# Creating A Fake Me From My Emails

Some twitter robot or another got me thinking about Markov Chains the other day (in the text generator sense), and it occurred to me that it shouldn't be too hard to create one which (poorly) simulates me.

Markov chains are basically a set of states and probabilities of moving from one state to another. If you build one out of a body of text, you can map the likelihood of a given set of words following another set of words. The upshot of this is that if you start from a random set of words and follow the map (choosing your next state at each node randomly in proportion to its likelihood from the intial text), you can end up with something that sounds (sort of) like it came from the original body text. It's a popular way to create twitter bots. Markov chains have other, much more practical uses, but I'm not concerned about them today.

I've got 10 years of my emails already sitting in a .csv file; step one was loading them up and discarding all the ones I didn't send. After that, most of the work was cleaning up the data - most of the stuff in the bodies of my emails is actually pretty useless for this purpose. I had to remove all the quotes parts of other people's messages, all the HTML messages (even when cleaned up, they polluted the Markov chain too much), 'Forwarded Message' sections, URLs, and my own signatures.

After passing all the emails through the removeJunk function below, I globbed all the texts together into one giant string and fed it into this Markov generator from Amanda Pickering. With that done, I could just call generate_words() over and over to see what kind of nonsense fake me would spew out.

So here's my final code for taking my emails and creating a fake me, Black Mirror-style:

import pandas as pd
import numpy as np
import re
from markovgenerator import MarkovGenerator

# Read in our email data file

# Only use mail I sent
emails = df.query('FromEmail == "[my email]"').copy()

# Blank out any missing body text
emails.Body.fillna(' ', inplace = True)

# Regexes for truncating messages
# If any of these are found, the rest of the message is stuff I didn't write
quoteHeaderRegex = re.compile('On.*?wrote:', re.DOTALL)
originalMessageRegex = re.compile('^\s?\-.*?(Original|Forwarded) Message.*?\-\s?$', re.MULTILINE | re.IGNORECASE) htmlRegex = re.compile('^\<html\>', re.MULTILINE) googleReaderRegex = re.compile('^E\.Z\. Hart - Google Reader', re.MULTILINE) # Other things in emails that aren't relevant # If these are found, replace them with empty string fromAndToRegex = re.compile('^(from:|to:|sent:).*?$', re.MULTILINE | re.IGNORECASE)
sigRegex = re.compile('^\-[\-\s]{1,4}E\.Z\.', re.MULTILINE)
dividerRegex = re.compile('\-{3,}')
urlRegex = re.compile('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*,]|(?:%[0-9a-fA-F][0-9a-fA-F]))+') def markovText(row): text = row['Body'] if(row['Format'] == 'Html'): return '' return removeJunk(text) def removeJunk(text): text = stripAfter(text, quoteHeaderRegex) text = stripAfter(text, originalMessageRegex) text = stripAfter(text, googleReaderRegex) text = stripAfter(text, htmlRegex) text = re.sub(fromAndToRegex, '', text) text = re.sub(sigRegex, '', text) text = re.sub(dividerRegex, '', text) text = re.sub(urlRegex, '', text) return text def stripAfter(text, regex): target = regex.search(text) if(target): return text[:target.start()] return text # Run all the emails through the cleanup function emails['Markov'] = emails.apply(markovText, axis=1) # Concatenate all the emails into one giant input string input = emails['Markov'][:].str.cat() markov_gen = MarkovGenerator(input, 200, 3) markov_gen.generate_words()  And here are a few of my favorite phrases from the results: "Use cheap rum. Cheap rum is going to get the crab wontons- otherwise I can't guarantee your safety:)" "And more important than anything else, has been what has kept me employed and made me successful. Anyway, I'm glad you took your flashlight." "We will begin working on the changes Tory has asked for, and I'll eventually start going full troll without her around:) Okay." "You're receiving this email because you're rewriting 10,000 lines of code that solved the first two weeks of August while in between leases." "At this point I'll need jumping and, ideally, that signs be installed? How would I go about making that request? Again, we completely understand if you don't have any SharePoint development experience, just experience as a user in each role just to make sure it was knitting and not crocheting- I don't know football that well, but any of them, they might actually turn into assets , though." Have fun creating your own email doppelgängers, but remember - cheap rum is going to get the crab wontons. I can't guarantee your safety. Comment ### E.Z. Hart # How Long Would It Take To Read All My Email? This is part of a series on mining your own Gmail data. For this post I want to tackle a fun question: how long would it take to read all of my email if that's all I did, 24/7? It's one of those questions that should interest anyone who's concerned about information overload or is looking to pare down their information consumption: "Just how much of my time is theoretically committed to my inbox?" First, the obvious: nobody actually does this. No one actually reads every email they receive from start to finish (as anyone who's dealt with email in a corporate environment knows all too well). Most of us have filters (both electronic and mental) set up to glean the info we need and skip the rest. And I'll bet that a lot of email is written without any expectation that the whole thing will be read; the author may be well aware that different portions of the email are relevant to different recipients, or that the email itself will only be interesting to a subset of the mailing list (e.g., many marketing emails). So it's not the one super-relevant data point that should make people completely re-think their information consumption habits or anything like that. But it is fun to think about, and as one data point among many others it might prove interesting or useful. On to the fun part - actually coming up with a number! Like most people, I'm getting new emails all the time. So technically I should be taking into account all the new emails I receive while I'm still reading through my old ones. But that's hard, so I'm not going to bother. Instead, I'm just going to assume I've stopped getting emails at all while I'm reading. Which means that getting a basic number is easy - I just have to count all the words in all my emails, divide that by the number of words per minute I read, and I've got the number of minutes it would take to read everything. The first thing I need to do is go back to my PowerShell script and pull in the body of each email. This is where we hit the first snag - HTML emails. For doing word counts, I really don't want to look at HTML emails, because there's a ton of junk in there which a human won't be reading. Luckily, most email clients which send HTML emails also include a text version; in those cases, we'll just extract that text portion of the email and ignore the HTML. Unfortunately, this isn't always the case; when there's not a text version available, we'll just have to get the HTML and figure out how to deal with it later. As usual, MimeKit will be doing most of the work. This version of the script is pretty similar to our previous ones, except that we have to loop through the possible body formats for each message to figure out which formats are available. We always check for the 'Text' format first, because that's the one we really want. If that's not available, we run through the others until we find one that works. The relevant changes are the hash of the possible formats, which we use for iteration and for tracking the number of emails of each type: $formats = @{
[MimeKit.Text.TextFormat]::Text = 0;
[MimeKit.Text.TextFormat]::Flowed = 0;
[MimeKit.Text.TextFormat]::Html = 0;
[MimeKit.Text.TextFormat]::Enriched = 0;
[MimeKit.Text.TextFormat]::RichText = 0;
[MimeKit.Text.TextFormat]::CompressedRichText = 0
}


And the section where we determine what the actual format is and store it:

    $bodyText =$null
$actualFormat =$null

# Run through all the enumeration values
# The pipe through sort ensures that we check them in the enum order,
# which is great because we prefer text over flowed over HTML, etc.
$formats.Keys | sort | % { # try each Format until we find one that works if($actualFormat -eq $null) { # Try to get the body in the current format$bodyText = $mimeMessage.GetTextBody($_)
if($bodyText) {$actualFormat = $_ } } } if($actualFormat -eq $null) {$unknownFormat += 1;
$actualFormat = "Unknown" } else {$formats[$actualFormat] += 1; }  You can find the full script here. A couple of notes: 1. This isn't perfect; sometimes MimeKit can't really figure out what the format is. For example, I have some Skype notification emails which MimeKit thinks are HTML only, but are in fact text. I'm not sure why MimeKit gets confused (probably incorrect headers in the original emails), but out of about 43,000 emails only a couple dozen seem to have issues, so I'm not going to worry about it. 2. In all of my emails, the only two formats returned were Text and HTML. This might have something to do with what Gmail supports; I've seen some posts that suggest Gmail doesn't support Flowed, though those may be outdated. In any case, I'm only really dealing with Text and HTML in my word counts. Once we've got the data, we can load it up in pandas and do some counting. Doing a naive count of the words in the plain text emails is trivial; we just define a method that uses Python's split method with None as the delimiter argument, and then look at the length of the returned list. Here's what textWordCount looks like: def textWordCount(text): if not(isinstance(text, str)): return 0 return len(text.split(None))  But the HTML emails are problematic because most of the content is markup that the user will never actually read. So we need to strip all that markup out and just count the words in the text portions of the HTML. To do that, we create another method which parses the HTML email content using the amazing Beautiful Soup library, strips away the style, script, head, and title parts, and extracts the text from what's left using get_text(). Once we've got the actual human-readable text, we can run it through our usual word counting method: def htmlWordCount(text): if not(isinstance(text, str)): return 0 soup = bsoup(text, 'html.parser') if soup is None: return 0 stripped = soup.get_text(" ", strip=True) [s.extract() for s in soup(['style', 'script', 'head', 'title'])] stripped = soup.get_text(" ", strip=True) return textWordCount(stripped)  I took a couple of online tests to get an idea of how fast I read and came up with 350 words per minute. With that bit of data, we can now add some more columns to our data and figure out the total time to read all the emails: def wordCount(row): if(row['Format'] == 'Html'): return htmlWordCount(row['Body']) return textWordCount(row['Body']) averageWordsPerMinute = 350 # Count the words in each message body emails['WordCount'] = emails.apply(wordCount, axis=1) emails['MinutesToRead'] = emails['WordCount'] / averageWordsPerMinute # Get total number of minutes required to read all these emails totalMinutes = emails['MinutesToRead'].sum() # And convert that to a more human-readable timespan timeToRead = humanfriendly.format_timespan(totalMinutes * 60)  The full script is here, if you're playing at home. Running that against all of my Gmail gives me: >>> timeToRead '2 weeks, 6 days and 18 hours'  So if I sat down and read at my fastest speed 24/7 for three weeks straight with no breaks, no sleep, and never slowing down, I could finish reading every word of every email I've ever received in my Gmail account. If I only read them 8 hours a day, it'd take me about 9 weeks to finish. That's actually less than I expected, though "two whole months of your life spent just reading your email" is a still a bit sobering. Sobering enough that I'm not going to try to compute this for my other four email accounts, anyway. Comment ### E.Z. Hart # Mining Your Gmail Data - Part 6 First off, let's take a look at the second question that came up at the end of the last post: ignoring the Media Type (the 'application/', 'video/', etc.) from the MIME type. That turns out to be pretty easy - the script from last time already collected that data, because MimeKit already made it available. We just need to adjust our pandas script to group on 'MediaSubtype' instead of 'MimeType': types = notFromMe.groupby(['MediaSubtype'])  That cleaned things up a lot. But we still have the second question from the last post: what's behind octet-stream? Application/octet-stream is basically the generic binary file option; most likely the original client which uploaded the file didn't specify the type. But we can make an educated guess about the type based on the file name extension, where we have it. So we'll write a quick function which takes a row of data and, if the Media Subtype is 'octet-stream', returns the file name extension from the FileName column: import os.path ... def filetype(row): if not(isinstance(row['ContentTypeName'], str)): return '' if row['MediaSubtype'] == 'octet-stream': return os.path.splitext(row['ContentTypeName'])[1] return row['MediaSubtype']  We can run that function against our data and put the results in a new column which we'll call 'FileType': notFromMe['FileType'] = notFromMe.apply(lambda row: filetype(row), axis = 1)  Now, instead of grouping by MediaSubtype, we just group by FileType. This isn't perfect - some of our data is getting discarded because there's not enough info between Media Subtype and FileName to figure out what kind of attachment it is. But the data is mostly good, and gives us a much more useful chart: I'm also running this chart with a threshold of 0.02 for the 'other' section, to clean up the less-frequent file types. The whole script can be found here. So, if I'm looking to downsize my Gmail backup, I should probably concentrate on JPEGs, videos (wmv and mpeg), and PDFs. Comment ### E.Z. Hart # Mining Your Gmail Data - Part 5 Last time I talked about mining my Gmail data, I figured out that most of my received attachments (by size) were from friends and family. This time I'm going to break down the data a little more so I can figure out what types of data are in those attachments. On a practical level, I could reduce my Gmail backup size by deleting old messages with photos I already have backed up elsewhere. In reality, I just want to screw around with the charting capabilities in pandas. This time I need all the data broken down by individual attachments. The script for this is similar to the last one, but instead of aggregating the attachments per message, I'm keeping them separate. I'm also grabbing some additional data, like the MIME type of each attachment and the file name. And because I'll need counts of individual attachments, I need to make sure I've got a unique ID for them. I can't use the message ID, because a single message can have multiple attachments. And I can't use the file name, because that may be repeated across messages. So I'm concatenating the two into a new field called AttachmentId. I'm also ignoring all the emails without any attachments. Here's the new script: $emails = @()
$gmvaultdb = "C:\Users\hartez\gmvault-db\db"$total = (Get-ChildItem $gmvaultdb -Recurse -Filter *.eml | measure).Count Add-Type -Path "MimeKit.1.2.10.0\lib\net45\MimeKit.dll" Get-ChildItem$gmvaultdb -Recurse -Filter *.eml | ForEach-Object {$i=0} { Write-Host "Processing$_ ($i of$total)"
$mimeMessage = [MimeKit.MimeMessage]::Load($_.FullName)
$attachments = @()$mimeMessage.Attachments | % {
$attachment = @{ Id =$mimeMessage.MessageId + $_.ContentDisposition.FileName FileName =$_.ContentDisposition.FileName
ContentTypeName = $_.ContentType.Name MimeType =$_.ContentType.MimeType
MediaType = $_.ContentType.MediaType MediaSubtype =$_.ContentType.MediaSubtype
Length = $_.ContentObject.Stream.Length ContentType =$_.ContentType
}
$attachments += (New-Object PSObject -Property$attachment)
}

$mimeMessage.From.ToString() -match '"\s<(.*)>$' | Out-Null;
$fromEmail =$Matches[1]

if($attachments.Count -gt 0) {$attachments | % {
$props = @{ Id =$mimeMessage.MessageId
To = $mimeMessage.To.ToString() From =$mimeMessage.From.ToString()
FromEmail = $fromEmail AttachmentId =$_.Id
FileName = $_.FileName ContentTypeName =$_.ContentTypeName
MimeType = $_.MimeType MediaType =$_.MediaType
MediaSubtype = $_.MediaSubtype Size =$_.Length
ContentType = $_.ContentType } }$emails += (New-Object PSObject -Property $props) }$i++
}

\$emails | Select Id, To, From, FromEmail, FileName, AttachmentId, ContentTypeName, MimeType, MediaType, MediaSubtype, Size, ContentType | Export-Csv attachments2.csv -NoTypeInformation


In pandas, I'm doing the usual import of the data and filtering down to messages which aren't from myself. Then I group the data by MIME type:

    types = notFromMe.groupby(['MimeType'])


and create aggregate columns for the total number of attachments of each type and the total size of those attachments:

    types = types.agg({'AttachmentId' : 'count', 'Size' : 'sum'})


I'll also need the total number of attachments and the total size of all attachments so I can calculate percentages:

    totalCount = types['AttachmentId'].sum()
totalSize = types['Size'].sum()


With that in place, adding the percentage columns is easy:

    types['percentCount'] = types['AttachmentId'] / totalCount
types['percentSize'] = types['Size'] / totalSize


At the top of the script I've already imported matplotlib:

    import matplotlib.pyplot as plt
plt.style.use('ggplot')


Now we're all set to plot this data so I can see the relative sizes of each file type. I'll start with 'percentSize':

    types['percentSize'].plot(kind='pie', figsize=(6, 6))
plt.show()


Wow. That's ... pretty ugly. Let's see if we can un-clutter that a bit by combining all of the really low-percentage stuff into a slice called "other". While we're at it, we'll create a method that we can re-use when we want to graph the percentages by count:

    def combinedPlot(df, col, cutoff):

# Just get the mime types which are 1% or more
overCutoff = df[col]
overCutoff = df.query(col + '>' + str(cutoff))

# Fill in the 'other' section
remaining = 1 - (major[col].sum())
other = pd.DataFrame({col : pd.Series([remaining], index = ['other'])})

# Add the 'other' section to our main data
both = overCutoff.append(other)

# Plot it
both[col].plot(kind='pie', figsize=(6, 6))
plt.show()


To use it, we just drop in our types data frame, the column we want to graph, and where we want to cut off the data:

    combinedPlot(types, 'percentSize', 0.01)


That's much better. But it raises a couple of questions:

1. What's with application/octet-stream? That could be any sort of file; can we dig down into that?
2. Can we get some more succinct labels? We don't really need the 'application/; and 'video/' prefixes.

This is already long; we'll get to those questions next time.

Comment