patsy/patsy/constraint.py at splines · IBMZ-Linux-OSS-Python/patsy · GitHub

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# This file is part of Patsy

# See file COPYING for license information.

# Interpreting linear constraints like "2*x1 + x2 = 0"

# These are made available in the patsy.* namespace

__all__ = ["LinearConstraint"]

import re

import numpy as np

from patsy import PatsyError

from patsy.origin import Origin

from patsy.util import (atleast_2d_column_default,

repr_pretty_delegate, repr_pretty_impl,

SortAnythingKey)

from patsy.infix_parser import Token, Operator, ParseNode, infix_parse

from patsy.compat import Scanner, Mapping

class LinearConstraint(object):

"""A linear constraint in matrix form.

This object represents a linear constraint of the form `Ax = b`.

Usually you won't be constructing these by hand, but instead get them as

the return value from :meth:`DesignInfo.linear_constraint`.

.. attribute:: coefs

A 2-dimensional ndarray with float dtype, representing `A`.

.. attribute:: constants

A 2-dimensional single-column ndarray with float dtype, representing

`b`.

.. attribute:: variable_names

A list of strings giving the names of the variables being

constrained. (Used only for consistency checking.)

"""

def __init__(self, variable_names, coefs, constants=None):

self.variable_names = list(variable_names)

self.coefs = np.atleast_2d(np.asarray(coefs, dtype=float))

if constants is None:

constants = np.zeros(self.coefs.shape[0], dtype=float)

constants = np.asarray(constants, dtype=float)

self.constants = atleast_2d_column_default(constants)

if self.constants.ndim != 2 or self.constants.shape[1] != 1:

raise ValueError("constants is not (convertible to) a column matrix")

if self.coefs.ndim != 2 or self.coefs.shape[1] != len(variable_names):

raise ValueError("wrong shape for coefs")

if self.coefs.shape[0] == 0:

raise ValueError("must have at least one row in constraint matrix")

if self.coefs.shape[0] != self.constants.shape[0]:

raise ValueError("shape mismatch between coefs and constants")

if np.any(np.all(self.coefs == 0, axis=1)):

raise ValueError("can't test a constant constraint")

__repr__ = repr_pretty_delegate

def _repr_pretty_(self, p, cycle):

assert not cycle

return repr_pretty_impl(p, self,

[self.variable_names, self.coefs, self.constants])

@classmethod

def combine(cls, constraints):

"""Create a new LinearConstraint by ANDing together several existing

LinearConstraints.

:arg constraints: An iterable of LinearConstraint objects. Their

:attr:`variable_names` attributes must all match.

:returns: A new LinearConstraint object.

"""

if not constraints:

raise ValueError("no constraints specified")

variable_names = constraints[0].variable_names

for constraint in constraints:

if constraint.variable_names != variable_names:

raise ValueError("variable names don't match")

coefs = np.row_stack([c.coefs for c in constraints])

constants = np.row_stack([c.constants for c in constraints])

return cls(variable_names, coefs, constants)

def test_LinearConstraint():

from numpy.testing.utils import assert_equal

lc = LinearConstraint(["foo", "bar"], [1, 1])

assert lc.variable_names == ["foo", "bar"]

assert_equal(lc.coefs, [[1, 1]])

assert_equal(lc.constants, [[0]])

lc = LinearConstraint(["foo", "bar"], [[1, 1], [2, 3]], [10, 20])

assert_equal(lc.coefs, [[1, 1], [2, 3]])

assert_equal(lc.constants, [[10], [20]])

assert lc.coefs.dtype == np.dtype(float)

assert lc.constants.dtype == np.dtype(float)

from nose.tools import assert_raises

assert_raises(ValueError, LinearConstraint, ["a"], [[1, 2]])

assert_raises(ValueError, LinearConstraint, ["a"], [[[1]]])

assert_raises(ValueError, LinearConstraint, ["a"], [[1, 2]], [3, 4])

assert_raises(ValueError, LinearConstraint, ["a", "b"], [[1, 2]], [3, 4])

assert_raises(ValueError, LinearConstraint, ["a"], [[0]])

assert_raises(ValueError, LinearConstraint, ["a"], [[1]], [[]])

assert_raises(ValueError, LinearConstraint, ["a", "b"], [])

assert_raises(ValueError, LinearConstraint, ["a", "b"],

np.zeros((0, 2)))

def test_LinearConstraint_combine():

comb = LinearConstraint.combine([LinearConstraint(["a", "b"], [1, 0]),

LinearConstraint(["a", "b"], [0, 1], [1])])

assert comb.variable_names == ["a", "b"]

from numpy.testing.utils import assert_equal

assert_equal(comb.coefs, [[1, 0], [0, 1]])

assert_equal(comb.constants, [[0], [1]])

from nose.tools import assert_raises

assert_raises(ValueError, LinearConstraint.combine, [])

assert_raises(ValueError, LinearConstraint.combine,

[LinearConstraint(["a"], [1]), LinearConstraint(["b"], [1])])

_ops = [

Operator(",", 2, -100),

Operator("=", 2, 0),

Operator("+", 1, 100),

Operator("-", 1, 100),

Operator("+", 2, 100),

Operator("-", 2, 100),

Operator("*", 2, 200),

Operator("/", 2, 200),

]

_atomic = ["NUMBER", "VARIABLE"]

def _token_maker(type, string):

def make_token(scanner, token_string):

if type == "__OP__":

actual_type = token_string

else:

actual_type = type

return Token(actual_type,

Origin(string, *scanner.match.span()),

token_string)

return make_token

def _tokenize_constraint(string, variable_names):

lparen_re = r"\("

rparen_re = r"\)"

op_re = "|".join([re.escape(op.token_type) for op in _ops])

num_re = r"[-+]?[0-9]*\.?[0-9]+([eE][-+]?[0-9]+)?"

whitespace_re = r"\s+"

# Prefer long matches:

variable_names = sorted(variable_names, key=len, reverse=True)

variable_re = "|".join([re.escape(n) for n in variable_names])

lexicon = [

(lparen_re, _token_maker(Token.LPAREN, string)),

(rparen_re, _token_maker(Token.RPAREN, string)),

(op_re, _token_maker("__OP__", string)),

(variable_re, _token_maker("VARIABLE", string)),

(num_re, _token_maker("NUMBER", string)),

(whitespace_re, None),

]

scanner = Scanner(lexicon)

tokens, leftover = scanner.scan(string)

if leftover:

offset = len(string) - len(leftover)

raise PatsyError("unrecognized token in constraint",

Origin(string, offset, offset + 1))

return tokens

def test__tokenize_constraint():

code = "2 * (a + b) = q"

tokens = _tokenize_constraint(code, ["a", "b", "q"])

expecteds = [("NUMBER", 0, 1, "2"),

("*", 2, 3, "*"),

(Token.LPAREN, 4, 5, "("),

("VARIABLE", 5, 6, "a"),

("+", 7, 8, "+"),

("VARIABLE", 9, 10, "b"),

(Token.RPAREN, 10, 11, ")"),

("=", 12, 13, "="),

("VARIABLE", 14, 15, "q")]

for got, expected in zip(tokens, expecteds):

assert isinstance(got, Token)

assert got.type == expected[0]

assert got.origin == Origin(code, expected[1], expected[2])

assert got.extra == expected[3]

from nose.tools import assert_raises

assert_raises(PatsyError, _tokenize_constraint, "1 + @b", ["b"])

# Shouldn't raise an error:

_tokenize_constraint("1 + @b", ["@b"])

# Check we aren't confused by names which are proper prefixes of other

# names:

for names in (["a", "aa"], ["aa", "a"]):

tokens = _tokenize_constraint("a aa a", names)

assert len(tokens) == 3

assert [t.extra for t in tokens] == ["a", "aa", "a"]

# Check that embedding ops and numbers inside a variable name works

tokens = _tokenize_constraint("2 * a[1,1],", ["a[1,1]"])

assert len(tokens) == 4

assert [t.type for t in tokens] == ["NUMBER", "*", "VARIABLE", ","]

assert [t.extra for t in tokens] == ["2", "*", "a[1,1]", ","]

def parse_constraint(string, variable_names):

return infix_parse(_tokenize_constraint(string, variable_names),

_ops, _atomic)

class _EvalConstraint(object):

def __init__(self, variable_names):

self._variable_names = variable_names

self._N = len(variable_names)

self._dispatch = {

("VARIABLE", 0): self._eval_variable,

("NUMBER", 0): self._eval_number,

("+", 1): self._eval_unary_plus,

("-", 1): self._eval_unary_minus,

("+", 2): self._eval_binary_plus,

("-", 2): self._eval_binary_minus,

("*", 2): self._eval_binary_multiply,

("/", 2): self._eval_binary_div,

("=", 2): self._eval_binary_eq,

(",", 2): self._eval_binary_comma,

}

# General scheme: there are 2 types we deal with:

# - linear combinations ("lincomb"s) of variables and constants,

# represented as ndarrays with size N+1

# The last entry is the constant, so [10, 20, 30] means 10x + 20y +

# 30.

# - LinearConstraint objects

def is_constant(self, coefs):

return np.all(coefs[:self._N] == 0)

def _eval_variable(self, tree):

var = tree.token.extra

coefs = np.zeros((self._N + 1,), dtype=float)

coefs[self._variable_names.index(var)] = 1

return coefs

def _eval_number(self, tree):

coefs = np.zeros((self._N + 1,), dtype=float)

coefs[-1] = float(tree.token.extra)

return coefs

def _eval_unary_plus(self, tree):

return self.eval(tree.args[0])

def _eval_unary_minus(self, tree):

return -1 * self.eval(tree.args[0])

def _eval_binary_plus(self, tree):

return self.eval(tree.args[0]) + self.eval(tree.args[1])

def _eval_binary_minus(self, tree):

return self.eval(tree.args[0]) - self.eval(tree.args[1])

def _eval_binary_div(self, tree):

left = self.eval(tree.args[0])

right = self.eval(tree.args[1])

if not self.is_constant(right):

raise PatsyError("Can't divide by a variable in a linear "

"constraint", tree.args[1])

return left / right[-1]

def _eval_binary_multiply(self, tree):

left = self.eval(tree.args[0])

right = self.eval(tree.args[1])

if self.is_constant(left):

return left[-1] * right

elif self.is_constant(right):

return left * right[-1]

else:

raise PatsyError("Can't multiply one variable by another "

"in a linear constraint", tree)

def _eval_binary_eq(self, tree):

# Handle "a1 = a2 = a3", which is parsed as "(a1 = a2) = a3"

args = list(tree.args)

constraints = []

for i, arg in enumerate(args):

if arg.type == "=":

constraints.append(self.eval(arg, constraint=True))

# make our left argument be their right argument, or

# vice-versa

args[i] = arg.args[1 - i]

left = self.eval(args[0])

right = self.eval(args[1])

coefs = left[:self._N] - right[:self._N]

if np.all(coefs == 0):

raise PatsyError("no variables appear in constraint", tree)

constant = -left[-1] + right[-1]

constraint = LinearConstraint(self._variable_names, coefs, constant)

constraints.append(constraint)

return LinearConstraint.combine(constraints)

def _eval_binary_comma(self, tree):

left = self.eval(tree.args[0], constraint=True)

right = self.eval(tree.args[1], constraint=True)

return LinearConstraint.combine([left, right])

def eval(self, tree, constraint=False):

key = (tree.type, len(tree.args))

assert key in self._dispatch

val = self._dispatch[key](tree)

if constraint:

# Force it to be a constraint

if isinstance(val, LinearConstraint):

return val

else:

assert val.size == self._N + 1

if np.all(val[:self._N] == 0):

raise PatsyError("term is constant, with no variables",

tree)

return LinearConstraint(self._variable_names,

val[:self._N],

-val[-1])

else:

# Force it to *not* be a constraint

if isinstance(val, LinearConstraint):

raise PatsyError("unexpected constraint object", tree)

return val

def linear_constraint(constraint_like, variable_names):

"""This is the internal interface implementing

DesignInfo.linear_constraint, see there for docs."""

if isinstance(constraint_like, LinearConstraint):

if constraint_like.variable_names != variable_names:

raise ValueError("LinearConstraint has wrong variable_names "

"(got %r, expected %r)"

% (constraint_like.variable_names,

variable_names))

return constraint_like

if isinstance(constraint_like, Mapping):

# Simple conjunction-of-equality constraints can be specified as

# dicts. {"x": 1, "y": 2} -> tests x = 1 and y = 2. Keys can be

# either variable names, or variable indices.

coefs = np.zeros((len(constraint_like), len(variable_names)),

dtype=float)

constants = np.zeros(len(constraint_like))

used = set()

for i, (name, value) in enumerate(constraint_like.iteritems()):

if name in variable_names:

idx = variable_names.index(name)

elif isinstance(name, (int, long)):

idx = name

else:

raise ValueError("unrecognized variable name/index %r"

% (name,))

if idx in used:

raise ValueError("duplicated constraint on %r"

% (variable_names[idx],))

used.add(idx)

coefs[i, idx] = 1

constants[i] = value

return LinearConstraint(variable_names, coefs, constants)

if isinstance(constraint_like, basestring):

constraint_like = [constraint_like]

# fall-through

if (isinstance(constraint_like, list)

and constraint_like

and isinstance(constraint_like[0], basestring)):

constraints = []

for code in constraint_like:

if not isinstance(code, basestring):

raise ValueError("expected a string, not %r" % (code,))

tree = parse_constraint(code, variable_names)

evaluator = _EvalConstraint(variable_names)

constraints.append(evaluator.eval(tree, constraint=True))

return LinearConstraint.combine(constraints)

if isinstance(constraint_like, tuple):

if len(constraint_like) != 2:

raise ValueError, "constraint tuple must have length 2"

coef, constants = constraint_like

return LinearConstraint(variable_names, coef, constants)

# assume a raw ndarray

coefs = np.asarray(constraint_like, dtype=float)

return LinearConstraint(variable_names, coefs)

def _check_lincon(input, varnames, coefs, constants):

from numpy.testing.utils import assert_equal

got = linear_constraint(input, varnames)

print "got", got

expected = LinearConstraint(varnames, coefs, constants)

print "expected", expected

assert_equal(got.variable_names, expected.variable_names)

assert_equal(got.coefs, expected.coefs)

assert_equal(got.constants, expected.constants)

assert_equal(got.coefs.dtype, np.dtype(float))

assert_equal(got.constants.dtype, np.dtype(float))

def test_linear_constraint():

from nose.tools import assert_raises

from patsy.compat import OrderedDict

t = _check_lincon

t(LinearConstraint(["a", "b"], [2, 3]), ["a", "b"], [[2, 3]], [[0]])

assert_raises(ValueError, linear_constraint,

LinearConstraint(["b", "a"], [2, 3]),

["a", "b"])

t({"a": 2}, ["a", "b"], [[1, 0]], [[2]])

t(OrderedDict([("a", 2), ("b", 3)]),

["a", "b"], [[1, 0], [0, 1]], [[2], [3]])

t(OrderedDict([("a", 2), ("b", 3)]),

["b", "a"], [[0, 1], [1, 0]], [[2], [3]])

t({0: 2}, ["a", "b"], [[1, 0]], [[2]])

t(OrderedDict([(0, 2), (1, 3)]), ["a", "b"], [[1, 0], [0, 1]], [[2], [3]])

t(OrderedDict([("a", 2), (1, 3)]),

["a", "b"], [[1, 0], [0, 1]], [[2], [3]])

assert_raises(ValueError, linear_constraint, {"q": 1}, ["a", "b"])

assert_raises(ValueError, linear_constraint, {"a": 1, 0: 2}, ["a", "b"])

t(np.array([2, 3]), ["a", "b"], [[2, 3]], [[0]])

t(np.array([[2, 3], [4, 5]]), ["a", "b"], [[2, 3], [4, 5]], [[0], [0]])

t("a = 2", ["a", "b"], [[1, 0]], [[2]])

t("a - 2", ["a", "b"], [[1, 0]], [[2]])

t("a + 1 = 3", ["a", "b"], [[1, 0]], [[2]])

t("a + b = 3", ["a", "b"], [[1, 1]], [[3]])

t("a = 2, b = 3", ["a", "b"], [[1, 0], [0, 1]], [[2], [3]])

t("b = 3, a = 2", ["a", "b"], [[0, 1], [1, 0]], [[3], [2]])

t(["a = 2", "b = 3"], ["a", "b"], [[1, 0], [0, 1]], [[2], [3]])

assert_raises(ValueError, linear_constraint, ["a", {"b": 0}], ["a", "b"])

# Actual evaluator tests

t("2 * (a + b/3) + b + 2*3/4 = 1 + 2*3", ["a", "b"],

[[2, 2.0/3 + 1]], [[7 - 6.0/4]])

t("+2 * -a", ["a", "b"], [[-2, 0]], [[0]])

t("a - b, a + b = 2", ["a", "b"], [[1, -1], [1, 1]], [[0], [2]])

t("a = 1, a = 2, a = 3", ["a", "b"],

[[1, 0], [1, 0], [1, 0]], [[1], [2], [3]])

t("a * 2", ["a", "b"], [[2, 0]], [[0]])

t("-a = 1", ["a", "b"], [[-1, 0]], [[1]])

t("(2 + a - a) * b", ["a", "b"], [[0, 2]], [[0]])

t("a = 1 = b", ["a", "b"], [[1, 0], [0, -1]], [[1], [-1]])

t("a = (1 = b)", ["a", "b"], [[0, -1], [1, 0]], [[-1], [1]])

t("a = 1, a = b = c", ["a", "b", "c"],

[[1, 0, 0], [1, -1, 0], [0, 1, -1]], [[1], [0], [0]])

# One should never do this of course, but test that it works anyway...

t("a + 1 = 2", ["a", "a + 1"], [[0, 1]], [[2]])

t(([10, 20], [30]), ["a", "b"], [[10, 20]], [[30]])

t(([[10, 20], [20, 40]], [[30], [35]]), ["a", "b"],

[[10, 20], [20, 40]], [[30], [35]])

# wrong-length tuple

assert_raises(ValueError, linear_constraint,

([1, 0], [0], [0]), ["a", "b"])

assert_raises(ValueError, linear_constraint, ([1, 0],), ["a", "b"])

t([10, 20], ["a", "b"], [[10, 20]], [[0]])

t([[10, 20], [20, 40]], ["a", "b"], [[10, 20], [20, 40]], [[0], [0]])

t(np.array([10, 20]), ["a", "b"], [[10, 20]], [[0]])

t(np.array([[10, 20], [20, 40]]), ["a", "b"],

[[10, 20], [20, 40]], [[0], [0]])

# unknown object type

assert_raises(ValueError, linear_constraint, None, ["a", "b"])

_parse_eval_error_tests = [

# Bad token

"a + <f>oo",

# No pure constant equalities

"a = 1, <1 = 1>, b = 1",

"a = 1, <b * 2 - b + (-2/2 * b)>",

"a = 1, <1>, b = 2",

"a = 1, <2 * b = b + b>, c",

# No non-linearities

"a + <a * b> + c",

"a + 2 / <b> + c",

# Constraints are not numbers

"a = 1, 2 * <(a = b)>, c",

"a = 1, a + <(a = b)>, c",

"a = 1, <(a, b)> + 2, c",

]

from patsy.parse_formula import _parsing_error_test

def test_eval_errors():

def doit(bad_code):

return linear_constraint(bad_code, ["a", "b", "c"])

_parsing_error_test(doit, _parse_eval_error_tests)

patsy/patsy/constraint.py at splines · IBMZ-Linux-OSS-Python/patsy · GitHub

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