def test_out_shapes(): """test_evaluation.py: program output is correct size """ # load test data set boston = load_boston() # boston.data = boston.data[::10] # boston.target = boston.target[::10] n_features = boston.data.shape[1] # function set # terminal set term_set = [] # numbers represent column indices of features for i in np.arange(n_features): term_set.append(node('x', loc=i)) # features # term_set.append(('k',0,np.random.rand())) # ephemeral random constants # initialize population pop_size = 5 few = FEW(population_size=pop_size, seed_with_ml=False) few.term_set = term_set few.n_features = n_features pop = few.init_pop() pop.X = np.asarray( list(map(lambda I: few.out(I, boston.data), pop.individuals))) #pop.X = out(pop.individuals[0],boston.data,boston.target) print("pop.X.shape:", pop.X.shape) print("boston.target.shape", boston.target.shape) assert pop.X.shape == (pop_size, boston.target.shape[0])
def test_calc_fitness_shape(): """test_evaluation.py: calc_fitness correct shapes """ # load test data set boston = load_boston() # boston.data = boston.data[::10] # boston.target = boston.target[::10] n_features = boston.data.shape[1] # terminal set term_set = [] # numbers represent column indices of features for i in np.arange(n_features): term_set.append(node('x', loc=i)) # features # term_set.append(('k',0,np.random.rand())) # ephemeral random constants # initialize population pop_size = 5 few = FEW(population_size=pop_size, seed_with_ml=False) few.term_set = term_set few.n_features = n_features pop = few.init_pop() pop.X = np.asarray( list(map(lambda I: few.out(I, boston.data), pop.individuals))) fitnesses = few.calc_fitness(pop.X, boston.target, 'mse', 'tournament') assert len(fitnesses) == len(pop.individuals) # test vectorized fitnesses vec_fitnesses = few.calc_fitness(pop.X, boston.target, 'mse', 'lexicase') fitmat = np.asarray(vec_fitnesses) print("fitmat.shape:", fitmat.shape) assert fitmat.shape == (len(pop.individuals), boston.target.shape[0])
def test_out_is_correct(): """test_evaluation.py: output matches known function outputs """ boston = load_boston() n_features = boston.data.shape[1] X = boston.data Y = boston.target p1 = Ind() p2 = Ind() p3 = Ind() p4 = Ind() p5 = Ind() p1.stack = [ node('x', loc=4), node('x', loc=5), node('-'), node('k', value=0.175), node('log'), node('-') ] p2.stack = [node('x', loc=7), node('x', loc=8), node('*')] p3.stack = [ node('x', loc=0), node('exp'), node('x', loc=5), node('x', loc=7), node('*'), node('/') ] p4.stack = [node('x', loc=12), node('sin')] p5.stack = [ node('k', value=178.3), node('x', loc=8), node('*'), node('x', loc=7), node('cos'), node('+') ] few = FEW() y1 = few.safe(np.log(0.175) - (X[:, 5] - X[:, 4])) y2 = few.safe(X[:, 7] * X[:, 8]) y3 = few.safe(divs(X[:, 5] * X[:, 7], np.exp(X[:, 0]))) y4 = few.safe(np.sin(X[:, 12])) y5 = few.safe(178.3 * X[:, 8] + np.cos(X[:, 7])) # y1,y2,y3,y4,y5 = safe(y1),safe(y2),safe(y3),safe(y4),safe(y5) few = FEW() assert np.array_equal(y1, few.out(p1, X)) print("y1 passed") assert np.array_equal(y2, few.out(p2, X)) print("y2 passed") assert np.array_equal(y3, few.out(p3, X)) print("y3 passed") # print("y4:",y4,"y4hat:",few.out(p4,X,Y)) assert np.array_equal(y4, few.out(p4, X)) print("y4 passed") assert np.array_equal(y5, few.out(p5, X))