import libraries

import os
import pandas as pd
import math

Import Libraries for Graph, GNN (Graph Neural Network), and GCN (Graph Convolutional Network)

import stellargraph as sg
from stellargraph import StellarGraph
from stellargraph.layer import DeepGraphCNN
from stellargraph.mapper import FullBatchNodeGenerator
from stellargraph.mapper import PaddedGraphGenerator
from stellargraph.layer import GCN

Machine Learning related library Imports (Tensorflow)

from tensorflow.keras import layers, optimizers, losses, metrics, Model
from sklearn import preprocessing, model_selection
from IPython.display import display, HTML
import matplotlib.pyplot as plt
%matplotlib inline
from tensorflow.keras.layers import Dense, Conv1D, MaxPool1D, Dropout, Flatten
from tensorflow import keras

#how to read data from csv files
df_s = pd.read_csv("./data/" + data_file, low_memory = False);
df_s.head()

Convert Data Type and Sort Data

convert Date field to be a Date Type

df_s["Date"] = df_s["Date"].astype(‘datetime64[ns]’)

Sort data by date although this is no longer needed as data already is sorted when I generated data

df_s = df_s.sort_values( by = ['Ticker','Date'], ascending = True )

df_s = df_s.sort_values( by = ‘Date’, ascending = True )
df_s.head()

drop not available data

df_s_transpose = df_s_transpose.dropna(axis = 1);

Core building blocks across Javascript, PHP, C++, Java, Python, and Ruby: Ref: https://codesensei.medium.com/core-building-blocks-and-which-programming-language-to-study-first-446977d119a8

Click on the images to see them clearly

#!/usr/bin/env python

coding: utf-8

In[1]:

k-means clustering

from numpy import unique
from numpy import where
from sklearn.datasets import make_classification
from sklearn.cluster import KMeans
from matplotlib import pyplot
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
get_ipython().run_line_magic(‘matplotlib’, ‘inline’)
import pandas as pd
import numpy as np
import numpy as np
from sklearn.cluster import KMeans
from sklearn import datasets
from sklearn.preprocessing import StandardScaler

In[2]:

import warnings
warnings.filterwarnings(‘ignore’)

In[3]:

the combined data

data_folder = ‘./nhanes_input_data/’

import the CSV as a pandas dataframe

df = pd.read_csv( data_folder + ‘0_dietaryIntakeDataForClassificationAndAnalysisData.csv’)
df.shape

In[4]:

df.head(5)

In[5]:

parameters to be used for KMeans clustring: centres

X and/or kdf will have only features we want to create cluster around

kdf = df[
   
    [
        'RIDAGEYR_Age_in_years_at_screening'
        ,'URDACT_Albumin_creatinine_ratio_mg_g'
    ]
]
X = kdf
X[:5]

In[6]:

ref: internet (not my code, using as a library)

def clean_dataset(df):
assert isinstance(df, pd.DataFrame), "df needs to be a pd.DataFrame"
df.dropna(inplace=True)
indices_to_keep = ~df.isin([np.nan, np.inf, -np.inf]).any(1)
return df[indices_to_keep].astype(np.float64)

In[7]:

X has the features to cluster around (centres: Age, ACR). df has the complete data

after clustering is done using features in X, we find positions (index) for each data

in a cluster then we use those index positions to cluster the data from df

X.shape, df.shape

In[8]:

X = clean_dataset(X)

In[9]:

define the model

model = KMeans(n_clusters = 10) #,random_state=0, n_init="auto"

fit the model

model.fit(X)
#model.labels_

In[10]:

Create csv files with the cluster daya

One csv for one Cluster

In[11]:

howManyClusters = 10
for clusterId in range (howManyClusters):
ind_list = np.where(model.labels_ == clusterId )[0]
cluster = df.iloc[ind_list]
cluster.to_csv(‘./nhanes_output_data/classifiedGroups/kmeanscluster/cluster-‘
+ str(clusterId) + ‘.csv’);

In[12]:

model.cluster_centers_

In[13]:

Scatter plot to see each cluster points visually

std_data = StandardScaler().fit_transform(X)
plt.scatter(std_data[:,0], std_data[:,1], c = model.labels_, cmap = "rainbow")
plt.title("K-means Clustering of Diet and ACR data")
plt.show()

# References:

# print("Shape of cluster:", model.cluster_centers_.shape)

# https://stackoverflow.com/questions/50297142/get-cluster-points-after-kmeans-in-a-list-format

#

# https://machinelearningmastery.com/clustering-algorithms-with-python/

# https://stackoverflow.com/questions/50297142/get-cluster-points-after-kmeans-in-a-list-format

# https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html

# https://datascience.stackexchange.com/questions/48693/perform-k-means-clustering-over-multiple-columns

# https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html

#

>>> from sklearn.cluster import KMeans

>>> import numpy as np

>>> X = np.array([[1, 2], [1, 4], [1, 0],

… [10, 2], [10, 4], [10, 0]])

>>> kmeans = KMeans(n_clusters=2, random_state=0, n_init="auto").fit(X)

>>> kmeans.labels_

array([1, 1, 1, 0, 0, 0], dtype=int32)

>>> kmeans.predict([[0, 0], [12, 3]])

array([1, 0], dtype=int32)

>>> kmeans.cluster_centers_

array([[10.,  2.],

[ 1.,  2.]])

In[ ]:

Raise Error in Oracle

RAISE VALUE_ERROR;

Raise Application Error in Oracle

Create custom exception and raise it.

Handle Exception

You could also insert into error log table and RAISE

Reference:
https://blogs.oracle.com/connect/post/error-management

"Collaboration diagrams (known as Communication Diagram in UML 2.x) are used to show how objects interact to perform the behavior of a particular use case, or a part of a use case. Along with sequence diagrams, collaboration are used by designers to define and clarify the roles of the objects that perform a particular flow of events of a use case. They are the primary source of information used to determining class responsibilities and interfaces."

Ref: Check Below

https://www.visual-paradigm.com/servlet/editor-content/guide/uml-unified-modeling-language/what-is-uml-collaboration-diagram/sites/7/2018/12/collaboration-diagram-example.png

https://www.visual-paradigm.com/guide/uml-unified-modeling-language/what-is-uml-collaboration-diagram/

Ref: Software Engineering Course: https://www.cs.ox.ac.uk/people/michael.wooldridge/teaching/soft-eng/
https://www.cs.ox.ac.uk/people/michael.wooldridge/teaching/soft-eng/lect07-4up.pdf

How it works: if, elsif, else (then) (Click on the images to see them clearly)

Ref: https://docs.oracle.com/cd/B13789_01/appdev.101/b10807/13_elems024.htm

Example:

Ref: https://docs.oracle.com/cd/B13789_01/appdev.101/b10807/13_elems024.htm

Oracle: CASE, WHEN, THEN

Simple:

Searched:
”
Else:

Reverse For Loop

Ref: For Loop Examples in Oracle

https://docs.oracle.com/cd/E11882_01/appdev.112/e25519/controlstatements.htm#BABEFFDC

Oracle While Loop

While loop example from the referenced url

DECLARE
done BOOLEAN := FALSE;
BEGIN
WHILE done LOOP
DBMS_OUTPUT.PUT_LINE (‘This line does not print.’);
done := TRUE; — This assignment is not made.
END LOOP;

WHILE NOT done LOOP
DBMS_OUTPUT.PUT_LINE (‘Hello, world!’);
done := TRUE;
END LOOP;
END;
/

Ref: https://docs.oracle.com/en/database/oracle/oracle-database/19/lnpls/plsql-control-statements.html#GUID-3F69F563-BCAE-4D3E-8E03-F53C8D64D093