Get Started: Metrics, Parameters, and Plots

DVC makes it easy to track metrics, update parameters, and visualize performance with plots. These concepts are introduced below.

All of the above can be combined into experiments to run and compare many iterations of your ML project.

Collecting metrics

First, let's see what is the mechanism to capture values for these ML attributes. Let's add a final evaluation stage to our pipeline from before:

$ dvc run -n evaluate \
          -d src/evaluate.py -d model.pkl -d data/features \
          -M scores.json \
          --plots-no-cache prc.json \
          --plots-no-cache roc.json \
          python src/evaluate.py model.pkl \
                 data/features scores.json prc.json roc.json

evaluate:
  cmd: python src/evaluate.py model.pkl data/features ...
  deps:
    - data/features
    - model.pkl
    - src/evaluate.py
  metrics:
    - scores.json:
        cache: false
  plots:
    - prc.json:
        cache: false
    - roc.json:
        cache: false

evaluate.py writes the model's ROC-AUC and average precision to scores.json, which in turn is marked as a metrics file with -M. Its contents are:

{ "avg_prec": 0.5204838673030754, "roc_auc": 0.9032012604172255 }

evaluate.py also writes precision, recall, and thresholds arrays (obtained using precision_recall_curve) into the plots file prc.json:

{
  "prc": [
    { "precision": 0.021473008227975116, "recall": 1.0, "threshold": 0.0 },
    ...,
    { "precision": 1.0, "recall": 0.009345794392523364, "threshold": 0.6 }
  ]
}

Similarly, it writes arrays for the roc_curve into roc.json for an additional plot.

DVC doesn't force you to use any specific file names, nor does it enforce a format or structure of a metrics or plots file. It's completely user/case-defined. Refer to dvc metrics and dvc plots for more details.

You can view tracked metrics and plots with DVC. Let's start with the metrics:

$ dvc metrics show
Path         avg_prec    roc_auc
scores.json  0.52048     0.9032

To view plots, first specify which arrays to use as the plot axes. We only need to do this once, and DVC will save our plot configurations.

$ dvc plots modify prc.json -x recall -y precision
Modifying stage 'evaluate' in 'dvc.yaml'
$ dvc plots modify roc.json -x fpr -y tpr
Modifying stage 'evaluate' in 'dvc.yaml'

Now let's view the plots:

$ dvc plots show
file:///Users/dvc/example-get-started/plots.html

Let's save this iteration, so we can compare it later:

$ git add scores.json prc.json roc.json
$ git commit -a -m "Create evaluation stage"

Later we will see how to compare and visualize different pipeline iterations. For now, let's see how can we capture another important piece of information which will be useful for comparison: parameters.

Defining stage parameters

It's pretty common for data science pipelines to include configuration files that define adjustable parameters to train a model, do pre-processing, etc. DVC provides a mechanism for stages to depend on the values of specific sections of such a config file (YAML, JSON, TOML, and Python formats are supported).

Luckily, we should already have a stage with parameters in dvc.yaml:

featurize:
  cmd: python src/featurization.py data/prepared data/features
  deps:
    - data/prepared
    - src/featurization.py
  params:
    - featurize.max_features
    - featurize.ngrams
  outs:
    - data/features

$ dvc run -n featurize \
          -p featurize.max_features,featurize.ngrams \
          -d src/featurization.py -d data/prepared \
          -o data/features \
          python src/featurization.py data/prepared data/features

The params section defines the parameter dependencies of the featurize stage. By default, DVC reads those values (featurize.max_features and featurize.ngrams) from a params.yaml file. But as with metrics and plots, parameter file names and structure can also be user- and case-defined.

Here's the contents of our params.yaml file:

prepare:
  split: 0.20
  seed: 20170428

featurize:
  max_features: 500
  ngrams: 1

train:
  seed: 20170428
  n_est: 50
  min_split: 2

Updating params and iterating

We are definitely not happy with the AUC value we got so far! Let's edit the params.yaml file to use bigrams and increase the number of features:

 featurize:
-  max_features: 500
-  ngrams: 1
+  max_features: 1500
+  ngrams: 2

The beauty of dvc.yaml is that all you need to do now is run:

$ dvc repro

It'll analyze the changes, use existing results from the run-cache, and execute only the commands needed to produce new results (model, metrics, plots).

The same logic applies to other possible adjustments — edit source code, update datasets — you do the changes, use dvc repro, and DVC runs what needs to be.

Comparing iterations

Finally, let's see how the updates improved performance. DVC has a few commands to see changes in and visualize metrics, parameters, and plots. These commands can work for one or across multiple pipeline iteration(s). Let's compare the current "bigrams" run with the last committed "baseline" iteration:

$ dvc params diff
Path         Param                   HEAD  workspace
params.yaml  featurize.max_features  500   1500
params.yaml  featurize.ngrams        1     2

dvc params diff can show how params in the workspace differ vs. the last commit.

dvc metrics diff does the same for metrics:

$ dvc metrics diff
Path         Metric    HEAD     workspace  Change
scores.json  avg_prec  0.52048  0.55259    0.03211
scores.json  roc_auc   0.9032   0.91536    0.01216

And finally, we can compare both precision recall and roc curves with a single command!

$ dvc plots diff
file:///Users/dvc/example-get-started/plots.html

See dvc plots diff for more info on its options.

All these commands also accept Git revisions (commits, tags, branch names) to compare.

On the next page, you can learn advanced ways to track, organize, and compare more experiment iterations.