Remove import * statements (#1257)

* Removed import * statements from most files

* Additional import fixes

* Additional import fixes

* Fix for relative import of sampling

* Most tests pass

* Changed relative to absolute import for sampling within ATMCMC

* Fixed circular import issue

* Fixed import syntax error

* Fixes for additional import errors

* MAINT Add failing imports.

* MAINT Add failing imports.

* More namespace fixes for tests

Author: Chris Fonnesbeck

pymc3/__init__.py

@@ -1,15 +1,15 @@
 __version__ = "3.0"
 
-from .core import *
 from .distributions import *
 from .math import *
-
+from .model import *
 from .stats import *
 from .sampling import *
 from .interactive_sampling import *
 from .step_methods import *
 from .tuning import *
 from .variational import *
+from . import sampling
 
 from .debug import *
 

pymc3/core.py

@@ -1,5 +1,5 @@
 import numpy as np
-from .blocking import *
+from .blocking import DictToVarBijection
 
 # TODO I could not locate this function used anywhere in the code base
 # do we need it?

pymc3/debug.py

@@ -1,8 +1,112 @@
 from . import timeseries
 from . import transforms
 
-from .continuous import *
-from .discrete import *
-from .distribution import *
-from .multivariate import *
-from .timeseries import *
+from .continuous import Uniform
+from .continuous import Flat
+from .continuous import Normal
+from .continuous import Beta
+from .continuous import Exponential
+from .continuous import Laplace
+from .continuous import StudentT
+from .continuous import Cauchy
+from .continuous import HalfCauchy
+from .continuous import Gamma
+from .continuous import Weibull
+from .continuous import Bound
+from .continuous import StudentTpos
+from .continuous import Lognormal
+from .continuous import ChiSquared
+from .continuous import HalfNormal
+from .continuous import Wald
+from .continuous import Pareto
+from .continuous import InverseGamma
+from .continuous import ExGaussian
+from .continuous import VonMises
+
+from .discrete import Binomial
+from .discrete import BetaBinomial
+from .discrete import Bernoulli
+from .discrete import Poisson
+from .discrete import NegativeBinomial
+from .discrete import ConstantDist
+from .discrete import ZeroInflatedPoisson
+from .discrete import DiscreteUniform
+from .discrete import Geometric
+from .discrete import Categorical
+
+from .distribution import DensityDist
+from .distribution import Distribution
+from .distribution import Continuous
+from .distribution import Discrete
+from .distribution import NoDistribution
+from .distribution import TensorType
+from .distribution import draw_values
+
+from .multivariate import MvNormal
+from .multivariate import MvStudentT
+from .multivariate import Dirichlet
+from .multivariate import Multinomial
+from .multivariate import Wishart
+from .multivariate import WishartBartlett
+from .multivariate import LKJCorr
+
+from .timeseries import AR1
+from .timeseries import GaussianRandomWalk
+from .timeseries import GARCH11
+
+from .transforms import transform
+from .transforms import stick_breaking
+from .transforms import logodds
+from .transforms import log
+from .transforms import sum_to_1
+
+__all__ = ['Uniform',
+    'Flat',
+    'Normal',
+    'Beta',
+    'Exponential',
+    'Laplace',
+    'StudentT',
+    'Cauchy',
+    'HalfCauchy',
+    'Gamma',
+    'Weibull',
+    'Bound',
+    'StudentTpos',
+    'Lognormal',
+    'ChiSquared',
+    'HalfNormal',
+    'Wald',
+    'Pareto',
+    'InverseGamma',
+    'ExGaussian',
+    'VonMises',
+    'Binomial',
+    'BetaBinomial',
+    'Bernoulli',
+    'Poisson',
+    'NegativeBinomial',
+    'ConstantDist',
+    'ZeroInflatedPoisson',
+    'DiscreteUniform',
+    'Geometric',
+    'Categorical',
+    'DensityDist',
+    'Distribution',
+    'Continuous',
+    'Discrete',
+    'NoDistribution',
+    'TensorType',
+    'MvNormal',
+    'MvStudentT',
+    'Dirichlet',
+    'Multinomial',
+    'Wishart',
+    'WishartBartlett',
+    'LKJCorr',
+    'AR1',
+    'GaussianRandomWalk',
+    'GARCH11'
+]
+           
+           

pymc3/distributions/__init__.py

@@ -6,7 +6,7 @@
 from ..model import Model, get_named_nodes
 
 
-__all__ = ['DensityDist', 'Distribution', 'Continuous', 'Discrete', 'NoDistribution', 'TensorType']
+__all__ = ['DensityDist', 'Distribution', 'Continuous', 'Discrete', 'NoDistribution', 'TensorType', 'draw_values']
 
 
 class Distribution(object):

pymc3/distributions/distribution.py

@@ -6,7 +6,7 @@
 from ..math import logit
 import numpy as np
 
-__all__ = ['transform', 'stick_breaking', 'logodds', 'log']
+__all__ = ['transform', 'stick_breaking', 'logodds', 'log', 'sum_to_1']
 
 
 class Transform(object):

pymc3/distributions/transforms.py

@@ -55,8 +55,8 @@ def run(n=3000):
 
         start = Point({
                  'groupmean': obs_means.mean(),
-                 'groupsd_interval': 0,
-                 'sd_interval': 0,
+                 'groupsd_interval_': 0,
+                 'sd_interval_': 0,
                  'means': np.array(obs_means),
                  'u_m': np.array([.72]),
                  'floor_m': 0.,

pymc3/examples/ARM12_6uranium.py

@@ -1,6 +1,6 @@
 import pymc3 as pm
 import numpy as np
-from pymc3.step_methods import ATMCMC as atmcmc
+from ..step_methods import ATMCMC, ATMIP_sample
 import theano.tensor as tt
 from matplotlib import pylab as plt
 
@@ -28,7 +28,7 @@
 def two_gaussians(x):
     log_like1 = - 0.5 * n * tt.log(2 * np.pi) \
                 - 0.5 * tt.log(dsigma) \
-                - 0.5 * (x - mu1).tt.dot(isigma).dot(x - mu1)
+                - 0.5 * (x - mu1).T.dot(isigma).dot(x - mu1)
     log_like2 = - 0.5 * n * tt.log(2 * np.pi) \
                 - 0.5 * tt.log(dsigma) \
                 - 0.5 * (x - mu2).T.dot(isigma).dot(x - mu2)
@@ -45,10 +45,10 @@ def two_gaussians(x):
     llk = pm.Potential('like', like)
 
 with ATMIP_test:
-    step = atmcmc.ATMCMC(n_chains=n_chains, tune_interval=tune_interval,
+    step = ATMCMC(n_chains=n_chains, tune_interval=tune_interval,
                          likelihood_name=ATMIP_test.deterministics[0].name)
 
-trcs = atmcmc.ATMIP_sample(
+trcs = ATMIP_sample(
                         n_steps=n_steps,
                         step=step,
                         njobs=njobs,

pymc3/examples/ATMIP_2gaussians.py

@@ -62,7 +62,7 @@ def interpolate(x0,y0, x, group):
 with Model() as model:
     coeff_sd = StudentT('coeff_sd', 10, 1, 5**-2)
 
-    y = GaussianRandomWalk('y', sd=coeff_sd, shape = (nknots, ncountries))
+    y = GaussianRandomWalk('y', sd=coeff_sd, shape=(nknots, ncountries))
 
     p = interpolate(knots, y, age, group)
 
@@ -78,14 +78,14 @@ def interpolate(x0,y0, x, group):
 # <codecell>
 
 with model:
-    s = find_MAP( vars=[sd, y])
+    s = find_MAP(vars=[sd, y])
 
     step = NUTS(scaling = s)
     trace = sample(100, step, s)
 
     s = trace[-1]
 
-    step = NUTS(scaling = s)
+    step = NUTS(scaling=s)
 
 def run(n=3000):
     if n == "short":
@@ -106,13 +106,6 @@ def run(n=3000):
 
         ylim(0,rate.max())
 
-    # <codecell>
-
-    traceplot(trace[100:], varnames = [coeff_sd,sd ]);
-
-    # <codecell>
-
-    autocorrplot(trace, varnames = [coeff_sd,sd ])
 
 if __name__ == '__main__':
     run()

pymc3/examples/GHME_2013.py

@@ -1,7 +1,7 @@
 import numpy as np
 import scipy.optimize as opt
 
-from pymc3 import *
+from pymc3 import Model, glm, find_MAP, sample
 
 # Generate data
 size = 50