The main goal for this project is to make it easy to add basic Prometheus instrumentation to your sklearn models. This way we can lower the entry barrier for instrumenting ML systems and thereby raise the quality of ML in production.

from skprometheus.pipeline import make_pipeline

clf_pipeline = make_pipeline(
  StandardScaler(),
  LogisticRegression(),
)

This will expose the following prometheus metrics

For every instance that passed through predict, the predicted probabilities are logged in a histogram. This histogram has 10 bins, evenly spaced between 0.0 and 1.0

For every instance that passed through predict, the time it took to make a prediction is logged in a histogram. This histogram has 10 bins, evenly spaced between 0 and 250 ms.

We don't automatically infer the buckets of the histogram. The disadvantage there is that a new model fit will therefore have new hist buckets which make tracking different models over time difficult

The amount of instances that the model made predictions for.

The amount of times the model threw an exception while predicting for new instances

In the example below we've used the OneHotEncoder, but this also works for the OrdinalEncoder

from skprometheus.pipeline import make_pipeline
from skprometheus.preprocessing import OneHotEncoder

clf_pipeline = make_pipeline(
  OneHotEncoder(),
  LogisticRegression(),
)

Counts the number of times each category is observed for each feature when transform is called on the encoder Note that apart from the categories observed in the training data, an additional category called missing is added as a label. This counter gets increased whenever transform is called on data that contains a category that wasn't observed during training time.

Counts the number of times each category is observed for each feature when the encoder is fitted. TODO: fit time metrics need to be worked out further!