Python Model

The program template will guide you in implementing the steps involved in extracting linguistic features, reducing dimensionality, modeling cognitive traits, and optimizing the entire pipeline.

Model

pythonCopy code# Install required libraries first (if not installed)
# !pip install torch transformers scikit-learn

import torch
from torch import nn
from transformers import BertTokenizer, BertModel
from sklearn.decomposition import PCA
from sklearn.metrics import mean_squared_error
from torch.utils.data import DataLoader, TensorDataset
from tqdm import tqdm

# Step 1: Load pre-trained BERT model and tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
bert_model = BertModel.from_pretrained('bert-base-uncased')

# Step 2: Function to extract BERT embeddings from text
def extract_bert_embeddings(text_list):
    inputs = tokenizer(text_list, padding=True, truncation=True, return_tensors="pt")
    with torch.no_grad():
        outputs = bert_model(**inputs)
    # Use mean pooling to get sentence embeddings
    embeddings = outputs.last_hidden_state.mean(dim=1)
    return embeddings

# Step 3: Reduce dimensionality of embeddings using PCA
def perform_pca(embeddings, num_components=50):
    pca = PCA(n_components=num_components)
    reduced_embeddings = pca.fit_transform(embeddings)
    return reduced_embeddings

# Step 4: Define the neural network model for cognitive trait prediction
class CognitiveTraitModel(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(CognitiveTraitModel, self).__init__()
        self.fc1 = nn.Linear(input_dim, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, output_dim)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# Step 5: Generate dummy data (replace with actual data)
texts = ["This is an example sentence.", "How are you doing today?", "I love learning about AI."]
labels = [[0.5], [0.7], [0.8]]  # Example cognitive trait labels (replace with real data)

# Step 6: Process the data (convert text to embeddings and apply PCA)
embeddings = extract_bert_embeddings(texts).numpy()
reduced_embeddings = perform_pca(embeddings)

# Step 7: Prepare the dataset and DataLoader
input_tensor = torch.tensor(reduced_embeddings, dtype=torch.float32)
label_tensor = torch.tensor(labels, dtype=torch.float32)
dataset = TensorDataset(input_tensor, label_tensor)
train_loader = DataLoader(dataset, batch_size=1, shuffle=True)

# Step 8: Initialize the model, loss function, and optimizer
input_dim = reduced_embeddings.shape[1]
output_dim = 1  # Predicting a single cognitive trait (can be extended)
model = CognitiveTraitModel(input_dim, output_dim)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# Step 9: Train the model
num_epochs = 100
for epoch in range(num_epochs):
    model.train()
    running_loss = 0.0
    for inputs, labels in tqdm(train_loader, desc=f"Epoch {epoch+1}/{num_epochs}"):
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()

    print(f"Epoch {epoch+1}, Loss: {running_loss/len(train_loader)}")

# Step 10: Evaluate the model
model.eval()
predictions = []
true_labels = []
with torch.no_grad():
    for inputs, labels in train_loader:
        outputs = model(inputs)
        predictions.append(outputs.numpy())
        true_labels.append(labels.numpy())

predictions = np.concatenate(predictions, axis=0)
true_labels = np.concatenate(true_labels, axis=0)

# Step 11: Compute the mean squared error (MSE) for evaluation
mse = mean_squared_error(true_labels, predictions)
print(f"Mean Squared Error: {mse}")

Instructions for Usage:

1. Install Libraries: Ensure that you have installed the necessary Python libraries, including torch, transformers, and scikit-learn. You can install them via pip:

   bashCopy codepip install torch transformers scikit-learn

2. Run the Code: Copy and paste the above code directly into your GitBook or Python environment. Make sure you have access to the internet as it downloads the BERT model and tokenizer from Hugging Face.

3. Replace Dummy Data: The code currently uses dummy text data and corresponding cognitive trait labels. Replace texts and labels with your actual dataset for meaningful results.

4. Modify Parameters: You can adjust the number of PCA components and the number of epochs for training based on your dataset size and complexity.

What to Expect:

• The code extracts BERT embeddings from text data.

• It reduces the dimensionality of the embeddings using PCA to improve model efficiency.

• It trains a simple neural network to predict cognitive traits based on the reduced embeddings.

• The model is evaluated using Mean Squared Error (MSE) to measure its accuracy.

Key

Key Components of the Framework:

1. Linguistic Feature Extraction (BERT Embeddings):

   • Tokenizer & Model: We use Hugging Face's BERT model to tokenize and generate sentence-level embeddings. Each sentence is transformed into a high-dimensional vector reflecting semantic and syntactic meaning.

   • Mean Pooling: The embedding for each sentence is computed using mean pooling, averaging token embeddings to form a singular vector that represents the sentence's cognitive content.

2. Dimensionality Reduction (PCA):

   • Principal Component Analysis (PCA) is used to reduce the high-dimensional BERT embeddings to a lower-dimensional space (e.g., 50 dimensions). PCA ensures that the most important variance in the data is retained, improving computational efficiency without losing essential information.

3. Cognitive Trait Prediction (Neural Network Model):

   • Architecture: A fully connected neural network model with three layers (128, 64, output_dim) processes the reduced embeddings. The ReLU activation is used to introduce non-linearity.

   • Output Layer: The network predicts cognitive traits as a continuous variable (e.g., personality, problem-solving style, cognitive flexibility).

4. Training & Evaluation:

   • Optimization: The model is trained using Adam optimizer with a learning rate of 0.001 and Mean Squared Error (MSE) as the loss function, which is standard for regression tasks.

   • Performance Evaluation: MSE is computed between predicted and actual cognitive trait values to evaluate model performance. This provides a direct metric for how well the model predicts the latent cognitive signatures.

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Detailed Methodology & Computation:

1. Feature Extraction Using BERT:

   • Input: Text is tokenized using BertTokenizer, and BertModel extracts contextual embeddings.

   • Embeddings are obtained from outputs.last_hidden_state and aggregated using mean pooling across token embeddings.

2. Dimensionality Reduction via PCA:

   • The high-dimensional BERT embeddings (typically 768-dimensional) are reduced to a smaller space (e.g., 50 dimensions) using PCA to preserve variance and computational efficiency.

3. Neural Network for Trait Prediction:

   • Input: Reduced embeddings are passed through a 3-layer feedforward network.

   • Each layer consists of Linear transformations followed by ReLU activations.

   • The final layer outputs predicted cognitive trait values in a continuous range (scaled based on real-world application).

4. Model Training:

   • The training loop iterates through the dataset in mini-batches (batch size = 1) using PyTorch DataLoader.

   • The optimizer adjusts the weights using backpropagation and the Adam optimizer.

   • The model is trained for a specified number of epochs (e.g., 100 epochs).

5. Evaluation:

   • Once trained, the model is evaluated using Mean Squared Error (MSE), which quantifies the difference between predicted and actual cognitive trait values.

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Code Implementation Highlights:

1. Text to Embedding Pipeline:

   • Tokenization is done via BertTokenizer, and sentence embeddings are extracted using BertModel.

   • This process is computationally intensive but provides rich semantic features necessary for cognitive signature analysis.

2. PCA for Dimensionality Reduction:

   • PCA reduces the dimensionality from 768 (BERT) to 50 components, making the embeddings more manageable for the neural network while retaining critical information.

3. Simple Neural Network Architecture:

   • A fully connected neural network with ReLU activation functions predicts cognitive traits based on the reduced embeddings. The model architecture is designed for flexibility, enabling extension for additional traits or more complex cognitive measures.

4. Optimizer and Loss Function:

   • The Adam optimizer and MSE loss function ensure the model converges effectively during training, as they are optimal for continuous output prediction tasks.

5. Evaluation via MSE:

   • After training, predictions are compared with actual values using Mean Squared Error (MSE), providing a clear metric of model performance.

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Computational Complexity:

• Embedding Extraction: Extracting BERT embeddings is computationally intensive with a time complexity of O(n), where n is the number of tokens in the text.

• PCA: PCA runs with a complexity of O(m * n^2), where m is the number of dimensions in the embeddings, and n is the number of data points. This is efficient for a typical dataset size but may require optimization for very large datasets.

• Neural Network Training: The training time complexity is O(n * e * d), where n is the number of samples, e is the number of epochs, and d is the dimensionality of the input (after PCA).