在 Python 中以表格形式打印数据

Question

我正在寻找 accuarcy/PRecision/Reacll 等等... 所以我使用了这段代码，它对我来说效果很好，但实际上我想将输出形式更改为表格 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)

NB: c 是列

Answer 1

使用此代码绘制PrettyTable:

from prettytable import PrettyTable
pt = PrettyTable()
pt.field_names = ['Feature','Precision','Recall','Accuracy']
pt.add_row([c,p,r,c_acc])

最后你想要的代码和输出如下：

from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score, recall_score
from 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：

Answer 2

您可以使用 pandas 数据框。

import pandas as pd

df = pd.DataFrame({
    "Feature": col,
    "Precision": prec,
    "Recall": rec,
    "Accuracy": acc
})

print(df)

注意: col, prec, rec, acc --> 列出数据类型。 从你的 for 循环创建这些列表，然后只将它们转换成数据帧，仅此而已。

在 Python 中以表格形式打印数据

Printing data in Tabular Form in Python

python

machine-learning

tabular-form

scikit-learn

prettytable