def add_features(self):
f = Features.Features(self.df)
f.add_bbands()
f.compute_side_min()
f.add_momantum()
f.add_volatility()
f.add_serial_correlation()
f.add_log_returns()
f.mov_average()
f.add_rsi()
f.add_srsi()
f.add_trending_signal()
def do_job(i, chunk):
file_name = "features_{}.h5".format(i)
df = pd.read_hdf(path.join(chunk_path, chunk), key = 'table')
features_for_this_chunk = []
for driver, trip in df.groupby(level = ['Driver', 'Trip']):
# Extract all features for each trip
series_features = feat.Features(trip, features).extract_all_features()
features_for_this_chunk.append(series_features)
# Create a data frame with the features
df_features_for_this_chunk = pd.DataFrame()
df_features_for_this_chunk = df_features_for_this_chunk.from_dict(features_for_this_chunk)
# Write data frames containing the features to HDF5 file
df_features_for_this_chunk.to_hdf('/scratch/vstrobel/features32_small/' + file_name, 'table')
print("Written to", file_name)
def get_xy(file_conllu, file_features, file_embedding=None):
mcd = get_mcd()
print("Chargement des arbres")
obj_generateAlltree = ConstructAllTree(file_conllu, mcd, True)
# print(obj_generateAlltree.get_corpus())
# print(obj_generateAlltree.get_vocabulary())
all_tree = obj_generateAlltree.get_allTreeProjectiviser()
# print(all_tree[0].print_tree())
print("Arbres charger : ", len(all_tree))
print("Création du dataset")
features = Features(file_features)
i = 0
for tree in all_tree:
# tree.print_tree()
# if i != 43 and i != 61:
A = Oracle(tree, features)
A.run()
#
# print(features.datas)
#
# print(features.labels_encoders)
print("Convertion du dataset")
print("file_embedding : ", file_embedding)
X, Y = features.get_Data_Set(file_embedding)
labels_encoderX = features.get_label_encoderX()
labels_encoderY = features.get_label_encoderY()
print("X_train_shape", X.shape)
print("Y_train_shape", Y.shape)
return X, Y, labels_encoderX, labels_encoderY, all_tree
def ExtractAllFeatures(self, hours):
#Create the Object of Feature Class
features = Features.Features()
#1. Need to sort the posts dict in ascending order of timestamp, then view the posts.
allPosts = self.documentDict[self.POSTS]
postDict, sortedKeys = self.SortByPostCreationTime(allPosts)
#2. Questioners Reputation
self.GetQuestionersReputation()
#3. Questions asked by questionaire
userDict = {} #this will store userID and questions asked by the user
userList = self.documentDict[self.USERS]
for postKey in sortedKeys: #posts sorted by creation time
if postDict[
postKey].PostTypeId == '1': # If id is 1, it is a question
#search the user who has asked the question
userID = postDict[postKey].OwnerUserId
#for this userID enter a new user in dictionary or update the 'question asked until this post' in the existing entry
try: #try updating
userDict[userID].questionAsked += 1
except:
#create new pbject of User class
newUser = QA.User(userID)
newUser.questionAsked = 0
userDict[
userID] = newUser #initialize the number of questions asked by the user previous to asking this question
#we know the post ID of this question. We can append this feature value in the self.QuestionAnswerPairs dict
currentPostId = postDict[postKey].Id
#To the current Post ID add the feature - question asked by the questionaire before asking the current question
self.QuestionAnswerPairs[
currentPostId].F2_QuesAskedByQuestionaire = userDict[
userID].questionAsked
#self.QuestionAnswerPairs[currentPostId].Owner = userList[userID] #add the original users object
a = 9
#4. Num Answers to Questions within one hour and Sum of their Scores
self.FindNumAnswerAndTheirScoresInXHours(hours)
#8. NumComments in Q/A of highest reputation user of the answer of current post
self.NumCommentsInQAOfHighRepUser(hours)
for key, qaPairs in self.QuestionAnswerPairs.items():
feature = Features.Feature()
feature.F1_QuestionersReputation = qaPairs.F1_QuestionersReputation
feature.F2_QuesAskedByQuestionaire = qaPairs.F2_QuesAskedByQuestionaire
feature.F3_NumAnswerToQuestionInXHours = qaPairs.F3_NumAnswerToQuestionInXHours
feature.F4_SumScores = qaPairs.F4_SumScores
feature.F5_BestScoreAnswerLength = qaPairs.F5_BestScoreAnswerLength
feature.F6_BestScoreNumComments = qaPairs.F6_BestScoreNumComments
feature.F7_BestScoreTimeDiff = qaPairs.F7_BestScoreTimeDiff
feature.F8_ReputedUserNumComments = qaPairs.F8_ReputedUserNumComments
feature.Y_Label_FrequentlyViewed = qaPairs.Y_Label_FrequentlyViewed
features.featureList.append(feature)
return features.featureList
pass
def generateFeaturesLine(self, lineSegment, pDict):
"""Generate the features for only a bisegment"""
sLang = pDict["sourceLanguage"]
dLang = pDict["targetLanguage"]
featuresDict = {}
id1, id2, sourceSegment, targetSegment, detectedSLangs, detectedDLangs = lineSegment.split(
"@#@")
sourceSegment = self.cleanSegment(sourceSegment)
targetSegment = self.cleanSegment(targetSegment)
features = Features(sourceSegment, targetSegment, pDict["fileRe"])
#Church Gale score
featuresDict["cgscore"] = features.getCGSore()
featuresDict["same"] = features.isEqual()
#Capital Letters
featuresDict["hassourcecl"], featuresDict[
"hastargetcl"] = features.haveCL()
featuresDict["caplettersworddif"] = features.difWordsCapitalLetters()
featuresDict[
"onlycaplettersdif"] = features.difWholeWordsCapitalLetters()
#Language Related Features: 0.0
featuresDict["langdif"] = features.getLangScore(
detectedSLangs, detectedDLangs, sLang, dLang)
#URL and Similarity
featuresDict["hassourceurl"], featuresDict[
"hastargeturl"] = features.haveItem("urlRe")
featuresDict["urlsim"] = round(features.getItemSimilarity("urlRe"), 2)
#TAG and Similarity
featuresDict["hassourcetag"], featuresDict[
"hastargettag"] = features.haveItem("tagRe")
featuresDict["tagsim"] = round(features.getItemSimilarity("tagRe"), 2)
#EMAIL and Similarity
featuresDict["hassourceemail"], featuresDict[
"hastargetemail"] = features.haveItem("emailRe")
featuresDict["emailsim"] = round(features.getItemSimilarity("emailRe"),
2)
#NUMBER and Similarity
featuresDict["hassourcenumber"], featuresDict[
"hastargetnumber"] = features.haveItem("numberRe")
featuresDict["numbersim"] = round(
features.getNumberSimilarity("numberRe"), 2)
#PUNCTUATION and Similarity
featuresDict["hassourcepunctuation"], featuresDict[
"hastargetpunctuation"] = features.havePunctuation()
featuresDict["punctsim"] = round(features.getPunctuationSimilarity(),
2)
#Name Entity Detection and Similarity
featuresDict["hassourcenameentity"], featuresDict[
"hastargetnameentity"] = features.haveNameEntity()
featuresDict["nersimilarity"] = features.getNameEntitiesSimilarity()
return featuresDict
def generateFeatures(self, pDict):
"""It reads the bisegments from the file and computes the features for each of them"""
sLang = pDict["sourceLanguage"]
dLang = pDict["targetLanguage"]
segmentFeaturesDict = collections.OrderedDict()
fs = codecs.open(pDict["segmentsLangFile"], "r", "utf-8")
for lineSegment in fs:
featuresDict = {}
lineSegment = lineSegment.rstrip()
dictKey1, dictKey2, sourceSegment, targetSegment, detectedSLangs, detectedDLangs = lineSegment.split(
"@#@")
dictKey = dictKey1 + "-" + dictKey2
sourceSegment = self.cleanSegment(sourceSegment)
targetSegment = self.cleanSegment(targetSegment)
features = Features(sourceSegment, targetSegment, pDict["fileRe"])
#---------Features that are not used in ML ---------------
featuresDict["sourcesegment"] = sourceSegment
featuresDict["targetsegment"] = targetSegment
featuresDict["nwordssource"], featuresDict[
"nwordstarget"] = features.getNWords()
#--------Features used in ML------------------
#Church Gale score
featuresDict["cgscore"] = features.getCGSore()
featuresDict["same"] = features.isEqual()
#Capital Letters
featuresDict["hassourcecl"], featuresDict[
"hastargetcl"] = features.haveCL()
featuresDict[
"caplettersworddif"] = features.difWordsCapitalLetters()
featuresDict[
"onlycaplettersdif"] = features.difWholeWordsCapitalLetters()
#Language Related Features: 0.0
featuresDict["langdif"] = features.getLangScore(
detectedSLangs, detectedDLangs, sLang, dLang)
#URL and Similarity
featuresDict["hassourceurl"], featuresDict[
"hastargeturl"] = features.haveItem("urlRe")
featuresDict["urlsim"] = round(features.getItemSimilarity("urlRe"),
2)
#TAG and Similarity
featuresDict["hassourcetag"], featuresDict[
"hastargettag"] = features.haveItem("tagRe")
featuresDict["tagsim"] = round(features.getItemSimilarity("tagRe"),
2)
#EMAIL and Similarity
featuresDict["hassourceemail"], featuresDict[
"hastargetemail"] = features.haveItem("emailRe")
featuresDict["emailsim"] = round(
features.getItemSimilarity("emailRe"), 2)
#NUMBER and Similarity
featuresDict["hassourcenumber"], featuresDict[
"hastargetnumber"] = features.haveItem("numberRe")
featuresDict["numbersim"] = round(
features.getNumberSimilarity("numberRe"), 2)
#PUNCTUATION and Similarity
featuresDict["hassourcepunctuation"], featuresDict[
"hastargetpunctuation"] = features.havePunctuation()
featuresDict["punctsim"] = round(
features.getPunctuationSimilarity(), 2)
#Name Entity Detection and Similarity
featuresDict["hassourcenameentity"], featuresDict[
"hastargetnameentity"] = features.haveNameEntity()
featuresDict["nersimilarity"] = features.getNameEntitiesSimilarity(
)
segmentFeaturesDict[dictKey] = featuresDict
return segmentFeaturesDict
def main(filetrainConllu,
filetestConllu,
features_file,
file_genarate,
model_file=None):
"""
construire le dataset
train le model
automate classfier , testdaset
build file conllu from tree
build scrip evaluation
:param model_file: fichier h5
:param filetestConllu: fichier conllu pour compare le resultat
:param filetesttxt:
:param features_file:
:return:
"""
print("Chargement de données ...")
# conllu_file = "Data/fr_gsd-ud-train.conllu"
# conllu_file = "Data/fr_gsd-ud-train.conllu"
# weight_embedding_file = "embd_file_vectors/embd.vec"
x_train, y_train, labels_encoderX, labels_encoderY, all_tree = get_data(
features_file, filetrainConllu)
# x_test, y_test,features_Xtest=get_data (features_file, filetestConllu)
all_tree_automate = list()
print("Train model ....")
input_dim = x_train.shape[1]
nb_class = y_train.shape[1]
if (model_file == None):
model = get_model(x_train, y_train, nb_class, input_dim)
exit()
else:
model = load_model(model_file)
# features = Features(features_file)
print(filetestConllu)
obj_generateAlltree = ConstructAllTree(filetestConllu, get_mcd(), False)
all_tree = obj_generateAlltree.get_allTree()
features = Features(features_file)
features.set_label_encoderX(labels_encoderX)
features.set_label_encoderY(labels_encoderY)
for id, tree in enumerate(all_tree):
all_vertices = tree.get_vertices()[1:]
liste_word = list()
for index, vertice in enumerate(all_vertices):
word = vertice.get_word()
liste_word.append(word)
print("INDEX=", id, " ", len(liste_word))
A = Automate(model, features=features, sentence=liste_word)
tree = A.run("Data/embd_fr_50.vec")
print("tree=", tree)
all_tree_automate.append(tree)
print("Generation du fichier conllu....")
TransformTreeConllu(all_tree, "generate_data.txt", filetestConllu)
print("Scrip Evaluation ...")
def generateFeatures(self, line):
"""It generates the features for a bisegment"""
featuresDict = {}
components = line.split("\t")
sourceSegment = components[0]
targetSegment = components[1]
saScore = components[2]
perplexitySource = components[3]
perplexityTarget = components[4]
category = ""
if len(components) == 6:
category = components[5]
featuresDict["category"] = category
sourceSegment = self.cleanSegment(sourceSegment)
targetSegment = self.cleanSegment(targetSegment)
features = Features(sourceSegment, targetSegment, self.pDict["fileRe"])
nWordsSource, nWordsTarget = features.getNWords()
#--------Features used in ML------------------
#Sentence alignment score is already provided in the file
featuresDict["saScore"] = saScore
#The Ken-LM language model score (perplexity) for source and target segment
featuresDict["perplexitySource"] = perplexitySource
featuresDict["perplexityTarget"] = perplexityTarget
#Gale-Church score
featuresDict["cgscore"] = features.getCGSore()
featuresDict["same"] = features.isEqual()
#Capital Letters
featuresDict["hassourcecl"], featuresDict[
"hastargetcl"] = features.haveCL()
featuresDict["caplettersworddif"] = features.difWordsCapitalLetters()
featuresDict[
"onlycaplettersdif"] = features.difWholeWordsCapitalLetters()
#URL and Similarity
featuresDict["hassourceurl"], featuresDict[
"hastargeturl"] = features.haveItem("urlRe")
featuresDict["urlsim"] = features.getItemSimilarity("urlRe")
#TAG and Similarity
featuresDict["hassourcetag"], featuresDict[
"hastargettag"] = features.haveItem("tagRe")
featuresDict["tagsim"] = features.getItemSimilarity("tagRe")
#EMAIL and Similarity
featuresDict["hassourceemail"], featuresDict[
"hastargetemail"] = features.haveItem("emailRe")
featuresDict["emailsim"] = features.getItemSimilarity("emailRe")
#NUMBER and Similarity
featuresDict["hassourcenumber"], featuresDict[
"hastargetnumber"] = features.haveItem("numberRe")
featuresDict["numbersim"] = features.getNumberSimilarity("numberRe")
#PUNCTUATION and Similarity
featuresDict["hassourcepunctuation"], featuresDict[
"hastargetpunctuation"] = features.havePunctuation()
featuresDict["punctsim"] = features.getPunctuationSimilarity()
#Name Entity Detection and Similarity
featuresDict["hassourcenameentity"], featuresDict[
"hastargetnameentity"] = features.haveNameEntity()
featuresDict["nersimilarity"] = features.getNameEntitiesSimilarity()
return featuresDict
import pandas as pd
import numpy as np
import Features as feat
from os import path
import matplotlib.pyplot as plt
dfs = pd.read_hdf(path.join("..", "chunks_big", "dataframe_0.h5"), key='table')
df1 = pd.DataFrame()
for index, trip in dfs.groupby(level=['Driver', 'Trip']):
features = feat.Features(trip, [])
# In km/h
velocities = features.euclidean_helper() * 2.2369
plt.hist(velocities, bins=np.arange(0, 100, 5))
plt.show()
filename='run.log',
level=log_level)
console = logging.StreamHandler()
console.setFormatter(
logging.Formatter('%(levelname)7s - %(name)-8s: %(message)s'))
logging.getLogger('').addHandler(console)
log = logging.getLogger('main')
# ----------------------------------------------------------------
# Load various components, and configure the modules that control
# the crawling process
#
corpus_table = CorpusTable.CorpusTable(args.dbdir) # Storage layer
spider = HTTPClient.HTTPClient() # Retrieval code
url_normaliser = Normalisation.URLNormaliser() # URL normaliser
feature_extractor = Features.Features(url_normaliser,
['title', 'h1']) # Feature extractor
# URL Fitness Function
#url_rank_function = SimplicityURLRank.SimplicityURLRank() # Prefer simple URLs
#url_rank_function = SampleURLRank.SampleURLRank() # Sample code
url_rank_function = HumanReadableURLRank.HumanReadableURLRank(
) # Prefer human-readable URLs
page_filters = [ # Filters for page rejection
# FuzzyDuplicateFilter.FuzzyDuplicateFilter(corpus_table), # Fuzzy hash using ssdeep
DuplicateFilter.DuplicateFilter(corpus_table), # Perfect duplicate checker
MinimumLengthFilter.MinimumLengthFilter(100), # Min length
MaximumLengthFilter.MaximumLengthFilter(800000), # Max length
URLCountFilter.URLCountFilter(0, 1000), # URL count
MetadataRegexpFilter.MetadataRegexpFilter(
'content_type', 'text\/(x?html|plain)'), # Content type
]
url_filters = [ # Filters for URL rejection
import numpy as np
import pandas as pd
from skimage import io, color, img_as_ubyte
from skimage.feature import greycomatrix, greycoprops
from sklearn.metrics.cluster import entropy
import skimage
import os
from Features import *
from camera import *
print(" Gerarando nova classe!")
controler = input (" Deseja adicionar uma nova classe?: y/n ")
if controler == 'y':
nome= input("Insira o nome da nova classe: ")
nova_classe= camera(nome)
nova_classe.save_img()
features = Features()
features.gerar_csv()
def ExtractFeature(row):
instanceFeature = Features.Features(row.Path)
conn2 = pyodbc.connect(
"Driver={SQL Server};Server=.\SQLEXPRESS;Trusted_Connection=yes;database=GraphColoringPortfolio"
)
cursor2 = conn2.cursor()
############################################################################################# Nodes, Edges and Density Features
#cursor2.execute("SELECT * FROM InstancesFeatures WHERE GraphID=?",row.Id)
#existingInstance = cursor2.fetchone()
#NumberOfNodes = instanceFeature.NumberOfNodes()
#NumberOfEdges = instanceFeature.NumberOfEdges()
#RatioNodesEdges = instanceFeature.RatioNodesEdges()
#RatioEdgesNodes = instanceFeature.RatioEdgesNodes()
#Density = instanceFeature.Density()
#if (existingInstance==None) :
# pk = uuid.uuid4()
# cursor2.execute("insert into InstancesFeatures(Id, GraphID, numberofnodes, numberofedges, rationodesedges, ratioedgesnodes, density) values (?, ?, ?, ?, ?, ?, ?)",
# str(pk), row.Id, NumberOfNodes, NumberOfEdges, RatioNodesEdges, RatioEdgesNodes, Density)
#else :
# cursor2.execute("UPDATE InstancesFeatures SET numberofnodes=?, numberofedges=?, rationodesedges=?, ratioedgesnodes=?, density=? where GraphID=?",
# NumberOfNodes, NumberOfEdges, RatioNodesEdges, RatioEdgesNodes, Density, existingInstance.GraphID)
#conn2.commit()
############################################################################################## Degree Centrality Features
#cursor2.execute("SELECT * FROM InstancesFeatures WHERE GraphID=?",row.Id)
#existingInstance = cursor2.fetchone()
#instanceFeature.ComputeDegree()
#MinDegree = instanceFeature.MinDegree()
#MaxDegree = instanceFeature.MaxDegree()
#MeanDegree = instanceFeature.MeanDegree()
#StdDegree = instanceFeature.StdDegree()
#VcDegree = instanceFeature.VcDegree()
#MedianDegree = instanceFeature.MedianDegree()
#Q1Degree = instanceFeature.Q1Degree()
#Q3Degree = instanceFeature.Q3Degree()
#if (existingInstance==None) :
# pk = uuid.uuid4()
# cursor2.execute("insert into InstancesFeatures(Id, GraphID, mindegree, maxdegree, meandegree, stddegree, vcdegree, mediandegree, q1degree, q3degree) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
# str(pk), row.Id, MinDegree, MaxDegree, MeanDegree, StdDegree, VcDegree, MedianDegree, Q1Degree, Q3Degree)
#else :
# cursor2.execute("UPDATE InstancesFeatures SET mindegree=?, maxdegree=?, meandegree=?, stddegree=?, vcdegree=?, mediandegree=?, q1degree=?, q3degree=? where GraphID=?",
# MinDegree, MaxDegree, MeanDegree, StdDegree, VcDegree, MedianDegree, Q1Degree, Q3Degree, existingInstance.GraphID)
#conn2.commit()
############################################################################################## Betweenness Centrality Features
#cursor2.execute("SELECT * FROM InstancesFeatures WHERE GraphID=?",row.Id)
#existingInstance = cursor2.fetchone()
#instanceFeature.ComputeBtwnsCentrality()
#MinBtwnsCentrality = instanceFeature.MinBtwnsCentrality()
#MaxBtwnsCentrality = instanceFeature.MaxBtwnsCentrality()
#MeanBtwnsCentrality = instanceFeature.MeanBtwnsCentrality()
#StdBtwnsCentrality = instanceFeature.StdBtwnsCentrality()
#VcBtwnsCentrality = instanceFeature.VcBtwnsCentrality()
#MedianBtwnsCentrality = instanceFeature.MedianBtwnsCentrality()
#Q1BtwnsCentrality = instanceFeature.Q1BtwnsCentrality()
#Q3BtwnsCentrality = instanceFeature.Q3BtwnsCentrality()
#if (existingInstance==None) :
# pk = uuid.uuid4()
# cursor2.execute("insert into InstancesFeatures(Id, GraphID, minbtwns, maxbtwns, meanbtwns, stdbtwns, vcbtwns, medianbtwns, q1btwns, q3btwns) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
# str(pk), row.Id, MinBtwnsCentrality, MaxBtwnsCentrality, MeanBtwnsCentrality, StdBtwnsCentrality, VcBtwnsCentrality, MedianBtwnsCentrality, Q1BtwnsCentrality, Q3BtwnsCentrality)
#else :
# cursor2.execute("UPDATE InstancesFeatures SET minbtwns=?, maxbtwns=?, meanbtwns=?, stdbtwns=?, vcbtwns=?, medianbtwns=?, q1btwns=?, q3btwns=? where GraphID=?",
# MinBtwnsCentrality, MaxBtwnsCentrality, MeanBtwnsCentrality, StdBtwnsCentrality, VcBtwnsCentrality, MedianBtwnsCentrality, Q1BtwnsCentrality, Q3BtwnsCentrality, existingInstance.GraphID)
#conn2.commit()
############################################################################################## Closeness Centrality Features
##cursor2.execute("SELECT * FROM InstancesFeatures WHERE GraphID=?",row.Id)
##existingInstance = cursor2.fetchone()
##instanceFeature.ComputeCloseness()
##MinCloseness = instanceFeature.MinClosenessCentrality()
##MaxCloseness = instanceFeature.MaxClosnessCentrality()
##MeanCloseness = instanceFeature.MeanClosenessCentrality()
##StdCloseness = instanceFeature.StdClosenessCentrality()
##VcCloseness = instanceFeature.VcClosenessCentrality()
##MedianCloseness = instanceFeature.MedianClosenessCentrality()
##Q1Closeness = instanceFeature.Q1ClosenessCentrality()
##Q3Closeness = instanceFeature.Q3ClosenessCentrality()
##if (existingInstance==None) :
## pk = uuid.uuid4()
## cursor2.execute("insert into InstancesFeatures(Id, GraphID, mincloseness, maxcloseness, meancloseness, stdcloseness, vccloseness, mediancloseness, q1closeness, q3closeness) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
## str(pk), row.Id, MinCloseness, MaxCloseness, MeanCloseness, StdCloseness, VcCloseness, MedianCloseness, Q1Closeness, Q3Closeness)
##else :
## cursor2.execute("UPDATE InstancesFeatures SET mincloseness=?, maxcloseness=?, meancloseness=?, stdcloseness=?, vccloseness=?, mediancloseness=?, q1closeness=?, q3closeness=? where GraphID=?",
## MinCloseness, MaxCloseness, MeanCloseness, StdCloseness, VcCloseness, MedianCloseness, Q1Closeness, Q3Closeness, existingInstance.GraphID)
##conn2.commit()
############################################################################################## Eigenvector Centrality Features
#cursor2.execute("SELECT * FROM InstancesFeatures WHERE GraphID=?",row.Id)
#existingInstance = cursor2.fetchone()
#instanceFeature.ComputeEgvCentrality()
#MinEgvCentrality = instanceFeature.MinEgvCentrality()
#MaxEgvCentrality = instanceFeature.MaxEgvCentrality()
#MeanEgvCentrality = instanceFeature.MeanEgvCentrality()
#StdEgvCentrality = instanceFeature.StdEgvCentrality()
#VcEgvCentrality = instanceFeature.VcEgvCentrality()
#MedianEgvCentrality = instanceFeature.MedianEgvCentrality()
#Q1EgvCentrality = instanceFeature.Q1EgvCentrality()
#Q3EgvCentrality = instanceFeature.Q3EgvCentrality()
#if (existingInstance==None) :
# pk = uuid.uuid4()
# cursor2.execute("insert into InstancesFeatures(Id, GraphID, minegvcentrality, maxegvcentrality, meanegvcentrality, stdegvcentrality, vcegvcentrality, medianegvcentrality, q1egvcentrality, q3egvcentrality) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
# str(pk), row.Id, MinEgvCentrality, MaxEgvCentrality, MeanEgvCentrality, StdEgvCentrality, VcEgvCentrality, MedianEgvCentrality, Q1EgvCentrality, Q3EgvCentrality)
#else :
# cursor2.execute("UPDATE InstancesFeatures SET minegvcentrality=?, maxegvcentrality=?, meanegvcentrality=?, stdegvcentrality=?, vcegvcentrality=?, medianegvcentrality=?, q1egvcentrality=?, q3egvcentrality=? where GraphID=?",
# MinEgvCentrality, MaxEgvCentrality, MeanEgvCentrality, StdEgvCentrality, VcEgvCentrality, MedianEgvCentrality, Q1EgvCentrality, Q3EgvCentrality, existingInstance.GraphID)
#conn2.commit()
############################################################################################## Eccentricity Features
#cursor2.execute("SELECT * FROM InstancesFeatures WHERE GraphID=?",row.Id)
#existingInstance = cursor2.fetchone()
#instanceFeature.ComputeEccentricity()
#MinEccentricity = instanceFeature.MinEccentricity()
#MaxEccentricity = instanceFeature.MaxEccentricity()
#MeanEccentricity = instanceFeature.MeanEccentricity()
#StdEccentricity = instanceFeature.StdEccentricity()
#VcEccentricity = instanceFeature.VcEccentricity()
#MedianEccentricity = instanceFeature.MedianEccentricity()
#Q1Eccentricity = instanceFeature.Q1Eccentricity()
#Q3Eccentricity = instanceFeature.Q3Eccentricity()
#if (existingInstance==None) :
# pk = uuid.uuid4()
# cursor2.execute("insert into InstancesFeatures(Id, GraphID, mineccentricity, maxeccentricity, meaneccentricity, stdeccentricity, vceccentricity, medianeccentricity, q1eccentricity, q3eccentricity) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
# str(pk), row.Id, MinEccentricity, MaxEccentricity, MeanEccentricity, StdEccentricity, VcEccentricity, MedianEccentricity, Q1Eccentricity, Q3Eccentricity)
#else :
# cursor2.execute("UPDATE InstancesFeatures SET mineccentricity=?, maxeccentricity=?, meaneccentricity=?, stdeccentricity=?, vceccentricity=?, medianeccentricity=?, q1eccentricity=?, q3eccentricity=? where GraphID=?",
# MinEccentricity, MaxEccentricity, MeanEccentricity, StdEccentricity, VcEccentricity, MedianEccentricity, Q1Eccentricity, Q3Eccentricity, existingInstance.GraphID)
#conn2.commit()
############################################################################################# Clustering Features
#cursor2.execute("SELECT GraphID FROM InstancesFeatures WHERE GraphID=?",row.Id)
#existingInstance = cursor2.fetchone()
#instanceFeature.ComputeClustering()
#MinClustering = instanceFeature.MinClustering()
#MaxClustering = instanceFeature.MaxClustering()
#MeanClustering = instanceFeature.MeanClustering()
#StdClustering = instanceFeature.StdClustering()
#VcClustering = instanceFeature.VcClustering()
#MedianClustering = instanceFeature.MedianClustering()
#Q1Clustering = instanceFeature.Q1Clustering()
#Q3Clustering = instanceFeature.Q3Clustering()
#if (existingInstance==None) :
# pk = uuid.uuid4()
# cursor2.execute("insert into InstancesFeatures(Id, GraphID, minlocalclustering, maxlocalclustering, meanlocalclustering, stdlocalclustering, vclocalclustering, medianlocalclustering, q1clustering, q3clustering) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
# str(pk), row.Id, MinClustering, MaxClustering, MeanClustering, StdClustering, VcClustering, MedianClustering, Q1Clustering, Q3Clustering)
#else :
# cursor2.execute("UPDATE InstancesFeatures SET minlocalclustering=?, maxlocalclustering=?, meanlocalclustering=?, stdlocalclustering=?, vclocalclustering=?, medianlocalclustering=?, q1clustering=?, q3clustering=? where GraphID=?",
# MinClustering, MaxClustering, MeanClustering, StdClustering, VcClustering, MedianClustering, Q1Clustering, Q3Clustering, existingInstance.GraphID)
#conn2.commit()
############################################################################################# Weighted Clustering Features
#instanceFeature.ComputeWeightedClustering()
#MinWeightedClustering = instanceFeature.MinWeightedClustering()
#MaxWeightedClustering = instanceFeature.MaxWeightedClustering()
#MeanWeightedClustering = instanceFeature.MeanWeightedClustering()
#StdWeightedClustering = instanceFeature.StdWeightedClustering()
#VcWeightedClustering = instanceFeature.VcWeightedClustering()
#MedianWeightedClustering = instanceFeature.MedianWeightedClustering()
#Q1WClustering = instanceFeature.Q1WeightedClustering()
#Q3WClustering = instanceFeature.Q3WeightedClustering()
#cursor2.execute("SELECT GraphID FROM InstancesFeatures WHERE GraphID=?",row.Id)
#existingInstance = cursor2.fetchone()
#if (existingInstance==None) :
# pk = uuid.uuid4()
# cursor2.execute("insert into InstancesFeatures(Id, GraphID, minlocalwclustering, maxlocalwclustering, meanlocalwclustering, stdlocalwclustering, vclocalwclustering, medianlocalwclustering, q1wclustering, q3wclustering) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
# str(pk), row.Id, MinWeightedClustering, MaxWeightedClustering, MeanWeightedClustering, StdWeightedClustering, VcWeightedClustering, MedianWeightedClustering, Q1WClustering, Q3WClustering)
#else :
# cursor2.execute("UPDATE InstancesFeatures SET minlocalwclustering=?, maxlocalwclustering=?, meanlocalwclustering=?, stdlocalwclustering=?, vclocalwclustering=?, medianwclustering=?, q1wclustering=?, q3wclustering=? where GraphID=?",
# MinWeightedClustering, MaxWeightedClustering, MeanWeightedClustering, StdWeightedClustering, VcWeightedClustering, MedianWeightedClustering, Q1WClustering, Q3WClustering, existingInstance.GraphID)
#conn2.commit()
############################################################################################# Adjacency Matrix Features
#instanceFeature.ComputeAdjacencyMatrixEigenvalues()
#Index = instanceFeature.Index()
#SecondLargestAdjEgv = instanceFeature.SecondLargestAdjEgv()
#SecondSmallestAdjEgv = instanceFeature.SecondSmallestAdjEgv()
#SmallestAdjEgv = instanceFeature.SmallestAdjEgv()
#MeanSpectrum = instanceFeature.MeanSpectrum()
#StdSpectrum = instanceFeature.StdSpectrum()
#Energy = instanceFeature.Energy()
#GapLargestAndSecondLargestAdjEgv = instanceFeature.GapLargestAndSecondLargestAdjEgv()
#cursor2.execute("SELECT GraphID FROM InstancesFeatures WHERE GraphID=?",row.Id)
#existingInstance = cursor2.fetchone()
#if (existingInstance==None) :
# pk = uuid.uuid4()
# cursor2.execute("insert into InstancesFeatures(Id, GraphID, adjindex, seclargestadjegv, secsmallestadjegv, smallestadjegv, meanspectrum, stdspectrum, energy, gaplargestand2ndlargestadj) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
# str(pk), row.Id, Index, SecondLargestAdjEgv, SecondSmallestAdjEgv, SmallestAdjEgv, MeanSpectrum, StdSpectrum, Energy, GapLargestAndSecondLargestAdjEgv)
#else :
# cursor2.execute("UPDATE InstancesFeatures SET adjindex=?, seclargestadjegv=?, secsmallestadjegv=?, smallestadjegv=?, meanspectrum=?, stdspectrum=?, energy=?, gaplargestand2ndlargestadj=? where GraphID=?",
# Index, SecondLargestAdjEgv, SecondSmallestAdjEgv, SmallestAdjEgv, MeanSpectrum, StdSpectrum, Energy, GapLargestAndSecondLargestAdjEgv, existingInstance.GraphID)
#conn2.commit()
############################################################################################## Laplacian Matrix Features
#instanceFeature.ComputeLaplacianMatrixEigenvalues()
#LaplacianIndex = instanceFeature.LaplacianIndex()
#SecondLargestLapEgv = instanceFeature.SecondLargestLapEgv()
#SecondSmallestNZLapEgv = instanceFeature.SecondSmallestNZLapEgv()
#SmallestNZLapEgv = instanceFeature.SmallestNZLapEgv()
#AlgebraicConnectivity = instanceFeature.AlgebraicConnectivity()
#SmallestLapEgv = instanceFeature.SmallestLapEgv()
#GapLargestAndSmallestNZLapEgv = instanceFeature.GapLargestAndSmallestNZLapEgv()
#cursor2.execute("SELECT GraphID FROM InstancesFeatures WHERE GraphID=?",row.Id)
#existingInstance = cursor2.fetchone()
#if (existingInstance==None) :
# pk = uuid.uuid4()
# cursor2.execute("insert into InstancesFeatures(Id, GraphID, laplacianindex, seclargestlapegv, secondsmallestnzlapegv, smallestnzlapegv, algconnectivity, smallestlapegv, gaplargestandsmallestnzlap) values (?, ?, ?, ?, ?, ?, ?, ?, ?)",
# str(pk), row.Id, LaplacianIndex, SecondLargestLapEgv, SecondSmallestNZLapEgv, SmallestNZLapEgv, AlgebraicConnectivity, SmallestLapEgv, GapLargestAndSmallestNZLapEgv)
#else :
# cursor2.execute("UPDATE InstancesFeatures SET laplacianindex=?, seclargestlapegv=?, secondsmallestnzlapegv=?, smallestnzlapegv=?, algconnectivity=?, smallestlapegv=?, gaplargestandsmallestnzlap=? where GraphID=?",
# LaplacianIndex, SecondLargestLapEgv, SecondSmallestNZLapEgv, SmallestNZLapEgv, AlgebraicConnectivity, SmallestLapEgv, GapLargestAndSmallestNZLapEgv, existingInstance.GraphID)
#conn2.commit()
############################################################################################### Wiener Index Feature
#WienerIndex = instanceFeature.WienerIndex()
#cursor2.execute("SELECT GraphID FROM InstancesFeatures WHERE GraphID=?",row.Id)
#existingInstance = cursor2.fetchone()
#if (existingInstance==None) :
# pk = uuid.uuid4()
# cursor2.execute("insert into InstancesFeatures(Id, GraphID, wienerindex) values (?, ?, ?)",
# str(pk), row.Id, WienerIndex)
#else :
# cursor2.execute("UPDATE InstancesFeatures SET wienerindex=? where GraphID=?",
# WienerIndex, existingInstance.GraphID)
#conn2.commit()
############################################################################################## Average Path Length Feature
AveragePathLength = instanceFeature.AveragePathLength()
cursor2.execute("SELECT GraphID FROM InstancesFeatures WHERE GraphID=?",
row.Id)
existingInstance = cursor2.fetchone()
if (existingInstance == None):
pk = uuid.uuid4()
cursor2.execute(
"insert into InstancesFeatures(Id, GraphID, avgpathlength) values (?, ?, ?)",
str(pk), row.Id, AveragePathLength)
else:
cursor2.execute(
"UPDATE InstancesFeatures SET avgpathlength=? where GraphID=?",
AveragePathLength, existingInstance.GraphID)
conn2.commit()
############################################################################################# Degeneracy Feature
#Degeneracy = instanceFeature.Degeneracy()
#cursor2.execute("SELECT GraphID FROM InstancesFeatures WHERE GraphID=?",row.Id)
#existingInstance = cursor2.fetchone()
#if (existingInstance==None) :
# pk = uuid.uuid4()
# cursor2.execute("insert into InstancesFeatures(Id, GraphID, degeneracy) values (?, ?, ?)",
# str(pk), row.Id, Degeneracy)
#else :
# cursor2.execute("UPDATE InstancesFeatures SET degeneracy=? where GraphID=?",
# Degeneracy, existingInstance.GraphID)
#conn2.commit()
############################################################################################## Girth Feature
#Girth = instanceFeature.Girth()
#cursor2.execute("SELECT GraphID FROM InstancesFeatures WHERE GraphID=?",row.Id)
#existingInstance = cursor2.fetchone()
#if (existingInstance==None) :
# pk = uuid.uuid4()
# cursor2.execute("insert into InstancesFeatures(Id, GraphID, girth) values (?, ?, ?)",
# str(pk), row.Id, Girth)
#else :
# cursor2.execute("UPDATE InstancesFeatures SET girth=? where GraphID=?",
# Girth, existingInstance.GraphID)
#conn2.commit()
############################################################################################## Connected Components, Rank and CoRank Features
#ConnectedComponents = instanceFeature.ConnectedComponents()
#Rank = instanceFeature.Rank()
#CoRank = instanceFeature.CoRank()
#cursor2.execute("SELECT GraphID FROM InstancesFeatures WHERE GraphID=?",row.Id)
#existingInstance = cursor2.fetchone()
#if (existingInstance==None) :
# pk = uuid.uuid4()
# cursor2.execute("insert into InstancesFeatures(Id, GraphID, connectedcomponents, rank, corank) values (?, ?, ?, ?, ?)",
# str(pk), row.Id, ConnectedComponents, Rank, CoRank)
#else :
# cursor2.execute("UPDATE InstancesFeatures SET connectedcomponents=?, rank=?, corank=? where GraphID=?",
# ConnectedComponents, Rank, CoRank, existingInstance.GraphID)
#conn2.commit()
############################################################################################## Maximal Clique Size
##MaxCliqueSize = instanceFeature.MaxCliqueSize()
##cursor2.execute("SELECT GraphID FROM InstancesFeatures WHERE GraphID=?",row.Id)
##existingInstance = cursor2.fetchone()
##if (existingInstance==None) :
## pk = uuid.uuid4()
## cursor2.execute("insert into InstancesFeatures(Id, GraphID, maxcliquesize) values (?, ?, ?)",
## str(pk), row.Id, MaxCliqueSize)
## conn2.commit()
##else :
## cursor2.execute("UPDATE InstancesFeatures SET maxcliquesize=? where GraphID=?",
## MaxCliqueSize, existingInstance.GraphID)
## conn2.commit()
############################################################################################# Szeged Index Feature
#SzegedIndex = instanceFeature.SzegedIndex()
#cursor2.execute("SELECT GraphID FROM InstancesFeatures WHERE GraphID=?",row.Id)
#existingInstance = cursor2.fetchone()
#if (existingInstance==None) :
# pk = uuid.uuid4()
# cursor2.execute("insert into InstancesFeatures(Id, GraphID, szegedindex) values (?, ?, ?)",
# str(pk), row.Id, SzegedIndex)
#else :
# cursor2.execute("UPDATE InstancesFeatures SET szegedindex=? where GraphID=?",
# SzegedIndex, existingInstance.GraphID)
#conn2.commit()
############################################################################################## Spectral Bipartivity (Beta) Feature
#Beta = instanceFeature.Beta()
#cursor2.execute("SELECT GraphID FROM InstancesFeatures WHERE GraphID=?",row.Id)
#existingInstance = cursor2.fetchone()
#if (existingInstance==None) :
# pk = uuid.uuid4()
# cursor2.execute("insert into InstancesFeatures(Id, GraphID, beta) values (?, ?, ?)",
# str(pk), row.Id, Beta)
#else :
# cursor2.execute("UPDATE InstancesFeatures SET beta=? where GraphID=?",
# Beta, existingInstance.GraphID)
#conn2.commit()
#del instanceFeature
#conn2.close()
print(row.Name, " processed.")
