2021-05-04 23:02:28 +02:00
{
"cells": [
2021-09-27 12:34:44 +02:00
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"<div class=\"alert alert-block alert-info\">\n",
"<h1> Ekstrakcja informacji </h1>\n",
"<h2> 8. <i>Regresja logistyczna</i> [ćwiczenia]</h2> \n",
"<h3> Jakub Pokrywka (2021)</h3>\n",
"</div>\n",
"\n",
""
]
},
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{
"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",
" loss = criterion(Y_predictions, Y)\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",
2021-05-10 10:55:55 +02:00
" x = torch.relu(x)\n",
2021-05-04 23:02:28 +02:00
" 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)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"1"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(0.4341224195120214, 0.896168108776267)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(0.3649017943276299, 0.9074074074074074)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"2"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(0.18619558424660096, 0.9765142150803461)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(0.16293201995668588, 0.9888888888888889)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"3"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(0.09108264647580784, 0.9962917181705809)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(0.08985773311858927, 0.9962962962962963)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"4"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(0.053487053708540566, 0.9987639060568603)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(0.05794332528279887, 1.0)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"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)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Zadanie domowe\n",
"\n",
"- wybrać jedno z poniższych repozytoriów i je sforkować:\n",
" - https://git.wmi.amu.edu.pl/kubapok/paranormal-or-skeptic-ISI-public\n",
" - https://git.wmi.amu.edu.pl/kubapok/sport-text-classification-ball-ISI-public\n",
"- stworzyć klasyfikator bazujący na prostej sieci neuronowej feed forward w pytorchu (można bazować na tym jupyterze). Zamiast tfidf proszę skorzystać z jakieś reprezentacji gęstej (np. word2vec).\n",
"- stworzyć predykcje w plikach dev-0/out.tsv oraz test-A/out.tsv\n",
"- wynik accuracy sprawdzony za pomocą narzędzia geval (patrz poprzednie zadanie) powinien wynosić conajmniej 0.67\n",
"- proszę umieścić predykcję oraz skrypty generujące (w postaci tekstowej a nie jupyter) w repo, a w MS TEAMS umieścić link do swojego repo\n",
"termin 25.05, 70 punktów\n"
]
}
],
"metadata": {
2021-09-27 12:34:44 +02:00
"author": "Jakub Pokrywka",
"email": "kubapok@wmi.amu.edu.pl",
2021-05-04 23:02:28 +02:00
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
2021-09-27 12:34:44 +02:00
"lang": "pl",
2021-05-04 23:02:28 +02:00
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
2021-09-27 12:34:44 +02:00
"version": "3.8.3"
},
"subtitle": "8.Regresja logistyczna[ćwiczenia]",
"title": "Ekstrakcja informacji",
"year": "2021"
2021-05-04 23:02:28 +02:00
},
"nbformat": 4,
"nbformat_minor": 4
}
