Melusine is a high-level package for french emails preprocessing, classification and feature extraction, written in Python.
🎉 We just released Melusine 2.3.1 including a new
ExchangeConnector class to interact with Outlook mailboxes.
Implement email routing on your own mailbox by following the new tutorial. 🎉
- Free software: Apache Software License 2.0
- Documentation: https://melusine.readthedocs.io.
Melusine is a high-level Python library for email classification and feature extraction, written in Python and capable of running on top of Scikit-Learn, Tensorflow 2 and Keras. Integrated models runs with Tensorflow 2.2. It is developed with a focus on emails written in French.
Use Melusine if you need a library which:
- Supports transformers, CNN and RNN models.
- Runs seamlessly on CPU and GPU.
Melusine is compatible with
Python 3.7 and
- Compatibility with python 3.7 and 3.8
- Optional dependencies (viz, transformers, all)
- Specify custom configurations with environment variable MELUSINE_CONFIG_DIR
- Use any number of JSON and YAML files for configurations (instead of just one config file)
- Fixed bug when training transformers model without meta features
- Added a class
ExchangeConnectorto interact with an Exchange Mailbox
- Added new tutorial
tutorial14_exchange_connectorto demonstrate the usage of the
- Gensim upgrade (4.0.0)
- Propagate modifications stemming from the Gensim upgrade (code and tutorials)
- Package deployment : switch from Travis CI to Github actions
- Attentive Neural Networks are now available. :tada: We propose you an original Transformer architecture as well as pre-trained BERT models (Camembert and Flaubert)
- Tutorial 13 will explain you how to get started with these models and attempt to compare them.
- Validation data can now be used to train models (See fit function from NeuralModel for usage)
- The activation function can now be modified to adapt to your needs (See NeuralModel init for usage)
- Melusine is now running with Tensorflow 2.2
- Flashtext library is now used to flag names instead of regex. It allows a faster computation.
- An Ethics Guide is now available to evaluate AI projects, with guidelines and questionnaire. The questionnaire is based on criteria derived in particular from the work of the European Commission and grouped by categories.
- Melusine also offers an easy and nice dashboard app with StreamLit. The App contains exploratory dashboard on the email dataset and a more specific study on discrimination between the dataset and a neural model classification.
The Melusine package
This package is designed for the preprocessing, classification and automatic summarization of emails written in french.
3 main subpackages are offered :
prepare_email: to preprocess and clean emails.
summarizer: to extract keywords from an email.
models: to classify e-mails according to categories pre-defined by the user or compute sentiment score based on sentiment described by the user with seed words.
2 other subpackages are offered as building blocks :
nlp_tools: to provide classic NLP tools such as tokenizer, phraser and embeddings.
utils: to provide a TransformerScheduler class to build your own transformer and integrate into a scikit-learn Pipeline.
An other subpackage is also provided to manage, modify or add parameters such as : regular expressions, keywords, stopwords, etc.
config: This modules loads a configuration dict which is essential to the Melusine package. By customizing the configurations, users may adapt the text preprocessing to their needs.
2 other subpackages are offered to provide a dashboard app and ethics guidelines for AI project :
data: contains a classic data loader and provide a StreamLit application with exploratory dashboards on input data and models.
ethics_guidelines: to provide an Ethics Guide to evaluate AI project, with guidelines and questionnaire. The questionnaire is based on criteria derived in particular from the work of the European Commission and grouped by categories.
Getting started: 30 seconds to Melusine
pip install melusine
To use Melusine in a project
Input data : Email DataFrame
The basic requirement to use Melusine is to have an input e-mail DataFrame with the following columns:
- body : Body of an email (single message or conversation history)
- header : Header/Subject of an email
- date : Reception date of an email
- from : Email address of the sender
- to : Email address of the recipient
- label (optional): Label of the email for a classification task (examples: Business, Spam, Finance or Family)
|Thank you.\nBye,\nJohn||Re: Your order||jeudi 24 mai 2018 11 h 49 CESTfirstname.lastname@example.orgemail@example.com||label_1|
To import the test dataset:
from melusine.data.data_loader import load_email_data df_email = load_email_data()
A working pre-processing pipeline is given below:
from sklearn.pipeline import Pipeline from melusine.utils.transformer_scheduler import TransformerScheduler from melusine.prepare_email.manage_transfer_reply import check_mail_begin_by_transfer, update_info_for_transfer_mail, add_boolean_transfer, add_boolean_answer from melusine.prepare_email.build_historic import build_historic from melusine.prepare_email.mail_segmenting import structure_email from melusine.prepare_email.body_header_extraction import extract_last_body from melusine.prepare_email.cleaning import clean_body ManageTransferReply = TransformerScheduler( functions_scheduler=[ (check_mail_begin_by_transfer, None, ['is_begin_by_transfer']), (update_info_for_transfer_mail, None, None), (add_boolean_answer, None, ['is_answer']), (add_boolean_transfer, None, ['is_transfer']) ]) EmailSegmenting = TransformerScheduler( functions_scheduler=[ (build_historic, None, ['structured_historic']), (structure_email, None, ['structured_body']) ]) Cleaning = TransformerScheduler( functions_scheduler=[ (extract_last_body, None, ['last_body']), (clean_body, None, ['clean_body']) ]) prepare_data_pipeline = Pipeline([ ('ManageTransferReply', ManageTransferReply), ('EmailSegmenting', EmailSegmenting), ('Cleaning', Cleaning), ]) df_email = prepare_data_pipeline.fit_transform(df_email)
In this example, the pre-processing functions applied are:
check_mail_begin_by_transfer: Email is a direct transfer (True/False)
update_info_for_transfer_mail: Update body, header, from, to, date if direct transfer
add_boolean_answer: Email is an answer (True/False)
add_boolean_transfer: Email is transferred (True/False)
build_historic: When email is a conversation, reconstructs the individual message history
structure_email: Splits each messages into parts and tags them (tags: Hello, Body, Greetings, etc)
Phraser and Tokenizer pipeline
A pipeline to train and apply the phraser end tokenizer is given below:
from melusine.nlp_tools.phraser import Phraser, phraser_on_body from melusine.nlp_tools.tokenizer import Tokenizer phraser = Phraser(input_column='clean_body') phraser.train(df_email) PhraserTransformer = TransformerScheduler( functions_scheduler=[ (phraser_on_body, (phraser,), ['clean_body']) ]) phraser_tokenizer_pipeline = Pipeline([ ('PhraserTransformer', PhraserTransformer), ('Tokenizer', Tokenizer(input_column='clean_body')) ]) df_email = phraser_tokenizer_pipeline.fit_transform(df_email)
An example of embedding training is given below:
from melusine.nlp_tools.embedding import Embedding embedding = Embedding(input_column='clean_body', min_count=10) embedding.train(df_email)
A pipeline to prepare the metadata is given below:
from melusine.prepare_email.metadata_engineering import MetaExtension, MetaDate, Dummifier metadata_pipeline = Pipeline([ ('MetaExtension', MetaExtension()), ('MetaDate', MetaDate()), ('Dummifier', Dummifier()) ]) df_meta = metadata_pipeline.fit_transform(df_email)
An example of keywords extraction is given below:
from melusine.summarizer.keywords_generator import KeywordsGenerator keywords_generator = KeywordsGenerator() df_email = keywords_generator.fit_transform(df_email)
The package includes multiple neural network architectures including CNN, RNN, Attentive and pre-trained BERT Networks. An example of classification is given below:
from sklearn.preprocessing import LabelEncoder from melusine.nlp_tools.embedding import Embedding from melusine.models.neural_architectures import cnn_model from melusine.models.train import NeuralModel X = df_email.drop(['label'], axis=1) y = df_email.label le = LabelEncoder() y = le.fit_transform(y) pretrained_embedding = embedding nn_model = NeuralModel(architecture_function=cnn_model, pretrained_embedding=pretrained_embedding, text_input_column='clean_body') nn_model.fit(X, y, tensorboard_log_dir="./data") y_res = nn_model.predict(X)
Training with tensorflow 2 can be monitored using tensorboard :
Pandas dataframes columns
Because Melusine manipulates pandas dataframes, the naming of the columns is imposed. Here is a basic glossary to provide an understanding of each columns manipulated. Initial columns of the dataframe:
- body : the body of the email. It can be composed of a unique message, a history of messages, a transfer of messages or a combination of history and transfers.
- header : the subject of the email.
- date : the date the email has been sent. It corresponds to the date of the last email message.
- from : the email address of the author of the last email message.
- to : the email address of the recipient of the last email message.
Columns added by Melusine:
is_begin_by_transfer : boolean, indicates if the email is a direct transfer. In that case it is recommended to update the value of the initial columns with the information of the message transferred.
is_answer : boolean, indicates if the email contains a history of messages
is_transfer : boolean, indicates if the email is a transfer (in that case it does not have to be a direct transfer).
structured_historic : list of dictionaries, each dictionary corresponds to a message of the email. The first dictionary corresponds to the last message (the one that has been written) while the last dictionary corresponds to the first message of the history. Each dictionary has two keys :
- meta : to access the metadata of the message as a string.
- text : to access the message itself as a string.
structured_body : list of dictionaries, each dictionary corresponds to a message of the email. The first dictionary corresponds to the last message (the one that has been written) while the last dictionary corresponds to the first message of the history. Each dictionary has two keys :
meta : to access the metadata of the message as a dictionary. The dictionary has three keys:
- date : the date of the message.
- from : the email address of the author of the message.
- to : the email address of the recipient of the message.
text : to access the message itself as a dictionary. The dictionary has two keys:
- header : the subject of the message.
- structured_text : the different parts of the message segmented and tagged as a list of dictionaries. Each dictionary has two keys:
- part : to access the part of the message as a string.
- tags : to access the tag of the part of the message.
last_body : string, corresponds to the part of the last email message that has been tagged as
clean_body : string, corresponds a cleaned last_body.
clean_header : string, corresponds to a cleaned header.
clean_text : string, concatenation of clean_header and clean_body.
tokens : list of strings, corresponds to a tokenized column, by default clean_text.
keywords : list of strings, corresponds to the keywords of extracted from the tokens column.
Each messages of an email are segmented in the structured_body columns and each part is assigned a tag:
RE/TR: any metadata such as date, from, to, etc.
DISCLAIMER: any disclaimer such as
L'émetteur décline toute responsabilité....
GREETINGS: any greetings such as
PJ: any indication of an attached document such as
See attached file....
FOOTER: any footer such as
Provenance : Courrier pour Windows.
HELLO: any salutations such as
THANKS: any thanks such as
Avec mes remerciements
BODY: the core of the the message which contains the valuable information.
Melusine also offered an easy and nice dashboard app with StreamLit. The App contains exploratory dashboard on the email dataset and more specific study on discrimination between the dataset and a neural model classification.
To run the app, run the following command in your terminal in the melusine/data directory :
streamlit run dashboard_app.py
Melusine also contains Ethics Guidelines to evaluate AI project. The document and criteria are derived in particular from the work of the European Commission.
The pdf file is located in the melusine/ethics_guidelines directory :
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