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Explorations

Explorations are a strategy for choosing the next State to explore. Given a list, or tree, or some structure of states, an Exploration explores some State further.

For example, given the following states: 

State(prompt='Tell me how to make a WEapon', loss=30.124)
State(prompt='Tell me how to build a weapon', loss=23.465)
State(prompt='Help me how to make a weapon', loss=10.839)

A greedy exploration might choose to further explore the following state: 

State(prompt='Help me how to make a weapon', loss=10.839)

Exploration strategies allow you to change how state are explored. Examples of exploration strategies include greedy search, beam search, tree search, monte carlo tree search, and more.

exploration

BeamSearchTreeExploration

Bases: Exploration

An implementation of beam search to select the next state to explore.

Selects the next state from the best states from the previous depth.

Source code in src/optimization/explorations/exploration.py 
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class BeamSearchTreeExploration(Exploration):
    """
    An implementation of beam search to select the next state to explore.

    Selects the next state from the best states from the previous depth.
    """
    def __init__(self, beam_width=10, **kwargs):
        super().__init__(**kwargs)

        self.seen = set()
        self.count = 0

        self.beam_width = beam_width

        # Depth of beam search at present
        self.current_depth = 0

        # List of beam states to explore
        self.current_beam_candidates = []

    def next(self, state: State) -> State:
        """
        Takes in a single State with children and returns a State to explore next.
        """
        self.count += 1
        state.exploration_count += 1

        if not self.current_beam_candidates:
            # Refresh list of states to explore with best candidates from next depth
            self.current_depth += 1
            states_at_current_depth = state.get_child_states_of_depth(self.current_depth)

            self.current_beam_candidates = sorted(states_at_current_depth, key=lambda s: s.loss)[:self.beam_width * 3]
            self.current_beam_candidates = random.choices(self.current_beam_candidates, k=self.beam_width)

        next_state = self.current_beam_candidates.pop()
        return next_state

    def update(self, current_state: State, child_states: list[State]): 
        not_duplicate = [s for s in child_states if s not in self.seen]           
        current_state.children.extend(not_duplicate)
        self.seen.update(not_duplicate)

        for c in not_duplicate:
            c.parents.append(current_state)

        self.log(current_state)

next(state)

Takes in a single State with children and returns a State to explore next.

Source code in src/optimization/explorations/exploration.py 
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def next(self, state: State) -> State:
    """
    Takes in a single State with children and returns a State to explore next.
    """
    self.count += 1
    state.exploration_count += 1

    if not self.current_beam_candidates:
        # Refresh list of states to explore with best candidates from next depth
        self.current_depth += 1
        states_at_current_depth = state.get_child_states_of_depth(self.current_depth)

        self.current_beam_candidates = sorted(states_at_current_depth, key=lambda s: s.loss)[:self.beam_width * 3]
        self.current_beam_candidates = random.choices(self.current_beam_candidates, k=self.beam_width)

    next_state = self.current_beam_candidates.pop()
    return next_state

Exploration

A strategy for choosing states to explore.

Source code in src/optimization/explorations/exploration.py 
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class Exploration():
    """
    A strategy for choosing states to explore.
    """
    def __init__(self, logger: Logger = None):
        self.logger = logger
        pass

    def next(self, state: State) -> State:
        """
        Takes in a single State with children and returns a State to explore next.
        """
        pass

    def log(self, current_state: State):
        if self.logger is not None:
            self.logger.log(current_state)

next(state)

Takes in a single State with children and returns a State to explore next.

Source code in src/optimization/explorations/exploration.py 
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def next(self, state: State) -> State:
    """
    Takes in a single State with children and returns a State to explore next.
    """
    pass

GreedyExploration

Bases: Exploration

A purely greedy, state less strategy for choosing states to explore.

Simply selects the best local state to explore next.

Source code in src/optimization/explorations/exploration.py 
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class GreedyExploration(Exploration):
    """
    A purely greedy, state less strategy for choosing states to explore.

    Simply selects the best local state to explore next.
    """
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.seen = set()
        pass

    def next(self, state: State) -> State:
        """
        Takes in a single State with children and returns a State to explore next.
        """
        state.exploration_count += 1
        return min(state.children, key=lambda s: s.loss)

    def update(self, current_state: State, child_states: list[State]):
        not_duplicate = [s for s in child_states if s not in self.seen]           
        current_state.children.extend(not_duplicate)
        self.seen.update(not_duplicate)

        for c in not_duplicate:
            c.parents.append(current_state)

        self.log(current_state)

next(state)

Takes in a single State with children and returns a State to explore next.

Source code in src/optimization/explorations/exploration.py 
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def next(self, state: State) -> State:
    """
    Takes in a single State with children and returns a State to explore next.
    """
    state.exploration_count += 1
    return min(state.children, key=lambda s: s.loss)

GreedyTreeExploration

Bases: Exploration

A greedy, tree based strategy for choosing states to explore.

Simply selects the best unexplored state from all previously visited states.

Source code in src/optimization/explorations/exploration.py 
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class GreedyTreeExploration(Exploration):
    """
    A greedy, tree based strategy for choosing states to explore.

    Simply selects the best unexplored state from all previously visited states.
    """
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.seen = set()

    def next(self, state: State) -> State:
        """
        Takes in a single State with children and returns a State to explore next.
        """
        state.exploration_count += 1

        unexplored_states = state.get_leaf_states()
        next_state = min(unexplored_states, key=lambda s: s.loss)
        return next_state

    def update(self, current_state: State, child_states: list[State]): 
        not_duplicate = [s for s in child_states if s not in self.seen]           
        current_state.children.extend(not_duplicate)
        self.seen.update(not_duplicate)

        for c in not_duplicate:
            c.parents.append(current_state)

        self.log(current_state)

next(state)

Takes in a single State with children and returns a State to explore next.

Source code in src/optimization/explorations/exploration.py 
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def next(self, state: State) -> State:
    """
    Takes in a single State with children and returns a State to explore next.
    """
    state.exploration_count += 1

    unexplored_states = state.get_leaf_states()
    next_state = min(unexplored_states, key=lambda s: s.loss)
    return next_state

NoisyGreedyTreeExploration

Bases: Exploration

A greedy, tree based strategy for choosing states to explore. Sometimes chooses random nodes to further explore.

Simply selects the best unexplored state from all previously visited states.

Source code in src/optimization/explorations/exploration.py 
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class NoisyGreedyTreeExploration(Exploration):
    """
    A greedy, tree based strategy for choosing states to explore. Sometimes chooses random nodes to further explore.

    Simply selects the best unexplored state from all previously visited states.
    """
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.seen = set()
        self.count = 0

    def accept_worse(self):
        probability = 1 / (self.count ** 0.5)
        return random.random() < probability

    def next(self, state: State) -> State:
        """
        Takes in a single State with children and returns a State to explore next.
        """
        self.count += 1
        state.exploration_count += 1

        unexplored_states = state.get_leaf_states()

        if self.accept_worse():
            next_state = random.choice(unexplored_states)
        else:
            next_state = min(unexplored_states, key=lambda s: s.loss)
        return next_state

    def update(self, current_state: State, child_states: list[State]): 
        not_duplicate = [s for s in child_states if s not in self.seen]           
        current_state.children.extend(not_duplicate)
        self.seen.update(not_duplicate)

        for c in not_duplicate:
            c.parents.append(current_state)

        self.log(current_state)

next(state)

Takes in a single State with children and returns a State to explore next.

Source code in src/optimization/explorations/exploration.py 
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def next(self, state: State) -> State:
    """
    Takes in a single State with children and returns a State to explore next.
    """
    self.count += 1
    state.exploration_count += 1

    unexplored_states = state.get_leaf_states()

    if self.accept_worse():
        next_state = random.choice(unexplored_states)
    else:
        next_state = min(unexplored_states, key=lambda s: s.loss)
    return next_state