PanCake is a Python package that allows users to stack scikit-learn models over a number of folds and train stacker models using out-of-sample predictions of input models.
The stacking tool provides the construction of a stacking module composed of in-layer (models being stacked)
and out-layer (stacker models) models. The output is a list or matrix of predictions from training of the module,
which can either be used as the final results, or fed into a different module.
After cloning the repository, install from the directory of the package by
pip install .
stacker = Stacker(X, y, splitter, evalMetric, family)where X is the data matrix (numpy array), y is target vector (numpy array),
splitter is a scikit-learn cross-validation generator (KFold or StratifiedKFold),
evalMetric is the metric to be maximized during training, and family is the
type of the problem (currently "regression" or "binary").
Add a scikit-learn model modelObj to in-layer by
stacker.addModelIn(modelObj, trainable, hyperParameters)If trainable is set to True then the model will be trained across folds using the
hyperParameters which is a dictionary of hyper-parameter grid for the
model (check scikit-learn's documentation for the model). If it is set
to False then the model is assumed fixed and is only fitted across folds.
Add a scikit-learn model modelObj to out-layer by
stacker.addModelOut(modelObj, hyperParameters)Again, hyperParameters is a dictionary containig the grid
of hyper-parameters for the model.
To train the model and get predictions on the training data, use
predsTrain = stacker.stackTrain(matrixOut)which yields final predictions for each out-layer model as a list when
matrixOut is set to False. When it is set to True, predictions
for each out-model is appended as column vectors is a an array.
For predictions on the test set, use:
predsTest = stacker.stackTest(X_ts, matrixOut)where X_ts is the test data and matrixOut is the same as above.
To get a summary on CV scores, fit and training times for each in-layer and out-layer model, use
stacker.summary()To save the trained stacker for later use, call
saveModel(stacker, savePath)To load a trained model from disk, call
stacker = loadModel(savePath)Jupyter notebooks analyzing the Boston Housing data is included in the repo:
- Multi-class classification problems
- Parallelization at the model and/or hyper-parameter level