Use sigma instead of sd, remove deprecationwarning (#4344)

* remove sd deprecation warning

* sd -> sigma in pymc3/tests/test_models_linear.py::TestGLM

* update cls.sd in tests

* noop

* don't delete self.sd

pymc3/distributions/continuous.py

@@ -478,7 +478,6 @@ class Normal(Continuous):
     def __init__(self, mu=0, sigma=None, tau=None, sd=None, **kwargs):
         if sd is not None:
             sigma = sd
-            warnings.warn("sd is deprecated, use sigma instead", DeprecationWarning)
         tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
         self.sigma = self.sd = tt.as_tensor_variable(sigma)
         self.tau = tt.as_tensor_variable(tau)
@@ -640,7 +639,6 @@ def __init__(
     ):
         if sd is not None:
             sigma = sd
-            warnings.warn("sd is deprecated, use sigma instead", DeprecationWarning)
         tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
         self.sigma = self.sd = tt.as_tensor_variable(sigma)
         self.tau = tt.as_tensor_variable(tau)
@@ -835,7 +833,6 @@ class HalfNormal(PositiveContinuous):
     def __init__(self, sigma=None, tau=None, sd=None, *args, **kwargs):
         if sd is not None:
             sigma = sd
-            warnings.warn("sd is deprecated, use sigma instead", DeprecationWarning)
         super().__init__(*args, **kwargs)
         tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
 
@@ -1218,7 +1215,6 @@ def __init__(self, alpha=None, beta=None, mu=None, sigma=None, sd=None, *args, *
         super().__init__(*args, **kwargs)
         if sd is not None:
             sigma = sd
-            warnings.warn("sd is deprecated, use sigma instead", DeprecationWarning)
         alpha, beta = self.get_alpha_beta(alpha, beta, mu, sigma)
         self.alpha = alpha = tt.as_tensor_variable(floatX(alpha))
         self.beta = beta = tt.as_tensor_variable(floatX(beta))
@@ -1724,7 +1720,6 @@ def __init__(self, mu=0, sigma=None, tau=None, sd=None, *args, **kwargs):
         super().__init__(*args, **kwargs)
         if sd is not None:
             sigma = sd
-            warnings.warn("sd is deprecated, use sigma instead", DeprecationWarning)
 
         tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
 
@@ -1884,11 +1879,9 @@ class StudentT(Continuous):
     """
 
     def __init__(self, nu, mu=0, lam=None, sigma=None, sd=None, *args, **kwargs):
-        super().__init__(*args, **kwargs)
         super().__init__(*args, **kwargs)
         if sd is not None:
             sigma = sd
-            warnings.warn("sd is deprecated, use sigma instead", DeprecationWarning)
         self.nu = nu = tt.as_tensor_variable(floatX(nu))
         lam, sigma = get_tau_sigma(tau=lam, sigma=sigma)
         self.lam = lam = tt.as_tensor_variable(lam)
@@ -2397,7 +2390,6 @@ def __init__(self, alpha=None, beta=None, mu=None, sigma=None, sd=None, *args, *
         super().__init__(*args, **kwargs)
         if sd is not None:
             sigma = sd
-            warnings.warn("sd is deprecated, use sigma instead", DeprecationWarning)
 
         alpha, beta = self.get_alpha_beta(alpha, beta, mu, sigma)
         self.alpha = alpha = tt.as_tensor_variable(floatX(alpha))
@@ -2545,7 +2537,6 @@ def __init__(self, alpha=None, beta=None, mu=None, sigma=None, sd=None, *args, *
 
         if sd is not None:
             sigma = sd
-            warnings.warn("sd is deprecated, use sigma instead", DeprecationWarning)
 
         alpha, beta = InverseGamma._get_alpha_beta(alpha, beta, mu, sigma)
         self.alpha = alpha = tt.as_tensor_variable(floatX(alpha))
@@ -2902,7 +2893,6 @@ def __init__(self, nu=1, sigma=None, lam=None, sd=None, *args, **kwargs):
         super().__init__(*args, **kwargs)
         if sd is not None:
             sigma = sd
-            warnings.warn("sd is deprecated, use sigma instead", DeprecationWarning)
 
         self.mode = tt.as_tensor_variable(0)
         lam, sigma = get_tau_sigma(lam, sigma)
@@ -3041,7 +3031,6 @@ def __init__(self, mu=0.0, sigma=None, nu=None, sd=None, *args, **kwargs):
 
         if sd is not None:
             sigma = sd
-            warnings.warn("sd is deprecated, use sigma instead", DeprecationWarning)
 
         self.mu = mu = tt.as_tensor_variable(floatX(mu))
         self.sigma = self.sd = sigma = tt.as_tensor_variable(floatX(sigma))
@@ -3317,7 +3306,6 @@ def __init__(self, mu=0.0, sigma=None, tau=None, alpha=1, sd=None, *args, **kwar
 
         if sd is not None:
             sigma = sd
-            warnings.warn("sd is deprecated, use sigma instead", DeprecationWarning)
 
         tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
         self.mu = mu = tt.as_tensor_variable(floatX(mu))
@@ -3721,7 +3709,6 @@ def __init__(self, nu=None, sigma=None, b=None, sd=None, *args, **kwargs):
         super().__init__(*args, **kwargs)
         if sd is not None:
             sigma = sd
-            warnings.warn("sd is deprecated, use sigma instead", DeprecationWarning)
 
         nu, b, sigma = self.get_nu_b(nu, b, sigma)
         self.nu = nu = tt.as_tensor_variable(floatX(nu))
@@ -3994,7 +3981,6 @@ class LogitNormal(UnitContinuous):
     def __init__(self, mu=0, sigma=None, tau=None, sd=None, **kwargs):
         if sd is not None:
             sigma = sd
-            warnings.warn("sd is deprecated, use sigma instead", DeprecationWarning)
         self.mu = mu = tt.as_tensor_variable(floatX(mu))
         tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
         self.sigma = self.sd = tt.as_tensor_variable(sigma)

pymc3/distributions/mixture.py

@@ -12,8 +12,6 @@
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
 
-import warnings
-
 from collections.abc import Iterable
 
 import numpy as np
@@ -632,7 +630,6 @@ class NormalMixture(Mixture):
     def __init__(self, w, mu, sigma=None, tau=None, sd=None, comp_shape=(), *args, **kwargs):
         if sd is not None:
             sigma = sd
-            warnings.warn("sd is deprecated, use sigma instead", DeprecationWarning)
         _, sigma = get_tau_sigma(tau=tau, sigma=sigma)
 
         self.mu = mu = tt.as_tensor_variable(mu)

pymc3/distributions/timeseries.py

@@ -12,8 +12,6 @@
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
 
-import warnings
-
 import numpy as np
 import theano.tensor as tt
 
@@ -116,7 +114,6 @@ def __init__(
         super().__init__(*args, **kwargs)
         if sd is not None:
             sigma = sd
-            warnings.warn("sd is deprecated, use sigma instead", DeprecationWarning)
 
         tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
         self.sigma = self.sd = tt.as_tensor_variable(sigma)
@@ -211,7 +208,6 @@ def __init__(self, tau=None, init=Flat.dist(), sigma=None, mu=0.0, sd=None, *arg
             raise TypeError("GaussianRandomWalk must be supplied a non-zero shape argument!")
         if sd is not None:
             sigma = sd
-            warnings.warn("sd is deprecated, use sigma instead", DeprecationWarning)
         tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
         self.tau = tt.as_tensor_variable(tau)
         sigma = tt.as_tensor_variable(sigma)

pymc3/tests/test_glm.py

@@ -46,9 +46,9 @@ def setup_class(cls):
         super().setup_class()
         cls.intercept = 1
         cls.slope = 3
-        cls.sd = 0.05
+        cls.sigma = 0.05
         x_linear, cls.y_linear = generate_data(cls.intercept, cls.slope, size=1000)
-        cls.y_linear += np.random.normal(size=1000, scale=cls.sd)
+        cls.y_linear += np.random.normal(size=1000, scale=cls.sigma)
         cls.data_linear = pd.DataFrame(dict(x=x_linear, y=cls.y_linear))
 
         x_logistic, y_logistic = generate_data(cls.intercept, cls.slope, size=3000)
@@ -73,7 +73,7 @@ def test_linear_component(self):
 
             assert round(abs(np.mean(trace["Intercept"]) - self.intercept), 1) == 0
             assert round(abs(np.mean(trace["x"]) - self.slope), 1) == 0
-            assert round(abs(np.mean(trace["sigma"]) - self.sd), 1) == 0
+            assert round(abs(np.mean(trace["sigma"]) - self.sigma), 1) == 0
 
     def test_glm(self):
         with Model() as model:
@@ -83,7 +83,7 @@ def test_glm(self):
 
             assert round(abs(np.mean(trace["Intercept"]) - self.intercept), 1) == 0
             assert round(abs(np.mean(trace["x"]) - self.slope), 1) == 0
-            assert round(abs(np.mean(trace["sd"]) - self.sd), 1) == 0
+            assert round(abs(np.mean(trace["sd"]) - self.sigma), 1) == 0
 
     def test_glm_offset(self):
         offset = 1.0

pymc3/tests/test_models_linear.py

@@ -33,9 +33,9 @@ def setup_class(cls):
         super().setup_class()
         cls.intercept = 1
         cls.slope = 3
-        cls.sd = 0.05
+        cls.sigma = 0.05
         x_linear, cls.y_linear = generate_data(cls.intercept, cls.slope, size=1000)
-        cls.y_linear += np.random.normal(size=1000, scale=cls.sd)
+        cls.y_linear += np.random.normal(size=1000, scale=cls.sigma)
         cls.data_linear = dict(x=x_linear, y=cls.y_linear)
 
         x_logistic, y_logistic = generate_data(cls.intercept, cls.slope, size=3000)
@@ -59,7 +59,7 @@ def test_linear_component(self):
 
             assert round(abs(np.mean(trace["lm_Intercept"]) - self.intercept), 1) == 0
             assert round(abs(np.mean(trace["lm_x0"]) - self.slope), 1) == 0
-            assert round(abs(np.mean(trace["sigma"]) - self.sd), 1) == 0
+            assert round(abs(np.mean(trace["sigma"]) - self.sigma), 1) == 0
         assert vars_to_create == set(model.named_vars.keys())
 
     def test_linear_component_from_formula(self):
@@ -75,7 +75,7 @@ def test_linear_component_from_formula(self):
 
             assert round(abs(np.mean(trace["Intercept"]) - self.intercept), 1) == 0
             assert round(abs(np.mean(trace["x"]) - self.slope), 1) == 0
-            assert round(abs(np.mean(trace["sigma"]) - self.sd), 1) == 0
+            assert round(abs(np.mean(trace["sigma"]) - self.sigma), 1) == 0
 
     def test_glm(self):
         with Model() as model:
@@ -88,7 +88,7 @@ def test_glm(self):
             )
             assert round(abs(np.mean(trace["glm_Intercept"]) - self.intercept), 1) == 0
             assert round(abs(np.mean(trace["glm_x0"]) - self.slope), 1) == 0
-            assert round(abs(np.mean(trace["glm_sd"]) - self.sd), 1) == 0
+            assert round(abs(np.mean(trace["glm_sd"]) - self.sigma), 1) == 0
             assert vars_to_create == set(model.named_vars.keys())
 
     def test_glm_from_formula(self):
@@ -103,7 +103,7 @@ def test_glm_from_formula(self):
 
             assert round(abs(np.mean(trace["%s_Intercept" % NAME]) - self.intercept), 1) == 0
             assert round(abs(np.mean(trace["%s_x" % NAME]) - self.slope), 1) == 0
-            assert round(abs(np.mean(trace["%s_sd" % NAME]) - self.sd), 1) == 0
+            assert round(abs(np.mean(trace["%s_sd" % NAME]) - self.sigma), 1) == 0
 
     def test_strange_types(self):
         with Model():