Metadata-Version: 2.1
Name: gammy
Version: 0.4.0
Summary: Generalized additive models with a Bayesian twist
Home-page: https://github.com/malmgrek/gammy
Author: Stratos Staboulis
License: UNKNOWN
Description: # Gammy – Generalized additive models in Python with a Bayesian twist
        
        A Generalized additive model is a predictive mathematical model defined as a sum
        of terms that are calibrated (fitted) with observation data. 
        
        Generalized additive models form a surprisingly general framework for building
        models for both production software and scientific research. This Python package
        offers tools for building the model terms as decompositions of various basis
        functions. It is possible to model the terms e.g. as Gaussian processes (with
        reduced dimensionality) of various kernels, as piecewise linear functions, and
        as B-splines, among others. Of course, very simple terms like lines and
        constants are also supported (these are just very simple basis functions).
        
        The uncertainty in the weight parameter distributions is modeled using Bayesian
        statistic with the help of the superb package
        [BayesPy](http://www.bayespy.org/index.html).
        
        ## Documentation
        
        A detailed documentation of the package can be found in
        <https://malmgrek.github.io/gammy>.
        
        
        ## Installation
        
        The package is found in PyPi.
        
        ``` shell
        pip install gammy
        ```
        
        
        ## Quick glance
        
        Let's try to estimate the MATLAB function from pseudo-random samples that are
        corrupted with pseudo-random noise. 
        
        ``` python
        import matplotlib.pyplot
        import numpy as np
        
        import gammy
        from gammy.arraymapper import x
        
        
        # Simulate data
        n = 100
        input_data = 6 * np.vstack((np.random.rand(n), np.random.rand(n))).T - 3
        y = (
            gammy.peaks(input_data[:, 0], input_data[:, 1]) + 4 
            + 0.3 * np.random.randn(n)
        )
        
        # Fit a model
        gp = gammy.ExpSquared1d(
            grid=np.arange(-3, 3, 0.1),
            corrlen=0.5,
            sigma=4.0,
            energy=0.9
        )
        model = gammy.models.bayespy.GAM(
            # Define a bivariate Gaussian Process prior with a
            # Kronecker structure
            gammy.Kron(gp(x[:, 0]), gp(x[:, 1])) + gammy.Scalar()
        ).fit(input_data, y)
        
        err = model.predict(input_data) - y  # Prediction error
        err.mean()
        # 1.00842e-08
        err.std()
        # 0.739
        
        # Noise std estimated by the model
        np.sqrt(model.inv_mean_tau)
        # 0.913
        
        ```
        
        Plot generated with `gammy.plot.validation_plot`:
        
        ![Marginal posterior densities of parameters](./doc/source/quick.png "Densities")
        `
        
        <!-- ## To-be-added features -->
        
        <!-- - **TODO** Quick model template functions (e.g. splines, GPs) -->
        <!-- - **TODO** Shorter overview and examples in README. Other docs inside `docs`. -->
        <!-- - **TODO** Support indicator models in plotting -->
        <!-- - **TODO** Fixed ordering for GP related basis functions. -->
        <!-- - **TODO** Hyperpriors for model parameters – Start from diagonal precisions. -->
        <!--            Instead of `(μ, Λ)` pairs, the arguments could be just -->
        <!--            BayesPy node. -->
        <!-- - **TODO** Support non-linear GAM models. -->
        <!-- - **TODO** Multi-dimensional observations. -->
        <!-- - **TODO** Dynamically changing models. -->
        
Keywords: bayesian statistics modeling gaussian process splines
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Provides-Extra: test
