[jira] [Updated] (SYSTEMML-855) Add a "Get Started" tutorial for Python users

["Niketan Pansare (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SYSTEMML-855?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Niketan Pansare updated SYSTEMML-855:
-------------------------------------
    Description: 
As an example, this tutorial could have following sections:
1. Steps to start Python shell (or cloud service like datascientistworkbench) with SystemML support:
wget https://raw.githubusercontent.com/apache/incubator-systemml/master/src/main/java/org/apache/sysml/api/python/SystemML.py
wget https://sparktc.ibmcloud.com/repo/latest/SystemML.jar

2. Give context for one of the algorithm: For example: Linear regression. We can borrow the technical detail from http://apache.github.io/incubator-systemml/algorithms-regression.html#description

3. Explain steps to download data we will use and how to implement Linear regression DS using embedded Python DSL:
https://github.com/apache/incubator-systemml/pull/197

<code>
import numpy as np
from sklearn import datasets
# Load the diabetes dataset
diabetes = datasets.load_diabetes()
# Use only one feature
diabetes_X = diabetes.data[:, np.newaxis, 2]
# Split the data into training/testing sets
diabetes_X_train = diabetes_X[:-20]
diabetes_X_test = diabetes_X[-20:]
# Split the targets into training/testing sets
diabetes_y_train = diabetes.target[:-20]
diabetes_y_test = diabetes.target[-20:]
</code>

4. Explain how to use our algorithm instead:
http://apache.github.io/incubator-systemml/algorithms-regression.html#examples

5. To explain tradeoffs of using NumPy or Scikit-Learn v/s SystemML's embedded DSL or SystemML's mllearn, increase the data size. For example: use twitter feed.

  was:
As an example, this tutorial could have following sections:
1. Steps to start Python shell (or cloud service like datascientistworkbench) with SystemML support:
wget https://raw.githubusercontent.com/apache/incubator-systemml/master/src/main/java/org/apache/sysml/api/python/SystemML.py
wget https://sparktc.ibmcloud.com/repo/latest/SystemML.jar

2. Give context for one of the algorithm: For example: Linear regression. We can borrow the technical detail from http://apache.github.io/incubator-systemml/algorithms-regression.html#description

3. Explain steps to download data we will use and how to implement Linear regression DS using embedded Python DSL:
https://github.com/apache/incubator-systemml/pull/197

```
import numpy as np
from sklearn import datasets
# Load the diabetes dataset
diabetes = datasets.load_diabetes()
# Use only one feature
diabetes_X = diabetes.data[:, np.newaxis, 2]
# Split the data into training/testing sets
diabetes_X_train = diabetes_X[:-20]
diabetes_X_test = diabetes_X[-20:]
# Split the targets into training/testing sets
diabetes_y_train = diabetes.target[:-20]
diabetes_y_test = diabetes.target[-20:]
```

4. Explain how to use our algorithm instead:
http://apache.github.io/incubator-systemml/algorithms-regression.html#examples

5. To explain tradeoffs of using NumPy or Scikit-Learn v/s SystemML's embedded DSL or SystemML's mllearn, increase the data size. For example: use twitter feed.


> Add a "Get Started" tutorial for Python users
> ---------------------------------------------
>
>                 Key: SYSTEMML-855
>                 URL: https://issues.apache.org/jira/browse/SYSTEMML-855
>             Project: SystemML
>          Issue Type: Task
>            Reporter: Niketan Pansare
>
> As an example, this tutorial could have following sections:
> 1. Steps to start Python shell (or cloud service like datascientistworkbench) with SystemML support:
> wget https://raw.githubusercontent.com/apache/incubator-systemml/master/src/main/java/org/apache/sysml/api/python/SystemML.py
> wget https://sparktc.ibmcloud.com/repo/latest/SystemML.jar
> 2. Give context for one of the algorithm: For example: Linear regression. We can borrow the technical detail from http://apache.github.io/incubator-systemml/algorithms-regression.html#description
> 3. Explain steps to download data we will use and how to implement Linear regression DS using embedded Python DSL:
> https://github.com/apache/incubator-systemml/pull/197
> <code>
> import numpy as np
> from sklearn import datasets
> # Load the diabetes dataset
> diabetes = datasets.load_diabetes()
> # Use only one feature
> diabetes_X = diabetes.data[:, np.newaxis, 2]
> # Split the data into training/testing sets
> diabetes_X_train = diabetes_X[:-20]
> diabetes_X_test = diabetes_X[-20:]
> # Split the targets into training/testing sets
> diabetes_y_train = diabetes.target[:-20]
> diabetes_y_test = diabetes.target[-20:]
> </code>
> 4. Explain how to use our algorithm instead:
> http://apache.github.io/incubator-systemml/algorithms-regression.html#examples
> 5. To explain tradeoffs of using NumPy or Scikit-Learn v/s SystemML's embedded DSL or SystemML's mllearn, increase the data size. For example: use twitter feed.



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