我正在基于此breast cancer dataset构建ML预测仪表板应用程序。
从下拉菜单中，我希望能够选择我的模型之一，运行拟合，并返回更新的混淆矩阵（热图）。
我计划将脚本扩展到表格，roc-curves，learning-curves等（即multi output callback）-但首先我希望这部分工作，然后再实现其他元素。
我尝试了不同的事情。
例如，在当前代码（如下）之前，我尝试直接从下拉菜单中调用模型，然后在回调内部进行所有cm计算，从而导致 AttributeError：'str'对象具有没有属性“适合” ：
@app.callback(Output('conf_matrix', 'figure'), [Input('dropdown-5', 'value')])
def update_cm_matix(model):
class_names=[0,1]
fitModel = model.fit(X_train, y_train)
y_pred = fitModel.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
return {'data': [go.Heatmap(x=class_names, y=class_names, z=cm, showscale=True, colorscale='blues')],
'layout': dict(width=350, height=280, margin={'t': 10},
xaxis=dict(title='Predicted class', tickvals=[0, 1]),
yaxis=dict(title='True class', tickvals=[0, 1], autorange='reversed'))}
（在下面的脚本中替换app.callback和函数）。
我正在苦苦挣扎的当前版本是：
# -*- coding: utf-8 -*-
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.feature_selection import RFE
import plotly.graph_objs as go
from dash.dependencies import Input, Output
app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
server = app.server
app.config.suppress_callback_exceptions = True
df = pd.read_csv("breast_cancer.csv")
y = np.array(df.diagnosis.tolist())
data = df.drop('diagnosis', 1)
X = np.array(data.values)
scaler = StandardScaler()
X = scaler.fit_transform(X)
random_state = 42
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=random_state)
# First model: logistic model + optimize hyperparameters
log = LogisticRegression(random_state=random_state)
param_grid = {'penalty': ['l2', 'l1'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}
CV_log = GridSearchCV(estimator=log, param_grid=param_grid,, scoring='accuracy', verbose=1, n_jobs=-1)
CV_log.fit(X_train, y_train)
log_best_params = CV_log.best_params_
log_clf = LogisticRegression(C=log_best_params['C'], penalty=log_best_params['penalty'], random_state=random_state)
# Second model: logistic model with recursive features elimination (just for illustration purposes, other models will be included)
rfe_selector = RFE(log_clf)
# app layout
app.layout = html.Div([
html.Div([
dcc.Dropdown(
id='dropdown-5',
options=[{'label': 'Logistic', 'value': 'log_clf'},
{'label': 'RFE', 'value': 'rfe_selector'}],
value='log_clf',
style={'width': '150px', 'height': '35px', 'fontSize': '10pt'}
)], style={}),
html.Div([
dcc.Graph(id='conf_matrix')
])
])
# function to run selected model
def ClassTrainEval(model):
fitModel = model.fit(X_train, y_train)
y_pred = fitModel.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
return fitModel, y_pred, y_score, cm
models = [log_clf, rfe_selector]
class_names = [0,1]
# dash callback
@app.callback(Output('conf_matrix', 'figure'), [Input('dropdown-5', 'value')])
def update_cm_matix(model):
for model in models:
ClassTrainEval(model)
return {'data': [go.Heatmap(x=class_names, y=class_names, z=cm, showscale=True, colorscale='blues')],
'layout': dict(width=350, height=280, margin={'t': 10},
xaxis=dict(title='Predicted class', tickvals=[0, 1]),
yaxis=dict(title='True class', tickvals=[0, 1], autorange='reversed'))}
if __name__ == '__main__':
app.run_server(debug=True)
哪里出现 NameError：未定义名称'cm' 错误。
我不确定如何使它正常工作-所以我希望有人可以指出正确的方向。
谢谢！
答案 0 :(得分：1)
您的代码中有多个错误。让我们先进行两次尝试。
dcc.Dropdown(
id='dropdown-5',
options=[{'label': 'Logistic', 'value': 'log_clf'},
{'label': 'RFE', 'value': 'rfe_selector'}],
value='log_clf',
style={'width': '150px', 'height': '35px', 'fontSize': '10pt'}
)], style={})
在下拉菜单中，模型是字符串（type('log_clf') == str），因此您无法对其进行训练。您需要编写如下的回调：
models = {'Logistic':log_clf, 'RFE':rfe_selector}
""""i jumped some line of code"""
dcc.Dropdown(
id='dropdown-5',
options=[{'label': v, 'value': v} for v in ['Logistic','RFE']],
value='Logistic',
style={'width': '150px', 'height': '35px', 'fontSize': '10pt'}
)
第二次尝试，您还需要一行内容以适应我所做的更改：
错误是：NameError: name 'cm' is not defined error（我假设它发生在回调中），并且由于您没有将函数的输出分配给变量而发生：
该功能是
# function to run selected model
def ClassTrainEval(model):
fitModel = model.fit(X_train, y_train)
y_pred = fitModel.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
return fitModel, y_pred, y_score, cm #Note that y_score is never defined so you need to remove this
，然后在回调中具有：
# dash callback
@app.callback(Output('conf_matrix', 'figure'), [Input('dropdown-5', 'value')])
def update_cm_matix(model):
for model in models: #<-------No loop needed
ClassTrainEval(model) #<-------Here You need to assigne the output
return {'data': [go.Heatmap(x=class_names, y=class_names, z=cm, showscale=True, colorscale='blues')],
'layout': dict(width=350, height=280, margin={'t': 10},
xaxis=dict(title='Predicted class', tickvals=[0, 1]),
yaxis=dict(title='True class', tickvals=[0, 1], autorange='reversed'))}
您可能要写：
@app.callback(Output('conf_matrix', 'figure'), [Input('dropdown-5', 'value')])
def update_cm_matix(v):
model = models[v]
fitModel, y_pred, cm = ClassTrainEval(model)
return {'data': [go.Heatmap(x=class_names, y=class_names, z=cm, showscale=True, colorscale='blues')],
'layout': dict(width=350, height=280, margin={'t': 10},
xaxis=dict(title='Predicted class', tickvals=[0, 1]),
yaxis=dict(title='True class', tickvals=[0, 1], autorange='reversed'))}