def test_classify(self):
k = 3
model = kNN.train(xs, ys, k)
result = kNN.classify(model, [6, -173.143442352])
self.assertEqual(result, 1)
result = kNN.classify(model, [309, -271.005880394])
self.assertEqual(result, 0)
def test_classify(self):
k = 3
model = kNN.train(xs, ys, k)
result = kNN.classify(model, [6, -173.143442352])
self.assertEqual(result, 1)
result = kNN.classify(model, [309, -271.005880394])
self.assertEqual(result, 0)
def test_model_accuracy(self):
correct = 0
k = 3
model = kNN.train(xs, ys, k)
predictions = [1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]
for i in range(len(predictions)):
prediction = kNN.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
k = 3
model = kNN.train(xs, ys, k)
predictions = [1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]
for i in range(len(predictions)):
prediction = kNN.classify(model, xs[i])
self.assertEqual(prediction, predictions[i])
if prediction == ys[i]:
correct += 1
self.assertEqual(correct, 15)
def test_leave_one_out(self):
correct = 0
k = 3
model = kNN.train(xs, ys, k)
predictions = [1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1]
for i in range(len(predictions)):
model = kNN.train(xs[:i] + xs[i + 1 :], ys[:i] + ys[i + 1 :], k)
prediction = kNN.classify(model, xs[i])
self.assertEqual(prediction, predictions[i])
if prediction == ys[i]:
correct += 1
self.assertEqual(correct, 13)
def test_leave_one_out(self):
correct = 0
k = 3
model = kNN.train(xs, ys, k)
predictions = [1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1]
for i in range(len(predictions)):
model = kNN.train(xs[:i] + xs[i + 1:], ys[:i] + ys[i + 1:], k)
prediction = kNN.classify(model, xs[i])
self.assertEqual(prediction, predictions[i])
if prediction == ys[i]:
correct += 1
self.assertEqual(correct, 13)
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)
features = tfidf.toarray() #将tf-idf矩阵抽取出来，元素a[i][j]表示j词在i类文本中的tf-idf权重
print(features.shape)
target = [c for (d, c) in documents]
train_set1 = features[:1500, :]
target_train = target[:1500]
test_set1 = features[1500:, :]
target_test = target[1500:]
from sklearn.svm import SVC
svclf = SVC(kernel='linear', probability=True)
svclf.fit(train_set1, target_train)
pred_svc = svclf.predict(test_set1)
print('SVM=', sum(pred_svc == target_test) / len(target_test))
from Bio import kNN
from scipy import spatial
model = kNN.train(train_set1, target_train, 7)
dd = [
kNN.classify(model, t, distance_fn=spatial.distance.cosine)
for t in test_set1
]
print('KNN_cos=',
sum(np.array(dd) == np.array(target_test)) / len(target_test))
from sklearn.neighbors import KNeighborsClassifier
knnclf = KNeighborsClassifier(n_neighbors=7) #default with k=5
knnclf.fit(train_set1, target_train)
pred_knn = knnclf.predict(test_set1)
print('KNN_eu=', sum(pred_knn == target_test) / len(target_test))
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)
