- This repo is an introductory project to understand the creation of ML models using DVC pipelines
- With DagsHub user can visualise and compare their experiments
- Clone the repo:
- Initialise dvc
- Configure dvc remote
- Version data file with dvc
- You are good to go. No define pipelines and build your first ML model and pipeline. Enjoy the reproducibility of experiments.
Idea is to build multiple model and ensemble them to create a stable strong model.
Your final pipeline structure will look like this in the end:
+-------------------+
| data/iris.csv.dvc |
+-------------------+
*
*
*
+-----------+
| split.dvc |
+-----------+
*
*
*
+---------------+
****| featurize.dvc |****
******** +---------------+ ********
******** ** *** *********
******** *** ** ********
***** ** ** ********
+--------------------+ +---------------+ +-------------------+ *****
| train_logistic.dvc |** | train_svc.dvc | | train_forrest.dvc | ********
+--------------------+ ******** +---------------+ +-------------------+ ********
******** ** *** *********
******** *** ** ********
***** ** ** *****
+--------------------+
| train_ensemble.dvc |
+--------------------+Order is data -> train_test_split -> feature_extraction -> 3 models -> ensemble_model and Done!!
With data/iris.csv versioned in DVC you can start with your first pipeline.
Define Stage1 => split.dvc i.e train_test_split.
dvc run -f split.dvc\
-d data/iris.csv\
-d src/train_test_split.py\
-o data/split\
python src/train_test_split.py -i "data/iris.csv" -o "data/split/"Define Stage2 => featurize.dvc i.e Feature Engineering.
dvc run -f featurize.dvc\
-d data/split\
-d src/feature_engineering.py\
-p pca\
-o data/features\
-o data/models/pca/model.gz\
-M data/models/pca/metrics.csv\
python src/feature_engineering.py -i "data/split/" -o "data/features/" -o "data/models/pca/"Define Stage3.a => train_logistic.dvc i.e Fit Logistic Regression Model.
dvc run -f train_logistic.dvc\
-d src/logistic_regression.py\
-d data/features\
-p logistic\
-o data/models/logistic/model.gz\
-M data/models/logistic/metrics.csv\
python src/logistic_regression.py -i "data/features/" -o "data/models/logistic/"Define Stage3.b => train_svc.dvc i.e Fit Linear SVC Model.
dvc run -f train_svc.dvc\
-d src/linear_svc.py\
-d data/features\
-p svc\
-o data/models/svc/model.gz\
-M data/models/svc/metrics.csv\
python src/linear_svc.py -i "data/features/" -o "data/models/svc/"Define Stage3.c => train_forrest.dvc i.e Fit Random Forrest Model.
dvc run -f train_forrest.dvc\
-d src/random_forrest.py\
-d data/features\
-p forrest\
-o data/models/r_forrest/model.gz\
-M data/models/r_forrest/metrics.csv\
python src/random_forrest.py -i "data/features/" -o "data/models/r_forrest/"Define Stage4 => train_ensemble.dvc i.e Create an Ensemble Model.
dvc run -f train_ensemble.dvc\
-d src/ensemble.py\
-d data/features\
-d data/models/logistic/model.gz\
-d data/models/svc/model.gz\
-d data/models/r_forrest/model.gz\
-p ensemble\
-o data/models/ensemble/model.gz\
-M data/models/ensemble/metrics.csv\
python src/ensemble.py -i "data/features/" -m "data/models/" -o "data/models/ensemble/"