diff --git a/.gitignore b/.gitignore
index ed1537f039bd0b1e28e3d3fd73f3cae685e3e067..f7db4b641de813a678b90840deee40b361228a41 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,9 +3,4 @@ __pycache__
pages/application/DecisionTree/utils/__pycache__
pages/application/DecisionTree/__pycache__
pages/application/__pycache__
-decision_tree_classifier_20170212.pkl
-push_command
-adult.pkl
-adult_data_00000.inst
-iris_00000.txt
-tests
\ No newline at end of file
+tests/push_command
\ No newline at end of file
diff --git a/README.md b/README.md
index 9f48666b427cdfa4ea4f9d3f92eb9c512a386643..73cd73236adf1dd8aff083939e563d94c7fe862d 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,18 @@
# Pnria Projet Deeplever
-## Requirements
+## How to use it
+
+Set the parameters and upload the models.
+The instance should either be a .txt at format (feature1=...,feature2=...) or a json file
+
+You will find possible models in the tests file
+
+## Trying the app
+
+### Requirements
`pip install -r requirements.txt`
-## Running
+### Running
Run app.py then visit localhost
diff --git a/assets/header.css b/assets/header.css
index b7dcabbc23dda219222a7fe465f8efa167df437e..3a882eae1b7fdffd40dd37f87941a5508a4f29d1 100644
--- a/assets/header.css
+++ b/assets/header.css
@@ -36,6 +36,12 @@ div.sidebar.col-3 {
background-color:gray;
}
+.sidebar .check-boxes{
+ width: 100%;
+ height: 40px;
+ text-align: center;
+}
+
.sidebar .upload {
width: 100%;
height: 50px;
@@ -49,7 +55,6 @@ div.sidebar.col-3 {
.sidebar .Select-control {
width: 100%;
- height: 30px;
line-height: 30px;
border-width: 1px;
border-radius: 5px;
@@ -60,7 +65,7 @@ div.sidebar.col-3 {
background-color: rgb(26,26,26);
}
-.sidebar .sidebar-dropdown{
+.sidebar .dropdown{
width: 100%;
height: 30px;
line-height: 30px;
diff --git a/callbacks.py b/callbacks.py
index 53779188ee9bd9b4de03049a0058529ba89d4787..5ef62d601526f58dbf4d39f45ca698ee867d80ad 100644
--- a/callbacks.py
+++ b/callbacks.py
@@ -4,7 +4,7 @@ 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
+from utils import parse_contents_graph, parse_contents_instance, parse_contents_data
def register_callbacks(page_home, page_course, page_application, app):
@@ -31,12 +31,15 @@ def register_callbacks(page_home, page_course, page_application, app):
@app.callback(
Output('pretrained_model_filename', 'children'),
+ Output('info_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('model_info_choice', 'contents'),
+ State('model_info_choice', 'filename'),
Input('ml_instance_choice', 'contents'),
State('ml_instance_choice', 'filename'),
Input('number_explanations', 'value'),
@@ -45,55 +48,73 @@ def register_callbacks(page_home, page_course, page_application, app):
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):
+ def update_ml_type(value_ml_model, pretrained_model_contents, pretrained_model_filename, model_info, model_info_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
+
+ # Choice of model
if ihm_id == 'ml_model_choice' :
- model_application.update_ml_model(value_ml_model)
- return None, None, None, None
+ model_application.update_ml_model(value_ml_model)
+ return None, None, None, None, None
+ # Choice of pkl pretrained model
elif ihm_id == 'ml_pretrained_model_choice':
- if value_ml_model is None :
+ if model_application.ml_model is None :
+ raise PreventUpdate
+ graph = parse_contents_graph(pretrained_model_contents, pretrained_model_filename)
+ model_application.update_pretrained_model(graph)
+ if not model_application.add_info :
+ model_application.update_pretrained_model_layout()
+ return pretrained_model_filename, None, None, model_application.component.network, None
+ else :
+ return pretrained_model_filename, None, None, None, None
+
+ # Choice of information for the model
+ elif ihm_id == 'model_info_choice':
+ if model_application.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
+ model_info = parse_contents_data(model_info, model_info_filename)
+ model_application.update_pretrained_model_layout_with_info(model_info, model_info_filename)
+ return pretrained_model_filename, model_info_filename, None, model_application.component.network, None
+ # Choice of instance to explain
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:
+ if model_application.ml_model is None or model_application.pretrained_model is None or model_application.enum<=0 or model_application.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
+ model_application.update_instance(instance)
+ return pretrained_model_filename, model_info_filename, instance_filename, model_application.component.network, model_application.component.explanation
+ # Choice of number of expls
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:
+ if model_application.ml_model is None or model_application.pretrained_model is None or len(model_application.instance)==0 or model_application.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
+ model_application.update_enum(enum)
+ return pretrained_model_filename, model_info_filename, instance_filename, model_application.component.network, model_application.component.explanation
+ # Choice of AxP or CxP
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 :
+ if model_application.ml_model is None or model_application.pretrained_model is None or len(model_application.instance)==0 or model_application.enum<=0 :
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
+ model_application.update_xtype(xtype)
+ return pretrained_model_filename, model_info_filename, instance_filename, model_application.component.network, model_application.component.explanation
+ # Choice of solver
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:
+ if model_application.ml_model is None or model_application.pretrained_model is None or len(model_application.instance)==0 or model_application.enum<=0 or len(model_application.xtype)==0:
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
+ model_application.update_solver(solver)
+ return pretrained_model_filename, model_info_filename, instance_filename, model_application.component.network, model_application.component.explanation
+ # Choice of AxP to draw
elif ihm_id == 'expl_choice' :
- if instance_contents is None :
+ if model_application.ml_model is None or model_application.pretrained_model is None or len(model_application.instance)==0 or model_application.enum<=0 or len(model_application.xtype)==0:
raise PreventUpdate
model_application.update_expl(expl_choice)
- return pretrained_model_filename, instance_filename, model_application.component.network, model_application.component.explanation
+ return pretrained_model_filename, model_info_filename, instance_filename, model_application.component.network, model_application.component.explanation
@app.callback(
@@ -116,3 +137,16 @@ def register_callbacks(page_home, page_course, page_application, app):
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
+
+ @app.callback(
+ Output('choice_info_div', 'hidden'),
+ Input('add_info_model_choice', 'on'),
+ prevent_initial_call=True
+ )
+ def add_model_info(add_info_model_choice):
+ model_application = page_application.model
+ model_application.update_info_needed(add_info_model_choice)
+ if add_info_model_choice:
+ return False
+ else :
+ return True
diff --git a/callbacks_detached.py b/callbacks_detached.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8db16ff713479399892471d56224bd0cd9456b2
--- /dev/null
+++ b/callbacks_detached.py
@@ -0,0 +1,172 @@
+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, parse_contents_data
+
+
+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('graph', 'children'),
+ Input('ml_model_choice', 'value'),
+ prevent_initial_call=True
+ )
+ def update_ml_type(value_ml_model):
+ model_application = page_application.model
+ model_application.update_ml_model(value_ml_model)
+ return None
+
+ @app.callback(
+ Output('pretrained_model_filename', 'children'),
+ Output('graph', 'children'),
+ Input('ml_pretrained_model_choice', 'contents'),
+ State('ml_pretrained_model_choice', 'filename'),
+ prevent_initial_call=True
+ )
+ def update_ml_pretrained_model(pretrained_model_contents, pretrained_model_filename):
+ model_application = page_application.model
+ if model_application.ml_model is None :
+ raise PreventUpdate
+ graph = parse_contents_graph(pretrained_model_contents, pretrained_model_filename)
+ model_application.update_pretrained_model(graph)
+ if not model_application.add_info :
+ model_application.update_pretrained_model_layout()
+ return pretrained_model_filename, model_application.component.network
+ else :
+ return pretrained_model_filename, None
+
+ @app.callback(
+ Output('info_filename', 'children'),
+ Output('graph', 'children'),
+ Input('model_info_choice', 'contents'),
+ State('model_info_choice', 'filename'),
+ prevent_initial_call=True
+ )
+ def update_info_model(model_info, model_info_filename):
+ model_application = page_application.model
+ if model_application.ml_model is None :
+ raise PreventUpdate
+ model_info = parse_contents_data(model_info, model_info_filename)
+ model_application.update_pretrained_model_layout_with_info(model_info, model_info_filename)
+ return model_info_filename, model_application.component.network
+
+ @app.callback(
+ Output('instance_filename', 'children'),
+ Output('graph', 'children'),
+ Output('explanation', 'children'),
+ Input('ml_instance_choice', 'contents'),
+ State('ml_instance_choice', 'filename'),
+ prevent_initial_call=True
+ )
+ def update_instance(instance_contents, instance_filename):
+ model_application = page_application.model
+ if model_application.ml_model is None or model_application.pretrained_model is None or model_application.enum<=0 or model_application.xtype is None :
+ raise PreventUpdate
+ instance = parse_contents_instance(instance_contents, instance_filename)
+ model_application.update_instance(instance)
+ return instance_filename, model_application.component.network, model_application.component.explanation
+
+ @app.callback(
+ Output('explanation', 'children'),
+ Input('number_explanations', 'value'),
+ prevent_initial_call=True
+ )
+ def update_enum(enum):
+ model_application = page_application.model
+ if model_application.ml_model is None or model_application.pretrained_model is None or len(model_application.instance)==0 or model_application.xtype is None:
+ raise PreventUpdate
+ model_application.update_enum(enum)
+ return model_application.component.explanation
+
+ @app.callback(
+ Output('explanation', 'children'),
+ Input('explanation_type', 'value'),
+ prevent_initial_call=True
+ )
+ def update_xtype(xtype):
+ model_application = page_application.model
+ if model_application.ml_model is None or model_application.pretrained_model is None or len(model_application.instance)==0 or model_application.enum<=0 :
+ raise PreventUpdate
+ model_application.update_xtype(xtype)
+ return model_application.component.explanation
+
+ @app.callback(
+ Output('explanation', 'children'),
+ Input('solver_sat', 'value'),
+ prevent_initial_call=True
+)
+ def update_solver(solver):
+ model_application = page_application.model
+ if model_application.ml_model is None or model_application.pretrained_model is None or len(model_application.instance)==0 or model_application.enum<=0 or len(model_application.xtype)==0:
+ raise PreventUpdate
+ model_application.update_solver(solver)
+ return model_application.component.explanation
+
+ @app.callback(
+ Output('graph', 'children'),
+ Input('expl_choice', 'value'),
+ prevent_initial_call=True
+ )
+ def update_expl_choice( expl_choice):
+ model_application = page_application.model
+ if model_application.ml_model is None or model_application.pretrained_model is None or len(model_application.instance)==0 or model_application.enum<=0 or len(model_application.xtype)==0:
+ raise PreventUpdate
+ model_application.update_expl(expl_choice)
+ return model_application.component.network
+
+ @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 len(explanation_type)==0:
+ return True, True, True, {}
+ elif "AXp" not in explanation_type and "CXp" in explanation_type:
+ return False, 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
+
+ @app.callback(
+ Output('choice_info_div', 'hidden'),
+ Input('add_info_model_choice', 'on'),
+ prevent_initial_call=True
+ )
+ def add_model_info(add_info_model_choice):
+ model_application = page_application.model
+ model_application.update_info_needed(add_info_model_choice)
+ if add_info_model_choice:
+ return False
+ else :
+ return True
diff --git a/pages/application/DecisionTree/DecisionTreeComponent.py b/pages/application/DecisionTree/DecisionTreeComponent.py
index 662884f4a755556a110e97b99456adaf860b100e..8a56c32349fced19e8c03889bb26fdc176b79973 100644
--- a/pages/application/DecisionTree/DecisionTreeComponent.py
+++ b/pages/application/DecisionTree/DecisionTreeComponent.py
@@ -1,40 +1,139 @@
from os import path
+import base64
import dash_bootstrap_components as dbc
import dash_interactive_graphviz
import numpy as np
from dash import dcc, html
+from pages.application.DecisionTree.utils.upload_tree import UploadedDecisionTree
+from pages.application.DecisionTree.utils.data import Data
from pages.application.DecisionTree.utils.dtree import DecisionTree
+
from pages.application.DecisionTree.utils.dtviz import (visualize,
visualize_expl,
visualize_instance)
-
class DecisionTreeComponent():
- def __init__(self, tree, typ_data):
+ def __init__(self, tree, type_tree='SKL', info=None, type_info=''):
+
+ if info is not None and '.csv' in type_info:
+ self.categorical = True
+ data = Data(info)
+ fvmap = data.mapping_features()
+ feature_names = data.names[:-1]
+ self.uploaded_dt = UploadedDecisionTree(tree, type_tree, maxdepth=tree.get_depth(), feature_names=feature_names, nb_classes=tree.n_classes_)
+ self.dt_format, self.map, features_names_mapping = self.uploaded_dt.dump(fvmap, feat_names=feature_names)
- self.dt = DecisionTree(from_pickle = tree)
+ elif info is not None and '.txt' in type_info :
+ self.categorical = True
+ fvmap = {}
+ feature_names = []
+ for i,line in enumerate(info.split('\n')):
+ fid, TYPE = line.split(',')[:2]
+ dom = line.split(',')[2:]
+ assert (fid not in feature_names)
+ feature_names.append(fid)
+ assert (TYPE in ['Binary', 'Categorical'])
+ fvmap[f'f{i}'] = dict()
+ dom = sorted(dom)
+ for j,v in enumerate(dom):
+ fvmap[f'f{i}'][j] = (fid, True, v)
+ self.uploaded_dt = UploadedDecisionTree(tree, type_tree, maxdepth=tree.get_depth(), feature_names=feature_names, nb_classes=tree.n_classes_)
+ self.dt_format, self.map, features_names_mapping = self.uploaded_dt.dump(fvmap, feat_names=feature_names)
+ else :
+ self.categorical = False
+ try:
+ feature_names = tree.feature_names_in_
+ except:
+ feature_names = [f'f{i}' for i in range(tree.n_features_in_)]
+ self.uploaded_dt = UploadedDecisionTree(tree, type_tree, maxdepth=tree.get_depth(), feature_names=feature_names, nb_classes=tree.n_classes_)
+ self.dt_format, self.map, features_names_mapping = self.uploaded_dt.convert_dt(feat_names=feature_names)
+
+ self.mapping_instance = self.create_fvmap_inverse(features_names_mapping)
+ self.dt = DecisionTree(from_dt=self.dt_format, mapfile = self.map, feature_names = feature_names)
dot_source = visualize(self.dt)
- self.network = [dbc.Row(dash_interactive_graphviz.DashInteractiveGraphviz(dot_source=dot_source, style = {"width": "60%",
+ self.network = html.Div([dash_interactive_graphviz.DashInteractiveGraphviz(dot_source=dot_source, style = {"width": "60%",
"height": "90%",
- "background-color": "transparent"}))]
+ "background-color": "transparent"})])
self.explanation = []
+
+ def create_fvmap_inverse(self, instance):
+ def create_fvmap_inverse_with_info(features_names_mapping) :
+ mapping_instance = {}
+ for feat in features_names_mapping :
+ feat_dic = {}
+ feature_description = feat.split(',')
+ name_feat, id_feat = feature_description[1].split(':')
+
+ for mapping in feature_description[2:]:
+ real_value, mapped_value = mapping.split(':')
+ feat_dic[np.float32(real_value)] = int(mapped_value)
+ mapping_instance[name_feat] = feat_dic
+
+ return mapping_instance
+
+ def create_fvmap_inverse_threashold(features_names_mapping) :
+ mapping_instance = {}
+ for feat in features_names_mapping :
+ feature_description = feat.split(',')
+ name_feat, id_feat = feature_description[1].split(':')
+ mapping_instance[name_feat] = float(feature_description[2].split(':')[0])
+
+ return mapping_instance
+
+ if self.categorical :
+ return create_fvmap_inverse_with_info(instance)
+ else :
+ return create_fvmap_inverse_threashold(instance)
+
+
+ def translate_instance(self, instance):
+ def translate_instance_categorical(instance):
+ instance_translated = []
+ for feat, real_value in instance :
+ instance_translated.append((feat, self.mapping_instance[feat][real_value]))
+ return instance_translated
+
+ def translate_instance_threasholds(instance):
+ instance_translated = []
+ for feat, real_value in instance :
+ try:
+ if real_value <= self.mapping_instance[feat]:
+ instance_translated.append((feat, 0))
+ else :
+ instance_translated.append((feat, 1))
+ except:
+ instance_translated.append((feat, real_value))
+ return instance_translated
+
+ if self.categorical :
+ return translate_instance_categorical(instance)
+ else :
+ return translate_instance_threasholds(instance)
+
+
def update_with_explicability(self, instance, enum, xtype, solver) :
- dot_source = visualize_instance(self.dt, instance)
- self.network = [dbc.Row(dash_interactive_graphviz.DashInteractiveGraphviz(
+
+ instance_translated = self.translate_instance(instance)
+ self.explanation = []
+ list_explanations_path=[]
+ explanation = self.dt.explain(instance_translated, enum=enum, xtype = xtype, solver=solver)
+
+ dot_source = visualize_instance(self.dt, instance_translated)
+ self.network = html.Div([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
+ #instance plotting
+ self.explanation.append(html.H4("Instance : \n"))
+ self.explanation.append(html.P(str([str(instance[i]) for i in range (len(instance))])))
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)"] :
@@ -51,9 +150,10 @@ class DecisionTreeComponent():
return list_explanations_path
def draw_explanation(self, instance, expl) :
+ instance = self.translate_instance(instance)
dot_source = visualize_expl(self.dt, instance, expl)
- self.network = [dbc.Row(dash_interactive_graphviz.DashInteractiveGraphviz(
+ self.network = html.Div([dash_interactive_graphviz.DashInteractiveGraphviz(
dot_source=dot_source,
style = {"width": "50%",
"height": "80%",
- "background-color": "transparent"}))]
+ "background-color": "transparent"})])
diff --git a/pages/application/DecisionTree/utils/data.py b/pages/application/DecisionTree/utils/data.py
new file mode 100644
index 0000000000000000000000000000000000000000..91aded54aaa39c6239d3a0696beafb217dc9a8a4
--- /dev/null
+++ b/pages/application/DecisionTree/utils/data.py
@@ -0,0 +1,98 @@
+#!/usr/bin/env python
+#-*- coding:utf-8 -*-
+##
+## data.py
+##
+## Created on: Sep 20, 2017
+## Author: Alexey Ignatiev, Nina Narodytska
+## E-mail: aignatiev@ciencias.ulisboa.pt, narodytska@vmware.com
+##
+
+#
+#==============================================================================
+from __future__ import print_function
+import collections
+import itertools
+import pickle
+import six
+import gzip
+from six.moves import range
+import numpy as np
+import pandas as pd
+
+#
+#==============================================================================
+class Data(object):
+ """
+ Class for representing data (transactions).
+ """
+
+ def __init__(self, data, separator=','):
+ """
+ Constructor and parser.
+ """
+ self.names = None
+ self.nm2id = None
+ self.feats = None
+ self.targets = None
+ self.samples = None
+
+ self.parse(data, separator)
+
+ def parse(self, data, separator):
+ """
+ Parse input file.
+ """
+
+ # reading data set from file
+ lines = data.split('\n')
+
+ # reading preamble
+ self.names = [name.replace('"','').strip() for name in lines[0].strip().split(separator)]
+ self.feats = [set([]) for n in self.names[:-1]]
+ self.targets = set([])
+
+ lines = lines[1:]
+
+ # filling name to id mapping
+ self.nm2id = {name: i for i, name in enumerate(self.names)}
+
+ self.nonbin2bin = {}
+ for name in self.nm2id:
+ spl = name.rsplit(':',1)
+ if (spl[0] not in self.nonbin2bin):
+ self.nonbin2bin[spl[0]] = [name]
+ else:
+ self.nonbin2bin[spl[0]].append(name)
+
+ # reading training samples
+ self.samples = []
+
+ for line, w in six.iteritems(collections.Counter(lines)):
+ inst = [v.strip() for v in line.strip().split(separator)]
+ self.samples.append(inst)
+ for i, v in enumerate(inst[:-1]):
+ if v:
+ self.feats[i].add(str(v))
+ assert(inst[-1])
+ self.targets.add(str(inst[-1]))
+
+ self.nof_feats = len(self.names[:-1])
+
+ def mapping_features(self):
+ """
+ feature-value mapping
+ """
+ fvmap = {}
+
+ for i in range(self.nof_feats):
+ fvmap[f'f{i}'] = dict()
+ for j, v in enumerate(sorted(self.feats[i])):
+ fvmap[f'f{i}'][j] = (self.names[i], True, v)
+
+ if len(self.feats[i]) > 2:
+ m = len(self.feats[i])
+ for j, v in enumerate(sorted(self.feats[i])):
+ fvmap[f'f{i}'][j+m] = (self.names[i], False, v)
+
+ return fvmap
diff --git a/pages/application/DecisionTree/utils/dtree.py b/pages/application/DecisionTree/utils/dtree.py
index 6bd118dfd8cb6ca577338d8a8189cb00cea33a68..79088c2cce1cbb0c4c6e65183195a4a110f357f6 100644
--- a/pages/application/DecisionTree/utils/dtree.py
+++ b/pages/application/DecisionTree/utils/dtree.py
@@ -11,47 +11,32 @@
#
#==============================================================================
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
-from torch import threshold
try: # for Python2
from cStringIO import StringIO
except ImportError: # for Python3
from io import StringIO
-
-import numpy as np
-from dash import dcc, html
from sklearn.tree import _tree
+import numpy as np
-
-#
-#==============================================================================
class Node():
"""
Node class.
"""
- def __init__(self, feat='', vals=None, threshold=None, children_left= None, children_right=None):
+ def __init__(self, feat='', vals=[]):
"""
Constructor.
"""
self.feat = feat
- if threshold is not None :
- self.threshold = threshold
- self.children_left = 0
- self.children_right = 0
- else :
- self.vals = {}
-
+ self.vals = vals
#
#==============================================================================
@@ -60,13 +45,12 @@ class DecisionTree():
Simple decision tree class.
"""
- def __init__(self, from_pickle=None, verbose=0):
+ def __init__(self, from_dt=None, mapfile=None, feature_names=None, verbose=0):
"""
Constructor.
"""
self.verbose = verbose
- self.typ=""
self.nof_nodes = 0
self.nof_terms = 0
@@ -76,112 +60,266 @@ class DecisionTree():
self.paths = {}
self.feats = []
self.feids = {}
+ self.fdoms = {}
+ self.fvmap = {}
+ self.feature_names = {f'f{i}' : feature_names[i] for i, f in enumerate(feature_names)}
- if from_pickle:
- self.typ="pkl"
- self.tree_ = ''
- self.from_pickle_file(from_pickle)
-
- #problem de feature names et problem de vals dans node
- def from_pickle_file(self, tree):
- #help(_tree.Tree)
- self.tree_ = tree.tree_
- #print(sklearn.tree.export_text(tree))
- try:
- feature_names = tree.feature_names_in_
- except:
- print("You did not dump the model with the features names")
- feature_names = [str(i) for i in range(tree.n_features_in_)]
-
- class_names = tree.classes_
- self.nodes = collections.defaultdict(lambda: Node(feat='', threshold=int(0), children_left=int(0), children_right=int(0)))
- self.terms={}
- self.nof_nodes = self.tree_.node_count
- self.root_node = 0
- self.feats = feature_names
-
- feature_name = [
- feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!"
- for i in self.tree_.feature]
-
- def recurse(node):
- if self.tree_.feature[node] != _tree.TREE_UNDEFINED:
- name = feature_name[node]
- val = self.tree_.threshold[node]
-
- #faire une boucle for des vals ?
- self.nodes[int(node)].feat = name
- self.nodes[int(node)].threshold = np.round(val, 4)
- self.nodes[int(node)].children_left = int(self.tree_.children_left[node])
- self.nodes[int(node)].children_right = int(self.tree_.children_right[node])
-
- recurse(self.tree_.children_left[node])
- recurse(self.tree_.children_right[node])
+ # OHE mapping
+ OHEMap = collections.namedtuple('OHEMap', ['dir', 'opp'])
+ self.ohmap = OHEMap(dir={}, opp={})
- else:
- self.terms[node] = class_names[np.argmax(self.tree_.value[node])]
-
- recurse(self.root_node)
+ if from_dt:
+ self.from_dt(from_dt)
+
+ if mapfile:
+ self.parse_mapping(mapfile)
+ else: # no mapping is given
+ for f in self.feats:
+ for v in self.fdoms[f]:
+ self.fvmap[tuple([f, v])] = '{0}={1}'.format(f, v)
+
+ def from_dt(self, data):
+ """
+ Get the tree from a file pointer.
+ """
+
+ contents = StringIO(data)
+
+ lines = contents.readlines()
+
+ # filtering out comment lines (those that start with '#')
+ lines = list(filter(lambda l: not l.startswith('#'), lines))
+ # number of nodes
+ self.nof_nodes = int(lines[0].strip())
+
+ # root node
+ self.root_node = int(lines[1].strip())
+
+ # number of terminal nodes (classes)
+ self.nof_terms = len(lines[3][2:].strip().split())
+
+ # the ordered list of terminal nodes
+ self.terms = {}
+ for i in range(self.nof_terms):
+ nd, _, t = lines[i + 4].strip().split()
+ self.terms[int(nd)] = t #int(t)
+
+ # finally, reading the nodes
+ self.nodes = collections.defaultdict(lambda: Node(feat='', vals={}))
+ self.feats = set([])
+ self.fdoms = collections.defaultdict(lambda: set([]))
+ for line in lines[(4 + self.nof_terms):]:
+ # reading the tuple
+ nid, fid, fval, child = line.strip().split()
+
+ # inserting it in the nodes list
+ self.nodes[int(nid)].feat = fid
+ self.nodes[int(nid)].vals[int(fval)] = int(child)
+
+ # updating the list of features
+ self.feats.add(fid)
+
+ # updaing feature domains
+ self.fdoms[fid].add(int(fval))
+
+ # adding complex node connections into consideration
+ for n1 in self.nodes:
+ conns = collections.defaultdict(lambda: set([]))
+ for v, n2 in self.nodes[n1].vals.items():
+ conns[n2].add(v)
+ self.nodes[n1].vals = {frozenset(v): n2 for n2, v in conns.items()}
+
+ # simplifying the features and their domains
self.feats = sorted(self.feats)
- self.feids = {f: i for i, f in enumerate(self.feats)}
- self.nof_terms = len(self.terms)
- self.nof_nodes -= len(self.terms)
+ #self.feids = {f: i for i, f in enumerate(self.feats)}
+ self.fdoms = {f: sorted(self.fdoms[f]) for f in self.fdoms}
+
+ # here we assume all features are present in the tree
+ # if not, this value will be rewritten by self.parse_mapping()
self.nof_feats = len(self.feats)
self.paths = collections.defaultdict(lambda: [])
self.extract_paths(root=self.root_node, prefix=[])
+ def parse_mapping(self, mapfile):
+ """
+ Parse feature-value mapping from a file.
+ """
+ self.fvmap = {}
+
+ lines = mapfile.split('\n')
+
+ if lines[0].startswith('OHE'):
+ for i in range(int(lines[1])):
+ feats = lines[i + 2].strip().split(',')
+ orig, ohe = feats[0], tuple(feats[1:])
+ self.ohmap.dir[orig] = tuple(ohe)
+ for f in ohe:
+ self.ohmap.opp[f] = orig
+
+ lines = lines[(int(lines[1]) + 2):]
+
+ elif lines[0].startswith('Categorical'):
+ # skipping the first comment line if necessary
+ lines = lines[1:]
+
+ elif lines[0].startswith('Ordinal'):
+ # skipping the first comment line if necessary
+ lines = lines[1:]
+
+ # number of features
+ self.nof_feats = int(lines[0].strip())
+ self.feids = {}
+
+ for line in lines[1:]:
+ feat, val, real = line.split()
+ self.fvmap[tuple([feat, int(val)])] = '{0}{1}'.format(self.feature_names[feat], real)
+ #if feat not in self.feids:
+ # self.feids[feat] = len(self.feids)
+
+ #assert len(self.feids) == self.nof_feats
+
+ def convert_to_multiedges(self):
+ """
+ Convert ITI trees with '!=' edges to multi-edges.
+ """
+ # new feature domains
+ fdoms = collections.defaultdict(lambda: [])
+
+ # tentative mapping relating negative and positive values
+ nemap = collections.defaultdict(lambda: collections.defaultdict(lambda: [None, None]))
+
+ for fv, tval in self.fvmap.items():
+ if '!=' in tval:
+ nemap[fv[0]][tval.split('=')[1]][0] = fv[1]
+ else:
+ fdoms[fv[0]].append(fv[1])
+ nemap[fv[0]][tval.split('=')[1]][1] = fv[1]
+
+ # a mapping from negative values to sets
+ fnmap = collections.defaultdict(lambda: {})
+ for f in nemap:
+ for t, vals in nemap[f].items():
+ if vals[0] != None:
+ fnmap[(f, frozenset({vals[0]}))] = frozenset(set(fdoms[f]).difference({vals[1]}))
+
+ # updating node connections
+ for n in self.nodes:
+ vals = {}
+ for v in self.nodes[n].vals.keys():
+ fn = (self.nodes[n].feat, v)
+ if fn in fnmap:
+ vals[fnmap[fn]] = self.nodes[n].vals[v]
+ else:
+ vals[v] = self.nodes[n].vals[v]
+ self.nodes[n].vals = vals
+
+ # updating the domains
+ self.fdoms = fdoms
+
+ # extracting the paths again
+ self.paths = collections.defaultdict(lambda: [])
+ self.extract_paths(root=self.root_node, prefix=[])
+
def extract_paths(self, root, prefix):
"""
Traverse the tree and extract explicit paths.
"""
-
- if root in self.terms.keys():
+ if root in self.terms:
# store the path
term = self.terms[root]
self.paths[term].append(prefix)
else:
# select next node
- feat, threshold, children_left, children_right = self.nodes[root].feat, self.nodes[root].threshold, self.nodes[root].children_left, self.nodes[root].children_right
- self.extract_paths(children_left, prefix + [tuple([feat, "<=" + str(threshold)])])
- self.extract_paths(children_right, prefix + [tuple([feat, ">"+ str(threshold)])])
-
- def execute(self, inst):
- inst = np.array([inst])
- path = self.tree_.decision_path(inst)
- term_id_node = self.tree_.apply(inst)
- term_id_node = term_id_node[0]
- path = path.indices[path.indptr[0] : path.indptr[0 + 1]]
- return path, term_id_node
+ feat, vals = self.nodes[root].feat, self.nodes[root].vals
+ for val in vals:
+ self.extract_paths(vals[val], prefix + [tuple([feat, val])])
+
+ def execute(self, inst, pathlits=False):
+ """
+ Run the tree and obtain the prediction given an input instance.
+ """
+ root = self.root_node
+ depth = 0
+ path = []
+
+ # this array is needed if we focus on the path's literals only
+ visited = [False for f in inst]
+
+ while not root in self.terms:
+ path.append(root)
+ feat, vals = self.nodes[root].feat, self.nodes[root].vals
+ visited[self.feids[feat]] = True
+ tval = inst[self.feids[feat]][1]
+ ###############
+ # assert(len(vals) == 2)
+ next_node = root
+ neq = None
+ for vs, dest in vals.items():
+ if tval in vs:
+ next_node = dest
+ break
+ else:
+ for v in vs:
+ if '!=' in self.fvmap[(feat, v)]:
+ neq = dest
+ break
+ else:
+ next_node = neq
+ # if tval not in vals:
+ # # go to the False branch (!=)
+ # for i in vals:
+ # if "!=" in self.fvmap[(feat,i)]:
+ # next_node = vals[i]
+ # break
+ # else:
+ # next_node = vals[tval]
+
+ assert (next_node != root)
+ ###############
+ root = next_node
+ depth += 1
+
+ if pathlits:
+ # filtering out non-visited literals
+ for i, v in enumerate(visited):
+ if not v:
+ inst[i] = None
+
+ return path, self.terms[root], depth
def prepare_sets(self, inst, term):
"""
Hitting set based encoding of the problem.
(currently not incremental -- should be fixed later)
"""
-
sets = []
for t, paths in self.paths.items():
# ignoring the right class
- if term in self.terms.keys() and self.terms[term] == t:
+ if t == term:
continue
# computing the sets to hit
for path in paths:
to_hit = []
for item in path:
+ fv = inst[self.feids[item[0]]]
# if the instance disagrees with the path on this item
- if ("<=" in item[1] and (inst[item[0]] > np.float32(item[1][2:]))) or (">" in item[1] and (inst[item[0]] <= np.float32(item[1][1:]))) :
- if "<=" in item[1] :
- fv = tuple([item[0], str(inst[item[0]]), ">" , str(np.float32(item[1][2:]))])
- else :
- fv = tuple([item[0], str(inst[item[0]]) , "<=" , str(np.float32(item[1][1:]))])
- to_hit.append(fv)
+ if fv and not fv[1] in item[1]:
+ if fv[0] in self.ohmap.opp:
+ to_hit.append(tuple([self.ohmap.opp[fv[0]], None]))
+ else:
+ to_hit.append(fv)
- if len(to_hit)>0 :
- to_hit = sorted(set(to_hit))
- sets.append(tuple(to_hit))
+ to_hit = sorted(set(to_hit))
+ sets.append(tuple(to_hit))
+
+ if self.verbose:
+ if self.verbose > 1:
+ print('c trav. path: {0}'.format(path))
+
+ print('c set to hit: {0}'.format(to_hit))
# returning the set of sets with no duplicates
return list(dict.fromkeys(sets))
@@ -190,38 +328,26 @@ class DecisionTree():
"""
Compute a given number of explanations.
"""
-
- inst_values = [np.float32(i[1]) for i in inst]
- inst_dic = {}
- for i in range(len(inst)):
- inst_dic[inst[i][0]] = np.float32(inst[i][1])
- path, term = self.execute(inst_values)
-
#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:
- continue
- decision_path_str +="(inst[{feature}] = {value}) {inequality} {threshold}) AND ".format(
- feature=self.nodes[node_id].feat,
- value=inst_dic[self.nodes[node_id].feat],
- inequality="<=" if inst_dic[self.nodes[node_id].feat] <= self.nodes[node_id].threshold else ">" ,
- threshold=self.nodes[node_id].threshold)
+ self.feids = {f'f{i}': i for i, f in enumerate(inst)}
+ inst = [(f'f{i}', int(inst[i][1])) for i,f in enumerate(inst)]
+ path, term, depth = self.execute(inst, pathlits)
- decision_path_str += "THEN " + str(self.terms[term])
+ #decision path
+ decision_path_str = 'IF {0} THEN class={1}'.format(' AND '.join([self.fvmap[inst[self.feids[self.nodes[n].feat]]] for n in path]), term)
explanation_dic["Decision path of instance : "] = decision_path_str
- explanation_dic["Decision path length : "] = 'Path length is :'+ str(len(path))
+ explanation_dic["Decision path length : "] = 'Path length is :'+ str(depth)
- # computing the sets to hit
- to_hit = self.prepare_sets(inst_dic, term)
+ if self.ohmap.dir:
+ f2v = {fv[0]: fv[1] for fv in inst}
+ # updating fvmap for printing ohe features
+ for fo, fis in self.ohmap.dir.items():
+ self.fvmap[tuple([fo, None])] = '(' + ' AND '.join([self.fvmap[tuple([fi, f2v[fi]])] for fi in fis]) + ')'
+ # computing the sets to hit
+ to_hit = self.prepare_sets(inst, term)
for type in xtype :
if type == "AXp":
explanation_dic.update(self.enumerate_abductive(to_hit, enum, solver, htype, term))
@@ -240,12 +366,9 @@ class DecisionTree():
with Hitman(bootstrap_with=to_hit, solver='m22', htype=htype) as hitman:
expls = []
for i, expl in enumerate(hitman.enumerate(), 1):
- 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])))
+ list_expls.append([self.fvmap[p] for p in sorted(expl, key=lambda p: p[0])])
+ list_expls_str.append('Explanation: IF {0} THEN class={1}'.format(' AND '.join([self.fvmap[p] for p in sorted(expl, key=lambda p: p[0])]), term))
+
expls.append(expl)
if i == enum:
break
@@ -262,7 +385,6 @@ class DecisionTree():
"""
Enumerate contrastive explanations.
"""
-
def process_set(done, target):
for s in done:
if s <= target:
@@ -277,10 +399,8 @@ class DecisionTree():
list_expls_str = []
explanation = {}
for expl in expls:
- 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])))
+ list_expls_str.append('Contrastive: IF {0} THEN class!={1}'.format(' OR '.join(['!{0}'.format(self.fvmap[p]) for p in sorted(expl, key=lambda p: p[0])]), 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]))
diff --git a/pages/application/DecisionTree/utils/dtviz.py b/pages/application/DecisionTree/utils/dtviz.py
index aacf7b646dd1384157116c138a125a069670c772..21ea63920338159b71e45c44a1cd2b772084bc9f 100755
--- a/pages/application/DecisionTree/utils/dtviz.py
+++ b/pages/application/DecisionTree/utils/dtviz.py
@@ -8,12 +8,16 @@
## E-mail: alexey.ignatiev@monash.edu
##
-import numpy as np
+#
+#==============================================================================
+import getopt
import pygraphviz
+
#
#==============================================================================
def create_legend(g):
legend = g.subgraphs()[-1]
+ legend.graph_attr.update(size="2,2")
legend.add_node("a", style = "invis")
legend.add_node("b", style = "invis")
legend.add_node("c", style = "invis")
@@ -31,55 +35,47 @@ def create_legend(g):
edge.attr["style"] = "dashed"
+#
+#==============================================================================
def visualize(dt):
"""
Visualize a DT with graphviz.
"""
- g = pygraphviz.AGraph(name='root', rankdir="TB")
- g.is_directed()
- g.is_strict()
-
- #g = pygraphviz.AGraph(name = "main", directed=True, strict=True)
+ 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))
+ g.add_node(n, label=dt.feature_names[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]))
+ g.add_node(n, label=dt.terms[n])
node = g.get_node(n)
node.attr['shape'] = 'square'
node.attr['fontsize'] = 13
+ # transitions
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
-
- 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
-
- g.add_subgraph(name='legend')
- create_legend(g)
+ for v in dt.nodes[n1].vals:
+ n2 = dt.nodes[n1].vals[v]
+ g.add_edge(n1, n2)
+ edge = g.get_edge(n1, n2)
+ if len(v) == 1:
+ edge.attr['label'] = dt.fvmap[tuple([dt.nodes[n1].feat, tuple(v)[0]])]
+ else:
+ edge.attr['label'] = '{0}'.format('\n'.join([dt.fvmap[tuple([dt.nodes[n1].feat, val])] for val in tuple(v)]))
+ edge.attr['fontsize'] = 10
+ edge.attr['arrowsize'] = 0.8
# saving file
g.layout(prog='dot')
- return(g.string())
+ return(g.to_string())
#
#==============================================================================
@@ -87,53 +83,50 @@ def visualize_instance(dt, instance):
"""
Visualize a DT with graphviz and plot the running instance.
"""
+ #path that follows the instance - colored in blue
+ path, term, depth = dt.execute(instance)
+ edges_instance = []
+ for i in range (len(path)-1) :
+ edges_instance.append((path[i], path[i+1]))
+ edges_instance.append((path[-1],"term:"+term))
+
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))
+ g.add_node(n, label=dt.feature_names[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]))
+ g.add_node(n, label=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]))
-
+ # transitions
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'
-
- 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'
+ for v in dt.nodes[n1].vals:
+ n2 = dt.nodes[n1].vals[v]
+ n2_type = g.get_node(n2).attr['shape']
+
+ g.add_edge(n1, n2)
+ edge = g.get_edge(n1, n2)
+ if len(v) == 1:
+ edge.attr['label'] = dt.fvmap[tuple([dt.nodes[n1].feat, tuple(v)[0]])]
+ else:
+ edge.attr['label'] = '{0}'.format('\n'.join([dt.fvmap[tuple([dt.nodes[n1].feat, val])] for val in tuple(v)]))
+
+ #instance path in dashed
+ if ((n1,n2) in edges_instance) or (n2_type=='square' and (n1, "term:"+ dt.terms[n2]) in edges_instance):
+ edge.attr['style'] = 'dashed'
+
+ edge.attr['fontsize'] = 10
+ edge.attr['arrowsize'] = 0.8
g.add_subgraph(name='legend')
create_legend(g)
@@ -141,66 +134,63 @@ def visualize_instance(dt, instance):
# 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.
"""
+ #path that follows the instance - colored in blue
+ path, term, depth = dt.execute(instance)
+ edges_instance = []
+ for i in range (len(path)-1) :
+ edges_instance.append((path[i], path[i+1]))
+ edges_instance.append((path[-1],"term:"+term))
+
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))
+ g.add_node(n, label=dt.feature_names[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]))
+ g.add_node(n, label=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]))
-
+ # transitions
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'
+ for v in dt.nodes[n1].vals:
+ n2 = dt.nodes[n1].vals[v]
+ n2_type = g.get_node(n2).attr['shape']
+ g.add_edge(n1, n2)
+ edge = g.get_edge(n1, n2)
+ if len(v) == 1:
+ edge.attr['label'] = dt.fvmap[tuple([dt.nodes[n1].feat, tuple(v)[0]])]
+ else:
+ edge.attr['label'] = '{0}'.format('\n'.join([dt.fvmap[tuple([dt.nodes[n1].feat, val])] for val in tuple(v)]))
+
+ #instance path in dashed
+ if ((n1,n2) in edges_instance) or (n2_type=='square' and (n1, "term:"+ dt.terms[n2]) in edges_instance):
+ edge.attr['style'] = 'dashed'
+ for label in edge.attr['label'].split('\n'):
+ if label in expl:
+ edge.attr['color'] = 'blue'
+
+ 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.to_string())
diff --git a/pages/application/DecisionTree/utils/upload_tree.py b/pages/application/DecisionTree/utils/upload_tree.py
new file mode 100644
index 0000000000000000000000000000000000000000..98bf190cfc588cae331fff2caeeb85df06b87645
--- /dev/null
+++ b/pages/application/DecisionTree/utils/upload_tree.py
@@ -0,0 +1,481 @@
+#!/usr/bin/env python
+#-*- coding:utf-8 -*-
+##
+## tree.py (reuses parts of the code of SHAP)
+##
+## Created on: Dec 7, 2018
+## Author: Nina Narodytska
+## E-mail: narodytska@vmware.com
+##
+
+#
+#==============================================================================
+from anytree import Node, RenderTree,AsciiStyle
+import json
+import numpy as np
+import math
+import six
+
+
+#
+#==============================================================================
+class xgnode(Node):
+ def __init__(self, id, parent = None):
+ Node.__init__(self, id, parent)
+ self.id = id # node value
+ self.name = None
+ self.left_node_id = -1 # left child
+ self.right_node_id = -1 # right child
+
+ self.feature = -1
+ self.threshold = None
+ self.values = -1
+ #iai
+ self.split = None
+
+ def __str__(self):
+ pref = ' ' * self.depth
+ if (len(self.children) == 0):
+ return (pref+ f"leaf:{self.id} {self.values}")
+ else:
+ if(self.name is None):
+ if (self.threshold is None):
+ return (pref+ f"({self.id}) f{self.feature}")
+ else:
+ return (pref+ f"({self.id}) f{self.feature} = {self.threshold}")
+ else:
+ if (self.threshold is None):
+ return (pref+ f"({self.id}) \"{self.name}\"")
+ else:
+ return (pref+ f"({self.id}) \"{self.name}\" = {self.threshold}")
+
+#
+#==============================================================================
+
+def walk_tree(node):
+ if (len(node.children) == 0):
+ # leaf
+ print(node)
+ else:
+ print(node)
+ walk_tree(node.children[0])
+ walk_tree(node.children[1])
+
+
+#
+#==============================================================================
+def scores_tree(node, sample):
+ if (len(node.children) == 0):
+ # leaf
+ return node.values
+ else:
+ feature_branch = node.feature
+ sample_value = sample[feature_branch]
+ assert(sample_value is not None)
+ if(sample_value < node.threshold):
+ return scores_tree(node.children[0], sample)
+ else:
+ return scores_tree(node.children[1], sample)
+
+
+
+#
+#==============================================================================
+def get_json_tree(model, tool, maxdepth=None):
+ """
+ returns the dtree in JSON format
+ """
+ jt = None
+ if tool == "DL85":
+ jt = model.tree_
+ elif tool == "IAI":
+ fname = os.path.splitext(os.path.basename(fname))[0]
+ dir_name = os.path.join("temp", f"{tool}{maxdepth}")
+ try:
+ os.stat(dir_name)
+ except:
+ os.makedirs(dir_name)
+ iai_json = os.path.join(dir_name, fname+'.json')
+ model.write_json(iai_json)
+ print(f'load JSON tree from {iai_json} ...')
+ with open(iai_json) as fp:
+ jt = json.load(fp)
+ elif tool == "ITI":
+ print(f'load JSON tree from {model.json_name} ...')
+ with open(model.json_name) as fp:
+ jt = json.load(fp)
+ #else:
+ # assert False, 'Unhandled model type: {0}'.format(self.tool)
+
+ return jt
+
+#
+#==============================================================================
+class UploadedDecisionTree:
+ """ A decision tree.
+ This object provides a common interface to many different types of models.
+ """
+ def __init__(self, model, tool, maxdepth, feature_names=None, nb_classes = 0):
+ self.tool = tool
+ self.model = model
+ self.tree = None
+ self.depth = None
+ self.n_nodes = None
+ json_tree = get_json_tree(self.model, self.tool, maxdepth)
+ self.tree, self.n_nodes, self.depth = self.build_tree(json_tree, feature_names)
+
+ def print_tree(self):
+ print("DT model:")
+ walk_tree(self.tree)
+
+
+ def dump(self, fvmap, filename=None, maxdepth=None, feat_names=None):
+ """
+ save the dtree and data map in .dt/.map file
+ """
+
+ def walk_tree(node, domains, internal, terminal):
+ """
+ extract internal (non-term) & terminal nodes
+ """
+ if (len(node.children) == 0): # leaf node
+ terminal.append((node.id, node.values))
+ else:
+ assert (node.children[0].id == node.left_node_id)
+ assert (node.children[1].id == node.right_node_id)
+
+ f = f"f{node.feature}"
+
+ if self.tool == "DL85":
+ l,r = (1,0)
+ internal.append((node.id, f, l, node.children[0].id))
+ internal.append((node.id, f, r, node.children[1].id))
+
+ elif self.tool == "ITI":
+ #l,r = (0,1)
+ if len(fvmap[f]) > 2:
+ n = 0
+ for v in fvmap[f]:
+ if (fvmap[f][v][2] == node.threshold) and \
+ (fvmap[f][v][1] == True):
+ l = v
+ n = n + 1
+ if (fvmap[f][v][2] == node.threshold) and \
+ (fvmap[f][v][1] == False):
+ r = v
+ n = n + 1
+
+ assert (n == 2)
+
+ elif (fvmap[f][0][2] == node.threshold):
+ l,r = (0,1)
+ else:
+ assert (fvmap[f][1][2] == node.threshold)
+ l,r = (1,0)
+
+ internal.append((node.id, f, l, node.children[0].id))
+ internal.append((node.id, f, r, node.children[1].id))
+
+ elif self.tool == "IAI":
+ left, right = [], []
+ for p in fvmap[f]:
+ if fvmap[f][p][1] == True:
+ assert (fvmap[f][p][2] in node.split)
+ if node.split[fvmap[f][p][2]]:
+ left.append(p)
+ else:
+ right.append(p)
+
+ internal.extend([(node.id, f, l, node.children[0].id) for l in left])
+ internal.extend([(node.id, f, r, node.children[1].id) for r in right])
+
+ elif self.tool == 'SKL':
+ left, right = [], []
+ for j in domains[f]:
+ if np.float32(fvmap[f][j][2]) <= np.float32(node.threshold):
+ left.append(j)
+ else:
+ right.append(j)
+
+ internal.extend([(node.id, f, l, node.children[0].id) for l in left])
+ internal.extend([(node.id, f, r, node.children[1].id) for r in right])
+
+ dom0, dom1 = dict(), dict()
+ dom0.update(domains)
+ dom1.update(domains)
+ dom0[f] = left
+ dom1[f] = right
+
+ else:
+ assert False, 'Unhandled model type: {0}'.format(self.tool)
+
+
+ internal, terminal = walk_tree(node.children[0], dom0, internal, terminal)
+ internal, terminal = walk_tree(node.children[1], dom1, internal, terminal)
+
+ return internal, terminal
+
+ domains = {f:[j for j in fvmap[f] if((fvmap[f][j][1]))] for f in fvmap}
+ internal, terminal = walk_tree(self.tree, domains, [], [])
+
+
+ dt = f"{self.n_nodes}\n{self.tree.id}\n"
+ dt += f"I {' '.join(dict.fromkeys([str(i) for i,_,_,_ in internal]))}\n"
+ dt +=f"T {' '.join([str(i) for i,_ in terminal ])}\n"
+ for i,c in terminal:
+ dt +=f"{i} T {c}\n"
+ for i,f, j, n in internal:
+ dt +=f"{i} {f} {j} {n}\n"
+
+ map = "Categorical\n"
+ map += f"{len(fvmap)}"
+ for f in fvmap:
+ for v in fvmap[f]:
+ if (fvmap[f][v][1] == True):
+ map += f"\n{f} {v} ={fvmap[f][v][2]}"
+ if (fvmap[f][v][1] == False) and self.tool == "ITI":
+ map += f"\n{f} {v} !={fvmap[f][v][2]}"
+
+
+ if feat_names is not None:
+ features_names_mapping = []
+ for i,fid in enumerate(feat_names):
+ feat=f'f{i}'
+ f = f'T:C,{fid}:{feat},'+",".join([f'{fvmap[feat][v][2]}:{v}' for v in fvmap[feat] if(fvmap[feat][v][1])])
+ features_names_mapping.append(f)
+
+ return dt, map, features_names_mapping
+
+ def convert_dt(self, feat_names):
+ """
+ save dtree in .dt format & generate dtree map from the tree
+ """
+ def walk_tree(node, domains, internal, terminal):
+ """
+ extract internal (non-term) & terminal nodes
+ """
+ if not len(node.children): # leaf node
+ terminal.append((node.id, node.values))
+ else:
+ # internal node
+ f = f"f{node.feature}"
+ left, right = [], []
+ for j in domains[f]:
+ if self.intvs[f][j] <= node.threshold:
+ left.append(j)
+ else:
+ right.append(j)
+
+ internal.extend([(node.id, f, l, node.children[0].id) for l in left])
+ internal.extend([(node.id, f, r, node.children[1].id) for r in right])
+
+ dom0, dom1 = dict(), dict()
+ dom0.update(domains)
+ dom1.update(domains)
+ dom0[f] = left
+ dom1[f] = right
+ #
+ internal, terminal = walk_tree(node.children[0], dom0, internal, terminal)
+ internal, terminal = walk_tree(node.children[1], dom1, internal, terminal)
+
+ return internal, terminal
+
+ assert (self.tool == 'SKL')
+ domains = {f:[j for j in range(len(self.intvs[f]))] for f in self.intvs}
+ internal, terminal = walk_tree(self.tree, domains, [], [])
+
+ dt = f"{self.n_nodes}\n{self.tree.id}\n"
+ dt += f"I {' '.join(dict.fromkeys([str(i) for i,_,_,_ in internal]))}\n"
+ dt += f"T {' '.join([str(i) for i,_ in terminal ])}"
+ for i,c in terminal:
+ dt += f"\n{i} T {c}"
+ for i,f, j, n in internal:
+ dt += f"\n{i} {f} {j} {n}"
+
+ map = "Ordinal\n"
+ map += f"{len(self.intvs)}"
+ for f in self.intvs:
+ for j,t in enumerate(self.intvs[f][:-1]):
+ map += f"\n{f} {j} <={np.round(float(t),4)}"
+ map += f"\n{f} {j+1} >{np.round(float(t),4)}"
+
+
+ if feat_names is not None:
+ features_names_mapping = []
+ for i,fid in enumerate(feat_names):
+ feat=f'f{i}'
+ if feat in self.intvs:
+ f = f'T:O,{fid}:{feat},'
+ f += ",".join([f'{t}:{j}' for j,t in enumerate(self.intvs[feat])])
+ features_names_mapping.append(f)
+
+ return dt, map, features_names_mapping
+
+
+ def build_tree(self, json_tree=None, feature_names=None):
+
+ def extract_data(json_node, idx, depth=0, root=None, feature_names=None):
+ """
+ Incremental Tree Inducer / DL8.5
+ """
+ if (root is None):
+ node = xgnode(idx)
+ else:
+ node = xgnode(idx, parent = root)
+
+ if "feat" in json_node:
+
+ if self.tool == "ITI": #f0, f1, ...,fn
+ node.feature = json_node["feat"][1:]
+ else:
+ node.feature = json_node["feat"] #json DL8.5
+ if (feature_names is not None):
+ node.name = feature_names[node.feature]
+
+ if self.tool == "ITI":
+ node.threshold = json_node[json_node["feat"]]
+
+ node.left_node_id = idx + 1
+ _, idx, d1 = extract_data(json_node['left'], idx+1, depth+1, node, feature_names)
+ node.right_node_id = idx + 1
+ _, idx, d2 = extract_data(json_node['right'], idx+1, depth+1, node, feature_names)
+ depth = max(d1, d2)
+
+ elif "value" in json_node:
+ node.values = json_node["value"]
+
+ return node, idx, depth
+
+
+ def extract_iai(lnr, json_tree, feature_names = None):
+ """
+ Interpretable AI tree
+ """
+
+ json_tree = json_tree['tree_']
+ nodes = []
+ depth = 0
+ for i, json_node in enumerate(json_tree["nodes"]):
+ if json_node["parent"] == -2:
+ node = xgnode(json_node["id"])
+ else:
+ root = nodes[json_node["parent"] - 1]
+ node = xgnode(json_node["id"], parent = root)
+
+ assert (json_node["parent"] > 0)
+ assert (root.id == json_node["parent"])
+
+ if json_node["split_type"] == "LEAF":
+ #node.values = target[json_node["fit"]["class"] - 1]
+ ##assert json_node["fit"]["probs"][node.values] == 1.0
+ node.values = lnr.get_classification_label(node.id)
+ depth = max(depth, lnr.get_depth(node.id))
+
+ assert (json_node["lower_child"] == -2 and json_node["upper_child"] == -2)
+
+ elif json_node["split_type"] == "MIXED":
+ #node.feature = json_node["split_mixed"]["categoric_split"]["feature"] - 1
+ #node.left_node_id = json_node["lower_child"]
+ #node.right_node_id = json_node["upper_child"]
+
+ node.feature = lnr.get_split_feature(node.id)
+ node.left_node_id = lnr.get_lower_child(node.id)
+ node.right_node_id = lnr.get_upper_child(node.id)
+ node.split = lnr.get_split_categories(node.id)
+
+
+ assert (json_node["split_mixed"]["categoric_split"]["feature"] > 0)
+ assert (json_node["lower_child"] > 0)
+ assert (json_node["upper_child"] > 0)
+
+ else:
+ assert False, 'Split feature is not \"categoric_split\"'
+
+ nodes.append(node)
+
+ return nodes[0], json_tree["node_count"], depth
+
+
+ def extract_skl(tree_, classes_, feature_names=None):
+ """
+ scikit-learn tree
+ """
+
+ def get_CART_tree(tree_):
+ n_nodes = tree_.node_count
+ children_left = tree_.children_left
+ children_right = tree_.children_right
+ #feature = tree_.feature
+ #threshold = tree_.threshold
+ #values = tree_.value
+ node_depth = np.zeros(shape=n_nodes, dtype=np.int64)
+
+ is_leaf = np.zeros(shape=n_nodes, dtype=bool)
+ stack = [(0, -1)] # seed is the root node id and its parent depth
+ while len(stack) > 0:
+ node_id, parent_depth = stack.pop()
+ node_depth[node_id] = parent_depth + 1
+
+ # If we have a test node
+ if (children_left[node_id] != children_right[node_id]):
+ stack.append((children_left[node_id], parent_depth + 1))
+ stack.append((children_right[node_id], parent_depth + 1))
+ else:
+ is_leaf[node_id] = True
+
+ return children_left, children_right, is_leaf, node_depth
+
+ children_left, children_right, is_leaf, node_depth = get_CART_tree(tree_)
+ feature = tree_.feature
+ threshold = tree_.threshold
+ values = tree_.value
+ m = tree_.node_count
+ assert (m > 0), "Empty tree"
+ ##
+ self.intvs = {f'f{feature[i]}':set([]) for i in range(tree_.node_count) if not is_leaf[i]}
+ for i in range(tree_.node_count):
+ if not is_leaf[i]:
+ self.intvs[f'f{feature[i]}'].add(threshold[i])
+ self.intvs = {f: sorted(self.intvs[f])+[math.inf] for f in six.iterkeys(self.intvs)}
+
+ def extract_data(idx, root = None, feature_names = None):
+ i = idx
+ assert (i < m), "Error index node"
+ if (root is None):
+ node = xgnode(i)
+ else:
+ node = xgnode(i, parent = root)
+ if is_leaf[i]:
+ node.values = classes_[np.argmax(values[i])]
+ else:
+ node.feature = feature[i]
+ if (feature_names):
+ node.name = feature_names[feature[i]]
+ node.threshold = threshold[i]
+ node.left_node_id = children_left[i]
+ node.right_node_id = children_right[i]
+ extract_data(node.left_node_id, node, feature_names)
+ extract_data(node.right_node_id, node, feature_names)
+
+ return node
+
+ root = extract_data(0, None, feature_names)
+ return root, tree_.node_count, tree_.max_depth
+
+
+
+ root, node_count, maxdepth = None, None, None
+
+ if(self.tool == 'SKL'):
+ if "feature_names_in_" in dir(self.model):
+ feature_names = self.model.feature_names_in_
+ root, node_count, maxdepth = extract_skl(self.model.tree_, self.model.classes_, feature_names)
+
+ if json_tree:
+ if self.tool == "IAI":
+ root, node_count, maxdepth = extract_iai(self.model, json_tree, feature_names)
+ else:
+ root,_,maxdepth = extract_data(json_tree, 1, 0, None, feature_names)
+ node_count = json.dumps(json_tree).count('feat') + json.dumps(json_tree).count('value')
+
+ return root, node_count, maxdepth
\ No newline at end of file
diff --git a/pages/application/application.py b/pages/application/application.py
index 16996a6aa067aa7bf5c30bd20b93793995d256c1..db54577aa48a31e87069afd59ebf34ed6a346ea0 100644
--- a/pages/application/application.py
+++ b/pages/application/application.py
@@ -1,5 +1,6 @@
from dash import dcc, html
import dash_bootstrap_components as dbc
+import dash_daq as daq
from pages.application.DecisionTree.DecisionTreeComponent import DecisionTreeComponent
@@ -19,7 +20,13 @@ class Model():
self.ml_model = ''
self.pretrained_model = ''
- self.typ_data = ''
+
+ self.add_info = False
+ self.model_info = ''
+
+ self.enum=1
+ self.xtype = ['AXp', 'CXp']
+ self.solver="g3"
self.instance = ''
@@ -34,15 +41,35 @@ class Model():
self.component_class = self.dict_components[self.ml_model]
self.component_class = globals()[self.component_class]
- def update_pretrained_model(self, pretrained_model_update, typ_data):
+ def update_pretrained_model(self, pretrained_model_update):
self.pretrained_model = pretrained_model_update
- self.typ_data = typ_data
- self.component = self.component_class(self.pretrained_model, self.typ_data)
- def update_instance(self, instance, enum, xtype, solver="g3"):
+ def update_info_needed(self, add_info):
+ self.add_info = add_info
+
+ def update_pretrained_model_layout(self):
+ self.component = self.component_class(self.pretrained_model)
+
+ def update_pretrained_model_layout_with_info(self, model_info, model_info_filename):
+ self.model_info = model_info
+ self.component = self.component_class(self.pretrained_model, info=self.model_info, type_info=model_info_filename)
+
+ def update_instance(self, instance):
self.instance = instance
- self.list_expls = self.component.update_with_explicability(self.instance, enum, xtype, solver)
+ self.list_expls = self.component.update_with_explicability(self.instance, self.enum, self.xtype, self.solver)
+
+ def update_enum(self, enum):
+ self.enum = enum
+ self.list_expls = self.component.update_with_explicability(self.instance, self.enum, self.xtype, self.solver)
+ def update_xtype(self, xtype):
+ self.xtype = xtype
+ self.list_expls = self.component.update_with_explicability(self.instance, self.enum, self.xtype, self.solver)
+
+ def update_solver(self, solver):
+ self.solver = solver
+ self.list_expls = self.component.update_with_explicability(self.instance, self.enum, self.xtype, self.solver)
+
def update_expl(self, expl):
self.expl = expl
self.component.draw_explanation(self.instance, expl)
@@ -52,11 +79,18 @@ class View():
def __init__(self, model):
self.model = model
- self.ml_menu_models = dcc.Dropdown(self.model.ml_models,
+ self.ml_menu_models = html.Div([
+ html.Br(),
+ html.Label("Choose the Machine Learning algorithm :"),
+ html.Br(),
+ dcc.Dropdown(self.model.ml_models,
id='ml_model_choice',
- className="sidebar-dropdown")
-
+ className="dropdown")])
+
self.pretrained_model_upload = html.Div([
+ html.Hr(),
+ html.Label("Choose the pretrained model : "),
+ html.Br(),
dcc.Upload(
id='ml_pretrained_model_choice',
children=html.Div([
@@ -67,7 +101,32 @@ class View():
),
html.Div(id='pretrained_model_filename')])
+ self.add_model_info_choice = html.Div([
+ html.Hr(),
+ html.Label("Do you wish to upload more info for your model ? : "),
+ html.Br(),
+ daq.BooleanSwitch(id='add_info_model_choice', on=False, color="#000000",)])
+
+ self.model_info = html.Div(id="choice_info_div",
+ hidden=True,
+ children=[
+ html.Hr(),
+ html.Label("Choose the pretrained model dataset (csv) or feature definition file (txt): "),
+ html.Br(),
+ dcc.Upload(
+ id='model_info_choice',
+ children=html.Div([
+ 'Drag and Drop or ',
+ html.A('Select File')
+ ]),
+ className="upload"
+ ),
+ html.Div(id='info_filename')])
+
self.instance_upload = html.Div([
+ html.Hr(),
+ html.Label("Choose the instance to explain : "),
+ html.Br(),
dcc.Upload(
id='ml_instance_choice',
children=html.Div([
@@ -78,21 +137,7 @@ class View():
),
html.Div(id='instance_filename')])
- 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 pretrained model : "),
- html.Br(),
- self.pretrained_model_upload,
- html.Hr(),
- html.Label("Choose the instance to explain : "),
- html.Br(),
- self.instance_upload,
- html.Hr(),
+ self.num_explanation = html.Div([
html.Label("Choose the number of explanations : "),
html.Br(),
dcc.Input(
@@ -100,32 +145,48 @@ class View():
value=1,
type="number",
placeholder="How many explanations ?",
- className="sidebar-dropdown"),
- html.Hr(),
+ className="dropdown"),
+ html.Hr()])
+
+ self.type_explanation = html.Div([
html.Label("Choose the kind of explanation : "),
html.Br(),
dcc.Checklist(
id="explanation_type",
options={'AXp' : "Abductive Explanation", 'CXp': "Contrastive explanation"},
value = ['AXp', 'CXp'],
- className="sidebar-dropdown",
- inline=True)], className="sidebar"),
- dcc.Tab(label='Advanced Parameters', children = [
- html.Hr(),
- html.Label("Choose the SAT solver : "),
+ className="check-boxes",
+ inline=True),
+ html.Hr()])
+
+ self.solver = html.Div([ html.Label("Choose the SAT solver : "),
html.Br(),
- dcc.Dropdown(['g3', 'g4', 'lgl', 'mcb', 'mcm', 'mpl', 'm22', 'mc', 'mgh'], 'g3', id='solver_sat')
- ], className="sidebar")
- ])
+ dcc.Dropdown(['g3', 'g4', 'lgl', 'mcb', 'mcm', 'mpl', 'm22', 'mc', 'mgh'], 'g3', id='solver_sat') ])
+
+ self.sidebar = dcc.Tabs(children=[
+ dcc.Tab(label='Basic Parameters', children = [
+ self.ml_menu_models,
+ self.pretrained_model_upload,
+ self.add_model_info_choice,
+ self.model_info,
+ self.instance_upload], className="sidebar"),
+ dcc.Tab(label='Advanced Parameters', children = [
+ html.Br(),
+ self.num_explanation,
+ self.type_explanation,
+ self.solver
+ ], className="sidebar")])
-
- self.expl_choice = dcc.Dropdown(self.model.list_expls,
+ self.expl_choice = html.Div([html.H5(id = "navigate_label", hidden=True, children="Navigate through the explanations and plot them on the tree : "),
+ html.Div(id='navigate_dropdown', hidden=True,
+ children = [dcc.Dropdown(self.model.list_expls,
id='expl_choice',
- className="dropdown")
+ className="dropdown")])])
- self.layout = dbc.Row([ dbc.Col([self.sidebar], width=3, class_name="sidebar"),
+ 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.Row(self.expl_choice)],
+ 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/requirements.txt b/requirements.txt
index db4cd1fddde22135bee76ca884fda1e72ab6f2c2..4f70ae69cdd7f78304ed576c0745589b7d79e127 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -11,4 +11,6 @@ scipy>=1.2.1
dash_bootstrap_components
dash_interactive_graphviz
python-sat[pblib,aiger]
-pygraphviz
\ No newline at end of file
+pygraphviz==1.9
+anytree==2.8.0
+dash_daq==0.5.0
\ No newline at end of file
diff --git a/tests/adult/adult.pkl b/tests/adult/adult.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..b18b4d6e77cfc501320ebd5650158dd268530d1d
Binary files /dev/null and b/tests/adult/adult.pkl differ
diff --git a/tests/adult/adult_2.pkl b/tests/adult/adult_2.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..5221943e347e0a7b2b83e0ee33e5c603396536a3
Binary files /dev/null and b/tests/adult/adult_2.pkl differ
diff --git a/tests/adult/adult_3.pkl b/tests/adult/adult_3.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..b17bc94372fe8d7b6db981e73bf34d6f37b1d07b
Binary files /dev/null and b/tests/adult/adult_3.pkl differ
diff --git a/tests/adult/adult_data_00000.inst b/tests/adult/adult_data_00000.inst
new file mode 100644
index 0000000000000000000000000000000000000000..c72f4e9e0aec1da4c12fdaa3b585d6d96054a30a
--- /dev/null
+++ b/tests/adult/adult_data_00000.inst
@@ -0,0 +1 @@
+f0=1,f1=9,f2=9,f3=7,f4=9,f5=5,f6=7,f7=0,f8=5,f9=3,f10=0,f11=15
\ No newline at end of file
diff --git a/tests/iris/decision_tree_classifier_20170212.pkl b/tests/iris/decision_tree_classifier_20170212.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..006ccc214168eed2a37f7201ccca66648f1112f7
Binary files /dev/null and b/tests/iris/decision_tree_classifier_20170212.pkl differ
diff --git a/tests/iris/iris.csv b/tests/iris/iris.csv
new file mode 100644
index 0000000000000000000000000000000000000000..1b9d0294d6d589f667daf28efecbd0cb0276c352
--- /dev/null
+++ b/tests/iris/iris.csv
@@ -0,0 +1,151 @@
+"sepal.length","sepal.width","petal.length","petal.width","variety"
+5.1,3.5,1.4,.2,"Setosa"
+4.9,3,1.4,.2,"Setosa"
+4.7,3.2,1.3,.2,"Setosa"
+4.6,3.1,1.5,.2,"Setosa"
+5,3.6,1.4,.2,"Setosa"
+5.4,3.9,1.7,.4,"Setosa"
+4.6,3.4,1.4,.3,"Setosa"
+5,3.4,1.5,.2,"Setosa"
+4.4,2.9,1.4,.2,"Setosa"
+4.9,3.1,1.5,.1,"Setosa"
+5.4,3.7,1.5,.2,"Setosa"
+4.8,3.4,1.6,.2,"Setosa"
+4.8,3,1.4,.1,"Setosa"
+4.3,3,1.1,.1,"Setosa"
+5.8,4,1.2,.2,"Setosa"
+5.7,4.4,1.5,.4,"Setosa"
+5.4,3.9,1.3,.4,"Setosa"
+5.1,3.5,1.4,.3,"Setosa"
+5.7,3.8,1.7,.3,"Setosa"
+5.1,3.8,1.5,.3,"Setosa"
+5.4,3.4,1.7,.2,"Setosa"
+5.1,3.7,1.5,.4,"Setosa"
+4.6,3.6,1,.2,"Setosa"
+5.1,3.3,1.7,.5,"Setosa"
+4.8,3.4,1.9,.2,"Setosa"
+5,3,1.6,.2,"Setosa"
+5,3.4,1.6,.4,"Setosa"
+5.2,3.5,1.5,.2,"Setosa"
+5.2,3.4,1.4,.2,"Setosa"
+4.7,3.2,1.6,.2,"Setosa"
+4.8,3.1,1.6,.2,"Setosa"
+5.4,3.4,1.5,.4,"Setosa"
+5.2,4.1,1.5,.1,"Setosa"
+5.5,4.2,1.4,.2,"Setosa"
+4.9,3.1,1.5,.2,"Setosa"
+5,3.2,1.2,.2,"Setosa"
+5.5,3.5,1.3,.2,"Setosa"
+4.9,3.6,1.4,.1,"Setosa"
+4.4,3,1.3,.2,"Setosa"
+5.1,3.4,1.5,.2,"Setosa"
+5,3.5,1.3,.3,"Setosa"
+4.5,2.3,1.3,.3,"Setosa"
+4.4,3.2,1.3,.2,"Setosa"
+5,3.5,1.6,.6,"Setosa"
+5.1,3.8,1.9,.4,"Setosa"
+4.8,3,1.4,.3,"Setosa"
+5.1,3.8,1.6,.2,"Setosa"
+4.6,3.2,1.4,.2,"Setosa"
+5.3,3.7,1.5,.2,"Setosa"
+5,3.3,1.4,.2,"Setosa"
+7,3.2,4.7,1.4,"Versicolor"
+6.4,3.2,4.5,1.5,"Versicolor"
+6.9,3.1,4.9,1.5,"Versicolor"
+5.5,2.3,4,1.3,"Versicolor"
+6.5,2.8,4.6,1.5,"Versicolor"
+5.7,2.8,4.5,1.3,"Versicolor"
+6.3,3.3,4.7,1.6,"Versicolor"
+4.9,2.4,3.3,1,"Versicolor"
+6.6,2.9,4.6,1.3,"Versicolor"
+5.2,2.7,3.9,1.4,"Versicolor"
+5,2,3.5,1,"Versicolor"
+5.9,3,4.2,1.5,"Versicolor"
+6,2.2,4,1,"Versicolor"
+6.1,2.9,4.7,1.4,"Versicolor"
+5.6,2.9,3.6,1.3,"Versicolor"
+6.7,3.1,4.4,1.4,"Versicolor"
+5.6,3,4.5,1.5,"Versicolor"
+5.8,2.7,4.1,1,"Versicolor"
+6.2,2.2,4.5,1.5,"Versicolor"
+5.6,2.5,3.9,1.1,"Versicolor"
+5.9,3.2,4.8,1.8,"Versicolor"
+6.1,2.8,4,1.3,"Versicolor"
+6.3,2.5,4.9,1.5,"Versicolor"
+6.1,2.8,4.7,1.2,"Versicolor"
+6.4,2.9,4.3,1.3,"Versicolor"
+6.6,3,4.4,1.4,"Versicolor"
+6.8,2.8,4.8,1.4,"Versicolor"
+6.7,3,5,1.7,"Versicolor"
+6,2.9,4.5,1.5,"Versicolor"
+5.7,2.6,3.5,1,"Versicolor"
+5.5,2.4,3.8,1.1,"Versicolor"
+5.5,2.4,3.7,1,"Versicolor"
+5.8,2.7,3.9,1.2,"Versicolor"
+6,2.7,5.1,1.6,"Versicolor"
+5.4,3,4.5,1.5,"Versicolor"
+6,3.4,4.5,1.6,"Versicolor"
+6.7,3.1,4.7,1.5,"Versicolor"
+6.3,2.3,4.4,1.3,"Versicolor"
+5.6,3,4.1,1.3,"Versicolor"
+5.5,2.5,4,1.3,"Versicolor"
+5.5,2.6,4.4,1.2,"Versicolor"
+6.1,3,4.6,1.4,"Versicolor"
+5.8,2.6,4,1.2,"Versicolor"
+5,2.3,3.3,1,"Versicolor"
+5.6,2.7,4.2,1.3,"Versicolor"
+5.7,3,4.2,1.2,"Versicolor"
+5.7,2.9,4.2,1.3,"Versicolor"
+6.2,2.9,4.3,1.3,"Versicolor"
+5.1,2.5,3,1.1,"Versicolor"
+5.7,2.8,4.1,1.3,"Versicolor"
+6.3,3.3,6,2.5,"Virginica"
+5.8,2.7,5.1,1.9,"Virginica"
+7.1,3,5.9,2.1,"Virginica"
+6.3,2.9,5.6,1.8,"Virginica"
+6.5,3,5.8,2.2,"Virginica"
+7.6,3,6.6,2.1,"Virginica"
+4.9,2.5,4.5,1.7,"Virginica"
+7.3,2.9,6.3,1.8,"Virginica"
+6.7,2.5,5.8,1.8,"Virginica"
+7.2,3.6,6.1,2.5,"Virginica"
+6.5,3.2,5.1,2,"Virginica"
+6.4,2.7,5.3,1.9,"Virginica"
+6.8,3,5.5,2.1,"Virginica"
+5.7,2.5,5,2,"Virginica"
+5.8,2.8,5.1,2.4,"Virginica"
+6.4,3.2,5.3,2.3,"Virginica"
+6.5,3,5.5,1.8,"Virginica"
+7.7,3.8,6.7,2.2,"Virginica"
+7.7,2.6,6.9,2.3,"Virginica"
+6,2.2,5,1.5,"Virginica"
+6.9,3.2,5.7,2.3,"Virginica"
+5.6,2.8,4.9,2,"Virginica"
+7.7,2.8,6.7,2,"Virginica"
+6.3,2.7,4.9,1.8,"Virginica"
+6.7,3.3,5.7,2.1,"Virginica"
+7.2,3.2,6,1.8,"Virginica"
+6.2,2.8,4.8,1.8,"Virginica"
+6.1,3,4.9,1.8,"Virginica"
+6.4,2.8,5.6,2.1,"Virginica"
+7.2,3,5.8,1.6,"Virginica"
+7.4,2.8,6.1,1.9,"Virginica"
+7.9,3.8,6.4,2,"Virginica"
+6.4,2.8,5.6,2.2,"Virginica"
+6.3,2.8,5.1,1.5,"Virginica"
+6.1,2.6,5.6,1.4,"Virginica"
+7.7,3,6.1,2.3,"Virginica"
+6.3,3.4,5.6,2.4,"Virginica"
+6.4,3.1,5.5,1.8,"Virginica"
+6,3,4.8,1.8,"Virginica"
+6.9,3.1,5.4,2.1,"Virginica"
+6.7,3.1,5.6,2.4,"Virginica"
+6.9,3.1,5.1,2.3,"Virginica"
+5.8,2.7,5.1,1.9,"Virginica"
+6.8,3.2,5.9,2.3,"Virginica"
+6.7,3.3,5.7,2.5,"Virginica"
+6.7,3,5.2,2.3,"Virginica"
+6.3,2.5,5,1.9,"Virginica"
+6.5,3,5.2,2,"Virginica"
+6.2,3.4,5.4,2.3,"Virginica"
+5.9,3,5.1,1.8,"Virginica"
\ No newline at end of file
diff --git a/tests/iris/iris.pkl b/tests/iris/iris.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..cf2d521581ec57be284bb87b75316940c16fdcb7
Binary files /dev/null and b/tests/iris/iris.pkl differ
diff --git a/tests/iris/iris.txt b/tests/iris/iris.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d356f5d1d011df170da9dacee5ac4b2d6bfc844b
--- /dev/null
+++ b/tests/iris/iris.txt
@@ -0,0 +1,4 @@
+sepal.length,Categorical,7.6,6.8,7.1,4.9,4.4,6.2,6,7.3,5.9,7.4,5.2,5.6,4.8,6.5,5.5,4.6,6.6,6.4,7,4.5,7.2,5.1,5.8,5.3,6.9,6.1,6.7,4.7,7.7,6.3,5.7,7.9,5.4,4.3,5
+sepal.width,Categorical,4.2,4.4,3.1,2.4,2.9,2,3.8,4.1,4,3.2,2.7,3.3,2.2,2.5,2.3,3.6,3.5,3.9,2.8,2.6,3.7,3,3.4
+petal.length,Categorical,4.2,4.9,4.4,6,5.9,5.2,5.6,4.8,1,5.5,4.6,6.6,1.1,3.8,1.5,6.4,4.1,4,4.5,1.6,3.3,1.4,5.1,1.7,5.8,3.5,3.6,5.3,1.9,6.9,6.1,6.7,4.7,3.9,1.2,1.3,6.3,5.7,3.7,5.4,3,4.3,5
+petal.width,Categorical,2.4,.2,1,2,1.1,1.5,.6,.5,2.2,.3,1.6,1.4,2.5,1.7,2.3,1.8,2.1,1.9,1.2,1.3,.1,.4
\ No newline at end of file
diff --git a/tests/iris/iris01.json b/tests/iris/iris01.json
new file mode 100644
index 0000000000000000000000000000000000000000..59c0840b5597ccb4347c3c2041707f0ae1d11aa0
--- /dev/null
+++ b/tests/iris/iris01.json
@@ -0,0 +1,4 @@
+{"sepal.length":4.9,
+"sepal.width":3,
+"petal.length":1.4,
+"petal.width":0.2}
diff --git a/tests/iris/iris2.pkl b/tests/iris/iris2.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..cf2d521581ec57be284bb87b75316940c16fdcb7
Binary files /dev/null and b/tests/iris/iris2.pkl differ
diff --git a/tests/iris/iris_00000.txt b/tests/iris/iris_00000.txt
new file mode 100644
index 0000000000000000000000000000000000000000..108004d0deba35123ce4bdbcb9fa37930d6164ba
--- /dev/null
+++ b/tests/iris/iris_00000.txt
@@ -0,0 +1 @@
+sepal.length=4.3,sepal.width=2.0,petal.length=1.0,petal.width=0.1
\ No newline at end of file
diff --git a/tests/zoo/inst/zoo_00.inst b/tests/zoo/inst/zoo_00.inst
new file mode 100644
index 0000000000000000000000000000000000000000..8d6abc5618927fb2ddf49400932809448ebef26c
--- /dev/null
+++ b/tests/zoo/inst/zoo_00.inst
@@ -0,0 +1 @@
+f0=1,f1=0,f1=0,f1=1,f1=0,f1=0,f1=1,f1=1,f1=1,f1=1,f1=0,f1=0,f1=4,f1=0,f1=0,f1=1
\ No newline at end of file
diff --git a/tests/zoo/inst/zoo_01.inst b/tests/zoo/inst/zoo_01.inst
new file mode 100644
index 0000000000000000000000000000000000000000..4a6df5103d348fbf79e75654cb20f9594d47e842
--- /dev/null
+++ b/tests/zoo/inst/zoo_01.inst
@@ -0,0 +1 @@
+1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1
\ No newline at end of file
diff --git a/tests/zoo/inst/zoo_02.inst b/tests/zoo/inst/zoo_02.inst
new file mode 100644
index 0000000000000000000000000000000000000000..d72c0ae09ad51c4f6ec073b66e773beb18515be4
--- /dev/null
+++ b/tests/zoo/inst/zoo_02.inst
@@ -0,0 +1 @@
+0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0
\ No newline at end of file
diff --git a/tests/zoo/inst/zoo_11.inst b/tests/zoo/inst/zoo_11.inst
new file mode 100644
index 0000000000000000000000000000000000000000..0fa9d7c8a871929cbe518a7be16966fc2f7b9a6d
--- /dev/null
+++ b/tests/zoo/inst/zoo_11.inst
@@ -0,0 +1 @@
+0,1,1,0,1,0,0,0,1,1,0,0,2,1,1,0
\ No newline at end of file
diff --git a/tests/zoo/zoo.csv b/tests/zoo/zoo.csv
new file mode 100644
index 0000000000000000000000000000000000000000..7eb9774cdc5f452baac8ed8d4ea0efe83a466386
--- /dev/null
+++ b/tests/zoo/zoo.csv
@@ -0,0 +1,102 @@
+hair,feathers,eggs,milk,airborne,aquatic,predator,toothed,backbone,breathes,venomous,fins,legs,tail,domestic,catsize,class_type
+1,0,0,1,0,0,1,1,1,1,0,0,4,0,0,1,mammal
+1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,mammal
+0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,fish
+1,0,0,1,0,0,1,1,1,1,0,0,4,0,0,1,mammal
+1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,mammal
+1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,mammal
+1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,1,mammal
+0,0,1,0,0,1,0,1,1,0,0,1,0,1,1,0,fish
+0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,fish
+1,0,0,1,0,0,0,1,1,1,0,0,4,0,1,0,mammal
+1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,mammal
+0,1,1,0,1,0,0,0,1,1,0,0,2,1,1,0,bird
+0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,fish
+0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,invertebrate
+0,0,1,0,0,1,1,0,0,0,0,0,4,0,0,0,invertebrate
+0,0,1,0,0,1,1,0,0,0,0,0,6,0,0,0,invertebrate
+0,1,1,0,1,0,1,0,1,1,0,0,2,1,0,0,bird
+1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,mammal
+0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,1,fish
+0,0,0,1,0,1,1,1,1,1,0,1,0,1,0,1,mammal
+0,1,1,0,1,0,0,0,1,1,0,0,2,1,1,0,bird
+0,1,1,0,1,1,0,0,1,1,0,0,2,1,0,0,bird
+1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,mammal
+0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,1,bird
+0,0,1,0,0,0,0,0,0,1,0,0,6,0,0,0,bug
+0,0,1,0,0,1,1,1,1,1,0,0,4,0,0,0,amphibian
+0,0,1,0,0,1,1,1,1,1,1,0,4,0,0,0,amphibian
+1,0,0,1,1,0,0,1,1,1,0,0,2,1,0,0,mammal
+1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,mammal
+1,0,0,1,0,0,1,1,1,1,0,0,2,0,1,1,mammal
+0,0,1,0,1,0,0,0,0,1,0,0,6,0,0,0,bug
+1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,1,mammal
+1,0,0,1,0,0,0,1,1,1,0,0,2,0,0,1,mammal
+0,1,1,0,1,1,1,0,1,1,0,0,2,1,0,0,bird
+0,0,1,0,0,1,0,1,1,0,0,1,0,1,0,0,fish
+1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,0,mammal
+1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,0,mammal
+0,1,1,0,1,0,1,0,1,1,0,0,2,1,0,0,bird
+0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,fish
+1,0,1,0,1,0,0,0,0,1,1,0,6,0,1,0,bug
+1,0,1,0,1,0,0,0,0,1,0,0,6,0,0,0,bug
+0,1,1,0,0,0,1,0,1,1,0,0,2,1,0,0,bird
+0,0,1,0,1,0,1,0,0,1,0,0,6,0,0,0,bug
+0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,0,bird
+1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,mammal
+1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,mammal
+0,0,1,0,0,1,1,0,0,0,0,0,6,0,0,0,invertebrate
+1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,mammal
+1,0,0,1,0,1,1,1,1,1,0,0,4,1,0,1,mammal
+1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,0,mammal
+1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,mammal
+1,0,1,0,1,0,0,0,0,1,0,0,6,0,0,0,bug
+0,0,1,0,0,1,1,1,1,1,0,0,4,1,0,0,amphibian
+0,0,1,0,0,1,1,0,0,0,0,0,8,0,0,1,invertebrate
+1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,0,mammal
+1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,mammal
+0,1,1,0,0,0,0,0,1,1,0,0,2,1,0,1,bird
+0,1,1,0,1,0,0,0,1,1,0,0,2,1,1,0,bird
+0,1,1,0,0,1,1,0,1,1,0,0,2,1,0,1,bird
+0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,0,bird
+0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,1,fish
+0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,fish
+0,0,1,0,0,0,1,1,1,1,1,0,0,1,0,0,reptile
+1,0,1,1,0,1,1,0,1,1,0,0,4,1,0,1,mammal
+1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,mammal
+1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,1,mammal
+0,0,0,1,0,1,1,1,1,1,0,1,0,1,0,1,mammal
+1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,mammal
+1,0,0,1,0,0,1,1,1,1,0,0,4,1,1,1,mammal
+1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,mammal
+1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,1,mammal
+0,1,1,0,0,0,1,0,1,1,0,0,2,1,0,1,bird
+0,0,0,0,0,0,1,0,0,1,1,0,8,1,0,0,invertebrate
+0,0,1,0,0,1,0,1,1,0,0,1,0,1,0,0,fish
+1,0,0,1,0,1,1,1,1,1,0,1,0,0,0,1,mammal
+1,0,0,1,0,1,1,1,1,1,0,1,2,1,0,1,mammal
+0,0,0,0,0,1,1,1,1,0,1,0,0,1,0,0,reptile
+0,0,1,0,0,1,1,0,0,0,1,0,0,0,0,0,invertebrate
+0,1,1,0,1,1,1,0,1,1,0,0,2,1,0,0,bird
+0,1,1,0,1,1,1,0,1,1,0,0,2,1,0,0,bird
+0,0,1,0,0,0,1,1,1,1,0,0,0,1,0,0,reptile
+0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,invertebrate
+0,0,1,0,0,1,0,1,1,0,0,1,0,1,0,0,fish
+0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,0,bird
+1,0,0,1,0,0,0,1,1,1,0,0,2,1,0,0,mammal
+0,0,1,0,0,1,1,0,0,0,0,0,5,0,0,0,invertebrate
+0,0,1,0,0,1,1,1,1,0,1,1,0,1,0,1,fish
+0,1,1,0,1,1,0,0,1,1,0,0,2,1,0,1,bird
+0,0,1,0,0,0,0,0,0,1,0,0,6,0,0,0,bug
+0,0,1,0,0,1,0,1,1,1,0,0,4,0,0,0,amphibian
+0,0,1,0,0,0,0,0,1,1,0,0,4,1,0,1,reptile
+0,0,1,0,0,0,1,1,1,1,0,0,4,1,0,0,reptile
+0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,1,fish
+1,0,0,1,1,0,0,1,1,1,0,0,2,1,0,0,mammal
+1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,0,mammal
+0,1,1,0,1,0,1,0,1,1,0,0,2,1,0,1,bird
+1,0,0,1,0,0,0,1,1,1,0,0,2,1,0,1,mammal
+1,0,1,0,1,0,0,0,0,1,1,0,6,0,0,0,bug
+1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,mammal
+0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,invertebrate
+0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,0,bird
\ No newline at end of file
diff --git a/tests/zoo/zoo.dt b/tests/zoo/zoo.dt
new file mode 100644
index 0000000000000000000000000000000000000000..a1ee6427913de0385b71077c93d9889e0479c78d
--- /dev/null
+++ b/tests/zoo/zoo.dt
@@ -0,0 +1,85 @@
+41
+1
+I 1 2 3 6 9 10 12 14 17 18 20 21 22 24 28 30 33 35 37 38
+T 4 5 7 8 11 13 15 16 19 23 25 26 27 29 31 32 34 36 39 40 41
+4 T bug
+5 T mammal
+7 T bird
+8 T bug
+11 T mammal
+13 T fish
+15 T bird
+16 T reptile
+19 T mammal
+23 T reptile
+25 T reptile
+26 T bird
+27 T fish
+29 T amphibian
+31 T amphibian
+32 T reptile
+34 T invertebrate
+36 T invertebrate
+39 T invertebrate
+40 T bug
+41 T invertebrate
+1 f4 0 2
+1 f4 1 9
+2 f0 0 3
+2 f0 1 6
+3 f10 0 4
+3 f10 1 5
+6 f12 1 7
+6 f12 5 7
+6 f12 9 7
+6 f12 11 7
+6 f12 3 8
+6 f12 7 8
+9 f15 0 10
+9 f15 1 17
+10 f3 0 11
+10 f3 1 12
+12 f11 0 13
+12 f11 1 14
+14 f12 1 15
+14 f12 9 15
+14 f12 3 16
+14 f12 5 16
+14 f12 7 16
+14 f12 11 16
+17 f8 0 18
+17 f8 1 33
+18 f0 0 19
+18 f0 1 20
+20 f12 1 21
+20 f12 9 21
+20 f12 3 28
+20 f12 5 28
+20 f12 7 28
+20 f12 11 28
+21 f6 0 22
+21 f6 1 27
+22 f10 0 23
+22 f10 1 24
+24 f12 9 25
+24 f12 1 26
+24 f12 3 26
+24 f12 5 26
+24 f12 7 26
+24 f12 11 26
+28 f10 0 29
+28 f10 1 30
+30 f5 0 31
+30 f5 1 32
+33 f5 0 34
+33 f5 1 35
+35 f13 0 36
+35 f13 1 37
+37 f9 0 38
+37 f9 1 41
+38 f12 1 39
+38 f12 5 39
+38 f12 9 39
+38 f12 11 39
+38 f12 3 40
+38 f12 7 40
diff --git a/tests/zoo/zoo.json b/tests/zoo/zoo.json
new file mode 100644
index 0000000000000000000000000000000000000000..506568b6a24d9f5066f1673d69e24a45d1109299
--- /dev/null
+++ b/tests/zoo/zoo.json
@@ -0,0 +1,18 @@
+{
+"hair":1,
+"feathers":0,
+"eggs":0,
+"milk":1,
+"airborne":0,
+"aquatic":0,
+"predator":0,
+"toothed":1,
+"backbone":1,
+"breathes":1,
+"venomous":0,
+"fins":0,
+"legs":6,
+"tail":1,
+"domestic":0,
+"catsize":1
+}
diff --git a/tests/zoo/zoo.map b/tests/zoo/zoo.map
new file mode 100644
index 0000000000000000000000000000000000000000..89838b19a6ef9d3425d66ce4c6ec27f5c3981fdd
--- /dev/null
+++ b/tests/zoo/zoo.map
@@ -0,0 +1,38 @@
+Categorical
+16
+f0 0 =1
+f0 1 =0
+f1 0 =1
+f1 1 =0
+f2 0 =1
+f2 1 =0
+f3 0 =1
+f3 1 =0
+f4 0 =1
+f4 1 =0
+f5 0 =1
+f5 1 =0
+f6 0 =1
+f6 1 =0
+f7 0 =1
+f7 1 =0
+f8 0 =1
+f8 1 =0
+f9 0 =1
+f9 1 =0
+f10 0 =1
+f10 1 =0
+f11 0 =1
+f11 1 =0
+f12 1 =2
+f12 3 =6
+f12 5 =5
+f12 7 =8
+f12 9 =0
+f12 11 =4
+f13 0 =1
+f13 1 =0
+f14 0 =1
+f14 1 =0
+f15 0 =1
+f15 1 =0
diff --git a/tests/zoo/zoo.pkl b/tests/zoo/zoo.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..edfd6540dd861d1260292b0bce1efe6d6a809461
Binary files /dev/null and b/tests/zoo/zoo.pkl differ
diff --git a/utils.py b/utils.py
index a3afff7ac45293e228d2a82e510de33d05ec77b5..42b30c99af3af1b8463b3aa8e29f1834e974e0b3 100644
--- a/utils.py
+++ b/utils.py
@@ -1,6 +1,8 @@
import base64
import io
import pickle
+import joblib
+import json
import numpy as np
from dash import html
@@ -11,28 +13,47 @@ def parse_contents_graph(contents, filename):
decoded = base64.b64decode(content_string)
try:
if '.pkl' in filename:
- data = pickle.load(io.BytesIO(decoded))
- typ = 'pkl'
+ data = joblib.load(io.BytesIO(decoded))
except Exception as e:
print(e)
return html.Div([
'There was an error processing this file.'
])
- return data, typ
+ return data
+
+def parse_contents_data(contents, filename):
+ content_type, content_string = contents.split(',')
+ decoded = base64.b64decode(content_string)
+ try:
+ if '.csv' in filename:
+ data = decoded.decode('utf-8')
+ if '.txt' in filename:
+ data = decoded.decode('utf-8')
+ except Exception as e:
+ print(e)
+ return html.Div([
+ 'There was an error processing this file.'
+ ])
+
+ return data
def parse_contents_instance(contents, filename):
content_type, content_string = contents.split(',')
decoded = base64.b64decode(content_string)
try:
- if 'csv' in filename:
+ if '.csv' in filename:
data = decoded.decode('utf-8')
- elif 'txt' in filename:
+ data = str(data).strip().split(',')
+ data = list(map(lambda i: tuple([i[0], np.float32(i[1])]), [i.split('=') for i in data]))
+ elif '.txt' in filename:
data = decoded.decode('utf-8')
- else :
+ data = str(data).strip().split(',')
+ data = list(map(lambda i: tuple([i[0], np.float32(i[1])]), [i.split('=') for i in data]))
+ elif '.json' in filename:
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]))
+ data = json.loads(data)
+ data = list(tuple(data.items()))
except Exception as e:
print(e)
return html.Div([