paranormal-or-skeptic-ISI-p.../main.py
2021-05-30 14:53:39 +00:00

108 lines
3.1 KiB
Python

import numpy
from sklearn.preprocessing import LabelEncoder
from sklearn.naive_bayes import GaussianNB, MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
import torch
from gensim import downloader
from nltk.tokenize import word_tokenize
import pandas as pd
class NetworkModel(torch.nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NetworkModel, self).__init__()
self.fc1 = torch.nn.Linear(input_size, hidden_size)
self.fc2 = torch.nn.Linear(hidden_size, num_classes)
def forward(self, x):
x = self.fc1(x)
x = torch.relu(x)
x = self.fc2(x)
x = torch.sigmoid(x)
return x
word2vec = downloader.load("word2vec-google-news-300")
def word2vecOnDoc(document):
return numpy.mean(
[word2vec[token] for token in document if token in word2vec] or [numpy.zeros(300)],
axis=0,
)
def prepareData(data):
data = [word_tokenize(row) for row in data.content.str.lower()]
data = [word2vecOnDoc(document) for document in data]
return data
def trainModel(trainFileIn, trainFileExpected):
inData = pd.read_table(
trainFileIn,
error_bad_lines=False,
header=None,
quoting=3,
usecols=["content"],
names=["content", "id"],
nrows=225000,
)
expectedData = pd.read_table(
trainFileExpected,
error_bad_lines=False,
header=None,
quoting=3,
usecols=["label"],
names=["label"],
nrows=225000,
)
# expectedData = prepareData(expectedData)
inData = prepareData(inData)
networkModel = NetworkModel(300, 300, 1)
criterion = torch.nn.BCELoss()
optim = torch.optim.SGD(networkModel.parameters(), lr=0.02)
epochs = 1
batchSize = 2
for _ in range(epochs):
networkModel.train()
for i in range(0, expectedData.shape[0], batchSize):
x = inData[i : i + batchSize]
x = torch.tensor(x)
y = expectedData[i : i + batchSize]
y = torch.tensor(y.astype(numpy.float32).to_numpy()).reshape(-1, 1)
outputs = networkModel(x.float())
loss = criterion(outputs, y)
# print(loss)
optim.zero_grad()
loss.backward()
optim.step()
return networkModel
def evaluateModel(model, inFile, outFile):
inData = pd.read_table(
inFile,
error_bad_lines=False,
header=None,
quoting=3,
usecols=["content"],
names=["content", "id"],
)
inData = prepareData(inData)
batchSize = 2
pred = []
with torch.no_grad():
for i in range(0, len(inData), batchSize):
x = inData[i : i + batchSize]
x = torch.tensor(x)
outputs = model(x.float())
prediction = outputs >= 0.5
pred += prediction.tolist()
numpy.asarray(pred, dtype=numpy.int32).tofile(outFile, sep="\n")
model = trainModel("train/in.tsv", "train/expected.tsv")
evaluateModel(model, "dev-0/in.tsv", "dev-0/out.tsv")
evaluateModel(model, "test-A/in.tsv", "test-A/out.tsv")