Plotly Dash table 回调

Question

我试图让滑块、用户输入和 table 之间的依赖关系起作用。我试过输出数据并使用回调来更新它。我被建议只在回调中创建 table 并只使用 "Div." 来定义它在显示中的位置。

其他信息：

table 是使用 dash_table 库从 pandas DataFrame 创建的。
数据为字典格式。
变量 threshold 是用户输入（滑块或输入）调整的值

如果有人能帮我找出为什么 table 没有显示，我将不胜感激？

这是我的代码：


import dash
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
from dash.dependencies import Input, Output
import dash_table

threshold = 0.5
################################################################
###################### Table Data ##############################
################################################################

metrics_index = ["AUC", "Accuracy", "Kappa", "Sensitivity (Recall)", "Specificity", "Precision", "F1"]

algo_columns = ["Test-SVM+Naïve B", "RF"]

table_data = {"AUC": [threshold * 0.8, threshold * 0.83],
              "Accuracy": [threshold * 0.85, threshold * 0.86],
              "Kappa": [threshold * 0.66, threshold * 0.69],
              "Sensitivity (Recall)": [threshold * 0.82, threshold * 0.83],
              "Specificity": [threshold * 0.78, threshold * 0.79],
              "Precision": [threshold * 0.78, threshold * 0.79],
              "F1": [threshold * 0.81, threshold * 0.82]}

data = [i for i in table_data]
table = pd.DataFrame(columns=algo_columns, index=metrics_index, data=[table_data[i] for i in metrics_index])
# display(table)


################################################################
########################  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', type='float', 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):
    threshold = float(value)
    table_data = {"AUC": [threshold * 0.8, threshold * 0.83],
                  "Accuracy": [threshold * 0.85, threshold * 0.86],
                  "Kappa": [threshold * 0.66, threshold * 0.69],
                  "Sensitivity (Recall)": [threshold * 0.82, threshold * 0.83],
                  "Specificity": [threshold * 0.78, threshold * 0.79],
                  "Precision": [threshold * 0.78, threshold * 0.79],
                  "F1": [threshold * 0.81, threshold * 0.82]}


    return dash_table.DataTable(
                            id='update-table',
                            data= table_data.to_dict('records'),
                            columns=[{'id': x, 'name': x} for x in table.columns]
                )


if __name__ == "__main__":
    app.run_server()

Answer 1

[screenshot of table live dynamic editing]

import dash
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
import dash_table
import pandas as pd

from dash.dependencies import Input, Output

threshold = 0.5
################################################################
###################### Table Data ##############################
################################################################

metrics_index = [
    "AUC",
    "Accuracy",
    "Kappa",
    "Sensitivity (Recall)",
    "Specificity",
    "Precision",
    "F1",
]

algo_columns = ["Test-SVM+Naïve B", "RF"]

table_data = {
    "AUC": [threshold * 0.8, threshold * 0.83],
    "Accuracy": [threshold * 0.85, threshold * 0.86],
    "Kappa": [threshold * 0.66, threshold * 0.69],
    "Sensitivity (Recall)": [threshold * 0.82, threshold * 0.83],
    "Specificity": [threshold * 0.78, threshold * 0.79],
    "Precision": [threshold * 0.78, threshold * 0.79],
    "F1": [threshold * 0.81, threshold * 0.82],
}

data = [i for i in table_data]
table = pd.DataFrame(
    columns=algo_columns,
    index=metrics_index,
    data=[table_data[i] for i in metrics_index],
)
# display(table)


################################################################
########################  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):
    threshold = float(value)
    table_data = pd.DataFrame.from_dict(
        {
            "AUC": [threshold * 0.8, threshold * 0.83],
            "Accuracy": [threshold * 0.85, threshold * 0.86],
            "Kappa": [threshold * 0.66, threshold * 0.69],
            "Sensitivity (Recall)": [threshold * 0.82, threshold * 0.83],
            "Specificity": [threshold * 0.78, threshold * 0.79],
            "Precision": [threshold * 0.78, threshold * 0.79],
            "F1": [threshold * 0.81, threshold * 0.82],
        }
    )

    return html.Div(
        [
            dash_table.DataTable(
                data=table_data.to_dict("rows"),
                columns=[{"id": x, "name": x} for x in table_data.columns],
            )
        ]
    )


if __name__ == "__main__":
    app.run_server(host="0.0.0.0", port=8050, debug=True, dev_tools_hot_reload=True)

我试过了，上面的代码稍作修改后似乎可以正常工作；我必须做出的改变是：

将 dict table_data 转换为数据帧（这允许 .to_dict() 方法，这是一种 pd.DataFrame 方法！）

    table_data = pd.DataFrame.from_dict(
        {
            "AUC": [threshold * 0.8, threshold * 0.83],
            "Accuracy": [threshold * 0.85, threshold * 0.86],
            "Kappa": [threshold * 0.66, threshold * 0.69],
            "Sensitivity (Recall)": [threshold * 0.82, threshold * 0.83],
            "Specificity": [threshold * 0.78, threshold * 0.79],
            "Precision": [threshold * 0.78, threshold * 0.79],
            "F1": [threshold * 0.81, threshold * 0.82],
        }
    )

也在 update_output 回调 fxn 中：
- 一个。将 df 的 'records' 更改为 'rows'。to_dict 方法调用
- 乙。你有 table 而不是 table_data 用于列 param
- C。在此处删除 id Dash 参数的使用，b/c 它已经在布局中

    return html.Div(
        [
            dash_table.DataTable(
                data=table_data.to_dict("rows"),
                columns=[{"id": x, "name": x} for x in table_data.columns],
            )
        ]
    )

看起来你调换了最大值和最小值！（最大零不会留下很多可能的输入！[实际上，none..]）；放置小数点和匹配精度也可能很重要，我添加以防万一。

                                html.Div(
                                    dcc.Input(
                                        id="input-box",
                                        max=1.00,
                                        min=0.00,
                                        step=0.01,
                                        value=threshold,
                                        type="number"
                                    )
                                ),

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