add logistic regression
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.ipynb_checkpoints/logistic-regression-checkpoint.ipynb
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.ipynb_checkpoints/logistic-regression-checkpoint.ipynb
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{
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"cells": [],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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dev-0/in.tsv
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dev-0/in.tsv
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dev-0/in.tsv.xz
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dev-0/out.tsv
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dev-0/out.tsv
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logistic-regression.ipynb
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logistic-regression.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 34,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import torch\n",
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"import gensim.downloader as gn\n",
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"import csv\n",
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"from nltk.tokenize import word_tokenize"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"STEP 3 - PREPROCESSING\n"
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]
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}
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],
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"source": [
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"names = ['content', 'id', 'label']\n",
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"train_data_content = pd.read_table('train/in.tsv', error_bad_lines = False, header = None, quoting = csv.QUOTE_NONE, names = ['content', 'id'])\n",
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"train_data_labels = pd.read_table('train/expected.tsv', error_bad_lines = False, header = None, quoting=csv.QUOTE_NONE, names = ['label'])\n",
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"dev_data = pd.read_table('dev-0/in.tsv', error_bad_lines = False, header = None, quoting = csv.QUOTE_NONE, names = ['content', 'id'])\n",
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"test_data = pd.read_table('test-A/in.tsv', error_bad_lines = False, header = None, quoting = csv.QUOTE_NONE, names = ['content', 'id'])\n",
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"\n",
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"print('STEP 3 - PREPROCESSING')\n",
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"# lowercase all content\n",
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"X_train = train_data_content['content'].str.lower()\n",
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"y_train = train_data_labels['label']\n",
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"X_dev = dev_data['content'].str.lower()\n",
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"X_test = test_data['content'].str.lower()\n",
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"\n",
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"# tokenize datasets\n",
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"X_train = [word_tokenize(content) for content in X_train]\n",
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"X_dev = [word_tokenize(content) for content in X_dev]\n",
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"X_test = [word_tokenize(content) for content in X_test]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[==================================================] 100.0% 1662.8/1662.8MB downloaded\n"
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]
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}
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],
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"source": [
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"w2v = gn.load('word2vec-google-news-300')\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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logistic-regression.py
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logistic-regression.py
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import pandas as pd
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import numpy as np
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import csv
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import torch
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from nltk.tokenize import word_tokenize
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from gensim import downloader
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FEATURES = ['content', 'id', 'label']
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PATHS = ['train/in.tsv', 'train/expected.tsv', 'dev-0/in.tsv', 'test-A/in.tsv', './dev-0/out.tsv', './test-A/out.tsv']
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PRE_TRAINED = 'word2vec-google-news-300'
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class NeuralNetwork(torch.nn.Module):
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def __init__(self, INPUT_DIM):
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super(NeuralNetwork, self).__init__()
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self.l1 = torch.nn.Linear(INPUT_DIM, 500)
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self.l2 = torch.nn.Linear(500, 1)
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def forward(self, x):
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x = self.l1(x)
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x = torch.relu(x)
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x = self.l2(x)
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x = torch.sigmoid(x)
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return x
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def get_data(FEATURES, PATHS):
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x_train = pd.read_table(PATHS[0], error_bad_lines = False, header = None, quoting = csv.QUOTE_NONE, names = FEATURES[:2])
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y_train = pd.read_table(PATHS[1], error_bad_lines = False, header = None, quoting = csv.QUOTE_NONE, names = FEATURES[2:])
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x_dev = pd.read_table(PATHS[2], error_bad_lines = False, header = None, quoting = csv.QUOTE_NONE, names = FEATURES[:2])
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x_test = pd.read_table(PATHS[3], error_bad_lines = False, header = None, quoting = csv.QUOTE_NONE, names = FEATURES[:2])
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return x_train, y_train, x_dev, x_test
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def preprocess(x_train, y_train, x_dev, x_test):
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x_train = x_train[FEATURES[0]].str.lower()
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x_dev = x_dev[FEATURES[0]].str.lower()
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x_test = x_test[FEATURES[0]].str.lower()
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y_train = y_train[FEATURES[2]]
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return x_train, y_train, x_dev, x_test
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def tokenize(x_train, x_dev, x_test):
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x_train = [word_tokenize(i) for i in x_train]
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x_dev = [word_tokenize(i) for i in x_dev]
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x_test = [word_tokenize(i) for i in x_test]
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return x_train, x_dev, x_test
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def use_word2vec():
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w2v = downloader.load(PRE_TRAINED)
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return w2v
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def document_vector(w2v, x_train, x_dev, x_test):
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x_train = [np.mean([w2v[w] for w in doc if w in w2v] or [np.zeros(300)], axis = 0) for doc in x_train]
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x_dev = [np.mean([w2v[w] for w in doc if w in w2v] or [np.zeros(300)], axis = 0) for doc in x_dev]
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x_test = [np.mean([w2v[w] for w in doc if w in w2v] or [np.zeros(300)], axis = 0) for doc in x_test]
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return x_train, x_dev, x_test
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def basic_config():
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INPUT_DIM = 300
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BATCH_SIZE = 5
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return INPUT_DIM, BATCH_SIZE
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def init_model(INPUT_DIM):
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nn_model = NeuralNetwork(INPUT_DIM)
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criterion = torch.nn.BCELoss()
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optimizer = torch.optim.SGD(nn_model.parameters(), lr = 0.1)
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return nn_model, optimizer, criterion
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def train(nn_model, BATCH_SIZE, criterion, optimizer, x_train, y_train):
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for epoch in range(5):
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nn_model.train()
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for i in range(0, y_train.shape[0], BATCH_SIZE):
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X = x_train[i:i+BATCH_SIZE]
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X = torch.tensor(X)
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y = y_train[i:i+BATCH_SIZE]
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y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)
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outputs = nn_model(X.float())
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loss = criterion(outputs, y)
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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def prediction(nn_model, BATCH_SIZE, x_dev, x_test):
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y_dev, y_test = [], []
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nn_model.eval()
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with torch.no_grad():
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for i in range(0, len(x_dev), BATCH_SIZE):
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X = x_dev[i:i+BATCH_SIZE]
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X = torch.tensor(X)
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outputs = nn_model(X.float())
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prediction = (outputs > 0.5)
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y_dev += prediction.tolist()
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for i in range(0, len(x_test), BATCH_SIZE):
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X = x_test[i:i+BATCH_SIZE]
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X = torch.tensor(X)
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outputs = nn_model(X.float())
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prediction = (outputs > 0.5)
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y_test += prediction.tolist()
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return y_dev, y_test
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def get_result(y_dev, y_test):
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np.asarray(y_dev, dtype = np.int32).tofile(PATHS[4], sep='\n')
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np.asarray(y_test, dtype = np.int32).tofile(PATHS[5], sep='\n')
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def main():
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x_train, y_train, x_dev, x_test = get_data(FEATURES, PATHS)
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x_train, y_train, x_dev, x_test = preprocess(x_train, y_train, x_dev, x_test)
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x_train, x_dev, x_test = tokenize(x_train, x_dev, x_test)
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w2v = use_word2vec()
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x_train, x_dev, x_test = document_vector(w2v, x_train, x_dev, x_test)
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INPUT_DIM, BATCH_SIZE = basic_config()
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nn_model, optimizer, criterion = init_model(INPUT_DIM)
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train(nn_model, BATCH_SIZE, criterion, optimizer, x_train, y_train)
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y_dev, y_test = prediction(nn_model, BATCH_SIZE, x_dev, x_test)
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get_result(y_dev, y_test)
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if __name__ == '__main__':
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main()
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test-A/in.tsv
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test-A/out.tsv
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test-A/out.tsv
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train/in.tsv
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train/in.tsv
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