# Clojure Naive Bayes under 100 LOC

October 09, 2020

Recently I was learning more about Bayes algorithms and how to implement a simple spam filter.

I wanted to have a simple and small implementation to help solidify the understanding of it.

I found an implementation in JS and decided to port it, the result is the following:

```
(ns com.wsscode.bayes
"Simple naive bayes implementation in Clojure.
Implementation ported from: https://github.com/ttezel/bayes/blob/master/lib/naive_bayes.js"
(:require [clojure.string :as str]))
(defn- math-log [n] #?(:clj (Math/log n) :cljs (js/Math.log n)))
(defn tokenize [s]
(into []
(comp (map str/lower-case)
(remove #(re-find #"^\d+$" %)))
(str/split s #"[.\s,]+")))
(defn classifier []
{::vocabulary #{}
::vocabulary-size 0
::total-documents 0
::doc-count {}
::word-count {}
::word-frequency-count {}
::categories #{}})
(defn initialize-category
[{::keys [categories] :as classifier} category]
(cond-> classifier
(not (contains? categories category))
(-> (assoc-in [::doc-count category] 0)
(assoc-in [::word-count category] 0)
(assoc-in [::word-frequency-count category] {})
(update ::categories conj category))))
(defn add-token [{::keys [vocabulary] :as classifier} token]
(cond-> classifier
(not (contains? vocabulary token))
(-> (update ::vocabulary conj token)
(update ::vocabulary-size inc))))
(defn learn [classifier text category]
(let [tokens (tokenize text)
table (frequencies tokens)]
(-> classifier
(initialize-category category)
(update-in [::doc-count category] inc)
(update ::total-documents inc)
(as-> <>
(reduce-kv
(fn [classifier token occurrences]
(-> classifier
(add-token token)
(update-in [::word-frequency-count category token] #(+ (or % 0) occurrences))
(update-in [::word-count category] + occurrences)))
<>
table)))))
(defn token-probability
[{::keys [vocabulary-size] :as classifier} token category]
(let [word-frequency-count (get-in classifier [::word-frequency-count category token] 0)
word-count (get-in classifier [::word-count category])]
(/ (inc word-frequency-count) (+ word-count vocabulary-size))))
(defn categorize
[{::keys [doc-count total-documents categories] :as classifier} text]
(let [tokens (tokenize text)
table (frequencies tokens)]
(-> (reduce
(fn [{:keys [max-probability] :as acc} category]
(let [category-probability (/ (get doc-count category) total-documents)
log-probability (reduce-kv
(fn [log-probability token occurrences]
(let [token-probability (token-probability classifier token category)]
; determine the log of the P( w | c ) for this word
(+ log-probability (* occurrences (math-log token-probability)))))
(math-log category-probability)
table)]
; now determine P( w | c ) for each word `w` in the text
(if (> log-probability max-probability)
{:max-probability log-probability
:chosen-category category}
acc)))
{:max-probability ##-Inf
:chosen-category nil}
categories)
:chosen-category)))
```

Example usage:

```
(-> (classifier)
(learn "amazing, awesome movie!! Yeah!! Oh boy." ::ham)
(learn "Sweet, this is incredibly, amazing, perfect, great!!" ::ham)
(learn "terrible, shitty thing. Damn. Sucks!!" ::spam)
(categorize "awesome, cool, amazing!! Yay."))
; => :ham
```

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