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 don't know if you still need a solution or not but this is best I have done to keep it in perfect format and still save it:

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))