mini-sisso 1.4.1

High-performance SISSO implementation with Rust backend.

mini-sisso

mini-sisso is a lightweight and user-friendly Python implementation of the SISSO (Sure Independence Screening and Sparsifying Operator) symbolic regression algorithm. It offers full compatibility with the scikit-learn ecosystem for discovering interpretable mathematical models from data.

Inheriting the advanced exploration capabilities of the original C++/Fortran-based implementation, mini-sisso provides these features in a more modern and accessible package, powered by a blazing-fast Rust backend:

• 🚀 Easy Adoption: Simple pip install. The default CPU version has minimal dependencies (NumPy/SciPy), ensuring a hassle-free setup.
• 🦀 High-Performance Rust Backend:
  • The computationally intensive exhaustive search is implemented in Rust, delivering performance far superior to pure Python implementations while remaining completely transparent to the user.
• 🧠 Memory Efficiency & Fast Exploration:
  • A "recipe-based" architecture dramatically reduces memory consumption during Feature Expansion.
  • The "Level-wise SIS" feature (toggleable) speeds up exploration by pruning unpromising features early.
• ⚖️ Balanced Feature Selection:
  • Implements a "Split Selection" strategy during SIS. This ensures that unary operators (like sin, exp) are not "crowded out" by the overwhelming number of binary combinations, preserving feature diversity.
• 🤝 Full scikit-learn Compatibility: Seamlessly integrates with powerful tools like GridSearchCV and Pipeline, in addition to the standard fit()/predict() interface.
• ⚡ Optional GPU Support: Achieve significant speedups with GPU acceleration by installing the optional PyTorch backend.

📥 Installation

CPU Version (Default, Recommended)

Installs the lightweight CPU version from PyPI. It includes the optimized Rust backend.

pip install mini-sisso

GPU Version (Optional)

To enable GPU acceleration with the PyTorch backend, install with the [gpu] option.

pip install "mini-sisso[gpu]"

🚀 Quick Start

Discover a mathematical model from your data in just a few lines of code.

import pandas as pd
import numpy as np
from mini_sisso.model import MiniSisso

# 1. Prepare data
np.random.seed(42) # Set seed for reproducibility
X_df = pd.DataFrame(np.random.rand(100, 2) * [2, 3], columns=["feature_A", "feature_B"])
# True equation: y = 2*sin(feature_A) + feature_B^2 + noise
y_series = pd.Series(2 * np.sin(X_df["feature_A"]) + X_df["feature_B"]**2 + np.random.randn(100) * 0.1)

# 2. Instantiate the Model (Full Hyperparameter List)
# Uncomment the parameters you need to change.
model = MiniSisso(
    # --- Control the fundamental search space ---
    n_expansion=2,                      # Depth of feature expansion (deeper finds more complex equations)
    operators=["+", "sin", "pow2"],     # List of operators for feature expansion
    
    # --- Select the main search strategy ---
    so_method="exhaustive",             # Model search strategy ('exhaustive', 'lasso', 'lightgbm')
    
    # --- Detailed settings for each strategy (selection_params) ---
    selection_params={
        # -- Parameters for "exhaustive" method --
        'n_term': 2,                    # Maximum number of terms in the discovered equation
        'n_sis_features': 10,           # Number of SIS candidates for each term
        
        # -- Parameters for "lasso" method --
        # 'alpha': 0.01,                # Regularization strength for Lasso
        
        # -- Parameters for "lightgbm" method --
        # 'n_features_to_select': 20,   # Number of features to select with LightGBM
        # 'lightgbm_params': {'n_estimators': 100, 'random_state': 42}, # Parameters for the LightGBM model itself
        
        # -- Optional preprocessing filters for "lasso"/"lightgbm" --
        # 'n_global_sis_features': 200, # Number of candidates to pre-screen based on correlation with target
        # 'collinearity_filter': 'mi',  # Method to calculate correlation between candidates ('mi' or 'dcor')
        # 'collinearity_threshold': 0.9, # Correlation threshold for the above filter
    },
    
    # --- Control computational efficiency ---
    use_levelwise_sis=True,             # Use staged search for speed (strongly recommended)
    n_level_sis_features=50,            # Number of promising features to keep at each expansion level
    
    # --- Select the execution environment ---
    # device="cuda",                      # Specify 'cuda' to use GPU
)

# 3. Fit the model
model.fit(X_df, y_series)

# 4. Check the results
print("\n--- Fit Results ---")
print(f"Discovered Equation: {model.equation_}")
print(f"Training RMSE: {model.rmse_:.4f}")
print(f"Training R2 Score: {model.r2_:.4f}")

# 5. Make predictions
print("\n--- Prediction ---")
X_test_df = pd.DataFrame(np.array([[0.5, 1.0], [1.0, 2.0]]), columns=["feature_A", "feature_B"])
predictions = model.predict(X_test_df)
print(f"Predictions for new data ([0.5, 1.0], [1.0, 2.0]): {predictions}")

Example Output:

Using NumPy/SciPy backend for CPU execution.
*** Starting Level-wise Recipe Generation (Level-wise SIS: ON, k_per_level=50) ***
Level 1: Generated 5, selected top 5. Total promising: 7. Time: 0.00s
Level 2: Generated 30, selected top 30. Total promising: 37. Time: 0.00s
***************** Starting SISSO Regressor (NumPy/SciPy Backend, Method: exhaustive) *****************

===== Searching for 1-term models =====
...
===== Searching for 2-term models =====
...
Best 2-term model: RMSE=0.092124, Eq: +0.998492 * ^2(feature_B) +1.971237 * sin(feature_A) +0.030610
Time: 0.01 seconds

==================================================
SISSO fitting finished. Total time: 0.02s
==================================================

Best Model Found (2 terms):
  RMSE: 0.092124
  R2:   0.998806
  Equation: +0.998492 * ^2(feature_B) +1.971237 * sin(feature_A) +0.030610

--- Fit Results ---
Discovered Equation: +0.998492 * ^2(feature_B) +1.971237 * sin(feature_A) +0.030610
Training RMSE: 0.0921
Training R2 Score: 0.9988

--- Predictions ---
Predictions for new data ([0.5, 1.0], [1.0, 2.0]): [2.0016012 5.6796584]

🛠️ Usage Guide: Controlling the Search with Hyperparameters

The mini-sisso search process follows this workflow, with each step controlled by hyperparameters.

Workflow Overview

1. Feature Expansion: Generates a large number of candidate features based on operators and n_expansion.
   • This process is made efficient by use_levelwise_sis=True and n_level_sis_features.
2. [Optional] Preprocessing Filters: A set of filters to prune candidate features when using lasso or lightgbm. (Configured in selection_params).
   • Global SIS: Removes features with low correlation to the target y.
   • Collinearity Filter: Removes highly correlated features from each other.
3. Model Search (Sparsifying Operator): The final model is discovered from the pruned candidates using the strategy specified by so_method.

---

Main Hyperparameters

so_method: The Three Model Search Strategies

The so_method parameter determines the core search approach.

1. so_method="exhaustive" (Default)

The classic SISSO approach. It uses iterative SIS and an exhaustive search powered by Rust to find the optimal model. Best for finding simple, interpretable models.

# Exhaustively search for models up to 3 terms
model = MiniSisso(
    so_method="exhaustive",
    selection_params={
        'n_term': 3,          # Max number of terms to search for
        'n_sis_features': 15  # Number of candidates to add to the pool at each SIS step
    }
)

2. so_method="lasso"

Uses Lasso regression as a feature selector to build a model quickly. Effective for large feature spaces.

# Select features using Lasso
model = MiniSisso(
    so_method="lasso",
    selection_params={
        'alpha': 0.01 # Regularization parameter for Lasso
    }
)

3. so_method="lightgbm"

Uses LightGBM as a feature selector. Excels at capturing non-linear relationships.

# Select top 20 features using LightGBM
model = MiniSisso(
    so_method="lightgbm",
    selection_params={
        'n_features_to_select': 20
    }
)

---

selection_params: Detailed Control for Each Strategy

The selection_params dictionary allows you to apply preprocessing filters and fine-tune each so_method.

Preprocessing Filters (for lasso/lightgbm)

• n_global_sis_features: Pre-screens candidates by removing those with low correlation to the target y.
• collinearity_filter: Removes highly correlated features to stabilize Lasso/LightGBM. Can be 'mi' (Mutual Information) or 'dcor' (Distance Correlation).

# Before running LightGBM, pre-screen to the top 200 features,
# then remove pairs with an MI score > 0.9
model = MiniSisso(
    so_method='lightgbm',
    selection_params={
        'n_global_sis_features': 200,
        'collinearity_filter': 'mi',
        'collinearity_threshold': 0.9,
        'n_features_to_select': 20
    }
)

Expert Settings (for lightgbm)

You can also directly specify the internal hyperparameters for lightgbm.

model = MiniSisso(
    so_method='lightgbm',
    selection_params={
        'n_features_to_select': 20,
        'lightgbm_params': {
            'n_estimators': 200,         # Number of trees
            'num_leaves': 31,            # Max number of leaves in one tree
            'learning_rate': 0.05,       # Learning rate
            'colsample_bytree': 0.8,     # Fraction of features to be considered for each tree
            'subsample': 0.8,            # Fraction of data to be used for each tree
            'reg_alpha': 0.1,            # L1 regularization
            'reg_lambda': 0.1,           # L2 regularization
            'random_state': 42,
            'n_jobs': -1,
            'verbosity': -1,
        }
    }
)

---

Other Key Parameters

• use_levelwise_sis (bool, default=True): Strongly recommended. Speeds up feature generation and saves memory.
• n_level_sis_features (int, default=50): Number of features to keep at each stage when use_levelwise_sis=True.
• device (str, default="cpu"): Set to "cuda" to use the GPU backend.

Available Operators

Specify the operators argument as a list of strings.

Operator	Description
'+'	Addition (a + b)
'-'	Subtraction (a - b)
'*'	Multiplication (a * b)
'/'	Division (a / b)
'sin'	Sine (sin(a))
'cos'	Cosine (cos(a))
'exp'	Exponential (e^a)
'log'	Natural logarithm (ln(a))
'sqrt'	Square root (sqrt(
'pow2'	Square (a^2)
'pow3'	Cube (a^3)
'inv'	Reciprocal (1/a)
'|-|'	Absolute difference (|a - b|)
'cbrt'	Cube root (a^(1/3))
'abs'	Absolute value (|a|)
'scd'	Standard Cauchy Distribution (1 / (π * (1 + a^2)))

🤝 scikit-learn Ecosystem Integration

mini-sisso inherits BaseEstimator and RegressorMixin from scikit-learn, allowing it to seamlessly integrate with the powerful tools provided by scikit-learn.

More detailed usage of Pipeline

Pipeline is a tool for connecting multiple processing steps and treating them as a single estimator.

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from mini_sisso.model import MiniSisso

# Pipeline definition
# Note: MiniSisso is sensitive to the scale of input features, so preprocessing such as StandardScaler
# may impair the interpretability of the discovered formula. It is generally not recommended.
# Here is an example to demonstrate how Pipeline technically works.
pipeline = Pipeline([
# Step 1: Run standardization using the name 'scaler'
('scaler', StandardScaler()), # Usually unnecessary/not recommended for MiniSisso
# Step 2: Run MiniSisso using the name 'sisso'
('sisso', MiniSisso(n_expansion=2, selection_params={'n_term': 2}, operators=["+", "sin", "pow2"]))
])

# Train the entire pipeline: X -> scaler.fit_transform -> sisso.fit
pipeline.fit(X_df, y_series)

# Predict using the pipeline: X -> scaler.transform -> sisso.predict
predictions = pipeline.predict(X_df)

# You can also access and change parameters for each step of the pipeline.
# Example: Changing the number of SISSO terms after training
# pipeline.set_params(sisso__selection_params={'n_term': 3})
print(f"Number of terms in the SISSO step of the pipeline: {pipeline.named_steps['sisso'].selection_params['n_term']}")

Advanced GridSearchCV Usage

GridSearchCV can automatically find the best combination of hyperparameters, including the so_method itself. The __ (double underscore) syntax allows you to search nested parameters within selection_params.

from sklearn.model_selection import GridSearchCV

# Define a list of parameter grids to search over
param_grid = [
    # Case 1: Search patterns for exhaustive method
    {
        'so_method': ['exhaustive'],
        'selection_params': [
            {'n_term': 2, 'n_sis_features': 10},
            {'n_term': 3, 'n_sis_features': 15}
        ]
    },
    # Case 2: Search patterns for lasso method
    {
        'so_method': ['lasso'],
        'selection_params': [
            {'alpha': 0.01, 'collinearity_filter': 'mi'},
            {'alpha': 0.005}
        ]
    },
    # Case 3: Search patterns for lightgbm method
    {
        'so_method': ['lightgbm'],
        'selection_params__n_features_to_select':,
        'selection_params__lightgbm_params__n_estimators':,
    }
]

grid_search = GridSearchCV(
    MiniSisso(n_expansion=2, operators=['+', 'sin', 'pow2']),
    param_grid, cv=3, scoring='neg_root_mean_squared_error', n_jobs=-1, verbose=1
)

print("Starting GridSearchCV to find the best method and parameters...")
grid_search.fit(X_df, y_series)

print(f"\nBest search method and params: {grid_search.best_params_}")
print(f"Equation from the best model: {grid_search.best_estimator_.equation_}")

⚙️ API Reference

MiniSisso

class MiniSisso(BaseEstimator, RegressorMixin):
    def __init__(self, n_expansion: int = 2, operators: list = None,
                 so_method: str = "exhaustive", selection_params: dict = None,
                 use_levelwise_sis: bool = True, n_level_sis_features: int = 50,
                 device: str = "cpu"):

MiniSisso

• n_expansion (int, default=2): Max level of feature expansion.
• operators (list[str], required): List of operators for feature generation.
• so_method (str, default="exhaustive"): Model search strategy ("exhaustive", "lasso", "lightgbm").
• selection_params (dict, optional): Dictionary of detailed parameters for the selected so_method and preprocessing filters.
• use_levelwise_sis (bool, default=True): Toggles the level-wise SIS feature.
• n_level_sis_features (int, default=50): Number of features to keep at each level if use_levelwise_sis=True.
• device (str, default="cpu"): Computation device ("cpu" or "cuda").

---

fit(X, y)

Fits the model to the training data.

Parameters

• X (array-like or pd.DataFrame): The feature data, shape (n_samples, n_features).
• y (array-like or pd.Series): The target variable data, shape (n_samples,).

Returns

• self: The fitted MiniSisso instance.

---

predict(X)

Makes predictions using the fitted model.

Parameters

• X (array-like or pd.DataFrame): The data to make predictions on.

Returns

• np.ndarray: A NumPy array of the predictions.

---

score(X, y)

Returns the coefficient of determination (R² score) of the prediction.

Parameters

• X (array-like or pd.DataFrame): The feature data.
• y (array-like or pd.Series): The true target variable data.

Returns

• float: The R² score.

---

Fitted Attributes

After calling fit(), you can access the following attributes:

• model.equation_ (str): The best mathematical model found.
• model.rmse_ (float): The RMSE of the best model on the training data.
• model.r2_ (float): The R2 score of the best model on the training data.
• model.coef_ (np.ndarray): The coefficients for each term in the best model.
• model.intercept_ (float): The intercept of the best model.

📜 License

This project is licensed under the MIT License.

🙏 Acknowledgements

This library was greatly inspired by the original SISSO algorithm paper and is built upon the fantastic open-source projects NumPy, SciPy, Pandas, scikit-learn, and PyTorch.