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"""Testing the Genetic Programming functions module."""
# Author: Trevor Stephens <trevorstephens.com>
#
# License: BSD 3 clause
import pickle
import numpy as np
import pytest
from numpy import maximum
from sklearn.datasets import load_diabetes, load_breast_cancer
from sklearn.utils.validation import check_random_state
from gplearn.functions import _protected_sqrt, make_function
from gplearn.genetic import SymbolicRegressor, SymbolicTransformer
from gplearn.genetic import SymbolicClassifier
# load the diabetes dataset and randomly permute it
rng = check_random_state(0)
diabetes = load_diabetes()
perm = rng.permutation(diabetes.target.size)
diabetes.data = diabetes.data[perm]
diabetes.target = diabetes.target[perm]
# load the breast cancer dataset and randomly permute it
cancer = load_breast_cancer()
perm = check_random_state(0).permutation(cancer.target.size)
cancer.data = cancer.data[perm]
cancer.target = cancer.target[perm]
def test_validate_function():
"""Check that valid functions are accepted & invalid ones raise error"""
# Check arity tests
_ = make_function(function=_protected_sqrt, name='sqrt', arity=1)
# non-integer arity
with pytest.raises(ValueError):
make_function(function=_protected_sqrt, name='sqrt', arity='1')
with pytest.raises(ValueError):
make_function(function=_protected_sqrt, name='sqrt', arity=1.0)
# non-bool wrap
with pytest.raises(ValueError):
make_function(function=_protected_sqrt, name='sqrt', arity=1, wrap='f')
# non-matching arity
with pytest.raises(ValueError):
make_function(function=_protected_sqrt, name='sqrt', arity=2)
with pytest.raises(ValueError):
make_function(function=maximum, name='max', arity=1)
# Check name test
with pytest.raises(ValueError):
make_function(function=_protected_sqrt, name=2, arity=1)
# Check return type tests
def bad_fun1(x1, x2):
return 'ni'
with pytest.raises(ValueError):
make_function(function=bad_fun1, name='ni', arity=2)
# Check return shape tests
def bad_fun2(x1):
return np.ones((2, 1))
with pytest.raises(ValueError):
make_function(function=bad_fun2, name='ni', arity=1)
# Check closure for negatives test
def _unprotected_sqrt(x1):
with np.errstate(divide='ignore', invalid='ignore'):
return np.sqrt(x1)
with pytest.raises(ValueError):
make_function(function=_unprotected_sqrt, name='sqrt', arity=1)
# Check closure for zeros test
def _unprotected_div(x1, x2):
with np.errstate(divide='ignore', invalid='ignore'):
return np.divide(x1, x2)
with pytest.raises(ValueError):
make_function(function=_unprotected_div, name='div', arity=2)
def test_function_in_program():
"""Check that using a custom function in a program works"""
def logic(x1, x2, x3, x4):
return np.where(x1 > x2, x3, x4)
logical = make_function(function=logic,
name='logical',
arity=4)
function_set = ['add', 'sub', 'mul', 'div', logical]
est = SymbolicTransformer(generations=2, population_size=2000,
hall_of_fame=100, n_components=10,
function_set=function_set,
parsimony_coefficient=0.0005,
max_samples=0.9, random_state=0)
est.fit(diabetes.data[:300, :], diabetes.target[:300])
formula = est._programs[0][3].__str__()
expected_formula = ('add(X3, logical(div(X5, sub(X5, X5)), '
'add(X9, -0.621), X8, X4))')
assert(expected_formula == formula)
def test_parallel_custom_function():
"""Regression test for running parallel training with custom functions"""
def _logical(x1, x2, x3, x4):
return np.where(x1 > x2, x3, x4)
logical = make_function(function=_logical,
name='logical',
arity=4)
est = SymbolicRegressor(generations=2,
function_set=['add', 'sub', 'mul', 'div', logical],
random_state=0,
n_jobs=2)
est.fit(diabetes.data, diabetes.target)
_ = pickle.dumps(est)
# Unwrapped functions should fail
logical = make_function(function=_logical,
name='logical',
arity=4,
wrap=False)
est = SymbolicRegressor(generations=2,
function_set=['add', 'sub', 'mul', 'div', logical],
random_state=0,
n_jobs=2)
est.fit(diabetes.data, diabetes.target)
with pytest.raises(AttributeError):
pickle.dumps(est)
# Single threaded will also fail in non-interactive sessions
est = SymbolicRegressor(generations=2,
function_set=['add', 'sub', 'mul', 'div', logical],
random_state=0)
est.fit(diabetes.data, diabetes.target)
with pytest.raises(AttributeError):
pickle.dumps(est)
def test_parallel_custom_transformer():
"""Regression test for running parallel training with custom transformer"""
def _sigmoid(x1):
with np.errstate(over='ignore', under='ignore'):
return 1 / (1 + np.exp(-x1))
sigmoid = make_function(function=_sigmoid,
name='sig',
arity=1)
est = SymbolicClassifier(generations=2,
transformer=sigmoid,
random_state=0,
n_jobs=2)
est.fit(cancer.data, cancer.target)
_ = pickle.dumps(est)
# Unwrapped functions should fail
sigmoid = make_function(function=_sigmoid,
name='sig',
arity=1,
wrap=False)
est = SymbolicClassifier(generations=2,
transformer=sigmoid,
random_state=0,
n_jobs=2)
est.fit(cancer.data, cancer.target)
with pytest.raises(AttributeError):
pickle.dumps(est)
# Single threaded will also fail in non-interactive sessions
est = SymbolicClassifier(generations=2,
transformer=sigmoid,
random_state=0)
est.fit(cancer.data, cancer.target)
with pytest.raises(AttributeError):
pickle.dumps(est)
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