def main():
# Create Problem
polynomial = Polynomial(num_terms=5, num_dimensions=2)
log.info(polynomial)
# Define Search Range
search_range = SearchRange()
search_range.set_feasible_range(10000, 2)
# Solvers
# brute_force_solver = BruteForceSolver(search_range)
genetic_algorithm_solver = GeneticAlgorithmSolver(search_range)
gui = Gui(polynomial, search_range)
# benchmark = Benchmark([brute_force_solver, genetic_algorithm_solver])
benchmark = Benchmark([genetic_algorithm_solver])
# Benchmark
benchmark.evaluate(polynomial)
# Visualize Solvers
# gui.create_animation(brute_force_solver)
animation = gui.create_animation(genetic_algorithm_solver)
# Create Default Visual
# gui.plot_problem()
gui.show()
def run(max_df, min_df, max_features, lowercase, stop_words, analyzer,
strip_accents, use_idf, sublinear_tf, norm, data_provider):
count_vector = CountVectorizer(min_df=min_df,
max_df=max_df,
max_features=max_features,
lowercase=lowercase,
stop_words=stop_words,
analyzer=analyzer,
strip_accents=strip_accents)
tfidf_transformer = TfidfTransformer(use_idf=use_idf,
sublinear_tf=sublinear_tf,
norm=norm)
feature_extractor = FeatureExtractor(PorterStemmer(), count_vector,
tfidf_transformer)
benchmark = Benchmark(DataProvider.get_data_provider('reuters'),
feature_extractor, False)
return benchmark.evaluate()
def run_benchmark(root_folder, times, feasible_ranges, polynomial=None):
if not os.path.exists(root_folder):
log.info('Creating Folder')
os.makedirs(root_folder)
# Create Problem
if not polynomial:
polynomial = Polynomial(num_terms=5, num_dimensions=2)
log.info(polynomial)
for feasible_range in feasible_ranges:
search_range = SearchRange()
search_range.set_feasible_range(feasible_range, 2)
folder = os.path.join(root_folder,
'feasible_range_{}'.format(feasible_range))
solution_payload = {}
if not os.path.exists(folder):
os.mkdir(folder)
for i in range(times):
# Create Folder
run_folder = os.path.join(folder, 'run_{}'.format(i))
if not os.path.exists(run_folder):
os.mkdir(run_folder)
# Solvers
brute_force_solver = BruteForceSolver(search_range)
genetic_algorithm_solver = GeneticAlgorithmSolver(search_range)
solvers = [brute_force_solver, genetic_algorithm_solver]
# Benchmark
benchmark = Benchmark(solvers)
benchmark.evaluate(polynomial)
# Add to Benchmark
for solver in solvers:
metric = benchmark.get_metrics(solver._id)
add_to_benchmark_payload(solution_payload, solver, metric)
# Save Animations
gui = Gui(polynomial, search_range)
genetic_animation = gui.create_animation(
genetic_algorithm_solver)
genetic_animation.save(os.path.join(run_folder, 'genetic.gif'),
writer='imagemagick',
fps=30)
brute_animation = gui.create_animation(brute_force_solver)
brute_animation.save(os.path.join(run_folder, 'brute.gif'),
writer='imagemagick',
fps=30)
# Save Problem
figure, plot = gui.plot_problem()
figure.savefig(os.path.join(run_folder, 'problem.png'))
# Pickle benchmark && solvers && polynomial
pickle_object(
genetic_algorithm_solver,
os.path.join(run_folder, 'genetic_solver.pickle'))
pickle_object(brute_force_solver,
os.path.join(run_folder, 'brute_solver.pickle'))
pickle_object(benchmark,
os.path.join(run_folder, 'brute_solver.pickle'))
pickle_object(gui, os.path.join(run_folder, 'gui.pickle'))
# Clear Matplotlib figures
gui.close()
pickle_object(solution_payload,
os.path.join(folder, 'benchmark.pickle'))
from benchmark import Benchmark
from data_provider import NewsgroupsDataProvider, ReutersDataProvider
from nltk import PorterStemmer, WordNetLemmatizer
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from feature_extractor import FeatureExtractor
max_df = 0.50 # ignore terms that appear in more than 50% of the documents
min_df = 0.01 # ignore terms that appear in less than 1% of the documents
count_vector = CountVectorizer(min_df=min_df,
max_df=max_df,
max_features=1000,
lowercase=True,
stop_words='english',
analyzer='word',
strip_accents='unicode')
tfidf_transformer = TfidfTransformer(use_idf=True,
sublinear_tf=True,
norm=None)
feature_extractor = FeatureExtractor(PorterStemmer(), count_vector,
tfidf_transformer)
# dataProvider = NewsgroupsDataProvider()
dataProvider = ReutersDataProvider()
benchmark = Benchmark(dataProvider, feature_extractor)
print('evaluation started')
benchmark.evaluate()
