purewords 0.1.1

A NLP preprocessing package

# purewords

Purewords is a package used to clean raw texts for all languages.

## Install

`pip install purewords`


## Usage

### Module usage:

```python
import purewords

# raw sentence
inputs = "ha hi!!hello I\'m at http:www.google.com.tw\n\n"
+ "you know yahoo? my_computer is great. My phone number"
+ "is 02-3366-5678. <br>的啦<br> my password: 123-abc$99&^%Y)\'_\'(Y "
```
#### Treat inputs as a sentence and clean it.

Word tokens are splitted with whitespace
```python
# result: string
purewords.clean_sentence(inputs)
'ha hi hello i am at _url_ you know yahoo my computer is great my phone number is _phone_ 的 my password _num_ abc _num_ y y'
```

#### Treat inputs as a document and clean it.

Split document with some confident splitting token such as '.' or '?'.
```python
# result: list of cleaned string
purewords.clean_document(inputs)
['ha hi', 'hello i am at _url_', 'you know yahoo', 'my computer is great', 'my phone number is _phone_', '的 my password _num_ abc _num_ y y']
```

### Customed your purewords

You can use different setting in purewords.

```python
import purewords
from purewords.tokenizer import YoctolTokenizer
from purewords.filter_collection import document_filters
from purewords.filter_collection import token_filters

tokenizer = YoctolTokenizer()
pw = purewords.PureWords(
tokenizer=tokenizer, # select your tokenizer
document_filters=document_filters, # select your document filters
token_filters=token_filters, # select your token filters
max_len=200, # cut long sentence whose length exceed max_len
min_len=1 # ignore short sentence
)

inputs = 'This is a sentence.'

pw.clean_sentence(inputs)
pw.clean_document(inputs)
```

#### Tokenizer

##### Select your tokenizer in purewords

You can select `WhitespaceTokenizer` tokenizer if you prefer tokenize
sentences with whitespace or `JiebaTokenizer` for default jieba setting.

Otherwise, we use yoctol jeiba tokenizer as our default setting.

```python
from purewords.tokenizer import WhitespaceTokenizer

tokenizer = WhitespaceTokenizer()
pw = purewords.PureWords(
tokenizer=tokenizer
)
```

##### Add new words in JiebaTokenizer

You can add new word in JiebaTokenizer to customize your tokenizer.

```python
from purewords.tokenizer import JiebaTokenizer

tokenizer = JiebaTokenizer()
tokenizer.add_word(new_word, freq, tag) # The setting is same with jieba.add_word
tokenizer.add_words(new_word_list, freq, tag)

pw = purewords.PureWords(
tokenizer=tokenizer
)
```

#### Filter collection

You can customize your preprocesing ways in purewords.

* document_filters: preprocess the raw sentence before sentence splitting
* token_filters: preprocess tokens after tokenization of each sentence

##### Organize your filters

You can create your customized filters by adding your filters in our filter collection class.

Filter means a callable object which receives a raw sentence and returns the processed one.

The preprocessing order is consistent with the adding order of filters.

```python
from purewords.filter_collection import BaseFilterCollection

custom_filters = BaseFilterCollection()
custom_filters.add(filter_1)
custom_filters.add(filter_2)
...
custom_filters.add(filter_n)

pw = purewords.PureWords(
tokenizer=tokenizer,
document_filters=custom_filters,
)
```

#### Stopwords

You can add stopwords in `purewords/config/stopwords.txt`.


### Command line usage:

Preprocess text files into a single cleaned document from command line.

Usage:

#### Clean single txt files
```
python -m purewords input_file_path
```

#### Clean text files in a directory

Or, you can use following command to clean all the txt files in your directory.
```
python -m purewords -d your_raw_text_dir
```

#### Ignore short sentences

If you prefer long sentences and want to ignore short sentences less than 5 words, you can try this.
```
python -m purewords -min 5 your_text_file
```

#### Cut long sentences

Or you prefer short sentences less than 30 words and want to cut long sentences into short sentences.

You can set up the maximun sentence length like this.
```
python -m purewords -max 30 your_text_file
```

#### Use multi-thread to speed up

You can also use multi-trhead to speed up the cleaning process.

In the follwoing example, you clean all the text files with 4 threads
```
python -m purewords -j 4 -d your_raw_text_dir
```