Example Python Projects

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Example Python Projects

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Now, let’s know some new trends in Python,

• Graph based Algorithms
• Semantic Web Usage
• Deep Learning and Machine Learning
• Multi-Criteria/Objective based Optimization
• Bio-informatics
• Twitter Data Analysis using Machine Learning

      Here, we take one real-time example in Twitter Data Analysis using Twython (Python Library),

Supported Features:

• Support Twitter’s Streaming API
• Python 3 Seamless support
• Read only application (OAuth2) support
• Support query data for Twitter lists, Timelines, Direct Messages, User Information, etc.

Install Twython using: easy_install twython

-Now, let’s start coding using twython library,

# Import Twython Package

from twython import Twython

twitter = Twython()

# Getting User’s Timeline

Twitter =Twython()

User_Timeline =twitter.getUserTimeline(screen_name=”pythoncentral”)

# Print twitter user tweets

for tweet in User_Timeline:

print(tweet[‘text’])

# Run Python Twython Script

python twythonExample.py

Recently Developed Algorithms:

• Bee’s Algorithm (Maximal Itemsets Mining)
• ConvNet (Deep Convolutional Network)
• Decision Tree with Scikit-Learn classifier (Blind Image Denoising)
• Linear Algorithm (Feature Interaction)
• Naïve Bayes Classifier (Classification)
• Accumulative Motion Context Network
• Online Python Algorithms using Flink (Online SVM and KMeans)
• Offline/Batch Algorithms using Flink (Random Forest, PCA, KMeans)

Given one program example in the field of “Social Sensor Networks”,

# Import Required Package

import snap

print “—– vector —– ”

v = snap.TIntV()

v.Add(1)

v.Add(2)

v.Add(3)

v.Add(4)

v.Add(5)

print v.Len()

print v[2]

v.SetVal(2, 2*v[2])

print v[2]

for item in v:

print item

for i in range(0, v.Len()):

print i, v[i]

print “—– hash table —– ”

h = snap.TIntStrH()

h[5] = “five”

h[3] = “three”

h[9] = “nine”

h[6] = “six”

h[1] = “one”

print h.Len()

print “h[3] =”, h[3]

h[3] = “four”

print “h[3] =”, h[3]

for key in h:

print key, h[key]

print “—– pair —– ”

p = snap.TIntStrPr(1, “one”);

print p.GetVal1()

print p.GetVal2()

print “—– graphs —– ”

G1 = snap.TUNGraph.New()

G2 = snap.TNGraph.New()

N1 = snap.TNEANet.New()

G1.AddNode(1)

G1.AddNode(5)

G1.AddNode(32)

G1.AddEdge(1,5)

G1.AddEdge(5,1)

G1.AddEdge(5,32)

# Create a directed random graph on 100 nodes and 1k edges

G2 = snap.GenRndGnm(snap.PNGraph, 100, 1000)

print “G2: Nodes %d, Edges %d” % (G2.GetNodes(), G2.GetEdges())

# Traverse the nodes

for NI in G2.Nodes():

print “node id %d with out-degree %d and in-degree %d” % (

NI.GetId(), NI.GetOutDeg(), NI.GetInDeg())

# Traverse the edges

for EI in G2.Edges():

print “edge (%d, %d)” % (EI.GetSrcNId(), EI.GetDstNId())

# Traverse the edges by nodes

for NI in G2.Nodes():

for Id in NI.GetOutEdges():

print “edge (%d %d)” % (NI.GetId(), Id)

# Save and load binary

FOut = snap.TFOut(“test.graph”)

G2.Save(FOut)

FOut.Flush()

FIn = snap.TFIn(“test.graph”)

G4 = snap.TNGraph.Load(FIn)

print “G4: Nodes %d, Edges %d” % (G4.GetNodes(), G4.GetEdges())

# Save and load from a text file

snap.SaveEdgeList(G4, “test.txt”, “Save as tab-separated list of edges”)

G5 = snap.LoadEdgeList(snap.PNGraph, “test.txt”, 0, 1)

print “G5: Nodes %d, Edges %d” % (G5.GetNodes(), G5.GetEdges())

# Create a directed random graph on 10k nodes and 5k edges

G6 = snap.GenRndGnm(snap.PNGraph, 10000, 5000)

print “G6: Nodes %d, Edges %d” % (G6.GetNodes(), G6.GetEdges())

# Convert to undirected graph

G7 = snap.ConvertGraph(snap.PUNGraph, G6)

print “G7: Nodes %d, Edges %d” % (G7.GetNodes(), G7.GetEdges())

# Get largest weakly connected component

WccG = snap.GetMxWcc(G6)

# Generate a network using Forest Fire model

G8 = snap.GenForestFire(1000, 0.35, 0.35)

print “G8: Nodes %d, Edges %d” % (G8.GetNodes(), G8.GetEdges())

# Get a subgraph induced on nodes {0,1,2,3,4}

SubG = snap.GetSubGraph(G8, snap.TIntV.GetV(0,1,2,3,4))

# Get 3-core of G8

Core3 = snap.GetKCore(G8, 3)

print “Core3: Nodes %d, Edges %d” % (Core3.GetNodes(), Core3.GetEdges())

# Delete nodes of out degree 3 and in degree 2

snap.DelDegKNodes(G8, 3, 2)

# Create a directed random graph on 10k nodes and 1k edges

G9 = snap.GenRndGnm(snap.PNGraph, 10000, 1000)

print “G9: Nodes %d, Edges %d” % (G9.GetNodes(), G9.GetEdges())

# Define a vector of pairs of integers (size, count) and get a distribution of connected components (component size, count)

CntV = snap.TIntPrV()

snap.GetWccSzCnt(G9, CntV)

for p in CntV:

print “size %d: count %d” % (p.GetVal1(), p.GetVal2())

# Get degree distribution pairs (out-degree, count):

snap.GetOutDegCnt(G9, CntV)

for p in CntV:

print “degree %d: count %d” % (p.GetVal1(), p.GetVal2())

# Generate a Preferential Attachment graph on 100 nodes and out-degree of 3

G10 = snap.GenPrefAttach(100, 3)

print “G10: Nodes %d, Edges %d” % (G10.GetNodes(), G10.GetEdges())

# Define a vector of floats and get first eigenvector of graph adjacency matrix

EigV = snap.TFltV()

snap.GetEigVec(G10, EigV)

nr = 0

for f in EigV:

nr += 1

print “%d: %.6f” % (nr, f)

# Get an approximation of graph diameter

diam = snap.GetBfsFullDiam(G10, 10)

print “diam”, diam

# Count the number of triads:

triads = snap.GetTriads(G10)

print “triads”, triads

# gGet the clustering coefficient

cf = snap.GetClustCf(G10)

print “cf”, cf

Evergreen Example Python Projects Topics:

• An effective mechanism for Social Sensors Based on Online Attention Computing of Public Safety Events
• An efficient mechanism for Discovering Knowledge based on Behavioral Analytics by Elderly Care
• A new mechanism for SynCam used by Capturing sub-frame synchronous media in smartphones
• An effective usage of Frugal based on Building Degree-Constrained Overlay Topology from Social Graphs
• An effective performance for Detection of spoofed identities based on smartphones using sociability metrics
• A new mechanism for Human dynamics of mobile crowd sensing by experimental datasets
• An efficient mechanism for Platform design based on social IoT
• An efficient usage of Social Behaviometrics for Personalized Devices into Internet of Things epoch
• On the use of Recognizing social touch gestures for recurrent and convolutional neural networks
• An effective mechanism for Joint resource and price competition in wireless sensor network-based on service provision

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