scikit learn output metrics.classification_report into CSV/tab-delimited format

Question

I'm doing a multiclass text classification in Scikit-Learn. The dataset is being trained using the Multinomial Naive Bayes classifier having hundreds of labels. Here's an extract from the Scikit Learn script for fitting the MNB model

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))

And a simplified output of the metrics.classification_report on command line screen looks like this:

             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

I was wondering if there was any way to get the report output into a standard csv file with regular column headers

When I send the command line output into a csv file or try to copy/paste the screen output into a spreadsheet - Openoffice Calc or Excel, It lumps the results in one column. Looking like this:

Answer 1

As of scikit-learn v0.20, the easiest way to convert a classification report to a pandas Dataframe is by simply having the report returned as a dict:

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

and then construct a Dataframe and transpose it:

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

From here on, you are free to use the standard pandas methods to generate your desired output formats (CSV, HTML, LaTeX, ...).

See the documentation.

Answer 2

If you want the individual scores this should do the job just fine.

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 3

We can get the actual values from the precision_recall_fscore_support function and then put them into data frames. the below code will give the same result, but now in a pandas dataframe:

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

Just import pandas as pd and make sure that you set the output_dict parameter which by default is False to True when computing the classification_report. This will result in an classification_report dictionary which you can then pass to a pandas DataFrame method. You may want to transpose the resulting DataFrame to fit the fit the output format that you want. The resulting DataFrame may then be written to a csv file as you wish.

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)

Answer 5

While the previous answers are probably all working I found them a bit verbose. The following stores the individual class results as well as the summary line in a single dataframe. Not very sensitive to changes in the report but did the trick for me.

#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)

Which gives the desired output:

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 6

It's obviously a better idea to just output the classification report as dict:

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

But here's a function I made to convert all classes (only classes) results to a pandas dataframe.

def report_to_df(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 df

Answer 7

As mentioned in one of the posts in here, precision_recall_fscore_support is analogous to classification_report.

Then it suffices to use pandas to easily format the data in a columnar format, similar to what classification_report does. Here is an example:

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

With classification_report You'll get something like:

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

Output:

             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

Then with our custom funtion pandas_classification_report:

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

Output:

             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

Then just save it to csv format (refer to here for other separator formating like sep=';'):

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

I open my_csv_file.csv with LibreOffice Calc (although you could use any tabular/spreadsheet editor like excel):

Answer 8

Another option is to calculate the underlying data and compose the report on your own. All the statistics you will get by

precision_recall_fscore_support

Answer 9

I also found some of the answers a bit verbose. Here is my three line solution, using precision_recall_fscore_support as others have suggested.

import pandas as pd
from sklearn.metrics import precision_recall_fscore_support

report = pd.DataFrame(list(precision_recall_fscore_support(y_true, y_pred)),
            index=['Precision', 'Recall', 'F1-score', 'Support']).T

# Now add the 'Avg/Total' row
report.loc['Avg/Total', :] = precision_recall_fscore_support(y_true, y_test,
    average='weighted')
report.loc['Avg/Total', 'Support'] = report['Support'].sum()

Answer 10

Along with example input-output, here's the other function metrics_report_to_df(). Implementing precision_recall_fscore_support from Sklearn metrics should do:

# 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 11

The simplest and best way I found is:

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 12

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)

returns a numpy array which can be turned to pandas dataframe or just be saved as csv file.

Answer 13

This is my code for 2 classes(pos,neg) classification

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 14

I have modified @kindjacket's answer. Try this:

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

You can just export that df to csv using pandas

Answer 15

I had the same problem what i did was, paste the string output of metrics.classification_report into google sheets or excel and split the text into columns by custom 5 whitespaces.

Answer 16

I have written below code to extract the classification report and save it to an excel file:

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)

To call the function below line can be used anywhere in the program:

saving_CL_report_naive_bayes = classifcation_report_processing(classification_report(y_val, prediction))

The output looks like below:

Answer 17

Definitely worth using:

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

But a slightly revised version of the function by Yash Nag is as follows. The function includes the accuracy, macro accuracy and weighted accuracy rows along with the classes:

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

The output for a test classification report may be found here

Answer 18

The way I have always solved output problems is like what I've mentioned in my previous comment, I've converted my output to a DataFrame. Not only is it incredibly easy to send to files (see here), but Pandas is really easy to manipulate the data structure. The other way I have solved this is writing the output line-by-line using CSV and specifically using writerow.

If you manage to get the output into a dataframe it would be

dataframe_name_here.to_csv()

or if using CSV it would be something like the example they provide in the CSV link.