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[WIP] Seeded Random Number Generation #4

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030ded7
establish pattern: use existing parameter passing to enable optional …
KirbyG Mar 30, 2024
8a8abea
random seeding used everywhere
KirbyG Apr 1, 2024
3dcd7aa
random seeding used everywhere
KirbyG Apr 1, 2024
7c0e014
Merge branch 'random-seed' of github.com:KirbyG/alogos into random-seed
KirbyG Apr 1, 2024
75d3572
Merge branch 'random-seed' of github.com:KirbyG/alogos into random-seed
KirbyG Apr 1, 2024
91bdf64
Merge branch 'random-seed' of github.com:KirbyG/alogos into random-seed
KirbyG Apr 1, 2024
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1 change: 1 addition & 0 deletions .gitignore
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*.pyc
5 changes: 4 additions & 1 deletion alogos/_grammar/data_structures.py
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Expand Up @@ -126,6 +126,7 @@ class Grammar:
"production_rules",
"start_symbol",
"_cache",
"rng",
)

# Initialization and reset
Expand Down Expand Up @@ -246,6 +247,8 @@ def __init__(
):
_warnings._warn_multiple_grammar_specs()

self.rng = kwargs.pop("rng", _random.Random())

# Transformation
if bnf_text is not None:
self.from_bnf_text(bnf_text, **kwargs)
Expand Down Expand Up @@ -2560,7 +2563,7 @@ def node_seq_repr(node_seq):
derivation = [[self.root_node]]
stack = [self.root_node]
while stack:
idx = _random.randint(0, len(stack) - 1)
idx = rng.randint(0, len(stack) - 1)
nt_node = stack.pop(idx)
derivation = next_derivation_step(derivation, nt_node, nt_node.children)
new_nt_nodes = [node for node in nt_node.children if node.children]
Expand Down
3 changes: 1 addition & 2 deletions alogos/_optimization/ea/algorithm.py
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@@ -1,6 +1,5 @@
"""Data structure for a evolutionary algorithm that uses G3P systems."""

import random as _random
from numbers import Number as _Number

import pylru as _pylru
Expand Down Expand Up @@ -970,7 +969,7 @@ def _cross_over(self, parent_population):
n = len(parent_population)
while len(crossed_over_individuals) < n:
if n > 2:
parent0, parent1 = _random.sample(parent_population.individuals, 2)
parent0, parent1 = pr["rng"].sample(parent_population.individuals, 2)
elif n == 2:
parent0, parent1 = parent_population.individuals
else:
Expand Down
19 changes: 9 additions & 10 deletions alogos/_optimization/ea/operators/selection.py
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@@ -1,4 +1,3 @@
import random as _random
from functools import lru_cache as _lru_cache
from math import isfinite as _isfinite
from math import isnan as _isnan
Expand All @@ -17,7 +16,7 @@ def uniform(individuals, sample_size, objective, parameters, state):
- Eiben, Introduction to Evolutionary Computing (2e 2015): p. 86

"""
sel_inds = _uniform_sampling_with_replacement(individuals, sample_size)
sel_inds = _uniform_sampling_with_replacement(individuals, sample_size, parameters)
return sel_inds


Expand Down Expand Up @@ -60,15 +59,15 @@ def tournament(individuals, sample_size, objective, parameters, state):
sel_inds = []
if objective == "min":
for _ in range(sample_size):
competitors = _uniform_sampling_with_replacement(individuals, ts)
competitors = _uniform_sampling_with_replacement(individuals, ts, parameters)
winner = competitors[0]
for competitor in competitors[1:]:
if competitor.less_than(winner, objective):
winner = competitor
sel_inds.append(winner)
else:
for _ in range(sample_size):
competitors = _uniform_sampling_with_replacement(individuals, ts)
competitors = _uniform_sampling_with_replacement(individuals, ts, parameters)
winner = competitors[0]
for competitor in competitors[1:]:
if competitor.greater_than(winner, objective):
Expand All @@ -89,7 +88,7 @@ def rank_proportional(individuals, sample_size, objective, parameters, state):
probabilities = _calculate_rank_probabilities(num_inds, mu, eta_plus)

# Sampling
sel_inds = _stochastic_universal_sampling(srt_inds, probabilities, sample_size)
sel_inds = _stochastic_universal_sampling(srt_inds, probabilities, sample_size, parameters)
return sel_inds


Expand All @@ -115,7 +114,7 @@ def fitness_proportional(individuals, sample_size, objective, parameters, state)
probabilities = _calculate_fitness_probabilities(scaled_fitnesses, usage_tracking)

# Sampling
sel_inds = _stochastic_universal_sampling(individuals, probabilities, sample_size)
sel_inds = _stochastic_universal_sampling(individuals, probabilities, sample_size, parameters)
return sel_inds


Expand Down Expand Up @@ -231,13 +230,13 @@ def _sort_individuals(individuals, reverse=False):
# Sampling algorithms: draw individuals from a population according to a probability distribution


def _uniform_sampling_with_replacement(population, sample_size):
def _uniform_sampling_with_replacement(population, sample_size, parameters):
"""Uniform sampling with replacement."""
selected_individuals = [_random.choice(population) for _ in range(sample_size)]
selected_individuals = [parameters["rng"].choice(population) for _ in range(sample_size)]
return selected_individuals


def _stochastic_universal_sampling(population, probabilities, sample_size):
def _stochastic_universal_sampling(population, probabilities, sample_size, parameters):
"""Stochastic universal sampling (SUS) for drawing individuals from a probability distribution.

A sampling algorithm by James Baker, designed as improvement to roulette wheel sampling.
Expand All @@ -251,7 +250,7 @@ def _stochastic_universal_sampling(population, probabilities, sample_size):
lambda_inv = 1.0 / sample_size

# Draw from uniform distribution
rand_uniform = _random.uniform(0.0, lambda_inv)
rand_uniform = parameters["rng"].uniform(0.0, lambda_inv)

# Decide which individual gets how many children (=copies of itself)
num_children = []
Expand Down
2 changes: 2 additions & 0 deletions alogos/_optimization/ea/parameters.py
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"""Default parameters for an evolutionary algorithm."""

import random as _random
from ..._utilities.parametrization import ParameterCollection as _ParameterCollection


Expand All @@ -9,6 +10,7 @@
population_size=100,
offspring_size=100,
verbose=False,
rng=_random.Random(),
# Storage
database_on=False,
database_location=":memory:",
Expand Down
12 changes: 5 additions & 7 deletions alogos/systems/_shared/crossover.py
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"""Shared crossover functions for several systems."""

from random import randint as _ri

from ... import exceptions as _exceptions


Expand Down Expand Up @@ -51,11 +49,11 @@ def two_point_length_preserving(grammar, gt1, gt2, parameters, representation):
_exceptions.raise_crossover_lp_error2()

# Get a random segment in genotype 1: choose two valid random points
s1, e1 = _get_two_different_points(l1)
s1, e1 = _get_two_different_points(l1, parameters)

# Get a random segment in genotype 2: choose a valid start position for a same-sized segment
lseg = e1 - s1
s2 = _ri(0, l2 - lseg)
s2 = grammar.rng.randint(0, l2 - lseg)
e2 = s2 + lseg

# Crossover: Swap two randomly positioned, but equally long segments
Expand All @@ -64,11 +62,11 @@ def two_point_length_preserving(grammar, gt1, gt2, parameters, representation):
return _GT(n1), _GT(n2)


def _get_two_different_points(n):
def _get_two_different_points(n, parameters):
"""Get two different numbers between 0 and n-1 to use as indices."""
while True:
p1 = _ri(0, n)
p2 = _ri(0, n)
p1 = grammar.rng.randint(0, n)
p2 = grammar.rng.randint(0, n)
if p1 == p2:
continue
if (p1 == 0 and p2 == n) or (p1 == n and p2 == 0):
Expand Down
6 changes: 2 additions & 4 deletions alogos/systems/_shared/init_individual.py
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"""Shared individual initialization functions of different systems."""

import random as _random

from ... import exceptions as _exceptions
from ..._utilities.parametrization import get_given_or_default as _get_given_or_default
from . import init_tree as _init_tree
Expand Down Expand Up @@ -193,12 +191,12 @@ def random_genotype(grammar, parameters, default_parameters, representation, map
# Parameter extraction
gl = _get_given_or_default("genotype_length", parameters, default_parameters)
cs = _get_given_or_default("codon_size", parameters, default_parameters)

# Transformation
try:
assert cs > 0
max_int = 2**cs - 1
random_genotype = [_random.randint(0, max_int) for _ in range(gl)]
random_genotype = [grammar.rng.randint(0, max_int) for _ in range(gl)]
except Exception:
_exceptions.raise_init_ind_rand_gt_error()

Expand Down
17 changes: 8 additions & 9 deletions alogos/systems/_shared/init_tree.py
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Expand Up @@ -2,7 +2,6 @@

import copy as _copy
import itertools as _itertools
import random as _random

from ... import exceptions as _exceptions
from ..._grammar import data_structures as _data_structures
Expand Down Expand Up @@ -59,7 +58,7 @@ def uniform(grammar, max_expansions=10_000):
# 2) Choose rule: randomly
rules = grammar.production_rules[chosen_nt_node.symbol]
if len(rules) > 1:
chosen_rule_idx = _random.randint(0, len(rules) - 1)
chosen_rule_idx = grammar.rng.randint(0, len(rules) - 1)
else:
chosen_rule_idx = 0
chosen_rule = rules[chosen_rule_idx]
Expand Down Expand Up @@ -123,7 +122,7 @@ def weighted(grammar, max_expansions=10_000, reduction_factor=0.96):
def weighted_choice(lhs, weights):
rhs_list = grammar.production_rules[lhs]
weight_list = weights[lhs]
chosen_rule_cumulative_weight = sum(weight_list) * _random.random()
chosen_rule_cumulative_weight = sum(weight_list) * grammar.rng.random()
chosen_rule_idx = 0
for cumulative_weight in _itertools.accumulate(weight_list):
if cumulative_weight >= chosen_rule_cumulative_weight:
Expand Down Expand Up @@ -207,7 +206,7 @@ def ptc2(grammar, max_expansions=100):
expansions = 0
while stack:
# 1) Choose nonterminal: random
chosen_nt_idx = _random.choice(range(len(stack)))
chosen_nt_idx = grammar.rng.choice(range(len(stack)))
chosen_nt_node = stack.pop(chosen_nt_idx)
# 2) Choose rule: randomly from those that do not lead over the wanted expansions
rules = grammar.production_rules[chosen_nt_node.symbol]
Expand All @@ -222,7 +221,7 @@ def ptc2(grammar, max_expansions=100):
min_expansions_per_symbol,
stack,
)
chosen_rule_idx = _random.randint(0, len(rules) - 1)
chosen_rule_idx = grammar.rng.randint(0, len(rules) - 1)
chosen_rule = rules[chosen_rule_idx]
# 3) Expand the chosen nonterminal with the rhs of the chosen rule
new_nodes = dt._expand(chosen_nt_node, chosen_rule)
Expand Down Expand Up @@ -267,7 +266,7 @@ def grow_one_branch_to_max_depth(grammar, max_depth=20):
stack = [(dt.root_node, 0)]
while stack:
# 1) Choose nonterminal: random
chosen_nt_idx = _random.choice(range(len(stack)))
chosen_nt_idx = grammar.rng.choice(range(len(stack)))
chosen_nt_node, depth = stack.pop(chosen_nt_idx)
# 2) Choose rule: randomly from those that do not lead over the wanted depth
rules = grammar.production_rules[chosen_nt_node.symbol]
Expand All @@ -288,7 +287,7 @@ def grow_one_branch_to_max_depth(grammar, max_depth=20):
rules = _filter_rules_for_grow(
chosen_nt_node.symbol, rules, depth, max_depth, min_depths
)
chosen_rule_idx = _random.randint(0, len(rules) - 1)
chosen_rule_idx = grammar.rng.randint(0, len(rules) - 1)
chosen_rule = rules[chosen_rule_idx]
# 3) Expand the chosen nonterminal with the rhs of the chosen rule
new_nodes = dt._expand(chosen_nt_node, chosen_rule)
Expand Down Expand Up @@ -333,7 +332,7 @@ def grow_all_branches_within_max_depth(grammar, max_depth=20):
rules = _filter_rules_for_grow(
chosen_nt_node.symbol, rules, depth, max_depth, min_depths
)
chosen_rule_idx = _random.randint(0, len(rules) - 1)
chosen_rule_idx = grammar.rng.randint(0, len(rules) - 1)
chosen_rule = rules[chosen_rule_idx]
# 3) Expand the chosen nonterminal with the rhs of the chosen rule
new_nodes = dt._expand(chosen_nt_node, chosen_rule)
Expand Down Expand Up @@ -381,7 +380,7 @@ def grow_all_branches_to_max_depth(grammar, max_depth=20):
rules = _filter_rules_for_full(
chosen_nt_node.symbol, rules, depth, max_depth, min_depths, is_recursive
)
chosen_rule_idx = _random.randint(0, len(rules) - 1)
chosen_rule_idx = grammar.rng.randint(0, len(rules) - 1)
chosen_rule = rules[chosen_rule_idx]
# 3) Expand the chosen nonterminal with the rhs of the chosen rule
new_nodes = dt._expand(chosen_nt_node, chosen_rule)
Expand Down
8 changes: 4 additions & 4 deletions alogos/systems/_shared/neighborhood.py
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Expand Up @@ -3,10 +3,9 @@
import functools as _functools
import itertools as _itertools
import operator as _operator
import random as _random


def generate_combinations(num_choices_per_pos, distance, max_size=None):
def generate_combinations(num_choices_per_pos, distance, parameters, max_size=None):
"""Generate all combinations of choices available at each position.

In each returned combination, the value 0 means that the original choice
Expand Down Expand Up @@ -59,6 +58,7 @@ def generate_combinations(num_choices_per_pos, distance, max_size=None):
max_size,
count,
count_per_pos_comb,
parameters
)
else:
# If no, generate the entire set
Expand All @@ -75,11 +75,11 @@ def _product(iterable):


def _generate_some_combinations(
choices_per_pos, pos_combinations, num_pos, max_size, count, count_per_pos_comb
choices_per_pos, pos_combinations, num_pos, max_size, count, count_per_pos_comb, parameters
):
"""Generate a random subset of all possible combinations."""
# Choose random neighbors by selecting indices from the enumerated possibilities
chosen = sorted(_random.sample(range(count), max_size))
chosen = sorted(parameters["rng"].sample(range(count), max_size))

# Construct the chosen neighbors
if chosen:
Expand Down
8 changes: 3 additions & 5 deletions alogos/systems/cfggp/crossover.py
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@@ -1,13 +1,11 @@
"""Crossover functions for CFG-GP."""

import random as _random

from ..._utilities.parametrization import get_given_or_default as _get_given_or_default
from . import default_parameters as _default_parameters
from . import representation as _representation


def subtree_exchange(grammar, genotype1, genotype2, parameters=None):
def subtree_exchange(grammar, genotype1, genotype2, parameters):
"""Generate new CFG-GP genotypes by exchanging suitable subtrees.

Randomly select nodes containing the same nonterminal in two trees
Expand Down Expand Up @@ -89,10 +87,10 @@ def subtree_exchange(grammar, genotype1, genotype2, parameters=None):
si = s1.intersection(s2)
if si:
# Randomly select a non-terminal in the first tree, which is also part of second tree
n1 = _random.choice([n for n in ns1 if n.symbol.text in si])
n1 = grammar.rng.choice([n for n in ns1 if n.symbol.text in si])
t1 = n1.symbol.text
# Randomly select the same non-terminal in the second genotype
n2 = _random.choice([n for n in ns2 if n.symbol.text == t1])
n2 = grammar.rng.choice([n for n in ns2 if n.symbol.text == t1])
# Swap subtrees by exchanging the list of child nodes
n1.children, n2.children = n2.children, n1.children
# Ensure max_depth constraint is not violated
Expand Down
6 changes: 2 additions & 4 deletions alogos/systems/cfggp/mutation.py
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@@ -1,7 +1,5 @@
"""Mutation functions for CFG-GP."""

import random as _random

from ..._grammar import data_structures as _data_structures
from ..._utilities.parametrization import get_given_or_default as _get_given_or_default
from .._shared import _cached_calculations
Expand Down Expand Up @@ -59,7 +57,7 @@ def subtree_replacement(grammar, genotype, parameters=None):
nodes_and_depths.append((node, depth))
stack = stack + [(node, depth + 1) for node in node.children]
# - Randomly select a node for mutation
node, depth = _random.choice(nodes_and_depths)
node, depth = grammar.rng.choice(nodes_and_depths)
# - Replace the node's subtree with a randomly generated new one
node.children = []
_grow_random_subtree(grammar, max_depth, start_depth=depth, root_node=node)
Expand All @@ -85,7 +83,7 @@ def _grow_random_subtree(grammar, max_depth, start_depth, root_node):
rules = _filter_rules_for_grow(
chosen_nt_node.symbol, rules, depth, max_depth, min_depths
)
chosen_rule_idx = _random.randint(0, len(rules) - 1)
chosen_rule_idx = grammar.rng.randint(0, len(rules) - 1)
chosen_rule = rules[chosen_rule_idx]
# 3) Expand the chosen nonterminal with the rhs of the chosen rule
new_nodes = dt._expand(chosen_nt_node, chosen_rule)
Expand Down
4 changes: 2 additions & 2 deletions alogos/systems/cfggp/neighborhood.py
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Expand Up @@ -13,7 +13,7 @@
_T = _grammar.data_structures.TerminalSymbol


def subtree_replacement(grammar, genotype, parameters=None):
def subtree_replacement(grammar, genotype, parameters):
"""Systematically change a chosen number of nodes.

Parameters
Expand Down Expand Up @@ -58,7 +58,7 @@ def subtree_replacement(grammar, genotype, parameters=None):

# Generate combinations of choices
combinations = _shared.neighborhood.generate_combinations(
num_choices_per_pos, distance, max_size
num_choices_per_pos, distance, max_size, parameters
)

# Neighborhood construction
Expand Down
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