scikit将输出metrics.classification_report学习为CSV /制表符分隔格式

Question

我在Scikit-Learn中进行多类文本分类。使用具有数百个标签的Multinomial Naive Bayes分类器训练数据集。这是Scikit Learn脚本的摘录，用于拟合MNB模型

from __future__ import print_function

# Read **`file.csv`** into a pandas DataFrame

import pandas as pd
path = 'data/file.csv'
merged = pd.read_csv(path, error_bad_lines=False, low_memory=False)

# define X and y using the original DataFrame
X = merged.text
y = merged.grid

# split X and y into training and testing sets;
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)

# import and instantiate CountVectorizer
from sklearn.feature_extraction.text import CountVectorizer
vect = CountVectorizer()

# create document-term matrices using CountVectorizer
X_train_dtm = vect.fit_transform(X_train)
X_test_dtm = vect.transform(X_test)

# import and instantiate MultinomialNB
from sklearn.naive_bayes import MultinomialNB
nb = MultinomialNB()

# fit a Multinomial Naive Bayes model
nb.fit(X_train_dtm, y_train)

# make class predictions
y_pred_class = nb.predict(X_test_dtm)

# generate classification report
from sklearn import metrics
print(metrics.classification_report(y_test, y_pred_class))

在命令行界面上，metrics.classification_report的简化输出如下所示：

             precision  recall   f1-score   support
     12       0.84      0.48      0.61      2843
     13       0.00      0.00      0.00        69
     15       1.00      0.19      0.32       232
     16       0.75      0.02      0.05       965
     33       1.00      0.04      0.07       155
      4       0.59      0.34      0.43      5600
     41       0.63      0.49      0.55      6218
     42       0.00      0.00      0.00       102
     49       0.00      0.00      0.00        11
      5       0.90      0.06      0.12      2010
     50       0.00      0.00      0.00         5
     51       0.96      0.07      0.13      1267
     58       1.00      0.01      0.02       180
     59       0.37      0.80      0.51      8127
      7       0.91      0.05      0.10       579
      8       0.50      0.56      0.53      7555      
    avg/total 0.59      0.48      0.45     35919

我想知道是否有办法将报告输出到带有常规列标题的标准csv文件中

当我将命令行输出发送到csv文件或尝试将屏幕输出复制/粘贴到电子表格 - Openoffice Calc或Excel时，它会将结果整合到一列中。看起来像这样：

帮助表示感谢。谢谢！

Answer 1

如果你想要个人得分，这应该可以胜任。

import pandas as pd

def classification_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('classification_report.csv', index = False)

report = classification_report(y_true, y_pred)
classification_report_csv(report)

Answer 2

从scikit-learn v0.20开始，将分类报告转换为pandas数据框的最简单方法是简单地将报告返回为dict：

report = classification_report(y_test, y_pred, output_dict=True)

然后构造一个数据框并转置它：

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

从这里开始，您可以随意使用标准的pandas方法来生成所需的输出格式（CSV，HTML，LaTeX等）。

另请参见https://scikit-learn.org/0.20/modules/generated/sklearn.metrics.classification_report.html

上的文档

Answer 3

我们可以从precision_recall_fscore_support函数中获取实际值，然后将它们放入数据框中。 下面的代码将给出相同的结果，但现在在pandas df :)中。

clf_rep = metrics.precision_recall_fscore_support(true, pred)
out_dict = {
             "precision" :clf_rep[0].round(2)
            ,"recall" : clf_rep[1].round(2)
            ,"f1-score" : clf_rep[2].round(2)
            ,"support" : clf_rep[3]
            }
out_df = pd.DataFrame(out_dict, index = nb.classes_)
avg_tot = (out_df.apply(lambda x: round(x.mean(), 2) if x.name!="support" else  round(x.sum(), 2)).to_frame().T)
avg_tot.index = ["avg/total"]
out_df = out_df.append(avg_tot)
print out_df

Answer 4

虽然之前的答案可能都有效但我发现它们有点冗长。以下内容将单个类结果以及摘要行存储在单个数据框中。对报告中的变化不是很敏感，但对我来说就是诀窍。

#init snippet and fake data
from io import StringIO
import re
import pandas as pd
from sklearn import metrics
true_label = [1,1,2,2,3,3]
pred_label = [1,2,2,3,3,1]

def report_to_df(report):
    report = re.sub(r" +", " ", report).replace("avg / total", "avg/total").replace("\n ", "\n")
    report_df = pd.read_csv(StringIO("Classes" + report), sep=' ', index_col=0)        
    return(report_df)

#txt report to df
report = metrics.classification_report(true_label, pred_label)
report_df = report_to_df(report)

#store, print, copy...
print (report_df)

给出了所需的输出：

Classes precision   recall  f1-score    support
1   0.5 0.5 0.5 2
2   0.5 0.5 0.5 2
3   0.5 0.5 0.5 2
avg/total   0.5 0.5 0.5 6

Answer 5

正如此处的其中一篇文章所述，precision_recall_fscore_support类似于classification_report。

然后使用python库pandas以列式格式轻松格式化数据就足够了，类似于classification_report。这是一个例子：

import numpy as np
import pandas as pd

from sklearn.metrics import classification_report
from  sklearn.metrics import precision_recall_fscore_support

np.random.seed(0)

y_true = np.array([0]*400 + [1]*600)
y_pred = np.random.randint(2, size=1000)

def pandas_classification_report(y_true, y_pred):
    metrics_summary = precision_recall_fscore_support(
            y_true=y_true, 
            y_pred=y_pred)

    avg = list(precision_recall_fscore_support(
            y_true=y_true, 
            y_pred=y_pred,
            average='weighted'))

    metrics_sum_index = ['precision', 'recall', 'f1-score', 'support']
    class_report_df = pd.DataFrame(
        list(metrics_summary),
        index=metrics_sum_index)

    support = class_report_df.loc['support']
    total = support.sum() 
    avg[-1] = total

    class_report_df['avg / total'] = avg

    return class_report_df.T

使用classification_report你会得到类似的内容：

print(classification_report(y_true=y_true, y_pred=y_pred, digits=6))

输出：

             precision    recall  f1-score   support

          0   0.379032  0.470000  0.419643       400
          1   0.579365  0.486667  0.528986       600

avg / total   0.499232  0.480000  0.485248      1000

然后使用我们的自定义功能pandas_classification_report：

df_class_report = pandas_classification_report(y_true=y_true, y_pred=y_pred)
print(df_class_report)

输出：

             precision    recall  f1-score  support
0             0.379032  0.470000  0.419643    400.0
1             0.579365  0.486667  0.528986    600.0
avg / total   0.499232  0.480000  0.485248   1000.0

然后将其保存为csv格式（请参阅here以获取其他分隔符格式，如sep =';'）：

df_class_report.to_csv('my_csv_file.csv',  sep=',')

我使用LibreOffice Calc打开my_csv_file.csv（尽管您可以使用任何表格/电子表格编辑器，如excel）：

Answer 6

另一个选择是计算基础数据并自行编写报告。

您将获得的所有统计数据

precision_recall_fscore_support

Answer 7

除了示例输入输出外， 还有另一个功能 metrics_report_to_df（） 。从Sklearn指标实施precision_recall_fscore_support应该：

# Generates classification metrics using precision_recall_fscore_support:
from sklearn import metrics
import pandas as pd
import numpy as np; from numpy import random

# Simulating true and predicted labels as test dataset: 
np.random.seed(10)
y_true = np.array([0]*300 + [1]*700)
y_pred = np.random.randint(2, size=1000)

# Here's the custom function returning classification report dataframe:
def metrics_report_to_df(ytrue, ypred):
    precision, recall, fscore, support = metrics.precision_recall_fscore_support(ytrue, ypred)
    classification_report = pd.concat(map(pd.DataFrame, [precision, recall, fscore, support]), axis=1)
    classification_report.columns = ["precision", "recall", "f1-score", "support"] # Add row w "avg/total"
    classification_report.loc['avg/Total', :] = metrics.precision_recall_fscore_support(ytrue, ypred, average='weighted')
    classification_report.loc['avg/Total', 'support'] = classification_report['support'].sum() 
    return(classification_report)

# Provide input as true_label and predicted label (from classifier)
classification_report = metrics_report_to_df(y_true, y_pred)

# Here's the output (metrics report transformed to dataframe )
In [1047]: classification_report
Out[1047]: 
           precision    recall  f1-score  support
0           0.300578  0.520000  0.380952    300.0
1           0.700624  0.481429  0.570703    700.0
avg/Total   0.580610  0.493000  0.513778   1000.0

Answer 8

我还发现一些答案有点冗长。这是我的三线解决方案，正如其他人所建议的那样使用avatar at bottom left > team name > Settings > Find integrations > Bitbucket Notebook Viewer > Add 。

precision_recall_fscore_support

Answer 9

这是我的2类（pos，neg）分类的代码

report = metrics.precision_recall_fscore_support(true_labels,predicted_labels,labels=classes)
        rowDicionary["precision_pos"] = report[0][0]
        rowDicionary["recall_pos"] = report[1][0]
        rowDicionary["f1-score_pos"] = report[2][0]
        rowDicionary["support_pos"] = report[3][0]
        rowDicionary["precision_neg"] = report[0][1]
        rowDicionary["recall_neg"] = report[1][1]
        rowDicionary["f1-score_neg"] = report[2][1]
        rowDicionary["support_neg"] = report[3][1]
        writer = csv.DictWriter(file, fieldnames=fieldnames)
        writer.writerow(rowDicionary)

Answer 10

def to_table(report):
    report = report.splitlines()
    res = []
    res.append(['']+report[0].split())
    for row in report[2:-2]:
       res.append(row.split())
    lr = report[-1].split()
    res.append([' '.join(lr[:3])]+lr[3:])
    return np.array(res)

返回一个numpy数组，该数组可以转换为pandas数据帧或只保存为csv文件。

Answer 11

我修改了@kindjacket的答案。 试试这个：

import collections
def classification_report_df(report):
    report_data = []
    lines = report.split('\n')
    del lines[-5]
    del lines[-1]
    del lines[1]
    for line in lines[1:]:
        row = collections.OrderedDict()
        row_data = line.split()
        row_data = list(filter(None, row_data))
        row['class'] = row_data[0] + " " + row_data[1]
        row['precision'] = float(row_data[2])
        row['recall'] = float(row_data[3])
        row['f1_score'] = float(row_data[4])
        row['support'] = int(row_data[5])
        report_data.append(row)
    df = pd.DataFrame.from_dict(report_data)
    df.set_index('class', inplace=True)
    return df

您可以使用熊猫将df导出到csv

Answer 12

我不知道您是否仍然需要解决方案，但这是我最好的做法，可以将其保持为完美格式并仍然保存：

def classifcation_report_processing(model_to_report):
    tmp = list()
    for row in model_to_report.split("\n"):
        parsed_row = [x for x in row.split("  ") if len(x) > 0]
        if len(parsed_row) > 0:
            tmp.append(parsed_row)

    # Store in dictionary
    measures = tmp[0]

    D_class_data = defaultdict(dict)
    for row in tmp[1:]:
        class_label = row[0]
        for j, m in enumerate(measures):
            D_class_data[class_label][m.strip()] = float(row[j + 1].strip())
    save_report = pd.DataFrame.from_dict(D_class_data).T
    path_to_save = os.getcwd() +'/Classification_report.xlsx'
    save_report.to_excel(path_to_save, index=True)
    return save_report.head(5)


saving_CL_report_naive_bayes = classifcation_report_processing(classification_report(y_val, prediction))

Answer 13

我遇到了同样的问题，将 metrics.classification_report 的字符串输出粘贴到google工作表或excel中，然后通过自定义5个空格将文本拆分为列。

Answer 14

只需import pandas as pd，并确保在计算output_dict时将False参数（默认为True设置为classification_report。这将产生一个classification_report dictionary，然后您可以将其传递给pandas DataFrame方法。您可能想要transpose生成的DataFrame以适合您想要的输出格式。然后可以根据需要将生成的DataFrame写入csv文件。

clsf_report = pd.DataFrame(classification_report(y_true = your_y_true, y_pred = your_y_preds5, output_dict=True)).transpose()
clsf_report.to_csv('Your Classification Report Name.csv', index= True)

我希望这会有所帮助。

Answer 15

最好将分类报告输出为 dict ：

sklearn.metrics.classification_report(y_true, y_pred, output_dict=True)

但这是我制作的用于将所有类（仅限类）结果转换为熊猫数据框的功能。

def report_to_csv(report):
    report = [x.split(' ') for x in report.split('\n')]
    header = ['Class Name']+[x for x in report[0] if x!='']
    values = []
    for row in report[1:-5]:
        row = [value for value in row if value!='']
        if row!=[]:
            values.append(row)
    df = pd.DataFrame(data = values, columns = header)
    return None

希望这对您来说很好。

Answer 16

我发现的最简单，最好的方法是：

classes = ['class 1','class 2','class 3']

report = classification_report(Y[test], Y_pred, target_names=classes)

report_path = "report.txt"

text_file = open(report_path, "w")
n = text_file.write(report)
text_file.close()

Answer 17

绝对值得使用：

sklearn.metrics.classification_report(y_true, y_pred, output_dict=True)

但是Yash Nag对该函数的稍微修改版本如下。该函数包括精度、宏精度和加权精度行以及类：

def classification_report_to_dataframe(str_representation_of_report):
    split_string = [x.split(' ') for x in str_representation_of_report.split('\n')]
    column_names = ['']+[x for x in split_string[0] if x!='']
    values = []
    for table_row in split_string[1:-1]:
        table_row = [value for value in table_row if value!='']
        if table_row!=[]:
            values.append(table_row)
    for i in values:
        for j in range(len(i)):
            if i[1] == 'avg':
                i[0:2] = [' '.join(i[0:2])]
            if len(i) == 3:
                i.insert(1,np.nan)
                i.insert(2, np.nan)
            else:
                pass
    report_to_df = pd.DataFrame(data=values, columns=column_names)
    return report_to_df

可以找到测试分类报告的输出here

Answer 18

我总是解决输出问题的方式就像我在之前的评论中提到的那样，我已将输出转换为DataFrame。这不仅非常容易发送到文件（see here），而且Pandas非常容易操作数据结构。我解决这个问题的另一种方法是使用CSV逐行编写输出，并使用writerow。

如果您设法将输出输入数据帧，那么它将是

dataframe_name_here.to_csv()

或者如果使用CSV，它将类似于他们在CSV链接中提供的示例。