def getClusteringEvalPlots(dataset):
noOfClusters = range(2, 11, 1)
for ds in dataset:
sse = [[]]
sil = [[[], []]]
scores = [[[], []], [[], []], [[], []], [[], []], [[], []]]
for cluster in noOfClusters:
kmLearner = Clustering.KM(n_clusters=cluster)
kmLearner.getLearner().fit(ds.training_x)
emLearner = Clustering.EM(n_components=cluster)
emLearner.getLearner().fit(ds.training_x)
clustringY_KM = kmLearner.getLearner().predict(ds.training_x)
clustringY_EM = emLearner.getLearner().predict(ds.training_x)
homogeneityKM, completenessKM, v_measureKM = homogeneity_completeness_v_measure(ds.training_y, clustringY_KM)
AMISKM = adjusted_mutual_info_score(ds.training_y, clustringY_KM)
ARSKM = adjusted_rand_score(ds.training_y, clustringY_KM)
silhouetteKM = silhouette_score(ds.training_x, clustringY_KM)
homogeneityEM, completenessEM, v_measureEM = homogeneity_completeness_v_measure(ds.training_y, clustringY_EM)
AMISEM = adjusted_mutual_info_score(ds.training_y, clustringY_EM)
ARSEM = adjusted_rand_score(ds.training_y, clustringY_EM)
silhouetteEM = silhouette_score(ds.training_x, clustringY_EM)
sse.append(kmLearner.getLearner().inertia_)
sil[0][0].append(silhouetteKM)
scores[0][0].append(v_measureKM)
scores[1][0].append(AMISKM)
scores[2][0].append(ARSKM)
scores[3][0].append(homogeneityKM)
sil[0][1].append(silhouetteEM)
scores[0][1].append(v_measureEM)
scores[1][1].append(AMISEM)
scores[2][1].append(ARSEM)
scores[3][1].append(homogeneityEM)
plt.style.use('seaborn-whitegrid')
plt.plot(noOfClusters, sil[0][0], label='Silhouette Score, KM', marker='o')
plt.plot(noOfClusters, sil[0][1], label='Silhouette Score, EM', marker='o', linestyle='--')
plt.ylabel('Silhouette Score', fontsize=12)
plt.xlabel('K', fontsize=12)
plt.title('Silhouette Plot for ' + ds.name, fontsize=12, y=1.03)
plt.legend()
plt.savefig('Figures/Clustering/Silhouette for ' + ds.name + '.png')
plt.close()
plt.style.use('seaborn-whitegrid')
plt.plot(noOfClusters, scores[0][0], label='V Measure, KM', marker='o')
plt.plot(noOfClusters, scores[1][0], label='Adj. Mutual Info, KM', marker='o')
plt.plot(noOfClusters, scores[2][0], label='Adj. Rand. Score, KM', marker='o')
plt.plot(noOfClusters, scores[0][1], label='V Measure, EM', marker='o', linestyle='--')
plt.plot(noOfClusters, scores[1][1], label='Adj. Mutual Info, EM', marker='o', linestyle='--')
plt.plot(noOfClusters, scores[2][1], label='Adj. Rand. Score, EM', marker='o', linestyle='--')
plt.ylabel('Score', fontsize=12)
plt.xlabel('K', fontsize=12)
plt.title('Score Plot for ' + ds.name, fontsize=12, y=1.03)
plt.legend()
plt.savefig('Figures/Clustering/Score for ' + ds.name + '.png')
plt.close()
def calcClusterAdded(iDataset):
retDSs = []
for ds in iDataset:
if 'Income' in ds.name:
clusterKM = 3
clusterEM = 2
if 'FA' in ds.name:
clusterKM = 2
clusterEM = 2
if 'ICA' in ds.name:
clusterKM = 2
clusterEM = 2
if 'PCA' in ds.name:
clusterKM = 3
clusterEM = 3
if 'RP' in ds.name:
clusterKM = 2
clusterEM = 3
elif 'Wine' in ds.name:
clusterKM = 2
clusterEM = 2
if 'FA' in ds.name:
clusterKM = 4
clusterEM = 2
if 'ICA' in ds.name:
clusterKM = 2
clusterEM = 2
if 'PCA' in ds.name:
clusterKM = 2
clusterEM = 2
if 'RP' in ds.name:
clusterKM = 3
clusterEM = 2
retDS = dataset()
emLearner = Clustering.KM(n_clusters=clusterKM)
emLearner.getLearner().fit(ds.training_x)
clustringY_KM = emLearner.getLearner().predict(ds.training_x)
xTransformed = pd.concat([pd.DataFrame(ds.training_x), pd.DataFrame(clustringY_KM)], axis=1).to_numpy()
retDS.training_x = xTransformed
retDS.training_y = ds.training_y
retDS.name = ds.name + ' with KM Clusters Added'
retDS.build_train_test_splitSecond()
retDSs.append(retDS)
retDS = dataset()
emLearner = Clustering.EM(n_components=clusterEM)
emLearner.getLearner().fit(ds.training_x)
clustringY_EM = emLearner.getLearner().predict(ds.training_x)
xTransformed = pd.concat([pd.DataFrame(ds.training_x), pd.DataFrame(clustringY_EM)], axis=1).to_numpy()
retDS.training_x = xTransformed
retDS.training_y = ds.training_y
retDS.name = ds.name + ' with EM Clusters Added'
retDS.build_train_test_splitSecond()
retDSs.append(retDS)
return retDSs[0:2], retDSs[2:4]
def getTsnePlot(dataset):
for ds in dataset:
if 'Income' in ds.name:
clusterKM = 3
clusterEM = 2
markerEM = ['+', 'x']
paletteKM = ['red', 'green', 'blue']
paletteEM = ['red', 'blue']
if 'FA' in ds.name:
clusterKM = 2
clusterEM = 2
paletteKM = ['red', 'blue']
paletteEM = ['red', 'blue']
if 'ICA' in ds.name:
clusterKM = 2
clusterEM = 2
paletteKM = ['red', 'blue']
paletteEM = ['red', 'blue']
if 'PCA' in ds.name:
clusterKM = 3
clusterEM = 3
paletteKM = ['red', 'green', 'blue']
paletteEM = ['red', 'green', 'blue']
if 'RP' in ds.name:
clusterKM = 2
clusterEM = 3
paletteKM = ['red', 'blue']
paletteEM = ['red', 'green', 'blue']
elif 'Wine' in ds.name:
clusterKM = 2
clusterEM = 2
paletteKM = ['red', 'blue']
paletteEM = ['red', 'blue']
if 'FA' in ds.name:
clusterKM = 4
clusterEM = 2
paletteKM = ['red', 'green', 'blue', 'orange']
paletteEM = ['red', 'blue']
if 'ICA' in ds.name:
clusterKM = 2
clusterEM = 2
paletteKM = ['red', 'blue']
paletteEM = ['red', 'blue']
if 'PCA' in ds.name:
clusterKM = 2
clusterEM = 2
paletteKM = ['red', 'blue']
paletteEM = ['red', 'blue']
if 'RP' in ds.name:
clusterKM = 3
clusterEM = 2
paletteKM = ['red', 'green', 'blue']
paletteEM = ['red', 'blue']
kmLearner = Clustering.KM(n_clusters=clusterKM)
kmLearner.getLearner().fit(ds.training_x)
emLearner = Clustering.EM(n_components=clusterEM)
emLearner.getLearner().fit(ds.training_x)
clustringY_KM = kmLearner.getLearner().predict(ds.training_x)
clustringY_EM = emLearner.getLearner().predict(ds.training_x)
homogeneityKM, completenessKM, v_measureKM = homogeneity_completeness_v_measure(ds.training_y, clustringY_KM)
AMISKM = adjusted_mutual_info_score(ds.training_y, clustringY_KM)
ARSKM = adjusted_rand_score(ds.training_y, clustringY_KM)
silhouetteKM = silhouette_score(ds.training_x, clustringY_KM)
homogeneityEM, completenessEM, v_measureEM = homogeneity_completeness_v_measure(ds.training_y, clustringY_EM)
AMISEM = adjusted_mutual_info_score(ds.training_y, clustringY_EM)
ARSEM = adjusted_rand_score(ds.training_y, clustringY_EM)
silhouetteEM = silhouette_score(ds.training_x, clustringY_EM)
print('For dataset ' + ds.name + ' using KM, the v_measure, AMIS, ARS and silhouette are: ',
v_measureKM, AMISKM, ARSKM, silhouetteKM)
print('For dataset ' + ds.name + ' using EM, the v_measure, AMIS, ARS and silhouette are: ',
v_measureEM, AMISEM, ARSEM, silhouetteEM)
fig = plt.figure()
ax = fig.add_subplot()
tsne = TSNE(n_components=2, random_state=0)
tsne_obj = tsne.fit_transform(ds.training_x)
ax.scatter(tsne_obj[:, 0], tsne_obj[:, 1], alpha=0.4, c=[paletteKM[x] for x in clustringY_KM])
plt.xlabel('X')
plt.xlabel('Y')
if 'FA' in ds.name and 'Income' in ds.name:
plt.scatter(tsne_obj[ds.training_y == 1, 0], tsne_obj[ds.training_y == 1, 1], marker='+', c='k')
plt.xlabel('X')
plt.xlabel('Y')
ax.set_title('t-SNE Plot for ' + ds.name + ' using KM')
plt.legend()
plt.savefig('Figures/Clustering/TSNE for ' + ds.name + ' using KM.png')
plt.close()
fig = plt.figure()
ax = fig.add_subplot()
tsne = TSNE(n_components=2, random_state=0)
tsne_obj = tsne.fit_transform(ds.training_x)
ax.scatter(tsne_obj[:, 0], tsne_obj[:, 1], alpha=0.4, c=[paletteEM[x] for x in clustringY_EM])
plt.xlabel('X')
plt.xlabel('Y')
if 'FA' in ds.name and 'Income' in ds.name:
plt.scatter(tsne_obj[ds.training_y == 1, 0], tsne_obj[ds.training_y == 1, 1], marker='+', c='k')
plt.xlabel('X')
plt.xlabel('Y')
ax.set_title('t-SNE Plot for ' + ds.name + ' using EM')
plt.legend()
plt.savefig('Figures/Clustering/TSNE for ' + ds.name + ' using EM.png')
plt.close()
if 'Wine' in ds.name:
plt.style.use('seaborn-whitegrid')
classDf = pd.DataFrame(clustringY_KM)
classDf.columns = ['Class']
datasetDf = pd.concat((pd.DataFrame(ds.training_x), classDf), axis=1)
parallel_coordinates(datasetDf, 'Class', color=paletteKM, alpha=0.3)
plt.xlabel('Features', fontsize=12)
plt.title('Parallel Coordinates Plot for ' + ds.name + ' using KM', fontsize=12, y=1.03)
plt.legend()
plt.savefig('Figures/Clustering/Parallel Coord for ' + ds.name + ' using KM.png')
plt.close()
plt.style.use('seaborn-whitegrid')
classDf = pd.DataFrame(clustringY_EM)
classDf.columns = ['Class']
datasetDf = pd.concat((pd.DataFrame(ds.training_x), classDf), axis=1)
parallel_coordinates(datasetDf, 'Class', color=paletteEM, alpha=0.3)
plt.xlabel('Features', fontsize=12)
plt.title('Parallel Coordinates Plot for ' + ds.name + ' using EM', fontsize=12, y=1.03)
plt.legend()
plt.savefig('Figures/Clustering/Parallel Coord for ' + ds.name + ' using EM.png')
plt.close()
