【发布时间】：2020-03-17 14:22:16
【问题描述】：
我在滑块、用户输入和工作表之间存在依赖关系。虽然，因为更新我只能描述从字典创建的基本熊猫数据帧，并在字典中进行简单的计算（基于阈值）。我无法让表格显示，我不确定我哪里出错了。
** 附加信息：**
- 阈值默认为 0.5，并通过用户输入（滑块或手动输入）的回调进行更新
- 额外的列被添加到 DataFrame
df作为基于阈值的二进制输出 -
metrics是y_true和y_pred的计算结果，即 fr ，这是尝试输出数据并使用回调更新数据的阈值。有人建议我只在回调中创建表并使用“Div”。定义其在显示中的位置。
我的代码如下：
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
import numpy as np
from scipy import stats
import plotly.graph_objs as go
from plotly.offline import init_notebook_mode, iplot
from datetime import datetime as dt
import dash_table
import pandas as pd
from sklearn import svm, datasets
import base64
import numpy as np
from sklearn.metrics import roc_auc_score, accuracy_score, cohen_kappa_score, recall_score, accuracy_score, precision_score, f1_score
from sklearn import metrics
from dash.dependencies import Input, Output```
import pandas as pd
from dash.dependencies import Input, Output
threshold = 0.5
# read data
data = pd.read_csv('data.csv')
#count columns in dataframe
count_algo = len(data.columns)
################################################################
######################## Body ################################
################################################################
body = dbc.Container(
[
dbc.Row(
[
dbc.Col(
[
html.H2("Slider + Manual entry test"),
dcc.Slider(
id="my-slider",
min=0,
max=1,
step=0.01,
marks={"0": "0", "0.5": "0.5", "1": "1"},
value=threshold,
),
html.Div(id="update-table"),
]
),
dbc.Col(
[
html.Div(
[
html.Div(
dcc.Input(
id="input-box",
max=0,
min=1,
step=0.01,
value=threshold,
)
),
html.Div(id="slider-output-container"),
]
)
]
),
]
)
]
)
app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
app.layout = html.Div([body])
##############################################################
######################## callbacks ###########################
##############################################################
@app.callback(
dash.dependencies.Output("slider-output-container", "children"),
[dash.dependencies.Input("my-slider", "value")],
)
def update_output(value):
threshold = float(value)
return threshold
# call back for slider to update based on manual input
@app.callback(
dash.dependencies.Output(component_id="my-slider", component_property="value"),
[dash.dependencies.Input("input-box", "value")],
)
def update_output(value):
threshold = float(value)
return threshold
# call back to update table
@app.callback(
dash.dependencies.Output("update-table", "children"),
[dash.dependencies.Input("my-slider", "value")],
)
def update_output(value):
#take value of threshold
threshold = float(value)
# add predicted
for i in data.iloc[:,1:]:
data['predicted_"{}"'.format(i)] = (data[i] >= threshold).astype('int')
table_data = pd.DataFrame.from_dict({
"AUC":[roc_auc_score(data.actual_label, data[i]) for i in data.iloc[:,count_algo:]],
"Accuracy":[accuracy_score(data.actual_label, data[i])for i in data.iloc[:,count_algo:]],
"Kappa":[cohen_kappa_score(data.actual_label, data[i])for i in data.iloc[:,count_algo:]],
"Sensitivity (Recall)": [recall_score(data.actual_label, data[i], average = 'weighted')for i in data.iloc[:,count_algo:]],
"Specificity": [accuracy_score(data.actual_label, data[i])for i in data.iloc[:,count_algo:]],
"Precision": [precision_score(data.actual_label, data[i], average = 'weighted')for i in data.iloc[:,count_algo:]],
"F1": [f1_score(data.actual_label, data[i], average = 'weighted')for i in data.iloc[:,count_algo:]]
}, orient = 'index').reset_index()
return html.Div([
dash_table.DataTable(
data=table_data.to_dict("rows"),
columns=[{"id": x, "name": x} for x in table_data.columns],
style_table={'overflowX': 'scroll'},
style_cell={'width':100},
)
])
if __name__ == "__main__":
app.run_server()
【问题讨论】：
标签： python callback plotly-dash