Data analysis

Recorded session

A recorded session of this example is appended below.

Freeact can use any Python package available in the execution environment. This example demonstrates using scikit-learn and matplotlib directly in code actions to fit a Gaussian Process Regressor to noisy sine wave data and visualize the results with uncertainty bounds.

Create a workspace with a virtual environment and install the required dependencies:

uv pip install scikit-learn matplotlib

Start the CLI tool:

uv run freeact

In the recording below, the agent performs Gaussian Process Regression in response to a single prompt:

Generate 30 noisy samples from a sine function and fit a Gaussian process regressor to the data. Save the result as a plot with uncertainty bounds to output/gpr_sine.png.

The agent generates the samples, fits a GaussianProcessRegressor with an RBF kernel, and creates a visualization showing the true sine function, noisy samples, model predictions, and uncertainty bounds.

A follow-up prompt asks for model statistics:

print the stats

The agent prints the log-marginal-likelihood and other attributes from the fitted model.

The resulting plot shows the GPR fit with a confidence interval: