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How to use _log_phase_statistics method in hypothesis

Best Python code snippet using hypothesis
engine.py
Source:engine.py
...125        self.__pending_call_explanation = explanation126    def clear_call_explanation(self):127        self.__pending_call_explanation = None128    @contextmanager129    def _log_phase_statistics(self, phase):130        self.stats_per_test_case.clear()131        start_time = time.perf_counter()132        try:133            yield134        finally:135            self.statistics[phase + "-phase"] = {136                "duration-seconds": time.perf_counter() - start_time,137                "test-cases": list(self.stats_per_test_case),138                "distinct-failures": len(self.interesting_examples),139                "shrinks-successful": self.shrinks,140            }141    @property142    def should_optimise(self):143        return Phase.target in self.settings.phases144    def __tree_is_exhausted(self):145        return self.tree.is_exhausted146    def __stoppable_test_function(self, data):147        """Run ``self._test_function``, but convert a ``StopTest`` exception148        into a normal return and avoid raising Flaky for RecursionErrors.149        """150        depth = stack_depth_of_caller()151        # Because we add to the recursion limit, to be good citizens we also add152        # a check for unbounded recursion.  The default limit is 1000, so this can153        # only ever trigger if something really strange is happening and it's hard154        # to imagine an intentionally-deeply-recursive use of this code.155        assert depth <= 1000, (156            "Hypothesis would usually add %d to the stack depth of %d here, "157            "but we are already much deeper than expected.  Aborting now, to "158            "avoid extending the stack limit in an infinite loop..."159            % (self.__recursion_limit, depth)160        )161        try:162            sys.setrecursionlimit(depth + self.__recursion_limit)163            self._test_function(data)164        except StopTest as e:165            if e.testcounter == data.testcounter:166                # This StopTest has successfully stopped its test, and can now167                # be discarded.168                pass169            else:170                # This StopTest was raised by a different ConjectureData. We171                # need to re-raise it so that it will eventually reach the172                # correct engine.173                raise174        finally:175            sys.setrecursionlimit(self.__recursion_limit)176    def test_function(self, data):177        if self.__pending_call_explanation is not None:178            self.debug(self.__pending_call_explanation)179            self.__pending_call_explanation = None180        assert isinstance(data.observer, TreeRecordingObserver)181        self.call_count += 1182        interrupted = False183        try:184            self.__stoppable_test_function(data)185        except KeyboardInterrupt:186            interrupted = True187            raise188        except BaseException:189            self.save_buffer(data.buffer)190            raise191        finally:192            # No branch, because if we're interrupted we always raise193            # the KeyboardInterrupt, never continue to the code below.194            if not interrupted:  # pragma: no branch195                data.freeze()196                call_stats = {197                    "status": data.status.name.lower(),198                    "runtime": data.finish_time - data.start_time,199                    "drawtime": math.fsum(data.draw_times),200                    "events": sorted({self.event_to_string(e) for e in data.events}),201                }202                self.stats_per_test_case.append(call_stats)203                self.__data_cache[data.buffer] = data.as_result()204        self.debug_data(data)205        if self.pareto_front is not None and self.pareto_front.add(data.as_result()):206            self.save_buffer(data.buffer, sub_key=b"pareto")207        assert len(data.buffer) <= BUFFER_SIZE208        if data.status >= Status.VALID:209            for k, v in data.target_observations.items():210                self.best_observed_targets[k] = max(self.best_observed_targets[k], v)211                if k not in self.best_examples_of_observed_targets:212                    self.best_examples_of_observed_targets[k] = data.as_result()213                    continue214                existing_example = self.best_examples_of_observed_targets[k]215                existing_score = existing_example.target_observations[k]216                if v < existing_score:217                    continue218                if v > existing_score or sort_key(data.buffer) < sort_key(219                    existing_example.buffer220                ):221                    self.best_examples_of_observed_targets[k] = data.as_result()222        if data.status == Status.VALID:223            self.valid_examples += 1224        if data.status == Status.INTERESTING:225            key = data.interesting_origin226            changed = False227            try:228                existing = self.interesting_examples[key]229            except KeyError:230                changed = True231                self.last_bug_found_at = self.call_count232                if self.first_bug_found_at is None:233                    self.first_bug_found_at = self.call_count234            else:235                if sort_key(data.buffer) < sort_key(existing.buffer):236                    self.shrinks += 1237                    self.downgrade_buffer(existing.buffer)238                    self.__data_cache.unpin(existing.buffer)239                    changed = True240            if changed:241                self.save_buffer(data.buffer)242                self.interesting_examples[key] = data.as_result()243                self.__data_cache.pin(data.buffer)244                self.shrunk_examples.discard(key)245            if self.shrinks >= MAX_SHRINKS:246                self.exit_with(ExitReason.max_shrinks)247        if (248            not self.ignore_limits249            and self.finish_shrinking_deadline is not None250            and self.finish_shrinking_deadline < time.perf_counter()251        ):252            # See https://github.com/HypothesisWorks/hypothesis/issues/2340253            report(254                "WARNING: Hypothesis has spent more than five minutes working to shrink "255                "a failing example, and stopped because it is making very slow "256                "progress.  When you re-run your tests, shrinking will resume and "257                "may take this long before aborting again.\n"258                "PLEASE REPORT THIS if you can provide a reproducing example, so that "259                "we can improve shrinking performance for everyone."260            )261            self.exit_with(ExitReason.very_slow_shrinking)262        if not self.interesting_examples:263            # Note that this logic is reproduced to end the generation phase when264            # we have interesting examples.  Update that too if you change this!265            # (The doubled implementation is because here we exit the engine entirely,266            #  while in the other case below we just want to move on to shrinking.)267            if self.valid_examples >= self.settings.max_examples:268                self.exit_with(ExitReason.max_examples)269            if self.call_count >= max(270                self.settings.max_examples * 10,271                # We have a high-ish default max iterations, so that tests272                # don't become flaky when max_examples is too low.273                1000,274            ):275                self.exit_with(ExitReason.max_iterations)276        if self.__tree_is_exhausted():277            self.exit_with(ExitReason.finished)278        self.record_for_health_check(data)279    def on_pareto_evict(self, data):280        self.settings.database.delete(self.pareto_key, data.buffer)281    def generate_novel_prefix(self):282        """Uses the tree to proactively generate a starting sequence of bytes283        that we haven't explored yet for this test.284        When this method is called, we assume that there must be at285        least one novel prefix left to find. If there were not, then the286        test run should have already stopped due to tree exhaustion.287        """288        return self.tree.generate_novel_prefix(self.random)289    def record_for_health_check(self, data):290        # Once we've actually found a bug, there's no point in trying to run291        # health checks - they'll just mask the actually important information.292        if data.status == Status.INTERESTING:293            self.health_check_state = None294        state = self.health_check_state295        if state is None:296            return297        state.draw_times.extend(data.draw_times)298        if data.status == Status.VALID:299            state.valid_examples += 1300        elif data.status == Status.INVALID:301            state.invalid_examples += 1302        else:303            assert data.status == Status.OVERRUN304            state.overrun_examples += 1305        max_valid_draws = 10306        max_invalid_draws = 50307        max_overrun_draws = 20308        assert state.valid_examples <= max_valid_draws309        if state.valid_examples == max_valid_draws:310            self.health_check_state = None311            return312        if state.overrun_examples == max_overrun_draws:313            fail_health_check(314                self.settings,315                (316                    "Examples routinely exceeded the max allowable size. "317                    "(%d examples overran while generating %d valid ones)"318                    ". Generating examples this large will usually lead to"319                    " bad results. You could try setting max_size parameters "320                    "on your collections and turning "321                    "max_leaves down on recursive() calls."322                )323                % (state.overrun_examples, state.valid_examples),324                HealthCheck.data_too_large,325            )326        if state.invalid_examples == max_invalid_draws:327            fail_health_check(328                self.settings,329                (330                    "It looks like your strategy is filtering out a lot "331                    "of data. Health check found %d filtered examples but "332                    "only %d good ones. This will make your tests much "333                    "slower, and also will probably distort the data "334                    "generation quite a lot. You should adapt your "335                    "strategy to filter less. This can also be caused by "336                    "a low max_leaves parameter in recursive() calls"337                )338                % (state.invalid_examples, state.valid_examples),339                HealthCheck.filter_too_much,340            )341        draw_time = sum(state.draw_times)342        if draw_time > 1.0:343            fail_health_check(344                self.settings,345                (346                    "Data generation is extremely slow: Only produced "347                    "%d valid examples in %.2f seconds (%d invalid ones "348                    "and %d exceeded maximum size). Try decreasing "349                    "size of the data you're generating (with e.g."350                    "max_size or max_leaves parameters)."351                )352                % (353                    state.valid_examples,354                    draw_time,355                    state.invalid_examples,356                    state.overrun_examples,357                ),358                HealthCheck.too_slow,359            )360    def save_buffer(self, buffer, sub_key=None):361        if self.settings.database is not None:362            key = self.sub_key(sub_key)363            if key is None:364                return365            self.settings.database.save(key, bytes(buffer))366    def downgrade_buffer(self, buffer):367        if self.settings.database is not None and self.database_key is not None:368            self.settings.database.move(self.database_key, self.secondary_key, buffer)369    def sub_key(self, sub_key):370        if self.database_key is None:371            return None372        if sub_key is None:373            return self.database_key374        return b".".join((self.database_key, sub_key))375    @property376    def secondary_key(self):377        return self.sub_key(b"secondary")378    @property379    def pareto_key(self):380        return self.sub_key(b"pareto")381    def debug(self, message):382        if self.settings.verbosity >= Verbosity.debug:383            base_report(message)384    @property385    def report_debug_info(self):386        return self.settings.verbosity >= Verbosity.debug387    def debug_data(self, data):388        if not self.report_debug_info:389            return390        stack = [[]]391        def go(ex):392            if ex.length == 0:393                return394            if len(ex.children) == 0:395                stack[-1].append(int_from_bytes(data.buffer[ex.start : ex.end]))396            else:397                node = []398                stack.append(node)399                for v in ex.children:400                    go(v)401                stack.pop()402                if len(node) == 1:403                    stack[-1].extend(node)404                else:405                    stack[-1].append(node)406        go(data.examples[0])407        assert len(stack) == 1408        status = repr(data.status)409        if data.status == Status.INTERESTING:410            status = "%s (%r)" % (status, data.interesting_origin)411        self.debug(412            "%d bytes %r -> %s, %s" % (data.index, stack[0], status, data.output)413        )414    def run(self):415        with local_settings(self.settings):416            try:417                self._run()418            except RunIsComplete:419                pass420            for v in self.interesting_examples.values():421                self.debug_data(v)422            self.debug(423                "Run complete after %d examples (%d valid) and %d shrinks"424                % (self.call_count, self.valid_examples, self.shrinks)425            )426    @property427    def database(self):428        if self.database_key is None:429            return None430        return self.settings.database431    def has_existing_examples(self):432        return self.database is not None and Phase.reuse in self.settings.phases433    def reuse_existing_examples(self):434        """If appropriate (we have a database and have been told to use it),435        try to reload existing examples from the database.436        If there are a lot we don't try all of them. We always try the437        smallest example in the database (which is guaranteed to be the438        last failure) and the largest (which is usually the seed example439        which the last failure came from but we don't enforce that). We440        then take a random sampling of the remainder and try those. Any441        examples that are no longer interesting are cleared out.442        """443        if self.has_existing_examples():444            self.debug("Reusing examples from database")445            # We have to do some careful juggling here. We have two database446            # corpora: The primary and secondary. The primary corpus is a447            # small set of minimized examples each of which has at one point448            # demonstrated a distinct bug. We want to retry all of these.449            # We also have a secondary corpus of examples that have at some450            # point demonstrated interestingness (currently only ones that451            # were previously non-minimal examples of a bug, but this will452            # likely expand in future). These are a good source of potentially453            # interesting examples, but there are a lot of them, so we down454            # sample the secondary corpus to a more manageable size.455            corpus = sorted(456                self.settings.database.fetch(self.database_key), key=sort_key457            )458            factor = 0.1 if (Phase.generate in self.settings.phases) else 1459            desired_size = max(2, ceil(factor * self.settings.max_examples))460            if len(corpus) < desired_size:461                extra_corpus = list(self.settings.database.fetch(self.secondary_key))462                shortfall = desired_size - len(corpus)463                if len(extra_corpus) <= shortfall:464                    extra = extra_corpus465                else:466                    extra = self.random.sample(extra_corpus, shortfall)467                extra.sort(key=sort_key)468                corpus.extend(extra)469            for existing in corpus:470                data = self.cached_test_function(existing)471                if data.status != Status.INTERESTING:472                    self.settings.database.delete(self.database_key, existing)473                    self.settings.database.delete(self.secondary_key, existing)474            # If we've not found any interesting examples so far we try some of475            # the pareto front from the last run.476            if len(corpus) < desired_size and not self.interesting_examples:477                desired_extra = desired_size - len(corpus)478                pareto_corpus = list(self.settings.database.fetch(self.pareto_key))479                if len(pareto_corpus) > desired_extra:480                    pareto_corpus = self.random.sample(pareto_corpus, desired_extra)481                pareto_corpus.sort(key=sort_key)482                for existing in pareto_corpus:483                    data = self.cached_test_function(existing)484                    if data not in self.pareto_front:485                        self.settings.database.delete(self.pareto_key, existing)486                    if data.status == Status.INTERESTING:487                        break488    def exit_with(self, reason):489        if self.ignore_limits:490            return491        self.statistics["stopped-because"] = reason.describe(self.settings)492        if self.best_observed_targets:493            self.statistics["targets"] = dict(self.best_observed_targets)494        self.debug("exit_with(%s)" % (reason.name,))495        self.exit_reason = reason496        raise RunIsComplete()497    def should_generate_more(self):498        # End the generation phase where we would have ended it if no bugs had499        # been found.  This reproduces the exit logic in `self.test_function`,500        # but with the important distinction that this clause will move on to501        # the shrinking phase having found one or more bugs, while the other502        # will exit having found zero bugs.503        if self.valid_examples >= self.settings.max_examples or self.call_count >= max(504            self.settings.max_examples * 10, 1000505        ):  # pragma: no cover506            return False507        # If we haven't found a bug, keep looking - if we hit any limits on508        # the number of tests to run that will raise an exception and stop509        # the run.510        if not self.interesting_examples:511            return True512        # If we've found a bug and won't report more than one, stop looking.513        elif not self.settings.report_multiple_bugs:514            return False515        assert self.first_bug_found_at <= self.last_bug_found_at <= self.call_count516        # Otherwise, keep searching for between ten and 'a heuristic' calls.517        # We cap 'calls after first bug' so errors are reported reasonably518        # soon even for tests that are allowed to run for a very long time,519        # or sooner if the latest half of our test effort has been fruitless.520        return self.call_count < MIN_TEST_CALLS or self.call_count < min(521            self.first_bug_found_at + 1000, self.last_bug_found_at * 2522        )523    def generate_new_examples(self):524        if Phase.generate not in self.settings.phases:525            return526        if self.interesting_examples:527            # The example database has failing examples from a previous run,528            # so we'd rather report that they're still failing ASAP than take529            # the time to look for additional failures.530            return531        self.debug("Generating new examples")532        assert self.should_generate_more()533        zero_data = self.cached_test_function(bytes(BUFFER_SIZE))534        if zero_data.status > Status.OVERRUN:535            self.__data_cache.pin(zero_data.buffer)536        if zero_data.status == Status.OVERRUN or (537            zero_data.status == Status.VALID and len(zero_data.buffer) * 2 > BUFFER_SIZE538        ):539            fail_health_check(540                self.settings,541                "The smallest natural example for your test is extremely "542                "large. This makes it difficult for Hypothesis to generate "543                "good examples, especially when trying to reduce failing ones "544                "at the end. Consider reducing the size of your data if it is "545                "of a fixed size. You could also fix this by improving how "546                "your data shrinks (see https://hypothesis.readthedocs.io/en/"547                "latest/data.html#shrinking for details), or by introducing "548                "default values inside your strategy. e.g. could you replace "549                "some arguments with their defaults by using "550                "one_of(none(), some_complex_strategy)?",551                HealthCheck.large_base_example,552            )553        self.health_check_state = HealthCheckState()554        # We attempt to use the size of the minimal generated test case starting555        # from a given novel prefix as a guideline to generate smaller test556        # cases for an initial period, by restriscting ourselves to test cases557        # that are not much larger than it.558        #559        # Calculating the actual minimal generated test case is hard, so we560        # take a best guess that zero extending a prefix produces the minimal561        # test case starting with that prefix (this is true for our built in562        # strategies). This is only a reasonable thing to do if the resulting563        # test case is valid. If we regularly run into situations where it is564        # not valid then this strategy is a waste of time, so we want to565        # abandon it early. In order to do this we track how many times in a566        # row it has failed to work, and abort small test case generation when567        # it has failed too many times in a row.568        consecutive_zero_extend_is_invalid = 0569        # We control growth during initial example generation, for two570        # reasons:571        #572        # * It gives us an opportunity to find small examples early, which573        #   gives us a fast path for easy to find bugs.574        # * It avoids low probability events where we might end up575        #   generating very large examples during health checks, which576        #   on slower machines can trigger HealthCheck.too_slow.577        #578        # The heuristic we use is that we attempt to estimate the smallest579        # extension of this prefix, and limit the size to no more than580        # an order of magnitude larger than that. If we fail to estimate581        # the size accurately, we skip over this prefix and try again.582        #583        # We need to tune the example size based on the initial prefix,584        # because any fixed size might be too small, and any size based585        # on the strategy in general can fall afoul of strategies that586        # have very different sizes for different prefixes.587        small_example_cap = clamp(10, self.settings.max_examples // 10, 50)588        optimise_at = max(self.settings.max_examples // 2, small_example_cap + 1)589        ran_optimisations = False590        while self.should_generate_more():591            prefix = self.generate_novel_prefix()592            assert len(prefix) <= BUFFER_SIZE593            if (594                self.valid_examples <= small_example_cap595                and self.call_count <= 5 * small_example_cap596                and not self.interesting_examples597                and consecutive_zero_extend_is_invalid < 5598            ):599                minimal_example = self.cached_test_function(600                    prefix + bytes(BUFFER_SIZE - len(prefix))601                )602                if minimal_example.status < Status.VALID:603                    consecutive_zero_extend_is_invalid += 1604                    continue605                consecutive_zero_extend_is_invalid = 0606                minimal_extension = len(minimal_example.buffer) - len(prefix)607                max_length = min(len(prefix) + minimal_extension * 10, BUFFER_SIZE)608                # We could end up in a situation where even though the prefix was609                # novel when we generated it, because we've now tried zero extending610                # it not all possible continuations of it will be novel. In order to611                # avoid making redundant test calls, we rerun it in simulation mode612                # first. If this has a predictable result, then we don't bother613                # running the test function for real here. If however we encounter614                # some novel behaviour, we try again with the real test function,615                # starting from the new novel prefix that has discovered.616                try:617                    trial_data = self.new_conjecture_data(618                        prefix=prefix, max_length=max_length619                    )620                    self.tree.simulate_test_function(trial_data)621                    continue622                except PreviouslyUnseenBehaviour:623                    pass624                # If the simulation entered part of the tree that has been killed,625                # we don't want to run this.626                if trial_data.observer.killed:627                    continue628                # We might have hit the cap on number of examples we should629                # run when calculating the minimal example.630                if not self.should_generate_more():631                    break632                prefix = trial_data.buffer633            else:634                max_length = BUFFER_SIZE635            data = self.new_conjecture_data(prefix=prefix, max_length=max_length)636            self.test_function(data)637            self.generate_mutations_from(data)638            # Although the optimisations are logically a distinct phase, we639            # actually normally run them as part of example generation. The640            # reason for this is that we cannot guarantee that optimisation641            # actually exhausts our budget: It might finish running and we642            # discover that actually we still could run a bunch more test cases643            # if we want.644            if (645                self.valid_examples >= max(small_example_cap, optimise_at)646                and not ran_optimisations647            ):648                ran_optimisations = True649                self.optimise_targets()650    def generate_mutations_from(self, data):651        # A thing that is often useful but rarely happens by accident is652        # to generate the same value at multiple different points in the653        # test case.654        #655        # Rather than make this the responsibility of individual strategies656        # we implement a small mutator that just takes parts of the test657        # case with the same label and tries replacing one of them with a658        # copy of the other and tries running it. If we've made a good659        # guess about what to put where, this will run a similar generated660        # test case with more duplication.661        if (662            # An OVERRUN doesn't have enough information about the test663            # case to mutate, so we just skip those.664            data.status >= Status.INVALID665            # This has a tendency to trigger some weird edge cases during666            # generation so we don't let it run until we're done with the667            # health checks.668            and self.health_check_state is None669        ):670            initial_calls = self.call_count671            failed_mutations = 0672            while (673                self.should_generate_more()674                # We implement fairly conservative checks for how long we675                # we should run mutation for, as it's generally not obvious676                # how helpful it is for any given test case.677                and self.call_count <= initial_calls + 5678                and failed_mutations <= 5679            ):680                groups = data.examples.mutator_groups681                if not groups:682                    break683                group = self.random.choice(groups)684                ex1, ex2 = [685                    data.examples[i] for i in sorted(self.random.sample(group, 2))686                ]687                assert ex1.end <= ex2.start688                replacements = [data.buffer[e.start : e.end] for e in [ex1, ex2]]689                replacement = self.random.choice(replacements)690                try:691                    # We attempt to replace both the the examples with692                    # whichever choice we made. Note that this might end693                    # up messing up and getting the example boundaries694                    # wrong - labels matching are only a best guess as to695                    # whether the two are equivalent - but it doesn't696                    # really matter. It may not achieve the desired result697                    # but it's still a perfectly acceptable choice sequence.698                    # to try.699                    new_data = self.cached_test_function(700                        data.buffer[: ex1.start]701                        + replacement702                        + data.buffer[ex1.end : ex2.start]703                        + replacement704                        + data.buffer[ex2.end :],705                        # We set error_on_discard so that we don't end up706                        # entering parts of the tree we consider redundant707                        # and not worth exploring.708                        error_on_discard=True,709                        extend=BUFFER_SIZE,710                    )711                except ContainsDiscard:712                    failed_mutations += 1713                    continue714                if (715                    new_data.status >= data.status716                    and data.buffer != new_data.buffer717                    and all(718                        k in new_data.target_observations719                        and new_data.target_observations[k] >= v720                        for k, v in data.target_observations.items()721                    )722                ):723                    data = new_data724                    failed_mutations = 0725                else:726                    failed_mutations += 1727    def optimise_targets(self):728        """If any target observations have been made, attempt to optimise them729        all."""730        if not self.should_optimise:731            return732        from hypothesis.internal.conjecture.optimiser import Optimiser733        # We want to avoid running the optimiser for too long in case we hit734        # an unbounded target score. We start this off fairly conservatively735        # in case interesting examples are easy to find and then ramp it up736        # on an exponential schedule so we don't hamper the optimiser too much737        # if it needs a long time to find good enough improvements.738        max_improvements = 10739        while True:740            prev_calls = self.call_count741            any_improvements = False742            for target, data in list(self.best_examples_of_observed_targets.items()):743                optimiser = Optimiser(744                    self, data, target, max_improvements=max_improvements745                )746                optimiser.run()747                if optimiser.improvements > 0:748                    any_improvements = True749            if self.interesting_examples:750                break751            max_improvements *= 2752            if any_improvements:753                continue754            self.pareto_optimise()755            if prev_calls == self.call_count:756                break757    def pareto_optimise(self):758        if self.pareto_front is not None:759            ParetoOptimiser(self).run()760    def _run(self):761        with self._log_phase_statistics("reuse"):762            self.reuse_existing_examples()763        with self._log_phase_statistics("generate"):764            self.generate_new_examples()765            # We normally run the targeting phase mixed in with the generate phase,766            # but if we've been asked to run it but not generation then we have to767            # run it explciitly on its own here.768            if Phase.generate not in self.settings.phases:769                self.optimise_targets()770        with self._log_phase_statistics("shrink"):771            self.shrink_interesting_examples()772        self.exit_with(ExitReason.finished)773    def new_conjecture_data(self, prefix, max_length=BUFFER_SIZE, observer=None):774        return ConjectureData(775            prefix=prefix,776            max_length=max_length,777            random=self.random,778            observer=observer or self.tree.new_observer(),779        )780    def new_conjecture_data_for_buffer(self, buffer):781        return ConjectureData.for_buffer(buffer, observer=self.tree.new_observer())782    def shrink_interesting_examples(self):783        """If we've found interesting examples, try to replace each of them784        with a minimal interesting example with the same interesting_origin....
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