esem.emulator.Emulator¶

class esem.emulator.Emulator(model, training_params, training_data, name='', gpu=0)¶

A class wrapping a statistical emulator

training_data¶

A wrapped representation of the training data

model¶

The underlying model which performs the emulation

name¶

A human-readable name for the model

__init__(model, training_params, training_data, name='', gpu=0)¶

Parameters

model (ModelAdaptor) – The (compiled but not trained) model to be wrapped
training_params (pd.DataFrame or array-like) – The training parameters (X)
training_data (esem.wrappers.DataWrapper or xarray.DataArray or iris.Cube or array-like) – The training data - the leading dimension should represent training samples (Y)
name (str) – Human readable name for the model
gpu (int) – The machine GPU to assign this model to

Methods

`__init__`(model, training_params, training_data)	Parameters model (`ModelAdaptor`) – The (compiled but not trained) model to be wrapped
`batch_stats`(sample_points[, batch_size])	Return mean and standard deviation in model predictions over samples, without storing the intermediate predicions in memory to enable evaluating large models over more samples than could fit in memory
`predict`(x, args, *kwargs)	Make a prediction using a trained emulator
`train`([verbose])	Train on the training data

esem