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Patch for newer python version #262

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Patch for newer python version #262

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9294faf
Fix type casting and prevent errors in genetic algorithm
rogeriog Mar 24, 2026
fe0d361
Fix missing tab __init__ method definition in Individual class
rogeriog Mar 24, 2026
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25 changes: 15 additions & 10 deletions modnet/hyper_opt/fit_genetic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -77,11 +77,12 @@ def __init__(
b = int(max_feat / 2)
self.genes["n_feat"] = random.randint(1, b) + b
elif max_feat > 100 and max_feat < 2000:
max = max_feat
self.genes["n_feat"] = 10 * random.randint(1, int(max / 10))
max_val = max_feat
self.genes["n_feat"] = 10 * random.randint(1, int(max_val / 10))
else:
max = np.sqrt(max_feat)
self.genes["n_feat"] = random.randint(1, max) ** 2
# FIX: Cast np.sqrt to int to prevent random.randint float crash
max_val = int(np.sqrt(max_feat))
self.genes["n_feat"] = random.randint(1, max_val) ** 2

def crossover(self, partner: Individual) -> Individual:
"""Does the crossover of two parents and returns a 'child' which has a mix of the parents hyperparams.
Expand All @@ -91,8 +92,8 @@ def crossover(self, partner: Individual) -> Individual:
Returns:
Individual: Child.
"""
# creates indices to take randomly half the genes from one parent, and half the genes from the other
mother_genes = random.sample(self.genes.keys(), k=len(self.genes) // 2)
# FIX: Cast dict_keys to list for random.sample in Python 3.9+
mother_genes = random.sample(list(self.genes.keys()), k=len(self.genes) // 2)

child_genes = {
gene: self.genes[gene] if gene in mother_genes else partner.genes[gene]
Expand Down Expand Up @@ -575,13 +576,17 @@ def run(
1 / lw**5 for lw in val_loss[ranking]
] # **5 in order to give relatively more importance to the best individuals
weights = [1e-5 if math.isnan(weight) else weight for weight in weights]
weights = [w / sum(weights) for w in weights]
# selection: weighted choice of the parents -> parents with a low MAE have more chance to be selected

# FIX: Add tiny epsilon to sum to prevent ZeroDivisionError
weight_sum = sum(weights) + 1e-10
weights = [w / weight_sum for w in weights]

# FIX: wrap NumPy arrays in list() for random.choices compatibility
parents_1 = random.choices(
individuals[ranking], weights=weights, k=size_pop
list(individuals[ranking]), weights=weights, k=size_pop
)
parents_2 = random.choices(
individuals[ranking], weights=weights, k=size_pop
list(individuals[ranking]), weights=weights, k=size_pop
)

# crossover
Expand Down