Merge pull request #106301 from likebupt/update-0303

Update 0303

Author: GitHubber17

articles/machine-learning/algorithm-module-reference/create-python-model.md

@@ -30,7 +30,7 @@ Use of this module requires intermediate or expert knowledge of Python. The modu
 
 This article will show how to use the **Create Python Model** with a simple pipeline. Below is the graph of the pipeline.
 
-![create-python-model](./media/module/aml-create-python-model.png)
+![create-python-model](./media/module/create-python-model.png)
 
 1.  Click **Create Python Model**, edit the script to implement your modeling or data management process. You can base the model on any learner that is included in a Python package in the Azure Machine Learning environment.
 

articles/machine-learning/algorithm-module-reference/enter-data-manually.md

@@ -84,11 +84,9 @@ This module can be helpful in scenarios such as these:
         |0.00016|0.004|0.999961|0.00784|1|  
         |0|0.004|0.999955|0.008615|1|  
   
-4.  Press ENTER after each row, to start a new line.  
-  
-     **Be sure to press ENTER after the final row.** 
+4.  Press ENTER after each row, to start a new line.      
      
-     If you press ENTER multiple times to add multiple empty trailing rows, the final empty row is removed trimmed, but other empty rows are treated as missing values.  
+     If you press ENTER multiple times to add multiple empty trailing rows, the empty rows will be removed trimmed.  
   
      If you create rows with missing values, you can always filter them out later.  
   

articles/machine-learning/algorithm-module-reference/media/module/create-python-model.png

@@ -149,9 +149,9 @@ This option is used when you have divided a dataset into multiple partitions and
 
 5. If you are working with multiple partitions, you must add additional instances of the **Partition and Sample** module to handle each partition.
 
-    For example, let's say previously partitioned patients into four folds using age. To work with each individual fold, you need four copies of the **Partition and Sample** module, and in each, you select a different fold, as shown below. It's not correct to use the **Assign to Folds** output directly.  
+    For example, the **Partition and Sample** module in the second row is set to **Assign to Folds**, and the modules in the third row is set to **Pick Fold**.   
 
-    [![Partition and sample](./media/partition-and-sample/partition-and-sample.png)](./media/partition-and-sample/partition-and-sample-lg.png#lightbox)
+    ![Partition and sample](./media/module/partition-and-sample.png)
 
 5. Run the pipeline.
 

articles/machine-learning/algorithm-module-reference/media/module/partition-and-sample.png

@@ -79,35 +79,75 @@ This module is particularly useful when you need to separate data into training
 
     Based on the regular expression you provide, the dataset is divided into two sets of rows: rows with values that match the expression and all remaining rows. 
 
+The following examples demonstrate how to divide a dataset using the **Regular Expression** option. 
+
+### Single whole word 
+
+This example puts into the first dataset all rows that contain the text `Gryphon` in the column `Text`, and puts other rows into the second output of **Split Data**:
+
+```text
+    \"Text" Gryphon  
+```
+
+### Substring
+
+This example looks for the specified string in any position within the second column of the dataset, denoted here by the index value of 1. The match is case-sensitive.
+
+```text
+(\1) ^[a-f]
+```
+
+The first result dataset contains all rows where the index column begins with one of these characters: `a`, `b`, `c`, `d`, `e`, `f`. All other rows are directed to the second output.
+
 ## Relative expression split.
 
 1. Add the [Split Data](./split-data.md) module to your pipeline, and connect it as input to the dataset you want to split.
 
 2. For **Splitting mode**, select **relative expression split**.
 
-3. In the **Relational expression** text box, type an expression that performs a comparison operation, on a single column:
+3. In the **Relational expression** text box, type an expression that performs a comparison operation on a single column:
+
+   For the **Numeric column**:
+   - The column contains numbers of any numeric data type, including date and time data types.
+   - The expression can reference a maximum of one column name.
+   - Use the ampersand character `&` for the AND operation. Use the pipe character `|` for the OR operation.
+   - The following operators are supported: `<`, `>`, `<=`, `>=`, `==`, `!=`.
+   - You cannot group operations by using `(` and `)`.
+   
+   For the **String column**:
+   - The following operators are supported: `==`, `!=`.
 
+4. Run the pipeline.
 
- - Numeric column:
-    - The column contains numbers of any numeric data type, including date/time data types.
+    The expression divides the dataset into two sets of rows: rows with values that meet the condition, and all remaining rows.
 
-    - The expression can reference a maximum of one column name.
+The following examples demonstrate how to divide a dataset using the **Relative Expression** option in the **Split Data** module:  
 
-    - Use the ampersand character (&) for the AND operation and use the pipe character (|) for the OR operation.
+### Using calendar year
 
-    - The following operators are supported: `<`, `>`, `<=`, `>=`, `==`, `!=`
+A common scenario is to divide a dataset by years. The following expression selects all rows where the values in the column `Year` are greater than `2010`.
 
-    - You cannot group operations by using `(` and `)`.
+```text
+\"Year" > 2010
+```
 
- - String column: 
-    - The following operators are supported: `==`, `!=`
+The date expression must account for all date parts that are included in the data column, and the format of dates in the data column must be consistent. 
 
+For example, in a date column using the format `mmddyyyy`, the expression should be something like this:
 
+```text
+\"Date" > 1/1/2010
+```
 
-4. Run the pipeline.
+### Using column indices
+
+The following expression demonstrates how you can use the column index to select all rows in the first column of the dataset that contain values less than or equal to 30, but not equal to 20.
+
+```text
+(\0)<=30 & !=20
+```
 
-    The expression divides the dataset into two sets of rows: rows with values that meet the condition, and all remaining rows.
 
 ## Next steps
 
-See the [set of modules available](module-reference.md) to Azure Machine Learning. 
+See the [set of modules available](module-reference.md) to Azure Machine Learning. 

articles/machine-learning/algorithm-module-reference/partition-and-sample.md

@@ -114,15 +114,17 @@ Use the following steps to submit a pipeline endpoint run from the designer:
 
 1. Select the pipeline you want to run.
 
-1. Select **Run**.
+1. Select **Submit**.
 
 1. In the setup dialog, you can specify a new input data path value, which points to your new dataset.
 
 ![Screenshot showing how to set up a parameterized pipeline run in the designer](./media/how-to-retrain-designer/published-pipeline-run.png)
 
 ### Submit runs with code
 
-There are multiple ways to access your REST endpoint programatically depending on your development environment. You can find code samples that show you how to submit pipeline runs with parameters in the **Consume** tab of your pipeline.
+You can find the REST endpoint of a published pipeline in the overview panel. By calling the endpoint, you can retrain the published pipeline.
+
+To make a REST call, you will need an OAuth 2.0 bearer-type authentication header. See the following [tutorial section](tutorial-pipeline-batch-scoring-classification.md#publish-and-run-from-a-rest-endpoint) for more detail on setting up authentication to your workspace and making a parameterized REST call.
 
 ## Next steps