import pandas as pd
import numpy as np
import glob
import math
import re
import sys
import multiprocessing
[docs]def downsampleRow(args):
row, targetSum = args
currentCount = row.sum()
downsampledRow = row.copy()
while currentCount > targetSum and currentCount != 0:
possible = downsampledRow[(downsampledRow > 0)]
desiredTossCount = int(currentCount - targetSum)
probabilities = [p / currentCount for p in possible]
for indexToLower in np.random.choice(
possible.index, max(0, desiredTossCount),
replace=True, p=probabilities):
if downsampledRow[indexToLower] > 0:
downsampledRow[indexToLower] -= 1
currentCount = downsampledRow.sum()
return downsampledRow
# downsample_to = sample to this amount of counts per column
# min_feature_abundance = remove all rows which have less than these counts
[docs]def downsampleDataFrame(df, downsample_to, min_feature_abundance=50):
pool = multiprocessing.Pool(8)
try:
df = df.loc[:, df.sum() > downsample_to]
df = df.loc[df.sum(1) > min_feature_abundance, :]
subset = df.transpose()
dfDownsampled = subset.copy()
for idx, drow in enumerate(
pool.map(
downsampleRow, [
(row, downsample_to) for i, row in subset.iterrows()])):
dfDownsampled.iloc[idx, :] = drow
except Exception as e:
print(e)
pool.close()
return dfDownsampled.transpose()