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utils.py
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utils.py
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import random
import os
import numpy as np
import torch
import scanpy as sc
import pandas as pd
from scipy import stats
import scipy.stats as st
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
import os
def seed_everything(seed: int):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def cal_ssim(im1,im2,M):
assert len(im1.shape) == 2 and len(im2.shape) == 2
assert im1.shape == im2.shape
mu1 = im1.mean()
mu2 = im2.mean()
sigma1 = np.sqrt(((im1 - mu1) ** 2).mean())
sigma2 = np.sqrt(((im2 - mu2) ** 2).mean())
sigma12 = ((im1 - mu1) * (im2 - mu2)).mean()
k1, k2, L = 0.01, 0.03, M
C1 = (k1*L) ** 2
C2 = (k2*L) ** 2
C3 = C2/2
l12 = (2*mu1*mu2 + C1)/(mu1 ** 2 + mu2 ** 2 + C1)
c12 = (2*sigma1*sigma2 + C2)/(sigma1 ** 2 + sigma2 ** 2 + C2)
s12 = (sigma12 + C3)/(sigma1*sigma2 + C3)
ssim = l12 * c12 * s12
return ssim
# TODO remove zeros
def scale_max(df):
result = pd.DataFrame()
for label, content in df.items():
content = content/content.max()
result = pd.concat([result, content],axis=1)
return result
def scale_z_score(df):
result = pd.DataFrame()
for label, content in df.items():
content = st.zscore(content)
content = pd.DataFrame(content,columns=[label])
result = pd.concat([result, content],axis=1)
return result
def scale_plus(df):
result = pd.DataFrame()
for label, content in df.items():
content = content/content.sum()
result = pd.concat([result,content],axis=1)
return result
def logNorm(df):
df = np.log1p(df)
df = st.zscore(df)
return df
class CalculateMeteics:
def __init__(self, raw_count, impute_count, prefix, metric, name, read_file=False):
# self.impute_count_file = impute_count_file
# if read_file:
# self.raw_count = pd.read_csv(raw_count_file, header=0, index_col=0).T
# self.raw_count = raw_count_file
# self.raw_count.columns = [x.upper() for x in self.raw_count.columns]
# # self.raw_count = self.make_average(self.raw_count)
# self.raw_count = self.raw_count.T
# self.raw_count = self.raw_count.loc[~self.raw_count.index.duplicated(keep='first')].T
# self.raw_count = self.raw_count.fillna(1e-20)
# # idx = self.raw_count > 0
# if read_file:
# self.impute_count = pd.read_csv(impute_count_file, header = 0, index_col = 0)
# self.impute_count = impute_count_file
# self.impute_count.columns = [x.upper() for x in self.impute_count.columns]
# # self.impute_count = self.make_average(self.impute_count)
# self.impute_count = self.impute_count.T
# self.impute_count = self.impute_count.loc[~self.impute_count.index.duplicated(keep='first')].T
# self.impute_count = self.impute_count.fillna(1e-20)
# self.impute_count[self.raw_count==0] = 0.0
self.raw_count = raw_count
self.impute_count = impute_count
self.impute_count.index = list(self.raw_count.index)
self.prefix = prefix
self.metric = metric
self.name = name
# print(self.raw_count.shape, self.impute_count.shape)
def SSIM(self, raw, impute, scale = 'scale_max'):
if scale == 'scale_max': # large time consumer
raw = scale_max(raw)
impute = scale_max(impute)
else:
print ('Please note you do not scale data by scale max')
print(raw.shape, impute.shape) # (92614, 1890)
if raw.shape[0] == impute.shape[0]:
result = pd.DataFrame()
for label in raw.columns:
# print(label) # SERPINA3 should be single column, it is gene
if label not in impute.columns:
ssim = 0
else:
raw_col = raw.loc[:,label] # return 'label' column
impute_col = impute.loc[:,label]
impute_col = impute_col.fillna(1e-20)
raw_col = raw_col.fillna(1e-20)
# print("PPP",raw_col.shape, impute_col.shape) # PPP (92614, 5) should be (92614,)
M = [raw_col.max(),impute_col.max()][raw_col.max()>impute_col.max()]
# print("PPP",raw_col.shape, impute_col.shape)
raw_col_2 = np.array(raw_col)
raw_col_2 = raw_col_2.reshape(raw_col_2.shape[0],1)
impute_col_2 = np.array(impute_col)
impute_col_2 = impute_col_2.reshape(impute_col_2.shape[0],1)
ssim = cal_ssim(raw_col_2,impute_col_2,M)
ssim_df = pd.DataFrame(ssim, index=["SSIM"],columns=[label])
result = pd.concat([result, ssim_df],axis=1)
else:
print("columns error")
return result
def PCC(self, raw, impute, scale = None):
if raw.shape[0] == impute.shape[0]:
result = pd.DataFrame()
for label in raw.columns:
if label not in impute.columns:
pearsonr = 0
else:
raw_col = raw.loc[:,label]
impute_col = impute.loc[:,label]
impute_col = impute_col.fillna(1e-20)
raw_col = raw_col.fillna(1e-20)
# print('raw_col',raw_col)
# print('impute_col',impute_col)
try:
pearsonr, _ = st.pearsonr(raw_col,impute_col)
except:
pearsonr = 0
pearson_df = pd.DataFrame(pearsonr, index=["PCC"],columns=[label])
result = pd.concat([result, pearson_df],axis=1)
else:
print("columns error")
return result
def JS(self, raw, impute, scale = 'scale_plus'):
if scale == 'scale_plus':
raw = scale_plus(np.expm1(raw))
impute = scale_plus(np.expm1(impute))
else:
print ('Please note you do not scale data by plus')
if raw.shape[0] == impute.shape[0]:
result = pd.DataFrame()
for label in raw.columns:
if label not in impute.columns:
JS = 1
else:
raw_col = raw.loc[:,label]
impute_col = impute.loc[:,label]
raw_col = raw_col.fillna(1e-20)
impute_col = impute_col.fillna(1e-20)
M = (raw_col.to_numpy() + impute_col.to_numpy())/2
t1 = st.entropy(raw_col, M)
t2 = st.entropy(impute_col, M)
# t1 = st.entropy(raw_col, M)
# t2 = st.entropy(impute_col, M)
# t1 = np.sum(raw_col* np.log(raw_col / M))
# t2 = np.sum(impute_col * np.log(impute_col/M))
JS = (t1 + t2) / 2
JS_df = pd.DataFrame(JS, index=["JS"],columns=[label])
result = pd.concat([result, JS_df],axis=1)
else:
print("columns error")
return result
def RMSE(self, raw, impute, scale = 'zscore'):
if scale == 'zscore':
raw = scale_z_score(raw)
impute = scale_z_score(impute)
else:
print ('Please note you do not scale data by zscore')
if raw.shape[0] == impute.shape[0]:
result = pd.DataFrame()
for label in raw.columns:
if label not in impute.columns:
RMSE = 1.5
else:
raw_col = raw.loc[:,label]
impute_col = impute.loc[:,label]
impute_col = impute_col.fillna(1e-20)
raw_col = raw_col.fillna(1e-20)
RMSE = np.sqrt(((raw_col - impute_col) ** 2).mean())
RMSE_df = pd.DataFrame(RMSE, index=["RMSE"],columns=[label])
result = pd.concat([result, RMSE_df],axis=1)
else:
print("columns error")
return result
def compute_all(self, K='none'):
raw = self.raw_count
impute = self.impute_count
impute[impute < 0] = 0
# print(impute.index)
# print(raw.index)
prefix = self.prefix
# print('SSIM')
SSIM = self.SSIM(raw,impute)
# exit()
# print('PCC')
Pearson = self.PCC(raw, impute)
# print('Pearson', Pearson) # [1 rows x 117 columns], 展示每一个gene（177） 的PCC指数
JS = self.JS(raw, impute)
# print('RMSE')
RMSE = self.RMSE(raw, impute)
result_all = pd.concat([Pearson, SSIM, RMSE, JS],axis=0)
# result_all = pd.concat([JS],axis=0)
if K == 'none':
save_path = os.path.join(prefix, self.name+"_Metrics.txt")
else:
save_path = os.path.join(prefix, self.name+"_Metrics_%d.txt"%(K))
print(save_path)
result_all.T.to_csv(save_path, sep='\t', header = 1, index = 1)
self.accuracy = result_all
return result_all