--Before we start, we need to restore the DB for this tutorial.
--Step1:Download the compressed backup file
--Save the file on a location where SQL Server can access it. For example:C:\Program Files \Microsoft SQL Server \MSSQL13.MSSQLSERVER\MSSQL\Backup\
--In a new query window in SSMS, execute the following restore statement, but REMEMBER TO CHANGE THE FILE PATHS
--to match the directories of your installation!
USE master;
GO
RESTORE DATABASE TutorialDB
FROM DISK = 'C:\Program Files\Microsoft SQL Server\MSSQL13.MSSQLSERVER\MSSQL\Backup\TutorialDB.bak'
WITH
MOVE 'TutorialDB' TO 'C:\Program Files\Microsoft SQL Server\MSSQL13.MSSQLSERVER\MSSQL\DATA\TutorialDB.mdf'
                , MOVE 'TutorialDB_log' TO 'C:\Program Files\Microsoft SQL Server\MSSQL13.MSSQLSERVER\MSSQL\DATA\TutorialDB.ldf';
GO

USE tutorialdb;
SELECT * FROM [dbo].[rental_data];


-- Operationalize
USE tutorialdb;
GO
-- Setup model table
DROP TABLE IF EXISTS rental_rx_models;
GO
CREATE TABLE rental_rx_models (
                model_name VARCHAR(30) NOT NULL DEFAULT('default model') PRIMARY KEY,
                model VARBINARY(MAX) NOT NULL
);
GO

-- Stored procedure that trains and generates a model using the rental_data and a decision tree algorithm
DROP PROCEDURE IF EXISTS generate_rental_rx_model;
go
CREATE PROCEDURE generate_rental_rx_model (@trained_model varbinary(max) OUTPUT)
AS
BEGIN
    EXECUTE sp_execute_external_script
      @language = N'R'
    , @script = N'
        require("RevoScaleR");

        rental_train_data$Holiday = factor(rental_train_data$Holiday);
            rental_train_data$Snow = factor(rental_train_data$Snow);
            rental_train_data$WeekDay = factor(rental_train_data$WeekDay);

        #Create a dtree model and train it using the training data set
        model_dtree <- rxDTree(RentalCount ~ Month + Day + WeekDay + Snow + Holiday, data = rental_train_data);
        #Before saving the model to the DB table, we need to serialize it
        trained_model <- as.raw(serialize(model_dtree, connection=NULL));'

    , @input_data_1 = N'select "RentalCount", "Month", "Day", "WeekDay", "Snow", "Holiday" from dbo.rental_data where Year < 2015'
    , @input_data_1_name = N'rental_train_data'
    , @params = N'@trained_model varbinary(max) OUTPUT'
    , @trained_model = @trained_model OUTPUT;
END;
GO
TRUNCATE TABLE rental_rx_models;
--Script to call the stored procedure that generates the rxDTree model and save the model in a table in SQL Server
DECLARE @model VARBINARY(MAX);
EXEC generate_rental_rx_model @model OUTPUT;
INSERT INTO rental_rx_models (model_name, model) VALUES('rxDTree', @model);
SELECT * FROM rental_rx_models;
GO

--Stored procedure that takes model name and new data as inout parameters and predicts the rental count for the new data
DROP PROCEDURE IF EXISTS predict_rentals;
GO
CREATE PROCEDURE predict_rentals (@model VARCHAR(100),@q NVARCHAR(MAX))
AS
BEGIN
    DECLARE @rx_model VARBINARY(MAX) = (SELECT model FROM rental_rx_models WHERE model_name = @model);
    EXECUTE sp_execute_external_script
        @language = N'R'
        , @script = N'
            require("RevoScaleR");

            #The InputDataSet contains the new data passed to this stored proc. We will use this data to predict.
            rentals = InputDataSet;

        #Convert types to factors
            rentals$Holiday = factor(rentals$Holiday);
            rentals$Snow = factor(rentals$Snow);
            rentals$WeekDay = factor(rentals$WeekDay);

            #Before using the model to predict, we need to unserialize it
            rental_model = unserialize(rx_model);

            #Call prediction function
            rental_predictions = rxPredict(rental_model, rentals);'
                , @input_data_1 = @q
        , @output_data_1_name = N'rental_predictions'
                , @params = N'@rx_model varbinary(max)'
                , @rx_model = @rx_model
                WITH RESULT SETS (("RentalCount_Predicted" FLOAT));

END;
GO

--Execute the predict_rentals stored proc and pass the modelname and a query string with a set of features we want to use to predict the rental count
EXEC dbo.predict_rentals @model = 'rxDTree',
       @q ='SELECT CONVERT(INT, 3) AS Month, CONVERT(INT, 24) AS Day, CONVERT(INT, 4) AS WeekDay, CONVERT(INT, 1) AS Snow, CONVERT(INT, 1) AS Holiday';
GO