The Subsequent AI Revolution: A Tutorial Utilizing VAEs to Generate Excessive-High quality Artificial Information

What’s artificial information?

Information created by a pc supposed to copy or increase current information.

Why is it helpful?

We have now all skilled the success of ChatGPT, Llama, and extra not too long ago, DeepSeek. These language fashions are getting used ubiquitously throughout society and have triggered many claims that we’re quickly approaching Synthetic Common Intelligence — AI able to replicating any human perform. 

Earlier than getting too excited, or scared, relying in your perspective — we’re additionally quickly approaching a hurdle to the development of those language fashions. In line with a paper revealed by a bunch from the analysis institute, Epoch [1], we’re operating out of knowledge. They estimate that by 2028 we can have reached the higher restrict of potential information upon which to coach language fashions. 

What occurs if we run out of knowledge?

Nicely, if we run out of knowledge then we aren’t going to have something new with which to coach our language fashions. These fashions will then cease enhancing. If we need to pursue Synthetic Common Intelligence then we’re going to should provide you with new methods of enhancing AI with out simply growing the amount of real-world coaching information. 

One potential saviour is artificial information which may be generated to imitate current information and has already been used to enhance the efficiency of fashions like Gemini and DBRX. 

Artificial information past LLMs

Past overcoming information shortage for giant language fashions, artificial information can be utilized within the following conditions: 

• Delicate Information — if we don’t need to share or use delicate attributes, artificial information may be generated which mimics the properties of those options whereas sustaining anonymity.
• Costly information — if amassing information is dear we will generate a big quantity of artificial information from a small quantity of real-world information.
• Lack of knowledge — datasets are biased when there’s a disproportionately low variety of particular person information factors from a specific group. Artificial information can be utilized to stability a dataset. 

Imbalanced datasets

Imbalanced datasets can (*however not all the time*) be problematic as they could not comprise sufficient data to successfully practice a predictive mannequin. For instance, if a dataset comprises many extra males than ladies, our mannequin could also be biased in the direction of recognising males and misclassify future feminine samples as males. 

On this article we present the imbalance within the well-liked UCI Grownup dataset [2], and the way we will use a variational auto-encoder to generate Artificial Information to enhance classification on this instance. 

We first obtain the Grownup dataset. This dataset comprises options similar to age, schooling and occupation which can be utilized to foretell the goal final result ‘revenue’. 

# Obtain dataset right into a dataframe
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/grownup/grownup.information"
columns = [
   "age", "workclass", "fnlwgt", "education", "education-num", "marital-status",
   "occupation", "relationship", "race", "sex", "capital-gain",
   "capital-loss", "hours-per-week", "native-country", "income"
]
information = pd.read_csv(url, header=None, names=columns, na_values=" ?", skipinitialspace=True)

# Drop rows with lacking values
information = information.dropna()

# Cut up into options and goal
X = information.drop(columns=["income"])
y = information['income'].map({'>50K': 1, '<=50K': 0}).values

# Plot distribution of revenue
plt.determine(figsize=(8, 6))
plt.hist(information['income'], bins=2, edgecolor="black")
plt.title('Distribution of Revenue')
plt.xlabel('Revenue')
plt.ylabel('Frequency')
plt.present()

Within the Grownup dataset, revenue is a binary variable, representing people who earn above, and under, $50,000. We plot the distribution of revenue over all the dataset under. We are able to see that the dataset is closely imbalanced with a far bigger variety of people who earn lower than $50,000. 

Regardless of this imbalance we will nonetheless practice a machine studying classifier on the Grownup dataset which we will use to find out whether or not unseen, or take a look at, people must be categorised as incomes above, or under, 50k. 

# Preprocessing: One-hot encode categorical options, scale numerical options
numerical_features = ["age", "fnlwgt", "education-num", "capital-gain", "capital-loss", "hours-per-week"]
categorical_features = [
   "workclass", "education", "marital-status", "occupation", "relationship",
   "race", "sex", "native-country"
]

preprocessor = ColumnTransformer(
   transformers=[
       ("num", StandardScaler(), numerical_features),
       ("cat", OneHotEncoder(), categorical_features)
   ]
)

X_processed = preprocessor.fit_transform(X)

# Convert to numpy array for PyTorch compatibility
X_processed = X_processed.toarray().astype(np.float32)
y_processed = y.astype(np.float32)
# Cut up dataset in practice and take a look at units
X_model_train, X_model_test, y_model_train, y_model_test = train_test_split(X_processed, y_processed, test_size=0.2, random_state=42)


rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)
rf_classifier.match(X_model_train, y_model_train)

# Make predictions
y_pred = rf_classifier.predict(X_model_test)

# Show confusion matrix
plt.determine(figsize=(6, 4))
sns.heatmap(cm, annot=True, fmt="d", cmap="YlGnBu", xticklabels=["Negative", "Positive"], yticklabels=["Negative", "Positive"])
plt.xlabel("Predicted")
plt.ylabel("Precise")
plt.title("Confusion Matrix")
plt.present()

Printing out the confusion matrix of our classifier exhibits that our mannequin performs pretty nicely regardless of the imbalance. Our mannequin has an general error fee of 16% however the error fee for the optimistic class (revenue > 50k) is 36% the place the error fee for the unfavourable class (revenue < 50k) is 8%. 

This discrepancy exhibits that the mannequin is certainly biased in the direction of the unfavourable class. The mannequin is ceaselessly incorrectly classifying people who earn greater than 50k as incomes lower than 50k. 

Under we present how we will use a Variational Autoencoder to generate artificial information of the optimistic class to stability this dataset. We then practice the identical mannequin utilizing the synthetically balanced dataset and scale back mannequin errors on the take a look at set. 

How can we generate artificial information?

There are many totally different strategies for producing artificial information. These can embody extra conventional strategies similar to SMOTE and Gaussian Noise which generate new information by modifying current information. Alternatively Generative fashions similar to Variational Autoencoders or Common Adversarial networks are predisposed to generate new information as their architectures be taught the distribution of actual information and use these to generate artificial samples.

On this tutorial we use a variational autoencoder to generate artificial information.

Variational Autoencoders

Variational Autoencoders (VAEs) are nice for artificial information technology as a result of they use actual information to be taught a steady latent area. We are able to view this latent area as a magic bucket from which we will pattern artificial information which carefully resembles current information. The continuity of this area is one in every of their huge promoting factors because it means the mannequin generalises nicely and doesn’t simply memorise the latent area of particular inputs.

A VAE consists of an encoder, which maps enter information right into a likelihood distribution (imply and variance) and a decoder, which reconstructs the info from the latent area. 

For that steady latent area, VAEs use a reparameterization trick, the place a random noise vector is scaled and shifted utilizing the realized imply and variance, making certain easy and steady representations within the latent area.

Under we assemble a BasicVAE class which implements this course of with a easy structure.

•  The encoder compresses the enter right into a smaller, hidden illustration, producing each a imply and log variance that outline a Gaussian distribution aka creating our magic sampling bucket. As an alternative of straight sampling, the mannequin applies the reparameterization trick to generate latent variables, that are then handed to the decoder. 
• The decoder reconstructs the unique information from these latent variables, making certain the generated information maintains traits of the unique dataset. 

class BasicVAE(nn.Module):
   def __init__(self, input_dim, latent_dim):
       tremendous(BasicVAE, self).__init__()
       # Encoder: Single small layer
       self.encoder = nn.Sequential(
           nn.Linear(input_dim, 8),
           nn.ReLU()
       )
       self.fc_mu = nn.Linear(8, latent_dim)
       self.fc_logvar = nn.Linear(8, latent_dim)
      
       # Decoder: Single small layer
       self.decoder = nn.Sequential(
           nn.Linear(latent_dim, 8),
           nn.ReLU(),
           nn.Linear(8, input_dim),
           nn.Sigmoid()  # Outputs values in vary [0, 1]
       )

   def encode(self, x):
       h = self.encoder(x)
       mu = self.fc_mu(h)
       logvar = self.fc_logvar(h)
       return mu, logvar

   def reparameterize(self, mu, logvar):
       std = torch.exp(0.5 * logvar)
       eps = torch.randn_like(std)
       return mu + eps * std

   def decode(self, z):
       return self.decoder(z)

   def ahead(self, x):
       mu, logvar = self.encode(x)
       z = self.reparameterize(mu, logvar)
       return self.decode(z), mu, logvar

Given our BasicVAE structure we assemble our loss features and mannequin coaching under. 

def vae_loss(recon_x, x, mu, logvar, tau=0.5, c=1.0):
   recon_loss = nn.MSELoss()(recon_x, x)
 
   # KL Divergence Loss
   kld_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
   return recon_loss + kld_loss / x.measurement(0)

def train_vae(mannequin, data_loader, epochs, learning_rate):
   optimizer = optim.Adam(mannequin.parameters(), lr=learning_rate)
   mannequin.practice()
   losses = []
   reconstruction_mse = []

   for epoch in vary(epochs):
       total_loss = 0
       total_mse = 0
       for batch in data_loader:
           batch_data = batch[0]
           optimizer.zero_grad()
           reconstructed, mu, logvar = mannequin(batch_data)
           loss = vae_loss(reconstructed, batch_data, mu, logvar)
           loss.backward()
           optimizer.step()
           total_loss += loss.merchandise()

           # Compute batch-wise MSE for comparability
           mse = nn.MSELoss()(reconstructed, batch_data).merchandise()
           total_mse += mse

       losses.append(total_loss / len(data_loader))
       reconstruction_mse.append(total_mse / len(data_loader))
       print(f"Epoch {epoch+1}/{epochs}, Loss: {total_loss:.4f}, MSE: {total_mse:.4f}")
   return losses, reconstruction_mse

combined_data = np.concatenate([X_model_train.copy(), y_model_train.cop
y().reshape(26048,1)], axis=1)

# Practice-test cut up
X_train, X_test = train_test_split(combined_data, test_size=0.2, random_state=42)

batch_size = 128

# Create DataLoaders
train_loader = DataLoader(TensorDataset(torch.tensor(X_train)), batch_size=batch_size, shuffle=True)
test_loader = DataLoader(TensorDataset(torch.tensor(X_test)), batch_size=batch_size, shuffle=False)

basic_vae = BasicVAE(input_dim=X_train.form[1], latent_dim=8)

basic_losses, basic_mse = train_vae(
   basic_vae, train_loader, epochs=50, learning_rate=0.001,
)

# Visualize outcomes
plt.determine(figsize=(12, 6))
plt.plot(basic_mse, label="Fundamental VAE")
plt.ylabel("Reconstruction MSE")
plt.title("Coaching Reconstruction MSE")
plt.legend()
plt.present()

vae_loss consists of two elements: reconstruction loss, which measures how nicely the generated information matches the unique enter utilizing Imply Squared Error (MSE), and KL divergence loss, which ensures that the realized latent area follows a standard distribution.

train_vae optimises the VAE utilizing the Adam optimizer over a number of epochs. Throughout coaching, the mannequin takes mini-batches of knowledge, reconstructs them, and computes the loss utilizing vae_loss. These errors are then corrected by way of backpropagation the place the mannequin weights are up to date. We practice the mannequin for 50 epochs and plot how the reconstruction imply squared error decreases over coaching.

We are able to see that our mannequin learns shortly tips on how to reconstruct our information, evidencing environment friendly studying. 

Now we’ve skilled our BasicVAE to precisely reconstruct the Grownup dataset we will now use it to generate artificial information. We need to generate extra samples of the optimistic class (people who earn over 50k) to be able to stability out the lessons and take away the bias from our mannequin.

To do that we choose all of the samples from our VAE dataset the place revenue is the optimistic class (earn greater than 50k). We then encode these samples into the latent area. As we’ve solely chosen samples of the optimistic class to encode, this latent area will mirror properties of the optimistic class which we will pattern from to create artificial information. 

We pattern 15000 new samples from this latent area and decode these latent vectors again into the enter information area as our artificial information factors. 

# Create column names
col_number = sample_df.form[1]
col_names = [str(i) for i in range(col_number)]
sample_df.columns = col_names

# Outline the characteristic worth to filter
feature_value = 1.0  # Specify the characteristic worth - right here we set the revenue to 1

# Set all revenue values to 1 : Over 50k
selected_samples = sample_df[sample_df[col_names[-1]] == feature_value]
selected_samples = selected_samples.values
selected_samples_tensor = torch.tensor(selected_samples, dtype=torch.float32)

basic_vae.eval()  # Set mannequin to analysis mode
with torch.no_grad():
   mu, logvar = basic_vae.encode(selected_samples_tensor)
   latent_vectors = basic_vae.reparameterize(mu, logvar)

# Compute the imply latent vector for this characteristic
mean_latent_vector = latent_vectors.imply(dim=0)


num_samples = 15000  # Variety of new samples
latent_dim = 8
latent_samples = mean_latent_vector + 0.1 * torch.randn(num_samples, latent_dim)

with torch.no_grad():
   generated_samples = basic_vae.decode(latent_samples)

Now we’ve generated artificial information of the optimistic class, we will mix this with the unique coaching information to generate a balanced artificial dataset. 

new_data = pd.DataFrame(generated_samples)

# Create column names
col_number = new_data.form[1]
col_names = [str(i) for i in range(col_number)]
new_data.columns = col_names

X_synthetic = new_data.drop(col_names[-1],axis=1)
y_synthetic = np.asarray([1 for _ in range(0,X_synthetic.shape[0])])

X_synthetic_train = np.concatenate([X_model_train, X_synthetic.values], axis=0)
y_synthetic_train = np.concatenate([y_model_train, y_synthetic], axis=0)

mapping = {1: '>50K', 0: '<=50K'}
map_function = np.vectorize(lambda x: mapping[x])
# Apply mapping
y_mapped = map_function(y_synthetic_train)

plt.determine(figsize=(8, 6))
plt.hist(y_mapped, bins=2, edgecolor="black")
plt.title('Distribution of Revenue')
plt.xlabel('Revenue')
plt.ylabel('Frequency')
plt.present()

We are able to now use our balanced coaching artificial dataset to retrain our random forest classifier. We are able to then consider this new mannequin on the unique take a look at information to see how efficient our artificial information is at decreasing the mannequin bias.

rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)
rf_classifier.match(X_synthetic_train, y_synthetic_train)

# Step 5: Make predictions
y_pred = rf_classifier.predict(X_model_test)

cm = confusion_matrix(y_model_test, y_pred)

# Create heatmap
plt.determine(figsize=(6, 4))
sns.heatmap(cm, annot=True, fmt="d", cmap="YlGnBu", xticklabels=["Negative", "Positive"], yticklabels=["Negative", "Positive"])
plt.xlabel("Predicted")
plt.ylabel("Precise")
plt.title("Confusion Matrix")
plt.present()

Our new classifier, skilled on the balanced artificial dataset makes fewer errors on the unique take a look at set than our authentic classifier skilled on the imbalanced dataset and our error fee is now diminished to 14%.

Nevertheless, we’ve not been capable of scale back the discrepancy in errors by a major quantity, our error fee for the optimistic class continues to be 36%. This may very well be as a consequence of to the next causes: 

• We have now mentioned how one of many advantages of VAEs is the educational of a steady latent area. Nevertheless, if the bulk class dominates, the latent area may skew in the direction of the bulk class.
• The mannequin could not have correctly realized a definite illustration for the minority class as a result of lack of knowledge, making it laborious to pattern from that area precisely.

On this tutorial we’ve launched and constructed a BasicVAE structure which can be utilized to generate artificial information which improves the classification accuracy on an imbalanced dataset. 

Observe for future articles the place I’ll present how we will construct extra refined VAE architectures which tackle the above issues with imbalanced sampling and extra.

[1] Villalobos, P., Ho, A., Sevilla, J., Besiroglu, T., Heim, L., & Hobbhahn, M. (2024). Will we run out of knowledge? Limits of LLM scaling based mostly on human-generated information. arXiv preprint arXiv:2211.04325, 3.

[2] Becker, B. & Kohavi, R. (1996). Grownup [Dataset]. UCI Machine Studying Repository. https://doi.org/10.24432/C5XW20.