Update docs for transform plugins (#1599)

- Update comments for `Correct` and `AstyneAnnotations` of `transform`
- Update documentation for `transform` plugins

CHANGELOG.md

@@ -14,6 +14,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Enhancements
 - Raise an appropriate error when exporting a datumaro dataset if its subset name contains path separators.
   (<https://github.com/openvinotoolkit/datumaro/pull/1615>)
+- Update docs for transform plugins
+  (<https://github.com/openvinotoolkit/datumaro/pull/1599>)
 
 ### Bug fixes
 

docs/source/docs/command-reference/context_free/transform.md

@@ -101,8 +101,10 @@ Basic dataset item manipulations:
 - [`remove_images`](#remove_images) - Removes specific images
 - [`remove_annotations`](#remove_annotations) - Removes annotations
 - [`remove_attributes`](#remove_attributes) - Removes attributes
-- [`astype_annotations`](#astype_annotations) - Convert annotation type
-- [`pseudo_labeling`](#pseudo_labeling) - Generate pseudo labels for unlabeled data
+- [`astype_annotations`](#astype_annotations) - Transforms annotation types
+- [`pseudo_labeling`](#pseudo_labeling) - Generates pseudo labels for unlabeled data
+- [`correct`](#correct) - Corrects annotaiton types
+- [`clean`](#clean) - Removes noisy data for tabular dataset
 
 Subset manipulations:
 - [`random_split`](#random_split) - Splits dataset into subsets
@@ -827,19 +829,6 @@ bbox_values_decrement [-h]
 Optional arguments:
 - `-h`, `--help` (flag) - Show this help message and exit
 
-#### `correct`
-
-Correct the dataset from a validation report
-
-Usage:
-```console
-correct [-h] [-r REPORT_PATH]
-```
-
-Optional arguments:
-- `-h`, `--help` (flag) - Show this help message and exit
-- `-r`, `--reports` (str) - A validation report from a 'validate' CLI (default=validation_reports.json)
-
 #### `pseudo_labeling`
 
 Assigns pseudo-labels to items in a dataset based on their similarity to predefined labels. This class is useful for semi-supervised learning when dealing with missing or uncertain labels.
@@ -858,7 +847,6 @@ Attributes:
 Usage:
 ```console
 pseudo_labeling [-h] [--labels LABELS]
-```
 
 Optional arguments:
 - `-h`, `--help` (flag) - Show this help message and exit
@@ -869,3 +857,40 @@ Examples:
   ```console
   datum transform -t pseudo_labeling -- --labels 'label1,label2'
   ```
+
+#### `correct`
+
+Correct the dataset from a validation report
+
+Usage:
+```console
+correct [-h] [-r REPORT_PATH]
+```
+
+Optional arguments:
+- `-h`, `--help` (flag) - Show this help message and exit
+- `-r`, `--reports` (str) - A validation report from a 'validate' CLI (default=validation_reports.json)
+
+#### `clean`
+
+Refines and preprocesses media items in a dataset, focusing on string, numeric, and categorical data. This transform is designed to clean and improve the quality of the data, making it more suitable for analysis and modeling.
+
+The cleaning process includes:
+
+- String Data: Removes unnecessary characters using NLP techniques.
+- Numeric Data: Identifies and handles outliers and missing values.
+- Categorical Data: Cleans and refines categorical information.
+
+Usage:
+```console
+clean [-h]
+```
+
+Optional arguments:
+- `-h`, `--help` (flag) - Show this help message and exit
+
+Examples:
+- Clean and preprocess dataset items
+  ```console
+  datum transform -t clean
+  ```

src/datumaro/plugins/transforms.py

@@ -1351,9 +1351,21 @@ def transform_item(self, item: DatasetItem):
 
 class Correct(Transform, CliPlugin):
     """
-    Correct the dataset from a validation report.
-    A user can should feed into validation_reports.json from validator to correct the dataset.
-    This helps to refine the dataset by rejecting undefined labels, missing annotations, and outliers.
+    This class provides functionality to correct and refine a dataset based on a validation report.|n
+    It processes a validation report (typically in JSON format) to identify and rectify various |n
+    dataset issues, such as undefined labels, missing annotations, outliers, empty labels/captions,|n
+    and unnecessary characters in captions. The correction process includes:|n
+    |n
+    - Adding missing labels and attributes.|n
+    - Removing or adjusting annotations with invalid or anomalous values.|n
+    - Filling in missing labels and captions with appropriate values.|n
+    - Removing unnecessary characters from text-based annotations like captions.|n
+    - Handling outliers by capping values within specified bounds.|n
+    - Updating dataset categories and annotations according to the corrections.|n
+    |n
+    The class is designed to be used as part of a command-line interface (CLI) and can be |n
+    configured with different validation reports. It integrates with the dataset extraction |n
+    process, ensuring that corrections are applied consistently across the dataset.|n
     """
 
     @classmethod
@@ -1749,13 +1761,15 @@ def __iter__(self):
 
 class AstypeAnnotations(ItemTransform):
     """
-    Enables the conversion of annotation types for the categories and individual items within a dataset.|n
+    Converts the types of annotations within a dataset based on a specified mapping.|n
     |n
-    Based on a specified mapping, it transforms the annotation types,|n
-    changing them to 'Label' if they are categorical, and to 'Caption' if they are of type string, float, or integer.|n
+    This transform changes annotations to 'Label' if they are categorical, and to 'Caption'
+    if they are of type string, float, or integer. This is particularly useful when working
+    with tabular data that needs to be converted into a format suitable for specific machine
+    learning tasks.|n
     |n
     Examples:|n
-        - Convert type of `title` annotation|n
+        - Converts the type of a `title` annotation:|n
 
         .. code-block::