happymath 0.2.1

Happymath is a high-level mathematical modeling Python library. Its core philosophy lies in reducing users' learning costs through high-level encapsulation, enabling efficient mathematical modeling. It is particularly suitable for mathematical modeling competitions and applied mathematics fields.

HappyMath

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🌐 Language: English | 中文

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HappyMath is a comprehensive mathematical computing and machine learning library that provides unified interfaces for automated machine learning, multi-criteria decision making, differential equations, and mathematical optimization.

⚠️ WARNING: PREVIEW VERSION ⚠️

This is currently a preview/development version of HappyMath.

Please be advised that:

• This version contains numerous bugs and issues
• Performance and stability are not guaranteed
• API may change without notice
• Documentation may be incomplete or inaccurate

For production use, please wait for the stable 1.0.0 release.

We appreciate your interest in testing our library, but use at your own risk!

Features

🤖 AutoML - Automated Machine Learning

• Classification: Automated model selection and hyperparameter tuning for classification tasks
• Regression: Intelligent regression model building with feature engineering
• Clustering: Unsupervised learning with automatic algorithm selection
• Anomaly Detection: Outlier and anomaly identification algorithms
• Time Series: Specialized time series forecasting and analysis

📊 Decision - Multi-Criteria Decision Making (MCDM)

A comprehensive framework for multi-criteria decision analysis with 80+ algorithms:

• Subjective Weighting: AHP, BWM, FUCOM, ROC, and more
• Objective Weighting: CRITIC, Entropy, MEREC, PSI, and others
• Scoring Methods: TOPSIS, VIKOR, SAW, MOORA, and 30+ algorithms
• Outranking Methods: ELECTRE and PROMETHEE families
• Fuzzy Decision Making: Complete fuzzy methodology support

🔧 DiffEq - Differential Equations

Unified interface for solving differential equations:

• Ordinary Differential Equations (ODE): Initial value and boundary value problems
• Partial Differential Equations (PDE): Various numerical methods
• Symbolic Analysis: Symbolic computation and analysis tools
• Multiple Solvers: SciPy, SymPy, and custom implementations

⚙️ Opt - Mathematical Optimization

Comprehensive optimization framework supporting:

• Linear Programming: Simplex and interior point methods
• Nonlinear Programming: Gradient-based and derivative-free methods
• Multi-objective Optimization: Pareto front analysis
• Constraint Handling: Various constraint types and formulations
• Solver Integration: Pyomo, Pymoo, and specialized solvers

Installation

⭐️ RECOMMENDED: Conda Installation

This is the recommended installation method for optimal compatibility and performance.

conda install -c conda-forge happymath

Alternative: Pip Installation

pip install happymath

⚠️ Important: When installing with pip, the following issues may occur:

• The ipopt solver is not included by default
• LightGBM models cannot be properly installed
• This may cause AutoML errors and reduced functionality

If you used pip installation or want to ensure all optional dependencies are available, install these packages via conda:

# Install ipopt solver for optimization problems
conda install -c conda-forge ipopt

# Install LightGBM for enhanced AutoML performance
conda install -c conda-forge lightgbm

Requirements

• Python 3.11+
• All core dependencies are automatically installed

Quick Start

AutoML Example

from happymath.AutoML import ClassificationML
from sklearn.datasets import load_iris
import pandas as pd

# Load data
iris = load_iris(as_frame=True)
data = iris.data.copy()
data["target"] = iris.target

# Create a classification experiment
clf = ClassificationML(
    data=data,
    target="target",
    train_size=0.8,
    fold=2,
    seed=42,
    verbose=False,
    html=False,
)

# Train a logistic regression model and predict
model = clf.create("lr", verbose=False)
predictions = clf.predict(data=data.head())
print(predictions[["target", "prediction_label"]].head())

Decision Analysis Example

from happymath.Decision import ObjWeighting, ScoringDecision
import numpy as np

# Decision matrix and criteria types
dm_data = np.array([[250, 16, 12], [200, 16, 8], [300, 32, 16]])
criteria = ["min", "max", "max"]

# Calculate objective weights using entropy
weighting = ObjWeighting(methods=["entropy"])
weights = weighting.decide(
    dataset=dm_data, criterion_type=criteria
).get_weights(method="entropy")
print("Weights:", weights)

# Rank using TOPSIS
scoring = ScoringDecision(methods=["topsis"])
rankings = scoring.decide(
    dataset=dm_data, weights=weights, criterion_type=criteria
).get_rankings(method="topsis")
print("Rankings:", rankings)

Differential Equations Example

import sympy
import numpy as np
from scipy.integrate import solve_ivp
from happymath.DiffEq.ODE.ODEModule import ODEModule

# Define dy/dt = 2*y + t, y(0) = 1
t = sympy.symbols("t")
y = sympy.Function("y")
ode_expr = -y(t).diff(t, 1) + 2 * y(t) + t
ics = {y(0): 1}

ode_obj = ODEModule(ode_expr)
t_span = np.linspace(0, 5, 50)

# Convert to SciPy format and solve
func, y0, const = ode_obj.ode2scipy("IVP", ics)
sol = solve_ivp(func, (0, 5), y0, t_eval=t_span, args=const)
print("y at t=5 ≈", sol.y[0, -1])

Optimization Example

import sympy as sp
from happymath.Opt.OptModule import OptModule

x1, x2 = sp.symbols("x1 x2", real=True)
obj = {"min": (x1 - 1) ** 2 + (x2 - 2) ** 2}
constraints = [x1 >= -5, x1 <= 5, x2 >= -5, x2 <= 5]

opt = OptModule(obj, constraints, mode="pymoo", default_search_range=5.0)
res = opt.solve(solver="GA", use_auto_solvers=False, max_solvers=1)
print("Optimal variables:", res.variables)
print("Optimal value:", res.objective_value)

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use HappyMath in your research, please cite:

@software{happymath2024,
  title={HappyMath: A Comprehensive Mathematical Computing Library},
  author={HappyMathLabs},
  year={2024},
  url={https://github.com/HappyMathLabs/happymath}
}