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Do not monkey-patch Ipython pretty representation on model variables #7712

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Do not monkey-patch Ipython pretty representation on model variables #7712

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9 changes: 0 additions & 9 deletions pymc/distributions/distribution.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,10 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import contextvars
import functools
import re
import sys
import types
import warnings

from abc import ABCMeta
Expand Down Expand Up @@ -53,7 +51,6 @@
from pymc.logprob.abstract import MeasurableOp, _icdf, _logcdf, _logprob
from pymc.logprob.basic import logp
from pymc.logprob.rewriting import logprob_rewrites_db
from pymc.printing import str_for_dist
from pymc.pytensorf import (
collect_default_updates_inner_fgraph,
constant_fold,
Expand Down Expand Up @@ -506,12 +503,6 @@ def __new__(
default_transform=default_transform,
initval=initval,
)

# add in pretty-printing support
rv_out.str_repr = types.MethodType(str_for_dist, rv_out)
rv_out._repr_latex_ = types.MethodType(
functools.partial(str_for_dist, formatting="latex"), rv_out
)
return rv_out

@classmethod
Expand Down
46 changes: 13 additions & 33 deletions pymc/model/core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,6 @@
import functools
import sys
import threading
import types
import warnings

from collections.abc import Iterable, Sequence
Expand Down Expand Up @@ -496,13 +495,6 @@ def __init__(
for name, values in coords_mutable.items():
self.add_coord(name, values, mutable=True)

from pymc.printing import str_for_model

self.str_repr = types.MethodType(str_for_model, self)
self._repr_latex_ = types.MethodType(
functools.partial(str_for_model, formatting="latex"), self
)

@classmethod
def get_context(
cls, error_if_none: bool = True, allow_block_model_access: bool = False
Expand Down Expand Up @@ -2026,6 +2018,19 @@ def to_graphviz(
dpi=dpi,
)

def _repr_pretty_(self, p, cycle):
from pymc.printing import str_for_model

output = str_for_model(self)
# Find newlines and replace them with p.break_()
# (see IPython.lib.pretty._repr_pprint)
lines = output.splitlines()
with p.group():
for idx, output_line in enumerate(lines):
if idx:
p.break_()
p.text(output_line)


class BlockModelAccess(Model):
"""Can be used to prevent user access to Model contexts."""
Expand Down Expand Up @@ -2252,19 +2257,6 @@ def Deterministic(name, var, model=None, dims=None):
var = var.copy(model.name_for(name))
model.deterministics.append(var)
model.add_named_variable(var, dims)

from pymc.printing import str_for_potential_or_deterministic

var.str_repr = types.MethodType(
functools.partial(str_for_potential_or_deterministic, dist_name="Deterministic"), var
)
var._repr_latex_ = types.MethodType(
functools.partial(
str_for_potential_or_deterministic, dist_name="Deterministic", formatting="latex"
),
var,
)

return var


Expand Down Expand Up @@ -2377,16 +2369,4 @@ def normal_logp(value, mu, sigma):
model.potentials.append(var)
model.add_named_variable(var, dims)

from pymc.printing import str_for_potential_or_deterministic

var.str_repr = types.MethodType(
functools.partial(str_for_potential_or_deterministic, dist_name="Potential"), var
)
var._repr_latex_ = types.MethodType(
functools.partial(
str_for_potential_or_deterministic, dist_name="Potential", formatting="latex"
),
var,
)

return var
60 changes: 14 additions & 46 deletions pymc/printing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,7 +35,10 @@


def str_for_dist(
dist: TensorVariable, formatting: str = "plain", include_params: bool = True
dist: TensorVariable,
formatting: str = "plain",
include_params: bool = True,
model: Model | None = None,
) -> str:
"""Make a human-readable string representation of a Distribution in a model.

Expand All @@ -47,12 +50,12 @@ def str_for_dist(
dist.owner.op, "extended_signature", None
):
dist_args = [
_str_for_input_var(x, formatting=formatting)
_str_for_input_var(x, formatting=formatting, model=model)
for x in dist.owner.op.dist_params(dist.owner)
]
else:
dist_args = [
_str_for_input_var(x, formatting=formatting)
_str_for_input_var(x, formatting=formatting, model=model)
for x in dist.owner.inputs
if not isinstance(x.type, RandomType | NoneTypeT)
]
Expand Down Expand Up @@ -106,7 +109,7 @@ def str_for_model(model: Model, formatting: str = "plain", include_params: bool
including parameter values.
"""
# Wrap functions to avoid confusing typecheckers
sfd = partial(str_for_dist, formatting=formatting, include_params=include_params)
sfd = partial(str_for_dist, formatting=formatting, include_params=include_params, model=model)
sfp = partial(
str_for_potential_or_deterministic, formatting=formatting, include_params=include_params
)
Expand Down Expand Up @@ -169,18 +172,14 @@ def str_for_potential_or_deterministic(
return rf"{print_name} ~ {dist_name}"


def _str_for_input_var(var: Variable, formatting: str) -> str:
def _str_for_input_var(var: Variable, formatting: str, model: Model | None = None) -> str:
# Avoid circular import
from pymc.distributions.distribution import SymbolicRandomVariable

def _is_potential_or_deterministic(var: Variable) -> bool:
if not hasattr(var, "str_repr"):
return False
try:
return var.str_repr.__func__.func is str_for_potential_or_deterministic
except AttributeError:
# in case other code overrides str_repr, fallback
if model is None:
return False
return var in model.deterministics or var in model.potentials

if isinstance(var, Constant | SharedVariable):
return _str_for_constant(var, formatting)
Expand All @@ -190,18 +189,18 @@ def _is_potential_or_deterministic(var: Variable) -> bool:
# show the names for RandomVariables, Deterministics, and Potentials, rather
# than the full expression
assert isinstance(var, TensorVariable)
return _str_for_input_rv(var, formatting)
return _str_for_input_rv(var, formatting, model=model)
elif isinstance(var.owner.op, DimShuffle):
return _str_for_input_var(var.owner.inputs[0], formatting)
return _str_for_input_var(var.owner.inputs[0], formatting, model=model)
else:
return _str_for_expression(var, formatting)


def _str_for_input_rv(var: TensorVariable, formatting: str) -> str:
def _str_for_input_rv(var: TensorVariable, formatting: str, model: Model | None = None) -> str:
_str = (
var.name
if var.name is not None
else str_for_dist(var, formatting=formatting, include_params=True)
else str_for_dist(var, formatting=formatting, include_params=True, model=model)
)
if "latex" in formatting:
return _latex_text_format(_latex_escape(_str.strip("$")))
Expand Down Expand Up @@ -277,37 +276,6 @@ def _latex_escape(text: str) -> str:
return text.replace("$", r"\$")


def _default_repr_pretty(obj: TensorVariable | Model, p, cycle):
"""Handy plug-in method to instruct IPython-like REPLs to use our str_repr above."""
# we know that our str_repr does not recurse, so we can ignore cycle
try:
if not hasattr(obj, "str_repr"):
raise AttributeError
output = obj.str_repr()
# Find newlines and replace them with p.break_()
# (see IPython.lib.pretty._repr_pprint)
lines = output.splitlines()
with p.group():
for idx, output_line in enumerate(lines):
if idx:
p.break_()
p.text(output_line)
except AttributeError:
# the default fallback option (no str_repr method)
IPython.lib.pretty._repr_pprint(obj, p, cycle)


try:
# register our custom pretty printer in ipython shells
import IPython

IPython.lib.pretty.for_type(TensorVariable, _default_repr_pretty)
IPython.lib.pretty.for_type(Model, _default_repr_pretty)
except (ModuleNotFoundError, AttributeError):
# no ipython shell
pass


def _format_underscore(variable: str) -> str:
"""Escapes all unescaped underscores in the variable name for LaTeX representation."""
return re.sub(r"(?<!\\)_", r"\\_", variable)
27 changes: 15 additions & 12 deletions tests/test_printing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,15 +30,17 @@
)
from pymc.math import dot
from pymc.model import Deterministic, Model, Potential
from pymc.printing import str_for_dist, str_for_model
from pymc.pytensorf import floatX


class BaseTestStrAndLatexRepr:
def test__repr_latex_(self):
for distribution, tex in zip(self.distributions, self.expected[("latex", True)]):
assert distribution._repr_latex_() == tex
for model_variable, tex in zip(self.distributions, self.expected[("latex", True)]):
if model_variable in self.model.basic_RVs:
assert str_for_dist(model_variable, formatting="latex", model=self.model) == tex

model_tex = self.model._repr_latex_()
model_tex = str_for_model(self.model, formatting="latex")

# make sure each variable is in the model
for tex in self.expected[("latex", True)]:
Expand All @@ -47,10 +49,11 @@ def test__repr_latex_(self):

def test_str_repr(self):
for str_format in self.formats:
for dist, text in zip(self.distributions, self.expected[str_format]):
assert dist.str_repr(*str_format) == text
for model_variable, text in zip(self.distributions, self.expected[str_format]):
if model_variable in self.model.basic_RVs:
assert str_for_dist(model_variable, *str_format, model=self.model) == text

model_text = self.model.str_repr(*str_format)
model_text = str_for_model(self.model, *str_format)
for text in self.expected[str_format]:
if str_format[0] == "latex":
for segment in text.strip("$").split(r"\sim"):
Expand Down Expand Up @@ -252,7 +255,7 @@ def test_model_latex_repr_three_levels_model():
"censored_normal", normal_dist, lower=-2.0, upper=2.0, observed=[1, 0, 0.5]
)

latex_repr = censored_model.str_repr(formatting="latex")
latex_repr = str_for_model(censored_model, formatting="latex")
expected = [
"$$",
"\\begin{array}{rcl}",
Expand All @@ -270,7 +273,7 @@ def test_model_latex_repr_mixture_model():
w = Dirichlet("w", [1, 1])
mix = Mixture("mix", w=w, comp_dists=[Normal.dist(0.0, 5.0), StudentT.dist(7.0)])

latex_repr = mix_model.str_repr(formatting="latex")
latex_repr = str_for_model(mix_model, formatting="latex")
expected = [
"$$",
"\\begin{array}{rcl}",
Expand All @@ -291,15 +294,15 @@ def test_model_repr_variables_without_monkey_patched_repr():
model = Model()
model.register_rv(x, "x")

str_repr = model.str_repr()
str_repr = str_for_model(model)
assert str_repr == "x ~ Normal(0, 1)"


def test_truncated_repr():
with Model() as model:
x = Truncated("x", Gamma.dist(1, 1), lower=0, upper=20)

str_repr = model.str_repr(include_params=False)
str_repr = str_for_model(model, include_params=False)
assert str_repr == "x ~ TruncatedGamma"


Expand All @@ -315,7 +318,7 @@ def random(rng, mu, size):
x = CustomDist("x", 0, dist=dist, class_name="CustomDistNormal")
x = CustomDist("y", 0, random=random, class_name="CustomRandomNormal")

str_repr = model.str_repr(include_params=False)
str_repr = str_for_model(model, include_params=False)
assert str_repr == "\n".join(["x ~ CustomDistNormal", "y ~ CustomRandomNormal"])


Expand All @@ -333,6 +336,6 @@ def test_latex_escaped_underscore(self):
Ensures that all underscores in model variable names are properly escaped for LaTeX representation
"""
model = self.simple_model()
model_str = model.str_repr(formatting="latex")
model_str = str_for_model(model, formatting="latex")
assert "\\_" in model_str
assert "_" not in model_str.replace("\\_", "")
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