dirGalaxies = shelve.open(path + '/BDs/listeGalaxies')
for galaxie in range(len(liste_galaxies)):
for node in dirGalaxies[str(liste_galaxies[galaxie])]:
matrix[index[node]][galaxie] += 1
matrix[:,galaxie] = matrix[:,galaxie] / len(dirGalaxies[str(liste_galaxies[galaxie])])
dirGalaxies.close()
label = np.array([i for i in range(len(t))])
tsne = TSNEVisualizer(decompose='svd',decompose_by=15)
tsne.fit(matrix, label)
print(tsne.transformer_)
tsne.poof()
svd = TruncatedSVD(n_components=15)
svd_matrix = svd.fit_transform(matrix)
tsne = ts.TSNE()
y = tsne.fit_transform(svd_matrix)
kmeans = Kmeans(5,200,0.1)
kmeans.fit(y)
for i in range(kmeans.nb_cluster):
print("Cluster ",i)
print((np.where(kmeans.which_cluster == i))[0])
print()
plt.scatter(y[:, 0], y[:, 1], c=kmeans.which_cluster.reshape(-1,1), s=50, cmap='viridis')
plt.title("Resultat du clustering")
plt.savefig("clustering")
plt.show()
newdf_countvectorizer.shape print(vectorizer.get_feature_names()) print(len(vectorizer.get_feature_names())) """**Display TSNE**""" from yellowbrick.text import TSNEVisualizer from sklearn.feature_extraction.text import TfidfVectorizer data = newdf['newPreprocessed'] tfidf = TfidfVectorizer() docs = tfidf.fit_transform(data) labels = newdf['feedback'] tsne = TSNEVisualizer() tsne.fit_transform(docs, labels) tsne.poof() # show the distribution of negative and positive reviews newdf.drop(['reviews.text'], axis=1, inplace=True) reviews = pd.DataFrame(newdf_countvectorizer.toarray()) newdf.head(1) """**Set Feature X and Target Y**""" newdf.reset_index(drop=True, inplace=True) newdf = pd.concat([newdf, reviews], axis=1) X = newdf.drop(['reviews.rating','feedback','preprocessed','preprocessedStr','preprocessedStr','newPreprocessed','keepAdj','posTag'],axis=1) y = newdf['feedback'] """*Split Test & Train Set*"""