johngian

              precision    recall  f1-score   support

           0       0.99      1.00      0.99      4995
           1       0.93      0.73      0.82       243

    accuracy                           0.98      5238
   macro avg       0.96      0.86      0.91      5238
weighted avg       0.98      0.98      0.98      5238

[[4981   14]

johngian

              precision    recall  f1-score   support

           0       0.99      1.00      0.99      4995
           1       0.93      0.73      0.82       243

    accuracy                           0.98      5238
   macro avg       0.96      0.86      0.91      5238
weighted avg       0.98      0.98      0.98      5238

johngian

              precision    recall  f1-score   support

           0       0.99      1.00      0.99      4444
           1       0.96      0.77      0.85       271

    accuracy                           0.98      4715
   macro avg       0.97      0.88      0.92      4715
weighted avg       0.98      0.98      0.98      4715

[[4435    9]

johngian

import time

import dateparser

from scrapy.downloadermiddlewares.retry import RetryMiddleware
from scrapy.http import Request

# Defering requests might cause spider to be idle and close
# Workaround to keep spider open
class ImmortalSpiderMiddleware(object):

johngian

# Shortcut for running emacsclient in the background
e() { emacsclient -c "$@" & disown}

johngian

import argparse
import json

from bugbug import bugzilla
from datetime import date, timedelta


def get_untriaged(args):
    # Calculare daterange
    today = date.today()

johngian

(bugbug) nemo@ip-172-31-8-187:~/workspace/bugbug$ python run.py --goal component --classifier nn --train --classify
Using TensorFlow backend.
144 components
Core::Graphics: 3646
Firefox: 3448
Core::Layout: 3248
Core::JavaScript: 2779
Core::DOM: 2715
WebExtensions: 2507
Core::CSS Parsing and Computation: 2167

johngian

@@ -110,6 +110,11 @@ class Model():
         if self.calculate_classification_metrics:
             y_pred = self.clf.predict(X_test)
             print(f'No confidence threshold - {len(y_test)} classified')
+
+            # HACK
+            if y_test.ndim == 2:
+                y_test = np.argmax(y_test, axis=1)
+
             print(metrics.confusion_matrix(y_test, y_pred, labels=class_names))

johngian

SUMO playstore

Estimate how much effort we need to use IAM

What needs to be done

Replace signup/login/logout using custom auth0-sdk with Django and OIDC

Disable RemoteUserMiddleware and RemoteUserBackend

Add django rest framework endpoint for signup/get_profile

Replace auth0.js “class Auth()“ to use local django signup

Replace auth0.js “class Auth()“ to get user profile from rest framework

Adapt frontend for LDAP/Passwordless login/signup

johngian

from nameparser import HumanName

def get_access_information(obj):
    from mozillians.groups.models import GroupMembership

    memberships = GroupMembership.objects.filter(userprofile=obj, status=GroupMembership.MEMBER, group__is_access_group=True)
    output = {}
    for m in memberships:
        output[m.group.url] = None
    return output