Kedro Tf Text 📜

This package consists of Kedro pipelines for preprocessing text and tabular data for multimodal machine learning in healthcare. This package preprocesses BERT and CNN text models. Use kedro-tf-image for preprocessing image models. The kedro-tf-utils creates fusion models for training. The kedro-multimodal template uses these pipelines. End users can just fork kedro-multimodal template to build multimodal pipelines!

How to install


pip install git+https://github.com/dermatologist/kedro-tf-text.git

Pipelines

Name	Input	Output	Description	Params
bert.download_bert	["bert_model", "params:bert_model"]	"bert_model_saved"	Download and save bert model (See bert_model and bert_model_saved in catalog)	None
cnn.cnn_text_pipeline	["glove_embedding", "params:cnn_text_model"]	cnn_text_model	creates a CNN text model from GloVe embedding layer	MAX_SEQ_LENGTH
preprocess.glove_embedding	["text_data", "params:embedding"]	"glove_embedding" (Pickle)	Create GloVe embedding	REPORT_FIELD, ID, TARGET
preprocess.process_text_pipeline	["text_data", "word2vec_embedding", "params:embedding"]	"processed_text" (Pickle)	process text using the word index from Word2Vec model	REPORT_FIELD, ID, TARGET
tabular.tabular_model_pipeline	["tabular_data", "params:tabular"]	tabular_model (Pickle)	Create a model from tabular csv data	DROP, TARGET, EPOCHS, DENSE_LAYER

Catalog

bert_model:
  type: kedro_tf_text.extras.datasets.bert_model_download.BertModelDownload
  preprocessor_url: "https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3"
  encoder_url: "https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/4"

bert_model_saved:
  type: tensorflow.TensorFlowModelDataset
  filepath: data/06_models/bert-tf

## ID | Long line of text | outcome (y)
text_data:
  type: pandas.CSVDataSet
  filepath: data/01_raw/test_report.csv

## ID | included | fields | excluded | fields | outcome (y)
tabular_data:
  type: pandas.CSVDataSet
  filepath: data/01_raw/test_dataset.csv

word2vec_embedding:
  type: pickle.PickleDataSet
  filepath: data/06_models/word2vec-embedding.pkl

glove_embedding:
  type: pickle.PickleDataSet
  filepath: data/06_models/glove-embedding.pkl

tabular_model:
  type: pickle.PickleDataSet
  filepath: data/06_models/tabular_model.pkl

processed_text:
  type: pickle.PickleDataSet
  filepath: data/03_primary/processed-text.pkl

fusion_model:
  type: tensorflow.TensorFlowModelDataset
  filepath: data/07_model_output/fusion

datasetinmemory:
  type: MemoryDataSet
  copy_mode: assign

Author

Bell Eapen

Overview

This is your new Kedro project, which was generated using Kedro 0.18.1.

Take a look at the Kedro documentation to get started.

Rules and guidelines

In order to get the best out of the template:

Don't remove any lines from the .gitignore file we provide
Make sure your results can be reproduced by following a data engineering convention
Don't commit data to your repository
Don't commit any credentials or your local configuration to your repository. Keep all your credentials and local configuration in conf/local/

How to install dependencies

Declare any dependencies in src/requirements.txt for pip installation and src/environment.yml for conda installation.

To install them, run:

pip install -r src/requirements.txt

How to run your Kedro pipeline

You can run your Kedro project with:

kedro run

How to test your Kedro project

Have a look at the file src/tests/test_run.py for instructions on how to write your tests. You can run your tests as follows:

kedro test

To configure the coverage threshold, go to the .coveragerc file.

Project dependencies

To generate or update the dependency requirements for your project:

kedro build-reqs

This will pip-compile the contents of src/requirements.txt into a new file src/requirements.lock. You can see the output of the resolution by opening src/requirements.lock.

After this, if you'd like to update your project requirements, please update src/requirements.txt and re-run kedro build-reqs.

Further information about project dependencies

How to work with Kedro and notebooks

Note: Using kedro jupyter or kedro ipython to run your notebook provides these variables in scope: context, catalog, and startup_error.

Jupyter, JupyterLab, and IPython are already included in the project requirements by default, so once you have run pip install -r src/requirements.txt you will not need to take any extra steps before you use them.

Jupyter

To use Jupyter notebooks in your Kedro project, you need to install Jupyter:

pip install jupyter

After installing Jupyter, you can start a local notebook server:

kedro jupyter notebook

JupyterLab

To use JupyterLab, you need to install it:

pip install jupyterlab

You can also start JupyterLab:

kedro jupyter lab

IPython

And if you want to run an IPython session:

kedro ipython

How to convert notebook cells to nodes in a Kedro project

You can move notebook code over into a Kedro project structure using a mixture of cell tagging and Kedro CLI commands.

By adding the node tag to a cell and running the command below, the cell's source code will be copied over to a Python file within src/<package_name>/nodes/:

kedro jupyter convert <filepath_to_my_notebook>

Note: The name of the Python file matches the name of the original notebook.

Alternatively, you may want to transform all your notebooks in one go. Run the following command to convert all notebook files found in the project root directory and under any of its sub-folders:

kedro jupyter convert --all

How to ignore notebook output cells in `git`

To automatically strip out all output cell contents before committing to git, you can run kedro activate-nbstripout. This will add a hook in .git/config which will run nbstripout before anything is committed to git.