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Using max_time in test population #195

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Oct 15, 2024
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Using max_time in test population #195

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b70a076
Using `max_time` in test population
gAldeia Oct 15, 2024
0e9751a
Printing console in test population
gAldeia Oct 15, 2024
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2 changes: 1 addition & 1 deletion experiment/test.sh
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Original file line number Diff line number Diff line change
@@ -1,2 +1,2 @@
python -m pytest -v test_algorithm.py --ml ${ALGORITHM}
python -m pytest -v test_population.py --ml ${ALGORITHM}
python -m pytest -s -v test_population.py --ml ${ALGORITHM}
42 changes: 33 additions & 9 deletions experiment/test_population.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,15 +20,14 @@
if 'MKL_NUM_THREADS' not in os.environ.keys():
os.environ['MKL_NUM_THREADS'] = '1'


def test_population(ml):
"""Sympy compatibility of model string"""

dataset = 'test/192_vineyard_small.tsv.gz'
random_state = 42

algorithm = importlib.__import__(f'methods.{ml}.regressor',globals(),
locals(),
algorithm = importlib.__import__(f'methods.{ml}.regressor',
globals(), locals(),
['est','hyper_params','complexity'])

features, labels, feature_names = read_file(
Expand All @@ -49,21 +48,46 @@ def test_population(ml):
y_train = y_train[sample_idx]
X_train = X_train.iloc[sample_idx]

##################################################
# fit with max_time
##################################################
MAXTIME = 3600 # in seconds
if hasattr(algorithm.est, 'max_time'):
algorithm.est.max_time = MAXTIME
print('max time:',MAXTIME)
else:
print('max time not set')

algorithm.est.fit(X_train.values, y_train)

if 'get_population' not in dir(algorithm):
algorithm.get_population = lambda est: [est]
##################################################
# get best solution
##################################################
if 'get_best_solution' not in dir(algorithm):
algorithm.get_best_solution = lambda est: est
print(f"{ml} does not implement get_best_solution")
else:
print(f"{ml} has get_best_solution method. Using implemented method")

population = algorithm.get_population(algorithm.est)
best_model = algorithm.get_best_solution(algorithm.est)
print('Best model')
print(algorithm.model(best_model, X_train))
print(algorithm.est.predict(X_train.values))

# assert that population has at least 1 and no more than 100 individuals
assert 1 <= len(population) <= 100, "Population size is not within the expected range"
##################################################
# get population
##################################################
if 'get_population' not in dir(algorithm):
algorithm.get_population = lambda est: [est]
print(f"{ml} does not implement get_population")
else:
print(f"{ml} has get_population method. Using implemented method")

population = algorithm.get_population(algorithm.est)
assert 1 <= len(population) <= 100, \
"Population size is not within the expected range"

for p in population:
for i, p in enumerate(population):
print(f"Individual {i}")
print(algorithm.model(p, X_train))
print(p.predict(X_train.values))
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