pyPhenology.models.Ensemble

class pyPhenology.models.Ensemble(core_models)

Fit an ensemble of different models.

This model can fit multiple models into an ensemble where the weights are equal among all ensemble members.

Note that the core models must be passed initialized. They will be fit within the Ensemble model:

from pyPhenology import models, utils
observations, predictors = utils.load_test_data(name='vaccinium')

m1 = models.Thermaltime(parameters={'T':0})
m2 = models.Thermaltime(parameters={'T':5})
m3 = models.Uniforc(parameters={'t1':1})
m4 = models.Uniforc(parameters={'t1':30})

ensemble = models.Ensemble(core_models=[m1,m2,m3,m4])
ensemble.fit(observations, predictors)

__init__(core_models)

Ensemble model

core_models : list of pyPhenology models, or a saved model file

Methods

__init__(core_models)	Ensemble model
ensemble_shape([shape])	Returns a tuple signifying the layers of submodels ie.
fit(observations, predictors[, n_jobs, …])	Fit the underlying core models Parameters: observations : dataframe pandas dataframe of phenology observations
get_params()	This returns list of dictionaries with parameters of each model in the ensemble
predict([to_predict, predictors, …])	Make predictions..
save_params(filename[, overwrite])	Save model parameters
score([metric, doy_observed, to_predict, …])	Get the scoring metric for fitted data Get the score on the dataset used for fitting (if fitting was done), otherwise set to_predict, and predictors as used in model.predict().

fit(observations, predictors, n_jobs=1, verbose=False, debug=False, **kwargs)

Fit the underlying core models Parameters:

observations : dataframe

pandas dataframe of phenology observations

predictors : dataframe

pandas dataframe of associated predictors

n_jobs : int

number of parallel processes to use

kwargs :

Other arguments passed to core model fitting (eg. optimzer methods)

get_params()

This returns list of dictionaries with parameters of each model in the ensemble

predict(to_predict=None, predictors=None, aggregation='mean', n_jobs=1, **kwargs)

Make predictions..

Predictions will be made using each core models, then a final prediction for each observation using the specified aggregation method.

Parameters:

see core model description

aggregation : str

Either ‘mean’, ‘median’, or ‘none’. If using ‘none’ this returns an array of tuple of size (number of members, predictions).

n_jobs : int

number of parallel processes to use