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ipd_extended/data_generation.py
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import numpy as np | |
import pandas as pd | |
import os.path | |
# synthetic case from uds | |
def correlated_data(m, n, sigma, f): | |
l = int(n / 2) | |
Z = np.random.normal(0, 1, (m, l)) | |
A = np.matrix(np.random.uniform(0, 1, (l, l))) | |
X1 = Z * A | |
B = np.matrix(np.random.uniform(0, 0.5, (l, l))) | |
W = X1 * B | |
E = np.random.normal(0, sigma, (m, l)) | |
X2 = f(W) + E | |
result = np.append(X1, X2, axis=1) | |
print(result) | |
return result | |
def generate_uncorrelated_data(m, n): | |
return np.random.normal(0, 1, (m, n)) | |
def func1(X): | |
return 2 * X + 1 | |
def func2(X): | |
return np.log2(np.abs(X) + 1) | |
def synthetic_data_uni(m, r, s, sigma=0.1): | |
r_dims = np.random.uniform(-0.5, 0.5, (m, r)) if r > 0 else np.empty((m, r)) | |
parity_dim = -(np.count_nonzero(r_dims > 0, axis=1) % 2 * 2 - 1).reshape(m, 1) * np.random.uniform(0, 0.5, | |
(m, | |
1)) if r > 0 else np.empty( | |
(m, r)) | |
s_dims = np.random.uniform(-0.5, 0.5, (m, s)) | |
data = np.concatenate((r_dims, parity_dim, s_dims), axis=1) | |
if sigma: | |
e = np.random.normal(0, sigma, (m, r + s + 1)) | |
data = data + e | |
return data | |
def synthetic_data_uni_negative(m, r, s, sigma=0.1): | |
r_dims = np.random.uniform(-0.5, 0.5, (m, r)) if r > 0 else np.empty((m, r)) | |
parity_dim = (np.count_nonzero(r_dims > 0, axis=1) % 2 * 2 - 1).reshape(m, 1) * np.random.uniform(0, 0.5, | |
(m, | |
1)) if r > 0 else np.empty( | |
(m, r)) | |
s_dims = np.random.uniform(-0.5, 0.5, (m, s)) | |
data = np.concatenate((r_dims, parity_dim, s_dims), axis=1) | |
if sigma: | |
e = np.random.normal(0, sigma, (m, r + s + 1)) | |
data = data + e | |
return data | |
def synthetic_data_gauss(m, r, s, sigma=0.1): | |
r_dims = np.random.normal(0, 1, (m, r)) if r > 0 else np.empty((m, r)) | |
parity_dim = -(np.count_nonzero(r_dims > 0, axis=1) % 2 * 2 - 1).reshape(m, 1) * np.abs(np.random.normal(0, 1, | |
(m, | |
1))) if r > 0 else np.empty( | |
(m, r)) | |
s_dims = np.random.normal(0, 1, (m, s)) | |
data = np.concatenate((r_dims, parity_dim, s_dims), axis=1) | |
if sigma: | |
e = np.random.normal(0, sigma, (m, r + s + 1)) | |
data = data + e | |
return data | |
def synthetic_with_nearcopies(m, k, l, sigma=0.1): | |
k_dims = np.repeat(np.random.uniform(-0.5, 0, (m, 1)), k, axis=1) if k > 0 else np.empty((m, k)) | |
l_dims = np.repeat(np.random.uniform(0, 0.5, (m, 1)), l, axis=1) if l > 0 else np.empty((m, l)) | |
data = np.concatenate((k_dims, l_dims), axis=1) | |
if sigma: | |
e = np.random.normal(0, sigma, (m, k + l)) | |
data = data + e | |
return data | |
def synthetic_cube_in_cube(m, r, i, side, sigma=0.5): | |
if r < 1: | |
raise ValueError | |
h = int(m * sigma) | |
range = [-0.5, 0] if side == 'l' else [-0.25, 0.25] if side == 'm' else [0, 0.5] | |
contra_range = [0, 0.5] if side == 'l' else [-0.25, 0.25] if side == 'm' else [-0.5, 0] | |
r_dims = np.concatenate(( | |
# np.concatenate( | |
# (np.random.uniform(range[0], range[1], (h, 1)), | |
# np.random.uniform(contra_range[0], contra_range[1], (h, 1))), | |
# axis=1) | |
np.random.uniform(range[0], range[1], (h, r)) | |
, np.random.uniform(-0.5, 0.5, (m - h, r))), axis=0) | |
i_dims = np.random.uniform(-0.5, 0.5, (m, i)) if i > 0 else np.empty((m, i)) | |
data = np.concatenate((r_dims, i_dims), axis=1) | |
return data | |
def synthetic_cjs(): | |
return np.concatenate((np.concatenate((np.random.normal(0, 1, (100, 1)), np.random.normal(2, 1, (100, 1))), axis=1), | |
np.concatenate((np.random.normal(4, 1, (100, 1)), np.random.normal(5, 1, (100, 1))), | |
axis=1)), axis=0) | |
# def blobs(rows): | |
# blobs_number = 4 | |
# dims = 4 | |
# l = int(rows/blobs_number) | |
# blob1 = np.random.normal(0, 1, (l, dims)) + np.concatenate((np.ones((l, 1)) * -3, np.ones((l, 1)) * -3, np.ones((l, 1)) * -3, np.ones((l, 1)) * -3), axis=1) | |
# blob2 = np.random.normal(0, 1, (l, dims)) + np.concatenate((np.ones((l, 1)) * 0, np.ones((l, 1)) * 0, np.ones((l, 1)) * 0, np.ones((l, 1)) * 0), axis=1) | |
# blob3 = np.random.normal(0, 1, (l, dims)) + np.concatenate((np.ones((l, 1)) * 3, np.ones((l, 1)) * 3, np.ones((l, 1)) * 3, np.ones((l, 1)) * 3), axis=1) | |
# blob4 = np.random.normal(0, 1, (l, dims)) + np.concatenate((np.ones((l, 1)) * 6, np.ones((l, 1)) * 6, np.ones((l, 1)) * 6, np.ones((l, 1)) * 6), axis=1) | |
# | |
# return np.concatenate((blob1, blob2, blob3, blob4), axis=0) | |
# # return np.concatenate((blob1, blob2, blob3), axis=0) | |
def cubes(rows): | |
cubes_number = 4 | |
dims = 4 | |
l = int(rows/cubes_number) | |
blob1 = np.random.uniform(0, 1, (l, dims)) + np.concatenate((np.ones((l, 1)) * -1.7, np.ones((l, 1)) * -1.7, np.ones((l, 1)) * -1.7, np.ones((l, 1)) * -1.7), axis=1) | |
blob2 = np.random.uniform(0, 1, (l, dims)) + np.concatenate((np.ones((l, 1)) * 0, np.ones((l, 1)) * 0, np.ones((l, 1)) * 0, np.ones((l, 1)) * 0), axis=1) | |
blob3 = np.random.uniform(0, 1, (l, dims)) + np.concatenate((np.ones((l, 1)) * 1.5, np.ones((l, 1)) * 1.5, np.ones((l, 1)) * 1.5, np.ones((l, 1)) * 1.5), axis=1) | |
blob4 = np.random.uniform(0, 1, (l, dims)) + np.concatenate((np.ones((l, 1)) * 3, np.ones((l, 1)) * 3, np.ones((l, 1)) * 3, np.ones((l, 1)) * 3), axis=1) | |
background = np.random.uniform(-2, 4, (l, dims)) | |
# blob3 = np.random.normal(0, 1, (l, dims)) + np.concatenate((np.ones((l, 1)) * 3, np.ones((l, 1)) * 3, np.ones((l, 1)) * 3, np.ones((l, 1)) * 3), axis=1) | |
# blob4 = np.random.normal(0, 1, (l, dims)) + np.concatenate((np.ones((l, 1)) * 6, np.ones((l, 1)) * 6, np.ones((l, 1)) * 6, np.ones((l, 1)) * 6), axis=1) | |
# return np.concatenate((blob1, blob2, background), axis=0) | |
return np.concatenate((blob1, blob2, blob3, blob4, background), axis=0) | |
# return np.concatenate((blob1, blob2, blob3, background), axis=0) | |
def append_irrelevant_features(file, n): | |
if n == 0: | |
raise ValueError("# of irrelevant features is 0") | |
data = pd.read_csv(file, delimiter=";", header=None, na_values='?') | |
rows = data.shape[0] | |
last_dim = data.shape[1] - 1 | |
irrel_data = np.random.uniform(-0.5, 0.5, (rows, n)) | |
return np.concatenate([data.loc[:, :last_dim - 1], irrel_data, data.loc[:, last_dim].to_frame()], axis=1) if (data[last_dim] == 0).all() \ | |
else np.concatenate([data, irrel_data], axis=1) | |
def generate(): | |
# -------generating dataset | |
# data = synthetic_cube_in_cube(rows, rel_features, irrel_features, 'l') | |
# data__ = synthetic_cjs() | |
# data = correlated_data(rows, rel_features + irrel_features, 1, func1) | |
# data = cubes(rows) | |
# # add zeroes as default class | |
# data = np.concatenate((data, np.zeros((data.shape[0], 1))), axis=1) | |
# -------appending irrelevant features to existing dataset | |
data = append_irrelevant_features(source, irrel_features) | |
# storing to disk | |
pd.DataFrame(data).to_csv(file, sep=';', header=False, index=False, float_format='%.2f') | |
if __name__ == '__main__': | |
# ------APPENDING IRRELEVANT FEATURES | |
for source_name in [ | |
"2d_2_cubes_aligned.csv", | |
"2d_2_cubes_xor.csv", | |
"2d_3_cubes_aligned_xor.csv", | |
"3d_2_cubes_aligned.csv", | |
"3d_2_cubes_xor.csv", | |
"3d_3_cubes_aligned.csv", | |
"3d_3_cubes_aligned_xor.csv", | |
"3d_3_cubes_xor.csv", | |
"3d_4_cubes_1_aligned_xor.csv", | |
"3d_4_cubes_2_aligned.csv", | |
"3d_4_cubes_xor.csv", | |
"4d_2_cubes_aligned.csv", | |
"4d_3_cubes_aligned_xor.csv", | |
"4d_3_cubes_xor.csv", | |
"4d_4_cubes_2_aligned.csv", | |
"4d_4_cubes_aligned_xor.csv", | |
"4d_4_cubes_xor.csv", | |
]: | |
for i in [4,5,6,7,8,9,10]: | |
# file = 'synthetic_cases/synthetic_cube_in_cube_10.csv' | |
source = 'synthetic_cases/cubes/' + source_name | |
file = 'synthetic_cases/cubes/' + source_name.replace(".csv", "") + '_' + str(i) + '.csv' | |
if os.path.isfile(file): | |
raise ValueError(file + " already exists!") | |
# parameters | |
rows = 4000 | |
# rel_features = 10 | |
irrel_features = i | |
generate() | |
# ----GENERATION---- | |
# file = 'synthetic_cases/cubes/4d_4_cubes_xor.csv' | |
# | |
# if os.path.isfile(file): | |
# raise ValueError(file + " already exists!") | |
# | |
# # parameters | |
# rows = 4000 | |
# rel_features = 2 | |
# irrel_features = 0 | |
# | |
# generate() |