PyMC Data Handling and Backends

import pymc as pm

def Data(name, value, *, dims=None, export_index_as_coords=True, **kwargs):
    """
    Create a data container for observed or input data.
    
    Parameters:
    - name (str): Name of the data variable
    - value (array-like): Initial data values
    - dims (tuple, optional): Dimension names for the data
    - export_index_as_coords (bool): Export indices as coordinates
    - **kwargs: Additional PyTensor shared variable options
    
    Returns:
    - data_var: PyTensor shared variable containing the data
    """

# Basic data container
import numpy as np

X_train = np.random.randn(100, 5)
y_train = np.random.randn(100)

with pm.Model() as model:
    # Create data containers
    X_data = pm.Data('X_data', X_train)
    y_data = pm.Data('y_data', y_train)
    
    # Model using data containers
    beta = pm.Normal('beta', 0, 1, shape=X_train.shape[1])
    mu = pm.math.dot(X_data, beta)
    sigma = pm.HalfNormal('sigma', 1)
    
    # Likelihood using data container
    likelihood = pm.Normal('likelihood', mu=mu, sigma=sigma, observed=y_data)

# Data containers with dimensions
coords = {'obs': range(100), 'features': ['f1', 'f2', 'f3', 'f4', 'f5']}

with pm.Model(coords=coords) as dimensional_model:
    # Data with explicit dimensions
    X_data = pm.Data('X_data', X_train, dims=['obs', 'features'])
    y_data = pm.Data('y_data', y_train, dims='obs')
    
    # Model components with matching dimensions
    beta = pm.Normal('beta', 0, 1, dims='features')
    mu = pm.math.dot(X_data, beta)
    likelihood = pm.Normal('likelihood', mu=mu, sigma=1, observed=y_data)

def set_data(new_data, *, model=None, coords=None):
    """
    Update data values in existing data containers.
    
    Parameters:
    - new_data (dict): Dictionary mapping data names to new values
    - model (Model, optional): Target model (default: current context)
    - coords (dict, optional): New coordinate values
    """

# Update data for out-of-sample prediction
X_test = np.random.randn(50, 5)

# Sample with training data
with model:
    trace = pm.sample(1000)

# Update data containers for prediction
pm.set_data({'X_data': X_test, 'y_data': None})

# Generate predictions with new data
with model:
    posterior_predictive = pm.sample_posterior_predictive(trace, var_names=['likelihood'])

# Restore original data
pm.set_data({'X_data': X_train, 'y_data': y_train})

def Minibatch(name, value, batch_size=None, *, dims=None, random_seed=None, **kwargs):
    """
    Create a minibatch data container for large datasets.
    
    Parameters:
    - name (str): Name of the minibatch variable
    - value (array-like): Full dataset
    - batch_size (int, optional): Size of each minibatch
    - dims (tuple, optional): Dimension names
    - random_seed (int, optional): Seed for batch sampling
    - **kwargs: Additional options
    
    Returns:
    - minibatch_var: Minibatch data container
    """

# Large dataset minibatching
X_large = np.random.randn(10000, 20)
y_large = np.random.randn(10000)

with pm.Model() as minibatch_model:
    # Create minibatch containers
    X_mb = pm.Minibatch('X_mb', X_large, batch_size=128)
    y_mb = pm.Minibatch('y_mb', y_large, batch_size=128)
    
    # Model parameters
    beta = pm.Normal('beta', 0, 1, shape=20)
    sigma = pm.HalfNormal('sigma', 1)
    
    # Linear predictor with minibatch data
    mu = pm.math.dot(X_mb, beta)
    
    # Likelihood (automatically scaled for minibatching)
    likelihood = pm.Normal('likelihood', mu=mu, sigma=sigma, 
                          observed=y_mb, total_size=len(X_large))
    
    # Variational inference works well with minibatches
    approx = pm.fit(n=50000, method='advi')
    trace = approx.sample(2000)

def get_data(dataset_name):
    """
    Load example datasets for testing and tutorials.
    
    Parameters:
    - dataset_name (str): Name of the dataset to load
    
    Returns:
    - data: Loaded dataset as appropriate data structure
    """

# Load example datasets
boston_housing = pm.get_data('boston_housing')
iris_data = pm.get_data('iris')
radon_data = pm.get_data('radon')

print("Available datasets:")
for name, data in [('boston', boston_housing), ('iris', iris_data)]:
    print(f"  {name}: {data.shape if hasattr(data, 'shape') else type(data)}")

from pymc.backends import NDArray

class NDArray:
    """
    In-memory trace storage backend.
    
    Parameters:
    - vars (list, optional): Variables to trace
    - model (Model, optional): PyMC model
    
    Attributes:
    - samples (dict): Dictionary of samples by variable name
    - sampler_stats (dict): Sampler statistics
    """
    
    def __init__(self, vars=None, model=None):
        pass
    
    def setup(self, draws, chain, sampler_vars=None):
        """Set up storage for sampling."""
        pass
    
    def record(self, point, sampler_stats=None):
        """Record a sample point."""
        pass
    
    def close(self):
        """Finalize the trace."""
        pass

# Basic in-memory sampling
with pm.Model() as model:
    alpha = pm.Normal('alpha', 0, 1)
    beta = pm.Normal('beta', 0, 1)
    
    # Use in-memory backend (default)
    trace = pm.sample(1000, trace=pm.backends.NDArray())

# Access samples directly
print(f"Alpha samples shape: {trace.posterior['alpha'].shape}")
print(f"Beta mean: {trace.posterior['beta'].mean():.3f}")

from pymc.backends import ZarrTrace

class ZarrTrace:
    """
    Zarr-based persistent trace storage.
    
    Parameters:
    - dir_path (str): Directory path for Zarr storage
    - vars (list, optional): Variables to trace
    - model (Model, optional): PyMC model
    - append (bool): Append to existing trace
    """
    
    def __init__(self, dir_path=None, vars=None, model=None, append=False):
        pass

# Persistent storage with Zarr
import tempfile
import os

with tempfile.TemporaryDirectory() as tmp_dir:
    zarr_path = os.path.join(tmp_dir, 'trace.zarr')
    
    with pm.Model() as model:
        # Large model...
        theta = pm.Normal('theta', 0, 1, shape=100)
        
        # Store trace persistently
        trace = pm.sample(10000, trace=pm.backends.ZarrTrace(zarr_path))
    
    # Trace is automatically saved to disk
    print(f"Trace stored at: {zarr_path}")
    
    # Load trace later
    loaded_trace = pm.backends.ZarrTrace(zarr_path)
    print(f"Loaded trace shape: {loaded_trace.posterior['theta'].shape}")

from pymc.backends.base import BaseTrace, MultiTrace

class BaseTrace:
    """
    Base class for all trace backends.
    
    Methods:
    - setup: Initialize trace storage
    - record: Record sample point
    - close: Finalize trace
    - __getitem__: Access samples by variable name
    """

class MultiTrace:
    """
    Container for multiple chain traces.
    
    Parameters:
    - straces (list): List of single-chain traces
    
    Methods:
    - get_values: Extract values for variables
    - get_sampler_stats: Extract sampler statistics
    - add_values: Add new samples
    """
    
    def __init__(self, straces):
        self.straces = straces
    
    def get_values(self, varname, burn=0, thin=1, combine=True, chains=None, squeeze=True):
        """
        Get values for a variable across chains.
        
        Parameters:
        - varname (str): Variable name
        - burn (int): Burn-in samples to exclude
        - thin (int): Thinning factor
        - combine (bool): Combine chains into single array
        - chains (list, optional): Specific chains to extract
        - squeeze (bool): Squeeze singleton dimensions
        
        Returns:
        - values: Variable samples
        """
        pass

# Working with MultiTrace
trace = pm.sample(2000, chains=4)  # Returns MultiTrace

# Extract specific variable values
alpha_samples = trace.get_values('alpha', burn=500, thin=2)
beta_samples = trace.get_values('beta', chains=[0, 1], combine=False)

# Get sampler statistics
diverging = trace.get_sampler_stats('diverging')
energy = trace.get_sampler_stats('energy')

print(f"Total divergences: {diverging.sum()}")
print(f"Mean energy: {energy.mean():.3f}")

def to_inference_data(trace=None, *, prior=None, posterior_predictive=None,
                     predictions=None, log_likelihood=None, coords=None,
                     dims=None, model=None, save_warmup=False, **kwargs):
    """
    Convert PyMC trace to ArviZ InferenceData object.
    
    Parameters:
    - trace: PyMC trace object
    - prior: Prior samples
    - posterior_predictive: Posterior predictive samples  
    - predictions: Out-of-sample predictions
    - log_likelihood: Log-likelihood values
    - coords (dict): Coordinate specifications
    - dims (dict): Dimension specifications
    - model: PyMC model
    - save_warmup (bool): Include warmup samples
    
    Returns:
    - idata: ArviZ InferenceData object
    """

# Convert to ArviZ InferenceData
with pm.Model() as model:
    alpha = pm.Normal('alpha', 0, 1)
    beta = pm.Normal('beta', 0, 1, shape=3)
    sigma = pm.HalfNormal('sigma', 1)
    
    mu = alpha + pm.math.dot(X_data, beta)
    y_obs = pm.Normal('y_obs', mu=mu, sigma=sigma, observed=y_data)
    
    # Sample
    trace = pm.sample(1000)
    
    # Prior samples
    prior_samples = pm.sample_prior_predictive(500)
    
    # Posterior predictive samples
    post_pred = pm.sample_posterior_predictive(trace)

# Convert to comprehensive InferenceData
idata = pm.to_inference_data(
    trace,
    prior=prior_samples,
    posterior_predictive=post_pred,
    coords={'obs': range(len(y_data)), 'features': ['f1', 'f2', 'f3']},
    dims={'beta': ['features'], 'y_obs': ['obs']},
    model=model
)

print("InferenceData groups:")
for group_name in idata._groups:
    print(f"  {group_name}: {list(idata[group_name].data_vars.keys())}")

def predictions_to_inference_data(predictions, *, posterior_trace=None,
                                model=None, coords=None, dims=None, **kwargs):
    """
    Convert predictions to InferenceData format.
    
    Parameters:
    - predictions: Prediction samples
    - posterior_trace: Original posterior trace
    - model: PyMC model
    - coords (dict): Coordinate specifications
    - dims (dict): Dimension specifications
    
    Returns:
    - pred_idata: InferenceData with predictions group
    """

# Out-of-sample predictions with proper metadata
X_test = np.random.randn(25, 3)
pm.set_data({'X_data': X_test, 'y_data': None})

with model:
    # Generate predictions
    predictions = pm.sample_posterior_predictive(
        trace, 
        var_names=['y_obs'],
        predictions=True
    )

# Convert predictions to InferenceData
pred_idata = pm.predictions_to_inference_data(
    predictions,
    posterior_trace=trace,
    coords={'test_obs': range(25), 'features': ['f1', 'f2', 'f3']},
    dims={'y_obs': ['test_obs']},
    model=model
)

from pymc.blocking import DictToArrayBijection

class DictToArrayBijection:
    """
    Bijection between dictionary and array representations of parameters.
    
    Parameters:
    - ordering: Variable ordering specification
    - vmap: Variable mapping
    
    Methods:
    - map: Convert dictionary to array
    - rmap: Convert array to dictionary
    """
    
    def __init__(self, ordering, vmap=None):
        pass
    
    def map(self, point_dict):
        """Convert parameter dictionary to array."""
        pass
    
    def rmap(self, point_array):  
        """Convert parameter array to dictionary."""
        pass

# Create bijection for efficient array operations
with pm.Model() as model:
    alpha = pm.Normal('alpha', 0, 1)
    beta = pm.Normal('beta', 0, 1, shape=3)
    
    # Create bijection
    bijection = pm.DictToArrayBijection(model.free_RVs)
    
    # Convert point formats
    point_dict = {'alpha': 0.5, 'beta': np.array([1.0, -0.5, 0.2])}
    point_array = bijection.map(point_dict)
    recovered_dict = bijection.rmap(point_array)
    
    print(f"Array representation: {point_array}")
    print(f"Recovered dict: {recovered_dict}")

class DataLoader:
    """Custom data loader for streaming/dynamic data."""
    
    def __init__(self, data_source, batch_size=128):
        self.data_source = data_source
        self.batch_size = batch_size
        self.current_batch = 0
    
    def get_batch(self):
        """Get next batch of data."""
        start_idx = self.current_batch * self.batch_size
        end_idx = start_idx + self.batch_size
        
        batch_data = self.data_source[start_idx:end_idx]
        self.current_batch += 1
        
        return batch_data
    
    def reset(self):
        """Reset batch counter."""
        self.current_batch = 0

# Use with PyMC for streaming inference
data_loader = DataLoader(large_dataset, batch_size=256)

with pm.Model() as streaming_model:
    # Initialize with first batch
    initial_batch = data_loader.get_batch()
    X_mb = pm.Minibatch('X_mb', initial_batch['X'], batch_size=256)
    y_mb = pm.Minibatch('y_mb', initial_batch['y'], batch_size=256)
    
    # Model definition...
    
    # Stream through data for variational inference
    for epoch in range(10):
        data_loader.reset()
        
        # Process all batches in epoch
        while True:
            try:
                batch = data_loader.get_batch()
                pm.set_data({'X_mb': batch['X'], 'y_mb': batch['y']})
                
                # Update variational approximation
                approx = pm.fit(n=1000, method='advi')
                
            except IndexError:  # End of data
                break

class CustomBackend(pm.backends.base.BaseTrace):
    """Custom trace backend for specialized storage needs."""
    
    def __init__(self, custom_storage, vars=None, model=None):
        self.custom_storage = custom_storage
        super().__init__(vars=vars, model=model)
    
    def setup(self, draws, chain, sampler_vars=None):
        """Initialize custom storage."""
        self.chain = chain
        self.draws = draws
        self.draw_idx = 0
        
        # Initialize storage structures
        self.custom_storage.init_chain(chain, draws, self.varnames)
    
    def record(self, point, sampler_stats=None):
        """Record sample to custom storage."""
        self.custom_storage.store_sample(
            self.chain, 
            self.draw_idx, 
            point, 
            sampler_stats
        )
        self.draw_idx += 1
    
    def close(self):
        """Finalize custom storage."""
        self.custom_storage.finalize_chain(self.chain)
    
    def __getitem__(self, key):
        """Access stored samples."""
        return self.custom_storage.get_samples(self.chain, key)

# Use custom backend
# custom_storage = YourCustomStorage()  # Implement your storage system
# trace = pm.sample(1000, trace=CustomBackend(custom_storage))

# Memory-efficient sampling for large models
with pm.Model() as large_model:
    # Very large parameter space
    theta = pm.Normal('theta', 0, 1, shape=10000)
    
    # Use memory-mapped storage
    with tempfile.TemporaryDirectory() as tmp_dir:
        zarr_path = os.path.join(tmp_dir, 'large_trace.zarr')
        
        # Sample with disk storage
        trace = pm.sample(
            draws=5000,
            trace=pm.backends.ZarrTrace(zarr_path),
            compute_convergence_checks=False,  # Save memory
            progressbar=True
        )
        
        # Process trace in chunks to avoid memory overflow
        chunk_size = 1000
        for i in range(0, 5000, chunk_size):
            chunk = trace.posterior['theta'][..., i:i+chunk_size, :]
            # Process chunk...
            processed_chunk = chunk.mean(dim='draw')
            print(f"Processed chunk {i//chunk_size + 1}")