Merge pull request #326 from ariddell/feature/speedup-tests

TST: streamline some tests to avoid CI time limit

Author: ariddell

pystan/tests/test_misc_args.py

@@ -7,16 +7,19 @@
 
 
 class TestArgs(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        model_code = 'parameters {real y;} model {y ~ normal(0,1);}'
+        cls.model = pystan.StanModel(model_code=model_code)
 
     def test_control(self):
+        model = self.model
         assertRaisesRegex = self.assertRaisesRegexp if PY2 else self.assertRaisesRegex
-        model_code = 'parameters {real y;} model {y ~ normal(0,1);}'
-
         with assertRaisesRegex(ValueError, '`control` must be a dictionary'):
             control_invalid = 3
-            pystan.stan(model_code=model_code, control=control_invalid)
+            model.sampling(control=control_invalid)
         with assertRaisesRegex(ValueError, '`control` contains unknown'):
             control_invalid = dict(foo=3)
-            pystan.stan(model_code=model_code, control=control_invalid)
+            model.sampling(control=control_invalid)
         with assertRaisesRegex(ValueError, '`metric` must be one of'):
-            pystan.stan(model_code=model_code, control={'metric': 'lorem-ipsum'})
+            model.sampling(control={'metric': 'lorem-ipsum'})

pystan/tests/test_user_inits.py

@@ -3,58 +3,53 @@
 import numpy as np
 
 import pystan
-from pystan._compat import PY2
 
 
 class TestUserInits(unittest.TestCase):
 
-    model_code = """
-    data {
-      real x;
-    }
-    parameters {
-      real mu;
-    }
-    model {
-      x ~ normal(mu,1);
-    }
-    """
-
-    data = dict(x=2)
+    @classmethod
+    def setUpClass(cls):
+        model_code = """
+            data {
+              real x;
+            }
+            parameters {
+              real mu;
+            }
+            model {
+              x ~ normal(mu,1);
+            }
+        """
+        cls.data = dict(x=2)
+        cls.model = pystan.StanModel(model_code=model_code)
 
     def test_user_init(self):
-        model_code = self.model_code
-        data = self.data
-        fit1 = pystan.stan(model_code=model_code, iter=10, chains=1, seed=2,
-                           data=data, init=[dict(mu=4)], warmup=0)
+        model, data = self.model, self.data
+        fit1 = model.sampling(iter=10, chains=1, seed=2, data=data, init=[dict(mu=4)], warmup=0)
         self.assertEqual(fit1.get_inits()[0]['mu'], 4)
-        fit2 = pystan.stan(model_code=model_code, iter=10, chains=1, seed=2,
-                           data=data, init=[dict(mu=400)], warmup=0)
+        fit2 = model.sampling(iter=10, chains=1, seed=2, data=data, init=[dict(mu=400)], warmup=0)
         self.assertEqual(fit2.get_inits()[0]['mu'], 400)
         self.assertFalse(all(fit1.extract()['mu'] == fit2.extract()['mu']))
 
     def test_user_initfun(self):
-        model_code = self.model_code
-        data = self.data
+        model, data = self.model, self.data
 
         def make_inits(chain_id):
             return dict(mu=chain_id)
 
-        fit1 = pystan.stan(model_code=model_code, iter=10, chains=4, seed=2,
-                           data=data, init=make_inits, warmup=0)
+        fit1 = model.sampling(iter=10, chains=4, seed=2, data=data, init=make_inits, warmup=0)
         for i, inits in enumerate(fit1.get_inits()):
             self.assertEqual(inits['mu'], i)
 
     def test_user_initfun_chainid(self):
-        model_code = self.model_code
-        data = self.data
+        model, data = self.model, self.data
 
         def make_inits(chain_id):
             return dict(mu=chain_id)
 
         chain_id = [9, 10, 11, 12]
-        fit1 = pystan.stan(model_code=model_code, iter=10, chains=4, seed=2,
-                           data=data, init=make_inits, warmup=0, chain_id=chain_id)
+        fit1 = model.sampling(iter=10, chains=4, seed=2, data=data,
+                              init=make_inits, warmup=0, chain_id=chain_id)
         for i, inits in zip(chain_id, fit1.get_inits()):
             self.assertEqual(inits['mu'], i)
 
@@ -79,44 +74,44 @@ def test_user_init_unspecified(self):
 
 class TestUserInitsMatrix(unittest.TestCase):
 
-    model_code = """
-    data {
-    int<lower=2> K;
-    int<lower=1> D;
-    }
-    parameters {
-    matrix[K,D] beta;
-    }
-    model {
-    for (k in 1:K)
-        for (d in 1:D)
-          beta[k,d] ~ normal(if_else(d==2,100, 0),1);
-    }"""
-    model = pystan.StanModel(model_code=model_code)
-    data = dict(K=3, D=4)
+    @classmethod
+    def setUpClass(cls):
+        model_code = """
+            data {
+              int<lower=2> K;
+              int<lower=1> D;
+            }
+            parameters {
+              matrix[K,D] beta;
+            }
+            model {
+            for (k in 1:K)
+                for (d in 1:D)
+                  beta[k,d] ~ normal(if_else(d==2,100, 0),1);
+           }
+        """
+        cls.model = pystan.StanModel(model_code=model_code)
+        cls.data = dict(K=3, D=4)
 
     def test_user_init(self):
-        model_code = self.model_code
-        data = self.data
+        model, data = self.model, self.data
         beta = np.ones((data['K'], data['D']))
-        fit1 = pystan.stan(model_code=model_code, iter=10, chains=1, seed=2,
-                           data=data, init=[dict(beta=beta)], warmup=0)
+        fit1 = model.sampling(iter=10, chains=1, seed=2,
+                              data=data, init=[dict(beta=beta)], warmup=0)
         np.testing.assert_equal(fit1.get_inits()[0]['beta'], beta)
         beta = 5 * np.ones((data['K'], data['D']))
-        fit2 = pystan.stan(model_code=model_code, iter=10, chains=1, seed=2,
-                           data=data, init=[dict(beta=beta)], warmup=0)
+        fit2 = model.sampling(iter=10, chains=1, seed=2,
+                              data=data, init=[dict(beta=beta)], warmup=0)
         np.testing.assert_equal(fit2.get_inits()[0]['beta'], beta)
 
     def test_user_initfun(self):
-        model_code = self.model_code
-        data = self.data
+        model, data = self.model, self.data
 
         beta = np.ones((data['K'], data['D']))
 
         def make_inits(chain_id):
             return dict(beta=beta * chain_id)
 
-        fit1 = pystan.stan(model_code=model_code, iter=10, chains=4, seed=2,
-                           data=data, init=make_inits, warmup=0)
+        fit1 = model.sampling(iter=10, chains=4, seed=2, data=data, init=make_inits, warmup=0)
         for i, inits in enumerate(fit1.get_inits()):
             np.testing.assert_equal(beta * i, inits['beta'])