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Interactive-Kmeans-app.py
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# *********************************************************************************************************************
# K-MEANS CLUSTERING : INTERACTIVE VISUALIZATION
# *********************************************************************************************************************
# ***************************************
# IMPORTS
# ***************************************
import json
import numpy as np
import dash
from dash.dependencies import Input, Output, State
import dash_core_components as dcc
import dash_html_components as html
import plotly.graph_objs as go
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_samples, silhouette_score
from HelpFunctions import create_dataset, init_centroids, Kmeans_EStep, Kmeans_MStep, make_kmeans_viz_data
# ***************************************
# INITIALIZATION
# ***************************************
app = dash.Dash(__name__)
# ***************************************
# APPLICATION
# ***************************************
## COLOR PALETTES
clusColorMap = {0:'#6395EC', 1:'#FFA27A', 2:'#DDA618', 3:'#DFA5DD', 4:'#61CCAB', 5:'#CD5C5C', 6:'#719D2F', 7:'#8E4D2C', 8:'#472F63'}
lineColors = {0: 'rgba(99,149,236, .7)', 1: 'rgba(255,162,122, .7)', 2: 'rgba(221,166,24, .7)', 3: 'rgba(223,165,221, .7)',
4: 'rgba(97,204,171, .7)', 5: 'rgba(205,92,92, .7)', 6: 'rgba(113,157,47, .7)', 7:'rgba(142,77,44, .7)',
8: 'rgba(71,47,99, .7)'}
## BUILD DASH APPLICATION
## APP LAYOUT
app.layout = html.Div(children=[
# COLUMN 1 (TITLE + PARAMETERS BLOCK + K-MEANS GRAPH)
html.Div([
# ROW 1 (TITLE)
html.Div([
html.H1('K-means : Interactive Visualization', style = {'color' : '#4B4D55', 'font-family':'savoye LET', 'fontSize' : 80, 'fontWeight' : 100, 'textAlign' : 'center', 'marginTop':'2%', 'marginBottom':'0%'})
], style = {'height' : '14%', 'border-radius':10, 'borderStyle' : 'solid', 'borderWidth' : 2, 'borderColor' : 'black', 'background-color':'white'}
),
# ROW 2 (PARAMETERS+GRAPH)
html.Div([
# ROW 2 - FIRST PART (PARAMETERS)
html.Div([
# CREATE DATASET
html.Div([
html.H6('CREATE DATASET', style = {'text-align':'center', 'marginTop' : 15, 'marginBottom':15, 'fontSize':15, 'fontWeight':800, 'color' : '#4B4D55'}),
html.Div([
html.Div('Shape :', style={'marginLeft':2}),
dcc.Dropdown(
id='shape-dropdown',
options=[
{'label': 'Gaussian Mixture', 'value': 'gmm'},
{'label': 'Circles', 'value': 'circle'},
{'label': 'Moons', 'value': 'moon'},
{'label': 'Anisotropicly distributed', 'value': 'anisotropic'},
{'label': 'No Structure', 'value': 'noStructure'},
],
value='gmm'
)
], style = {'marginLeft': 10, 'marginRight': 10, 'marginBottom':15}),
html.Div([
html.Div('Sample Size :', style={'marginLeft':2}),
dcc.Slider(id='sample-slider', min=50, max=500, step=50, value=200, marks={50:'50', 500:'500'})
], style = {'marginLeft': 10, 'marginRight': 10, 'marginBottom':25}),
html.Div([
html.Div('Nb of Clusters :', style={'marginLeft':2}),
dcc.Slider(id='cluster-slider', min=2, max=9, marks={i: '{}'.format(i) for i in range(2, 10)}, value=3)
], style = {'marginLeft': 10, 'marginRight': 10}),
html.Div([
html.Button('GENERATE DATA', id='regenData-button', style={'width':130, 'font-size':10, 'text-align':'center', 'padding': 0}),
], style={'marginLeft':'22%', 'marginTop':40}),
]),
html.Hr(style={'marginTop':25, 'marginBottom':15}),
# K-MEANS INITIALIZATION
html.Div([
html.H6('INITIALIZE K-MEANS', style = {'text-align':'center', 'marginBottom':15, 'fontSize':15, 'fontWeight':800, 'color' : '#4B4D55'}),
html.Div([
html.Div('Initialization Method :', style={'marginLeft':2}),
dcc.Dropdown(
id='init-dropdown',
options=[
{'label': 'Random', 'value': 'random'},
{'label': 'K-means++', 'value': 'kmeans++'}
],
value='random'
)
], style = {'marginLeft': 10, 'marginRight': 10, 'marginBottom':15}),
html.Div([
html.Div('Nb of Centroids :', style={'marginLeft':2}),
dcc.Slider(id='centroid-slider', min=2, max=9, marks={i: '{}'.format(i) for i in range(2, 10)}, value=3)
], style = {'marginLeft': 10, 'marginRight': 10, 'marginBottom':30}),
html.Div([
html.Div('Max Iterations :', style={'marginLeft':2}),
dcc.Slider(id='iter-slider', min=5, max=20, step=1, value=10, marks={5:'5', 20:'20'})
], style = {'marginLeft': 10, 'marginRight': 10, 'marginBottom':15}),
html.Div([
html.Button('GENERATE CENTROIDS', id='regenCentroids-button', style={'width':130, 'font-size':8, 'text-align':'center', 'padding': 0}),
], style={'marginLeft':'22%', 'marginTop':30}),
]),
], className='three columns', style = {'background-color':'white', 'height' : '100%', 'borderStyle' : 'solid', 'border-radius':10, 'borderWidth' : 2, 'borderColor' : 'black'}),
# ROW 2 - SECOND PART (K-MEANS SCATTERPLOT)
html.Div([
html.H6('K-MEANS SCATTER PLOT', style = {'text-align':'center', 'marginTop' : 30, 'marginBottom':5, 'fontSize':15, 'fontWeight':800, 'color' : '#4B4D55'}),
html.Button('Play', id='play-button', style={'marginLeft':45, 'marginTop':30, 'marginBottom':10, 'marginRight':5, 'width':'5%', 'font-size':10, 'text-align': 'center', 'padding': 0}),
html.Button('Pause', id='pause-button', style={'marginRight':5, 'width':'8%', 'font-size':10, 'text-align':'center', 'padding': 0}),
html.Button('<<', id='prevStep-button', style={'marginRight':5, 'width':'5%', 'font-size':10, 'text-align':'center', 'padding': 0}),
html.Button('>>', id='nextStep-button', style={'marginRight':5, 'width':'5%', 'font-size':10, 'text-align':'center', 'padding': 0}),
html.Button('Restart', id='restart-button', style={'width':'10%', 'font-size':10, 'text-align':'center', 'padding': 0}),
html.Button(id='iter-text', disabled=True, style={'marginLeft':'14.5%', 'width':'22%', 'background-color':'#4B4D55', 'pointer-events': 'none', 'color':'white', 'font-size':10, 'text-align':'center', 'padding': 0}),
dcc.Graph(id='kmeans-graph', animate=True, config={'displayModeBar': False}, style={'marginLeft':5, 'marginRight':15}),
dcc.Interval(id='interval-component', interval=3600*1000, n_intervals=0),
html.Div([
html.Img(src=app.get_asset_url('Signature_Logo.png'), style={'height':'100%', 'float':'right'})
], style={'height':'10%', 'top':'0%', 'position':'relative'})
], className = 'nine columns', style = {'background-color':'white', 'height' : '100%', 'border-width':2, 'borderStyle' : 'solid', 'border-radius':10, 'borderColor' : 'black'}),
], style = {'height' : '84%', 'position':'relative', 'top':'2%'})
], className = 'eight columns', style = {'height':'96%'}),
# COLUMN 2 (COST FUNCTION GRAPH + SILHOUETTE GRAPH)
html.Div([
html.Div([
html.H6('K-MEANS COST FUNCTION', style = {'text-align':'center', 'marginTop' : 15, 'marginBottom':5, 'fontSize':15, 'fontWeight':800, 'color' : '#4B4D55'}),
dcc.Graph(id='inertia-graph', config={'displayModeBar': False}, style={'marginLeft':5, 'marginRight':5}),
], style = {'background-color':'white', 'height' : '49%', 'border-radius':10, 'borderStyle' : 'solid', 'borderWidth' : 2, 'borderColor' : 'black'}),
html.Div([
html.H6('SILHOUETTE ANALYSIS', style = {'text-align':'center', 'marginTop' : 15, 'marginBottom':5, 'fontSize':15, 'fontWeight':800, 'color' : '#4B4D55'}),
dcc.Graph(id='silhouette-graph', config={'displayModeBar': False}, style={'marginLeft':5, 'marginRight':5})
], style = {'background-color':'white', 'height' : '49%', 'position':'relative', 'top':'1.6%', 'border-radius':10, 'borderStyle' : 'solid', 'borderWidth' : 2, 'borderColor' : 'black'}),
], className = 'four columns', style={ 'height':'96%', 'marginLeft':'1.5%', 'width':'32.5%'}),
# COPYRIGHT
html.Div([
html.H6('Copyright © 2019 Samy TAFASCA', style={'color':'white', 'font-size':10, 'text-align':'right'}),
# HIDDEN DIVISIONS FOR STORING INTERMEDIATE DATA
html.Div(id='dataset-value', style={'display': 'none'}),
html.Div(id='kmeansCentroids-value', style={'display': 'none'}),
html.Div(id='kmeansFrames-counter', style={'display': 'none'}),
], className= 'twelve columns', style={'marginTop':'1%'})
], style={'height':820})
# *************************************** #
# CALLBACKS #
# *************************************** #
# *********************************************************************************************************
# CALLBACK 1 : UPDATE DATASET AND STORE IN HIDDEN DIV
# *********************************************************************************************************
@app.callback(Output('dataset-value', 'children'),
[Input('shape-dropdown', 'value'), Input('sample-slider', 'value'), Input('cluster-slider', 'value'), Input('regenData-button', 'n_clicks')])
def update_dataset(sampleShape, sampleSize, n_clusters, regenData_n_clicks):
# CREATE DATASET
X = create_dataset(shape=sampleShape, sampleSize=sampleSize, n_clusters=n_clusters)
X = json.dumps(X.tolist())
return X
# *********************************************************************************************************
# *********************************************************************************************************
# CALLBACK 2 : UPDATE CENTROIDS AND STORE IN HIDDEN DIV
# *********************************************************************************************************
@app.callback(Output('kmeansCentroids-value', 'children'),
[Input('init-dropdown', 'value'), Input('centroid-slider', 'value'), Input('dataset-value', 'children'), Input('regenCentroids-button', 'n_clicks')])
def update_kmeans_centroids(initMethod, n_centroids, dataset, regenCentroids_n_clicks):
X = np.array(json.loads(dataset))
centroids = init_centroids(X, k=n_centroids, initMethod=initMethod)
centroids = json.dumps(centroids.tolist())
return centroids
# *********************************************************************************************************
# *********************************************************************************************************
# CALLBACK 3 : UPDATE K-MEANS FRAMES, INERTIA, SILHOUETTE
# *********************************************************************************************************
globalKmeansFramesCounter = -1
globalFrames = []
globalInertia = []
globalSilhouette = tuple()
@app.callback(Output('kmeansFrames-counter', 'children'),
[Input('dataset-value', 'children'), Input('kmeansCentroids-value', 'children'), Input('iter-slider', 'value')])
def update_kmeansFrames(dataset, kmeans_centroids, max_iter):
global globalKmeansFramesCounter, globalFrames, globalInertia, globalSilhouette
# UPDATE COUNTER
globalKmeansFramesCounter = globalKmeansFramesCounter + 1
# LOAD DATASET & CENTROIDS **************************
X = np.array(json.loads(dataset))
centroids = np.array(json.loads(kmeans_centroids))
n_centroids = centroids.shape[0]
labels = [-1]*X.shape[0]
# RUN K-MEANS ***************************************************
inertia_hist = []
kmeans_frames = []
kmeans_frames.append(make_kmeans_viz_data(X, labels, centroids, clusColorMap))
for i in range(max_iter):
# Expectation Step
labels = Kmeans_EStep(X, centroids)
kmeans_frames.append(make_kmeans_viz_data(X, labels, centroids, clusColorMap))
# Maximization Step
centroids = Kmeans_MStep(X, centroids, labels)
kmeans_frames.append(make_kmeans_viz_data(X, labels, centroids, clusColorMap))
# Compute Inertia
inertia = 0
for j in range(n_centroids):
inertia = inertia + np.power(np.linalg.norm(X[labels==j,:]-centroids[j], axis=1), 2).sum()
inertia_hist.append(inertia)
globalInertia = inertia_hist
globalInertia = (globalInertia, KMeans(n_clusters=len(centroids)).fit(X).inertia_)
# COMPUTE SILHOUETTE ********************************************
silhouetteValues = silhouette_samples(X, labels)
globalSilhouette = ({k:silhouetteValues[labels==k] for k in range(n_centroids)}, silhouette_score(X, labels))
# CREATE FRAMES *************************************************
globalFrames = [{'data':kmeans_frames[0], 'layout':{**layout, 'title':'Intialization...'}}]
globalFrames = globalFrames + [{'data':d, 'layout':{**layout, 'title':'Step {} : {}'.format(idx//2+1, 'Expectation')}} if idx%2==0
else {'data':d, 'layout':{**layout, 'title':'Step {} : {}'.format(idx//2+1, 'Maximization')}}
for idx,d in enumerate(kmeans_frames[1:])]
return globalKmeansFramesCounter
# *********************************************************************************************************
# *********************************************************************************************************
# CALLBACK 4 : UPDATE K-MEANS GRAPH
# *********************************************************************************************************
globalCurrentStep = 0
globalPrevClicks = 0
globalNextClicks = 0
globalRestartClicks=0
globalNumIntervals = 0
globalFramesCounter = 0
layout = dict(
xaxis = dict(zeroline=False, showgrid=False, showline=True, showticklabels=True, linecolor='#4B4D55', linewidth=2, mirror='ticks', range=[-17,17]),
yaxis = dict(zeroline=False, showgrid=False, showline=True, showticklabels=True, linecolor='#4B4D55', linewidth=2, mirror='ticks', range=[-17,17]),
height = 450,
margin = {'t':10, 'b':30, 'l': 40},
plot_bgcolor = "rgba(75,77,85,1)"
)
@app.callback(Output('kmeans-graph', 'figure'),
[
Input('nextStep-button', 'n_clicks'), Input('prevStep-button', 'n_clicks'), Input('restart-button', 'n_clicks_timestamp'),
Input('kmeansFrames-counter', 'children'), Input('interval-component', 'n_intervals')
])
def update_kmeans_graph(nextStep_n_clicks, prevStep_n_clicks, restart_n_clicks, frames_counter, n_intervals):
global globalPrevClicks, globalNextClicks, globalCurrentStep, globalRestartClicks, globalNumIntervals, globalFramesCounter
if prevStep_n_clicks == None: prevStep_n_clicks=0
if nextStep_n_clicks == None: nextStep_n_clicks=0
if restart_n_clicks == None: restart_n_clicks=0
if n_intervals == None: n_intervals = 0
if (globalRestartClicks != restart_n_clicks) or (globalFramesCounter != frames_counter):
globalRestartClicks = restart_n_clicks
globalFramesCounter = frames_counter
globalCurrentStep = 0
d = globalFrames[globalCurrentStep]['data']
fig = dict(data=d, layout=layout)
return fig
elif (globalNextClicks != nextStep_n_clicks) or (globalNumIntervals != n_intervals):
globalNextClicks = nextStep_n_clicks
globalNumIntervals = n_intervals
globalCurrentStep = min(globalCurrentStep + 1, len(globalFrames)-1)
d = globalFrames[globalCurrentStep]['data']
fig = dict(data=d, layout=layout)
return fig
elif globalPrevClicks != prevStep_n_clicks:
globalPrevClicks = prevStep_n_clicks
globalCurrentStep = max(globalCurrentStep - 1, 0)
d = globalFrames[globalCurrentStep]['data']
fig = dict(data=d, layout=layout)
return fig
d = globalFrames[globalCurrentStep]['data']
fig = dict(data=d, layout=layout)
return fig
# *********************************************************************************************************
# *********************************************************************************************************
# CALLBACK 5 : UPDATE STEP/ITERATION TEXT
# *********************************************************************************************************
@app.callback(Output('iter-text', 'children'), [Input('kmeans-graph', 'figure')])
def update_iter_text(kmeans_fig):
text = globalFrames[globalCurrentStep]['layout']['title']
return text
# *********************************************************************************************************
# *********************************************************************************************************
# CALLBACK 6 : DISABLE NB CLUSTERS FOR CERTAIN SHAPES
# *********************************************************************************************************
@app.callback(Output('cluster-slider', 'disabled'), [Input('shape-dropdown', 'value')])
def disable_component(shape):
if shape in ['moon', 'circle', 'noStructure']:
return True
return False
# *********************************************************************************************************
# *********************************************************************************************************
# CALLBACK 7 : CONTROL ANIMATION USING PLAY/PAUSE BUTTONS
# *********************************************************************************************************
globalPlayClicks = 0
globalPauseClicks = 0
@app.callback(Output('interval-component', 'interval'), [Input('play-button', 'n_clicks'), Input('pause-button', 'n_clicks')])
def play_kmeans(play_clicks, pause_clicks):
global globalPlayClicks, globalPauseClicks, currentStepIter
if play_clicks==None: play_clicks=0
if pause_clicks==None: pause_clicks=0
if globalPlayClicks != play_clicks:
globalPlayClicks = play_clicks
return 1000
return 3600*1000
# *********************************************************************************************************
# *********************************************************************************************************
# CALLBACK 8 : STATE OF PLAY BUTTON
# *********************************************************************************************************
@app.callback(Output('play-button', 'style'), [Input('interval-component', 'interval')])
def update_play_state(interval):
if interval == 1000:
style = {'background-color':'#33C3F0', 'border-color':'white', 'marginLeft':45, 'marginTop':30, 'marginBottom':10, 'marginRight':5, 'width':60, 'font-size':10, 'color':'white', 'text-align': 'center', 'padding': 0}
else:
style = {'marginLeft':45, 'marginTop':30, 'marginBottom':10, 'marginRight':5, 'width':60, 'font-size':10, 'text-align': 'center', 'padding': 0}
return style
# *********************************************************************************************************
# *********************************************************************************************************
# CALLBACK 9 : INERTIA GRAPH
# *********************************************************************************************************
layoutInertia = dict(
#title = 'K-Means Cost Function',
xaxis = dict(title='Iteration', zeroline=False, showgrid=False, showline=True, linecolor='#4B4D55', linewidth=2, mirror='ticks'),
yaxis = dict(title='Inertia', zeroline=False, showgrid=True, showline=True, linecolor='#4B4D55', linewidth=2, mirror='ticks', gridcolor="silver", tickformat='.0f'),
height = 320,
margin = {'t':10, 'l':60, 'r':10, 'b':40},
showlegend = False,
plot_bgcolor = "rgba(75,77,85,1)"
)
@app.callback(Output('inertia-graph', 'figure'), [Input('kmeansFrames-counter', 'children')])
def update_inertia_graph(frames_counter):
data1 = go.Scatter(
x = list(range(1, len(globalInertia[0])+1)),
y = globalInertia[0],
mode = 'markers+lines',
marker = dict(color='white', size=10, line = dict(width=2, color='rgb(205,92,92,1)')),
line = dict(color='rgba(205,92,92,1)'),
name = 'Distorsion')
data2 = go.Scatter(
x = [len(globalFrames)//2-1],
y = [globalInertia[1]+0.1*(globalInertia[0][0]-globalInertia[1])],
mode = 'text',
text = 'Global Minimum',
name = str(round(globalInertia[1])),
hoverinfo = 'name',
textfont = dict(
color = "white"
)
)
layoutInertia['shapes'] = [dict(type='line', x0=0, y0=globalInertia[1], x1=len(globalFrames)//2+1, y1=globalInertia[1], line={'color':'white', 'width':1, 'dash':'dot'})]
fig = dict(data=[data1, data2], layout=layoutInertia)
return fig
# *********************************************************************************************************
# *********************************************************************************************************
# CALLBACK 10 : UPDATE SILHOUETTE GRAPH
# *********************************************************************************************************
layoutSilhouette = dict(
xaxis = dict(title='Silhouette Coefficient', zeroline=False, showgrid=True, showline=True, linecolor='#4B4D55', linewidth=2, mirror='ticks', gridcolor="silver"),
yaxis = dict(title='Cluster', showticklabels=False, zeroline=False, showgrid=False, showline=True, linecolor='#4B4D55', linewidth=2, mirror='ticks'),
height = 320,
margin = {'t':20, 'l':50, 'r':10, 'b':40},
showlegend = False,
plot_bgcolor = "rgba(75,77,85,1)"
)
@app.callback(Output('silhouette-graph', 'figure'), [Input('kmeansFrames-counter', 'children')])
def update_silhouette_graph(frames_counter):
data = []
y_lower = 5
nCentroids = len(globalSilhouette[0])
sampleSize = len([x for sublist in globalSilhouette[0].values() for x in sublist])
for i in range(nCentroids):
silhouetteValues = globalSilhouette[0][i]
silhouetteValues.sort()
y_upper = y_lower + len(silhouetteValues)
filled_area = go.Scatter(y=np.arange(y_lower, y_upper),
x=silhouetteValues,
mode='lines',
showlegend=False,
line=dict(width=1, color=clusColorMap[i]),
fill='tozerox',
fillcolor= lineColors[i])
data.append(filled_area)
y_lower = y_upper + 5
trace = go.Scatter(
x = [globalSilhouette[1]-.1],
y = [.2*sampleSize],
mode = 'text',
text = 'Avg. Score',
textfont = dict(
color="white"
)
)
data.append(trace)
layoutSilhouette['yaxis']['range'] = [0, sampleSize+5*(nCentroids+1)]
layoutSilhouette['shapes'] = [dict(type='line', x0=globalSilhouette[1], y0=0, x1=globalSilhouette[1], y1=sampleSize+6*nCentroids, line={'color':'white', 'width':2, 'dash':'dot'})]
fig = dict(data=data, layout=layoutSilhouette)
return fig
# *********************************************************************************************************
# ***************************************
# EXECUTION
# ***************************************
if __name__ == '__main__':
app.run_server(debug=True)