Feature Engineering

Feature Engineering Framework

Comprehensive, modular feature engineering framework general tabular datasets. Provides strategy-based operations including numerical scaling, categorical encoding, polynomial features, and feature selection through a configurable pipeline.

Core Components

FeatureEngineeringStrategies

Collection of static methods for feature engineering operations:

Numerical Features (if intepretability is not a concern)

• scale_numerical(df, columns, method) - Scale using 'standard', 'minmax', or 'robust'
• create_bins(df, columns, n_bins, strategy) - Discretize using 'uniform', 'quantile', or 'kmeans'
• create_polynomial_features(df, columns, degree) - Generate polynomial and interaction terms
• create_interaction_features(df, column_pairs) - Create multiplication interactions
• create_log_features(df, columns) - Log-transform for skewed distributions

Categorical Features

• encode_categorical(df, columns, method) - Encode using 'onehot', 'label', 'frequency', or 'hash'
• create_category_aggregations(df, categorical_col, numerical_cols, agg_funcs) - Group-level statistics

Binary Features

• convert_to_binary(df, columns) - Convert Yes/No, True/False to 0/1 (data type to int)

Data Quality Validation

• validate_numeric_features(df, exclude_cols) - Verify all features are numeric (except ID columns)
• validate_no_constants(df, exclude_cols) - Remove constant columns with no variance

Feature Selection

• select_features_variance(df, columns, threshold) - Remove low-variance features (default: 0.01). For some columns that consist of almost the same values, we might consider to drop due to the low variance it brings in order to reduce dimensionality.
• select_features_correlation(df, columns, threshold) - Remove highly correlated features

FeatureEngineeringPipeline

Orchestrates multiple feature engineering steps with logging.

CRITICAL REQUIREMENTS:

1. ALL output features MUST be numeric (int or float) - DID analysis cannot use string/object columns
2. Preview data types BEFORE processing: df.dtypes and df.head() to check actual values
3. Encode ALL categorical variables - strings like "degree", "age_range" must be converted to numbers
4. Verify output: Final dataframe should have df.select_dtypes(include='number').shape[1] == df.shape[1] - 1 (excluding ID column)

Usage Example

from feature_engineering import FeatureEngineeringStrategies, FeatureEngineeringPipeline

# Create pipeline
pipeline = FeatureEngineeringPipeline(name="Demographics")

# Add feature engineering steps
pipeline.add_step(
    FeatureEngineeringStrategies.convert_to_binary,
    columns=['<column5>', '<column2>'],
    description="Convert binary survey responses to 0/1"
).add_step(
    FeatureEngineeringStrategies.encode_categorical,
    columns=['<column3>', '<column7>'],
    method='onehot',
    description="One-hot encode categorical features"
).add_step(
    FeatureEngineeringStrategies.scale_numerical,
    columns=['<column10>', '<column1>'],
    method='standard',
    description="Standardize numerical features"
).add_step(
    FeatureEngineeringStrategies.validate_numeric_features,
    exclude_cols=['<ID Column>'],
    description="Verify all features are numeric before modeling"
).add_step(
    FeatureEngineeringStrategies.validate_no_constants,
    exclude_cols=['<ID Column>'],
    description="Remove constant columns with no predictive value"
).add_step(
    FeatureEngineeringStrategies.select_features_variance,
    columns=[],  # Empty = auto-select all numerical
    threshold=0.01,
    description="Remove low-variance features"
)

# Execute pipeline
# df_complete: complete returns original columns and the engineered features
df_complete = pipeline.execute(your_cleaned_df, verbose=True)

# Shortcut: Get the ID Column with the all needed enigneered features
engineered_features = pipeline.get_engineered_features()
df_id_pure_features = df_complete[['<ID Column>']+engineered_features]

# Get execution log
log_df = pipeline.get_log()

Input

• A valid dataFrame that would be sent to feature engineering after any data processing, imputation, or drop (A MUST)

Output

• DataFrame with both original and engineered columns
• Engineered feature names accessible via pipeline.get_engineered_features()
• Execution log available via pipeline.get_log()

Key Features

• Multiple encoding methods for categorical variables
• Automatic handling of high-cardinality categoricals
• Polynomial and interaction feature generation
• Built-in feature selection for dimensionality reduction
• Pipeline pattern for reproducible transformations

Best Practices

• Always validate data types before downstream analysis: Use validate_numeric_features() after encoding
• Check for constant columns that provide no information: Use validate_no_constants() before modeling
• Convert binary features before other transformations
• Use one-hot encoding for low-cardinality categoricals
• Use KNN imputation if missing value could be inferred from other relevant columns
• Use hash encoding for high-cardinality features (IDs, etc.)
• Apply variance threshold to remove constant features
• Check correlation matrix before modeling to avoid multicollinearity
• MAKE SURE ALL ENGINEERED FEATURES ARE NUMERICAL

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