drug_target_affinity_prediction_DeepCov_DTI.ipynb

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      "metadata": {
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      "source": [
        "!pip install PyTDC\n",
        "!pip install datasets\n",
        "\n",
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "PaSFOc7BAtSc"
      },
      "outputs": [],
      "source": [
        "import tqdm\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import plotly.express as px\n",
        "from tdc.multi_pred import DTI\n",
        "import plotly.figure_factory as ff\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from sklearn.metrics import accuracy_score, precision_recall_fscore_support"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eI5aPCcx_o42"
      },
      "outputs": [],
      "source": [
        "data = DTI(name = 'Davis')\n",
        "split = data.get_split()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T3TLl-kYve4m"
      },
      "source": [
        "## Data Provision"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "hp1b0qS5_va9"
      },
      "outputs": [],
      "source": [
        "new_data=data.get_data()\n",
        "# new_data['weights']=1/new_data['Y']\n",
        "# new_data=new_data.sample(frac=0.5,weights='weights')\n",
        "y_max=new_data['Y'].max()\n",
        "y_min=new_data['Y'].min()\n",
        "new_data['Y']=(new_data['Y']-y_min)/(y_max-y_min)\n",
        "# new_data['Y']=-np.log10(new_data['Y']/1e9)\n",
        "new_data=new_data.sample(frac=1)\n",
        "new_data=new_data.reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Some basic Analysis"
      ],
      "metadata": {
        "id": "S7MM7TgE55Cx"
      }
    },
    {
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      "execution_count": 5,
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        {
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          "data": {
            "text/plain": [
              "             Drug_l      Target_l             Y\n",
              "count  25772.000000  25772.000000  25772.000000\n",
              "mean      54.176471    744.849604      0.755811\n",
              "std       10.962637    372.813592      0.399002\n",
              "min       32.000000    244.000000      0.000000\n",
              "25%       45.000000    479.000000      0.377499\n",
              "50%       53.000000    632.000000      1.000000\n",
              "75%       61.250000    954.000000      1.000000\n",
              "max       81.000000   2549.000000      1.000000"
            ],
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              "      <td>25772.000000</td>\n",
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              "      <td>744.849604</td>\n",
              "      <td>0.755811</td>\n",
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              "      <th>25%</th>\n",
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      "source": [
        "new_data['Drug_l']=new_data.Drug.apply(len)\n",
        "new_data['Target_l']=new_data.Target.apply(len)\n",
        "new_data[['Drug_l','Target_l','Y']].describe()"
      ]
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              "                                                    Drug  \\\n",
              "25767  Cc1nc(Nc2ncc(C(=O)Nc3c(C)cccc3Cl)s2)cc(N2CCN(C...   \n",
              "25768  CCN1CCN(Cc2ccc(NC(=O)Nc3ccc(Oc4cc(NC)ncn4)cc3)...   \n",
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              "\n",
              "                                                  Target  \n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "new_data[['Drug',\"Target\"]].tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "CHaH9ovHPuJK",
        "outputId": "85addfd6-cf2d-45a6-d411-2f164fa0f1ef"
      },
      "outputs": [
        {
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig = px.histogram(new_data['Y'], nbins=200,marginal=\"box\")\n",
        "\n",
        "# Show the plot\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A2wTDUkq8gl5"
      },
      "source": [
        "## Let's create the tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "5AFnyd2n6Zl3"
      },
      "outputs": [],
      "source": [
        "def tokenize(input_string):\n",
        "  return [ord(char) for char in input_string]\n",
        "def encode(input_string,max_length=128,padding=True):\n",
        "  tokens=tokenize(input_string)\n",
        "  if len(tokens)>max_length:\n",
        "    tokens=tokens[:max_length]\n",
        "  if (len(tokens)<max_length) & padding:\n",
        "    tokens.extend([0 for _ in range(max_length-len(tokens))])\n",
        "  return tokens\n",
        "def decode(input_tokens):\n",
        "  return ''.join(list(map(lambda x:chr(x), input_tokens)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "9IUIelUOAJZq"
      },
      "outputs": [],
      "source": [
        "l_tokenizer=encode('z',padding=False)[0]+1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bLHVshvy8nJi"
      },
      "source": [
        "## Now, let's create the dataset object"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "WzH02MkR4WYz"
      },
      "outputs": [],
      "source": [
        "class DTIA_Dataset(Dataset):\n",
        "    def __init__(self, df,drug_max_length,target_max_length):\n",
        "        self.df = df\n",
        "        self.dml=drug_max_length\n",
        "        self.tml=target_max_length\n",
        "    def __len__(self):\n",
        "        return len(self.df)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        row=self.df.iloc[idx]\n",
        "        input_drug=torch.tensor(encode(row['Drug'],max_length=self.dml))\n",
        "        input_target=torch.tensor(encode(row['Target'],max_length=self.tml))\n",
        "        y=torch.tensor(row['Y'],dtype=torch.float32)\n",
        "        return {'input_drug':input_drug,\n",
        "         'input_target': input_target,\n",
        "         'y':y}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "B3u_fHBhAjxD"
      },
      "outputs": [],
      "source": [
        "dml=45\n",
        "tml=700"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "MdyWuihg_J65"
      },
      "outputs": [],
      "source": [
        "l=int(new_data.shape[0]*0.8)\n",
        "train_p=DTIA_Dataset(new_data[:l],drug_max_length=45,target_max_length=700)\n",
        "test_p=DTIA_Dataset(new_data[l:],drug_max_length=45,target_max_length=700)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "JbQ1aU6Z-jMx"
      },
      "outputs": [],
      "source": [
        "train_loader=DataLoader(train_p,batch_size=32,shuffle=True)\n",
        "test_loader=DataLoader(test_p,batch_size=32)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-JeV556v_rAf"
      },
      "source": [
        "## Now a model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "PhMN7rXQ-veI"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "class Drug_Model(torch.nn.Module):\n",
        "    def __init__(self, embed_dim=16,dim1=32):\n",
        "        super(Drug_Model, self).__init__()\n",
        "        self.embeddings = nn.Embedding(l_tokenizer, embed_dim)\n",
        "        self.conv1=nn.Conv1d(embed_dim,dim1,kernel_size=4)\n",
        "        self.bn1=nn.BatchNorm1d(dim1)\n",
        "\n",
        "        self.conv2=nn.Conv1d(dim1,dim1*2,kernel_size=4)\n",
        "        self.bn2=nn.BatchNorm1d(dim1*2)\n",
        "\n",
        "        self.conv3=nn.Conv1d(dim1*2,dim1*4,kernel_size=4)\n",
        "        self.bn3=nn.BatchNorm1d(dim1*4)\n",
        "\n",
        "        self.fpool=nn.MaxPool1d(36)\n",
        "\n",
        "    def forward(self, input_ids):\n",
        "        x = self.embeddings(input_ids)\n",
        "        x=torch.transpose(x,1,2)\n",
        "        x=F.relu((self.bn1(self.conv1(x))))\n",
        "        x=F.relu((self.bn2(self.conv2(x))))\n",
        "        x=F.relu((self.bn3(self.conv3(x))))\n",
        "        x = torch.squeeze(self.fpool(x),2)\n",
        "        return x\n",
        "\n",
        "\n",
        "class Target_Model(torch.nn.Module):\n",
        "    def __init__(self, embed_dim=16,dim1=32):\n",
        "        super(Target_Model, self).__init__()\n",
        "        self.embeddings = nn.Embedding(l_tokenizer, embed_dim)\n",
        "        self.conv1=nn.Conv1d(embed_dim,dim1,kernel_size=4)\n",
        "        self.bn1=nn.BatchNorm1d(dim1)\n",
        "\n",
        "        self.conv2=nn.Conv1d(dim1,dim1*2,kernel_size=4)\n",
        "        self.bn2=nn.BatchNorm1d(dim1*2)\n",
        "\n",
        "        self.conv3=nn.Conv1d(dim1*2,dim1*4,kernel_size=4)\n",
        "        self.bn3=nn.BatchNorm1d(dim1*4)\n",
        "\n",
        "        self.fpool=nn.MaxPool1d(691)\n",
        "\n",
        "    def forward(self, input_ids):\n",
        "        x = self.embeddings(input_ids)\n",
        "        x=torch.transpose(x,1,2)\n",
        "        x=F.relu((self.bn1(self.conv1(x))))\n",
        "        x=F.relu((self.bn2(self.conv2(x))))\n",
        "        x=F.relu((self.bn3(self.conv3(x))))\n",
        "        x = torch.squeeze(self.fpool(x),2)\n",
        "        return x\n",
        "\n",
        "class DTIA_Model(torch.nn.Module):\n",
        "    def __init__(self, embed_dim=32,dim1=64):\n",
        "        super(DTIA_Model, self).__init__()\n",
        "        self.drug_encoder=Drug_Model(embed_dim=embed_dim,dim1=dim1)\n",
        "        self.target_encoder=Target_Model(embed_dim=embed_dim,dim1=dim1)\n",
        "        # self.drug_encoder=Drug_Model_att(embed_dim=embed_dim,dim1=dim1)\n",
        "        # self.target_encoder=Target_Model_att(embed_dim=embed_dim,dim1=dim1)\n",
        "        self.dense1 = nn.Linear(dim1*8, 1024)\n",
        "        # self.dense1 = nn.Linear(embed_dim*2, 1024)\n",
        "        self.dp1=nn.Dropout(0.1)\n",
        "        self.dense2 = nn.Linear(1024, 1024)\n",
        "        self.dp2=nn.Dropout(0.1)\n",
        "        self.dense3 = nn.Linear(1024, 512)\n",
        "        self.dp3=nn.Dropout(0.1)\n",
        "        self.fdense = nn.Linear(512, 1)\n",
        "\n",
        "\n",
        "    def forward(self, drug_inputs,target_inputs):\n",
        "        drug_encoded=self.drug_encoder(drug_inputs)\n",
        "        target_encoded=self.target_encoder(target_inputs)\n",
        "        x=torch.concat((drug_encoded,target_encoded),dim=-1)\n",
        "        x = F.relu(self.dp1(self.dense1(x)))\n",
        "        x = F.relu(self.dp2(self.dense2(x)))\n",
        "        x = F.relu(self.dp3(self.dense3(x)))\n",
        "        x=self.fdense(x)\n",
        "\n",
        "        return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wV5_o3ysCYN4",
        "outputId": "a7eacf4d-1b5c-4f1f-a5a1-8b676a7e5208"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DTIA_Model(\n",
              "  (drug_encoder): Drug_Model(\n",
              "    (embeddings): Embedding(123, 128)\n",
              "    (conv1): Conv1d(128, 64, kernel_size=(4,), stride=(1,))\n",
              "    (bn1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (conv2): Conv1d(64, 128, kernel_size=(4,), stride=(1,))\n",
              "    (bn2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (conv3): Conv1d(128, 256, kernel_size=(4,), stride=(1,))\n",
              "    (bn3): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (fpool): MaxPool1d(kernel_size=36, stride=36, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (target_encoder): Target_Model(\n",
              "    (embeddings): Embedding(123, 128)\n",
              "    (conv1): Conv1d(128, 64, kernel_size=(4,), stride=(1,))\n",
              "    (bn1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (conv2): Conv1d(64, 128, kernel_size=(4,), stride=(1,))\n",
              "    (bn2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (conv3): Conv1d(128, 256, kernel_size=(4,), stride=(1,))\n",
              "    (bn3): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (fpool): MaxPool1d(kernel_size=691, stride=691, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (dense1): Linear(in_features=512, out_features=1024, bias=True)\n",
              "  (dp1): Dropout(p=0.1, inplace=False)\n",
              "  (dense2): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "  (dp2): Dropout(p=0.1, inplace=False)\n",
              "  (dense3): Linear(in_features=1024, out_features=512, bias=True)\n",
              "  (dp3): Dropout(p=0.1, inplace=False)\n",
              "  (fdense): Linear(in_features=512, out_features=1, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "\n",
        "model=DTIA_Model(embed_dim=128,dim1=64)\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "# device='cpu'\n",
        "model.to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nsOVtHKlCtiR"
      },
      "source": [
        "## Now, training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "X22cDgFtCk1R"
      },
      "outputs": [],
      "source": [
        "def train(num_epochs=10):\n",
        "    criterion= nn.MSELoss()\n",
        "    optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
        "    best_test_loss = float('inf')\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        model.train()\n",
        "        train_loss = 0.0\n",
        "        train_total=0.0\n",
        "        test_loss=0.0\n",
        "        test_total=0.0\n",
        "\n",
        "        for batch in tqdm.tqdm(train_loader):\n",
        "            input_drug=batch['input_drug'].to(device)\n",
        "            input_target=batch['input_target'].to(device)\n",
        "            y=batch['y'].to(device)\n",
        "            optimizer.zero_grad()\n",
        "            outputs = model(input_drug,input_target).view(-1,)\n",
        "            loss = criterion(outputs,y)\n",
        "            train_loss += loss.item()\n",
        "            train_total+=outputs.size(0)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "        avg_train_loss = train_loss / len(train_loader)\n",
        "\n",
        "\n",
        "\n",
        "        model.eval()\n",
        "        with torch.no_grad():\n",
        "            for batch in test_loader:\n",
        "                input_drug=batch['input_drug'].to(device)\n",
        "                input_target=batch['input_target'].to(device)\n",
        "                y=batch['y'].to(device)\n",
        "                outputs = model(input_drug,input_target).view(-1,)\n",
        "                loss = criterion(outputs,y)\n",
        "                test_total+=outputs.size(0)\n",
        "                test_loss += loss.item()\n",
        "        avg_test_loss = test_loss / len(test_loader)\n",
        "\n",
        "        if avg_test_loss < best_test_loss:\n",
        "            best_test_loss = avg_test_loss\n",
        "            torch.save(model.state_dict(), \"best_model.pt\")\n",
        "        print(f\"Epoch {epoch+1}/{num_epochs}: \"\n",
        "              f\"Train Loss: {avg_train_loss:.4f}, Valid Loss: {avg_test_loss:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HpZ9x_c1ELIh",
        "outputId": "7cdb837c-0514-40f0-f650-da2a7e3ab417"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 645/645 [00:25<00:00, 25.39it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10: Train Loss: 0.1642, Valid Loss: 0.1144\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 645/645 [00:17<00:00, 36.52it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 2/10: Train Loss: 0.1218, Valid Loss: 0.1210\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 645/645 [00:17<00:00, 36.92it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 3/10: Train Loss: 0.1164, Valid Loss: 0.1062\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 645/645 [00:18<00:00, 35.19it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 4/10: Train Loss: 0.1147, Valid Loss: 0.1100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 645/645 [00:18<00:00, 34.83it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 5/10: Train Loss: 0.1118, Valid Loss: 0.1054\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 645/645 [00:18<00:00, 35.46it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 6/10: Train Loss: 0.1087, Valid Loss: 0.1064\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 645/645 [00:17<00:00, 37.12it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 7/10: Train Loss: 0.1055, Valid Loss: 0.0999\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 645/645 [00:17<00:00, 37.13it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 8/10: Train Loss: 0.1028, Valid Loss: 0.1049\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 645/645 [00:17<00:00, 36.57it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 9/10: Train Loss: 0.1011, Valid Loss: 0.0971\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 645/645 [00:17<00:00, 36.94it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 10/10: Train Loss: 0.1007, Valid Loss: 0.0980\n"
          ]
        }
      ],
      "source": [
        "train()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## VISUAL EVALUATION"
      ],
      "metadata": {
        "id": "qQDCPW6Q4Q4Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ys=[]\n",
        "preds=[]\n",
        "for batch in tqdm.tqdm(test_loader):\n",
        "    input_drug=batch['input_drug'].to(device)\n",
        "    input_target=batch['input_target'].to(device)\n",
        "    y=batch['y'].to(device)\n",
        "    outputs = model(input_drug,input_target).view(-1,)\n",
        "    ys.extend(list(y.detach().cpu().numpy()))\n",
        "    preds.extend(list(outputs.detach().cpu().numpy()))\n",
        "temp_df=pd.DataFrame({'ys':ys,'preds':preds})\n",
        "px.scatter(temp_df,x='ys',y='preds')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "lBSilFju4QQY",
        "outputId": "a9fac1f1-5613-483d-a4de-00d7a7ea3526"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 162/162 [00:03<00:00, 40.85it/s]\n"
          ]
        },
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          "output_type": "display_data",
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      "source": [
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        "id": "CdKzz2buk00m",
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      "execution_count": 20,
      "outputs": [
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              "             ys     preds\n",
              "ys     1.000000  0.615017\n",
              "preds  0.615017  1.000000"
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-91d01310-683b-48a6-a62b-4925714157c1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yh1WxK54HXrQ"
      },
      "source": [
        "## FUTURE WORK\n",
        "\n",
        "\n",
        "1.   Add figures for training history and some evaluations\n",
        "2.   Improve the tokenizer\n",
        "3.   Improve the model\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T_HSBT48EL1D"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}