lr_and_nn/regresja_logistyczna_i_sieci_neuronowe.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regresja logistyczna"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## import bibliotek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/media/kuba/ssdsam/anaconda3/lib/python3.8/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import gensim\n",
    "import torch\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.datasets import fetch_20newsgroups\n",
    "# https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "CATEGORIES = ['soc.religion.christian', 'alt.atheism']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "newsgroups_train_dev = fetch_20newsgroups(subset = 'train', categories=CATEGORIES)\n",
    "newsgroups_test = fetch_20newsgroups(subset = 'test', categories=CATEGORIES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "newsgroups_train_dev_text = newsgroups_train_dev['data']\n",
    "newsgroups_test_text = newsgroups_test['data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train_dev = newsgroups_train_dev['target']\n",
    "Y_test = newsgroups_test['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "newsgroups_train_text, newsgroups_dev_text, Y_train, Y_dev = train_test_split(newsgroups_train_dev_text, Y_train_dev, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_names = newsgroups_train_dev['target_names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alt.atheism', 'soc.religion.christian']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## zadanie (5 minut)\n",
    "\n",
    "- stworzyć baseline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PYTANIE: co jest nie tak z regresją liniową?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regresja logistyczna"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### wektoryzacja"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## zadanie (5 minut)\n",
    "\n",
    "- na podstawie newsgroups_train_text stworzyć tfidf wektoryzer ze słownikiem max 10_000\n",
    "- wygenerować wektory: X_train, X_dev, X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### model - inicjalizacja "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LogisticRegressionModel(torch.nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(LogisticRegressionModel, self).__init__()\n",
    "        self.fc = torch.nn.Linear(FEAUTERES,1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.fc(x)\n",
    "        x = torch.sigmoid(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_model = LogisticRegressionModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.4978],\n",
       "        [0.5009],\n",
       "        [0.4998],\n",
       "        [0.4990],\n",
       "        [0.5018]], grad_fn=<SigmoidBackward>)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_model(torch.Tensor(X_train[0:5].astype(np.float32).todense()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegressionModel(\n",
       "  (fc): Linear(in_features=10000, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Parameter containing:\n",
       " tensor([[-0.0059,  0.0035,  0.0021,  ..., -0.0042, -0.0057, -0.0049]],\n",
       "        requires_grad=True),\n",
       " Parameter containing:\n",
       " tensor([-0.0023], requires_grad=True)]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(lr_model.parameters())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## model - trenowanie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = torch.nn.BCELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = torch.optim.SGD(lr_model.parameters(), lr = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "809"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "loss_score = 0\n",
    "acc_score = 0\n",
    "items_total = 0\n",
    "lr_model.train()\n",
    "for i in range(0, Y_train.shape[0], BATCH_SIZE):\n",
    "    X = X_train[i:i+BATCH_SIZE]\n",
    "    X = torch.tensor(X.astype(np.float32).todense())\n",
    "    Y = Y_train[i:i+BATCH_SIZE]\n",
    "    Y = torch.tensor(Y.astype(np.float32)).reshape(-1,1)\n",
    "    Y_predictions = lr_model(X)\n",
    "    acc_score += torch.sum((Y_predictions > 0.5) == Y).item()\n",
    "    items_total += Y.shape[0] \n",
    "    \n",
    "    optimizer.zero_grad()\n",
    "    loss = criterion(Y_predictions, Y)\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    \n",
    "\n",
    "    loss_score += loss.item() * Y.shape[0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.5667],\n",
       "        [0.5802],\n",
       "        [0.5757],\n",
       "        [0.5670]], grad_fn=<SigmoidBackward>)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.],\n",
       "        [1.],\n",
       "        [1.],\n",
       "        [0.]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "452"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "809"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.5587144622991347\n"
     ]
    }
   ],
   "source": [
    "print(f'accuracy: {acc_score / items_total}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BCE loss: 0.6745463597170355\n"
     ]
    }
   ],
   "source": [
    "print(f'BCE loss: {loss_score / items_total}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### model - ewaluacja"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_loss_acc(model, X_dataset, Y_dataset):\n",
    "    loss_score = 0\n",
    "    acc_score = 0\n",
    "    items_total = 0\n",
    "    model.eval()\n",
    "    for i in range(0, Y_dataset.shape[0], BATCH_SIZE):\n",
    "        X = X_dataset[i:i+BATCH_SIZE]\n",
    "        X = torch.tensor(X.astype(np.float32).todense())\n",
    "        Y = Y_dataset[i:i+BATCH_SIZE]\n",
    "        Y = torch.tensor(Y.astype(np.float32)).reshape(-1,1)\n",
    "        Y_predictions = model(X)\n",
    "        acc_score += torch.sum((Y_predictions > 0.5) == Y).item()\n",
    "        items_total += Y.shape[0] \n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss = criterion(Y_predictions, Y)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "\n",
    "        loss_score += loss.item() * Y.shape[0] \n",
    "    return (loss_score / items_total), (acc_score / items_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6443227143826974, 0.622991347342398)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_loss_acc(lr_model, X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6369243131743537, 0.6037037037037037)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_loss_acc(lr_model, X_dev, Y_dev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6323775731785694, 0.6499302649930265)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_loss_acc(lr_model, X_test, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### wagi modelu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Parameter containing:\n",
       " tensor([[ 0.0314, -0.0375,  0.0131,  ..., -0.0057, -0.0008, -0.0089]],\n",
       "        requires_grad=True),\n",
       " Parameter containing:\n",
       " tensor([0.0563], requires_grad=True)]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(lr_model.parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 0.0314, -0.0375,  0.0131,  ..., -0.0057, -0.0008, -0.0089],\n",
       "       grad_fn=<SelectBackward>)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(lr_model.parameters())[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.return_types.topk(\n",
       "values=tensor([0.3753, 0.2305, 0.2007, 0.2006, 0.1993, 0.1952, 0.1930, 0.1898, 0.1831,\n",
       "        0.1731, 0.1649, 0.1647, 0.1543, 0.1320, 0.1314, 0.1303, 0.1296, 0.1261,\n",
       "        0.1245, 0.1243], grad_fn=<TopkBackward>),\n",
       "indices=tensor([8942, 6336, 1852, 9056, 1865, 4039, 7820, 5002, 8208, 1857, 9709,  803,\n",
       "        1046,  130, 4306, 6481, 4370, 4259, 4285, 1855]))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.topk(list(lr_model.parameters())[0][0], 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the\n",
      "of\n",
      "christ\n",
      "to\n",
      "church\n",
      "god\n",
      "rutgers\n",
      "jesus\n",
      "sin\n",
      "christians\n",
      "we\n",
      "and\n",
      "athos\n",
      "1993\n",
      "hell\n",
      "our\n",
      "his\n",
      "he\n",
      "heaven\n",
      "christian\n"
     ]
    }
   ],
   "source": [
    "for i in torch.topk(list(lr_model.parameters())[0][0], 20)[1]:\n",
    "    print(vectorizer.get_feature_names()[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.return_types.topk(\n",
       "values=tensor([-0.3478, -0.2578, -0.2455, -0.2347, -0.2330, -0.2265, -0.2205, -0.2050,\n",
       "        -0.2044, -0.1979, -0.1876, -0.1790, -0.1747, -0.1745, -0.1734, -0.1647,\n",
       "        -0.1639, -0.1617, -0.1601, -0.1592], grad_fn=<TopkBackward>),\n",
       "indices=tensor([5119, 8096, 5420, 4436, 6194, 1627, 6901, 5946, 9970, 3116, 1036, 9906,\n",
       "        5654, 8329, 7869, 1039, 1991, 4926, 5035, 4925]))"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.topk(list(lr_model.parameters())[0][0], 20, largest = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "keith\n",
      "sgi\n",
      "livesey\n",
      "host\n",
      "nntp\n",
      "caltech\n",
      "posting\n",
      "morality\n",
      "you\n",
      "edu\n",
      "atheism\n",
      "wpd\n",
      "mathew\n",
      "solntze\n",
      "sandvik\n",
      "atheists\n",
      "com\n",
      "islamic\n",
      "jon\n",
      "islam\n"
     ]
    }
   ],
   "source": [
    "for i in torch.topk(list(lr_model.parameters())[0][0], 20, largest = False)[1]:\n",
    "    print(vectorizer.get_feature_names()[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sieć neuronowa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NeuralNetworkModel(torch.nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(NeuralNetworkModel, self).__init__()\n",
    "        self.fc1 = torch.nn.Linear(FEAUTERES,500)\n",
    "        self.fc2 = torch.nn.Linear(500,1)\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": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "nn_model = NeuralNetworkModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = torch.nn.BCELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = torch.optim.SGD(nn_model.parameters(), lr = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.6605833534551934, 0.5908529048207664)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.6379233609747004, 0.6481481481481481)"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.4341224195120214, 0.896168108776267)"
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     "metadata": {},
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       "(0.05794332528279887, 1.0)"
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     "metadata": {},
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   ],
   "source": [
    "for epoch in range(5):\n",
    "    loss_score = 0\n",
    "    acc_score = 0\n",
    "    items_total = 0\n",
    "    nn_model.train()\n",
    "    for i in range(0, Y_train.shape[0], BATCH_SIZE):\n",
    "        X = X_train[i:i+BATCH_SIZE]\n",
    "        X = torch.tensor(X.astype(np.float32).todense())\n",
    "        Y = Y_train[i:i+BATCH_SIZE]\n",
    "        Y = torch.tensor(Y.astype(np.float32)).reshape(-1,1)\n",
    "        Y_predictions = nn_model(X)\n",
    "        acc_score += torch.sum((Y_predictions > 0.5) == Y).item()\n",
    "        items_total += Y.shape[0] \n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss = criterion(Y_predictions, Y)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "\n",
    "        loss_score += loss.item() * Y.shape[0] \n",
    "\n",
    "    display(epoch)\n",
    "    display(get_loss_acc(nn_model, X_train, Y_train))\n",
    "    display(get_loss_acc(nn_model, X_dev, Y_dev))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.16834938257537793, 0.9428172942817294)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_loss_acc(nn_model, X_test, Y_test)"
   ]
  }
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