util.print_latex_statistics_clusters(dataset_kmed, 'cluster', ['Bx', 'By', 'Bz'], 'label')
# And the hierarchical clustering is the last one we try
clusteringH = HierarchicalClustering()
k_values = range(2, 10)
silhouette_values = []
# Do some initial runs to determine the right number for the mBximum number of clusters.
print '===== agglomaritive clustering ====='
for k in k_values:
print 'k = ', k
dataset_cluster, l = clusteringH.agglomerative_over_instances(copy.deepcopy(dataset), ['Bx', 'By', 'Bz'], k, 'euclidean', use_prev_linkage=True, link_function='ward')
silhouette_score = dataset_cluster['silhouette'].mean()
print 'silhouette = ', silhouette_score
silhouette_values.append(silhouette_score)
if k == k_values[0]:
DataViz.plot_dendrogram(dataset_cluster, l)
plot.plot(k_values, silhouette_values, 'b-')
plot.ylim([0,1])
plot.xlabel('mBx number of clusters')
plot.ylabel('silhouette score')
plot.show()
# And we select the outcome dataset of the knn clustering....
dataset_knn.to_csv(dataset_path + 'chapter5_our_result.csv')
def main():
# Read the result from the previous chapter convert the index to datetime
try:
dataset = pd.read_csv(DATA_PATH / DATASET_FNAME, index_col=0)
dataset.index = pd.to_datetime(dataset.index)
except IOError as e:
print(
'File not found, try to run previous crowdsignals scripts first!')
raise e
# Create an instance of visualization class to plot the results
DataViz = VisualizeDataset(__file__)
# Create objects for clustering
clusteringNH = NonHierarchicalClustering()
clusteringH = HierarchicalClustering()
if FLAGS.mode == 'kmeans':
# Do some initial runs to determine the right number for k
k_values = range(2, 10)
silhouette_values = []
print('Running k-means clustering')
for k in k_values:
print(f'k = {k}')
dataset_cluster = clusteringNH.k_means_over_instances(
dataset=copy.deepcopy(dataset),
cols=['acc_phone_x', 'acc_phone_y', 'acc_phone_z'],
k=k,
distance_metric='default',
max_iters=20,
n_inits=10)
silhouette_score = dataset_cluster['silhouette'].mean()
print(f'silhouette = {silhouette_score}')
silhouette_values.append(silhouette_score)
DataViz.plot_xy(x=[k_values],
y=[silhouette_values],
xlabel='k',
ylabel='silhouette score',
ylim=[0, 1],
line_styles=['b-'])
# Run the knn with the highest silhouette score
k = k_values[np.argmax(silhouette_values)]
print(f'Highest K-Means silhouette score: k = {k}')
print('Use this value of k to run the --mode=final --k=?')
if FLAGS.mode == 'kmediods':
# Do some initial runs to determine the right number for k
k_values = range(2, 10)
silhouette_values = []
print('Running k-medoids clustering')
for k in k_values:
print(f'k = {k}')
dataset_cluster = clusteringNH.k_medoids_over_instances(
dataset=copy.deepcopy(dataset),
cols=['acc_phone_x', 'acc_phone_y', 'acc_phone_z'],
k=k,
distance_metric='default',
max_iters=20,
n_inits=10)
silhouette_score = dataset_cluster['silhouette'].mean()
print(f'silhouette = {silhouette_score}')
silhouette_values.append(silhouette_score)
DataViz.plot_xy(x=[k_values],
y=[silhouette_values],
xlabel='k',
ylabel='silhouette score',
ylim=[0, 1],
line_styles=['b-'])
# Run k medoids with the highest silhouette score
k = k_values[np.argmax(silhouette_values)]
print(f'Highest K-Medoids silhouette score: k = {k}')
dataset_kmed = clusteringNH.k_medoids_over_instances(
dataset=copy.deepcopy(dataset),
cols=['acc_phone_x', 'acc_phone_y', 'acc_phone_z'],
k=k,
distance_metric='default',
max_iters=20,
n_inits=50)
DataViz.plot_clusters_3d(
data_table=dataset_kmed,
data_cols=['acc_phone_x', 'acc_phone_y', 'acc_phone_z'],
cluster_col='cluster',
label_cols=['label'])
DataViz.plot_silhouette(data_table=dataset_kmed,
cluster_col='cluster',
silhouette_col='silhouette')
util.print_latex_statistics_clusters(
dataset=dataset_kmed,
cluster_col='cluster',
input_cols=['acc_phone_x', 'acc_phone_y', 'acc_phone_z'],
label_col='label')
# Run hierarchical clustering
if FLAGS.mode == 'agglomerative':
k_values = range(2, 10)
silhouette_values = []
# Do some initial runs to determine the right number for the maximum number of clusters
print('Running agglomerative clustering')
for k in k_values:
print(f'k = {k}')
dataset_cluster, link = clusteringH.agglomerative_over_instances(
dataset=dataset,
cols=['acc_phone_x', 'acc_phone_y', 'acc_phone_z'],
max_clusters=k,
distance_metric='euclidean',
use_prev_linkage=True,
link_function='ward')
silhouette_score = dataset_cluster['silhouette'].mean()
print(f'silhouette = {silhouette_score}')
silhouette_values.append(silhouette_score)
if k == k_values[0]:
DataViz.plot_dendrogram(dataset_cluster, link)
# Plot the clustering results
DataViz.plot_xy(x=[k_values],
y=[silhouette_values],
xlabel='k',
ylabel='silhouette score',
ylim=[0, 1],
line_styles=['b-'])
if FLAGS.mode == 'final':
# Select the outcome dataset of the knn clustering
clusteringNH = NonHierarchicalClustering()
dataset = clusteringNH.k_means_over_instances(
dataset=dataset,
cols=['acc_phone_x', 'acc_phone_y', 'acc_phone_z'],
k=FLAGS.k,
distance_metric='default',
max_iters=50,
n_inits=50)
# Plot the results
DataViz.plot_clusters_3d(dataset,
['acc_phone_x', 'acc_phone_y', 'acc_phone_z'],
'cluster', ['label'])
DataViz.plot_silhouette(dataset, 'cluster', 'silhouette')
# Print table statistics
util.print_latex_statistics_clusters(
dataset, 'cluster', ['acc_phone_x', 'acc_phone_y', 'acc_phone_z'],
'label')
del dataset['silhouette']
# Store the final dataset
dataset.to_csv(DATA_PATH / RESULT_FNAME)