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Fix issue#42-(1) #44

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merged 1 commit into from
Nov 10, 2017
Merged

Fix issue#42-(1) #44

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5 changes: 4 additions & 1 deletion gplearn/genetic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -471,7 +471,10 @@ def fit(self, X, y, sample_weight=None):

if isinstance(self, RegressorMixin):
# Find the best individual in the final generation
self._program = self._programs[-1][np.argmin(fitness)]
if self._metric.greater_is_better:
self._program = self._programs[-1][np.argmax(fitness)]
else:
self._program = self._programs[-1][np.argmin(fitness)]

if isinstance(self, TransformerMixin):
# Find the best individuals in the final generation
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Can we expand the PR to cover the transformer as well? I think that the hall_of_fame = fitness.argsort()[:self.hall_of_fame] should be the place to do this. Should be adequate to cut the results in the opposite direction for the bottom end using hall_of_fame = fitness.argsort()[self.population_size - self.hall_of_fame:]

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Presumably all the existing tests should pass with this change, just add an additional regression test to keep this bug removed in the future as well.

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25 changes: 25 additions & 0 deletions gplearn/tests/test_genetic.py
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Expand Up @@ -1010,6 +1010,31 @@ def test_warm_start():
assert_equal(cold_program, warm_program)


def test_customizied_regressor_metrics():
"""Check whether parameter greater_is_better works fine"""

x_data = rng.uniform(-1, 1, 100).reshape(50, 2)
y_true = x_data[:, 0] ** 2 + x_data[:, 1] ** 2

est_gp = SymbolicRegressor(metric='mean absolute error', stopping_criteria=0.000001, random_state=415,
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Can we reduce these line lengths to 79 chars or less for PEP8?

parsimony_coefficient=0.001, verbose=0, init_method='full', init_depth=(2, 4))
est_gp.fit(x_data, y_true)
formula = est_gp.__str__()
assert_equal("add(mul(X1, X1), mul(X0, X0))", formula, True)

def neg_mean_absolute_error(y, y_pred, sample_weight):
return -1 * mean_absolute_error(y, y_pred, sample_weight)

customizied_fitness = make_fitness(neg_mean_absolute_error, greater_is_better=True)

c_est_gp = SymbolicRegressor(metric=customizied_fitness, stopping_criteria=-0.000001, random_state=415,
parsimony_coefficient=0.001, verbose=0, init_method='full', init_depth=(2, 4))
c_est_gp.fit(x_data, y_true)
c_formula = c_est_gp.__str__()

assert_equal("add(mul(X1, X1), mul(X0, X0))", c_formula, True)

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Does this test fail on master?

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Just checked and it does. Might be a better test to ensure the formulas are equal to each other though?

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@eggachecat eggachecat Aug 15, 2017 •
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@trevorstephens Sorry for my late feedback.. Maybe I should not compare the string of the formula considering other possible solutions. I will test whether the outputs of the formula given X almost equal to the right answer

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@eggachecat eggachecat Aug 15, 2017 •
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@trevorstephens Hi it just occurred to me that why would test failed anyway?Since I specify function call a fix random_state parameter, shouldn't it give the same result forever?Just curious...

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No worries @eggachecat ... It's open source lol, happens when people have the time to work on it! I think checking the string is fine for a negative version of the same metric.

It's a regression check so its intention is to ensure the bug does not return in the future. So yes, it should always pass unless something changes later that brings the bug back. This check would ensure that the bug is detected before merging code later on that brings back the problem.


if __name__ == "__main__":
import nose
nose.runmodule()