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neural-network.py

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+import os
+import pandas as pd
+import tensorflow as tf
+import numpy as np
+import torch
+import torch.nn as nn
+from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+
+print('debug 1')
+train_df = pd.read_csv('train/in.tsv', header=None, sep='\t')
+test_df = pd.read_csv('test-A/in.tsv', header=None, sep='\t')
+dev_df = pd.read_csv('dev-0/in.tsv', header=None, sep='\t')
+train_expected = pd.read_csv('train/expected.tsv', header=None, sep='\t')
+
+train_text = train_df[0].tolist()
+test_text = test_df[0].tolist()
+dev_text = test_df[0].tolist()
+
+text_data = train_text + test_text + dev_text
+
+vectorize_layer = TextVectorization(max_tokens=5, output_mode="int")
+text_data = tf.data.Dataset.from_tensor_slices(text_data)
+vectorize_layer.adapt(text_data.batch(64))
+
+inputs = tf.keras.layers.Input(shape=(1,), dtype=tf.string, name="text")
+outputs = vectorize_layer(inputs)
+model = tf.keras.Model(inputs, outputs)
+
+print('uwaga debug')
+x_train = list(map(model.predict, train_text))
+y_train = train_expected[0]
+x_test = list(map(model.predict, test_text))
+loss_function = nn.CrossEntropyLoss()
+
+x_train = pd.DataFrame(x_train)
+x_test = pd.DataFrame(x_test)
+y_train = pd.DataFrame(y_train[0])
+
+# (model.predict(["Murder in the forset!"]))
+
+
+class FeedforwardNeuralNetModel(nn.Module):
+    def __init__(self, input_dim, hidden_dim, output_dim):
+        super(FeedforwardNeuralNetModel, self).__init__()
+        # Linear function
+        self.fc1 = nn.Linear(input_dim, hidden_dim)
+
+        # Non-linearity
+        self.sigmoid = nn.Sigmoid()
+
+        # Linear function (readout)
+        self.fc2 = nn.Linear(hidden_dim, output_dim)
+
+    def forward(self, x):
+        # Linear function  # LINEAR
+        out = self.fc1(x)
+
+        # Non-linearity  # NON-LINEAR
+        out = self.sigmoid(out)
+
+        # Linear function (readout)  # LINEAR
+        out = self.fc2(out)
+        return out
+
+num_epochs = 2
+
+for epoch in range(num_epochs):
+    if (epoch + 1) % 25 == 0:
+        print("Epoch completed: " + str(epoch + 1))
+    print(f"Epoch number: {epoch}")
+    train_loss = 0
+    for index, row in x_train.iterrows():
+        print(index)
+
+        # Forward pass to get output
+        probs = x_train[0][index]
+        # Get the target label
+        target = y_train[0][index]
+
+        # Calculate Loss: softmax --> cross entropy loss
+        loss = loss_function(probs, target)
+        # Accumulating the loss over time
+        train_loss += loss.item()
+
+        # Getting gradients w.r.t. parameters
+        loss.backward()
+
+
+
+    train_loss = 0
+
+
+bow_ff_nn_predictions = []
+original_lables_ff_bow = []
+with torch.no_grad():
+    for index, row in x_test.iterrows():
+        probs = x_test[0][index]
+        bow_ff_nn_predictions.append(torch.argmax(probs, dim=1).cpu().numpy()[0])
+
+print(bow_ff_nn_predictions)

neural-network2.py

@@ -0,0 +1,151 @@
+import pandas as pd
+from gensim.utils import simple_preprocess
+from gensim.parsing.porter import PorterStemmer
+from gensim import corpora
+
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import torch
+
+
+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 F.softmax(out, dim=1)
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+train_expected = pd.read_csv('train/expected.tsv', header=None, sep='\t')
+train_df = pd.read_csv('train/in.tsv', header=None, sep='\t')
+# test_df = pd.read_csv('test-A/in.tsv', header=None, sep='\t')
+test_df = pd.read_csv('dev-0/in.tsv', header=None, sep='\t')
+y_train = pd.DataFrame(train_expected[0])
+
+
+train_df[0] = [simple_preprocess(text, deacc=True) for text in train_df[0]]
+porter_stemmer = PorterStemmer()
+train_df['stemmed_tokens'] = [[porter_stemmer.stem(word) for word in tokens] for tokens in train_df[0]]
+test_df[0] = [simple_preprocess(text, deacc=True) for text in test_df[0]]
+test_df['stemmed_tokens'] = [[porter_stemmer.stem(word) for word in tokens] for tokens in test_df[0]]
+
+x_test = pd.DataFrame(test_df['stemmed_tokens'])
+x_train = pd.DataFrame(train_df['stemmed_tokens'])
+
+
+def make_dict(top_data_df_small, padding=True):
+    if padding:
+        print("Dictionary with padded token added")
+        review_dict = corpora.Dictionary([['pad']])
+        review_dict.add_documents(top_data_df_small['stemmed_tokens'])
+    else:
+        print("Dictionary without padding")
+        review_dict = corpora.Dictionary(top_data_df_small['stemmed_tokens'])
+    return review_dict
+
+
+# Make the dictionary without padding for the basic models
+review_dict = make_dict(train_df, padding=False)
+
+VOCAB_SIZE = len(review_dict)
+NUM_LABELS = 2
+
+
+# Function to make bow vector to be used as input to network
+def make_bow_vector(review_dict, sentence):
+    vec = torch.zeros(VOCAB_SIZE, dtype=torch.float64, device=device)
+    for word in sentence:
+        vec[review_dict.token2id[word]] += 1
+    return vec.view(1, -1).float()
+
+
+input_dim = VOCAB_SIZE
+hidden_dim = 10
+output_dim = 2
+num_epochs = 2
+
+ff_nn_bow_model = FeedforwardNeuralNetModel(input_dim, hidden_dim, output_dim)
+ff_nn_bow_model.to(device)
+
+loss_function = nn.CrossEntropyLoss()
+optimizer = optim.SGD(ff_nn_bow_model.parameters(), lr=0.001)
+
+losses = []
+iter = 0
+
+def make_target(label):
+    if label == 0:
+        return torch.tensor([0], dtype=torch.long, device=device)
+    elif label == 1:
+        return torch.tensor([1], dtype=torch.long, device=device)
+
+
+# Start training
+for epoch in range(num_epochs):
+    if (epoch + 1) % 25 == 0:
+        print("Epoch completed: " + str(epoch + 1))
+    print(f"Epoch number: {epoch}")
+    train_loss = 0
+    for index, row in x_train.iterrows():
+        print(index)
+        # Clearing the accumulated gradients
+        optimizer.zero_grad()
+
+        # Make the bag of words vector for stemmed tokens
+        bow_vec = make_bow_vector(review_dict, row['stemmed_tokens'])
+
+        # Forward pass to get output
+        probs = ff_nn_bow_model(bow_vec)
+
+        # Get the target label
+        target = make_target(y_train[0][index])
+
+        # Calculate Loss: softmax --> cross entropy loss
+        loss = loss_function(probs, target)
+        # Accumulating the loss over time
+        train_loss += loss.item()
+
+        # Getting gradients w.r.t. parameters
+        loss.backward()
+
+        # Updating parameters
+        optimizer.step()
+
+    train_loss = 0
+
+
+bow_ff_nn_predictions = []
+original_lables_ff_bow = []
+with torch.no_grad():
+    for index, row in x_test.iterrows():
+        bow_vec = make_bow_vector(review_dict, row['stemmed_tokens'])
+        probs = ff_nn_bow_model(bow_vec)
+        bow_ff_nn_predictions.append(torch.argmax(probs, dim=1).cpu().numpy()[0])
+