Refactored API access.

1.0.1

Merge branch 'refactor'

Cutting a release to be in sync with the latest pypi version

0.3.8

Updating documentation

Allowing the package to be accessible as an AWS lambda layer.

v0.3.5

Read the API key from environment variables if needed

Limiting dependency on seaborn

v0.3.4

Limiting dependency to seaborn

Adding support for RMSE

Adding regression root mean square error (RMSE) in the list of metrics whose achievable values we calculate.

Improving maximum entropy predictions.

v0.3.1

Preparing for release

Adding maximum-entropy predictive models

Adding a maximum-entropy based classifier (kxy.MaxEntClassifier) and regressor (kxy.MaxEntRegressor) following the scikit-learn signature for fitting and predicting.

These models estimate the posterior mean E[u_y|x] and the posterior standard deviation sqrt(Var[u_y|x]) for any specific value of x, where the copula-uniform representations (u_y, u_x) follow the maximum-entropy distribution.

Predictions in the primal are derived from E[u_y|x].

Improving support for categorical variables

• Regression analyses now fully support categorical variables.
• Foundations for multi-output regressions are laid.
• Categorical variables are now systematically encoded and treated as continuous, consistent with what's done at the learning stage.
• Regression and classification are further normalized, and most the compute for classification problems now takes place on the API side, and should be considerably faster.

Making the mutual information analysis abide by variable groups.

v0.1.3

Version update for pypi release

Emergency bugfix

v0.1.2

Version update for pypi release