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kashenfelter jdyen ebcg1

greta.gp's Issues

catch incorrect length of lengthscale vector

When a kernel is specified with a single lengthscale, but evaluated on a design matrix with multiple columns, only the first column is used and no warning is issued. We should let the user know about a dimension mismatch, and either error, or in the case of a single lengthscale bing provided, just replicate it and assume they want an isotropic kernel.

See here for a reprex: https://forum.greta-stats.org/t/gaussian-process-in-greta-matern-covariance/151/17

Cannot run example code from repo: [AttributeError: Unknown setting; coming from dag$define_tf()]

Issue

I can't seem to be able to run the example in the repo's README, with the following error when I try to create the model object.

>     m <- model(f_plot)
Error in py_call_impl(callable, dots$args, dots$keywords) : 
  AttributeError: Unknown setting.

Detailed traceback: 
  File "/opt/conda/lib/python3.6/site-packages/gpflow-1.1.0-py3.6.egg/gpflow/core/compilable.py", line 91, in __init__
    self.initialize(force=True)
  File "/opt/conda/lib/python3.6/site-packages/gpflow-1.1.0-py3.6.egg/gpflow/core/node.py", line 77, in initialize
    session = self.enquire_session(session)
  File "/opt/conda/lib/python3.6/site-packages/gpflow-1.1.0-py3.6.egg/gpflow/core/node.py", line 127, in enquire_session
    session = session_manager.get_default_session()
  File "/opt/conda/lib/python3.6/site-packages/gpflow-1.1.0-py3.6.egg/gpflow/session_manager.py", line 95, in get_default_session
    _DefaultSessionKeeper.session = get_session(*args, **kwargs)
  File "/opt/conda/lib/python3.6/site-packages/gpflow-1.1.0-py3.6.egg/gpflow/session_manager.py", line 104, in get_session
    kwargs['config'] = tf.ConfigProto(**settings.session)
  File "/opt/conda/lib/python3.6/site-packages/gpflow-1.1.0-py3.6.egg/gpflow

Version Informations:

gpflow: 1.1.0 (installed from the gpflow repo)
tensorflow: 1.6.0 (PyPI pip install)
greta.gp: 0.1.3
greta: 0.2.4

I can run examples from gpflow's documentations, so I think my gpflow installation should be good.. Any thoughts?

Cannot run example code from repo: Error in py_set_attr_impl(x, name, value) : ValueError: Value has different shape. Parameter shape (1,), value shape (1, 1).

I begin the example code somewhat differently as follows:

library(reticulate)         
gpflow <- import("gpflow")     # to verify that gpflow can be found by reticulate
# also, without the above, I get 
#    "Error: the GPflow python package and the gpflowr R package must be installed to plot greta models"
library(gpflowr)
library (greta)
library (greta.gp)

Then when the example code reaches the following line, an error is reported:

m <- model(f_plot)

Error in py_set_attr_impl(x, name, value) : ValueError: Value has different shape. Parameter shape (1,), value shape (1, 1).
40. stop(structure(list(message = "ValueError: Value has different shape. Parameter shape (1,), value shape (1, 1).", call = py_set_attr_impl(x, name, value), cppstack = structure(list( file = "", line = -1L, stack = "C++ stack not available on this system"), .Names = c("file", "line", "stack"), class = "Rcpp_stack_trace")), .Names = c("message", ...
39. py_set_attr_impl(x, name, value)
38. py_set_attr(x, name, value)
37. `[[<-.python.builtin.object`(`*tmp*`, name, value = <environment>)
36. `[[<-`(`*tmp*`, name, value = <environment>)
35. recurse_kernel(greta_kernel$components[[1]], tf_parameters, counter)
34. recurse_kernel(greta_kernel, tf_parameters, counter)
33. compile_gpflow_kernel(greta_kernel, tf_parameters)
32. (function (X, ..., greta_kernel) { tf_parameters <- list(...) gpflow_kernel <- compile_gpflow_kernel(greta_kernel, tf_parameters) ...
31. do.call(self$operation, args)
30. self$tf(dag)
29. x$define_tf(dag)
28. FUN(X[[i]], ...)
27. lapply(self$children[which(!children_defined)], function(x) x$define_tf(dag))
26. x$define_tf(dag)
25. FUN(X[[i]], ...)
24. lapply(self$children[which(!children_defined)], function(x) x$define_tf(dag))
23. x$define_tf(dag)
22. FUN(X[[i]], ...)
21. lapply(self$children[which(!children_defined)], function(x) x$define_tf(dag))
20. x$define_tf(dag)
19. FUN(X[[i]], ...)
18. lapply(self$children[which(!children_defined)], function(x) x$define_tf(dag))
17. x$define_tf(dag)
16. FUN(X[[i]], ...)
15. lapply(self$children[which(!children_defined)], function(x) x$define_tf(dag))
14. x$define_tf(dag)
13. FUN(X[[i]], ...)
12. lapply(self$children[which(!children_defined)], function(x) x$define_tf(dag))
11. x$define_tf(self)
10. FUN(X[[i]], ...)
9. lapply(self$node_list, function(x) x$define_tf(self))
8. force(expr)
7. tryCatchList(expr, classes, parentenv, handlers)
6. tryCatch(force(expr), finally = { data$`__exit__`(NULL, NULL, NULL) if (!is.null(as)) { remove(list = as, envir = envir) ...
5. with.python.builtin.object(self$tf_graph$as_default(), expr)
4. with(self$tf_graph$as_default(), expr)
3. self$on_graph(lapply(self$node_list, function(x) x$define_tf(self)))
2. dag$define_tf()
1. model(f_plot)

switch to version not using GPflow

As GPflow is only being used for the kernel definitions, it can easily be ported to pure R/greta.
That would ease installation woes for R users and make it far easier to add more kernels.

Release greta.gp 0.2.0

Updating to current toolchain

  • use github actions
  • tidy up description
  • use markdown roxygen
  • use cli for messages
  • tidy up README
  • create basic vignette
  • use pkgdown
  • use snapshot tests where appropriate

First release:

Prepare for release:

  • git pull
  • Check if any deprecation processes should be advanced, as described in Gradual deprecation
  • devtools::build_readme()
  • urlchecker::url_check()
  • devtools::check(remote = TRUE, manual = TRUE)
  • devtools::check_win_devel()
  • rhub::check_for_cran()
  • git push
  • Draft blog post

Submit to CRAN:

  • usethis::use_version('minor')
  • devtools::submit_cran()
  • Approve email

Wait for CRAN...

  • Accepted 🎉
  • git push
  • usethis::use_github_release()
  • usethis::use_dev_version()
  • usethis::use_news_md()
  • git push
  • Finish blog post
  • Tweet
  • Add link to blog post in pkgdown news menu

Fix package alias

Dear maintainer,

You have file 'greta.gp/man/greta.gp.Rd' with \docType{package}, likely
intended as a package overview help file, but without the appropriate
PKGNAME-package \alias as per "Documenting packages" in R-exts.

This seems to be the consequence of the breaking change

Using @doctype package no longer automatically adds a -package alias.
Instead document _PACKAGE to get all the defaults for package
documentation.

in roxygen2 7.0.0 (2019-11-12) having gone unnoticed, see
r-lib/roxygen2#1491.

As explained in the issue, to get the desired PKGNAME-package \alias
back, you should either change to the new approach and document the new
special sentinel

  "_PACKAGE"

or manually add

  @aliases greta.gp-package

if remaining with the old approach.

Cannot run example code (from this git or jdyen 0.3.0.x one)

Hi.

Switching from JAGS to Greta and this extension is exactly what I was looking for. However, when I try to run the example code I get the following error:

f <- gp(x, kernel)
Error: objects of class function cannot be coerced to greta arrays

I thought it may be due to the fact that I have greta 0.3.0.9002 installed. So I tried jdyen's forked version and got a different error

m <- model(f_plot)
Error in tf$gather(X, dims, axis = -1L) : attempt to apply non-function

I tried to find solutions to similar R<->Tensorflow issues via google, but have been unable to make any progress.

I'm on Windows 10. Tensorflow 1.13.1 is set up to use my GPU in a virtual conda environment. I haven't had any issues running models in greta, just greta.gp.

Have you run into any of these issues?

Thanks so much

H

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