Standardize class names in interfaces

R/bridgestan.R

@@ -1,5 +1,5 @@
 
-Bridge <- R6::R6Class("Bridge",
+StanModel <- R6::R6Class("StanModel",
   public = list(
     initialize = function(lib, data, rng_seed, chain_id) {
       if (.Platform$OS.type == "windows"){

R/tests/test_bridgestan.R

@@ -1,7 +1,7 @@
 source("../bridgestan.R")
 library(testthat)
 
-simple <- Bridge$new("../../stan/simple/simple_model.so", "../../stan/simple/simple.data.json", 1234, 0)
+simple <- StanModel$new("../../stan/simple/simple_model.so", "../../stan/simple/simple.data.json", 1234, 0)
 test_that("simple_model name is correct", {
     expect_identical(simple$name(), "simple_model")
 })
@@ -26,9 +26,7 @@ test_that("simple_model Hessian is -I",{
 })
 
 
-bernoulli <- Bridge$new("../../stan/bernoulli/bernoulli_model.so", "../../stan/bernoulli/bernoulli.data.json", 1234, 0)
-
-
+bernoulli <- StanModel$new("../../stan/bernoulli/bernoulli_model.so", "../../stan/bernoulli/bernoulli.data.json", 1234, 0)
 
 test_that("loading another library didn't break prior ones", {
     if (.Platform$OS.type == "windows"){

python/bridgestan/__init__.py

@@ -9,7 +9,7 @@
 FloatArray = npt.NDArray[np.float64]
 double_array = ndpointer(dtype=ctypes.c_double, flags=("C_CONTIGUOUS"))
 
-__all__ = ["Bridge"]
+__all__ = ["StanModel"]
 
 
 def validate_readable(f: str) -> bool:
@@ -23,9 +23,9 @@ def validate_readable(f: str) -> bool:
         raise FileNotFoundError("could not open file f =", f)
 
 
-class Bridge:
+class StanModel:
     """
-    A Bridge instance encapsulates a Stan model instantiated with data
+    A StanModel instance encapsulates a Stan model instantiated with data
     and provides methods to access parameter names, transforms, log
     densities, gradients, and Hessians.
 
@@ -51,7 +51,7 @@ def __init__(
         chain_id: int = 0,
     ) -> None:
         """
-        Construct a Bridge object to a Stan model and data given
+        Construct a StanModel object for a Stan model and data given
         constructor arguments.
 
         :param model_lib: A system path to compiled shared object.

python/docs/languages/python.rst

@@ -3,5 +3,5 @@
 Python Interface: bridgestan.py
 ===============================
 
-.. autoclass:: bridgestan.Bridge
+.. autoclass:: bridgestan.StanModel
    :members:

python/example.py

@@ -3,7 +3,7 @@
 import numpy as np
 
 # small shim to add dims(), required by MCMC impl
-class BridgeDims(bs.Bridge):
+class StaModelDims(bs.StanModel):
     def dims(self) -> int:
         return self.param_unc_num()
 
@@ -13,7 +13,7 @@ def dims(self) -> int:
 bernoulli_lib = "./stan/bernoulli/bernoulli_model.so"
 bernoulli_data = "./stan/bernoulli/bernoulli.data.json"
 
-smb = BridgeDims(bernoulli_lib, bernoulli_data)
+smb = StaModelDims(bernoulli_lib, bernoulli_data)
 x = np.random.uniform(size = smb.dims())
 q = np.log(x / (1 - x))         # unconstrained scale
 
@@ -31,7 +31,7 @@ def dims(self) -> int:
 multi_lib = "./stan/multi/multi_model.so"
 multi_data = "./stan/multi/multi.data.json"
 
-smm = BridgeDims(multi_lib, multi_data)
+smm = StaModelDims(multi_lib, multi_data)
 x = np.random.uniform(size = smm.param_num())
 
 print("log_density and gradient of Multivariate Gaussian model:")
@@ -43,7 +43,7 @@ def dims(self) -> int:
 
 # HMC
 
-model = BridgeDims(multi_lib, multi_data, seed=1234)
+model = StaModelDims(multi_lib, multi_data, seed=1234)
 
 stepsize = 0.25
 steps = 10

python/test/test_bridgestan.py

@@ -9,41 +9,41 @@ def test_constructor():
 
     # test empty data
     std_so = "../stan/stdnormal/stdnormal_model.so"
-    b1 = bs.Bridge(std_so)
+    b1 = bs.StanModel(std_so)
     np.testing.assert_allclose(bool(b1), True)
 
     # test load data
     bernoulli_so = "../stan/bernoulli/bernoulli_model.so"
     bernoulli_data = "../stan/bernoulli/bernoulli.data.json"
-    b2 = bs.Bridge(bernoulli_so, bernoulli_data)
+    b2 = bs.StanModel(bernoulli_so, bernoulli_data)
     np.testing.assert_allclose(bool(b2), True)
 
     # test missing so file
     with np.testing.assert_raises(FileNotFoundError):
-        b3 = bs.Bridge("nope, not going to find it")
+        b3 = bs.StanModel("nope, not going to find it")
 
     # test missing data file
     with np.testing.assert_raises(FileNotFoundError):
-        b3 = bs.Bridge(bernoulli_so, "nope, not going to find it")
+        b3 = bs.StanModel(bernoulli_so, "nope, not going to find it")
 
     # test data load exception
     throw_data_so = "../stan/throw_data/throw_data_model.so"
     print("construct() EXCEPTION MSG ON NEXT LINE IS NOT AN ERROR")
     with np.testing.assert_raises(RuntimeError):
-        b4 = bs.Bridge(throw_data_so)
+        b4 = bs.StanModel(throw_data_so)
 
     # TODO(carpenter): test get right error message on stderr
 
 
 def test_name():
     std_so = "../stan/stdnormal/stdnormal_model.so"
-    b = bs.Bridge(std_so)
+    b = bs.StanModel(std_so)
     np.testing.assert_equal("stdnormal_model", b.name())
 
 
 def test_param_num():
     full_so = "../stan/full/full_model.so"
-    b = bs.Bridge(full_so)
+    b = bs.StanModel(full_so)
     np.testing.assert_equal(1, b.param_num())
     np.testing.assert_equal(1, b.param_num(include_tp=False))
     np.testing.assert_equal(1, b.param_num(include_gq=False))
@@ -57,14 +57,14 @@ def test_param_num():
 
 def test_param_unc_num():
     simplex_so = "../stan/simplex/simplex_model.so"
-    b = bs.Bridge(simplex_so)
+    b = bs.StanModel(simplex_so)
     np.testing.assert_equal(5, b.param_num())
     np.testing.assert_equal(4, b.param_unc_num())
 
 
 def test_param_names():
     matrix_so = "../stan/matrix/matrix_model.so"
-    b = bs.Bridge(matrix_so)
+    b = bs.StanModel(matrix_so)
     np.testing.assert_array_equal(
         ["A.1.1", "A.2.1", "A.3.1", "A.1.2", "A.2.2", "A.3.2"], b.param_names()
     )
@@ -144,13 +144,13 @@ def test_param_names():
 
 def test_param_unc_names():
     matrix_so = "../stan/matrix/matrix_model.so"
-    b1 = bs.Bridge(matrix_so)
+    b1 = bs.StanModel(matrix_so)
     np.testing.assert_array_equal(
         ["A.1.1", "A.2.1", "A.3.1", "A.1.2", "A.2.2", "A.3.2"], b1.param_unc_names()
     )
 
     simplex_so = "../stan/simplex/simplex_model.so"
-    b2 = bs.Bridge(simplex_so)
+    b2 = bs.StanModel(simplex_so)
     np.testing.assert_array_equal(
         ["theta.1", "theta.2", "theta.3", "theta.4"], b2.param_unc_names()
     )
@@ -168,7 +168,7 @@ def cov_constrain(v, D):
 def test_param_constrain():
     fr_gaussian_so = "../stan/fr_gaussian/fr_gaussian_model.so"
     fr_gaussian_data = "../stan/fr_gaussian/fr_gaussian.data.json"
-    bridge = bs.Bridge(fr_gaussian_so, fr_gaussian_data)
+    bridge = bs.StanModel(fr_gaussian_so, fr_gaussian_data)
 
     D = 4
     size = 16
@@ -193,7 +193,7 @@ def test_param_constrain():
     np.testing.assert_allclose(B_expected, B)
 
     full_so = "../stan/full/full_model.so"
-    bridge2 = bs.Bridge(full_so)
+    bridge2 = bs.StanModel(full_so)
 
     b2 = bridge.param_constrain(a)
     np.testing.assert_equal(1, bridge2.param_constrain(a).size)
@@ -214,7 +214,7 @@ def test_param_constrain():
 
     # exception handling test in transformed parameters/model (compiled same way)
     throw_tp_so = "../stan/throw_tp/throw_tp_model.so"
-    bridge2 = bs.Bridge(throw_tp_so)
+    bridge2 = bs.StanModel(throw_tp_so)
 
     y = np.array(np.random.uniform(1))
     bridge2.param_constrain(y, include_tp=False)
@@ -223,7 +223,7 @@ def test_param_constrain():
         bridge2.param_constrain(y, include_tp=True)
 
     throw_gq_so = "../stan/throw_gq/throw_gq_model.so"
-    bridge3 = bs.Bridge(throw_gq_so)
+    bridge3 = bs.StanModel(throw_gq_so)
     bridge3.param_constrain(y, include_gq=False)
     print("param_constrain() EXCEPTION MSG ON NEXT LINE IS NOT AN ERROR")
     with np.testing.assert_raises(RuntimeError):
@@ -233,7 +233,7 @@ def test_param_constrain():
 def test_param_unconstrain():
     fr_gaussian_so = "../stan/fr_gaussian/fr_gaussian_model.so"
     fr_gaussian_data = "../stan/fr_gaussian/fr_gaussian.data.json"
-    bridge = bs.Bridge(fr_gaussian_so, fr_gaussian_data)
+    bridge = bs.StanModel(fr_gaussian_so, fr_gaussian_data)
 
     unc_size = 10
     a = np.random.normal(size=unc_size)
@@ -252,7 +252,7 @@ def test_param_unconstrain():
 def test_param_unconstrain_json():
     gaussian_so = "../stan/gaussian/gaussian_model.so"
     gaussian_data = "../stan/gaussian/gaussian.data.json"
-    bridge = bs.Bridge(gaussian_so, gaussian_data)
+    bridge = bs.StanModel(gaussian_so, gaussian_data)
 
     # theta = np.array([0.2, 1.9])
     theta_unc = np.array([0.2, np.log(1.9)])
@@ -284,7 +284,7 @@ def _bernoulli_jacobian(y, p):
 def test_log_density():
     bernoulli_so = "../stan/bernoulli/bernoulli_model.so"
     bernoulli_data = "../stan/bernoulli/bernoulli.data.json"
-    bridge = bs.Bridge(bernoulli_so, bernoulli_data)
+    bridge = bs.StanModel(bernoulli_so, bernoulli_data)
     y = np.asarray([0, 1, 0, 0, 0, 0, 0, 0, 0, 1])
     for _ in range(2):
         x = np.random.uniform(size=bridge.param_unc_num())
@@ -299,7 +299,7 @@ def test_log_density():
         np.testing.assert_allclose(lp4, _bernoulli(y, x))
 
     throw_lp_so = "../stan/throw_lp/throw_lp_model.so"
-    bridge2 = bs.Bridge(throw_lp_so)
+    bridge2 = bs.StanModel(throw_lp_so)
     y2 = np.array(np.random.uniform(1))
     print("log_density() EXCEPTION MSG ON NEXT LINE IS NOT AN ERROR")
     with np.testing.assert_raises(RuntimeError):
@@ -328,7 +328,7 @@ def _grad_jacobian_true(y_unc):
         return 1
 
     jacobian_so = "../stan/jacobian/jacobian_model.so"
-    bridge = bs.Bridge(jacobian_so)
+    bridge = bs.StanModel(jacobian_so)
 
     y = np.abs(np.random.normal(1))
     y_unc = np.log(y)
@@ -418,7 +418,7 @@ def _hess_jacobian_true(y_unc):
         return 0
 
     jacobian_so = "../stan/jacobian/jacobian_model.so"
-    bridge = bs.Bridge(jacobian_so)
+    bridge = bs.StanModel(jacobian_so)
 
     # test value, gradient, hessian, all combos +/- propto, +/- jacobian
     y = np.abs(np.random.normal(1))
@@ -494,7 +494,7 @@ def _hess_jacobian_true(y_unc):
     # test with 5 x 5 Hessian
     simple_so = "../stan/simple/simple_model.so"
     simple_data = "../stan/simple/simple.data.json"
-    bridge2 = bs.Bridge(simple_so, simple_data)
+    bridge2 = bs.StanModel(simple_so, simple_data)
 
     D = 5
     y = np.random.uniform(size=D)
@@ -506,7 +506,7 @@ def _hess_jacobian_true(y_unc):
 def test_out_behavior():
     bernoulli_so = "../stan/bernoulli/bernoulli_model.so"
     bernoulli_data = "../stan/bernoulli/bernoulli.data.json"
-    smb = bs.Bridge(bernoulli_so, bernoulli_data)
+    smb = bs.StanModel(bernoulli_so, bernoulli_data)
 
     grads = []
     for _ in range(2):
@@ -542,7 +542,7 @@ def _bernoulli(y, p):
 
     bernoulli_so = "../stan/bernoulli/bernoulli_model.so"
     bernoulli_data = "../stan/bernoulli/bernoulli.data.json"
-    smb = bs.Bridge(bernoulli_so, bernoulli_data)
+    smb = bs.StanModel(bernoulli_so, bernoulli_data)
     np.testing.assert_string_equal(smb.name(), "bernoulli_model")
     np.testing.assert_allclose(smb.param_unc_num(), 1)
     np.testing.assert_allclose(smb.param_num(include_tp=False, include_gq=False), 1)
@@ -568,7 +568,7 @@ def _grad_multi(x):
     multi_so = "../stan/multi/multi_model.so"
     multi_data = "../stan/multi/multi.data.json"
 
-    smm = bs.Bridge(multi_so, multi_data)
+    smm = bs.StanModel(multi_so, multi_data)
     x = np.random.normal(size=smm.param_unc_num())
     logdensity, grad = smm.log_density_gradient(x)
     np.testing.assert_allclose(logdensity, _multi(x))
@@ -580,7 +580,7 @@ def test_gaussian():
     lib = "../stan/gaussian/gaussian_model.so"
     data = "../stan/gaussian/gaussian.data.json"
 
-    model = bs.Bridge(lib, data)
+    model = bs.StanModel(lib, data)
 
     theta = np.array([0.2, 1.9])
     theta_unc = np.array([0.2, np.log(1.9)])
@@ -607,7 +607,7 @@ def cov_constrain(v, D):
 
     lib = "../stan/fr_gaussian/fr_gaussian_model.so"
     data = "../stan/fr_gaussian/fr_gaussian.data.json"
-    model = bs.Bridge(lib, data)
+    model = bs.StanModel(lib, data)
 
     size = 16
     unc_size = 10