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TPM.py
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TPM.py
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## for system
from codecs import getencoder
import contextlib
import sys
from this import d
import time
import datetime
import pickle as pick
import csv
from pathlib import Path
import os
import openpyxl
from pandas.core.frame import DataFrame
import xlsxwriter
## for data
import operator
import math
import pandas as pd
import numpy as np
import scipy
import scipy.spatial as sp
from operator import itemgetter
import random
from sklearn.preprocessing import PowerTransformer, minmax_scale
## for visulization
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.axes3d import Axes3D
import plotly.graph_objs as go
import plotly.express as px
import plotly.figure_factory as ff
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
## for clustering
from sklearn.cluster import KMeans, AgglomerativeClustering, SpectralClustering
from scipy.cluster.hierarchy import dendrogram, linkage, fcluster
from sklearn.preprocessing import normalize
from kmodes.kprototypes import KPrototypes
## for dimension reduction
from sklearn.manifold import MDS
from sklearn.decomposition import PCA
## for summarizing
from statsmodels.stats.weightstats import DescrStatsW
## for R
import rpy2.robjects as ro
from rpy2.robjects import pandas2ri, numpy2ri
#numpy2ri.activate()
#pandas2ri.activate()
from rpy2.robjects.conversion import localconverter
from rpy2.robjects.packages import importr
from rpy2.robjects.lib import grid
#grid.activate()
R = ro.r
R('install.packages("cluster")')
R('install.packages("tidyverse")')
R('install.packages("factoextra")')
R('install.packages("dendextend")')
R('install.packages("stats")')
R('install.packages("base")')
R('install.packages("NbClust")')
R('install.packages("fpc")')
R('install.packages("ggplot2")')
R('install.packages("ggdendro")')
R('install.packages("grDevices")')
r_cluster=importr('cluster')
r_tidyverse=importr('tidyverse')
r_factoextra=importr('factoextra')
r_dendextend=importr('dendextend')
r_stats=importr('stats')
r_base=importr('base')
r_NbClust=importr('NbClust')
r_fpc=importr('fpc')
r_ggplot2=importr('ggplot2')
r_ggdendro=importr('ggdendro')
r_grDevices=importr('grDevices')
def numpy_to_rpy2(numpy):
with localconverter(ro.default_converter + numpy2ri.converter):
return ro.conversion.py2rpy(numpy)
def pandas_to_rpy2(dataframe):
with localconverter(ro.default_converter + pandas2ri.converter):
return ro.conversion.py2rpy(dataframe)
class DatabasePreparation(object):
def loadingDatabase(self, url):
np.seterr(divide='ignore', invalid='ignore')
self.data = pd.read_csv(url, sep=";", encoding="utf-8")
print(self.data.head())
print(self.data.info())
self.percent_missing = dict(self.data.isnull().sum() * 100 / len(self.data))
print(self.percent_missing)
def preprocessing(self):
self.data = self.data.drop(['date_visit'], axis = 1)
self.data['date_review'] = self.data['date_review'].replace(['0000-00-00'],'0001-01-01')
self.data['date_review'] = pd.to_datetime(self.data['date_review'], format='%Y-%m-%d', yearfirst = True, errors = 'coerce')
self.data['country'] = self.data['country'].replace(np.nan, '-', regex=True)
self.data = self.data[self.data["country"] != "-"]
self.data = self.data.sort_values('date_review', ascending=True)
self.data = self.data.dropna(subset=['date_review', 'memberID', 'id', 'age', 'sexe'])
self.data = self.data.reset_index(drop=True)
print(self.data.head())
print(self.data.info())
self.percent_missing = dict(self.data.isnull().sum() * 100 / len(self.data))
print(self.percent_missing)
print(len(self.data.groupby(['id']).groups.keys()))
def sortTouristsNumberComments(self, limite_commentaires):
self.dictionnaire = self.data.groupby(['memberID']).groups
self.dict_sorted = {}
dict_len = {key: len(value) for key, value in self.dictionnaire.items()}
sorted_key_list = sorted(dict_len.items(), key=operator.itemgetter(1), reverse=True)
for i in sorted_key_list:
if (i[1] < limite_commentaires):
break
else:
self.dict_sorted[i[0]] = self.dictionnaire[i[0]]
self.data = self.data[self.data['memberID'].isin(list(self.dict_sorted.keys()))]
self.data =self.data.reset_index(drop=True)
print(self.data.head())
print(self.data.info())
self.percent_missing = dict(self.data.isnull().sum() * 100 / len(self.data))
print(self.percent_missing)
def breakdownDatabase(self, breakdown_natio, breakdown_date, date, natio):
# POUR CONSIDERER TOUTES LES NATIONALITES METTRE A FALSE
if (breakdown_natio == "True"):
print("Découpage par nationalité")
liste_natios = self.data.groupby("country").size().sort_values(ascending = False)
liste_natios = {k : v for k, v in liste_natios.items()}
print(list(liste_natios.keys())[0:15])
self.data = self.data[self.data["country"] == natio]
self.data = self.data.sort_values('date_review', ascending=True)
self.data = self.data.reset_index(drop=True)
print(self.data.head())
print(self.data.info())
self.percent_missing = dict(self.data.isnull().sum() * 100 / len(self.data))
print(self.percent_missing)
else:
print("Pas de découpage par nationalité")
if (breakdown_date == "True"):
print("Découpage par année")
self.data = self.data[self.data['date_review'] >= str(date)+'-01-01']
self.data = self.data[self.data['date_review'] < str(date+1)+'-01-01']
else:
print("Pas de découpage par année")
self.data = self.data.sort_values('date_review', ascending=True)
self.data = self.data.reset_index(drop=True)
print(self.data.head())
print(self.data.info())
self.percent_missing = dict(self.data.isnull().sum() * 100 / len(self.data))
print(self.percent_missing)
def locationsList(self, seuil):
list_locations = self.data.groupby("id").size().sort_values(ascending = False)
list_locations = {k : v for k, v in list_locations.items()}
self.top = []
if (seuil < len(list_locations.keys())):
for i in range(0, seuil):
self.top.append(list(list_locations.keys())[i])
else:
print('Erreur, seuil trop grand')
def run(self, limite_commentaires, breakdown_natio, breakdown_date, date, natio, seuil, url):
self.loadingDatabase(url)
self.preprocessing()
self.sortTouristsNumberComments(limite_commentaires)
self.breakdownDatabase(breakdown_natio, breakdown_date, date, natio)
self.locationsList(seuil)
return self.top
def locationsDatabase(self, locations, naive = False):
id_locations = list(locations['id'])
self.data = self.data[self.data['id'].isin(id_locations)]
self.data = self.data.reset_index(drop=True)
print(self.data.head())
print(self.data.info())
self.percent_missing = dict(self.data.isnull().sum() * 100 / len(self.data))
print(self.percent_missing)
if naive:
time = self.data.date_review - self.data.date_review[0]
self.data.date_review = pd.Series([i.days for i in time])
self.data = self.data.drop(['nom', 'latitude', 'longitude', 'typeR', 'dateLastScanReviews',
'gadm36_gid', 'gid', 'name_1', 'name_2', 'name_3', 'name_4', 'name_5',
'region', 'memberID', 'location'], axis = 1)
self.data = pd.get_dummies(self.data, prefix=['id', 'age', 'sexe', 'country'], columns=['id', 'age', 'sexe', 'country'])
self.data.date_review = minmax_scale(self.data.date_review)
return self.data
class LocationsClassification(object):
def loadingClassification(self):
url = 'data/Classification_locations.csv'
self.classification = pd.read_csv(url, sep=";", encoding="latin-1")
print(self.classification.head())
print(self.classification.info())
def differenceClassificationLists(self, top):
if not list(set(top).difference(list(self.classification.id))):
print("La différence entre la liste de monuments classifiés et ceux du top est bien nulle")
else:
print(list(set(top).difference(list(self.classification.id))))
print(top)
sys.exit("La différence entre la liste de monuments classifiés et ceux du top n'est pas nulle !")
def creationClassificationCode(self):
self.classification = self.classification.replace({'Monument' : 'A', 'Park/garden' : 'B', 'Urbanism' : 'C',
'Art gallerie/museum' : 'D', 'Holy site/place of worship' : 'E',
'Historic building' : 'F', 'Theater/auditorium' : 'G',
'Shop' : 'H', 'Restaurant/bar' : 'I', 'Gastronomy' : 'J',
'Hotel' : 'K', 'Wood' : 'L', 'Watering place' : 'M',
'Beach' : 'N', 'Mountain' : 'O', 'Music building' : 'P',
'Cinema' : 'Q', 'Amusement park/aquarium' : 'R', 'Sport' : 'S', 'Viewpoint' : 'T'}, regex = True)
self.classification['categorie'] = self.classification['categorie'].str.split('_')
def run(self, top):
self.loadingClassification()
self.differenceClassificationLists(top)
self.creationClassificationCode()
return self.classification
def conversionClassificationId(self, id, data):
return list(data[data.id == id].categorie)[0]
def classificationOntology(self, voyage):
classes = [["A", "B", "C"],
["D", "E", "F", "G"],
["H", "I", "J", "K"],
["L", "M", "N", "O"],
["P", "Q", "R", "S"],
["T"]]
index = [np.array(np.pad([voyage.count(j) if j in voyage else 0 for j in i], (0,4-len(i)),'constant', constant_values=0), dtype = 'float') for i in classes]
return index
class TouristsStaticProfiles(object):
def staticList(self, data):
self.static_profiles = list(data.groupby(['memberID', 'country', 'age', 'sexe']).groups.keys())
self.static_profiles = {i[0]:(i[1], i[2], i[3]) for i in self.static_profiles}
def run(self, data):
self.staticList(data)
return self.static_profiles
class Seasonality(object):
def seasonDetection(self, date):
self.season = None
seasons = ["Winter", "Spring", "Summer", "Autumn"]
if ((date.month == 1) or (date.month == 2)):
self.season = seasons[0]
else:
if (date.month == 3):
if (date.day < 19):
self.season = seasons[0]
else:
self.season = seasons[1]
else:
if ((date.month == 4) or (date.month == 5)):
self.season = seasons[1]
else:
if (date.month == 6):
if (date.day < 21):
self.season = seasons[1]
else:
self.season = seasons[2]
else:
if ((date.month == 7) or (date.month == 8)):
self.season = seasons[2]
else:
if (date.month == 9):
if (date.day < 19):
self.season = seasons[2]
else:
self.season = seasons[3]
else:
if ((date.month == 10) or (date.month == 11)):
self.season = seasons[3]
else:
if (date.month == 12):
if (date.day < 19):
self.season = seasons[3]
else:
self.season = seasons[0]
def run(self, date):
self.seasonDetection(date)
return self.season
class TripsList(object):
def touristsProfiles(self, data):
self.tourists = data.groupby(['memberID']).groups
self.tourists = dict(sorted(self.tourists.items(), key=operator.itemgetter(0), reverse=True))
self.tourists_infos = data.groupby(['memberID', 'id', 'date_review']).groups
self.tourists_infos = dict(sorted(self.tourists_infos.items(), key=operator.itemgetter(0, 0), reverse=True))
def tripsCreation(self):
liste_infos_commentaire = list(self.tourists_infos.keys()) # liste des commentaires contenant pour chaque commentaire le memberID du touriste,
# l'id du monument et la date du commentaire
liste_index = list(self.tourists_infos.values()) # liste des commentaires contenant l'index de chaque commentaire dans la database
self.tourists_raw_dynamic_profiles = {}
compteur = 0
for i in self.tourists.items():
tab = []
for j in range(compteur, len(liste_index)):
index = liste_index[j][0]
if (index in i[1]):
date = liste_infos_commentaire[j][2]
id = liste_infos_commentaire[j][1]
couple = (date, id)
tab.append(couple)
else:
break
compteur = j
if (len(tab) > 1):
self.tourists_raw_dynamic_profiles[i[0]] = sorted(tab, key=lambda x: x[0])
def tripsOrganisation(self, threshold):
self.tourists_dynamic_profiles = {}
seuil = pd.Timedelta(days = threshold)
for i in self.tourists_raw_dynamic_profiles.items():
tab1 = []
tab2 = []
tab3 = []
timer = i[1][0][0]
for j in range(0, len(i[1])):
date = i[1][j][0]
name = i[1][j][1]
couple = (date, name)
delta1 = pd.to_timedelta(date.floor('D') - timer.floor('D'))
tab2.append(couple)
if (delta1 <= seuil):
tab3.extend(tab2)
tab2 = []
timer = date
else:
if(len(tab3) > 1):
tab1.append(tab3)
timer = tab2[-1][0]
tab3 = []
tab3 = tab2
tab2 = []
if (len(tab3) > 1):
tab1.append(tab3)
if (len(tab1) > 0):
self.tourists_dynamic_profiles[i[0]] = tab1
def dynamicProfiles(self, classification):
classification_data = LocationsClassification()
season = Seasonality()
self.dynamic_profiles = {}
id = 0
for i in self.tourists_dynamic_profiles.items():
self.dynamic_profiles[i[0]] = []
for j in i[1]:
classifications = []
saison = season.run(j[0][0])
duree = j[-1][0] - j[0][0]
for k in j:
classifications.extend(classification_data.conversionClassificationId(k[1], classification))
self.dynamic_profiles[i[0]].append((classifications, saison, duree, id))
id = id + 1
def tripsDatabase(self, classification):
self.list_trips = {}
for i in self.dynamic_profiles.items():
for j in i[1]:
self.list_trips[j[3]] = (classification.classificationOntology(j[0]), j[1], j[2].days, i[0])
print("Il y a " + str(len(self.list_trips)) + "trips dans la base de données")
def dynamicProfilesWithoutClassification(self):
season = Seasonality()
self.dynamic_profiles_no_classification = {}
id = 0
for i in self.tourists_dynamic_profiles.items():
self.dynamic_profiles_no_classification[i[0]] = []
for j in i[1]:
classifications = []
saison = season.run(j[0][0])
duree = j[-1][0] - j[0][0]
for k in j:
classifications.append(k[1])
self.dynamic_profiles_no_classification[i[0]].append((classifications, saison, duree, id))
id = id + 1
def topMonumentsCluster(self, top, clusters, classification, region, date, a, b):
self.list_locations = {k:[] for k,v in clusters.items()}
self.list_tops = {k:[] for k,v in clusters.items()}
for k,v in clusters.items():
for i in self.dynamic_profiles_no_classification.values():
if (i[0][-1] in v):
self.list_locations[k].extend(i[0][0])
for i in set(self.list_locations[k]):
self.list_tops[k].append((i, self.list_locations[k].count(i)))
self.list_tops[k].sort(key = lambda x: x[1], reverse=True)
self.list_tops[k] = self.list_tops[k][0:top]
tpm = []
for i in self.list_tops[k]:
tpm.append(list(classification[classification.id == i[0]].nom)[0])
self.list_tops[k] = tpm
self.list_tops = pd.DataFrame.from_dict(self.list_tops, orient = "columns")
print(self.list_tops)
self.list_tops.to_csv("res/" + region + "/" + str(date) + "/" + str(a) + "_" + str(b) + "_top.csv", encoding="latin_1")
def run(self, data, data_classification, threshold, classification):
self.touristsProfiles(data)
self.tripsCreation()
self.tripsOrganisation(threshold)
self.dynamicProfiles(data_classification)
self.tripsDatabase(classification)
return self.list_trips
def getTop(self, data, threshold, top, clusters, classification, region, date, a, b):
self.touristsProfiles(data)
self.tripsCreation()
self.tripsOrganisation(threshold)
self.dynamicProfilesWithoutClassification()
self.topMonumentsCluster(top, clusters, classification, region, date, a, b)
class TripsDatabase(object):
def flatten(self, t):
return [item for sublist in t for item in sublist]
def visualization_PCA(self, date, region, file, labels):
color = None
if (labels is not None):
if ("color" in self.df.columns):
self.df.drop(["color"], axis = 1)
self.df['color'] = labels
color = 'color'
fig = px.scatter(self.df, x = "x", y = "y", color = color)
fig.update_traces(mode="markers")
fig.show()
fig.write_html("res/" + region + "/" + str(date) + "/" + file + ".html")
def database_voyages(self, liste, a, b, date, region):
new_dict = {k:self.flatten([self.flatten(i) if isinstance(i, list) else [i] for i in v]) for k,v in liste.items()}
self.database = pd.DataFrame.from_dict(new_dict, orient='index')
self.database.columns = ["A", "B", "C", "C#", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "T#", "T#2", "T#3", "Saison", "Duree", "Id"]
self.database[["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "Duree"]] = minmax_scale(self.database[["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "Duree"]])
print(self.database.head())
print(self.database.info())
categorical = pd.get_dummies(self.database.Saison)
self.database_num = self.database.drop(["Saison"], axis = 1)
self.database_num[["Saison_Autumn", "Saison_Spring", "Saison_Summer", "Saison_Winter"]] = categorical
self.database_num = self.database_num.drop(["Id"], axis = 1)
print(self.database_num.head())
self.matrixProjection(a, b, date, region)
self.list_trips = {}
for i in range(0, len(self.database.values)):
self.list_trips[i] = (np.array([self.database.values[i][j] for j in range(0, 24)]), self.database.values[i][-3], self.database.values[i][-2], self.database.values[i][-1])
return self.database, self.database_num, self.list_trips
def matrixProjection(self, a, b, date, region):
mds = MDS(random_state=0, dissimilarity = "euclidean")
df_transform = pd.DataFrame(mds.fit_transform(self.database_num), columns = ["x", "y"])
fig = px.scatter(df_transform, x="x", y="y")
fig.update_traces(mode="markers")
fig.show()
fig.write_html("res/" + region + "/" + str(date) + "/" + str(a) + "_" + str(b) + "_" + "projection.html")
def embedding_PCA(self, date, region):
pca = PCA(n_components=2)
self.df = pd.DataFrame(pca.fit_transform(self.database_num), columns = ["x", "y"])
self.visualization_PCA(date, region, "pca", None)
def run(self, liste, a, b, date, region):
self.database_voyages(liste, a, b, date, region)
#self.embedding_PCA(date, region)
class AlgoClustering(object):
def hierarchical_clustering(self, database, k, date, region):
modelhierarchical = AgglomerativeClustering(n_clusters=k)
modelhierarchical.fit_predict(database)
#self.matrixProjection(a = "hierarchical", b = "", date = date, region = region, database = database)
return modelhierarchical.labels_
def kmeans_clustering(self, database, k, date, region):
modelkmeans = KMeans(n_clusters = k, init = 'k-means++', n_init = 100)
modelkmeans.fit_predict(database)
#self.matrixProjection(a = "kmeans", b = "", date = date, region = region, database = database)
return modelkmeans.labels_
def spectral_clustering(self, database, k, date, region):
modelspectral = SpectralClustering(n_clusters= k, assign_labels='cluster_qr')
modelspectral.fit_predict(database)
#self.matrixProjection(a = "spectral", b = "", date = date, region = region, database = database)
return modelspectral.labels_
def kprototypes_clustering(self, database, k, date, region):
modelkproto = KPrototypes(n_clusters = k, init='Cao', n_jobs = 4)
modelkproto.fit_predict(database, categorical=[24])
#self.matrixProjection(a = "kmeans", b = "", date = date, region = region, database = database)
return modelkproto.labels_
def matrixProjection(self, a, b, date, region, database):
mds = MDS(random_state=0, dissimilarity = "euclidean")
df_transform = pd.DataFrame(mds.fit_transform(database), columns = ["x", "y"])
fig = px.scatter(df_transform, x="x", y="y")
fig.update_traces(mode="markers")
fig.show()
fig.write_html("res/" + region + "/" + str(date) + "/" + str(a) + "_" + str(b) + "_" + "projection.html")
class TouristProfileMeasure(object):
def distanceMatrix(self, list_trips, a, b):
content_matrix = np.zeros((len(list_trips), len(list_trips)))
context_matrix = np.zeros((len(list_trips), len(list_trips)))
start_time = time.time()
tabA1 = np.array([i[0] for i in list_trips.values()]) #monumentcat
tabB1 = np.array([i[1] for i in list_trips.values()]) #saison
tabC1 = np.array([i[2] for i in list_trips.values()]) #duree
for i in range(0, len(list_trips) - 1):
idA = list_trips[i][0] #monument
idB = list_trips[i][1] #saison
idC = list_trips[i][2] #duree
tabA = tabA1[i+1:] #monumentcat
tabB = tabB1[i+1:] #saison
tabC = tabC1[i+1:] #duree
resA = self.classificationDistance(tabA, idA)
resB = self.seasonDistance(tabB, idB)
resC = self.durationDistance(tabC, idC)
content_matrix[i+1:,i] = resA
context_matrix[i+1:,i] = resB + resC
content_matrix = content_matrix/np.max(content_matrix)
context_matrix = context_matrix/np.max(context_matrix)
self.matrix = a*content_matrix + b*context_matrix
self.matrix = normalize(self.matrix, axis=1, norm='l1')
#self.tril = matrix.T[np.triu_indices_from(matrix.T, 1)] #On transpose la matrice pour récupérer les éléments de la matrice dans leurs
#ordres d'insertion !!
self.r_matrix = numpy_to_rpy2(self.matrix)
self.r_tril = r_stats.as_dist(self.r_matrix, diag = False, upper = False)
print("--- %s seconds ---" % (time.time() - start_time))
return self.r_tril
def entanglementMatrix(self, list_trips):
self.ent = np.zeros((11, 11))
start_time = time.time()
for score_a in range(0, 11):
score_b = 1 - score_a/10
score_a = score_a/10
score_a = round(score_a,1)
score_b = round(score_b,1)
self.distanceMatrix(list_trips, score_a, score_b)
Path("matrice").mkdir(parents=True, exist_ok=True)
with open("matrice/" + str(score_a) + "_" + str(score_b) + ".pkl", "wb") as tf:
pick.dump(self.matrix, tf)
for i in range(0, 11):
print(i)
with open("matrice/" + str(round(i/10,1)) + "_" + str(round(1 - i/10,1)) + ".pkl", "rb") as tf: #i puis i puisque la matrice d'intrication est symétrique
matrix_a = pick.load(tf)
print(matrix_a)
r_matrix_a = numpy_to_rpy2(matrix_a)
r_tril_a = r_stats.as_dist(r_matrix_a, diag = False, upper = False)
r_hc_a = r_stats.hclust(r_tril_a, "ward.D2")
r_dend_a = r_stats.as_dendrogram(r_hc_a)
print(self.ent)
print("--- %s seconds ---" % (time.time() - start_time))
for j in range(i, 11):
print(j)
with open("matrice/" + str(round(j/10,1)) + "_" + str(round(1 - j/10,1)) + ".pkl", "rb") as tf:
matrix_b = pick.load(tf)
r_matrix_b = numpy_to_rpy2(matrix_b)
r_tril_b = r_stats.as_dist(r_matrix_b, diag = False, upper = False)
r_hc_b = r_stats.hclust(r_tril_b, "ward.D2")
r_dend_b = r_stats.as_dendrogram(r_hc_b)
dend_list = r_dendextend.dendlist(r_dend_a, r_dend_b)
dend_list = r_dendextend.untangle(dend_list, method = "random")
ent_value = float(r_dendextend.entanglement(dend_list)[0])
self.ent[i, j] = round(ent_value, 3)
print("--- %s seconds ---" % (time.time() - start_time))
def entanglementVisualization(self, date, region):
v = self.ent[np.triu_indices(self.ent.shape[0], k = 0)]
nr, nc = self.ent.shape
matrice = np.zeros((nr,nc))
matrice[np.triu_indices(matrice.shape[0], k = 0)] = v
matrice = matrice + matrice.T - np.diag(np.diag(matrice))
print(matrice)
x=np.arange(0,1.1,0.1)
y=np.arange(0,1.1,0.1)
#y = 1 - y
fig = px.imshow(matrice, text_auto=True,
x = ["(1.0,0.0)", "(0.9,0.1)", "(0.8,0.2)", "(0.7,0.3)", "(0.6,0.4)", "(0.5,0.5)", "(0.4,0.6)", "(0.3,0.7)", "(0.2,0.8)", "(0.1,0.9)", "(0.0,1.0)"],
y = ["(1.0,0.0)", "(0.9,0.1)", "(0.8,0.2)", "(0.7,0.3)", "(0.6,0.4)", "(0.5,0.5)", "(0.4,0.6)", "(0.3,0.7)", "(0.2,0.8)", "(0.1,0.9)", "(0.0,1.0)"],
labels={"x" : "First hyperparameter couple", "y": "Second hyperparameter couple"})
fig.show()
fig.write_html("res/" + region + "/" + str(date) + "_" + "entanglement.html")
def matrixProjection(self, a, b, date, region):
matrice = self.matrix + self.matrix.T - np.diag(np.diag(self.matrix))
mds = MDS(random_state=0, dissimilarity = "precomputed")
df_transform = pd.DataFrame(mds.fit_transform(matrice), columns = ["x", "y"])
fig = px.scatter(df_transform, x="x", y="y")
fig.update_traces(mode="markers")
fig.show()
fig.write_html("res/" + region + "/" + str(date) + "/" + str(a) + "_" + str(b) + "_" + "projection.html")
def classificationDistance(self, tab, id, distance = "euclidean"):
res = None
if (distance == "euclidean"):
sub = tab - id
res = np.sum(np.sqrt(np.einsum('ki,ki->ki', sub, sub)), axis = 1)
#res = np.sum(np.sqrt(np.einsum('kij,kij->ki', sub, sub)), axis = 1)
else:
if (distance == "cosine"):
res = np.sum(1 - np.nan_to_num((np.einsum('kij,ij->ki', tab, id)/(np.linalg.norm(tab, axis=2)*np.linalg.norm(id, axis=1))), nan = 1), axis = 1)
return res
def seasonDistance(self, tab, id, distance = "graph"):
saisons = {"Winter":0, "Spring":1, "Summer":2, "Autumn":3}
res = None
if (distance == "discrete"):
res = [int(id == i) for i in tab]
else:
if (distance == "graph"):
res = 1 - abs(1 - abs(saisons[id] - np.array(itemgetter(*tab)(saisons)))/2)
return res
def durationDistance(self, tab, id):
res = np.sqrt((tab - id)**2)
return res
def hierarchicalClustering(self, k):
r_hc = r_stats.hclust(self.r_tril, method = "ward.D2")
self.r_groups = r_dendextend.cutree(r_hc, k)
#self.cluster = fcluster(Z = self.linked, t = self.k, criterion = "maxclust")
def run(self, list_trips, a, b, date, region, k):
self.distanceMatrix(list_trips, a, b)
self.matrixProjection(a, b, date, region)
self.hierarchicalClustering(k)
return self.r_groups
def entanglement(self, list_trips, date, region):
self.entanglementMatrix(list_trips)
self.entanglementVisualization(date, region)
class ClusteringMeasures(object):
def numberClusters(self, a, b, date, region, database):
r_database = pandas_to_rpy2(database)
#r_index = r_base.list('dunn', 'silhouette', 'frey', 'mcclain', "cindex")
#r_index = r_base.list('sdbw')
r_index = r_base.list('silhouette')
r_length = r_base.length(r_index)
r_liste = r_base.vector("list", r_length)
min = 2
max = 15
for i in range(0,r_length[0]):
#r_nb = r_NbClust.NbClust(data = ro.NULL, diss = r_tril, method = "ward.D2", distance = ro.NULL, min_nc = min, max_nc = max, index = r_index[i])
r_nb = r_NbClust.NbClust(data = r_database, method = "ward.D2", distance = "euclidean", min_nc = min, max_nc = max, index = r_index[i]) #pour algo usuels
#r_liste[i] = r_nb.rx('Best.nc')[0]
#r_liste[i] = r_liste[i].rx('Number_clusters')
r_liste[i] = r_nb.rx('All.index')
x = np.array(r_liste[0])[0]
y = np.arange(min,max+1)
df = pd.DataFrame([x,y])
df = df.transpose()
df.columns = ['y', 'x']
Path("res/" + region + "/" + str(date)).mkdir(parents=True, exist_ok=True)
fig = px.scatter(df, x="x", y="y")
fig.update_traces(mode="markers+lines")
fig.write_html("res/" + region + "/" + str(date) + "/" + str(a) + "_" + str(b) + "_" + "silhouette.html")
fig.show()
#self.k = r_base.mean(r_base.unlist(r_liste))
#self.k = round(float(self.k[0]))
self.k = float(input('Quel est la valeur de k ?'))
internal = list(df[df.x == self.k].y)[0]
return self.k, internal
def run(self, a, b, distance_classification, distance_season, date, region, database):
self.numberClusters(a, b, distance_classification, distance_season, date, region, database)
class TPMAlgorithm(object):
def extractionClusters(self, r_groups):
groups = list(r_groups)
maximum = max(groups)
minimum = min(groups)
id = list(range(minimum, maximum + 1))
cluster = [0]*len(id)
zip_iterator = zip(id, cluster)
self.clusters = dict(zip_iterator)
for i in range(0, len(groups)):
if (self.clusters[groups[i]] == 0):
self.clusters[groups[i]] = []
self.clusters[groups[i]].append(i)
else:
self.clusters[groups[i]].append(i)
def informationMerge(self, list_trips, static):
self.profiles = {}
for i in self.clusters.items():
tab = []
for j in i[1]:
tab.append((list_trips[j][:-1] + static[list_trips[j][-1]]))
self.profiles[i[0]] = tab
def summaryClusters(self):
classes = [['Monument', 'Park/garden', 'Urbanism'],
['Art gallerie/museum', 'Holy site/place of worship', 'Historic building', 'Theater/auditorium'],
['Shop', 'Restaurant/bar', 'Gastronomy', 'Hotel'],
['Wood', 'Watering place', 'Beach', 'Mountain'],
['Music building', 'Cinema', 'Amusement park/aquarium', 'Sport'],
["Viewpoint"]]
self.summary = {}
self.natio = []
cluster = 1
for i in self.profiles.items():
size = len(i[1])
comments = {}
for j in i[1]:
com = 0
for k in j[0]:
com = com + np.sum(k)
if(com in comments.keys()):
comments[com] = comments[com] + 1
else:
comments[com] = 1
weighted_stats = DescrStatsW(list(comments.keys()), weights=list(comments.values()), ddof=0)
locations = {}
for a in i[1]:
for b in range(0, len(a[0])):
for c in range(0, len(classes[b])): #on doit itérer sur classes plutot que sur a[0][b] du fait
#de la taille standardisée des vecteurs de classification
classe = classes[b][c]
if (classe in locations.keys()):
locations[classe] = locations[classe] + a[0][b][c]
else:
locations[classe] = 1
locations = {k:round((v/np.sum(np.array(list(locations.values()))))*100, 3) for k,v in locations.items()}
locations = dict(sorted(locations.items(), key=operator.itemgetter(0), reverse=False))
seasons = {}
for l in i[1]:
if (l[1] in seasons.keys()):
seasons[l[1]] = seasons[l[1]] + 1
else:
seasons[l[1]] = 1
seasons = {k:round((v/size)*100, 3) for k,v in seasons.items()}
seasons = dict(sorted(seasons.items(), key=operator.itemgetter(1), reverse=True))
durations = [j[2] for j in i[1]]
nationalities = {}
for m in i[1]:
if (m[3] in nationalities.keys()):
nationalities[m[3]] = nationalities[m[3]] + 1
else:
nationalities[m[3]] = 1
nationalities = {k:round((v/size)*100, 3) for k,v in nationalities.items()}
nationalities = dict(sorted(nationalities.items(), key=operator.itemgetter(1), reverse=True)[0:10])
nationalities = dict(sorted(nationalities.items(), key=operator.itemgetter(0), reverse=False))
self.natio.extend(list(nationalities.keys()))
ages = {}
for n in i[1]:
if (n[4] in ages.keys()):
ages[n[4]] = ages[n[4]] + 1
else:
ages[n[4]] = 1
ages = {k:round((v/size)*100, 3) for k,v in ages.items()}
ages = dict(sorted(ages.items(), key=operator.itemgetter(1), reverse=True))
sexes = {}
for o in i[1]:
if (o[5] in sexes.keys()):
sexes[o[5]] = sexes[o[5]] + 1
else:
sexes[o[5]] = 1
sexes = {k:round((v/size)*100, 3) for k,v in sexes.items()}
sexes = dict(sorted(sexes.items(), key=operator.itemgetter(1), reverse=True))
self.summary[i[0]] = {"Cluster" : cluster,
"Duration_Mean" : round(np.mean(durations), 3),
"Duration_Standard_Deviation" : round(np.std(durations), 3),
"Comments_Mean": round(weighted_stats.mean, 3),
"Comments_Standard_Deviation": round(weighted_stats.std, 3),
"Taille": size,
"Summer" : 0,
"Autumn" : 0,
"Winter" : 0,
"Spring" : 0,
"man" : 0,
"woman" : 0,
"35-49" : 0,
"50-64" : 0,
"25-34" : 0,
"18-24" : 0,
"13-17" : 0,
"4-2a" : 0,
'Monument' : 0,
'Park/garden' : 0,
'Urbanism' : 0,
'Art gallerie/museum' : 0,
'Holy site/place of worship' : 0,
'Historic building' : 0,
'Theater/auditorium' : 0,
'Shop' : 0,
'Restaurant/bar' : 0,
'Gastronomy' : 0,
'Hotel' : 0,
'Wood' : 0,
'Watering place' : 0,
'Beach' : 0,
'Mountain' : 0,
'Music building' : 0,
'Cinema' : 0,
'Amusement park/aquarium' : 0,
'Sport' : 0,
'Viewpoint' : 0}
self.summary[i[0]].update(seasons)
self.summary[i[0]].update(locations)
self.summary[i[0]].update(nationalities)
self.summary[i[0]].update(ages)
self.summary[i[0]].update(sexes)
cluster = cluster + 1
self.natio = list(set(self.natio))
self.natio = dict.fromkeys(self.natio, 0)
for i in self.profiles.items():
res = self.natio.copy()
res.update(self.summary[i[0]])
self.summary[i[0]] = res
mean = np.array([[np.sum([self.summary[i][j] if j == k else 0 for j in self.summary[i].keys()]) for i in self.summary.keys()] for k in self.summary[1].keys()])
mean_size = np.array([[np.sum([self.summary[i][j] if j == "Taille" else 0 for j in self.summary[i].keys()]) for i in self.summary.keys()]])
self.summary["Mean"] = dict(zip(list(self.summary[1].keys()), np.around(np.sum(mean*mean_size, axis = 1)/np.sum(mean_size), 3)))
self.summary["Mean"]["Cluster"] = "Mean"
def saveResults(self, a, b, date, region):
path = "res/" + region + "/" + str(date) + "/"
file = str(a) + "_" + str(b)
Path(path).mkdir(parents=True, exist_ok=True)
with open(path + file + "_clusters.pkl", "wb") as tf:
pick.dump(self.clusters,tf)
self.summary = list(self.summary.values())
with open(path + file + "_summary.pkl", "wb") as tf:
pick.dump(self.summary,tf)
def openResults(self, a, b, date, region, file):
#file = input("Ouvrir summary ou clusters ?")
path = "res/" + region + "/" + str(date)
file = str(a) + "_" + str(b) + "_" + file + ".pkl"
with open(path + file, "rb") as tf:
new_dict = pick.load(tf)
return new_dict
def convertSummaryToCsv(self, a, b, date, region):
csv_columns = list(self.summary[1].keys())
path = "res/" + region + "/" + str(date)
file = str(a) + "_" + str(b) + "_clusters.csv"
Path(path).mkdir(parents=True, exist_ok=True)
csv_file = path + "/" + file
try:
with open(csv_file, 'w') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=csv_columns)
writer.writeheader()
for data in self.summary:
writer.writerow(data)
except IOError:
print("I/O error")
def convertCsvToExcel(self, a, b, date, region):
path = "res/" + region + "/" + str(date)
file = str(a) + "_" + str(b) + "_clusters.csv"
data = pd.read_csv(path + '/' + file)
data = pd.pivot_table(data,index=["Cluster"])
saisons = data[['Winter', 'Spring', 'Autumn', 'Summer']]
durees = data[["Duration_Mean", "Duration_Standard_Deviation"]]
attractions = data[['Monument', 'Park/garden', 'Urbanism',
'Art gallerie/museum', 'Holy site/place of worship', 'Historic building', 'Theater/auditorium',
'Shop', 'Restaurant/bar', 'Gastronomy', 'Hotel',
'Wood', 'Watering place', 'Beach', 'Mountain',
'Music building', 'Cinema', 'Amusement park/aquarium', 'Sport',
"Viewpoint"]]
commentaires = data[['Taille', "Comments_Mean", "Comments_Standard_Deviation"]]
demographiques = data[["man","woman","35-49","50-64","25-34","18-24","13-17","4-2a"]]
nationalites = data.drop(['Winter', 'Spring', 'Autumn', 'Summer', "Duration_Mean", "Duration_Standard_Deviation", 'Monument', 'Park/garden', 'Urbanism',
'Art gallerie/museum', 'Holy site/place of worship', 'Historic building', 'Theater/auditorium',
'Shop', 'Restaurant/bar', 'Gastronomy', 'Hotel',
'Wood', 'Watering place', 'Beach', 'Mountain',
'Music building', 'Cinema', 'Amusement park/aquarium', 'Sport',
"Viewpoint", 'Taille', "Comments_Mean", "Comments_Standard_Deviation",
"man","woman","35-49","50-64","25-34","18-24","13-17","4-2a"], 1)
path = "res/" + region + "/" + str(date)
file = str(a) + "_" + str(b) + "_clusters.xlsx"
excelfilename = path + "/" + file
Path(path).mkdir(parents=True, exist_ok=True)
liste = [saisons, durees, attractions, commentaires, demographiques, nationalites]
names = ["saisons", "durees", "attractions", "commentaires", "demographiques", "nationalites"]
try:
os.remove(excelfilename)
except OSError:
pass
writer = pd.ExcelWriter(excelfilename, engine='xlsxwriter')
for i in range (0, len(liste)):
file = liste[i]
name = names[i]
file.to_excel(writer, index=True, sheet_name=name)
writer.save()
def run(self, list_trips, static, a, b, date, region, r_groups):
self.extractionClusters(r_groups)
self.informationMerge(list_trips, static)
self.summaryClusters()
self.saveResults(a, b, date, region)
self.convertSummaryToCsv(a, b, date, region)
self.convertCsvToExcel(a, b, date, region)
class NaiveAlgorithm(object):
def extractionClusters(self, groups):
maximum = max(groups)
minimum = min(groups)
id = list(range(minimum, maximum + 1))
cluster = [0]*len(id)
zip_iterator = zip(id, cluster)
self.clusters = dict(zip_iterator)
for i in range(0, len(groups)):
if (self.clusters[groups[i]] == 0):
self.clusters[groups[i]] = []
self.clusters[groups[i]].append(i)
else:
self.clusters[groups[i]].append(i)
def informationMerge(self, database, classification):
self.profiles = {}
for i in self.clusters.items():
tab = []
for j in i[1]:
data = database[database.index == j]
tab.append((database_classification[database_classification.id == data.id.values[0]].nom.values[0], data.date_review.values[0], data.sexe.values[0],
data.age.values[0], data.country.values[0]))
self.profiles[i[0]] = tab
def summaryClusters(self):
self.summary = {}
self.natio = []
self.loca = []
cluster = 1
for i in self.profiles.items():
locations = [j[0] for j in i[1]]
locations = {item:locations.count(item) for item in locations}
locations = dict(sorted(locations.items(), key=operator.itemgetter(1), reverse=True))
locations = {k:round((v/np.sum(np.array(list(locations.values()))))*100, 3) for k,v in locations.items()}
locations = dict(list(locations.items())[0: 10])
self.loca.extend(list(locations.keys()))
date = pd.DataFrame([j[1] for j in i[1]], columns=["date"]).date
m = pd.Timestamp(0)
mean = (m + (date - m).mean()).floor('s')
gender = [j[2] for j in i[1]]
gender = {item:gender.count(item) for item in gender}
gender = dict(sorted(gender.items(), key=operator.itemgetter(1), reverse=True))
gender = {k:round((v/np.sum(np.array(list(gender.values()))))*100, 3) for k,v in gender.items()}
age = [j[3] for j in i[1]]
age = {item:age.count(item) for item in age}
age = dict(sorted(age.items(), key=operator.itemgetter(1), reverse=True))
age = {k:round((v/np.sum(np.array(list(age.values()))))*100, 3) for k,v in age.items()}