bassa-reg 0.1.14

The BASSA algorithm as presented in the paper Sparse Linear Bayesian Models for Organic Chemistry

BASSA: Bayesian Analysis with Spike-and-Slab Arrangements

Overview

Most chemical datasets are small and high-dimensional, making deep learning impractical. Linear regression remains interpretable and effective, but feature selection is critical. Traditional methods pick a single “best” model, overlooking the fact that multiple plausible models may exist.

BASSA combines Bayesian spike-and-slab regression with a filtering method to efficiently discover and organize many valid regression models. This reveals diverse interpretations hidden in chemical data without overcommitting to a single solution.

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Installation

pip install bassa-reg

Plots are generated through JavaScript/Vega renderers, so no LaTeX or Matplotlib installation is required for package plots.

Example Use

import os

import numpy as np
import pandas as pd
from bassa_reg import Bassa
from bassa_reg.spike_and_slab.spike_and_slab import SpikeAndSlabConfigurations, SpikeAndSlabRegression
from bassa_reg.spike_and_slab.spike_and_slab_util_models import SpikeAndSlabPriors, TestSet

def generate_data(N, M, K, noise_level=0.1):
    X = pd.DataFrame(np.random.randn(N, M), columns=[fr's_{i}' for i in range(M)])
    coefficients = np.random.randn(K)
    Y = pd.Series(X.iloc[:, :K].dot(coefficients) + np.random.randn(N) * noise_level)
    return X, Y

x_train, y_train = generate_data(100, 20, 5)

priors = SpikeAndSlabPriors()
config = SpikeAndSlabConfigurations(sampler_iterations=5000)
abs_dir = os.path.dirname(os.path.abspath(__file__))

regression = SpikeAndSlabRegression(x=x_train,
                                    y=y_train,
                                    priors=priors,
                                    config=config,
                                    project_path=abs_dir,
                                    experiment_name="demo")

regression.run()
bassa = Bassa(model=regression)
bassa.run()

Results

After running both the spike-and-slab regression and BASSA, results are saved in the specified project directory.
The main output is the bassa_plot.png file, which represents the models chosen by BASSA. An interactive viewer is also saved at bassa_plot_viewer/index.html.

This chart visualizes the different models found by BASSA, with their feature combinations and performance metrics.
Key additional outputs include:

Markov Chain Visualization

The markov_chain_visualization.png file shows the exploration of the top 20 features over iterations. An HTML/SVG version is also saved as markov_chain_visualization.html.
It is sorted by feature inclusion frequency, highlighting the most commonly selected features.
Precise feature inclusion frequencies are also provided in a separate file named feature_stats.csv.

Survival Process Plot

The survival_plot.png file, accompanied by the survival_table.csv file, illustrates the survival process of models over iterations. An HTML/SVG version is also saved as survival_plot.html.

This is an auxiliary output that helps understand how models persist or change.

Additional Data

The meta_data.csv file contains information about the Spike-and-Slab regression run, including the number of iterations, and other configuration details. It also includes some metrics about the regression performance on the training data.

Prediction on New Data

In order to make predictions on new data, create a new TestSet object.

import os

import numpy as np
import pandas as pd
from bassa_reg.spike_and_slab.spike_and_slab import SpikeAndSlabConfigurations, SpikeAndSlabRegression
from bassa_reg.spike_and_slab.spike_and_slab_util_models import SpikeAndSlabPriors, TestSet

def generate_data(N, M, K, noise_level=0.1):
    X = pd.DataFrame(np.random.randn(N, M), columns=[fr's_{i}' for i in range(M)])
    coefficients = np.random.randn(K)
    Y = pd.Series(X.iloc[:, :K].dot(coefficients) + np.random.randn(N) * noise_level)
    x_test = pd.DataFrame(np.random.randn(int(N/2), M), columns=[fr's_{i}' for i in range(M)])
    return X, Y, x_test

x_train, y_train, x_test = generate_data(100, 20, 5)

priors = SpikeAndSlabPriors()
config = SpikeAndSlabConfigurations(sampler_iterations=5000)
abs_dir = os.path.dirname(os.path.abspath(__file__))

test_set = TestSet(x_test=x_test,
                   samples_per_y=100,
                   iterations=200)

regression = SpikeAndSlabRegression(x=x_train,
                                    y=y_train,
                                    priors=priors,
                                    config=config,
                                    test_set=test_set,
                                    project_path=abs_dir,
                                    experiment_name="prediction_demo")

regression.run()

The sampler will run an extra numbers of iterations set by the iterations parameter in the TestSet object.
In every iteration, the sampler will sample samples_per_y values of y for each sample in the test set.
The average of these samples will be the predicted value for each sample in the test set.

Continuing a Previous Run

In order to continue a previous run, you first need to set save_samples=True on the SpikeAndSlabConfigurations object.
Then, you can load the previous run using the SpikeAndSlabLoader object and pass it to the SpikeAndSlabRegression object.

import os

import numpy as np
import pandas as pd
from bassa_reg.spike_and_slab.spike_and_slab import SpikeAndSlabConfigurations, SpikeAndSlabRegression
from bassa_reg.spike_and_slab.spike_and_slab_util_models import SpikeAndSlabPriors, SpikeAndSlabLoader


def generate_data(N, M, K, noise_level=0.1):
    X = pd.DataFrame(np.random.randn(N, M), columns=[fr's_{i}' for i in range(M)])
    coefficients = np.random.randn(K)
    Y = pd.Series(X.iloc[:, :K].dot(coefficients) + np.random.randn(N) * noise_level)
    return X, Y

x_train, y_train = generate_data(100, 10, 6, noise_level=0.6)
priors = SpikeAndSlabPriors()
config = SpikeAndSlabConfigurations(sampler_iterations=5000,
                                    save_meta_data=True,
                                    save_samples=True)
abs_dir = os.path.dirname(os.path.abspath(__file__))
regression = SpikeAndSlabRegression(x=x_train,
                            y=y_train,
                            priors=priors,
                            config=config,
                            project_path=abs_dir,
                            experiment_name="example_run")
regression.run()
loader = SpikeAndSlabLoader(path = f"{abs_dir}/{regression.full_experiment_name}")
regression = SpikeAndSlabRegression(x=x_train,
                                    y=y_train,
                                    priors=priors,
                                    config=config,
                                    project_path=abs_dir,
                                    experiment_name="example_run",
                                    load_experiment=loader)
regression.run()

Choosing Priors For Spike-and-Slab

There are 3 latent variables in the spike-and-slab model that need priors:

BASSA Thresholds

TBD