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# 返回图片测试
from sklearn.linear_model import LinearRegression
from sklearn.neural_network import MLPRegressor
import lightgbm as lgb
from xgboost import XGBRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import pandas as pd
from fastapi.middleware.cors import CORSMiddleware # 跨域
from fastapi import FastAPI, Response, BackgroundTasks
import json
import matplotlib.pyplot as plt
import io
import matplotlib
matplotlib.use('AGG')
app = FastAPI()
# 配置跨域白名单
origins = [
"http://127.0.0.1:5500"
]
# 图片测试
app.add_middleware(
CORSMiddleware,
allow_origins=origins,
allow_credentials=True,
allow_methods=["POST"],
allow_headers=["*"],
)
def create_img():
plt.rcParams['figure.figsize'] = [7.50, 3.50]
plt.rcParams['figure.autolayout'] = True
plt.plot([1, 2])
img_buf = io.BytesIO()
plt.savefig(img_buf, format='png')
plt.close()
return img_buf
@app.get('/png')
async def get_img(background_tasks: BackgroundTasks):
img_buf = create_img()
# get the entire buffer content
# because of the async, this will await the loading of all content
bufContents: bytes = img_buf.getvalue()
background_tasks.add_task(img_buf.close)
headers = {'Content-Disposition': 'inline; filename="out.png"'}
return Response(bufContents, headers=headers, media_type='image/png')
# 机器学习测试
# 多元线性回归
@app.post("/mlr")
async def mlr():
# 引入excel数据
# 可能存在空行问题，dropna(axis=0)删除空行
df = pd.read_csv("./1.csv").dropna(axis=0)
# 取xy
x = df.iloc[:, :12]
# y = df.loc[:, "BSR"]
y = df.loc[:, "SBR"]
# 划分数据集
x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=0.3, random_state=0)
# 标准化
standardscaler = StandardScaler()
standardscaler.fit(x_train)
x_train = standardscaler.transform(x_train)
x_test = standardscaler.transform(x_test)
# 多元线性回归
model = LinearRegression()
model.fit(x_train, y_train)
# 测试样本预测
y_pred = model.predict(x_test)
return json.dumps(y_pred.tolist())
# 随机森林
@app.post("/rf")
async def rf():
# 引入excel数据
# 可能存在空行问题，dropna(axis=0)删除空行
df = pd.read_csv("./1.csv").dropna(axis=0)
# 取xy
x = df.iloc[:, :12]
# y = df.loc[:, "BSR"]
y = df.loc[:, "SBR"]
# 划分数据集
x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=0.3, random_state=0)
# 标准化
standardscaler = StandardScaler()
standardscaler.fit(x_train)
x_train = standardscaler.transform(x_train)
x_test = standardscaler.transform(x_test)
# 随机森林
model = RandomForestRegressor()
model.fit(x_train, y_train)
# 测试样本预测
y_pred = model.predict(x_test)
return json.dumps(y_pred.tolist())
# BP神经网络
@app.post("/bpn")
async def rf():
# 引入excel数据
# 可能存在空行问题，dropna(axis=0)删除空行
df = pd.read_csv("./1.csv").dropna(axis=0)
# 取xy
x = df.iloc[:, :12]
# y = df.loc[:, "BSR"]
y = df.loc[:, "SBR"]
# 划分数据集
x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=0.3, random_state=0)
# 标准化
standardscaler = StandardScaler()
standardscaler.fit(x_train)
x_train = standardscaler.transform(x_train)
x_test = standardscaler.transform(x_test)
# BP神经网络
model = MLPRegressor(hidden_layer_sizes=(10,), random_state=10,learning_rate_init=0.1) # BP神经网络回归模型
model.fit(x_train,y_train) # 训练模型
# 测试样本预测
y_pred = model.predict(x_test)
return json.dumps(y_pred.tolist())
# XGBoost
@app.post("/xgboost")
async def rf():
# 引入excel数据
# 可能存在空行问题，dropna(axis=0)删除空行
df = pd.read_csv("./1.csv").dropna(axis=0)
# 取xy
x = df.iloc[:, :12]
# y = df.loc[:, "BSR"]
y = df.loc[:, "SBR"]
# 划分数据集
x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=0.3, random_state=0)
# 标准化
standardscaler = StandardScaler()
standardscaler.fit(x_train)
x_train = standardscaler.transform(x_train)
x_test = standardscaler.transform(x_test)
# XGBoost
model = XGBRegressor(max_depth=5, learning_rate=0.1, n_estimators=160, objective='reg:gamma')
model.fit(x_train, y_train)
# 测试样本预测
y_pred = model.predict(x_test)
return json.dumps(y_pred.tolist())
# LightGBM
@app.post("/lightgbm")
async def rf():
# 引入excel数据
# 可能存在空行问题，dropna(axis=0)删除空行
df = pd.read_csv("./1.csv").dropna(axis=0)
# 取xy
x = df.iloc[:, :12]
# y = df.loc[:, "BSR"]
y = df.loc[:, "SBR"]
# 划分数据集
x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=0.3, random_state=0)
# 标准化
standardscaler = StandardScaler()
standardscaler.fit(x_train)
x_train = standardscaler.transform(x_train)
x_test = standardscaler.transform(x_test)
# LightGBM
model = lgb.LGBMRegressor(objective='regression',num_leaves=31,learning_rate=0.05,n_estimators=20)
model.fit(x_train, y_train)
# 测试样本预测
y_pred = model.predict(x_test)
return json.dumps(y_pred.tolist())
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