def main(to_show: bool = True,
to_save: bool = False,
normalize=True,
standardize=True):
print('norm: {}'.format(normalize))
print('stand: {}'.format(standardize))
# os.mkdir('plot')
positive_data_path = 'data/all_positive_X.txt'
negative_data_path = 'data/all_negative_X.txt'
# data: Tuple[np.ndarray, np.ndarray] = prepare_data(positive_data_path, negative_data_path, normalize=normalize,
# standardize=standardize)
#
# X = data[0]
# y = data[1]
# X, y = load_data()
# pca_3(X, y, to_show)
# pca_2(X, y, to_show=to_show, to_save=to_save)
# pca_2_incremental(X, y, to_show=to_show, to_save=to_save)
# t_sne_2(X, y, to_show=to_show, to_save=to_save)
index = 1
# n_neighbors = 200
# d = 0.8
# m = "euclidean"
n_neighbors = 15
d = 0.1
m = "euclidean"
ts = time.time()
print('iteration: {}'.format(index))
data: Tuple[np.ndarray, np.ndarray] = prepare_data(positive_data_path,
negative_data_path,
normalize=normalize,
standardize=standardize)
X = data[0]
y = data[1]
print(' << data ready')
name = m if type(m) is str else m.__name__
# title = 'UMAP_{}_{}_{}'.format(name, n_neighbors, d)
title = 'UMAP - Unsafe and Legitimate websites'.format(
name, n_neighbors, d)
umap_2(X,
y,
title=title,
to_show=to_show,
to_save=to_save,
metric=m,
min_dist=d,
n_neighbors=n_neighbors)
print(' << done in {}'.format(time.time() - ts))
def get_model(i: int):
models = [XGBClassifier(), RandomForestClassifier()]
return models[i]
def ratio_of_pos_and_neg(y_train: np.ndarray):
positive_samples_count = np.sum(y_train)
negative_samples_count = y_train.shape[0] - np.sum(y_train)
assert y_train.shape[0] == positive_samples_count + negative_samples_count
return positive_samples_count, negative_samples_count
positive_data_path = 'data/train_positive_X.txt'
negative_data_path = 'data/train_negative_X.txt'
data: Tuple[np.ndarray, np.ndarray] = prepare_data(positive_data_path, negative_data_path, normalize=False, standardize=False)
X = data[0]
y = data[1]
positive = y.sum()
negative = y.shape[0] - positive
print(positive)
print(negative)
idx = np.random.RandomState(seed=11).permutation(X.shape[0])
# idx = np.random.permutation(X.shape[0])
X, y = X[idx], y[idx]
# print(X[0])
log_file_name = 'log.txt'
log_file = open(result_folder + '/' + log_file_name, 'a')
def __print(text: str):
log_file.write(text + '\n')
print(text)
features_and_indexes = get_feature_names_and_indexes()
train_positive_data_path = 'data/train_positive_X.txt'
train_negative_data_path = 'data/train_negative_X.txt'
data: Tuple[np.ndarray, np.ndarray] = prepare_data(train_positive_data_path,
train_negative_data_path,
normalize=False,
standardize=False)
X_train = data[0]
y_train = data[1]
print('training data: {}'.format(X_train.shape))
test_positive_data_path = 'data/test_positive_X.txt'
test_negative_data_path = 'data/test_negative_X.txt'
data: Tuple[np.ndarray, np.ndarray] = prepare_data(test_positive_data_path,
test_negative_data_path,
normalize=False,
standardize=False)
X_test = data[0]
y_test = data[1]
print('testing data: {}'.format(X_test.shape))
