LouisdeBruijn

def prior_probabilities(classes):
    """Compute prior probabilities for all class labels."""
    dic = {}
    freq = FreqDist(classes)
    for k, v in freq.items():
        prior = freq[k] / len(classes)
        dic[k] = round(prior, 3)

    return dic

LouisdeBruijn

def shuffle_split(documents, labels, split):
    """Shuffle data to ensure random class distribution in train/test split."""
    tuples = [[doc, label] for doc, label in zip(documents, labels)]
    random.shuffle(tuples)
    X, Y = zip(*tuples)

    split_point = int(split*len(X))

    Xtrain = X[:split_point]
    Ytrain = Y[:split_point]

LouisdeBruijn

def tokenize_pos(tokens):
    """Add POS-tags to each token."""
    return [token+"_POS-"+tag for token, tag in nltk.pos_tag(tokens)]


class LengthFeatures(BaseEstimator, TransformerMixin):
    """Feature engineer the length of each feature."""
    def fit(self, x, y=None):
        return self

LouisdeBruijn

def tabular_results(Xtest, Ytest, Yguess, prior_prob, posterior_prob):
    """Return table with classification results."""
    maximum_array = posterior_prob.max(axis=1)

    df = pd.DataFrame(columns=['sentence', 'true_label', 'prior_probabilities', 'predicted_label', 'posterior_probabilities'])
    df['sentence'] = Xtest
    df['true_label'] = Ytest
    for index, row in df.iterrows():
        row['prior_probabilities'] = prior_prob[row['true_label']]
    df['predicted_label'] = Yguess

LouisdeBruijn

def baseline_classifier(Xtest, Ytest):
    """Baseline by randomly assigning a label to each document."""
    label_list = [label for label in set(Ytest)]  # random.choice cannot index a set, so needs to be a list
    baseline = [random.choice(label_list) for sent in Xtest]

    return baseline

LouisdeBruijn

def main():

    X, Y = read_corpus(args.input, args.binary)

    Xtrain, Xtest, Ytrain, Ytest = shuffle_split(X, Y, 0.8)

    prior_prob = prior_probabilities(Y)
    info('Prior probabilities per class: {0}'.format(prior_prob))

    classifier = feature_union(count=True, tfidf=True, textstats=True)

LouisdeBruijn

@require_http_methods(["GET"])
@login_required(login_url='/login', redirect_field_name='')
def index(request):

    #  Verbose user permission authentication!
    groups = [group.name for group in request.user.groups.all()]
    if not any(x in groups for x in ['PERMISSION_GR_1', 'PERMISSION_GR_2']):
        messages.add_message(request, messages.WARNING, '{0} does not have permission.'.format(request.user))
        return redirect('home')
    

LouisdeBruijn

from functools import wraps
from django.contrib import messages
from django.contrib.auth.views import redirect_to_login
from django.contrib.auth import REDIRECT_FIELD_NAME

from django.shortcuts import resolve_url
from urllib.parse import urlparse
from [projectname].settings import config
import [projectname].settings as settings
import requests

LouisdeBruijn

def superuser_required(view_func=None, redirect_field_name=REDIRECT_FIELD_NAME, login_url='/login', message=default_message):
    """
    Decorator for views that checks that the user is logged in and is a
    superuser, displaying message if provided.
    """
    actual_decorator = user_passes_test(
        lambda u: u.is_active and u.is_superuser and u.is_authenticated,
        login_url=login_url,
        redirect_field_name=redirect_field_name,
        message=message

LouisdeBruijn

from decorators import staff_required, superser_required

@login_required(login_url='/login', redirect_field_name='')
@staff_required(login_url='/', redirect_field_name='', message='You are not authorised to view this page.')
def staff_required_page(request):
    // Do something meaningful


@require_http_methods(["POST"])
@login_required(login_url='/login', redirect_field_name='')