This is the fourth part of the ML series. For your convenience you can find other parts in the table of contents in Part 1 – Linear regression in MXNet

This time we are going to implement linear regression as a function. This gives us a little more flexibility in terms of debugging the code and reading it later, also, we can implement much more complex algorithms. Too bad, we can’t use this in Redshift at this time as it doesn’t support such functions or stored procedures. So I will use T-SQL and test the code with MS SQL 2017. I assume you have table `samples`

with Iris dataset.

We start with declaring a type for the function parameter:

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CREATE TYPE SamplesTable AS TABLE (id int, feature int, value float, target float) |

Next, let’s prepare samples for training:

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DECLARE @numbers TABLE (N int) INSERT INTO @numbers SELECT TOP 5 row_number() OVER(ORDER BY t1.number) AS N FROM master..spt_values AS t1 CROSS JOIN master..spt_values AS t2 DECLARE @samples TABLE( sepal_length float ,sepal_width float ,petal_length float ,petal_width float ,iris varchar(255) ,is_setosa float ,is_virginica float ,sample_id int ) INSERT INTO @samples SELECT TOP 100 S.*, CASE WHEN S.iris = 'setosa' THEN 1.0 ELSE 0.0 END AS is_setosa, CASE WHEN S.iris = 'virginica' THEN 1.0 ELSE 0.0 END AS is_virginica, row_number() OVER(ORDER BY (SELECT NULL)) AS sample_id FROM samples AS S ORDER BY (SELECT ABS(CHECKSUM(NewId()))) DECLARE @samplesPivoted SamplesTable INSERT INTO @samplesPivoted SELECT S.sample_id, N.N, CASE WHEN N.N = 1 THEN S.sepal_width WHEN N.N = 2 THEN S.petal_length WHEN N.N = 3 THEN S.petal_width WHEN N.N = 4 THEN S.is_setosa ELSE S.is_virginica END, S.sepal_length FROM @samples AS S CROSS JOIN @numbers AS N |

We generate table with numbers, next add more features, and then pivot them just like in the last part.

Finally, our function:

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CREATE FUNCTION Train(@samplesPivoted SamplesTable READONLY) RETURNS @coefficients TABLE(feature int, w float, b float, mse float) AS BEGIN DECLARE @featureIds TABLE(feature int) INSERT INTO @featureIds SELECT DISTINCT feature from @samplesPivoted INSERT INTO @coefficients SELECT feature, 0.0, 0.0, -1.0 FROM @featureIds DECLARE @gradients TABLE(feature int, gw float, gb float) INSERT INTO @gradients SELECT feature, 0.0, 0.0 FROM @featureIds DECLARE @learningRate float SELECT @learningRate = 0.01 DECLARE @iterations int SELECT @iterations = 500 DECLARE @currentIteration int SELECT @currentIteration = 0 DECLARE @newCoefficients TABLE(feature int, w float, b float) DECLARE @distances TABLE(id int, distance float) DECLARE @mse float WHILE @currentIteration < @iterations BEGIN DELETE FROM @newCoefficients INSERT INTO @newCoefficients SELECT C.feature, C.w - @learningRate * G.gw, C.b - @learningRate * G.gb FROM @coefficients AS C JOIN @gradients AS G ON C.feature = G.feature DELETE FROM @distances; INSERT INTO @distances SELECT S.id, SUM(N.w * S.value + N.b) - MAX(S.target) FROM @samplesPivoted AS S JOIN @newCoefficients AS N ON S.feature = N.feature GROUP BY S.id SELECT @mse = AVG(D.distance * D.distance) FROM @distances AS D DELETE FROM @gradients; INSERT INTO @gradients SELECT S.feature, AVG(S.value * D.distance), AVG(D.distance) FROM @samplesPivoted AS S JOIN @distances AS D ON S.id = D.id GROUP BY S.feature DELETE FROM @coefficients; INSERT INTO @coefficients SELECT *, @mse FROM @newCoefficients SELECT @currentIteration = @currentIteration + 1 END RETURN END |

We extract `featureIds`

so we can pass basically any dataset for training and it should work. We initialize coefficients with default values, do the same with gradients, and prepare some bookkeeping like iterations count or learning rate.

Next, in every iteration we start with calculating new coefficients based on old coefficients and old gradients. We clear `distances`

table and calculate distance (which is the difference between predicted value and expected value) for each sample. Next, we calculate mean squared error.

Next, we need to calculate new gradients. For each feature we calculate the derivatives and we are done. We just need to store new coefficients and increase the counter.

Now we can execute the code:

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SELECT * FROM Train(@samplesPivoted) |

And the result is:

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feature w b mse ----------- ---------------------- ---------------------- ---------------------- 1 0.746997439342549 0.282176586393152 0.098274347087078 2 0.563235001391582 0.282176586393152 0.098274347087078 3 0.0230764649956309 0.282176586393152 0.098274347087078 4 0.193704294614636 0.282176586393152 0.098274347087078 5 -0.110068224303597 0.282176586393152 0.098274347087078 |