167 lines
4.8 KiB
Python
167 lines
4.8 KiB
Python
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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from sklearn.feature_extraction.text import HashingVectorizer
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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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from torch.optim import optimizer
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vectorizer = HashingVectorizer(n_features=20)
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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
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# print(train_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('model loaded')
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train_text = train_df[0].apply(lambda x: vectorizer.transform([x]))
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test_text = test_df[0].apply(lambda x: vectorizer.transform([x]))
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x_train = train_text.tolist()
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x_test = test_text.tolist()
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# x_train = list(map(model.predict, train_text))
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# x_train = [model.predict([x]) for x in train_text]
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y_train = train_expected[0].astype(np.float32)
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# x_test = list(map(model.predict, test_text))
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# x_test = [model.predict([x]) for x in 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)
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print("End of vectorization")
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# print((model.predict("Murder in the forset!")))
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class FeedforwardNeuralNetModel(nn.Module):
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def __init__(self):
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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(FEAUTERES, 500)
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# Non-linearity 1
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self.fc2 = nn.Linear(500,1)
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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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return out
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num_epochs = 2
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x_dict = x_train.to_dict()
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y_train = y_train.to_dict()
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nn_model = FeedforwardNeuralNetModel()
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BATCH_SIZE = 5
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criterion = torch.nn.BCELoss()
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optimizer = torch.optim.SGD(nn_model.parameters(), lr = 0.1)
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for epoch in range(5):
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loss_score = 0
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acc_score = 0
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items_total = 0
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nn_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.astype(np.float32).todense())
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Y = Y_train[i:i+BATCH_SIZE]
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Y = torch.tensor(Y.astype(np.float32)).reshape(-1,1)
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Y_predictions = nn_model(X)
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acc_score += torch.sum((Y_predictions > 0.5) == Y).item()
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items_total += Y.shape[0]
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optimizer.zero_grad()
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loss = criterion(Y_predictions, Y)
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loss.backward()
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optimizer.step()
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loss_score += loss.item() * Y.shape[0]
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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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# # for index, row in x_train.iterrows():
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#
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#
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# print(row, index)
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# # Forward pass to get output
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# probs = x_train[0][index]
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# # probs = torch.tensor(probs.astype(np.float32))
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# # Get the target label
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# target = y_train[0][index]
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# print(target)
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# # target = np.array(target).astype(np.float32)
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# print(type(target))
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# # target = .astype(np.float32).reshape(-1,1)
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# # target
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# # target = torch.tensor(target.astype(np.float32)).reshape(-1,1)
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#
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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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#
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# # Getting gradients w.r.t. parameters
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# loss.backward()
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#
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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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#
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# print(bow_ff_nn_predictions)
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