04_BERT.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "Collapsed": "false",
        "id": "EGpVHzuQsYoL"
      },
      "source": [
        "## 0. Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G8IYpSiMsiqb",
        "outputId": "49dd67f0-bc4c-4b86-90d6-dda5b8263e25"
      },
      "source": [
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "if gpu_info.find('failed') >= 0:\n",
        "  print('Select the Runtime > \"Change runtime type\" menu to enable a GPU accelerator, ')\n",
        "  print('and then re-execute this cell.')\n",
        "else:\n",
        "  print(gpu_info)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thu Feb 22 00:01:53 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  NVIDIA A100-SXM4-40GB          Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   30C    P0              47W / 350W |      2MiB / 40960MiB |      0%      Default |\n",
            "|                                         |                      |             Disabled |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Vj-3pXFsifl",
        "outputId": "4265762f-7299-48d3-a520-5a00b4124082"
      },
      "source": [
        "from psutil import virtual_memory\n",
        "ram_gb = virtual_memory().total / 1e9\n",
        "print('Your runtime has {:.1f} gigabytes of available RAM\\n'.format(ram_gb))\n",
        "\n",
        "if ram_gb < 20:\n",
        "  print('To enable a high-RAM runtime, select the Runtime > \"Change runtime type\"')\n",
        "  print('menu, and then select High-RAM in the Runtime shape dropdown. Then, ')\n",
        "  print('re-execute this cell.')\n",
        "else:\n",
        "  print('You are using a high-RAM runtime!')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Your runtime has 89.6 gigabytes of available RAM\n",
            "\n",
            "You are using a high-RAM runtime!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fC5AG-pesiVy",
        "outputId": "e1e571d4-99e1-464d-f53f-9995573ef6ba"
      },
      "source": [
        "import tensorflow as tf\n",
        "import torch\n",
        "\n",
        "# If there's a GPU available...\n",
        "if torch.cuda.is_available():\n",
        "\n",
        "    # Tell PyTorch to use the GPU.\n",
        "    device = torch.device(\"cuda\")\n",
        "\n",
        "    print('There are %d GPU(s) available.' % torch.cuda.device_count())\n",
        "\n",
        "    print('We will use the GPU:', torch.cuda.get_device_name(0))\n",
        "\n",
        "# If not...\n",
        "else:\n",
        "    print('No GPU available, using the CPU instead.')\n",
        "    device = torch.device(\"cpu\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 1 GPU(s) available.\n",
            "We will use the GPU: NVIDIA A100-SXM4-40GB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MeJNGjMEszTw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ee8d2ef-54e2-4e7f-8572-b0698b16ca53"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "R2hhY37ZsYoM"
      },
      "source": [
        "import torch\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tqdm.notebook import tqdm\n",
        "from functools import reduce\n",
        "from datetime import datetime\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import f1_score, confusion_matrix"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 01. Data"
      ],
      "metadata": {
        "id": "s-gngUoqdZNE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"../tweets.csv\")\n",
        "\n",
        "df = df.drop_duplicates(subset=[\"text\"])\n",
        "\n",
        "df = df.sample(frac=1)\n",
        "\n",
        "df = df.reset_index(drop=True)\n",
        "\n",
        "# replace all words starting with \"@\" in the 'text' column with \"[NAME]\"\n",
        "df['text'] = df['text'].str.replace(r'@\\w+', '[NAME]', regex=True)\n",
        "\n",
        "df['text'] = df['text'].str.replace(r'\\s*http://\\S+(\\s+|$)', '', regex=True).str.strip()\n",
        "\n",
        "# replace all phone numbers in the 'text' column with \"[PHONE]\"\n",
        "df['text'] = df['text'].str.replace(r'(\\d{3})[-.\\s]?(\\d{3})[-.\\s]?(\\d{4})', '[PHONE]', regex=True)\n",
        "\n",
        "# get tweet text length\n",
        "df[\"text_length\"] = df.text.str.len()\n",
        "\n",
        "# remove meaningless tweet\n",
        "df = df[df[\"text_length\"]>15]\n",
        "\n",
        "# drop text_length column\n",
        "df = df.drop(\"text_length\", axis=1)\n",
        "\n",
        "df"
      ],
      "metadata": {
        "id": "rEHNiW0bUs48"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "sns.set_style('ticks')\n",
        "sns.set(rc={'figure.figsize':(6,3)})\n",
        "sns.set(font_scale=1)\n",
        "\n",
        "g = sns.histplot(data=df, y=\"category\", shrink=0.7)\n",
        "g.set_xlabel(\"Count\")\n",
        "g.set_ylabel(\"Category\")"
      ],
      "metadata": {
        "id": "crRsS6NjRKPB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_test = df.sample(frac = 0.1)\n",
        "df_train_val = df.drop(df_test.index)\n",
        "\n",
        "# rename the dataframes\n",
        "\n",
        "df = df_train_val\n",
        "df_prediction = df_test"
      ],
      "metadata": {
        "id": "t6k3ujIZRL1X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# If you want to assign numerical values automatically\n",
        "\n",
        "df = df[~df.category.str.contains('\\|')]\n",
        "df = df[df.category != 'nocode']\n",
        "possible_labels = df.category.unique()\n",
        "\n",
        "label_dict = {}\n",
        "for index, possible_label in enumerate(possible_labels):\n",
        "    label_dict[possible_label] = index\n",
        "\n",
        "df['label'] = df.category.replace(label_dict)\n",
        "\n",
        "df.category.value_counts()"
      ],
      "metadata": {
        "id": "VhjqjxoWRORF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.label.value_counts()"
      ],
      "metadata": {
        "id": "ypEvDrH2RQWp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "Collapsed": "false",
        "id": "ebz_aM4VsYoS"
      },
      "source": [
        "### Training/Validation Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "LRPdp3u4sYoT"
      },
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split(df.index.values,\n",
        "                                                  df.label.values,\n",
        "                                                  test_size =0.1125,\n",
        "                                                  random_state = 425, #864 #768 425\n",
        "                                                  stratify = df.label.values)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['data_type'] = ['not_set'] * df.shape[0]\n",
        "df.loc[X_train, 'data_type'] = 'train'\n",
        "df.loc[X_val, 'data_type'] = 'val'\n",
        "df.groupby(['category', 'label', 'data_type']).count()"
      ],
      "metadata": {
        "id": "k1KQHzxIRTbm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "Collapsed": "false",
        "id": "cv-vD9w-sYoV"
      },
      "source": [
        "## 02. Loading Tokenizer and Encoding our Data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers"
      ],
      "metadata": {
        "id": "6_LfNvBvxcOr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "x3d16WVYsYoV"
      },
      "source": [
        "from torch.utils.data import TensorDataset\n",
        "from transformers import RobertaTokenizer, RobertaConfig, RobertaForSequenceClassification, AdamW\n",
        "from transformers import AutoTokenizer, AutoModelForMaskedLM"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "9vDHuaTLsYoW"
      },
      "source": [
        "print('Loading RoBERTa tokenizer...')\n",
        "tokenizer = RobertaTokenizer.from_pretrained('roberta-base', do_lower_case=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "onP7hLjVsYoW"
      },
      "source": [
        "# Text strings returned to a dictionary with the encoded and processed inputs in the form of PyTorch tensors.\n",
        "\n",
        "encoded_data_train = tokenizer.batch_encode_plus(\n",
        "    df[df.data_type=='train'].text.values,\n",
        "    add_special_tokens=True,\n",
        "    return_attention_mask=True,\n",
        "    pad_to_max_length=True,\n",
        "    max_length=256, #Inputs that are longer than this are truncated and inputs that are shorter are padded.\n",
        "    return_tensors='pt' #pytorch tensors\n",
        ")\n",
        "\n",
        "encoded_data_val = tokenizer.batch_encode_plus(\n",
        "    df[df.data_type=='val'].text.values,\n",
        "    add_special_tokens=True,\n",
        "    return_attention_mask=True,\n",
        "    pad_to_max_length=True,\n",
        "    max_length=256,\n",
        "    return_tensors='pt'\n",
        ")\n",
        "\n",
        "input_ids_train = encoded_data_train['input_ids']\n",
        "attention_masks_train = encoded_data_train['attention_mask']\n",
        "labels_train = torch.tensor(df[df.data_type=='train'].label.values)\n",
        "\n",
        "input_ids_val = encoded_data_val['input_ids']\n",
        "attention_masks_val = encoded_data_val['attention_mask']\n",
        "labels_val = torch.tensor(df[df.data_type=='val'].label.values)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "aDVr-kGVsYoX"
      },
      "source": [
        "# Creating PyTorch TensorDataset objects for the training and validation data.\n",
        "\n",
        "dataset_train = TensorDataset(input_ids_train, attention_masks_train, labels_train)\n",
        "dataset_val = TensorDataset(input_ids_val, attention_masks_val, labels_val)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "Collapsed": "false",
        "id": "DXd65Rw5sYoY"
      },
      "source": [
        "### Setting up the RoBERTa Pretrained Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "lFWOPnLNsYoZ"
      },
      "source": [
        "model = RobertaForSequenceClassification.from_pretrained(\"roberta-base\",\n",
        "                                                      num_labels=len(label_dict),\n",
        "                                                      output_attentions=False,\n",
        "                                                      output_hidden_states=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "Collapsed": "false",
        "id": "0qz6gHKtsYoa"
      },
      "source": [
        "### Creating Data Loaders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "bTnyum1YsYoa"
      },
      "source": [
        "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "fhs4BA3ZsYoa"
      },
      "source": [
        "batch_size = 64# parameter1\n",
        "\n",
        "dataloader_train = DataLoader(dataset_train,\n",
        "                              sampler=RandomSampler(dataset_train),\n",
        "                              batch_size=batch_size)\n",
        "\n",
        "dataloader_validation = DataLoader(dataset_val,\n",
        "                                   sampler=SequentialSampler(dataset_val),\n",
        "                                   batch_size=batch_size)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "Collapsed": "false",
        "id": "6es257ZNsYoa"
      },
      "source": [
        "### Setting Up Optimiser and Scheduler\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "zaykl9x0sYob"
      },
      "source": [
        "from transformers import AdamW, get_linear_schedule_with_warmup"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "HbKUksissYob"
      },
      "source": [
        "lr = 7e-06\n",
        "eps = 1e-08\n",
        "optimizer = AdamW(model.parameters(),\n",
        "                  lr = lr,\n",
        "                  eps = eps)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "2dPjZC-PsYob"
      },
      "source": [
        "epochs = 16\n",
        "\n",
        "scheduler = get_linear_schedule_with_warmup(optimizer,\n",
        "                                            num_warmup_steps=0,\n",
        "                                            num_training_steps=len(dataloader_train)*epochs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "Collapsed": "false",
        "id": "95Qky2WBsYoc"
      },
      "source": [
        "## 03. Performance metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oeZ3maKZPLr0"
      },
      "source": [
        "Accuracy metric approach originally used in accuracy function in [this tutorial](https://mccormickml.com/2019/07/22/BERT-fine-tuning/#41-bertforsequenceclassification).\n",
        "\n",
        "[Argmax function](https://deeplizard.com/learn/video/K3lX3Cltt4c)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score\n",
        "\n",
        "def compute_metrics(preds, labels):\n",
        "    preds_flat = np.argmax(preds, axis=1)\n",
        "    labels_flat = labels.flatten()\n",
        "\n",
        "    label_dict_inverse = {v: k for k, v in label_dict.items()}\n",
        "\n",
        "    # Compute overall accuracy\n",
        "    overall_accuracy = (preds_flat == labels_flat).mean()\n",
        "\n",
        "    # Compute per-class accuracy\n",
        "    per_class_accuracy = {}\n",
        "    for label in np.unique(labels_flat):\n",
        "        y_preds = preds_flat[labels_flat==label]\n",
        "        y_true = labels_flat[labels_flat==label]\n",
        "        per_class_accuracy[label_dict_inverse[label]] = (y_preds == y_true).mean()\n",
        "\n",
        "    # Compute weighted F1 score\n",
        "    weighted_f1_score = f1_score(labels_flat, preds_flat, average='weighted')\n",
        "\n",
        "    return overall_accuracy, per_class_accuracy, weighted_f1_score"
      ],
      "metadata": {
        "id": "4nFh7TfADGjt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "Collapsed": "false",
        "id": "Jesr2n6zsYod"
      },
      "source": [
        "## 04. Creating a training loop"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "Collapsed": "false",
        "id": "BD9heEXXsYoe"
      },
      "source": [
        "Approach adapted from an older version of HuggingFace's `run_glue.py` script. Accessible [here](https://github.com/huggingface/transformers/blob/5bfcd0485ece086ebcbed2d008813037968a9e58/examples/run_glue.py#L128)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "nhv1asIPsYoe"
      },
      "source": [
        "import random\n",
        "\n",
        "seed_val = 4\n",
        "random.seed(seed_val)\n",
        "np.random.seed(seed_val)\n",
        "torch.manual_seed(seed_val)\n",
        "torch.cuda.manual_seed_all(seed_val)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "l137s9YRsYoe"
      },
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model.to(device)\n",
        "\n",
        "print(device)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "6BVxOKcDsYof"
      },
      "source": [
        "def evaluate(dataloader_val):\n",
        "\n",
        "    model.eval()\n",
        "\n",
        "    loss_val_total = 0\n",
        "    predictions, true_vals = [], []\n",
        "\n",
        "    for batch in dataloader_val:\n",
        "\n",
        "        batch = tuple(b.to(device) for b in batch)\n",
        "\n",
        "        inputs = {'input_ids':      batch[0],\n",
        "                  'attention_mask': batch[1],\n",
        "                  'labels':         batch[2],\n",
        "                 }\n",
        "\n",
        "        with torch.no_grad():\n",
        "            outputs = model(**inputs)\n",
        "\n",
        "        loss = outputs[0]\n",
        "        logits = outputs[1]\n",
        "        loss_val_total += loss.item()\n",
        "\n",
        "        logits = logits.detach().cpu().numpy()\n",
        "        label_ids = inputs['labels'].cpu().numpy()\n",
        "        predictions.append(logits)\n",
        "        true_vals.append(label_ids)\n",
        "\n",
        "    loss_val_avg = loss_val_total/len(dataloader_val)\n",
        "\n",
        "    predictions = np.concatenate(predictions, axis=0)\n",
        "    true_vals = np.concatenate(true_vals, axis=0)\n",
        "\n",
        "    return loss_val_avg, predictions, true_vals"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epoch_list = []\n",
        "f1_list = []\n",
        "acc_list = []\n",
        "train_loss_list = []\n",
        "val_loss_list = []\n",
        "\n",
        "for epoch in tqdm(range(1, epochs+1)):\n",
        "\n",
        "    model.train()\n",
        "\n",
        "    loss_train_total = 0\n",
        "\n",
        "    progress_bar = tqdm(dataloader_train, desc='Epoch {:1d}'.format(epoch), leave=False, disable=False)\n",
        "    for batch in progress_bar:\n",
        "\n",
        "        model.zero_grad()\n",
        "\n",
        "        batch = tuple(b.to(device) for b in batch)\n",
        "\n",
        "        inputs = {'input_ids':      batch[0],\n",
        "                  'attention_mask': batch[1],\n",
        "                  'labels':         batch[2],\n",
        "                 }\n",
        "\n",
        "        outputs = model(**inputs)\n",
        "\n",
        "        loss = outputs[0]\n",
        "        loss_train_total += loss.item()\n",
        "        loss.backward()\n",
        "\n",
        "        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
        "\n",
        "        optimizer.step()\n",
        "        scheduler.step()\n",
        "\n",
        "        progress_bar.set_postfix({'training_loss': '{:.3f}'.format(loss.item()/len(batch))})\n",
        "\n",
        "\n",
        "    torch.save(model.state_dict(), f'finetuned_RoBERTa_epoch_{epoch}.model')\n",
        "\n",
        "    tqdm.write(f'\\nEpoch {epoch}')\n",
        "\n",
        "    loss_train_avg = loss_train_total/len(dataloader_train)\n",
        "    tqdm.write(f'Training loss: {loss_train_avg}')\n",
        "\n",
        "    val_loss, predictions, true_vals = evaluate(dataloader_validation)\n",
        "    overall_accuracy, per_class_accuracy, weighted_f1_score = compute_metrics(predictions, true_vals)\n",
        "\n",
        "    tqdm.write(f'Validation loss: {val_loss}')\n",
        "    tqdm.write(f'F1 Score (Weighted): {weighted_f1_score}')\n",
        "    tqdm.write(f'Accuracy: {overall_accuracy}')\n",
        "\n",
        "    epoch_list.append(epoch)\n",
        "    f1_list.append(weighted_f1_score)\n",
        "    acc_list.append(overall_accuracy)\n",
        "    train_loss_list.append(loss_train_avg)\n",
        "    val_loss_list.append(val_loss)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        "outputId": "acdb7511-6c98-463c-dcc8-cf21390d844d"
      },
      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "  0%|          | 0/16 [00:00<?, ?it/s]"
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        {
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          "data": {
            "text/plain": [
              "Epoch 1:   0%|          | 0/156 [00:00<?, ?it/s]"
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          "text": [
            "\n",
            "Epoch 1\n",
            "Training loss: 0.6979742772304095\n",
            "Validation loss: 0.4647481471300125\n",
            "F1 Score (Weighted): 0.8254454028698659\n",
            "Accuracy: 0.8310864393338621\n"
          ]
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        {
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          "data": {
            "text/plain": [
              "Epoch 2:   0%|          | 0/156 [00:00<?, ?it/s]"
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          "text": [
            "\n",
            "Epoch 2\n",
            "Training loss: 0.4132355805964042\n",
            "Validation loss: 0.422037111967802\n",
            "F1 Score (Weighted): 0.8455788450897703\n",
            "Accuracy: 0.8445678033306899\n"
          ]
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        {
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          "data": {
            "text/plain": [
              "Epoch 3:   0%|          | 0/156 [00:00<?, ?it/s]"
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          "text": [
            "\n",
            "Epoch 3\n",
            "Training loss: 0.3419231155361885\n",
            "Validation loss: 0.44072495251893995\n",
            "F1 Score (Weighted): 0.8375544814476049\n",
            "Accuracy: 0.8461538461538461\n"
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        {
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          "data": {
            "text/plain": [
              "Epoch 4:   0%|          | 0/156 [00:00<?, ?it/s]"
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          "text": [
            "\n",
            "Epoch 4\n",
            "Training loss: 0.2900476536880701\n",
            "Validation loss: 0.39379721507430077\n",
            "F1 Score (Weighted): 0.8670599543687678\n",
            "Accuracy: 0.8683584456780333\n"
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        },
        {
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          "data": {
            "text/plain": [
              "Epoch 5:   0%|          | 0/156 [00:00<?, ?it/s]"
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          "text": [
            "\n",
            "Epoch 5\n",
            "Training loss: 0.24914398865822035\n",
            "Validation loss: 0.3903368629515171\n",
            "F1 Score (Weighted): 0.8697872534393498\n",
            "Accuracy: 0.8723235527359239\n"
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        {
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          "data": {
            "text/plain": [
              "Epoch 6:   0%|          | 0/156 [00:00<?, ?it/s]"
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          "text": [
            "\n",
            "Epoch 6\n",
            "Training loss: 0.21622334602169502\n",
            "Validation loss: 0.4050272352993488\n",
            "F1 Score (Weighted): 0.8620838324360075\n",
            "Accuracy: 0.8636003172085647\n"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch 7:   0%|          | 0/156 [00:00<?, ?it/s]"
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          "text": [
            "\n",
            "Epoch 7\n",
            "Training loss: 0.18980220924967375\n",
            "Validation loss: 0.4170766308903694\n",
            "F1 Score (Weighted): 0.8721216186004919\n",
            "Accuracy: 0.8723235527359239\n"
          ]
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch 8:   0%|          | 0/156 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "bc60924f5da64aa68e5fb7e94cb2e5c7"
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        {
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          "name": "stdout",
          "text": [
            "\n",
            "Epoch 8\n",
            "Training loss: 0.16383287432388619\n",
            "Validation loss: 0.4377089455723763\n",
            "F1 Score (Weighted): 0.8745394280036636\n",
            "Accuracy: 0.8762886597938144\n"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch 9:   0%|          | 0/156 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "9cd64e365eeb4a2b8b71178ec8d64ea8"
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          "text": [
            "\n",
            "Epoch 9\n",
            "Training loss: 0.14744308024931413\n",
            "Validation loss: 0.451511362940073\n",
            "F1 Score (Weighted): 0.8731923777532685\n",
            "Accuracy: 0.8747026169706582\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch 10:   0%|          | 0/156 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "ddfa8ffe1d584e4ea4a633170dacb80d"
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          "name": "stdout",
          "text": [
            "\n",
            "Epoch 10\n",
            "Training loss: 0.12665507983034238\n",
            "Validation loss: 0.46041497886180877\n",
            "F1 Score (Weighted): 0.879013231440849\n",
            "Accuracy: 0.8810467882632831\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch 11:   0%|          | 0/156 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "6d1d84464e424829a88b8bba760ab7c0"
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          "text": [
            "\n",
            "Epoch 11\n",
            "Training loss: 0.11384418974511135\n",
            "Validation loss: 0.4754087805747986\n",
            "F1 Score (Weighted): 0.8722800850831363\n",
            "Accuracy: 0.8739095955590801\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch 12:   0%|          | 0/156 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "be62d7aa1f29465c875b5dd50f82b1f6"
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          "name": "stdout",
          "text": [
            "\n",
            "Epoch 12\n",
            "Training loss: 0.10430945611845416\n",
            "Validation loss: 0.4821273498237133\n",
            "F1 Score (Weighted): 0.8754817382116922\n",
            "Accuracy: 0.8770816812053925\n"
          ]
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch 13:   0%|          | 0/156 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "701e92fe4bcc46e9aee07ecae15cc6ae"
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          "metadata": {}
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        {
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          "name": "stdout",
          "text": [
            "\n",
            "Epoch 13\n",
            "Training loss: 0.09297555262366167\n",
            "Validation loss: 0.4840932711958885\n",
            "F1 Score (Weighted): 0.8765935883290435\n",
            "Accuracy: 0.8778747026169706\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch 14:   0%|          | 0/156 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d3f2eb0a68cb4551aea9da5a615c1949"
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          },
          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Epoch 14\n",
            "Training loss: 0.08725397955053128\n",
            "Validation loss: 0.5057309344410896\n",
            "F1 Score (Weighted): 0.8733464501063928\n",
            "Accuracy: 0.8747026169706582\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch 15:   0%|          | 0/156 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6b45fb65d37e47268e8ff1a4c65c9598"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Epoch 15\n",
            "Training loss: 0.08073633130735312\n",
            "Validation loss: 0.5094402015209198\n",
            "F1 Score (Weighted): 0.8767705210914294\n",
            "Accuracy: 0.8778747026169706\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch 16:   0%|          | 0/156 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "73e6c143d51c436086a0b60e46978521"
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          },
          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Epoch 16\n",
            "Training loss: 0.07572929578451201\n",
            "Validation loss: 0.5135170474648476\n",
            "F1 Score (Weighted): 0.8739752830526585\n",
            "Accuracy: 0.8754956383822363\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.load_state_dict(torch.load('/content/finetuned_RoBERTa_epoch_10.model', map_location=torch.device('cpu')))"
      ],
      "metadata": {
        "id": "-DWka6TQOsWw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_sum = pd.DataFrame({'F1':f1_list, \"Accuracy\": acc_list, \"train_loss\": train_loss_list, \"val_loss\": val_loss_list, \"epoch\": epoch_list})\n",
        "\n",
        "df_sum[\"learning_rate\"] = lr\n",
        "df_sum[\"eps\"] = eps\n",
        "df_sum[\"batch_size\"] = batch_size\n",
        "df_sum"
      ],
      "metadata": {
        "id": "hXK8PuXHV706"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 05. Testing"
      ],
      "metadata": {
        "id": "WDRg058jpj81"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_test[\"label\"] = df_test.category.replace(label_dict)\n",
        "df_test[\"label\"] = df_test[\"label\"].astype(int)\n",
        "df_test[\"data_type\"] = \"test\""
      ],
      "metadata": {
        "id": "eeWCXQSxlvsV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_test"
      ],
      "metadata": {
        "id": "eHU2fFWuwf32"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Using the tokenizer to encode the test data\n",
        "encoded_data_test = tokenizer.batch_encode_plus(\n",
        "    df_test[df_test.data_type=='test'].text.values,\n",
        "    add_special_tokens=True,\n",
        "    return_attention_mask=True,\n",
        "    pad_to_max_length=True,\n",
        "    truncation=True,\n",
        "    max_length=256,\n",
        "    return_tensors='pt'\n",
        ")\n",
        "\n",
        "# Separating the input_ids, attention_masks and labels from the encoded data\n",
        "\n",
        "input_ids_test = encoded_data_test['input_ids']\n",
        "attention_masks_test = encoded_data_test['attention_mask']\n",
        "labels_test = torch.tensor(df_test[df_test.data_type=='test'].label.values)\n",
        "\n",
        "# Creating a Tensor Dataset from the input_ids, attention_masks and labels\n",
        "dataset_test = TensorDataset(input_ids_test, attention_masks_test, labels_test)\n",
        "\n",
        "# Creating a Dataloader from the Tensor Dataset for test data\n",
        "dataloader_test = DataLoader(dataset_test,\n",
        "                             sampler=SequentialSampler(dataset_test),\n",
        "                            batch_size=batch_size)"
      ],
      "metadata": {
        "id": "wC5Iqc1dog9A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "_, predictions, true_vals = evaluate(dataloader_test)"
      ],
      "metadata": {
        "id": "ZtcH3cKb9hmb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def predicted_list_generate(preds, labels):\n",
        "    label_dict_inverse = {v: k for k, v in label_dict.items()}\n",
        "\n",
        "    preds_flat = np.argmax(preds, axis=1).flatten()\n",
        "    labels_flat = labels.flatten()\n",
        "    df_label_created = pd.DataFrame()\n",
        "\n",
        "    predicted = preds_flat.tolist()\n",
        "\n",
        "    return predicted\n",
        "\n",
        "prediction = predicted_list_generate(predictions, true_vals)\n",
        "df_test['predicted_label'] = prediction"
      ],
      "metadata": {
        "id": "IWYn09WMf1UO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction = predicted_list_generate(predictions, true_vals)\n",
        "df_test['predicted_label'] = prediction"
      ],
      "metadata": {
        "id": "qPIr9QDqqJPL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "overall_accuracy, per_class_accuracy, weighted_f1_score = compute_metrics(predictions, true_vals)\n",
        "\n",
        "print(\"Overall accuracy:\")\n",
        "print(overall_accuracy)\n",
        "\n",
        "print(\"Per Class Accuracy:\")\n",
        "print(per_class_accuracy)\n",
        "\n",
        "print(\"weighted_f1_score:\")\n",
        "print(per_class_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ni9meJ_det3",
        "outputId": "2895995f-8258-4c7c-9c11-684dca0875be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overall accuracy:\n",
            "0.8883534136546185\n",
            "Per Class Accuracy:\n",
            "{'Neutral': 0.7359154929577465, 'Positive': 0.9489932885906041, 'Negative': 0.8796296296296297}\n",
            "weighted_f1_score:\n",
            "{'Neutral': 0.7359154929577465, 'Positive': 0.9489932885906041, 'Negative': 0.8796296296296297}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 06. Confusion Matrix"
      ],
      "metadata": {
        "id": "shSeliBRZHHe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "metadata": {
        "id": "cQwK_fEBapHU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def conf(dataloader_val):\n",
        "\n",
        "    model.eval()\n",
        "\n",
        "    loss_val_total = 0\n",
        "    predictions, true_vals = [], []\n",
        "\n",
        "    for batch in dataloader_val:\n",
        "\n",
        "        batch = tuple(b.to(device) for b in batch)\n",
        "\n",
        "        inputs = {'input_ids':      batch[0],\n",
        "                  'attention_mask': batch[1],\n",
        "                  'labels':         batch[2],\n",
        "                 }\n",
        "\n",
        "        with torch.no_grad():\n",
        "            outputs = model(**inputs)\n",
        "\n",
        "        loss = outputs[0]\n",
        "        logits = outputs[1]\n",
        "        loss_val_total += loss.item()\n",
        "\n",
        "        logits = logits.detach().cpu().numpy()\n",
        "        label_ids = inputs['labels'].cpu().numpy()\n",
        "        predictions.append(logits)\n",
        "        true_vals.append(label_ids)\n",
        "\n",
        "    loss_val_avg = loss_val_total/len(dataloader_val)\n",
        "\n",
        "    predictions = np.concatenate(predictions, axis=0)\n",
        "    pred_vals = np.argmax(predictions, axis=1).flatten() # note\n",
        "    true_vals = np.concatenate(true_vals, axis=0)\n",
        "\n",
        "    return loss_val_avg, pred_vals, true_vals"
      ],
      "metadata": {
        "id": "oJi7KHtOZXa-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "_, pred_vals, true_vals = conf(dataloader_validation)\n",
        "\n",
        "cf_matrix = confusion_matrix(true_vals, pred_vals)"
      ],
      "metadata": {
        "id": "ShGwKkHB1pI5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H4-3eadLemFV",
        "outputId": "cebc3802-adf6-4e2f-a36c-f194e4571e18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[214,  67,  11],\n",
              "       [ 25, 705,  22],\n",
              "       [  6,  19, 192]])"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = [\"Neutral\",  'Positive', \"Negative\"]\n",
        "df_cm = pd.DataFrame(cf_matrix, index = labels,\n",
        "                  columns = labels)"
      ],
      "metadata": {
        "id": "haR4pFOlZdIo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "sns.set(font_scale=1.6)\n",
        "fig, ax = plt.subplots(figsize=(10,10))\n",
        "sns.heatmap(df_cm, annot=True,\n",
        "            linewidths=.5, ax=ax, fmt='g', annot_kws={\"fontsize\":17})"
      ],
      "metadata": {
        "id": "FOMjts4-fwDr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),\n",
        "             (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),\n",
        "             (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),\n",
        "             (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),\n",
        "             (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]\n",
        "\n",
        "for i in range(len(tableau20)):\n",
        "    r, g, b = tableau20[i]\n",
        "    tableau20[i] = (r / 255., g / 255., b / 255.)"
      ],
      "metadata": {
        "id": "2hza-YswwpoD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epoch_csv3 = pd.melt(df_sum, id_vars='epoch', value_vars=['train_loss', \"val_loss\"])\n",
        "epoch_csv3.loc[(epoch_csv3.variable == \"train_loss\"), \"variable\"] = \"Training Loss\"\n",
        "epoch_csv3.loc[(epoch_csv3.variable == \"val_loss\"), \"variable\"] = \"Validation Loss\""
      ],
      "metadata": {
        "id": "0CvX25JRvWOy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "#sns.set_style('ticks')\n",
        "sns.set(style=\"whitegrid\")\n",
        "sns.set(rc={'figure.figsize':(9.4,8.27)})\n",
        "sns.set(font_scale=1.4)\n",
        "g = sns.lineplot(data=epoch_csv3, x=\"epoch\", y=\"value\", hue=\"variable\", palette=[tableau20[8], tableau20[10]], linewidth=2)\n",
        "g.set_xlabel(\"Epoch\")\n",
        "g.set_ylabel(\"\")\n",
        "handles, labels = g.get_legend_handles_labels()\n",
        "g.legend(handles=handles[0:], labels=labels[0:])"
      ],
      "metadata": {
        "id": "ZrVe8u0uwoo_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 752
        },
        "outputId": "4fc1685f-51cd-4588-d3e0-36bc6d56eaf5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x791e42241f30>"
            ]
          },
          "metadata": {},
          "execution_count": 127
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 940x827 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}