OML4Py Dataset Creation.dsnb

[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Dataset Creation","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":["%md"," "],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# OML4Py Demonstration Dataset Creation","","This script loads the sample datasets `BREASTCANCER` and `IRIS` provided with the SciKit-Learn Python package and imports into them into the Oracle Database by using the oml.create() function. ","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries","message":["%python","","import oml","","from sklearn import datasets","import pandas as pd"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Load breast cancer data and create table","message":["%python","","bc = datasets.load_breast_cancer()","bc_data = bc.data.astype(float)","X = pd.DataFrame(bc_data, columns = bc.feature_names)","y = pd.DataFrame(bc.target, columns = ['TARGET'])","","try:"," oml.drop(table='BREASTCANCER')","except:"," pass","BREASTCANCER = oml.create(pd.concat([X, y], axis=1), table = 'BREASTCANCER')","print(\"Shape:\",BREASTCANCER.shape)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Load IRIS dataset and create table","message":["%python","","iris = datasets.load_iris()","X = pd.DataFrame(iris.data, "," columns = [\"SEPAL_LENGTH\", \"SEPAL_WIDTH\", \"PETAL_LENGTH\", \"PETAL_WIDTH\"])","y = pd.DataFrame(list(map(lambda x: {0:'setosa', 1: 'versicolor', 2:'virginica'}[x], iris.target)), "," columns = ['SPECIES'])","","try:"," oml.drop(table='IRIS')","except:"," pass","IRIS = oml.create(pd.concat([X, y], axis=1), table = 'IRIS')","print(\"Shape:\",IRIS.shape)"],"enabled":true,"result":null,"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":null,"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]