From 88dd2e632d2957f3f38f773bd9958f0c6f284f67 Mon Sep 17 00:00:00 2001 From: elenasamuylova <67064421+elenasamuylova@users.noreply.github.com> Date: Mon, 27 May 2024 22:02:14 +0100 Subject: [PATCH] Docs - LLM and main page updates (#1131) --- docs/book/.gitbook/assets/main/dashboard.gif | Bin 0 -> 987555 bytes .../book/.gitbook/assets/main/reports-min.png | Bin 0 -> 130458 bytes docs/book/.gitbook/assets/main/tests.gif | Bin 0 -> 1342440 bytes docs/book/README.md | 153 ++++++++++------ docs/book/get-started/quickstart-cloud.md | 14 +- docs/book/get-started/quickstart-llm.md | 48 +++-- docs/book/get-started/tutorial-llm.md | 61 ++++--- docs/book/reference/all-metrics.md | 72 +++++++- docs/book/reference/all-tests.md | 165 +++++++++++++----- 9 files changed, 373 insertions(+), 140 deletions(-) create mode 100644 docs/book/.gitbook/assets/main/dashboard.gif create mode 100644 docs/book/.gitbook/assets/main/reports-min.png create mode 100644 docs/book/.gitbook/assets/main/tests.gif diff 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cL_rN4001f`0J8UvXzwh8irEvT5rFc40Op9}5C8xG literal 0 HcmV?d00001 diff --git a/docs/book/README.md b/docs/book/README.md index db7980b2a1..31870293d8 100644 --- a/docs/book/README.md +++ b/docs/book/README.md @@ -1,90 +1,135 @@ -Evidently is an open-source Python library for data scientists and ML engineers. +Evidently helps evaluate, test, and monitor data and ML-powered systems. +* Predictive tasks: classification, regression, ranking, recommendations. +* Generative tasks: chatbots, RAGs, Q&A, summarization. +* Data monitoring: data quality and data drift for text, tabular, texts, embeddings. -It helps evaluate, test, and monitor data and ML models from validation to production. It works with tabular, text data and embeddings. +Evidently is available both as an open-source Python library and Evidently Cloud platform. # Quick Start -New to Evidently? Pick your Quickstart (each takes 1 min) or a Tutorial (15 min). +Choose a Quickstart (1-2min) or a Tutorial (15 min) to start. -| | | | -| ------- | ------------------------------------------------------ | - | -| **LLM evaluations**

    Run checks on text data and LLM outputs. Open-source and Cloud.

    [-> **LLM Quickstart**](get-started/quickstart-llm.md)
    [-> **LLM Tutorial**](get-started/tutorial-llm.md)| **Tabular data checks**

    Create Reports and Test Suites for tabular data. Open-source.

    [-> **Tabular Quickstart**](get-started/hello-world.md)
    [-> **Tabular Tutorial**](get-started/tutorial.md) | **Monitoring Dashboard**

    Get a live dashboard to track metrics over time.

    [-> **Monitoring Quickstart**](quickstart-cloud.md)
    [-> **Monitoring Tutorial**](tutorial-cloud.md)| +
    LLM evaluationsRun checks for text data and generative LLM outputs.

    → LLM Quickstart
    → LLM Tutorial

    Tabular data checksRun evaluations for tabular data and ML models.

    → Tabular Quickstart
    → Tabular Tutorial

    Monitoring DashboardGet a live dashboard to track evaluation results over time.

    → Monitoring Quickstart
    → Monitoring Tutorial

    You can explore more code [examples](examples/examples.md). # How it works -Evidently helps evaluate and test data and ML model quality throughout the model lifecycle. +Evidently helps evaluate and track quality of ML-based systems, from experimentation to production. -Evidently has a modular approach with 3 components: **Reports**, **Test Suites**, and a **Monitoring Dashboard**. They cover different usage scenarios: from ad hoc analysis to automated pipeline testing and continuous monitoring. +Evidently is both a library of 100+ ready-made evaluations, and a framework to easily implement yours: from Python functions to LLM judges. -Evidently has a simple, declarative API and a library of in-built metrics, tests, and visualizations. +Evidently has a modular architecture, and you can start with ad hoc checks without complex installations. There are 3 key components: Reports, Test Suites and Monitoring Dashboard. -# 1. Tests suites: batch model checks +## Reports -Tests perform structured data and ML model quality checks. You can set the conditions manually or let Evidently generate them based on the reference dataset. Tests will return an explicit **pass** or **fail** result. - -You can create a **Test Suite** from 50+ tests or run one of the **Presets**. For example, to test Data Stability or Regression Performance. +Reports compute different metrics on data and ML quality. You can use Reports for visual analysis and debugging, or as a computation layer for the monitoring dashboard. -Tests are best for automated batch checks. - -![](.gitbook/assets/main/evidently_tests_main-min.png) +You can be as hands-off or hands-on as you like: start with Presets, and customize metrics as you go. -**Input**: one or two datasets as pandas.DataFrames or csv. - -**How you get the output**: inside Jupyter notebook or Colab, as an exportable HTML, JSON, or Python dictionary. - -**Primary use case: test-based ML monitoring**. You can run tests as a step in the ML pipeline. For example, when you receive a new batch of data, labels, or generate predictions. You can build a conditional workflow based on the results, e.g., to trigger an alert, retrain, or get a report. +![](.gitbook/assets/main/reports-min.png) -**Read more**: -* [Overview: what is a test and a test suite](introduction/core-concepts.md) -* [User guide: how to generate tests](tests-and-reports/run-tests.md) -* [Reference: available tests and presets](reference/all-tests.md) +

    -# 2. Reports: interactive visualizations +More on Reports -Reports calculate various metrics and provide rich interactive visualizations. - -You can create a custom **Report** from individual metrics or run one of the **Presets** that cover a specific aspect of the model or data performance. For example, Data Quality or Classification Performance. - -Reports are best for exploratory analysis, debugging, and documentation. +* You can pass a single dataset or two for side-by-side comparison. + * Pass data as a CSV, pandas or Spark dataframe. +* You can get pre-built Reports with [Presets](presets/all-presets.md), or combine [individual Metrics](reference/all-metrics.md). +* You can use Reports as a standalone tool: + * For exploration and debugging: view results in Python or export as HTML. + * As a computation layer: export results to Python dictionary, JSON or dataframe. + * For documentation: add text comments and save Model Card. +* You can also use Reports as a logging component of ML Monitoring system: + * Compute Reports on a cadence over live data and save as JSON snapshots. + * Visualize results from multiple Reports over time on the Monitoring Dashboard. + * Configure alerts when metrics are out of bounds. -![](.gitbook/assets/main/evidently_reports_main-min.png) +**Docs**: +* [Quickstart - LLM and text evals](get-started/quickstart-llm.md) +* [Quickstart - ML and tabular](get-started/hello-world.md) +* [Reference: available Metrics](reference/all-metrics.md) +* [User guide: how to get Reports](tests-and-reports/get-reports.md) +
    -**Input**: one or two datasets as pandas.DataFrames or csv. - -**How you get the output**: inside Jupyter notebook or Colab, as an exportable HTML file, JSON, or Python dictionary. - -**Primary use case**: analysis and exploration. Reports help visually evaluate the data or model performance. For example, during exploratory data analysis, model evaluation on the training set, when debugging the model quality decay, or comparing several models. +## Tests suites + +Tests verify whether computed metrics satisfy defined conditions. Each Test in a suite returns a pass or fail result. + +This interface helps automate your evaluations for regression testing, checks during CI/CD, or validation steps in data pipelines.  -**Secondary use cases**: -* **Reporting and documentation**. You can generate visual HTML reports and ML model cards. -* **Performance logging**. You can integrate an evaluation step in the data/ML pipeline, get outputs as JSON, and log it for further analysis or to visualize using BI tools. +![](.gitbook/assets/main/tests.gif) -**Read more**: -* [Overview: what is a report and a metric](introduction/core-concepts.md) -* [User guide: how to run reports](tests-and-reports/get-reports.md) -* [Reference: available metrics and metric presets](reference/all-metrics.md) +
    -# 3. ML monitoring dashboard +More on Test Suites -*Available starting from v0.4.0*. +* You can set Test conditions manually or auto-generate them from a reference dataset.  +* You can get pre-built Test Suites with [Presets](presets/all-presets.md), or combine [individual Tests](reference/all-tests.md). +* You can see Test results in a visual report or get a JSON or Python export. +* You can use Test Suites as a standalone tool: + * Regression testing during experimentation. + * Automated CI/CD checks after you get new labeled data or update models. + * Pipeline testing: add a validation step to your data pipelines.   +* You can also use Test Suites as a logging component of ML Monitoring system: + * Run automated Test Suites and save results as JSON snapshots. + * Show test outcomes and metrics metrics on the Monitoring Dashboard.  + * Configure alerts on failed Tests. -You can self-host an ML monitoring dashboard to visualize metrics and test results over time. This functionality sits on top of Reports and Test Suites. You must store their outputs as Evidently JSON `snapshots` that serve as a data source for the Evidently Monitoring UI. +**Docs**: +* [Tutorial - LLM and text evals](get-started/tutorial-llm.md) +* [Quickstart - ML and tabular](get-started/tutorial.md) +* [Reference: available Tests](reference/all-tests.md) +* [User guide: how to generate Tests](tests-and-reports/run-tests.md) +
    -You can visualize any and track 100+ metrics available in Evidently, from number nulls in data to text sentiment and embedding drift. -![](.gitbook/assets/main/evidently_ml_monitoring_main.png) +# 3. ML monitoring dashboard -**Input**: Evidently `snapshots`, logged to an object storage. +The monitoring dashboard helps visualize ML system performance over time and detect issues. You can track key metrics and test outcomes. -**Output**: a monitoring dashboard available as a web app. - -**Primary use case: continuous monitoring**. When you need a live dashboard to see all your models and metrics over time. +You can use Evidently Cloud or self-host. Evidently Cloud offers extra features like user authentication and roles, built-in alerting, and a no-code interface.  -**Read more**: +![](.gitbook/assets/main/dashboard.gif) + +
    + +More on Monitoring Dashboard + +* You save Reports or Test Suites as JSON snapshots. The monitoring dashboard runs over these evaluation results as a data source. +* You can create custom combinations of Panels and choose what exactly to plot. +* You can get dashboards as code for version control and reproducibility.  +* You can send data in near real-time using a Collector service or in batches.  +* For Evidently Cloud: send alerts to Slack, Discord, and email.  +* For Evidently Cloud: get pre-built Tabs and manage everything in the UI. + +**Docs**: * [Get Started - Evidently Cloud](get-started/tutorial-cloud.md) * [Get Started - Self-hosting](get-started/tutorial-monitoring.md) +* [Monitoring user guide](monitoring/monitoring_overview.md) +
    + +# What can you evaluate? +Evidently Reports, Test Suites and ML Monitoring dashboard rely on the shared set of metrics. + +For every single evaluation - be it share of nulls, text sentiment, or embedding drift distance - you can get a `Report` with a pre-built visualization, use it in a `TestSuite` to define a conditional check (“tell me if the value is out of bounds”), and plot the values over time on a `Dashboard`. + +| Evaluation group | Examples | +|------|------| +| **Tabular Data Quality** | Missing values, duplicates, empty rows or columns, min-max ranges, new categorical values, correlation changes, etc. | +| **Text Descriptors** | Text length, out-of-vocabulary words, share of special symbols, regular expressions matches. | +| **Data Drift** | Statistical test and distance metrics to compare distributions of model predictions, numerical and categorical features, text data, or embeddings. | +| **Classification Quality** | From accuracy, precision, recall, ROC AUC, confusion matrices to classification bias and comparisons to dummy model quality. | +| **Regression Quality** | From MAE, RMSE, and error distributions to analyzing under- and over-predictions. | +| **Ranking and Recommendations quality** | From NDCG, MAP, and Hit Rate to serendipity, novelty, and diversity of recommendations. | +| **LLM Output Quality** | Model-based scoring with external models and LLMs to detect toxicity, sentiment, evaluate retrieval relevance, etc. | + +You can also implement custom Metrics and Tests as a Python function or define your prompts for LLM-as-a-judge. + +**See more**: +* [Reference: available Metrics](reference/all-metrics.md) +* [Reference: available Tests](reference/all-tests.md) +* [Presets: pre-built evaluation suites](presets/all-presets.md) # Community and support diff --git a/docs/book/get-started/quickstart-cloud.md b/docs/book/get-started/quickstart-cloud.md index 8a6fd6fdd0..e26f97bfb6 100644 --- a/docs/book/get-started/quickstart-cloud.md +++ b/docs/book/get-started/quickstart-cloud.md @@ -4,17 +4,15 @@ description: ML Monitoring “Hello world.” From data to dashboard in a couple # 1. Create an account -If not already, [sign up for an Evidently Cloud account](https://app.evidently.cloud/signup). +If not already, [sign up for an Evidently Cloud account](https://app.evidently.cloud/signup). Create your Organization. # 2. Create a team -Go to the main page, and click "plus" to create a new Team. For example, "Personal". - -Copy and save the Team ID from the [Teams page](https://app.evidently.cloud/teams). +Click on the **Teams** icon on the left menu. Create a Team - for example, "Personal". Copy and save the team ID. ([Team page](https://app.evidently.cloud/teams)). # 3. Get an access token -Click on the left menu with a key sign, select "personal token," generate and save the token. +Click the **Key** icon in the left menu to go. Generate and save the token. ([Token page](https://app.evidently.cloud/token)). # 4. Install the Python library @@ -37,11 +35,15 @@ from evidently.metric_preset import DataQualityPreset # 5. Create a new Project -Connect to Evidently Cloud using your access token and create a Project inside your Team. +Connect to Evidently Cloud using your access token. ```python ws = CloudWorkspace(token="YOUR_TOKEN_HERE", url="https://app.evidently.cloud") +``` +Create a new Project inside your Team. Pass the `team_id`. + +```python project = ws.create_project("My test project", team_id="YOUR_TEAM_ID") project.description = "My project description" project.save() diff --git a/docs/book/get-started/quickstart-llm.md b/docs/book/get-started/quickstart-llm.md index 3b8b7efc3d..35b8c94744 100644 --- a/docs/book/get-started/quickstart-llm.md +++ b/docs/book/get-started/quickstart-llm.md @@ -1,13 +1,15 @@ --- -description: LLM evaluation “Hello world.” Open-source and cloud workflow. +description: LLM evaluation "Hello world." --- +You can run this example in Colab or any Python environment. + # 1. Installation -Install the Evidently Python library. You can run this example in Colab or any Python environment. +Install the Evidently Python library. ``` -!pip install evidently +!pip install evidently[llm] ``` Import the necessary components: @@ -33,17 +35,23 @@ from evidently.ui.workspace.cloud import CloudWorkspace # 2. Import the toy dataset -Import a toy dataset with e-commerce reviews. +Import a toy dataset with e-commerce reviews. It contains a column with "Review_Text" that you'll analyze. ```python reviews_data = datasets.fetch_openml(name='Womens-E-Commerce-Clothing-Reviews', version=2, as_frame='auto') reviews = reviews_data.frame[:100] ``` + # 3. Run the evals -Run basic numeric evaluations to check text sentiment (on a scale of -1 to 1), text length, etc., for the "Reviews" column. +Run an evaluation Preset to check basic text descriptive text properties: +* text sentiment (scale -1 to 1) +* text length (number of symbols) +* number of sentences in a text +* percentage of out-of-vocabulary words (scale 0 to 100) +* percentage of non-letter characters (scale 0 to 100) -``` +```python text_evals_report = Report(metrics=[ TextEvals(column_name="Review_Text") ] @@ -52,7 +60,7 @@ text_evals_report = Report(metrics=[ text_evals_report.run(reference_data=None, current_data=reviews) ``` -This runs a pre-built set of checks. You can pick others or create custom evaluations, including LLM-as-a-judge. +There are more evals to choose from. You can also create custom ones, including LLM-as-a-judge. View a Report in Python: @@ -60,26 +68,36 @@ View a Report in Python: text_evals_report ``` -You can export results as HTML, JSON, or a Python dictionary to use elsewhere, or send to Evidently Cloud for monitoring. +You will see a summary distribution of results for each evaluation. # 4. Send results to Evidently Cloud -To record and track evaluation results over time, send them to Evidently Cloud. You need an API key. -* Sign up for [an Evidently Cloud account](https://app.evidently.cloud/signup). -* Once you log in, click "plus" to create a new Team. For example, "Personal". Copy the team ID from [Team's page](https://app.evidently.cloud/teams). -* Click the key icon in the left menu, select "personal token," generate and save the token. +To record and monitor evaluations over time, send them to Evidently Cloud. You'll need an API key. +* Sign up for an [Evidently Cloud account](https://app.evidently.cloud/signup), and create your Organization. +* Click on the **Teams** icon on the left menu. Create a Team - for example, "Personal". Copy and save the team ID. ([Team page](https://app.evidently.cloud/teams)). +* Click the **Key** icon in the left menu to go. Generate and save the token. ([Token page](https://app.evidently.cloud/token)). -Connect to Evidently Cloud using your token and create a Project inside your Team: +Connect to Evidently Cloud using your token. ```python ws = CloudWorkspace(token="YOUR_TOKEN_HERE", url="https://app.evidently.cloud") +``` + +Create a Project inside your Team. Pass the `team_id`: +```python project = ws.create_project("My test project", team_id="YOUR_TEAM_ID") project.description = "My project description" project.save() ``` -Visit Evidently Cloud, open your Project, and navigate to the "Report" to see evaluation results. +Send the Report to the Cloud: + +```python +ws.add_report(project.id, text_evals_report) +``` + +Go to the Evidently Cloud. Open your Project and head to the "Reports" in the left menu. ([Cloud home](https://app.evidently.cloud/)). ![](../.gitbook/assets/cloud/toy_text_report_preview.gif) @@ -87,7 +105,7 @@ In the future, you can log ongoing evaluation results to build monitoring panels # Want to see more? -Check out a more in-depth tutorial to learn key workflows. It covers using LLM-as-a-judge, running conditional test suites, and more. +Check out a more in-depth tutorial to learn key workflows. It covers using LLM-as-a-judge, running conditional test suites, monitoring results over time and more. {% content-ref url="tutorial-llm.md" %} [Evidently LLM Tutorial](tutorial-llm.md). diff --git a/docs/book/get-started/tutorial-llm.md b/docs/book/get-started/tutorial-llm.md index 1019c78d15..930da735be 100644 --- a/docs/book/get-started/tutorial-llm.md +++ b/docs/book/get-started/tutorial-llm.md @@ -2,9 +2,9 @@ description: Evaluate and test your LLM use case in 15 minutes. --- -Evaluating output quality is a crucial element of building production-grade LLM applications. During development, you must compare different prompts and models and catch issues as you tweak them. Once your application is live, you must monitor its performance on actual data to ensure it's safe and accurate and learn from user interactions. +Evaluating output quality is a crucial element of building production-grade LLM applications. During development, you must compare different prompts and models and catch issues as you tweak them. Once your app is live, you must monitor its performance on actual data to ensure it's safe and accurate. -Simple "vibe checks" of individual outputs don't scale well. This tutorial shows how you can automate LLM evaluations from experiments to productions. +Simple "vibe checks" of individual outputs don't scale well. This tutorial shows how you can automate LLM evaluations from experiments to production. {% hint style="success" %} **Want a very simple example first?** This ["Hello World"](quickstart-llm.md) will take a couple minutes. @@ -69,13 +69,13 @@ nltk.download('omw-1.4') nltk.download('vader_lexicon') ``` -**Optional**. To be able to send results to Evidently Cloud. +**Optional**. To be able to send results to Evidently Cloud: ```python from evidently.ui.workspace.cloud import CloudWorkspace ``` -**Optional**. To remotely manage the dashboard design. +**Optional**. To remotely manage the dashboard design in Evidently Cloud: ```python from evidently.ui.dashboards import DashboardPanelTestSuite @@ -88,7 +88,9 @@ from evidently.renderers.html_widgets import WidgetSize # 2. Prepare a dataset -We'll use an example dialogue dataset imitating a company Q&A system where employees ask questions about HR, finance, etc. Download the CSV file from GitHub: +We'll use an example dialogue dataset that imitates a company Q&A system in which employees ask questions about HR, finance, etc. + +Download the CSV file from GitHub: * [Example dataset](https://github.com/evidentlyai/evidently/blob/main/examples/how_to_questions/chat_df.csv) Import it as a pandas DataFrame and add a datetime index: @@ -114,21 +116,26 @@ Here is a preview with `assistant_logs.head()`: To be able to save and share results and get a live monitoring dashboard, create a Project in Evidently Cloud. Here's how to set it up: -1. **Sign up**: Create an [Evidently Cloud account](https://app.evidently.cloud/signup)). -2. **Add a Team**: Click “plus” on the homepage. Copy the team ID from the [team page](https://app.evidently.cloud/teams). -3. **Get your API token**: Click the key icon in the left menu. Generate and save a token. -4. **Create a Project**: Connect to Evidently Cloud using the API key. Create a new Project inside your Team, adding your title and description. +* **Sign up**. If you do not have one yet, create an [Evidently Cloud account](https://app.evidently.cloud/signup) and your Organization. +* **Add a Team**. Click on the **Teams** icon on the left menu. Create a Team, copy and save the team ID. ([Team page](https://app.evidently.cloud/teams)). +* **Get your API token**. Click the **Key** icon in the left menu to go. Generate and save the token. ([Token page](https://app.evidently.cloud/token)). +* **Connect to Evidently Cloud**. Pass your API key to connect. ```python ws = CloudWorkspace(token="YOUR_API_TOKEN", url="https://app.evidently.cloud") +``` +* **Create a Project**. Create a new Project inside your Team, adding your title and description: +```python project = ws.create_project("My сhatbot project", team_id="YOUR_TEAM_ID") project.description = "My project description" project.save() ``` # 4. Run your first eval -**Column mapping**. This optional step helps identify specific columns in your data. For example, pointing to a "datetime" column will add a time index to the plots. +You will now run a few simple out-of-the-box evals and generate a visual Report in your Python environment. + +**Create column mapping**. This optional step helps identify specific columns in your data. For example, pointing to a "datetime" column will add a time index to the plots. ```python column_mapping = ColumnMapping( @@ -150,13 +157,20 @@ text_evals_report.run(reference_data=None, current_data=assistant_logs[:100], co text_evals_report ``` -The Report will show stats like text length, sentiment and % of out-of-vocabulary words. +The Report will show stats like: +* text sentiment (scale -1 to 1) +* text length (number of symbols) +* number of sentences in a text +* percentage of out-of-vocabulary words (scale 0 to 100) +* percentage of non-letter characters (scale 0 to 100) ![](../.gitbook/assets/cloud/llm_report_preview-min.gif) We call these generated statistics `descriptors`. They can be numerical or categorical. -This simple example is just a starting point. We’ll show more evaluations later on. +{% hint style="success" %} +**What else is there?** This simple example is just a starting point. We’ll show more complex evaluations later in the tutorial. There are also other interfaces beyond visual Reports: we'll also cover how to automate pass/fail checks with Test Suites and get a monitoring dashboard.  +{% endhint %} # 5. Export results @@ -197,7 +211,9 @@ To see it in the UI, go to the Reports section using the left menu. # 6. Customize evaluations -You can create a custom evaluation suite with different types of checks: +You will now learn to create a custom evaluation suite for your LLM system inputs and outputs. + +You can combine different types of checks: * **Rule-based**. Detect specific words or patterns in your data. * **ML-based**. Use external models to score data (e.g., for toxicity, topic, tone). * **LLM-as-a-judge**. Prompt LLMs to categorize or score texts. @@ -206,8 +222,6 @@ You can create a custom evaluation suite with different types of checks: Evidently provides a library of ready-made descriptors to parametrize. The following section will show a few examples. For clarity, we'll generate separate Reports for each group of checks. In practice, you can put all evals together in a single Report.  -**Note**: These are not just visual Reports! Later sections will also cover automating pass/fail checks with Test Suites and monitoring dashboards.  - ## Rule-based evals  These evals are fast and cheap to compute at scale. Evidently has built-in descriptors for: @@ -243,10 +257,10 @@ Here is an example result for `IncludesWords(words_list=['salary'])` descriptor. ## ML models You can also use any pre-trained machine learning model to score your texts. Evidently has: -* In-built model-basded descriptors like `Sentiment` (scores texts from negative -1 to positive 1). +* In-built model-based descriptors like `Sentiment`. * Wrappers to call external Python functions or models published on HuggingFace (`HuggingFaceModel` descriptor). -Let's score the data by Sentiment (in-built model) and a Toxicity (exteranl HuggingFace model). +Let's evaluate the responses for Sentiment (in-built model, scores from - 1 to 1) and Toxicity (using [external HuggingFace classifier model](https://huggingface.co/spaces/evaluate-measurement/toxicity) that returns the score between 0 to 1 for the "toxic" class). ```python text_evals_report = Report(metrics=[ @@ -263,8 +277,7 @@ text_evals_report.run(reference_data=None, current_data=assistant_logs[:100], co text_evals_report ``` -This code downloads the Hugging Face model to score your data locally. -Example result with distribution of toxicity scores: +This code downloads the Hugging Face model to score your data locally. Example result with the distribution of toxicity scores: ![](../.gitbook/assets/cloud/llm_toxicity_hf-min.png) @@ -320,7 +333,7 @@ text_evals_report ## Metadata columns -Our dataset also includes pre-existing user evaluations in a categorical `feedback` column with upvotes and downvotes. You can include summaries for any numerical or categorical column in the Report. +Our dataset also includes pre-existing user evaluations in a categorical `feedback` column with upvotes and downvotes. You can add summaries for any numerical or categorical column in the Report. To add a summary on the “feedback” column, use `ColumnSummaryMetric()`: @@ -344,7 +357,7 @@ You might want to compare two datasets using the same criteria. For example, you ## Side-by-side Reports -You can generate similar Report as before but with two datasets. This lets you visualize the distributions side by side. +You can generate similar Reports as before but with two datasets. This lets you visualize the distributions side by side. For simplicity, let's take the first 100 rows as "reference" and the next 100 as "current". You can combine text evals and metadata summary. @@ -413,9 +426,9 @@ drift_report **Data Drift Preset**. You can also use `DataDriftPreset()` to compare distribution of all columns - text and metadata - in the dataset at once. {% endhint %} -To detect drift on raw data, Evidently will train and evaluate a classifier model to differentiate the two text datasets. If the model can identify if a text sample belongs to the “reference” or “current” dataset, you can consider them different enough. The resulting score is the [ROC AUC](https://www.evidentlyai.com/classification-metrics/explain-roc-curve) of the classifier. If drift is detected, Evidently shows phrases that help differentiate between the two datasets. +To detect drift on raw data, Evidently will train and evaluate a classifier model to differentiate the two text datasets. If the model can identify if a text sample belongs to the “reference” or “current” dataset, you can consider them different enough. The resulting score is the [ROC AUC](https://www.evidentlyai.com/classification-metrics/explain-roc-curve) of the classifier. (0.5 means the classifier is no better than random, and the datasets are very similar. Values between 0.5 and 1 show the model is able to differentiate between the two datasets, and there is a likely change between the datasets.) -In our case, there is no drift, so there is no interpretation. +If drift is detected, Evidently shows phrases that help differentiate between the two datasets. In our case, there is no drift, so there is no interpretation.
    @@ -447,7 +460,7 @@ drift_report.run(reference_data=df_random_sample, current_data=df_salary_hr, col drift_report ``` -The result will now show drift with a score of 0.631. You will also see examples of texts that help differentiate between the two datasets. For instance, questions about the "employee portal" are typical for the "current" dataset, indicating that HR-related questions became more frequent. +The result will now detect some drift. You will also see examples of texts that help differentiate between the two datasets. For instance, questions about the "employee portal" are typical for the "current" dataset, indicating that HR-related questions became more frequent. ![](../.gitbook/assets/cloud/llm_text_drift-min.png) diff --git a/docs/book/reference/all-metrics.md b/docs/book/reference/all-metrics.md index 64cdbc82f1..195409c7f0 100644 --- a/docs/book/reference/all-metrics.md +++ b/docs/book/reference/all-metrics.md @@ -30,16 +30,86 @@ We are doing our best to maintain this page up to date. In case of discrepancies **Defaults**: each Metric in a Preset uses the default parameters for this Metric. You can see them in the tables below. +
    + +Data Quality Preset + | Preset name and Description | Parameters | |---|---| | **`DataQualityPreset`**

    Evaluates the data quality and provides descriptive stats.

    Input features are required. Prediction and target are optional.

    **Contents:**
    • `DatasetSummaryMetric()`
    • `ColumnSummaryMetric(column_name=column_name)` for `all` or `сolumns` if provided
    • `DatasetMissingValuesMetric()`
    • `DatasetCorrelationsMetric()`
    | **Optional**:
    `columns`
    | + +
    + +
    + +Data Drift Preset + +| Preset name and Description | Parameters | +|---|---| | **`DataDriftPreset`**
    Evaluates the data drift in the individual columns and the dataset.

    Input features are required.

    **Contents**:
    • `DataDriftTable(сolumns=сolumns)` or `all` if not listed
    • `DatasetDriftMetric(сolumns=сolumns)` or `all` if not listed
    | **Optional**:
    • `columns`
    • `stattest`
    • `cat_stattest`
    • `num_stattest`
    • `per_column_stattest`
    • `text_stattest`
    • `stattest_threshold`
    • `cat_stattest_threshold`
    • `num_stattest_threshold`
    • `per_column_stattest_threshold`
    • `text_stattest_threshold`
    • `embeddings`
    • `embeddings_drift_method`
    • `drift_share`
    [How to set data drift parameters](../customization/options-for-statistical-tests.md), [embeddings drift parameters](../customization/embeddings-drift-parameters.md).| + +
    + +
    + +Target Drift Preset + +| Preset name and Description | Parameters | +|---|---| | **`TargetDriftPreset`**

    Evaluates the prediction or target drift.

    Target or prediction is required. Input features are optional.

    **Contents**:
    • `ColumnDriftMetric(column_name=target, prediction)`
    • `ColumnCorrelationsMetric(column_name=target, prediction)`
    • `TargetByFeaturesTable(columns=columns)` or `all` if not listed
    If regression:
    • `ColumnValuePlot(column_name=target, prediction)`
    | **Optional**:
    • `columns`
    • `stattest`
    • `cat_stattest`
    • `num_stattest`
    • `per_column_stattest`
    • `stattest_threshold`
    • `cat_stattest_threshold`
    • `num_stattest_threshold`
    • `per_column_stattest_threshold`
    [How to set data drift parameters](../customization/options-for-statistical-tests.md). | + +
    + +
    + +Regression Preset + +| Preset name and Description | Parameters | +|---|---| | **`RegressionPreset`**
    Evaluates the quality of a regression model.

    Prediction and target are required. Input features are optional.

    **Contents**:
    • `RegressionQualityMetric()`
    • `RegressionPredictedVsActualScatter()`
    • `RegressionPredictedVsActualPlot()`
    • `RegressionErrorPlot()`
    • `RegressionAbsPercentageErrorPlot()`
    • `RegressionErrorDistribution()`
    • `RegressionErrorNormality()`
    • `RegressionTopErrorMetric()`
    • `RegressionErrorBiasTable(columns=columns)` or `all` if not listed
    | **Optional**:
    `columns` | + +
    + +
    + +Classification Preset + +| Preset name and Description | Parameters | +|---|---| | **`ClassificationPreset`**
    Evaluates the quality of a classification model.

    Prediction and target are required. Input features are optional.

    **Contents**:
    • `ClassificationQualityMetric()`
    • `ClassificationClassBalance()`
    • `ClassificationConfusionMatrix()`
    • `ClassificationQualityByClass()`
    If probabilistic classification, also:
    • `ClassificationClassSeparationPlot()`
    • `ClassificationProbDistribution()`
    • `ClassificationRocCurve()`
    • `ClassificationPRCurve()`
    • `ClassificationPRTable()`
    • `ClassificationQualityByFeatureTable(columns=columns)` or `all` if not listed
    | **Optional**:
    • `columns`
    • `probas_threshold`
    • `k`
    | -|**`TextOverviewPreset(column_name=”text”)`**
    Evaluates data drift and descriptive statistics for text data.

    Input features (text) are required.

    **Contents**:
    • `ColumnSummaryMetric()`
    • `TextDescriptorsDistribution()`
    • `TextDescriptorsCorrelation()`
    If reference data is provided, also:
    • `ColumnDriftMetric()`
    • `TextDescriptorsDriftMetric()`
    | **Required**:
    `column_name` | + +
    + +
    + +Text Overview Preset + +| Preset name and Description | Parameters | +|---|---| +|**`TextOverviewPreset(column_name=”text”)`**
    Evaluates descriptive statistics for all text columns in the dataset.

    Text columns (inputs and/or outputs) are required.

    **Contents**:
    • `ColumnSummaryMetric()`
    • for text descriptors: `Sentiment()`, `SentenceCount()`, `OOV()`, `TextLength()`, `NonLetterCharacterPercentage()`
    If more than one text column is provided, also for:
    • `SemanticSimilarity` between the columns
    | **Required**:
    `column_name` or `columns` | + +
    + +
    + +Text Evals + +| Preset name and Description | Parameters | +|---|---| +|**`TextEvals(column_name=”text”)`**
    Evaluates descriptive statistics a defined text column. Simplifies interface to add other descriptors

    A text column (input or output) is required.

    **Contents**:
    • `ColumnSummaryMetric()`
    • for text descriptors: `Sentiment()`, `SentenceCount()`, `OOV()`, `TextLength()`, `NonLetterCharacterPercentage()`| **Required**:
      `column_name`| + +
    + +
    + +RecSys (Recommender System) Preset + +| Preset name and Description | Parameters | +|---|---| |**`RecsysPreset`**
    Evaluates the quality of the recommender system.

    Recommendations and true relevance scores are required. For some metrics, training data and item features are required.

    **Contents**:
    • `PrecisionTopKMetric()`
    • `RecallTopKMetric()`
    • `FBetaTopKMetric()`
    • `MAPKMetric()`
    • `NDCGKMetric()`
    • `MRRKMetric()`
    • `HitRateKMetric()`
    • `PersonalizationMetric()`
    • `PopularityBias()`
    • `RecCasesTable()`
    • `ScoreDistribution()`
    • `DiversityMetric()`
    • `SerendipityMetric()`
    • `NoveltyMetric()`
    • `ItemBiasMetric()` (pass column as a parameter)
    • `UserBiasMetric()`(pass column as a parameter)
    | **Required**:
    `k`
    **Optional**:
    • `min_rel_score: Optional[int]`
    • `no_feedback_users: bool`
    • `normalize_arp: bool`
    • `user_ids: Optional[List[Union[int, str]]]`
    • `display_features: Optional[List[str]]`
    • `item_features: Optional[List[str]]`
    • `user_bias_columns: Optional[List[str]]`
    • `item_bias_columns: Optional[List[str]]`
    +
    + # Data Integrity **Defaults for Missing Values**. The metrics that calculate the number or share of missing values detect four types of the values by default: Pandas nulls (None, NAN, etc.), "" (empty string), Numpy "-inf" value, Numpy "inf" value. You can also pass a custom missing values as a parameter and specify if you want to replace the default list. Example: diff --git a/docs/book/reference/all-tests.md b/docs/book/reference/all-tests.md index 533940b07b..d2f2e14b14 100644 --- a/docs/book/reference/all-tests.md +++ b/docs/book/reference/all-tests.md @@ -1,5 +1,5 @@ --- -description: List of all tests and test presets available in Evidently. +description: List of all Tests and Test Presets available in Evidently. ---
    @@ -7,25 +7,25 @@ description: List of all tests and test presets available in Evidently. How to use this page This is a reference page. You can return here: -* To discover **available tests** and choose which to include in a custom test suite. -* To understand which **parameters** you can change for a specific test or preset. -* To verify which tests are included in a **test preset**. +* To discover **available Tests** and choose which to include in a custom Test suite. +* To understand which **parameters** you can change for a specific Test or Preset. +* To verify which tests are included in a **Test Preset**. -You can use the menu on the right to navigate the sections. We organize individual tests into groups, e.g. Data Quality, Data Integrity, Regression, etc. Note that these groups do **not** match the presets with similar names. For example, there are more Data Quality tests than in the `DataQualityTestPreset`. +You can use the menu on the right to navigate the sections. # How to read the tables -* **Name**: the name of the test or test preset. -* **Description**: plain text explanation of the test, or the content of the preset. For tests, we specify whether it applies to the whole dataset or individual columns. +* **Name**: the name of the Test or Test preset. +* **Description**: plain text explanation. For Tests, we specify whether it applies to the whole dataset or individual columns. * **Parameters**: available configurations. * Required parameters are necessary for calculations, e.g. a column name for a column-level test. * Optional parameters modify how the underlying metric is calculated, e.g. which statistical test or correlation method is used. - * *Test condition parameters* help set the conditions (e.g. equal, not equal, greater than, etc.) that define the expectations from the test output. If the condition is violated, the test returns a fail. Here you can see the complete list of the [standard condition parameteres](../tests-and-reports/custom-test-suite.md#custom-conditions). They apply to most of the tests, and are optional. -* **Default tests condition**: they apply if you do not set a custom сondition. - * With reference: the test conditions that apply when you pass a reference dataset and Evidently can derive expectations from it. - * No reference: the test conditions that apply if you do not provide the reference. They are based on heuristics. + * [*Test condition parameters*](../tests-and-reports/custom-test-suite.md#custom-conditions) set the conditions (e.g. equal, not equal, greater than, etc.) that define the expectations from the Test output. If the condition is violated, the test returns a fail. They apply to most of the Tests, and are optional. +* **Default Test condition**: they apply if you do not set a custom сondition. + * With reference: the Test conditions that apply when you pass a reference dataset and Evidently can derive conditions from it. (E.g. expect +/- 10% from reference). + * No reference: the Test conditions that apply if you do not provide the reference. They are based on heuristics. -**Test visualizations**. Each test also includes a default render. If you want to see the visualization, navigate to the [example notebooks](../examples/examples.md). +**Test visualizations**. Each Test also includes a default render. If you want to see the visualization, navigate to the [example notebooks](../examples/examples.md).
    @@ -37,21 +37,122 @@ We are doing our best to maintain this page up to date. In case of discrepancies Default conditions for each Test in the Preset match the Test's defaults. You can see them in the tables below. The listed Preset parameters apply to the relevant individual Tests inside the Preset. +
    + +NoTargetPerformance Test Preset + | Preset name and Description | Parameters | |---|---| | **`NoTargetPerformanceTestPreset`**
    • `TestShareOfDriftedColumns()`
    • `TestColumnDrift(column_name=prediction)`
    • `TestColumnShareOfMissingValues(column_name=column_name)` for `all` or `сolumns` if provided
    • `TestShareOfOutRangeValues(column_name=column_name)` for all `numerical_columns` or among `columns` if provided
    • `TestShareOfOutListValues(column_name=column_name)` for all `categorical_columns` or among `columns` if provided
    • `TestMeanInNSigmas(column_name=column_name, n=2)` for all `numerical_columns` or among `columns` if provided
    | **Optional**:
    • `columns`
    • `stattest`
    • `cat_stattest`
    • `num_stattest`
    • `per_column_stattest`
    • `text_stattest`
    • `stattest_threshold`
    • `cat_stattest_threshold`
    • `num_stattest_threshold`
    • `per_column_stattest_threshold`
    • `text_stattest_threshold`
    • `embeddings`
    • `embeddings_drift_method`
    • `drift_share`
    [How to set data drift parameters](../customization/options-for-statistical-tests.md), [embeddings drift parameters](../customization/embeddings-drift-parameters.md).| + +
    + +
    + +Data Stability Test Preset + +| Preset name and Description | Parameters | +|---|---| | **`DataStabilityTestPreset`**
    • `TestNumberOfRows()`
    • `TestNumberOfColumns()`
    • `TestColumnsType()`
    • `TestColumnShareOfMissingValues()`
    • `TestShareOfOutRangeValues(column_name=column_name)` for all `numerical_columns` or among `columns` if provided
    • `TestShareOfOutListValues(column_name=column_name)` for all `categorical_columns` or among `columns` if provided
    • `TestMeanInNSigmas(column_name=column_name, n=2)` for all `numerical_columns` or among `columns` if provided
    | **Optional**:
    • `columns`
    | + +
    + +
    + +Data Quality Test Preset + +| Preset name and Description | Parameters | +|---|---| | **`DataQualityTestPreset`**
    • `TestColumnShareOfMissingValues(column_name=column_name)` for `all` or `columns`
    • `TestMostCommonValueShare(column_name=column_name)` for `all` or `columns`
    • `TestNumberOfConstantColumns()`
    • `TestNumberOfDuplicatedColumns()`
    • `TestNumberOfDuplicatedRows()`
    • `TestHighlyCorrelatedColumns()`
    | **Optional:**
    • `columns`
    | + +
    + +
    + +Data Drift Test Preset + +| Preset name and Description | Parameters | +|---|---| | **`DataDriftTestPreset`**
    • `TestShareOfDriftedColumns()`
    • `TestColumnDrift(column_name=column_name)` for `all` or `сolumns` if provided
    | **Optional**:
    • `columns`
    • `stattest`
    • `cat_stattest`
    • `num_stattest`
    • `per_column_stattest`
    • `text_stattest`
    • `stattest_threshold`
    • `cat_stattest_threshold`
    • `num_stattest_threshold`
    • `per_column_stattest_threshold`
    • `text_stattest_threshold`
    • `embeddings`
    • `embeddings_drift_method`
    • `drift_share`
    [How to set data drift parameters](../customization/options-for-statistical-tests.md), [embeddings drift parameters](../customization/embeddings-drift-parameters.md).| + +
    + +
    + +Regression Test Preset + +| Preset name and Description | Parameters | +|---|---| | **`RegressionTestPreset`**
    • `TestValueMeanError()`
    • `TestValueMAE()`
    • `TestValueRMSE()`
    • `TestValueMAPE()`
    | N/A | + +
    + +
    + +Multiclass Classification Test Preset + +| Preset name and Description | Parameters | +|---|---| | **`MulticlassClassificationTestPreset`**
    • `TestAccuracyScore()`
    • `TestF1Score()`
    • `TestPrecisionByClass()`
    • `TestRecallByClass()`
    • `TestColumnDrift(column_name=target)`
    • `TestNumberOfRows()`
    If probabilistic classification, also:
    • `TestLogLoss()`
    • `TestRocAuc()`
    | **Optional**:
    • `stattest`
    • `stattest_threshold`
    [How to set data drift parameters](../customization/options-for-statistical-tests.md). | + +
    + +
    + +Binary Classification (Top K) Test Preset + +| Preset name and Description | Parameters | +|---|---| | **`BinaryClassificationTopKTestPreset`**
    • `TestAccuracyScore(k=k)`
    • `TestPrecisionScore(k=k)`
    • `TestRecallScore(k=k)`
    • `TestF1Score(k=k)`
    • `TestColumnDrift(column_name=target)`
    • `TestRocAuc()`
    • `TestLogLoss()`
    |**Required**:
    • `k`
    **Optional**:
    • `stattest`
    • `stattest_threshold`
    • `probas_threshold`
    [How to set data drift parameters](../customization/options-for-statistical-tests.md). | + +
    + +
    + +Binary Classification Test Preset + +| Preset name and Description | Parameters | +|---|---| | **`BinaryClassificationTestPreset`**
    • `TestColumnDrift(column_name=target)`
    • `TestPrecisionScore()`
    • `TestRecallScore()`
    • `TestF1Score()`
    • `TestAccuracyScore()`
    If probabilistic classification, also:
    • `TestRocAuc()`
    | **Optional**:
    • `stattest`
    • `stattest_threshold`
    • `probas_threshold`
    [How to set data drift parameters](../customization/options-for-statistical-tests.md)| + +
    + +
    + +RecSys (Recommender Systems) Test Preset + +| Preset name and Description | Parameters | +|---|---| | **`RecsysTestPreset`**
    • `TestPrecisionTopK()`
    • `TestRecallTopK()`
    • `TestMAPK()`
    • `TestNDCGK()`
    • `TestHitRateK()`
    | **Required:**
    • `k`
    **Optional:**
    • `min_rel_score: Optional[int]`
    • `no_feedback_users: bool`
    -# Data Integrity +
    + +{% hint style="info" %} +**How to set custom Test conditions?** Use parameters (e.g. equal, not equal, greater than, etc.) to set [Test Conditions](../tests-and-reports/custom-test-suite.md#custom-conditions). +{% endhint %} + +# Data Quality + +| Test name | Description | Parameters | Default test condition | +|---|---|---|---| +| **TestNumberOfRows()** | Dataset-level.

    Tests the number of rows against the reference or a defined condition.| **Required**:
    N/A

    **Optional**:
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects +/-10% or >30.

    **With reference**: the test fails if the number of rows differs by over 10% from the reference.

    **No reference**: the test fails if the number of rows is <= 30.| +| **TestNumberOfColumns()** | Dataset-level.

    Tests the number of columns against the reference or a defined condition. | **Required**:
    N/A

    **Optional**:
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects the same or non-zero.

    **With reference**: the test fails if the number of columns differs from the reference.

    **No reference**: the test fails if the number of columns is 0.| +| **TestNumberOfConstantColumns()** | Dataset-level.

    Tests the number of columns with all constant values against reference or a defined condition. |**Required**:
    N/A

    **Optional**:
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects =< or none.

    **With reference**: the test fails if the number of constant columns is higher than in the reference.

    **No reference**: the test fails if there is at least one constant column.| +| **TestNumberOfEmptyRows()** | Dataset-level.

    Tests the number of empty rows against reference or a defined condition. |**Required**:
    N/A

    **Optional**:
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects +/- 10% or none.

    **With reference**: the test fails if the share of empty rows is over 10% higher or lower than in the reference.

    **No reference**: the test fails if there is at least one empty row.| +| **TestNumberOfEmptyColumns()** | Dataset-level.

    Tests the number of empty columns against reference or a defined condition.|**Required**:
    N/A

    **Optional**:
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects =< or none.

    **With reference**: the test fails if the number of empty columns is higher than in the reference.

    **No reference**: the test fails if there is at least one empty column.| +| **TestNumberOfDuplicatedRows()** | Dataset-level.

    Tests the number of duplicate rows against reference or a defined condition. |**Required**:
    N/A

    **Optional**:
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects +/- 10% or none.

    **With reference**: the test fails if the share of duplicate rows is over 10% higher or lower than in the reference.

    **No reference**: the test fails if there is at least one duplicate row. | +| **TestNumberOfDuplicatedColumns()** | Dataset-level.

    Tests the number of duplicate columns against reference or a defined condition. |**Required**:
    N/A

    **Optional**:
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects =< or none.

    **With reference**: the test fails if the number of duplicate columns is higher than in the reference.

    **No reference**: the test fails if there is at least one duplicate column.| +| **TestConflictTarget()**| Dataset-level.

    Tests if there are conflicts in the target (instances where a different label is assigned for an identical input). | N/A | Expects no conflicts in the target (with or without reference). | +| **TestConflictPrediction()**| Dataset-level.

    Tests if there are conflicts in the prediction (instances where a different prediction is made for an identical input). | N/A | Expects no conflicts in the target (with or without reference). | +| **TestColumnsType()**| Dataset-level.

    Tests the types of all columns against the reference.| **Required**:
    N/A

    **Optional**:
    `columns_type: dict`

    **Test conditions**:
    N/A | Expects types to match.

    **With reference**: the test fails if at least one column type does not match.
    **No reference**: N/A | +| **TestColumnAllConstantValues**(column_name='name') | Column-level.

    Tests if all the values in a given column are constant.|**Required**:
    • `column_name`
    **Optional**:
    N/A

    **Test conditions**:
    N/A| Expects non-constant.

    The test fails if all values in a given column are constant.| +| **TestColumnAllUniqueValues**(column_name='name') | Column-level.

    Tests if all the values in a given column are unique.| **Required**:
    • `column_name`
    **Optional**:
    N/A

    **Test conditions**:
    N/A | Expects all unique (e.g., IDs).

    The test fails if at least one value in a given column is not unique.| +| **TestColumnRegExp**(column_name='name, reg_exp='^[0..9]') | Column-level.

    Tests the number of values in a column that do not match a defined regular expression, against reference or a defined condition. |**Required**:
    • `column_name`
    • `reg_exp`
    **Optional**:
    N/A

    **Test conditions**:
    • *standard parameters*
    | **With reference**: the test fails if the share of values that match a regular expression is over 10% higher or lower than in the reference.

    **No reference**: the test fails if at least one of the values does not match a regular expression. | +| **TestNumberOfUniqueValues**(column_name='name')
    | Column-level.

    Tests the number of unique values in a given column against reference or a defined condition. | **Required**:
    • `column_name`
    **Optional:**
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects +/-10%.

    **With reference**: the test fails if the share of unique values is different by more than 10%.

    **No reference**: N/A | +| **TestUniqueValuesShare**(column_name='name') | Column-level.

    Tests the share of unique values in a given column against reference or a defined condition. | **Required**:
    • `column_name`
    **Optional:**
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects +/-10%.

    **With reference**: the test fails if the share of unique values is different by more than 10%.

    **No reference**: N/A | +| **TestMostCommonValueShare**(column_name='name') | Column-level.

    Tests the share of the most common value in a given column against reference or a defined condition. | **Required**:
    • `column_name`
    **Optional:**
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects +/-10%.

    **With reference**: the test fails if the share of the most common value is different by more than 10% from the reference.

    **No reference**: the test fails if the share of the most common value is >= 80%. | -**Defaults for Data Integrity**. If there is no reference data or defined conditions, data integrity will be checked against a set of heuristics. If you pass the reference data, Evidently will automatically derive all relevant statistics (e.g., number of columns, rows, share of missing values etc.) and apply default test conditions. You can also pass custom test conditions. +# Missing Values **Defaults for Missing Values**. The metrics that calculate the number or share of missing values detect four types of the values by default: Pandas nulls (None, NAN, etc.), "" (empty string), Numpy "-inf" value, Numpy "inf" value. You can also pass a custom missing values as a parameter and specify if you want to replace the default list. Example: @@ -61,8 +162,6 @@ TestNumberOfMissingValues(missing_values=["", 0, "n/a", -9999, None], replace=Tr | Test name | Description | Parameters | Default test condition | |---|---|---|---| -| **TestNumberOfRows()** | Dataset-level.

    Tests the number of rows against the reference or a defined condition.| **Required**:
    N/A

    **Optional**:
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects +/-10% or >30.

    **With reference**: the test fails if the number of rows differs by over 10% from the reference.

    **No reference**: the test fails if the number of rows is <= 30.| -| **TestNumberOfColumns()** | Dataset-level.

    Tests the number of columns against the reference or a defined condition. | **Required**:
    N/A

    **Optional**:
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects the same or non-zero.

    **With reference**: the test fails if the number of columns differs from the reference.

    **No reference**: the test fails if the number of columns is 0.| | **TestNumberOfMissingValues()** | Dataset-level.

    Tests the number of missing values in the dataset against the reference or a defined condition.|**Required**:
    N/A

    **Optional**:
    • `missing_values = [], replace = True/False` (default = default list)
    **Test conditions**:
    • *standard parameters*
    | Expects up to +10% or 0.

    **With reference**: the test fails if the share of missing values is over 10% higher than in reference.

    **No reference**: the test fails if the dataset contains missing values.| | **TestShareOfMissingValues()**| Dataset-level.

    Tests the share of missing values in the dataset against the reference or a defined condition.|**Required**:
    N/A

    **Optional**:
    • `missing_values = [], replace = True/False` (default = default list)
    **Test conditions**:
    • *standard parameters*
    | Expects up to +10% or 0.

    **With reference**: the test fails if the share of missing values is over 10% higher than in reference.

    **No reference**: the test fails if the dataset contains missing values.| | **TestNumberOfColumnsWithMissingValues()**| Dataset-level.

    Tests the number of columns that contain missing values in the dataset against the reference or a defined condition.|**Required**:
    N/A

    **Optional**:
    • `missing_values = [], replace = True/False` (default = default list)
    **Test conditions**:
    • *standard parameters*
    | Expects <= or 0.

    **With reference**: the test fails if the number of columns with missing values is higher than in reference.
    **No reference**: the test fails if the dataset contains columns with missing values.| @@ -70,56 +169,43 @@ TestNumberOfMissingValues(missing_values=["", 0, "n/a", -9999, None], replace=Tr | **TestNumberOfRowsWithMissingValues()** | Dataset-level.

    Tests the number of rows that contain missing values against the reference or a defined condition. | **Required**:
    N/A

    **Optional**:
    • `missing_values = [], replace = True/False` (default = default list)
    **Test conditions**
    • *standard parameters*
    | Expects up to +10% or 0.

    **With reference**: the test fails if the share of rows with missing values is over 10% higher than in reference.

    **No reference**: the test fails if the dataset contains rows with missing values.| | **TestShareOfRowsWithMissingValues()** | Dataset-level.

    Tests the share of rows that contain missing values against the reference or a defined condition. | **Required**:
    N/A

    **Optional**:
    • `missing_values = [], replace = True/False` (default = default list)
    **Test conditions**
    • *standard parameters*
    | Expects up to +10% or 0.

    **With reference**: the test fails if the share of rows with missing values is over 10% higher than in reference.

    **No reference**: the test fails if the dataset contains rows with missing values.| | **TestNumberOfDifferentMissingValues()**| Dataset-level.

    Tests the number of differently encoded missing values in the dataset against the reference or a defined condition. Detects 4 types of missing values by default and/or values from a user list. | **Required**:
    N/A

    **Optional**:
    • `missing_values: list
      replace: bool = True`(default = default list)
    **Test conditions**
    • *standard parameters*
    | Expects <= or none.

    **With reference**: the test fails if the current dataset has more types of missing values.

    **No reference**: the test fails if the current dataset contains missing values. -| **TestNumberOfConstantColumns()** | Dataset-level.

    Tests the number of columns with all constant values against reference or a defined condition. |**Required**:
    N/A

    **Optional**:
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects =< or none.

    **With reference**: the test fails if the number of constant columns is higher than in the reference.

    **No reference**: the test fails if there is at least one constant column.| -| **TestNumberOfEmptyRows()** | Dataset-level.

    Tests the number of empty rows against reference or a defined condition. |**Required**:
    N/A

    **Optional**:
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects +/- 10% or none.

    **With reference**: the test fails if the share of empty rows is over 10% higher or lower than in the reference.

    **No reference**: the test fails if there is at least one empty row.| -| **TestNumberOfEmptyColumns()** | Dataset-level.

    Tests the number of empty columns against reference or a defined condition.|**Required**:
    N/A

    **Optional**:
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects =< or none.

    **With reference**: the test fails if the number of empty columns is higher than in the reference.

    **No reference**: the test fails if there is at least one empty column.| -| **TestNumberOfDuplicatedRows()** | Dataset-level.

    Tests the number of duplicate rows against reference or a defined condition. |**Required**:
    N/A

    **Optional**:
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects +/- 10% or none.

    **With reference**: the test fails if the share of duplicate rows is over 10% higher or lower than in the reference.

    **No reference**: the test fails if there is at least one duplicate row. | -| **TestNumberOfDuplicatedColumns()** | Dataset-level.

    Tests the number of duplicate columns against reference or a defined condition. |**Required**:
    N/A

    **Optional**:
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects =< or none.

    **With reference**: the test fails if the number of duplicate columns is higher than in the reference.

    **No reference**: the test fails if there is at least one duplicate column.| -| **TestColumnsType()**| Dataset-level.

    Tests the types of all columns against the reference.| **Required**:
    N/A

    **Optional**:
    `columns_type: dict`

    **Test conditions**:
    N/A | Expects types to match.

    **With reference**: the test fails if at least one column type does not match.
    **No reference**: N/A | | **TestColumnNumberOfMissingValues**(column_name='name')| Column-level.

    Tests the number of missing values in a given column against the reference or a defined condition.| **Required**:
    • `column_name`
    **Optional**:
    • `missing_values = [], replace = True/False` (default = default list)
    **Test conditions**
    • *standard parameters*
    | Expects up to 10% or none.

    **With reference**: the test fails if the share of missing values in a column is over 10% higher than in reference.

    **No reference**: the test fails if the column contains missing values.| | **TestColumnShareOfMissingValues**(column_name='name')| Column-level.

    Tests the share of missing values in a given column against the reference or a defined condition.| **Required**:
    • `column_name`
    **Optional**:
    • `missing_values = [], replace = True/False` (default = default list)
    **Test conditions**
    • *standard parameters*
    | Expects up to 10% or none.

    **With reference**: the test fails if the share of missing values in a column is over 10% higher than in reference.
    **No reference**: the test fails if the column contains missing values.| | **TestColumnNumberOfDifferentMissingValues**(column_name='name')| Column-level.

    Tests the number of differently encoded missing values in the column against reference or a defined condition. Detects 4 types of missing values by default and/or values from a user list. | **Required**:
    • `column_name`
    **Optional**:
    • `missing_values = [], replace = True/False` (default = default list)
    **Test conditions**:
    • *standard parameters*
    | Expects <= or none.

    **With reference**: the test fails if the current column has more types of missing values.

    **No reference**: The test fails if the column contains missing values.| -| **TestColumnAllConstantValues**(column_name='name') | Column-level.

    Tests if all the values in a given column are constant.|**Required**:
    • `column_name`
    **Optional**:
    N/A

    **Test conditions**:
    N/A| Expects non-constant.

    The test fails if all values in a given column are constant.| -| **TestColumnAllUniqueValues**(column_name='name') | Column-level.

    Tests if all the values in a given column are unique.| **Required**:
    • `column_name`
    **Optional**:
    N/A

    **Test conditions**:
    N/A | Expects all unique (e.g., IDs).

    The test fails if at least one value in a given column is not unique.| -| **TestColumnRegExp**(column_name='name, reg_exp='^[0..9]') | Column-level.

    Tests the number of values in a column that do not match a defined regular expression, against reference or a defined condition. |**Required**:
    • `column_name`
    • `reg_exp`
    **Optional**:
    N/A

    **Test conditions**:
    • *standard parameters*
    | **With reference**: the test fails if the share of values that match a regular expression is over 10% higher or lower than in the reference.

    **No reference**: the test fails if at least one of the values does not match a regular expression. | -| **TestCategoryShare**(column_name='education', category='Some-college', lt=0.5)) | Column-level.

    Tests if the number of objects belonging to a defined category (or having a defined numerical value) is within the threshold.|**Required**:
    • `column_name`
    • `category`
    **Optional**:
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects the category to be present.

    The test fails if the category is not present.| -| **TestCategoryCount**(column_name='education', category='Some-college', lt=0.5)) | Column-level.

    Tests if the share of objects belonging to a defined category (or having a defined numerical value) is within the threshold.|**Required**:
    • `column_name`
    • `category`
    **Optional**:
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects the category to be present.

    The test fails if the category is not present.| - -# Data Quality -**Defaults for data quality**. If there is no reference data or defined conditions, data quality will be checked against a set of heuristics. If you pass the reference data, Evidently will automatically derive all relevant statistics (e.g., min value, max value, value range, value list, etc.) and apply default test conditions. You can also pass custom test conditions. +# Correlations | Test name | Description | Parameters | Default test conditions | |---|---|---|---| -| **TestConflictTarget()**| Dataset-level.

    Tests if there are conflicts in the target (instances where a different label is assigned for an identical input). | N/A | Expects no conflicts in the target (with or without reference). | -| **TestConflictPrediction()**| Dataset-level.

    Tests if there are conflicts in the prediction (instances where a different prediction is made for an identical input). | N/A | Expects no conflicts in the target (with or without reference). | | **TestTargetPredictionCorrelation()** | Dataset-level.

    Tests the strength of correlation between the target and prediction.| **Required**:
    N/A

    **Optional:**
    • `method` (default = `pearson`, available = `pearson`, `spearman`, `kendall`, `cramer_v`)
    **Test conditions**:
    • *standard parameters*
    | Expects +/- 0.25 in correlation strength, or > 0.

    **With reference**: the test fails if there is a 0.25+ change in the correlation strength between target and prediction.

    **No reference**: the test fails if the correlation between target and prediction <=0 | | **TestHighlyCorrelatedColumns()**| Dataset-level.

    Tests the strongest correlation between a pair of features, against reference or a defined condition.
    | **Required**:
    N/A

    **Optional:**
    • `method` (default = `pearson`, available = `pearson`, `spearman`, `kendall`, `cramer_v`)
    **Test conditions**:
    • *standard parameters*
    | Expects +/- 10% in max correlation strength, or < 0.9.

    **With reference**: the test fails if there is a 10%+ change in the correlation strength for the most correlated feature pair.

    **No reference**: the test fails if there is at least one pair of features with the correlation >= 0.9 | | **TestTargetFeaturesCorrelations()**| Dataset-level.

    Tests if any of the features is highly correlated with the target.
    Example use: to detect target leak. | **Required**:
    N/A

    **Optional:**
    • '`method` (default = `pearson`, available = `pearson`, `spearman`, `kendall`, `cramer_v`)
    **Test conditions**:
    • *standard parameters*
    | Expects +/- 10% in max correlation strength, or < 0.9.

    **With reference**: the test fails if there is a 10%+ change in the correlation strength for the feature most correlated with the target.

    **No reference**: the test fails if at least one feature is correlated with the target >= 0.9 | | **TestPredictionFeaturesCorrelations()**| Dataset-level.

    Tests if any of the features is highly correlated with the prediction
    Example use: to detect when predictions rely on a single feature. | **Required**:
    N/A

    **Optional:**
    • `method` (default = `pearson`, available = `pearson`, `spearman`, `kendall`, `cramer_v`)
    **Test conditions**:
    • *standard parameters*
    | Expects +/- 10% in max correlation strength, or < 0.9.

    **With reference**: the test fails if there is a 10%+ change in the correlation strength for the feature most correlated with the prediction.

    **No reference**: the test fails if at least one feature is correlated with the prediction >= 0.9 | | **TestCorrelationChanges()** | Dataset-level.

    Tests the number of correlation violations (significant change in the correlation strength between any two columns).| **Required**:
    N/A

    **Optional:**
    • `method` (default = `pearson`, available = `pearson`, `spearman`, `kendall`, `cramer_v`)
    • `corr_diff` (default = 0.25)
    • `column_name`(checks for correlation changes only between a chosen column and other columns in the dataset)
    **Test conditions**:
    • *standard parameters*
    | Expects none.

    **With reference**: the test fails if at least 1 correlation violation is detected.

    **No reference**: N/A | + +# Column Value Checks + +| Test name | Description | Parameters | Default test conditions | +|---|---|---|---| | **TestColumnValueMin**(column_name='num-column') | Column-level.

    Tests the minimum value of a given numerical column against reference or a defined condition. | **Required**:
    • `column_name`
    **Optional:** N/A

    **Test conditions**:
    • *standard parameters*
    | Expects not lower.

    **With reference**: the test fails if the minimum value is lower than in the reference.

    **No reference**: N/A | | **TestColumnValueMax**(column_name='num-column') | Column-level.

    Tests the maximum value of a given numerical column against reference or a defined condition. | **Required**:
    • `column_name`
    **Optional:** N/A

    **Test conditions**:
    • *standard parameters*
    | Expects not higher.

    **With reference**: the test fails if the maximum value is higher than in the reference.

    **No reference**: N/A | | **TestColumnValueMean**(column_name='num-column') | Column-level.

    Tests the mean value of a given numerical column against reference or a defined condition. | **Required**:
    • `column_name`
    **Optional:**
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects +/-10%.

    **With reference**: the test fails if the mean value is different by more than 10%.

    **No reference**: N/A | | **TestColumnValueMedian**(column_name='num-column') | Column-level.

    Tests the median value of a given numerical column against reference or a defined condition. | **Required**:
    • `column_name`
    **Optional:**
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects +/-10%.

    **With reference**: the test fails if the median value is different by more than 10%.

    **No reference**: N/A | | **TestColumnValueStd**(column_name='num-column')
    | Column-level.

    Tests the standard deviation of a given numerical column against reference or a defined condition. | **Required**:
    • `column_name`
    **Optional:**
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects +/-10%.

    **With reference**: the test fails if the standard deviation is different by more than 10%.

    **No reference**: N/A | -| **TestNumberOfUniqueValues**(column_name='name')
    | Column-level.

    Tests the number of unique values in a given column against reference or a defined condition. | **Required**:
    • `column_name`
    **Optional:**
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects +/-10%.

    **With reference**: the test fails if the share of unique values is different by more than 10%.

    **No reference**: N/A | -| **TestUniqueValuesShare**(column_name='name') | Column-level.

    Tests the share of unique values in a given column against reference or a defined condition. | **Required**:
    • `column_name`
    **Optional:**
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects +/-10%.

    **With reference**: the test fails if the share of unique values is different by more than 10%.

    **No reference**: N/A | -| **TestMostCommonValueShare**(column_name='name') | Column-level.

    Tests the share of the most common value in a given column against reference or a defined condition. | **Required**:
    • `column_name`
    **Optional:**
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects +/-10%.

    **With reference**: the test fails if the share of the most common value is different by more than 10% from the reference.

    **No reference**: the test fails if the share of the most common value is >= 80%. | +|**TestColumnQuantile**(column_name='num_column', quantile=0.25) | Column-level.

    Computes a quantile value and compares it to the reference or against a defined condition. | **Required**:
    • `column_name`
    • `quantile`
    **Optional:** N/A

    **Test conditions**:
    • *standard parameters*
    | Expects +/-10%.

    **With reference**: the test fails if the quantile value is over 10% higher or lower.

    **No reference**: N/A | | **TestMeanInNSigmas**(column_name='num-column') | Column-level.

    Tests if the mean value in a given numerical column is within the expected range , defined in standard deviations. This test requires reference. | **Required**:
    • `column_name`
    **Optional:**
    • `n_sigmas`
    | Expects +/- 2 std dev.

    **With reference**: the test fails if the current mean value is out of the +/- 2 std dev interval from the reference mean value.

    **No reference**: N/A | | **TestValueRange**(column_name='num_column') | Column-level.

    Tests if a numerical column contains values out of the min-max range. | **Required**:
    • `column_name`
    **Optional:**
    • `left`
    • `right`
    **Test conditions**:
    N/A | Expects all values to be in range.

    **With reference**: the test fails if the column contains values out of the min-max range as seen in the reference.

    **No reference**: N/A | | **TestShareOfOutRangeValues**(column_name='num_column')| Column-level.

    Tests the share of values out of the min-max range against reference or a defined condition. | **Required**:
    • `column_name`
    **Optional:**
    • `left`
    • `right`
    **Test conditions**:
    • *standard parameters*
    || Expects all values to be in range.

    | Expects all values to be in range.

    **With reference**: the test fails if at least 1 value is out of range (as seen in reference).

    **No reference**: N/A | | **TestNumberOfOutRangeValues**(column_name='num_column') | Column-level.

    Tests the number of values out of the min-max range against reference or a defined condition. | **Required**:
    • `column_name`
    **Optional:**
    • `left`
    • `right`
    **Test conditions**:
    • *standard parameters*
    || Expects all values to be in range.

    **With reference**: the test fails if at least 1 value is out of range (as seen in reference).

    **No reference**: N/A | +| **TestCategoryShare**(column_name='education', category='Some-college', lt=0.5)) | Column-level.

    Tests if the number of objects belonging to a defined category (or having a defined numerical value) is within the threshold.|**Required**:
    • `column_name`
    • `category`
    **Optional**:
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects the category to be present.

    The test fails if the category is not present.| +| **TestCategoryCount**(column_name='education', category='Some-college', lt=0.5)) | Column-level.

    Tests if the share of objects belonging to a defined category (or having a defined numerical value) is within the threshold.|**Required**:
    • `column_name`
    • `category`
    **Optional**:
    N/A

    **Test conditions**:
    • *standard parameters*
    | Expects the category to be present.

    The test fails if the category is not present.| | **TestValueList**(column_name='cat_column') | Column-level.

    Tests if a categorical column contains values out of the list. | **Required**:
    • `column_name`
    **Optional:**
    • `values: List[str]`
    **Test conditions**:
    N/A| Expects all values to be in the list.

    **With reference**: the test fails if the column contains values out of the list (as seen in reference).

    **No reference**: N/A | | **TestNumberOfOutListValues**(column_name='cat_column')| Column-level.

    Tests the number of values in a given column that are out of list, against reference or a defined condition. | **Required**:
    • `column_name`
    **Optional:**
    • `values: List[str]`
    **Test conditions**:
    • *standard parameters*
    | Expects all values to be in the list.

    **With reference**: the test fails if the column contains values out of the list (as seen in reference).

    **No reference**: N/A | | **TestShareOfOutListValues**(column_name='cat_column')| Column-level.

    Tests the share of values in a given column that are out of list against reference or a defined condition. | **Required**:
    • `column_name`
    **Optional:**
    • `values: List[str]`
    **Test conditions**:
    • *standard parameters*
    | Expects all values to be in the list.

    **With reference**: the test fails if the column contains values out of the list (as seen in reference).
    **No reference**: N/A | -|**TestColumnQuantile**(column_name='num_column', quantile=0.25) | Column-level.

    Computes a quantile value and compares it to the reference or against a defined condition. | **Required**:
    • `column_name`
    • `quantile`
    **Optional:** N/A

    **Test conditions**:
    • *standard parameters*
    | Expects +/-10%.

    **With reference**: the test fails if the quantile value is over 10% higher or lower.

    **No reference**: N/A | # Data Drift -**Defaults for Data Drift**. By default, all data drift tests use the Evidently [drift detection logic](data-drift-algorithm.md) that selects a different statistical test or metric based on feature type and volume. You always need a reference dataset. - -To modify the logic or select a different test, you should set [data drift parameters](../customization/options-for-statistical-tests.md). +By default, all data drift tests use the Evidently [drift detection logic](data-drift-algorithm.md) that selects a different statistical test or metric based on feature type and volume. You always need a reference dataset. To modify the logic or select a different test, you should set [data drift parameters](../customization/options-for-statistical-tests.md). | Test name | Description | Parameters | Default test conditions | |---|---|---|---| @@ -128,7 +214,6 @@ To modify the logic or select a different test, you should set [data drift param | **TestColumnDrift(column_name='name')**| Column-level.

    Tests if there is a distribution shift in a given column compared to the reference.| **Required**:
    • column_name
    **Optional**:
    • `stattest`(default=automated selection)
    • `stattest_threshold`(default=test default)
    | Expects no drift.

    **With reference:** the test fails if the distribution drift is detected in a given column.

    **No reference:** N/A | | **TestEmbeddingsDrift(embeddings_name='small_subset')**| Column-level.

    Tests if there is drift in embeddings compared to reference.| **Required**:
    • `embeddings_name`
    **Optional**:
    • `drift_method`(default=model)
    | Expects no drift.

    **With reference:** the test fails if the drift is detected in a given subset of columns.

    **No reference:** N/A | - # Regression **Defaults for Regression tests**: if there is no reference data or defined conditions, Evidently will compare the model performance to a dummy model that predicts the optimal constant (varies by the metric). You can also pass the reference dataset and run the test with default conditions, or define custom test conditions.

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