def mainWork(path,savePath):
pbar = tqdm.tqdm(os.listdir(path), desc='dirs')
for file in pbar:
pbar.set_description("Processing %s" % file)
if file.endswith('xlsx') or file.endswith('csv'):
originData = readAllTypeFile(os.path.join(path, file))
for missPattern in ['normal']:
result = {}
for missRate in [0.05, 0.1, 0.2, 0.3, 0.4, 0.5]:
if missPattern == 'normal':
missData = gene_missingdata(rate=missRate, data=originData)
elif missPattern == 'taxa':
missData = gene_missingdata_taxa_bias(rate=missRate, data=originData)
elif missPattern == 'chara':
missData = gene_missingdata_chara_bias(rate=missRate, data=originData)
elif missPattern == 'block':
missData = gene_missingdata_block_bias(rate=missRate, data=originData)
else:
raise Exception("缺失模式错误，请在'normal','taxa','chara','block'中选择对应模式")
result, MICEImputedData = MICE(result, originData, missData, missRate, missPattern)
for firstImputedMethod in ['mice']:
if firstImputedMethod=='mice':
firstImputedData = MICEImputedData
for loss in ['MSELoss']:
#for autoMethod in ['Autoencoder','ResAutoencoder','StockedAutoencoder','StockedResAutoencoder']:
for autoMethod in ['StockedResAutoencoder']:
start=time.time()
result,_=TAI(result=result,firstImputedMethod=firstImputedMethod,
firstImputedData=firstImputedData,
loss=loss,autoMethod=autoMethod,
originData=originData,missData=missData,
missRate=missRate,missPattern=missPattern,
)
logger.info("{}-{}-{}训练耗时:{}".format(firstImputedMethod,loss,autoMethod,time.time() - start))
if not os.path.exists(savePath):
os.makedirs(savePath)
saveJson(result, os.path.join(savePath,"{}_{}_{}_{}.json".format("allMethod", missPattern,file, datetime.datetime.now().strftime('%Y%m%d-%H%M%S'))))
varname = "{}_{}_{}".format(first_imputed_method, loss,
method)
globals()[varname] = [[] for _ in range(4)]
methed_names_half.append(varname)
methed_names_all.append(varname)
# for i in [0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]:
for i in [0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]:
if missPattern == 'normal':
missData = gene_missingdata(rate=i, data=originData)
elif missPattern == 'taxa':
missData = gene_missingdata_taxa_bias(rate=i, data=originData)
elif missPattern == 'chara':
missData = gene_missingdata_chara_bias(rate=i, data=originData)
elif missPattern == 'block':
missData = gene_missingdata_block_bias(rate=i, data=originData)
else:
raise Exception(
"缺失模式错误，请在'normal','taxa','chara','block'中选择对应模式")
mark = [
temp[0] for temp in pd.DataFrame(np.unique(missData)).dropna(
axis=0).values
]
try:
imputedData = mice.MICE().complete(missData)
imputedData = modifier(imputedData, mark)
score = evaluate.RMSE(originData, imputedData)
mice_misc[0].append(score)
mice_misc[1].append(MAE(originData, imputedData))
def mainWork(path, savePath):
for file in os.listdir(path):
originData = readAllTypeFile(os.path.join(path, file))
for missPattern in ['normal']:
# for missPattern in ['normal','block', 'taxa', 'chara']:
# for i in [0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]:
result = {}
for missRate in [0.05, 0.1, 0.2, 0.3, 0.4, 0.5]:
if missPattern == 'normal':
missData = gene_missingdata(rate=missRate, data=originData)
elif missPattern == 'taxa':
missData = gene_missingdata_taxa_bias(rate=missRate,
data=originData)
elif missPattern == 'chara':
missData = gene_missingdata_chara_bias(rate=missRate,
data=originData)
elif missPattern == 'block':
missData = gene_missingdata_block_bias(rate=missRate,
data=originData)
else:
raise Exception(
"缺失模式错误，请在'normal','taxa','chara','block'中选择对应模式")
result, _ = Random(result, originData, missData, missRate,
missPattern, 'disperse')
result, _ = Medain(result, originData, missData, missRate,
missPattern, 'disperse')
result, KNNImputedData = KNN(result, originData, missData,
missRate, missPattern, 'disperse')
result, EMImputedData = EM(result, originData, missData,
missRate, missPattern, 'disperse')
result, IIImputedData = II(result, originData, missData,
missRate, missPattern, 'disperse')
result, _ = GAIN(result, originData, missData, missRate,
missPattern, 'disperse')
result, _ = MIDA(result, originData, missData, missRate,
missPattern, 'disperse')
result, MICEImputedData = MICE(result, originData, missData,
missRate, missPattern,
'disperse')
# for firstImputedMethod in ['ii', 'mice']:
for firstImputedMethod in ['knn', 'ii', 'mice']:
if firstImputedMethod == 'knn':
firstImputedData = KNNImputedData
elif firstImputedMethod == 'ii':
firstImputedData = IIImputedData
elif firstImputedMethod == 'mice':
firstImputedData = MICEImputedData
for loss in ['MSELoss']:
# for autoMethod in ['Autoencoder','ResAutoencoder','StockedAutoencoder','StockedResAutoencoder']:
for autoMethod in ['Autoencoder']:
start = time.time()
result = TAI(
result=result,
firstImputedMethod=firstImputedMethod,
firstImputedData=firstImputedData.copy(),
loss=loss,
autoMethod=autoMethod,
originData=originData,
missData=missData,
missRate=missRate,
missPattern=missPattern)
logger.info("改后{}-{}-{}训练耗时:{}".format(
firstImputedMethod, loss, autoMethod,
time.time() - start))
saveJson(
result,
os.path.join(
savePath, "{}_{}_{}_{}.json".format(
"allmethod", missPattern, file,
datetime.datetime.now().strftime('%Y%m%d-%H%M%S'))))
