/usr/lib/python2.7/dist-packages/patsy/desc.py is in python-patsy 0.4.1+git34-ga5b54c2-1.
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# Copyright (C) 2011-2012 Nathaniel Smith <njs@pobox.com>
# See file LICENSE.txt for license information.
# This file defines the ModelDesc class, which describes a model at a high
# level, as a list of interactions of factors. It also has the code to convert
# a formula parse tree (from patsy.parse_formula) into a ModelDesc.
from __future__ import print_function
import six
from patsy import PatsyError
from patsy.parse_formula import ParseNode, Token, parse_formula
from patsy.eval import EvalEnvironment, EvalFactor
from patsy.util import uniqueify_list
from patsy.util import repr_pretty_delegate, repr_pretty_impl
from patsy.util import no_pickling, assert_no_pickling
# These are made available in the patsy.* namespace
__all__ = ["Term", "ModelDesc", "INTERCEPT"]
# One might think it would make more sense for 'factors' to be a set, rather
# than a tuple-with-guaranteed-unique-entries-that-compares-like-a-set. The
# reason we do it this way is that it preserves the order that the user typed
# and is expecting, which then ends up producing nicer names in our final
# output, nicer column ordering, etc. (A similar comment applies to the
# ordering of terms in ModelDesc objects as a whole.)
class Term(object):
"""The interaction between a collection of factor objects.
This is one of the basic types used in representing formulas, and
corresponds to an expression like ``"a:b:c"`` in a formula string.
For details, see :ref:`formulas` and :ref:`expert-model-specification`.
Terms are hashable and compare by value.
Attributes:
.. attribute:: factors
A tuple of factor objects.
"""
def __init__(self, factors):
self.factors = tuple(uniqueify_list(factors))
def __eq__(self, other):
return (isinstance(other, Term)
and frozenset(other.factors) == frozenset(self.factors))
def __ne__(self, other):
return not self == other
def __hash__(self):
return hash((Term, frozenset(self.factors)))
__repr__ = repr_pretty_delegate
def _repr_pretty_(self, p, cycle):
assert not cycle
repr_pretty_impl(p, self, [list(self.factors)])
def name(self):
"""Return a human-readable name for this term."""
if self.factors:
return ":".join([f.name() for f in self.factors])
else:
return "Intercept"
__getstate__ = no_pickling
INTERCEPT = Term([])
class _MockFactor(object):
def __init__(self, name):
self._name = name
def name(self):
return self._name
def test_Term():
assert Term([1, 2, 1]).factors == (1, 2)
assert Term([1, 2]) == Term([2, 1])
assert hash(Term([1, 2])) == hash(Term([2, 1]))
f1 = _MockFactor("a")
f2 = _MockFactor("b")
assert Term([f1, f2]).name() == "a:b"
assert Term([f2, f1]).name() == "b:a"
assert Term([]).name() == "Intercept"
assert_no_pickling(Term([]))
class ModelDesc(object):
"""A simple container representing the termlists parsed from a formula.
This is a simple container object which has exactly the same
representational power as a formula string, but is a Python object
instead. You can construct one by hand, and pass it to functions like
:func:`dmatrix` or :func:`incr_dbuilder` that are expecting a formula
string, but without having to do any messy string manipulation. For
details see :ref:`expert-model-specification`.
Attributes:
.. attribute:: lhs_termlist
rhs_termlist
Two termlists representing the left- and right-hand sides of a
formula, suitable for passing to :func:`design_matrix_builders`.
"""
def __init__(self, lhs_termlist, rhs_termlist):
self.lhs_termlist = uniqueify_list(lhs_termlist)
self.rhs_termlist = uniqueify_list(rhs_termlist)
__repr__ = repr_pretty_delegate
def _repr_pretty_(self, p, cycle):
assert not cycle
return repr_pretty_impl(p, self,
[],
[("lhs_termlist", self.lhs_termlist),
("rhs_termlist", self.rhs_termlist)])
def describe(self):
"""Returns a human-readable representation of this :class:`ModelDesc`
in pseudo-formula notation.
.. warning:: There is no guarantee that the strings returned by this
function can be parsed as formulas. They are best-effort
descriptions intended for human users. However, if this ModelDesc
was created by parsing a formula, then it should work in
practice. If you *really* have to.
"""
def term_code(term):
if term == INTERCEPT:
return "1"
else:
return term.name()
result = " + ".join([term_code(term) for term in self.lhs_termlist])
if result:
result += " ~ "
else:
result += "~ "
if self.rhs_termlist == [INTERCEPT]:
result += term_code(INTERCEPT)
else:
term_names = []
if INTERCEPT not in self.rhs_termlist:
term_names.append("0")
term_names += [term_code(term) for term in self.rhs_termlist
if term != INTERCEPT]
result += " + ".join(term_names)
return result
@classmethod
def from_formula(cls, tree_or_string):
"""Construct a :class:`ModelDesc` from a formula string.
:arg tree_or_string: A formula string. (Or an unevaluated formula
parse tree, but the API for generating those isn't public yet. Shh,
it can be our secret.)
:returns: A new :class:`ModelDesc`.
"""
if isinstance(tree_or_string, ParseNode):
tree = tree_or_string
else:
tree = parse_formula(tree_or_string)
value = Evaluator().eval(tree, require_evalexpr=False)
assert isinstance(value, cls)
return value
__getstate__ = no_pickling
def test_ModelDesc():
f1 = _MockFactor("a")
f2 = _MockFactor("b")
m = ModelDesc([INTERCEPT, Term([f1])], [Term([f1]), Term([f1, f2])])
assert m.lhs_termlist == [INTERCEPT, Term([f1])]
assert m.rhs_termlist == [Term([f1]), Term([f1, f2])]
print(m.describe())
assert m.describe() == "1 + a ~ 0 + a + a:b"
assert_no_pickling(m)
assert ModelDesc([], []).describe() == "~ 0"
assert ModelDesc([INTERCEPT], []).describe() == "1 ~ 0"
assert ModelDesc([INTERCEPT], [INTERCEPT]).describe() == "1 ~ 1"
assert (ModelDesc([INTERCEPT], [INTERCEPT, Term([f2])]).describe()
== "1 ~ b")
def test_ModelDesc_from_formula():
for input in ("y ~ x", parse_formula("y ~ x")):
md = ModelDesc.from_formula(input)
assert md.lhs_termlist == [Term([EvalFactor("y")]),]
assert md.rhs_termlist == [INTERCEPT, Term([EvalFactor("x")])]
class IntermediateExpr(object):
"This class holds an intermediate result while we're evaluating a tree."
def __init__(self, intercept, intercept_origin, intercept_removed, terms):
self.intercept = intercept
self.intercept_origin = intercept_origin
self.intercept_removed =intercept_removed
self.terms = tuple(uniqueify_list(terms))
if self.intercept:
assert self.intercept_origin
assert not (self.intercept and self.intercept_removed)
__repr__ = repr_pretty_delegate
def _pretty_repr_(self, p, cycle): # pragma: no cover
assert not cycle
return repr_pretty_impl(p, self,
[self.intercept, self.intercept_origin,
self.intercept_removed, self.terms])
__getstate__ = no_pickling
def _maybe_add_intercept(doit, terms):
if doit:
return (INTERCEPT,) + terms
else:
return terms
def _eval_any_tilde(evaluator, tree):
exprs = [evaluator.eval(arg) for arg in tree.args]
if len(exprs) == 1:
# Formula was like: "~ foo"
# We pretend that instead it was like: "0 ~ foo"
exprs.insert(0, IntermediateExpr(False, None, True, []))
assert len(exprs) == 2
# Note that only the RHS gets an implicit intercept:
return ModelDesc(_maybe_add_intercept(exprs[0].intercept, exprs[0].terms),
_maybe_add_intercept(not exprs[1].intercept_removed,
exprs[1].terms))
def _eval_binary_plus(evaluator, tree):
left_expr = evaluator.eval(tree.args[0])
if tree.args[1].type == "ZERO":
return IntermediateExpr(False, None, True, left_expr.terms)
else:
right_expr = evaluator.eval(tree.args[1])
if right_expr.intercept:
return IntermediateExpr(True, right_expr.intercept_origin, False,
left_expr.terms + right_expr.terms)
else:
return IntermediateExpr(left_expr.intercept,
left_expr.intercept_origin,
left_expr.intercept_removed,
left_expr.terms + right_expr.terms)
def _eval_binary_minus(evaluator, tree):
left_expr = evaluator.eval(tree.args[0])
if tree.args[1].type == "ZERO":
return IntermediateExpr(True, tree.args[1], False,
left_expr.terms)
elif tree.args[1].type == "ONE":
return IntermediateExpr(False, None, True, left_expr.terms)
else:
right_expr = evaluator.eval(tree.args[1])
terms = [term for term in left_expr.terms
if term not in right_expr.terms]
if right_expr.intercept:
return IntermediateExpr(False, None, True, terms)
else:
return IntermediateExpr(left_expr.intercept,
left_expr.intercept_origin,
left_expr.intercept_removed,
terms)
def _check_interactable(expr):
if expr.intercept:
raise PatsyError("intercept term cannot interact with "
"anything else", expr.intercept_origin)
def _interaction(left_expr, right_expr):
for expr in (left_expr, right_expr):
_check_interactable(expr)
terms = []
for l_term in left_expr.terms:
for r_term in right_expr.terms:
terms.append(Term(l_term.factors + r_term.factors))
return IntermediateExpr(False, None, False, terms)
def _eval_binary_prod(evaluator, tree):
exprs = [evaluator.eval(arg) for arg in tree.args]
return IntermediateExpr(False, None, False,
exprs[0].terms
+ exprs[1].terms
+ _interaction(*exprs).terms)
# Division (nesting) is right-ward distributive:
# a / (b + c) -> a/b + a/c -> a + a:b + a:c
# But left-ward, in S/R it has a quirky behavior:
# (a + b)/c -> a + b + a:b:c
# This is because it's meaningless for a factor to be "nested" under two
# different factors. (This is documented in Chambers and Hastie (page 30) as a
# "Slightly more subtle..." rule, with no further elaboration. Hopefully we
# will do better.)
def _eval_binary_div(evaluator, tree):
left_expr = evaluator.eval(tree.args[0])
right_expr = evaluator.eval(tree.args[1])
terms = list(left_expr.terms)
_check_interactable(left_expr)
# Build a single giant combined term for everything on the left:
left_factors = []
for term in left_expr.terms:
left_factors += list(term.factors)
left_combined_expr = IntermediateExpr(False, None, False,
[Term(left_factors)])
# Then interact it with everything on the right:
terms += list(_interaction(left_combined_expr, right_expr).terms)
return IntermediateExpr(False, None, False, terms)
def _eval_binary_interact(evaluator, tree):
exprs = [evaluator.eval(arg) for arg in tree.args]
return _interaction(*exprs)
def _eval_binary_power(evaluator, tree):
left_expr = evaluator.eval(tree.args[0])
_check_interactable(left_expr)
power = -1
if tree.args[1].type in ("ONE", "NUMBER"):
expr = tree.args[1].token.extra
try:
power = int(expr)
except ValueError:
pass
if power < 1:
raise PatsyError("'**' requires a positive integer", tree.args[1])
all_terms = left_expr.terms
big_expr = left_expr
# Small optimization: (a + b)**100 is just the same as (a + b)**2.
power = min(len(left_expr.terms), power)
for i in range(1, power):
big_expr = _interaction(left_expr, big_expr)
all_terms = all_terms + big_expr.terms
return IntermediateExpr(False, None, False, all_terms)
def _eval_unary_plus(evaluator, tree):
return evaluator.eval(tree.args[0])
def _eval_unary_minus(evaluator, tree):
if tree.args[0].type == "ZERO":
return IntermediateExpr(True, tree.origin, False, [])
elif tree.args[0].type == "ONE":
return IntermediateExpr(False, None, True, [])
else:
raise PatsyError("Unary minus can only be applied to 1 or 0", tree)
def _eval_zero(evaluator, tree):
return IntermediateExpr(False, None, True, [])
def _eval_one(evaluator, tree):
return IntermediateExpr(True, tree.origin, False, [])
def _eval_number(evaluator, tree):
raise PatsyError("numbers besides '0' and '1' are "
"only allowed with **", tree)
def _eval_python_expr(evaluator, tree):
factor = EvalFactor(tree.token.extra, origin=tree.origin)
return IntermediateExpr(False, None, False, [Term([factor])])
class Evaluator(object):
def __init__(self):
self._evaluators = {}
self.add_op("~", 2, _eval_any_tilde)
self.add_op("~", 1, _eval_any_tilde)
self.add_op("+", 2, _eval_binary_plus)
self.add_op("-", 2, _eval_binary_minus)
self.add_op("*", 2, _eval_binary_prod)
self.add_op("/", 2, _eval_binary_div)
self.add_op(":", 2, _eval_binary_interact)
self.add_op("**", 2, _eval_binary_power)
self.add_op("+", 1, _eval_unary_plus)
self.add_op("-", 1, _eval_unary_minus)
self.add_op("ZERO", 0, _eval_zero)
self.add_op("ONE", 0, _eval_one)
self.add_op("NUMBER", 0, _eval_number)
self.add_op("PYTHON_EXPR", 0, _eval_python_expr)
# Not used by Patsy -- provided for the convenience of eventual
# user-defined operators.
self.stash = {}
# This should not be considered a public API yet (to use for actually
# adding new operator semantics) because I wrote in some of the relevant
# code sort of speculatively, but it isn't actually tested.
def add_op(self, op, arity, evaluator):
self._evaluators[op, arity] = evaluator
def eval(self, tree, require_evalexpr=True):
result = None
assert isinstance(tree, ParseNode)
key = (tree.type, len(tree.args))
if key not in self._evaluators:
raise PatsyError("I don't know how to evaluate this "
"'%s' operator" % (tree.type,),
tree.token)
result = self._evaluators[key](self, tree)
if require_evalexpr and not isinstance(result, IntermediateExpr):
if isinstance(result, ModelDesc):
raise PatsyError("~ can only be used once, and "
"only at the top level",
tree)
else:
raise PatsyError("custom operator returned an "
"object that I don't know how to "
"handle", tree)
return result
#############
_eval_tests = {
"": (True, []),
" ": (True, []),
" \n ": (True, []),
"a": (True, ["a"]),
"1": (True, []),
"0": (False, []),
"- 1": (False, []),
"- 0": (True, []),
"+ 1": (True, []),
"+ 0": (False, []),
"0 + 1": (True, []),
"1 + 0": (False, []),
"1 - 0": (True, []),
"0 - 1": (False, []),
"1 + a": (True, ["a"]),
"0 + a": (False, ["a"]),
"a - 1": (False, ["a"]),
"a - 0": (True, ["a"]),
"1 - a": (True, []),
"a + b": (True, ["a", "b"]),
"(a + b)": (True, ["a", "b"]),
"a + ((((b))))": (True, ["a", "b"]),
"a + ((((+b))))": (True, ["a", "b"]),
"a + ((((b - a))))": (True, ["a", "b"]),
"a + a + a": (True, ["a"]),
"a + (b - a)": (True, ["a", "b"]),
"a + np.log(a, base=10)": (True, ["a", "np.log(a, base=10)"]),
# Note different spacing:
"a + np.log(a, base=10) - np . log(a , base = 10)": (True, ["a"]),
"a + (I(b) + c)": (True, ["a", "I(b)", "c"]),
"a + I(b + c)": (True, ["a", "I(b + c)"]),
"a:b": (True, [("a", "b")]),
"a:b:a": (True, [("a", "b")]),
"a:(b + c)": (True, [("a", "b"), ("a", "c")]),
"(a + b):c": (True, [("a", "c"), ("b", "c")]),
"a:(b - c)": (True, [("a", "b")]),
"c + a:c + a:(b - c)": (True, ["c", ("a", "c"), ("a", "b")]),
"(a - b):c": (True, [("a", "c")]),
"b + b:c + (a - b):c": (True, ["b", ("b", "c"), ("a", "c")]),
"a:b - a:b": (True, []),
"a:b - b:a": (True, []),
"1 - (a + b)": (True, []),
"a + b - (a + b)": (True, []),
"a * b": (True, ["a", "b", ("a", "b")]),
"a * b * a": (True, ["a", "b", ("a", "b")]),
"a * (b + c)": (True, ["a", "b", "c", ("a", "b"), ("a", "c")]),
"(a + b) * c": (True, ["a", "b", "c", ("a", "c"), ("b", "c")]),
"a * (b - c)": (True, ["a", "b", ("a", "b")]),
"c + a:c + a * (b - c)": (True, ["c", ("a", "c"), "a", "b", ("a", "b")]),
"(a - b) * c": (True, ["a", "c", ("a", "c")]),
"b + b:c + (a - b) * c": (True, ["b", ("b", "c"), "a", "c", ("a", "c")]),
"a/b": (True, ["a", ("a", "b")]),
"(a + b)/c": (True, ["a", "b", ("a", "b", "c")]),
"b + b:c + (a - b)/c": (True, ["b", ("b", "c"), "a", ("a", "c")]),
"a/(b + c)": (True, ["a", ("a", "b"), ("a", "c")]),
"a ** 2": (True, ["a"]),
"(a + b + c + d) ** 2": (True, ["a", "b", "c", "d",
("a", "b"), ("a", "c"), ("a", "d"),
("b", "c"), ("b", "d"), ("c", "d")]),
"(a + b + c + d) ** 3": (True, ["a", "b", "c", "d",
("a", "b"), ("a", "c"), ("a", "d"),
("b", "c"), ("b", "d"), ("c", "d"),
("a", "b", "c"), ("a", "b", "d"),
("a", "c", "d"), ("b", "c", "d")]),
"a + +a": (True, ["a"]),
"~ a + b": (True, ["a", "b"]),
"~ a*b": (True, ["a", "b", ("a", "b")]),
"~ a*b + 0": (False, ["a", "b", ("a", "b")]),
"~ -1": (False, []),
"0 ~ a + b": (True, ["a", "b"]),
"1 ~ a + b": (True, [], True, ["a", "b"]),
"y ~ a + b": (False, ["y"], True, ["a", "b"]),
"0 + y ~ a + b": (False, ["y"], True, ["a", "b"]),
"0 + y * z ~ a + b": (False, ["y", "z", ("y", "z")], True, ["a", "b"]),
"-1 ~ 1": (False, [], True, []),
"1 + y ~ a + b": (True, ["y"], True, ["a", "b"]),
# Check precedence:
"a + b * c": (True, ["a", "b", "c", ("b", "c")]),
"a * b + c": (True, ["a", "b", ("a", "b"), "c"]),
"a * b - a": (True, ["b", ("a", "b")]),
"a + b / c": (True, ["a", "b", ("b", "c")]),
"a / b + c": (True, ["a", ("a", "b"), "c"]),
"a*b:c": (True, ["a", ("b", "c"), ("a", "b", "c")]),
"a:b*c": (True, [("a", "b"), "c", ("a", "b", "c")]),
# Intercept handling:
"~ 1 + 1 + 0 + 1": (True, []),
"~ 0 + 1 + 0": (False, []),
"~ 0 - 1 - 1 + 0 + 1": (True, []),
"~ 1 - 1": (False, []),
"~ 0 + a + 1": (True, ["a"]),
"~ 1 + (a + 0)": (True, ["a"]), # This is correct, but perhaps surprising!
"~ 0 + (a + 1)": (True, ["a"]), # Also correct!
"~ 1 - (a + 1)": (False, []),
}
# <> mark off where the error should be reported:
_eval_error_tests = [
"a <+>",
"a + <(>",
"b + <(-a)>",
"a:<1>",
"(a + <1>)*b",
"a + <2>",
"a + <1.0>",
# eh, catching this is a hassle, we'll just leave the user some rope if
# they really want it:
#"a + <0x1>",
"a ** <b>",
"a ** <(1 + 1)>",
"a ** <1.5>",
"a + b <# asdf>",
"<)>",
"a + <)>",
"<*> a",
"a + <*>",
"a + <foo[bar>",
"a + <foo{bar>",
"a + <foo(bar>",
"a + <[bar>",
"a + <{bar>",
"a + <{bar[]>",
"a + foo<]>bar",
"a + foo[]<]>bar",
"a + foo{}<}>bar",
"a + foo<)>bar",
"a + b<)>",
"(a) <.>",
"<(>a + b",
"<y ~ a> ~ b",
"y ~ <(a ~ b)>",
"<~ a> ~ b",
"~ <(a ~ b)>",
"1 + <-(a + b)>",
"<- a>",
"a + <-a**2>",
]
def _assert_terms_match(terms, expected_intercept, expecteds): # pragma: no cover
if expected_intercept:
expecteds = [()] + expecteds
assert len(terms) == len(expecteds)
for term, expected in zip(terms, expecteds):
if isinstance(term, Term):
if isinstance(expected, str):
expected = (expected,)
assert term.factors == tuple([EvalFactor(s) for s in expected])
else:
assert term == expected
def _do_eval_formula_tests(tests): # pragma: no cover
for code, result in six.iteritems(tests):
if len(result) == 2:
result = (False, []) + result
model_desc = ModelDesc.from_formula(code)
print(repr(code))
print(result)
print(model_desc)
lhs_intercept, lhs_termlist, rhs_intercept, rhs_termlist = result
_assert_terms_match(model_desc.lhs_termlist,
lhs_intercept, lhs_termlist)
_assert_terms_match(model_desc.rhs_termlist,
rhs_intercept, rhs_termlist)
def test_eval_formula():
_do_eval_formula_tests(_eval_tests)
def test_eval_formula_error_reporting():
from patsy.parse_formula import _parsing_error_test
parse_fn = lambda formula: ModelDesc.from_formula(formula)
_parsing_error_test(parse_fn, _eval_error_tests)
def test_formula_factor_origin():
from patsy.origin import Origin
desc = ModelDesc.from_formula("a + b")
assert (desc.rhs_termlist[1].factors[0].origin
== Origin("a + b", 0, 1))
assert (desc.rhs_termlist[2].factors[0].origin
== Origin("a + b", 4, 5))
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