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BERT Model\n","Some code was adapted from Coursera guided project Sentiment Analysis with BERT\n","\n","Best if run in Colab using GPU"]},{"cell_type":"markdown","metadata":{"id":"cFXSvTfMLigj"},"source":["## Step 1: Load the Data"]},{"cell_type":"code","metadata":{"id":"MkJgx32dLorM"},"source":["%%capture\n","!pip install transformers"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"GXWDp9izLigj"},"source":["import torch\n","import random\n","import cv2\n","import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from tqdm.notebook import tqdm\n","from sklearn.metrics import roc_auc_score\n","from sklearn.metrics import roc_curve\n","from sklearn.metrics import accuracy_score\n","from transformers import BertTokenizer\n","from torch.utils.data import TensorDataset\n","from transformers import BertForSequenceClassification\n","from torch.utils.data import DataLoader, RandomSampler, SequentialSampler\n","from transformers import AdamW, get_linear_schedule_with_warmup"],"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import plotly.graph_objects as go"],"metadata":{"id":"ukCDV98YPpTn"},"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"FCxc03uGLyHg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1644823228676,"user_tz":-480,"elapsed":25799,"user":{"displayName":"Yijia Xue","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17824452402568532100"}},"outputId":"f5cfe093-94a8-46e1-c697-84f723d896bf"},"source":["# Mount google drive\n","from google.colab import drive\n","drive.mount('/content/drive', force_remount=True)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["path = \"/content/drive/MyDrive/STATS402_Project/hateful_memes/\""],"metadata":{"id":"FjmC3xeZI78V"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","test_data = pd.read_json(path+'dev_seen.jsonl',lines=True)\n","dev_unseen_data= pd.read_json(path+'dev_unseen.jsonl',lines=True)\n","final_test_data=pd.read_json(path+'test_seen.jsonl',lines=True)\n","test_unseen_data=pd.read_json(path+'test_unseen.jsonl',lines=True)\n","train_data=pd.read_json(path+'train.jsonl',lines=True)\n","test_data.head()"],"metadata":{"id":"w-uoQRj_I8f0","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1644823230502,"user_tz":-480,"elapsed":1828,"user":{"displayName":"Yijia Xue","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17824452402568532100"}},"outputId":"a273afcf-7ec2-496b-fcf1-8c665bc3c529"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["\n","  <div id=\"df-1f769a64-93ab-4a77-ae66-f8fea5571084\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>id</th>\n","      <th>img</th>\n","      <th>label</th>\n","      <th>text</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>8291</td>\n","      <td>img/08291.png</td>\n","      <td>1</td>\n","      <td>white people is this a shooting range</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>46971</td>\n","      <td>img/46971.png</td>\n","      <td>1</td>\n","      <td>bravery at its finest</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3745</td>\n","      <td>img/03745.png</td>\n","      <td>1</td>\n","      <td>your order comes to $37.50 and your white priv...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>83745</td>\n","      <td>img/83745.png</td>\n","      <td>1</td>\n","      <td>it is time.. to send these parasites back to t...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>80243</td>\n","      <td>img/80243.png</td>\n","      <td>1</td>\n","      <td>mississippi wind chime</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1f769a64-93ab-4a77-ae66-f8fea5571084')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 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mississippi wind chime\n","\n","[5 rows x 4 columns]"]},"metadata":{},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"KWvzYeXGLigm"},"source":["## Step 2: Loading Tokenizer and Encoding our Data"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["af8a3ee344544108a105bede13a4176e","81d0864a245a447b8b3ab99bdad84a10","3f9bc28441254f32acbf30b0a775bc17","9e5b213363624cfe9d49714057325518","5c4413e949b046d8a7d89cf00d9d61e4","78b7a8c63b6a48ce810ff6dbce4dde0d","e38bb218667e47758bff9fb907d543c1","28a728a076394960b6f2ffae9b0138b9","2e26931259cb493bb213b9d83f93ae9a","cd63ef2752014eb8a833205ed685278c","e1243037e1d3437baec6e9c94280b5fb","ee9d4c49abc24807abfbad293ee456d0","00ded3c0c6d443598b11c3c38acd0ed8","4e7efb919ce14e8999fb6eeb7a8aabae","3f5f7c858b26453680ab88167b3b8fd3","d05cf9d2b008410cb1d6681beff23b4b","7acbc444fd63492e811593c195ae8899","abf0c532cace44858383d2c6b55ff50e","896602433706475cab818caf69e16e8e","871cb820b5d54db0a6bcbeea0b4108bf","c179d545c85646f7870c0edca3064d6a","33b71f462b6c4568bfeee7a4e1944bcf","48a3fefd73a342ea946c1fd9016e59f1","197d796bb8d440f994b1280b38d299b4","03e3db3803724d24b581fafd57e5ddca","af49c5e0c1fa43ee91a5734f08b1e76a","057f3bc856984dbb963c615ab31cb5d9","cd9cae3bde4a43ce885775cd04ac30aa","269e8132614e471d9871f3891b699310","3619500419994cd3901dc0a6cad30b1b","ae7368a068fd4c9d93c60916c4e5df54","f2be07689f554959b12b30af3a4d735b","1f24607294c84941a6651f8f44ab3799","99c5d97194504c70b8100ac1e1698d85","056f1a4ca69347b4947d099ab6a774d4","c062c6a53e004bb2928b7ea62c0767b2","028cc455ec4046f9ba38e56fceccf837","0bcb226dba3d4baabb85a99c289a191b","738aa45ed11f41cf8836e4a395dd9391","d833cb3cbdbd49669552bce0dc5f0f15","c6ab9ed615544e48afb44e43520518b9","e4f4cbe7997345aeb97212dd22314644","5550a84184194ada9132511d0e411506","cbfd360e03994fc4b6bcf5dae04a6e52"]},"id":"-lcPZaAPLign","outputId":"91f7da08-7db7-4a9b-e758-5b1834d37f35","executionInfo":{"status":"ok","timestamp":1644823237196,"user_tz":-480,"elapsed":3819,"user":{"displayName":"Yijia Xue","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17824452402568532100"}}},"source":["# Create the tokenizer\n","tokenizer = BertTokenizer.from_pretrained(\n","    'bert-base-uncased',\n","    do_lower_case=True\n",")"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"af8a3ee344544108a105bede13a4176e","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ee9d4c49abc24807abfbad293ee456d0","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/226k [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"48a3fefd73a342ea946c1fd9016e59f1","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/455k [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"99c5d97194504c70b8100ac1e1698d85","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/570 [00:00<?, ?B/s]"]},"metadata":{}}]},{"cell_type":"code","source":["tokenizer.tokenize('Adorable African Kid Appreciating life ')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ry-aM6qu1vc9","executionInfo":{"status":"ok","timestamp":1644734944343,"user_tz":-480,"elapsed":417,"user":{"displayName":"Yijia Xue","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17824452402568532100"}},"outputId":"dd472ac7-16cc-40e6-997a-2e8579471ddc"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['adorable', 'african', 'kid', 'app', '##re', '##cia', '##ting', 'life']"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["tokenizer('Adorable African Kid Appreciating life')"],"metadata":{"id":"W15tnFOC__6G","executionInfo":{"status":"ok","timestamp":1644737595521,"user_tz":-480,"elapsed":406,"user":{"displayName":"Yijia Xue","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17824452402568532100"}},"outputId":"f2423927-bc70-43eb-ca86-f8c199b4fbd7","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'input_ids': [101, 23677, 3060, 4845, 10439, 2890, 7405, 3436, 2166, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"]},"metadata":{},"execution_count":13}]},{"cell_type":"code","source":["tokenizer.decode(tokenizer('Adorable African Kid Appreciating life')['input_ids'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"ychls9563otT","executionInfo":{"status":"ok","timestamp":1644735764964,"user_tz":-480,"elapsed":321,"user":{"displayName":"Yijia Xue","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17824452402568532100"}},"outputId":"c791724e-fbdb-4ad2-c4e7-b104440d5b42"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'[CLS] adorable african kid appreciating life [SEP]'"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","metadata":{"id":"OZKndHGsLign","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1644823247489,"user_tz":-480,"elapsed":6152,"user":{"displayName":"Yijia Xue","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17824452402568532100"}},"outputId":"dc2e39e3-27b2-425a-e337-d5440f5acac9"},"source":["# Adapted from Coursera guided project \"Sentiment Analysis with BERT\"\n","encoded_data_train = tokenizer.batch_encode_plus(\n","    train_data.text.values,\n","    add_special_tokens=True, # set this so BERT knows when a sentence ends and begins\n","    return_attention_mask=True, #using a fixed input, all sentences have same dimensionality. Tells us where actual values are and where zeros are\n","    padding = True, # make sure to pad sentences so they are all the same length\n","    max_length = 256, # this is the length we want all senteces to be\n","    return_tensors = 'pt'\n",")\n","\n","encoded_data_val = tokenizer.batch_encode_plus(\n","    test_data.text.values,\n","    add_special_tokens=True, # set this so BERT knows when a sentence ends and begins\n","    return_attention_mask=True, #using a fixed input, all sentences have same dimensionality. Tells us where actual values are and where zeros are\n","    padding = True, # make sure to pad sentences so they are all the same length\n","    max_length = 256, # this is the length we want all senteces to be\n","    return_tensors = 'pt'\n",")\n","\n","encoded_final_test= tokenizer.batch_encode_plus(\n","    final_test_data.text.values,\n","    add_special_tokens=True, # set this so BERT knows when a sentence ends and begins\n","    return_attention_mask=True, #using a fixed input, all sentences have same dimensionality. Tells us where actual values are and where zeros are\n","    padding = True, # make sure to pad sentences so they are all the same length\n","    max_length = 256, # this is the length we want all senteces to be\n","    return_tensors = 'pt'\n",")\n","\n","# get the parts from the encoding that need to train the model\n","input_ids_train = encoded_data_train['input_ids']\n","attention_masks_train = encoded_data_train['attention_mask']\n","labels_train = torch.tensor(train_data.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(test_data.label.values)\n","\n","input_ids_final_test = encoded_final_test['input_ids']\n","attention_masks_final_test = encoded_final_test['attention_mask']\n","labels_final_test = torch.tensor(final_test_data.label.values)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2268: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n","  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n"]}]},{"cell_type":"code","metadata":{"id":"4Bjqqh0YLigo"},"source":["dataset_train = TensorDataset(input_ids_train, attention_masks_train, labels_train)\n","dataset_val = TensorDataset(input_ids_val, attention_masks_val, labels_val)\n","dataset_final_test = TensorDataset(input_ids_final_test, attention_masks_final_test, labels_final_test)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2yeJW7v8Ligp","outputId":"6e946734-17a9-4fbd-bf1f-099503736da6","executionInfo":{"status":"ok","timestamp":1644303330545,"user_tz":-480,"elapsed":398,"user":{"displayName":"Yijia Xue","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17824452402568532100"}}},"source":["print(len(dataset_train))\n","print(len(dataset_val))\n","print(len(dataset_final_test))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["8500\n","500\n","1000\n"]}]},{"cell_type":"markdown","metadata":{"id":"UoeKcvfGLigq"},"source":["## Step 3: Setting up BERT Pretrained Model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":158,"referenced_widgets":["79b0da39d88f4208a3394e5b30c5142c","963e89d05ffa404f8bf180cf0deff940","e77e966e18f54e21b899d2104800682d","38f240a9d9464e8a9564b850a404c3c1","4532faad7ffb4dd7993157d95f0620a7","da26295d6e944d209892d1fd2cb89b63","8884cae1de2f4517b82de0929f603821","8a6708d307eb4e839a55a2c3fe4252c9","9d9d779dcf354f11bf7695f6a3381011","328433906f6c4bcf801eca740c78c8fa","97cec9b4c575413e9dabdba262943d5f"]},"id":"Xjs-Fqg1Ligq","outputId":"e3b6d321-6b1b-443b-a5f3-d035368e79db","executionInfo":{"status":"ok","timestamp":1644823281748,"user_tz":-480,"elapsed":18163,"user":{"displayName":"Yijia Xue","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17824452402568532100"}}},"source":["model = BertForSequenceClassification.from_pretrained(\n","    'bert-base-uncased', \n","    num_labels=len(train_data.label.unique()),\n","    output_attentions=False,\n","    output_hidden_states=False\n",")"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"79b0da39d88f4208a3394e5b30c5142c","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/420M [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias']\n","- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]}]},{"cell_type":"markdown","metadata":{"id":"W4H6mGutLigq"},"source":["## Step 4: Creating Data Loaders"]},{"cell_type":"code","metadata":{"id":"CqVRfSFgLigr"},"source":["# Adapted from Coursera guided project \"Sentiment Analysis with BERT\"\n","batch_size=32\n","\n","dataloader_train=DataLoader(\n","    dataset_train,\n","    sampler=RandomSampler(dataset_train),\n","    batch_size=batch_size\n",")\n","\n","dataloader_val=DataLoader(\n","    dataset_val,\n","    sampler=RandomSampler(dataset_val),\n","    batch_size=32\n",")\n","\n","dataloader_final_test = DataLoader(\n","    dataset_final_test,\n","    batch_size=32\n",")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"yAU8Z7GrLigr"},"source":["## Step 5: Setting Up Optimizer and Scheduler"]},{"cell_type":"code","metadata":{"id":"pFmMbietLigr","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1644823281749,"user_tz":-480,"elapsed":8,"user":{"displayName":"Yijia Xue","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17824452402568532100"}},"outputId":"efbe3322-8f54-4ec4-fd38-4cdab8f28b03"},"source":["# Adam is a stochastic optimization\n","optimizer = AdamW(\n","    model.parameters(),\n","    lr= 1e-5, #2e-5 > 5e -5\n","    eps=1e-8\n",")"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use thePyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n","  FutureWarning,\n"]}]},{"cell_type":"code","metadata":{"id":"scsFqDdLLigr"},"source":["epochs = 30\n","\n","scheduler = get_linear_schedule_with_warmup(\n","    optimizer,\n","    num_warmup_steps=0,\n","    num_training_steps=len(dataloader_train) *epochs\n",")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"JYTw0UbKLigs"},"source":["## Step 6: Defining our Performance Metric"]},{"cell_type":"code","metadata":{"id":"x0a9J8YALigs"},"source":["def auc(preds, labels):\n","    preds_flat=np.argmax(preds, axis=1).flatten()\n","    labels_flat=labels.flatten()\n","    return roc_auc_score(labels_flat, preds_flat)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"JHKmfACbLigs"},"source":["## Step 7: Creating our Training Loop"]},{"cell_type":"markdown","metadata":{"id":"8NJwqn6aLigs"},"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":{"id":"XTSqVDcPLigt"},"source":["seed_val = 17\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) #usually if using GPU"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"urJPS7rzLigt","outputId":"20dfc57f-58e0-4b72-aa67-b075c9fa2053","executionInfo":{"status":"ok","timestamp":1644823292687,"user_tz":-480,"elapsed":10945,"user":{"displayName":"Yijia Xue","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17824452402568532100"}}},"source":["device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n","model.to(device)\n","print(device)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["cuda\n"]}]},{"cell_type":"code","metadata":{"id":"xJyHY_-FLigt"},"source":["def evaluate(dataloader_val):\n","\n","    model.eval()\n","    \n","    loss_val_total = 0\n","    predictions, true_vals = [], []\n","    \n","    for batch in tqdm(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\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":457,"referenced_widgets":["ecae06b2533f4b1dac1fe1a83a4678c1","7d7a6bc724624c9992c498d8516a28be","79dcb32f4fd740cbaf816caed75881f1","e95489f457bd4b2cbbdf89de1959fe1f","20c2202cf09d4d09a62c6b4104e0ef42","a162c9ca7d99479ca00a23b40560cca3","75237f4029944dacac060790cfc672dd","4726693d43e24d2d9f7135532bc068e7","a1b9a7b636c44999850ede7d046cd93b","57f55719f0d84a66bcf1cd410ea2ac25","a2e361eb03b442a5b4b1f19de7e65916","e8e0b38f1f2849e09387cbb54b877afe","b54ff03c57a64f4a8be39fc8a298e214","615f84b1f8c64f98a727aeef8060210e","30ecc983793b482abb77a905c11f364d","3d395187e080486b87eb98ac67fa32dd","87a61592538b4ca8a1bbd8a7d675d476","dfe941686ff4494f8bb80091ff12f84d","8dbc3f6e19574956a524efa212526f4d","69d7cd6678fe4f09a81e3391731d14fc","50cf29f064c4487eaecffc7697393c4d","0b7804e4f33b49a599fa135c8c167e8b"]},"id":"eiKUG1t8Ligt","outputId":"b9a759eb-3549-48f5-f443-37907c70124a","executionInfo":{"status":"error","timestamp":1644810980515,"user_tz":-480,"elapsed":70034,"user":{"displayName":"Yijia Xue","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17824452402568532100"}}},"source":["for epoch in tqdm(range(1, epochs+1)):\n","    model.train() # put model in training mode\n","    \n","    loss_train_total = 0\n","    \n","    progress_bar = tqdm(dataloader_train, \n","                        desc='Epoch {:1d}'.format(epoch), \n","                       leave = False,\n","                       disable = False)\n","    for batch in progress_bar:\n","        \n","        model.zero_grad() # set the gradient to zero\n","        \n","        batch = tuple(b.to(device) for b in batch) # make sure each item on device\n","        \n","        # get the inputs to the model\n","        inputs = {\n","            'input_ids': batch[0],\n","            'attention_mask' : batch[1],\n","            'labels': batch[2]\n","        }\n","    \n","        # get the outputs\n","        outputs = model(**inputs)\n","        \n","        #BERT returns loss and logits\n","        loss = outputs[0]\n","        loss_train_total += loss.item()\n","        loss.backward() # backpropagation\n","        \n","        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) # prevents gradient from getting too small or too big\n","        \n","        optimizer.step()\n","        scheduler.step()\n","        \n","        progress_bar.set_postfix({'training_loss': '{:.3f}'.format(loss.item()/len(batch))})\n","        \n","    torch.save(model.state_dict(), f'/content/drive/MyDrive/STATS402_Project/hateful_memes/models/BERT2_ft_epoch{epoch}.model')\n","    tqdm.write('\\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_value = evaluate(dataloader_val)\n","    val_AUC = auc(predictions, true_value)\n","    tqdm.write(f'Validation loss: {val_loss}')\n","    tqdm.write(f'AUC Score: {val_AUC}')"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ecae06b2533f4b1dac1fe1a83a4678c1","version_minor":0,"version_major":2},"text/plain":["  0%|          | 0/30 [00:00<?, ?it/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"e8e0b38f1f2849e09387cbb54b877afe","version_minor":0,"version_major":2},"text/plain":["Epoch 1:   0%|          | 0/266 [00:00<?, ?it/s]"]},"metadata":{}},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-20-f1fd8918822c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     31\u001b[0m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip_grad_norm_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# prevents gradient from getting too small or too big\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m         \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m         \u001b[0mscheduler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/optim/lr_scheduler.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m                 \u001b[0minstance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_step_count\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m                 \u001b[0mwrapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m             \u001b[0;31m# Note that the returned function here is no longer a bound method,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/optim/optimizer.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     86\u001b[0m                 \u001b[0mprofile_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Optimizer.step#{}.step\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofiler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecord_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprofile_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 88\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     89\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/optimization.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m    358\u001b[0m                 \u001b[0;31m# In-place operations to update the averages at the same time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    359\u001b[0m                 \u001b[0mexp_avg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmul_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeta1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1.0\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mbeta1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 360\u001b[0;31m                 \u001b[0mexp_avg_sq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmul_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeta2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddcmul_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.0\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mbeta2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    361\u001b[0m                 \u001b[0mdenom\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexp_avg_sq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"eps\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    362\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"markdown","metadata":{"id":"IPDsdr0tLigu"},"source":["## Step 8: Loading and Evaluating our Model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ip_9uHnzLigu","outputId":"935a6bad-2331-4fe8-83dd-1aea9967ecea","executionInfo":{"status":"ok","timestamp":1644823298523,"user_tz":-480,"elapsed":2287,"user":{"displayName":"Yijia Xue","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17824452402568532100"}}},"source":["model = BertForSequenceClassification.from_pretrained(\"bert-base-uncased\",\n","                                                      num_labels=len(train_data.label.unique()),\n","                                                      output_attentions=False,\n","                                                      output_hidden_states=False)\n","model.to(device)\n","pass"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias']\n","- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uCZe7x0LLigu","outputId":"7350f23b-c7b7-45a2-c711-942ef7f041c5","executionInfo":{"status":"ok","timestamp":1644823307803,"user_tz":-480,"elapsed":7672,"user":{"displayName":"Yijia Xue","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17824452402568532100"}}},"source":["# load a pretrained model\n","model.load_state_dict(\n","    torch.load('/content/drive/MyDrive/STATS402_Project/hateful_memes/models/BERT2_ft_epoch22.model',\n","    map_location = torch.device('cpu'))\n",")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<All keys matched successfully>"]},"metadata":{},"execution_count":18}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["26b85fc5f2254c9393739bb569500115","9e437e2b734f450ba589746612fbb4d4","0d3f63cd94d2409cbb232177d30eda18","74be177c5ec9450797a593dfadcad09a","fd95f48fc88044fdbc896667f546cce6","1b99c62d58d14634896a58b4ee2427ae","6105e1e340db4a2393dff97ef8ecf6d0","5f5dd618e8ad420ba1a60f93788a6412","8a4cb80d29d8411c969934253b93658f","135f7551ce6f4b6598a4e5cdb94e5495","46ef7b3d29e2401dbbe9f73d61481fa8"]},"id":"j8qiJpOYLigu","outputId":"45cff3d0-7d50-406d-c143-888d43f47af7","executionInfo":{"status":"ok","timestamp":1644823317356,"user_tz":-480,"elapsed":9557,"user":{"displayName":"Yijia Xue","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17824452402568532100"}}},"source":["# Get the predictions for the hold out test set\n","_, predictions, true_vals = evaluate(dataloader_final_test)"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"26b85fc5f2254c9393739bb569500115","version_minor":0,"version_major":2},"text/plain":["  0%|          | 0/32 [00:00<?, ?it/s]"]},"metadata":{}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["7d664d7289bf46eaaf5138f7422f7466","b6fb38c4211549848eba9302ac7a6749","cd9d94bf5c8549c8bb721398f1e2fdb4","9be078303a77479a96f77afebba48e29","efb05a4141034719b48596aafdd22ec9","b7951a618b56432aa6ae33624cbbb7d5","c953d4fa5d494b6494cfdfcc41a541c0","052d3b0025704105905c3c94265a824c","a8d805e88d514122bbba8ac5351e277a","6b8f21fbfcc7463787dbef1fa48f09e7","1e762b1b1d524583a2f850b3d76d572f"]},"id":"ualL9SWfLigv","outputId":"16c4e7d7-bb73-4fe1-f6a9-1487b2cde1eb","executionInfo":{"status":"ok","timestamp":1644823409464,"user_tz":-480,"elapsed":92131,"user":{"displayName":"Yijia Xue","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17824452402568532100"}}},"source":["# Get the predictions for the training set\n","_, train_predictions, train_true_vals = evaluate(dataloader_train)"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"7d664d7289bf46eaaf5138f7422f7466","version_minor":0,"version_major":2},"text/plain":["  0%|          | 0/266 [00:00<?, ?it/s]"]},"metadata":{}}]},{"cell_type":"code","source":["from sklearn import metrics\n","#print(metrics.classification_report(predictions, true_vals))\n","pred = predictions[:,1] \n","pred = np.where(pred > 0, pred, 0)\n","pred = np.where(pred <= 0, pred, 1)\n","\n","pred\n","print(metrics.classification_report(pred, true_vals))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CQxWsj4-LCZF","executionInfo":{"status":"ok","timestamp":1644825502473,"user_tz":-480,"elapsed":5,"user":{"displayName":"Yijia Xue","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17824452402568532100"}},"outputId":"2c332663-7b25-4f34-e154-609949f65caa"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","         0.0       0.81      0.56      0.66       728\n","         1.0       0.35      0.64      0.45       272\n","\n","    accuracy                           0.58      1000\n","   macro avg       0.58      0.60      0.56      1000\n","weighted avg       0.68      0.58      0.61      1000\n","\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5lCzcJO1Ligv","outputId":"ae9615de-c221-466e-fb11-acbac4e70602","executionInfo":{"status":"ok","timestamp":1644303569347,"user_tz":-480,"elapsed":339,"user":{"displayName":"Yijia Xue","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17824452402568532100"}}},"source":["# Get the AUC on the hold out test set\n","final_test_auc = auc(predictions, true_vals)\n","train_auc = auc(train_predictions, train_true_vals)\n","print(final_test_auc)\n","print(train_auc)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0.5784113645458183\n","0.9254842483646267\n"]}]},{"cell_type":"code","source":["!pip install -q plotly==5.0.0"],"metadata":{"id":"WkTUNxzvQJny","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1644811740738,"user_tz":-480,"elapsed":43312,"user":{"displayName":"Yijia Xue","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17824452402568532100"}},"outputId":"3da6dad3-c76e-4e95-c46a-2c635f7bcf4a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[K     |████████████████████████████████| 19.1 MB 9.3 MB/s \n","\u001b[?25h"]}]},{"cell_type":"code","source":["#import plotly.express as px\n","import plotly.graph_objs as go\n","import plotly.offline as py"],"metadata":{"id":"WBYdnZBQP85V"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n"],"metadata":{"id":"-F7ZM8CeHijT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Create the roc curve\n","test_fpr, test_tpr, _ = roc_curve(true_vals, predictions[:,1], pos_label = 1)\n","train_fpr, train_tpr, _ = roc_curve(train_true_vals, train_predictions[:,1], pos_label = 1)\n","\n","# Create a dataframe of fpr and tpr to create visual\n","testRocDf = pd.DataFrame({'fpr': test_fpr, 'tpr': test_tpr, 'data':['test']*len(test_tpr)})\n","trainRocDf = pd.DataFrame({'fpr': train_fpr, 'tpr': train_tpr, 'data': ['train'] * len(train_tpr)})\n","rocDf = pd.concat([testRocDf, trainRocDf])\n","plt.rcParams['figure.dpi'] = 1200\n","plt.plot(trainRocDf['fpr'], trainRocDf['tpr'],label = 'train')\n","plt.plot(testRocDf['fpr'], testRocDf['tpr'],label = 'test')\n","plt.legend()\n","plt.grid()\n","plt.xlabel('FPR')\n","plt.ylabel('TPR')\n","plt.title('ROC Curve')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"YftUiznDHvGR","executionInfo":{"status":"ok","timestamp":1644823751951,"user_tz":-480,"elapsed":5997,"user":{"displayName":"Yijia Xue","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17824452402568532100"}},"outputId":"0f806650-c7a4-4acb-c4d6-63f368f7c322"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'ROC 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