Module tf.search.searchexe
Search execution management
Expand source code Browse git
"""
# Search execution management
"""
from .relations import basicRelations
from .syntax import syntax
from .semantics import semantics
from .graph import connectedness, displayPlan
from .spin import spinAtoms, spinEdges
from .stitch import setStrategy, stitch
from ..parameters import SEARCH_FAIL_FACTOR, YARN_RATIO, TRY_LIMIT_FROM, TRY_LIMIT_TO
from ..core.timestamp import DEEP
PROGRESS = 100
class SearchExe:
perfDefaults = dict(
yarnRatio=YARN_RATIO,
tryLimitFrom=TRY_LIMIT_FROM,
tryLimitTo=TRY_LIMIT_TO,
)
perfParams = dict(**perfDefaults)
@classmethod
def setPerfParams(cls, params):
cls.perfParams = params
def __init__(
self,
api,
searchTemplate,
outerTemplate=None,
quKind=None,
offset=0,
level=0,
sets=None,
shallow=False,
silent=DEEP,
showQuantifiers=False,
_msgCache=False,
setInfo={},
):
self.api = api
TF = api.TF
setSilent = TF.setSilent
self.searchTemplate = searchTemplate
self.outerTemplate = outerTemplate
self.quKind = quKind
self.level = level
self.offset = offset
self.sets = sets
self.shallow = 0 if not shallow else 1 if shallow is True else shallow
self.silent = silent
setSilent(silent)
self.showQuantifiers = showQuantifiers
self._msgCache = (
_msgCache if type(_msgCache) is list else -1 if _msgCache else 0
)
self.good = True
self.setInfo = setInfo
basicRelations(self, api)
# API METHODS ###
def search(self, limit=None):
api = self.api
TF = api.TF
setSilent = TF.setSilent
setSilent(True)
self.study()
setSilent(self.silent)
return self.fetch(limit=limit)
def study(self, strategy=None):
api = self.api
TF = api.TF
info = TF.info
indent = TF.indent
isSilent = TF.isSilent
setSilent = TF.setSilent
_msgCache = self._msgCache
indent(level=0, reset=True)
self.good = True
wasSilent = isSilent()
setStrategy(self, strategy)
if not self.good:
return
info("Checking search template ...", cache=_msgCache)
self._parse()
self._prepare()
if not self.good:
return
info(
f"Setting up search space for {len(self.qnodes)} objects ...",
cache=_msgCache,
)
spinAtoms(self)
# in spinAtoms an inner call to study may have happened due to quantifiers
# That will restore the silent level to what we had outside
# study(). So we have to make it deep again.
setSilent(wasSilent)
info(
f"Constraining search space with {len(self.qedges)} relations ...",
cache=_msgCache,
)
spinEdges(self)
info(f"\t{len(self.thinned)} edges thinned", cache=_msgCache)
info(
f"Setting up retrieval plan with strategy {self.strategyName} ...",
cache=_msgCache,
)
stitch(self)
if self.good:
yarnContent = sum(len(y) for y in self.yarns.values())
info(f"Ready to deliver results from {yarnContent} nodes", cache=_msgCache)
info("Iterate over S.fetch() to get the results", tm=False, cache=_msgCache)
info("See S.showPlan() to interpret the results", tm=False, cache=_msgCache)
def fetch(self, limit=None):
api = self.api
TF = api.TF
F = api.F
error = TF.error
_msgCache = self._msgCache
if limit and limit < 0:
limit = 0
if not self.good:
queryResults = set() if self.shallow else []
elif self.shallow:
queryResults = self.results
else:
failLimit = limit if limit else SEARCH_FAIL_FACTOR * F.otype.maxNode
def limitedResults():
for (i, result) in enumerate(self.results()):
if i < failLimit:
yield result
else:
if not limit:
error(
f"cut off at {failLimit} results. There are more ...",
cache=_msgCache,
)
return
queryResults = (
limitedResults() if limit is None else tuple(limitedResults())
)
return queryResults
def count(self, progress=None, limit=None):
TF = self.api.TF
info = TF.info
error = TF.error
_msgCache = self._msgCache
indent = TF.indent
indent(level=0, reset=True)
if limit and limit < 0:
limit = 0
if not self.good:
error(
"This search has problems. No results to count.",
tm=False,
cache=_msgCache,
)
return
if progress is None:
progress = PROGRESS
if limit:
failLimit = limit
msg = f" up to {failLimit}"
else:
failLimit = SEARCH_FAIL_FACTOR * self.api.F.otype.maxNode
msg = ""
info(
f"Counting results per {progress}{msg} ...",
cache=_msgCache,
)
indent(level=1, reset=True)
j = 0
good = True
for (i, r) in enumerate(self.results(remap=False)):
if i >= failLimit:
if not limit:
good = False
break
j += 1
if j == progress:
j = 0
info(i + 1, cache=_msgCache)
indent(level=0)
if good:
info(f"Done: {i + 1} results", cache=_msgCache)
else:
error(
f"cut off at {failLimit} results. There are more ...", cache=_msgCache
)
# SHOWING WITH THE SEARCH GRAPH ###
def showPlan(self, details=False):
displayPlan(self, details=details)
def showOuterTemplate(self, _msgCache):
error = self.api.TF.error
offset = self.offset
outerTemplate = self.outerTemplate
quKind = self.quKind
if offset and outerTemplate is not None:
for (i, line) in enumerate(outerTemplate.split("\n")):
error(f"{i:>2} {line}", tm=False, cache=_msgCache)
error(f"line {offset:>2}: Error under {quKind}:", tm=False, cache=_msgCache)
# TOP-LEVEL IMPLEMENTATION METHODS
def _parse(self):
syntax(self)
semantics(self)
def _prepare(self):
if not self.good:
return
self.yarns = {}
self.spreads = {}
self.spreadsC = {}
self.uptodate = {}
self.results = None
connectedness(self)Classes
class SearchExe (api, searchTemplate, outerTemplate=None, quKind=None, offset=0, level=0, sets=None, shallow=False, silent='deep', showQuantifiers=False, setInfo={})-
Expand source code Browse git
class SearchExe: perfDefaults = dict( yarnRatio=YARN_RATIO, tryLimitFrom=TRY_LIMIT_FROM, tryLimitTo=TRY_LIMIT_TO, ) perfParams = dict(**perfDefaults) @classmethod def setPerfParams(cls, params): cls.perfParams = params def __init__( self, api, searchTemplate, outerTemplate=None, quKind=None, offset=0, level=0, sets=None, shallow=False, silent=DEEP, showQuantifiers=False, _msgCache=False, setInfo={}, ): self.api = api TF = api.TF setSilent = TF.setSilent self.searchTemplate = searchTemplate self.outerTemplate = outerTemplate self.quKind = quKind self.level = level self.offset = offset self.sets = sets self.shallow = 0 if not shallow else 1 if shallow is True else shallow self.silent = silent setSilent(silent) self.showQuantifiers = showQuantifiers self._msgCache = ( _msgCache if type(_msgCache) is list else -1 if _msgCache else 0 ) self.good = True self.setInfo = setInfo basicRelations(self, api) # API METHODS ### def search(self, limit=None): api = self.api TF = api.TF setSilent = TF.setSilent setSilent(True) self.study() setSilent(self.silent) return self.fetch(limit=limit) def study(self, strategy=None): api = self.api TF = api.TF info = TF.info indent = TF.indent isSilent = TF.isSilent setSilent = TF.setSilent _msgCache = self._msgCache indent(level=0, reset=True) self.good = True wasSilent = isSilent() setStrategy(self, strategy) if not self.good: return info("Checking search template ...", cache=_msgCache) self._parse() self._prepare() if not self.good: return info( f"Setting up search space for {len(self.qnodes)} objects ...", cache=_msgCache, ) spinAtoms(self) # in spinAtoms an inner call to study may have happened due to quantifiers # That will restore the silent level to what we had outside # study(). So we have to make it deep again. setSilent(wasSilent) info( f"Constraining search space with {len(self.qedges)} relations ...", cache=_msgCache, ) spinEdges(self) info(f"\t{len(self.thinned)} edges thinned", cache=_msgCache) info( f"Setting up retrieval plan with strategy {self.strategyName} ...", cache=_msgCache, ) stitch(self) if self.good: yarnContent = sum(len(y) for y in self.yarns.values()) info(f"Ready to deliver results from {yarnContent} nodes", cache=_msgCache) info("Iterate over S.fetch() to get the results", tm=False, cache=_msgCache) info("See S.showPlan() to interpret the results", tm=False, cache=_msgCache) def fetch(self, limit=None): api = self.api TF = api.TF F = api.F error = TF.error _msgCache = self._msgCache if limit and limit < 0: limit = 0 if not self.good: queryResults = set() if self.shallow else [] elif self.shallow: queryResults = self.results else: failLimit = limit if limit else SEARCH_FAIL_FACTOR * F.otype.maxNode def limitedResults(): for (i, result) in enumerate(self.results()): if i < failLimit: yield result else: if not limit: error( f"cut off at {failLimit} results. There are more ...", cache=_msgCache, ) return queryResults = ( limitedResults() if limit is None else tuple(limitedResults()) ) return queryResults def count(self, progress=None, limit=None): TF = self.api.TF info = TF.info error = TF.error _msgCache = self._msgCache indent = TF.indent indent(level=0, reset=True) if limit and limit < 0: limit = 0 if not self.good: error( "This search has problems. No results to count.", tm=False, cache=_msgCache, ) return if progress is None: progress = PROGRESS if limit: failLimit = limit msg = f" up to {failLimit}" else: failLimit = SEARCH_FAIL_FACTOR * self.api.F.otype.maxNode msg = "" info( f"Counting results per {progress}{msg} ...", cache=_msgCache, ) indent(level=1, reset=True) j = 0 good = True for (i, r) in enumerate(self.results(remap=False)): if i >= failLimit: if not limit: good = False break j += 1 if j == progress: j = 0 info(i + 1, cache=_msgCache) indent(level=0) if good: info(f"Done: {i + 1} results", cache=_msgCache) else: error( f"cut off at {failLimit} results. There are more ...", cache=_msgCache ) # SHOWING WITH THE SEARCH GRAPH ### def showPlan(self, details=False): displayPlan(self, details=details) def showOuterTemplate(self, _msgCache): error = self.api.TF.error offset = self.offset outerTemplate = self.outerTemplate quKind = self.quKind if offset and outerTemplate is not None: for (i, line) in enumerate(outerTemplate.split("\n")): error(f"{i:>2} {line}", tm=False, cache=_msgCache) error(f"line {offset:>2}: Error under {quKind}:", tm=False, cache=_msgCache) # TOP-LEVEL IMPLEMENTATION METHODS def _parse(self): syntax(self) semantics(self) def _prepare(self): if not self.good: return self.yarns = {} self.spreads = {} self.spreadsC = {} self.uptodate = {} self.results = None connectedness(self)Class variables
var perfDefaultsvar perfParams
Static methods
def setPerfParams(params)
Methods
def count(self, progress=None, limit=None)def fetch(self, limit=None)def search(self, limit=None)def showOuterTemplate(self, _msgCache)def showPlan(self, details=False)def study(self, strategy=None)