def test():
theta = readTheta()
count = 0
reader = BinReader(FILENAME)
reader.open()
result = [0] * reader.LineCount
for i in xrange(reader.LineCount):
(x,userid,itemid,label) = reader.readline()
x[0] = 1
y = np.dot(x[:37],theta)
result[i] = (userid,itemid,y)
if i % 10000 == 0:
print '%d/%d' % (i,reader.LineCount)
result.sort(key=lambda x:x[2],reverse=True)
result = result[:6500]
print ur'样本总数:',count
print ur'正在输出...'
with open('result.csv','w') as f:
for item in result:
f.write('%d,%d\n' % (item[0],item[1]))
print ur'测试结束，输出个数:',6500
def test():
theta = readTheta()
count = 0
reader = BinReader(FILENAME)
reader.open()
result = [0] * reader.LineCount
for i in xrange(reader.LineCount):
(x, userid, itemid, label) = reader.readline()
x[0] = 1
y = np.dot(x[:37], theta)
result[i] = (userid, itemid, y)
if i % 10000 == 0:
print '%d/%d' % (i, reader.LineCount)
result.sort(key=lambda x: x[2], reverse=True)
result = result[:6500]
print ur'样本总数:', count
print ur'正在输出...'
with open('result.csv', 'w') as f:
for item in result:
f.write('%d,%d\n' % (item[0], item[1]))
print ur'测试结束，输出个数:', 6500
def load(self):
reader = BinReader(self._filename)
reader.open()
self.LineCount = reader.LineCount
self.data = [0] * reader.LineCount
self.PosiCount = 0
for i in xrange(reader.LineCount):
self.data[i] = reader.readline()
self.data[i][0][0] = 1
if self.data[i][3] == 1:
self.PosiCount+=1
reader.close()
class Normailzer(object):
"""description of class"""
def __init__(self,filename):
self.filename = filename
self.reader = BinReader(filename)
def getMaxMin(self):
self.reader.open()
dim = self.reader.XDim - 1 #去除常数项
max = [0] * dim
min = [0] * dim
for k in xrange(self.reader.LineCount):
(x,userid,itemid,label) = self.reader.readline()
for i in xrange(dim):
if x[i + 1] > max[i]:
max[i] = x[i + 1]
if x[i + 1] < min[i]:
min[i] = x[i + 1]
if k % 10000 == 0:
print '%d/%d' % (k,self.reader.LineCount)
self.reader.close()
return (max,min)
##调用该函数，实现归一化
def transform(self):
(max,min) = self.getMaxMin()
self.reader.open()
dim = self.reader.XDim - 1 #去除常数项
dis = [0] * dim
for i in xrange(dim):
dis[i] = max[i] - min[i]
self.writer = BinWriter(self.filename.rstrip('.bin') + '.norm.bin')
self.writer.open(self.reader.LineCount,self.reader.Dim)
for k in xrange(self.reader.LineCount):
(x,userid,itemid,label) = self.reader.readline()
for i in xrange(dim):
x[i + 1] = (x[i + 1] - min[i]) / dis[i]
self.writer.writeline(x,userid,itemid,label)
if k % 10000 == 0:
print '%d/%d' % (k,self.reader.LineCount)
self.writer.close()
class Normailzer(object):
"""description of class"""
def __init__(self, filename):
self.filename = filename
self.reader = BinReader(filename)
def getMaxMin(self):
self.reader.open()
dim = self.reader.XDim - 1 #去除常数项
max = [0] * dim
min = [0] * dim
for k in xrange(self.reader.LineCount):
(x, userid, itemid, label) = self.reader.readline()
for i in xrange(dim):
if x[i + 1] > max[i]:
max[i] = x[i + 1]
if x[i + 1] < min[i]:
min[i] = x[i + 1]
if k % 10000 == 0:
print '%d/%d' % (k, self.reader.LineCount)
self.reader.close()
return (max, min)
##调用该函数，实现归一化
def transform(self):
(max, min) = self.getMaxMin()
self.reader.open()
dim = self.reader.XDim - 1 #去除常数项
dis = [0] * dim
for i in xrange(dim):
dis[i] = max[i] - min[i]
self.writer = BinWriter(self.filename.rstrip('.bin') + '.norm.bin')
self.writer.open(self.reader.LineCount, self.reader.Dim)
for k in xrange(self.reader.LineCount):
(x, userid, itemid, label) = self.reader.readline()
for i in xrange(dim):
x[i + 1] = (x[i + 1] - min[i]) / dis[i]
self.writer.writeline(x, userid, itemid, label)
if k % 10000 == 0:
print '%d/%d' % (k, self.reader.LineCount)
self.writer.close()
from sklearn.ensemble import GradientBoostingClassifier
from BinReader import BinReader
import numpy as np
from sklearn.ensemble.gradient_boosting import GradientBoostingRegressor
(data,label,items) = BinReader.readData(ur'F:\AliRecommendHomeworkData\1212新版\train1217.expand.norm.bin')
X_train = np.array(data)
label = [item[0] for item in label]
y_train = np.array(label)
est = GradientBoostingRegressor(n_estimators=150, learning_rate=0.1,max_depth=3, random_state=0, loss='ls',verbose=1).fit(X_train, y_train)
print 'testing...'
reader = BinReader(ur'F:\AliRecommendHomeworkData\1212新版\test18.expand.norm.bin')
reader.open()
result = [0] * reader.LineCount
for i in xrange(reader.LineCount):
(x,userid,itemid,label) = reader.readline()
x[0] = 1
y = est.predict([x])[0]
result[i] = (userid,itemid,y)
if i % 10000 == 0:
print '%d/%d' % (i,reader.LineCount)
result.sort(key=lambda x:x[2],reverse=True)
result = result[:7000]
print ur'正在输出...'
with open('result.csv','w') as f:
for item in result:
from sklearn.ensemble.gradient_boosting import GradientBoostingRegressor
path_train = ur'data\temp_train.bin'
path_test = ur'data\temp_test.bin'
(data, label, items) = BinReader.readData(path_train)
X_train = np.array(data)
label = [item[0] for item in label]
y_train = np.array(label)
est = GradientBoostingRegressor(n_estimators=150, learning_rate=0.1, max_depth=3, random_state=0, loss='ls',
verbose=1).fit(X_train, y_train)
print 'testing...'
reader = BinReader(path_test)
reader.open()
result = [0] * reader.LineCount
for i in xrange(reader.LineCount):
(x, userid, itemid, label) = reader.readline()
x[0] = 1
y = est.predict([x])[0]
result[i] = (userid, itemid, y)
if i % 10000 == 0:
print '%d/%d' % (i, reader.LineCount)
result.sort(key=lambda x: x[2], reverse=True)
result = result[:7000]
print "input"
with open('result.csv', 'w') as f:
for item in result:
