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fixed fitnesses being invalid for mutate with fitness #30

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Jun 18, 2023
Merged

fixed fitnesses being invalid for mutate with fitness #30

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51 changes: 35 additions & 16 deletions src/p2lab/genetic/genetic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,9 +6,9 @@
import numpy as np

from p2lab.genetic.matching import dense
from p2lab.genetic.operations import fitness_mutate, mutate
from p2lab.genetic.operations import selection, fitness_mutate, mutate
from p2lab.pokemon.battle import run_battles
from p2lab.pokemon.team import Team
from p2lab.team import Team


# Psuedocode for the genetic algorithm, some placeholder functions below to
Expand All @@ -17,6 +17,7 @@ def genetic_team(
pokemon_population: list[str], # list of all valid pokemon names
num_pokemon: int,
num_teams: int,
match_fn: Callable,
fitness_fn: Callable,
crossover_fn: Callable = None,
crossover_prob: float = 0.95,
Expand Down Expand Up @@ -44,6 +45,8 @@ def genetic_team(
pokemon_population: A list of strings containing all valid pokemon names for the population
num_pokemon: Number of pokmeon in each team
num_teams: Number of pokemon teams to generate
match_fn: A function that generates an array of matches. See p2lab.genetic.matching for
choices
fitness_fn: A function to define fitness scores after each round
crossover_fn: A function that defines the crossover step in each round. Set to
None if this step should be ignored.
Expand Down Expand Up @@ -77,7 +80,7 @@ def genetic_team(
num_pokemon,
num_teams,
)
matches = generate_matches(teams)
matches = match_fn(teams)

# Run initial simulations
results = run_battles(matches)
Expand All @@ -90,29 +93,45 @@ def genetic_team(

# Genetic Loop
for _iter in range(num_evolutions):
# Step 1: selection
# An odd number of teams is an edge case for crossover, as it uses 2 teams
# at a time. This adds an extra team to selection if we are using crossover.
# the extra team will be removed by the crossover function.
extra = num_teams % 2 - mutate_with_fitness

# Returns new fitnesses in case we are doing fitness-mutate
new_teams, new_fitness = selection(
teams=teams,
fitness=fitness,
num_teams=num_teams + extra,
)

# Step 2: crossover
# Only do this step if not mutating with fitness, as fitness scores become
# invalid after crossover if doing so
if not mutate_with_fitness:
new_teams = crossover_fn(
teams=new_teams,
num_teams=num_teams,
num_pokemon=num_pokemon,
crossover_prob=crossover_prob,
allow_all=allow_all,
)

# Step 3: mutate
# If mutating with fitness, skip the crossover step. Otherwise, crossover +
# mutate.
if mutate_with_fitness:
teams = fitness_mutate(
teams=teams,
teams=new_teams,
num_pokemon=num_pokemon,
fitness=fitness,
fitness=new_fitness,
pokemon_population=pokemon_population,
allow_all=allow_all,
k=mutate_k,
)

else:
# Crossover based on fitness func
new_teams = crossover_fn(
teams=teams,
fitness=fitness,
num_teams=num_teams,
num_pokemon=num_pokemon,
crossover_prob=crossover_prob,
allow_all=allow_all,
)

# Mutate the new teams
teams = mutate(
teams=new_teams,
Expand All @@ -124,7 +143,7 @@ def genetic_team(
)

# Generate matches from list of teams
matches = generate_matches(teams)
matches = match_fn(teams)

# Run simulations
results = run_battles(matches)
Expand Down
40 changes: 34 additions & 6 deletions src/p2lab/genetic/operations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,36 @@
from p2lab.team import Team


### Selection Operation
def selection(
teams: list[Team],
fitness: np.ndarray,
num_teams: int,
) -> tuple[list[Team], np.ndarray]:
"""
This function performs the selection genetic operation. Its purpose is to
pass on good chromosomes/genes to the next population as a function of
fitness.
Args:
teams: A list containing all team objects
fitness: A numpy array of shape (N_team,) containing team fitness
scores
num_teams: The number of teams
"""

# Sample indices with replacement to produce new teams + fitnesses
old_indices = list(range(num_teams))
new_indices = random.choices(old_indices, k=num_teams)

# New teams and fitness
new_teams = [teams[i] for i in new_indices]
new_fitness = fitness[new_indices]

# Return
return new_teams, new_fitness


### Crossover Operations
def build_crossover_fn(
crossover_method: Callable,
Expand All @@ -25,7 +55,6 @@ def build_crossover_fn(

def crossover_fn(
teams: list[Team],
fitness: np.ndarray,
num_teams: int,
num_pokemon: int,
crossover_prob: float,
Expand All @@ -52,11 +81,10 @@ def crossover_fn(

# Each loop produces 2 new teams, so needs half this number to produce
# enough teams.
for _i in range(math.ceil(num_teams / 2)):
# Sample conditional on fitness
teams_old = np.random.choice(teams, size=2, replace=False, p=fitness)
team1_old = teams_old[0]
team2_old = teams_old[1]
for i in range(math.ceil(num_teams / 2)):
# Loop over two teams at a time
team1_old = teams[i*2]
team2_old = teams[i*2 + 1]

# Extract list of pokemon to crossover
team1_pokemon = team1_old.pokemon
Expand Down