AE_MultiTask.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9d5797c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch [1/300], loss: 0.5186\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch [280/300], loss: 0.4182\n",
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     ]
    }
   ],
   "source": [
    "import torch\n",
    "import pandas as pd\n",
    "from torch import nn, optim\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "\n",
    "from src.config import CLEANED_PATH, DataType, UNCLEANED_PATH, RAW_FILE, INFERENCE_FILE\n",
    "\n",
    "\n",
    "class MultiNewsDataset(Dataset):\n",
    "    \n",
    "    def __init__(self, data_type):\n",
    "        if data_type == DataType.train.name:\n",
    "            train_path = CLEANED_PATH.format(data_type=DataType.train.name) + INFERENCE_FILE        \n",
    "            train_df = pd.read_csv(train_path, sep=',')\n",
    "            train_df.drop(columns=['pair_id'], inplace=True)\n",
    "\n",
    "\n",
    "            l_cols = ['entities', 'narrative', 'time', 'geography', 'overall']\n",
    "            y = train_df[l_cols]\n",
    "            train_df.drop(columns=l_cols, inplace=True)\n",
    "\n",
    "            y = y.values\n",
    "            x = train_df.values\n",
    "        else:\n",
    "            test_path = CLEANED_PATH.format(data_type=DataType.test.name) + INFERENCE_FILE\n",
    "            test_df = pd.read_csv(test_path, sep=',', \n",
    "                                  usecols=['sentences_mean', 'sentences_min', 'sentences_max',\n",
    "                                           'sentences_med', 'title', 'n1_title_n2_text',\n",
    "                                           'n2_title_n1_text', 'n1_title_n1_text',\n",
    "                                           'n2_title_n2_text', 'start_para', 'end_para',\n",
    "                                           'ner', 'tf_idf', 'wmd_dist'])\n",
    "            x = test_df.values\n",
    "            y = 0\n",
    "        \n",
    "        self.x = torch.tensor(x, dtype=torch.float32)\n",
    "        self.y = torch.tensor(y, dtype=torch.float32)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.y)\n",
    "\n",
    "    def __getitem__(self,idx):\n",
    "        return self.x[idx], self.y[idx]\n",
    "\n",
    "\n",
    "class Autoencoder(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.encoder = nn.Sequential(\n",
    "            nn.Linear(14, 22),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(22, 14),\n",
    "            nn.ReLU()\n",
    "        )\n",
    "        \n",
    "        self.bn = nn.Sequential(nn.Linear(14, 4), nn.ReLU())\n",
    "        \n",
    "        self.decoder = nn.Sequential(\n",
    "            nn.Linear(4, 14),\n",
    "            nn.ReLU(),\n",
    "        )\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = self.encoder(x)\n",
    "        \n",
    "        out = self.bn(x)\n",
    "        \n",
    "        recon = self.decoder(out)\n",
    "        return out, recon\n",
    "\n",
    "\n",
    "class TaskRegressor(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.mlp = nn.Sequential(nn.Linear(4, 1), nn.ReLU())\n",
    "        \n",
    "    def forward(self, x):\n",
    "        return self.mlp(x)\n",
    "\n",
    "\n",
    "ae = Autoencoder()\n",
    "geo = TaskRegressor()\n",
    "ent = TaskRegressor()\n",
    "tim = TaskRegressor()\n",
    "nar = TaskRegressor()\n",
    "ovr = TaskRegressor()\n",
    "\n",
    "# MSE Loss \n",
    "loss = nn.MSELoss()\n",
    "optimizer = optim.Adam((list(ae.parameters())+list(geo.parameters())+list(ent.parameters())+\n",
    "                        list(tim.parameters())+list(nar.parameters())+list(ovr.parameters())),lr=0.01)\n",
    "max_epoch = 300\n",
    "lmbda = 0.01\n",
    "\n",
    "myDs = MultiNewsDataset(DataType.train.name)\n",
    "train_loader = DataLoader(myDs, batch_size=100, shuffle=False)\n",
    "\n",
    "for ep in range(max_epoch):\n",
    "    for data in train_loader:\n",
    "        optimizer.zero_grad()\n",
    "        x, y = data\n",
    "        out, recon = ae(x)\n",
    "\n",
    "        # Encoder-Decoder forward pass\n",
    "        recon_loss = loss(x, recon)\n",
    "        \n",
    "#         'entities', 'narrative', 'time', 'geography', 'overall'\n",
    "        gold_data_ent, gold_data_nar, gold_data_tim, gold_data_geo, gold_data_ovr = y[:][0], y[:][1], y[:][2], y[:][3], y[:][4]\n",
    "        # Regressor forward-pass\n",
    "        geo_loss = loss(geo(out), gold_data_geo)\n",
    "        ent_loss = loss(ent(out), gold_data_ent)\n",
    "        tim_loss = loss(tim(out), gold_data_tim)\n",
    "        nar_loss = loss(nar(out), gold_data_nar)\n",
    "        ovr_loss = loss(ovr(out), gold_data_ovr)\n",
    "\n",
    "        # Accumulate Loss\n",
    "#         main_loss = recon_loss + 0.5*(geo_loss+ent_loss+tim_loss+nar_loss+ovr_loss)\n",
    "        main_loss = 0.5*recon_loss + 0.5*(ovr_loss)\n",
    "\n",
    "        main_loss.backward()  # Compute gradients\n",
    "        optimizer.step()  # Back-prop\n",
    "    print(f'epoch [{ep + 1}/{max_epoch}], loss:{main_loss.item(): .4f}')\n",
    "\n",
    "#sentences_mean,sentences_min,sentences_max,sentences_med,\n",
    "#title,n1_title_n2_text,n2_title_n1_text,n1_title_n1_text,n2_title_n2_text,\n",
    "#start_para,end_para,ner,tf_idf,wmd_dist,\n",
    "\n",
    "\n",
    "#geography,entities,time,narrative,overall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dbcfbd4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "        [2.4471],\n",
      "        [2.4468]], grad_fn=<ReluBackward0>)\n"
     ]
    }
   ],
   "source": [
    "testDS = MultiNewsDataset(DataType.test.name)\n",
    "test_loader = DataLoader(testDS, batch_size=100, shuffle=False)\n",
    "\n",
    "for data in train_loader:\n",
    "    torch.no_grad()\n",
    "    x, _ = data\n",
    "    out, _ = ae(x)\n",
    "    pred = ovr(out)\n",
    "    print(pred)"
   ]
  }
 ],
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