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nn.py
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# -*- coding: utf8 -*-
import sys
import csv
import glob
import random
import zipfile
import numpy as np
import mxnet as mx
from mxnet.gluon import nn
from data import *
from nnHelper import *
logging.basicConfig(
level=logging.INFO,
datefmt='%Y%m%d-%H:%M:%S',
format='%(asctime)s: %(message)s',
)
###################################################
class MyData(object):
def __init__(self, ctx, isTrain=True):
self.ctx = ctx
self.isTrain = isTrain
global name
bs = getBS(name)
self.batchSize = bs
self.loadDatas()
def loadDatas(self):
logging.info('Loading datas ...')
self.datas = loadSparse(
0 if self.isTrain else 1)
self.len = self.datas.shape[0]
self.indices = list(range(self.len))
self.iters = self.len / self.batchSize
logging.info('Loading datas done ...')
def __next__(self):
index, batchSize = self.index, self.batchSize
indices = self.indices[index:index+batchSize]
self.index += batchSize
if self.index > self.len: raise StopIteration
sData = self.datas[indices].toarray()
datas = mx.nd.array(
sData[:, 1:], self.ctx, dtype='int32')
labels = mx.nd.array(sData[:, 0], self.ctx)
return datas, labels
def reset(self):
self.index = 0
#if self.isTrain:
# #self.indices = random.sample(self.indices,len(self.indices))
# random.#shuffle(self.indices)
def __iter__(self):
return self
class MyDataVal(object):
def __init__(self, ctx):
self.ctx = ctx
global name
bs = getBS(name)
self.batchSize = bs
self.loadDatas()
def loadDatas(self):
logging.info('Loading datas ...')
keys = ['aid', 'uid']
self.ids = getMerged(*keys, kind=4).values
self.datas = loadSparse(1)
self.len = self.datas.shape[0]
logging.info('Loading datas done ...')
def __next__(self):
index, batchSize = self.index, self.batchSize
if index >= self.len: raise StopIteration
end = min(index + batchSize, self.len)
ids = self.ids[index:end]
ids = mx.nd.array(
ids, self.ctx, dtype='int32')
sData = self.datas[index:end].toarray()
datas = mx.nd.array(
sData[:, 1:], self.ctx, dtype='int32')
self.index += self.batchSize
return ids, datas
def reset(self):
self.index = 0
def __iter__(self):
return self
class MyDataTest(object):
def __init__(self, ctx):
self.ctx = ctx
global name
bs = getBS(name)
self.batchSize = bs
self.loadDatas()
def loadDatas(self):
logging.info('Loading datas ...')
keys = ['aid', 'uid']
self.ids = getMerged(*keys, kind=2).values
self.datas = loadSparse(2)
self.len = self.datas.shape[0]
logging.info('Loading datas done ...')
def __next__(self):
index, batchSize = self.index, self.batchSize
if index >= self.len: raise StopIteration
end = min(index + batchSize, self.len)
ids = self.ids[index:end]
ids = mx.nd.array(
ids, self.ctx, dtype='int32')
sData = self.datas[index:end].toarray()
datas = mx.nd.array(
sData[:, 1:], self.ctx, dtype='int32')
self.index += self.batchSize
return ids, datas
def reset(self):
self.index = 0
def __iter__(self):
return self
###################################################
class MyNet(nn.HybridSequential):
def __init__(self, ctx):
super(MyNet, self).__init__()
global name
self.init(ctx)
if getSorN(name):
self.hybridize()
#self.hybridize()
self.collect_params().initialize(
#mx.init.MSRAPrelu(slope=0),
mx.init.Xavier(),
ctx
)
def init(self, ctx):
with self.name_scope():
global name
global lossKind
n = lossKind + 1
self.add(getStart(name))
self.add(MyIBA(128, 'relu'))#全连接最后一层，另外正则可选+在loss
self.add(MyIBA(128, 'relu'))
self.add(nn.Dense(n))
#self.add(MyIBA(n, 'self'))
class MyNN(MyModel):
def getNet(self, ctx):
global lossKind
return MyNet(ctx), getLoss(lossKind)
def getModel(self):
global model; return model
def getMetric(self):
global lossKind
return getMetric(lossKind), 'aucMetric'
def getData(self, ctx):
return MyData(ctx, True), MyData(ctx, False)
def forDebug(self, out):
pass
def getTrainer(self, params, iters):
opt = getOpt(iters)
return mx.gluon.Trainer(params, opt)
#return mx.gluon.Trainer(params, 'adam',{ 'clip_gradient': 2})
class MyNNV(MyPredict):
def __init__(self, gpu=0):
super(MyNNV, self).__init__(gpuID)
model = self.getModel()
self.name = '%s/val.csv' % model
self.f = open(self.name, 'w')
self.csv = csv.writer(self.f)
self.csv.writerow(['aid', 'uid', 'score'])
def getModel(self):
global model; return model
def onDone(self):
self.f.close()
def getData(self, ctx):
return MyDataVal(ctx)
def getNet(self, ctx):
model = self.getModel()
name = sorted(glob.glob(
'%s/*.params' % model))[-1]
net = MyNet(ctx)
net.load_params(name, ctx)
logging.info('Load %s ...' % name)
return net
def preProcess(self, data):
return data[1]
def postProcess(self, data, pData, output):
ids = data[0].asnumpy()
if lossKind == 0:
output = mx.nd.sigmoid(output)
output = output.asnumpy()[:,0]
if lossKind == 1:
output = mx.nd.softmax(output)
output = output.asnumpy()[:,1]
if lossKind == 2:
output1 = output[:, 0:2]
output2 = output[:, 2:3]
output1 = mx.nd.softmax(output1)
output1 = output1.asnumpy()[:,1]
output2 = mx.nd.sigmoid(output2)
output2 = output2.asnumpy()[:,0]
output = 0.5*(output1 + output2)
for id, out in zip(ids, output):
self.csv.writerow([id[0], id[1], '%.6f'%out])
class MyNNP(MyPredict):
def __init__(self, gpu=0):
super(MyNNP, self).__init__(gpuID)
model = self.getModel()
self.name = '%s/submission.csv' % model
self.f = open(self.name, 'w')
self.csv = csv.writer(self.f)
self.csv.writerow(['aid', 'uid', 'score'])
def getModel(self):
global model; return model
def onDone(self):
self.f.close()
model = self.getModel()
zipName = '%s/submission.zip' % model
with zipfile.ZipFile(zipName, 'w') as f:
f.write(
self.name, 'submission.csv',
compress_type=zipfile.ZIP_DEFLATED
)
def getData(self, ctx):
return MyDataTest(ctx)
def getNet(self, ctx):
model = self.getModel()
name = sorted(glob.glob(
'%s/*.params' % model))[-1]
net = MyNet(ctx)
net.load_params(name, ctx)
print( 'Load %s ...' % name)
return net
def preProcess(self, data):
return data[1]
def postProcess(self, data, pData, output):
ids = data[0].asnumpy()
if lossKind == 0:
output = mx.nd.sigmoid(output)
output = output.asnumpy()[:,0]
if lossKind == 1:
output = mx.nd.softmax(output)
output = output.asnumpy()[:,1]
if lossKind == 2:
output1 = output[:, 0:2]
output2 = output[:, 2:3]
output1 = mx.nd.softmax(output1)
output1 = output1.asnumpy()[:,1]
output2 = mx.nd.sigmoid(output2)
output2 = output2.asnumpy()[:,0]
output = 0.5*(output1 + output2)
for id, out in zip(ids, output):
self.csv.writerow([id[0], id[1], '%.6f'%out])
###################################################
if __name__ == '__main__':
mx.random.seed(2018)
random.seed(2018)
logging.info('All start...')
assert len(sys.argv)==5, 'Failed ...'
kind, gpuID = list(map(int, sys.argv[1:3]))
name = sys.argv[3]
model = './modelNN_%s' % name
lossKind = int(sys.argv[4])
if lossKind == 1:
model += '#Softmax'
if lossKind == 2:
model += '#S2SLoss'
if kind==1:
m = MyNN(gpuID); m.train()
if kind==2:
m = MyNNV(gpuID); m.predict()
if kind==0:
m = MyNNP(gpuID); m.predict()
logging.info('All done ...')