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ipd_extended/data_generation.py

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import numpy as np
import pandas as pd
import re
import os
from pathlib import Path
from os import listdir
def artificial():
# a = [str(i) + ";" + str(int((i / 200) % 2)) + ";0" + "\n" for i in range(20000)]
# with open("/Users/tatyanadembelova/Documents/study/thesis/ipd_extended/new_cubes/artificial20000.csv", "w") as f:
# f.writelines(a)
a = [str(i) + ";0" + ";0" + "\n" for i in range(10000)]
size = len(a)
a.extend([str(i + size) + ";1;0" + "\n" for i in range(10000)])
with open("/Users/tatyanadembelova/Documents/study/thesis/ipd_extended/new_cubes/artificialtrick10000.csv", "w") as f:
f.writelines(a)
# synthetic case from uds
def correlated_data(m, n, sigma, f):
# l1 = int(n / 2)
# l2 = n - l1
# Z = np.random.normal(0, 1, (m, l1))
# A = np.matrix(np.random.uniform(1, 2, (l1, l1)))
# X1 = Z * A
# B = np.matrix(np.random.uniform(1, 2, (l1, l2)))
# W = X1 * B
# E = np.random.normal(0, sigma, (m, l2))
# X2 = f(W) + E
# result = np.append(X1, X2, axis=1)
# print(result)
l1 = int(n / 2)
l2 = n - l1
Z = np.random.normal(0, 1, (m, l1))
A = np.matrix(np.random.uniform(1, 2, (l1, l1)))
X1 = Z * A
# A = np.matrix(np.random.uniform(1, 2, (m, l1)))
# X1 = A
B = np.matrix(np.random.uniform(1, 2, (l1, l2)))
W = X1 * B
E = np.random.normal(0, sigma, (m, l2))
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 func3(X):
return np.power(X, 2)
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)
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')
def cube_func(r):
if -1.5 <= r[0] <= -0.5 and -1.5 <= r[1] <= -0.5 and -1.5 <= r[2] <= -0.5:
return 1
if 0 <= r[0] <= 1 and 0 <= r[1] <= 1 and 0 <= r[2] <= 1:
return 2
if 1.5 <= r[0] <= 2.5 and 1.5 <= r[1] <= 2.5 and 1.5 <= r[2] <= 2.5:
return 3
if 3.0 <= r[0] <= 4.0 and 3.0 <= r[1] <= 4.0 and 3.0 <= r[2] <= 4.0:
return 3
# if -1.7 <= r[0] <= -0.7 and -1.7 <= r[1] <= -0.7 and -1.7 <= r[2] <= -0.7:
# return 1
# if 0 <= r[0] <= 1 and 0 <= r[1] <= 1 and 0 <= r[2] <= 1:
# return 2
# if 1.5 <= r[0] <= 2.5 and 1.5 <= r[1] <= 2.5 and 1.5 <= r[2] <= 2.5:
# return 3
# elif 3 <= r[0] <= 4 and 3 <= r[1] <= 4 and 3 <= r[2] <= 4:
# return 4
# elif 0 <= r[0] <= 1 and 0 <= r[1] <= 1:
# return 3
else:
return 0
def update_class(file, new_file):
data = pd.read_csv(file, delimiter=";", header=None, na_values='?')
zeros_dim = data.shape[1] - 1
classes = data.apply(cube_func, axis=1).to_frame()
new_data = np.concatenate([data.loc[:, :zeros_dim - 1], classes], axis=1)
pd.DataFrame(new_data).to_csv(new_file, sep=';', header=False, index=False, float_format='%.2f')
# def update_out_txt(directory):
# directory = str(directory)
# if not os.path.exists(directory + "/out.txt"):
# return
# search = re.search("(\dd_(\d).*.csv)", directory)
# run = search.group(1)
# classes_count = int(search.group(2)) + 1
# classes = pd.read_csv("synthetic_cases/cubes/" + run, delimiter=";", header=None, na_values='?')
# classes = classes[classes.shape[1] - 1]
#
# class_row = 0
# with open(directory + "/out.txt", "r") as old_out:
# with open(directory + "/out2.txt", "w") as new_out:
# for line in old_out:
# if "@attribute class {" in line:
# line = "@attribute class {" + ",".join(['"' + str(i) + '.0"' for i in range(classes_count)]) + "}"
# if ',"0.0"' in line:
# line = line.replace(',"0.0"', ',"' + str(classes[class_row]) + '"')
# class_row += 1
# new_out.write(line)
# def export_out(directory):
# directory = str(directory)
# if not os.path.exists(directory + "/out2.txt"):
# return
# destination = "logs_test/arff/" + directory.replace(".csv", "").replace("logs_test/", "") + ".arff"
# search = re.search("_(\d)d_.*", directory)
# rel_dim_count = int(search.group(1))
#
# with open(directory + "/out2.txt", "r") as out_txt:
# with open(destination, "w") as new_out:
# for line in out_txt:
# if line.startswith("@attribute class"):
# line = line + "\n"
# if "@attribute dim" in line:
# if int(re.search("@attribute dim(\d+) ", line).group(1)) >= rel_dim_count:
# continue
#
# if not line.startswith("@") and line.strip() != "":
# split = line.split(",")
# line = ",".join(split[:rel_dim_count]) + ',' + split[-1]
# new_out.write(line)
def correct_weka_output():
file = "test_experiment_all.arff"
file2 = "test_experiment_all2.arff"
pathlist = listdir("logs_quality/arff")
with open(file, "r") as old_out:
with open(file2, "w") as output2:
path_id = 0
counter = 0
for line in old_out:
if line.startswith("DB"):
line = line.replace("DB", str(pathlist[path_id].replace(".arff", "")))
counter += 1
if counter == 100:
path_id += 1
counter = 0
output2.write(line)
if __name__ == '__main__':
# artificial()
# exit(1)
# file = 'synthetic_cases/synthetic_cube_in_cube_10.csv'
for source_name in ["gas_small.csv"]:
source = 'data/' + source_name
file = 'data/' + source_name.replace(".csv", "") + '_r128.csv'
if os.path.isfile(file):
raise ValueError(file + " already exists!")
# parameters
# rows = 6000
# rel_features = 10
irrel_features = 100
generate()
# pd.DataFrame(correlated_data(4000, 3, 0.5, func3)).to_csv("synthetic_cases/uds_new.csv", index=False)
# update_out_txt("logs_test/ID_PERFECT_0.3_4d_4_cubes_xor_9.csv")
# export_out("logs_test/ID_PERFECT_0.3_4d_4_cubes_xor_9.csv")
# ------UPDATE THE EXISTING CUBE DATASETS WITH ACTUAL CLASSES
#
# problem = "3d_4_cubes_xor"
# update_class("synthetic_cases/cubes/" + problem + ".csv",
# "synthetic_cases/sup_cubes/" + problem + ".csv")
# for i in range(1, 11):
# update_class("synthetic_cases/cubes/" + problem + "_" + str(i) + ".csv",
# "synthetic_cases/sup_cubes/" + problem + "_" + str(i) + ".csv")
# -----CORRECT WEKA OUTPUT (PUT CORRECT DB NAMES)
# correct_weka_output()
# -----UPDATE OUT FILE
# pathlist = Path("logs_quality").glob('*.csv')
# for path in pathlist:
# export_out(path)
# ------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()