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Added simple linear regression sample analytic for Python 3 support
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‎README.md‎

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- **[demo-timeseries-adder-with-model](analytics/demo-timeseries-adder-with-model):** Takes 2 arrays of timeseries data and returns a timeseries array that contains the sums at each timestamp, adjusted by a threshold value provided in a trained model. Currently available in Java and Python.
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- **[demo-RTM-loco](analytics/demo-RTM-loco):** A reference analytic that is used to calculate locomotive efficiency using a linear regression model. It has been implemented in Java, Matlab (r2011b), and Python.
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- **[miners-rule](analytics/miners-rule):** A sample analytic that performs a Miner's Rule operation on 2 timeseries arrays and returns a timeseries array. Currently only available in Java.
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- **[sample-linear-regression](analytics/sample-linear-regression):**:new: A sample analytic that performs a simple linear regression with two arrays as input and returns the p-value, r-value, slope, intercept and standard error. Currently only available in Python 3.
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For more information on developing analytics for use with Predix Analytics, see [Analytic Development](https://docs.predix.io/en-US/content/service/analytics_services/analytics_framework/analytic-development) on Predix IO.
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‎analytics/README.md‎

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- **[demo-timeseries-adder-with-model](demo-timeseries-adder-with-model):** Takes 2 arrays of timeseries data and returns a timeseries array that contains the sums at each timestamp, adjusted by a threshold value provided in a trained model. Currently available in Java and Python.
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- **[demo-RTM-loco](demo-RTM-loco):** A reference analytic that is used to calculate locomotive efficiency using a linear regression model. It has been implemented in Java, Matlab (r2011b), and Python.
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- **[miners-rule](miners-rule):** A sample analytic that performs a Miner's Rule operation on 2 timeseries arrays and returns a timeseries array. Currently only available in Java.
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- **[sample-linear-regression](sample-linear-regression):**:new: A sample analytic that performs a simple linear regression with two arrays as input and returns the p-value, r-value, slope, intercept and standard error. Currently only available in Python 3.
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For more information on developing analytics for use with Predix Analytics, see [Analytic Development](https://docs.predix.io/en-US/content/service/analytics_services/analytics_framework/analytic-development) on Predix IO.

‎analytics/simple-linear-regression/README.MD‎

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#Simple Linear Regression
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A Python3-based sample analytic performing simple linear regression computation which can be deployed to Predix Analytics platform.
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## Pre-requisites
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To run this analytic locally, you will need to have the following:
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- Python 3.6+
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- scipy 0.19.1
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## Running unit tests
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```bash
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$ cd <location>/simple-linear-regression/analytics
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$ python -m unittest test_simple_linear_regression.py
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```
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## Building, deploying and running the analytic
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1. Zip the contents of this directory or if you have Maven3 installed and configured, do a `mvn clean install` to generate the `py3-simple-linear-regression-0.1-bin.zip` in the `target` folder.
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2. Create an analytic in Analytics Catalog with the name "simple-linregress", the version "v1" and the supported language to "PYTHON_3".
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3. Upload the zip file and attach it to the created analytic.
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4. Deploy and test the analytic on Predix Analytics platform.
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## Analytic template
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This analytic takes in 2 arrays and returns the p-value, r-value, slope, intercept and standard error. This structure is outlined in this [analytic template](simple_linear_regression_template.json).
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### Input format
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The expected JSON input data format is as follows
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```json
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{
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"y": [19.0, 20.3, 20.5, 21.5, 22.45, 23.0, 23.0, 25.5, 24.0],
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"x": [1, 2, 3, 4, 5, 6, 7, 8, 9]
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}
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```
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### Output format
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The JSON output format from the analytic is as follows:
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```json
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{
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"slope": 0.7166666666666667,
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"intercept": 18.472222222222225,
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"r_value": 0.9559490311973318,
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"p_value": 5.6576124923241295e-05,
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"std_err": 0.08317445171439007
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}
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```
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## Developing a Python-based analytic
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1. Implement the analytic (and test functions) according to your development guidelines.
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2. Create an entry method in your analytic class. The entry method signature must be in one of the following two formats:
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* For analytics that do not use trained models, use the following signature for your entry method:
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`def entry_method(self, inputJson):`
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* For analytics that use trained models, use the following signature for your entry method:
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`def entry_method(self, inputJson, inputModels):`
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* In either case, the `entry_method` can be any method name. `inputJson` is the JSON string input that will be passed to the analytic. The output of this method must also be a JSON string.
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* `inputModels` contains a dict() of trained models as defined in the port-to-field map. The entry method should properly handle the case of an empty dict.
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3. Create a config.json file in the top level of the project directory. Specify the entry method in the format of `<subdirectory>.<className>.<methodName>`, conda-libs, and non-conda-libs.
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4. Package all the analytic files and the config.json file into a ZIP file.
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For more information on developing analytics for use with the Predix Analytics platform, please visit the **[Analytic Development](https://docs.predix.io/en-US/content/service/analytics_services/analytics_framework/analytic-development)** section of the Predix Analytics Services documentation on predix.io.
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‎analytics/simple-linear-regression/analytics/__init__.py‎

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from analytics.simple_linear_regression import SimpleLinearRegression

‎analytics/simple-linear-regression/analytics/simple_linear_regression.py‎

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import json
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from scipy import stats
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class SimpleLinearRegression:
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def compute(self, data):
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data_json = json.loads(data)
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x = data_json.get("x")
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y = data_json.get("y")
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slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
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output = {
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"slope": slope,
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"intercept": intercept,
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"r_value": r_value,
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"p_value": p_value,
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"std_err": std_err}
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return json.dumps(output)

‎analytics/simple-linear-regression/analytics/test_simple_linear_regression.py‎

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import json
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from unittest import TestCase
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from simple_linear_regression import SimpleLinearRegression
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class SimpleLinearRegressionTest(TestCase):
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def setUp(self):
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pass
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def test_compute(self):
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input_data_dict = {"y": [19, 20, 20.5, 21.5, 22, 23, 23, 25.5, 24],
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"x": [1, 2, 3, 4, 5, 6, 7, 8, 9]}
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simple_linear_regression = SimpleLinearRegression()
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output_data = simple_linear_regression.compute(json.dumps(input_data_dict))
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analytic_output_dict = json.loads(output_data)
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print(analytic_output_dict)
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self.assertEqual(0.7166666666666667, analytic_output_dict["slope"])
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self.assertEqual(0.9559490311973318, analytic_output_dict["r_value"])
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self.assertEqual(5.6576124923241295e-05, analytic_output_dict["p_value"])
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self.assertEqual(18.472222222222225, analytic_output_dict["intercept"])
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self.assertEqual(0.08317445171439007, analytic_output_dict["std_err"])

‎analytics/simple-linear-regression/assembly.xml‎

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<?xml version="1.0" encoding="UTF-8"?>
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<!--
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~ Copyright (c) 2015 - 2016 General Electric Company. All rights reserved.
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~
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~ The copyright to the computer software herein is the property of
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~ General Electric Company. The software may be used and/or copied only
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~ with the written permission of General Electric Company or in accordance
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~ with the terms and conditions stipulated in the agreement/contract
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~ under which the software has been supplied.
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-->
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<assembly xmlns="http://maven.apache.org/plugins/maven-assembly-plugin/assembly/2.5.5">
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<id>bin</id>
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<formats>
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<format>zip</format>
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</formats>
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<includeBaseDirectory>false</includeBaseDirectory>
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<fileSets>
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<fileSet>
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<directory>${project.basedir}</directory>
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<excludes>
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<exclude>pom.xml</exclude>
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<exclude>assembly.xml</exclude>
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<exclude>target/</exclude>
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<exclude>.idea/</exclude>
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<exclude>*.iml</exclude>
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<exclude>**/**.pyc</exclude>
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<exclude>**/**/test_*.py</exclude>
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<exclude>**/**/*_pycache_*</exclude>
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<exclude>README.MD</exclude>
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</excludes>
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</fileSet>
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</fileSets>
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</assembly>

‎analytics/simple-linear-regression/config.json‎

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{
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"entry-method": "analytics.SimpleLinearRegression.compute",
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"conda-libs": [
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"scipy==0.19.1"
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]
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}
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‎analytics/simple-linear-regression/pom.xml‎

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<?xml version="1.0" encoding="UTF-8"?>
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<!--
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~ Copyright (c) 2015 - 2016 General Electric Company. All rights reserved.
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~
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~ The copyright to the computer software herein is the property of
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~ General Electric Company. The software may be used and/or copied only
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~ with the written permission of General Electric Company or in accordance
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~ with the terms and conditions stipulated in the agreement/contract
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~ under which the software has been supplied.
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-->
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<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
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xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
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<modelVersion>4.0.0</modelVersion>
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<groupId>com.ge.predix.insight</groupId>
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<artifactId>py3-simple-linear-regression</artifactId>
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<name>Sample Simple Linear regression in Python 3</name>
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<packaging>pom</packaging>
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<description>Implementation of Simple Linear regression analytic service in Python 3</description>
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<version>0.1</version>
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<build>
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<plugins>
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<plugin>
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<groupId>org.apache.maven.plugins</groupId>
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<artifactId>maven-assembly-plugin</artifactId>
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<version>2.5.5</version>
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<executions>
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<execution>
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<id>create-distribution</id>
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<phase>package</phase>
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<goals>
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<goal>single</goal>
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</goals>
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<configuration>
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<descriptors>
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<descriptor>assembly.xml</descriptor>
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</descriptors>
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</configuration>
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</execution>
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</executions>
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</plugin>
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</plugins>
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</build>
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</project>
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‎analytics/simple-linear-regression/simple_linear_regression_template.json‎

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{
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"analyticName": "SimpleLinearRegression",
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"analyticVersion": "V1.0",
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"inputPortDefinitions": [
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{
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"portType": "FIELD",
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"portName": "x",
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"variable": false,
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"dataType": "DOUBLE_ARRAY",
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"required": true
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},
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{
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"portType": "FIELD",
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"portName": "y",
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"variable": false,
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"dataType": "INTEGER_ARRAY",
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"required": true
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}
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],
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"outputPortDefinitions": [
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{
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"portType": "FIELD",
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"portName": "slope",
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"variable": false,
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"dataType": "DOUBLE",
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"required": true
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},
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{
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"portType": "FIELD",
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"portName": "intercept",
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"variable": false,
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"dataType": "DOUBLE",
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"required": true
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},
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{
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"portType": "FIELD",
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"portName": "r_value",
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"variable": false,
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"dataType": "DOUBLE",
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"required": true
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},
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{
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"portType": "FIELD",
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"portName": "p_value",
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"variable": false,
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"dataType": "DOUBLE",
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"required": true
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},
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{
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"portType": "FIELD",
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"portName": "std_err",
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"variable": false,
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"dataType": "DOUBLE",
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"required": true
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}
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]
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}

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