# Maintainable Machine Learning Code

Creating a machine learning pipeline requires almost no thought these days but maintaining a reusable and understandable codebase the whole way through is much more challenging. However there are some steps and ideas that can be borrowed from traditional programming that are still relevant.

## Big Ball of Mud - Machine Learning Edition

Let's say you're starting out with Machine Learning and you need an immediate fix for a small problem, or a quick prototype or proof of concept. Easy, below is a sample program to make predictions on the Iris dataset

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
iris_csv = ("https://raw.githubusercontent.com/"
"uiuc-cse/data-fa14/gh-pages/data/iris.csv")
y = LabelEncoder().fit_transform(df[species])
df = df.drop("species", axis=1)
clf = RandomForestClassifier()
clf.fit(df, y).predict(df)


Even to non programmers the above lines of code are pretty easy to read. It's simple enough to just put that into a python script in some directory with the data files next to it and start churning out predictions.

Later on you realize there's some steps you can do to make your model better. It starts off innocently enough, perhaps just one line to normalize the features, maybe a loop to perform cross validation but within a couple of hours you'll end up with one large python script that has true false statements all over the place, code chunks that are commented out, and incoherent comments all over the place. Suddenly the piecemeal growth of our project dawns on you.

Hopefully you're using version control but even then all your code commits will be on the master branch with commit messages like "model 1" or "added pickling column". You'll find some features are engineered off other features, or portions of your code relied on a datatype coercion you made somewhere between the third and sixth commit. As your code progresses Different artifacts change at different rates and you're trying to remember which processing steps were required to run one of the predictions you had that other day.

Your code is now a Big Ball of Mud. Big Ball of Mud is an excellent paper written in 1999 about what happens when code is haphazardly written. The paper is still relevant today, with all the text in italics copied and pasted straight from the paper. At the core the paper is warning us that if architecture is not persistently considered the final code base will become unmaintainable that it will eventually need to be totally torn down. Unfortunately this is how I've seen so many machine learning implementations turn out. The issue is present even at large companies like Uber who have built dedicated platforms for creating maintainable machine learning pipelines.

Even if you're just writing code for yourself there's still benefits intentionally writing your code in a reusable way. You'll be able to iterate faster, structure your experiments, and be able to revisit your code with minimal effort later on.

# General approaches

With some experimentation I've been able to find an approach that works for me.

Whether you're using R, Python, or any other language really, study up on what is typically done to package the code.

With Python I immediately do four things at a minimum

• Create a requirements.txt or environment.yml file
• Create a source code directory
• Initialize a git repo and commit everything

These are the minimum steps for reproducibility. Imagine how hard it would be if someone handed you a flash drive full of Python files. How do you know what is required to run it, or what packages are needed? Rather than worry if they were using Python 2 or 3, or pandas .19, etc With these four basic steps you're guaranteeing that the environment is reproducible.

A great tool that helps with this is CookieCutter templates, specifically this Data Science Template It's got even more than detailed here but really will make setting up a reusable package easy.

## Write a library for reusable components of your code

Don't put everything in your main.py file. For code that loads datasets, or writes predictions to a file, write a package that lets you abstract that away from your machine learning code. Although it takes more effort up front, later on it means your actual prediction script will be much easier to read.

In the example above we can write a function that looks like this

def load_iris():
"""Loads iris dataset from github and returns (df,y_raw)"""
iris_csv = ("https://raw.githubusercontent.com/"
"uiuc-cse/data-fa14/gh-pages/data/iris.csv")
y_raw = df["species"]
df = df.drop("species", axis=1)
return df, y_raw


Doing this has multiple benefits, if written in the package you can import this into any script without any duplication. It also clearly demarcates that this set of code has no dependencies on other lines.

## Use a testing package

Unittests are still relevant with machine learning code. You can use tests for your own code. There are many examples of test driven development. However I also use tests to test my own understanding of how machine learning libraries work. By writing the following tests I can double-check my understanding of word lemmatizers and CountVectorizer in scikit-learn.

def test_lemmatization():
"""Test that Lemmatizer works"""
wnl = WordNetLemmatizer()
assert wnl.lemmatize('dogs') == 'dog'

def test_lemmatization_with_countvectorizer():
"""Test Lemmatization with Stop Words removal"""
strings = ["I like Dogs and", "I like Churches and"]
count = CountVectorizer(tokenizer=lemmatizer, stop_words='english')
count.fit(strings)
assert {'church', 'dog', 'like'} == set(count.get_feature_names())


It's not about my doubt that the code is implemented poorly, but moreso to double check and reinforce my own understanding. By writing series of these tests I find that I both understand what other libraries are doing and that I double check my understanding fundamentals of the theory.

# Scikit-learn specific

If you use scikit there are some additional tools that are helpful. scikit-learn itself is written in a way that makes machine learning composable. It's so good that other non scikit-learn libraries tend to build similar APIs to maintain compatibility, for example XGBoost implements a scikit-learn abstraction layer.

## Use pipelines

scikit-learn makes it very easy to tie data processing steps together, without storing intermediate results. It also makes it easy to turn off or turn on portions of your pipeline without having to comment entire blocks of code. And lastly it makes it very easy to use parameter grid searches and cross validation in one or two lines.

## Write your own predictors and transformers

Most of the time however data will require some data munging before scikit-learns transformers and predictors can use it. For these activities scikit-learn provides all the scaffolding you need in its base module By utilizing these it keeps you within the pipeline framework which again makes it easy to use all the built-in tools for experimentation.

For example here is an implementation of a transformer I used in a Natural Language Processing Problem that found word count and string length for a given string.

class TextTransformer(BaseEstimator, TransformerMixin):

def __init__(self, features):
"""Takes column of text and returns back array of features. Features list is specified by features
Parameters
----------
features : iter
Iterable of features strings that correspond to class
"""

self.features = features
return

def fit(self, X, y):
return self

def transform(self, X):
"""Accepts Pandas Series and returns back a dataframe where each column is a feature"""

if self.features is None:
raise ValueError("Features must be a list of features that will be used in this model")

features = pd.DataFrame()

for feature in self.features:
transformer = getattr(self, feature)
col = transformer(X)

features[feature] = col

return features.values

@staticmethod
def word_count(X):
"""Count the number of words in each string"""
return X.apply(lambda x: len(x.split(" ")))

@staticmethod
def sentence_length(X):
"""Get the length of the string for each author"""
return X.apply(lambda x: len(x))


While seemingly verbose, if using packages in my machine learning code this entire feature processing portion of logic looked like this

features = FeatureUnion(
[
('basic_preprocessing', TextTransformer(features=['word_count', 'sentence_length'])),
("tfidf", TfidfVectorizer())
]
)


It made it dead simple for me to add or remove features with just one line of code, making my experimentation and feature selection process that much quicker.

## Conclusion

It's very much worth the effort to spend the extra effort to make your machine learning code be more like a package and less like a script. You'll save anyone using your code time figuring out how to reproduce your results, but you'll also save yourself a bunch of time also trying to reproduce your results! The extra effort goes a long way in protecting your sanity and also ensuring you're able to build more than just a prototype prediction engine.