Spaces:
Runtime error
Runtime error
File size: 17,030 Bytes
93c029f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 |
import matplotlib.pyplot as plt
import pickle
from src.cocktails.utilities.cocktail_generation_utilities.population import *
from src.cocktails.utilities.glass_and_volume_utilities import glass_volume
from src.cocktails.config import RECIPE2FEATURES_PATH
def test_mutation_params(cocktail_reps):
indexes = np.arange(cocktail_reps.shape[0])
np.random.shuffle(indexes)
perfs = []
mutated_perfs = []
pop_params = dict(mutation_params=dict(p_add_ing=0.7,
p_remove_ing=0.7,
p_switch_ing=0.5,
p_change_q=0.7,
delta_change_q=0.3,
asexual_rep=True,
crossover=True,
ingredient_addition=(0.1, 0.05)),
nb_generations=100,
pop_size=100,
nb_elites=10,
dist='mse',
n_neighbors=5)
for i in indexes[:20]:
target = cocktail_reps[i]
for j in range(100):
parent = IndividualCocktail(pop_params=pop_params,
target_affective_cluster=None,
target=target.copy())
perfs.append(parent.perf)
child = parent.get_child()[0]
# child.compute_cocktail_rep()
# child.compute_perf()
if perfs[-1] != child.perf:
mutated_perfs.append(child.perf)
else:
perfs.pop(-1)
filtered_children = np.argwhere(np.array(mutated_perfs)==-100).flatten()
non_filtered_ids = np.argwhere(np.logical_and(np.array(perfs)!=-100, np.array(mutated_perfs)!=-100)).flatten()
print(f'Proportion of filtered: {filtered_children.size} / {len(mutated_perfs)} = {int(filtered_children.size / len(mutated_perfs)*100)}%')
plt.figure()
plt.scatter(np.array(perfs)[non_filtered_ids], np.array(mutated_perfs)[non_filtered_ids], s=100, alpha=0.5)
plt.xlabel('parent perf')
plt.ylabel('child perf')
print(np.corrcoef(np.array(perfs)[non_filtered_ids], np.array(mutated_perfs)[non_filtered_ids])[0, 1])
plt.show()
stop = 1
def test_crossover(cocktail_reps):
indexes = np.arange(cocktail_reps.shape[0])
np.random.shuffle(indexes)
perfs = []
mutated_perfs = []
pop_params = dict(mutation_params=dict(p_add_ing=0.7,
p_remove_ing=0.7,
p_switch_ing=0.5,
p_change_q=0.7,
delta_change_q=0.3,
asexual_rep=True,
crossover=True,
ingredient_addition=(0.1, 0.05)),
nb_generations=100,
pop_size=100,
nb_elites=10,
dist='mse',
n_neighbors=5)
for i in indexes[:20]:
for j in range(100):
target = cocktail_reps[i]
parent1 = IndividualCocktail(pop_params=pop_params,
target_affective_cluster=None,
target=target.copy())
parent2 = IndividualCocktail(pop_params=pop_params,
target_affective_cluster=None,
target=target.copy())
child = parent1.get_child_with(parent2)[0]
# child.compute_cocktail_rep()
# child.compute_perf()
perfs.append((parent1.perf + parent2.perf)/2)
if perfs[-1] != child.perf:
mutated_perfs.append(child.perf)
else:
perfs.pop(-1)
filtered_children = np.argwhere(np.array(mutated_perfs)==-100).flatten()
non_filtered_ids = np.argwhere(np.logical_and(np.array(perfs)>-45, np.array(mutated_perfs)!=-100)).flatten()
print(f'Proportion of filtered: {filtered_children.size} / {len(mutated_perfs)} = {int(filtered_children.size / len(mutated_perfs)*100)}%')
plt.figure()
plt.scatter(np.array(perfs)[non_filtered_ids], np.array(mutated_perfs)[non_filtered_ids], s=100, alpha=0.5)
plt.xlabel('parent perf')
plt.ylabel('child perf')
print(np.corrcoef(np.array(perfs)[non_filtered_ids], np.array(mutated_perfs)[non_filtered_ids])[0, 1])
plt.show()
stop = 1
def run_comparisons():
np.random.seed(0)
indexes = np.arange(cocktail_reps.shape[0])
np.random.shuffle(indexes)
for n_neighbors in [0, 5]:
id_str_neigh = '5neigh_' if n_neighbors == 5 else '0_neigh_'
for asexual_rep in [True, False]:
id_str_as = id_str_neigh + 'asexual_' if asexual_rep else id_str_neigh
for crossover in [True, False]:
id_str = id_str_as + 'crossover_' if crossover else id_str_as
if crossover or asexual_rep:
mutation_params = dict(p_add_ing = 0.5,
p_remove_ing = 0.5,
p_change_q = 0.5,
delta_change_q = 0.3,
asexual_rep=asexual_rep,
crossover=crossover,
ingredient_addition = (0.1, 0.05))
nb_generations = 100
pop_size=100
nb_elites=10
dist = 'mse'
results = dict()
print(id_str)
for i, ind in enumerate(indexes[:30]):
print(i+1)
target_ing_str = data['ingredients_str'][ind]
target = cocktail_reps[ind]
population = Population(nb_generations=nb_generations, pop_size=pop_size, nb_elite=nb_elites,
target=target, dist=dist, mutation_params=mutation_params,
n_neighbors=n_neighbors, target_ing_str=target_ing_str, true_prep_type=data['category'][ind])
population.run_evolution(verbose=False)
best_scores, best_ind = population.get_best_score()
recipes = [ind.get_recipe()[3] for ind in best_ind[:5]]
results[str(ind)] = dict(best_scores=best_scores[:5], recipes=recipes, target=population.target_individual.get_recipe()[3])
with open(f'/home/cedric/Desktop/ga_tests_{id_str}.pickle', 'wb') as f:
pickle.dump(results, f)
def get_cocktail_distribution(cocktail_reps):
return (np.mean(cocktail_reps, axis=0), np.cov(cocktail_reps, rowvar=0))
def sample_cocktails(cocktail_reps, n=10, target_affective_cluster=None, to_print=True):
distrib = get_cocktail_distribution(cocktail_reps)
sampled_cocktail_reps = np.random.multivariate_normal(distrib[0], distrib[1], size=n)
recipes = []
closest_recipes = []
for i_c, cr in enumerate(sampled_cocktail_reps):
population = setup_recipe_generation(cr.copy(), target_affective_cluster=target_affective_cluster)
closest_recipes.append(population.nn_recipes[0])
best_scores, best_individuals = population.run_evolution()
recipes.append(best_individuals[0].get_recipe()[3])
if to_print:
print(f'Sample #{len(recipes)}:')
print(recipes[-1])
print('Closest from dataset:')
print(closest_recipes[-1])
stop = 1
return recipes, closest_recipes
def setup_recipe_generation(target, known_target_dict=None, target_affective_cluster=None):
# pop_params = dict(mutation_params=dict(p_add_ing=0.7,
# p_remove_ing=0.7,
# p_switch_ing=0.5,
# p_change_q=0.7,
# delta_change_q=0.3,
# asexual_rep=True,
# crossover=True,
# ingredient_addition=(0.1, 0.05)),
# nb_generations=2, #100
# pop_size=5, #100
# nb_elites=2, #10
# dist='mse',
# n_neighbors=3) #5
pop_params = dict(mutation_params=dict(p_add_ing=0.4,
p_remove_ing=1,
p_switch_ing=0.5,
p_change_q=1,
delta_change_q=0.3,
asexual_rep=True,
crossover=True,
ingredient_addition=(0.1, 0.05)),
nb_generations=100, # 100
pop_size=100, # 100
nb_elites=10, # 10
dist='mse',
n_neighbors=5) # 5
population = Population(target=target, target_affective_cluster=target_affective_cluster, known_target_dict=known_target_dict, pop_params=pop_params)
return population
def cocktailrep2recipe(cocktail_rep, unit='mL', target_affective_cluster=None, known_target_dict=None, n_output=1, return_ind=False, verbose=True, full_verbose=False, level=0):
init_time = time.time()
if verbose: print(' ' * level + 'Generating cocktail..')
if cocktail_rep.ndim > 1:
assert cocktail_rep.shape[0] == 1
cocktail_rep = cocktail_rep.flatten()
# target_affective_cluster = target_affective_cluster[0]
population = setup_recipe_generation(cocktail_rep.copy(), known_target_dict=known_target_dict, target_affective_cluster=target_affective_cluster)
if full_verbose:
print(' ' * (level + 2) + '3 nearest neighbors:')
for i, recipe, score in zip(range(3), population.nn_recipes[:3], population.nn_scores[:3]):
print(' ' * (level + 4) + f'#{i+1}, score: {score:.2f}')
print(' ' * (level + 4) + recipe[1:].replace('None ()', '').replace('\t\t', ' ' * (level + 6)))
best_scores, best_individuals = population.run_evolution(verbose=full_verbose, level=level+2)
for i in range(n_output):
best_individuals[i].make_recipe_fit_the_glass()
instructions = [ind.get_instructions() for ind in best_individuals[:n_output]]
recipes = [ind.get_recipe(unit=unit)[3] for ind in best_individuals[:n_output]]
glasses = [ind.glass for ind in best_individuals[:n_output]]
prep_types = [ind.prep_type for ind in best_individuals[:n_output]]
for i, g, p, inst in zip(range(len(recipes)), glasses, prep_types, instructions):
recipes[i] = recipes[i].replace('Recipe', 'Ingredients') + f'Serve in:\n {g.capitalize()} glass.\n' + inst
if full_verbose:
print(f'\n--------------\n{n_output} best results:')
for i, recipe, score in zip(range(n_output), recipes, best_scores[:n_output]):
print(f'#{i+1}, score: {score:.2f}')
print(recipe)
if verbose: print(' ' * (level + 2) + f'Generated in {int(time.time() - init_time)} seconds.')
if return_ind:
return recipes, best_scores[:n_output], best_individuals[:n_output]
else:
return recipes, best_scores[:n_output]
def interpolate(cocktail_rep1, cocktail_rep2, alpha, verbose=False):
recipe, score = cocktailrep2recipe(alpha * cocktail_rep1 + (1 - alpha) * cocktail_rep2, verbose=verbose)
return recipe[0], score
def interpolation_study(n_steps, cocktail_reps):
alphas = np.arange(0, 1 + 1e-6, 1/(n_steps + 1))
indexes = np.random.choice(np.arange(cocktail_reps.shape[0]), size=2, replace=False)
target_ing_str1, target_ing_str2 = data['ingredients_str'][indexes[0]], data['ingredients_str'][indexes[1]]
cocktail_rep1, cocktail_rep2 = cocktail_reps[indexes[0]], cocktail_reps[indexes[1]]
recipes, scores = [], []
for alpha in alphas:
recipe, score = interpolate(cocktail_rep1, cocktail_rep2, alpha)
recipes.append(recipe)
scores.append(score[0])
print('Point A:')
print_recipe(ingredient_str=target_ing_str2)
for i, alpha in enumerate(alphas):
print(f'Alpha = {alpha}, score = {scores[i]}')
print(recipes[i])
print('Point B:')
print_recipe(ingredient_str=target_ing_str1)
stop = 1
def test_robustness_affective_cluster(cocktail_reps):
indexes = np.arange(cocktail_reps.shape[0])
np.random.shuffle(indexes)
matches = []
for i in indexes:
target_ing_str = data['ingredients_str'][i]
true_prep_type = data['category'][i]
target = cocktail_reps[i]
# get affective cluster
recipes, best_scores, best_inds = cocktailrep2recipe(cocktail_rep=target, target_ing_str=target_ing_str, true_prep_type=true_prep_type, n_output=1, verbose=False,
return_ind=True)
matches.append(best_inds[0].does_affective_cluster_match())
print(np.mean(matches))
def test(cocktail_reps):
indexes = np.arange(these_cocktail_reps.shape[0])
unnormalized_cr = np.array([data[k] for k in rep_keys]).transpose()
for i in indexes:
target_ing_str = data['ingredients_str'][i]
true_prep_type = data['category'][i]
target = these_cocktail_reps[i]
# print('preptype:', true_prep_type)
# print('cocktail unnormalized', np.sum(unnormalized_cr[i]), unnormalized_cr[i])
# print('cocktail hand normalized', np.sum(normalize_cocktail(unnormalized_cr[i])), normalize_cocktail(unnormalized_cr[i]))
# print('cocktail rep normalized', np.sum(these_cocktail_reps[i]), these_cocktail_reps[i])
# print('cocktail rep normalized', np.sum(all_reps[i]), all_reps[i])
population = setup_recipe_generation(target.copy(), target_ing_str=target_ing_str, target_affective_cluster=None, true_prep_type=true_prep_type)
target = population.target_individual
target.compute_perf()
if target.perf < -50:
print(i)
print_recipe(target_ing_str)
if not target.is_alcohol_present(): print('No alcohol')
if not target.is_total_volume_enough(): print('small volume')
if not target.does_fit_glass():
print(target.end_volume)
print(glass_volume[target.get_glass_type()] * 0.81)
print('too much volume')
if not target.is_alcohol_reasonable():
print(f'amount of alcohol too small or too large: {target.alcohol_precentage}')
stop = 1
if __name__ == '__main__':
these_cocktail_reps = COCKTAIL_REPS.copy()
# test_crossover(these_cocktail_reps)
# test_mutation_params(these_cocktail_reps)
# test(these_cocktail_reps)
# recipes, closest_recipes = sample_cocktails(these_cocktail_reps, n=10)
# interpolation_study(n_steps=4, cocktail_reps=these_cocktail_reps)
# test_robustness_affective_cluster(these_cocktail_reps)
indexes = np.arange(these_cocktail_reps.shape[0])
np.random.shuffle(indexes)
# test_crossover(mutation_params, dist)
# test_mutation_params(mutation_params, dist)
stop = 1
unnormalized_cr = np.array([data[k] for k in rep_keys]).transpose()
for i in indexes:
print(i)
target_ing_str = data['ingredients_str'][i]
target_prep_type = data['category'][i]
target_glass = data['glass'][i]
print('preptype:', target_prep_type)
print('cocktail unnormalized', np.sum(unnormalized_cr[i]), unnormalized_cr[i])
print('cocktail hand normalized', np.sum(normalize_cocktail(unnormalized_cr[i])), normalize_cocktail(unnormalized_cr[i]))
print('cocktail rep normalized', np.sum(these_cocktail_reps[i]), these_cocktail_reps[i])
print('cocktail rep normalized', np.sum(all_reps[i]), all_reps[i])
print(i)
print('___________Target')
nn_model = NearestNeighbors()
nn_model.fit(these_cocktail_reps)
dists, indexes = nn_model.kneighbors(these_cocktail_reps[i].reshape(1, -1))
print(indexes)
print_recipe(target_ing_str)
target = these_cocktail_reps[i]
known_target_dict = dict(prep_type=target_prep_type,
ing_str=target_ing_str,
glass=target_glass)
recipes, best_scores = cocktailrep2recipe(cocktail_rep=target, known_target_dict=known_target_dict, n_output=1, verbose=True, full_verbose=True)
stop = 1 |