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Commit da034c90 authored by Caroline DE POURTALES's avatar Caroline DE POURTALES
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pickle done, cleaning, customizing to do

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.gitignore
+ 2
− 1
View file @ da034c90
......@@ -7,4 +7,5 @@ decision_tree_classifier_20170212.pkl
push_command
adult.pkl
adult_data_00000.inst
iris_00000.txt
\ No newline at end of file
iris_00000.txt
tests
\ No newline at end of file
app.py
+ 37
− 88
View file @ da034c90
......@@ -4,103 +4,52 @@ import json
import dash
import dash_bootstrap_components as dbc
import pandas as pd
from dash import Input, Output, State, dcc, html
from dash.exceptions import PreventUpdate
from dash import dcc, html
from pages.application.layout_application import Model, View
from utils import extract_data, parse_contents_instance, parse_contents_tree
from callbacks import register_callbacks
from pages.application.application import Application, Model, View
from utils import extract_data
'''
Loading data
'''
app = dash.Dash(external_stylesheets=[dbc.themes.LUX], suppress_callback_exceptions=True)
#################################################################################
############################# Layouts ###########################################
#################################################################################
models_data = open('data_retriever.json')
data = json.load(models_data)["data"]
app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
'''
Construction of the layout
'''
#For home directory
page_home = dbc.Row([])
#For course directory
page_course = dbc.Row([])
#For the application
names_models, dict_components = extract_data(data)
model = Model(names_models, dict_components)
view = View(model)
tabs = dcc.Tabs([
dcc.Tab(label='Course on Explainable AI', children=[]),
view.tab,
])
model_application = Model(names_models, dict_components)
view_application = View(model_application)
page_application = Application(view_application)
app.layout = html.Div([
html.H1('FXToolKit'),
tabs])
'''
Callback for the app
'''
@app.callback(
Output('dataset_filename', 'children'),
Output('instance_filename', 'children'),
Output('graph', 'children'),
Output('explanation', 'children'),
Input('ml_model_choice', 'value'),
Input('ml_dataset_choice', 'contents'),
Input('ml_instance_choice', 'contents'),
Input('number_explanations', 'value'),
Input('explanation_type', 'value'),
Input('solver_sat', 'value'),
State('ml_dataset_choice', 'filename'),
State('ml_instance_choice', 'filename'),
prevent_initial_call=True
)
def update_ml_type(value_ml_model, dataset_contents, instance_contents, enum, xtype, solver, dataset_filename, instance_filename):
ctx = dash.callback_context
if ctx.triggered:
ihm_id = ctx.triggered[0]['prop_id'].split('.')[0]
if ihm_id == 'ml_model_choice' :
model.update_ml_model(value_ml_model)
return "", "", "", ""
elif ihm_id == 'ml_dataset_choice':
if value_ml_model == None :
raise PreventUpdate
tree, typ = parse_contents_tree(dataset_contents, dataset_filename)
model.update_dataset(tree, typ)
return dataset_filename, "", model.component.network, ""
elif ihm_id == 'ml_instance_choice' :
if value_ml_model == None or dataset_contents == None or enum == None or xtype==None:
raise PreventUpdate
instance = parse_contents_instance(instance_contents, instance_filename)
model.update_instance(instance, enum, xtype)
return dataset_filename, instance_filename, model.component.network, model.component.explanation
elif ihm_id == 'number_explanations' :
if value_ml_model == None or dataset_contents == None or instance_contents == None or xtype==None:
raise PreventUpdate
instance = parse_contents_instance(instance_contents, instance_filename)
model.update_instance(instance, enum, xtype)
return dataset_filename, instance_filename, model.component.network, model.component.explanation
elif ihm_id == 'explanation_type' :
if value_ml_model == None or dataset_contents == None or instance_contents == None or enum == None :
raise PreventUpdate
instance = parse_contents_instance(instance_contents, instance_filename)
model.update_instance(instance, enum, xtype)
return dataset_filename, instance_filename, model.component.network, model.component.explanation
elif ihm_id == 'solver_sat' :
if value_ml_model == None or dataset_contents == None or instance_contents == None or enum == None or xtype == None:
raise PreventUpdate
instance = parse_contents_instance(instance_contents, instance_filename)
model.update_instance(instance, enum, xtype, solver=solver)
return dataset_filename, instance_filename, model.component.network, model.component.explanation
dcc.Location(id='url', refresh=False),
html.Nav(id='navbar-container',
children=[dbc.NavbarSimple(
children=[
dbc.NavItem(dbc.NavLink("Home", id="home-link", href="/")),
dbc.NavItem(dbc.NavLink("Course", id="course-link", href="/course")),
dbc.NavItem(dbc.NavLink("Application on explainable AI", id="application-link", href="/application")),
],
brand="FX ToolKit",
color="primary",
dark=True,)]),
html.Div(id='page-content')
])
#################################################################################
################################# Callback for the app ##########################
#################################################################################
register_callbacks(page_home, page_course, page_application, app)
'''
Launching app
'''
#################################################################################
################################# Launching app #################################
#################################################################################
if __name__ == '__main__':
app.run_server(debug=True)
assets/header.css 0 → 100644
+ 103
− 0
View file @ da034c90
/* NAVBAR */
.navbar-dark .navbar-brand {
color: #fff;
font-size: 30px;
}
/* SIDEBAR */
.sidebar {
padding: 2rem;
padding-top:0.5rem;
color: rgb(255, 255, 255);
font-weight: 300;
background-color: black;
}
.sidebar .tab.jsx-3468109796 {
color:rgb(255, 255, 255);
font-weight: 500;
background-color: #1a1c1d;
}
.sidebar .tab--selected.jsx-3468109796:hover {
background-color:gray;
}
.sidebar .upload {
width: 100%;
height: 50px;
line-height: 50px;
border-width: 1px;
border-style: dashed;
border-radius: 5px;
text-align: center;
margin: 10px
}
.sidebar .Select-control {
width: 100%;
height: 30px;
line-height: 30px;
border-width: 1px;
border-radius: 5px;
text-align: center;
margin: 10px;
color:rgb(255, 255, 255);
font-weight: 400;
background-color: black;
}
.sidebar .sidebar-dropdown{
width: 100%;
height: 30px;
line-height: 30px;
border-width: 1px;
border-radius: 5px;
text-align: center;
margin: 10px;
color:rgb(255, 255, 255);
font-weight: 400;
background-color: black;
}
.sidebar .has-value.Select--single > .Select-control .Select-value .Select-value-label, .has-value.is-pseudo-focused.Select--single > .sidebar .Select-control .Select-value .Select-value-label {
color:rgb(255, 255, 255);
}
.sidebar .Select-menu-outer{
width: 100%;
border-width: 1px;
border-radius: 5px;
text-align: center;
margin: 10px;
color:rgb(255, 255, 255);
font-weight: 400;
background-color: black;
}
/* EXPLANATION */
main#explanation {
width: 95%;
margin-bottom: 5rem;
margin-top: 5rem;
border-width: 4px;
border-style:double;
border-radius: 5px;
padding: 2rem;
border-radius: 10px;
}
/* GRAPH */
.column_graph {
margin-top: 5rem;
}
assets/typography.css 0 → 100644
+ 21
− 0
View file @ da034c90
body {
font-family: sans-serif;
}
H4 {
font-size: 20px;
text-decoration-line:underline;
text-decoration-thickness:2px;
text-decoration-style:solid;
color: hsl(229, 58%, 19%)
}
H5 {
font-size: 16px;
color: hsl(228, 58%, 12%);
}
p {
font-size: 15px;
color: hsl(0, 0%, 0%)
}
\ No newline at end of file
callbacks.py 0 → 100644
+ 116
− 0
View file @ da034c90
import dash
import pandas as pd
from dash import Input, Output, State
from dash.dependencies import Input, Output, State
from dash.exceptions import PreventUpdate
from utils import parse_contents_graph, parse_contents_instance
def register_callbacks(page_home, page_course, page_application, app):
page_list = ['home', 'course', 'application']
@app.callback(
Output('page-content', 'children'),
Input('url', 'pathname'))
def display_page(pathname):
if pathname == '/':
return page_home
if pathname == '/application':
return page_application.view.layout
if pathname == '/course':
return page_course
@app.callback(Output('home-link', 'active'),
Output('course-link', 'active'),
Output('application-link', 'active'),
Input('url', 'pathname'))
def navbar_state(pathname):
active_link = ([pathname == f'/{i}' for i in page_list])
return active_link[0], active_link[1], active_link[2]
@app.callback(
Output('pretrained_model_filename', 'children'),
Output('instance_filename', 'children'),
Output('graph', 'children'),
Output('explanation', 'children'),
Input('ml_model_choice', 'value'),
Input('ml_pretrained_model_choice', 'contents'),
State('ml_pretrained_model_choice', 'filename'),
Input('ml_instance_choice', 'contents'),
State('ml_instance_choice', 'filename'),
Input('number_explanations', 'value'),
Input('explanation_type', 'value'),
Input('solver_sat', 'value'),
Input('expl_choice', 'value'),
prevent_initial_call=True
)
def update_ml_type(value_ml_model, pretrained_model_contents, pretrained_model_filename, instance_contents, instance_filename, enum, xtype, solver, expl_choice):
ctx = dash.callback_context
if ctx.triggered:
ihm_id = ctx.triggered[0]['prop_id'].split('.')[0]
model_application = page_application.model
if ihm_id == 'ml_model_choice' :
model_application.update_ml_model(value_ml_model)
return None, None, None, None
elif ihm_id == 'ml_pretrained_model_choice':
if value_ml_model is None :
raise PreventUpdate
tree, typ = parse_contents_graph(pretrained_model_contents, pretrained_model_filename)
model_application.update_pretrained_model(tree, typ)
return pretrained_model_filename, None, model_application.component.network, None
elif ihm_id == 'ml_instance_choice' :
if value_ml_model is None or pretrained_model_contents is None or enum is None or xtype is None:
raise PreventUpdate
instance = parse_contents_instance(instance_contents, instance_filename)
model_application.update_instance(instance, enum, xtype)
return pretrained_model_filename, instance_filename, model_application.component.network, model_application.component.explanation
elif ihm_id == 'number_explanations' :
if value_ml_model is None or pretrained_model_contents is None or instance_contents is None or xtype is None:
raise PreventUpdate
instance = parse_contents_instance(instance_contents, instance_filename)
model_application.update_instance(instance, enum, xtype)
return pretrained_model_filename, instance_filename, model_application.component.network, model_application.component.explanation
elif ihm_id == 'explanation_type' :
if value_ml_model is None or pretrained_model_contents is None or instance_contents is None or enum is None :
raise PreventUpdate
instance = parse_contents_instance(instance_contents, instance_filename)
model_application.update_instance(instance, enum, xtype)
return pretrained_model_filename, instance_filename, model_application.component.network, model_application.component.explanation
elif ihm_id == 'solver_sat' :
if value_ml_model is None or pretrained_model_contents is None or instance_contents is None or enum is None or xtype is None:
raise PreventUpdate
instance = parse_contents_instance(instance_contents, instance_filename)
model_application.update_instance(instance, enum, xtype, solver=solver)
return pretrained_model_filename, instance_filename, model_application.component.network, model_application.component.explanation
elif ihm_id == 'expl_choice' :
if instance_contents is None :
raise PreventUpdate
model_application.update_expl(expl_choice)
return pretrained_model_filename, instance_filename, model_application.component.network, model_application.component.explanation
@app.callback(
Output('explanation', 'hidden'),
Output('navigate_label', 'hidden'),
Output('navigate_dropdown', 'hidden'),
Output('expl_choice', 'options'),
Input('explanation', 'children'),
Input('explanation_type', 'value'),
prevent_initial_call=True
)
def layout_buttons_navigate_expls(explanation, explanation_type):
if explanation is None or "AXp" not in explanation_type:
return True, True, True, {}
else :
options = {}
model_application = page_application.model
for i in range (len(model_application.list_expls)):
options[str(model_application.list_expls[i])] = model_application.list_expls[i]
return False, False, False, options
pages/__init__.py 0 → 100644
+ 0
− 0
View file @ da034c90
pages/application/DecisionTree/DecisionTreeComponent.py
+ 47
− 29
View file @ da034c90
from dash import dcc
from pages.application.DecisionTree.utils.dtviz import visualize, visualize_instance
from pages.application.DecisionTree.utils.dtree import DecisionTree
from os import path
import dash_bootstrap_components as dbc
import dash_interactive_graphviz
import numpy as np
from dash import dcc, html
from pages.application.DecisionTree.utils.dtree import DecisionTree
from pages.application.DecisionTree.utils.dtviz import (visualize,
visualize_expl,
visualize_instance)
import os.path
from os import path
import numpy as np
class DecisionTreeComponent():
def __init__(self, tree, typ_data):
if typ_data == "dt" :
self.dt = DecisionTree(from_dt = tree)
elif typ_data == "pkl" :
self.dt = DecisionTree(from_pickle = tree)
self.dt = DecisionTree(from_pickle = tree)
dot_source = visualize(self.dt)
self.network = dash_interactive_graphviz.DashInteractiveGraphviz(
dot_source=dot_source
)
self.explanation = dcc.Textarea(value = "", style = { "font_size" : "15px",
"width": "40rem",
"height": "40rem",
"margin-bottom": "5rem",
"background-color": "#f8f9fa",
})
self.network = [dbc.Row(dash_interactive_graphviz.DashInteractiveGraphviz(dot_source=dot_source, style = {"width": "60%",
"height": "90%",
"background-color": "transparent"}))]
self.explanation = []
def update_with_explicability(self, instance, enum, xtype, solver) :
instance = str(instance).strip().split(',')
instance = list(map(lambda i: tuple([i[0], np.float32(i[1])]), [i.split('=') for i in instance]))
dot_source = visualize_instance(self.dt, instance)
self.network = dash_interactive_graphviz.DashInteractiveGraphviz(
dot_source=dot_source
)
self.explanation.value = self.dt.explain(instance, enum=enum, xtype = xtype, solver=solver)
self.network = [dbc.Row(dash_interactive_graphviz.DashInteractiveGraphviz(
dot_source=dot_source, style = {"width": "50%",
"height": "80%",
"background-color": "transparent"}
))]
self.explanation = []
list_explanations_path=[]
explanation = self.dt.explain(instance, enum=enum, xtype = xtype, solver=solver)
#Creating a clean and nice text component
for k in explanation.keys() :
if k != "List of path explanation(s)":
if k in ["List of abductive explanation(s)","List of contrastive explanation(s)"] :
self.explanation.append(html.H4(k))
for expl in explanation[k] :
self.explanation.append(html.Hr())
self.explanation.append(html.P(expl))
self.explanation.append(html.Hr())
else :
self.explanation.append(html.P(k + explanation[k]))
else :
list_explanations_path = explanation["List of path explanation(s)"]
return list_explanations_path
def draw_explanation(self, instance, expl) :
dot_source = visualize_expl(self.dt, instance, expl)
self.network = [dbc.Row(dash_interactive_graphviz.DashInteractiveGraphviz(
dot_source=dot_source,
style = {"width": "50%",
"height": "80%",
"background-color": "transparent"}))]
pages/application/DecisionTree/utils/dtree.py
+ 44
− 31
View file @ da034c90
......@@ -11,21 +11,26 @@
#
#==============================================================================
from __future__ import print_function
import collections
from functools import reduce
import sklearn
from pysat.card import *
from pysat.examples.hitman import Hitman
from pysat.formula import CNF, IDPool
from pysat.solvers import Solver
import sklearn
from torch import threshold
try: # for Python2
from cStringIO import StringIO
except ImportError: # for Python3
from io import StringIO
from sklearn.tree import _tree
import numpy as np
from dash import dcc, html
from sklearn.tree import _tree
#
#==============================================================================
......@@ -81,7 +86,7 @@ class DecisionTree():
def from_pickle_file(self, tree):
#help(_tree.Tree)
self.tree_ = tree.tree_
print(sklearn.tree.export_text(tree))
#print(sklearn.tree.export_text(tree))
try:
feature_names = tree.feature_names_in_
except:
......@@ -132,7 +137,7 @@ class DecisionTree():
Traverse the tree and extract explicit paths.
"""
if root in self.terms:
if root in self.terms.keys():
# store the path
term = self.terms[root]
self.paths[term].append(prefix)
......@@ -159,7 +164,7 @@ class DecisionTree():
sets = []
for t, paths in self.paths.items():
# ignoring the right class
if t == term:
if term in self.terms.keys() and self.terms[term] == t:
continue
# computing the sets to hit
......@@ -190,11 +195,15 @@ class DecisionTree():
inst_dic = {}
for i in range(len(inst)):
inst_dic[inst[i][0]] = np.float32(inst[i][1])
inst_orig = inst[:]
path, term = self.execute(inst_values)
explanation = str(inst_dic) + "\n \n"
decision_path_str = "c inst : IF : "
#contaiins all the elements for explanation
explanation_dic = {}
#instance plotting
explanation_dic["Instance : "] = str(inst_dic)
#decision path
decision_path_str = "IF : "
for node_id in path:
# continue to the next node if it is a leaf node
if term == node_id:
......@@ -207,43 +216,45 @@ class DecisionTree():
threshold=self.nodes[node_id].threshold)
decision_path_str += "THEN " + str(self.terms[term])
explanation += decision_path_str + "\n \n"
explanation +='c path len:'+ str(len(path))+ "\n \n \n"
explanation_dic["Decision path of instance : "] = decision_path_str
explanation_dic["Decision path length : "] = 'Path length is :'+ str(len(path))
# computing the sets to hit
to_hit = self.prepare_sets(inst_dic, term)
for type in xtype :
if type == "AXp":
explanation += "Abductive explanation : " + "\n \n"
explanation += self.enumerate_abductive(to_hit, enum, solver, htype, term)+ "\n \n"
explanation_dic.update(self.enumerate_abductive(to_hit, enum, solver, htype, term))
else :
explanation += "Contrastive explanation : "+ "\n \n"
explanation += self.enumerate_contrastive(to_hit, term)+ "\n \n"
explanation_dic.update(self.enumerate_contrastive(to_hit, term))
return explanation
return explanation_dic
def enumerate_abductive(self, to_hit, enum, solver, htype, term):
"""
Enumerate abductive explanations.
"""
explanation = ""
list_expls = []
list_expls_str = []
explanation = {}
with Hitman(bootstrap_with=to_hit, solver='m22', htype=htype) as hitman:
expls = []
for i, expl in enumerate(hitman.enumerate(), 1):
explanation += 'c expl: IF {0} THEN class={1}'.format(' AND '.join(["(inst[{feature}] = {value}) {inequality} {threshold})".format(feature=p[0],
list_expls.append([ p[0] + p[2] + p[3] for p in expl])
list_expls_str.append('Explanation: IF {0} THEN class={1}'.format(' AND '.join(["(inst[{feature}] = {value}) {inequality} {threshold})".format(feature=p[0],
value=p[1],
inequality=p[2],
threshold=p[3])
for p in sorted(expl, key=lambda p: p[0])]), str(self.terms[term]))+ "\n"
for p in sorted(expl, key=lambda p: p[0])]), str(self.terms[term])))
expls.append(expl)
if i == enum:
break
explanation += 'c nof expls:' + str(i)+ "\n"
explanation += 'c min expl:'+ str( min([len(e) for e in expls]))+ "\n"
explanation += 'c max expl:'+ str( max([len(e) for e in expls]))+ "\n"
explanation += 'c avg expl: {0:.2f}'.format(sum([len(e) for e in expls]) / len(expls))+ "\n \n \n"
explanation["List of path explanation(s)"] = list_expls
explanation["List of abductive explanation(s)"] = list_expls_str
explanation["Number of abductive explanation(s) : "] = str(i)
explanation["Minimal abductive explanation : "] = str( min([len(e) for e in expls]))
explanation["Maximal abductive explanation : "] = str( max([len(e) for e in expls]))
explanation["Average abductive explanation : "] = '{0:.2f}'.format(sum([len(e) for e in expls]) / len(expls))
return explanation
......@@ -263,15 +274,17 @@ class DecisionTree():
to_hit = [set(s) for s in to_hit]
to_hit.sort(key=lambda s: len(s))
expls = list(reduce(process_set, to_hit, []))
explanation = ""
list_expls_str = []
explanation = {}
for expl in expls:
explanation += 'c expl: IF {0} THEN class!={1}'.format(' OR '.join(["inst[{feature}] {inequality} {threshold})".format(feature=p[0],
list_expls_str.append('Contrastive: IF {0} THEN class!={1}'.format(' OR '.join(["inst[{feature}] {inequality} {threshold})".format(feature=p[0],
inequality="<=" if p[2]==">" else ">",
threshold=p[3])
for p in sorted(expl, key=lambda p: p[0])]), str(self.terms[term]))+ "\n"
explanation +='c nof expls:'+ str(len(expls))+ "\n"
explanation +='c min expl:'+ str( min([len(e) for e in expls]))+ "\n"
explanation +='c max expl:'+ str( max([len(e) for e in expls]))+ "\n"
explanation +='c avg expl: {0:.2f}'.format(sum([len(e) for e in expls]) / len(expls))+ "\n"
for p in sorted(expl, key=lambda p: p[0])]), str(self.terms[term])))
explanation["List of contrastive explanation(s)"] = list_expls_str
explanation["Number of contrastive explanation(s) : "]=str(len(expls))
explanation["Minimal contrastive explanation : "]= str( min([len(e) for e in expls]))
explanation["Maximal contrastive explanation : "]= str( max([len(e) for e in expls]))
explanation["Average contrastive explanation : "]='{0:.2f}'.format(sum([len(e) for e in expls]) / len(expls))
return explanation
\ No newline at end of file
return explanation
pages/application/DecisionTree/utils/dtviz.py
+ 96
− 9
View file @ da034c90
......@@ -8,22 +8,40 @@
## E-mail: alexey.ignatiev@monash.edu
##
#
#==============================================================================
from pages.application.DecisionTree.utils.dtree import DecisionTree
import pygraphviz
import numpy as np
import pandas as pd
import pygraphviz
#
#==============================================================================
def create_legend(g):
legend = g.subgraphs()[-1]
legend.add_node("a", style = "invis")
legend.add_node("b", style = "invis")
legend.add_node("c", style = "invis")
legend.add_node("d", style = "invis")
legend.add_edge("a","b")
edge = legend.get_edge("a","b")
edge.attr["label"] = "instance"
edge.attr["style"] = "dashed"
legend.add_edge("c","d")
edge = legend.get_edge("c","d")
edge.attr["label"] = "instance with explanation"
edge.attr["color"] = "blue"
edge.attr["style"] = "dashed"
def visualize(dt):
"""
Visualize a DT with graphviz.
"""
g = pygraphviz.AGraph(directed=True, strict=True)
g = pygraphviz.AGraph(name='root', rankdir="TB")
g.is_directed()
g.is_strict()
#g = pygraphviz.AGraph(name = "main", directed=True, strict=True)
g.edge_attr['dir'] = 'forward'
g.graph_attr['rankdir'] = 'TB'
# non-terminal nodes
for n in dt.nodes:
......@@ -56,6 +74,9 @@ def visualize(dt):
edge.attr['fontsize'] = 10
edge.attr['arrowsize'] = 0.8
g.add_subgraph(name='legend')
create_legend(g)
# saving file
g.layout(prog='dot')
return(g.string())
......@@ -102,7 +123,7 @@ def visualize_instance(dt, instance):
edge.attr['arrowsize'] = 0.8
#instance path in blue
if ((n1,children_left) in edges_instance):
edge.attr['color'] = 'blue'
edge.attr['style'] = 'dashed'
children_right = dt.nodes[n1].children_right
g.add_edge(n1, children_right)
......@@ -112,8 +133,74 @@ def visualize_instance(dt, instance):
edge.attr['arrowsize'] = 0.8
#instance path in blue
if ((n1,children_right) in edges_instance):
edge.attr['color'] = 'blue'
edge.attr['style'] = 'dashed'
g.add_subgraph(name='legend')
create_legend(g)
# saving file
g.layout(prog='dot')
return(g.to_string())
#
#==============================================================================
def visualize_expl(dt, instance, expl):
"""
Visualize a DT with graphviz and plot the running instance.
"""
g = pygraphviz.AGraph(directed=True, strict=True)
g.edge_attr['dir'] = 'forward'
g.graph_attr['rankdir'] = 'TB'
# non-terminal nodes
for n in dt.nodes:
g.add_node(n, label=str(dt.nodes[n].feat))
node = g.get_node(n)
node.attr['shape'] = 'circle'
node.attr['fontsize'] = 13
# terminal nodes
for n in dt.terms:
g.add_node(n, label=str(dt.terms[n]))
node = g.get_node(n)
node.attr['shape'] = 'square'
node.attr['fontsize'] = 13
#path that follows the instance - colored in blue
instance = [np.float32(i[1]) for i in instance]
path, term_id_node = dt.execute(instance)
edges_instance = []
for i in range (len(path)-1) :
edges_instance.append((path[i], path[i+1]))
for n1 in dt.nodes:
threshold = dt.nodes[n1].threshold
children_left = dt.nodes[n1].children_left
g.add_edge(n1, children_left)
edge = g.get_edge(n1, children_left)
edge.attr['label'] = str(dt.nodes[n1].feat) + "<=" + str(threshold)
edge.attr['fontsize'] = 10
edge.attr['arrowsize'] = 0.8
#instance path in blue
if ((n1,children_left) in edges_instance):
edge.attr['style'] = 'dashed'
if edge.attr['label'] in expl :
edge.attr['color'] = 'blue'
children_right = dt.nodes[n1].children_right
g.add_edge(n1, children_right)
edge = g.get_edge(n1, children_right)
edge.attr['label'] = str(dt.nodes[n1].feat) + ">" + str(threshold)
edge.attr['fontsize'] = 10
edge.attr['arrowsize'] = 0.8
#instance path in blue
if ((n1,children_right) in edges_instance):
edge.attr['style'] = 'dashed'
if edge.attr['label'] in expl :
edge.attr['color'] = 'blue'
g.add_subgraph(name='legend')
create_legend(g)
g.layout(prog='dot')
return(g.to_string())
pages/application/layout_application.py → pages/application/application.py
+ 49
− 50
View file @ da034c90
from dash import dcc, html
import dash
import dash_bootstrap_components as dbc
from pages.application.DecisionTree.DecisionTreeComponent import DecisionTreeComponent
SIDEBAR_STYLE = {
}
class Application():
def __init__(self, view):
self.view = view
self.model = view.model
CONTENT_STYLE = {
}
class Model():
......@@ -19,11 +18,14 @@ class Model():
self.ml_models = names_models
self.ml_model = ''
self.dataset = ''
self.pretrained_model = ''
self.typ_data = ''
self.instance = ''
self.list_expls = []
self.expl_path = []
self.component_class = ''
self.component = ''
......@@ -32,41 +34,38 @@ class Model():
self.component_class = self.dict_components[self.ml_model]
self.component_class = globals()[self.component_class]
def update_dataset(self, dataset_update, typ_data):
self.dataset = dataset_update
def update_pretrained_model(self, pretrained_model_update, typ_data):
self.pretrained_model = pretrained_model_update
self.typ_data = typ_data
self.component = self.component_class(self.dataset, self.typ_data)
self.component = self.component_class(self.pretrained_model, self.typ_data)
def update_instance(self, instance, enum, xtype, solver="g3"):
self.instance = instance
self.component.update_with_explicability(self.instance, enum, xtype, solver)
self.list_expls = self.component.update_with_explicability(self.instance, enum, xtype, solver)
def update_expl(self, expl):
self.expl = expl
self.component.draw_explanation(self.instance, expl)
class View():
def __init__(self, model):
self.model = model
self.ml_menu_models = dcc.Dropdown(self.model.ml_models, id='ml_model_choice')
self.ml_menu_models = dcc.Dropdown(self.model.ml_models,
id='ml_model_choice',
className="sidebar-dropdown")
self.dataset_upload = html.Div([
dcc.Upload(
id='ml_dataset_choice',
self.pretrained_model_upload = html.Div([
dcc.Upload(
id='ml_pretrained_model_choice',
children=html.Div([
'Drag and Drop or ',
html.A('Select File')
]),
style={
'width': '100%',
'height': '60px',
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px',
'textAlign': 'center',
'margin': '10px'
}
className="upload"
),
html.Div(id='dataset_filename')])
html.Div(id='pretrained_model_filename')])
self.instance_upload = html.Div([
dcc.Upload(
......@@ -75,29 +74,20 @@ class View():
'Drag and Drop or ',
html.A('Select instance')
]),
style={
'width': '100%',
'height': '60px',
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px',
'textAlign': 'center',
'margin': '10px'
}
className="upload"
),
html.Div(id='instance_filename')])
self.sidebar = dbc.Col([
dcc.Tabs(children=[
dcc.Tab(label='Basic Parameters', children = [
self.sidebar = dcc.Tabs(children=[
dcc.Tab(label='Basic Parameters', children = [
html.Br(),
html.Label("Choose the Machine Learning algorithm :"),
html.Br(),
self.ml_menu_models,
html.Hr(),
html.Label("Choose the dataset : "),
html.Label("Choose the pretrained model : "),
html.Br(),
self.dataset_upload,
self.pretrained_model_upload,
html.Hr(),
html.Label("Choose the instance to explain : "),
html.Br(),
......@@ -107,8 +97,10 @@ class View():
html.Br(),
dcc.Input(
id="number_explanations",
value=1,
type="number",
placeholder="How many explanations ?"),
placeholder="How many explanations ?",
className="sidebar-dropdown"),
html.Hr(),
html.Label("Choose the kind of explanation : "),
html.Br(),
......@@ -116,17 +108,24 @@ class View():
id="explanation_type",
options={'AXp' : "Abductive Explanation", 'CXp': "Contrastive explanation"},
value = ['AXp', 'CXp'],
inline=True)]),
className="sidebar-dropdown",
inline=True)], className="sidebar"),
dcc.Tab(label='Advanced Parameters', children = [
html.Hr(),
html.Label("Choose the SAT solver : "),
html.Br(),
dcc.Dropdown(['g3', 'g4', 'lgl', 'mcb', 'mcm', 'mpl', 'm22', 'mc', 'mgh'], 'g3', id='solver_sat')
])
])],width=3)
self.layout = dbc.Row([self.sidebar,
dbc.Col(html.Div(id = "graph", children=" "), width=4),
dbc.Col(html.Div(id = "explanation", children=" "), width=3)])
self.tab = dcc.Tab(label='Application on Explainable AI', children=self.layout)
], className="sidebar")
])
self.expl_choice = dcc.Dropdown(self.model.list_expls,
id='expl_choice',
className="dropdown")
self.layout = dbc.Row([ dbc.Col([self.sidebar], width=3, class_name="sidebar"),
dbc.Col([dbc.Row(id = "graph", children=[]),
dbc.Row(html.Div([html.H5(id = "navigate_label", hidden=True, children="Navigate through the explanations and plot them on the tree : "),
html.Div(self.expl_choice, id='navigate_dropdown', hidden=True)]))], width=5, class_name="column_graph"),
dbc.Col(html.Main(id = "explanation", children=[], hidden=True), width=4)])
\ No newline at end of file
utils.py
+ 5
− 4
View file @ da034c90
import base64
import io
import pickle
import pandas as pd
import sklearn
import numpy as np
from dash import html
def parse_contents_tree(contents, filename):
def parse_contents_graph(contents, filename):
content_type, content_string = contents.split(',')
decoded = base64.b64decode(content_string)
try:
......@@ -31,7 +31,8 @@ def parse_contents_instance(contents, filename):
data = decoded.decode('utf-8')
else :
data = decoded.decode('utf-8')
data = str(data).strip().split(',')
data = list(map(lambda i: tuple([i[0], np.float32(i[1])]), [i.split('=') for i in data]))
except Exception as e:
print(e)
return html.Div([
......
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