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# [SDS-2.x, Scalable Data Engineering Science](https://lamastex.github.io/scalable-data-science/sds/2/x/)

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### Power Plant ML Pipeline Application - DataFrame Part
This is the Spark SQL parts of an end-to-end example of using a number of different machine learning algorithms to solve a supervised regression problem.

This is a break-down of *Power Plant ML Pipeline Application* from databricks.

**This will be a recurring example in the sequel**

##### Table of Contents

- **Step 1: Business Understanding** 
- **Step 2: Load Your Data**
- **Step 3: Explore Your Data**
- **Step 4: Visualize Your Data**
- *Step 5: Data Preparation*
- *Step 6: Data Modeling*
- *Step 7: Tuning and Evaluation*
- *Step 8: Deployment*

*We are trying to predict power output given a set of readings from various sensors in a gas-fired power generation plant.  Power generation is a complex process, and understanding and predicting power output is an important element in managing a plant and its connection to the power grid.*

* Given this business problem, we need to translate it to a Machine Learning task (actually a *Statistical* Machine Learning task).  
* The ML task here is *regression* since the label (or target) we will be trying to predict takes a *continuous numeric* value 
   * Note: if the labels took values from a finite discrete set, such as, `Spam`/`Not-Spam` or `Good`/`Bad`/`Ugly`, then the ML task would be *classification*.

**Today, we will only cover Steps 1, 2, 3 and 4 above**. You need introductions to linear algebra, stochastic gradient descent and decision trees before we can accomplish the **applied ML task** with some intuitive understanding. If you can't wait for ML then **check out [Spark MLLib Programming Guide](https://spark.apache.org/docs/latest/mllib-guide.html) for comming attractions!**

The example data is provided by UCI at [UCI Machine Learning Repository Combined Cycle Power Plant Data Set](https://archive.ics.uci.edu/ml/datasets/Combined+Cycle+Power+Plant)

You can read the background on the UCI page, but in summary:

* we have collected a number of readings from sensors at a Gas Fired Power Plant (also called a Peaker Plant) and 
* want to use those sensor readings to predict how much power the plant will generate in a couple weeks from now.
* Again, today we will just focus on Steps 1-4 above that pertain to DataFrames.

More information about Peaker or Peaking Power Plants can be found on Wikipedia [https://en.wikipedia.org/wiki/Peaking_power_plant](https://en.wikipedia.org/wiki/Peaking_power_plant).

//This allows easy embedding of publicly available information into any other notebook
//when viewing in git-book just ignore this block - you may have to manually chase the URL in frameIt("URL").
//Example usage:
// displayHTML(frameIt("https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation#Topics_in_LDA",250))
def frameIt( u:String, h:Int ) : String = {
      """<iframe 
 src=""""+ u+""""
 width="95%" height="""" + h + """"
 sandbox>
  <p>
    <a href="http://spark.apache.org/docs/latest/index.html">
      Fallback link for browsers that, unlikely, don't support frames
    </a>
  </p>
</iframe>"""
   }
displayHTML(frameIt("https://en.wikipedia.org/wiki/Peaking_power_plant",300))

displayHTML(frameIt("https://archive.ics.uci.edu/ml/datasets/Combined+Cycle+Power+Plant",500))

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##Step 1: Business Understanding
The first step in any machine learning task is to understand the business need. 

As described in the overview we are trying to predict power output given a set of readings from various sensors in a gas-fired power generation plant.

The problem is a regression problem since the label (or target) we are trying to predict is numeric

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##Step 2: Load Your Data
Now that we understand what we are trying to do, we need to load our data and describe it, explore it and verify it.

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Data was downloaded already as these five Tab-separated-variable or tsv files.

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Now let us load the data from the Tab-separated-variable or tsv text file into an `RDD[String]` using the familiar `textFile` method.

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### 2.1. Alternatively, load data via the upload GUI feature in databricks 
## USE THIS FOR OTHER SMALLish DataSets you want to import to your CE
Since the dataset is relatively small, we will use the upload feature in Databricks to upload the data as a table.

First download the Data Folder from [UCI Machine Learning Repository Combined Cycle Power Plant Data Set](https://archive.ics.uci.edu/ml/datasets/Combined+Cycle+Power+Plant)

The file is a multi-tab Excel document so you will need to save each tab as a Text file export. 

I prefer exporting as a Tab-Separated-Values (TSV) since it is more consistent than CSV.

Call each file Folds5x2_pp<Sheet 1..5>.tsv and save to your machine.

Go to the Databricks Menu > Tables > Create Table

Select Datasource as "File"

Upload *ALL* 5 files at once.

See screenshots below (but refer [https://docs.databricks.com/user-guide/importing-data.html](https://docs.databricks.com/user-guide/importing-data.html) for latest methods to import data):


**2.1.1. Create Table**
  _________________

When you import your data, name your table `power_plant`, specify all of the columns with the datatype `Double` and make sure you check the `First row is header` box.

![alt text](http://training.databricks.com/databricks_guide/1_4_ML_Power_Plant_Import_Table.png)

**2.1.2. Review Schema**
  __________________

Your table schema and preview should look like this after you click ```Create Table```:

![alt text](http://training.databricks.com/databricks_guide/1_4_ML_Power_Plant_Import_Table_Schema.png)

%md Now that your data is loaded let's explore it.

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##Step 3: Explore Your Data
Now that we understand what we are trying to do, we need to load our data and describe it, explore it and verify it.

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#### Viewing the table as text
By uisng `.show` method we can see some of the contents of the table in plain text.

This works in pure Apache Spark, say in `Spark-Shell` without any notebook layer on top of Spark like databricks, zeppelin or jupyter.

It is a good idea to use this method when possible.

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#### Viewing as DataFrame

This is almost necessary for a data scientist to gain visual insights into all pair-wise relationships between the several (3 to 6 or so) variables in question.

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#### Viewing as Table via SQL
Let us look at what tables are already available, as follows:

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We can also access the list of tables and databases using `spark.catalog` methods as explained here:

* [https://databricks.com/blog/2016/08/15/how-to-use-sparksession-in-apache-spark-2-0.html](https://databricks.com/blog/2016/08/15/how-to-use-sparksession-in-apache-spark-2-0.html)

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We need to create a temporary view of the DataFrame as a table before being able to access it via SQL.

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Note that table names are in lower-case only!

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**You Try!**

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The following SQL statement simply selects all the columns (due to `*`) from `powerPlantTable`.

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Note that the output of the above command is the same as `display(powerPlantDF)` we did earlier.

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We can use the SQL `desc` command to describe the schema. This is the SQL equivalent of `powerPlantDF.printSchema` we saw earlier.

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**Schema Definition**

Our schema definition from UCI appears below:

- AT = Atmospheric Temperature in C
- V = Exhaust Vaccum Speed
- AP = Atmospheric Pressure
- RH = Relative Humidity
- PE = Power Output

PE is our label or target. This is the value we are trying to predict given the measurements.

*Reference [UCI Machine Learning Repository Combined Cycle Power Plant Data Set](https://archive.ics.uci.edu/ml/datasets/Combined+Cycle+Power+Plant)*

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Let's do some basic statistical analysis of all the columns. 

We can use the describe function with no parameters to get some basic stats for each column like count, mean, max, min and standard deviation.  More information can be found in the [Spark API docs](https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.sql.DataFrame)

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##Step 4: Visualize Your Data

To understand our data, we will look for correlations between features and the label.  This can be important when choosing a model.  E.g., if features and a label are linearly correlated, a linear model like Linear Regression can do well; if the relationship is very non-linear, more complex models such as Decision Trees or neural networks can be better. We use the Databricks built in visualization to view each of our predictors in relation to the label column as a scatter plot to see the correlation between the predictors and the label.

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From the above plot, it looks like there is strong linear correlation between temperature and Power Output!

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The linear correlation is not as strong between Exhaust Vacuum Speed and Power Output but there is some semblance of a pattern.

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...and atmospheric pressure and relative humidity seem to have little to no linear correlation.

These pairwise plots can also be done directly using `display` on `select`ed columns of the DataFrame `powerPlantDF`.

In general **we will shy from SQL as much as possible** to focus on ML pipelines written with DataFrames and DataSets with occassional in-and-out of RDDs.  

The illustations in `%sql` above are to mainly reassure those with a RDBMS background and SQL that their SQL expressibility can be directly used in Apache Spark and in databricks notebooks.

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Furthermore, you can interactively start playing with `display` on the full DataFrame!

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We will do the following steps in the sequel.

- *Step 5: Data Preparation*
- *Step 6: Data Modeling*
- *Step 7: Tuning and Evaluation*
- *Step 8: Deployment*