upgrade to new lens version
This commit is contained in:
Marcell Vazquez-Chanlatte
parent
212a8c195a
commit
d78037816b
5 changed files with 86 additions and 61 deletions
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@ -1,7 +1,7 @@
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-e git://github.com/mvcisback/hypothesis-cfg@master#egg=hypothesis-cfg
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bitarray==0.8.1
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funcy==1.7.2
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lenses==0.1.7
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funcy==1.9.1
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lenses==0.3.0
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pandas==0.19.2
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parsimonious==0.7.0
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sympy==1.0
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40
stl/ast.py
40
stl/ast.py
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@ -1,5 +1,4 @@
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# -*- coding: utf-8 -*-
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# TODO: create iso lens between sugar and non-sugar
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# TODO: supress + given a + (-b). i.e. want a - b
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from collections import namedtuple, deque
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@ -38,8 +37,9 @@ class AST(object):
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def __invert__(self):
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return Neg(self)
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@property
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def children(self):
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return []
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return set()
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class _Top(AST):
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@ -70,9 +70,10 @@ class AtomicPred(namedtuple("AP", ["id"]), AST):
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def __repr__(self):
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return f"{self.id}"
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@property
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def children(self):
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return []
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return set()
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class LinEq(namedtuple("LinEquality", ["terms", "op", "const"]), AST):
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@ -80,9 +81,10 @@ class LinEq(namedtuple("LinEquality", ["terms", "op", "const"]), AST):
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def __repr__(self):
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return " + ".join(map(str, self.terms)) + f" {self.op} {self.const}"
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@property
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def children(self):
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return []
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return set()
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def __hash__(self):
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# TODO: compute hash based on contents
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@ -101,9 +103,10 @@ class Interval(namedtuple('I', ['lower', 'upper'])):
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def __repr__(self):
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return f"[{self.lower},{self.upper}]"
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@property
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def children(self):
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return [self.lower, self.upper]
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return {self.lower, self.upper}
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class NaryOpSTL(namedtuple('NaryOp', ['args']), AST):
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@ -112,9 +115,10 @@ class NaryOpSTL(namedtuple('NaryOp', ['args']), AST):
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OP = "?"
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def __repr__(self):
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return f" {self.OP} ".join(f"({x})" for x in self.args)
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@property
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def children(self):
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return self.args
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return set(self.args)
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class Or(NaryOpSTL):
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@ -141,8 +145,9 @@ class ModalOp(namedtuple('ModalOp', ['interval', 'arg']), AST):
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def __repr__(self):
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return f"{self.OP}{self.interval}({self.arg})"
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@property
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def children(self):
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return [self.arg]
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return {self.arg}
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class F(ModalOp):
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@ -168,8 +173,9 @@ class Until(namedtuple('ModalOp', ['arg1', 'arg2']), AST):
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def __repr__(self):
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return f"({self.arg1}) U ({self.arg2})"
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@property
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def children(self):
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return [self.arg1, self.arg2]
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return {self.arg1, self.arg2}
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def __hash__(self):
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# TODO: compute hash based on contents
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@ -181,9 +187,10 @@ class Neg(namedtuple('Neg', ['arg']), AST):
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def __repr__(self):
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return f"¬({self.arg})"
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@property
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def children(self):
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return [self.arg]
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return {self.arg}
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def __hash__(self):
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# TODO: compute hash based on contents
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@ -195,9 +202,10 @@ class Next(namedtuple('Next', ['arg']), AST):
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def __repr__(self):
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return f"X({self.arg})"
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@property
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def children(self):
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return [self.arg]
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return {self.arg}
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def __hash__(self):
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# TODO: compute hash based on contents
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@ -13,7 +13,7 @@ import stl
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oo = float('inf')
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def pointwise_sat(phi):
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ap_names = [z.id.name for z in stl.utils.AP_lens(phi).get_all()]
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ap_names = [z.id.name for z in stl.utils.AP_lens(phi).Each().collect()]
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def _eval_stl(x, t):
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evaluated = stl.utils.eval_lineqs(phi, x)
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evaluated.update(fn.project(x, ap_names))
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@ -1,7 +1,7 @@
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from hypothesis_cfg import ContextFreeGrammarStrategy
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import hypothesis.strategies as st
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from hypothesis.searchstrategy.strategies import SearchStrategy
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from hypothesis.strategies import integers
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import stl
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@ -15,11 +15,32 @@ GRAMMAR = {
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}
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def build_lineq(params):
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pass
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LinEqStrategy = st.builds(
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lambda x: stl.ast.Lineq(*x),
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st.tuples(
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st.lists(
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st.tuples(
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st.sampled_from(["x", "y", "z","w"]),
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st.integers(min_value=-5, max_value=5)),
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min_size=1, max_size=4, unique=True),
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st.sampled_from([">=", "<=", "<", ">", "="]),
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st.integers(min_value=-5, max_value=5)
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))
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class SignalTemporalLogicStategy(SearchStrategy):
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def __init__(self, max_length: int):
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super(SearchStrategy, self).__init__()
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self.cfg_gen = ContextFreeGrammarStrategy(
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GRAMMAR, max_length=max_length, start='phi')
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self.ap_gen = st.builds(
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lambda i: stl.ast.AtomicPred(f"AP{i}"),
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st.integers(min_value=0, max_value=max_length))
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def do_draw(self, data):
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# TODO: randomly assign all intervals
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@ -27,4 +48,6 @@ class SignalTemporalLogicStategy(SearchStrategy):
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# TODO: randomly generate boolean predicate
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# TODO: randomly generate linear predicate
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phi = stl.parse("".join(self.cfg_gen.do_draw(data)))
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ap_lens = stl.utils.AP_lens(phi).Each()
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phi = ap_lens.modify(lambda _: self.ap_gen.do_draw(data))
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return phi
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76
stl/utils.py
76
stl/utils.py
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@ -1,9 +1,10 @@
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from typing import List, Type, Dict, Mapping, T
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from typing import List, Type, Dict, Mapping, T, TypeVar
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from collections import deque
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import operator as op
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from functools import reduce
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from lenses import lens, Lens
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import lenses
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from lenses import lens
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import funcy as fn
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import sympy
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import traces
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@ -13,59 +14,52 @@ from stl.ast import (LinEq, And, Or, NaryOpSTL, F, G, Interval, Neg,
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AtomicPred)
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from stl.types import STL, STL_Generator, MTL
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Lens = TypeVar('Lens')
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def walk(phi:STL) -> STL_Generator:
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"""DSF walk of the AST."""
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"""Walk of the AST."""
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pop = deque.pop
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children = deque([phi])
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while len(children) > 0:
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node = pop(children)
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yield node
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children.extend(node.children())
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children.extend(node.children)
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def vars_in_phi(phi):
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focus = stl.terms_lens(phi)
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return set(focus.tuple_(lens().id, lens().time).get_all())
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return set(focus.tuple_(lens.id, lens.time).get_all())
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def type_pred(*args:List[Type]) -> Mapping[Type, bool]:
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ast_types = set(args)
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return lambda x: type(x) in ast_types
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def _child_lens(psi:STL, focus:Lens) -> STL_Generator:
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if psi is None:
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return
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elif psi is stl.TOP or psi is stl.BOT:
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return
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elif isinstance(psi, stl.ast.Until):
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yield from [focus.arg1, focus.arg2]
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elif isinstance(psi, NaryOpSTL):
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for j, _ in enumerate(psi.args):
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yield focus.args[j]
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else:
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yield focus.arg
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def ast_lens(phi:STL, bind:bool=True, *,
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pred:Mapping[T, bool], focus_lens:Lens=None) -> Lens:
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def ast_lens(phi:STL, bind=True, *, pred=None, focus_lens=None) -> Lens:
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if focus_lens is None:
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focus_lens = lambda x: [lens()]
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tls = list(fn.flatten(_ast_lens(phi, pred=pred, focus_lens=focus_lens)))
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tl = lens().tuple_(*tls).each_()
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return tl.bind(phi) if bind else tl
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focus_lens = lambda _: [lens]
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if pred is None:
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pred = lambda _: False
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l = lenses.bind(phi) if bind else lens
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return l.Tuple(*_ast_lens(phi, pred=pred, focus_lens=focus_lens))
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def _ast_lens(phi:STL, pred, focus_lens) -> Lens:
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if pred(phi):
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yield from focus_lens(phi)
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if phi is None or not phi.children:
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return
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def _ast_lens(phi, *, pred, focus=lens(), focus_lens):
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psi = focus.get(state=phi)
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ret_lens = [focus.add_lens(l) for l in focus_lens(psi)] if pred(psi) else []
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if isinstance(psi, (LinEq, stl.ast.AtomicPred)):
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return ret_lens
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child_lenses = list(_child_lens(psi, focus=focus))
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ret_lens += [_ast_lens(phi, pred=pred, focus=cl, focus_lens=focus_lens)
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for cl in child_lenses]
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return ret_lens
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if phi is stl.TOP or phi is stl.BOT:
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child_lenses = [lens]
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elif isinstance(phi, stl.ast.Until):
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child_lenses = [lens.GetAttr('arg1'), lens.GetAttr('arg2')]
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elif isinstance(phi, NaryOpSTL):
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child_lenses = [lens.GetAttr('args')[j] for j, _ in enumerate(phi.args)]
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else:
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child_lenses = [lens.GetAttr('arg')]
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for l in child_lenses:
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yield from [l & cl for cl in _ast_lens(l.get()(phi), pred, focus_lens)]
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lineq_lens = fn.partial(ast_lens, pred=type_pred(LinEq))
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AP_lens = fn.partial(ast_lens, pred=type_pred(stl.ast.AtomicPred))
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@ -78,7 +72,7 @@ def terms_lens(phi:STL, bind:bool=True) -> Lens:
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def param_lens(phi:STL) -> Lens:
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is_sym = lambda x: isinstance(x, sympy.Symbol)
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def focus_lens(leaf):
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return [lens().const] if isinstance(leaf, LinEq) else [lens().interval[0], lens().interval[1]]
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return [lens.const] if isinstance(leaf, LinEq) else [lens().interval[0], lens().interval[1]]
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return ast_lens(phi, pred=type_pred(LinEq, F, G),
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focus_lens=focus_lens).filter_(is_sym)
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@ -93,7 +87,7 @@ def f_neg_or_canonical_form(phi:STL) -> STL:
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if isinstance(phi, LinEq):
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return phi
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children = [f_neg_or_canonical_form(s) for s in phi.children()]
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children = [f_neg_or_canonical_form(s) for s in phi.children]
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if isinstance(phi, (And, G)):
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children = [Neg(s) for s in children]
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children = tuple(children)
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@ -160,7 +154,7 @@ def eval_lineq(lineq, x, times=None, compact=True):
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return float(term.coeff)*x[term.id.name][t]
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output = traces.TimeSeries(domain=traces.Domain(times[0], times[-1]))
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terms = lens(lineq).terms.each_().get_all()
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terms = lens(lineq).Each().terms.Each().collect()
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for t in times:
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lhs = sum(eval_term(term, t) for term in terms)
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output[t] = op_lookup[lineq.op](lhs, lineq.const)
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@ -172,7 +166,7 @@ def eval_lineq(lineq, x, times=None, compact=True):
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def eval_lineqs(phi, x, times=None):
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if times is None:
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times = get_times(x)
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lineqs = set(lineq_lens(phi).get_all())
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lineqs = set(lineq_lens(phi).Each().collect())
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return {lineq: eval_lineq(lineq, x, times=times) for lineq in lineqs}
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