Things I learned while following this tutorial on how to build reusable models with scikit-learn.
- When in doubt, go back to pandas.
- When in doubt, write tests.
- When in doubt, write helper methods to wrap existing objects, rather than creating new objects.
Ingesting "clean" data is easy, right?
Step 1 of this tutorial began with downloading data using requests, and saving that to a csv file. So I did that. I've used requests before, I had no reason to think it wouldn't work. It looked like it worked.
Step 2 was to read the file into pandas. I've read lots of csv files into pandas before, so I had no reason to think it wouldn't work.
It didn't work.
I double-checked that I had followed the instructions correctly, and then checked a few more times before concluding that something was not quite right about the data.
I went back and did the easy thing, just printing out the response from requests.
After some digging, I figured out that
response.content is not the same as
The tutorial said to use
response.text seemed to have actually parsed the strings.
Even with that fix, pandas was refusing to read in more than the first row of data, due to a couple of problems:
- pandas wasn't finding the line terminators (nothing special, just
- pandas wasn't finding equal numbers of items per row
Unexpectedly, when I went back to what I usually do, just plain old
pandas.read_csv, this time going directly from the url, and including the column names, that actually worked.
So it was actually better, and a lot less code, to completely skip using
Testing always gets me unstuck
I really liked the end-to-end structure of this tutorial, and was frankly embarrassed that I had so much trouble getting the initial ingestion to work.
I liked that the tutorial gave me an excuse to walk through how the author actually uses scikit-learn models in production. With the data firmly in hand, the data visualization steps were easy - they worked as advertised, and anyway I'm very familiar with using
seaborn to make charts in python.
I had never created a Bunch object before, so that was new for me. That seemed to work, but then the next steps again failed, and I had to back up a few steps.
I wasn't sure what the problem was, so I did what I always do with complicated problems, and wrote some tests to rule out user error and make sure I understood what the code was doing. That helped a lot, and identified what was actually broken.
The problem: how to apply
LabelEncoder to help convert categorical data, and
Imputer to help fill missing data, to multiple columns.
Because the idea was to do this in the context of a
Pipeline object, the author demonstrated how to create our own Encoder and Imputer objects, with multiple inheritance. I understand the goal of this: take advantage of the nice clean syntax you get from making a Pipeline. But it was failing at the
fit_transform step, and it wasn't obvious why.
transform() steps both seemed to be working individually and sequentially, and it wasn't easy to figure out how the
fit_transform step was supposed to do anything more than chain them together.
After banging my head on this at the end of a long day, even going back to the original scikit-learn source code in an effort to design tests to help me figure out what was wrong, I decided to sleep on it.
Simple and working is better than complicated and broken
I seriously considered writing tests for our custom Encoder and Imputer objects, but then it dawned on me that I really didn't need to do that. I decided that the Pipeline functionality was so simple that I didn't really need it, so I just stripped the objects down into simple functions to run the
transform steps, which was really all I needed anyway.
That got me through the rest of the steps, so I could practice pickling a model and re-loading it, which seemed to work just fine.
I don't know if the scikit-learn folks have plans to extend these methods, or if everyone normally does these kinds of acrobatics to encode and impute on multiple columns - normally I would just use pandas for that, too.