regression.py

@@ -1,100 +0,0 @@
-import numpy as np
-import pandas as pd
-import torch
-from csv import QUOTE_NONE
-from nltk.tokenize import word_tokenize
-import gensim.downloader
-
-#Based on source material from classes
-class MyNeuralNetwork(torch.nn.Module):
-    def __init__(self, input_size, hidden_size, num_classes):
-        super(MyNeuralNetwork, 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 = gensim.downloader.load('word2vec-google-news-300')
-def get_word2vec(document):
-
-    return np.mean([word2vec[token] for token in document if token in word2vec] or [np.zeros(300)], axis=0)
-
-#Basic paths + reading from files
-XtrainingData = pd.read_table('train/in.tsv.xz', error_bad_lines=False, header=None, quoting=QUOTE_NONE, names=['content', 'id'])
-YtrainingData = pd.read_table('train/expected.tsv', error_bad_lines=False, header=None, quoting=QUOTE_NONE, names=['label'])['label']
-
-XtestData = pd.read_table('test-A/in.tsv.xz', error_bad_lines=False, header=None, quoting=QUOTE_NONE, names=['content', 'id'])
-XdevData = pd.read_table('dev-0/in.tsv.xz', error_bad_lines=False, header=None, quoting=QUOTE_NONE, names=['content', 'id'])
-
-
-#Data filltering and preprocessing
-XtrainingData = [word_tokenize(row) for row in XtrainingData['content'].str.lower()]
-XtestData = [word_tokenize(row) for row in XtestData['content'].str.lower()]
-XdevData = [word_tokenize(row) for row in XdevData['content'].str.lower()]
-XtrainingData = [get_word2vec(document) for document in XtrainingData]
-XtestData = [get_word2vec(document) for document in XtestData]
-XdevData = [get_word2vec(document) for document in XdevData]
-
-#Basic parameters for the model
-eph = 30
-batches = 5
-network = MyNeuralNetwork(300, 600, 1)
-crit = torch.nn.BCELoss()
-opt = torch.optim.SGD(network.parameters(), lr=0.03)
-
-########Accuracy for different parameters according to Geval###########
-#0.7561 for 5 epochs and 5 batches
-#0.7728 for 30 epochs and 5 batches
-#0.7712 for 30 epochs and 15 batches
-#######################################################################
-#Model training according to source files from classes
-for epoch in range(eph):
-    network.train()
-
-    for i in range(0, YtrainingData.shape[0], batches):
-        x = XtrainingData[i :i + batches]
-        x = torch.tensor(x)
-        y = YtrainingData[i :i + batches]
-        y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)
-
-        outcome = network(x.float())
-        loss = crit(outcome, y)
-        opt.zero_grad()
-        loss.backward()
-        opt.step()
-
-#Basic evaluation
-YtestPred = []
-YpredDev = []
-
-with torch.no_grad():
-    for i in range(0, len(XdevData), batches):
-        x = XdevData[i :i + batches]
-        x = torch.tensor(x)
-        outcome = network(x.float())
-        predict = outcome > 0.5
-
-        YpredDev += predict.tolist()
-
-    for i in range(0, len(XtestData), batches):
-        x = XtestData[i :i + batches]
-        x = torch.tensor(x)
-        outcome = network(x.float())
-        predict = outcome > 0.5
-
-        YtestPred += predict.tolist()
-
-#Saving outputs
-np.asarray(YpredDev, dtype=np.int32).tofile('./dev-0/out.tsv', sep='\n')
-np.asarray(YtestPred, dtype=np.int32).tofile('./test-A/out.tsv', sep='\n')
-
-########Accuracy for different parameters according to Geval###########
-#0.7561 for 5 epochs and 5 batches
-#0.7728 for 30 epochs and 5 batches
-#0.7712 for 30 epochs and 15 batches
-#######################################################################