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Add support for maintaining pop fitness in CSV file #333

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Add support for maintaining pop fitness in CSV file #333

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56 changes: 49 additions & 7 deletions mach_opt/mach_opt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@
import pickle
import time
import sys
import pathlib
import multiprocessing as mp

__all__ = [
Expand All @@ -35,7 +36,14 @@ def initial_pop(self, pop_size):
pop = pg.population(self.prob, size=pop_size)
return pop

def run_optimization(self, pop, gen_size, filepath=None, pg_neighbors=20):
def run_optimization(
self,
pop,
gen_size,
pop_filepath: pathlib.Path,
pop_fitness_filepath: pathlib.Path,
pg_neighbors=20,
):
algo = pg.algorithm(
pg.moead(
gen=1,
Expand All @@ -50,27 +58,61 @@ def run_optimization(self, pop, gen_size, filepath=None, pg_neighbors=20):
preserve_diversity=True,
)
)

for _ in range(0, gen_size):
print("This is iteration", _)

# Evolve population
pop = algo.evolve(pop)

# Save state
print("Saving current generation")
self.save_pop(filepath, pop)
self.save_pop(pop_filepath, pop)
self.save_pop_fitness(pop_fitness_filepath, pop)

return pop

# methods to save and load latest generation for resuming optimization
def save_pop(self, filepath, pop):
df = pd.DataFrame(pop.get_x())
df.to_csv(filepath)

def load_pop(self, filepath, pop_size):
def save_pop_fitness(self, filepath, pop):
df = pd.DataFrame(pop.get_f())
df.to_csv(filepath)

def load_pop(
self,
pop_filepath: pathlib.Path,
pop_fitness_filepath: pathlib.Path,
pop_size: int,
print_to_console=False,
):
try:
df = pd.read_csv(filepath, index_col=0)
df_x = pd.read_csv(pop_filepath, index_col=0)
except FileNotFoundError:
return None

try:
df_fit = pd.read_csv(pop_fitness_filepath, index_col=0)
except FileNotFoundError:
print("NO FITNESS VALUES FOUND FOR POPULATION...")
print("NOW COMPUTING FITNESS DURING POPULATION LOAD...")
df_fit = None

pop = pg.population(self.prob)

for i in range(pop_size):
print(df.iloc[i])
pop.push_back(df.iloc[i])
x = df_x.iloc[i]

if print_to_console:
print(x)

if df_fit is not None:
f = df_fit.iloc[i]
pop.push_back(x, f=f)
else:
pop.push_back(x)

return pop


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