怎么把几个classification report放在列表里
时间: 2024-02-29 16:51:33 浏览: 12
将几个classification report放在列表里,您可以直接使用Python语法来创建一个列表,将每个classification report作为一个元素添加到列表中。例如:
```python
# 假设您有3个classification_report,分别为classif_report_1、classif_report_2和classif_report_3
classif_report_1 = {'precision': 0.8, 'recall': 0.8, 'f1-score': 0.8, 'support': 100}
classif_report_2 = {'precision': 0.9, 'recall': 0.9, 'f1-score': 0.9, 'support': 100}
classif_report_3 = {'precision': 0.7, 'recall': 0.7, 'f1-score': 0.7, 'support': 100}
# 将这3个classification_report放在一个列表中
results = [classif_report_1, classif_report_2, classif_report_3]
```
这样,您就可以通过索引来访问列表中的每个classification report,例如`results[0]`就是classif_report_1。
相关问题
怎么求10个classification report的平均classification report
你可以使用scikit-learn库的`classification_report`函数和numpy库的`mean`函数来求10个分类报告的平均分类报告。
假设你已经得到了10个分类报告,每个分类报告都是一个字符串,可以将它们保存在一个列表中,例如:
``` python
reports = [report1, report2, report3, ..., report10]
```
然后,可以使用`classification_report`函数和numpy库的`mean`函数来求10个分类报告的平均分类报告:
``` python
from sklearn.metrics import classification_report
import numpy as np
# 将10个分类报告转换为字典类型
report_dict = {}
for i, report in enumerate(reports):
report_dict[f'report_{i+1}'] = classification_report_to_dict(report)
# 将分类报告的字典类型转换为DataFrame类型
report_df = pd.DataFrame.from_dict(report_dict, orient='index')
# 计算平均分类报告
avg_report_dict = report_df.mean().to_dict()
# 将平均分类报告转换为字符串类型
avg_report_str = classification_report_from_dict(avg_report_dict)
```
其中,`classification_report_to_dict`和`classification_report_from_dict`是两个自定义函数,用于将分类报告字符串转换为字典类型,和将字典类型转换为分类报告字符串。这两个函数的实现可以参考下面的代码:
``` python
def classification_report_to_dict(report):
lines = report.split('\n')
lines = [line.strip() for line in lines if line.strip()]
classes = []
class_dict = {}
for line in lines[1:]:
t = line.split()
if len(t) == 1:
class_name = t[0]
classes.append(class_name)
class_dict[class_name] = {}
else:
class_dict[class_name]['precision'] = float(t[0])
class_dict[class_name]['recall'] = float(t[1])
class_dict[class_name]['f1-score'] = float(t[2])
class_dict[class_name]['support'] = int(t[3])
macro_avg = lines[-3].split()
micro_avg = lines[-2].split()
class_dict['macro avg'] = {'precision': float(macro_avg[1]), 'recall': float(macro_avg[2]), 'f1-score': float(macro_avg[3]), 'support': int(macro_avg[4])}
class_dict['micro avg'] = {'precision': float(micro_avg[1]), 'recall': float(micro_avg[2]), 'f1-score': float(micro_avg[3]), 'support': int(micro_avg[4])}
return class_dict
def classification_report_from_dict(report_dict):
classes = list(report_dict.keys())
classes.remove('macro avg')
classes.remove('micro avg')
lines = [' precision recall f1-score support\n\n']
for class_name in classes:
line = f"{class_name.ljust(15)}{report_dict[class_name]['precision']:.2f} {report_dict[class_name]['recall']:.2f} {report_dict[class_name]['f1-score']:.2f} {report_dict[class_name]['support']:5d}\n"
lines.append(line)
lines.append('\n')
macro_avg = report_dict['macro avg']
line = f"{'macro avg'.ljust(15)}{macro_avg['precision']:.2f} {macro_avg['recall']:.2f} {macro_avg['f1-score']:.2f} {macro_avg['support']:5d}\n"
lines.append(line)
micro_avg = report_dict['micro avg']
line = f"{'micro avg'.ljust(15)}{micro_avg['precision']:.2f} {micro_avg['recall']:.2f} {micro_avg['f1-score']:.2f} {micro_avg['support']:5d}\n"
lines.append(line)
report_str = ''.join(lines)
return report_str
```
这样,就可以得到10个分类报告的平均分类报告了,保存在`avg_report_str`变量中。
怎么把classification report如何全部存为excel表格
您可以使用Python中的pandas库将classification report转换为数据框,并将其转换为Excel表格。请按照以下步骤操作:
1. 导入所需的库:
```
import pandas as pd
from sklearn.metrics import classification_report
```
2. 生成classification report:
```
target_names = ['class 0', 'class 1', 'class 2']
y_true = [0, 1, 2, 2, 0, 1]
y_pred = [0, 0, 2, 2, 0, 2]
report = classification_report(y_true, y_pred, target_names=target_names, output_dict=True)
```
在这个例子中,我们生成了一个classification report,并将结果存储在`report`中。`target_names`是类别标签,`y_true`和`y_pred`是真实标签和预测标签。
3. 将classification report转换为数据框:
```
df = pd.DataFrame(report).transpose()
```
将生成一个名为`df`的数据框,其中包含classification report的所有重要统计信息。
4. 将数据框保存为Excel表格:
```
writer = pd.ExcelWriter('classification_report.xlsx')
df.to_excel(writer, index=True)
writer.save()
```
以上代码将生成一个名为`classification_report.xlsx`的Excel表格,其中包含classification report的所有重要统计信息。
希望这可以帮助您将classification report保存为Excel表格。