Metadata-Version: 2.1
Name: ml4ir
Version: 0.0.1
Summary: Machine Learning libraries for Information Retrieval
Home-page: https://www.salesforce.com/
Author: Search Relevance, Salesforce
Author-email: searchrelevancyscrumteam@salesforce.com
License: ASL 2.0
Description: # ml4ir: Machine Learning Library for Information Retrieval
        
        ## Setup
        #### Requirements
        * python3.6+
        * pip3
        * docker (version 18.09+ tested)
        
        
        #### Using PIP
        ml4ir can be installed as a pip package by using the following command
        
        ```
        pip install  'git+https://git@github.com/salesforce/ml4ir#egg=ml4ir&subdirectory=python'
        ```
        
        This will install ml4ir-0.0.1 (the current version). In future, when this package is available on PyPI, it will be as simple as pip install ml4ir
        
        
        #### Docker (Recommended)
        We have set up a `docker-compose.yml` file for building and using docker containers to train models.
        
        To run unit tests
        ```
        docker-compose up
        ```
        
        To invoke ml4ir with custom arguments with docker, run
        ```
        /bin/bash tools/run_docker.sh ml4ir \
        	python3 ml4ir/base/pipeline.py
            <args>
        ```
        
        For ranking applications, specifically, use
        ```
        /bin/bash tools/run_docker.sh ml4ir \
        	python3 ml4ir/applications/ranking/pipeline.py
            <args>
        ```
        
        Refer to usage section below for details on how to run ml4ir - ranking
        
        Check `ml4ir/applications/ranking/scripts/example_run.sh` for a predefined example run.
        
        To run example invocation of ranking application with docker,
        ```
        /bin/bash python/ml4ir/applications/ranking/scripts/example_run.sh
        ```
        
        #### Virtual Environment
        Install virtualenv
        ```
        pip3 install virtualenv
        ```
        
        Create new python3 virtual environment inside your git repo (it's .gitignored, don't worry)
        ```
        cd $PLACE_YOU_CAlLED_GIT_CLONE/ml4ir
        python3 -m venv python/env/.ml4ir_venv3
        ```
        
        Activate virtualenv
        ```
        cd python/
        source env/.ml4ir_venv3/bin/activate
        ```
        
        Install all dependencies (carefully)
        ```
        pip3 install --upgrade setuptools
        pip install --upgrade pip
        pip3 install -r requirements.txt
        ```
        
        Note, there are some AWS incompatibilities, gotta fix that, but you can ignore them for now
        ```
        ERROR: botocore 1.14.9 has requirement docutils<0.16,>=0.10, but you'll have docutils 0.16 which is incompatible.
        ERROR: awscli 1.17.9 has requirement docutils<0.16,>=0.10, but you'll have docutils 0.16 which is incompatible.
        ERROR: awscli 1.17.9 has requirement rsa<=3.5.0,>=3.1.2, but you'll have rsa 4.0 which is incompatible.
        ERROR: tensorflow-probability 0.8.0 has requirement cloudpickle==1.1.1, but you'll have cloudpickle 1.2.2 which is incompatible.
        ERROR: apache-beam 2.18.0 has requirement dill<0.3.2,>=0.3.1.1, but you'll have dill 0.3.0 which is incompatible.
        ERROR: apache-beam 2.18.0 has requirement httplib2<=0.12.0,>=0.8, but you'll have httplib2 0.17.0 which is incompatible.
        ERROR: apache-beam 2.18.0 has requirement pyarrow<0.16.0,>=0.15.1; python_version >= "3.0" or platform_system != "Windows", but you'll have pyarrow 0.14.1 which is incompatible.
        ERROR: tfx-bsl 0.15.3 has requirement absl-py<0.9,>=0.7, but you'll have absl-py 0.9.0 which is incompatible.
        ERROR: tfx-bsl 0.15.3 has requirement apache-beam[gcp]<2.17,>=2.16, but you'll have apache-beam 2.18.0 which is incompatible.
        ERROR: tensorflow-transform 0.15.0 has requirement absl-py<0.9,>=0.7, but you'll have absl-py 0.9.0 which is incompatible.
        ```
        
        Note that pre-commit-hooks are required, and installed as a requirement if needed. 
        If an error results that they didn't install, execute `pre-commit install` to install git hooks in your .git/ directory.
        
        
        Set the PYTHONPATH environment variable
        ```
        export PYTHONPATH=$PYTHONPATH:`pwd`/python
        ```
        
        ## Usage
        The entrypoint into the training or evaluation functionality of ml4ir is through `ml4ir/base/pipeline.py` and for application specific overrides, look at `ml4ir/applications/<eg: ranking>/pipeline.py
        
        ### ml4ir Library
        To use ml4ir as a deep learning library to build relevance models, look at the walkthrough under `notebooks/PointwiseRankingDemo.ipynb` or `notebooks/PointwiseRankingDemo.html`(contains architecture diagrams). The notebook walks one through building, training, saving, and the entire life cycle of a `RelevanceModel` from the bottom up. Additionally, the HTML version also sheds light on the design of ml4ir and the data format used.
        
        ### Applications - Ranking
        #### Examples
        Using TFRecord
        ```
        python ml4ir/applications/ranking/pipeline.py \
        --data_dir ml4ir/applications/ranking/tests/data/tfrecord \
        --feature_config ml4ir/applications/ranking/tests/data/config/feature_config.yaml \
        --run_id test \
        --data_format tfrecord \
        --execution_mode train_inference_evaluate
        ```
        
        Using CSV
        ```
        python ml4ir/applications/ranking/pipeline.py \
        --data_dir ml4ir/applications/ranking/tests/data/csv \
        --feature_config ml4ir/applications/ranking/tests/data/config/feature_config.yaml \
        --run_id test \
        --data_format csv \
        --execution_mode train_inference_evaluate
        ```
        
        Running in inference mode using the default serving signature
        ```
        python ml4ir/applications/ranking/pipeline.py \
        --data_dir ml4ir/applications/ranking/tests/data/tfrecord \
        --feature_config ml4ir/applications/ranking/tests/data/config/feature_config.yaml \
        --run_id test \
        --data_format tfrecord \
        --model_file `pwd`/models/test/final/default \
        --execution_mode inference_only
        
        NOTE: Make sure to add the right data and feature config before training models.
        TODO: describe how to do this
        
        ```
        ## Running Tests
        To run all the python based tests under `ml4ir`
        ```
        python3 -m pytest
        ```
        
        To run specific tests, 
        ```
        python3 -m pytest /path/to/test/module
        ```
        
        ## Project Organization
        The following structure is a little out of date (TODO(jake) - fix it!)
        
            ├── LICENSE
            ├── Makefile           <- Makefile with commands like `make data` or `make train`
            ├── README.md          <- The top-level README for developers using this project.
            ├── data
            │   ├── external       <- Data from third party sources.
            │   ├── interim        <- Intermediate data that has been transformed.
            │   ├── processed      <- The final, canonical data sets for modeling.
            │   └── raw            <- The original, immutable data dump.
            │
            ├── docs               <- A default Sphinx project; see sphinx-doc.org for details
            │
            ├── models             <- Trained and serialized models, model predictions, or model summaries
            │
            ├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
            │                         the creator's initials, and a short `-` delimited description, e.g.
            │                         `1.0-jqp-initial-data-exploration`.
            │
            ├── references         <- Data dictionaries, manuals, and all other explanatory materials.
            │
            ├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
            │   └── figures        <- Generated graphics and figures to be used in reporting
            │
            ├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
            │                         generated with `pip freeze > requirements.txt`
            │
            ├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
            ├── ml4ir                <- Source code for use in this project.
            │   ├── __init__.py    <- Makes ml4ir a Python module
            │   │
            │   ├── data           <- Scripts to download or generate data
            │   │   └── make_dataset.py
            │   │
            │   ├── features       <- Scripts to turn raw data into features for modeling
            │   │   └── build_features.py
            │   │
            │   ├── models         <- Scripts to train models and then use trained models to make
            │   │   │                 predictions
            │   │   ├── predict_model.py
            │   │   └── train_model.py
            │   │
            │   └── visualization  <- Scripts to create exploratory and results oriented visualizations
            │       └── visualize.py
            │
            └── tox.ini            <- tox file with settings for running tox; see tox.testrun.org
        
        
        --------
        
        <p><small>Project based on the <a target="_blank" href="https://drivendata.github.io/cookiecutter-data-science/">cookiecutter data science project template</a>. #cookiecutterdatascience</small></p>
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: >=3.6
Description-Content-Type: text/markdown
