def test_calculate_probability(self):
model = LogisticRegression.train(xs, ys)
q, p = LogisticRegression.calculate(model, [6, -173.143442352])
self.assertAlmostEqual(p, 0.993242, 6)
self.assertAlmostEqual(q, 0.006758, 6)
q, p = LogisticRegression.calculate(model, [309, -271.005880394])
self.assertAlmostEqual(p, 0.000321, 6)
self.assertAlmostEqual(q, 0.999679, 6)
def test_calculate_probability(self):
model = LogisticRegression.train(xs, ys)
q, p = LogisticRegression.calculate(model, [6,-173.143442352])
self.assertAlmostEqual(p, 0.993242, places=6)
self.assertAlmostEqual(q, 0.006758, places=6)
q, p = LogisticRegression.calculate(model, [309, -271.005880394])
self.assertAlmostEqual(p, 0.000321, places=6)
self.assertAlmostEqual(q, 0.999679, places=6)
def test_model_accuracy(self):
correct = 0
model = LogisticRegression.train(xs, ys)
predictions = [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]
for i in range(len(predictions)):
prediction = LogisticRegression.classify(model, xs[i])
self.assertEqual(prediction, predictions[i])
if prediction == ys[i]:
correct += 1
self.assertEqual(correct, 16)
def test_leave_one_out(self):
correct = 0
predictions = [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0]
for i in range(len(predictions)):
model = LogisticRegression.train(xs[:i]+xs[i+1:], ys[:i]+ys[i+1:])
prediction = LogisticRegression.classify(model, xs[i])
self.assertEqual(prediction, predictions[i])
if prediction==ys[i]:
correct+=1
self.assertEqual(correct, 15)
def test_model_accuracy(self):
correct = 0
model = LogisticRegression.train(xs, ys)
predictions = [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]
for i in range(len(predictions)):
prediction = LogisticRegression.classify(model, xs[i])
self.assertEqual(prediction, predictions[i])
if prediction==ys[i]:
correct+=1
self.assertEqual(correct, 16)
def test_leave_one_out(self):
correct = 0
predictions = [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0]
for i in range(len(predictions)):
model = LogisticRegression.train(xs[:i] + xs[i + 1:], ys[:i] + ys[i + 1:])
prediction = LogisticRegression.classify(model, xs[i])
self.assertEqual(prediction, predictions[i])
if prediction == ys[i]:
correct += 1
self.assertEqual(correct, 15)
def test_classify(self):
model = LogisticRegression.train(xs, ys)
result = LogisticRegression.classify(model, [6, -173.143442352])
self.assertEqual(result, 1)
result = LogisticRegression.classify(model, [309, -271.005880394])
self.assertEqual(result, 0)
def test_calculate_model(self):
model = LogisticRegression.train(xs, ys)
beta = model.beta
self.assertAlmostEqual(beta[0], 8.9830, 4)
self.assertAlmostEqual(beta[1], -0.0360, 4)
self.assertAlmostEqual(beta[2], 0.0218, 4)
def post(self):
alldata = self.getRequestData()
user = self.objUserInfo
s = Entity.model(self.db)
print(alldata)
if alldata['model_type'] == 1:
xss = alldata['xs'].split()
xs = []
ys = []
q = 0
for i in xss:
xs.append([float(i.split(',')[0]), float(i.split(',')[1])])
for i in range(len(xs)):
ys.append(int(alldata['ys'].split(',')[q]))
q = q + 1
print(len(xs), len(ys))
model = LogisticRegression.train(xs, ys)
if model.beta:
lsData = {
"create_id": user['id'],
"name": alldata['name'],
"beta": str(model.beta),
"note": alldata['note']
}
id = s.save(lsData, table='public.logistis')
self.response(id)
elif alldata['model_type'] == 2:
xss = alldata['xs'].split()
xs = []
ys = []
q = 0
for i in xss:
xs.append([float(i.split(',')[0]), float(i.split(',')[1])])
for i in range(len(xs)):
ys.append(int(alldata['ys'].split(',')[q]))
q = q + 1
print(xs, ys)
print(xs, ys)
count = 1
while count >= 0:
rpath = str(random.randint(10000, 90000))
pyfile = '/home/ubuntu/pythonff/mdt/mdt/mdtproject/trunk/service/data_mining/' + rpath + '.py'
if not os.path.isfile(pyfile):
count = -1
else:
count = 1
f = open(pyfile, 'w')
text = 'from Bio import kNN' + '\n' + 'class model():' + '\n' + ' def knn(self):' + '\n' + ' xs = ' + str(
xs
) + '\n' + ' ys =' + str(ys) + '\n' + ' k=' + str(
alldata['k']
) + '\n' + ' model = kNN.train(xs,ys,k)' + '\n' + ' return model'
print(text)
f.write(text)
f.close()
if os.path.isfile(pyfile):
lsData = {
"create_id": user['id'],
"name": alldata['name'],
"file_name": rpath,
"packpath": pyfile,
"type": '2',
"note": alldata['note']
}
id = s.save(lsData, table='public.pymodel')
self.response(id)
elif alldata['model_type'] == 3:
xss = alldata['xs'].split()
xs = []
ys = []
q = 0
for i in xss:
xs.append([float(i.split(',')[0]), float(i.split(',')[1])])
for i in range(len(xs)):
ys.append(int(alldata['ys'].split(',')[q]))
q = q + 1
print(xs, ys)
count = 1
while count >= 0:
rpath = str(random.randint(10000, 90000))
pyfile = '/home/ubuntu/pythonff/mdt/mdt/mdtproject/trunk/service/data_mining/' + rpath + '.py'
if not os.path.isfile(pyfile):
count = -1
else:
count = 1
f = open(pyfile, 'w')
text = 'from Bio import NaiveBayes' + '\n' + 'class model():' + '\n' + ' def bayes(self):' + '\n' + ' xs = ' + str(
xs
) + '\n' + ' ys =' + str(
ys
) + '\n' + ' model = NaiveBayes.train(xs,ys)' + '\n' + ' return model'
print(text)
f.write(text)
f.close()
if os.path.isfile(pyfile):
lsData = {
"create_id": user['id'],
"name": alldata['name'],
"file_name": rpath,
"packpath": pyfile,
"type": '3',
"note": alldata['note']
}
id = s.save(lsData, table='public.pymodel')
self.response(id)
def test_calculate_model_with_update_callback(self):
model = LogisticRegression.train(xs, ys, update_fn=show_progress)
beta = model.beta
self.assertAlmostEqual(beta[0], 8.9830, places=4)
def test_classify(self):
model = LogisticRegression.train(xs, ys)
result = LogisticRegression.classify(model, [6,-173.143442352])
self.assertEqual(result, 1)
result = LogisticRegression.classify(model, [309, -271.005880394])
self.assertEqual(result, 0)
def test_calculate_model(self):
model = LogisticRegression.train(xs, ys)
beta = model.beta
self.assertAlmostEqual(beta[0], 8.9830, places=4)
self.assertAlmostEqual(beta[1], -0.0360, places=4)
self.assertAlmostEqual(beta[2], 0.0218, places=4)
def test_calculate_model_with_update_callback(self):
model = LogisticRegression.train(xs, ys, update_fn=show_progress)
beta = model.beta
self.assertAlmostEqual(beta[0], 8.9830, places=4)
from Bio import LogisticRegression
import numpy as np
all_data = np.loadtxt("../datasets/iris/iris.data", delimiter=",",
dtype="float, float, float, float, S11")
xs = []
ys = []
for i in all_data:
if 'virgi' not in str(i[-1]):
xs.append([i[0], i[1], i[2], i[3]])
if 'setosa' in str(i[-1]):
ys.append(0)
else:
ys.append(1)
test_xs = xs.pop()
test_ys = ys.pop()
def show_progress(iteration, loglikelihood):
print("Iteration:", iteration, "Log-likelihood function:", loglikelihood)
model = LogisticRegression.train(xs, ys, update_fn=show_progress)
print("This should be Iris-versic (1): {}".format(LogisticRegression.classify(model, test_xs)))
def post(self):
alldata = self.getRequestData()
user = self.objUserInfo
s=Entity.model(self.db)
print(alldata)
if alldata['model_type']==1:
xss=alldata['xs'].split()
xs=[]
ys=[]
q=0
for i in xss:
xs.append([float(i.split(',')[0]),float(i.split(',')[1])])
for i in range(len(xs)):
ys.append(int(alldata['ys'].split(',')[q]))
q=q+1
print(len(xs),len(ys))
model=LogisticRegression.train(xs,ys)
if model.beta:
lsData={
"create_id" : user['id'],
"name" : alldata['name'],
"beta" : str(model.beta),
"note" : alldata['note']
}
id = s.save(lsData,table='public.logistis')
self.response(id)
elif alldata['model_type']==2:
xss=alldata['xs'].split()
xs=[]
ys=[]
q=0
for i in xss:
xs.append([float(i.split(',')[0]),float(i.split(',')[1])])
for i in range(len(xs)):
ys.append(int(alldata['ys'].split(',')[q]))
q=q+1
print(xs,ys)
print(xs,ys)
count=1
while count >= 0 :
rpath = str(random.randint(10000, 90000))
pyfile='/home/ubuntu/pythonff/mdt/mdt/mdtproject/trunk/service/data_mining/'+rpath+'.py'
if not os.path.isfile(pyfile):
count=-1
else:
count=1
f=open(pyfile,'w')
text = 'from Bio import kNN'+'\n'+'class model():'+'\n'+' def knn(self):'+'\n'+' xs = '+str(xs)+'\n'+' ys ='+str(ys)+'\n'+' k='+str(alldata['k'])+'\n'+' model = kNN.train(xs,ys,k)'+'\n'+' return model'
print(text)
f.write(text)
f.close()
if os.path.isfile(pyfile):
lsData={
"create_id" : user['id'],
"name" : alldata['name'],
"file_name" : rpath,
"packpath" : pyfile,
"type" : '2',
"note" : alldata['note']
}
id = s.save(lsData,table='public.pymodel')
self.response(id)
elif alldata['model_type']==3:
xss=alldata['xs'].split()
xs=[]
ys=[]
q=0
for i in xss:
xs.append([float(i.split(',')[0]),float(i.split(',')[1])])
for i in range(len(xs)):
ys.append(int(alldata['ys'].split(',')[q]))
q=q+1
print(xs,ys)
count=1
while count >= 0 :
rpath = str(random.randint(10000, 90000))
pyfile='/home/ubuntu/pythonff/mdt/mdt/mdtproject/trunk/service/data_mining/'+rpath+'.py'
if not os.path.isfile(pyfile):
count=-1
else:
count=1
f=open(pyfile,'w')
text = 'from Bio import NaiveBayes'+'\n'+'class model():'+'\n'+' def bayes(self):'+'\n'+' xs = '+str(xs)+'\n'+' ys ='+str(ys)+'\n'+' model = NaiveBayes.train(xs,ys)'+'\n'+' return model'
print(text)
f.write(text)
f.close()
if os.path.isfile(pyfile):
lsData={
"create_id" : user['id'],
"name" : alldata['name'],
"file_name" : rpath,
"packpath" : pyfile,
"type" : '3',
"note" : alldata['note']
}
id = s.save(lsData,table='public.pymodel')
self.response(id)