.ipynb_checkpoints/Untitled-checkpoint.ipynb

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-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 4
-}

.ipynb_checkpoints/Untitled1-checkpoint.ipynb

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-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 4
-}

.ipynb_checkpoints/logistic-regression-checkpoint.ipynb

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-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 4
-}

logistic-regression.ipynb

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-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "import gensim.downloader as gn\n",
-    "import csv\n",
-    "from nltk.tokenize import word_tokenize"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "STEP 3 - PREPROCESSING\n"
-     ]
-    }
-   ],
-   "source": [
-    "names = ['content', 'id', 'label']\n",
-    "train_data_content = pd.read_table('train/in.tsv', error_bad_lines = False, header = None, quoting = csv.QUOTE_NONE, names = ['content', 'id'])\n",
-    "train_data_labels = pd.read_table('train/expected.tsv', error_bad_lines = False, header = None, quoting=csv.QUOTE_NONE, names = ['label'])\n",
-    "dev_data = pd.read_table('dev-0/in.tsv', error_bad_lines = False, header = None, quoting = csv.QUOTE_NONE, names = ['content', 'id'])\n",
-    "test_data = pd.read_table('test-A/in.tsv', error_bad_lines = False, header = None, quoting = csv.QUOTE_NONE, names = ['content', 'id'])\n",
-    "\n",
-    "print('STEP 3 - PREPROCESSING')\n",
-    "# lowercase all content\n",
-    "X_train = train_data_content['content'].str.lower()\n",
-    "y_train = train_data_labels['label']\n",
-    "X_dev = dev_data['content'].str.lower()\n",
-    "X_test = test_data['content'].str.lower()\n",
-    "\n",
-    "# tokenize datasets\n",
-    "X_train = [word_tokenize(content) for content in X_train]\n",
-    "X_dev = [word_tokenize(content) for content in X_dev]\n",
-    "X_test = [word_tokenize(content) for content in X_test]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[==================================================] 100.0% 1662.8/1662.8MB downloaded\n"
-     ]
-    }
-   ],
-   "source": [
-    "w2v = gn.load('word2vec-google-news-300')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

logistic-regression.py

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-import pandas as pd
-import numpy as np
-import csv
-import torch
-from nltk.tokenize import word_tokenize
-from gensim import downloader
-
-FEATURES = ['content', 'id', 'label']
-PATHS = ['train/in.tsv', 'train/expected.tsv', 'dev-0/in.tsv', 'test-A/in.tsv', './dev-0/out.tsv', './test-A/out.tsv']
-PRE_TRAINED = 'word2vec-google-news-300'
-
-class NeuralNetwork(torch.nn.Module):
-    def __init__(self, INPUT_DIM):
-        super(NeuralNetwork, self).__init__()
-        self.l1 = torch.nn.Linear(INPUT_DIM, 500)
-        self.l2 = torch.nn.Linear(500, 1)
-
-    def forward(self, x):
-        x = self.l1(x)
-        x = torch.relu(x)
-        x = self.l2(x)
-        x = torch.sigmoid(x)
-        return x
-
-def get_data(FEATURES, PATHS):
-  x_train = pd.read_table(PATHS[0], error_bad_lines = False, header = None, quoting = csv.QUOTE_NONE, names = FEATURES[:2])
-  y_train = pd.read_table(PATHS[1], error_bad_lines = False, header = None, quoting = csv.QUOTE_NONE, names = FEATURES[2:])
-  x_dev = pd.read_table(PATHS[2], error_bad_lines = False, header = None, quoting = csv.QUOTE_NONE, names = FEATURES[:2])
-  x_test = pd.read_table(PATHS[3], error_bad_lines = False, header = None, quoting = csv.QUOTE_NONE, names = FEATURES[:2])
-
-  return x_train, y_train, x_dev, x_test
-
-def preprocess(x_train, y_train, x_dev, x_test):
-  x_train = x_train[FEATURES[0]].str.lower()
-  x_dev = x_dev[FEATURES[0]].str.lower()
-  x_test = x_test[FEATURES[0]].str.lower()
-  y_train = y_train[FEATURES[2]]
-
-  return x_train, y_train, x_dev, x_test
-
-def tokenize(x_train, x_dev, x_test):
-  x_train = [word_tokenize(i) for i in x_train]
-  x_dev = [word_tokenize(i) for i in x_dev]
-  x_test = [word_tokenize(i) for i in x_test]
-
-  return x_train, x_dev, x_test
-
-def use_word2vec():
-  w2v = downloader.load(PRE_TRAINED)
-
-  return w2v
-
-def document_vector(w2v, x_train, x_dev, x_test):
-  x_train = [np.mean([w2v[word] for word in doc if word in w2v] or [np.zeros(300)], axis = 0) for doc in x_train]
-  x_dev = [np.mean([w2v[word] for word in doc if word in w2v] or [np.zeros(300)], axis = 0) for doc in x_dev]
-  x_test = [np.mean([w2v[word] for word in doc if word in w2v] or [np.zeros(300)], axis = 0) for doc in x_test]
-
-  return x_train, x_dev, x_test
-
-def basic_config():
-  INPUT_DIM = 300
-  BATCH_SIZE = 5
-
-  return INPUT_DIM, BATCH_SIZE
-
-def init_model(INPUT_DIM):
-  nn_model = NeuralNetwork(INPUT_DIM)
-  criterion = torch.nn.BCELoss()
-  optimizer = torch.optim.SGD(nn_model.parameters(), lr = 0.1)
-
-  return nn_model, optimizer, criterion
-
-def train(nn_model, BATCH_SIZE, criterion, optimizer, x_train, y_train):
-  for epoch in range(5):
-    nn_model.train()
-    for i in range(0, y_train.shape[0], BATCH_SIZE):
-        X = x_train[i:i+BATCH_SIZE]
-        X = torch.tensor(X)
-        y = y_train[i:i+BATCH_SIZE]
-        y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)
-        outputs = nn_model(X.float())
-        loss = criterion(outputs, y)
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()
-
-def prediction(nn_model, BATCH_SIZE, x_dev, x_test):
-  y_dev, y_test = [], []
-  nn_model.eval()
-  with torch.no_grad():
-      for i in range(0, len(x_dev), BATCH_SIZE):
-          X = x_dev[i:i+BATCH_SIZE]
-          X = torch.tensor(X)
-          outputs = nn_model(X.float())
-          prediction = (outputs > 0.5)
-          y_dev += prediction.tolist()
-      for i in range(0, len(x_test), BATCH_SIZE):
-          X = x_test[i:i+BATCH_SIZE]
-          X = torch.tensor(X)
-          outputs = nn_model(X.float())
-          prediction = (outputs > 0.5)
-          y_test += prediction.tolist()
-
-  return y_dev, y_test
-
-def get_result(y_dev, y_test):
-  np.asarray(y_dev, dtype = np.int32).tofile(PATHS[4], sep='\n')
-  np.asarray(y_test, dtype = np.int32).tofile(PATHS[5], sep='\n')
-
-def main():
-  x_train, y_train, x_dev, x_test = get_data(FEATURES, PATHS)
-  x_train, y_train, x_dev, x_test = preprocess(x_train, y_train, x_dev, x_test)
-  x_train, x_dev, x_test = tokenize(x_train, x_dev, x_test)
-  w2v = use_word2vec()
-  x_train, x_dev, x_test = document_vector(w2v, x_train, x_dev, x_test)
-  INPUT_DIM, BATCH_SIZE = basic_config()
-  nn_model, optimizer, criterion = init_model(INPUT_DIM)
-  train(nn_model, BATCH_SIZE, criterion, optimizer, x_train, y_train)
-  y_dev, y_test = prediction(nn_model, BATCH_SIZE, x_dev, x_test)
-  get_result(y_dev, y_test)
-
-if _name_ == '_main_':
-  main()

roberta.py

@@ -1,84 +0,0 @@
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
-import torch
-
-PATHS = ['train/in.tsv', 'train/expected.tsv', 'dev-0/in.tsv', 'test-A/in.tsv', './dev-0/out.tsv', './test-A/out.tsv']
-OUTPUT_PATHS = ['dev-0/out.tsv', 'test-A/out.tsv']
-PRE_TRAINED = ['roberta-base']
-
-def get_data(path):
-    data = []
-    with open(path, encoding='utf-8') as f:
-        data = f.readlines()
-
-    return data
-
-def generate_output(path, trainer, X_data):
-    data = []
-    with open(path, encoding='utf-8') as f:
-        for result in trainer.predict(X_data):
-            f.write(str(result) + '\n')
-
-class IMDbDataset(torch.utils.data.Dataset):
-    def __init__(self, encodings, labels):
-        self.encodings = encodings
-        self.labels = labels
-
-    def __getitem__(self, idx):
-        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
-        item['labels'] = torch.tensor(self.labels[idx])
-        return item
-
-    def __len__(self):
-        return len(self.labels)
-
-def prepare(data_train_X, data_train_Y):
-    tokenizer = AutoTokenizer.from_pretrained(PRE_TRAINED[0])
-    model = AutoModelForSequenceClassification.from_pretrained(PRE_TRAINED[0], num_labels=2)
-    device = torch.device("cpu")
-    model.to(device)
-    encoded_input  = tokenizer([text[0] for text in list(zip(data_train_X, data_train_Y))], truncation=True, padding=True)
-    train_dataset = IMDbDataset(encoded_input , [int(text[1]) for text in list(zip(data_train_X, data_train_Y))])
-
-    return train_dataset, model
-    
-def training(train_dataset, model):
-    training_args = TrainingArguments(
-    output_dir='./results',          # output directory
-    num_train_epochs=3,              # total number of training epochs
-    per_device_train_batch_size=16,  # batch size per device during training
-    per_device_eval_batch_size=64,   # batch size for evaluation
-    warmup_steps=500,                # number of warmup steps for learning rate scheduler
-    weight_decay=0.01,               # strength of weight decay
-    logging_dir='./logs',            # directory for storing logs
-    logging_steps=10,
-    )
-
-    trainer = Trainer(
-        model=model,                         # the instantiated Transformers model to be trained
-        args=training_args,                  # training arguments, defined above
-        train_dataset=train_dataset,         # training dataset
-    )
-
-    trainer.train()
-
-    return trainer
-
-def main():
-    #data
-    X_train = get_data(PATHS[0])
-    y_train = get_data(PATHS[1])
-    X_dev = get_data(PATHS[2])
-    X_test = get_data(PATHS[3])
-
-    #prepare
-    train_dataset, model = prepare(X_train, y_train)
-
-    #trainer
-    trainer = training(train_dataset, model)
-
-    #output
-    generate_output(OUTPUT_PATHS[0], trainer, X_dev)
-    generate_output(OUTPUT_PATHS[1], trainer, X_test)
-
-if __name__ == '__main__':
-    main()