From c3433fa9e6ea314bc004e82aa296fcaf0aa532a8 Mon Sep 17 00:00:00 2001
From: Caroline DE POURTALES <cdepourt@montana.irit.fr>
Date: Wed, 8 Jun 2022 12:08:24 +0200
Subject: [PATCH] cleaning instances
---
.../DecisionTree/DecisionTreeComponent.py | 22 ++++----
pages/application/DecisionTree/utils/dtviz.py | 4 +-
.../RandomForest/RandomForestComponent.py | 53 +++++++++----------
.../RandomForest/utils/xrf/xforest.py | 4 --
pages/application/application.py | 2 +-
utils.py | 33 +++++++++---
6 files changed, 63 insertions(+), 55 deletions(-)
diff --git a/pages/application/DecisionTree/DecisionTreeComponent.py b/pages/application/DecisionTree/DecisionTreeComponent.py
index 23dc317..19c7904 100644
--- a/pages/application/DecisionTree/DecisionTreeComponent.py
+++ b/pages/application/DecisionTree/DecisionTreeComponent.py
@@ -4,10 +4,7 @@ from dash import html
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,
- visualize_contrastive_expl)
+from pages.application.DecisionTree.utils.dtviz import *
from pages.application.DecisionTree.utils.upload_tree import UploadedDecisionTree
@@ -116,19 +113,14 @@ class DecisionTreeComponent:
def update_with_explicability(self, instance, enum, xtype, solver):
+ instance = instance[0]
instance_translated = self.translate_instance(instance)
+
self.explanation = []
list_explanations_path = []
list_contrastive_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"}
- )])
-
# Creating a clean and nice text component
# instance plotting
self.explanation.append(html.H4("Instance : \n"))
@@ -147,6 +139,14 @@ class DecisionTreeComponent:
list_explanations_path = explanation["List of path explanation(s)"]
list_contrastive_explanations_path = explanation["List of path contrastive explanation(s)"]
+ # Create graph
+ 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"}
+ )])
+
return list_explanations_path, list_contrastive_explanations_path
def draw_explanation(self, instance, expl):
diff --git a/pages/application/DecisionTree/utils/dtviz.py b/pages/application/DecisionTree/utils/dtviz.py
index 02aa847..5390899 100755
--- a/pages/application/DecisionTree/utils/dtviz.py
+++ b/pages/application/DecisionTree/utils/dtviz.py
@@ -32,13 +32,13 @@ def create_legend(G):
legend.add_edge(edge)
edge = pydot.Edge("e", "f")
- edge.obj_dict['attributes']["label"] = "contrastive explanation"
+ edge.obj_dict['attributes']["label"] = "contrastive \n explanation"
edge.obj_dict['attributes']["color"] = "red"
edge.obj_dict['attributes']["style"] = "dashed"
legend.add_edge(edge)
edge = pydot.Edge("c", "d")
- edge.obj_dict['attributes']["label"] = "instance with explanation"
+ edge.obj_dict['attributes']["label"] = "instance with \n explanation"
edge.obj_dict['attributes']["color"] = "blue"
edge.obj_dict['attributes']["style"] = "dashed"
legend.add_edge(edge)
diff --git a/pages/application/RandomForest/RandomForestComponent.py b/pages/application/RandomForest/RandomForestComponent.py
index fc548d9..dc532d4 100644
--- a/pages/application/RandomForest/RandomForestComponent.py
+++ b/pages/application/RandomForest/RandomForestComponent.py
@@ -11,16 +11,14 @@ class RandomForestComponent:
# Conversion model
self.data = Dataset(info)
- self.data.mapping_features()
if info is not None and 'csv' in type_info:
self.random_forest = XRF(model, self.data.feature_names, self.data.target_name)
# encoding here so not in the explanation
-
self.tree_to_plot = 0
dot_source = tree.export_graphviz(self.random_forest.cls.estimators()[self.tree_to_plot],
- feature_names=self.data.feature_names, class_names=self.data.class_names,
+ feature_names=self.data.feature_names, class_names=list(map(lambda cl : str(cl), self.data.target_name)),
impurity=False, filled=False, rounded=True)
self.network = html.Div([dash_interactive_graphviz.DashInteractiveGraphviz(
dot_source=dot_source, style={"width": "50%",
@@ -29,41 +27,38 @@ class RandomForestComponent:
)])
self.explanation = []
- def update_with_explicability(self, instance, enum_feats=None, xtype=None, solver=None):
-
- instance = instance[0]
- if "=" in instance:
- splitted_instance = [float(v.split('=')[1].strip()) for v in instance.split(',')]
- else:
- splitted_instance = [float(v.strip()) for v in instance.split(',')]
+ def update_with_explicability(self, instances, enum_feats=None, xtype=None, solver=None):
- # Call instance
+ instances = [list(map(lambda feature: feature[1], instance)) for instance in instances]
self.explanation = []
- self.explanation.append(html.H5("Instance"))
- self.explanation.append(html.Hr())
- self.explanation.append(
- html.P(str([tuple((self.data.feature_names[i], str(splitted_instance[i]))) for i in
- range(len(splitted_instance) - 1)]) + " THEN " + str(tuple((self.data.target_name, str(splitted_instance[-1]))))))
- self.explanation.append(html.Hr())
-
- # Call explanation
- explanation_result = None
- if isinstance(self.random_forest, XRF):
- explanation_result = self.random_forest.explain(splitted_instance)
- list_explanations_path = []
- # Creating a clean and nice text component
- for k in explanation_result.keys():
- self.explanation.append(html.H5(k))
+ for instance in instances:
+ self.explanation.append(html.H4("Sample : "))
+ # Call instance
+ self.explanation.append(html.H5("Instance"))
self.explanation.append(html.Hr())
- self.explanation.append(html.P(explanation_result[k]))
+ self.explanation.append(
+ html.P(str([tuple((self.data.feature_names[i], str(instance[i]))) for i in
+ range(len(instance) - 1)]) + " THEN " + str(
+ tuple((self.data.target_name, str(instance[-1]))))))
self.explanation.append(html.Hr())
- return list_explanations_path, []
+ # Call explanation
+ explanation_result = None
+ if isinstance(self.random_forest, XRF):
+ explanation_result = self.random_forest.explain(instance)
+ # Creating a clean and nice text component
+ for k in explanation_result.keys():
+ self.explanation.append(html.H5(k))
+ self.explanation.append(html.Hr())
+ self.explanation.append(html.P(explanation_result[k]))
+ self.explanation.append(html.Hr())
+
+ return [], []
def update_plotted_tree(self, tree_to_plot):
self.tree_to_plot = tree_to_plot
dot_source = tree.export_graphviz(self.random_forest.cls.estimators()[self.tree_to_plot],
- feature_names=self.data.feature_names, class_names=self.data.class_names,
+ feature_names=self.data.feature_names, class_names=list(map(lambda cl : str(cl), self.data.target_name)),
impurity=False, filled=False, rounded=True)
self.network = html.Div([dash_interactive_graphviz.DashInteractiveGraphviz(
dot_source=dot_source, style={"width": "50%",
diff --git a/pages/application/RandomForest/utils/xrf/xforest.py b/pages/application/RandomForest/utils/xrf/xforest.py
index a15685c..749e3c4 100644
--- a/pages/application/RandomForest/utils/xrf/xforest.py
+++ b/pages/application/RandomForest/utils/xrf/xforest.py
@@ -43,10 +43,6 @@ class Dataset(Data):
le.fit(samples[:, -1])
samples[:, -1] = le.transform(samples[:, -1])
self.class_names = le.classes_
- print(le.classes_)
- print(samples[1:4, :])
- else :
- self.class_names = np.unique(samples[:, -1])
samples = np.asarray(samples, dtype=np.float32)
self.X = samples[:, 0: self.nb_features]
diff --git a/pages/application/application.py b/pages/application/application.py
index 2edf406..3f88c12 100644
--- a/pages/application/application.py
+++ b/pages/application/application.py
@@ -219,7 +219,7 @@ class View:
self.tree_to_plot = html.Div(id="choosing_tree", hidden=True,
children=[html.H5("Choose a tree to plot: "),
- html.Div(children=[dcc.Slider(0, 100, 1,
+ html.Div(children=[dcc.Slider(0, 50, 1,
value=0,
id='choice_tree')])])
diff --git a/utils.py b/utils.py
index d171f1b..67e9af7 100644
--- a/utils.py
+++ b/utils.py
@@ -10,6 +10,7 @@ from pages.application.RandomForest.utils import xrf
from pages.application.RandomForest.utils.xrf import *
sys.modules['xrf'] = xrf
+
def parse_contents_graph(contents, filename):
content_type, content_string = contents.split(',')
decoded = base64.b64decode(content_string)
@@ -17,7 +18,7 @@ def parse_contents_graph(contents, filename):
if '.pkl' in filename:
try:
data = joblib.load(io.BytesIO(decoded))
- except :
+ except:
data = pickle.load(io.BytesIO(decoded))
elif '.txt' in filename:
data = decoded.decode('utf-8').strip()
@@ -47,31 +48,47 @@ def parse_contents_data(contents, filename):
return data
+def split_instance_according_to_format(instance, features_names=None):
+ if "=" in instance:
+ splitted_instance = [tuple((v.split('=')[0].strip(), float(v.split('=')[1].strip()))) for v in
+ instance.split(',')]
+ else:
+ if features_names:
+ splitted_instance = [tuple((features_names[i], float(instance.split(',')[i].strip()))) for i in
+ range(len(instance.split(',')))]
+ else:
+ splitted_instance = [tuple(("feature_{0}".format(i), float(instance.split(',')[i].strip()))) for i in
+ range(len(instance.split(',')))]
+
+ return splitted_instance
+
+
def parse_contents_instance(contents, filename):
content_type, content_string = contents.split(',')
decoded = base64.b64decode(content_string)
try:
if '.csv' in filename:
data = decoded.decode('utf-8')
- features_names, data = str(data).strip().split('\n')[:2]
+ features_names = str(data).strip().split('\n')[0]
features_names = str(features_names).strip().split(',')
- data = str(data).strip().split(',')
- data = list(tuple([features_names[i], np.float32(data[i])]) for i in range(len(data)))
+ data = str(data).strip().split('\n')[1:]
+ data = list(map(lambda inst: split_instance_according_to_format(inst, features_names), data))
elif '.txt' 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 = str(data).split('\n')
+ data = list(map(lambda inst: split_instance_according_to_format(inst), data))
elif '.json' in filename:
data = decoded.decode('utf-8').strip()
data = json.loads(data)
- data = list(tuple(data.items()))
+ data = [tuple(data.items())]
elif '.inst' in filename:
data = decoded.decode('utf-8').strip()
data = json.loads(data)
- data = list(tuple(data.items()))
+ data = [tuple(data.items())]
elif '.samples' in filename:
decoded = decoded.decode('utf-8').strip()
data = str(decoded).split('\n')
+ data = list(map(lambda inst: split_instance_according_to_format(inst), data))
except Exception as e:
print(e)
return html.Div([
--
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