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 extr

Answer 1

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