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 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()
def pca_movies(self, color):
# Preparation de la methode PCA pour une projection sur 3 dimensions
pca = PCA(n_components=3)
# PCA pour l'ensemble des films
# Calcul de la projection a partir des donnees
pca.fit(self._model.u)
# Application de la projection aux donnees
newMovies = pca.transform(self._model.u)
# Creation de la figure dans laquelle nous allons representer le nuage de point
fig=plt.figure()
ax = fig.add_subplot(111, projection='3d')
if color == 0:
# colorer selon la moyenne donnée au film
moyennes = [row[0] for row in self._movieMean]
moyennes = [0 if math.isnan(x) else x for x in moyennes]
colors = ['blue','green','cyan','yellow','magenta','red']
categories = np.unique(moyennes)
colordict = dict(zip(categories, colors))
listColors = [colordict[x] for x in moyennes]
elif color ==1:
# colorer selon le genre du film
genres = functions.getGenres(self._moviesInformations)
categories = ['Action','Adventure','Animation','Children\'s','Comedy','Crime','Documentary','Drama','Fantasy','Film-Noir','Horror','Musical','Mystery','Romance','Sci-Fi','Thriller','War','Western']
colors = ['IndianRed','Red','Pink','PaleVioletRed','LightSalmon','Orange','Gold','Violet','Purple','DarkSlateBlue','GreenYellow','DarkOliveGreen','MediumAquamarine','DarkCyan', 'CornflowerBlue','Navy','MistyRose','Peru','Maroon']
colordict = dict(zip(categories, colors))
#faire la correspondance entre les ids
genres2 = list()
for i in range (0, self._model.u.shape[0]):
genres2.append(genres[str(self._original_movieIDs[i])])
listColors = [colordict[x] for x in genres2]
ax.scatter(newMovies[:,0],newMovies[:,1],newMovies[:,2], c=listColors)
#fig.add_axes(ax)
plt.title('3D PCA of movies from model')
plt.show()
pca.fit(self._subModel)
# Application de la projection aux donnees
newMovies = pca.transform(self._subModel)
# Creation de la figure dans laquelle nous allons representer le nuage de point
fig=plt.figure()
ax = p3.Axes3D(fig)
j=0
listColors2 =list()
for i,x in enumerate(listColors) :
if (i in self._mostRated):
listColors2.append(x)
j=j+1
ax.scatter3D(newMovies[:,0],newMovies[:,1],newMovies[:,2],c=listColors2)
fig.add_axes(ax)
plt.show()
pca2 = PCA(n_components=2)
pca2.fit(self._subModel)
# Application de la projection aux donnees
newMovies = pca2.transform(self._subModel)
# Percentage of variance explained for each components
print "explained variance ratio (first two components): ", pca2.explained_variance_ratio_
#plot with labels
labels = functions.getMoviesNames(self._moviesInformations,self._original_movieIDs[self._mostRated])
plt.figure()
plt.scatter(newMovies[:, 0], newMovies[:, 1],c=listColors2)
plt.legend()
plt.title('2D PCA of most rated movies from model')
for label, x, y in zip(labels, newMovies[:, 0], newMovies[:, 1]):
plt.annotate(
label.decode('utf-8'),
xy = (x, y), xytext = (30, 10),
textcoords = 'offset points', ha = 'right', va = 'bottom',
arrowprops = dict(arrowstyle = '-'))
plt.show()
center = pca2.transform(centroids)
# some plotting using numpy's logical indexing
plt.plot(newMovies[idx==0,0],newMovies[idx==0,1],'oc',
newMovies[idx==1,0],newMovies[idx==1,1],'or',
newMovies[idx==2,0],newMovies[idx==2,1],'ob',
newMovies[idx==3,0],newMovies[idx==3,1],'om',
newMovies[idx==4,0],newMovies[idx==4,1],'oy',
newMovies[idx==5,0],newMovies[idx==5,1],'ok',
newMovies[idx==6,0],newMovies[idx==6,1],color="#aff666", marker="o",
newMovies[idx==7,0],newMovies[idx==7,1],color="#efe986", marker="o",
newMovies[idx==8,0],newMovies[idx==8,1],color="#b34ee", marker="o",
newMovies[idx==9,0],newMovies[idx==9,1],color="#bbbccc", marker="o")
plt.plot(center[:,0],center[:,1],'sg',markersize=8)
plt.title(dis)
#plot with labels
labels = functions.getMoviesNames(a._moviesInformations,a._original_movieIDs)
for label, x, y in zip(labels, newMovies[:, 0], newMovies[:, 1]):
plt.annotate(
label.decode('utf-8'),
xy = (x, y), xytext = (30, 10),
textcoords = 'offset points', ha = 'right', va = 'bottom',
arrowprops = dict(arrowstyle = '-'))
plt.show()
print dis
def bestK(self):
all_dis = dict()
for k in range(1,100):
centroids,dis = kmeans(self._model.u,k)