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https://github.com/d-one/nlpeasy

Easy Peasy Language Squeezy
https://github.com/d-one/nlpeasy

datascience elasticsearch kibana nlp spacy

Last synced: 22 days ago
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Easy Peasy Language Squeezy

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README 

        
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NLPeasy

=======



Build NLP pipelines the easy way



> **Disclaimer:** This is in Alpha stage, lot of things can go wrong.

> It could possibly change your Elasticsearch Data the API is not fixed yet

> and even the name NLPeasy might change.



* Free software: Apache Software License 2.0



Usage

-----



For this example to completely work you need to have Python at least in Version 3.6 installed.

Also you need to have install and start either



- **Docker** , direct download links for

    [Mac (DMG)](https://download.docker.com/mac/stable/Docker.dmg) and

    [Windows (exe)](https://download.docker.com/win/stable/Docker%20for%20Windows%20Installer.exe).

- **Elasticsearch** and **Kibana**:

     or

     (pure Apache licensed version)



Then on the terminal issue:

```bash

python -m venv venv

source venv/bin/activate

pip install nlpeasy scikit-learn

python -m spacy download en_core_web_md

```

The package `scikit-learn` is just used in this example to get the newsgroups data and preprocess it.

The last command downloads a spacy model for the english language -

for the following you need to have at least it's `md` (=medium) version which has wordvectors.



```python

import pandas as pd

import nlpeasy as ne

from sklearn.datasets import fetch_20newsgroups



# connect to running elastic or else start an Open Source stack on your docker

elk = ne.connect_elastic(docker_prefix='nlp', elk_version='7.10.2', mount_volume_prefix=None)

# If it is started on docker it will on the first time pull the images (1.3GB)!

# Setting mountVolumePrefix="./elastic-data/" would keep the data of elastic in your

# filesystems and then the data survives container restarts



# read data as Pandas data frame

news_raw = fetch_20newsgroups(remove=('headers', 'footers', 'quotes'))

news_groups = [news_raw['target_names'][i] for i in news_raw['target']]

news = pd.DataFrame({'newsgroup': news_groups, 'message': news_raw['data']})



# setup NLPeasy pipeline with name for the elastic index and set the text column

pipeline = ne.Pipeline(index='news', text_cols=['message'], tag_cols=['newsgroup'], elk=elk)



pipeline += ne.VaderSentiment('message', 'sentiment')

pipeline += ne.SpacyEnrichment(nlp='en_core_web_md', cols=['message'], vec=True)



# do the pipeline - just for first 100, the whole thing would take 10 minutes

news_enriched = pipeline.process(news.head(10000000), write_elastic=True)



# Create Kibana Dashboard of all the columns

pipeline.create_kibana_dashboard()



# open Kibana in webbrowser

elk.show_kibana()

```



Let's have some fun outside of Elastic/Kibana - but this needs `pip install matplotlib`

```python

import numpy as np

from scipy.cluster.hierarchy import dendrogram, linkage

import matplotlib.pyplot as plt

grouped = news_enriched.loc[~news_enriched.message_vec.isna()].groupby('newsgroup')

group_vec = grouped.apply(lambda z: np.stack(z.message_vec.values).mean(axis=0))

clust = linkage(np.stack(group_vec), 'ward')

# calculate full dendrogram

plt.figure(figsize=(10, 10))

plt.title('Hierarchical Clustering Dendrogram Newsgroups')

plt.xlabel('sample index')

plt.ylabel('distance')

dendrogram(

    clust,

    leaf_rotation=0.,  # rotates the x axis labels

    leaf_font_size=8.,  # font size for the x axis labels

    labels=group_vec.index,

    orientation='left'

)

plt.show()

```



Installation

------------



Prerequisites:



- Python 3 (we use Python 3.7)

- Elastic: Several possibilities

    - Have Docker installed - needs to have the docker package installed (see below).

    - Install and start Elasticsearch and Kibana:

         or

         (pure Apache licensed version)

    - Use any running Elasticsearch and Kibana (on premise or cloud)...

- Pretrained Models: See below for Spacy Language Models and WordVectors



It is recommended to use a virtual environment:

```bash

cd $PROJECT_DIR

python -m venv venv

source venv/bin/activate

```

The source statement has to be repeated whenever you open a new terminal.



Then install

```bash

pip install nlpeasy

```

Or the development version from GitHub:

```bash

pip install --upgrade git+https://github.com/d-one/nlpeasy

```



If you want to use spaCy language models download them (90-200 MB), e.g.

```bash

python -m spacy download en_core_web_md

# and/or

python -m spacy download de_core_news_md

```

If you want to use pretrained [FastText-Wordvectors](https://fasttext.cc/docs/en/pretrained-vectors.html) (each ~7GB):

```bash

curl -O https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.en.zip

curl -O https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.de.zip

```



If you want to use Jupyter, install it to the virtual environment:

```bash

pip install jupyterlab

```



### Development

To install this module in Dev-mode, i.e. change files and reload module:

```bash

git clone https://github.com/d-one/nlpeasy

cd nlpeasy

```



It is recommended to use a virtual environment:

```bash

python -m venv venv

source venv/bin/activate

```



Install the version in edit mode:

```bash

pip install -e .

```



In Jupyter you can have reloaded code when you change the files as in:

```python

%load_ext autoreload

%autoreload 2

```



Features

--------



* Pandas based pipeline

* Support for any extensions - now includes some for Regex, spaCy, VaderSentiment

* Write results to ElasticSearch

* Automatic Kibana dashboard generation

* Have Elastic started in Docker if it is not installed locally or remotely

* Apache License 2.0



Credits

-------



This package was created with [Cookiecutter]() and the [`audreyr/cookiecutter-pypackage`] project template.