API reference¶

This page provides an auto-generated summary of xarray’s API. For more details and examples, refer to the relevant chapters in the main part of the documentation.

Top-level functions¶

This provides the main interface for ESEm and should be the starting point for most users.

`gp_model`	Create a Gaussian process (GP) based emulator with provided training_params (X) and training_data (Y) which assumes independent inputs (and outputs).
`cnn_model`	Create a simple two layer Convolutional Neural Network Emulator using Keras.
`rf_model`	Create a simple Random Forest Emulator using sklearn.

Emulator¶

`Emulator`	A class wrapping a statistical emulator
`Emulator.train`	Train on the training data
`Emulator.predict`	Make a prediction using a trained emulator
`Emulator._predict`	The (internal) predict interface used by e.g., a sampler.
`Emulator.batch_stats`	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

Sampler¶

This class defines the sampling interface currently used by the ABC and MCMC sampling implementations.

`Sampler`	A class that efficiently samples a Model object for posterior inference
`Sampler.sample`	This is the call that does the actual inference.

MCMCSampler¶

`MCMCSampler`	Sample from the posterior using the TensorFlow Markov-Chain Monte-Carlo (MCMC) sampling tools.
`MCMCSampler.sample`	This is the call that does the actual inference.

ABCSampler¶

`ABCSampler`	Sample from the posterior using Approximate Bayesian Computation (ABC).
`ABCSampler.sample`	Sample the emulator over prior_x and compare with the observations, returning n_samples of the posterior distribution (those points for which the model is compatible with the observations).
`ABCSampler.get_implausibility`	Calculate the implausibility of the provided sample points, optionally in batches.
`ABCSampler.batch_constrain`	Constrain the supplied sample points based on the tolerance threshold, optionally in bathes.

Wrappers¶

`ProcessWrapper`	This class handles applying any data pre- and post-processing by any provided DataProcessor
`DataWrapper`	Provide a unified interface for numpy arrays, Iris Cube’s and xarray DataArrays.
`DataWrapper.name`
`DataWrapper.data`
`DataWrapper.dtype`
`DataWrapper.wrap`	Wrap back in a cube if one was provided
`CubeWrapper`
`DataArrayWrapper`

ModelAdaptor¶

`ModelAdaptor`	Provides a unified interface for all emulation engines within ESEm.
`SKLearnModel`	A wrapper around scikit-learn models.
`KerasModel`	A wrapper around Keras models
`GPFlowModel`	A wrapper around GPFlow regression models

DataProcessor¶

`DataProcessor`	A utility class for transparently processing (transforming) numpy arrays and un-processing TensorFlow Tensors to aid in emulation.
`Log`	Return log(x + c) where c can be specified.
`Whiten`	Scale the data to have zero mean and unit variance
`Normalise`	Linearly scale the data to lie between [0, 1]
`Flatten`	Flatten all dimensions except the leading one
`Reshape`	Ensure the training data is the right shape for the ConvNet
`Recast`	Cast the data to a given type

Utilities¶

A collection of associated utilities which might be of use when performing typical ESEm workflows.

`plot_results`	Validation plot for LeaveOneOut
`validation_plot`
`plot_parameter_space`
`get_uniform_params`	Slightly convoluted method for getting a flat set of points evenly
`get_random_params`	Get points randomly sampling a (unit) N-dimensional space
`ensemble_collocate`	Efficiently collocate (interpolate) many ensemble members on to a set of (un-gridded) observations
`leave_one_out`	Function to perform LeaveOneOut cross-validation with different models.
`get_param_mask`	Determine the most relevant parameters in the input space using a regularised linear model and either the Aikake or Baysian Information Criterion.