Connecting an Autonomous Data Warehouse With Python

In this article, I will connect to an Oracle database running in the cloud (Oracle Autonomous Data Warehouse) and make a simple regression application in python environment with a sample data taken from here.

First of all, I will make this application in Autonomous Data Warehouse (DB) which is offered as a service in Oracle Cloud. All I need is an Oracle Cloud account. You can get Autonomous Data Warehouse service, which is one of the services of Always Free (Oracle Free Tier), free of charge and you can provision and use it on the Cloud in minutes without any installation. You can follow the link for detailed information.

You can start Autonomous Data Warehouse service via Oracle Cloud Infrastructure as shown in the video below.

The second component I need is to install the cx_oracle package in python to connect to the Oracle database in the Cloud from my local environment. The next step is to install an Oracle Client on my machine.

You can do the above mentioned installations by following the link.

I need a data set and a problem to implement it. I choose Boston Housing Prices as a problem. To solve this problem, I will construct a regression model. I get the data set from Kaggle (Boston Housing).

Let's first examine the BOSTON_HOUSING dataset.

Now that we have reviewed the details with our dataset, let's load the BOSTON_HOUSING that we downloaded to our Oracle database.

First, create the Oracle table in which we will load the data set (train.csv) that we downloaded.

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CREATE TABLE BOSTON_HOUSING

(

   ID        NUMBER,

   CRIM      NUMBER,

   ZN        NUMBER,

   INDUS     NUMBER,

   CHAS      NUMBER,

   NOX       NUMBER,

   RM        NUMBER,

   AGE       NUMBER,

   DIS       NUMBER,

   RAD       NUMBER,

   TAX       NUMBER,

   PTRATIO   NUMBER,

   BLACK     NUMBER,

   LSTAT     NUMBER,

   MEDV      NUMBER

);

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CREATE TABLE BOSTON_HOUSING_TEST

(

   ID        NUMBER,

   CRIM      NUMBER,

   ZN        NUMBER,

   INDUS     NUMBER,

   CHAS      NUMBER,

   NOX       NUMBER,

   RM        NUMBER,

   AGE       NUMBER,

   DIS       NUMBER,

   RAD       NUMBER,

   TAX       NUMBER,

   PTRATIO   NUMBER,

   BLACK     NUMBER,

   LSTAT     NUMBER,

   MEDV      NUMBER

);

Now that we have created our table, we will load the dataset we downloaded as CSV into the table; we have multiple methods to do this:

• Using Oracle External Table.
• Using Oracle SQL Loader.
• Using SQL-PL/SQL editors (Oracle SQL Developer, Toad, PL/SQL Developer, etc).

I will load the data set with the help of the editor I use. I use Oracle SQL Developer as an editor. With Oracle SQL Developer, you can load data as follows.

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SELECT * FROM BOSTON_HOUSING;

We have completed the dataset loading process.

When we observe the data, we see the details according to the various characteristics of the houses. Each row contains information on the specific characteristics of the house. Our basic parameters for regression analysis are as presented in this table. In this table, we predict the result of the regression analysis. The MEDV column is the target variable that we will use in this analysis.

Now let's start writing the necessary code on the Python side.

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import cx_Oracle as cx

import pandas as pd

import warnings

warnings.filterwarnings('ignore')

#Connect to Autonomous Data Warehouse 

con = cx.connect("ADMIN","yourPass","mltest_high")

query = 'SELECT * from boston_housing'

data_train = pd.read_sql(query, con=con)

query = 'SELECT * from boston_housing_test'

data_test = pd.read_sql(query, con=con)

With cx_oracle, we connected to ADW, and from there we extracted the data from the relevant tables into the data frame via python.

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data_train.head()

data_test.head()

Now let's create and test our model with the data we have.

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######### Building Model with Boosting Method for Regression #######

from sklearn.ensemble import AdaBoostRegressor

X = data_train.iloc[:,1:14] # features

y = data_train.iloc[:,14:15] # target variable

#training

regr = AdaBoostRegressor(random_state=0, n_estimators=100)

regr.fit(X, y) 

#see feature importance

regr.feature_importances_

#model test with test data set from oracle database (data_test df)

data_test.iloc[1:2,1:14] # take one record from data set

Now let's take a test record and make an estimate using the model we created for this record.

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test_rec = data_test.iloc[1:2,1:14]

testid = data_test.iloc[1:2,0:1] # get test record id for updating result in ADW (database)

id_final = int(test_id["ID"])

#predict value using model

res = regr.predict(test_rec)

pred_medv = res[0]

Now let's update the MEDV value of the estimated record in the database.

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#update database record on ADW

query = 'update boston_housing_test set medv =:result where id=:testrecid'

cur = con.cursor()

cur.execute(query,[pred_medv,id_final])

con.commit()

con.close()

Yes, as we have seen, we extracted data from the Cloud database with Python and then used it in the training of the model we built with sklearn and tested it with a new value.