How to annotate a heatmap on plotly as a sub-plot?

Question

I'm trying to create subplots on one image, including confusion matrix(heatmap), ROC and feature importance. I want to display an annotated heatmap. But there is an error about trace1 when I ran the code. Can someone help me to figure out what's wrong? The error massage is

ValueError: Invalid element(s) received for the 'data' property of Invalid elements include: ......

#threshold_plot - if True returns threshold plot for model
def coupon_use_prediction(algorithm,training_x,testing_x,
                             training_y,testing_y,cols,cf,threshold_plot) :

    #model
    algorithm.fit(training_x,training_y)
    predictions   = algorithm.predict(testing_x)
    probabilities = algorithm.predict_proba(testing_x)
    #coeffs
    if   cf == "coefficients" :
        coefficients  = pd.DataFrame(algorithm.coef_.ravel())
    elif cf == "features" :
        coefficients  = pd.DataFrame(algorithm.feature_importances_)

    column_df     = pd.DataFrame(cols)
    coef_sumry    = (pd.merge(coefficients,column_df,left_index= True,
                              right_index= True, how = "left"))
    coef_sumry.columns = ["coefficients","features"]
    coef_sumry    = coef_sumry.sort_values(by = "coefficients",ascending = False)

    print (algorithm)
    print ("\n Classification report : \n",classification_report(testing_y,predictions))
    print ("Accuracy   Score : ",accuracy_score(testing_y,predictions))
    #confusion matrix
    conf_matrix = confusion_matrix(testing_y,predictions)
    #roc_auc_score
    model_roc_auc = roc_auc_score(testing_y,predictions) 
    print ("Area under curve : ",model_roc_auc,"\n")
    fpr,tpr,thresholds = roc_curve(testing_y,probabilities[:,1])

    #plot confusion matrix(x = predicted, y = actual)

    trace1 = ff.create_annotated_heatmap(z = conf_matrix,
                                         x = ["Not use","Use"],
                                         y = ["Not use","Use"],
                                         showscale  = False,name = "matrix")
    #plot roc curve
    trace2 = go.Scatter(x = fpr,y = tpr,
                        name = "Roc : " + str(model_roc_auc),
                        line = dict(color = ('rgb(22, 96, 167)'),width = 2))
    trace3 = go.Scatter(x = [0,1],y=[0,1],
                        line = dict(color = ('rgb(205, 12, 24)'),width = 2,
                        dash = 'dot'))

    #plot coeffs
    trace4 = go.Bar(x = coef_sumry["features"],y = coef_sumry["coefficients"],
                    name = "coefficients",
                    marker = dict(color = coef_sumry["coefficients"],
                                  colorscale = "Picnic",
                                  line = dict(width = .6,color = "black")))

    #subplots
    fig = tls.make_subplots(rows=2, cols=2, specs=[[{}, {}], [{'colspan': 2}, None]],
                            subplot_titles=('Confusion Matrix',
                                            'Receiver operating characteristic',
                                            'Feature Importances'))

    fig.append_trace(trace1,1,1)
    fig.append_trace(trace2,1,2)
    fig.append_trace(trace3,1,2)
    fig.append_trace(trace4,2,1)

    fig['layout'].update(showlegend=False, title="Model performance" ,
                         autosize = False,height = 900,width = 800,
                         plot_bgcolor = 'rgba(240,240,240, 0.95)',
                         paper_bgcolor = 'rgba(240,240,240, 0.95)',
                         margin = dict(b = 195))
    fig["layout"]["xaxis2"].update(dict(title = "false positive rate"))
    fig["layout"]["yaxis2"].update(dict(title = "true positive rate"))
    fig["layout"]["xaxis3"].update(dict(showgrid = True,tickfont = dict(size = 10),
                                        tickangle = 90))
    py.iplot(fig)

    if threshold_plot == True : 
        visualizer = DiscriminationThreshold(algorithm)
        visualizer.fit(training_x,training_y)
        visualizer.poof()

logit  = LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)

coupon_use_prediction(logit,train_X,test_X,train_Y,test_Y,
                         cols,"coefficients",threshold_plot = True)

Answer 1

trace1 is the output of ff.create_annotated_heatmap , which returns an object of type plotly.graph_objs._figure.Figure . Here it is the line of code where it happens. You can't add a whole figure as a trace, only the data part of it, something like:

fig.add_trace(go.Heatmap(trace1.data[0]))

The annotations are stored in the layout so you will have to modify those out too somehow.