|Ivan Shcheklein 9e05ab4590 Evaluate bigrams model||1 month ago|
|.dvc||1 month ago|
|data||1 month ago|
|src||1 month ago|
|.gitignore||1 month ago|
|README.md||1 month ago|
|dvc.lock||1 month ago|
|dvc.yaml||1 month ago|
|params.yaml||1 month ago|
|prc.json||1 month ago|
|scores.json||1 month ago|
|DVC Managed File|
|Git Managed File|
|DVC Managed File|
|Git Managed File|
This is an auto-generated repository for use in DVC Get Started. It is a step-by-step quick introduction into basic DVC concepts.
The project is a natural language processing (NLP) binary classifier problem of
predicting tags for a given StackOverflow question. For example, we want one
classifier which can predict a post that is about the Python language by tagging
🐛 Please report any issues found in this project here - example-repos-dev.
Python 3.6+ is required to run code from this repo.
$ git clone https://github.com/iterative/example-get-started $ cd example-get-started
Now let's install the requirements. But before we do that, we strongly recommend creating a virtual environment with a tool such as virtualenv:
$ virtualenv -p python3 .env $ source .env/bin/activate $ pip install -r src/requirements.txt
This DVC project comes with a preconfigured DVC remote storage that holds raw data (input), intermediate, and final results that are produced. This is a read-only HTTP remote.
$ dvc remote list storage https://remote.dvc.org/get-started
You can run
dvc pull to download the data:
$ dvc pull
$ dvc repro Data and pipelines are up to date.
If you'd like to test commands like
that require write access to the remote storage, the easiest way would be to set
up a "local remote" on your file system:
This kind of remote is located in the local file system, but is external to the DVC project.
$ mkdir -P /tmp/dvc-storage $ dvc remote add local /tmp/dvc-storage
You should now be able to run:
$ dvc push -r local
This project with the help of the Git tags reflects the sequence of actions that are run in the DVC get started guide. Feel free to checkout one of them and play with the DVC commands having the playground ready.
0-git-init: Empty Git repository initialized.
1-dvc-init: DVC has been initialized.
.dvc/with the cache directory created.
2-track-data: Raw data file
data.xmldownloaded and tracked with DVC using
dvc add. First
3-config-remote: Remote HTTP storage initialized. It's a shared read only storage that contains all data artifacts produced during next steps.
dvc importto get the same
data.xmlfrom the DVC data registry.
5-source-code: Source code downloaded and put into Git.
dvc.yamland the first pipeline stage with
dvc run. It transforms XML data into TSV.
8-ml-pipeline: Feature extraction and train stages created. It takes data in TSV format and produces two
.pklfiles that contain serialized feature matrices. Tain runs random forest classifier and creates the
9-evaluate: Evaluation stage. Runs the model on a test dataset to produce its performance AUC value. The result is dumped into a DVC metric file so that we can compare it with other experiments later.
10-bigrams-model: Bigrams experiment, code has been modified to extract more features. We run
dvc reprofor the first time to illustrate how DVC can reuse cached files and detect changes along the computational graph, regenerating the model with the updated data.
11-bigrams-experiment: Reproduce the evaluation stage with the bigrams based model.
There are two additional tags:
baseline-experiment: First end-to-end result that we have performance metric for.
bigrams-experiment: Second experiment (model trained using bigrams features).
These tags can be used to illustrate
-T options across different
The data files, DVC files, and results change as stages are created one by one.
After cloning and using
dvc pull to download data
tracked by DVC, the workspace should look like this:
$ tree . ├── README.md ├── data # <-- Directory with raw and intermediate data │ ├── data.xml # <-- Initial XML StackOverflow dataset (raw data) │ ├── data.xml.dvc # <-- .dvc file - a placeholder/pointer to raw data │ ├── features # <-- Extracted feature matrices │ │ ├── test.pkl │ │ └── train.pkl │ └── prepared # <-- Processed dataset (split and TSV formatted) │ ├── test.tsv │ └── train.tsv ├── dvc.lock ├── dvc.yaml # <-- DVC pipeline file ├── model.pkl # <-- Trained model file ├── params.yaml # <-- Parameters file ├── prc.json # <-- Precision-recall curve data points ├── scores.json # <-- Binary classifier final metrics (e.g. AUC) └── src # <-- Source code to run the pipeline stages ├── evaluate.py ├── featurization.py ├── prepare.py ├── requirements.txt # <-- Python dependencies needed in the project └── train.py