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 ..parameters import YARN_RATIO, TRY_LIMIT_FROM, TRY_LIMIT_TO
from ..core.timestamp import SILENT_D, AUTO, silentConvert


class Search(object):
    """ """

    def __init__(self, api, silent=SILENT_D):
        silent = silentConvert(silent)
        self.api = api
        self.silent = silent
        self.exe = None
        self.perfDefaults = dict(
            yarnRatio=YARN_RATIO,
            tryLimitFrom=TRY_LIMIT_FROM,
            tryLimitTo=TRY_LIMIT_TO,
        )
        self.perfParams = {}
        self.perfParams.update(self.perfDefaults)
        SearchExe.setPerfParams(self.perfParams)

    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 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 = self.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.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:
            messages = wrapMessages(_msgCache)
            self._msgCache = _msgCache
            return (queryResults, messages) if here else (queryResults, 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 asscociated search templates
        will be constructed and executed. These searches will be reported clearly.

        The resulting plan can be viewd 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.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 indicats 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='terse')
Expand source code Browse git
class Search(object):
    """ """

    def __init__(self, api, silent=SILENT_D):
        silent = silentConvert(silent)
        self.api = api
        self.silent = silent
        self.exe = None
        self.perfDefaults = dict(
            yarnRatio=YARN_RATIO,
            tryLimitFrom=TRY_LIMIT_FROM,
            tryLimitTo=TRY_LIMIT_TO,
        )
        self.perfParams = {}
        self.perfParams.update(self.perfDefaults)
        SearchExe.setPerfParams(self.perfParams)

    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 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 = self.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.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:
            messages = wrapMessages(_msgCache)
            self._msgCache = _msgCache
            return (queryResults, messages) if here else (queryResults, 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 asscociated search templates
        will be constructed and executed. These searches will be reported clearly.

        The resulting plan can be viewd 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.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 indicats 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() or 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 SEARCH_FAIL_FACTOR. If this fail limit is exceeded in cases where no positive limit has 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, 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.
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() or 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 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.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

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.

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.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 : tuple of int
The tuple of nodes in question.

Returns

string
The result indicats 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.

More ways of showing results

The advanced API offers much better ways of showing results. See show() and table().

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 indicats 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='terse', 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 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 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.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

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.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:
        messages = wrapMessages(_msgCache)
        self._msgCache = _msgCache
        return (queryResults, messages) if here else (queryResults, 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, 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.

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='terse')

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 asscociated search templates will be constructed and executed. These searches will be reported clearly.

The resulting plan can be viewd 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 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 SILENT_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 asscociated search templates
    will be constructed and executed. These searches will be reported clearly.

    The resulting plan can be viewd 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='terse', **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.searchdesign and 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:

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 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

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 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

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
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 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 = self.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)