Module tf.search.search
Search (top-level)
Expand source code Browse git
"""
# Search (top-level)
"""
from ..core.helpers import console, wrapMessages
from .searchexe import SearchExe
from ..core.timestamp import SILENT_D, AUTO, silentConvert
class Search:
""" """
def __init__(self, api, silent=SILENT_D):
silent = silentConvert(silent)
self.api = api
self.silent = silent
self.exe = None
perfDefaults = SearchExe.perfDefaults
self.perfParams = {}
self.perfParams.update(perfDefaults)
def tweakPerformance(self, silent=SILENT_D, **kwargs):
"""Tweak parameters that influence the search process.
!!! explanation "Theory"
Before the search engine retrieves result tuples of nodes,
there is a process to narrow down the search space.
See `tf.about.searchdesign` and remember that we use the term *yarn* for
the sets of candidate nodes from which we stitch our results together.
*Edge spinning* is the process of
transferring constraints on one node via edges to constraints on
another node. The one node lives in a yarn, i.e. a set of candidate nodes,
and the node at the other side of the edge lives in a yarn.
If the first yarn is small then we might be able to reduce the second yarn
by computing the counterparts of the nodes in the small yarn in the second
yarn. We can leave out all other nodes from the second yarn.
A big reduction!
The success of edge spinning depends mainly on two factors:
!!! info "Size difference"
Edge spinning works best if there is a big difference in size
between the candidate
sets for the nodes at both sides of an edge.
!!! info "Spread"
The spread of a relation is the number of different edges
that can start from the same node or end at the same node.
For example, the spread of the `equality` operator is just 1, but
the spread of the `inequality` operator is virtually as big
as the relevant yarn.
If there are constraints on a node in one yarn, and if there is an edge
between that yarn and another one, and if the spread is big,
not much of the constraint can be transferred to the other yarn.
!!! example "Example"
Suppose both yarns are words, the first yarn has been constrained
to verbs, and the equality relation holds must hold between the yarns.
Then in all results the node from the second yarn is also a verb.
So we can constrain the second yarn to verbs too.
But if the relation is inequality, we cannot impose any additional
restriction on nodes in the second yarn. All nodes in the second
yarn are unequal to at least one verb.
!!! info "Estimating the spread"
We estimate the spreads of edges over and over again, in a series
of iterations where we reduce yarns.
An exhaustive computation would be too expensive, so we take
a sample of a limited amount of relation computations.
If you do not pass a parameter, its value will not be changed.
If you pass `None` for a parameter, its value will be reset to the default value.
Here are the parameters that you can tweak:
Parameters
----------
yarnRatio: float
The `yarnRatio` is the minimal factor between the sizes of
the smallest and the biggest set of candidates of the nodes at both ends of
the edge. And that divided by the spread of the relation as estimated
by a sample.
!!! example "Example"
Suppose we set the `yarnRatio` to 1.5.
Then, if we have yarns of 100,000 and 10,000 members,
with a relation between them with spread 5,
then 100,000 / 10,000 / 5 = 2.
This is higher than the `yarnRatio` of 1.5,
so the search engine decides that edge spinning is worth it.
The reasoning is that the 10,000 nodes in the smallest yarn are expected
to reach only 10,000 * 5 nodes in the other yarn by the relation,
and so we can achieve a significant reduction.
If you have a very slow query, and you think that a bit more edge spinning
helps, decrease the `yarnRatio` towards 0.
If you find that a lot of queries spend too much time in edge spinning,
increase the `yarnRatio`.
tryLimitFrom: integer
In order to determine the spreads of the relations, TF takes
random samples and extrapolates the results. We grab some nodes
from the set at the *from* side of an edge, and some nodes at the
*to* side of the same edge, Then we compute in how many cases the relation
holds. That is a measure for the spread.
The parameters `tryLimitFrom` and `tryLimitTo` dictate how big these
samples are. The bigger, the better the estimation of the spread.
But also the more work it is.
If you find that your queries take consistently a tad too much time,
consider lowering these parameters to 10.
If you find that the times your queries take varies a lot,
increase these values to 10000.
tryLimitTo: integer
See `tryLimitFrom`
"""
silent = silentConvert(silent)
api = self.api
TF = api.TF
error = TF.error
info = TF.info
isSilent = TF.isSilent
setSilent = TF.setSilent
defaults = SearchExe.perfDefaults
wasSilent = isSilent()
setSilent(silent)
for (k, v) in kwargs.items():
if k not in defaults:
error(f'No such performance parameter: "{k}"', tm=False)
continue
if v is None:
v = defaults[k]
elif type(v) is not int and k != "yarnRatio":
error(
f'Performance parameter "{k}" must be set to an integer, not to "{v}"',
tm=False,
)
continue
self.perfParams[k] = v
info("Performance parameters, current values:", tm=False)
for (k, v) in sorted(self.perfParams.items()):
info(f"\t{k:<20} = {v:>7}", tm=False)
SearchExe.setPerfParams(self.perfParams)
setSilent(wasSilent)
def search(
self,
searchTemplate,
limit=None,
sets=None,
shallow=False,
silent=SILENT_D,
here=True,
_msgCache=False,
):
"""Searches for combinations of nodes that together match a search template.
If you can, you should use `tf.advanced.search.search` instead.
Parameters
----------
searchTemplate: string
A string that conforms to the rules described in `tf.about.searchusage`.
shallow: set | tuple
If `True` or `1`, the result is a set of things that match the
top-level element of the `query`.
If `2` or a bigger number `n`, return the set of truncated result tuples:
only the first `n` members of each tuple is retained.
If `False` or `0`, a sorted list of all result tuples will be returned.
sets: dict
If not `None`, it should be a dictionary of sets, keyed by a names.
limit: integer, optional None
If `limit` is a positive number, it will fetch only that many results.
If it is negative, 0, None, or absent, it will fetch arbitrary many results.
!!! caution "there is an upper *fail limit* for safety reasons.
The limit is a factor times the max node in your corpus.
See `tf.parameters.SEARCH_FAIL_FACTOR`.
If this *fail limit* is exceeded in cases where no positive `limit`
has been passed, you get a warning message.
Returns
-------
generator | tuple
Each result is a tuple of nodes, where each node corresponds to an
*atom*-line in your search template (see `tf.about.searchusage`).
If `limit` is not `None`, a *generator* is returned,
which yields the results one by one.
Otherwise, the results will be fetched up till `limit`
and delivered as a tuple.
Notes
-----
!!! hint "More info on the search plan"
Searching is complex. The search template must be parsed, interpreted,
and translated into a search plan. See `tf.search.search.Search.study`.
"""
exe = SearchExe(
self.api,
searchTemplate,
outerTemplate=searchTemplate,
quKind=None,
offset=0,
sets=sets,
shallow=shallow,
silent=silent,
_msgCache=_msgCache,
setInfo={},
)
if here:
self.exe = exe
queryResults = exe.search(limit=limit)
if type(_msgCache) is list:
(status, messages) = wrapMessages(_msgCache)
self._msgCache = _msgCache
return (
(queryResults, status, messages)
if here
else (queryResults, status, messages, exe)
)
return queryResults
def study(
self,
searchTemplate,
strategy=None,
sets=None,
shallow=False,
here=True,
silent=SILENT_D,
):
"""Studies a template to prepare for searching with it.
The search space will be narrowed down and a plan for retrieving the results
will be set up.
If the search template query has quantifiers, the associated search templates
will be constructed and executed. These searches will be reported clearly.
The resulting plan can be viewed by `tf.search.search.Search.showPlan`.
Parameters
----------
searchTemplate: string
A string that conforms to the rules described in `tf.about.searchusage`.
strategy: string
In order to tame the performance of search, the strategy by which results
are fetched matters a lot. The search strategy is an implementation detail,
but we bring it to the surface nevertheless.
To see the names of the available strategies, just call
`S.study('', strategy='x')` and you will get a list of options reported to
choose from.
Feel free to experiment. To see what the strategies do, see the
code in `tf.search.stitch`.
shallow: set | tuple
If `True` or `1`, the result is a set of things that match the
top-level element of the search template.
If `2` or a bigger number `n`, return the set of truncated result tuples:
only the first `n` members of each tuple is retained.
If `False` or `0`, a sorted list of all result tuples will be returned.
sets: dict
If not `None`, it should be a dictionary of sets, keyed by a names.
In the search template you can refer to those names to invoke those sets.
silent: string, optional tf.core.timestamp.SILENT_D
See `tf.core.timestamp.Timestamp`
See Also
--------
tf.about.searchusage: Search guide
"""
if silent is False:
silent = AUTO
exe = SearchExe(
self.api,
searchTemplate,
outerTemplate=searchTemplate,
quKind=None,
offset=0,
sets=sets,
shallow=shallow,
silent=SILENT_D,
showQuantifiers=True,
setInfo={},
)
if here:
self.exe = exe
return exe.study(strategy=strategy)
def fetch(self, limit=None, _msgCache=False):
"""Retrieves query results, up to a limit.
Must be called after a previous `tf.search.search.Search.search()` or
`tf.search.search.Search.study()`.
Parameters
----------
limit: integer, optional None
If `limit` is a positive number, it will fetch only that many results.
If it is negative, 0, None, or absent, it will fetch arbitrary many results.
!!! caution "there is an upper *fail limit* for safety reasons.
The limit is a factor times the max node in your corpus.
See `tf.parameters.SEARCH_FAIL_FACTOR`.
If this *fail limit* is exceeded in cases where no positive `limit`
has been passed, you get a warning message.
Returns
-------
generator | tuple
Each result is a tuple of nodes, where each node corresponds to an
*atom*-line in your search template (see `tf.about.searchusage`).
If `limit` is not `None`, a *generator* is returned,
which yields the results one by one.
Otherwise, the results will be fetched up till `limit`
and delivered as a tuple.
Notes
-----
!!! example "Iterating over the `fetch()` generator"
You typically fetch results by saying:
i = 0
for tup in S.results():
do_something(tup[0])
do_something_else(tup[1])
Alternatively, you can set the `limit` parameter, to ask for just so many
results. They will be fetched, and when they are all collected,
returned as a tuple.
!!! example "Fetching a limited amount of results"
This
S.fetch(limit=10)
gives you the first 10 results without further ado.
"""
exe = self.exe
TF = self.api.TF
if exe is None:
error = TF.error
error('Cannot fetch if there is no previous "study()"')
else:
queryResults = exe.fetch(limit=limit)
if type(_msgCache) is list:
messages = TF.cache(_asString=True)
return (queryResults, messages)
return queryResults
def count(self, progress=None, limit=None):
"""Counts the results, with progress messages, optionally up to a limit.
Must be called after a previous `tf.search.search.Search.search()` or
`tf.search.search.Search.study()`.
Parameters
----------
progress: integer, optional, default `100`
Every once for every `progress` results a progress message is shown
when fetching results.
limit: integer, optional None
If `limit` is a positive number, it will fetch only that many results.
If it is negative, 0, None, or absent, it will fetch arbitrary many results.
!!! caution "there is an upper *fail limit* for safety reasons.
The limit is a factor times the max node in your corpus.
See `tf.parameters.SEARCH_FAIL_FACTOR`.
If this *fail limit* is exceeded in cases where no positive `limit`
has been passed, you get a warning message.
!!! note "why needed"
You typically need this in cases where result fetching turns out to
be (very) slow.
!!! caution "generator versus list"
`len(S.results())` does not work in general, because `S.results()` is
usually a generator that delivers its results as they come.
Returns
-------
None
The point of this function is to show the counting of the results
on the screen in a series of timed messages.
"""
exe = self.exe
if exe is None:
error = self.api.TF.error
error('Cannot count if there is no previous "study()"')
else:
exe.count(progress=progress, limit=limit)
def showPlan(self, details=False):
"""Show the result of the latest study of a template.
Search results are tuples of nodes and the plan shows which part of the tuple
corresponds to which part of the search template.
Only meaningful after a previous `tf.search.search.Search.study`.
Parameters
----------
details: boolean, optional False
If `True`, more information will be provided:
an overview of the search space and a description of how the results
will be retrieved.
!!! note "after S.study()"
This function is only meaningful after a call to `S.study()`.
"""
exe = self.exe
if exe is None:
error = self.api.TF.error
error('Cannot show plan if there is no previous "study()"')
else:
exe.showPlan(details=details)
def relationsLegend(self):
"""Dynamic info about the basic relations that can be used in templates.
It includes the edge features that are available in your dataset.
Returns
-------
None
The legend will be shown in the output.
"""
exe = self.exe
if exe is None:
exe = SearchExe(self.api, "")
console(exe.relationLegend)
def glean(self, tup):
"""Renders a single result into something human readable.
A search result is just a tuple of nodes that correspond to your template, as
indicated by `showPlan()`. Nodes give you access to all information that the
corpus has about it.
This function is meant to just give you a quick first impression.
Parameters
----------
tup: tuple of int
The tuple of nodes in question.
Returns
-------
string
The result indicates where the tuple occurs in terms of sections,
and what text is associated with the tuple.
Notes
-----
!!! example "Inspecting results"
This
for result in S.fetch(limit=10):
TF.info(S.glean(result))
is a handy way to get an impression of the first bunch of results.
!!! hint "Universal"
This function works on all tuples of nodes, whether they have been
obtained by search or not.
!!! hint "More ways of showing results"
The advanced API offers much better ways of showing results.
See `tf.advanced.display.show` and `tf.advanced.display.table`.
"""
T = self.api.T
F = self.api.F
E = self.api.E
fOtype = F.otype.v
slotType = F.otype.slotType
maxSlot = F.otype.maxSlot
eoslots = E.oslots.data
lR = len(tup)
if lR == 0:
return ""
fields = []
for (i, n) in enumerate(tup):
otype = fOtype(n)
words = [n] if otype == slotType else eoslots[n - maxSlot - 1]
if otype == T.sectionTypes[2]:
field = "{} {}:{}".format(*T.sectionFromNode(n))
elif otype == slotType:
field = T.text(words)
elif otype in T.sectionTypes[0:2]:
field = ""
else:
field = "{}[{}{}]".format(
otype,
T.text(words[0:5]),
"..." if len(words) > 5 else "",
)
fields.append(field)
return " ".join(fields)Classes
class Search (api, silent='auto')-
Expand source code Browse git
class Search: """ """ def __init__(self, api, silent=SILENT_D): silent = silentConvert(silent) self.api = api self.silent = silent self.exe = None perfDefaults = SearchExe.perfDefaults self.perfParams = {} self.perfParams.update(perfDefaults) def tweakPerformance(self, silent=SILENT_D, **kwargs): """Tweak parameters that influence the search process. !!! explanation "Theory" Before the search engine retrieves result tuples of nodes, there is a process to narrow down the search space. See `tf.about.searchdesign` and remember that we use the term *yarn* for the sets of candidate nodes from which we stitch our results together. *Edge spinning* is the process of transferring constraints on one node via edges to constraints on another node. The one node lives in a yarn, i.e. a set of candidate nodes, and the node at the other side of the edge lives in a yarn. If the first yarn is small then we might be able to reduce the second yarn by computing the counterparts of the nodes in the small yarn in the second yarn. We can leave out all other nodes from the second yarn. A big reduction! The success of edge spinning depends mainly on two factors: !!! info "Size difference" Edge spinning works best if there is a big difference in size between the candidate sets for the nodes at both sides of an edge. !!! info "Spread" The spread of a relation is the number of different edges that can start from the same node or end at the same node. For example, the spread of the `equality` operator is just 1, but the spread of the `inequality` operator is virtually as big as the relevant yarn. If there are constraints on a node in one yarn, and if there is an edge between that yarn and another one, and if the spread is big, not much of the constraint can be transferred to the other yarn. !!! example "Example" Suppose both yarns are words, the first yarn has been constrained to verbs, and the equality relation holds must hold between the yarns. Then in all results the node from the second yarn is also a verb. So we can constrain the second yarn to verbs too. But if the relation is inequality, we cannot impose any additional restriction on nodes in the second yarn. All nodes in the second yarn are unequal to at least one verb. !!! info "Estimating the spread" We estimate the spreads of edges over and over again, in a series of iterations where we reduce yarns. An exhaustive computation would be too expensive, so we take a sample of a limited amount of relation computations. If you do not pass a parameter, its value will not be changed. If you pass `None` for a parameter, its value will be reset to the default value. Here are the parameters that you can tweak: Parameters ---------- yarnRatio: float The `yarnRatio` is the minimal factor between the sizes of the smallest and the biggest set of candidates of the nodes at both ends of the edge. And that divided by the spread of the relation as estimated by a sample. !!! example "Example" Suppose we set the `yarnRatio` to 1.5. Then, if we have yarns of 100,000 and 10,000 members, with a relation between them with spread 5, then 100,000 / 10,000 / 5 = 2. This is higher than the `yarnRatio` of 1.5, so the search engine decides that edge spinning is worth it. The reasoning is that the 10,000 nodes in the smallest yarn are expected to reach only 10,000 * 5 nodes in the other yarn by the relation, and so we can achieve a significant reduction. If you have a very slow query, and you think that a bit more edge spinning helps, decrease the `yarnRatio` towards 0. If you find that a lot of queries spend too much time in edge spinning, increase the `yarnRatio`. tryLimitFrom: integer In order to determine the spreads of the relations, TF takes random samples and extrapolates the results. We grab some nodes from the set at the *from* side of an edge, and some nodes at the *to* side of the same edge, Then we compute in how many cases the relation holds. That is a measure for the spread. The parameters `tryLimitFrom` and `tryLimitTo` dictate how big these samples are. The bigger, the better the estimation of the spread. But also the more work it is. If you find that your queries take consistently a tad too much time, consider lowering these parameters to 10. If you find that the times your queries take varies a lot, increase these values to 10000. tryLimitTo: integer See `tryLimitFrom` """ silent = silentConvert(silent) api = self.api TF = api.TF error = TF.error info = TF.info isSilent = TF.isSilent setSilent = TF.setSilent defaults = SearchExe.perfDefaults wasSilent = isSilent() setSilent(silent) for (k, v) in kwargs.items(): if k not in defaults: error(f'No such performance parameter: "{k}"', tm=False) continue if v is None: v = defaults[k] elif type(v) is not int and k != "yarnRatio": error( f'Performance parameter "{k}" must be set to an integer, not to "{v}"', tm=False, ) continue self.perfParams[k] = v info("Performance parameters, current values:", tm=False) for (k, v) in sorted(self.perfParams.items()): info(f"\t{k:<20} = {v:>7}", tm=False) SearchExe.setPerfParams(self.perfParams) setSilent(wasSilent) def search( self, searchTemplate, limit=None, sets=None, shallow=False, silent=SILENT_D, here=True, _msgCache=False, ): """Searches for combinations of nodes that together match a search template. If you can, you should use `tf.advanced.search.search` instead. Parameters ---------- searchTemplate: string A string that conforms to the rules described in `tf.about.searchusage`. shallow: set | tuple If `True` or `1`, the result is a set of things that match the top-level element of the `query`. If `2` or a bigger number `n`, return the set of truncated result tuples: only the first `n` members of each tuple is retained. If `False` or `0`, a sorted list of all result tuples will be returned. sets: dict If not `None`, it should be a dictionary of sets, keyed by a names. limit: integer, optional None If `limit` is a positive number, it will fetch only that many results. If it is negative, 0, None, or absent, it will fetch arbitrary many results. !!! caution "there is an upper *fail limit* for safety reasons. The limit is a factor times the max node in your corpus. See `tf.parameters.SEARCH_FAIL_FACTOR`. If this *fail limit* is exceeded in cases where no positive `limit` has been passed, you get a warning message. Returns ------- generator | tuple Each result is a tuple of nodes, where each node corresponds to an *atom*-line in your search template (see `tf.about.searchusage`). If `limit` is not `None`, a *generator* is returned, which yields the results one by one. Otherwise, the results will be fetched up till `limit` and delivered as a tuple. Notes ----- !!! hint "More info on the search plan" Searching is complex. The search template must be parsed, interpreted, and translated into a search plan. See `tf.search.search.Search.study`. """ exe = SearchExe( self.api, searchTemplate, outerTemplate=searchTemplate, quKind=None, offset=0, sets=sets, shallow=shallow, silent=silent, _msgCache=_msgCache, setInfo={}, ) if here: self.exe = exe queryResults = exe.search(limit=limit) if type(_msgCache) is list: (status, messages) = wrapMessages(_msgCache) self._msgCache = _msgCache return ( (queryResults, status, messages) if here else (queryResults, status, messages, exe) ) return queryResults def study( self, searchTemplate, strategy=None, sets=None, shallow=False, here=True, silent=SILENT_D, ): """Studies a template to prepare for searching with it. The search space will be narrowed down and a plan for retrieving the results will be set up. If the search template query has quantifiers, the associated search templates will be constructed and executed. These searches will be reported clearly. The resulting plan can be viewed by `tf.search.search.Search.showPlan`. Parameters ---------- searchTemplate: string A string that conforms to the rules described in `tf.about.searchusage`. strategy: string In order to tame the performance of search, the strategy by which results are fetched matters a lot. The search strategy is an implementation detail, but we bring it to the surface nevertheless. To see the names of the available strategies, just call `S.study('', strategy='x')` and you will get a list of options reported to choose from. Feel free to experiment. To see what the strategies do, see the code in `tf.search.stitch`. shallow: set | tuple If `True` or `1`, the result is a set of things that match the top-level element of the search template. If `2` or a bigger number `n`, return the set of truncated result tuples: only the first `n` members of each tuple is retained. If `False` or `0`, a sorted list of all result tuples will be returned. sets: dict If not `None`, it should be a dictionary of sets, keyed by a names. In the search template you can refer to those names to invoke those sets. silent: string, optional tf.core.timestamp.SILENT_D See `tf.core.timestamp.Timestamp` See Also -------- tf.about.searchusage: Search guide """ if silent is False: silent = AUTO exe = SearchExe( self.api, searchTemplate, outerTemplate=searchTemplate, quKind=None, offset=0, sets=sets, shallow=shallow, silent=SILENT_D, showQuantifiers=True, setInfo={}, ) if here: self.exe = exe return exe.study(strategy=strategy) def fetch(self, limit=None, _msgCache=False): """Retrieves query results, up to a limit. Must be called after a previous `tf.search.search.Search.search()` or `tf.search.search.Search.study()`. Parameters ---------- limit: integer, optional None If `limit` is a positive number, it will fetch only that many results. If it is negative, 0, None, or absent, it will fetch arbitrary many results. !!! caution "there is an upper *fail limit* for safety reasons. The limit is a factor times the max node in your corpus. See `tf.parameters.SEARCH_FAIL_FACTOR`. If this *fail limit* is exceeded in cases where no positive `limit` has been passed, you get a warning message. Returns ------- generator | tuple Each result is a tuple of nodes, where each node corresponds to an *atom*-line in your search template (see `tf.about.searchusage`). If `limit` is not `None`, a *generator* is returned, which yields the results one by one. Otherwise, the results will be fetched up till `limit` and delivered as a tuple. Notes ----- !!! example "Iterating over the `fetch()` generator" You typically fetch results by saying: i = 0 for tup in S.results(): do_something(tup[0]) do_something_else(tup[1]) Alternatively, you can set the `limit` parameter, to ask for just so many results. They will be fetched, and when they are all collected, returned as a tuple. !!! example "Fetching a limited amount of results" This S.fetch(limit=10) gives you the first 10 results without further ado. """ exe = self.exe TF = self.api.TF if exe is None: error = TF.error error('Cannot fetch if there is no previous "study()"') else: queryResults = exe.fetch(limit=limit) if type(_msgCache) is list: messages = TF.cache(_asString=True) return (queryResults, messages) return queryResults def count(self, progress=None, limit=None): """Counts the results, with progress messages, optionally up to a limit. Must be called after a previous `tf.search.search.Search.search()` or `tf.search.search.Search.study()`. Parameters ---------- progress: integer, optional, default `100` Every once for every `progress` results a progress message is shown when fetching results. limit: integer, optional None If `limit` is a positive number, it will fetch only that many results. If it is negative, 0, None, or absent, it will fetch arbitrary many results. !!! caution "there is an upper *fail limit* for safety reasons. The limit is a factor times the max node in your corpus. See `tf.parameters.SEARCH_FAIL_FACTOR`. If this *fail limit* is exceeded in cases where no positive `limit` has been passed, you get a warning message. !!! note "why needed" You typically need this in cases where result fetching turns out to be (very) slow. !!! caution "generator versus list" `len(S.results())` does not work in general, because `S.results()` is usually a generator that delivers its results as they come. Returns ------- None The point of this function is to show the counting of the results on the screen in a series of timed messages. """ exe = self.exe if exe is None: error = self.api.TF.error error('Cannot count if there is no previous "study()"') else: exe.count(progress=progress, limit=limit) def showPlan(self, details=False): """Show the result of the latest study of a template. Search results are tuples of nodes and the plan shows which part of the tuple corresponds to which part of the search template. Only meaningful after a previous `tf.search.search.Search.study`. Parameters ---------- details: boolean, optional False If `True`, more information will be provided: an overview of the search space and a description of how the results will be retrieved. !!! note "after S.study()" This function is only meaningful after a call to `S.study()`. """ exe = self.exe if exe is None: error = self.api.TF.error error('Cannot show plan if there is no previous "study()"') else: exe.showPlan(details=details) def relationsLegend(self): """Dynamic info about the basic relations that can be used in templates. It includes the edge features that are available in your dataset. Returns ------- None The legend will be shown in the output. """ exe = self.exe if exe is None: exe = SearchExe(self.api, "") console(exe.relationLegend) def glean(self, tup): """Renders a single result into something human readable. A search result is just a tuple of nodes that correspond to your template, as indicated by `showPlan()`. Nodes give you access to all information that the corpus has about it. This function is meant to just give you a quick first impression. Parameters ---------- tup: tuple of int The tuple of nodes in question. Returns ------- string The result indicates where the tuple occurs in terms of sections, and what text is associated with the tuple. Notes ----- !!! example "Inspecting results" This for result in S.fetch(limit=10): TF.info(S.glean(result)) is a handy way to get an impression of the first bunch of results. !!! hint "Universal" This function works on all tuples of nodes, whether they have been obtained by search or not. !!! hint "More ways of showing results" The advanced API offers much better ways of showing results. See `tf.advanced.display.show` and `tf.advanced.display.table`. """ T = self.api.T F = self.api.F E = self.api.E fOtype = F.otype.v slotType = F.otype.slotType maxSlot = F.otype.maxSlot eoslots = E.oslots.data lR = len(tup) if lR == 0: return "" fields = [] for (i, n) in enumerate(tup): otype = fOtype(n) words = [n] if otype == slotType else eoslots[n - maxSlot - 1] if otype == T.sectionTypes[2]: field = "{} {}:{}".format(*T.sectionFromNode(n)) elif otype == slotType: field = T.text(words) elif otype in T.sectionTypes[0:2]: field = "" else: field = "{}[{}{}]".format( otype, T.text(words[0:5]), "..." if len(words) > 5 else "", ) fields.append(field) return " ".join(fields)Methods
def count(self, progress=None, limit=None)-
Counts the results, with progress messages, optionally up to a limit.
Must be called after a previous
Search.search()orSearch.study().Parameters
progress:integer, optional, default100- Every once for every
progressresults a progress message is shown when fetching results. limit:integer, optionalNone-
If
limitis a positive number, it will fetch only that many results. If it is negative, 0, None, or absent, it will fetch arbitrary many results.!!! caution "there is an upper fail limit for safety reasons. The limit is a factor times the max node in your corpus. See
SEARCH_FAIL_FACTOR. If this fail limit is exceeded in cases where no positivelimithas been passed, you get a warning message.
why needed
You typically need this in cases where result fetching turns out to be (very) slow.
generator versus list
len(S.results())does not work in general, becauseS.results()is usually a generator that delivers its results as they come.Returns
None- The point of this function is to show the counting of the results on the screen in a series of timed messages.
Expand source code Browse git
def count(self, progress=None, limit=None): """Counts the results, with progress messages, optionally up to a limit. Must be called after a previous `tf.search.search.Search.search()` or `tf.search.search.Search.study()`. Parameters ---------- progress: integer, optional, default `100` Every once for every `progress` results a progress message is shown when fetching results. limit: integer, optional None If `limit` is a positive number, it will fetch only that many results. If it is negative, 0, None, or absent, it will fetch arbitrary many results. !!! caution "there is an upper *fail limit* for safety reasons. The limit is a factor times the max node in your corpus. See `tf.parameters.SEARCH_FAIL_FACTOR`. If this *fail limit* is exceeded in cases where no positive `limit` has been passed, you get a warning message. !!! note "why needed" You typically need this in cases where result fetching turns out to be (very) slow. !!! caution "generator versus list" `len(S.results())` does not work in general, because `S.results()` is usually a generator that delivers its results as they come. Returns ------- None The point of this function is to show the counting of the results on the screen in a series of timed messages. """ exe = self.exe if exe is None: error = self.api.TF.error error('Cannot count if there is no previous "study()"') else: exe.count(progress=progress, limit=limit) def fetch(self, limit=None)-
Retrieves query results, up to a limit.
Must be called after a previous
Search.search()orSearch.study().Parameters
limit:integer, optionalNone-
If
limitis a positive number, it will fetch only that many results. If it is negative, 0, None, or absent, it will fetch arbitrary many results.!!! caution "there is an upper fail limit for safety reasons. The limit is a factor times the max node in your corpus. See
SEARCH_FAIL_FACTOR. If this fail limit is exceeded in cases where no positivelimithas been passed, you get a warning message.
Returns
generator | tuple-
Each result is a tuple of nodes, where each node corresponds to an atom-line in your search template (see
tf.about.searchusage).If
limitis notNone, a generator is returned, which yields the results one by one.Otherwise, the results will be fetched up till
limitand delivered as a tuple.
Notes
Iterating over the
fetch()generatorYou typically fetch results by saying:
i = 0 for tup in S.results(): do_something(tup[0]) do_something_else(tup[1])Alternatively, you can set the
limitparameter, to ask for just so many results. They will be fetched, and when they are all collected, returned as a tuple.Fetching a limited amount of results
This
S.fetch(limit=10)gives you the first 10 results without further ado.
Expand source code Browse git
def fetch(self, limit=None, _msgCache=False): """Retrieves query results, up to a limit. Must be called after a previous `tf.search.search.Search.search()` or `tf.search.search.Search.study()`. Parameters ---------- limit: integer, optional None If `limit` is a positive number, it will fetch only that many results. If it is negative, 0, None, or absent, it will fetch arbitrary many results. !!! caution "there is an upper *fail limit* for safety reasons. The limit is a factor times the max node in your corpus. See `tf.parameters.SEARCH_FAIL_FACTOR`. If this *fail limit* is exceeded in cases where no positive `limit` has been passed, you get a warning message. Returns ------- generator | tuple Each result is a tuple of nodes, where each node corresponds to an *atom*-line in your search template (see `tf.about.searchusage`). If `limit` is not `None`, a *generator* is returned, which yields the results one by one. Otherwise, the results will be fetched up till `limit` and delivered as a tuple. Notes ----- !!! example "Iterating over the `fetch()` generator" You typically fetch results by saying: i = 0 for tup in S.results(): do_something(tup[0]) do_something_else(tup[1]) Alternatively, you can set the `limit` parameter, to ask for just so many results. They will be fetched, and when they are all collected, returned as a tuple. !!! example "Fetching a limited amount of results" This S.fetch(limit=10) gives you the first 10 results without further ado. """ exe = self.exe TF = self.api.TF if exe is None: error = TF.error error('Cannot fetch if there is no previous "study()"') else: queryResults = exe.fetch(limit=limit) if type(_msgCache) is list: messages = TF.cache(_asString=True) return (queryResults, messages) return queryResults def glean(self, tup)-
Renders a single result into something human readable.
A search result is just a tuple of nodes that correspond to your template, as indicated by
showPlan(). Nodes give you access to all information that the corpus has about it.This function is meant to just give you a quick first impression.
Parameters
tup:tupleofint- The tuple of nodes in question.
Returns
string- The result indicates where the tuple occurs in terms of sections, and what text is associated with the tuple.
Notes
Inspecting results
This
for result in S.fetch(limit=10): TF.info(S.glean(result))is a handy way to get an impression of the first bunch of results.
Universal
This function works on all tuples of nodes, whether they have been obtained by search or not.
Expand source code Browse git
def glean(self, tup): """Renders a single result into something human readable. A search result is just a tuple of nodes that correspond to your template, as indicated by `showPlan()`. Nodes give you access to all information that the corpus has about it. This function is meant to just give you a quick first impression. Parameters ---------- tup: tuple of int The tuple of nodes in question. Returns ------- string The result indicates where the tuple occurs in terms of sections, and what text is associated with the tuple. Notes ----- !!! example "Inspecting results" This for result in S.fetch(limit=10): TF.info(S.glean(result)) is a handy way to get an impression of the first bunch of results. !!! hint "Universal" This function works on all tuples of nodes, whether they have been obtained by search or not. !!! hint "More ways of showing results" The advanced API offers much better ways of showing results. See `tf.advanced.display.show` and `tf.advanced.display.table`. """ T = self.api.T F = self.api.F E = self.api.E fOtype = F.otype.v slotType = F.otype.slotType maxSlot = F.otype.maxSlot eoslots = E.oslots.data lR = len(tup) if lR == 0: return "" fields = [] for (i, n) in enumerate(tup): otype = fOtype(n) words = [n] if otype == slotType else eoslots[n - maxSlot - 1] if otype == T.sectionTypes[2]: field = "{} {}:{}".format(*T.sectionFromNode(n)) elif otype == slotType: field = T.text(words) elif otype in T.sectionTypes[0:2]: field = "" else: field = "{}[{}{}]".format( otype, T.text(words[0:5]), "..." if len(words) > 5 else "", ) fields.append(field) return " ".join(fields) def relationsLegend(self)-
Dynamic info about the basic relations that can be used in templates.
It includes the edge features that are available in your dataset.
Returns
None- The legend will be shown in the output.
Expand source code Browse git
def relationsLegend(self): """Dynamic info about the basic relations that can be used in templates. It includes the edge features that are available in your dataset. Returns ------- None The legend will be shown in the output. """ exe = self.exe if exe is None: exe = SearchExe(self.api, "") console(exe.relationLegend) def search(self, searchTemplate, limit=None, sets=None, shallow=False, silent='auto', here=True)-
Searches for combinations of nodes that together match a search template.
If you can, you should use
search()instead.Parameters
searchTemplate:string- A string that conforms to the rules described in
tf.about.searchusage. shallow:set | tuple-
If
Trueor1, the result is a set of things that match the top-level element of thequery.If
2or a bigger numbern, return the set of truncated result tuples: only the firstnmembers of each tuple is retained.If
Falseor0, a sorted list of all result tuples will be returned. sets:dict- If not
None, it should be a dictionary of sets, keyed by a names. limit:integer, optionalNone-
If
limitis a positive number, it will fetch only that many results. If it is negative, 0, None, or absent, it will fetch arbitrary many results.!!! caution "there is an upper fail limit for safety reasons. The limit is a factor times the max node in your corpus. See
SEARCH_FAIL_FACTOR. If this fail limit is exceeded in cases where no positivelimithas been passed, you get a warning message.
Returns
generator | tuple-
Each result is a tuple of nodes, where each node corresponds to an atom-line in your search template (see
tf.about.searchusage).If
limitis notNone, a generator is returned, which yields the results one by one.Otherwise, the results will be fetched up till
limitand delivered as a tuple.
Notes
More info on the search plan
Searching is complex. The search template must be parsed, interpreted, and translated into a search plan. See
Search.study().Expand source code Browse git
def search( self, searchTemplate, limit=None, sets=None, shallow=False, silent=SILENT_D, here=True, _msgCache=False, ): """Searches for combinations of nodes that together match a search template. If you can, you should use `tf.advanced.search.search` instead. Parameters ---------- searchTemplate: string A string that conforms to the rules described in `tf.about.searchusage`. shallow: set | tuple If `True` or `1`, the result is a set of things that match the top-level element of the `query`. If `2` or a bigger number `n`, return the set of truncated result tuples: only the first `n` members of each tuple is retained. If `False` or `0`, a sorted list of all result tuples will be returned. sets: dict If not `None`, it should be a dictionary of sets, keyed by a names. limit: integer, optional None If `limit` is a positive number, it will fetch only that many results. If it is negative, 0, None, or absent, it will fetch arbitrary many results. !!! caution "there is an upper *fail limit* for safety reasons. The limit is a factor times the max node in your corpus. See `tf.parameters.SEARCH_FAIL_FACTOR`. If this *fail limit* is exceeded in cases where no positive `limit` has been passed, you get a warning message. Returns ------- generator | tuple Each result is a tuple of nodes, where each node corresponds to an *atom*-line in your search template (see `tf.about.searchusage`). If `limit` is not `None`, a *generator* is returned, which yields the results one by one. Otherwise, the results will be fetched up till `limit` and delivered as a tuple. Notes ----- !!! hint "More info on the search plan" Searching is complex. The search template must be parsed, interpreted, and translated into a search plan. See `tf.search.search.Search.study`. """ exe = SearchExe( self.api, searchTemplate, outerTemplate=searchTemplate, quKind=None, offset=0, sets=sets, shallow=shallow, silent=silent, _msgCache=_msgCache, setInfo={}, ) if here: self.exe = exe queryResults = exe.search(limit=limit) if type(_msgCache) is list: (status, messages) = wrapMessages(_msgCache) self._msgCache = _msgCache return ( (queryResults, status, messages) if here else (queryResults, status, messages, exe) ) return queryResults def showPlan(self, details=False)-
Show the result of the latest study of a template.
Search results are tuples of nodes and the plan shows which part of the tuple corresponds to which part of the search template.
Only meaningful after a previous
Search.study().Parameters
details:boolean, optionalFalse- If
True, more information will be provided: an overview of the search space and a description of how the results will be retrieved.
after S.study()
This function is only meaningful after a call to
S.study().Expand source code Browse git
def showPlan(self, details=False): """Show the result of the latest study of a template. Search results are tuples of nodes and the plan shows which part of the tuple corresponds to which part of the search template. Only meaningful after a previous `tf.search.search.Search.study`. Parameters ---------- details: boolean, optional False If `True`, more information will be provided: an overview of the search space and a description of how the results will be retrieved. !!! note "after S.study()" This function is only meaningful after a call to `S.study()`. """ exe = self.exe if exe is None: error = self.api.TF.error error('Cannot show plan if there is no previous "study()"') else: exe.showPlan(details=details) def study(self, searchTemplate, strategy=None, sets=None, shallow=False, here=True, silent='auto')-
Studies a template to prepare for searching with it.
The search space will be narrowed down and a plan for retrieving the results will be set up.
If the search template query has quantifiers, the associated search templates will be constructed and executed. These searches will be reported clearly.
The resulting plan can be viewed by
Search.showPlan().Parameters
searchTemplate:string- A string that conforms to the rules described in
tf.about.searchusage. strategy:string-
In order to tame the performance of search, the strategy by which results are fetched matters a lot. The search strategy is an implementation detail, but we bring it to the surface nevertheless.
To see the names of the available strategies, just call
S.study('', strategy='x')and you will get a list of options reported to choose from.Feel free to experiment. To see what the strategies do, see the code in
tf.search.stitch. shallow:set | tuple-
If
Trueor1, the result is a set of things that match the top-level element of the search template.If
2or a bigger numbern, return the set of truncated result tuples: only the firstnmembers of each tuple is retained.If
Falseor0, a sorted list of all result tuples will be returned. sets:dict- If not
None, it should be a dictionary of sets, keyed by a names. In the search template you can refer to those names to invoke those sets. silent:string, optionalSILENT_D- See
Timestamp
See Also
tf.about.searchusage- Search guide
Expand source code Browse git
def study( self, searchTemplate, strategy=None, sets=None, shallow=False, here=True, silent=SILENT_D, ): """Studies a template to prepare for searching with it. The search space will be narrowed down and a plan for retrieving the results will be set up. If the search template query has quantifiers, the associated search templates will be constructed and executed. These searches will be reported clearly. The resulting plan can be viewed by `tf.search.search.Search.showPlan`. Parameters ---------- searchTemplate: string A string that conforms to the rules described in `tf.about.searchusage`. strategy: string In order to tame the performance of search, the strategy by which results are fetched matters a lot. The search strategy is an implementation detail, but we bring it to the surface nevertheless. To see the names of the available strategies, just call `S.study('', strategy='x')` and you will get a list of options reported to choose from. Feel free to experiment. To see what the strategies do, see the code in `tf.search.stitch`. shallow: set | tuple If `True` or `1`, the result is a set of things that match the top-level element of the search template. If `2` or a bigger number `n`, return the set of truncated result tuples: only the first `n` members of each tuple is retained. If `False` or `0`, a sorted list of all result tuples will be returned. sets: dict If not `None`, it should be a dictionary of sets, keyed by a names. In the search template you can refer to those names to invoke those sets. silent: string, optional tf.core.timestamp.SILENT_D See `tf.core.timestamp.Timestamp` See Also -------- tf.about.searchusage: Search guide """ if silent is False: silent = AUTO exe = SearchExe( self.api, searchTemplate, outerTemplate=searchTemplate, quKind=None, offset=0, sets=sets, shallow=shallow, silent=SILENT_D, showQuantifiers=True, setInfo={}, ) if here: self.exe = exe return exe.study(strategy=strategy) def tweakPerformance(self, silent='auto', **kwargs)-
Tweak parameters that influence the search process.
Theory
Before the search engine retrieves result tuples of nodes, there is a process to narrow down the search space.
See
tf.about.searchdesignand remember that we use the term yarn for the sets of candidate nodes from which we stitch our results together.Edge spinning is the process of transferring constraints on one node via edges to constraints on another node. The one node lives in a yarn, i.e. a set of candidate nodes, and the node at the other side of the edge lives in a yarn.
If the first yarn is small then we might be able to reduce the second yarn by computing the counterparts of the nodes in the small yarn in the second yarn. We can leave out all other nodes from the second yarn. A big reduction!
The success of edge spinning depends mainly on two factors:
Size difference
Edge spinning works best if there is a big difference in size between the candidate sets for the nodes at both sides of an edge.
Spread
The spread of a relation is the number of different edges that can start from the same node or end at the same node.
For example, the spread of the
equalityoperator is just 1, but the spread of theinequalityoperator is virtually as big as the relevant yarn.If there are constraints on a node in one yarn, and if there is an edge between that yarn and another one, and if the spread is big, not much of the constraint can be transferred to the other yarn.
Example
Suppose both yarns are words, the first yarn has been constrained to verbs, and the equality relation holds must hold between the yarns. Then in all results the node from the second yarn is also a verb. So we can constrain the second yarn to verbs too.
But if the relation is inequality, we cannot impose any additional restriction on nodes in the second yarn. All nodes in the second yarn are unequal to at least one verb.
Estimating the spread
We estimate the spreads of edges over and over again, in a series of iterations where we reduce yarns.
An exhaustive computation would be too expensive, so we take a sample of a limited amount of relation computations.
If you do not pass a parameter, its value will not be changed. If you pass
Nonefor a parameter, its value will be reset to the default value.Here are the parameters that you can tweak:
Parameters
yarnRatio:float-
The
yarnRatiois the minimal factor between the sizes of the smallest and the biggest set of candidates of the nodes at both ends of the edge. And that divided by the spread of the relation as estimated by a sample.Example
Suppose we set the
yarnRatioto 1.5. Then, if we have yarns of 100,000 and 10,000 members, with a relation between them with spread 5, then 100,000 / 10,000 / 5 = 2. This is higher than theyarnRatioof 1.5, so the search engine decides that edge spinning is worth it.The reasoning is that the 10,000 nodes in the smallest yarn are expected to reach only 10,000 * 5 nodes in the other yarn by the relation, and so we can achieve a significant reduction.
If you have a very slow query, and you think that a bit more edge spinning helps, decrease the
yarnRatiotowards 0.If you find that a lot of queries spend too much time in edge spinning, increase the
yarnRatio. tryLimitFrom:integer-
In order to determine the spreads of the relations, TF takes random samples and extrapolates the results. We grab some nodes from the set at the from side of an edge, and some nodes at the to side of the same edge, Then we compute in how many cases the relation holds. That is a measure for the spread.
The parameters
tryLimitFromandtryLimitTodictate how big these samples are. The bigger, the better the estimation of the spread. But also the more work it is.If you find that your queries take consistently a tad too much time, consider lowering these parameters to 10.
If you find that the times your queries take varies a lot, increase these values to 10000.
tryLimitTo:integer- See
tryLimitFrom
Expand source code Browse git
def tweakPerformance(self, silent=SILENT_D, **kwargs): """Tweak parameters that influence the search process. !!! explanation "Theory" Before the search engine retrieves result tuples of nodes, there is a process to narrow down the search space. See `tf.about.searchdesign` and remember that we use the term *yarn* for the sets of candidate nodes from which we stitch our results together. *Edge spinning* is the process of transferring constraints on one node via edges to constraints on another node. The one node lives in a yarn, i.e. a set of candidate nodes, and the node at the other side of the edge lives in a yarn. If the first yarn is small then we might be able to reduce the second yarn by computing the counterparts of the nodes in the small yarn in the second yarn. We can leave out all other nodes from the second yarn. A big reduction! The success of edge spinning depends mainly on two factors: !!! info "Size difference" Edge spinning works best if there is a big difference in size between the candidate sets for the nodes at both sides of an edge. !!! info "Spread" The spread of a relation is the number of different edges that can start from the same node or end at the same node. For example, the spread of the `equality` operator is just 1, but the spread of the `inequality` operator is virtually as big as the relevant yarn. If there are constraints on a node in one yarn, and if there is an edge between that yarn and another one, and if the spread is big, not much of the constraint can be transferred to the other yarn. !!! example "Example" Suppose both yarns are words, the first yarn has been constrained to verbs, and the equality relation holds must hold between the yarns. Then in all results the node from the second yarn is also a verb. So we can constrain the second yarn to verbs too. But if the relation is inequality, we cannot impose any additional restriction on nodes in the second yarn. All nodes in the second yarn are unequal to at least one verb. !!! info "Estimating the spread" We estimate the spreads of edges over and over again, in a series of iterations where we reduce yarns. An exhaustive computation would be too expensive, so we take a sample of a limited amount of relation computations. If you do not pass a parameter, its value will not be changed. If you pass `None` for a parameter, its value will be reset to the default value. Here are the parameters that you can tweak: Parameters ---------- yarnRatio: float The `yarnRatio` is the minimal factor between the sizes of the smallest and the biggest set of candidates of the nodes at both ends of the edge. And that divided by the spread of the relation as estimated by a sample. !!! example "Example" Suppose we set the `yarnRatio` to 1.5. Then, if we have yarns of 100,000 and 10,000 members, with a relation between them with spread 5, then 100,000 / 10,000 / 5 = 2. This is higher than the `yarnRatio` of 1.5, so the search engine decides that edge spinning is worth it. The reasoning is that the 10,000 nodes in the smallest yarn are expected to reach only 10,000 * 5 nodes in the other yarn by the relation, and so we can achieve a significant reduction. If you have a very slow query, and you think that a bit more edge spinning helps, decrease the `yarnRatio` towards 0. If you find that a lot of queries spend too much time in edge spinning, increase the `yarnRatio`. tryLimitFrom: integer In order to determine the spreads of the relations, TF takes random samples and extrapolates the results. We grab some nodes from the set at the *from* side of an edge, and some nodes at the *to* side of the same edge, Then we compute in how many cases the relation holds. That is a measure for the spread. The parameters `tryLimitFrom` and `tryLimitTo` dictate how big these samples are. The bigger, the better the estimation of the spread. But also the more work it is. If you find that your queries take consistently a tad too much time, consider lowering these parameters to 10. If you find that the times your queries take varies a lot, increase these values to 10000. tryLimitTo: integer See `tryLimitFrom` """ silent = silentConvert(silent) api = self.api TF = api.TF error = TF.error info = TF.info isSilent = TF.isSilent setSilent = TF.setSilent defaults = SearchExe.perfDefaults wasSilent = isSilent() setSilent(silent) for (k, v) in kwargs.items(): if k not in defaults: error(f'No such performance parameter: "{k}"', tm=False) continue if v is None: v = defaults[k] elif type(v) is not int and k != "yarnRatio": error( f'Performance parameter "{k}" must be set to an integer, not to "{v}"', tm=False, ) continue self.perfParams[k] = v info("Performance parameters, current values:", tm=False) for (k, v) in sorted(self.perfParams.items()): info(f"\t{k:<20} = {v:>7}", tm=False) SearchExe.setPerfParams(self.perfParams) setSilent(wasSilent)