def test_car_data(self):
"""Simple example using car data."""
# Car data from example 'Naive Bayes Classifier example'
# by Eric Meisner November 22, 2003
# http://www.inf.u-szeged.hu/~ormandi/teaching/mi2/02-naiveBayes-example.pdf
xcar = [
["Red", "Sports", "Domestic"],
["Red", "Sports", "Domestic"],
["Red", "Sports", "Domestic"],
["Yellow", "Sports", "Domestic"],
["Yellow", "Sports", "Imported"],
["Yellow", "SUV", "Imported"],
["Yellow", "SUV", "Imported"],
["Yellow", "SUV", "Domestic"],
["Red", "SUV", "Imported"],
["Red", "Sports", "Imported"],
]
ycar = [
"Yes",
"No",
"Yes",
"No",
"Yes",
"No",
"Yes",
"No",
"No",
"Yes",
]
carmodel = NaiveBayes.train(xcar, ycar)
self.assertEqual("Yes", NaiveBayes.classify(carmodel, ["Red", "Sports", "Domestic"]))
self.assertEqual("No", NaiveBayes.classify(carmodel, ["Red", "SUV", "Domestic"]))
def test_car_data(self):
"""Simple example using car data."""
# Car data from example 'Naive Bayes Classifier example'
# by Eric Meisner November 22, 2003
# http://www.inf.u-szeged.hu/~ormandi/teaching/mi2/02-naiveBayes-example.pdf
xcar = [
['Red', 'Sports', 'Domestic'],
['Red', 'Sports', 'Domestic'],
['Red', 'Sports', 'Domestic'],
['Yellow', 'Sports', 'Domestic'],
['Yellow', 'Sports', 'Imported'],
['Yellow', 'SUV', 'Imported'],
['Yellow', 'SUV', 'Imported'],
['Yellow', 'SUV', 'Domestic'],
['Red', 'SUV', 'Imported'],
['Red', 'Sports', 'Imported'],
]
ycar = [
'Yes',
'No',
'Yes',
'No',
'Yes',
'No',
'Yes',
'No',
'No',
'Yes',
]
carmodel = NaiveBayes.train(xcar, ycar)
self.assertEqual("Yes", NaiveBayes.classify(carmodel, ['Red', 'Sports', 'Domestic']))
self.assertEqual("No", NaiveBayes.classify(carmodel, ['Red', 'SUV', 'Domestic']))
def test_train_function_with_priors(self):
model = NaiveBayes.train(self.xs,
self.ys,
priors={
'male': 0.1,
'female': 0.9
})
result = NaiveBayes.calculate(model, self.test, scale=True)
expected = -692.0
self.assertEqual(expected, round(result['male']))
def setUp(self):
# Using example from https://en.wikipedia.org/wiki/Naive_Bayes_classifier
# height (feet), weight (lbs), foot size (inches)
self.xs = [
[6, 180, 12],
[5.92, 190, 11],
[5.58, 170, 12],
[5.92, 165, 10],
[5, 100, 6],
[5.5, 150, 8],
[5.42, 130, 7],
[5.75, 150, 9],
]
self.ys = [
'male',
'male',
'male',
'male',
'female',
'female',
'female',
'female',
]
self.model = NaiveBayes.train(self.xs, self.ys)
self.test = [6, 130, 8]
def setUp(self):
# Using example from https://en.wikipedia.org/wiki/Naive_Bayes_classifier
# height (feet), weight (lbs), foot size (inches)
self.xs = [
[6, 180, 12],
[5.92, 190, 11],
[5.58, 170, 12],
[5.92, 165, 10],
[5, 100, 6],
[5.5, 150, 8],
[5.42, 130, 7],
[5.75, 150, 9],
]
self.ys = [
'male',
'male',
'male',
'male',
'female',
'female',
'female',
'female',
]
self.model = NaiveBayes.train(self.xs, self.ys)
self.test = [6, 130, 8]
def test_calculate_function_with_scale(self):
result = NaiveBayes.calculate(self.model, self.test, scale=True)
expected = -689.0
self.assertEqual(expected, round(result['male']))
def test_classify_function(self):
expected = "female"
result = NaiveBayes.classify(self.model, self.test)
self.assertEqual(expected, result)
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_function(self):
expected = "female"
result = NaiveBayes.classify(self.model, self.test)
self.assertEqual(expected, result)
def test_train_function_with_priors(self):
model = NaiveBayes.train(self.xs, self.ys, priors={'male': 0.1, 'female': 0.9})
result = NaiveBayes.calculate(model, self.test, scale=True)
expected = -692.0
self.assertEqual(expected, round(result['male']))
def test_calculate_function_with_scale(self):
result = NaiveBayes.calculate(self.model, self.test, scale=True)
expected = -689.0
self.assertEqual(expected, round(result['male']))
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 bayes(self): xs = [[-53.0, -200.78], [117.0, -267.14], [57.0, -163.47], [16.0, -190.3], [11.0, -220.94], [85.0, -193.94], [16.0, -182.71], [15.0, -180.41], [-26.0, -181.73], [58.0, -259.87], [126.0, -414.53], [191.0, -249.57], [113.0, -265.28], [145.0, -312.99], [154.0, -213.83], [147.0, -380.85], [93.0, -291.13]] ys =[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0] model = NaiveBayes.train(xs,ys) return model