Use absolute imports

.pre-commit-config.yaml

@@ -54,3 +54,8 @@ repos:
       language: python
       name: Check no tests are ignored
       pass_filenames: false
+    - id: no-relative-imports
+      name: No relative imports
+      entry: from \.[\.\w]* import
+      types: [python]
+      language: pygrep

pymc3/__init__.py

@@ -38,16 +38,16 @@ def __set_compiler_flags():
 
 __set_compiler_flags()
 
-from . import gp, ode, sampling
-from .backends import load_trace, save_trace
-from .backends.tracetab import *
-from .blocking import *
-from .data import *
-from .distributions import *
-from .distributions import transforms
-from .exceptions import *
-from .glm import *
-from .math import (
+from pymc3 import gp, ode, sampling
+from pymc3.backends import load_trace, save_trace
+from pymc3.backends.tracetab import *
+from pymc3.blocking import *
+from pymc3.data import *
+from pymc3.distributions import *
+from pymc3.distributions import transforms
+from pymc3.exceptions import *
+from pymc3.glm import *
+from pymc3.math import (
     expand_packed_triangular,
     invlogit,
     invprobit,
@@ -56,15 +56,15 @@ def __set_compiler_flags():
     logsumexp,
     probit,
 )
-from .model import *
-from .model_graph import model_to_graphviz
-from .plots import *
-from .sampling import *
-from .smc import *
-from .stats import *
-from .step_methods import *
-from .tests import test
-from .theanof import *
-from .tuning import *
-from .variational import *
-from .vartypes import *
+from pymc3.model import *
+from pymc3.model_graph import model_to_graphviz
+from pymc3.plots import *
+from pymc3.sampling import *
+from pymc3.smc import *
+from pymc3.stats import *
+from pymc3.step_methods import *
+from pymc3.tests import test
+from pymc3.theanof import *
+from pymc3.tuning import *
+from pymc3.variational import *
+from pymc3.vartypes import *

pymc3/backends/__init__.py

@@ -60,4 +60,9 @@
 Saved backends can be loaded using `arviz.from_netcdf`
 
 """
-from ..backends.ndarray import NDArray, load_trace, point_list_to_multitrace, save_trace
+from pymc3.backends.ndarray import (
+    NDArray,
+    load_trace,
+    point_list_to_multitrace,
+    save_trace,
+)

pymc3/backends/base.py

@@ -26,9 +26,9 @@
 import numpy as np
 import theano.tensor as tt
 
-from ..model import modelcontext
-from ..util import get_var_name
-from .report import SamplerReport, merge_reports
+from pymc3.backends.report import SamplerReport, merge_reports
+from pymc3.model import modelcontext
+from pymc3.util import get_var_name
 
 logger = logging.getLogger("pymc3")
 

pymc3/backends/report.py

@@ -20,7 +20,7 @@
 
 import arviz
 
-from ..util import get_untransformed_name, is_transformed_name
+from pymc3.util import get_untransformed_name, is_transformed_name
 
 logger = logging.getLogger("pymc3")
 

pymc3/backends/tracetab.py

@@ -20,7 +20,7 @@
 import numpy as np
 import pandas as pd
 
-from ..util import get_default_varnames
+from pymc3.util import get_default_varnames
 
 __all__ = ["trace_to_dataframe"]
 

pymc3/blocking.py

@@ -22,7 +22,7 @@
 
 import numpy as np
 
-from .util import get_var_name
+from pymc3.util import get_var_name
 
 __all__ = ["ArrayOrdering", "DictToArrayBijection", "DictToVarBijection"]
 

pymc3/distributions/__init__.py

@@ -12,10 +12,10 @@
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
 
-from . import shape_utils, timeseries, transforms
-from .bart import BART
-from .bound import Bound
-from .continuous import (
+from pymc3.distributions import shape_utils, timeseries, transforms
+from pymc3.distributions.bart import BART
+from pymc3.distributions.bound import Bound
+from pymc3.distributions.continuous import (
     Beta,
     Cauchy,
     ChiSquared,
@@ -48,7 +48,7 @@
     Wald,
     Weibull,
 )
-from .discrete import (
+from pymc3.distributions.discrete import (
     Bernoulli,
     BetaBinomial,
     Binomial,
@@ -66,7 +66,7 @@
     ZeroInflatedNegativeBinomial,
     ZeroInflatedPoisson,
 )
-from .distribution import (
+from pymc3.distributions.distribution import (
     Continuous,
     DensityDist,
     Discrete,
@@ -76,8 +76,8 @@
     draw_values,
     generate_samples,
 )
-from .mixture import Mixture, MixtureSameFamily, NormalMixture
-from .multivariate import (
+from pymc3.distributions.mixture import Mixture, MixtureSameFamily, NormalMixture
+from pymc3.distributions.multivariate import (
     Dirichlet,
     KroneckerNormal,
     LKJCholeskyCov,
@@ -89,9 +89,9 @@
     Wishart,
     WishartBartlett,
 )
-from .posterior_predictive import fast_sample_posterior_predictive
-from .simulator import Simulator
-from .timeseries import (
+from pymc3.distributions.posterior_predictive import fast_sample_posterior_predictive
+from pymc3.distributions.simulator import Simulator
+from pymc3.distributions.timeseries import (
     AR,
     AR1,
     GARCH11,

pymc3/distributions/bart.py

@@ -14,8 +14,8 @@
 
 import numpy as np
 
-from .distribution import NoDistribution
-from .tree import LeafNode, SplitNode, Tree
+from pymc3.distributions.distribution import NoDistribution
+from pymc3.distributions.tree import LeafNode, SplitNode, Tree
 
 __all__ = ["BART"]
 

pymc3/distributions/continuous.py

@@ -27,11 +27,8 @@
 from scipy.interpolate import InterpolatedUnivariateSpline
 from scipy.special import expit
 
-from pymc3.theanof import floatX
-
-from ..math import invlogit, logdiffexp, logit
-from . import transforms
-from .dist_math import (
+from pymc3.distributions import transforms
+from pymc3.distributions.dist_math import (
     SplineWrapper,
     alltrue_elemwise,
     betaln,
@@ -46,8 +43,10 @@
     std_cdf,
     zvalue,
 )
-from .distribution import Continuous, draw_values, generate_samples
-from .special import log_i0
+from pymc3.distributions.distribution import Continuous, draw_values, generate_samples
+from pymc3.distributions.special import log_i0
+from pymc3.math import invlogit, logdiffexp, logit
+from pymc3.theanof import floatX
 
 __all__ = [
     "Uniform",

pymc3/distributions/discrete.py

@@ -19,12 +19,18 @@
 
 from scipy import stats
 
+from pymc3.distributions.dist_math import (
+    betaln,
+    binomln,
+    bound,
+    factln,
+    logpow,
+    random_choice,
+)
+from pymc3.distributions.distribution import Discrete, draw_values, generate_samples
+from pymc3.distributions.shape_utils import broadcast_distribution_samples
 from pymc3.math import log1pexp, logaddexp, logit, sigmoid, tround
-
-from ..theanof import floatX, intX, take_along_axis
-from .dist_math import betaln, binomln, bound, factln, logpow, random_choice
-from .distribution import Discrete, draw_values, generate_samples
-from .shape_utils import broadcast_distribution_samples
+from pymc3.theanof import floatX, intX, take_along_axis
 
 __all__ = [
     "Binomial",

pymc3/distributions/dist_math.py

@@ -31,11 +31,10 @@
 from theano.scan import until
 from theano.tensor.slinalg import Cholesky
 
+from pymc3.distributions.shape_utils import to_tuple
+from pymc3.distributions.special import gammaln
 from pymc3.theanof import floatX
 
-from .shape_utils import to_tuple
-from .special import gammaln
-
 f = floatX
 c = -0.5 * np.log(2.0 * np.pi)
 _beta_clip_values = {

pymc3/distributions/distribution.py

@@ -33,22 +33,22 @@
 
 from theano import function
 
-from ..memoize import memoize
-from ..model import (
+from pymc3.distributions.shape_utils import (
+    broadcast_dist_samples_shape,
+    get_broadcastable_dist_samples,
+    to_tuple,
+)
+from pymc3.memoize import memoize
+from pymc3.model import (
     ContextMeta,
     FreeRV,
     Model,
     MultiObservedRV,
     ObservedRV,
     build_named_node_tree,
 )
-from ..util import get_repr_for_variable, get_var_name
-from ..vartypes import string_types, theano_constant
-from .shape_utils import (
-    broadcast_dist_samples_shape,
-    get_broadcastable_dist_samples,
-    to_tuple,
-)
+from pymc3.util import get_repr_for_variable, get_var_name
+from pymc3.vartypes import string_types, theano_constant
 
 __all__ = [
     "DensityDist",

pymc3/distributions/mixture.py

@@ -20,23 +20,23 @@
 import theano
 import theano.tensor as tt
 
-from ..math import logsumexp
-from ..theanof import _conversion_map, take_along_axis
-from .continuous import Normal, get_tau_sigma
-from .dist_math import bound, random_choice
-from .distribution import (
+from pymc3.distributions.continuous import Normal, get_tau_sigma
+from pymc3.distributions.dist_math import bound, random_choice
+from pymc3.distributions.distribution import (
     Discrete,
     Distribution,
     _DrawValuesContext,
     _DrawValuesContextBlocker,
     draw_values,
     generate_samples,
 )
-from .shape_utils import (
+from pymc3.distributions.shape_utils import (
     broadcast_distribution_samples,
     get_broadcastable_dist_samples,
     to_tuple,
 )
+from pymc3.math import logsumexp
+from pymc3.theanof import _conversion_map, take_along_axis
 
 __all__ = ["Mixture", "NormalMixture", "MixtureSameFamily"]
 

pymc3/distributions/multivariate.py

@@ -30,22 +30,21 @@
 
 import pymc3 as pm
 
-from pymc3.theanof import floatX
-
-from ..math import kron_diag, kron_dot, kron_solve_lower, kronecker
-from ..model import Deterministic
-from . import transforms
-from .continuous import ChiSquared, Normal
-from .dist_math import bound, factln, logpow
-from .distribution import (
+from pymc3.distributions import transforms
+from pymc3.distributions.continuous import ChiSquared, Normal
+from pymc3.distributions.dist_math import bound, factln, logpow
+from pymc3.distributions.distribution import (
     Continuous,
     Discrete,
     _DrawValuesContext,
     draw_values,
     generate_samples,
 )
-from .shape_utils import broadcast_dist_samples_to, to_tuple
-from .special import gammaln, multigammaln
+from pymc3.distributions.shape_utils import broadcast_dist_samples_to, to_tuple
+from pymc3.distributions.special import gammaln, multigammaln
+from pymc3.math import kron_diag, kron_dot, kron_solve_lower, kronecker
+from pymc3.model import Deterministic
+from pymc3.theanof import floatX
 
 __all__ = [
     "MvNormal",

pymc3/distributions/posterior_predictive.py

@@ -27,24 +27,24 @@
 from typing_extensions import Literal, Protocol
 from xarray import Dataset
 
-from ..backends.base import MultiTrace
-from ..exceptions import IncorrectArgumentsError
-from ..model import (
-    Model,
-    MultiObservedRV,
-    ObservedRV,
-    get_named_nodes_and_relations,
-    modelcontext,
-)
-from ..util import chains_and_samples, dataset_to_point_list, get_var_name
-from ..vartypes import theano_constant
-from .distribution import (
+from pymc3.backends.base import MultiTrace
+from pymc3.distributions.distribution import (
     _compile_theano_function,
     _DrawValuesContext,
     _DrawValuesContextBlocker,
     is_fast_drawable,
     vectorized_ppc,
 )
+from pymc3.exceptions import IncorrectArgumentsError
+from pymc3.model import (
+    Model,
+    MultiObservedRV,
+    ObservedRV,
+    get_named_nodes_and_relations,
+    modelcontext,
+)
+from pymc3.util import chains_and_samples, dataset_to_point_list, get_var_name
+from pymc3.vartypes import theano_constant
 
 # Failing tests:
 #    test_mixture_random_shape::test_mixture_random_shape

pymc3/distributions/simulator.py

@@ -18,7 +18,7 @@
 
 from scipy.spatial import cKDTree
 
-from .distribution import NoDistribution, draw_values
+from pymc3.distributions.distribution import NoDistribution, draw_values
 
 __all__ = ["Simulator"]