def main():
# Parameters
images_directory = './data/images'
categories_file = '../categories.json'
if not os.path.isdir(images_directory):
loader = ImageLoader(output_directory=images_directory,
categories_file=categories_file)
loader.load()
experience_file = './data/experience-new-ratings.csv'
number_of_experiences, ids_for_categories = load_experience_data(
experience_file, n_ratings=2)
number_of_true_images_for_provider = 8
number_of_noise_images_for_provider = 2
number_of_images_in_collage = 6
output_directory = './data/generated-data'
features_original_images_file = './data/images/features-images-1'
image_generator = ImageGenerator(images_directory, categories_file,
features_original_images_file)
image_generator.generate(
number_of_true_images_for_provider=number_of_true_images_for_provider,
number_of_noise_images_for_provider=number_of_noise_images_for_provider,
number_of_images=number_of_experiences,
ids=ids_for_categories,
number_of_images_in_collage=number_of_images_in_collage,
output_directory=output_directory)
evaluator = Evaluator(output_directory,
features_file='./data/features-generated-data')
evaluator.visualize(show='ratings')
evaluator.classify()
def main():
# Parameters
words_directory = './data/words/'
categories_file = '../categories.json'
neutral_words_file = './data/neutral_words.csv'
if not os.path.isdir(words_directory):
loader = WordsLoader(output_directory=words_directory,
categories_file=categories_file)
loader.load()
number_of_texts = {
'leisure': {
1: 2,
2: 3,
3: 2
},
'adventure': {
1: 5,
2: 2,
3: 1
}
}
ids = {
'leisure': {
1: [1, 2],
2: [3, 5, 6],
3: [7, 8]
},
'adventure': {
1: [9, 10, 11, 12, 4],
2: [13, 15],
3: [100]
}
}
text_length = 100
noise_ratio = 0.3
neutral_words_ratio = 0.2
output_directory = './generated-data2'
#text_generator = TextGenerator(words_directory, neutral_words_file, categories_file)
#text_generator.generate(number_of_texts, ids, text_length, noise_ratio, neutral_words_ratio, output_directory)
evaluator = Evaluator(output_directory)
evaluator.visualize(show='ratings')
evaluator.classify(algorithm='knn')
