In [5]:
from time import perf_counter

import matplotlib.pyplot as plt
from matplotlib.ticker import StrMethodFormatter
import numpy as np
import pandas as pd
from tabulate import tabulate
from wmfdata import charting, hive
from wmfdata.charting import comma_fmt, pct_fmt
from wmfdata.utils import df_to_remarkup, pct_str

We are using the data starting on 14 July, after all our significant data quality issues were fixed.

Collecting data

Let's do the grouping into sessions and users in Hive since it takes a loooong time in Pandas.

In [2]:
sessions ="""
    event.editing_session_id as edit_attempt_id,
    collect_list(event.editor_interface) as interface,
    -- User IDs are not unique across wikis
    if(event.user_id != 0, concat(wiki, "-", event.user_id), event.anonymous_user_token) as user_id,
    event.user_id = 0 as user_is_anonymous,
    event.user_editcount as user_edit_count,
    max(event.revision_id) as max_rev_id,
    sum(cast(event.action = "ready" as int)) >= 1 as ready,
    sum(cast(event.action = "saveSuccess" as int)) >= 1 as completed,
    min(dt) as start_dt,
    unix_timestamp(max(dt), "yyyy-MM-dd'T'HH:mm:ss'Z'") - unix_timestamp(min(dt), "yyyy-MM-dd'T'HH:mm:ss'Z'") as duration
from event.editattemptstep
    event.bucket in ('default-visual', 'default-source') and
    year = 2019 and (
        month = 7 and day >= 14 or
        month >= 8
group by
    start_dt=lambda df: pd.to_datetime(df["start_dt"]),
    wiki=lambda df: df["wiki"].astype("category"),
    platform=lambda df: df["platform"].astype("category"),
    bucket=lambda df: df["bucket"].astype("category")

# Ensure that including extra data in the group by hasn't broken any sessions into multiple rows
assert sessions["edit_attempt_id"].is_unique
AttributeError                            Traceback (most recent call last)
/srv/home/neilpquinn-wmf/pkg/wmfdata/wmfdata/ in run(cmds, fmt)
     24     try:
---> 25         hive_conn = impala_conn(host='an-coord1001.eqiad.wmnet', port=10000, auth_mechanism='PLAIN')
     26         hive_cursor = hive_conn.cursor()

~/venv/lib/python3.5/site-packages/impala/ in connect(host, port, database, timeout, use_ssl, ca_cert, auth_mechanism, user, password, kerberos_service_name, use_ldap, ldap_user, ldap_password, use_kerberos, protocol)
    146                           kerberos_service_name=kerberos_service_name,
--> 147                           auth_mechanism=auth_mechanism)
    148     return hs2.HiveServer2Connection(service, default_db=database)

~/venv/lib/python3.5/site-packages/impala/ in connect(host, port, timeout, use_ssl, ca_cert, user, password, kerberos_service_name, auth_mechanism)
    757                               auth_mechanism, user, password)
--> 758
    759     protocol = TBinaryProtocol(transport)

~/venv/lib/python3.5/site-packages/thrift_sasl/ in open(self)
     66   def open(self):
---> 67     if not self._trans.isOpen():

AttributeError: 'TSocket' object has no attribute 'isOpen'

During handling of the above exception, another exception occurred:

UnboundLocalError                         Traceback (most recent call last)
<ipython-input-2-97c7075565c5> in <module>()
     30     event.anonymous_user_token,
     31     event.bucket
---> 32 """).assign(
     33     start_dt=lambda df: pd.to_datetime(df["start_dt"]),
     34     wiki=lambda df: df["wiki"].astype("category"),

/srv/home/neilpquinn-wmf/pkg/wmfdata/wmfdata/ in run(cmds, fmt)
     37                 result = hive_cursor.fetchall()
     38     finally:
---> 39         hive_conn.close()
     41     return result

UnboundLocalError: local variable 'hive_conn' referenced before assignment

There seems to be a pretty big imbalance in the number bucketed users and attempts. That doesn't seem right, but it's unclear what we can actually do about it.

In [207]:
    users=("user_id", "nunique"),
    attempts=("edit_attempt_id", len)
attempts users
default-source 1,822,026 1,302,187
default-visual 1,704,482 1,214,917
In [210]:
start = perf_counter()

users = sessions.assign(
    bucket=lambda df: df["bucket"].astype(object) # Work around Pandas bug aggregating categorical fields
    attempts=("edit_attempt_id", "nunique"),
    ready_attempts=("ready", "sum"),
    completed_attempts=("completed", "sum"),
    is_anonymous=("user_is_anonymous", "first"),
    bucket=("bucket", "first"),
    avg_duration=("duration", "mean")
    ready_rate=lambda df: df["ready_attempts"] / df["attempts"],
    completion_rate=lambda df: df["completed_attempts"] / df["ready_attempts"]

elapsed_time = perf_counter() - start

print("Completed in {:.1f} min.".format(elapsed_time / 60))
Completed in 0.3 min.
In [211]:
# We have some infinite completion rate because the users have saves but no readies
users = users.replace([np.inf, -np.inf], np.nan)
In [183]:
sessions_sample.groupby("user_id").agg({"user_is_anonymous": "first"}).head()
00005eececf789cffb2a9b7216fa4115 True
0005c9f5898dbaf5380fa365aa702bff True
000a0a1dbee10136ba77b6401f9f71be True
0013f3a8d69c67390f8f143691e8387f True
0014ee6476a15e409d26f590704353aa True
In [184]:
sessions_sample.groupby("user_id").agg({"bucket": "first"}).head()
0 default-source
1 default-visual
2 default-visual
3 default-source
4 default-source

In progress: uncensoring data

14 day horizon from first recorded attempt to deal with censoring:

In [160]:
sessions_sample = sessions.sample(10000)
In [163]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 10000 entries, 2108210 to 29624
Data columns (total 13 columns):
edit_attempt_id      10000 non-null object
wiki                 10000 non-null category
platform             10000 non-null category
interface            10000 non-null object
user_id              10000 non-null object
bucket               10000 non-null category
user_is_anonymous    10000 non-null bool
user_edit_count      10000 non-null int64
max_rev_id           10000 non-null int64
ready                10000 non-null bool
completed            10000 non-null bool
start_dt             10000 non-null datetime64[ns, UTC]
duration             10000 non-null int64
dtypes: bool(3), category(3), datetime64[ns, UTC](1), int64(3), object(3)
memory usage: 684.6+ KB
In [171]:
data_end = pd.Timestamp("2019-08-27 00:00:00Z")

def uncensor(group):
    user_entrance = group["start_dt"].min()
    user_exit = user_entrance + pd.Timedelta(days=14)
    if user_exit > data_end:
        return None
        return group[group["start_dt"] < user_exit].reset_index(drop=True).drop("user_id", axis=1)
uncensored_sessions = sessions_sample.groupby("user_id").apply(uncensor)
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 6619 entries, (00005eececf789cffb2a9b7216fa4115, 0) to (zh_yuewiki-179809, 0)
Data columns (total 12 columns):
edit_attempt_id      6619 non-null object
wiki                 6619 non-null category
platform             6619 non-null category
interface            6619 non-null object
bucket               6619 non-null category
user_is_anonymous    6619 non-null bool
user_edit_count      6619 non-null int64
max_rev_id           6619 non-null int64
ready                6619 non-null bool
completed            6619 non-null bool
start_dt             6619 non-null datetime64[ns, UTC]
duration             6619 non-null int64
dtypes: bool(3), category(3), datetime64[ns, UTC](1), int64(3), object(2)
memory usage: 446.9+ KB
In [1]:
NameError                                 Traceback (most recent call last)
<ipython-input-1-fd02fa451f27> in <module>
----> 1 uncensored_sessions.head()

NameError: name 'uncensored_sessions' is not defined

Average attempts and completion per user

In [213]:
users["init_completion_rate"] = users["completed_attempts"] / users["attempts"]
In [219]:
    avg_attempts=("attempts", "mean"),
    avg_completion_rate=("completion_rate", "mean"),
    avg_init_completion_rate=("init_completion_rate", "mean"),
    avg_completed_edits=("completed_attempts", "mean"),
    "avg_attempts": "{:.3f}",
    "avg_completed_edits": "{:.3f}",
    "avg_completion_rate": "{:.2%}",
    "avg_init_completion_rate": "{:.2%}"
avg_attempts avg_completed_edits avg_completion_rate avg_init_completion_rate
default-source 1.399 0.040 0.88% 0.87%
default-visual 1.403 0.037 1.23% 0.79%

Time to save

In [215]:
default-source    131.509963
default-visual    165.769435
Name: duration, dtype: float64

Edit quality

In [ ]:

Edit switching

In [ ]: