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!
pip install git+https://github.com/dermatologist/kedro-tf-text.git
| 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 |
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
This is your new Kedro project, which was generated using Kedro 0.18.1.
Take a look at the Kedro documentation to get started.
In order to get the best out of the template:
- Don't remove any lines from the
.gitignorefile 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/
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
You can run your Kedro project with:
kedro run
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.
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
Note: Using
kedro jupyterorkedro ipythonto run your notebook provides these variables in scope:context,catalog, andstartup_error.Jupyter, JupyterLab, and IPython are already included in the project requirements by default, so once you have run
pip install -r src/requirements.txtyou will not need to take any extra steps before you use them.
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
To use JupyterLab, you need to install it:
pip install jupyterlab
You can also start JupyterLab:
kedro jupyter lab
And if you want to run an IPython session:
kedro ipython
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
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.
Note: Your output cells will be retained locally.
Further information about building project documentation and packaging your project