final version
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README.md
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README.md
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Skeptic vs paranormal subreddits
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================================
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Classify a reddit as either from Skeptic subreddit or one of the
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"paranormal" subreddits (Paranormal, UFOs, TheTruthIsHere, Ghosts,
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,Glitch-in-the-Matrix, conspiracytheories).
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Output label is the probability of a paranormal subreddit.
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Sources
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-------
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Data taken from <https://archive.org/details/2015_reddit_comments_corpus>.
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config.txt
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config.txt
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--metric Likelihood --metric Accuracy --metric F1 --metric F0:N<Precision> --metric F9999999:N<Recall> --precision 4 --in-header in-header.tsv --out-header out-header.tsv
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dev-0/expected.tsv
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dev-0/expected.tsv
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dev-0/in.tsv.xz
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dev-0/out.tsv
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ffn.py
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ffn.py
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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import pickle
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import numpy as np
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import pandas as pd
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from word2vec import Word2Vec
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class FFN(nn.Module):
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def __init__(self, input_dim, output_dim, hidden1_size, hidden2_size, lr, epochs, batch_size):
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super(FFN, self).__init__()
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self.path = 'model1.pickle'
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self.lr = lr
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self.epochs = epochs
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self.output_dim = output_dim
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self.word2vec = Word2Vec()
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self.word2vec.load()
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self.batch_size = batch_size
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self.input_dim = input_dim
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self.fc1 = nn.Linear(batch_size, hidden1_size)
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self.fc2 = nn.Linear(hidden1_size, hidden2_size)
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self.fc3 = nn.Linear(hidden2_size, hidden2_size)
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self.fc4 = nn.Linear(hidden2_size, hidden2_size)
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self.fc5 = nn.Linear(hidden2_size, batch_size)
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def forward(self, data):
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data = F.relu(self.fc1(data))
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data = F.relu(self.fc2(data))
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data = F.relu(self.fc3(data))
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data = F.relu(self.fc4(data))
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data = F.sigmoid(self.fc5(data))
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return data
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def serialize(self):
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with open(self.path, 'wb') as file:
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pickle.dump(self, file)
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def load(self):
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with open(self.path, 'rb') as file:
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self = pickle.load(file)
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def batch(self, iterable, n=1):
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l = len(iterable)
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for ndx in range(0, l, n):
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yield iterable[ndx:min(ndx + n, l)]
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def train(self, data, expected):
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self.zero_grad()
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criterion = torch.nn.BCELoss()
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optimizer = optim.Adam(self.parameters(), lr=self.lr)
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batch_size = self.batch_size
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num_of_classes = self.output_dim
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for epoch in range(self.epochs):
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epoch_loss = 0.0
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idx = 0
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for i in range(0, int(len(data)/batch_size)*batch_size, batch_size):
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inputs = data[i:i + batch_size]
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labels = expected[i:i+ batch_size]
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optimizer.zero_grad()
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outputs = self.forward(torch.tensor(self.word2vec.list_of_sentences2vec(inputs)))
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target = torch.tensor(labels.values).double()
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loss = criterion(outputs.view(batch_size), target.view(-1,))
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loss.backward()
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optimizer.step()
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epoch_loss += loss.item()
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if(idx % 1000 == 0):
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print('epoch: {}, idx: {}, loss: {}'.format(epoch, idx, epoch_loss/1000))
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epoch_loss = 0
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idx += 1
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self.serialize()
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def test(self, data, expected, path):
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correct = 0
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incorrect = 0
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total = 0
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predictions = []
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batch_size = self.batch_size
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for i in range(0, int(len(data)/batch_size)*batch_size, batch_size):
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inputs = data[i:i + batch_size]
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labels = expected[i:i+ batch_size]
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predicted = self.forward(torch.tensor(self.word2vec.list_of_sentences2vec(inputs)))
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score = [1 if x > 0.5 else 0 for x in predicted]
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for x, y in zip(score, labels):
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if(x == y):
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correct += 1
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else:
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incorrect += 1
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predictions.append(score)
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print(correct)
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print(incorrect)
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print(correct/(incorrect + correct))
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df = pd.DataFrame(np.asarray(predictions).reshape(int(len(data)/batch_size)*batch_size))
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df.reset_index(drop=True, inplace=True)
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df.to_csv(path, sep="\t", index=False)
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in-header.tsv
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in-header.tsv
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PostText Timestamp
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main.py
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main.py
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import pandas as pd
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import spacy
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from ffn import FFN
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import numpy as np
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from nltk.tokenize import word_tokenize
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sp = spacy.load('en_core_web_sm')
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def word2vec(word):
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return sp(word).vector
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def create_embeddings_file(data, path, func):
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out = []
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for line in data:
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out.append(func(line))
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df = pd.DataFrame(out)
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df.to_csv(path)
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def load_embeddings_file(path):
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return pd.read_csv(path)
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train_data = pd.read_csv("train/in.tsv", sep='\t')
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train_data.columns = ['PostText', 'Timestamp']
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train_expected = pd.read_csv("train/expected.tsv", sep='\t')
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train_expected.columns = ['Label']
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test_data = pd.read_csv("test-A/in.tsv", sep='\t')
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test_data.columns = ['PostText', 'Timestamp']
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dev_data = pd.read_csv('dev-0/in.tsv', sep='\t')
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dev_data.columns = ['PostText', 'Timestamp']
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dev_expected = pd.read_csv('dev-0/expected.tsv', sep='\t')
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dev_expected.columns = ['Label']
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create_embeddings_file(dev_data['PostText'], 'dev-0/embeddings.csv', word2vec)
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create_embeddings_file(test_data['PostText'], 'test-A/embeddings.csv', word2vec)
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create_embeddings_file(train_data['PostText'], 'train/embeddings.csv', word2vec)
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train_data = load_embeddings_file('train/embeddings.csv').to_numpy()
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dev_data = load_embeddings_file('dev-0/embeddings.csv').to_numpy()
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test_data = load_embeddings_file('test-A/embeddings.csv').to_numpy()
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model = FFN(300, 1, 300, 300, 0.01, 4, 100)
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model.double()
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model.train([np.asarray(word_tokenize(x)) for x in train_data['PostText']], train_expected['Label'])
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model.load()
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model.double()
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model.test([np.asarray(word_tokenize(x)) for x in train_data['PostText']], train_expected['Label'], "train/out.tsv")
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out-header.tsv
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Label
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train/expected.tsv
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word2vec.py
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word2vec.py
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import gensim.downloader
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import numpy as np
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class Word2Vec():
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def __init__(self) -> None:
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pass
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def load(self):
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self.model = gensim.downloader.load('word2vec-google-news-300')
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def sentence2vec(self, sentence):
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return np.mean([self.model[word] if word in self.model else np.zeros(300) for word in sentence])
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def list_of_sentences2vec(self, sentences):
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return [self.sentence2vec(x) for x in sentences]
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