Update to 2.37 (#64)

* Updates for Pathfinder changes

* Update cache version

* Actually bump submodule

Author: WardBrian

.github/workflows/main.yml

@@ -8,7 +8,7 @@ on:
   workflow_dispatch: {}
 
 env:
-  CACHE_VERSION: 2
+  CACHE_VERSION: 3
 
 # only run one copy per PR
 concurrency:

Makefile

@@ -160,7 +160,7 @@ print-%  : ; @echo $* = $($*) ;
 STANC_DL_RETRY = 5
 STANC_DL_DELAY = 10
 STANC3_TEST_BIN_URL ?=
-STANC3_VERSION ?= v2.36.0
+STANC3_VERSION ?= v2.37.0
 
 ifeq ($(OS),Windows_NT)
  OS_TAG := windows

clients/R/R/zzz.R

@@ -1,7 +1,7 @@
 HMC_SAMPLER_VARIABLES = c("lp__", "accept_stat__", "stepsize__", "treedepth__", "n_leapfrog__",
     "divergent__", "energy__")
 
-PATHFINDER_VARIABLES = c("lp_approx__", "lp__")
+PATHFINDER_VARIABLES = c("lp_approx__", "lp__", "path__")
 
 OPTIMIZATION_VARIABLES = c("lp__")
 

clients/R/tests/testthat/test_pathfinder.R

@@ -50,10 +50,10 @@ test_that("seed works", {
     out1 <- pathfinder(bernoulli_model, BERNOULLI_DATA, seed = 123)
     out2 <- pathfinder(bernoulli_model, BERNOULLI_DATA, seed = 123)
 
-    expect_equal(out1$draws$theta, out2$draws$theta)
+    expect_equal(sort(posterior::draws_of(out1$draws$theta)), sort(posterior::draws_of(out2$draws$theta)))
 
     out3 <- pathfinder(bernoulli_model, BERNOULLI_DATA, seed = 456)
-    expect_error(expect_equal(out1$draws$theta, out3$draws$theta))
+    expect_error(expect_equal(sort(posterior::draws_of(out1$draws$theta)), sort(posterior::draws_of(out3$draws$theta))))
 
 })
 test_that("inits work", {

clients/julia/src/model.jl

@@ -32,7 +32,7 @@ const HMC_SAMPLER_VARIABLES = [
     "energy__",
 ]
 
-const PATHFINDER_VARIABLES = ["lp_approx__", "lp__"]
+const PATHFINDER_VARIABLES = ["lp_approx__", "lp__", "path__"]
 
 const OPTIMIZE_VARIABLES = ["lp__"]
 

clients/julia/test/test_pathfinder.jl

@@ -79,10 +79,10 @@
     @testset "Seed" begin
         out1 = pathfinder(bernoulli_model, BERNOULLI_DATA; seed = UInt32(123))
         out2 = pathfinder(bernoulli_model, BERNOULLI_DATA; seed = UInt32(123))
-        @test out1.draws == out2.draws
+        @test sortslices(out1.draws, dims=1) == sortslices(out2.draws, dims=1)
 
         out3 = pathfinder(bernoulli_model, BERNOULLI_DATA; seed = UInt32(456))
-        @test out1.draws != out3.draws
+        @test sortslices(out1.draws, dims=1) != sortslices(out3.draws, dims=1)
     end
 
     @testset "Inits" begin

clients/python/tests/test_laplace.py

@@ -77,7 +77,7 @@ def test_seed(bernoulli_model):
         seed=123,
     )
 
-    np.testing.assert_equal(out1["theta"], out2["theta"])
+    np.testing.assert_equal(out1.data, out2.data)
 
     out3 = bernoulli_model.laplace_sample(
         BERNOULLI_MODE,
@@ -114,7 +114,7 @@ def test_bad_mode_stanoutput(bernoulli_model):
 
 
 def test_bad_mode_array(bernoulli_model):
-    mode1 = np.array([2.0])
+    mode1 = np.array([2.0, 1.0])
     with pytest.raises(RuntimeError, match="Bounded variable is 2"):
         bernoulli_model.laplace_sample(mode1, BERNOULLI_DATA)
 

clients/python/tests/test_optimize.py

@@ -56,7 +56,7 @@ def test_seed(bernoulli_model):
         seed=123,
     )
 
-    np.testing.assert_equal(out1["theta"], out2["theta"])
+    np.testing.assert_equal(out1.data, out2.data)
 
     out3 = bernoulli_model.optimize(
         BERNOULLI_DATA,

clients/python/tests/test_pathfinder.py

@@ -25,16 +25,33 @@ def test_data(bernoulli_model):
     assert 0.2 < out2["theta"].mean() < 0.3
 
 
-def test_seed(bernoulli_model):
-    out1 = bernoulli_model.pathfinder(BERNOULLI_DATA, seed=123)
-    out2 = bernoulli_model.pathfinder(BERNOULLI_DATA, seed=123)
-
-    np.testing.assert_equal(out1["theta"], out2["theta"])
+@pytest.mark.parametrize("num_paths", [1, 4])
+@pytest.mark.parametrize("psis_resample", [True, False])
+def test_seed(bernoulli_model, num_paths, psis_resample):
+    out1 = bernoulli_model.pathfinder(
+        BERNOULLI_DATA,
+        seed=123,
+        num_paths=num_paths,
+        psis_resample=psis_resample,
+    )
+    out2 = bernoulli_model.pathfinder(
+        BERNOULLI_DATA,
+        seed=123,
+        num_paths=num_paths,
+        psis_resample=psis_resample,
+    )
+    assert out1.data.shape[1] == 5
+    np.testing.assert_equal(np.sort(out1.data, axis=0), np.sort(out2.data, axis=0))
 
-    out3 = bernoulli_model.pathfinder(BERNOULLI_DATA, seed=456)
+    out3 = bernoulli_model.pathfinder(
+        BERNOULLI_DATA,
+        seed=456,
+        num_paths=num_paths,
+        psis_resample=psis_resample,
+    )
 
     with pytest.raises(AssertionError):
-        np.testing.assert_equal(out1["theta"], out3["theta"])
+        np.testing.assert_equal(np.sort(out1["theta"]), np.sort(out3["theta"]))
 
 
 def test_output_sizes(bernoulli_model):
@@ -133,20 +150,18 @@ def test_inits(multimodal_model, temp_json):
     assert np.all(out3["mu"] < 0)
 
 
-def test_inits_mode(multimodal_model):
+@pytest.mark.parametrize("num_paths", [1, 4])
+@pytest.mark.parametrize("psis_resample", [True, False])
+def test_inits_mode(multimodal_model, num_paths, psis_resample):
     # initializing at mode means theres nowhere to go
     init1 = {"mu": -100}
 
     with pytest.raises(
         RuntimeError, match="None of the LBFGS iterations completed successfully"
     ):
-        multimodal_model.pathfinder(inits=init1)
-
-    # also for single path
-    with pytest.raises(
-        RuntimeError, match="None of the LBFGS iterations completed successfully"
-    ):
-        multimodal_model.pathfinder(inits=init1, num_paths=1, psis_resample=False)
+        multimodal_model.pathfinder(
+            inits=init1, num_paths=num_paths, psis_resample=psis_resample
+        )
 
 
 def test_bad_data(bernoulli_model):

clients/python/tests/test_sample.py

@@ -46,7 +46,7 @@ def test_seed(bernoulli_model):
         BERNOULLI_DATA, seed=123, num_warmup=100, num_samples=100
     )
 
-    np.testing.assert_equal(out1["theta"], out2["theta"])
+    np.testing.assert_equal(out1.data, out2.data)
 
     out3 = bernoulli_model.sample(
         BERNOULLI_DATA, seed=456, num_warmup=100, num_samples=100