diff --git a/.gitignore b/.gitignore
index ac7f262d44c6cf8e2e6c604978156a382186ff8c..ed1537f039bd0b1e28e3d3fd73f3cae685e3e067 100644
--- a/.gitignore
+++ b/.gitignore
@@ -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
diff --git a/app.py b/app.py
index e605e68bf0922f4f6ca56198ba817b533d2ddd50..9fc4fab2db76b7424e8fe6a01c0b5c4cb1c6458e 100644
--- a/app.py
+++ b/app.py
@@ -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)
diff --git a/assets/header.css b/assets/header.css
new file mode 100644
index 0000000000000000000000000000000000000000..63ccc73777c8d9462f49d5e3a64395233f7de102
--- /dev/null
+++ b/assets/header.css
@@ -0,0 +1,103 @@
+/* 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;
+}
+
+
+
diff --git a/assets/typography.css b/assets/typography.css
new file mode 100644
index 0000000000000000000000000000000000000000..be7111a86df23034aac7e48a4931610e19e37ce9
--- /dev/null
+++ b/assets/typography.css
@@ -0,0 +1,21 @@
+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
diff --git a/callbacks.py b/callbacks.py
new file mode 100644
index 0000000000000000000000000000000000000000..126e27e82da879906f65742006cc4685568371e2
--- /dev/null
+++ b/callbacks.py
@@ -0,0 +1,116 @@
+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
diff --git a/pages/__init__.py b/pages/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/pages/application/DecisionTree/DecisionTreeComponent.py b/pages/application/DecisionTree/DecisionTreeComponent.py
index 93716c0abca762b56602827cdf356fc3320eceba..662884f4a755556a110e97b99456adaf860b100e 100644
--- a/pages/application/DecisionTree/DecisionTreeComponent.py
+++ b/pages/application/DecisionTree/DecisionTreeComponent.py
@@ -1,41 +1,59 @@
-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"}))]
diff --git a/pages/application/DecisionTree/utils/dtree.py b/pages/application/DecisionTree/utils/dtree.py
index 2a8b75f672ac927851084ab14b50cbb46f066f8e..6bd118dfd8cb6ca577338d8a8189cb00cea33a68 100644
--- a/pages/application/DecisionTree/utils/dtree.py
+++ b/pages/application/DecisionTree/utils/dtree.py
@@ -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
diff --git a/pages/application/DecisionTree/utils/dtviz.py b/pages/application/DecisionTree/utils/dtviz.py
index 11ac6fb2be79bff4c93a1bbcff4b2147b4aa1095..aacf7b646dd1384157116c138a125a069670c772 100755
--- a/pages/application/DecisionTree/utils/dtviz.py
+++ b/pages/application/DecisionTree/utils/dtviz.py
@@ -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())
diff --git a/pages/application/layout_application.py b/pages/application/application.py
similarity index 57%
rename from pages/application/layout_application.py
rename to pages/application/application.py
index 78c5c019436a0e3ac2a5e74ea59434b872b586f6..16996a6aa067aa7bf5c30bd20b93793995d256c1 100644
--- a/pages/application/layout_application.py
+++ b/pages/application/application.py
@@ -1,14 +1,13 @@
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
diff --git a/utils.py b/utils.py
index 27f0b5ea4d3e8c657245b7e9e44aac709e6ea985..a3afff7ac45293e228d2a82e510de33d05ec77b5 100644
--- a/utils.py
+++ b/utils.py
@@ -1,12 +1,12 @@
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([