paranormal-or-skeptic-ISI-public/Feed-Forward.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "308eb052",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import gensim.downloader as downloader\n",
    "import pandas as pd\n",
    "import csv\n",
    "from nltk.tokenize import word_tokenize as tokenize\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "212c0f2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class NeuralNetworkModel(torch.nn.Module):\n",
    "    \n",
    "    def __init__(self, input_size, hidden_size, num_classes):\n",
    "        super(NeuralNetworkModel, self).__init__()\n",
    "        self.fc1 = torch.nn.Linear(input_size,hidden_size)\n",
    "        self.fc2 = torch.nn.Linear(hidden_size,num_classes)\n",
    "        \n",
    "    def forward(self,x):\n",
    "        x = self.fc1(x)\n",
    "        x = torch.relu(x)\n",
    "        x = self.fc2(x)\n",
    "        x = torch.sigmoid(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "afd13d8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[==================================================] 100.0% 1662.8/1662.8MB downloaded\n"
     ]
    }
   ],
   "source": [
    "w2v = downloader.load('word2vec-google-news-300')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f68b0a7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#model + settings\n",
    "\n",
    "nn = NeuralNetworkModel(300,300,1)\n",
    "crit = torch.nn.BCELoss()\n",
    "opti = torch.optim.SGD(nn.parameters(), lr=0.08)\n",
    "BATCH_SIZE = 5\n",
    "epochs = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f0713eab",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#trening\n",
    "\n",
    "#wczytanie danych\n",
    "train_data_in = pd.read_csv('train/in.tsv.xz', compression='xz', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\\t', nrows=3000)\n",
    "train_data_ex = pd.read_csv('train/expected.tsv', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\\t', nrows=3000)\n",
    "\n",
    "#preprocessing\n",
    "train_in = train_data_in[0].str.lower()\n",
    "train_in = [tokenize(line) for line in train_in]\n",
    "train_in = [np.mean([w2v[x] for x in data if x in w2v] or [np.zeros(300)], axis=0) for data in train_in]\n",
    "train_ex = train_data_ex[0]\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    nn.train()\n",
    "    for i in range(0,train_data_ex.shape[0],BATCH_SIZE):\n",
    "        x = train_in[i:i + BATCH_SIZE]\n",
    "        x = torch.tensor(x)\n",
    "        y = train_ex[i:i + BATCH_SIZE]\n",
    "        y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1,1)\n",
    "        \n",
    "        opti.zero_grad()\n",
    "        y_pred = nn(x.float())\n",
    "        loss = crit(y_pred,y)\n",
    "        loss.backward()\n",
    "        opti.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1ab1dce0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-27-b22b834acfd4>:21: FutureWarning: The input object of type 'Tensor' is an array-like implementing one of the corresponding protocols (`__array__`, `__array_interface__` or `__array_struct__`); but not a sequence (or 0-D). In the future, this object will be coerced as if it was first converted using `np.array(obj)`. To retain the old behaviour, you have to either modify the type 'Tensor', or assign to an empty array created with `np.empty(correct_shape, dtype=object)`.\n",
      "  np.asarray(dev0_y, dtype=np.int32).tofile('dev-0/out.tsv', sep='\\n')\n",
      "<ipython-input-27-b22b834acfd4>:21: DeprecationWarning: setting an array element with a sequence. This was supported in some cases where the elements are arrays with a single element. For example `np.array([1, np.array([2])], dtype=int)`. In the future this will raise the same ValueError as `np.array([1, [2]], dtype=int)`.\n",
      "  np.asarray(dev0_y, dtype=np.int32).tofile('dev-0/out.tsv', sep='\\n')\n"
     ]
    }
   ],
   "source": [
    "#dev-0 predict\n",
    "\n",
    "#wczytanie danych\n",
    "dev0_data = pd.read_csv('dev-0/in.tsv.xz', compression='xz', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\\t')\n",
    "dev0_data = dev0_data[0].str.lower()\n",
    "dev0_data = [tokenize(line) for line in dev0_data]\n",
    "dev0_data = [np.mean([w2v[x] for x in data if x in w2v] or [np.zeros(300)], axis=0) for data in dev0_data]\n",
    "\n",
    "dev0_y=[]\n",
    "nn.eval()\n",
    "with torch.no_grad():\n",
    "    for i in range(0, len(dev0_data), BATCH_SIZE):\n",
    "        x = dev0_data[i:i + BATCH_SIZE]\n",
    "        x = torch.tensor(x)\n",
    "        dev0_y_pred = nn(x.float())\n",
    "\n",
    "        dev0_y_prediction = (dev0_y_pred > 0.5)\n",
    "        dev0_y.extend(dev0_y_prediction)\n",
    "        \n",
    "#zapis wyników\n",
    "np.asarray(dev0_y, dtype=np.int32).tofile('dev-0/out.tsv', sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "22941828",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-28-ed4376367760>:21: FutureWarning: The input object of type 'Tensor' is an array-like implementing one of the corresponding protocols (`__array__`, `__array_interface__` or `__array_struct__`); but not a sequence (or 0-D). In the future, this object will be coerced as if it was first converted using `np.array(obj)`. To retain the old behaviour, you have to either modify the type 'Tensor', or assign to an empty array created with `np.empty(correct_shape, dtype=object)`.\n",
      "  np.asarray(testA_y, dtype=np.int32).tofile('test-A/out.tsv', sep='\\n')\n",
      "<ipython-input-28-ed4376367760>:21: DeprecationWarning: setting an array element with a sequence. This was supported in some cases where the elements are arrays with a single element. For example `np.array([1, np.array([2])], dtype=int)`. In the future this will raise the same ValueError as `np.array([1, [2]], dtype=int)`.\n",
      "  np.asarray(testA_y, dtype=np.int32).tofile('test-A/out.tsv', sep='\\n')\n"
     ]
    }
   ],
   "source": [
    "#test-A predict\n",
    "\n",
    "#wczytanie danych\n",
    "testA_data = pd.read_csv('test-A/in.tsv.xz', compression='xz', header=None, quoting=csv.QUOTE_NONE, sep='\\t')\n",
    "testA_data = testA_data[0].str.lower()\n",
    "testA_data = [tokenize(line) for line in testA_data]\n",
    "testA_data = [np.mean([w2v[x] for x in data if x in w2v] or [np.zeros(300)], axis=0) for data in testA_data]\n",
    "\n",
    "testA_y=[]\n",
    "nn.eval()\n",
    "with torch.no_grad():\n",
    "    for i in range(0, len(testA_data), BATCH_SIZE):\n",
    "        x = testA_data[i:i + BATCH_SIZE]\n",
    "        x = torch.tensor(x)\n",
    "        testA_y_pred = nn(x.float())\n",
    "\n",
    "        testA_y_prediction = (testA_y_pred > 0.5)\n",
    "        testA_y.extend(testA_y_prediction)\n",
    "        \n",
    "#zapis wyników\n",
    "np.asarray(testA_y, dtype=np.int32).tofile('test-A/out.tsv', sep='\\n')"
   ]
  }
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