autoFRK-python

autoFRK-python is a Python implementation of the R package autoFRK v1.4.3 (Tzeng S et al., 2021). autoFRK provides a Resolution Adaptive Fixed Rank Kriging (FRK) approach for handling regular and irregular spatial data, reducing computational cost through multi-resolution basis functions.

Features

Spatial modeling based on multi-resolution basis functions
Supports single or multiple time points
Offers approximate or EM-based model estimation
Suitable for global latitude-longitude data
Implemented in PyTorch, supporting CPU and GPU (requires PyTorch with CUDA support for GPU)

## Main Functions

AutoFRK

Automatic Fixed Rank Kriging.
MRTS

Multi-Resolution Thin-Plate Spline basis function.

Installation

Install via pip:

pip install autoFRK

Install directly from GitHub:

pip install git+https://github.com/Josh-test-lab/autoFRK-python.git

Or clone and install manually:

git clone https://github.com/Josh-test-lab/autoFRK-python.git
cd autoFRK-python
pip install .

Usage

1. Import and Initialize

import torch
from autoFRK import AutoFRK

# Initialize the autoFRK model
model = AutoFRK(dtype=torch.float64, device="cpu")

2. Model Fitting

# Assume `data` is (n, T) observations (NA allowed) and `loc` is (n, d) spatial coordinates  corresponding to n locations
data = torch.randn(100, 1)  # Example data
loc = torch.rand(100, 2)    # Example 2D coordinates

model_object = model.forward(
    data=data,
    loc=loc,
    maxit=50,
    tolerance=1e-6,
    method="fast",          # "fast" or "EM"
    n_neighbor=3
)

print(result.keys())
# ['M', 's', 'negloglik', 'w', 'V', 'G', 'LKobj']

forward() returns a dictionary including:

M: ML estimate of M.
s: Estimate for the scale parameter of measurement errors.
negloglik: Negative log-likelihood.
w: K by T matrix with w[t] as the t-th column.
V: K by K matrix of the prediction error covariance matrix of w[t].
G: User specified basis function matrix or an automatically generated object from MRTS.
LKobj: Not used yet.

3. Predicting New Data

# Assume `newloc` contains new spatial coordinates
newloc = torch.rand(20, 2)

pred = model.predict(
    obj=result,
    newloc=newloc,
    se_report=True
)

print(pred['pred.value'])  # Predicted values
print(pred.get('se'))            # Standard errors

predict() can optionally return standard errors (se_report=True). If obj is not provided, the most recent forward() result is used.

Arguments

AutoFRK

AutoFRK.forward() supports various parameters:

Parameter	Description	Type	Default
`data`	n by T data matrix (NA allowed) with z[t] as the t-th column.	`torch.Tensor`	(Required)
`loc`	n by d matrix of coordinates corresponding to n locations.	`torch.Tensor`	(Required)
`mu`	n-vector or scalar for µ.	`float \| torch.Tensor`	0
`D`	n by n matrix (preferably sparse) for the covariance matrix of the measurement errors up to a constant scale.	`torch.Tensor`	Identity matrix
`G`	n by K matrix of basis function values with each column being a basis function taken values at `loc`. Automatically determined if `None`.	`torch.Tensor`	`None`
`maxK`	Maximum number of basis functions considered. Default is `None`, which means 10 · √n (for n > 100) or n (for n ≤ 100).	`int`	`None`
`Kseq`	User-specified vector of numbers of basis functions considered. Default is `None`, which is determined from `maxK`.
`maxknot`	Maximum number of knots used in generating basis functions.	`torch.Tensor`	`None`
`method`	`"fast"` or `"EM"`; `"fast"` fills missing data using k-nearest-neighbor imputation, while `"EM"` handles missing data via the EM algorithm.	`str`	`"fast"`
`n_neighbor`	Number of neighbors used in the `"fast"` imputation method.	`int`	3
`maxit`	Maximum number of iterations used in the `"EM"` imputation method.	`int`	50
`tolerance`	Precision tolerance for convergence check used in the `"EM"` imputation method.	`float`	1e-6
`requires_grad`	If `True`, enables gradient computation for `data` tensor.	`bool`	`False`
`calculate_with_spherical`	If `True`, calculates thin-plate spline distances using spherical coordinates.	`bool`	`False`
`finescale`	Logical; if `True`, an (approximate) stationary finer-scale process η[t] will be included based on the LatticeKrig package. Only the diagonals of `D` are used. (Not used yet)	`bool`	`FALSE`
`dtype`	Data type used in computations (e.g.,`torch.float64`).	`torch.dtype`	`torch.float64`
`device`	Target computation device ("cpu", "cuda", "mps", etc.). If `None`, automatically selected.	`torch.device \| str`	`None`

AutoFRK.predict() supports various parameters:

Parameter	Description	Type	Default
`obj`	A model object obtained from `AutoFRK`. If `None`, the model object produced by the `forward` method will be used.	`dict \| None`	`None`
`obsData`	A vector with observed data used for prediction. Default is `None`, which uses the `data` input from `obj`.	`torch.Tensor \| None`	`None`
`obsloc`	A matrix with rows being coordinates of observation locations for `obsData`. Only objects using `mrts` basis functions can have `obsloc` different from the `loc` input of `object`. Default is `None`.	`torch.Tensor \| None`	`None`
`mu_obs`	A vector or scalar for the deterministic mean values at `obsloc`.	`float` \| `torch.Tensor`	0
`newloc`	A matrix with rows being coordinates of new locations for prediction. Default is `None`, which gives prediction at the locations of the observed data.	`torch.Tensor \| None`	`None`
`basis`	A matrix with each column being a basis function taken values at `newloc`. Can be omitted if `object` was fitted using default `MRTS` basis functions.	`torch.Tensor \| None`	`None`
`mu_new`	A vector or scalar for the deterministic mean values at `newloc`.	`float` \| `torch.Tensor`	0
`se_report`	Logical; if `True`, the standard error of prediction is reported.	`bool`	`False`
`dtype`	Data type used in computations (e.g., `torch.float64`). Defaults to the dtype of the model `obj` if available	`torch.dtype`	`torch.float64`
`device`	Target device for computations (e.g., 'cpu', 'cuda', 'mps'). If `None`, it will be selected automatically, with the device of the model `obj` used first if available.	`torch.device \| str`	`None`

MRTS

MRTS.forward() supports various parameters:

Parameter	Description	Type	Default
`knot`	m by d matrix (d ≤ 3) for m locations of d-dimensional knots as in ordinary splines. Missing values are not allowed.	`torch.Tensor`	(Required)
`k`	The number (≤m) of basis functions.	`int`	`None`
`x`	n by d matrix of coordinates corresponding to n locations where the values of basis functions are to be evaluated. Default is `None`, which uses the m by d matrix in `knot`.	`torch.Tensor \| None`	`None`
`maxknot`	Maximum number of knots to be used in generating basis functions. If `maxknot` <m, a deterministic subset selection of `knot`s will be used. To use all `knot`s, set `maxknot` ≥ m.	`int`	5000
`calculate_with_spherical`	If `True`, calculates thin-plate spline distances using spherical coordinates.	`bool`	`False`
`dtype`	Data type used in computations (e.g.,`torch.float64`).	`torch.dtype`	`torch.float64`
`device`	Target computation device ("cpu", "cuda", "mps", etc.). If `None`, automatically selected.	`torch.device \| str`	`None`

MRTS.predict() supports various parameters:

Parameter	Description	Type	Default
`obj`	A model object obtained from `MRTS`. If `None`, the model object produced by the `forward` method will be used.	`dict \| None`	`None`
`newx`	n by d matrix of coordinates corresponding to n locations where prediction is desired.	`torch.Tensor \| None`	`None`
`calculate_with_spherical`	If `True`, calculates thin-plate spline distances using spherical coordinates. The default `None` uses the method specified in `forward`.	`bool \| None`	`None`
`dtype`	Data type used in computations (e.g., `torch.float64`). Defaults to the dtype of the model `obj` if available	`torch.dtype`	`torch.float64`
`device`	Target device for computations (e.g., 'cpu', 'cuda', 'mps'). If `None`, it will be selected automatically, with the device of the model `obj` used first if available.	`torch.device \| str`	`None`

Example Code

AutoFRK

import torch
from autoFRK import AutoFRK

# Generate fake data
n, T = 200, 1
data = torch.randn(n, T)
loc = torch.rand(n, 2)

# Initialize model
model = AutoFRK(device="cpu")

# Fit model
res = model.forward(
    data=data,
    loc=loc
)

# Predict new data
newloc = torch.rand(10, 2)
pred = model.predict(
    newloc=newloc
)

print("Predicted values:", pred['pred.value'])

MRTS

import torch
from autoFRK import MRTS

Experimental Features

Spherical coordinate basis function computation
Gradient tracking (using torch's requires_grad_())

Authors

ShengLi Tzeng — Original Paper Author
Hsin-Cheng Huang — Original Paper Author
Wen-Ting Wang — R Package Author
Yao-Chih Hsu — Python Package Author

Contributors

Hao-Yun Huang — Spherical Coordinate Thin Plate Spline Provider
Yi-Xuan Xie — Python Package Tester
Xuan-Chun Wang — Python Package Tester

License

GPL (>= 3)

Development and Contribution

Report bugs or request features on GitHub issues

References

Tzeng S, Huang H, Wang W, Nychka D, Gillespie C (2021). autoFRK: Automatic Fixed Rank Kriging. R package version 1.4.3, https://CRAN.R-project.org/package=autoFRK
Wang, J. W.-T. (n.d.). autoFRK. GitHub. Retrieved January 7, 2023, from https://egpivo.github.io/autoFRK/
Tzeng, S. & Huang, H.-C. (2018). Resolution Adaptive Fixed Rank Kriging. Technometrics. https://doi.org/10.1080/00401706.2017.1345701
Nychka, D., Hammerling, D., Sain, S., & Lenssen, N. (2016). LatticeKrig: Multiresolution Kriging Based on Markov Random Fields

Citation

To cite the Python package autoFRK-python in publications use:

  Tzeng S, Huang H, Wang W, Hsu Y (2025). _autoFRK-python: Automatic Fixed Rank Kriging. The Python version with PyTorch_. Python package version 1.1.1, 
  <https://pypi.org/project/autoFRK/>.

A BibTeX entry for LaTeX users to cite the Python package is:

  @Manual{,
    title = {autoFRK-python: Automatic Fixed Rank Kriging. The Python version with PyTorch},
    author = {ShengLi Tzeng and Hsin-Cheng Huang and Wen-Ting Wang and Yao-Chih Hsu},
    year = {2025},
    note = {Python package version 1.1.1},
    url = {https://pypi.org/project/autoFRK/},
  }

To cite the R package autoFRK in publications use:

  Tzeng S, Huang H, Wang W, Nychka D, Gillespie C (2021). _autoFRK: Automatic Fixed Rank Kriging_. R package version 1.4.3,
  <https://CRAN.R-project.org/package=autoFRK>.

A BibTeX entry for LaTeX users to cite the R package is:

  @Manual{,
    title = {autoFRK: Automatic Fixed Rank Kriging},
    author = {ShengLi Tzeng and Hsin-Cheng Huang and Wen-Ting Wang and Douglas Nychka and Colin Gillespie},
    year = {2021},
    note = {R package version 1.4.3},
    url = {https://CRAN.R-project.org/package=autoFRK},
  }

Release Notes

v1.1.1

Fixed a ValueError caused by a missing v in the model object when using the "EM" method.
Fixed an issue with absent indices in the EM0miss function when using the "EM" method with missing data.
Fixed a bug in the EM0miss function where some variables could not be found when handling missing data with the "EM" method.
Improved the handling of device selection to reduce redundant checks and repeated triggers.
Added input validation for the mu and mu_new variable.
Updated additional functions to fully support requires_grad.
Update README.

v1.1.0

Added dtype and device parameters to AutoFRK.predict() and MRTS.predict().
Added logger_level parameter to AutoFRK.__init__() and MRTS.__init__() (default: 20). Options include NOTSET(0), DEBUG(10), INFO(20), WARNING(30), ERROR(40), CRITICAL(50).
Enhanced automatic device selection, including MPS support.
Fixed device assignment issue when device is not specified, preventing redundant parameter transfers.

v1.0.0

Ported R package autoFRK to Python.

Repositories

Python Repository:
https://github.com/Josh-test-lab/autoFRK-python
R Repository:
https://github.com/egpivo/autoFRK

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