comparison ml_visualization_ex.py @ 22:bfdf732091c1 draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 02087ce2966cf8b4aac9197a41171e7f986c11d1-dirty"

21:0f47ad52fcaf

        if len(df1.columns) > 1:
            precision["micro"], recall["micro"], _ = precision_recall_curve(
                df1.values.ravel(), df2.values.ravel(), pos_label=pos_label)
            ap['micro'] = average_precision_score(
                df1.values, df2.values, average='micro', pos_label=pos_label or 1)

        data = []
        for key in precision.keys():
            trace = go.Scatter(
                x=recall[key],

                name='%s (area = %.2f)' % (key, roc_auc[key]) if key == 'micro'
                     else 'column %s (area = %.2f)' % (key, roc_auc[key])
            )
            data.append(trace)

        trace = go.Scatter(x=[0, 1], y=[0, 1], 
                           mode='lines', 
                           line=dict(color='black', dash='dash'),
                           showlegend=False)
        data.append(trace)

22:bfdf732091c1

        if len(df1.columns) > 1:
            precision["micro"], recall["micro"], _ = precision_recall_curve(
                df1.values.ravel(), df2.values.ravel(), pos_label=pos_label)
            ap['micro'] = average_precision_score(
                df1.values, df2.values, average='micro',
                pos_label=pos_label or 1)

        data = []
        for key in precision.keys():
            trace = go.Scatter(
                x=recall[key],

                name='%s (area = %.2f)' % (key, roc_auc[key]) if key == 'micro'
                     else 'column %s (area = %.2f)' % (key, roc_auc[key])
            )
            data.append(trace)

        trace = go.Scatter(x=[0, 1], y=[0, 1],
                           mode='lines', 
                           line=dict(color='black', dash='dash'),
                           showlegend=False)
        data.append(trace)