 ))\n",
        "          y_train.extend(np.argmax(logits.cpu().detach().numpy(),axis =1 ))\n",
        "\n",
        "          loss = criterion1(outputs.view(-1,2), logits.view(-1,2))\n",
        "          \n",
        "\n",
        "          loss.backward()\n",
        "          train_loss_total+=loss.item()\n",
        "          torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
        "          optimizer.step()\n",
        "          \n",
        "      \n",
        "      # DEV\n",
        "      model.eval()\n",
        "      dev_loss_total = 0\n",
        "      y_hat_val = []\n",
        "      y_val = []\n",
        "      with torch.no_grad():\n",
        "          for batch in dev_data:\n",
        "              inputs,labels,logits = batch\n",
        "              logits = torch.tensor(logits, dtype = torch.float).to(device)\n",
        "\n",
        "              input_ids = inputs['input_ids']\n",
        "              attention_mask = inputs['attention_mask']\n",
        "\n",
        "              outputs = model(input_ids=input_ids.to(device),\n",
        "                          attention_mask=attention_mask.to(device))\n",
        "              \n",
        "              y_hat_val.extend(np.argmax(outputs.cpu().detach().numpy(),axis =1 ))\n",
        "              y_val.extend(np.argmax(logits.cpu().detach().numpy(),axis =1 ))\n",
        "              \n",
        "              loss = criterion2(outputs.view(-1,2), logits.view(-1, 2))\n",
        "\n",
        "              \n",
        "              \n",
        "              dev_loss_total += loss.item()\n",
        "      \n",
        "      \n",
        "      current_train_loss = train_loss_total/len(train_data)\n",
        "      current_val_loss = dev_loss_total/len(dev_data)\n",
        "      hist_loss_train.append(current_train_loss)\n",
        "      hist_loss_val.append(current_val_loss)\n",
        "\n",
        "      \n",
        "      tn, fp, fn, tp = confusion_matrix(y_train, y_hat_train).ravel()\n",
        "      hist_tp_train.append(tp)\n",
        "      hist_tn_train.append(tn)\n",
        "      hist_fp_train.append(fp)\n",
        "      hist_fn_train.append(fn)\n",
        "      print(f'Train : TP = {tp}, TN = {tn}, FP = {fp}, FN = {fn}')\n",
        "\n",
        "\n",
        "      \n",
        "      tn, fp, fn, tp = confusion_matrix(y_val, y_hat_val).ravel()\n",
        "      hist_tp_val.append(tp)\n",
        "      hist_tn_val.append(tn)\n",
        "      hist_fp_val.append(fp)\n",
        "      hist_fn_val.append(fn)\n",
        "      \n",
        "      print(f'Val : TP = {tp}, TN = {tn}, FP = {fp}, FN = {fn}')\n",
        "\n",
        "      print(f\"Epoch {e}, Train loss = {current_train_loss}, Dev loss = {current_val_loss}\")\n",
        "\n",
        "      # choose to use train loss or val loss for earlystopping\n",
        "      patience_loss = train_loss_total\n",
        "      if patience_loss >= best_loss:\n",
        "        print('Triggered')\n",
        "        trigger_times += 1\n",
        "        if trigger_times >= patience:\n",
        "            print('Early stopping!')\n",
        "            model.load_state_dict(best_model_dict)\n",
        "\n",
        "            df = pd.DataFrame()\n",
        "            df['tp_train'] = hist_tp_train\n",
        "            df['tn_train'] = hist_tn_train\n",
        "            df['fp_train'] = hist_fp_train\n",
        "            df['fn_train'] = hist_fn_train\n",
        "            \n",
        "            df['tp_val'] = hist_tp_val\n",
        "            df['tn_val'] = hist_tn_val\n",
        "            df['fp_val'] = hist_fp_val\n",
        "            df['fn_val'] = hist_fn_val\n",
        "\n",
        "            df['train_loss'] = hist_loss_train\n",
        "            df['val_loss'] = hist_loss_val\n",
        "            return df\n",
        "      else:\n",
        "        best_loss = patience_loss\n",
        "        best_model_dict = model.state_dict()\n",
        "        trigger_times = 0\n",
        "\n",
        "        \n",
        "        torch.save({\n",
        "        'model_state_dict': model.state_dict(),\n",
        "        'optimizer_state_dict': optimizer.state_dict(),\n",
        "        'hist_loss_train': hist_loss_train,\n",
        "        'hist_loss_val': hist_loss_val\n",
        "                    }, env+'best_1.pth')\n",
        "      \n",
        "  df = pd.DataFrame()\n",
        "  df['tp_train'] = hist_tp_train\n",
        "  df['tn_train'] = hist_tn_train\n",
        "  df['fp_train'] = hist_fp_train\n",
        "  df['fn_train'] = hist_fn_train\n",
        "  \n",
        "  df['tp_val'] = hist_tp_val\n",
        "  df['tn_val'] = hist_tn_val\n",
        "  df['fp_val'] = hist_fp_val\n",
        "  df['fn_val'] = hist_fn_val\n",
        "\n",
        "  df['train_loss'] = hist_loss_train\n",
        "  df['val_loss'] = hist_loss_val\n",
        "  return df"
      ],
      "metadata": {
        "id": "qGNvAx3Gfmj5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = train_loop_probe(bert_and_probe, n_epochs=1000,\n",
        "                 train_data=train, dev_data=test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tBz3X31G9cCW",
        "outputId": "87649261-eb6c-43d3-996b-9860907e03fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train : TP = 420, TN = 1107, FP = 1006, FN = 305\n",
            "Val : TP = 163, TN = 74, FP = 459, FN = 19\n",
            "Epoch 1, Train loss = 9.133581777636925, Dev loss = 12.520495953767195\n",
            "Train : TP = 482, TN = 1175, FP = 938, FN = 243\n",
            "Val : TP = 173, TN = 39, FP = 494, FN = 9\n",
            "Epoch 2, Train loss = 8.763163936272097, Dev loss = 15.23002288652503\n",
            "Train : TP = 473, TN = 1180, FP = 933, FN = 252\n",
            "Val : TP = 171, TN = 43, FP = 490, FN = 11\n",
            "Epoch 3, Train loss = 8.657809053913931, Dev loss = 15.616560314012611\n",
            "Train : TP = 509, TN = 1208, FP = 905, FN = 216\n",
            "Val : TP = 177, TN = 30, FP = 503, FN = 5\n",
            "Epoch 4, Train loss = 8.483783941590383, Dev loss = 17.734655504641324\n",
            "Train : TP = 537, TN = 1232, FP = 881, FN = 188\n",
            "Val : TP = 136, TN = 181, FP = 352, FN = 46\n",
            "Epoch 5, Train loss = 8.285139083862305, Dev loss = 11.0061674947324\n",
            "Train : TP = 525, TN = 1262, FP = 851, FN = 200\n",
            "Val : TP = 136, TN = 180, FP = 353, FN = 46\n",
            "Epoch 6, Train loss = 8.13501799508427, Dev loss = 10.917963898700217\n",
            "Train : TP = 538, TN = 1237, FP = 876, FN = 187\n",
            "Val : TP = 171, TN = 55, FP = 478, FN = 11\n",
            "Epoch 7, Train loss = 8.07829340924038, Dev loss = 15.855827455935271\n",
            "Train : TP = 547, TN = 1247, FP = 866, FN = 178\n",
            "Val : TP = 144, TN = 143, FP = 390, FN = 38\n",
            "Epoch 8, Train loss = 7.87728586089745, Dev loss = 12.465742442918861\n",
            "Train : TP = 564, TN = 1310, FP = 803, FN = 161\n",
            "Val : TP = 157, TN = 97, FP = 436, FN = 25\n",
            "Epoch 9, Train loss = 7.800293600961064, Dev loss = 13.82469890428626\n",
            "Train : TP = 571, TN = 1299, FP = 814, FN = 154\n",
            "Val : TP = 138, TN = 182, FP = 351, FN = 44\n",
            "Epoch 10, Train loss = 7.734397791744618, Dev loss = 11.26029574352762\n",
            "Train : TP = 592, TN = 1288, FP = 825, FN = 133\n",
            "Val : TP = 122, TN = 258, FP = 275, FN = 60\n",
            "Epoch 11, Train loss = 7.64933833111538, Dev loss = 9.680914277615754\n",
            "Train : TP = 586, TN = 1342, FP = 771, FN = 139\n",
            "Val : TP = 157, TN = 95, FP = 438, FN = 25\n",
            "Epoch 12, Train loss = 7.442661301473553, Dev loss = 13.350360124007516\n",
            "Train : TP = 589, TN = 1304, FP = 809, FN = 136\n",
            "Val : TP = 142, TN = 149, FP = 384, FN = 40\n",
            "Epoch 13, Train loss = 7.565655188614063, Dev loss = 12.324943086375361\n",
            "Triggered\n",
            "Train : TP = 590, TN = 1349, FP = 764, FN = 135\n",
            "Val : TP = 108, TN = 275, FP = 258, FN = 74\n",
            "Epoch 14, Train loss = 7.379528136735551, Dev loss = 9.174972865892494\n",
            "Train : TP = 618, TN = 1363, FP = 750, FN = 107\n",
            "Val : TP = 160, TN = 90, FP = 443, FN = 22\n",
            "Epoch 15, Train loss = 7.178385809566198, Dev loss = 14.853482661039934\n",
            "Train : TP = 604, TN = 1353, FP = 760, FN = 121\n",
            "Val : TP = 141, TN = 167, FP = 366, FN = 41\n",
            "Epoch 16, Train loss = 7.179253208503295, Dev loss = 12.107188100400178\n",
            "Triggered\n",
            "Train : TP = 617, TN = 1365, FP = 748, FN = 108\n",
            "Val : TP = 141, TN = 167, FP = 366, FN = 41\n",
            "Epoch 17, Train loss = 7.091732410902388, Dev loss = 12.258035991502846\n",
            "Train : TP = 613, TN = 1354, FP = 759, FN = 112\n",
            "Val : TP = 138, TN = 186, FP = 347, FN = 44\n",
            "Epoch 18, Train loss = 7.064891032958299, Dev loss = 11.967780776645826\n",
            "Train : TP = 622, TN = 1377, FP = 736, FN = 103\n",
            "Val : TP = 153, TN = 125, FP = 408, FN = 29\n",
            "Epoch 19, Train loss = 6.958768544572123, Dev loss = 13.950903685196586\n",
            "Train : TP = 598, TN = 1368, FP = 745, FN = 127\n",
            "Val : TP = 132, TN = 189, FP = 344, FN = 50\n",
            "Epoch 20, Train loss = 6.99133630281084, Dev loss = 11.681212881337041\n",
            "Triggered\n",
            "Train : TP = 636, TN = 1379, FP = 734, FN = 89\n",
            "Val : TP = 150, TN = 117, FP = 416, FN = 32\n",
            "Epoch 21, Train loss = 6.842331087991093, Dev loss = 14.213003863459049\n",
            "Train : TP = 627, TN = 1436, FP = 677, FN = 98\n",
            "Val : TP = 151, TN = 115, FP = 418, FN = 31\n",
            "Epoch 22, Train loss = 6.7119044721796275, Dev loss = 14.283708447995393\n",
            "Train : TP = 618, TN = 1413, FP = 700, FN = 107\n",
            "Val : TP = 150, TN = 132, FP = 401, FN = 32\n",
            "Epoch 23, Train loss = 6.710709127147546, Dev loss = 13.359688551529594\n",
            "Train : TP = 631, TN = 1420, FP = 693, FN = 94\n",
            "Val : TP = 156, TN = 108, FP = 425, FN = 26\n",
            "Epoch 24, Train loss = 6.590150334861841, Dev loss = 14.777369001637334\n",
            "Train : TP = 628, TN = 1409, FP = 704, FN = 97\n",
            "Val : TP = 151, TN = 123, FP = 410, FN = 31\n",
            "Epoch 25, Train loss = 6.583839245056838, Dev loss = 14.599951661151389\n",
            "Train : TP = 649, TN = 1421, FP = 692, FN = 76\n",
            "Val : TP = 157, TN = 93, FP = 440, FN = 25\n",
            "Epoch 26, Train loss = 6.491947525002983, Dev loss = 16.220160898954973\n",
            "Train : TP = 643, TN = 1433, FP = 680, FN = 82\n",
            "Val : TP = 154, TN = 111, FP = 422, FN = 28\n",
            "Epoch 27, Train loss = 6.560699200362302, Dev loss = 15.694908017697541\n",
            "Triggered\n",
            "Train : TP = 642, TN = 1449, FP = 664, FN = 83\n",
            "Val : TP = 130, TN = 206, FP = 327, FN = 52\n",
            "Epoch 28, Train loss = 6.477075801806503, Dev loss = 11.334931249203889\n",
            "Train : TP = 643, TN = 1462, FP = 651, FN = 82\n",
            "Val : TP = 121, TN = 240, FP = 293, FN = 61\n",
            "Epoch 29, Train loss = 6.31750128778179, Dev loss = 10.402281906293785\n",
            "Train : TP = 654, TN = 1465, FP = 648, FN = 71\n",
            "Val : TP = 133, TN = 205, FP = 328, FN = 49\n",
            "Epoch 30, Train loss = 6.311385015423379, Dev loss = 11.559894499571428\n",
            "Train : TP = 646, TN = 1462, FP = 651, FN = 79\n",
            "Val : TP = 141, TN = 156, FP = 377, FN = 41\n",
            "Epoch 31, Train loss = 6.298790599522966, Dev loss = 13.488096610359523\n",
            "Train : TP = 651, TN = 1427, FP = 686, FN = 74\n",
            "Val : TP = 172, TN = 63, FP = 470, FN = 10\n",
            "Epoch 32, Train loss = 6.310977539319671, Dev loss = 17.767093368198562\n",
            "Triggered\n",
            "Train : TP = 657, TN = 1460, FP = 653, FN = 68\n",
            "Val : TP = 151, TN = 112, FP = 421, FN = 31\n",
            "Epoch 33, Train loss = 6.120558320806268, Dev loss = 14.974734679512355\n",
            "Train : TP = 678, TN = 1480, FP = 633, FN = 47\n",
            "Val : TP = 131, TN = 200, FP = 333, FN = 51\n",
            "Epoch 34, Train loss = 6.01152308335465, Dev loss = 11.765249418175738\n",
            "Train : TP = 657, TN = 1495, FP = 618, FN = 68\n",
            "Val : TP = 132, TN = 209, FP = 324, FN = 50\n",
            "Epoch 35, Train loss = 6.016566598013545, Dev loss = 11.539894643037215\n",
            "Triggered\n",
            "Train : TP = 655, TN = 1485, FP = 628, FN = 70\n",
            "Val : TP = 144, TN = 149, FP = 384, FN = 38\n",
            "Epoch 36, Train loss = 5.936955149254103, Dev loss = 13.19957857546599\n",
            "Train : TP = 659, TN = 1504, FP = 609, FN = 66\n",
            "Val : TP = 144, TN = 146, FP = 387, FN = 38\n",
            "Epoch 37, Train loss = 5.936137402995249, Dev loss = 13.895377076190451\n",
            "Train : TP = 647, TN = 1484, FP = 629, FN = 78\n",
            "Val : TP = 154, TN = 104, FP = 429, FN = 28\n",
            "Epoch 38, Train loss = 5.975319101569358, Dev loss = 15.498881174170453\n",
            "Triggered\n",
            "Train : TP = 661, TN = 1522, FP = 591, FN = 64\n",
            "Val : TP = 143, TN = 142, FP = 391, FN = 39\n",
            "Epoch 39, Train loss = 5.843776788604393, Dev loss = 13.989270500514818\n",
            "Train : TP = 653, TN = 1506, FP = 607, FN = 72\n",
            "Val : TP = 90, TN = 349, FP = 184, FN = 92\n",
            "Epoch 40, Train loss = 5.874824931112568, Dev loss = 8.932672770127006\n",
            "Triggered\n",
            "Train : TP = 662, TN = 1522, FP = 591, FN = 63\n",
            "Val : TP = 136, TN = 181, FP = 352, FN = 46\n",
            "Epoch 41, Train loss = 5.839574963858958, Dev loss = 12.623815951140031\n",
            "Train : TP = 661, TN = 1505, FP = 608, FN = 64\n",
            "Val : TP = 139, TN = 180, FP = 353, FN = 43\n",
            "Epoch 42, Train loss = 5.731138154362025, Dev loss = 12.3917293963225\n",
            "Train : TP = 657, TN = 1515, FP = 598, FN = 68\n",
            "Val : TP = 130, TN = 199, FP = 334, FN = 52\n",
            "Epoch 43, Train loss = 5.783805340863346, Dev loss = 11.857432199561078\n",
            "Triggered\n",
            "Train : TP = 658, TN = 1548, FP = 565, FN = 67\n",
            "Val : TP = 125, TN = 232, FP = 301, FN = 57\n",
            "Epoch 44, Train loss = 5.712820235263096, Dev loss = 10.788920195206352\n",
            "Train : TP = 664, TN = 1527, FP = 586, FN = 61\n",
            "Val : TP = 93, TN = 342, FP = 191, FN = 89\n",
            "Epoch 45, Train loss = 5.587691548165311, Dev loss = 9.255130560501762\n",
            "Train : TP = 676, TN = 1523, FP = 590, FN = 49\n",
            "Val : TP = 134, TN = 193, FP = 340, FN = 48\n",
            "Epoch 46, Train loss = 5.550765016105737, Dev loss = 12.743536368660305\n",
            "Train : TP = 674, TN = 1543, FP = 570, FN = 51\n",
            "Val : TP = 154, TN = 113, FP = 420, FN = 28\n",
            "Epoch 47, Train loss = 5.54208560472124, Dev loss = 16.12099249466606\n",
            "Train : TP = 659, TN = 1528, FP = 585, FN = 66\n",
            "Val : TP = 148, TN = 131, FP = 402, FN = 34\n",
            "Epoch 48, Train loss = 5.698385986049524, Dev loss = 14.805840119071629\n",
            "Triggered\n",
            "Train : TP = 676, TN = 1553, FP = 560, FN = 49\n",
            "Val : TP = 115, TN = 275, FP = 258, FN = 67\n",
            "Epoch 49, Train loss = 5.530428133653791, Dev loss = 10.044912255328635\n",
            "Train : TP = 667, TN = 1565, FP = 548, FN = 58\n",
            "Val : TP = 151, TN = 122, FP = 411, FN = 31\n",
            "Epoch 50, Train loss = 5.485961169339298, Dev loss = 14.890315470488176\n",
            "Train : TP = 674, TN = 1564, FP = 549, FN = 51\n",
            "Val : TP = 129, TN = 214, FP = 319, FN = 53\n",
            "Epoch 51, Train loss = 5.411929044830665, Dev loss = 11.50382311447807\n",
            "Train : TP = 684, TN = 1554, FP = 559, FN = 41\n",
            "Val : TP = 157, TN = 107, FP = 426, FN = 25\n",
            "Epoch 52, Train loss = 5.405357529608051, Dev loss = 16.001031502433445\n",
            "Train : TP = 662, TN = 1554, FP = 559, FN = 63\n",
            "Val : TP = 147, TN = 128, FP = 405, FN = 35\n",
            "Epoch 53, Train loss = 5.493070797973804, Dev loss = 15.169918226159137\n",
            "Triggered\n",
            "Train : TP = 658, TN = 1549, FP = 564, FN = 67\n",
            "Val : TP = 105, TN = 320, FP = 213, FN = 77\n",
            "Epoch 54, Train loss = 5.468305027886723, Dev loss = 9.372226798016092\n",
            "Triggered\n",
            "Train : TP = 669, TN = 1524, FP = 589, FN = 56\n",
            "Val : TP = 135, TN = 189, FP = 344, FN = 47\n",
            "Epoch 55, Train loss = 5.531576231624303, Dev loss = 13.436703847802203\n",
            "Triggered\n",
            "Train : TP = 674, TN = 1546, FP = 567, FN = 51\n",
            "Val : TP = 141, TN = 156, FP = 377, FN = 41\n",
            "Epoch 56, Train loss = 5.459069710099295, Dev loss = 13.853229439776877\n",
            "Triggered\n",
            "Train : TP = 675, TN = 1572, FP = 541, FN = 50\n",
            "Val : TP = 110, TN = 306, FP = 227, FN = 72\n",
            "Epoch 57, Train loss = 5.281807811072703, Dev loss = 9.548859181611434\n",
            "Train : TP = 684, TN = 1577, FP = 536, FN = 41\n",
            "Val : TP = 168, TN = 84, FP = 449, FN = 14\n",
            "Epoch 58, Train loss = 5.239416484082683, Dev loss = 17.809746825176738\n",
            "Train : TP = 671, TN = 1567, FP = 546, FN = 54\n",
            "Val : TP = 129, TN = 209, FP = 324, FN = 53\n",
            "Epoch 59, Train loss = 5.346586771225661, Dev loss = 11.482962525409201\n",
            "Triggered\n",
            "Train : TP = 678, TN = 1572, FP = 541, FN = 47\n",
            "Val : TP = 160, TN = 95, FP = 438, FN = 22\n",
            "Epoch 60, Train loss = 5.180300602752172, Dev loss = 16.79462673353112\n",
            "Train : TP = 674, TN = 1566, FP = 547, FN = 51\n",
            "Val : TP = 105, TN = 313, FP = 220, FN = 77\n",
            "Epoch 61, Train loss = 5.164711470014594, Dev loss = 9.38565326773602\n",
            "Train : TP = 685, TN = 1585, FP = 528, FN = 40\n",
            "Val : TP = 140, TN = 165, FP = 368, FN = 42\n",
            "Epoch 62, Train loss = 5.1907971575019065, Dev loss = 14.328727639239768\n",
            "Triggered\n",
            "Train : TP = 680, TN = 1574, FP = 539, FN = 45\n",
            "Val : TP = 168, TN = 77, FP = 456, FN = 14\n",
            "Epoch 63, Train loss = 5.044138699435116, Dev loss = 18.611044427622918\n",
            "Train : TP = 681, TN = 1576, FP = 537, FN = 44\n",
            "Val : TP = 130, TN = 185, FP = 348, FN = 52\n",
            "Epoch 64, Train loss = 5.17721103550343, Dev loss = 12.740149373593537\n",
            "Triggered\n",
            "Train : TP = 674, TN = 1561, FP = 552, FN = 51\n",
            "Val : TP = 132, TN = 198, FP = 335, FN = 50\n",
            "Epoch 65, Train loss = 5.226539737722847, Dev loss = 12.148414943529213\n",
            "Triggered\n",
            "Train : TP = 679, TN = 1602, FP = 511, FN = 46\n",
            "Val : TP = 158, TN = 107, FP = 426, FN = 24\n",
            "Epoch 66, Train loss = 5.090420235408826, Dev loss = 16.84370414070461\n",
            "Triggered\n",
            "Train : TP = 679, TN = 1604, FP = 509, FN = 46\n",
            "Val : TP = 145, TN = 148, FP = 385, FN = 37\n",
            "Epoch 67, Train loss = 5.069262852829493, Dev loss = 13.71131009640901\n",
            "Triggered\n",
            "Train : TP = 684, TN = 1616, FP = 497, FN = 41\n",
            "Val : TP = 156, TN = 114, FP = 419, FN = 26\n",
            "Epoch 68, Train loss = 5.002237960193934, Dev loss = 16.548118010811184\n",
            "Train : TP = 684, TN = 1599, FP = 514, FN = 41\n",
            "Val : TP = 144, TN = 154, FP = 379, FN = 38\n",
            "Epoch 69, Train loss = 4.987315188632922, Dev loss = 14.34050348530645\n",
            "Train : TP = 681, TN = 1606, FP = 507, FN = 44\n",
            "Val : TP = 163, TN = 102, FP = 431, FN = 19\n",
            "Epoch 70, Train loss = 5.035150340433871, Dev loss = 17.164334089859672\n",
            "Triggered\n",
            "Train : TP = 676, TN = 1608, FP = 505, FN = 49\n",
            "Val : TP = 134, TN = 175, FP = 358, FN = 48\n",
            "Epoch 71, Train loss = 4.962872320346618, Dev loss = 12.907036200813625\n",
            "Train : TP = 674, TN = 1597, FP = 516, FN = 51\n",
            "Val : TP = 157, TN = 111, FP = 422, FN = 25\n",
            "Epoch 72, Train loss = 4.987426339910272, Dev loss = 15.685034503107486\n",
            "Triggered\n",
            "Train : TP = 684, TN = 1636, FP = 477, FN = 41\n",
            "Val : TP = 125, TN = 237, FP = 296, FN = 57\n",
            "Epoch 73, Train loss = 4.852427517430166, Dev loss = 11.265397776728092\n",
            "Train : TP = 672, TN = 1618, FP = 495, FN = 53\n",
            "Val : TP = 145, TN = 156, FP = 377, FN = 37\n",
            "Epoch 74, Train loss = 4.905427587166261, Dev loss = 13.895584728406822\n",
            "Triggered\n",
            "Train : TP = 689, TN = 1610, FP = 503, FN = 36\n",
            "Val : TP = 180, TN = 32, FP = 501, FN = 2\n",
            "Epoch 75, Train loss = 4.86607441741429, Dev loss = 25.488348836484164\n",
            "Triggered\n",
            "Train : TP = 689, TN = 1609, FP = 504, FN = 36\n",
            "Val : TP = 110, TN = 277, FP = 256, FN = 72\n",
            "Epoch 76, Train loss = 4.821651616793, Dev loss = 10.353785017262334\n",
            "Train : TP = 679, TN = 1614, FP = 499, FN = 46\n",
            "Val : TP = 113, TN = 257, FP = 276, FN = 69\n",
            "Epoch 77, Train loss = 4.927951531463794, Dev loss = 10.605978654778522\n",
            "Triggered\n",
            "Train : TP = 685, TN = 1622, FP = 491, FN = 40\n",
            "Val : TP = 137, TN = 184, FP = 349, FN = 45\n",
            "Epoch 78, Train loss = 4.7958713622575395, Dev loss = 13.32648754119873\n",
            "Train : TP = 689, TN = 1618, FP = 495, FN = 36\n",
            "Val : TP = 132, TN = 200, FP = 333, FN = 50\n",
            "Epoch 79, Train loss = 4.778199115496003, Dev loss = 12.490763581317404\n",
            "Train : TP = 685, TN = 1624, FP = 489, FN = 40\n",
            "Val : TP = 152, TN = 122, FP = 411, FN = 30\n",
            "Epoch 80, Train loss = 4.7841796821422795, Dev loss = 15.638225182243016\n",
            "Triggered\n",
            "Train : TP = 684, TN = 1631, FP = 482, FN = 41\n",
            "Val : TP = 157, TN = 106, FP = 427, FN = 25\n",
            "Epoch 81, Train loss = 4.792338604337714, Dev loss = 17.178056136421535\n",
            "Triggered\n",
            "Train : TP = 688, TN = 1649, FP = 464, FN = 37\n",
            "Val : TP = 118, TN = 266, FP = 267, FN = 64\n",
            "Epoch 82, Train loss = 4.686359073338884, Dev loss = 10.39630359152089\n",
            "Train : TP = 689, TN = 1662, FP = 451, FN = 36\n",
            "Val : TP = 143, TN = 152, FP = 381, FN = 39\n",
            "Epoch 83, Train loss = 4.710270986128389, Dev loss = 14.708764615266219\n",
            "Triggered\n",
            "Train : TP = 691, TN = 1620, FP = 493, FN = 34\n",
            "Val : TP = 100, TN = 307, FP = 226, FN = 82\n",
            "Epoch 84, Train loss = 4.738938219091866, Dev loss = 9.642227235047713\n",
            "Triggered\n",
            "Train : TP = 690, TN = 1613, FP = 500, FN = 35\n",
            "Val : TP = 130, TN = 219, FP = 314, FN = 52\n",
            "Epoch 85, Train loss = 4.838826388455509, Dev loss = 12.125266904416291\n",
            "Triggered\n",
            "Train : TP = 687, TN = 1617, FP = 496, FN = 38\n",
            "Val : TP = 93, TN = 340, FP = 193, FN = 89\n",
            "Epoch 86, Train loss = 4.744907052329417, Dev loss = 9.38141273415607\n",
            "Triggered\n",
            "Train : TP = 698, TN = 1659, FP = 454, FN = 27\n",
            "Val : TP = 100, TN = 311, FP = 222, FN = 82\n",
            "Epoch 87, Train loss = 4.692887807160281, Dev loss = 9.811163404713506\n",
            "Triggered\n",
            "Train : TP = 690, TN = 1670, FP = 443, FN = 35\n",
            "Val : TP = 149, TN = 104, FP = 429, FN = 33\n",
            "Epoch 88, Train loss = 4.514244893963418, Dev loss = 17.758559766023055\n",
            "Train : TP = 690, TN = 1655, FP = 458, FN = 35\n",
            "Val : TP = 108, TN = 296, FP = 237, FN = 74\n",
            "Epoch 89, Train loss = 4.554443150423886, Dev loss = 10.096911844999894\n",
            "Triggered\n",
            "Train : TP = 689, TN = 1632, FP = 481, FN = 36\n",
            "Val : TP = 77, TN = 378, FP = 155, FN = 105\n",
            "Epoch 90, Train loss = 4.674858208452718, Dev loss = 9.132899305094844\n",
            "Triggered\n",
            "Train : TP = 687, TN = 1640, FP = 473, FN = 38\n",
            "Val : TP = 121, TN = 252, FP = 281, FN = 61\n",
            "Epoch 91, Train loss = 4.591074530997973, Dev loss = 11.463618568752123\n",
            "Triggered\n",
            "Train : TP = 695, TN = 1617, FP = 496, FN = 30\n",
            "Val : TP = 133, TN = 212, FP = 321, FN = 49\n",
            "Epoch 92, Train loss = 4.580523857909642, Dev loss = 12.357800691024117\n",
            "Triggered\n",
            "Train : TP = 683, TN = 1666, FP = 447, FN = 42\n",
            "Val : TP = 135, TN = 210, FP = 323, FN = 47\n",
            "Epoch 93, Train loss = 4.605647036198819, Dev loss = 12.21232335463814\n",
            "Triggered\n",
            "Train : TP = 686, TN = 1656, FP = 457, FN = 39\n",
            "Val : TP = 103, TN = 313, FP = 220, FN = 79\n",
            "Epoch 94, Train loss = 4.542331272296691, Dev loss = 10.166835017826246\n",
            "Triggered\n",
            "Train : TP = 690, TN = 1652, FP = 461, FN = 35\n",
            "Val : TP = 136, TN = 163, FP = 370, FN = 46\n",
            "Epoch 95, Train loss = 4.5431858917300625, Dev loss = 13.79431790890901\n",
            "Triggered\n",
            "Train : TP = 696, TN = 1639, FP = 474, FN = 29\n",
            "Val : TP = 128, TN = 215, FP = 318, FN = 54\n",
            "Epoch 96, Train loss = 4.573548000850034, Dev loss = 12.389911610147227\n",
            "Triggered\n",
            "Train : TP = 706, TN = 1673, FP = 440, FN = 19\n",
            "Val : TP = 132, TN = 190, FP = 343, FN = 50\n",
            "Epoch 97, Train loss = 4.425057630860404, Dev loss = 13.081101376077402\n",
            "Train : TP = 689, TN = 1682, FP = 431, FN = 36\n",
            "Val : TP = 141, TN = 146, FP = 387, FN = 41\n",
            "Epoch 98, Train loss = 4.459457384066635, Dev loss = 14.35263496896495\n",
            "Triggered\n",
            "Train : TP = 690, TN = 1667, FP = 446, FN = 35\n",
            "Val : TP = 137, TN = 170, FP = 363, FN = 45\n",
            "Epoch 99, Train loss = 4.506863267234202, Dev loss = 14.192347775334897\n",
            "Triggered\n",
            "Train : TP = 691, TN = 1686, FP = 427, FN = 34\n",
            "Val : TP = 98, TN = 327, FP = 206, FN = 84\n",
            "Epoch 100, Train loss = 4.369790358489819, Dev loss = 9.516410412995711\n",
            "Train : TP = 689, TN = 1682, FP = 431, FN = 36\n",
            "Val : TP = 138, TN = 172, FP = 361, FN = 44\n",
            "Epoch 101, Train loss = 4.480932203571448, Dev loss = 14.450530383897865\n",
            "Triggered\n",
            "Train : TP = 688, TN = 1642, FP = 471, FN = 37\n",
            "Val : TP = 139, TN = 186, FP = 347, FN = 43\n",
            "Epoch 102, Train loss = 4.522467074769266, Dev loss = 13.455950902855914\n",
            "Triggered\n",
            "Train : TP = 688, TN = 1642, FP = 471, FN = 37\n",
            "Val : TP = 131, TN = 194, FP = 339, FN = 51\n",
            "Epoch 103, Train loss = 4.460502078024189, Dev loss = 12.43828707155974\n",
            "Triggered\n",
            "Train : TP = 700, TN = 1671, FP = 442, FN = 25\n",
            "Val : TP = 138, TN = 178, FP = 355, FN = 44\n",
            "Epoch 104, Train loss = 4.451751585756795, Dev loss = 13.778712770213252\n",
            "Triggered\n",
            "Train : TP = 702, TN = 1711, FP = 402, FN = 23\n",
            "Val : TP = 147, TN = 167, FP = 366, FN = 35\n",
            "Epoch 105, Train loss = 4.2607268135199385, Dev loss = 13.783204410387123\n",
            "Train : TP = 696, TN = 1678, FP = 435, FN = 29\n",
            "Val : TP = 115, TN = 280, FP = 253, FN = 67\n",
            "Epoch 106, Train loss = 4.4727896626076005, Dev loss = 10.595759184464164\n",
            "Triggered\n",
            "Train : TP = 699, TN = 1690, FP = 423, FN = 26\n",
            "Val : TP = 119, TN = 241, FP = 292, FN = 63\n",
            "Epoch 107, Train loss = 4.310755215334089, Dev loss = 11.262521992559018\n",
            "Triggered\n",
            "Train : TP = 702, TN = 1688, FP = 425, FN = 23\n",
            "Val : TP = 157, TN = 121, FP = 412, FN = 25\n",
            "Epoch 108, Train loss = 4.3783053440994095, Dev loss = 15.83029780180558\n",
            "Triggered\n",
            "Train : TP = 695, TN = 1675, FP = 438, FN = 30\n",
            "Val : TP = 110, TN = 283, FP = 250, FN = 72\n",
            "Epoch 109, Train loss = 4.321282593052039, Dev loss = 10.431444976640785\n",
            "Triggered\n",
            "Train : TP = 696, TN = 1677, FP = 436, FN = 29\n",
            "Val : TP = 90, TN = 355, FP = 178, FN = 92\n",
            "Epoch 110, Train loss = 4.411898816569468, Dev loss = 9.247030714283818\n",
            "Triggered\n",
            "Train : TP = 693, TN = 1724, FP = 389, FN = 32\n",
            "Val : TP = 110, TN = 288, FP = 245, FN = 72\n",
            "Epoch 111, Train loss = 4.222641998462462, Dev loss = 10.075398569521697\n",
            "Train : TP = 694, TN = 1687, FP = 426, FN = 31\n",
            "Val : TP = 110, TN = 303, FP = 230, FN = 72\n",
            "Epoch 112, Train loss = 4.367845031652558, Dev loss = 10.207183506177818\n",
            "Triggered\n",
            "Train : TP = 691, TN = 1684, FP = 429, FN = 34\n",
            "Val : TP = 105, TN = 313, FP = 220, FN = 77\n",
            "Epoch 113, Train loss = 4.271921305174239, Dev loss = 10.005923457767652\n",
            "Triggered\n",
            "Train : TP = 695, TN = 1672, FP = 441, FN = 30\n",
            "Val : TP = 141, TN = 164, FP = 369, FN = 41\n",
            "Epoch 114, Train loss = 4.350558109497756, Dev loss = 13.98124305061672\n",
            "Triggered\n",
            "Train : TP = 695, TN = 1699, FP = 414, FN = 30\n",
            "Val : TP = 82, TN = 371, FP = 162, FN = 100\n",
            "Epoch 115, Train loss = 4.38076175464673, Dev loss = 8.89822935021442\n",
            "Triggered\n",
            "Train : TP = 698, TN = 1680, FP = 433, FN = 27\n",
            "Val : TP = 140, TN = 171, FP = 362, FN = 42\n",
            "Epoch 116, Train loss = 4.266867150081677, Dev loss = 13.666888568712318\n",
            "Triggered\n",
            "Train : TP = 695, TN = 1709, FP = 404, FN = 30\n",
            "Val : TP = 93, TN = 331, FP = 202, FN = 89\n",
            "Epoch 117, Train loss = 4.24464524997754, Dev loss = 9.324330723803977\n",
            "Triggered\n",
            "Train : TP = 699, TN = 1692, FP = 421, FN = 26\n",
            "Val : TP = 132, TN = 211, FP = 322, FN = 50\n",
            "Epoch 118, Train loss = 4.329917950576611, Dev loss = 12.377223678257154\n",
            "Triggered\n",
            "Train : TP = 699, TN = 1684, FP = 429, FN = 26\n",
            "Val : TP = 117, TN = 253, FP = 280, FN = 65\n",
            "Epoch 119, Train loss = 4.2197919400890225, Dev loss = 11.310605836951215\n",
            "Train : TP = 688, TN = 1693, FP = 420, FN = 37\n",
            "Val : TP = 122, TN = 250, FP = 283, FN = 60\n",
            "Epoch 120, Train loss = 4.3010020309619685, Dev loss = 11.226355345352836\n",
            "Triggered\n",
            "Train : TP = 696, TN = 1708, FP = 405, FN = 29\n",
            "Val : TP = 138, TN = 181, FP = 352, FN = 44\n",
            "Epoch 121, Train loss = 4.194776516282157, Dev loss = 13.620787744936736\n",
            "Train : TP = 696, TN = 1694, FP = 419, FN = 29\n",
            "Val : TP = 117, TN = 272, FP = 261, FN = 65\n",
            "Epoch 122, Train loss = 4.294850135117434, Dev loss = 10.837114748747453\n",
            "Triggered\n",
            "Train : TP = 694, TN = 1686, FP = 427, FN = 31\n",
            "Val : TP = 126, TN = 223, FP = 310, FN = 56\n",
            "Epoch 123, Train loss = 4.200412972589557, Dev loss = 12.233489907306174\n",
            "Triggered\n",
            "Train : TP = 683, TN = 1724, FP = 389, FN = 42\n",
            "Val : TP = 141, TN = 175, FP = 358, FN = 41\n",
            "Epoch 124, Train loss = 4.177120267675164, Dev loss = 13.51598598646081\n",
            "Train : TP = 701, TN = 1676, FP = 437, FN = 24\n",
            "Val : TP = 124, TN = 252, FP = 281, FN = 58\n",
            "Epoch 125, Train loss = 4.251419313837973, Dev loss = 11.19642398668372\n",
            "Triggered\n",
            "Train : TP = 687, TN = 1701, FP = 412, FN = 38\n",
            "Val : TP = 111, TN = 305, FP = 228, FN = 71\n",
            "Epoch 126, Train loss = 4.2035039609737614, Dev loss = 9.9368547356647\n",
            "Triggered\n",
            "Train : TP = 689, TN = 1713, FP = 400, FN = 36\n",
            "Val : TP = 120, TN = 243, FP = 290, FN = 62\n",
            "Epoch 127, Train loss = 4.135388873936085, Dev loss = 11.524909185326617\n",
            "Train : TP = 700, TN = 1723, FP = 390, FN = 25\n",
            "Val : TP = 110, TN = 281, FP = 252, FN = 72\n",
            "Epoch 128, Train loss = 4.165629526202598, Dev loss = 10.585401783818783\n",
            "Triggered\n",
            "Train : TP = 692, TN = 1722, FP = 391, FN = 33\n",
            "Val : TP = 90, TN = 343, FP = 190, FN = 92\n",
            "Epoch 129, Train loss = 4.2147010484438265, Dev loss = 9.389093150263248\n",
            "Triggered\n",
            "Train : TP = 702, TN = 1712, FP = 401, FN = 23\n",
            "Val : TP = 157, TN = 102, FP = 431, FN = 25\n",
            "Epoch 130, Train loss = 4.116735169057096, Dev loss = 17.551883117012355\n",
            "Train : TP = 703, TN = 1707, FP = 406, FN = 22\n",
            "Val : TP = 100, TN = 315, FP = 218, FN = 82\n",
            "Epoch 131, Train loss = 4.084535483563884, Dev loss = 9.851187457209049\n",
            "Train : TP = 700, TN = 1700, FP = 413, FN = 25\n",
            "Val : TP = 116, TN = 291, FP = 242, FN = 66\n",
            "Epoch 132, Train loss = 4.209307895617538, Dev loss = 10.64410808812017\n",
            "Triggered\n",
            "Train : TP = 686, TN = 1694, FP = 419, FN = 39\n",
            "Val : TP = 95, TN = 336, FP = 197, FN = 87\n",
            "Epoch 133, Train loss = 4.206622550996502, Dev loss = 9.626516176306684\n",
            "Triggered\n",
            "Train : TP = 702, TN = 1718, FP = 395, FN = 23\n",
            "Val : TP = 122, TN = 252, FP = 281, FN = 60\n",
            "Epoch 134, Train loss = 4.091917962170719, Dev loss = 11.621660688649053\n",
            "Triggered\n",
            "Train : TP = 693, TN = 1693, FP = 420, FN = 32\n",
            "Val : TP = 93, TN = 342, FP = 191, FN = 89\n",
            "Epoch 135, Train loss = 4.223833879727996, Dev loss = 9.562008878459102\n",
            "Triggered\n",
            "Train : TP = 704, TN = 1734, FP = 379, FN = 21\n",
            "Val : TP = 148, TN = 121, FP = 412, FN = 34\n",
            "Epoch 136, Train loss = 4.046790171205328, Dev loss = 16.083253984865934\n",
            "Train : TP = 692, TN = 1705, FP = 408, FN = 33\n",
            "Val : TP = 133, TN = 199, FP = 334, FN = 49\n",
            "Epoch 137, Train loss = 4.163578162032567, Dev loss = 12.722163532091225\n",
            "Triggered\n",
            "Train : TP = 700, TN = 1715, FP = 398, FN = 25\n",
            "Val : TP = 104, TN = 308, FP = 225, FN = 78\n",
            "Epoch 138, Train loss = 4.148033128695541, Dev loss = 10.099828823753025\n",
            "Triggered\n",
            "Train : TP = 693, TN = 1691, FP = 422, FN = 32\n",
            "Val : TP = 121, TN = 251, FP = 282, FN = 61\n",
            "Epoch 139, Train loss = 4.206943789224946, Dev loss = 11.433211160742719\n",
            "Triggered\n",
            "Train : TP = 698, TN = 1730, FP = 383, FN = 27\n",
            "Val : TP = 71, TN = 389, FP = 144, FN = 111\n",
            "Epoch 140, Train loss = 4.125813243094455, Dev loss = 8.680862986523172\n",
            "Triggered\n",
            "Train : TP = 702, TN = 1724, FP = 389, FN = 23\n",
            "Val : TP = 124, TN = 243, FP = 290, FN = 58\n",
            "Epoch 141, Train loss = 3.977013068252735, Dev loss = 11.555149327153744\n",
            "Train : TP = 699, TN = 1736, FP = 377, FN = 26\n",
            "Val : TP = 100, TN = 320, FP = 213, FN = 82\n",
            "Epoch 142, Train loss = 4.038979598645414, Dev loss = 9.717180003290592\n",
            "Triggered\n",
            "Train : TP = 697, TN = 1709, FP = 404, FN = 28\n",
            "Val : TP = 66, TN = 396, FP = 137, FN = 116\n",
            "Epoch 143, Train loss = 4.082275621006998, Dev loss = 8.682813520016877\n",
            "Triggered\n",
            "Train : TP = 692, TN = 1696, FP = 417, FN = 33\n",
            "Val : TP = 112, TN = 299, FP = 234, FN = 70\n",
            "Epoch 144, Train loss = 4.192978491943873, Dev loss = 10.37208368467248\n",
            "Triggered\n",
            "Train : TP = 696, TN = 1700, FP = 413, FN = 29\n",
            "Val : TP = 151, TN = 129, FP = 404, FN = 31\n",
            "Epoch 145, Train loss = 4.046862227193425, Dev loss = 15.534096054408861\n",
            "Triggered\n",
            "Train : TP = 704, TN = 1739, FP = 374, FN = 21\n",
            "Val : TP = 99, TN = 320, FP = 213, FN = 83\n",
            "Epoch 146, Train loss = 3.9540002386221724, Dev loss = 9.614260507666547\n",
            "Train : TP = 700, TN = 1742, FP = 371, FN = 25\n",
            "Val : TP = 136, TN = 187, FP = 346, FN = 46\n",
            "Epoch 147, Train loss = 3.997047728367066, Dev loss = 12.643754005432129\n",
            "Triggered\n",
            "Train : TP = 698, TN = 1734, FP = 379, FN = 27\n",
            "Val : TP = 99, TN = 318, FP = 215, FN = 83\n",
            "Epoch 148, Train loss = 4.0077016380395785, Dev loss = 9.796107769012451\n",
            "Triggered\n",
            "Train : TP = 697, TN = 1737, FP = 376, FN = 28\n",
            "Val : TP = 117, TN = 281, FP = 252, FN = 65\n",
            "Epoch 149, Train loss = 3.970880397250143, Dev loss = 10.552210434623387\n",
            "Triggered\n",
            "Train : TP = 706, TN = 1725, FP = 388, FN = 19\n",
            "Val : TP = 97, TN = 334, FP = 199, FN = 85\n",
            "Epoch 150, Train loss = 4.024231924099869, Dev loss = 9.381071007770041\n",
            "Triggered\n",
            "Train : TP = 696, TN = 1736, FP = 377, FN = 29\n",
            "Val : TP = 89, TN = 352, FP = 181, FN = 93\n",
            "Epoch 151, Train loss = 3.9245335252097484, Dev loss = 9.235459410625955\n",
            "Train : TP = 706, TN = 1721, FP = 392, FN = 19\n",
            "Val : TP = 108, TN = 300, FP = 233, FN = 74\n",
            "Epoch 152, Train loss = 3.980136190907339, Dev loss = 9.889452395231828\n",
            "Triggered\n",
            "Train : TP = 715, TN = 1745, FP = 368, FN = 10\n",
            "Val : TP = 135, TN = 207, FP = 326, FN = 47\n",
            "Epoch 153, Train loss = 3.8446794815277787, Dev loss = 12.509813681892727\n",
            "Train : TP = 702, TN = 1729, FP = 384, FN = 23\n",
            "Val : TP = 118, TN = 269, FP = 264, FN = 64\n",
            "Epoch 154, Train loss = 3.9325786440559987, Dev loss = 10.610190101291822\n",
            "Triggered\n",
            "Train : TP = 694, TN = 1754, FP = 359, FN = 31\n",
            "Val : TP = 127, TN = 253, FP = 280, FN = 55\n",
            "Epoch 155, Train loss = 3.8559306672449862, Dev loss = 11.04794162252675\n",
            "Triggered\n",
            "Train : TP = 702, TN = 1734, FP = 379, FN = 23\n",
            "Val : TP = 123, TN = 255, FP = 278, FN = 59\n",
            "Epoch 156, Train loss = 3.9784246482206194, Dev loss = 10.910051719002102\n",
            "Triggered\n",
            "Train : TP = 698, TN = 1744, FP = 369, FN = 27\n",
            "Val : TP = 77, TN = 381, FP = 152, FN = 105\n",
            "Epoch 157, Train loss = 3.997594246703587, Dev loss = 8.810816806295644\n",
            "Triggered\n",
            "Train : TP = 699, TN = 1763, FP = 350, FN = 26\n",
            "Val : TP = 114, TN = 267, FP = 266, FN = 68\n",
            "Epoch 158, Train loss = 3.8560358754704507, Dev loss = 10.63103487180627\n",
            "Triggered\n",
            "Train : TP = 693, TN = 1738, FP = 375, FN = 32\n",
            "Val : TP = 132, TN = 202, FP = 331, FN = 50\n",
            "Epoch 159, Train loss = 3.9407063012712458, Dev loss = 12.570335015006687\n",
            "Triggered\n",
            "Train : TP = 697, TN = 1733, FP = 380, FN = 28\n",
            "Val : TP = 94, TN = 346, FP = 187, FN = 88\n",
            "Epoch 160, Train loss = 3.963449649596482, Dev loss = 9.226977742236594\n",
            "Triggered\n",
            "Train : TP = 702, TN = 1759, FP = 354, FN = 23\n",
            "Val : TP = 135, TN = 227, FP = 306, FN = 47\n",
            "Epoch 161, Train loss = 3.825698300693812, Dev loss = 11.809720536936885\n",
            "Train : TP = 688, TN = 1726, FP = 387, FN = 37\n",
            "Val : TP = 87, TN = 334, FP = 199, FN = 95\n",
            "Epoch 162, Train loss = 4.035250411944443, Dev loss = 9.583745085674783\n",
            "Triggered\n",
            "Train : TP = 703, TN = 1751, FP = 362, FN = 22\n",
            "Val : TP = 85, TN = 351, FP = 182, FN = 97\n",
            "Epoch 163, Train loss = 3.902861090188616, Dev loss = 9.383128041806428\n",
            "Triggered\n",
            "Train : TP = 704, TN = 1745, FP = 368, FN = 21\n",
            "Val : TP = 98, TN = 320, FP = 213, FN = 84\n",
            "Epoch 164, Train loss = 3.823871272333552, Dev loss = 9.624552788941756\n",
            "Train : TP = 709, TN = 1759, FP = 354, FN = 16\n",
            "Val : TP = 99, TN = 324, FP = 209, FN = 83\n",
            "Epoch 165, Train loss = 3.769456153505304, Dev loss = 9.657653041507887\n",
            "Train : TP = 704, TN = 1766, FP = 347, FN = 21\n",
            "Val : TP = 97, TN = 331, FP = 202, FN = 85\n",
            "Epoch 166, Train loss = 3.8550351603647295, Dev loss = 9.52125014429507\n",
            "Triggered\n",
            "Train : TP = 696, TN = 1763, FP = 350, FN = 29\n",
            "Val : TP = 88, TN = 350, FP = 183, FN = 94\n",
            "Epoch 167, Train loss = 3.819054918342762, Dev loss = 9.250263504360033\n",
            "Triggered\n",
            "Train : TP = 704, TN = 1752, FP = 361, FN = 21\n",
            "Val : TP = 126, TN = 260, FP = 273, FN = 56\n",
            "Epoch 168, Train loss = 3.7792633624559038, Dev loss = 11.027229640794838\n",
            "Triggered\n",
            "Train : TP = 708, TN = 1736, FP = 377, FN = 17\n",
            "Val : TP = 109, TN = 295, FP = 238, FN = 73\n",
            "Epoch 169, Train loss = 3.8576799202501104, Dev loss = 10.1360211994337\n",
            "Triggered\n",
            "Train : TP = 701, TN = 1734, FP = 379, FN = 24\n",
            "Val : TP = 143, TN = 160, FP = 373, FN = 39\n",
            "Epoch 170, Train loss = 3.883981088574013, Dev loss = 14.007499238719111\n",
            "Triggered\n",
            "Train : TP = 700, TN = 1768, FP = 345, FN = 25\n",
            "Val : TP = 121, TN = 250, FP = 283, FN = 61\n",
            "Epoch 171, Train loss = 3.7451183501254306, Dev loss = 11.475262641906738\n",
            "Train : TP = 702, TN = 1749, FP = 364, FN = 23\n",
            "Val : TP = 94, TN = 322, FP = 211, FN = 88\n",
            "Epoch 172, Train loss = 3.8980720605743064, Dev loss = 9.548416635264521\n",
            "Triggered\n",
            "Train : TP = 690, TN = 1762, FP = 351, FN = 35\n",
            "Val : TP = 94, TN = 323, FP = 210, FN = 88\n",
            "Epoch 173, Train loss = 3.8742690019393233, Dev loss = 9.474727547687033\n",
            "Triggered\n",
            "Train : TP = 697, TN = 1768, FP = 345, FN = 28\n",
            "Val : TP = 52, TN = 438, FP = 95, FN = 130\n",
            "Epoch 174, Train loss = 3.8188877092318587, Dev loss = 8.594348244045092\n",
            "Triggered\n",
            "Train : TP = 701, TN = 1762, FP = 351, FN = 24\n",
            "Val : TP = 95, TN = 335, FP = 198, FN = 87\n",
            "Epoch 175, Train loss = 3.919552853937899, Dev loss = 9.470150657322096\n",
            "Triggered\n",
            "Train : TP = 705, TN = 1769, FP = 344, FN = 20\n",
            "Val : TP = 131, TN = 195, FP = 338, FN = 51\n",
            "Epoch 176, Train loss = 3.748330295755622, Dev loss = 12.857483200404955\n",
            "Triggered\n",
            "Train : TP = 690, TN = 1795, FP = 318, FN = 35\n",
            "Val : TP = 100, TN = 315, FP = 218, FN = 82\n",
            "Epoch 177, Train loss = 3.7296487374252147, Dev loss = 9.826731142790422\n",
            "Train : TP = 704, TN = 1750, FP = 363, FN = 21\n",
            "Val : TP = 129, TN = 209, FP = 324, FN = 53\n",
            "Epoch 178, Train loss = 3.8017817963375133, Dev loss = 12.55832468945047\n",
            "Triggered\n",
            "Train : TP = 693, TN = 1785, FP = 328, FN = 32\n",
            "Val : TP = 127, TN = 217, FP = 316, FN = 55\n",
            "Epoch 179, Train loss = 3.713247372862998, Dev loss = 12.186341907667076\n",
            "Train : TP = 704, TN = 1742, FP = 371, FN = 21\n",
            "Val : TP = 98, TN = 321, FP = 212, FN = 84\n",
            "Epoch 180, Train loss = 3.816547758123848, Dev loss = 10.065795939901601\n",
            "Triggered\n",
            "Train : TP = 699, TN = 1757, FP = 356, FN = 26\n",
            "Val : TP = 116, TN = 266, FP = 267, FN = 66\n",
            "Epoch 181, Train loss = 3.791333069962062, Dev loss = 10.78862132196841\n",
            "Triggered\n",
            "Train : TP = 699, TN = 1773, FP = 340, FN = 26\n",
            "Val : TP = 143, TN = 168, FP = 365, FN = 39\n",
            "Epoch 182, Train loss = 3.7483740415465965, Dev loss = 14.353169026582137\n",
            "Triggered\n",
            "Train : TP = 703, TN = 1767, FP = 346, FN = 22\n",
            "Val : TP = 145, TN = 140, FP = 393, FN = 37\n",
            "Epoch 183, Train loss = 3.751714813575316, Dev loss = 14.801381277001422\n",
            "Triggered\n",
            "Train : TP = 697, TN = 1774, FP = 339, FN = 28\n",
            "Val : TP = 140, TN = 165, FP = 368, FN = 42\n",
            "Epoch 184, Train loss = 3.7500822758406738, Dev loss = 14.015313853388248\n",
            "Triggered\n",
            "Train : TP = 703, TN = 1759, FP = 354, FN = 22\n",
            "Val : TP = 129, TN = 210, FP = 323, FN = 53\n",
            "Epoch 185, Train loss = 3.740995010633147, Dev loss = 12.706960263459578\n",
            "Triggered\n",
            "Train : TP = 702, TN = 1758, FP = 355, FN = 23\n",
            "Val : TP = 147, TN = 159, FP = 374, FN = 35\n",
            "Epoch 186, Train loss = 3.7360439447874434, Dev loss = 14.69784919075344\n",
            "Triggered\n",
            "Train : TP = 699, TN = 1760, FP = 353, FN = 26\n",
            "Val : TP = 111, TN = 288, FP = 245, FN = 71\n",
            "Epoch 187, Train loss = 3.7802952327085344, Dev loss = 10.295982236447541\n",
            "Triggered\n",
            "Train : TP = 701, TN = 1777, FP = 336, FN = 24\n",
            "Val : TP = 111, TN = 277, FP = 256, FN = 71\n",
            "Epoch 188, Train loss = 3.723429986600126, Dev loss = 10.38423024053159\n",
            "Triggered\n",
            "Train : TP = 694, TN = 1788, FP = 325, FN = 31\n",
            "Val : TP = 106, TN = 308, FP = 225, FN = 76\n",
            "Epoch 189, Train loss = 3.753626587685574, Dev loss = 9.838729816934336\n",
            "Triggered\n",
            "Early stopping!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(len(df)),df['train_loss'],df['val_loss'])\n",
        "plt.legend(['train','validation'])\n",
        "plt.title('loss')\n",
        "plt.show()\n",
        "#plt.yscale('log')"
      ],
      "metadata": {
        "id": "ujSQSLH5B-ZS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5bc9c99b-8bc6-41a3-b82b-65b575e441f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(len(df)),df['train_loss'])\n",
        "plt.legend(['train'])\n",
        "plt.title('loss')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "88EJTg0GjWFZ",
        "outputId": "e756cc6b-fad0-479f-d782-a785a11553bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "recall_train = df['tp_train']/(df['tp_train'] + df['fn_train'])\n",
        "precision_train = df['tp_train']/(df['tp_train'] + df['fp_train'])\n",
        "\n",
        "\n",
        "recall_val = df['tp_val']/(df['tp_val'] + df['fn_val'])\n",
        "precision_val = df['tp_val']/(df['tp_val'] + df['fp_val'])"
      ],
      "metadata": {
        "id": "l2lY4zfPkDSV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(len(df)),recall_train,recall_val)\n",
        "plt.legend(['train','validation'])\n",
        "plt.title('recall')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "nzI7lkOpWYtx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "2cacff4a-d311-40ea-f98f-93ff7ea5cee4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(len(df)),precision_train,precision_val)\n",
        "plt.legend(['train','validation'])\n",
        "plt.title('precision')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "XKYi7UYwhK3v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "9a6b509a-35b6-49f6-9b51-11f9496a488e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save({\n",
        "        'df': df\n",
        "                    }, env+'results_df.pth')"
      ],
      "metadata": {
        "id": "ZEPcPjyo1rcv"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}