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Sep 18, 2020
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Black formatted 15 files #4113

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dd7017d
Black formatted 15 files
Sayam753 Sep 18, 2020
10d4760
Ran pyupgrade on all files
Sayam753 Sep 18, 2020
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137 changes: 36 additions & 101 deletions pymc3/tests/test_distributions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -157,9 +157,7 @@ def __mul__(self, other):
)

def __neg__(self):
return Domain(
[-v for v in self.vals], self.dtype, (-self.lower, -self.upper), self.shape
)
return Domain([-v for v in self.vals], self.dtype, (-self.lower, -self.upper), self.shape)


def product(domains, n_samples=-1):
Expand All @@ -177,9 +175,7 @@ def product(domains, n_samples=-1):
names, domains = zip(*domains.items())
except ValueError: # domains.items() is empty
return []
all_vals = [
zip(names, val) for val in itertools.product(*[d.vals for d in domains])
]
all_vals = [zip(names, val) for val in itertools.product(*[d.vals for d in domains])]
if n_samples > 0 and len(all_vals) > n_samples:
return (all_vals[j] for j in nr.choice(len(all_vals), n_samples, replace=False))
return all_vals
Expand Down Expand Up @@ -428,9 +424,7 @@ def invlogit(x, eps=sys.float_info.epsilon):

def orderedlogistic_logpdf(value, eta, cutpoints):
c = np.concatenate(([-np.inf], cutpoints, [np.inf]))
ps = np.array(
[invlogit(eta - cc) - invlogit(eta - cc1) for cc, cc1 in zip(c[:-1], c[1:])]
)
ps = np.array([invlogit(eta - cc) - invlogit(eta - cc1) for cc, cc1 in zip(c[:-1], c[1:])])
p = ps[value]
return np.where(np.all(ps >= 0), np.log(p), -np.inf)

Expand All @@ -445,9 +439,7 @@ def __init__(self, n):
class MultiSimplex:
def __init__(self, n_dependent, n_independent):
self.vals = []
for simplex_value in itertools.product(
simplex_values(n_dependent), repeat=n_independent
):
for simplex_value in itertools.product(simplex_values(n_dependent), repeat=n_independent):
self.vals.append(np.vstack(simplex_value))
self.shape = (n_independent, n_dependent)
self.dtype = Unit.dtype
Expand All @@ -468,16 +460,12 @@ def PdMatrix(n):

PdMatrix2 = Domain([np.eye(2), [[0.5, 0.05], [0.05, 4.5]]], edges=(None, None))

PdMatrix3 = Domain(
[np.eye(3), [[0.5, 0.1, 0], [0.1, 1, 0], [0, 0, 2.5]]], edges=(None, None)
)
PdMatrix3 = Domain([np.eye(3), [[0.5, 0.1, 0], [0.1, 1, 0], [0, 0, 2.5]]], edges=(None, None))


PdMatrixChol1 = Domain([np.eye(1), [[0.001]]], edges=(None, None))
PdMatrixChol2 = Domain([np.eye(2), [[0.1, 0], [10, 1]]], edges=(None, None))
PdMatrixChol3 = Domain(
[np.eye(3), [[0.1, 0, 0], [10, 100, 0], [0, 1, 10]]], edges=(None, None)
)
PdMatrixChol3 = Domain([np.eye(3), [[0.1, 0, 0], [10, 100, 0], [0, 1, 10]]], edges=(None, None))


def PdMatrixChol(n):
Expand Down Expand Up @@ -538,19 +526,15 @@ def logp(args):

self.check_logp(model, value, domain, paramdomains, logp, decimal=decimal)

def check_logp(
self, model, value, domain, paramdomains, logp_reference, decimal=None
):
def check_logp(self, model, value, domain, paramdomains, logp_reference, decimal=None):
domains = paramdomains.copy()
domains["value"] = domain
logp = model.fastlogp
for pt in product(domains, n_samples=100):
pt = Point(pt, model=model)
if decimal is None:
decimal = select_by_precision(float64=6, float32=3)
assert_almost_equal(
logp(pt), logp_reference(pt), decimal=decimal, err_msg=str(pt)
)
assert_almost_equal(logp(pt), logp_reference(pt), decimal=decimal, err_msg=str(pt))

def check_logcdf(
self,
Expand Down Expand Up @@ -615,17 +599,13 @@ def test_triangular(self):
Triangular,
Runif,
{"lower": -Rplusunif, "c": Runif, "upper": Rplusunif},
lambda value, c, lower, upper: sp.triang.logpdf(
value, c - lower, lower, upper - lower
),
lambda value, c, lower, upper: sp.triang.logpdf(value, c - lower, lower, upper - lower),
)
self.check_logcdf(
Triangular,
Runif,
{"lower": -Rplusunif, "c": Runif, "upper": Rplusunif},
lambda value, c, lower, upper: sp.triang.logcdf(
value, c - lower, lower, upper - lower
),
lambda value, c, lower, upper: sp.triang.logcdf(value, c - lower, lower, upper - lower),
)

def test_bound_normal(self):
Expand Down Expand Up @@ -774,9 +754,7 @@ def test_beta(self):
{"alpha": Rplus, "beta": Rplus},
lambda value, alpha, beta: sp.beta.logpdf(value, alpha, beta),
)
self.pymc3_matches_scipy(
Beta, Unit, {"mu": Unit, "sigma": Rplus}, beta_mu_sigma
)
self.pymc3_matches_scipy(Beta, Unit, {"mu": Unit, "sigma": Rplus}, beta_mu_sigma)
self.check_logcdf(
Beta,
Unit,
Expand All @@ -788,15 +766,10 @@ def test_kumaraswamy(self):
# Scipy does not have a built-in Kumaraswamy pdf
def scipy_log_pdf(value, a, b):
return (
np.log(a)
+ np.log(b)
+ (a - 1) * np.log(value)
+ (b - 1) * np.log(1 - value ** a)
np.log(a) + np.log(b) + (a - 1) * np.log(value) + (b - 1) * np.log(1 - value ** a)
)

self.pymc3_matches_scipy(
Kumaraswamy, Unit, {"a": Rplus, "b": Rplus}, scipy_log_pdf
)
self.pymc3_matches_scipy(Kumaraswamy, Unit, {"a": Rplus, "b": Rplus}, scipy_log_pdf)

def test_exponential(self):
self.pymc3_matches_scipy(
Expand All @@ -821,9 +794,7 @@ def test_negative_binomial(self):
def test_fun(value, mu, alpha):
return sp.nbinom.logpmf(value, alpha, 1 - mu / (mu + alpha))

self.pymc3_matches_scipy(
NegativeBinomial, Nat, {"mu": Rplus, "alpha": Rplus}, test_fun
)
self.pymc3_matches_scipy(NegativeBinomial, Nat, {"mu": Rplus, "alpha": Rplus}, test_fun)

def test_laplace(self):
self.pymc3_matches_scipy(
Expand All @@ -844,9 +815,7 @@ def test_lognormal(self):
Lognormal,
Rplus,
{"mu": R, "tau": Rplusbig},
lambda value, mu, tau: floatX(
sp.lognorm.logpdf(value, tau ** -0.5, 0, np.exp(mu))
),
lambda value, mu, tau: floatX(sp.lognorm.logpdf(value, tau ** -0.5, 0, np.exp(mu))),
)
self.check_logcdf(
Lognormal,
Expand Down Expand Up @@ -907,13 +876,9 @@ def test_gamma(self):
)

def test_fun(value, mu, sigma):
return sp.gamma.logpdf(
value, mu ** 2 / sigma ** 2, scale=1.0 / (mu / sigma ** 2)
)
return sp.gamma.logpdf(value, mu ** 2 / sigma ** 2, scale=1.0 / (mu / sigma ** 2))

self.pymc3_matches_scipy(
Gamma, Rplus, {"mu": Rplusbig, "sigma": Rplusbig}, test_fun
)
self.pymc3_matches_scipy(Gamma, Rplus, {"mu": Rplusbig, "sigma": Rplusbig}, test_fun)

self.check_logcdf(
Gamma,
Expand All @@ -939,9 +904,7 @@ def test_fun(value, mu, sigma):
alpha, beta = InverseGamma._get_alpha_beta(None, None, mu, sigma)
return sp.invgamma.logpdf(value, alpha, scale=beta)

self.pymc3_matches_scipy(
InverseGamma, Rplus, {"mu": Rplus, "sigma": Rplus}, test_fun
)
self.pymc3_matches_scipy(InverseGamma, Rplus, {"mu": Rplus, "sigma": Rplus}, test_fun)

def test_pareto(self):
self.pymc3_matches_scipy(
Expand Down Expand Up @@ -1001,9 +964,7 @@ def test_binomial(self):
)

# Too lazy to propagate decimal parameter through the whole chain of deps
@pytest.mark.xfail(
condition=(theano.config.floatX == "float32"), reason="Fails on float32"
)
@pytest.mark.xfail(condition=(theano.config.floatX == "float32"), reason="Fails on float32")
def test_beta_binomial(self):
self.checkd(BetaBinomial, Nat, {"alpha": Rplus, "beta": Rplus, "n": NatSmall})

Expand All @@ -1012,9 +973,7 @@ def test_bernoulli(self):
Bernoulli,
Bool,
{"logit_p": R},
lambda value, logit_p: sp.bernoulli.logpmf(
value, scipy.special.expit(logit_p)
),
lambda value, logit_p: sp.bernoulli.logpmf(value, scipy.special.expit(logit_p)),
)
self.pymc3_matches_scipy(
Bernoulli, Bool, {"p": Unit}, lambda value, p: sp.bernoulli.logpmf(value, p)
Expand Down Expand Up @@ -1047,21 +1006,15 @@ def test_bound_poisson(self):
assert np.isinf(x.logp({"x": 0}))

def test_constantdist(self):
self.pymc3_matches_scipy(
Constant, I, {"c": I}, lambda value, c: np.log(c == value)
)
self.pymc3_matches_scipy(Constant, I, {"c": I}, lambda value, c: np.log(c == value))

# Too lazy to propagate decimal parameter through the whole chain of deps
@pytest.mark.xfail(
condition=(theano.config.floatX == "float32"), reason="Fails on float32"
)
@pytest.mark.xfail(condition=(theano.config.floatX == "float32"), reason="Fails on float32")
def test_zeroinflatedpoisson(self):
self.checkd(ZeroInflatedPoisson, Nat, {"theta": Rplus, "psi": Unit})

# Too lazy to propagate decimal parameter through the whole chain of deps
@pytest.mark.xfail(
condition=(theano.config.floatX == "float32"), reason="Fails on float32"
)
@pytest.mark.xfail(condition=(theano.config.floatX == "float32"), reason="Fails on float32")
def test_zeroinflatednegativebinomial(self):
self.checkd(
ZeroInflatedNegativeBinomial,
Expand All @@ -1070,9 +1023,7 @@ def test_zeroinflatednegativebinomial(self):
)

# Too lazy to propagate decimal parameter through the whole chain of deps
@pytest.mark.xfail(
condition=(theano.config.floatX == "float32"), reason="Fails on float32"
)
@pytest.mark.xfail(condition=(theano.config.floatX == "float32"), reason="Fails on float32")
def test_zeroinflatedbinomial(self):
self.checkd(ZeroInflatedBinomial, Nat, {"n": NatSmall, "p": Unit, "psi": Unit})

Expand Down Expand Up @@ -1298,9 +1249,7 @@ def test_mvt(self, n):

@pytest.mark.parametrize("n", [2, 3, 4])
def test_AR1(self, n):
self.pymc3_matches_scipy(
AR1, Vector(R, n), {"k": Unit, "tau_e": Rplus}, AR1_logpdf
)
self.pymc3_matches_scipy(AR1, Vector(R, n), {"k": Unit, "tau_e": Rplus}, AR1_logpdf)

@pytest.mark.parametrize("n", [2, 3])
def test_wishart(self, n):
Expand All @@ -1325,9 +1274,7 @@ def test_lkj(self, x, eta, n, lp):

@pytest.mark.parametrize("n", [2, 3])
def test_dirichlet(self, n):
self.pymc3_matches_scipy(
Dirichlet, Simplex(n), {"a": Vector(Rplus, n)}, dirichlet_logpdf
)
self.pymc3_matches_scipy(Dirichlet, Simplex(n), {"a": Vector(Rplus, n)}, dirichlet_logpdf)

def test_dirichlet_shape(self):
a = tt.as_tensor_variable(np.r_[1, 2])
Expand Down Expand Up @@ -1529,9 +1476,7 @@ def logp(x):

def test_get_tau_sigma(self):
sigma = np.array([2])
assert_almost_equal(
continuous.get_tau_sigma(sigma=sigma), [1.0 / sigma ** 2, sigma]
)
assert_almost_equal(continuous.get_tau_sigma(sigma=sigma), [1.0 / sigma ** 2, sigma])

@pytest.mark.parametrize(
"value,mu,sigma,nu,logp",
Expand Down Expand Up @@ -1582,9 +1527,7 @@ def test_ex_gaussian_cdf(self, value, mu, sigma, nu, logcdf):
err_msg=str((value, mu, sigma, nu, logcdf)),
)

@pytest.mark.xfail(
condition=(theano.config.floatX == "float32"), reason="Fails on float32"
)
@pytest.mark.xfail(condition=(theano.config.floatX == "float32"), reason="Fails on float32")
def test_vonmises(self):
self.pymc3_matches_scipy(
VonMises,
Expand Down Expand Up @@ -1626,8 +1569,7 @@ def test_logitnormal(self):
Unit,
{"mu": R, "sigma": Rplus},
lambda value, mu, sigma: (
sp.norm.logpdf(logit(value), mu, sigma)
- (np.log(value) + np.log1p(-value))
sp.norm.logpdf(logit(value), mu, sigma) - (np.log(value) + np.log1p(-value))
),
decimal=select_by_precision(float64=6, float32=1),
)
Expand All @@ -1641,9 +1583,7 @@ def test_rice(self):
Rice,
Rplus,
{"nu": Rplus, "sigma": Rplusbig},
lambda value, nu, sigma: sp.rice.logpdf(
value, b=nu / sigma, loc=0, scale=sigma
),
lambda value, nu, sigma: sp.rice.logpdf(value, b=nu / sigma, loc=0, scale=sigma),
)
self.pymc3_matches_scipy(
Rice,
Expand All @@ -1652,9 +1592,7 @@ def test_rice(self):
lambda value, b, sigma: sp.rice.logpdf(value, b=b, loc=0, scale=sigma),
)

@pytest.mark.xfail(
condition=(theano.config.floatX == "float32"), reason="Fails on float32"
)
@pytest.mark.xfail(condition=(theano.config.floatX == "float32"), reason="Fails on float32")
def test_moyal(self):
self.pymc3_matches_scipy(
Moyal,
Expand All @@ -1669,9 +1607,7 @@ def test_moyal(self):
lambda value, mu, sigma: floatX(sp.moyal.logcdf(value, mu, sigma)),
)

@pytest.mark.xfail(
condition=(theano.config.floatX == "float32"), reason="Fails on float32"
)
@pytest.mark.xfail(condition=(theano.config.floatX == "float32"), reason="Fails on float32")
def test_interpolated(self):
for mu in R.vals:
for sigma in Rplus.vals:
Expand All @@ -1683,9 +1619,7 @@ class TestedInterpolated(Interpolated):
def __init__(self, **kwargs):
x_points = np.linspace(xmin, xmax, 100000)
pdf_points = sp.norm.pdf(x_points, loc=mu, scale=sigma)
super().__init__(
x_points=x_points, pdf_points=pdf_points, **kwargs
)
super().__init__(x_points=x_points, pdf_points=pdf_points, **kwargs)

def ref_pdf(value):
return np.where(
Expand Down Expand Up @@ -1896,9 +1830,10 @@ def func(x):
return -2 * (x ** 2).sum()

with pm.Model():
pm.Normal('x')
y = pm.DensityDist('y', func)
pm.Normal("x")
y = pm.DensityDist("y", func)
pm.sample(draws=5, tune=1, mp_ctx="spawn")

import pickle

pickle.loads(pickle.dumps(y))
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