def test_adapt_single_model(population_strategy: PopulationStrategy):
n = 10
df = pd.DataFrame([{"s": np.random.rand()} for _ in range(n)])
w = np.ones(n) / n
kernel = MultivariateNormalTransition()
kernel.fit(df, w)
population_strategy.update([kernel], np.array([1.]), t=0)
assert population_strategy(t=0) > 0
def test_adapt_two_models(population_strategy: PopulationStrategy):
n = 10
kernels = []
for _ in range(2):
df = pd.DataFrame([{"s": np.random.rand()} for _ in range(n)])
w = np.ones(n) / n
kernel = MultivariateNormalTransition()
kernel.fit(df, w)
kernels.append(kernel)
population_strategy.update(kernels, np.array([.7, .2]), t=0)
assert population_strategy(t=0) > 0
def test_no_parameters(population_strategy: PopulationStrategy):
n = 10
df = pd.DataFrame(index=list(range(n)))
w = np.ones(n) / n
kernels = []
for _ in range(2):
kernel = MultivariateNormalTransition()
kernel.fit(df, w)
kernels.append(kernel)
population_strategy.update(kernels, np.array([.7, .3]), t=0)
assert population_strategy(t=0) > 0
def test_one_with_one_without_parameters(
population_strategy: PopulationStrategy):
n = 10
kernels = []
df_without = pd.DataFrame(index=list(range(n)))
w_without = np.ones(n) / n
kernel_without = MultivariateNormalTransition()
kernel_without.fit(df_without, w_without)
kernels.append(kernel_without)
df_with = pd.DataFrame([{"s": np.random.rand()} for _ in range(n)])
w_with = np.ones(n) / n
kernel_with = MultivariateNormalTransition()
kernel_with.fit(df_with, w_with)
kernels.append(kernel_with)
population_strategy.update(kernels, np.array([.7, .3]), t=0)
assert population_strategy(t=0) > 0
def test_transitions_not_modified(population_strategy: PopulationStrategy):
n = 10
kernels = []
test_points = pd.DataFrame([{"s": np.random.rand()} for _ in range(n)])
for _ in range(2):
df = pd.DataFrame([{"s": np.random.rand()} for _ in range(n)])
w = np.ones(n) / n
kernel = MultivariateNormalTransition()
kernel.fit(df, w)
kernels.append(kernel)
test_weights = [k.pdf(test_points) for k in kernels]
population_strategy.update(kernels, np.array([.7, .2]))
after_adaptation_weights = [k.pdf(test_points) for k in kernels]
same = all((k1 == k2).all()
for k1, k2 in zip(test_weights, after_adaptation_weights))
err_msg = ("Population strategy {}"
" modified the transitions".format(population_strategy))
assert same, err_msg