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SynthRO_app.py
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SynthRO_app.py
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import os
import dash
import dash_bootstrap_components as dbc
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.figure_factory as ff
import plotly.graph_objs as go
import plotly.io as pio
import scipy.stats as stats
from dash import dcc, html, dash_table, ctx
from dash.dependencies import Input, Output, State, ALL
from dash.exceptions import PreventUpdate
from plotly.subplots import make_subplots
from sklearn.metrics import mean_squared_error, auc
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from multiprocessing import Pool
from itertools import repeat
from utils import GeneralUtils as utils, ResemblanceMetrics as ResMet, UtilityMetrics as UtiMet, PrivacyMetrics as PriMet
global real_data, synthetic_datasets, dict_features_type, num_features, cat_features, real_train_data, real_test_data, encoder_labels
real_data = pd.DataFrame()
synthetic_datasets = []
dict_features_type = {}
real_train_data = pd.DataFrame()
real_test_data = pd.DataFrame()
global path_user
path_user = os.getcwd().replace('\\', '/')
if not os.path.exists(os.path.join(path_user, 'data_figures')):
os.makedirs(os.path.join(path_user, 'data_figures'))
if not os.path.exists(os.path.join(path_user, 'data_report')):
os.makedirs(os.path.join(path_user, 'data_report'))
global n_cpu
n_cpu = os.cpu_count()
print("!! START !!")
app = dash.Dash(__name__, external_stylesheets=[dbc.themes.FLATLY], suppress_callback_exceptions=True,
prevent_initial_callbacks="initial_duplicate")
# PAGE LAYOUT (static elements)
app.layout = html.Div([
# Header application (top)
dbc.NavbarSimple([
html.H1("SynthRO", style={"color": "#ffffff"})
],
sticky="top",
color="primary",
dark=True,
style={"height": "10vh"}
),
# Actual user position
dcc.Location(id='user_position', refresh=False),
# Page content
html.Div(id='page_content', style={'minHeight': '82vh'}),
# Navigation bar (bottom)
dbc.NavbarSimple([
dbc.NavItem(dbc.NavLink('Load Data', href='/page_1'), style={"marginLeft": "2vw", "marginRight": "5vw"}),
dbc.DropdownMenu(
[
dbc.DropdownMenuItem('Univariate Resemblance Analysis', href='/page_2_ura'),
dbc.DropdownMenuItem(divider=True),
dbc.DropdownMenuItem('Multivariate Relationships Analysis', href='/page_2_mra'),
dbc.DropdownMenuItem(divider=True),
dbc.DropdownMenuItem('Data Labeling Analysis', href='/page_2_dla'),
],
style={"marginRight": "5vw"},
menu_variant="dark",
direction='up',
nav=True,
in_navbar=True,
disabled=True,
id="nav2",
label="Resemblance"),
dbc.NavItem(dbc.NavLink('Utility', href='/page_3', id="nav3", disabled=True), style={"marginRight": "5vw"}),
dbc.DropdownMenu(
[
dbc.DropdownMenuItem('Similarity Evaluation Analysis', href='/page_4_sea'),
dbc.DropdownMenuItem(divider=True),
dbc.DropdownMenuItem('Membership Inference Attack', href='/page_4_mia'),
dbc.DropdownMenuItem(divider=True),
dbc.DropdownMenuItem('Attribute Inference Attack', href='/page_4_aia'),
],
style={"marginRight": "5vw"},
menu_variant="dark",
direction='up',
nav=True,
in_navbar=True,
disabled=True,
id="nav4",
label="Privacy"),
dbc.NavItem(dbc.NavLink('Benchmarking', href='/page_5', id="nav5", disabled=True),
style={"marginRight": "3vw"}),
dbc.NavItem(dbc.Button(["Download Report"], id="button-report", color="primary", disabled=True),
id="item-report"),
dcc.Loading([dcc.Download(id="download-report")], type="circle", fullscreen=True),
],
style={"height": "8vh"},
color="primary",
dark=True,
sticky="bottom",
links_left=True
)
])
@app.callback(Output('item-report', 'style'),
Input('user_position', 'pathname'))
def update_download_visibility(pathname):
if pathname == "/page_1" or pathname == "/":
return {'display': 'none'}
else:
return {"display": "block", "position": "relative", "left": "20vw"}
@app.callback(Output('page_content', 'children'),
Input('user_position', 'pathname'))
def display_page(pathname):
if pathname == '/page_1':
return page_1
elif pathname == '/page_2_ura':
return page_2_ura
elif pathname == '/page_2_mra':
return page_2_mra
elif pathname == '/page_2_dla':
return page_2_dla
elif pathname == '/page_3':
return page_3
elif pathname == '/page_4_sea':
return page_4_sea
elif pathname == '/page_4_mia':
return page_4_mia
elif pathname == '/page_4_aia':
return page_4_aia
elif pathname == '/page_5':
return page_5
else:
return page_1
# PAGE 1 CONTENTS (load data)
page_1 = html.Div([
dbc.Container([
# Upload real dataset
dbc.Row([dbc.Col([
html.H2("Real dataset", style={'margin-left': '1vw', 'margin-top': '1vw'}),
dcc.Upload(
id='upload-data-real',
children=html.Div([
'Drag and drop the file here or ',
html.A('select a file')
]),
style={
'width': '80vw',
'height': '60px',
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px',
'textAlign': 'center',
'margin-left': '5vw',
'margin-right': '5vw'
},
multiple=False
),
dcc.Loading(html.Div(id='output-data-upload-real', style={'margin-bottom': '1.5vw'})),
])]),
dbc.Row([dbc.Col(html.Div([html.Br(), html.Br()]), width=True)]),
# Upload synthetic datasets
dbc.Row([dbc.Col([
html.H2("Synthetic datasets", style={'margin-left': '1vw'}),
dcc.Upload(
id='upload-data-syn',
children=html.Div([
'Drag and drop the files here or ',
html.A('select datasets (even more than one)')
]),
style={
'width': '80vw',
'height': '60px',
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px',
'textAlign': 'center',
'margin-left': '5vw',
'margin-right': '5vw'
},
multiple=True
),
dcc.Loading(html.Div(id='output-data-upload-syn', style={'margin-bottom': '1.5vw'})),
])]),
dbc.Row([dbc.Col(html.Div([html.Br(), html.Br()]), width=True)]),
# Upload features type
dbc.Row([dbc.Col([
html.H2(["Features data types "], style={'margin-left': '1vw', 'display': 'inline-block'}),
html.Span("ℹ", id="info-feature-type", style={'cursor': 'pointer'}),
dbc.Tooltip(
"The file must be structured into two columns named 'Feature' and 'Type': the first column should "
"contain names, while the second should contain a string either 'numerical' or 'categorical'. "
"Otherwise, select the type using the table below.",
target="info-feature-type",
),
dcc.Upload(
id='upload-data-type',
children=html.Div([
'Drag and drop the file here or ',
html.A('select a file')
]),
style={
'width': '80vw',
'height': '60px',
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px',
'textAlign': 'center',
'margin-left': '5vw',
'margin-right': '5vw'
},
multiple=False
),
html.Div(id='output-data-upload-type', style={'margin-bottom': '1.5vw'}),
html.Div(id='none-div', style={'display': 'none'})
])]),
dbc.Row([dbc.Col(html.Div([html.Br(), html.Br()]), width=True)]),
], fluid=True)
])
@app.callback(Output('output-data-upload-real', 'children'),
Input('upload-data-real', 'contents'))
def update_table_real(contents):
global real_data, synthetic_datasets
if contents is not None:
real_data = utils.parse_contents(contents)
global encoder_labels
encoder_labels = LabelEncoder()
for col in real_data.columns:
if real_data[col].dtype == 'object':
real_data[col] = encoder_labels.fit_transform(real_data[col])
# correction different dataset sizes
if synthetic_datasets:
if real_data.shape[0] < synthetic_datasets[0].shape[0]:
synthetic_datasets = [df.sample(n=len(real_data), random_state=80).reset_index(drop=True) for df in
synthetic_datasets]
elif real_data.shape[0] > synthetic_datasets[0].shape[0]:
real_data = real_data.sample(n=synthetic_datasets[0].shape[0], random_state=80).reset_index(drop=True)
children = [
html.Div([
dash_table.DataTable(
data=real_data.head(50).to_dict('records'),
columns=[{'name': col, 'id': col} for col in real_data.columns],
page_action='none',
style_table={'height': '300px',
'width': '80vw',
'margin-left': '5vw',
'margin-right': '5vw',
'overflowY': 'auto',
'overflowX': 'auto'
},
fixed_rows={'headers': True},
style_header={'text-align': 'center'},
style_cell={'minWidth': 100, 'maxWidth': 100, 'width': 100},
)
])
]
return children
@app.callback(Output('output-data-upload-syn', 'children'),
Input('upload-data-syn', 'contents'))
def update_table_syn(contents):
global synthetic_datasets, real_data
if contents is not None:
with Pool(round(n_cpu * 0.75)) as pool:
synthetic_datasets = pool.map(utils.parse_contents, contents)
# correction different synthetic dataset sizes
synthetic_datasets = [
df.sample(n=min(len(df) for df in synthetic_datasets), random_state=80).reset_index(drop=True) for df in
synthetic_datasets]
encoder_label = LabelEncoder()
for syn_dataset in synthetic_datasets:
for col in syn_dataset.columns:
if syn_dataset[col].dtype == 'object':
syn_dataset[col] = encoder_label.fit_transform(syn_dataset[col])
# correction different dataset sizes
if not real_data.empty:
if real_data.shape[0] < synthetic_datasets[0].shape[0]:
synthetic_datasets = [df.sample(n=len(real_data), random_state=80).reset_index(drop=True) for df in
synthetic_datasets]
elif real_data.shape[0] > synthetic_datasets[0].shape[0]:
real_data = real_data.sample(n=synthetic_datasets[0].shape[0], random_state=80).reset_index(drop=True)
tabs = []
for i, df in enumerate(synthetic_datasets):
tab_content = dbc.Card([
dbc.CardBody([
dash_table.DataTable(
data=df.head(50).to_dict('records'),
columns=[{'name': col, 'id': col} for col in df.columns],
page_action='none',
style_table={'height': '300px',
'overflowY': 'auto',
'overflowX': 'auto'
},
fixed_rows={'headers': True},
style_header={'text-align': 'center'},
style_cell={'minWidth': 100, 'maxWidth': 100, 'width': 100},
)
])
], color="primary", outline=True)
tabs.append(dbc.Tab(tab_content, label=f"Dataset {i + 1}", tab_id=f"tab{i + 1}"))
children = [
html.Div([dbc.Tabs(tabs)], style={'width': '80vw', 'margin-left': '5vw', 'margin-right': '5vw'})
]
return children
@app.callback(Output('output-data-upload-type', 'children'),
Input('upload-data-type', 'contents'),
Input('output-data-upload-real', 'children'))
def update_table_type(contents, children):
global dict_features_type, num_features, cat_features
if ctx.triggered[0]['prop_id'] == 'upload-data-type.contents' and contents:
df = utils.parse_contents(contents)
elif ctx.triggered[0]['prop_id'] == 'output-data-upload-real.children' and children:
df = pd.DataFrame({'Feature': real_data.columns.tolist(), 'Type': ''})
for c in real_data.columns.tolist():
if (real_data[c].dtype == 'int64' and len(real_data[c].unique()) <= 10) or real_data[c].dtype == 'object':
df.loc[df['Feature'] == c, 'Type'] = 'categorical'
else:
df.loc[df['Feature'] == c, 'Type'] = 'numerical'
else:
raise PreventUpdate
dict_features_type = df.set_index("Feature")["Type"].to_dict()
num_features = [key for key, value in dict_features_type.items() if value == "numerical"]
cat_features = [key for key, value in dict_features_type.items() if value == "categorical"]
children = [
html.Div([
dash_table.DataTable(
id="table-type-dropdown",
data=df.to_dict('records'),
columns=[{'name': "Feature", 'id': "Feature"},
{'name': "Type", 'id': "Type", 'presentation': 'dropdown'}],
page_action='none',
style_table={'height': '300px',
'width': '60vw',
'margin-left': '15vw',
'margin-right': '15vw',
'overflowY': 'auto',
'overflowX': 'auto'
},
fixed_rows={'headers': True},
style_header={'text-align': 'center'},
editable=True,
dropdown={
"Type": {
'options': [
{'label': 'numerical', 'value': 'numerical'},
{'label': 'categorical', 'value': 'categorical'}
],
'clearable': False
},
},
css=[
{
"selector": ".Select-menu-outer",
"rule": 'display : block !important',
},
{
"selector": ".Select-value",
"rule": 'max-width : 95%;'
}
]
)
])
]
return children
@app.callback(Output('none-div', 'children'),
Input('table-type-dropdown', 'data'),
prevent_initial_call=True)
def update_dict_type(data):
global dict_features_type, num_features, cat_features
d = pd.DataFrame(data).set_index("Feature")["Type"].to_dict()
dict_features_type = dict(zip(dict_features_type.keys(), d.values()))
num_features = [key for key, value in dict_features_type.items() if value == "numerical"]
cat_features = [key for key, value in dict_features_type.items() if value == "categorical"]
return []
@app.callback(Output('nav2', 'disabled'),
Output('nav3', 'disabled'),
Output('nav4', 'disabled'),
Output('nav5', 'disabled'),
Input('output-data-upload-real', 'children'),
Input('output-data-upload-syn', 'children'),
Input('output-data-upload-type', 'children'))
def active_nav(r, s, t):
if not real_data.empty and synthetic_datasets and dict_features_type:
return False, False, False, False
else:
return True, True, True, True
# PAGE 2 CONTENTS (resemblance metrics)
# URA metrics
page_2_ura = html.Div([
dbc.Container(
[
# header URA section
dbc.Row(
dbc.Col(html.H3("Univariate Resemblance Analysis",
style={'margin-left': '1vw', 'margin-top': '1vw', 'margin-bottom': '2vw'}),
width="auto")),
# Dropdown URA numerical test
dbc.Row(
[
dbc.Col(html.Div(
[
dbc.Label("Choose statistical test", html_for="dropdown"),
dcc.Dropdown(
id="dropdown-test-num",
options=[
{"label": "Kolmogorov–Smirnov test", "value": "ks_test"},
{"label": "Student T-test", "value": "t_test"},
{"label": "Mann Whitney U-test", "value": "u_test"},
],
clearable=False
),
],
className="mb-3",
), width={'size': 3, 'offset': 1}),
dbc.Col(html.Div(["Features type: ", html.I("numerical")]), width={'size': 'auto'}, align="center")
]
),
# Table and graphs URA numerical test
dbc.Row(
[
dbc.Col(dcc.Loading(html.Div(id="output-table-pvalue-num")), width={'size': 3, 'offset': 1},
align="center"),
dbc.Col(html.Div(id="output-graph-num"), width={'size': 5, 'offset': 2}, align="center")
],
style={'margin-bottom': '2vw'}),
dcc.Store(id={"type": "data-report", "index": 0}),
# Dropdown URA categorical test
dbc.Row(
[
dbc.Col(html.Div(
[
dbc.Label("Choose statistical test", html_for="dropdown"),
dcc.Dropdown(
id="dropdown-test-cat",
options=[
{"label": "Chi-square test", "value": "chi_test"},
],
clearable=False
),
],
className="mb-3",
), width={'size': 3, 'offset': 1}),
dbc.Col(html.Div(["Features type: ", html.I("categorical")]), width={'size': 'auto'},
align="center")
]
),
# Table and graphs URA categorical test
dbc.Row(
[
dbc.Col(dcc.Loading(html.Div(id="output-table-pvalue-cat")), width={'size': 3, 'offset': 1},
align="center"),
dbc.Col(html.Div(id="output-graph-cat"), width={'size': 5, 'offset': 2}, align="center")
],
style={'margin-bottom': '2vw'}),
dcc.Store(id={"type": "data-report", "index": 1}),
# Dropdown URA metrics distance
dbc.Row(
[
dbc.Col(html.Div(
[
dbc.Label("Choose distance metric", html_for="dropdown"),
dcc.Dropdown(
id="dropdown-dist",
options=[
{"label": "Cosine distance", "value": "cos_dist"},
{"label": "Jensen-Shannon distance", "value": "js_dist"},
{"label": "Wasserstein distance", "value": "w_dist"},
],
clearable=False
),
],
className="mb-3",
), width={'size': 3, 'offset': 1}),
dbc.Col(html.Div(["Features type: ", html.I("numerical")]), width={'size': 'auto'},
align="center")
]
),
# Table URA distances
dbc.Row(
[
dbc.Col(dcc.Loading(html.Div(id="output-table-dist")), width={'size': 3, 'offset': 1},
align="center"),
],
),
dcc.Store(id={"type": "data-report", "index": 2}),
dbc.Row([dbc.Col(html.Div([html.Br(), html.Br()]), width=True)])
], fluid=True
)
])
@app.callback(Output('output-table-pvalue-num', 'children'),
Output({"type": "data-report", "index": 0}, 'data'),
Input('dropdown-test-num', 'value'),
prevent_initial_call=True)
def update_table_pvalue_num(value):
if value is not None:
if value == 'ks_test':
with Pool(round(n_cpu * 0.75)) as pool:
dict_pvalues = pool.starmap(ResMet.URA.ks_tests, zip(repeat(real_data[num_features]), synthetic_datasets))
elif value == 't_test':
with Pool(round(n_cpu * 0.75)) as pool:
dict_pvalues = pool.starmap(ResMet.URA.student_t_tests, zip(repeat(real_data[num_features]), synthetic_datasets))
elif value == 'u_test':
with Pool(round(n_cpu * 0.75)) as pool:
dict_pvalues = pool.starmap(ResMet.URA.mann_whitney_tests,
zip(repeat(real_data[num_features]), synthetic_datasets))
dfs = [pd.DataFrame(list(d.items()), columns=['Feature', 'p value']) for d in dict_pvalues]
tabs = []
for i, df in enumerate(dfs):
tab_content = dbc.Card([
dbc.CardBody([
dash_table.DataTable(
data=df.to_dict('records'),
columns=[{'name': "Feature", 'id': "Feature"}, {'name': "p value", 'id': "p value"}],
page_action='none',
style_table={'height': '300px',
'overflowY': 'auto',
},
fixed_rows={'headers': True},
style_header={'text-align': 'center'},
style_cell={'minWidth': '50%', 'maxWidth': '50%', 'width': '50%'},
style_data_conditional=[
{
'if': {
'filter_query': '{p value} <= 0.05',
},
'backgroundColor': '#e74c3c',
'color': 'white'
},
{
'if': {
'filter_query': '{p value} > 0.05',
},
'backgroundColor': '#18bc9c',
'color': 'white'
},
{
'if': {'state': 'active'},
'backgroundColor': '#003153',
'border': '1px solid blue'
}
],
id={"type": "tbl-pvalue-num", "index": i}
)
])
], color="primary", outline=True)
tabs.append(dbc.Tab(tab_content, label=f"Dataset {i + 1}", tab_id=f"tab{i + 1}"))
children = [
html.Div([dbc.Tabs(tabs, active_tab="tab1")])
]
return children, [dict_pvalues, {'selected_opt': value}]
else:
return [], []
@app.callback(Output('output-table-pvalue-cat', 'children'),
Output({"type": "data-report", "index": 1}, 'data'),
Input('dropdown-test-cat', 'value'),
prevent_initial_call=True)
def update_table_pvalue_cat(value):
if value is not None:
if value == 'chi_test':
with Pool(round(n_cpu * 0.75)) as pool:
dict_pvalues = pool.starmap(ResMet.URA.chi_squared_tests, zip(repeat(real_data[cat_features]), synthetic_datasets))
dfs = [pd.DataFrame(list(d.items()), columns=['Feature', 'p value']) for d in dict_pvalues]
tabs = []
for i, df in enumerate(dfs):
tab_content = dbc.Card([
dbc.CardBody([
dash_table.DataTable(
data=df.to_dict('records'),
columns=[{'name': "Feature", 'id': "Feature"}, {'name': "p value", 'id': "p value"}],
page_action='none',
style_table={'height': '300px',
'overflowY': 'auto',
},
fixed_rows={'headers': True},
style_header={'text-align': 'center'},
style_cell={'minWidth': '50%', 'maxWidth': '50%', 'width': '50%'},
style_data_conditional=[
{
'if': {
'filter_query': '{p value} > 0.05',
},
'backgroundColor': '#e74c3c',
'color': 'white'
},
{
'if': {
'filter_query': '{p value} <= 0.05',
},
'backgroundColor': '#18bc9c',
'color': 'white'
},
{
'if': {'state': 'active'},
'backgroundColor': '#003153',
'border': '1px solid blue'
}
],
id={"type": "tbl-pvalue-cat", "index": i}
)
])
], color="primary", outline=True)
tabs.append(dbc.Tab(tab_content, label=f"Dataset {i + 1}", tab_id=f"tab{i + 1}"))
children = [
html.Div([dbc.Tabs(tabs, active_tab="tab1")])
]
return children, [dict_pvalues, {'selected_opt': value}]
else:
return [], []
@app.callback(Output('output-table-dist', 'children'),
Output({"type": "data-report", "index": 2}, 'data'),
Input('dropdown-dist', 'value'),
prevent_initial_call=True)
def update_table_dist(value):
if value is not None:
if value == 'cos_dist':
with Pool(round(n_cpu * 0.75)) as pool:
dict_distances = pool.starmap(ResMet.URA.cosine_distances,
zip(repeat(real_data[num_features]), synthetic_datasets))
elif value == 'js_dist':
with Pool(round(n_cpu * 0.75)) as pool:
dict_distances = pool.starmap(ResMet.URA.js_distances,
zip(repeat(real_data[num_features]), synthetic_datasets))
elif value == 'w_dist':
with Pool(round(n_cpu * 0.75)) as pool:
dict_distances = pool.starmap(ResMet.URA.wass_distances,
zip(repeat(real_data[num_features]), synthetic_datasets))
dfs = [pd.DataFrame(list(d.items()), columns=['Feature', 'Distance value']) for d in dict_distances]
tabs = []
for i, df in enumerate(dfs):
tab_content = dbc.Card([
dbc.CardBody([
dash_table.DataTable(
data=df.to_dict('records'),
columns=[{'name': "Feature", 'id': "Feature"},
{'name': "Distance value", 'id': "Distance value"}],
page_action='none',
sort_action="native",
sort_mode="multi",
style_table={'height': '300px',
'overflowY': 'auto',
},
fixed_rows={'headers': True},
style_header={'text-align': 'center'},
style_cell={'minWidth': '50%', 'maxWidth': '50%', 'width': '50%'},
id='tbl-dist'
)
])
], color="primary", outline=True)
tabs.append(dbc.Tab(tab_content, label=f"Dataset {i + 1}", tab_id=f"tab{i + 1}"))
children = [
html.Div([dbc.Tabs(tabs, active_tab="tab1")])
]
return children, [dict_distances, {'selected_opt': value}]
else:
return [], []
def create_fig_ura(col_real, col_syn, feature_name, feature_type):
if feature_type == "numerical":
fig_real = ff.create_distplot([col_real], ['Original Data'], show_hist=False, show_rug=False, colors=['blue'])
fig_syn = ff.create_distplot([col_syn], ['Synthetic Data'], show_hist=False, show_rug=False, colors=['red'])
fig_real.update_traces(fill='tozeroy', fillcolor='rgba(0, 0, 255, 0.5)', selector=dict(type='scatter'))
fig_syn.update_traces(fill='tozeroy', fillcolor='rgba(255, 0, 0, 0.5)', selector=dict(type='scatter'))
fig = fig_real.add_traces(fig_syn.data)
else:
plot_data = pd.DataFrame({
'value': col_real + col_syn,
'variable': ['Original Data'] * len(col_real) + ['Synthetic Data'] * len(col_syn)
})
fig = px.histogram(plot_data, x="value", color='variable', barmode='group', histnorm='percent',
color_discrete_map={'Original Data': 'blue', 'Synthetic Data': 'red'}, opacity=0.9)
fig.update_layout(title_text=feature_name, showlegend=True)
return fig
@app.callback(Output('output-graph-num', 'children'),
Input({"type": "tbl-pvalue-num", "index": ALL}, 'active_cell'),
prevent_initial_call=True)
def update_graphs(active_cell):
if any(active_cell):
selected_feature = num_features[active_cell[ctx.triggered_id.index]['row']]
col_real = real_data[selected_feature].tolist()
col_syn = synthetic_datasets[ctx.triggered_id.index][selected_feature].tolist()
fig = create_fig_ura(col_real, col_syn, selected_feature, "numerical")
children = [
html.Div([
dcc.Graph(figure=fig)
])
]
else:
children = [
html.Div([
dcc.Graph(
figure={
'data': [],
'layout': {
'annotations': [{
'x': 0.5,
'y': 0.5,
'xref': 'paper',
'yref': 'paper',
'text': 'Click on an adjacent table cell to view the corresponding chart,<br>which '
'compares real data with synthetic data for the selected feature.',
'showarrow': False,
'font': {'size': 16},
'xanchor': 'center',
'yanchor': 'middle',
'bgcolor': 'rgba(255, 255, 255, 0.7)',
'bordercolor': 'rgba(0, 0, 0, 0.2)',
'borderwidth': 1,
'borderpad': 4,
'borderline': {'width': 2}
}],
'xaxis': {'visible': False},
'yaxis': {'visible': False},
'template': 'plotly_white'
}
},
config={'displayModeBar': False}
)
])
]
return children
@app.callback(Output('output-graph-cat', 'children'),
Input({"type": "tbl-pvalue-cat", "index": ALL}, 'active_cell'),
prevent_initial_call=True)
def update_graphs(active_cell):
if any(active_cell):
selected_feature = cat_features[active_cell[ctx.triggered_id.index]['row']]
col_real = real_data[selected_feature].tolist()
col_syn = synthetic_datasets[ctx.triggered_id.index][selected_feature].tolist()
fig = create_fig_ura(col_real, col_syn, selected_feature, "categorical")
children = [
html.Div([
dcc.Graph(figure=fig)
])
]
else:
children = [
html.Div([
dcc.Graph(
figure={
'data': [],
'layout': {
'annotations': [{
'x': 0.5,
'y': 0.5,
'xref': 'paper',
'yref': 'paper',
'text': 'Click on an adjacent table cell to view the corresponding chart,<br>which '
'compares real data with synthetic data for the selected feature.',
'showarrow': False,
'font': {'size': 16},
'xanchor': 'center',
'yanchor': 'middle',
'bgcolor': 'rgba(255, 255, 255, 0.7)',
'bordercolor': 'rgba(0, 0, 0, 0.2)',
'borderwidth': 1,
'borderpad': 4,
'borderline': {'width': 2}
}],
'xaxis': {'visible': False},
'yaxis': {'visible': False},
'template': 'plotly_white'
}
},
config={'displayModeBar': False}
)
])
]
return children
# MRA metrics
page_2_mra = html.Div([
dbc.Container(
[
# Header MRA section
dbc.Row(
dbc.Col(html.H3("Multivariate Relationship Analysis",
style={'margin-left': '1vw', 'margin-top': '1vw', 'margin-bottom': '1vw'}),
width="auto")),
dbc.Row(
dbc.Col(html.H4("Comparison correlation matrices",
style={'margin-left': '1.5vw', 'margin-top': '2vw', 'margin-bottom': '1.5vw'}),
width="auto")),
# Dropdown numerical matrices/categorical matrices, Radio for graphical visualization
dbc.Row(
[
dbc.Col(html.Div(
[
dbc.Label("Choose correlation matrix type", html_for="dropdown"),
dcc.Dropdown(
id="dropdown-corr-mat",
options=[
{"label": "Pairwise Pearson correlation matrices", "value": "corr_num"},
{"label": "Normalized contingency tables", "value": "corr_cat"},
],
clearable=False
),
],
className="mb-3",
), width={'size': 3, 'offset': 1}),
dbc.Col(html.Div(["Features type: ", html.I(id="label-type")]),
width={'size': 2}, align="center"),
dbc.Col(html.Div(
[
dbc.RadioItems(
id="radios-mat",
className="btn-group",
inputClassName="btn-check",
labelClassName="btn btn-outline-primary",
labelCheckedClassName="active",
options=[
{"label": "Real vs Syn", "value": "rs"},
{"label": "Differences", "value": "diff"},
],
value="rs",
)
], className="btn-group"
), width={'size': 3}, align="center")
]
),
# Correlation matrices
dbc.Row(
[
dbc.Col(dcc.Loading(html.Div(id="output-corr-mat")), width={'size': 8, 'offset': 2}, align="center")
],
),
dcc.Store(id={"type": "data-report", "index": 3}),
dbc.Row(
dbc.Col([html.H4(["Comparison Outliers ",
html.Span("ℹ", id="info-outlier", style={'cursor': 'pointer'})],
style={'margin-left': '1.5vw', 'margin-top': '7vw', 'margin-bottom': '0vw'},
id="title-outlier"),
dbc.Tooltip(
"Unsupervised Outlier Detection using the Local Outlier Factor. It measures the "
"local deviation of the density of a given sample with respect to its neighbors.",
target="info-outlier",
)],
width="auto")),
# Boxplot LOF scores
dbc.Row(
[
dbc.Col(dcc.Loading(html.Div(id="output-boxplot")), width={'size': 8, 'offset': 2}, align="center")
],
),
dcc.Store(id={"type": "data-report", "index": 4}),
dbc.Row(
dbc.Col(html.H4("Comparison Principal Component Analysis",
style={'margin-left': '1.5vw', 'margin-top': '7vw', 'margin-bottom': '0vw'},
id="title-pca"),
width="auto")),
# PCA
dcc.Loading(dbc.Row(
[
dbc.Col(html.Div(id="output-pca"), width={'size': 6, 'offset': 1}, align="center"),
dbc.Col(html.Div(id="output-table-pca"), width={'size': 3, 'offset': 1}, align="center")
],
)),
dcc.Store(id={"type": "data-report", "index": 5}),
dbc.Row(
dbc.Col(html.H4("Comparison UMAP visualization",
style={'margin-left': '1.5vw', 'margin-top': '7vw', 'margin-bottom': '1.5vw'}),
width="auto")),
# Radio for UMAP visualization, input meta-parameters
dbc.Form(
dbc.Row(
[
dbc.Col(html.Div(
[
dbc.RadioItems(
id="radios-umap",