DataViz = VisualizeDataset()
# Read the result from the previous chapter, and make sure the index is of the type datetime.
dataset_path = './intermediate_datafiles/'
try:
dataset = pd.read_csv(dataset_path + 'chapter4_our_result.csv', index_col=0)
except IOError as e:
print('File not found, try to run previous crowdsignals scripts first!')
raise e
dataset.index = dataset.index.to_datetime()
# First let us use non hierarchical clustering.
clusteringNH = NonHierarchicalClustering()
# Let us look at k-means first.
k_values = range(2, 10)
silhouette_values = []
#
## Do some initial runs to determine the right number for k
#
print '===== kmeans clustering ====='
for k in k_values:
print 'k = ', k
dataset_cluster = clusteringNH.k_means_over_instances(copy.deepcopy(dataset), ['Bx', 'By', 'Bz'], k, 'default', 20, 10)
silhouette_score = dataset_cluster['silhouette'].mean()
print 'silhouette = ', silhouette_score
silhouette_values.append(silhouette_score)
DataViz = VisualizeDataset()
# Read the result from the previous chapter, and make sure the index is of the type datetime.
dataset_path = './intermediate_datafiles/'
try:
dataset = pd.read_csv(dataset_path + 'chapter4_result.csv', index_col=0)
except IOError as e:
print('File not found, try to run previous crowdsignals scripts first!')
raise e
dataset.index = dataset.index.to_datetime()
# # First let us use non hierarchical clustering.
#
clusteringNH = NonHierarchicalClustering()
#
# # Let us look at k-means first.
#
# k_values = range(2, 10)
# silhouette_values = []
# #
# ## Do some initial runs to determine the right number for k
# #
# print '===== kmeans clustering ====='
# for k in k_values:
# print 'k = ', k
# dataset_cluster = clusteringNH.k_means_over_instances(copy.deepcopy(dataset), ['gyr_phone_x', 'gyr_phone_y', 'gyr_phone_z'], k, 'default', 20, 10)
# silhouette_score = dataset_cluster['silhouette'].mean()
# print 'silhouette = ', silhouette_score
# silhouette_values.append(silhouette_score)
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)
# Read the result from the previous chapter, and make sure the index is of the type datetime.
dataset_path = './intermediate_datafiles/'
try:
dataset = pd.read_csv(dataset_path + 'processed_data.csv', index_col=0)
except IOError as e:
print('File not found, try to run previous crowdsignals scripts first!')
raise e
dataset.index = dataset.index.to_datetime()
selected_columns = ['gFx', 'gFy', 'gFz', 'ax', 'ay', 'az', 'Gain']
# First let us use non hierarchical clustering.
clusteringNH = NonHierarchicalClustering()
# Let us look at k-means first.
k_values = range(2, 10)
silhouette_values = []
#
## Do some initial runs to determine the right number for k
#
print '===== kmeans clustering ====='
for k in k_values:
print 'k = ', k
dataset_cluster = clusteringNH.k_means_over_instances(
copy.deepcopy(dataset), selected_columns, k, 'default', 20, 10)
silhouette_score = dataset_cluster['silhouette'].mean()
print 'silhouette = ', silhouette_score
'gyr_Gyroscope z (rad/s)', 'gyr_Gyroscope'
],
[
'linacc_Linear Acceleration x (m/s^2)',
'linacc_Linear Acceleration y (m/s^2)',
'linacc_Linear Acceleration z (m/s^2)',
'linacc_Linear Acceleration'
],
[
'mag_Magnetic field x (muT)', 'mag_Magnetic field y (muT)',
'mag_Magnetic field z (muT)', 'mag_Magnetic field'
]]
# First let us use non hierarchical clustering.
clusteringNH = NonHierarchicalClustering()
# Let us look at k-means first.
#
## Do some initial runs to determine the right number for k
#
for printing in params:
# print '===== kmeans clustering ====='
k_values = range(2, 10)
silhouette_values = []
for k in k_values:
# print 'k = ', k
dataset_cluster = clusteringNH.k_means_over_instances(
copy.deepcopy(dataset), [printing[0], printing[1], printing[2]], k,
# As usual, we set our program constants, read the input file and initialize a visualization object.
DATA_PATH = Path('./intermediate_datafiles/Assignment3')
DATASET_FNAME = sys.argv[1] if len(sys.argv) > 1 else 'chapter4_result.csv'
RESULT_FNAME = sys.argv[2] if len(sys.argv) > 2 else 'chapter5_result.csv'
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
DataViz = VisualizeDataset(__file__)
# We'll start by applying non-hierarchical clustering.
clusteringNH = NonHierarchicalClustering()
# Let us look at k-means first.
k_values = range(2, 4)
silhouette_values = []
## Do some initial runs to determine the right number for k
print('===== kmeans clustering =====')
for k in k_values:
print(f'k = {k}')
dataset_cluster = clusteringNH.k_means_over_instances(copy.deepcopy(dataset), ['acc_x', 'acc_y', 'acc_z'], k, 'default', 20, 10)
silhouette_score = dataset_cluster['silhouette'].mean()
print(f'silhouette = {silhouette_score}')
silhouette_values.append(silhouette_score)
import copy
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path
import pickle
from Load import *
# As usual, we set our program constants, read the input file and initialize a visualization object.
dataset = pd.read_csv(outlier_watch_data, index_col=[time_col]).dropna()
dataset.index = pd.to_datetime(dataset.index)
DataViz = VisualizeDataset(__file__, show=False)
# We'll start by applying non-hierarchical clustering.
clusteringNH = NonHierarchicalClustering()
# Let us look at k-means first.
k_values = range(2, 25)
silhouette_values = []
## Do some initial runs to determine the right number for k
attributes_to_cluster = [
'acc_x',
'acc_y',
'acc_z',
"gyr_x",
"gyr_y",
"gyr_z",
]
print('===== kmeans clustering =====')
DataViz = VisualizeDataset()
# Read the result from the previous chapter, and make sure the index is of the type datetime.
dataset_path = './intermediate_datafiles-own/'
try:
dataset = pd.read_csv(dataset_path + 'chapter4_result.csv', index_col=0)
except IOError as e:
print('File not found, try to run previous crowdsignals scripts first!')
raise e
dataset.index = dataset.index.to_datetime()
# First let us use non hierarchical clustering.
clusteringNH = NonHierarchicalClustering()
# Let us look at k-means first.
'''
k_values = range(2, 10)
silhouette_values = []
#
## Do some initial runs to determine the right number for k
#
print '===== kmeans clustering ====='
for k in k_values:
print 'k = ', k
dataset_cluster = clusteringNH.k_means_over_instances(copy.deepcopy(dataset), ['gyr_phone_x', 'gyr_phone_y', 'gyr_phone_z'], k, 'default', 20, 10)
silhouette_score = dataset_cluster['silhouette'].mean()
print 'silhouette = ', silhouette_score
silhouette_values.append(silhouette_score)
