def hclust(self):
dissim2 = functions.getSimilaritiesFromModel(self._subModel)
linkageMatrix = hier.linkage(dist.squareform(dissim2), method='single')
labels = functions.getMoviesNames(self._moviesInformations,self._original_movieIDs[self._mostRated])
dendro = hier.dendrogram(linkageMatrix, labels=labels, leaf_rotation=90)
plt.show()
def __init__(self):
self._model= RSVD.load("objects/")
self._original_movieIDs=np.load("objects/original_movieIDs")
self._ratings= np.load("objects/ratings")
self._moviesInformations = functions.loadMoviesInformations('data_movilens1m/movies.dat')
self._usersInformations = functions.loadUsersInformations('data_movilens1m/users.dat')
self._dissim = functions.getSimilaritiesFromModel(self._model.u)
self._movieMean=np.load("objects/movieMean")
self._userMean=np.load("objects/userMean")
# PCA pour les films les plus notes
self._mostRated = functions.getNMostRatedMovies(self._ratings,2000)
j = 0
self._subModel=np.empty((len(self._mostRated),self._model.u.shape[1])) #subRatings doit etre du meme type que model
self._subMovieMean = np.empty((len(self._mostRated),2))
for i,row in enumerate(self._model.u):
if i in self._mostRated:
self._subMovieMean[j] = self._movieMean[i]
self._subModel[j]=row
j= j +1
# PCA pour les films les plus notes
self._mostRating = functions.getNMostRatingUsers(self._ratings)
j = 0
self._subModel2=np.empty((len(self._mostRating),self._model.v.shape[1])) #subRatings doit etre du meme type que model
self._subMovieMean2 = np.empty((len(self._mostRating),2))
for i,row in enumerate(self._model.v):
if i in self._mostRating:
self._subMovieMean2[j] = self._userMean[i]
self._subModel2[j]=row
j= j +1
def graph(self) :
dissim2 = functions.getSimilaritiesFromModel(self._subModel)
maxi = np.amax(dissim2) #-1
A = dist.squareform(maxi-dissim2)
G = nx.from_numpy_matrix(A)
movieList = functions.getMoviesNames(self._moviesInformations,self._original_movieIDs[self._mostRated])
G = nx.relabel_nodes(G, dict(zip(range(len(G.nodes())),movieList)))
#G = nx.to_agraph(G)
#G.node_attr.update(color="red", style="filled")
#G.edge_attr.update(color="blue", width="2.0")
nx.draw(G, edge_color = "blue", font_weight="bold")
plt.show()
