Merge pull request #101300 from trevorbye/master

bug and git issue fixes

Author: PRMerger12

articles/machine-learning/how-to-configure-auto-train.md

@@ -79,10 +79,11 @@ The following code examples demonstrate how to store the data in these formats.
 * TabularDataset
   ```python
   from azureml.core.dataset import Dataset
+  from azureml.opendatasets import Diabetes
 
-  tabular_dataset = Dataset.Tabular.from_delimited_files("https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv")
-  train_dataset, test_dataset = tabular_dataset.random_split(percentage = 0.1, seed = 42)
-  label = "Label"
+  tabular_dataset = Diabetes.get_tabular_dataset()
+  train_dataset, test_dataset = tabular_dataset.random_split(percentage=0.1, seed=42)
+  label = "Y"
   ```
 
 * Pandas dataframe
@@ -91,9 +92,9 @@ The following code examples demonstrate how to store the data in these formats.
     import pandas as pd
     from sklearn.model_selection import train_test_split
 
-    df = pd.read_csv("https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv", delimiter="\t", quotechar='"')
-    train_data, test_data = train_test_split(df, test_size = 0.1, random_state = 42)
-    label = "Label"
+    df = pd.read_csv("your-local-file.csv")
+    train_data, test_data = train_test_split(df, test_size=0.1, random_state=42)
+    label = "label-col-name"
     ```
 
 ## Fetch data for running experiment on remote compute

articles/machine-learning/how-to-create-register-datasets.md

@@ -194,16 +194,7 @@ titanic_ds = titanic_ds.register(workspace=workspace,
 
 To create datasets with Azure Open Datasets from the SDK, make sure you've installed the package with `pip install azureml-opendatasets`. Each discrete data set is represented by its own class in the SDK, and certain classes are available as either a `TabularDataset`, `FileDataset`, or both. See the [reference documentation](https://docs.microsoft.com/python/api/azureml-opendatasets/azureml.opendatasets?view=azure-ml-py) for a full list of classes.
 
-Most classes inherit from and return an instance of `TabularDataset`. Examples of these classes include `PublicHolidays`, `BostonSafety`, and `UsPopulationZip`. To create a `TabularDataset` from these types of classes, use the constructor with no arguments. When you register a dataset created from Open Datasets, no data is immediately downloaded, but the data will be accessed later when requested (during training, for example) from a central storage location. 
-
-```python
-from azureml.opendatasets import UsPopulationZip
-
-tabular_dataset = UsPopulationZip()
-tabular_dataset = tabular_dataset.register(workspace=workspace, name="pop data", description="US population data by zip code")
-```
-
-You can retrieve certain classes as either a `TabularDataset` or `FileDataset`, which allows you to manipulate and/or download the files directly. Other classes can get a dataset only by using either the `get_tabular_dataset()` or `get_file_dataset()` functions. The following code sample shows a few examples of these types of classes:
+You can retrieve certain classes as either a `TabularDataset` or `FileDataset`, which allows you to manipulate and/or download the files directly. Other classes can get a dataset **only** by using one of `get_tabular_dataset()` or `get_file_dataset()` functions. The following code sample shows a few examples of these types of classes.
 
 ```python
 from azureml.opendatasets import MNIST
@@ -218,6 +209,8 @@ from azureml.opendatasets import Diabetes
 diabetes_tabular = Diabetes.get_tabular_dataset()
 ```
 
+When you register a dataset created from Open Datasets, no data is immediately downloaded, but the data will be accessed later when requested (during training, for example) from a central storage location.
+
 ### Use the UI
 
 You can also create datasets from Open Datasets classes through the UI. In your workspace, select the **Datasets** tab under **Assets**. On the **Create dataset** drop-down menu, select **From Open Datasets**.

articles/machine-learning/how-to-machine-learning-interpretability-aml.md

@@ -329,10 +329,10 @@ You can load the local, feature importance plot for any data point by selecting
     jupyter labextension install microsoft-mli-widget
     ```
 
-To load the visualization dashboard, use the following code:
+To load the visualization dashboard, use the following code.
 
 ```python
-from azureml.contrib.interpret.visualize import ExplanationDashboard
+from interpret_community.widget import ExplanationDashboard
 
 ExplanationDashboard(global_explanation, model, x_test)
 ```