113 lines
3.4 KiB
Python
113 lines
3.4 KiB
Python
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import pandas as pd
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import numpy as np
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import scipy
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import torch
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import pandas as pd
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from sklearn.model_selection import train_test_split
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import kaggle
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from sklearn.feature_extraction.text import TfidfVectorizer
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from torch import nn
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from torch import optim
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import matplotlib.pyplot as plt
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if __name__ == "__main__":
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# kaggle.api.authenticate()
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# kaggle.api.dataset_download_files('shivamb/real-or-fake-fake-jobposting-prediction', path='.',
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# unzip=True)
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data = pd.read_csv('fake_job_postings.csv', engine='python')
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data = data.replace(np.nan, '', regex=True)
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data_train, data_test = train_test_split(data, test_size=3000, random_state=1)
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data_dev, data_test = train_test_split(data_test, test_size=1500, random_state=1)
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x_train = data_train["title"]
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x_dev = data_dev["title"]
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x_test = data_test["title"]
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y_train = data_train["fraudulent"]
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y_dev = data_dev["fraudulent"]
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y_test = data_test["fraudulent"]
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x_train = np.array(x_train)
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x_dev = np.array(x_dev)
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y_train = np.array(y_train)
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y_dev = np.array(y_dev)
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vectorizer = TfidfVectorizer()
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x_train = vectorizer.fit_transform(x_train)
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x_dev = vectorizer.transform(x_dev)
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x_train = torch.tensor(scipy.sparse.csr_matrix.todense(x_train)).float()
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x_dev = torch.tensor(scipy.sparse.csr_matrix.todense(x_dev)).float()
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y_train = torch.tensor(y_train)
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y_dev = torch.tensor(y_dev)
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from torch import nn
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model = nn.Sequential(
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nn.Linear(x_train.shape[1], 64),
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nn.ReLU(),
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nn.Linear(64, data_train["title"].nunique()),
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nn.LogSoftmax(dim=1))
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# Define the loss
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criterion = nn.NLLLoss() # Forward pass, log
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logps = model(x_train) # Calculate the loss with the logits and the labels
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loss = criterion(logps, y_train)
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loss.backward() # Optimizers need parameters to optimize and a learning rate
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optimizer = optim.Adam(model.parameters(), lr=0.002)
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train_losses = []
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test_losses = []
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test_accuracies = []
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epochs = 5
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for e in range(epochs):
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optimizer.zero_grad()
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output = model.forward(x_train)
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loss = criterion(output, y_train)
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loss.backward()
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train_loss = loss.item()
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train_losses.append(train_loss)
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optimizer.step()
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# Turn off gradients for validation, saves memory and computations
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with torch.no_grad():
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model.eval()
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log_ps = model(x_dev)
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test_loss = criterion(log_ps, y_dev)
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test_losses.append(test_loss)
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ps = torch.exp(log_ps)
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top_p, top_class = ps.topk(1, dim=1)
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equals = top_class == y_dev.view(*top_class.shape)
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test_accuracy = torch.mean(equals.float())
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test_accuracies.append(test_accuracy)
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model.train()
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print(f"Epoch: {e + 1}/{epochs}.. ",
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f"Training Loss: {train_loss:.3f}.. ",
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f"Test Loss: {test_loss:.3f}.. ",
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f"Test Accuracy: {test_accuracy:.3f}")
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plt.figure(figsize=(12, 5))
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ax = plt.subplot(121)
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plt.xlabel('epochs')
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plt.ylabel('negative log likelihood loss')
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plt.plot(train_losses, label='Training loss')
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plt.plot(test_losses, label='Validation loss')
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plt.legend(frameon=False)
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plt.subplot(122)
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plt.xlabel('epochs')
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plt.ylabel('test accuracy')
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plt.plot(test_accuracies)
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plt.show()
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print('Succes')
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