Add final solution
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dev-0/out.tsv
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dev-0/out.tsv
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169
feed-forward-nn.py
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169
feed-forward-nn.py
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import csv
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import gensim.downloader
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import numpy as np
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import pandas as pd
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import torch
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import torch.nn as nn
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from nltk import word_tokenize
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# Feed forward neural network model
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class FeedforwardNeuralNetModel(nn.Module):
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def __init__(self, input_dim, hidden_dim, output_dim):
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super(FeedforwardNeuralNetModel, self).__init__()
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# Linear function 1: vocab_size --> 500
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self.fc1 = nn.Linear(input_dim, hidden_dim)
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# Non-linearity 1
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self.relu1 = nn.ReLU()
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# Linear function 2: 500 --> 500
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self.fc2 = nn.Linear(hidden_dim, hidden_dim)
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# Non-linearity 2
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self.relu2 = nn.ReLU()
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# Linear function 3 (readout): 500 --> 3
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self.fc3 = nn.Linear(hidden_dim, output_dim)
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def forward(self, x):
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# Linear function 1
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out = self.fc1(x)
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# Non-linearity 1
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out = self.relu1(out)
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# Non-linearity 2
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out = self.relu2(out)
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# Linear function 3 (readout)
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out = self.fc3(out)
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return torch.sigmoid(out)
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col_names = ["content", "id", "label"]
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# Loading dataset
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train_set_features = pd.read_table(
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"train/in.tsv.xz",
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error_bad_lines=False,
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quoting=csv.QUOTE_NONE,
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header=None,
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names=col_names[:2],
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)
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train_set_labels = pd.read_table(
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"train/expected.tsv",
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error_bad_lines=False,
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quoting=csv.QUOTE_NONE,
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header=None,
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names=col_names[2:],
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)
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dev_set = pd.read_table(
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"dev-0/in.tsv.xz",
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error_bad_lines=False,
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header=None,
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quoting=csv.QUOTE_NONE,
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names=col_names[:2],
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)
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test_set = pd.read_table(
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"test-A/in.tsv.xz",
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error_bad_lines=False,
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header=None,
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quoting=csv.QUOTE_NONE,
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names=col_names[:2],
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)
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# Lowercase text
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X_train = train_set_features["content"].str.lower()
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y_train = train_set_labels["label"]
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X_dev = dev_set["content"].str.lower()
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X_test = test_set["content"].str.lower()
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# Tokenize text with nltk
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X_train = [word_tokenize(content) for content in X_train]
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X_dev = [word_tokenize(content) for content in X_dev]
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X_test = [word_tokenize(content) for content in X_test]
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# Vectorize text
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word2vec = gensim.downloader.load("word2vec-google-news-300")
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X_train = [
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np.mean(
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[word2vec[word] for word in content if word in word2vec] or [np.zeros(300)],
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axis=0,
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)
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for content in X_train
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]
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X_dev = [
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np.mean(
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[word2vec[word] for word in content if word in word2vec] or [np.zeros(300)],
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axis=0,
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)
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for content in X_dev
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]
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X_test = [
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np.mean(
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[word2vec[word] for word in content if word in word2vec] or [np.zeros(300)],
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axis=0,
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)
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for content in X_test
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]
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# Model config
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input_dim = 300
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hidden_layer = 600
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output_dim = 1
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batch_size = 10
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epochs = 10
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# Model init
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model = FeedforwardNeuralNetModel(input_dim, hidden_layer, output_dim)
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optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
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criterion = torch.nn.BCELoss()
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# Learning model
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for epoch in range(epochs):
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model.train()
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for i in range(0, y_train.shape[0], batch_size):
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X = X_train[i : i + batch_size]
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X = torch.tensor(X)
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y = y_train[i : i + batch_size]
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y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)
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outputs = model(X.float())
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loss = criterion(outputs, y)
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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# Making predictions for dev-0 & and test-A
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test_prediction = []
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dev_prediction = []
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model.eval()
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with torch.no_grad():
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for i in range(0, len(X_test), batch_size):
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X = X_test[i : i + batch_size]
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X = torch.tensor(X)
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outputs = model(X.float())
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prediction = outputs > 0.5
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test_prediction += prediction.tolist()
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for i in range(0, len(X_dev), batch_size):
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X = X_dev[i : i + batch_size]
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X = torch.tensor(X)
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outputs = model(X.float())
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prediction = outputs > 0.5
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dev_prediction += prediction.tolist()
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test_prediction = np.asarray(test_prediction, dtype=np.int32)
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dev_prediction = np.asarray(dev_prediction, dtype=np.int32)
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test_prediction.tofile("./test-A/out.tsv", sep="\n")
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dev_prediction.tofile("./dev-0/out.tsv", sep="\n")
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import os
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import pandas as pd
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import tensorflow as tf
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import numpy as np
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import torch
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import torch.nn as nn
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from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
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os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
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print('debug 1')
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train_df = pd.read_csv('train/in.tsv', header=None, sep='\t')
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test_df = pd.read_csv('test-A/in.tsv', header=None, sep='\t')
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dev_df = pd.read_csv('dev-0/in.tsv', header=None, sep='\t')
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train_expected = pd.read_csv('train/expected.tsv', header=None, sep='\t')
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train_text = train_df[0].tolist()
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test_text = test_df[0].tolist()
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dev_text = test_df[0].tolist()
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text_data = train_text + test_text + dev_text
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vectorize_layer = TextVectorization(max_tokens=5, output_mode="int")
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text_data = tf.data.Dataset.from_tensor_slices(text_data)
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vectorize_layer.adapt(text_data.batch(64))
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inputs = tf.keras.layers.Input(shape=(1,), dtype=tf.string, name="text")
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outputs = vectorize_layer(inputs)
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model = tf.keras.Model(inputs, outputs)
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print('uwaga debug')
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x_train = list(map(model.predict, train_text))
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y_train = train_expected[0]
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x_test = list(map(model.predict, test_text))
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loss_function = nn.CrossEntropyLoss()
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x_train = pd.DataFrame(x_train)
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x_test = pd.DataFrame(x_test)
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y_train = pd.DataFrame(y_train[0])
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# (model.predict(["Murder in the forset!"]))
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class FeedforwardNeuralNetModel(nn.Module):
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def __init__(self, input_dim, hidden_dim, output_dim):
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super(FeedforwardNeuralNetModel, self).__init__()
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# Linear function
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self.fc1 = nn.Linear(input_dim, hidden_dim)
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# Non-linearity
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self.sigmoid = nn.Sigmoid()
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# Linear function (readout)
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self.fc2 = nn.Linear(hidden_dim, output_dim)
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def forward(self, x):
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# Linear function # LINEAR
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out = self.fc1(x)
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# Non-linearity # NON-LINEAR
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out = self.sigmoid(out)
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# Linear function (readout) # LINEAR
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out = self.fc2(out)
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return out
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num_epochs = 2
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for epoch in range(num_epochs):
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if (epoch + 1) % 25 == 0:
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print("Epoch completed: " + str(epoch + 1))
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print(f"Epoch number: {epoch}")
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train_loss = 0
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for index, row in x_train.iterrows():
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print(index)
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# Forward pass to get output
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probs = x_train[0][index]
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# Get the target label
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target = y_train[0][index]
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# Calculate Loss: softmax --> cross entropy loss
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loss = loss_function(probs, target)
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# Accumulating the loss over time
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train_loss += loss.item()
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# Getting gradients w.r.t. parameters
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loss.backward()
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train_loss = 0
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bow_ff_nn_predictions = []
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original_lables_ff_bow = []
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with torch.no_grad():
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for index, row in x_test.iterrows():
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probs = x_test[0][index]
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bow_ff_nn_predictions.append(torch.argmax(probs, dim=1).cpu().numpy()[0])
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print(bow_ff_nn_predictions)
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import pandas as pd
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from gensim.utils import simple_preprocess
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from gensim.parsing.porter import PorterStemmer
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from gensim import corpora
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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 torch
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class FeedforwardNeuralNetModel(nn.Module):
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def __init__(self, input_dim, hidden_dim, output_dim):
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super(FeedforwardNeuralNetModel, self).__init__()
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# Linear function 1: vocab_size --> 500
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self.fc1 = nn.Linear(input_dim, hidden_dim)
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# Non-linearity 1
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self.relu1 = nn.ReLU()
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# Linear function 2: 500 --> 500
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self.fc2 = nn.Linear(hidden_dim, hidden_dim)
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# Non-linearity 2
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self.relu2 = nn.ReLU()
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# Linear function 3 (readout): 500 --> 3
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self.fc3 = nn.Linear(hidden_dim, output_dim)
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def forward(self, x):
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# Linear function 1
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out = self.fc1(x)
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# Non-linearity 1
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out = self.relu1(out)
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# Non-linearity 2
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out = self.relu2(out)
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# Linear function 3 (readout)
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out = self.fc3(out)
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return F.softmax(out, dim=1)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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train_expected = pd.read_csv('train/expected.tsv', header=None, sep='\t')
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train_df = pd.read_csv('train/in.tsv', header=None, sep='\t')
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# test_df = pd.read_csv('test-A/in.tsv', header=None, sep='\t')
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test_df = pd.read_csv('dev-0/in.tsv', header=None, sep='\t')
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y_train = pd.DataFrame(train_expected[0])
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train_df[0] = [simple_preprocess(text, deacc=True) for text in train_df[0]]
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porter_stemmer = PorterStemmer()
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train_df['stemmed_tokens'] = [[porter_stemmer.stem(word) for word in tokens] for tokens in train_df[0]]
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test_df[0] = [simple_preprocess(text, deacc=True) for text in test_df[0]]
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test_df['stemmed_tokens'] = [[porter_stemmer.stem(word) for word in tokens] for tokens in test_df[0]]
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x_test = pd.DataFrame(test_df['stemmed_tokens'])
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x_train = pd.DataFrame(train_df['stemmed_tokens'])
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def make_dict(top_data_df_small, padding=True):
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if padding:
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print("Dictionary with padded token added")
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review_dict = corpora.Dictionary([['pad']])
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review_dict.add_documents(top_data_df_small['stemmed_tokens'])
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else:
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print("Dictionary without padding")
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review_dict = corpora.Dictionary(top_data_df_small['stemmed_tokens'])
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return review_dict
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# Make the dictionary without padding for the basic models
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review_dict = make_dict(train_df, padding=False)
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VOCAB_SIZE = len(review_dict)
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NUM_LABELS = 2
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# Function to make bow vector to be used as input to network
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def make_bow_vector(review_dict, sentence):
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vec = torch.zeros(VOCAB_SIZE, dtype=torch.float64, device=device)
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for word in sentence:
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vec[review_dict.token2id[word]] += 1
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return vec.view(1, -1).float()
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input_dim = VOCAB_SIZE
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hidden_dim = 10
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output_dim = 2
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num_epochs = 2
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ff_nn_bow_model = FeedforwardNeuralNetModel(input_dim, hidden_dim, output_dim)
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ff_nn_bow_model.to(device)
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loss_function = nn.CrossEntropyLoss()
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optimizer = optim.SGD(ff_nn_bow_model.parameters(), lr=0.001)
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losses = []
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iter = 0
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def make_target(label):
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if label == 0:
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return torch.tensor([0], dtype=torch.long, device=device)
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elif label == 1:
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return torch.tensor([1], dtype=torch.long, device=device)
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# Start training
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for epoch in range(num_epochs):
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if (epoch + 1) % 25 == 0:
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print("Epoch completed: " + str(epoch + 1))
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print(f"Epoch number: {epoch}")
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train_loss = 0
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for index, row in x_train.iterrows():
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print(index)
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# Clearing the accumulated gradients
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optimizer.zero_grad()
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# Make the bag of words vector for stemmed tokens
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bow_vec = make_bow_vector(review_dict, row['stemmed_tokens'])
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# Forward pass to get output
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probs = ff_nn_bow_model(bow_vec)
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# Get the target label
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target = make_target(y_train[0][index])
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# Calculate Loss: softmax --> cross entropy loss
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loss = loss_function(probs, target)
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# Accumulating the loss over time
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train_loss += loss.item()
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# Getting gradients w.r.t. parameters
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loss.backward()
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# Updating parameters
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optimizer.step()
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train_loss = 0
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bow_ff_nn_predictions = []
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original_lables_ff_bow = []
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with torch.no_grad():
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for index, row in x_test.iterrows():
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bow_vec = make_bow_vector(review_dict, row['stemmed_tokens'])
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probs = ff_nn_bow_model(bow_vec)
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bow_ff_nn_predictions.append(torch.argmax(probs, dim=1).cpu().numpy()[0])
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