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