Evidently

Interactive reports and JSON profiles to analyze, monitor and debug machine learning models.

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What is it?

Evidently helps evaluate machine learning models during validation and monitor them in production. The tool generates interactive visual reports and JSON profiles from pandas DataFrame or csv files.

You can use visual reports for ad hoc analysis, debugging and team sharing, and JSON profiles to integrate Evidently in prediction pipelines or with other visualization tools.

Currently 6 reports are available.

1. Data Drift

Detects changes in feature distribution.

2. Numerical Target Drift

Detects changes in numerical target and feature behavior.

3. Categorical Target Drift

Detects changes in categorical target and feature behavior.

4. Regression Model Performance

Analyzes the performance of a regression model and model errors.

5. Classification Model Performance

Analyzes the performance and errors of a classification model. Works both for binary and multi-class models.

6. Probabilistic Classification Model Performance

Analyzes the performance of a probabilistic classification model, quality of model calibration, and model errors. Works both for binary and multi-class models.

Installing from PyPI

MAC OS and Linux

Evidently is available as a PyPI package. To install it using pip package manager, run:

$ pip install evidently

The tool allows building interactive reports both inside a Jupyter notebook and as a separate HTML file. If you only want to generate interactive reports as HTML files or export as JSON profiles, the installation is now complete.

To enable building interactive reports inside a Jupyter notebook, we use jupyter nbextension. If you want to create reports inside a Jupyter notebook, then after installing evidently you should run the two following commands in the terminal from evidently directory.

To install jupyter nbextension, run:

$ jupyter nbextension install --sys-prefix --symlink --overwrite --py evidently

To enable it, run:

$ jupyter nbextension enable evidently --py --sys-prefix

That's it!

Note: a single run after the installation is enough. No need to repeat the last two commands every time.

Note 2: if you use Jupyter Lab, you may experience difficulties with exploring report inside a Jupyter notebook. However, the report generation in a separate .html file will work correctly.

Windows

Evidently is available as a PyPI package. To install it using pip package manager, run:

$ pip install evidently

The tool allows building interactive reports both inside a Jupyter notebook and as a separate HTML file. Unfortunately, building reports inside a Jupyter notebook is not yet possible for Windows. The reason is Windows requires administrator privileges to create symlink. In later versions we will address this issue.

Getting started

Jupyter Notebook

To start, prepare your data as two pandas DataFrames. The first should include your reference data, the second - current production data. The structure of both datasets should be identical.

• For Data Drift report, include the input features only.
• For Target Drift reports, include the column with Target and/or Prediction.
• For Model Performance reports, include the columns with Target and Prediction.

Calculation results can be available in one of the two formats:

• Option 1: an interactive Dashboard displayed inside the Jupyter notebook or exportable as a HTML report.
• Option 2: a JSON Profile that includes the values of metrics and the results of statistical tests.

Option 1: Dashboard

After installing the tool, import Evidently dashboard and required tabs:

import pandas as pd
from sklearn import datasets

from evidently.dashboard import Dashboard
from evidently.dashboard.tabs import (
    DataDriftTab,
    CatTargetDriftTab,
    RegressionPerformanceTab,
    ClassificationPerformanceTab,
    ProbClassificationPerformanceTab,
)

iris = datasets.load_iris()
iris_frame = pd.DataFrame(iris.data, columns = iris.feature_names)
iris_frame['target'] = iris.target

To generate the Data Drift report, run:

iris_data_drift_report = Dashboard(tabs=[DataDriftTab()])
iris_data_drift_report.calculate(iris_frame[:100], iris_frame[100:], column_mapping = None)
iris_data_drift_report.save("reports/my_report.html")

To generate the Data Drift and the Categorical Target Drift reports, run:

iris_data_and_target_drift_report = Dashboard(tabs=[DataDriftTab(), CatTargetDriftTab()])
iris_data_and_target_drift_report.calculate(iris_frame[:100], iris_frame[100:], column_mapping = None)
iris_data_and_target_drift_report.save("reports/my_report_with_2_tabs.html")

If you get a security alert, press "trust html". HTML report does not open automatically. To explore it, you should open it from the destination folder.

To generate the Regression Model Performance report, run:

regression_model_performance = Dashboard(tabs=[RegressionPerformanceTab()]) 
regression_model_performance.calculate(reference_data, current_data, column_mapping = column_mapping) 

You can also generate a Regression Model Performance for a single DataFrame. In this case, run:

regression_single_model_performance = Dashboard(tabs=[RegressionPerformanceTab()])
regression_single_model_performance.calculate(reference_data, None, column_mapping=column_mapping)

To generate the Classification Model Performance report, run:

classification_performance_report = Dashboard(tabs=[ClassificationPerformanceTab()])
classification_performance_report.calculate(reference_data, current_data, column_mapping = column_mapping)

For Probabilistic Classification Model Performance report, run:

classification_performance_report = Dashboard(tabs=[ProbClassificationPerformanceTab()])
classification_performance_report.calculate(reference_data, current_data, column_mapping = column_mapping)

You can also generate either of the Classification reports for a single DataFrame. In this case, run:

classification_single_model_performance = Dashboard(tabs=[ClassificationPerformanceTab()])
classification_single_model_performance.calculate(reference_data, None, column_mapping=column_mapping)

or

prob_classification_single_model_performance = Dashboard(tabs=[ProbClassificationPerformanceTab()])
prob_classification_single_model_performance.calculate(reference_data, None, column_mapping=column_mapping)

Option 2: Profile

After installing the tool, import Evidently profile and required sections:

import pandas as pd
from sklearn import datasets

from evidently.model_profile import Profile
from evidently.model_profile.sections import (
    DataDriftProfileSection,
    CatTargetDriftProfileSection,
    RegressionPerformanceProfileSection,
    ClassificationPerformanceProfileSection,
    ProbClassificationPerformanceProfileSection,
)

iris = datasets.load_iris()
iris_frame = pd.DataFrame(iris.data, columns = iris.feature_names)

To generate the Data Drift profile, run:

iris_data_drift_profile = Profile(sections=[DataDriftProfileSection()])
iris_data_drift_profile.calculate(iris_frame, iris_frame, column_mapping = None)
iris_data_drift_profile.json() 

To generate the Data Drift and the Categorical Target Drift profile, run:

iris_target_and_data_drift_profile = Profile(sections=[DataDriftProfileSection(), CatTargetDriftProfileSection()])
iris_target_and_data_drift_profile.calculate(iris_frame[:75], iris_frame[75:], column_mapping = None) 
iris_target_and_data_drift_profile.json() 

You can also generate a Regression Model Performance for a single DataFrame. In this case, run:

regression_single_model_performance = Profile(sections=[RegressionPerformanceProfileSection()])
regression_single_model_performance.calculate(reference_data, None, column_mapping=column_mapping)

To generate the Classification Model Performance profile, run:

classification_performance_profile = Profile(sections=[ClassificationPerformanceProfileSection()])
classification_performance_profile.calculate(reference_data, current_data, column_mapping = column_mapping)

For Probabilistic Classification Model Performance profile, run:

classification_performance_report = Profile(sections=[ProbClassificationPerformanceProfileSection()])
classification_performance_report.calculate(reference_data, current_data, column_mapping = column_mapping)

You can also generate either of the Classification profiles for a single DataFrame. In this case, run:

classification_single_model_performance = Profile(sections=[ClassificationPerformanceProfileSection()])
classification_single_model_performance.calculate(reference_data, None, column_mapping=column_mapping)

or

prob_classification_single_model_performance = Profile(sections=[ProbClassificationPerformanceProfileSection()])
prob_classification_single_model_performance.calculate(reference_data, None, column_mapping=column_mapping)

Community Reports

• A simple dashboard which contains two custom widgets with target distribution information link to repository

Google Colab, Kaggle Kernel, Deepnote

You can run evidently in Google Colab, Kaggle Notebook and Deepnote.

First, install evidently. Run the following command in the notebook cell: !pip install evidently

There is no need to enable nbextension for this case, because evidently uses an alternative way to display visuals in the hosted notebooks.

To build a Dashboard or a Profile simply repeat the steps described in the previous paragraph. For example, to build the Data Drift dashboard, run:

import pandas as pd
from sklearn import datasets

from evidently.dashboard import Dashboard
from evidently.dashboard.tabs import DataDriftTab

iris = datasets.load_iris()
iris_frame = pd.DataFrame(iris.data, columns = iris.feature_names)

iris_data_drift_report = Dashboard(tabs=[DataDriftTab()])
iris_data_drift_report.calculate(iris_frame[:100], iris_frame[100:], column_mapping = None)

To display the dashboard in the Google Colab, Kaggle Kernel, Deepnote, run:

iris_data_drift_report.show()

The show() method has the argument mode, which can take the following options:

• auto - the default option. Ideally, you will not need to specify the value for mode and use the default. But, if it does not work (in case we failed to determine the environment automatically), consider setting the correct value explicitly.
• nbextension - to show the UI using nbextension. Use this option to display dashboards in Jupyter notebooks (it should work automatically).
• inline - to insert the UI directly into the cell. Use this option for PyLab, Google Colab, Kaggle Kernels and Deepnote. For Google Colab, this should work automatically, for PyLab, Kaggle Kernels and Deepnote the option should be specified explicitly.

Telemetry

When you use Evidently in the command-line interface, we collect basic telemetry (starting from 0.1.21.dev0 version). It includes data on the environment (e.g. Python version) and usage (type of report or profile generated). You can read more about what we collect here.

You can opt-out from telemetry collection by setting the environment variable EVIDENTLY_DISABLE_TELEMETRY=1

Large datasets

As you can see from the above example, you can specify sampling parameters for large files. You can use different sampling strategies for reference and current data, or apply sampling only to one of the files. Currently we have 3 sampling types available:

• none - there will be no sampling for the file,
• nth - each Nth row of the file will be taken. This option works together with n parameter (see the example with the Dashboard above)
• random - random sampling will be applied. This option works together with ratio parameter (see the example with the Profile above)

Documentation

For more information, refer to a complete Documentation.

Examples

• See Data Drift Dashboard and Profile generation to explore the results both inside a Jupyter notebook and as a separate .html file: Iris, Boston

• See Categorical Target and Data Drift Dashboard and Profile generation to explore the results both inside a Jupyter notebook and as a separate file: Iris, Breast Cancer

• See Numerical Target and Data Drift Dashboard and Profile generation to explore the results both inside a Jupyter notebook and as a separate file: Boston

• See Regression Performance Dashboard and Profile generation to explore the results both inside a Jupyter notebook and as a separate file: Bike Sharing Demand

• See Classification Performance Dashboard and Profile generation to explore the results both inside a Jupyter notebook and as a separate file: Iris

• See Probabilistic Classification Performance Dashboard and Profile generation to explore the results both inside a Jupyter notebook and as a separate .html file: Iris, Breast Cancer

Stay updated

We will be releasing more reports soon. If you want to receive updates, follow us on Twitter, or sign up for our newsletter. You can also find more tutorials and explanations in our Blog. If you want to chat and connect, join our Discord community!