feed-forward-nn.py

@@ -1,169 +0,0 @@
-import csv
-
-import gensim.downloader
-import numpy as np
-import pandas as pd
-import torch
-import torch.nn as nn
-from nltk import word_tokenize
-
-
-# Feed forward neural network model
-class FeedforwardNeuralNetModel(nn.Module):
-    def __init__(self, input_dim, hidden_dim, output_dim):
-        super(FeedforwardNeuralNetModel, self).__init__()
-
-        # Linear function 1: vocab_size --> 500
-        self.fc1 = nn.Linear(input_dim, hidden_dim)
-        # Non-linearity 1
-        self.relu1 = nn.ReLU()
-
-        # Linear function 2: 500 --> 500
-        self.fc2 = nn.Linear(hidden_dim, hidden_dim)
-        # Non-linearity 2
-        self.relu2 = nn.ReLU()
-
-        # Linear function 3 (readout): 500 --> 3
-        self.fc3 = nn.Linear(hidden_dim, output_dim)
-
-    def forward(self, x):
-        # Linear function 1
-        out = self.fc1(x)
-        # Non-linearity 1
-        out = self.relu1(out)
-
-        # Non-linearity 2
-        out = self.relu2(out)
-
-        # Linear function 3 (readout)
-        out = self.fc3(out)
-
-        return torch.sigmoid(out)
-
-
-col_names = ["content", "id", "label"]
-
-# Loading dataset
-train_set_features = pd.read_table(
-    "train/in.tsv.xz",
-    error_bad_lines=False,
-    quoting=csv.QUOTE_NONE,
-    header=None,
-    names=col_names[:2],
-)
-train_set_labels = pd.read_table(
-    "train/expected.tsv",
-    error_bad_lines=False,
-    quoting=csv.QUOTE_NONE,
-    header=None,
-    names=col_names[2:],
-)
-dev_set = pd.read_table(
-    "dev-0/in.tsv.xz",
-    error_bad_lines=False,
-    header=None,
-    quoting=csv.QUOTE_NONE,
-    names=col_names[:2],
-)
-test_set = pd.read_table(
-    "test-A/in.tsv.xz",
-    error_bad_lines=False,
-    header=None,
-    quoting=csv.QUOTE_NONE,
-    names=col_names[:2],
-)
-
-
-# Lowercase text
-X_train = train_set_features["content"].str.lower()
-y_train = train_set_labels["label"]
-
-X_dev = dev_set["content"].str.lower()
-X_test = test_set["content"].str.lower()
-
-# Tokenize text with nltk
-X_train = [word_tokenize(content) for content in X_train]
-X_dev = [word_tokenize(content) for content in X_dev]
-X_test = [word_tokenize(content) for content in X_test]
-
-# Vectorize text
-word2vec = gensim.downloader.load("word2vec-google-news-300")
-X_train = [
-    np.mean(
-        [word2vec[word] for word in content if word in word2vec] or [np.zeros(300)],
-        axis=0,
-    )
-    for content in X_train
-]
-X_dev = [
-    np.mean(
-        [word2vec[word] for word in content if word in word2vec] or [np.zeros(300)],
-        axis=0,
-    )
-    for content in X_dev
-]
-X_test = [
-    np.mean(
-        [word2vec[word] for word in content if word in word2vec] or [np.zeros(300)],
-        axis=0,
-    )
-    for content in X_test
-]
-
-# Model config
-input_dim = 300
-hidden_layer = 600
-output_dim = 1
-batch_size = 10
-epochs = 10
-
-# Model init
-model = FeedforwardNeuralNetModel(input_dim, hidden_layer, output_dim)
-optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
-criterion = torch.nn.BCELoss()
-
-# Learning model
-for epoch in range(epochs):
-    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 = model(X.float())
-        loss = criterion(outputs, y)
-
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()
-
-
-# Making predictions for dev-0 & and test-A
-test_prediction = []
-dev_prediction = []
-model.eval()
-with torch.no_grad():
-    for i in range(0, len(X_test), batch_size):
-        X = X_test[i : i + batch_size]
-        X = torch.tensor(X)
-
-        outputs = model(X.float())
-
-        prediction = outputs > 0.5
-        test_prediction += prediction.tolist()
-
-    for i in range(0, len(X_dev), batch_size):
-        X = X_dev[i : i + batch_size]
-        X = torch.tensor(X)
-
-        outputs = model(X.float())
-
-        prediction = outputs > 0.5
-        dev_prediction += prediction.tolist()
-
-
-test_prediction = np.asarray(test_prediction, dtype=np.int32)
-dev_prediction = np.asarray(dev_prediction, dtype=np.int32)
-test_prediction.tofile("./test-A/out.tsv", sep="\n")
-dev_prediction.tofile("./dev-0/out.tsv", sep="\n")