fredwu / simple_bayes Goto Github PK

In order to save the bayes filter e.g. in a database/key-value store I think an universal save/load function would be great.

My suggestion is to adapt the storage behavior so that the save function returns a {:ok, pid, data} tuple. In case of the filesystem storage, which doesn't need to return data, the returned tuple is {:ok, pid, nil}.

The load function than needs to accepts the encoded data.

What do you think?

First, many thanks for your great Bayes library!

As an enhancement and to handle different use cases, it would be great to have the ability to select the Bayes algorithm to use.

I would suggest these two additional algorithms:

• Binarized Multinomial Naive Bayes (The Binarized Multinomial Naive Bayes can be used when the frequencies of the words don’t play a key role in a classification, e.g. sentiment analysis)
• Bernoulli Naive Bayes (The Bernoulli Naive Bayes can be used when the absence of a particular word matters, e.g. spam or adult content filtering)

What do you think?

This is minor but it seems as though allowing classify_one or classify is unnecessary. There could just be an option of :top (or something better) to specify how many to classify:

bayes |> SimpleBayes.classify("Maybe green maybe red but definitely round and sweet.", top: 3) # classify 3

bayes |> SimpleBayes.classify("Maybe green maybe red but definitely round and sweet.") # classify all

bayes |> SimpleBayes.classify("Maybe green maybe red but definitely round and sweet.", top: 1) # classify 1, rather than a separate function

Seems that being generic to the number to return would be handy, and trim down any special casing.

Heya!

Loving this library, except I've encountered an unfortunate pathological case.

I have trained a classifier on roughly 288,000 labeled texts. Cardinality of labels is 5, and length of the text ~5 words.

Here's how it's configured:

model:          :bernoulli,
storage:        :file_system,
file_path:      @storage,
default_weight: 1.0,
smoothing:      0.0,
stem:           false,
stop_words:     []

I then persisted this trained model to disk. It's roughly 11Mb when stored. Then I attempted to load it back into memory...

After 10 (!) minutes I received this error:

** (ArithmeticError) bad argument in arithmetic expression
    (stdlib) :math.pow(7318, 24158)
             lib/simple_bayes/classifier/models/bernoulli.ex:7: SimpleBayes.Classifier.Model.Bernoulli.probability_of/3
             lib/simple_bayes/classifier/probability.ex:37: anonymous fn/4 in SimpleBayes.Classifier.Probability.for_collection/3
    (elixir) lib/map.ex:114: Map.do_new_transform/3
             lib/simple_bayes/classifier.ex:13: SimpleBayes.Classifier.classify/3

Ouch.

So, there's two things here:

1. There's an issue prevent me from reloading the trained classifier.
2. Loading is painfully slow.

When I have the time, I'll explore the reasons behind (1). For now, I'll see how far dets can get me.