# 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()
result = test.test()
stopwatch.stop()
print(str(sample_size) + " Samples: " + str(result) + "% Präzision")
from classification.KNNClassification import KNNClassifier
from classification.ClassificationTest import ClassificationTest
# script for corresponding test case
# most test cases should be able to be executed without any further changes, if data is available
FEATURES = 100
SAMPLE_SIZE = 10000
EPOCHS = 5
BATCH_SIZE = 32
# change device to "cpu" if cuda not available
DEVICE = "cuda"
stopwatch = StopWatch()
# pregenerated embedding and labels
file_words = open("../data/unique_equations.json")
file_labels = open("../data/unique_labels.json")
file_weights = open("../data/weights_0.json")
data_provider = DataProviderLight(file_words, file_labels, sample_size=SAMPLE_SIZE, file_weights=file_weights)
processor = VectorProcessor(data_provider)
generator = DeterministicGenerator(data_provider, processor)
w2v_epochs = Word2Vec(data_provider, FEATURES, DEVICE)
stopwatch.start()
w2v_epochs.train(EPOCHS, BATCH_SIZE)
stopwatch.stop()
data_provider.weights = torch.tensor(w2v_epochs.get_weights())
dataset = generator.generate_dataset()
classifier = KNNClassifier(data_provider, dataset, DEVICE)
classifier.n_neighbours = 5
classifier.train()
test = ClassificationTest(dataset, classifier)
print("Präzision: " + str(test.test()) + "%")