### Install CTGAN from Source with Development Requirements

Source: https://github.com/sdv-dev/ctgan/blob/main/RELEASE.md

This snippet shows how to clone the CTGAN repository, navigate into the directory, checkout the main branch, and install development requirements. This is a prerequisite for the release process.

```bash
git clone https://github.com/sdv-dev/CTGAN.git
cd CTGAN
git checkout main
make install-develop
```

--------------------------------

### Install CTGAN Standalone Library (pip)

Source: https://github.com/sdv-dev/ctgan/blob/main/README.md

This command installs the CTGAN library directly using pip. It's a prerequisite for using CTGAN as a standalone tool, which may require manual data preprocessing.

```bash
pip install ctgan
```

--------------------------------

### Install CTGAN Standalone Library (conda)

Source: https://github.com/sdv-dev/ctgan/blob/main/README.md

This command installs the CTGAN library using conda, specifying the pytorch and conda-forge channels. This is an alternative installation method for users who prefer the conda package manager.

```bash
conda install -c pytorch -c conda-forge ctgan
```

--------------------------------

### Advanced CTGAN Training Configuration (Python)

Source: https://context7.com/sdv-dev/ctgan/llms.txt

Provides examples of configuring CTGAN for different dataset sizes and custom learning rates. Includes options for embedding dimensions, network sizes, batch sizes, epochs, and learning rate decay. Also shows how to access and plot training loss.

```python
from ctgan import CTGAN
import matplotlib.pyplot as plt

# Configure for large datasets
ctgan_large = CTGAN(
    embedding_dim=256,           # Larger embedding space
    generator_dim=(512, 512),    # Deeper generator network
    discriminator_dim=(512, 512),# Deeper discriminator network
    batch_size=1000,             # Larger batch size
    epochs=500,                  # More training epochs
    discriminator_steps=5,       # Multiple D updates per G update
    pac=10,                      # Pac parameter for discriminator
    log_frequency=True,          # Use log frequency for conditional sampling
    verbose=True
)

# Configure for small datasets
ctgan_small = CTGAN(
    embedding_dim=64,
    generator_dim=(128, 128),
    discriminator_dim=(128, 128),
    batch_size=100,
    epochs=1000,                 # More epochs for small data
    pac=5,
    verbose=True
)

# Configure with custom learning rates
ctgan_tuned = CTGAN(
    generator_lr=1e-4,           # Lower learning rate for generator
    generator_decay=1e-5,        # Weight decay for regularization
    discriminator_lr=1e-4,
    discriminator_decay=1e-5,
    epochs=300
)

# Assuming 'data' and 'discrete_columns' are defined elsewhere
ctgan_tuned.fit(data, discrete_columns)
samples = ctgan_tuned.sample(5000)

# Access loss history for analysis
loss_df = ctgan_tuned.loss_values
plt.plot(loss_df['Epoch'], loss_df['Generator Loss'], label='Generator')
plt.plot(loss_df['Epoch'], loss_df['Discriminator Loss'], label='Discriminator')
plt.legend()
plt.savefig('training_loss.png')
```

--------------------------------

### Generate Synthetic Data with CTGAN (Python)

Source: https://github.com/sdv-dev/ctgan/blob/main/README.md

This Python code demonstrates how to use the CTGAN model to generate synthetic data. It loads a demo dataset, specifies discrete columns, trains the CTGAN model, and then samples synthetic data. Ensure the 'ctgan' library is installed.

```python
from ctgan import CTGAN
from ctgan import load_demo

real_data = load_demo()

# Names of the columns that are discrete
discrete_columns = [
    'workclass',
    'education',
    'marital-status',
    'occupation',
    'relationship',
    'race',
    'sex',
    'native-country',
    'income'
]

ctgan = CTGAN(epochs=10)
ctgan.fit(real_data, discrete_columns)

# Create synthetic data
synthetic_data = ctgan.sample(1000)
```

--------------------------------

### Run Linting and Tests for CTGAN

Source: https://github.com/sdv-dev/ctgan/blob/main/RELEASE.md

This command executes both the test suite and linting tools for the CTGAN project. It ensures that all tests pass without errors and that the code adheres to the project's coding standards. Successful execution is indicated by a summary of passed tests and linting checks.

```bash
make test && make lint
```

--------------------------------

### Load Demo Dataset for CTGAN Testing (Python)

Source: https://context7.com/sdv-dev/ctgan/llms.txt

Loads the Adult Census demo dataset using the `load_demo` function for quick testing and experimentation with the CTGAN model. Demonstrates fitting the model and sampling synthetic data.

```python
from ctgan import load_demo

# Load demo dataset (Adult Census)
demo_data = load_demo()

print(demo_data.shape)
print(demo_data.head())
print(demo_data.columns.tolist())

# Demo data includes mixed types
print(demo_data.dtypes)

# Use demo data for quick testing
from ctgan import CTGAN

discrete_cols = [
    'workclass', 'education', 'marital-status',
    'occupation', 'relationship', 'race', 'sex',
    'native-country', 'income'
]

model = CTGAN(epochs=10, verbose=True)
model.fit(demo_data, discrete_cols)
synthetic = model.sample(100)
```

--------------------------------

### Create Release Candidate Branch in SDV

Source: https://github.com/sdv-dev/ctgan/blob/main/RELEASE.md

This sequence of bash commands demonstrates how to create a new branch in the SDV repository to test a CTGAN release candidate. It involves creating a new branch, updating the pyproject.toml file to specify the release candidate version, and pushing the new branch to the origin.

```bash
git checkout -b test-ctgan-X.Y.Z
git push --set-upstream origin test-ctgan-X.Y.Z
```

--------------------------------

### CTGAN Synthesizer for Synthetic Tabular Data

Source: https://context7.com/sdv-dev/ctgan/llms.txt

Initializes and trains the CTGAN model using a demo dataset. It handles discrete columns and generates synthetic samples. Requires the 'ctgan' library.

```python
from ctgan import CTGAN
from ctgan import load_demo

# Load demo dataset (Adult Census Dataset)
real_data = load_demo()

# Define discrete columns
discrete_columns = [
    'workclass',
    'education',
    'marital-status',
    'occupation',
    'relationship',
    'race',
    'sex',
    'native-country',
    'income'
]

# Initialize and train CTGAN model
ctgan = CTGAN(
    epochs=300,
    batch_size=500,
    generator_dim=(256, 256),
    discriminator_dim=(256, 256),
    embedding_dim=128,
    generator_lr=2e-4,
    discriminator_lr=2e-4,
    pac=10,
    verbose=True
)

# Fit the model to real data
ctgan.fit(real_data, discrete_columns)

# Generate synthetic samples
synthetic_data = ctgan.sample(1000)
print(synthetic_data.head())
```

--------------------------------

### Model Persistence for CTGAN and TVAE

Source: https://context7.com/sdv-dev/ctgan/llms.txt

Saves trained CTGAN and TVAE models to disk using pickle and loads them for later use without retraining. Requires 'ctgan' library.

```python
from ctgan import CTGAN, TVAE

# Train and save CTGAN model
ctgan = CTGAN(epochs=300)
ctgan.fit(train_data, discrete_columns)
ctgan.save('my_ctgan_model.pkl')

# Load saved model
loaded_ctgan = CTGAN.load('my_ctgan_model.pkl')
new_samples = loaded_ctgan.sample(1000)

# Same for TVAE
tvae = TVAE(epochs=200)
tvae.fit(train_data, discrete_columns)
tvae.save('my_tvae_model.pkl')

loaded_tvae = TVAE.load('my_tvae_model.pkl')
tvae_samples = loaded_tvae.sample(2000)
```

--------------------------------

### GPU/CPU Device Management for CTGAN

Source: https://context7.com/sdv-dev/ctgan/llms.txt

Demonstrates how to control device placement (GPU or CPU) for CTGAN model training and inference. Allows enabling GPU acceleration or forcing CPU usage. Requires 'ctgan' and 'torch' libraries.

```python
from ctgan import CTGAN
import torch

# Enable GPU acceleration (default)
ctgan_gpu = CTGAN(enable_gpu=True, epochs=100)
ctgan_gpu.fit(data, discrete_columns)

# Force CPU usage
ctgan_cpu = CTGAN(enable_gpu=False, epochs=100)
ctgan_cpu.fit(data, discrete_columns)

# Manually set device after initialization
ctgan = CTGAN()
ctgan.fit(data, discrete_columns)

# Switch to CPU
ctgan.set_device(torch.device('cpu'))
cpu_samples = ctgan.sample(500)

# Switch to GPU
if torch.cuda.is_available():
    ctgan.set_device(torch.device('cuda:0'))
    gpu_samples = ctgan.sample(1000)
```

--------------------------------

### Update SDV pyproject.toml for CTGAN Release Candidate

Source: https://github.com/sdv-dev/ctgan/blob/main/RELEASE.md

This TOML snippet illustrates how to modify the `pyproject.toml` file in the SDV project to specify a minimum version constraint for CTGAN. This is typically done when testing a release candidate to ensure compatibility.

```toml
'ctgan>=X.Y.Z.dev0'
```

--------------------------------

### Check CTGAN Release Readiness

Source: https://github.com/sdv-dev/ctgan/blob/main/RELEASE.md

This command performs a final check to ensure that the CTGAN project is ready for an official release. It should be executed after all previous release steps, including HISTORY.md updates, have been completed.

```bash
make check-release
```

--------------------------------

### Random State Control for Reproducibility

Source: https://context7.com/sdv-dev/ctgan/llms.txt

Illustrates setting random seeds for NumPy and PyTorch to ensure reproducible synthetic data generation with the CTGAN model. Requires 'ctgan', 'numpy', and 'torch' libraries.

```python
from ctgan import CTGAN
import numpy as np
import torch

# Example usage would involve setting seeds before model initialization or fitting.
# For instance:
# np.random.seed(42)
# torch.manual_seed(42)
# if torch.cuda.is_available():
#     torch.cuda.manual_seed_all(42)

# ctgan = CTGAN(seed=42) # If CTGAN supports a direct seed parameter.
# ctgan.fit(data, discrete_columns)
```

--------------------------------

### Set Random State for CTGAN Reproducibility (Python)

Source: https://context7.com/sdv-dev/ctgan/llms.txt

Demonstrates how to set a random state for the CTGAN model using either an integer seed or custom NumPy and PyTorch random state objects. This ensures reproducible synthetic data generation.

```python
from ctgan import CTGAN
import numpy as np
import torch

# Assuming 'data' and 'discrete_columns' are defined elsewhere
# Method 1: Set random state with integer seed
ctgan = CTGAN(epochs=100)
ctgan.set_random_state(42)
ctgan.fit(data, discrete_columns)
samples1 = ctgan.sample(1000)

# Rerun with same seed produces identical results
ctgan2 = CTGAN(epochs=100)
ctgan2.set_random_state(42)
ctgan2.fit(data, discrete_columns)
samples2 = ctgan2.sample(1000)

# samples1 equals samples2

# Method 2: Use custom random state objects
np_state = np.random.RandomState(seed=123)
torch_state = torch.Generator().manual_seed(123)

ctgan = CTGAN(epochs=100)
ctgan.set_random_state((np_state, torch_state))
ctgan.fit(data, discrete_columns)
reproducible_samples = ctgan.sample(500)
```

--------------------------------

### TVAE Synthesizer for Synthetic Tabular Data

Source: https://context7.com/sdv-dev/ctgan/llms.txt

Initializes and trains the TVAE model for synthetic tabular data generation. It requires a pandas DataFrame and a list of discrete columns. Uses custom parameters for model configuration. Requires 'ctgan' and 'pandas' libraries.

```python
from ctgan import TVAE
import pandas as pd

# Load your data
data = pd.read_csv('your_data.csv')

# Define discrete columns
discrete_cols = ['category_col1', 'category_col2']

# Initialize TVAE with custom parameters
tvae = TVAE(
    epochs=300,
    batch_size=500,
    embedding_dim=128,
    compress_dims=(128, 128),
    decompress_dims=(128, 128),
    l2scale=1e-5,
    loss_factor=2,
    verbose=True
)

# Train the model
tvae.fit(data, discrete_cols)

# Generate synthetic data
synthetic_samples = tvae.sample(5000)

# Access training loss values
print(tvae.loss_values)
```

--------------------------------

### Conditional Sampling with CTGAN

Source: https://context7.com/sdv-dev/ctgan/llms.txt

Generates synthetic data with specific conditions on discrete columns using the CTGAN model. Allows controlling the distribution of generated values by specifying condition columns and values. Requires 'ctgan' library.

```python
from ctgan import CTGAN, load_demo

# Setup and train model
real_data = load_demo()
discrete_columns = ['education', 'income', 'sex']

ctgan = CTGAN(epochs=100, verbose=False)
ctgan.fit(real_data, discrete_columns)

# Generate samples with conditional constraints
# Increase probability of specific category combinations
samples_high_income = ctgan.sample(
    n=1000,
    condition_column='income',
    condition_value='>50K'
)

samples_doctorate = ctgan.sample(
    n=500,
    condition_column='education',
    condition_value='Doctorate'
)

# Verify conditional generation worked
print(samples_high_income['income'].value_counts())
print(samples_doctorate['education'].value_counts())
```

--------------------------------

### Python Assertions in CTGAN Code

Source: https://github.com/sdv-dev/ctgan/blob/main/static_code_analysis.txt

These code snippets demonstrate the use of Python's 'assert' statement, which is flagged by the Bandit linter. Assertions are used for debugging and will be removed in optimized code, potentially impacting runtime checks.

```python
assert args.sample_condition_column_value is not None
```

```python
assert item[0] == 'D'
```

```python
assert idx in discrete
```

```python
assert idx in meta['discrete_columns']
```

```python
assert st == data.shape[1]
```

```python
assert input_.size()[0] % self.pac == 0
```

```python
assert batch_size % 2 == 0
```

```python
assert st == recon_x.size()[1]
```

--------------------------------

### CTGAN with NumPy Arrays (Python)

Source: https://context7.com/sdv-dev/ctgan/llms.txt

Demonstrates how to train and use the CTGAN model directly with NumPy arrays instead of pandas DataFrames. This involves specifying discrete columns by their indices.

```python
from ctgan import CTGAN
import numpy as np

# Create numpy array data
data_array = np.array([
    [25, 50000, 1, 0],
    [30, 60000, 0, 1],
    [35, 75000, 1, 1],
    [40, 90000, 0, 0]
])

# Specify discrete columns by index (not name)
discrete_column_indices = [2, 3]  # Third and fourth columns are discrete

# Train model with numpy array
ctgan = CTGAN(epochs=100)
ctgan.fit(data_array, discrete_column_indices)

# Generate synthetic numpy array
synthetic_array = ctgan.sample(1000)
print(type(synthetic_array))  # numpy.ndarray
print(synthetic_array.shape)

# Model preserves input type
assert isinstance(synthetic_array, np.ndarray)
```

--------------------------------

### DataTransformer for Tabular Data Transformation (Python)

Source: https://context7.com/sdv-dev/ctgan/llms.txt

Utilizes the DataTransformer class to convert tabular data into a model-ready format. It applies Bayesian GMM for continuous columns and one-hot encoding for discrete columns, with support for inverse transformation.

```python
from ctgan.data_transformer import DataTransformer
import pandas as pd

# Create sample data
data = pd.DataFrame({
    'age': [25, 30, 35, 40],
    'income': [50000, 60000, 75000, 90000],
    'education': ['Bachelors', 'Masters', 'PhD', 'Bachelors']
})

# Initialize transformer
transformer = DataTransformer(
    max_clusters=10,
    weight_threshold=0.005
)

# Fit and transform data
discrete_columns = ['education']
transformer.fit(data, discrete_columns)
transformed_data = transformer.transform(data)

print(f"Original shape: {data.shape}")
print(f"Transformed shape: {transformed_data.shape}")
print(f"Output dimensions: {transformer.output_dimensions}")

# Inverse transform back to original format
recovered_data = transformer.inverse_transform(transformed_data)
print(recovered_data)

# Convert column name/value to internal ID
column_info = transformer.convert_column_name_value_to_id('education', 'PhD')
print(column_info)
# Output: {'discrete_column_id': 0, 'column_id': 2, 'value_id': 2}
```

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