PlatIAgro Native Tasks

└── tasks
    ├── automl-classifier
    │   ├── Experiment.ipynb      <- Scripts to train models
    │   └── Deployment.ipynb      <- Scripts to make predictions
    ├── automl-regressor
    │   ├── Experiment.ipynb
    │   └── Deployment.ipynb
    ├── logistic-regression
    │   ├── Experiment.ipynb
    │   └── Deployment.ipynb
    ├── linear-regression
    │   ├── Experiment.ipynb
    │   └── Deployment.ipynb
    ├── svc
    │   ├── Experiment.ipynb
    │   └── Deployment.ipynb
    ├── svr
    │   ├── Experiment.ipynb
    │   └── Deployment.ipynb
    ├── mlp-classifier
    │   ├── Experiment.ipynb
    │   └── Deployment.ipynb
    ├── mlp-regressor
    │   ├── Experiment.ipynb
    │   └── Deployment.ipynb
    ├── random-forest-classifier
    │   ├── Experiment.ipynb
    │   └── Deployment.ipynb
    ├── random-forest-regressor
    │   ├── Experiment.ipynb
    │   └── Deployment.ipynb
    ├── descriptive-analysis
    │   └── Experiment.ipynb
    ├── feature-tools
    │   ├── Experiment.ipynb
    │   └── Deployment.ipynb
    ├── filter-selection
    │   ├── Experiment.ipynb
    │   └── Deployment.ipynb
    ├── grouping-categorical-features
    │   ├── Experiment.ipynb
    │   └── Deployment.ipynb
    ├── imputer
    │   ├── Experiment.ipynb
    │   └── Deployment.ipynb
    ├── isolation-forest-clustering
    │   ├── Experiment.ipynb
    │   └── Deployment.ipynb
    ├── kmeans-clustering
    │   ├── Experiment.ipynb
    │   └── Deployment.ipynb
    ├── normalizer
    │   ├── Experiment.ipynb
    │   └── Deployment.ipynb
    ├── pre-selection
    │   ├── Experiment.ipynb
    │   └── Deployment.ipynb
    ├── rfe-selector
    │   ├── Experiment.ipynb
    │   └── Deployment.ipynb
    ├── robust-scaler
    │   ├── Experiment.ipynb
    │   └── Deployment.ipynb
    ├── simulated-annealing
    │   ├── Experiment.ipynb
    │   └── Deployment.ipynb
    ├── transformation-graph
    │   ├── Experiment.ipynb
    │   └── Deployment.ipynb
    └── variance-threshold
       ├── Experiment.ipynb
       └── Deployment.ipynb

Testing

Install the testing requirements:

apt-get -y install tesseract-ocr tesseract-ocr-por tesseract-ocr-eng
pip install -r requirements.txt
pip install torch==1.5.1+cpu torchvision==0.6.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
pip install transformers==3.0.2

Export these environment variables:

export MINIO_ENDPOINT=localhost:9000
export MINIO_ACCESS_KEY=minio
export MINIO_SECRET_KEY=minio123

Start MinIO and Datasets API:

docker network create tasks

docker run -d -p 9000:9000 \
--name minio \
-e "MINIO_ACCESS_KEY=$MINIO_ACCESS_KEY" \
-e "MINIO_SECRET_KEY=$MINIO_SECRET_KEY" \
--network tasks \
minio/minio:RELEASE.2018-02-09T22-40-05Z server /data

docker run -d -p 8080:8080 \
--name datasets \
-e "MINIO_ENDPOINT=minio:9000" \
-e "MINIO_ACCESS_KEY=$MINIO_ACCESS_KEY" \
-e "MINIO_SECRET_KEY=$MINIO_SECRET_KEY" \
--network tasks \
platiagro/datasets:0.2.0

Use the following command to run all tests:

pytest