到目前为止，我有一个flask应用程序，在该应用程序中，我为给定的数据帧计算线性回归。然后，我使用JSON将图转储到HTML。单击时有一个按钮，显示线性回归图。我想在下拉列表中选择预测算法：Facebook先知，线性回归和多项式回归。但是，一旦我从列表中选择了另一种算法，如何删除图形呢？
非常感谢您的帮助！
app.py的代码是：
from flask import Flask, render_template # this has changed
import plotly
import plotly.graph_objs as go
import pandas as pd
import numpy as np
import json
# for another file...
import datetime as dt
import numpy as np
import pandas as pd
from distributed.deploy.ssh import bcolors
from pandas.plotting import register_matplotlib_converters
from plotly import graph_objs as go
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
register_matplotlib_converters()
def calculate_linear_regression(data, sensor_name):
data = pd.read_csv(data) #
data['day'] = pd.to_datetime(data['day'], format="%d/%m/%Y") #
data = data.sort_values(by=['readable time'])
group_by_df = pd.DataFrame([name, group.mean()[sensor_name]] for name, group in data.groupby('day'))
group_by_df.columns = ['day', sensor_name]
group_by_df['day'] = pd.to_datetime(group_by_df['day'])
group_by_df['day'] = group_by_df['day'].map(dt.datetime.toordinal)
def split(group_by_df):
X = group_by_df[['day']].values
y = group_by_df[[sensor_name]].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, shuffle=False)
return X_train, X_test, y_train, y_test
X_train, X_test, y_train, y_test = split(group_by_df)
def analyse_forecast():
print(bcolors.OKBLUE + "MSE linear regression(mean squared error)",
mean_squared_error(group_by_df[sensor_name], group_by_df['predicted']), bcolors.ENDC)
print("r2 score ", r2_score(group_by_df[sensor_name], group_by_df['predicted']))
rmse = np.sqrt(mean_squared_error(group_by_df[sensor_name], group_by_df['predicted']))
print(bcolors.WARNING + "RMSE for linear regression=", rmse, bcolors.ENDC)
print(bcolors.OKBLUE + "MSE TEST ", mean_squared_error(y_test, group_by_df['predicted'][len(X_train):]),
bcolors.ENDC)
print("r2 score TEST", r2_score(y_test, group_by_df['predicted'][len(X_train):]))
return mean_squared_error(group_by_df[sensor_name], group_by_df['predicted'])
def calculate_linear_reg():
group_by_df.reset_index(inplace=True)
mse_list = []
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(group_by_df[['day']])
group_by_df['predicted'] = y_pred
mse_list.append(analyse_forecast())
calculate_linear_reg()
return group_by_df, X_train, sensor_name
group_by_df, X_train, sensor_name = calculate_linear_regression(
"https://raw.githubusercontent.com/iulianastroia/csv_data/master/final_dataframe.csv",
"ch2o")
def create_figure(group_by_df, X_train, sensor_name):
linear_regression_fig = go.Figure()
# plot predicted values
linear_regression_fig.add_trace(go.Scatter(
x=group_by_df['day'].map(dt.datetime.fromordinal),
y=group_by_df['predicted'],
name="linear regression",
mode='lines+markers',
marker=dict(
color=np.where(group_by_df['day'].index < len(X_train), 'red', 'green'))))
# plot actual values
linear_regression_fig.add_trace(go.Scatter(
x=group_by_df['day'].map(dt.datetime.fromordinal),
y=group_by_df[sensor_name],
name='ACTUAL values',
mode='lines+markers'))
linear_regression_fig.update_layout(
title='Linear regression for ' + sensor_name,
yaxis_title=sensor_name,
xaxis_title='Day',
showlegend=True)
# linear_regression_fig.show()
graphJSON = json.dumps(linear_regression_fig, cls=plotly.utils.PlotlyJSONEncoder)
return graphJSON
def create_plot():
fig = go.Figure()
fig.add_trace(go.Scatter(
x=[0, 1, 2, 3, 4, 5, 6, 7, 8],
y=[0, 1, 2, 3, 4, 5, 6, 7, 8],
mode="lines+markers",
name="Name of Trace 1" # this sets its legend entry
))
fig.add_trace(go.Scatter(
x=[0, 1, 2, 3, 4, 5, 6, 7, 8],
y=[1, 0, 3, 2, 5, 4, 7, 6, 8],
name="Name of Trace 2"
))
fig.update_layout(
title="Plot Title",
xaxis_title="x Axis Title",
yaxis_title="y Axis Title",
font=dict(
family="bahnschrift",
size=18,
color="#7f7f7f"
)
)
graphJSON = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
return graphJSON
app = Flask(__name__)
@app.route('/')
def index():
bar = create_figure(group_by_df, X_train, sensor_name)
return render_template('index.html', scatter_plot=bar)
if __name__ == '__main__':
app.run()
而index.html是：
<!DOCTYPE html>
<html>
<head lang="en">
<meta charset="UTF-8">
<title>My First Dashboard</title>
<script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.6/d3.min.js"></script>
</head>
<body>
<input type="submit" id="send_message" name="send_message" class="btn btn-outline-secondary"
value="Linear Regression" onclick="show_plot()" />
<div class="chart" id="bargraph">
<script>
function show_plot(){
var graphs = {{scatter_plot | safe}};
Plotly.plot('bargraph',graphs,{});}
</script>
</div>
</body>
</html>
如果我理解正确，您有多组数据，并且希望能够选择每组数据以显示在网页上的表格中。我不会尝试在同一“页面”上加载不同的数据集。这是我为我的一个项目所做的一个示例。
我的索引页面具有以下形式，可用于从数据库中加载选项。 drpdwn对象包含我的选项列表。
menu_tol.py
...
@bp.route('/menu_tol')
def index(tol_id=0):
db=get_db()
menu_query='SELECT * FROM tol'
drpdwn=db.execute(menu_query).fetchall()
close_db()
return render_template(
'menu_tol/index.html',
drpdwn=drpdwn
)
menu_tol / index.html
<form name"tol_form" action="/menu_tol/update" method='POST'>
<select name="tol_select">
{% for tol in drpdwn %}
<option value={{tol.id}}>{{tol.name}}</option>
{% endfor %}
</select>
<input type="submit" value="Submit">
</form>
用户从此处选择一个对象，然后单击一个按钮。该脚本使用该值来生成特定于我选择的新URL。然后，第二页将包含该请求所独有的所有信息。这样，html页面仅显示其给出的内容，而我不必考虑多个可能性。
@bp.route('/menu_tol/update', methods=('GET','POST'))
def update():
if request.method=='POST':
tol_id=request.form['tol_select']
return redirect(url_for('.summary', tol_id=tol_id))
@bp.route('/menu_tol/<tol_id>/summary', methods=('GET','POST'))
def summary(tol_id):
db=get_db()
tol_query='SELECT name FROM tol WHERE id = ' + tol_id
...
return render_template(
'menu_tol/summary.html',
tol_title=tol_title,
result_list=result_list,
tol_id=tol_id
)
最后，如果要“清除”结果，只需创建一个对象，即可将您重定向到index页面。
<a style="color:blue" class="action" href="/menu_tol"><b>TOL</b></a> - TOL Archive