OSS Quickstart - Data and ML monitoring

Run your first evaluation using Evidently open-source, for tabular data.

You are looking at the old Evidently documentation: this API is available with versions 0.6.7 or lower and Evidently Cloud v1. Check the newer docs version here.

It's best to run this example in Jupyter Notebook or Google Colab so that you can render HTML Reports directly in a notebook cell.

Installation

Install Evidently using the pip package manager:

!pip install evidently

Imports

Import the Evidently components and a toy “Iris” dataset:

import pandas as pd

from sklearn import datasets

from evidently.test_suite import TestSuite
from evidently.test_preset import DataStabilityTestPreset

from evidently.report import Report
from evidently.metric_preset import DataDriftPreset

iris_data = datasets.load_iris(as_frame='auto')
iris_frame = iris_data.frame

Run a Test Suite

Split the data into two batches. Run a set of pre-built data quality Tests to evaluate the quality of the current_data:

data_stability= TestSuite(tests=[
    DataStabilityTestPreset(),
])
data_stability.run(current_data=iris_frame.iloc[:60], reference_data=iris_frame.iloc[60:], column_mapping=None)
data_stability

This will automatically generate tests on share of nulls, out-of-range values, etc. – with test conditions generated based on the first "reference" dataset.

Get a Report

Get a Data Drift Report to see if the data distributions shifted between two datasets:

data_drift_report = Report(metrics=[
    DataDriftPreset(),
])

data_drift_report.run(current_data=iris_frame.iloc[:60], reference_data=iris_frame.iloc[60:], column_mapping=None)
data_drift_report