def most_informative_feature_for_binary_classification(vectorizer, classifier, n=100):


 """Identify most important features if given a vectorizer and binary classifier. Set n to the number
    of weighted features you would like to show.
    """
    def read_counter():
        return loads(open("counter.json", "r").read()) + 1 if path.exists("counter.json") else 0

    def write_counter():
        with open("counter.json", "w") as f:
            f.write(dumps(counter))

    counter = 1

    counter = read_counter()
    atexit.register(write_counter)
    if counter >= 7:
        counter = 0

    # additional stopwords to be remove
    # Open a file and read it into memory
    file = open('..stopwords.txt')
    additional_stopwords = file.read()
    additional_stopwords = additional_stopwords.split()

    class_labels = classifier.classes_
    feature_names = vectorizer.get_feature_names()
    feature_names = [word for word in feature_names if word not in additional_stopwords]

    topn_class1 = sorted(zip(classifier.coef_[0], feature_names))[:n]
    topn_class2 = sorted(zip(classifier.coef_[0], feature_names))[-n:]

    # class_labels = category
    # coef = co-effecient
    # feat = most informative feature
    if counter == 1:
        for coef, feat in topn_class1:
            print(class_labels[0], coef, feat)
            # print(class_labels)
            # -> output: [2 3 4 5 6 7 8] index of this 0 1 2 3 4 5 6

        print()

        for coef, feat in reversed(topn_class2):
            print(class_labels[1], coef, feat)

    elif counter == 2:
        for coef, feat in topn_class1:
            print(class_labels[1], coef, feat)

        print()

        for coef, feat in reversed(topn_class2):
            print(class_labels[2], coef, feat)

    elif counter == 3:
        for coef, feat in topn_class1:
            print(class_labels[2], coef, feat)

        print()

        for coef, feat in reversed(topn_class2):
            print(class_labels[3], coef, feat)

    elif counter == 4:
        for coef, feat in topn_class1:
            print(class_labels[3], coef, feat)

        print()

        for coef, feat in reversed(topn_class2):
            print(class_labels[4], coef, feat)

    elif counter == 5:
        for coef, feat in topn_class1:
            print(class_labels[4], coef, feat)

        print()

        for coef, feat in reversed(topn_class2):
            print(class_labels[5], coef, feat)

    elif counter == 6:
        for coef, feat in topn_class1:
            print(class_labels[5], coef, feat)

        print()

        for coef, feat in reversed(topn_class2):
            print(class_labels[6], coef, feat)

    else:
        print("=== PLEASE RUN PROGRAM AGAIN TO VIEW THE CO-EFFICIENT FOR THE CHOSEN MODEL ===")

2 -8.322094697329087 aaa
2 -8.322094697329087 aaa cm
2 -8.322094697329087 aaa cm underwent
2 -8.322094697329087 aaa free
2 -8.322094697329087 aaa free ivc

3 -8.010764835561018 assymetry imp giddiness
3 -8.144858449457846 admitted feb year
3 -8.164330364141858 agreeable dre brown
3 -8.172447581146958 aerobic anaerobic labeled
3 -8.180391164585233 actually body

3 -8.322580751462969 aaa
3 -8.322580751462969 aaa cm
3 -8.322580751462969 aaa cm underwent
3 -8.322580751462969 aaa free
3 -8.322580751462969 aaa free ivc

4 -8.0112508896949 assymetry moving
4 -8.145344503591728 admitted feb year
4 -8.16481641827574 agreeable dre brown
4 -8.17293363528084 aerobic anaerobic labeled
4 -8.180877218719115 actually body

4 -8.322337753927105 aaa
4 -8.322337753927105 aaa cm
4 -8.322337753927105 aaa cm underwent
4 -8.322337753927105 aaa free
4 -8.322337753927105 aaa free ivc

5 -8.011007892159036 assymetry imp
5 -8.145101506055864 admitted frequent falls
5 -8.164573420739876 agreeable early review
5 -8.172690637744976 af anticoagulation
5 -8.18063422118325 actually body