Merge pull request #1679 from pymc-devs/init_case2

Make init argument to sample case-insensitive

pymc3/examples/arbitrary_stochastic.py

@@ -20,7 +20,7 @@ def run(n_samples=3000):
     start = model.test_point
     h = pm.find_hessian(start, model=model)
     step = pm.Metropolis(model.vars, h, blocked=True, model=model)
-    trace = pm.sample(n_samples, step, start, model=model)
+    trace = pm.sample(n_samples, step=step, start=start, model=model)
     return trace
 
 if __name__ == "__main__":

pymc3/examples/factor_potential.py

@@ -13,7 +13,7 @@ def run(n=3000):
     if n == "short":
         n = 50
     with model:
-        trace = sample(n, step, start)
+        trace = sample(n, step=step, start=start)
 
 if __name__ == '__main__':
     run()

pymc3/sampling.py

@@ -98,20 +98,20 @@ def sample(draws, step=None, init='advi', n_init=200000, start=None,
         A step function or collection of functions. If no step methods are
         specified, or are partially specified, they will be assigned
         automatically (defaults to None).
-    init : str {'advi', 'advi_map', 'map', 'nuts', None}
+    init : str {'ADVI', 'ADVI_MAP', 'MAP', 'NUTS', None}
         Initialization method to use.
-        * advi : Run ADVI to estimate starting points and diagonal covariance
+        * ADVI : Run ADVI to estimate starting points and diagonal covariance
         matrix. If njobs > 1 it will sample starting points from the estimated
         posterior, otherwise it will use the estimated posterior mean.
-        * advi_map: Initialize ADVI with MAP and use MAP as starting point.
-        * map : Use the MAP as starting point.
-        * nuts : Run NUTS to estimate starting points and covariance matrix. If
+        * ADVI_MAP: Initialize ADVI with MAP and use MAP as starting point.
+        * MAP : Use the MAP as starting point.
+        * NUTS : Run NUTS to estimate starting points and covariance matrix. If
         njobs > 1 it will sample starting points from the estimated posterior,
         otherwise it will use the estimated posterior mean.
         * None : Do not initialize.
     n_init : int
         Number of iterations of initializer
-        If 'advi', number of iterations, if 'nuts', number of draws.
+        If 'ADVI', number of iterations, if 'nuts', number of draws.
     start : dict
         Starting point in parameter space (or partial point)
         Defaults to trace.point(-1)) if there is a trace provided and
@@ -147,6 +147,8 @@ def sample(draws, step=None, init='advi', n_init=200000, start=None,
     """
     model = modelcontext(model)
 
+    if init is not None:
+        init = init.lower()
 
     if step is None and init is not None and pm.model.all_continuous(model.vars):
         # By default, use NUTS sampler
@@ -398,7 +400,7 @@ def sample_ppc(trace, samples=None, model=None, vars=None, size=None, random_see
     return {k: np.asarray(v) for k, v in ppc.items()}
 
 
-def init_nuts(init='advi', njobs=1, n_init=500000, model=None,
+def init_nuts(init='ADVI', njobs=1, n_init=500000, model=None,
               random_seed=-1, **kwargs):
     """Initialize and sample from posterior of a continuous model.
 
@@ -409,17 +411,17 @@ def init_nuts(init='advi', njobs=1, n_init=500000, model=None,
 
     Parameters
     ----------
-    init : str {'advi', 'advi_map', 'map', 'nuts'}
+    init : str {'ADVI', 'ADVI_MAP', 'MAP', 'NUTS'}
         Initialization method to use.
-        * advi : Run ADVI to estimate posterior mean and diagonal covariance matrix.
-        * advi_map: Initialize ADVI with MAP and use MAP as starting point.
-        * map : Use the MAP as starting point.
-        * nuts : Run NUTS and estimate posterior mean and covariance matrix.
+        * ADVI : Run ADVI to estimate posterior mean and diagonal covariance matrix.
+        * ADVI_MAP: Initialize ADVI with MAP and use MAP as starting point.
+        * MAP : Use the MAP as starting point.
+        * NUTS : Run NUTS and estimate posterior mean and covariance matrix.
     njobs : int
         Number of parallel jobs to start. 
     n_init : int
         Number of iterations of initializer
-        If 'advi', number of iterations, if 'metropolis', number of draws.
+        If 'ADVI', number of iterations, if 'metropolis', number of draws.
     model : Model (optional if in `with` context)
     **kwargs : keyword arguments
         Extra keyword arguments are forwarded to pymc3.NUTS.
@@ -439,6 +441,9 @@ def init_nuts(init='advi', njobs=1, n_init=500000, model=None,
     pm._log.info('Initializing NUTS using {}...'.format(init))
 
     random_seed = int(np.atleast_1d(random_seed)[0])
+
+    if init is not None:
+        init = init.lower()
 
     if init == 'advi':
         v_params = pm.variational.advi(n=n_init, random_seed=random_seed)

pymc3/tests/test_diagnostics.py

@@ -20,9 +20,9 @@ def get_ptrace(self, n_samples):
             # Run sampler
             step1 = Slice([model.early_mean_log_, model.late_mean_log_])
             step2 = Metropolis([model.switchpoint])
-            start = {'early_mean': 2., 'late_mean': 3., 'switchpoint': 90}
-            ptrace = sample(n_samples, [step1, step2], start, njobs=2, progressbar=False,
-                            random_seed=[1, 4])
+            start = {'early_mean': 7., 'late_mean': 1., 'switchpoint': 100}
+            ptrace = sample(n_samples, step=[step1, step2], start=start, njobs=2, 
+                    progressbar=False, random_seed=[20090425, 19700903])
         return ptrace
 
     def test_good(self):
@@ -53,7 +53,7 @@ def test_right_shape_python_float(self, shape=None, test_shape=None):
             # start sampling at the MAP
             start = find_MAP()
             step = NUTS(scaling=start)
-            ptrace = sample(n_samples, step, start,
+            ptrace = sample(n_samples, step=step, start=start,
                             njobs=n_jobs, random_seed=42)
 
         rhat = gelman_rubin(ptrace)['x']
@@ -92,7 +92,7 @@ def get_switchpoint(self, n_samples):
             # Run sampler
             step1 = Slice([model.early_mean_log_, model.late_mean_log_])
             step2 = Metropolis([model.switchpoint])
-            trace = sample(n_samples, [step1, step2], progressbar=False, random_seed=1)
+            trace = sample(n_samples, step=[step1, step2], progressbar=False, random_seed=1)
         return trace['switchpoint']
 
     def test_geweke_negative(self):
@@ -153,7 +153,7 @@ def test_effective_n(self):
             # start sampling at the MAP
             start = find_MAP()
             step = NUTS(scaling=start)
-            ptrace = sample(n_samples, step, start,
+            ptrace = sample(n_samples, step=step, start=start,
                             njobs=n_jobs, random_seed=42)
 
         n_effective = effective_n(ptrace)['x']
@@ -174,7 +174,7 @@ def test_effective_n_right_shape_python_float(self,
             # start sampling at the MAP
             start = find_MAP()
             step = NUTS(scaling=start)
-            ptrace = sample(n_samples, step, start,
+            ptrace = sample(n_samples, step=step, start=start,
                             njobs=n_jobs, random_seed=42)
 
         n_effective = effective_n(ptrace)['x']

pymc3/tests/test_examples.py

@@ -54,7 +54,7 @@ def test_run(self):
             h = H(start)
 
             step = pm.HamiltonianMC(model.vars, h)
-            pm.sample(50, step, start)
+            pm.sample(50, step=step, start=start)
 
 
 class TestARM12_6(SeededTest):
@@ -88,7 +88,7 @@ def too_slow(self):
             start = pm.find_MAP(start=start,
                                 vars=[model['groupmean'], model['sd_interval_'], model['floor_m']])
             step = pm.NUTS(model.vars, scaling=start)
-            pm.sample(50, step, start)
+            pm.sample(50, step=step, start=start)
 
 
 class TestARM12_6Uranium(SeededTest):
@@ -129,7 +129,7 @@ def too_slow(self):
             h = np.diag(H(start))
 
             step = pm.HamiltonianMC(model.vars, h)
-            pm.sample(50, step, start)
+            pm.sample(50, step=step, start=start)
 
 
 def build_disaster_model(masked=False):
@@ -263,7 +263,7 @@ def test_run(self):
             start = {'psi': 0.5, 'z': (self.y > 0).astype(int), 'theta': 5}
             step_one = pm.Metropolis([model.theta_interval_, model.psi_logodds_])
             step_two = pm.BinaryMetropolis([model.z])
-            pm.sample(50, [step_one, step_two], start)
+            pm.sample(50, step=[step_one, step_two], start=start)
 
 
 class TestRSV(SeededTest):

pymc3/tests/test_glm.py

@@ -34,7 +34,7 @@ def test_linear_component(self):
             Normal('y_obs', mu=y_est, sd=sigma, observed=self.y_linear)
             start = find_MAP(vars=[sigma])
             step = Slice(model.vars)
-            trace = sample(500, step, start, progressbar=False, random_seed=self.random_seed)
+            trace = sample(500, step=step, start=start, progressbar=False, random_seed=self.random_seed)
 
             self.assertAlmostEqual(np.mean(trace['Intercept']), self.intercept, 1)
             self.assertAlmostEqual(np.mean(trace['x']), self.slope, 1)

pymc3/tests/test_models_linear.py

@@ -44,7 +44,7 @@ def test_linear_component(self):
             Normal('y_obs', mu=lm.y_est, sd=sigma, observed=self.y_linear)  # yields y_obs
             start = find_MAP(vars=[sigma])
             step = Slice(model.vars)
-            trace = sample(500, step, start, progressbar=False, random_seed=self.random_seed)
+            trace = sample(500, step=step, start=start, progressbar=False, random_seed=self.random_seed)
 
             self.assertAlmostEqual(np.mean(trace['lm_Intercept']), self.intercept, 1)
             self.assertAlmostEqual(np.mean(trace['lm_x0']), self.slope, 1)
@@ -58,7 +58,7 @@ def test_linear_component_from_formula(self):
             Normal('y_obs', mu=lm.y_est, sd=sigma, observed=self.y_linear)
             start = find_MAP(vars=[sigma])
             step = Slice(model.vars)
-            trace = sample(500, step, start, progressbar=False, random_seed=self.random_seed)
+            trace = sample(500, step=step, start=start, progressbar=False, random_seed=self.random_seed)
 
             self.assertAlmostEqual(np.mean(trace['Intercept']), self.intercept, 1)
             self.assertAlmostEqual(np.mean(trace['x']), self.slope, 1)
@@ -79,7 +79,7 @@ def test_glm(self):
             )
             start = find_MAP()
             step = Slice(model.vars)
-            trace = sample(500, step, start, progressbar=False, random_seed=self.random_seed)
+            trace = sample(500, step=step, start=start, progressbar=False, random_seed=self.random_seed)
             self.assertAlmostEqual(np.mean(trace['glm_Intercept']), self.intercept, 1)
             self.assertAlmostEqual(np.mean(trace['glm_x0']), self.slope, 1)
             self.assertAlmostEqual(np.mean(trace['glm_sd']), self.sd, 1)
@@ -91,7 +91,7 @@ def test_glm_from_formula(self):
             Glm.from_formula('y ~ x', self.data_linear, name=NAME)
             start = find_MAP()
             step = Slice(model.vars)
-            trace = sample(500, step, start, progressbar=False, random_seed=self.random_seed)
+            trace = sample(500, step=step, start=start, progressbar=False, random_seed=self.random_seed)
 
             self.assertAlmostEqual(np.mean(trace['%s_Intercept' % NAME]), self.intercept, 1)
             self.assertAlmostEqual(np.mean(trace['%s_x' % NAME]), self.slope, 1)

pymc3/tests/test_plots.py

@@ -20,7 +20,7 @@ def test_plots():
         start = model.test_point
         h = find_hessian(start)
         step = Metropolis(model.vars, h)
-        trace = sample(3000, step, start)
+        trace = sample(3000, step=step, start=start)
 
         traceplot(trace)
         forestplot(trace)
@@ -34,7 +34,7 @@ def test_plots_multidimensional():
     with model as model:
         h = np.diag(find_hessian(start))
         step = Metropolis(model.vars, h)
-        trace = sample(3000, step, start)
+        trace = sample(3000, step=step, start=start)
 
         traceplot(trace)
         plot_posterior(trace)
@@ -47,7 +47,7 @@ def test_multichain_plots():
         step1 = Slice([model.early_mean_log_, model.late_mean_log_])
         step2 = Metropolis([model.switchpoint])
         start = {'early_mean': 2., 'late_mean': 3., 'switchpoint': 50}
-        ptrace = sample(1000, [step1, step2], start, njobs=2)
+        ptrace = sample(1000, step=[step1, step2], start=start, njobs=2)
 
     forestplot(ptrace, varnames=['early_mean', 'late_mean'])
     autocorrplot(ptrace, varnames=['switchpoint'])

pymc3/tests/test_sampling.py

@@ -59,7 +59,7 @@ def test_sample(self):
         with self.model:
             for njobs in test_njobs:
                 for steps in [1, 10, 300]:
-                    pm.sample(steps, self.step, {}, None, njobs=njobs, random_seed=self.random_seed)
+                    pm.sample(steps, step=self.step, njobs=njobs, random_seed=self.random_seed)
 
     def test_sample_init(self):
         with self.model: