alt-profanity-check-replit 0.1.1
A fast, robust library to check for offensive language in strings, mainly built for those who are using it in replit.

# profanity-check

[![Build Status](https://travis-ci.com/dimitrismistriotis/profanity-check.svg?branch=master)](https://travis-ci.com/dimitrismistriotis/profanity-check)
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A fast, robust Python library to check for profanity or offensive language in strings. Read more about how and why `profanity-check` was built in [this blog post](https://victorzhou.com/blog/better-profanity-detection-with-scikit-learn/). You can also test out `profanity-check` [in your browser](https://repl.it/@vzhou842/profanity-check-playground).

## Important 
This project was originally made by [Victor Zhou](https://github.com/vzhou842), but that [project](https://github.com/vzhou842/profanity-check) was not well maintained, hence gave many [errors](https://github.com/vzhou842/profanity-check/issues/28) on usage. This project was later saved by these group of [heros](https://github.com/dimitrismistriotis/profanity-check/graphs/contributors). They even released a package in [pypi](https://pypi.org/project/alt-profanity-check/). 

A new problem arised for those who were using a IDE called replit. Replit automatically installs packages from the imports section which is present at the topmost section of our code. Even though I have installed the [corrected pip package](https://pypi.org/project/alt-profanity-check/), replit would automatically install the [faulty package](https://pypi.org/project/profanity-check/) because these things were present at the topmost section of my code. 
```py
from profanity_check import predict, predict_prob
```
Hence I forked the corrected [library](https://github.com/dimitrismistriotis/profanity-check) and made some changes so that I can import the package like this in my repl.
```py
from alt_profanity_check import predict, predict_prob
```

## How It Works

`profanity-check` uses a linear SVM model trained on 200k human-labeled samples of clean and profane text strings. Its model is simple but surprisingly effective, meaning **`profanity-check` is both robust and extremely performant**.

## Why Use profanity-check?

### No Explicit Blacklist

Many profanity detection libraries use a hard-coded list of bad words to detect and filter profanity. For example, [profanity](https://pypi.org/project/profanity/) uses [this wordlist](https://github.com/ben174/profanity/blob/master/profanity/data/wordlist.txt), and even [better-profanity](https://pypi.org/project/better-profanity/) still uses [a wordlist](https://github.com/snguyenthanh/better_profanity/blob/master/better_profanity/profanity_wordlist.txt). There are obviously glaring issues with this approach, and, while they might be performant, **these libraries are not accurate at all**.

A simple example for which `profanity-check` is better is the phrase *"You cocksucker"* - `profanity` thinks this is clean because it doesn't have *"cocksucker"* in its wordlist.

### Performance

Other libraries like [profanity-filter](https://github.com/rominf/profanity-filter) use more sophisticated methods that are much more accurate but at the cost of performance. A benchmark (performed December 2018 on a new 2018 Macbook Pro) using [a Kaggle dataset of Wikipedia comments](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data) yielded roughly the following results:

| Package | 1 Prediction (ms) | 10 Predictions (ms) | 100 Predictions (ms)
| --------|-------------------|---------------------|-----------------------
| profanity-check | 0.2 | 0.5 | 3.5
| profanity-filter | 60 | 1200 | 13000
| profanity | 0.3 | 1.2 | 24

`profanity-check` is anywhere from **300 - 4000 times faster** than `profanity-filter` in this benchmark!

### Accuracy

This table speaks for itself:

| Package | Test Accuracy | Balanced Test Accuracy | Precision | Recall | F1 Score
| ------- | ------------- | ---------------------- | --------- | ------ | --------
| profanity-check | 95.0% | 93.0% | 86.1% | 89.6% | 0.88
| profanity-filter | 91.8% | 83.6% | 85.4% | 70.2% | 0.77
| profanity | 85.6% | 65.1% | 91.7% | 30.8% | 0.46

See the How section below for more details on the dataset used for these results.

## Installation

```
$ pip install alt-profanity-check-replit
```

## Usage

```python
from alt_profanity_check import predict, predict_prob

predict(['predict() takes an array and returns a 1 for each string if it is offensive, else 0.'])
# [0]

predict(['fuck you'])
# [1]

predict_prob(['predict_prob() takes an array and returns the probability each string is offensive'])
# [0.08686173]

predict_prob(['go to hell, you scum'])
# [0.7618861]
```

Note that both `predict()` and `predict_prob` return [`numpy`](https://pypi.org/project/numpy/) arrays.

## More on How/Why It Works

### How

Special thanks to the authors of the datasets used in this project. `profanity-check` was trained on a combined dataset from 2 sources:
- [t-davidson/hate-speech-and-offensive-language](https://github.com/t-davidson/hate-speech-and-offensive-language/tree/master/data), used in their paper *Automated Hate Speech Detection and the Problem of Offensive Language*
- the [Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data) on Kaggle.

`profanity-check` relies heavily on the excellent [`scikit-learn`](https://scikit-learn.org/) library. It's mostly powered by `scikit-learn` classes [`CountVectorizer`](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html), [`LinearSVC`](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html), and [`CalibratedClassifierCV`](https://scikit-learn.org/stable/modules/generated/sklearn.calibration.CalibratedClassifierCV.html). It uses a [Bag-of-words model](https://en.wikipedia.org/wiki/Bag-of-words_model) to vectorize input strings before feeding them to a linear classifier.

### Why

One simplified way you could think about why `profanity-check` works is this: during the training process, the model learns which words are "bad" and how "bad" they are because those words will appear more often in offensive texts. Thus, it's as if the training process is picking out the "bad" words out of all possible words and using those to make future predictions. This is better than just relying on arbitrary word blacklists chosen by humans!

## Caveats

This library is far from perfect. For example, it has a hard time picking up on less common variants of swear words like *"f4ck you"* or *"you b1tch"* because they don't appear often enough in the training corpus. **Never treat any prediction from this library as unquestionable truth, because it does and will make mistakes.** Instead, use this library as a heuristic.