feat: add synthetic data generation module and CLI command (closes #285)

Author: HeerakKashyap

synthetic_data.py

@@ -0,0 +1,94 @@
+"""
+Synthetic Data Generation Utilities
+
+- Tabular data generation
+- Time series data generation
+- Data with specific distributions
+"""
+
+import numpy as np
+import pandas as pd
+from datetime import datetime, timedelta
+
+def generate_tabular_data(n_samples=1000, n_features=10, target_column=True):
+    """
+    Generate synthetic tabular data for classification/regression.
+    
+    Args:
+        n_samples: Number of samples to generate
+        n_features: Number of features
+        target_column: Whether to include a target column
+    
+    Returns:
+        DataFrame with synthetic data
+    """
+    # Generate features
+    features = np.random.randn(n_samples, n_features)
+    feature_names = [f'feature_{i}' for i in range(n_features)]
+    
+    # Create DataFrame
+    df = pd.DataFrame(features, columns=feature_names)
+    
+    # Add target column if requested
+    if target_column:
+        # Simple target based on first feature
+        df['target'] = (df['feature_0'] > 0).astype(int)
+    
+    return df
+
+def generate_time_series_data(n_samples=1000, trend=True, seasonality=True):
+    """
+    Generate synthetic time series data.
+    
+    Args:
+        n_samples: Number of time points
+        trend: Whether to add trend
+        seasonality: Whether to add seasonality
+    
+    Returns:
+        DataFrame with time series data
+    """
+    # Base time series
+    time_index = pd.date_range(start='2020-01-01', periods=n_samples, freq='D')
+    
+    # Generate base signal
+    signal = np.random.randn(n_samples) * 0.1
+    
+    # Add trend
+    if trend:
+        trend_component = np.linspace(0, 2, n_samples)
+        signal += trend_component
+    
+    # Add seasonality
+    if seasonality:
+        seasonal_component = 0.5 * np.sin(2 * np.pi * np.arange(n_samples) / 365)
+        signal += seasonal_component
+    
+    # Create DataFrame
+    df = pd.DataFrame({
+        'date': time_index,
+        'value': signal
+    })
+    
+    return df
+
+def generate_categorical_data(n_samples=1000, n_categories=5):
+    """
+    Generate synthetic categorical data.
+    
+    Args:
+        n_samples: Number of samples
+        n_categories: Number of categories
+    
+    Returns:
+        DataFrame with categorical data
+    """
+    categories = [f'category_{i}' for i in range(n_categories)]
+    data = np.random.choice(categories, size=n_samples)
+    
+    df = pd.DataFrame({
+        'category': data,
+        'value': np.random.randn(n_samples)
+    })
+    
+    return df