Merge pull request #188641 from nibaccam/label-dataset

Labeled datasets| PM edits

Author: v-shils

articles/machine-learning/how-to-use-labeled-dataset.md

@@ -8,24 +8,23 @@ ms.service: machine-learning
 ms.subservice: mldata
 ms.topic: how-to
 ms.custom: data4ml
-ms.date: 10/21/2021
+ms.date: 02/15/2022
 
 # Customer intent: As an experienced Python developer, I need to export my data labels and use them for machine learning tasks.
 ---
 
 # Create and explore Azure Machine Learning dataset with labels
 
-In this article, you'll learn how to export the data labels from an Azure Machine Learning data labeling project and load them into popular formats such as, a pandas dataframe for data exploration or a Torchvision dataset for image transformation. 
+In this article, you'll learn how to export the data labels from an Azure Machine Learning data labeling project and load them into popular formats such as, a pandas dataframe for data exploration. 
 
 ## What are datasets with labels 
 
-We refer to Azure Machine Learning datasets with labels as labeled datasets. These specific dataset types of labeled datasets are only created as an output of Azure Machine Learning data labeling projects. Create a data labeling project [for image labeling](how-to-create-image-labeling-projects.md) or [text labeling](how-to-create-text-labeling-projects.md). Machine Learning supports data labeling projects for image classification, either multi-label or multi-class, and object identification together with bounded boxes.
+Azure Machine Learning datasets with labels are referred to as labeled datasets. These specific datasets are [TabularDatasets](/python/api/azureml-core/azureml.data.tabular_dataset.tabulardataset) with a dedicated label column and are only created as an output of Azure Machine Learning data labeling projects. Create a data labeling project [for image labeling](how-to-create-image-labeling-projects.md) or [text labeling](how-to-create-text-labeling-projects.md). Machine Learning supports data labeling projects for image classification, either multi-label or multi-class, and object identification together with bounded boxes.
 
 ## Prerequisites
 
 * An Azure subscription. If you don’t have an Azure subscription, create a [free account](https://azure.microsoft.com/free/) before you begin.
 * The [Azure Machine Learning SDK for Python](/python/api/overview/azure/ml/intro), or access to [Azure Machine Learning studio](https://ml.azure.com/).
-    * Install the [azure-contrib-dataset](/python/api/azureml-contrib-dataset/) package
 * A Machine Learning workspace. See [Create an Azure Machine Learning workspace](how-to-manage-workspace.md).
 * Access to an Azure Machine Learning data labeling project. If you don't have a labeling project, first create one for [image labeling](how-to-create-image-labeling-projects.md) or [text labeling](how-to-create-text-labeling-projects.md).
 
@@ -48,36 +47,30 @@ You can access the exported Azure Machine Learning dataset in the **Datasets** s
 
 Once you have exported your labeled data to an Azure Machine Learning dataset, you can use AutoML to build computer vision models trained on your labeled data. Learn more at [Set up AutoML to train computer vision models with Python (preview)](how-to-auto-train-image-models.md)
 
-## Explore labeled datasets
+## Explore labeled datasets via pandas dataframe
 
-Load your labeled datasets into a pandas dataframe or Torchvision dataset to leverage popular open-source libraries for data exploration, as well as PyTorch provided libraries for image transformation and training.
+Load your labeled datasets into a pandas dataframe to leverage popular open-source libraries for data exploration with the [`to_pandas_dataframe()`](/python/api/azureml-core/azureml.data.tabulardataset#to-pandas-dataframe-on-error--null---out-of-range-datetime--null--) method from the `azureml-dataprep` class. 
 
-### Pandas dataframe
-
-You can load labeled datasets into a pandas dataframe with the [`to_pandas_dataframe()`](/python/api/azureml-core/azureml.data.tabulardataset#to-pandas-dataframe-on-error--null---out-of-range-datetime--null--) method from the `azureml-contrib-dataset` class. Install the class with the following shell command: 
+Install the class with the following shell command: 
 
 ```shell
-pip install azureml-contrib-dataset
+pip install azureml-dataprep
 ```
 
->[!NOTE]
->The azureml.contrib namespace changes frequently, as we work to improve the service. As such, anything in this namespace should be considered as a preview, and not fully supported by Microsoft.
-
-Azure Machine Learning offers the following file handling options for file streams when converting to a pandas dataframe.
-* Download: Download your data files to a local path.
-* Mount: Mount your data files to a mount point. Mount only works for Linux-based compute, including Azure Machine Learning notebook VM and Azure Machine Learning Compute.
-
 In the following code, the `animal_labels` dataset is the output from a labeling project previously saved to the workspace.
+The exported dataset is a [TabularDataset](/python/api/azureml-core/azureml.data.tabular_dataset.tabulardataset). If you plan to use [download()] or [mount()] methods, be sure to set the parameter `stream column ='image_url'`
 
 ```Python
 import azureml.core
-import azureml.contrib.dataset
 from azureml.core import Dataset, Workspace
-from azureml.contrib.dataset import FileHandlingOption
+
 
 # get animal_labels dataset from the workspace
 animal_labels = Dataset.get_by_name(workspace, 'animal_labels')
-animal_pd = animal_labels.to_pandas_dataframe(file_handling_option=FileHandlingOption.DOWNLOAD, target_path='./download/', overwrite_download=True)
+animal_pd = animal_labels.to_pandas_dataframe()
+
+# download the images to local 
+animal_labels.download(stream_column='image_url') 
 
 import matplotlib.pyplot as plt
 import matplotlib.image as mpimg
@@ -87,33 +80,7 @@ img = mpimg.imread(animal_pd.loc[0,'image_url'])
 imgplot = plt.imshow(img)
 ```
 
-### Torchvision datasets
-
-You can load labeled datasets into Torchvision dataset with the [to_torchvision()](/python/api/azureml-contrib-dataset/azureml.contrib.dataset.tabulardataset#to-torchvision--) method also from the `azureml-contrib-dataset` class. To use this method, you need to have [PyTorch](https://pytorch.org/) installed. 
-
-In the following code, the `animal_labels` dataset is the output from a labeling project previously saved to the workspace.
-
-```python
-import azureml.core
-import azureml.contrib.dataset
-from azureml.core import Dataset, Workspace
-from azureml.contrib.dataset import FileHandlingOption
-
-from torchvision.transforms import functional as F
-
-# get animal_labels dataset from the workspace
-animal_labels = Dataset.get_by_name(workspace, 'animal_labels')
-
-# load animal_labels dataset into torchvision dataset
-pytorch_dataset = animal_labels.to_torchvision()
-img = pytorch_dataset[0][0]
-print(type(img))
-
-# use methods from torchvision to transform the img into grayscale
-pil_image = F.to_pil_image(img)
-gray_image = F.to_grayscale(pil_image, num_output_channels=3)
-
-imgplot = plt.imshow(gray_image)
-```
-
 ## Next steps
+
+* Learn to [train image classification models in Azure](./tutorial-train-deploy-notebook.md)
+* [Set up AutoML to train computer vision models with Python (preview)](how-to-auto-train-image-models.md)