Improve shrink ordering of text

hypothesis-python/RELEASE.rst

@@ -0,0 +1,5 @@
+RELEASE_TYPE: patch
+
+This release improves the behaviour of the :func:`~hypothesis.strategies.characters` strategy
+when shrinking, by changing which characters are considered smallest to prefer more "normal" ascii characters
+where available.

hypothesis-python/src/hypothesis/internal/conjecture/dfa/__init__.py

@@ -0,0 +1,115 @@
+# This file is part of Hypothesis, which may be found at
+# https://github.com/HypothesisWorks/hypothesis/
+#
+# Most of this work is copyright (C) 2013-2020 David R. MacIver
+# ([email protected]), but it contains contributions by others. See
+# CONTRIBUTING.rst for a full list of people who may hold copyright, and
+# consult the git log if you need to determine who owns an individual
+# contribution.
+#
+# This Source Code Form is subject to the terms of the Mozilla Public License,
+# v. 2.0. If a copy of the MPL was not distributed with this file, You can
+# obtain one at https://mozilla.org/MPL/2.0/.
+#
+# END HEADER
+
+import threading
+from collections import deque
+
+
+class DFA:
+    """Base class for implementations of deterministic finite
+    automata.
+
+    This is abstract to allow for the possibility of states
+    being calculated lazily as we traverse the DFA (which
+    we make heavy use of in our L* implementation - see
+    lstar.py for details).
+
+    States can be of any hashable type.
+    """
+
+    def __init__(self):
+        self.__caches = threading.local()
+
+    @property
+    def start(self):
+        """Returns the starting state."""
+        raise NotImplementedError()
+
+    def is_accepting(self, i):
+        """Returns if state ``i`` is an accepting one."""
+        raise NotImplementedError()
+
+    def transition(self, i, c):
+        """Returns the state that i transitions to on reading
+        character c from a string."""
+        raise NotImplementedError()
+
+    def transitions(self, i):
+        """Iterates over all pairs (byte, state) of transitions
+        which do not lead to dead states."""
+        for c, j in self.__raw_transitions(i):
+            if not self.is_dead(j):
+                yield c, j
+
+    def matches(self, s):
+        """Returns whether the string ``s`` is accepted
+        by this automaton."""
+        i = self.start
+        for c in s:
+            i = self.transition(i, c)
+        return self.is_accepting(i)
+
+    def is_dead(self, i):
+        """Returns True if no strings can be accepted
+        when starting from state ``i``."""
+        if self.is_accepting(i):
+            return False
+
+        try:
+            cache = self.__caches.dead
+        except AttributeError:
+            cache = {}
+            self.__caches.dead = cache
+
+        try:
+            return cache[i]
+        except KeyError:
+            pass
+        seen = set()
+        pending = deque([i])
+        result = True
+        while pending:
+            j = pending.popleft()
+            if j in seen:
+                continue
+            seen.add(j)
+            if self.is_accepting(j):
+                result = False
+                break
+            else:
+                for _, k in self.__raw_transitions(j):
+                    pending.append(k)
+        if result:
+            for j in seen:
+                cache[j] = True
+        else:
+            cache[i] = False
+        return result
+
+    def all_matching_strings(self):
+        """Iterate over all strings matched by this automaton
+        in shortlex-ascending order."""
+        queue = deque([(self.start, b"")])
+        while queue:
+            i, path = queue.popleft()
+            if self.is_accepting(i):
+                yield path
+            for c, j in self.transitions(i):
+                queue.append((j, path + bytes([c])))
+
+    def __raw_transitions(self, i):
+        for c in range(256):
+            j = self.transition(i, c)
+            yield c, j

hypothesis-python/src/hypothesis/internal/conjecture/dfa/lstar.py

@@ -0,0 +1,221 @@
+# This file is part of Hypothesis, which may be found at
+# https://github.com/HypothesisWorks/hypothesis/
+#
+# Most of this work is copyright (C) 2013-2020 David R. MacIver
+# ([email protected]), but it contains contributions by others. See
+# CONTRIBUTING.rst for a full list of people who may hold copyright, and
+# consult the git log if you need to determine who owns an individual
+# contribution.
+#
+# This Source Code Form is subject to the terms of the Mozilla Public License,
+# v. 2.0. If a copy of the MPL was not distributed with this file, You can
+# obtain one at https://mozilla.org/MPL/2.0/.
+#
+# END HEADER
+
+from hypothesis.internal.conjecture.dfa import DFA
+from hypothesis.internal.conjecture.junkdrawer import find_integer
+
+"""
+This module contains an implementation of the L* algorithm
+for learning a deterministic finite automaton based on an
+unknown membership function and a series of examples of
+strings that may or may not satisfy it.
+
+The two relevant papers for understanding this are:
+
+* Angluin, Dana. "Learning regular sets from queries and counterexamples."
+  Information and computation 75.2 (1987): 87-106.
+* Rivest, Ronald L., and Robert E. Schapire. "Inference of finite automata
+  using homing sequences." Information and Computation 103.2 (1993): 299-347.
+  Note that we only use the material from section 4.5 "Improving Angluin's L*
+  algorithm" (page 318), and all of the rest of the material on homing
+  sequences can be skipped.
+
+The former explains the core algorithm, the latter a modification
+we use (which we have further modified) which allows it to
+be implemented more efficiently.
+
+We have two major departures from the paper:
+
+1. We learn the automaton lazily as we traverse it. This is particularly
+   valuable because if we make many corrections on the same string we only
+   have to learn the transitions that correspond to the string we are
+   correcting on.
+2. We make use of our ``find_integer`` method rather than a binary search
+   as proposed in the Rivest and Schapire paper, as we expect that
+   usually most strings will be mispredicted near the beginning.
+
+A note on performance: This code is not really fast enough for
+us to ever want to run in production on large strings, and this
+is somewhat intrinsic. We should only use it in testing or for
+learning languages offline that we can record for later use.
+
+"""
+
+
+class LStar:
+    def __init__(self, member):
+        self.__experiments = []
+        self.__cache = {}
+        self.__member = member
+
+        self.__add_experiment(b"")
+
+    def member(self, s):
+        """Check whether this string is a member of the language
+        to be learned."""
+        s = bytes(s)
+        try:
+            return self.__cache[s]
+        except KeyError:
+            return self.__cache.setdefault(s, self.__member(s))
+
+    @property
+    def generation(self):
+        """Return an integer value that will be incremented
+        every time the DFA we predict changes."""
+        return len(self.__experiments)
+
+    @property
+    def dfa(self):
+        """Returns our current model of a DFA for matching
+        the language we are learning."""
+        if self.__dfa is None:
+            self.__dfa = ExperimentDFA(self.member, self.__experiments)
+        return self.__dfa
+
+    def learn(self, s):
+        """Learn to give the correct answer on this string.
+        That is, after this method completes we will have
+        ``self.dfa.matches(s) == self.member(s)``.
+
+        Note that we do not guarantee that this will remain
+        true in the event that learn is called again with
+        a different string. It is in principle possible that
+        future learning will cause us to make a mistake on
+        this string. However, repeatedly calling learn on
+        each of a set of strings until the generation stops
+        changing is guaranteed to terminate.
+        """
+        s = bytes(s)
+        correct_outcome = self.member(s)
+
+        # We don't want to check this inside the loop because it potentially
+        # causes us to evaluate more of the states than we actually need to,
+        # but if our model is mostly correct then this will be faster because
+        # we only need to evaluate strings that are of the form
+        # ``state + experiment``, which will generally be cached and/or needed
+        # later.
+        if self.dfa.matches(s) == correct_outcome:
+            return
+
+        # In the papers they assume that we only run this process
+        # once, but this is silly - often when you've got a messy
+        # string it will be wrong for many different reasons.
+        #
+        # Thus we iterate this to a fixed point where we repair
+        # the DFA by repeatedly adding experiments until the DFA
+        # agrees with the membership function on this string.
+        while True:
+            dfa = self.dfa
+
+            states = [dfa.start]
+
+            def seems_right(n):
+                """After reading n characters from s, do we seem to be
+                in the right state?
+
+                We determine this by replacing the first n characters
+                of s with the label of the state we expect to be in.
+                If we are in the right state, that will replace a substring
+                with an equivalent one so must produce the same answer.
+                """
+                if n > len(s):
+                    return False
+
+                # Populate enough of the states list to know where we are.
+                while n >= len(states):
+                    states.append(dfa.transition(states[-1], s[len(states) - 1]))
+
+                return self.member(dfa.label(states[n]) + s[n:]) == correct_outcome
+
+            n = find_integer(seems_right)
+
+            # We got to the end without ever finding ourself in a bad
+            # state, so we must correctly match this string.
+            if n == len(s):
+                assert dfa.matches(s) == correct_outcome
+                break
+
+            # Reading n characters does not put us in a bad state but
+            # reading n + 1 does. This means that the remainder of
+            # the string that we have not read yet is an experiment
+            # that allows us to distinguish the state that we ended
+            # up in from the state that we should have ended up in.
+            self.__add_experiment(s[n + 1 :])
+
+    def __add_experiment(self, e):
+        self.__experiments.append(e)
+        self.__dfa = None
+
+
+class ExperimentDFA(DFA):
+    """This implements a lazily calculated DFA where states
+    are labelled by some string that reaches them, and are
+    distinguished by a membership test and a set of experiments."""
+
+    def __init__(self, member, experiments):
+        DFA.__init__(self)
+        self.__experiments = tuple(experiments)
+        self.__member = member
+
+        self.__states = [b""]
+        self.__rows_to_states = {tuple(map(member, experiments)): 0}
+        self.__transition_cache = {}
+
+    def label(self, i):
+        return self.__states[i]
+
+    @property
+    def start(self):
+        return 0
+
+    def is_accepting(self, i):
+        return self.__member(self.__states[i])
+
+    def transition(self, i, c):
+        key = (i, c)
+        try:
+            return self.__transition_cache[key]
+        except KeyError:
+            pass
+        s = self.__states[i]
+
+        # t is either the string that labels our destination
+        # state or one equivalent to it.
+        t = s + bytes([c])
+
+        # A row is a tuple of booleans that corresponds to
+        # the information our experiments can reveal about
+        # this string. Two strings with different rows *must*
+        # correspond to different states in the DFA for our
+        # membership function, because the same path out
+        # of them (taken by one of the experiments) leads to
+        # different results.
+        row = tuple(self.__member(t + e) for e in self.__experiments)
+        try:
+            # If we have seen this row before, assume that this
+            # state is equivalent to that already discovered one.
+            # If it is not, we will have to find a new experiment
+            # to reveal that,
+            result = self.__rows_to_states[row]
+        except KeyError:
+            # This string is definitely not equivalent to any of
+            # those visited before, so it must be a new state and
+            # we add it to our list of states.
+            result = len(self.__states)
+            self.__states.append(t)
+            self.__rows_to_states[row] = result
+        self.__transition_cache[key] = result
+        return result

hypothesis-python/src/hypothesis/internal/conjecture/engine.py

@@ -87,7 +87,14 @@ class RunIsComplete(Exception):
 
 
 class ConjectureRunner:
-    def __init__(self, test_function, settings=None, random=None, database_key=None):
+    def __init__(
+        self,
+        test_function,
+        settings=None,
+        random=None,
+        database_key=None,
+        ignore_limits=False,
+    ):
         self._test_function = test_function
         self.settings = settings or Settings()
         self.shrinks = 0
@@ -96,6 +103,7 @@ def __init__(self, test_function, settings=None, random=None, database_key=None)
         self.valid_examples = 0
         self.random = random or Random(getrandbits(128))
         self.database_key = database_key
+        self.ignore_limits = ignore_limits
 
         # Global dict of per-phase statistics, and a list of per-call stats
         # which transfer to the global dict at the end of each phase.
@@ -273,7 +281,8 @@ def test_function(self, data):
                 self.exit_with(ExitReason.max_shrinks)
 
         if (
-            self.finish_shrinking_deadline is not None
+            not self.ignore_limits
+            and self.finish_shrinking_deadline is not None
             and self.finish_shrinking_deadline < time.perf_counter()
         ):
             # See https://github.com/HypothesisWorks/hypothesis/issues/2340
@@ -558,6 +567,8 @@ def reuse_existing_examples(self):
                         break
 
     def exit_with(self, reason):
+        if self.ignore_limits:
+            return
         self.statistics["stopped-because"] = reason.describe(self.settings)
         if self.best_observed_targets:
             self.statistics["targets"] = dict(self.best_observed_targets)