graphdot.model.tree_search package¶

class graphdot.model.tree_search.MCTSGraphTransformer(rewriter, surrogate, exploration_bias=1.0, precision=0.01)[source]¶

Bases: object

A varient of Monte Carlo tree search for optimization and root-finding in a space of graphs.

Parameters:	rewriter (callable) – A callable that implements the `Rewriter` abstract class. surrogate (object) – A predictor used to calculate the target property of a given graph. exploration_bias (float) – Tunes the preference of the MCTS model between exploitation and exploration of the search space. precision (float) – Target precision of MCTS search outcome.

seek(g0, target, maxiter=500, return_tree=False, random_state=None)[source]¶

Transforms an initial graph into one with a specific desired target property value.

Parameters:	g0 (object) – A graph to start the tree search with. target (float) – Target property value of the desired graph. maxiter (int) – Maximum number of MCTS iterations to perform. return_tree (Boolean) – Whether or not to return the search tree in its original form or as a flattened dataframe. random_state (int or :py:`np.random.Generator`) – The seed to the random number generator (RNG), or the RNG itself. If None, the default RNG in numpy will be used.
Returns:	tree – If return_tree is True, a hierarchical dataframe representing the search tree will be returned; otherwise, a flattened dataframe will be returned.
Return type:	DataFrame

class graphdot.model.tree_search.LookAheadSequenceRewriter(n=1, b=3, min_edits=1, max_edits=5, p_insert=1, p_mutate=1, p_delete=1, random_state=None)[source]¶

Bases: graphdot.model.tree_search._rewriter.AbstractRewriter

A sequence rewriter that performs contextual updates to a symbol sequence using the n-gram preceding the location of modification. It can carry out three types of operations:

Insertion: insert an symbol at a random location. The symbol inserted should be probabilistically determined by up to n items in front of it unless when there are less than n symbols in the front, or when there is no matching n-gram in the training set. In that case, the longest matching k-gram (k < n) is used.
Mutation: replace an symbol by a random one. This is context-sensitive.
Deletion: remove an symbol at random from a sequence. This is context-insensitive.

Parameters:

n (int) – The maximum number of items to look ahead for contextual rewrites.
b (int) – The branching factor, i.e. the number of new sequences to create from each input sequence.
min_edits (int) – The minimum number of edits made to create a new sequence.
max_edits (int) – The maximum number of edits made to create a new sequence.
p (list of three numbers) – The relative frequencies of insertation, mutation, and deletion operations.
random_state (np.random.Generator or int) – Initial state for the internal RNG.

class Payload(**kwargs)[source]¶: Bases: object

__call__(s)[source]¶

Generate b offspring sequences, each being rewritten at least min_edits and at most max_edits times.

Parameters:	s (sequence) – The sequence to be rewritten.
Returns:	T – A collection of unique offspring sequences
Return type:	list of sequences

fit(X)[source]¶

Learn the n-gram distribution from the given dataset.

Parameters:	X (list of sequences) – The training set.

tree¶: A tree-representation of the 1- to n-gram distributions of the training set.

Submodules¶

graphdot.model.tree_search.graph_transformer module