async def serve(q: Q):
if not q.app.lb_dict:
q.app.lb_dict = dict()
if not q.user.stop_watch:
q.user.stop_watch = StopWatch()
if q.args.start and not q.user.stop_watch.active:
await U.start_clock(q.user.stop_watch, q)
q.user.stop_watch.update_df()
await U.update_streak_history(q.user.stop_watch, q)
await U.update_leaderboard(q)
elif q.args.stop and q.user.stop_watch.active:
q.user.stop_watch.stop()
await U.update_stop_streak(q.user.stop_watch, q)
# q.user.stop_watch.update_df()
# await U.update_streak_history(q.user.stop_watch, q)
#
# await U.update_leaderboard(q)
if not q.client.initialized:
q.client.initialized = True
await U.responsive_layout(q)
await U.update_leaderboard(q)
await q.page.save()
from utils.StopWatch import StopWatch
from utils.RawDataProcessing import PregeneratedProcessor
from utils.DataProviderLight import DataProviderLight
from utils.DatasetGeneration import DeterministicGenerator
from classification.ClassificationTest import ClassificationTest
from classification.KNNClassification import KNNClassifier
# script for corresponding test case
# most test cases should be able to be executed without any further changes, if data is available
SAMPLE_SIZE = [5000, 10000, 20000, 50000]
N_NEIGHBORS = 1
# change device to "cpu" if cuda not available
DEVICE = "cuda"
stopwatch = StopWatch()
# pregenerated embedding and labels
for sample_size in SAMPLE_SIZE:
file_words = open("../tests/embedding7.json")
file_labels = open("../data/unique_labels.json")
data_provider = DataProviderLight(file_words, file_labels, sample_size=sample_size)
# embedding data, splitting up into train and test set
processor = PregeneratedProcessor(data_provider)
generator = DeterministicGenerator(data_provider, processor)
dataset = generator.generate_dataset()
print("Sample Size: " + str(sample_size))
# creating classifier, overwriting parameters
classifier = KNNClassifier(data_provider, dataset, DEVICE)
classifier.n_neighbours = N_NEIGHBORS
# train classifier and output progress
classifier.train()
# testing
test = ClassificationTest(dataset, classifier)
DEVICE = "cuda"
for sample_size in SAMPLE_SIZE:
# raw words and labels
file_words = open("../data/unique_equations.json")
file_labels = open("../data/unique_labels.json")
# pre calculated weight matrix
file_weights = open("../data/weights_0.json")
data_provider = DataProviderLight(file_words,
file_labels,
sample_size=sample_size,
file_weights=file_weights)
# embedding data, splitting up into train and test set
processor = VectorProcessor(data_provider)
generator = DeterministicGenerator(data_provider, processor)
stopwatch = StopWatch()
# training the word2vec net
word2vec = Word2Vec(data_provider, FEATURES, DEVICE)
word2vec.train(EPOCHS, BATCH_SIZE)
# extracting weights and injecting them into the data provider
data_provider.weights = torch.tensor(word2vec.get_weights())
# generate dataset
dataset = generator.generate_dataset()
# train knn classifier
classifier = KNNClassifier(data_provider, dataset, DEVICE)
classifier.n_neighbours = N_NEIGHBORS
classifier.train()
# test the classifier
test = ClassificationTest(dataset, classifier)
stopwatch.start()
def stopwatch() -> StopWatch:
return StopWatch()