def naive():
params = get_params(request.args)
X_train, X_test, y_train, y_test = createData(params[2],params[3],params[4])
start, end = params[0], params[1]
model = GaussianNB()
model.fit(X_train,np.ravel(y_train))
y_pred = model.predict(X_test)
res = result(X_test, y_test, y_pred)
res = res[start:end]
print("naive")
print(params)
return res.to_json(orient='index')
def svm():
params = get_params(request.args)
X_train, X_test, y_train, y_test = createData(params[2],params[3],params[4])
start, end = params[0], params[1]
clf = SVC(kernel=params[6])
clf.fit(X_train, np.ravel(y_train))
y_pred = clf.predict(X_test)
res = result(X_test, y_test, y_pred)
res = res[start:end]
print("svm")
print(params)
return res.to_json(orient='index')
def logistic():
params = get_params(request.args)
X_train, X_test, y_train, y_test = createData(params[2],params[3],params[4])
start, end = params[0], params[1]
model = linear_model.LogisticRegression(random_state=0)
model.fit(X_train, np.ravel(y_train))
y_pred = model.predict(X_test)
res = result(X_test, y_test, y_pred)
res = res[start:end]
print("log")
print(params)
return res.to_json(orient='index')
def rtree():
params = get_params(request.args)
X_train, X_test, y_train, y_test = createData(params[2],params[3],params[4])
start, end = params[0], params[1]
clf=RandomForestClassifier(n_estimators=params[8])
clf.fit(X_train,np.ravel(y_train))
y_pred=clf.predict(X_test)
res = result(X_test, y_test, y_pred)
res = res[start:end]
print("rtree")
print(params)
return res.to_json(orient='index')
def dtree():
params = get_params(request.args)
X_train, X_test, y_train, y_test = createData(params[2],params[3],params[4])
start, end = params[0], params[1]
clf = DecisionTreeClassifier(max_depth= params[7])
clf = clf.fit(X_train,np.ravel(y_train))
y_pred = clf.predict(X_test)
res = result(X_test, y_test, y_pred)
res = res[start:end]
print("dtree")
print(params)
return res.to_json(orient='index')
def knear():
params = get_params(request.args)
X_train, X_test, y_train, y_test = createData(params[2],params[3],params[4])
start, end = params[0], params[1]
classifier = KNeighborsClassifier(n_neighbors=params[5])
classifier.fit(X_train, np.ravel(y_train))
y_pred = classifier.predict(X_test)
res = result(X_test, y_test, y_pred)
res = res[start:end]
print("knear")
print(params)
return res.to_json(orient='index')