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_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 get(self):
offset = int(self.get_argument('o', default='1'))
rowcount = int(self.get_argument('r', default='10'))
offset = (offset - 1) * rowcount
no = self.get_argument('no', default='')
model_id = self.get_argument('model_id', default='')
model_type = self.get_argument('model_type', default='')
package = self.get_argument('model_name', default='')
cur = self.db.getCursor()
rowdata = {}
#查询
if no == '1':
if model_type == '1':
cur.execute(
" select b.name,a.create_id,a.name,a.note,a.beta from public.logistis a "
" left join public.account b on a.create_id = b.id "
"where a.id='%s' " % (model_id))
rows = cur.fetchall()
print(rows)
rowdata['struct'] = "id,create_id,name,note,beta "
rowdata['rows'] = rows
else:
cur.execute(
" select b.name,a.create_id,a.name,a.note,c.name,a.file_name from public.pymodel a "
" left join public.account b on a.create_id = b.id "
" left join public.model c on a.type = c.type "
" where a.id='%s' and a.type='%s' " %
(model_id, model_type))
rows = cur.fetchall()
rowdata['struct'] = "id,create_id,name,note,type,filename "
rowdata['rows'] = rows
self.response(rowdata)
elif no == '2':
if model_type == '1':
beta = self.get_argument('beta', default='')
model_data = self.get_argument('model', default='')
a = []
q = 0
print(model_data)
a = (list(eval(model_data)))
model = LogisticRegression.LogisticRegression()
model.beta = (list(eval(beta)))
rowdata = {}
rowdata['op'] = LogisticRegression.calculate(model, a)
rowdata['rows'] = LogisticRegression.classify(model, a)
elif model_type == '2':
pack = 'data_mining.' + package
import importlib
bb = importlib.import_module(pack)
ma = kNN.kNN()
model = bb.model.knn(ma)
model_data = self.get_argument('model', default='')
a = []
a = (list(eval(model_data)))
rowdata = {}
rowdata['op'] = kNN.calculate(model, a)
rowdata['rows'] = kNN.classify(model, a)
elif model_type == '3':
pack = 'data_mining.' + package
import importlib
bb = importlib.import_module(pack)
ma = NaiveBayes.NaiveBayes()
model = bb.model.bayes(ma)
model_data = self.get_argument('model', default='')
a = []
a = (list(eval(model_data)))
rowdata = {}
rowdata['op'] = NaiveBayes.calculate(model, a)
rowdata['rows'] = NaiveBayes.classify(model, a)
self.response(rowdata)
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)
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 get(self):
offset = int(self.get_argument('o',default='1'))
rowcount = int(self.get_argument('r',default='10'))
offset=(offset-1)*rowcount
no = self.get_argument('no', default='')
model_id = self.get_argument('model_id', default='')
model_type = self.get_argument('model_type', default='')
package=self.get_argument('model_name', default='')
cur=self.db.getCursor()
rowdata={}
#查询
if no=='1':
if model_type =='1':
cur.execute(" select b.name,a.create_id,a.name,a.note,a.beta from public.logistis a "
" left join public.account b on a.create_id = b.id "
"where a.id='%s' "% (model_id) )
rows = cur.fetchall()
print(rows)
rowdata['struct']="id,create_id,name,note,beta "
rowdata['rows']= rows
else:
cur.execute(" select b.name,a.create_id,a.name,a.note,c.name,a.file_name from public.pymodel a "
" left join public.account b on a.create_id = b.id "
" left join public.model c on a.type = c.type "
" where a.id='%s' and a.type='%s' "% (model_id,model_type) )
rows = cur.fetchall()
rowdata['struct']="id,create_id,name,note,type,filename "
rowdata['rows']= rows
self.response(rowdata)
elif no=='2':
if model_type=='1':
beta = self.get_argument('beta', default='')
model_data=self.get_argument('model', default='')
a=[]
q=0
print(model_data)
a=(list(eval(model_data)))
model=LogisticRegression.LogisticRegression()
model.beta=(list(eval(beta)))
rowdata={}
rowdata['op']=LogisticRegression.calculate(model,a)
rowdata['rows']=LogisticRegression.classify(model,a)
elif model_type=='2':
pack='data_mining.'+package
import importlib
bb=importlib.import_module(pack)
ma=kNN.kNN()
model=bb.model.knn(ma)
model_data=self.get_argument('model', default='')
a=[]
a=(list(eval(model_data)))
rowdata={}
rowdata['op']=kNN.calculate(model,a)
rowdata['rows']=kNN.classify(model,a)
elif model_type=='3':
pack='data_mining.'+package
import importlib
bb=importlib.import_module(pack)
ma=NaiveBayes.NaiveBayes()
model=bb.model.bayes(ma)
model_data=self.get_argument('model', default='')
a=[]
a=(list(eval(model_data)))
rowdata={}
rowdata['op']=NaiveBayes.calculate(model,a)
rowdata['rows']=NaiveBayes.classify(model,a)
self.response(rowdata)
