Data Preprocessing

class Discretize:
    """
    Discretize continuous attributes.
    
    Args:
        method: Discretization method
        n_intervals: Number of intervals for equal-width/frequency
        remove_const: Remove constant attributes
    """
    def __init__(self, method=None, n_intervals=4, remove_const=True): ...
    
    def __call__(self, data):
        """Apply discretization to data."""

class EqualFreq:
    """Equal frequency discretization."""
    def __init__(self, n=4): ...

class EqualWidth:
    """Equal width discretization."""
    def __init__(self, n=4): ...

class EntropyMDL:
    """Entropy-based discretization with MDL criterion."""
    def __call__(self, data, attribute): ...

class Continuize:
    """
    Convert discrete attributes to continuous.
    
    Args:
        zero_based: Use 0-based encoding
        multinomial_treatment: How to handle multinomial variables
    """
    def __init__(self, zero_based=False, multinomial_treatment=None): ...
    
    def __call__(self, data):
        """Apply continuization to data."""

class DomainContinuizer:
    """Domain-level continuization utilities."""
    def __init__(self, zero_based=False): ...
    
    def __call__(self, data):
        """Transform domain to continuous representation."""

class Impute:
    """
    Impute missing values.
    
    Args:
        method: Imputation method
    """
    def __init__(self, method=None): ...
    
    def __call__(self, data):
        """Apply imputation to data."""

class Average:
    """
    Impute with mean (continuous) or mode (discrete).
    """
    def __call__(self, data, variable): ...

class DoNotImpute:
    """Leave missing values as-is."""
    def __call__(self, data, variable): ...

class DropInstances:
    """Remove instances with missing values."""
    def __call__(self, data, variable): ...

class ReplaceUnknowns:
    """Replace unknown values with specified value."""
    def __init__(self, value): ...
    
    def __call__(self, data, variable): ...

class RemoveNaNRows:
    """Remove rows containing missing values."""
    def __call__(self, data):
        """Remove rows with NaN values."""

class RemoveNaNColumns:
    """Remove columns containing missing values."""
    def __call__(self, data):
        """Remove columns with NaN values."""

class Normalizer:
    """
    Normalize data features.
    
    Args:
        norm_type: Normalization type ('l1', 'l2', 'max')
        transform_class: Apply to class variables
        zero_based: Use zero-based scaling
    """
    def __init__(self, norm_type='l2', transform_class=False, zero_based=True): ...
    
    def __call__(self, data):
        """Apply normalization to data."""

class SelectBestFeatures:
    """
    Select k best features based on scoring function.
    
    Args:
        method: Feature scoring method
        k: Number of features to select
    """
    def __init__(self, method=None, k=5): ...
    
    def __call__(self, data):
        """Select best features from data."""

class SelectRandomFeatures:
    """
    Randomly select features.
    
    Args:
        k: Number of features to select
        random_state: Random seed
    """
    def __init__(self, k=5, random_state=None): ...
    
    def __call__(self, data):
        """Randomly select features."""

class Preprocess:
    """
    Preprocessing pipeline container.
    
    Args:
        preprocessors: List of preprocessing steps
    """
    def __init__(self, preprocessors=None): ...
    
    def __call__(self, data):
        """Apply all preprocessing steps sequentially."""

class FeatureConstructor:
    """Base class for feature construction."""
    def __call__(self, data): ...

class Polynomial:
    """Create polynomial features."""
    def __init__(self, degree=2): ...
    
    def __call__(self, data):
        """Generate polynomial features."""

# Basic preprocessing workflow
from Orange.data import Table
from Orange.preprocess import Discretize, Impute, Normalizer, SelectBestFeatures

# Load data
data = Table("iris")

# Discretization
discretizer = Discretize(method=Discretize.EqualFreq, n_intervals=3)
discrete_data = discretizer(data)

# Missing value imputation
imputer = Impute(method=Impute.Average())
clean_data = imputer(data)

# Normalization
normalizer = Normalizer(norm_type='l2')
normalized_data = normalizer(data)

# Feature selection
selector = SelectBestFeatures(k=3)
selected_data = selector(data)

# Preprocessing pipeline
from Orange.preprocess import Preprocess
pipeline = Preprocess([
    RemoveNaNRows(),
    Impute(method=Impute.Average()),
    Normalizer(norm_type='l2'),
    SelectBestFeatures(k=10)
])
processed_data = pipeline(data)

# Custom discretization
from Orange.preprocess import EqualWidth, EqualFreq, EntropyMDL
equal_width = Discretize(method=EqualWidth(n=5))
equal_freq = Discretize(method=EqualFreq(n=4))
entropy_disc = Discretize(method=EntropyMDL())

# Continuization example
from Orange.preprocess import Continuize
continuizer = Continuize(zero_based=True)
continuous_data = continuizer(discrete_data)

# Advanced imputation
from Orange.preprocess import ReplaceUnknowns, DropInstances
replace_imputer = Impute(method=ReplaceUnknowns(value=0))
drop_imputer = Impute(method=DropInstances())

# Feature selection with different methods
from Orange.preprocess import SelectBestFeatures
# Note: Different scoring methods would be available in actual implementation
chi2_selector = SelectBestFeatures(method='chi2', k=5)
f_score_selector = SelectBestFeatures(method='f_classif', k=8)

print(f"Original data shape: {data.X.shape}")
print(f"Processed data shape: {processed_data.X.shape}")
print(f"Selected features: {[var.name for var in selected_data.domain.attributes]}")