LinearRegressionModel __init__

---

__init__(self, name=None)

Create a ‘new’ instance of a Linear Regression model.

Parameters:

name : unicode (default=None)

User supplied name.

Returns:

: Model

A new instance of LinearRegressionModel

Linear Regression [R9] is used to model the relationship between a scalar dependent variable and one or more independent variables. The Linear Regression model is initialized, trained on columns of a frame and used to predict the value of the dependent variable given the independent observations of a frame.

footnotes

[R9]	https://en.wikipedia.org/wiki/Linear_regression

[R10]

https://spark.apache.org/docs/1.5.0/ml-linear-methods.html

Examples

Consider the following model trained and tested on the sample data set in frame ‘frame’.

Consider the following frame containing two columns.

>>> frame.inspect()
[#]  x1   y
===============
[0]  0.0    0.0
[1]  1.0    2.5
[2]  2.0    5.0
[3]  3.0    7.5
[4]  4.0   10.0
[5]  5.0   12.5
[6]  6.0   13.0
[7]  7.0  17.15
[8]  8.0   18.5
[9]  9.0   23.5

>>> model = ta.LinearRegressionModel()
[===Job Progress===]
>>> train_output = model.train(frame,'y',['x1'])
[===Job Progress===]
>>> train_output
{u'explained_variance': 49.27592803030301,
 u'intercept': -0.032727272727271384,
 u'iterations': 3,
 u'label': u'y',
 u'mean_absolute_error': 0.5299393939393939,
 u'mean_squared_error': 0.6300969696969692,
 u'objective_history': [0.5, 0.007324606455391047, 0.006312834669731454],
 u'observation_columns': [u'x1'],
 u'r_2': 0.9873743306605371,
 u'root_mean_squared_error': 0.7937864761363531,
 u'weights': [2.4439393939393934]}
>>> test_output = model.test(frame,'y')
[===Job Progress===]
>>> test_output
{u'explained_variance': 49.27592803030301,
 u'mean_absolute_error': 0.5299393939393939,
 u'mean_squared_error': 0.6300969696969692,
 u'r_2': 0.9873743306605371,
 u'root_mean_squared_error': 0.7937864761363531}
>>> predicted_frame = model.predict(frame, ["x1"])
[===Job Progress===]
>>> predicted_frame.inspect()
[#]  x1   y      predicted_value
==================================
[0]  4.0   10.0      9.74303030303
[1]  0.0    0.0   -0.0327272727273
[2]  1.0    2.5      2.41121212121
[3]  6.0   13.0      14.6309090909
[4]  3.0    7.5      7.29909090909
[5]  7.0  17.15      17.0748484848
[6]  9.0   23.5      21.9627272727
[7]  8.0   18.5      19.5187878788
[8]  5.0   12.5       12.186969697
[9]  2.0    5.0      4.85515151515

>>> model.publish()
[===Job Progress===]

Take the path to the published model and run it in the Scoring Engine

>>> import requests
>>> headers = {'Content-type': 'application/json', 'Accept': 'application/json,text/plain'}

Posting a request to get the metadata about the model

>>> r =requests.get('http://mymodel.demotrustedanalytics.com/v2/metadata')
>>> r.text
u'{"model_details":{"model_type":"Linear Regression Model","model_class":"org.apache.spark.ml.regression.LinearRegressionModel","model_reader":"org.trustedanalytics.atk.scoring.models.LinearRegressionModelReaderPlugin","custom_values":{}},"input":[{"name":"x1","value":"Double"}],"output":[{"name":"x1","value":"Double"},{"name":"Prediction","value":"Double"}]}'

Posting a request to version 1 of Scoring Engine supporting strings for requests and response:

>>> r = requests.post('http://mymodel.demotrustedanalytics.com/v1/score?data=4.0', headers=headers)
>>> r.text
u'9.743030303030302'

Posting a request to version 1 with multiple records to score:

>>> r = requests.post('http://mymodel.demotrustedanalytics.com/v1/score?data=4.0&data=0.0', headers=headers)
>>> r.text
u'9.743030303030302, -0.032727272727271384' #not working

Posting a request to version 2 of Scoring Engine supporting Json for requests and responses.

>>> r = requests.post("http://mymodel.demotrustedanalytics.com/v2/score", json={"records": [{"x1": 0.0}]})
>>> r.text
u'{"data":[{"x1":0.0,"Prediction":[-0.032727272727271384]}]}'

Posting a request to version 2 with multiple records to score:

>>> r = requests.post("http://mymodel.demotrustedanalytics.com/v2/score", json={"records": [{"x1": 0.0}, {"x1": 4.0}]})
>>> r.text
u'{"data":[{"x1":0.0,"Prediction":[-0.032727272727271384]},{"x1":4.0,"Prediction":[9.743030303030302]}]}'