我正在寻找准确率/精确率/召回率等指标…所以我使用了这段代码，效果很好，但实际上我想将输出形式改为表格形式。我的输出：
Column 2 acc: 1.0 Column 2 p: 1.0 Column 2 r: 1.0 Column 1 acc: 1.0 Column 1 p: 1.0 Column 1 r: 1.0 Column 3 acc: 1.0 Column 3 p: 1.0 Column 3 r: 1.0我想要的输出：
+----------+-----------+-------+---------+| Feature | Precision |Recall | Accuracy|+----------+-----------+-------+---------+| 1 | 1.0 | 1.0 | 1.0 || 2 | 1.0 | 1.0 | 1.0 || 3 | 1.0 | 1.0 | 1.0 |+----------+----------+--------+---------+我的代码：
def calc_acc(original, predect1): common_columns = list(set(original.columns).intersection(predect1.columns)) avg_a = 0.0 avg_p = 0.0 avg_r = 0.0 for c in common_columns: c_acc = accuracy_score(original[c], predect1[c]) p = precision_score(original[c], predect1[c], average='macro', labels=np.unique(predect1[c])) r = recall_score(original[c], predect1[c], average='macro', labels=np.unique(predect1[c])) print(f'Column {c} acc: {c_acc}') print(f'Column {c} p: {p}') print(f'Column {c} r: {r}') avg_a += c_acc/len(common_columns) avg_p += p/len(common_columns) avg_r += r/len(common_columns)注意：c 是列名
回答：
使用以下代码绘制 PrettyTable：
from prettytable import PrettyTablept = PrettyTable()pt.field_names = ['Feature','Precision','Recall','Accuracy']pt.add_row([c,p,r,c_acc])最终你想要的代码和输出如下：
from sklearn.metrics import accuracy_scorefrom sklearn.metrics import precision_score, recall_scorefrom prettytable import PrettyTable def calc_acc(original, predect1): common_columns = list(set(original.columns).intersection(predect1.columns)) avg_a = 0.0 avg_p = 0.0 avg_r = 0.0 pt = PrettyTable() pt.field_names = ['Feature','Precision','Recall','Accuracy'] for c in common_columns: c_acc = accuracy_score(original[c], predect1[c]) p = precision_score(original[c], predect1[c], average='macro', labels=np.unique(predect1[c])) r = recall_score(original[c], predect1[c], average='macro', labels=np.unique(predect1[c])) pt.add_row([c,p,r,c_acc]) avg_a += c_acc/len(common_columns) avg_p += p/len(common_columns) avg_r += r/len(common_columns) print(pt) pre = [[1, 1, 3], [2, 3, 4]]pre = pd.DataFrame(pre, columns= ['1', '2', '3'])calc_acc(pre, pre)输出：
