def home():
params = get_algo(request.args)
if params is None:
params = "linearRegression"
data_params = get_params(request.args)
if params == "linearRegression":
return redirect(
url_for('.linear',
start=data_params[0],
end=data_params[1],
rows=data_params[2],
cols=data_params[3],
clust=data_params[4]))
elif params == "logisticRegression":
return redirect(
url_for('.logistic',
start=data_params[0],
end=data_params[1],
rows=data_params[2],
cols=data_params[3],
clust=data_params[4]))
elif params == "ridge":
return redirect(
url_for('.ridge',
start=data_params[0],
end=data_params[1],
rows=data_params[2],
cols=data_params[3],
clust=data_params[4],
alpha=data_params[10]))
else:
return "select algo"
def linear():
params = get_params(request.args)
X_train, X_test, y_train, y_test = regressionData(params[2], params[3])
start, end = params[0], params[1]
model = linear_model.LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
res = result(X_test, y_test, y_pred)
res = res[start:end]
return res.to_json(orient='index')
def ridge():
params = get_params(request.args)
start, end = params[0], params[1]
X_train, X_test, y_train, y_test = regressionData(params[2], params[3])
ridgereg = linear_model.Ridge(alpha=params[10], normalize=True)
ridgereg.fit(X_train, y_train)
y_pred = ridgereg.predict(X_test)
res = result(X_test, y_test, y_pred)
res = res[start:end]
print("ridge")
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 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 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')
def home():
params = get_algo(request.args)
if params is None or "":
params = "logisticRegression"
data_params = get_params(request.args)
if params == "logisticRegression":
return redirect(url_for('.logistic',start=data_params[0],end=data_params[1], rows=data_params[2], cols=data_params[3], clust=data_params[4]))
elif params == "knear":
return redirect(url_for('.knear',start=data_params[0],end=data_params[1], rows=data_params[2], cols=data_params[3], clust=data_params[4], knear=data_params[5]))
elif params == "svm":
return redirect(url_for('.svm',start=data_params[0],end=data_params[1], rows=data_params[2], cols=data_params[3], clust=data_params[4], kernel=data_params[6]))
elif params == "naive":
return redirect(url_for('.naive',start=data_params[0],end=data_params[1], rows=data_params[2], cols=data_params[3], clust=data_params[4]))
elif params == "dtree":
return redirect(url_for('.dtree',start=data_params[0],end=data_params[1], rows=data_params[2], cols=data_params[3], clust=data_params[4], max_depth=data_params[7]))
elif params == "rtree":
return redirect(url_for('.rtree',start=data_params[0],end=data_params[1], rows=data_params[2], cols=data_params[3], clust=data_params[4], n_estimators=data_params[8]))