Each script or library is in a directory in this folder. For each one you will always find a readme explaining what's its purpose and usage, the actual whizzml code in a .whizzml file, and the JSON metadata needed to create BigML resources.

By convention, when the artifact is a library, the files are called library.whizzml and metadata.json, while for a script we use script.whizzml and metadata.json.

• covariate-shift Determine if there is a shift in data distribution between two datasets.
• model-or-ensemble Decide whether to use models or ensembles for predictions, given an input source.
• remove-anomalies Using Flatline and an anomaly detector, remove from an input dataset its anomalous rows.
• smacdown-branin Simple example of SMACdown, using the Branin function as evaluator.
• smacdown-ensemble Use SMACdown to discover the best possible ensemble to model a given dataset id.
• find-neighbors Using cluster distances as a metric, find instances in a dataset close to a given row.
• stacked-generalization Simple stacking using decision tree, ensembles and logistic regression.
• best-first Feature selection using a greedy algorithm.
• gradient-boosting Boosting algorithm using gradient descent.
• model-per-cluster Scripts and library to model data after clustering and make predictions using the resulting per-cluster model.
• best-k Scripts and library implementing Pham-Dimov-Nguyen algorithm for choosing the best k in k-means clusters.
• seeded-best-k Scripts and library implementing Pham-Dimov-Nguyen algorithm for choosing the best k in k-means clusters, with user-provided seeds.
• anomaly-shift Calculate the average anomaly between two given datasets.
• cross-validation Scripts for performing k-fold crossvalidation.
• clean-dataset Scripts and library for cleaning up a dataset.
• boruta Script for feature selection using the Boruta algorithm.
• cluster-classification Script that determines which input fields are most important for differentiating between clusters.
• anomaly-benchmarking Script that takes any dataset (classification or regression) and turns it into a binary classification problem with the two classes "normal" and "anomalous".

The makefile at the top level provides targets to register packages and run tests (when they're available). It needs a working installation of bigmler. Just type

make help

for a list of possibilities, including:

• tests to run all available test scripts (which live in the test subdirectory of some packages), which typically use bigmler.

• compile to use bigmler to register in BigML the resources associated with one or more packages in the repository.

• clean to delete resources and outputs (both remote and local) created by compile.

• distcheck combines most of the above to check that all the scripts in the repository are working: this target should build cleanly before merging into

The verbosity of the tests output can be controlled with the variable VERBOSITY, which runs from 0 (the default, mostly silent) to 2. E.g.:

make tests VERBOSITY=1

If you write your own test scripts, include test-utils.sh for shared utilities.