Text Evals

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

TL;DR: You can explore and compare text datasets.

• Report: for visual analysis or metrics export, use the TextEvals.

Text Evals Report

To visually explore the descriptive properties of text data, you can create a new Report object and generate TextEvals preset for the column containing the text data. It's best to define your own set of descriptors by passing them as a list to the TextEvals preset. For more details, see how descriptors work.

If you don’t specify descriptors, the Preset will use default statistics.

Code example

text_overview_report = Report(metrics=[
    TextEvals(column_name="Review_Text")
])

text_overview_report.run(reference_data=ref, current_data=cur)
text_overview_report

Note that to calculate some text-related metrics, you may also need to also import additional libraries:

import nltk
nltk.download('words')
nltk.download('wordnet')
nltk.download('omw-1.4')

Data Requirements

• You can pass one or two datasets. Evidently will compute descriptors both for the current production data and the reference data. If you pass a single dataset, there will be no comparison.

• To run this preset, you must have text columns in your dataset. Additional features and prediction/target are optional. Pass them if you want to analyze the correlations with text descriptors.

• Column mapping. Specify the columns that contain text features in column mapping.

How it looks

The report includes 5 components. All plots are interactive.

Aggregated visuals in plots. Starting from v 0.3.2, all visuals in the Evidently Reports are aggregated by default. This helps decrease the load time and report size for larger datasets. If you work with smaller datasets or samples, you can pass an option to generate plots with raw data. You can choose whether you want it on not based on the size of your dataset.

Text Descriptors Distribution

The report generates several features that describe different text properties and shows the distributions of these text descriptors.

Text length

Non-letter characters

Out-of-vocabulary words

Sentiment

Shows the distribution of text sentiment (-1 negative to 1 positive).

Sentence Count

Shows the sentence count.

Metrics output

You can also get the report output as a JSON or a Python dictionary.

Report customization

• You can choose your own descriptors.

• You can use a different color schema for the report.

• You can create a different report or test suite from scratch, taking this one as an inspiration.

Examples

• Head to an example how-to notebook to see an example Text Overview preset and other metrics and tests for text data.