Category: BigQuery

More Google Maps Semantic Location History Fun with BigQuery

In the last post, I covered the basics of how to transform and load your Google Maps data, including Google’s parsed history data, into BigQuery.

In this post, I’ll share a few interesting queries I’ve written to look at semantic history. These should work just fine for you too if you follow the steps in the previous post.

Visited U.S. States

This query will show when you first visited a state. Google Maps needs to have placed you at a specific location you visited for it to appear in this list, so if you just drive through a state it may not register. You could get that data with your raw location history, and I’ll probably write that up later, but this assures that you at least “went” somewhere in the state.

WITH statedate AS 
(SELECT sl.StateAbbrev state, 
        sh.timestampstart tstart, 
        sh.timestampend tend
FROM location.semantic_history sh
INNER JOIN location.statelookup sl
ON sh.address LIKE(CONCAT('%, ', sl.StateAbbrev, ' %')))
SELECT state, 
       COUNT(*) locations, 
       MIN(tstart) firstVisit, 
       MAX(tend) maxVisit
FROM statedate
GROUP BY state

This query requires a supplementary table, which I named statelookup. I just grabbed the query from and modified it for BigQuery (delete identity column and change the VARCHAR/CHAR columns to STRING.) Tons of the public data sets have states available so you could join to one of those too.

This will return a list of every state you’ve been to within your location history, ordered by the first time you went somewhere in that state. You can also tack on an ANY_VALUE(name) to pick a random place in that state that you went to. (It won’t actually be random, because repeated visits increase the weight, so statistically you’ll probably get a familiar place.)

Fill-Up Frequency

This query will show (almost) every time you went to a gas station, although it can be extended to any other Google Places category.

SELECT name, duration, date, pointstart 
FROM location.semantic_history
WHERE 'gas_station' IN UNNEST(locationTypes)

I noticed that it missed a bunch of rural places in which I have definitely purchased gas, so you might consider an OR filter with common names of gas stations.

OR REGEXP_CONTAINS(ARRAY_TO_STRING(locationTypes, ' '), '(phillips|valero|chevron|shell|sheetz|texaco)')

Add whatever gas station names are common in your area. Some may also be missing because you didn’t remain at the place long enough for Google to register it; I’ve seen that issue with other locations too. I think some of these show up as waypoint locations, but these queries don’t account for that.

Any Other Kind of Location

You can take the query above and replace ‘gas_station’ with a bunch of categories. Here are some examples:

  • airport
  • gym
  • grocery_or_supermarket
  • electronics_store
  • restaurant
  • movie_theater

The full list can be found here. You actually can use most of the types it says you can’t use in the type filter, like point_of_interest or food, because we obtained these locations from ID mapping, not from a search.

Google is pretty inclusive for these tags, so Target, Staples, and even a piano store I went to come up as electronics_store.

Favorite Restaurants

As a variation on this, you can see which restaurants you dine out at the most, and on average how often you visit them. Using placeid as the GROUP BY here ensures that different locations of the same chain are separated out.

DECLARE startdate DATE DEFAULT '2018-01-01';
DECLARE enddate DATE DEFAULT '2019-12-31';

       COUNT(*) visits,
       ROUND(DATE_DIFF(enddate, startdate, DAY) / COUNT(*),0) frequency
FROM location.semantic_history
WHERE 'restaurant' IN UNNEST(locationTypes)
AND date BETWEEN startdate AND enddate
GROUP BY placeid

Lastly, replacing the visits column with a duration check will show you your favorite places ordered by how many hours you spent there. There probably won’t be a huge difference unless you frequently disappear into your favorite bars for hours on end.

ROUND(SUM(duration)/3600, 2) totalTime,

Wrap Up

Other than the state abbreviation table, these queries don’t use anything besides the semantic data upload we already had. We didn’t even use any GIS queries to plot maps or intersections, which would give us all kinds of power to join other kinds of data.

For instance, you could join to weather data to see your experience with outdoor temperatures over time, by your personal location. If you missed a heat wave at home, your personal weather history would show that. You could even plot how close you were to every reported hurricane. Lots of fun data mining yet to come!

Google Maps Semantic History in BigQuery

In this post I talked about getting your location data from Google Maps and loading it into Google BigQuery. I mentioned that Google also provides its semantic history. This data structure includes Maps’ inferred place names and addresses and its guesses about mode of travel. It divides the data into two main event types, activitySegment and placeVisit.

placeVisit describes time spent stationary within a given latitude/longitude bound, and includes the Google Place ID, address, name, and a confidence score.

activitySegment includes start and end coordinates, as well as the waypoints and raw path, duration, and an activity type (“IN_PASSENGER_VEHICLE”, “IN_BUS”, “IN_TRAM”, et al.) with associated confidence score.

In order to load this data into BigQuery, I wanted to transform it to JSONL as before, but also to unify the two event types into a single structure so they can be queried together. Most of the important pieces, like location and duration, apply to both. This also allows for a single date partitioning scheme, so that you can query a single partition to get all activity for a single day. (You could easily store multiple people’s data in the same structure and continue to leverage the same partition scheme.)

Defining the Schema

The JSON schema / BigQuery table definition I decided on was:

dateDATELocal event date (adjusted for timezone)
typeSTRING“VISIT” for a placeVisit and “TRAVEL” for an activitySegment
latStartFLOATThe starting latitude coordinate of the event
lonStartFLOATThe starting longitude coordinate of the event
latEndFLOATThe ending latitude coordinate of the event (“VISIT” is the same as start)
lonEndFLOATThe ending longitude coordinate of the event (“VISIT” is the same as start)
moveTypeSTRINGThe activity type (“IN_VEHICLE”, etc.) “VISIT” always has “NONE”
distanceFLOATDistance traveled in meters during the activitySegment. 0 for “VISIT”.
semTypeSTRINGGoogle’s semantic type for the location. (“TYPE_WORK”, “TYPE_HOME”)
placeIdSTRINGGoogle’s place ID for the VISIT.
addressSTRINGThe geocoded address for the lat/lon pair for the VISIT.
nameSTRINGThe name of the location (“Wendy’s”, “Starbucks”, “The Colosseum”)
confidenceFLOATThe confidence score for the named location
timestampStartTIMESTAMPThe fixed UNIX epoch timestamp for the beginning of the event, in seconds
timestampEndTIMESTAMPThe fixed UNIX epoch timestamp for the end of the event, in seconds
durationINTEGER(timestampStart – timestampEnd), for convenience
latCenterFLOATVISITs have an extra lat/lon pair, which I loaded here just in case
lonCenterFLOATVISITs have an extra lat/lon pair, which I loaded here just in case
waypointPathGEOGRAPHYThe coordinates of any waypoints along the activitySegment
rawPathGEOGRAPHYA simplified linestring showing the path for activitySegments
pointStartGEOGRAPHYThe geography point for latStart/lonStart
pointEndGEOGRAPHYThe geography point for latEnd/lonEnd
pointCenterGEOGRAPHYThe geography point for latCenter/lonCenter
Tabular form of the schema.json file.

I kept the non-geographic columns to mirror the source data, but they’re redundant to ST_X(pointStart) or ST_Y(pointStart). Date, however, is not — whereas the raw timestamp values give the fixed start and end values for the activity, Date is localized to the appropriate timezone. In fact, using a nifty npm package called geo-tz, it will actually automatically look up the timezone in the lat/lon the event is in! So even as you move between timezones, it will still place the event in the appropriate local date. If it can’t find one, it just defaults to my local timezone, “America/Los_Angeles”.

Coding the Script

Unlike last time, I wanted both a one-stop shop and an idempotent module that I could run over and over again as I worked on the data, without having to use the UI to repeat manual steps. I wrote a nodejs script that performs the following steps:

  • Transform all semantic history files into JSONL format
  • Zip and load file to Google Cloud Storage
  • Create the BigQuery table according to the defined schema (dropping and rebuilding each time)
  • Load the GCS file to BigQuery
  • Perform any necessary post-processing in BigQuery to clean the data

There weren’t a lot of challenges here, just your regular data transformation pipeline. Google Takeout doesn’t have a native API (there are workarounds) so I didn’t bother writing a repeatable pipeline to refresh the data monthly or weekly. (And for those of you who read my book, note that despite writing the book in Python, I’m more comfortable in Node for personal projects. I chose Python for the book because it’s compatible across Jupyter notebooks, Dataflow, Google Cloud Functions, and App Engine and I didn’t want to be jumping around in languages. Despite that, I still ended up with Python, BQ SQL, ZetaSQL, MySQL SQL, Javascript, and Java…) A couple of interesting notes though.

While Takeout provides the regular location history as a single, massive file in JSON or KML, the semantic versions are segmented into monthly files, i.e. 2020_JANUARY.json, 2020_FEBRUARY.json, etc. I just used a single glob wildcard to grab all JSONs in the subfolders, so you can dump your “Semantic Location History” folder directly into the script folder. Don’t mix in any other JSONs, though because it just uses ‘./sem/**/*.json’ as the glob pattern.

There doesn’t seem to be any way to actually gzip files for use with the BigQuery Node SDK. I didn’t do a lot of research on this, but I couldn’t get the SDK to take the file if I gzipped it. The semantic files are much smaller — mine were 8MB unzipped and 2MB gzipped, so I didn’t fiddle too much with this and just commented it out.

Longitude/latitude are stored as E7 values in the Takeout JSON, so I simply divided by 1E7 to restore the normal decimal format. Timestamps are stored with millisecond precision, so I divided by 1000 to load them as BigQuery timestamp files.

Loading GEOGRAPHY types from JSONL is kind of interesting too. Loading ST_POINTs isn’t too bad; I just used a string template in the form `POINT(${lon} ${lat})` and BigQuery was happy. MULTIPOINTs and LINESTRINGs were a little harder, mostly because the data often repeats the same point or contains no unique points at all. The comma and parentheses are also strict, so it requires a bit of string finagling to get it working right. Ultimately I used these WKT string formats:

PointPOINT(30 10)
MultipointMULTIPOINT ((10 40), (40 30), (20 20), (30 10))
LinestringLINESTRING (30 10, 10 30, 40 40)
WKT formats BigQuery is okay with

You will need a GCP service account key with permissions to create/delete Google Cloud Storage objects and permission on BigQuery to run jobs, create, and delete tables in the dataset. Download it from the GCP IAM console and save it as service-account-key.json in the script folder to get access.

You also have to make your own GCS bucket, which you can do with gsutil or the Cloud console.

Running the Script

The script documents its progress, although it only takes about 45 seconds to run from my local machine with my file set. The majority of that time is spent in loading the file into BigQuery from GCS, which doesn’t take place on my machine anyway.

The script doesn’t have any real handling, so it’ll likely throw an UnhandledPromiseRejectionWarning if something goes wrong. If everything works correctly, you’ll be left with a yellow “Finished!” prompt and you can move into BigQuery.

Note also that there is an empty postprocessing step in which you could add any SQL you want to run to clean things up. For instance, some rows have starting and ending coordinates, but don’t have a distance, so you might want to UPDATE the ST_DISTANCE for those rows for cleaner distance statistics:

UPDATE `location.semantic_history`
SET distance = ST_DISTANCE(pointStart, pointEnd)
AND distance IS NULL

But now you have a neat table that contains all of your location history in an easily queryable form, with lots of helpful metadata.

Google Place IDs

Enabling the Places API and using an API call in the format{PLACE_ID}&key={GOOGLE_MAPS_KEY}

gives you all of the Google Maps data on a given place, including operating hours, price level, the geometry for the whole place, reviews, and a bunch of other stuff. You could join this data together with your location history for some really interesting queries like “how many places was I at after hours?” or “how many expensive restaurants do I go to per month?” I’ll tackle this part in a later post, using the Node SDK for Google Maps. You can cache this data too, so I can probably build some sort of rudimentary table that lets me collect and load places data wherever it exists.

Interesting Queries

Even with just duration, latitude, and names, I can do some pretty neat things with this data. Here are just a few examples I tried.

How many times do I go to Starbucks each week?

WITH weeks AS
     FROM UNNEST(GENERATE_DATE_ARRAY('2014-01-01','2021-01-01')) a),
     times AS
    (SELECT DATE_TRUNC(date,week) w, count(*) count
     FROM location.semantic_history
     WHERE LOWER(name) LIKE '%starbucks%'
     GROUP BY DATE_TRUNC(date,week))
SELECT weeks.w, IFNULL(times.count,0)
FROM weeks

Or any other place, just change the “WHERE LOWER(name) LIKE” to whatever clause you want. How about number of hours spent on planes?

SELECT SUM(duration)/3600 FROM location.semantic_history
WHERE moveType = 'FLYING'

SELECT rawPath from location.semantic_history
WHERE moveType = 'FLYING'

The second query can be passed directly to BigQuery Geo Viz for a full visualization of your flights. Google only began tracking the activity type somewhere in 2016, so data before that won’t have it. This also seems to be missing a bunch of flights I’ve taken since then, so it probably needs refinement. Or, how about all trips ordered by decreasing speed in miles per hour?

SELECT (ST_DISTANCE(pointStart,pointEnd)/duration)*2.23694 mph, * 
FROM location.semantic_history
WHERE distance > 200 AND duration > 0

Noting that the maps distance is sometimes wrong, I recalculated it to do this math. Even that yielded some amusing outliers, no doubt due to poor cellular or GPS signal. Apparently, on one New Year’s Eve, I traveled from SFO to the middle of the Pacific Ocean at 930 mph. (This appears to have been Google thinking I landed at SFO when I actually landed at LAX.)

Lastly, you can generate the Google Maps direction string for any trip — not the route you actually took, just the default route so you can visualize the locations:

SELECT FORMAT(',%s/%s,%s/', 
       CAST(latStart AS STRING), CAST(lonStart AS STRING), CAST(latEnd AS STRING), CAST(lonEnd AS STRING)) mapsString
FROM location.semantic_history

Using the Code

The code for all of this is available on github at Feel free to download and modify for your own use!

Google Maps Location History with BigQuery

Google Maps can be configured to record your location and show your history over time. If you don’t mind the privacy implications of this, it’s a powerful tool to see where you’ve been and how often you go to certain places. You can also use it to calculate historical travel times, visualize road trips, and so on.

Unfortunately, the stock interface leaves something to be desired. It’s hard to plot paths or to search by locations. You can’t get a list of all the times you’ve been to a certain place. There are some pretty interesting insights about your habits and behaviors that are locked up inside the interface.

I was thinking about the impact that quarantine has had on my travel habits. The stay-at-home order in California took effect here around March 19, but we had imposed mandatory work-from-home on the 16th. The only way I could see that in aggregate on Google Maps was the graph of visited locations.

Column graph showing number of places visited by day
Well, that’s depressing.

That got me thinking, is there a way to visualize this data over a longer period of time? Could I do more interesting things like calculate the maximum distance traveled from home on any given day?

Google Takeout

It is possible to get your location history exported as a single JSON. To do that, go to and click “Deselect All”. Then scroll down to Location History, recheck it, and make sure the settings look like this:

Options for Location History export

Export your data, and it will begin the process. It can take several minutes to hours to do this, but in my experience it was usually less than five minutes. Download the JSON file into a new folder on your desktop somewhere.

Loading to BigQuery

Unfortunately, the JSON that Google Takeout outputs isn’t directly compatible with BigQuery. BigQuery uses JSONL, which is basically a file consisting of a separate JSON object per line. This is so you can load massive datasets into BigQuery without invoking a parser. I guess Takeout didn’t get the message, because it generated one giant 600MB JSON object that choked several parsers as I tried to view it.

So. I guess we have to do some preprocessing on the file before BigQuery will accept it. Guess I’m breaking out the Node. I’m not sure how I’d want to handle this if the file were, say, 600GB, but we can handle 600MB locally without too much hassle. I just cobbled a quick script together that converts a history JSON file into a JSONL file.

const fs = require('fs')
const filename = './lh.json'

let jsonr = fs.readFileSync(filename);
let jp = JSON.parse(jsonr);
let array = jp.locations;

let newfile = filename.replace(/.json$/g, '.jsonl')
let stream = fs.createWriteStream(newfile); =>
        let y = { timestamp: parseInt(x.timestampMs / 1000),
                  lat: x.latitudeE7 / 10000000,
                  lon: x.longitudeE7 / 10000000,
                  accuracy: x.accuracy };
        // foreshadowing: think about timezones...


All this does is take a file named lh.json that holds the JSON-formatted Google Takeout file, loads it into memory, and converts each line to a separate JSON object. I also preprocessed the timestamps and latitude/longitude pairs into decimals for easier GEOGRAPHY conversion. This also has the effect of making the result file substantially smaller by shortening the names — the resulting file was only 203MB. I didn’t bother to gzip it, but I also have a fast Internet connection. If you don’t, gzip the file now before the next step.

Then I transferred it directly to Google Cloud Storage for loading into BigQuery.

gsutil -m cp lh.jsonl 'gs://my-bucket/lh.jsonl'

After that, it’s a simple bq call to get the data loaded into BigQuery.

bq load --source_format=NEWLINE_DELIMITED_JSON \
        --time_partitioning_field=timestamp \
        location.history \
        gs://my-bucket/lh.jsonl \

After I did that, I added additional columns for GEOGRAPHY and for the date in my local timezone — and then I realized my error. The partitioning and clustering aren’t going to help if they are always off by seven or eight hours, and since I want to look at data day-by-day, this could be a big problem. Well, how bad can it be…?

SELECT * FROM location.history
WHERE timestamp BETWEEN ‘2017-01-01’ AND ‘2017-01-02’
This query will process 246.2 KB when run.
SELECT * FROM location.history
WHERE date BETWEEN ‘2017-01-01’ AND ‘2017-01-02’
This query will process 260.8 MB when run.
Oh, only a thousand times worse.

That bad. Luckily, this is easy enough to fix by migrating the data to a new table and deleting the old one.

location.history2  (date DATE,
                    timestamp TIMESTAMP,
                    lat NUMERIC,
                    lon NUMERIC,
                    accuracy INT64,
                    point GEOGRAPHY)

INSERT INTO location.history2
SELECT date, timestamp, lat, lon, accuracy, point
FROM location.history;

DROP TABLE location.history;
-- bq cp location.history2 location.history
DROP TABLE location.history2;

Then I copied history2 back to history — this can be done via command-line or the UI, so I just used the UI to copy the table. Back in business!

Querying the Data

There are lots of ways to skin this cat. I thought the most interesting way to look at this, rather than distance from home, was to calculate the maximum distance inside the smallest polygon that encompassed all of my locations for the day.

INSERT INTO location.history_date
SELECT date, 
ST_CONVEXHULL(ST_UNION_AGG(point)) convex_hull, 

-- the maximum distance between two equivalent hulls is the longest vertex
-- inside of that hull
               ST_CONVEXHULL(ST_UNION_AGG(point))) max_distance

FROM location.history
WHERE accuracy < 100

This is a little abstract, but I had a couple thoughts in mind. One, I’ve moved several times since I started collecting location data, which means that distance from “home” is arbitrary across time. Two, I travel a lot too, and I want to show relative motion. So while a day I go to Chicago will have a large value, if I stay in Chicago the next several days, the values should be small again to reflect that I didn’t travel as much during the 24-hour period. This worked basically as I expected. For example, here’s the day I first saw Hamilton in Chicago:

From the excellent BigQuery geo viz at

This isn’t totally accurate, and the “convexification” eliminates internal boundaries, but it’s close enough for our purposes, which is just to determine the max length of a line inside the shape.

Applying this to the table on a daily basis and calculating a 7-day median value generates this graph for 2020:

After traveling cross-country around New Year’s, it was back to the daily commute with exceptions here and there. Then, during the week of 3/23, quarantine began and mobility dropped to zero. There’s a brief period of activity as restrictions were lifted in mid-June, but since then… not going anywhere.

As you’ve no doubt realized, this is the tip of the iceberg. The JSON export from the beginning also contains the semantic location data, which includes mode of travel with waypoints, inferred location with name, address, and confidence values, and the Google Place ID, which you can use to categorize all the locations.

That was sure a lot of work to make me feel depressed about the fact that I haven’t gone anywhere in four months.

The Classifying Hat — An ML Harry Potter Sorting Hat with BigQuery

The wizarding world is notoriously haphazard. If a muggle school ran like Hogwarts, it’d be sued out of existence within the first term. Law and order is a one-way ticket to Azkaban. And the sorting hat labels eleven year olds for the rest of their lives as brave, sneaky, intelligent, or… well, whatever Hufflepuff is.

A major consideration for machine learning models is the concept of “interpretability”, namely, can you understand and explain why the model makes certain choices. The sorting hat has almost no interpretability, except on the rare occasions that it stubbornly defends its own choices as accurate. Can we make something that chooses as well as the Sorting Hat does, but is also interpretable?

First of all, in machine learning terms, it’d be called a “Classifying Hat”, since the type of ML model used to predict a choice given a set of input features is a multi-class logistic regression. So let’s build ourselves a Classifying Hat.

To do this, we’ll use Google Cloud Platform’s BigQuery Machine Learning capabilities, because you can do it in pure SQL directly from Google Sheets, and it’s really easy to train and predict. Since this isn’t a serious analysis (if that wasn’t obvious) there’s no reason to go crazy with the feature engineering. I follow pretty much the same set of steps for all BQML models anyway.

1. Find the Data

First we’re gonna need some good old Harry Potter data. I want a mix of features that would presumably be helpful in prediction, but nothing so detail-oriented that it would be hard to replicate for arbitrary sorting (i.e., if you wanted to see how the Classifying Hat sorts you.)

I settled on a mix of sources:

  • This Kaggle dataset has names, genders, wand, birthright, eyes, and hair. Since house selection seems to be significantly related to blood status and pedigree, this will probably help a lot. You also receive your wand before you get sorted, so there’s probably some useful predictive capability there.
  • This remarkable Harry Potter aggression analysis weighting characters in the book based on how often they appear and how aggressive their actions are. Since personality type is clearly a factor in sorting, this will be helpful. (Of course, we don’t know cause vs. effect here! It’s possible that being labeled so early in life influences your later actions.)
  • This compilation of related datasets includes a graph showing the relationships among all of the characters. I ended up not using this. Similar to how Facebook works, you could use this to predict a house for someone who “isn’t on the network” by seeing who they associate with. I didn’t use it because the books cover only the time students are at Hogwarts, and by definition they are going to be closely associated with others in their house, since they share sleeping and living quarters. This would be far more useful on the wizarding social network (“Facegrimoire”?) where you could easily determine someone’s house affiliation based on their connections.

I also pulled scripts from the first few movies and the texts of the books, for a secondary analysis on how people’s vocabularies are related to their house selections. That can be done at a later date (yeah, okay, I’m never doing that).

2. Prepare the Data

Now that we have enough data to start with, we’ll get it into a format we can create a machine learning model from.

The Kaggle dataset, while being super useful, was not well formatted, having semicolon delimiters and a ton of jagged arrays. I preprocessed it in Google Sheets and generated a table with blank cells instead of “Unknown”s, and cleaned up overly descriptive labels to make them easier for the model to bucket.

Character names also didn’t match among the various files due to spelling, use of aliases, alternate names, or extra middle names, so I used the aggression dataset’s convention of assigning a four letter slug (i.e. “harr” for Harry Potter, “tonk” for Nymphadora Tonks, “vold” for Tom Riddle, and so on.) and matched up the data.

Since there are only a couple hundred rows, I opted to do this largely manually using Google Sheets, where I could edit all of the values directly. As we’ll see in a second, it’s possible to run BQML models directly out of Google Sheets, which is pretty neat.

3. Load the Data

I placed each filtered “table” into a separate sheet of the Google Sheets file (you can choose to load from a specific sheet). Then I copied the URL to the clipboard and loaded the tables into BigQuery.

I didn’t realize it at first, but when you make a table from a Google Sheet, it’s actually a federated source. That means that it’s actually querying live from the sheet whenever you access the data. This also means there isn’t really any transactional integrity, so if you are making changes to the sheet while the query is running it sometimes generates internal errors.

Note also that whatever schema you supply will govern how far out the table goes. So if you only define three columns but your sheet has twenty, the table will only have three columns. If you edit the schema then the columns will automatically get populated with data from the corresponding sheet columns. Kind of weird.

I prefer to use a view as an input to my machine learning models so I can tweak the view without rebuilding the model, and can also look at the raw input. The CREATE MODEL statement can take several minutes, even on small datasets, and it’s easier to do preliminary feature selection and extraction before getting there.

Accordingly, my final CREATE VIEW statement looked like this. I didn’t bother to specify column names in the table schema and am just transforming them back out via the view.

string_field_0 ID,
string_field_1 named,
string_field_2 gender,
string_field_3 role,
string_field_4 house,
string_field_5 wand_length,
string_field_6 wand_wood,
string_field_7 wand_animal,
string_field_8 patronus,
string_field_9 race,
string_field_10 blood,
string_field_11 hair,
string_field_12 eyes,
string_field_13 loyalty,
string_field_14 skills,
string_field_15 birthdate,
(SELECT SUM(mentions) FROM hp.aggression a WHERE = h.string_field_0) mentions,
(SELECT AVG(agg_weighted) FROM hp.aggression a WHERE = h.string_field_0) agg_weighted,
FROM hp.houses h
WHERE string_field_0 IS NOT NULL

Views (non-materialized ones, anyway) are still federated, so this will pull all the data together from multiple sheets and transform it every time you SELECT from it. That convenience will come in handy later.

4. Build the Model

Now I was ready to actually create and train the model. This meant transforming and selecting my final feature set and filters and building up a model. Per the above, I knew I wanted a multi-class logistic regression. This is a supervised model, which means I have to label input data with the correct class and use that to train the model. The typical split is 80/20, but for datasets with fewer than 500 rows, BQML defaults to using all the rows as training data. This isn’t great, since it raises the chances of overfitting. However, only ninety-odd people have been assigned to houses, so it’s all we get. (I was not inclined to sift through reams of fan fiction to plausibly tag any others.)

My first model creation statement:

OPTIONS (model_type='logistic_reg', input_label_cols=['house'])
SELECT * EXCEPT (id) FROM `hp.input` WHERE house IS NOT NULL

5. Evaluate the Model

The model ran for a few minutes (no hatstall here) and spit out the new model object. I went to the model evaluation, and lo and behold:

First attempt at ML sorting
The first model’s confusion matrix.

Hm. That smells like overfitting. Did I do something wrong? I was careful to remove ID numbers and tags that might bias the model. You could make a case that name shouldn’t be in there, and I did try that variation, but you could also make an argument that bias by last name is intentional by the Sorting Hat. Weasley? Gryffindor. Potter? Gryffindor. Malfoy? Slytherin. That may not be fair, but it probably does accurately reflect how the hat works.

Nonetheless, it still seemed a bit too on-the-nose, so I went back and checked what other columns I might have missed. Well, “loyalty” sure stood out. It turns out that if your loyalty includes “Death Eaters”, you are overwhelmingly likely to be in Slytherin. I nixed that column. I also nixed “species”, which was basically all human except for Hagrid, who was listed as “half-giant”, and immediately caused all non-humans to go to Gryffindor too. (That might also be right.)

For my second attempt, I made a few more changes. I compromised on sending only last name instead of full name. I also bucketized wand length by 2, so that 8-10 inches was a group, 10-12, and so on. Lastly, I removed birthdate extractions entirely. I thought zodiac sign or decade might be interesting, but the data is too vague to be of much use.

ML.QUANTILE_BUCKETIZE(CAST(wand_length AS NUMERIC), 2) OVER() wand_length_bucketized,
OPTIONS (model_type='logistic_reg', input_label_cols=['house'])
SELECT * EXCEPT (id, role, race) FROM `hp.input` WHERE house IS NOT NULL

A bit concerning; it’s still basically perfect.

Aw, someone got mis-sorted.

If this were a real-world data set, I would now be extremely concerned about implicit bias and the possibility that the model was making judgments purely superficially in a totally unfair way. I do think that’s happening, but I also think the Sorting Hat does too (and this kind of proves it) so we’ve reached the standard for novelty level.

As a last step, I took surname out of the model just to see how much that was really skewing things.


I guess there really is a secret class system.

6. Make Some Predictions!

Making a prediction is easy: we just select from ML.PREDICT with our model, and it’ll add the target feature prediction to the table. We have a bunch of characters in the table who don’t have houses, either because it was never defined, they’re in schools other than Hogwarts, or they’re muggles. Who cares, let’s predict the rest of the table.

SELECT house, predicted_house, fullname 
ML.PREDICT(MODEL ``, (SELECT * FROM `hp.input`))

This yields some entertaining and plausible results such as sorting Dobby the house elf into Hufflepuff and Nurse Pomfrey into Slytherin. It also places all the Dursleys in Gryffindor, oddly. You can add your own examples and see how it makes its decisions, which is at least one thing the Sorting Hat can’t/won’t do!

Next Steps

I was recently playing around with the use of Google Forms to automatically drop rows into Google Sheets, and then to write some Apps Script to do things with that. It wouldn’t be too hard to take inputs from a Google Form and trigger an automatic BQML prediction based on new data. In other words, you could turn this ML classifying hat into a charming little quiz!

It would also be interesting to drop this into a k-means model and see if it segments people into four distinct houses, or if it has something else in mind.

The Google Sheet with my data is here, so if you want to give it a spin, all you need to do is make a copy to your own Google Drive and import it into a BigQuery table (use the rightmost sheet, “Houses”.) Have fun!

This analysis was used as the basis for a talk at SQL Saturday Los Angeles on June 13, 2020. It also draws from my book, BigQuery for Data Warehousing, out now!

Sample Data Warehouse Charter

In my book, I repeatedly emphasize the importance of chartering and running a project to encapsulate your data program. The first artifact in this process is the project charter, around which business stakeholders can agree about what your program is and what it should do.

Appendix B of the book provides an outline of a sample charter. You can download a blank version of the charter here:

A sample charter
A sample charter