Added dockerfile

Dockerfile

@@ -0,0 +1,26 @@
+FROM python:3.7
+
+# Installing git
+RUN apt-get update -y
+RUN apt-get install -y git
+# Installing numpy and jupyterlab
+RUN pip install jupyterlab numpy Cython pyarrow
+
+# Installing requirements
+COPY ./requirements.txt requirements.txt
+RUN pip install -r requirements.txt
+
+# Copying examples
+COPY ./examples /examples
+
+# Copying this package
+COPY . /package
+RUN pip install -e /package
+
+# Download models
+RUN python -c "from lc_classifier.classifier.models import HierarchicalRandomForest;HierarchicalRandomForest({}).download_model()"
+
+WORKDIR /examples
+EXPOSE 8888
+
+CMD ["jupyter", "notebook", "--allow-root", "--ip", "0.0.0.0", "--NotebookApp.token=''"]

README.md

@@ -20,7 +20,7 @@ pip install -e .
 # Functionalities
 
 ## Augmentation
-If you want more samples you can use our implementation of data augmentation. For now you can use ShortTransientAugmenter for some transients. 
+If you want more samples you can use our implementation of data augmentation. For now you can use ShortTransientAugmenter for some transients.
 
 ## Classifier
 The classifier code contains BaseClassifier (a simple random forest) and HierarchicalRandomForest (a random forest with internal hierarchy), both with methods for fit and predict.
@@ -39,7 +39,7 @@ Before to get features, we preprocess the time series with filters and boundary
 ### Extractors:
 The extractors are the portion of code with the logic to extract features from time series. Each extractor do only one task, after that our CustomHierarchicalExtractor merge all extractors for get features to train the model.
 
-##### How can I add extractors to library? 
+##### How can I add extractors to library?
 You can use inheritance from `base extractors` and use it for create your own extractor. For now you can inherit:
 - `FeatureExtractor` is a generic extractor only fill methods.
 - `FeatureExtractorSingleBand` is a extractor that compute features by band.
@@ -83,6 +83,20 @@ After that you can see a report of tests:
 coverage report
 ```
 
+# Run a container
+
+This repository comes with a Dockerfile to test the model.
+
+To build the image run
+```
+docker build -t alerce/lc_classifier
+```
+Then run the container
+```
+docker run --rm -p 8888:8888 alerce/lc_classifier
+```
+The container comes with a jupyter notebook and some examples in `http://localhost:8888`
+
 # Reference
 
 If you use this library, please cite our work: