Here is my problem: i have to generate some synthetic data (like 7/8 columns), correlated each other (using pearson coefficient). I can do this easily, but next i have to insert a percentage of duplicates in each column (yes, pearson coefficent will be lower), different for each column. The problem is that i don't want to insert personally that duplicates, cause in my case it would be like to cheat.
Someone knows how to generate correlated data already duplicates ? I've searched but usually questions are about drop or avoid duplicates..
Language: python3 To generate correlated data i'm using this simple code: Generatin correlated data
i find out the solution. I post the code, it might be helpful for someone.
#this are the data, generated randomically with a given shape
rnd = np.random.random(size=(10**7, 8))
#that array represent a column of the covariance matrix (i want correlated data, so i randomically choose a number between 0.8 and 0.95)
#I added other 7 columns, with varing range of values (all upper than 0.7)
attr1 = np.random.uniform(0.8, .95, size = (8,1))
#attr2,3,4,5,6,7 like attr1
#corr_mat is the matrix, union of columns
corr_mat = np.column_stack((attr1,attr2,attr3,attr4,attr5, attr6,attr7,attr8))
from statsmodels.stats.correlation_tools import cov_nearest
#using that function i found the nearest covariance matrix to my matrix,
#to be sure that it's positive definite
a = cov_nearest(corr_mat)
from scipy.linalg import cholesky
upper_chol = cholesky(a)
# Finally, compute the inner product of upper_chol and rnd
ans = rnd @ upper_chol
#ans now has randomically correlated data (high correlation, but is customizable)
#next i create a pandas Dataframe with ans values
df = pd.DataFrame(ans, columns=['att1', 'att2', 'att3', 'att4',
'att5', 'att6', 'att7', 'att8'])
#last step is to truncate float values of ans in a variable way, so i got
#duplicates in varying percentage
a = df.values
for i in range(8):
trunc = np.random.randint(5,12)
print(trunc)
a.T[i] = a.T[i].round(decimals=trunc)
#float values of ans have 16 decimals, so i randomically choose an int
# between 5 and 12 and i use it to truncate each value
Finally, those are my duplicates percentages for each column:
duplicate rate attribute: att1 = 5.159390000000002
duplicate rate attribute: att2 = 11.852260000000001
duplicate rate attribute: att3 = 12.036079999999998
duplicate rate attribute: att4 = 35.10611
duplicate rate attribute: att5 = 4.6471599999999995
duplicate rate attribute: att6 = 35.46553
duplicate rate attribute: att7 = 0.49115000000000464
duplicate rate attribute: att8 = 37.33252