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Merged
merged 5 commits into from
Dec 26, 2020
+10 −31
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Use sigma instead of sd, remove deprecationwarning #4344

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6169b5b
remove sd deprecation warning
MarcoGorelli Dec 25, 2020
fbc9355
sd -> sigma in pymc3/tests/test_models_linear.py::TestGLM
MarcoGorelli Dec 25, 2020
392b83c
update cls.sd in tests
MarcoGorelli Dec 25, 2020
4173b5e
noop
MarcoGorelli Dec 25, 2020
b7ec8de
don't delete self.sd
MarcoGorelli Dec 26, 2020
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34 changes: 10 additions & 24 deletions pymc3/distributions/continuous.py
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Original file line number Diff line number Diff line change
Expand Up @@ -478,9 +478,8 @@ 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.sigma = tt.as_tensor_variable(sigma)
self.tau = tt.as_tensor_variable(tau)

self.mean = self.median = self.mode = self.mu = mu = tt.as_tensor_variable(floatX(mu))
Expand Down Expand Up @@ -640,9 +639,8 @@ 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)
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I think we can leave self.sd too.

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Sure, I can revert this, but wouldn't it be safer to have a single source of truth for the value of sigma?

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I agree with your sentiment, but from user perspective, setting sd=x I would expect there to be a y.sd, and if we're keeping it around, which isn't the cleanest anyway, I don't see what we gain by changing this one thing in a subtle way.

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makes sense, thanks for explaining!

self.sigma = tt.as_tensor_variable(sigma)
self.tau = tt.as_tensor_variable(tau)
self.lower_check = tt.as_tensor_variable(floatX(lower)) if lower is not None else lower
self.upper_check = tt.as_tensor_variable(floatX(upper)) if upper is not None else upper
Expand Down Expand Up @@ -835,11 +833,10 @@ class HalfNormal(PositiveContinuous):
def __init__(self, sigma=None, tau=None, sd=None, *args, **kwargs):
if sd is not None:
sigma = sd
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Instead of deleting, could you raise an error here and advice using sigma

The reason being that sd was a keyword for a REALLY long time so there are a lot of examples out there in the wild with this keyword.

warnings.warn("sd is deprecated, use sigma instead", DeprecationWarning)
super().__init__(*args, **kwargs)
tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)

self.sigma = self.sd = sigma = tt.as_tensor_variable(sigma)
self.sigma = sigma = tt.as_tensor_variable(sigma)
self.tau = tau = tt.as_tensor_variable(tau)

self.mean = tt.sqrt(2 / (np.pi * self.tau))
Expand Down Expand Up @@ -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))
Expand Down Expand Up @@ -1724,13 +1720,12 @@ 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)

self.mu = mu = tt.as_tensor_variable(floatX(mu))
self.tau = tau = tt.as_tensor_variable(tau)
self.sigma = self.sd = sigma = tt.as_tensor_variable(sigma)
self.sigma = sigma = tt.as_tensor_variable(sigma)

self.mean = tt.exp(self.mu + 1.0 / (2 * self.tau))
self.median = tt.exp(self.mu)
Expand Down Expand Up @@ -1884,15 +1879,13 @@ 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)
self.sigma = self.sd = sigma = tt.as_tensor_variable(sigma)
self.sigma = sigma = tt.as_tensor_variable(sigma)
self.mean = self.median = self.mode = self.mu = mu = tt.as_tensor_variable(mu)

self.variance = tt.switch((nu > 2) * 1, (1 / self.lam) * (nu / (nu - 2)), np.inf)
Expand Down Expand Up @@ -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))
Expand Down Expand Up @@ -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))
Expand Down Expand Up @@ -2902,12 +2893,11 @@ 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)
self.median = tt.as_tensor_variable(sigma)
self.sigma = self.sd = tt.as_tensor_variable(sigma)
self.sigma = tt.as_tensor_variable(sigma)
self.lam = tt.as_tensor_variable(lam)
self.nu = nu = tt.as_tensor_variable(floatX(nu))

Expand Down Expand Up @@ -3041,10 +3031,9 @@ 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))
self.sigma = sigma = tt.as_tensor_variable(floatX(sigma))
self.nu = nu = tt.as_tensor_variable(floatX(nu))
self.mean = mu + nu
self.variance = (sigma ** 2) + (nu ** 2)
Expand Down Expand Up @@ -3317,12 +3306,11 @@ 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))
self.tau = tt.as_tensor_variable(tau)
self.sigma = self.sd = tt.as_tensor_variable(sigma)
self.sigma = tt.as_tensor_variable(sigma)

self.alpha = alpha = tt.as_tensor_variable(floatX(alpha))

Expand Down Expand Up @@ -3721,11 +3709,10 @@ 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))
self.sigma = self.sd = sigma = tt.as_tensor_variable(floatX(sigma))
self.sigma = sigma = tt.as_tensor_variable(floatX(sigma))
self.b = b = tt.as_tensor_variable(floatX(b))

nu_sigma_ratio = -(nu ** 2) / (2 * sigma ** 2)
Expand Down Expand Up @@ -3994,10 +3981,9 @@ 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)
self.sigma = tt.as_tensor_variable(sigma)
self.tau = tau = tt.as_tensor_variable(tau)

self.median = invlogit(mu)
Expand Down
5 changes: 1 addition & 4 deletions pymc3/distributions/mixture.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -632,11 +630,10 @@ 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)
self.sigma = self.sd = sigma = tt.as_tensor_variable(sigma)
self.sigma = sigma = tt.as_tensor_variable(sigma)

super().__init__(w, Normal.dist(mu, sigma=sigma, shape=comp_shape), *args, **kwargs)

Expand Down
8 changes: 2 additions & 6 deletions pymc3/distributions/timeseries.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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

Expand Down Expand Up @@ -116,10 +114,9 @@ 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)
self.sigma = tt.as_tensor_variable(sigma)
self.tau = tt.as_tensor_variable(tau)

self.mean = tt.as_tensor_variable(0.0)
Expand Down Expand Up @@ -211,11 +208,10 @@ 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)
self.sigma = self.sd = sigma
self.sigma = sigma
self.mu = tt.as_tensor_variable(mu)
self.init = init
self.mean = tt.as_tensor_variable(0.0)
Expand Down
8 changes: 4 additions & 4 deletions pymc3/tests/test_glm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)
Expand All @@ -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:
Expand All @@ -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
Expand Down
12 changes: 6 additions & 6 deletions pymc3/tests/test_models_linear.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)
Expand All @@ -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):
Expand All @@ -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:
Expand All @@ -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):
Expand All @@ -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():
Expand Down