Question

有人知道有没有输出分类报告作为文本文件或CSV文件？这行＆＃34; print（metrics.classification_report（y_test，y_pred））＆＃34;在python中给我分类报告。我希望以csv格式提供此报告。我试图复制和粘贴，但列将集中在一起！任何帮助表示赞赏！

Answer 1

这是可能的，但您需要创建一个函数。

让我们说我想将报告写入我的report.csv文件（这需要在运行代码之前创建）

完整示例：

from sklearn.metrics import classification_report
import csv
import pandas as pd

y_true = [0, 1, 2, 2, 2]
y_pred = [0, 0, 2, 2, 1]
target_names = ['class 0', 'class 1', 'class 2']

def classifaction_report_csv(report):
    report_data = []
    lines = report.split('\n')
    for line in lines[2:-3]:
        row = {}
        row_data = line.split('      ')
        row['class'] = row_data[0]
        row['precision'] = float(row_data[1])
        row['recall'] = float(row_data[2])
        row['f1_score'] = float(row_data[3])
        row['support'] = float(row_data[4])
        report_data.append(row)
    dataframe = pd.DataFrame.from_dict(report_data)
    dataframe.to_csv('report.csv', index = False)

#call the classification_report first and then our new function

report = classification_report(y_true, y_pred, target_names=target_names)
classifaction_report_csv(report)

希望这会有所帮助。打开csv文件，然后看：

截图：

Answer 2

除了血清的回答，我发现以下方法很有用 - 无需使用precision_recall_fscore_support解析分类报告字符串：

from sklearn.metrics import precision_recall_fscore_support
from sklearn.utils.multiclass import unique_labels


def classification_report_to_csv_pandas_way(ground_truth,
                                            predictions,
                                            full_path="test_pandas.csv"):
    """
    Saves the classification report to csv using the pandas module.
    :param ground_truth: list: the true labels
    :param predictions: list: the predicted labels
    :param full_path: string: the path to the file.csv where results will be saved
    :return: None
    """
    import pandas as pd

    # get unique labels / classes
    # - assuming all labels are in the sample at least once
    labels = unique_labels(ground_truth, predictions)

    # get results
    precision, recall, f_score, support = precision_recall_fscore_support(ground_truth,
                                                                          predictions,
                                                                          labels=labels,
                                                                          average=None)
    # a pandas way:
    results_pd = pd.DataFrame({"class": labels,
                               "precision": precision,
                               "recall": recall,
                               "f_score": f_score,
                               "support": support
                               })

    results_pd.to_csv(full_path, index=False)


def classification_report_to_csv(ground_truth,
                                 predictions,
                                 full_path="test_simple.csv"):
    """
    Saves the classification report to csv.
    :param ground_truth: list: the true labels
    :param predictions: list: the predicted labels
    :param full_path: string: the path to the file.csv where results will be saved
    :return: None
    """
    # get unique labels / classes
    # - assuming all labels are in the sample at least once
    labels = unique_labels(ground_truth, predictions)

    # get results
    precision, recall, f_score, support = precision_recall_fscore_support(ground_truth,
                                                                          predictions,
                                                                          labels=labels,
                                                                          average=None)

    # or a non-pandas way:
    with open(full_path) as fp:
        for line in zip(labels, precision, recall, f_score, support):
            fp.write(",".join(line))

if __name__ == '__main__':
    # dummy data
    ground_truth = [1, 1, 4, 1, 3, 1, 4]
    prediction = [1, 1, 3, 4, 3, 1, 1]

    # test
    classification_report_to_csv(ground_truth, prediction)
    classification_report_to_csv_pandas_way(ground_truth, prediction)

两种情况下的输出：

class,f_score,precision,recall,support
1,0.75,0.75,0.75,4
3,0.666666666667,0.5,1.0,1
4,0.0,0.0,0.0,2

Answer 3

该函数具有一个可以解决此确切问题的参数。

import pandas as pd
from sklearn.metrics import classification_report

report_dict = classification_report(y_true, y_pred, output_dict=True)
pd.DataFrame(report_dict)

将字典转换为数据帧后，可以将其写入csv，轻松绘制，对其进行操作或执行其他操作。

Answer 4

我发现Rabeez Riaz解决方案要容易得多。我想补充一点，您可以使用report_dict作为参数转置到数据框。

df = pandas.DataFrame（report_dict）.transpose（）

从这里开始，您可以自由使用标准的pandas方法来生成所需的输出格式（CSV，HTML，LaTeX等）。您想要的输出格式（CSV，HTML，LaTeX等）。

源链接：https://intellipaat.com/community/15701/scikit-learn-output-metrics-classificationreport-into-csv-tab-delimited-format

Answer 5

要有一个类似于分类报告输出的csv，你可以这样使用：

    labels = list(set(targcol))
    report_dict = classification_report(targcol, predcol, output_dict=True)
    repdf = pd.DataFrame(report_dict).round(2).transpose()
    repdf.insert(loc=0, column='class', value=labels + ["accuracy", "macro avg", "weighted avg"])
    repdf.to_csv("results.csv", index=False)

Sklearn中的分类报告

5 个答案: