diff --git a/callbacks.py b/callbacks.py
index db26d680cc5aebd844896184f88ad7bc61a0c05d..51501420d7f4f33bc11fb603ef079e1db64cd666 100644
--- a/callbacks.py
+++ b/callbacks.py
@@ -6,6 +6,7 @@ from utils import parse_contents_graph, parse_contents_instance, parse_contents_
from pages.application.RandomForest.utils import xrf
from pages.application.RandomForest.utils.xrf import *
+
sys.modules['xrf'] = xrf
@@ -84,7 +85,7 @@ def register_callbacks(page_home, page_course, page_application, app):
prevent_initial_call=True
)
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,
+ model_info_filename, instance_contents, instance_filename, enum, xtype, solver, expl_choice,
cont_expl_choice, id_tree):
ctx = dash.callback_context
if ctx.triggered:
@@ -173,30 +174,15 @@ def register_callbacks(page_home, page_course, page_application, app):
@app.callback(
Output('explanation', 'hidden'),
- Output('interaction_graph', 'hidden'),
- Output('expl_choice', 'options'),
- Output('cont_expl_choice', 'options'),
- Input('ml_model_choice', 'value'),
Input('explanation', 'children'),
Input('explanation_type', 'value'),
prevent_initial_call=True
)
- def layout_buttons_navigate_expls(ml_type, explanation, explanation_type):
- if ml_type != "DecisionTree":
- return True, True, {}, {}
- elif explanation is None or len(explanation_type) == 0:
- return True, True, {}, {}
- elif "AXp" not in explanation_type and "CXp" in explanation_type:
- return False, True, {}, {}
+ def show_explanation_window(explanation, explanation_type):
+ if explanation is None or len(explanation_type) == 0:
+ return True
else:
- options_expls = {}
- options_cont_expls = {}
- model_application = page_application.model
- for i in range(len(model_application.list_expls)):
- options_expls[str(model_application.list_expls[i])] = model_application.list_expls[i]
- for i in range(len(model_application.list_cont_expls)):
- options_cont_expls[str(model_application.list_cont_expls[i])] = model_application.list_cont_expls[i]
- return False, False, options_expls, options_cont_expls
+ return False
@app.callback(
Output('choice_info_div', 'hidden'),
@@ -211,6 +197,7 @@ def register_callbacks(page_home, page_course, page_application, app):
else:
return True
+ ########### RandomForest ###########
@app.callback(
Output('choosing_tree', 'hidden'),
Input('graph', 'children'),
@@ -221,3 +208,25 @@ def register_callbacks(page_home, page_course, page_application, app):
return False
else:
return True
+
+ ########### DecistionTree ###########
+ @app.callback(
+ Output('interaction_graph', 'hidden'),
+ Output('expl_choice', 'options'),
+ Output('cont_expl_choice', 'options'),
+ Input('explanation', 'children'),
+ Input('explanation_type', 'value'),
+ prevent_initial_call=True
+ )
+ def layout_buttons_navigate_expls(explanation, explanation_type):
+ if page_application.model.ml_model == "DecisionTree" and explanation is not None and len(explanation_type) > 0:
+ options_expls = {}
+ options_cont_expls = {}
+ model_application = page_application.model
+ for i in range(len(model_application.list_expls)):
+ options_expls[str(model_application.list_expls[i])] = model_application.list_expls[i]
+ for i in range(len(model_application.list_cont_expls)):
+ options_cont_expls[str(model_application.list_cont_expls[i])] = model_application.list_cont_expls[i]
+ return False, options_expls, options_cont_expls
+ else:
+ return True, {}, {}
diff --git a/pages/RFxp/Classifiers/RF2001/pima/pima_nbestim_50_maxdepth_3.mod.pkl b/pages/RFxp/Classifiers/RF2001/pima/pima_nbestim_50_maxdepth_3.mod.pkl
deleted file mode 100644
index ac92a2373a6e27d1bf1b9537fcb42235914f452f..0000000000000000000000000000000000000000
Binary files a/pages/RFxp/Classifiers/RF2001/pima/pima_nbestim_50_maxdepth_3.mod.pkl and /dev/null differ
diff --git a/pages/RFxp/RFxp.py b/pages/RFxp/RFxp.py
deleted file mode 100755
index 5557e04fe0418a8eae77f70635a9849e46cc829b..0000000000000000000000000000000000000000
--- a/pages/RFxp/RFxp.py
+++ /dev/null
@@ -1,148 +0,0 @@
-#!/usr/bin/env python3
-#-*- coding:utf-8 -*-
-##
-## xprf.py
-##
-## Created on: Oct 08, 2020
-## Author: Yacine Izza
-## E-mail: yacine.izza@univ-toulouse.fr
-##
-
-#
-#==============================================================================
-from __future__ import print_function
-from data import Data
-from options import Options
-import os
-import sys
-import pickle
-import resource
-
-
-from xrf import XRF, RF2001, Dataset
-import numpy as np
-
-
-
-#
-#==============================================================================
-def show_info():
- """
- Print info message.
- """
- print("c RFxp: Random Forest explainer.")
- print('c')
-
-
-#
-#==============================================================================
-def pickle_save_file(filename, data):
- try:
- f = open(filename, "wb")
- pickle.dump(data, f)
- f.close()
- except:
- print("Cannot save to file", filename)
- exit()
-
-def pickle_load_file(filename):
- try:
- f = open(filename, "rb")
- data = pickle.load(f)
- f.close()
- return data
- except Exception as e:
- print(e)
- print("Cannot load from file", filename)
- exit()
-
-
-#
-#==============================================================================
-if __name__ == '__main__':
- # parsing command-line options
- options = Options(sys.argv)
-
- # making output unbuffered
- if sys.version_info.major == 2:
- sys.stdout = os.fdopen(sys.stdout.fileno(), 'w', 0)
-
- # showing head
- show_info()
-
-
-
- if options.files:
- cls = None
- xrf = None
-
- print("loading data ...")
- data = Dataset(filename=options.files[0],
- separator=options.separator, use_categorical = options.use_categorical)
-
- if options.train:
- '''
- data = Dataset(filename=options.files[0], mapfile=options.mapfile,
- separator=options.separator,
- use_categorical = options.use_categorical)
- '''
- params = {'n_trees': options.n_estimators,
- 'depth': options.maxdepth}
- cls = RF2001(**params)
- train_accuracy, test_accuracy = cls.train(data)
-
- if options.verb == 1:
- print("----------------------")
- print("Train accuracy: {0:.2f}".format(100. * train_accuracy))
- print("Test accuracy: {0:.2f}".format(100. * test_accuracy))
- print("----------------------")
-
- xrf = XRF(cls, data.feature_names, data.target_name, options.verb)
- #xrf.test_tree_ensemble()
-
- bench_name = os.path.basename(options.files[0])
- assert (bench_name.endswith('.csv'))
- bench_name = os.path.splitext(bench_name)[0]
- bench_dir_name = options.output + "/RF2001/" + bench_name
- try:
- os.stat(bench_dir_name)
- except:
- os.makedirs(bench_dir_name)
-
- basename = (os.path.join(bench_dir_name, bench_name +
- "_nbestim_" + str(options.n_estimators) +
- "_maxdepth_" + str(options.maxdepth)))
-
- modfile = basename + '.mod.pkl'
- print("saving model to ", modfile)
- pickle_save_file(modfile, cls)
-
-
- # read a sample from options.explain
- if options.explain:
- options.explain = [float(v.strip()) for v in options.explain.split(',')]
-
- if not xrf:
- print("loading model ...")
- cls = pickle_load_file(options.files[1])
- #print()
- #print("class skl:",cls.forest.classes_)
- #print("feat names:",data.feature_names)
- #print("extended name:",data.extended_feature_names_as_array_strings)
- #print("target:",data.target_name)
- #print()
- xrf = XRF(cls, data.feature_names, data.target_name, options.verb)
- if options.verb:
- # print test accuracy of the RF model
- _, X_test, _, y_test = data.train_test_split()
- X_test = data.transform(X_test)
- cls.print_accuracy(X_test, y_test)
-
- expl = xrf.explain(options.explain, options.xtype)
-
- print(f"expl len: {len(expl)}")
-
- del xrf.enc
- del xrf.x
-
-
\ No newline at end of file
diff --git a/pages/RFxp/data.py b/pages/RFxp/data.py
deleted file mode 100644
index 6c1546db94bb0fc26706bd197392c6babc40f114..0000000000000000000000000000000000000000
--- a/pages/RFxp/data.py
+++ /dev/null
@@ -1,168 +0,0 @@
-#!/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 os, pickle
-import six
-from six.moves import range
-import numpy as np
-
-
-#
-#==============================================================================
-class Data(object):
- """
- Class for representing data (transactions).
- """
-
- def __init__(self, filename=None, fpointer=None, mapfile=None,
- separator=',', use_categorical = False):
- """
- Constructor and parser.
- """
-
- self.names = None
- self.nm2id = None
- self.samps = None
- self.wghts = None
- self.feats = None
- self.fvmap = None
- self.ovmap = {}
- self.fvars = None
- self.fname = filename
- self.mname = mapfile
- self.deleted = set([])
-
- if filename:
- with open(filename, 'r') as fp:
- self.parse(fp, separator)
- elif fpointer:
- self.parse(fpointer, separator)
-
- if self.mname:
- self.read_orig_values()
-
- # check if we have extra info about categorical_features
-
- if (use_categorical):
- extra_file = filename+".pkl"
- try:
- f = open(extra_file, "rb")
- print("Attempt: loading extra data from ", extra_file)
- extra_info = pickle.load(f)
- print("loaded")
- f.close()
- self.categorical_features = extra_info["categorical_features"]
- self.categorical_names = extra_info["categorical_names"]
- self.class_names = extra_info["class_names"]
- self.categorical_onehot_names = extra_info["categorical_names"].copy()
-
- for i, name in enumerate(self.class_names):
- self.class_names[i] = str(name).replace("b'","'")
- for c in self.categorical_names.items():
- clean_feature_names = []
- for i, name in enumerate(c[1]):
- name = str(name).replace("b'","'")
- clean_feature_names.append(name)
- self.categorical_names[c[0]] = clean_feature_names
-
- except Exception as e:
- f.close()
- print("Please provide info about categorical features or omit option -c", e)
- exit()
-
- def parse(self, fp, separator):
- """
- Parse input file.
- """
-
- # reading data set from file
- lines = fp.readlines()
-
- # reading preamble
- self.names = lines[0].strip().split(separator)
- self.feats = [set([]) for n in self.names]
- del(lines[0])
-
- # 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.samps, self.wghts = [], []
-
- for line, w in six.iteritems(collections.Counter(lines)):
- sample = line.strip().split(separator)
- for i, f in enumerate(sample):
- if f:
- self.feats[i].add(f)
- self.samps.append(sample)
- self.wghts.append(w)
-
- # direct and opposite mappings for items
- idpool = itertools.count(start=0)
- FVMap = collections.namedtuple('FVMap', ['dir', 'opp'])
- self.fvmap = FVMap(dir={}, opp={})
-
- # mapping features to ids
- for i in range(len(self.names) - 1):
- feats = sorted(list(self.feats[i]), reverse=True)
- if len(feats) > 2:
- for l in feats:
- self.fvmap.dir[(self.names[i], l)] = l
- else:
- self.fvmap.dir[(self.names[i], feats[0])] = 1
- if len(feats) == 2:
- self.fvmap.dir[(self.names[i], feats[1])] = 0
-
- # opposite mapping
- for key, val in six.iteritems(self.fvmap.dir):
- self.fvmap.opp[val] = key
-
- # determining feature variables (excluding class variables)
- for v, pair in six.iteritems(self.fvmap.opp):
- if pair[0] == self.names[-1]:
- self.fvars = v - 1
- break
-
- def read_orig_values(self):
- """
- Read original values for all the features.
- (from a separate CSV file)
- """
-
- self.ovmap = {}
-
- for line in open(self.mname, 'r'):
- featval, bits = line.strip().split(',')
- feat, val = featval.split(':')
-
- for i, b in enumerate(bits):
- f = '{0}:b{1}'.format(feat, i + 1)
- v = self.fvmap.dir[(f, '1')]
-
- if v not in self.ovmap:
- self.ovmap[v] = [feat]
-
- if -v not in self.ovmap:
- self.ovmap[-v] = [feat]
-
- self.ovmap[v if b == '1' else -v].append(val)
diff --git a/pages/RFxp/options.py b/pages/RFxp/options.py
deleted file mode 100644
index 446eb71702f91876d0f8bef64fd90d048dd95282..0000000000000000000000000000000000000000
--- a/pages/RFxp/options.py
+++ /dev/null
@@ -1,154 +0,0 @@
-#!/usr/bin/env python
-#-*- coding:utf-8 -*-
-##
-## options.py
-##
-## Created on: Dec 7, 2018
-## Author: Alexey Ignatiev, Nina Narodytska
-## E-mail: aignatiev@ciencias.ulisboa.pt, narodytska@vmware.com
-##
-
-#
-#==============================================================================
-from __future__ import print_function
-import getopt
-import math
-import os
-import sys
-
-
-#
-#==============================================================================
-class Options(object):
- """
- Class for representing command-line options.
- """
-
- def __init__(self, command):
- """
- Constructor.
- """
-
- # actions
- self.train = False
- self.encode = 'none'
- self.explain = ''
- self.xtype = 'abd'
- self.use_categorical = False
-
- # training options
- self.accmin = 0.95
- self.n_estimators = 100
- self.maxdepth = 3
- self.testsplit = 0.2
- self.seed = 7
-
- # other options
- self.files = None
- self.output = 'Classifiers'
- self.mapfile = None
- self.separator = ','
- self.smallest = False
- self.solver = 'g3'
- self.verb = 0
-
-
- if command:
- self.parse(command)
-
- def parse(self, command):
- """
- Parser.
- """
-
- self.command = command
-
- try:
- opts, args = getopt.getopt(command[1:],
- 'e:hc:d:Mn:o:s:tvx:X:',
- ['encode=', 'help', 'use-categorical=',
- 'maxdepth=', 'minimum', 'nbestims=',
- 'output=', 'seed=', 'solver=', 'testsplit=',
- 'train', 'verbose', 'explain=', 'xtype=' ])
- except getopt.GetoptError as err:
- sys.stderr.write(str(err).capitalize())
- self.usage()
- sys.exit(1)
-
- for opt, arg in opts:
- if opt in ('-a', '--accmin'):
- self.accmin = float(arg)
- elif opt in ('-c', '--use-categorical'):
- self.use_categorical = True
- elif opt in ('-d', '--maxdepth'):
- self.maxdepth = int(arg)
- elif opt in ('-e', '--encode'):
- self.encode = str(arg)
- elif opt in ('-h', '--help'):
- self.usage()
- sys.exit(0)
-
- elif opt in ('-M', '--minimum'):
- self.smallest = True
- elif opt in ('-n', '--nbestims'):
- self.n_estimators = int(arg)
- elif opt in ('-o', '--output'):
- self.output = str(arg)
-
- elif opt == '--seed':
- self.seed = int(arg)
- elif opt == '--sep':
- self.separator = str(arg)
- elif opt in ('-s', '--solver'):
- self.solver = str(arg)
- elif opt == '--testsplit':
- self.testsplit = float(arg)
- elif opt in ('-t', '--train'):
- self.train = True
- elif opt in ('-v', '--verbose'):
- self.verb += 1
- elif opt in ('-x', '--explain'):
- self.explain = str(arg)
- elif opt in ('-X', '--xtype'):
- self.xtype = str(arg)
- else:
- assert False, 'Unhandled option: {0} {1}'.format(opt, arg)
-
- if self.encode == 'none':
- self.encode = None
-
- self.files = args
-
- def usage(self):
- """
- Print usage message.
- """
-
- print('Usage: ' + os.path.basename(self.command[0]) + ' [options] input-file')
- print('Options:')
- #print(' -a, --accmin=<float> Minimal accuracy')
- #print(' Available values: [0.0, 1.0] (default = 0.95)')
- #print(' -c, --use-categorical Treat categorical features as categorical (with categorical features info if available)')
- print(' -d, --maxdepth=<int> Maximal depth of a tree')
- print(' Available values: [1, INT_MAX] (default = 3)')
- #print(' -e, --encode=<smt> Encode a previously trained model')
- #print(' Available values: sat, maxsat, none (default = none)')
- print(' -h, --help Show this message')
-
- #print(' -m, --map-file=<string> Path to a file containing a mapping to original feature values. (default: none)')
- #print(' -M, --minimum Compute a smallest size explanation (instead of a subset-minimal one)')
- print(' -n, --nbestims=<int> Number of trees in the ensemble')
- print(' Available values: [1, INT_MAX] (default = 100)')
- print(' -o, --output=<string> Directory where output files will be stored (default: \'temp\')')
-
- print(' --seed=<int> Seed for random splitting')
- print(' Available values: [1, INT_MAX] (default = 7)')
- print(' --sep=<string> Field separator used in input file (default = \',\')')
- print(' -s, --solver=<string> A SAT oracle to use')
- print(' Available values: glucose3, minisat (default = g3)')
- print(' -t, --train Train a model of a given dataset')
- print(' --testsplit=<float> Training and test sets split')
- print(' Available values: [0.0, 1.0] (default = 0.2)')
- print(' -v, --verbose Increase verbosity level')
- print(' -x, --explain=<string> Explain a decision for a given comma-separated sample (default: none)')
- print(' -X, --xtype=<string> Type of explanation to compute: abductive or contrastive')
diff --git a/pages/RFxp/pima.csv b/pages/RFxp/pima.csv
deleted file mode 100644
index f3fac60936efb97c6c201c1d29b858c362e3d189..0000000000000000000000000000000000000000
--- a/pages/RFxp/pima.csv
+++ /dev/null
@@ -1,769 +0,0 @@
-Pregnant,plasma glucose,Diastolic blood pressure,Triceps skin fold thickness,2-Hour serum insulin,Body mass index,Diabetes pedigree function,Age,target
-4.0,117.0,62.0,12.0,0.0,29.7,0.38,30.0,1
-4.0,158.0,78.0,0.0,0.0,32.9,0.8029999999999999,31.0,1
-2.0,118.0,80.0,0.0,0.0,42.9,0.693,21.0,1
-13.0,129.0,0.0,30.0,0.0,39.9,0.569,44.0,1
-5.0,162.0,104.0,0.0,0.0,37.7,0.151,52.0,1
-7.0,114.0,64.0,0.0,0.0,27.4,0.732,34.0,1
-6.0,102.0,82.0,0.0,0.0,30.8,0.18,36.0,1
-1.0,196.0,76.0,36.0,249.0,36.5,0.875,29.0,1
-9.0,102.0,76.0,37.0,0.0,32.9,0.665,46.0,1
-7.0,161.0,86.0,0.0,0.0,30.4,0.165,47.0,1
-7.0,114.0,66.0,0.0,0.0,32.8,0.258,42.0,1
-4.0,184.0,78.0,39.0,277.0,37.0,0.264,31.0,1
-0.0,137.0,40.0,35.0,168.0,43.1,2.2880000000000003,33.0,1
-6.0,125.0,76.0,0.0,0.0,33.8,0.121,54.0,1
-11.0,155.0,76.0,28.0,150.0,33.3,1.3530000000000002,51.0,1
-7.0,187.0,50.0,33.0,392.0,33.9,0.826,34.0,1
-7.0,178.0,84.0,0.0,0.0,39.9,0.331,41.0,1
-0.0,180.0,66.0,39.0,0.0,42.0,1.893,25.0,1
-8.0,120.0,86.0,0.0,0.0,28.4,0.259,22.0,1
-2.0,105.0,80.0,45.0,191.0,33.7,0.711,29.0,1
-0.0,118.0,84.0,47.0,230.0,45.8,0.551,31.0,1
-7.0,150.0,78.0,29.0,126.0,35.2,0.6920000000000001,54.0,1
-1.0,149.0,68.0,29.0,127.0,29.3,0.349,42.0,1
-8.0,188.0,78.0,0.0,0.0,47.9,0.13699999999999998,43.0,1
-3.0,173.0,78.0,39.0,185.0,33.8,0.97,31.0,1
-0.0,189.0,104.0,25.0,0.0,34.3,0.435,41.0,1
-9.0,164.0,84.0,21.0,0.0,30.8,0.831,32.0,1
-4.0,131.0,68.0,21.0,166.0,33.1,0.16,28.0,0
-6.0,85.0,78.0,0.0,0.0,31.2,0.382,42.0,0
-5.0,143.0,78.0,0.0,0.0,45.0,0.19,47.0,0
-4.0,110.0,66.0,0.0,0.0,31.9,0.47100000000000003,29.0,0
-10.0,115.0,0.0,0.0,0.0,35.3,0.134,29.0,0
-5.0,73.0,60.0,0.0,0.0,26.8,0.268,27.0,0
-7.0,106.0,92.0,18.0,0.0,22.7,0.235,48.0,0
-0.0,98.0,82.0,15.0,84.0,25.2,0.299,22.0,0
-2.0,88.0,58.0,26.0,16.0,28.4,0.7659999999999999,22.0,0
-1.0,73.0,50.0,10.0,0.0,23.0,0.248,21.0,0
-6.0,144.0,72.0,27.0,228.0,33.9,0.255,40.0,0
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-1.0,95.0,82.0,25.0,180.0,35.0,0.233,43.0,1
-4.0,134.0,72.0,0.0,0.0,23.8,0.27699999999999997,60.0,1
-4.0,144.0,82.0,32.0,0.0,38.5,0.5539999999999999,37.0,1
-4.0,173.0,70.0,14.0,168.0,29.7,0.361,33.0,1
-0.0,105.0,84.0,0.0,0.0,27.9,0.741,62.0,1
-10.0,129.0,62.0,36.0,0.0,41.2,0.441,38.0,1
-1.0,199.0,76.0,43.0,0.0,42.9,1.3940000000000001,22.0,1
-0.0,109.0,88.0,30.0,0.0,32.5,0.855,38.0,1
-7.0,196.0,90.0,0.0,0.0,39.8,0.451,41.0,1
-7.0,159.0,66.0,0.0,0.0,30.4,0.38299999999999995,36.0,1
-1.0,115.0,70.0,30.0,96.0,34.6,0.529,32.0,1
-1.0,172.0,68.0,49.0,579.0,42.4,0.7020000000000001,28.0,1
-11.0,120.0,80.0,37.0,150.0,42.3,0.785,48.0,1
-2.0,134.0,70.0,0.0,0.0,28.9,0.542,23.0,1
-6.0,148.0,72.0,35.0,0.0,33.6,0.627,50.0,1
-1.0,126.0,60.0,0.0,0.0,30.1,0.349,47.0,1
-7.0,187.0,68.0,39.0,304.0,37.7,0.254,41.0,1
-9.0,119.0,80.0,35.0,0.0,29.0,0.263,29.0,1
-6.0,115.0,60.0,39.0,0.0,33.7,0.245,40.0,1
-7.0,136.0,74.0,26.0,135.0,26.0,0.647,51.0,0
-0.0,120.0,74.0,18.0,63.0,30.5,0.285,26.0,0
-5.0,116.0,74.0,0.0,0.0,25.6,0.201,30.0,0
-4.0,128.0,70.0,0.0,0.0,34.3,0.303,24.0,0
-6.0,96.0,0.0,0.0,0.0,23.7,0.19,28.0,0
-2.0,127.0,46.0,21.0,335.0,34.4,0.17600000000000002,22.0,0
-4.0,76.0,62.0,0.0,0.0,34.0,0.391,25.0,0
-3.0,96.0,56.0,34.0,115.0,24.7,0.9440000000000001,39.0,0
-6.0,137.0,61.0,0.0,0.0,24.2,0.151,55.0,0
-3.0,111.0,58.0,31.0,44.0,29.5,0.43,22.0,0
-2.0,81.0,60.0,22.0,0.0,27.7,0.29,25.0,0
-1.0,77.0,56.0,30.0,56.0,33.3,1.251,24.0,0
-3.0,111.0,62.0,0.0,0.0,22.6,0.142,21.0,0
-6.0,166.0,74.0,0.0,0.0,26.6,0.304,66.0,0
-1.0,143.0,86.0,30.0,330.0,30.1,0.892,23.0,0
-0.0,107.0,60.0,25.0,0.0,26.4,0.133,23.0,0
-2.0,99.0,70.0,16.0,44.0,20.4,0.235,27.0,0
-2.0,100.0,68.0,25.0,71.0,38.5,0.324,26.0,0
-2.0,120.0,54.0,0.0,0.0,26.8,0.455,27.0,0
-1.0,111.0,94.0,0.0,0.0,32.8,0.265,45.0,0
-6.0,108.0,44.0,20.0,130.0,24.0,0.813,35.0,0
-3.0,113.0,50.0,10.0,85.0,29.5,0.626,25.0,0
-4.0,141.0,74.0,0.0,0.0,27.6,0.244,40.0,0
-2.0,99.0,0.0,0.0,0.0,22.2,0.10800000000000001,23.0,0
-8.0,85.0,55.0,20.0,0.0,24.4,0.136,42.0,0
-1.0,89.0,76.0,34.0,37.0,31.2,0.192,23.0,0
-1.0,109.0,58.0,18.0,116.0,28.5,0.21899999999999997,22.0,0
-1.0,93.0,70.0,31.0,0.0,30.4,0.315,23.0,0
-12.0,140.0,85.0,33.0,0.0,37.4,0.244,41.0,0
-1.0,80.0,55.0,0.0,0.0,19.1,0.258,21.0,0
-4.0,99.0,72.0,17.0,0.0,25.6,0.294,28.0,0
-1.0,109.0,60.0,8.0,182.0,25.4,0.9470000000000001,21.0,0
-3.0,113.0,44.0,13.0,0.0,22.4,0.14,22.0,0
-0.0,95.0,80.0,45.0,92.0,36.5,0.33,26.0,0
-4.0,123.0,80.0,15.0,176.0,32.0,0.44299999999999995,34.0,0
-2.0,112.0,75.0,32.0,0.0,35.7,0.14800000000000002,21.0,0
-2.0,92.0,62.0,28.0,0.0,31.6,0.13,24.0,0
-1.0,144.0,82.0,40.0,0.0,41.3,0.607,28.0,0
-6.0,91.0,0.0,0.0,0.0,29.8,0.501,31.0,0
-0.0,124.0,56.0,13.0,105.0,21.8,0.452,21.0,0
-5.0,132.0,80.0,0.0,0.0,26.8,0.18600000000000005,69.0,0
-9.0,91.0,68.0,0.0,0.0,24.2,0.2,58.0,0
-3.0,128.0,78.0,0.0,0.0,21.1,0.268,55.0,0
-0.0,108.0,68.0,20.0,0.0,27.3,0.787,32.0,0
-2.0,112.0,68.0,22.0,94.0,34.1,0.315,26.0,0
-1.0,81.0,74.0,41.0,57.0,46.3,1.0959999999999999,32.0,0
-4.0,94.0,65.0,22.0,0.0,24.7,0.14800000000000002,21.0,0
-3.0,158.0,64.0,13.0,387.0,31.2,0.295,24.0,0
-0.0,57.0,60.0,0.0,0.0,21.7,0.735,67.0,0
-4.0,95.0,60.0,32.0,0.0,35.4,0.284,28.0,0
diff --git a/pages/RFxp/xrf/__init__.py b/pages/RFxp/xrf/__init__.py
deleted file mode 100644
index 9f52257095bc5c4ad22ff8810d2db39830109b31..0000000000000000000000000000000000000000
--- a/pages/RFxp/xrf/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-#from .tree import *
-from .rndmforest import *
-from .xforest import *
\ No newline at end of file
diff --git a/pages/RFxp/xrf/rndmforest.py b/pages/RFxp/xrf/rndmforest.py
deleted file mode 100644
index 62dd80f373fc971f0414fbe825c0058d6f6149c2..0000000000000000000000000000000000000000
--- a/pages/RFxp/xrf/rndmforest.py
+++ /dev/null
@@ -1,137 +0,0 @@
-from sklearn.ensemble._voting import VotingClassifier
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.preprocessing import OneHotEncoder, LabelEncoder
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import accuracy_score
-import numpy as np
-import sys
-import os
-import resource
-
-import collections
-from itertools import combinations
-from six.moves import range
-import six
-import math
-
-
-
-#
-#==============================================================================
-class VotingRF(VotingClassifier):
- """
- Majority rule classifier
- """
-
- def fit(self, X, y, sample_weight=None):
- self.estimators_ = []
- for _, est in self.estimators:
- self.estimators_.append(est)
-
- self.le_ = LabelEncoder().fit(y)
- self.classes_ = self.le_.classes_
-
-
- def predict(self, X):
- """Predict class labels for X.
- Parameters
- ----------
- X : {array-like, sparse matrix} of shape (n_samples, n_features)
- The input samples.
- Returns
- -------
- maj : array-like of shape (n_samples,)
- Predicted class labels.
- """
- #check_is_fitted(self)
-
- # 'hard' voting
- predictions = self._predict(X)
- predictions = np.asarray(predictions, np.int64) #NEED TO BE CHECKED
- maj = np.apply_along_axis(
- lambda x: np.argmax(
- np.bincount(x, weights=self._weights_not_none)),
- axis=1, arr=predictions)
-
- maj = self.le_.inverse_transform(maj)
-
- return maj
-
-
-#
-#==============================================================================
-class RF2001(object):
- """
- The main class to train Random Forest Classifier (RFC).
- """
-
- def __init__(self, **options):
- """
- Constructor.
- """
- self.forest = None
- self.voting = None
-
- param_dist = {'n_estimators':options['n_trees'],
- 'max_depth':options['depth'],
- 'criterion':'entropy',
- 'random_state':324089}
-
- self.forest = RandomForestClassifier(**param_dist)
-
- def fit(self, X_train, y_train):
- """
- building Breiman'01 Random Forest
- (similar to train(dataset) fnc)
- """
- self.forest.fit(X_train,y_train)
- rtrees = [ ('dt', dt) for i, dt in enumerate(self.forest.estimators_)]
- self.voting = VotingRF(estimators=rtrees)
- self.voting.fit(X_train,y_train)
-
- return self
-
-
- def train(self, dataset, verb=0):
- """
- Train a random forest.
- """
-
- X_train, X_test, y_train, y_test = dataset.train_test_split()
-
- X_train = dataset.transform(X_train)
- X_test = dataset.transform(X_test)
-
- print("Build a random forest.")
- self.forest.fit(X_train,y_train)
-
- rtrees = [ ('dt', dt) for i, dt in enumerate(self.forest.estimators_)]
- self.voting = VotingRF(estimators=rtrees)
- self.voting.fit(X_train,y_train)
-
- train_acc = accuracy_score(self.predict(X_train), y_train)
- test_acc = accuracy_score(self.predict(X_test), y_test)
-
- if verb > 1:
- self.print_acc_vote(X_train, X_test, y_train, y_test)
- self.print_acc_prob(X_train, X_test, y_train, y_test)
-
- return train_acc, test_acc
-
- def predict(self, X):
- return self.voting.predict(X)
-
- def predict_prob(self, X):
- self.forest.predict(X)
-
- def estimators(self):
- assert(self.forest.estimators_ is not None)
- return self.forest.estimators_
-
- def n_estimators(self):
- return self.forest.n_estimators
-
- def print_accuracy(self, X_test, y_test):
- test_acc = accuracy_score(self.predict(X_test), y_test)
- print("c Model accuracy: {0:.2f}".format(100. * test_acc))
- #print("----------------------")
\ No newline at end of file
diff --git a/pages/RFxp/xrf/tree.py b/pages/RFxp/xrf/tree.py
deleted file mode 100644
index 5fddabd0dc27b0be6672903cbdc6085fbbcaf898..0000000000000000000000000000000000000000
--- a/pages/RFxp/xrf/tree.py
+++ /dev/null
@@ -1,174 +0,0 @@
-#
-#==============================================================================
-from anytree import Node, RenderTree,AsciiStyle
-import json
-import numpy as np
-import math
-import os
-
-
-#
-#==============================================================================
-class dt_node(Node):
- def __init__(self, id, parent = None):
- Node.__init__(self, id, parent)
- self.id = id # The 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+ "leaf: {} {}".format(self.id, self.values))
- else:
- if(self.name is None):
- return (pref+ "{} f{}<{}".format(self.id, self.feature, self.threshold))
- else:
- return (pref+ "{} \"{}\"<{}".format(self.id, self.name, self.threshold))
-
-
-#==============================================================================
-def build_tree(tree_, feature_names = None):
- ##
- feature = tree_.feature
- threshold = tree_.threshold
- values = tree_.value
- n_nodes = tree_.node_count
- children_left = tree_.children_left
- children_right = tree_.children_right
- 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
- ##
-
- m = tree_.node_count
- assert (m > 0), "Empty tree"
-
- def extract_data(idx, root = None, feature_names = None):
- i = idx
- assert (i < m), "Error index node"
- if (root is None):
- node = dt_node(i)
- else:
- node = dt_node(i, parent = root)
- #node.cover = json_node["cover"]
- if is_leaf[i]:
- node.values = np.argmax(values[i])
- #if(inverse):
- # node.values = -node.values
- else:
- node.feature = feature[i]
- if (feature_names is not None):
- 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) #feat < threshold ( < 0.5 False)
- extract_data(node.right_node_id, node, feature_names) #feat >= threshold ( >= 0.5 True)
-
- return node
-
- root = extract_data(0, None, feature_names)
-
- return root
-
-
-#==============================================================================
-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 count_nodes(root):
- def count(node):
- if len(node.children):
- return sum([1+count(n) for n in node.children])
- else:
- return 0
- m = count(root) + 1
- return m
-
-#
-#==============================================================================
-def predict_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 predict_tree(node.children[0], sample)
- else:
- return predict_tree(node.children[1], sample)
-
-
-#
-#==============================================================================
-class Forest:
- """ An ensemble of decision trees.
-
- This object provides a common interface to many different types of models.
- """
- def __init__(self, rf, feature_names = None):
- #self.rf = rf
- self.trees = [ build_tree(dt.tree_, feature_names) for dt in rf.estimators()]
- self.sz = sum([dt.tree_.node_count for dt in rf.estimators()])
- self.md = max([dt.tree_.max_depth for dt in rf.estimators()])
- ####
- nb_nodes = [dt.tree_.node_count for dt in rf.estimators()]
- print("min: {0} | max: {1}".format(min(nb_nodes), max(nb_nodes)))
- assert([dt.tree_.node_count for dt in rf.estimators()] == [count_nodes(dt) for dt in self.trees])
- #self.print_trees()
-
- def print_trees(self):
- for i,t in enumerate(self.trees):
- print("tree number: ", i)
- walk_tree(t)
-
- def predict_inst(self, inst):
- scores = [predict_tree(dt, inst) for dt in self.trees]
- scores = np.asarray(scores)
- maj = np.argmax(np.bincount(scores))
- return maj
-
-
- def predict(self, samples):
- predictions = []
- print("#Trees: ", len(self.trees))
- for sample in np.asarray(samples):
- scores = []
- for i,t in enumerate(self.trees):
- s = predict_tree(t, sample)
- scores.append((s))
- scores = np.asarray(scores)
- predictions.append(scores)
- predictions = np.asarray(predictions)
- #print(predictions)
- #np.bincount(x, weights=self._weights_not_none)
- maj = np.apply_along_axis(lambda x: np.argmax(np.bincount(x)), axis=1, arr=predictions)
-
- return maj
-
diff --git a/pages/RFxp/xrf/xforest.py b/pages/RFxp/xrf/xforest.py
deleted file mode 100644
index b2bc978ce683396d578b5db3859de272640139c5..0000000000000000000000000000000000000000
--- a/pages/RFxp/xrf/xforest.py
+++ /dev/null
@@ -1,874 +0,0 @@
-
-#from sklearn.ensemble._voting import VotingClassifier
-#from sklearn.ensemble import RandomForestClassifier
-from sklearn.preprocessing import OneHotEncoder, LabelEncoder
-from sklearn.model_selection import train_test_split
-#from sklearn.metrics import accuracy_score
-import numpy as np
-import sys
-import os
-import resource
-
-import collections
-from itertools import combinations
-from six.moves import range
-import six
-import math
-
-from data import Data
-from .rndmforest import RF2001, VotingRF
-from .tree import Forest, predict_tree
-
-#from .encode import SATEncoder
-from pysat.formula import CNF, WCNF, IDPool
-from pysat.solvers import Solver
-from pysat.card import CardEnc, EncType
-from pysat.examples.lbx import LBX
-from pysat.examples.mcsls import MCSls
-from pysat.examples.rc2 import RC2
-
-
-
-
-#
-#==============================================================================
-class Dataset(Data):
- """
- Class for representing dataset (transactions).
- """
- def __init__(self, filename=None, fpointer=None, mapfile=None,
- separator=' ', use_categorical = False):
- super().__init__(filename, fpointer, mapfile, separator, use_categorical)
-
- # split data into X and y
- self.feature_names = self.names[:-1]
- self.nb_features = len(self.feature_names)
- self.use_categorical = use_categorical
-
- samples = np.asarray(self.samps)
- if not all(c.isnumeric() for c in samples[:, -1]):
- le = LabelEncoder()
- le.fit(samples[:, -1])
- samples[:, -1]= le.transform(samples[:, -1])
- self.class_names = le.classes_
- print(le.classes_)
- print(samples[1:4, :])
-
- samples = np.asarray(samples, dtype=np.float32)
- self.X = samples[:, 0: self.nb_features]
- self.y = samples[:, self.nb_features]
- self.num_class = len(set(self.y))
- self.target_name = list(range(self.num_class))
-
- print("c nof features: {0}".format(self.nb_features))
- print("c nof classes: {0}".format(self.num_class))
- print("c nof samples: {0}".format(len(self.samps)))
-
- # check if we have info about categorical features
- if (self.use_categorical):
- self.target_name = self.class_names
-
- self.binarizer = {}
- for i in self.categorical_features:
- self.binarizer.update({i: OneHotEncoder(categories='auto', sparse=False)})#,
- self.binarizer[i].fit(self.X[:,[i]])
- else:
- self.categorical_features = []
- self.categorical_names = []
- self.binarizer = []
- #feat map
- self.mapping_features()
-
-
-
- def train_test_split(self, test_size=0.2, seed=0):
- return train_test_split(self.X, self.y, test_size=test_size, random_state=seed)
-
-
- def transform(self, x):
- if(len(x) == 0):
- return x
- if (len(x.shape) == 1):
- x = np.expand_dims(x, axis=0)
- if (self.use_categorical):
- assert(self.binarizer != [])
- tx = []
- for i in range(self.nb_features):
- #self.binarizer[i].drop = None
- if (i in self.categorical_features):
- self.binarizer[i].drop = None
- tx_aux = self.binarizer[i].transform(x[:,[i]])
- tx_aux = np.vstack(tx_aux)
- tx.append(tx_aux)
- else:
- tx.append(x[:,[i]])
- tx = np.hstack(tx)
- return tx
- else:
- return x
-
- def transform_inverse(self, x):
- if(len(x) == 0):
- return x
- if (len(x.shape) == 1):
- x = np.expand_dims(x, axis=0)
- if (self.use_categorical):
- assert(self.binarizer != [])
- inverse_x = []
- for i, xi in enumerate(x):
- inverse_xi = np.zeros(self.nb_features)
- for f in range(self.nb_features):
- if f in self.categorical_features:
- nb_values = len(self.categorical_names[f])
- v = xi[:nb_values]
- v = np.expand_dims(v, axis=0)
- iv = self.binarizer[f].inverse_transform(v)
- inverse_xi[f] =iv
- xi = xi[nb_values:]
-
- else:
- inverse_xi[f] = xi[0]
- xi = xi[1:]
- inverse_x.append(inverse_xi)
- return inverse_x
- else:
- return x
-
- def transform_inverse_by_index(self, idx):
- if (idx in self.extended_feature_names):
- return self.extended_feature_names[idx]
- else:
- print("Warning there is no feature {} in the internal mapping".format(idx))
- return None
-
- def transform_by_value(self, feat_value_pair):
- if (feat_value_pair in self.extended_feature_names.values()):
- keys = (list(self.extended_feature_names.keys())[list( self.extended_feature_names.values()).index(feat_value_pair)])
- return keys
- else:
- print("Warning there is no value {} in the internal mapping".format(feat_value_pair))
- return None
-
- def mapping_features(self):
- self.extended_feature_names = {}
- self.extended_feature_names_as_array_strings = []
- counter = 0
- if (self.use_categorical):
- for i in range(self.nb_features):
- if (i in self.categorical_features):
- for j, _ in enumerate(self.binarizer[i].categories_[0]):
- self.extended_feature_names.update({counter: (self.feature_names[i], j)})
- self.extended_feature_names_as_array_strings.append("f{}_{}".format(i,j)) # str(self.feature_names[i]), j))
- counter = counter + 1
- else:
- self.extended_feature_names.update({counter: (self.feature_names[i], None)})
- self.extended_feature_names_as_array_strings.append("f{}".format(i)) #(self.feature_names[i])
- counter = counter + 1
- else:
- for i in range(self.nb_features):
- self.extended_feature_names.update({counter: (self.feature_names[i], None)})
- self.extended_feature_names_as_array_strings.append("f{}".format(i))#(self.feature_names[i])
- counter = counter + 1
-
- def readable_sample(self, x):
- readable_x = []
- for i, v in enumerate(x):
- if (i in self.categorical_features):
- readable_x.append(self.categorical_names[i][int(v)])
- else:
- readable_x.append(v)
- return np.asarray(readable_x)
-
-
- def test_encoding_transformes(self, X_train):
- # test encoding
-
- X = X_train[[0],:]
-
- print("Sample of length", len(X[0])," : ", X)
- enc_X = self.transform(X)
- print("Encoded sample of length", len(enc_X[0])," : ", enc_X)
- inv_X = self.transform_inverse(enc_X)
- print("Back to sample", inv_X)
- print("Readable sample", self.readable_sample(inv_X[0]))
- assert((inv_X == X).all())
-
- '''
- for i in range(len(self.extended_feature_names)):
- print(i, self.transform_inverse_by_index(i))
- for key, value in self.extended_feature_names.items():
- print(value, self.transform_by_value(value))
- '''
-#
-#==============================================================================
-class XRF(object):
- """
- class to encode and explain Random Forest classifiers.
- """
-
- def __init__(self, model, feature_names, class_names, verb=0):
- self.cls = model
- #self.data = dataset
- self.verbose = verb
- self.feature_names = feature_names
- self.class_names = class_names
- self.fnames = [f'f{i}' for i in range(len(feature_names))]
- self.f = Forest(model, self.fnames)
-
- if self.verbose > 2:
- self.f.print_trees()
- if self.verbose:
- print("c RF sz:", self.f.sz)
- print('c max-depth:', self.f.md)
- print('c nof DTs:', len(self.f.trees))
-
- def __del__(self):
- if 'enc' in dir(self):
- del self.enc
- if 'x' in dir(self):
- if self.x.slv is not None:
- self.x.slv.delete()
- del self.x
- del self.f
- self.f = None
- del self.cls
- self.cls = None
-
- def encode(self, inst):
- """
- Encode a tree ensemble trained previously.
- """
- if 'f' not in dir(self):
- self.f = Forest(self.cls, self.fnames)
- #self.f.print_tree()
-
- time = resource.getrusage(resource.RUSAGE_CHILDREN).ru_utime + \
- resource.getrusage(resource.RUSAGE_SELF).ru_utime
-
- self.enc = SATEncoder(self.f, self.feature_names, len(self.class_names), self.fnames)
-
- #inst = self.data.transform(np.array(inst))[0]
- formula, _, _, _ = self.enc.encode(np.array(inst))
-
- time = resource.getrusage(resource.RUSAGE_CHILDREN).ru_utime + \
- resource.getrusage(resource.RUSAGE_SELF).ru_utime - time
-
- if self.verbose:
- print('c nof vars:', formula.nv) # number of variables
- print('c nof clauses:', len(formula.clauses)) # number of clauses
- print('c encoding time: {0:.3f}'.format(time))
-
- def explain(self, inst, xtype='abd'):
- """
- Explain a prediction made for a given sample with a previously
- trained RF.
- """
-
- time = resource.getrusage(resource.RUSAGE_CHILDREN).ru_utime + \
- resource.getrusage(resource.RUSAGE_SELF).ru_utime
-
- if 'enc' not in dir(self):
- self.encode(inst)
-
- #inpvals = self.data.readable_sample(inst)
- inpvals = np.asarray(inst)
- preamble = []
- for f, v in zip(self.feature_names, inpvals):
- if f not in str(v):
- preamble.append('{0} = {1}'.format(f, v))
- else:
- preamble.append(v)
-
- inps = self.fnames # input (feature value) variables
- #print("inps: {0}".format(inps))
-
- self.x = SATExplainer(self.enc, inps, preamble, self.class_names, verb=self.verbose)
- #inst = self.data.transform(np.array(inst))[0]
- expl = self.x.explain(np.array(inst), xtype)
-
- time = resource.getrusage(resource.RUSAGE_CHILDREN).ru_utime + \
- resource.getrusage(resource.RUSAGE_SELF).ru_utime - time
-
- if self.verbose:
- print("c Total time: {0:.3f}".format(time))
-
- return expl
-
- def enumerate(self, inst, xtype='con', smallest=True):
- """
- list all XPs
- """
- if 'enc' not in dir(self):
- self.encode(inst)
-
- if 'x' not in dir(self):
- inpvals = np.asarray(inst)
- preamble = []
- for f, v in zip(self.feature_names, inpvals):
- if f not in str(v):
- preamble.append('{0} = {1}'.format(f, v))
- else:
- preamble.append(v)
-
- inps = self.fnames
- self.x = SATExplainer(self.enc, inps, preamble, self.class_names)
-
- for expl in self.x.enumerate(np.array(inst), xtype, smallest):
- yield expl
-
-#
-#==============================================================================
-class SATEncoder(object):
- """
- Encoder of Random Forest classifier into SAT.
- """
-
- def __init__(self, forest, feats, nof_classes, extended_feature_names, from_file=None):
- self.forest = forest
- #self.feats = {f: i for i, f in enumerate(feats)}
- self.num_class = nof_classes
- self.vpool = IDPool()
- self.extended_feature_names = extended_feature_names
-
- #encoding formula
- self.cnf = None
-
- # for interval-based encoding
- self.intvs, self.imaps, self.ivars, self.thvars = None, None, None, None
-
-
- def newVar(self, name):
- """
- If a variable named 'name' already exists then
- return its id; otherwise create a new var
- """
- if name in self.vpool.obj2id: #var has been already created
- return self.vpool.obj2id[name]
- var = self.vpool.id('{0}'.format(name))
- return var
-
- def nameVar(self, vid):
- """
- input a var id and return a var name
- """
- return self.vpool.obj(abs(vid))
-
- def printLits(self, lits):
- print(["{0}{1}".format("-" if p<0 else "",self.vpool.obj(abs(p))) for p in lits])
-
- def traverse(self, tree, k, clause):
- """
- Traverse a tree and encode each node.
- """
-
- if tree.children:
- f = tree.name
- v = tree.threshold
- pos = neg = []
- if f in self.intvs:
- d = self.imaps[f][v]
- pos, neg = self.thvars[f][d], -self.thvars[f][d]
- else:
- var = self.newVar(tree.name)
- pos, neg = var, -var
- #print("{0} => {1}".format(tree.name, var))
-
- assert (pos and neg)
- self.traverse(tree.children[0], k, clause + [-neg])
- self.traverse(tree.children[1], k, clause + [-pos])
- else: # leaf node
- cvar = self.newVar('class{0}_tr{1}'.format(tree.values,k))
- self.cnf.append(clause + [cvar])
- #self.printLits(clause + [cvar])
-
- def compute_intervals(self):
- """
- Traverse all trees in the ensemble and extract intervals for each
- feature.
-
- At this point, the method only works for numerical datasets!
- """
-
- def traverse_intervals(tree):
- """
- Auxiliary function. Recursive tree traversal.
- """
-
- if tree.children:
- f = tree.name
- v = tree.threshold
- if f in self.intvs:
- self.intvs[f].add(v)
-
- traverse_intervals(tree.children[0])
- traverse_intervals(tree.children[1])
-
- # initializing the intervals
- self.intvs = {'{0}'.format(f): set([]) for f in self.extended_feature_names if '_' not in f}
-
- for tree in self.forest.trees:
- traverse_intervals(tree)
-
- # OK, we got all intervals; let's sort the values
- self.intvs = {f: sorted(self.intvs[f]) + ([math.inf] if len(self.intvs[f]) else []) for f in six.iterkeys(self.intvs)}
-
- self.imaps, self.ivars = {}, {}
- self.thvars = {}
- for feat, intvs in six.iteritems(self.intvs):
- self.imaps[feat] = {}
- self.ivars[feat] = []
- self.thvars[feat] = []
- for i, ub in enumerate(intvs):
- self.imaps[feat][ub] = i
-
- ivar = self.newVar('{0}_intv{1}'.format(feat, i))
- self.ivars[feat].append(ivar)
- #print('{0}_intv{1}'.format(feat, i))
-
- if ub != math.inf:
- #assert(i < len(intvs)-1)
- thvar = self.newVar('{0}_th{1}'.format(feat, i))
- self.thvars[feat].append(thvar)
- #print('{0}_th{1}'.format(feat, i))
-
-
-
- def encode(self, sample):
- """
- Do the job.
- """
-
- ###print('Encode RF into SAT ...')
-
- self.cnf = CNF()
- # getting a tree ensemble
- #self.forest = Forest(self.model, self.extended_feature_names)
- num_tree = len(self.forest.trees)
- self.forest.predict_inst(sample)
-
- #introducing class variables
- #cvars = [self.newVar('class{0}'.format(i)) for i in range(self.num_class)]
-
- # define Tautology var
- vtaut = self.newVar('Tautology')
- self.cnf.append([vtaut])
-
- # introducing class-tree variables
- ctvars = [[] for t in range(num_tree)]
- for k in range(num_tree):
- for j in range(self.num_class):
- var = self.newVar('class{0}_tr{1}'.format(j,k))
- ctvars[k].append(var)
-
- # traverse all trees and extract all possible intervals
- # for each feature
- ###print("compute intervarls ...")
- self.compute_intervals()
-
- #print(self.intvs)
- #print([len(self.intvs[f]) for f in self.intvs])
- #print(self.imaps)
- #print(self.ivars)
- #print(self.thvars)
- #print(ctvars)
-
-
- ##print("encode trees ...")
- # traversing and encoding each tree
- for k, tree in enumerate(self.forest.trees):
- #print("Encode tree#{0}".format(k))
- # encoding the tree
- self.traverse(tree, k, [])
- # exactly one class var is true
- #self.printLits(ctvars[k])
- card = CardEnc.atmost(lits=ctvars[k], vpool=self.vpool,encoding=EncType.cardnetwrk)
- self.cnf.extend(card.clauses)
-
-
-
- # calculate the majority class
- self.cmaj = self.forest.predict_inst(sample)
-
- ##print("encode majority class ...")
- #Cardinality constraint AtMostK to capture a j_th class
-
- if(self.num_class == 2):
- rhs = math.floor(num_tree / 2) + 1
- if(self.cmaj==1 and not num_tree%2):
- rhs = math.floor(num_tree / 2)
- lhs = [ctvars[k][1 - self.cmaj] for k in range(num_tree)]
- atls = CardEnc.atleast(lits = lhs, bound = rhs, vpool=self.vpool, encoding=EncType.cardnetwrk)
- self.cnf.extend(atls)
- else:
- zvars = []
- zvars.append([self.newVar('z_0_{0}'.format(k)) for k in range (num_tree) ])
- zvars.append([self.newVar('z_1_{0}'.format(k)) for k in range (num_tree) ])
- ##
- rhs = num_tree
- lhs0 = zvars[0] + [ - ctvars[k][self.cmaj] for k in range(num_tree)]
- ##self.printLits(lhs0)
- atls = CardEnc.atleast(lits = lhs0, bound = rhs, vpool=self.vpool, encoding=EncType.cardnetwrk)
- self.cnf.extend(atls)
- ##
- #rhs = num_tree - 1
- rhs = num_tree + 1
- ###########
- lhs1 = zvars[1] + [ - ctvars[k][self.cmaj] for k in range(num_tree)]
- ##self.printLits(lhs1)
- atls = CardEnc.atleast(lits = lhs1, bound = rhs, vpool=self.vpool, encoding=EncType.cardnetwrk)
- self.cnf.extend(atls)
- #
- pvars = [self.newVar('p_{0}'.format(k)) for k in range(self.num_class + 1)]
- ##self.printLits(pvars)
- for k,p in enumerate(pvars):
- for i in range(num_tree):
- if k == 0:
- z = zvars[0][i]
- #self.cnf.append([-p, -z, vtaut])
- self.cnf.append([-p, z, -vtaut])
- #self.printLits([-p, z, -vtaut])
- #print()
- elif k == self.cmaj+1:
- z = zvars[1][i]
- self.cnf.append([-p, z, -vtaut])
-
- #self.printLits([-p, z, -vtaut])
- #print()
-
- else:
- z = zvars[0][i] if (k<self.cmaj+1) else zvars[1][i]
- self.cnf.append([-p, -z, ctvars[i][k-1] ])
- self.cnf.append([-p, z, -ctvars[i][k-1] ])
-
- #self.printLits([-p, -z, ctvars[i][k-1] ])
- #self.printLits([-p, z, -ctvars[i][k-1] ])
- #print()
-
- #
- self.cnf.append([-pvars[0], -pvars[self.cmaj+1]])
- ##
- lhs1 = pvars[:(self.cmaj+1)]
- ##self.printLits(lhs1)
- eqls = CardEnc.equals(lits = lhs1, bound = 1, vpool=self.vpool, encoding=EncType.cardnetwrk)
- self.cnf.extend(eqls)
-
-
- lhs2 = pvars[(self.cmaj + 1):]
- ##self.printLits(lhs2)
- eqls = CardEnc.equals(lits = lhs2, bound = 1, vpool=self.vpool, encoding=EncType.cardnetwrk)
- self.cnf.extend(eqls)
-
-
-
- ##print("exactly-one feat const ...")
- # enforce exactly one of the feature values to be chosen
- # (for categorical features)
- categories = collections.defaultdict(lambda: [])
- for f in self.extended_feature_names:
- if '_' in f:
- categories[f.split('_')[0]].append(self.newVar(f))
- for c, feats in six.iteritems(categories):
- # exactly-one feat is True
- self.cnf.append(feats)
- card = CardEnc.atmost(lits=feats, vpool=self.vpool, encoding=EncType.cardnetwrk)
- self.cnf.extend(card.clauses)
- # lits of intervals
- for f, intvs in six.iteritems(self.ivars):
- if not len(intvs):
- continue
- self.cnf.append(intvs)
- card = CardEnc.atmost(lits=intvs, vpool=self.vpool, encoding=EncType.cardnetwrk)
- self.cnf.extend(card.clauses)
- #self.printLits(intvs)
-
-
-
- for f, threshold in six.iteritems(self.thvars):
- for j, thvar in enumerate(threshold):
- d = j+1
- pos, neg = self.ivars[f][d:], self.ivars[f][:d]
-
- if j == 0:
- assert(len(neg) == 1)
- self.cnf.append([thvar, neg[-1]])
- self.cnf.append([-thvar, -neg[-1]])
- else:
- self.cnf.append([thvar, neg[-1], -threshold[j-1]])
- self.cnf.append([-thvar, threshold[j-1]])
- self.cnf.append([-thvar, -neg[-1]])
-
- if j == len(threshold) - 1:
- assert(len(pos) == 1)
- self.cnf.append([-thvar, pos[0]])
- self.cnf.append([thvar, -pos[0]])
- else:
- self.cnf.append([-thvar, pos[0], threshold[j+1]])
- self.cnf.append([thvar, -pos[0]])
- self.cnf.append([thvar, -threshold[j+1]])
-
-
-
- return self.cnf, self.intvs, self.imaps, self.ivars
-
-
-#
-#==============================================================================
-class SATExplainer(object):
- """
- An SAT-inspired minimal explanation extractor for Random Forest models.
- """
-
- def __init__(self, sat_enc, inps, preamble, target_name, verb=1):
- """
- Constructor.
- """
- self.enc = sat_enc
- self.inps = inps # input (feature value) variables
- self.target_name = target_name
- self.preamble = preamble
- self.verbose = verb
- self.slv = None
-
- def prepare_selectors(self, sample):
- # adapt the solver to deal with the current sample
- #self.csel = []
- self.assums = [] # var selectors to be used as assumptions
- self.sel2fid = {} # selectors to original feature ids
- self.sel2vid = {} # selectors to categorical feature ids
- self.sel2v = {} # selectors to (categorical/interval) values
-
- #for i in range(self.enc.num_class):
- # self.csel.append(self.enc.newVar('class{0}'.format(i)))
- #self.csel = self.enc.newVar('class{0}'.format(self.enc.cmaj))
-
- # preparing the selectors
- for i, (inp, val) in enumerate(zip(self.inps, sample), 1):
- if '_' in inp:
- # binarized (OHE) features
- assert (inp not in self.enc.intvs)
-
- feat = inp.split('_')[0]
- selv = self.enc.newVar('selv_{0}'.format(feat))
-
- self.assums.append(selv)
- if selv not in self.sel2fid:
- self.sel2fid[selv] = int(feat[1:])
- self.sel2vid[selv] = [i - 1]
- else:
- self.sel2vid[selv].append(i - 1)
-
- p = self.enc.newVar(inp)
- if not val:
- p = -p
- else:
- self.sel2v[selv] = p
-
- self.enc.cnf.append([-selv, p])
- #self.enc.printLits([-selv, p])
-
- elif len(self.enc.intvs[inp]):
- #v = None
- #for intv in self.enc.intvs[inp]:
- # if intv > val:
- # v = intv
- # break
- v = next((intv for intv in self.enc.intvs[inp] if intv > val), None)
- assert(v is not None)
-
- selv = self.enc.newVar('selv_{0}'.format(inp))
- self.assums.append(selv)
-
- assert (selv not in self.sel2fid)
- self.sel2fid[selv] = int(inp[1:])
- self.sel2vid[selv] = [i - 1]
-
- for j,p in enumerate(self.enc.ivars[inp]):
- cl = [-selv]
- if j == self.enc.imaps[inp][v]:
- cl += [p]
- self.sel2v[selv] = p
- else:
- cl += [-p]
-
- self.enc.cnf.append(cl)
- #self.enc.printLits(cl)
-
-
-
- def explain(self, sample, xtype='abd', smallest=False):
- """
- Hypotheses minimization.
- """
- if self.verbose:
- print(' explaining: "IF {0} THEN {1}"'.format(' AND '.join(self.preamble), self.target_name[self.enc.cmaj]))
-
-
- self.time = resource.getrusage(resource.RUSAGE_CHILDREN).ru_utime + \
- resource.getrusage(resource.RUSAGE_SELF).ru_utime
-
- self.prepare_selectors(sample)
-
- if xtype == 'abd':
- # abductive (PI-) explanation
- expl = self.compute_axp()
- else:
- # contrastive explanation
- expl = self.compute_cxp()
-
- self.time = resource.getrusage(resource.RUSAGE_CHILDREN).ru_utime + \
- resource.getrusage(resource.RUSAGE_SELF).ru_utime - self.time
-
- # delete sat solver
- self.slv.delete()
- self.slv = None
-
- if self.verbose:
- print(' time: {0:.3f}'.format(self.time))
-
- return expl
-
- def compute_axp(self, smallest=False):
- """
- Compute an Abductive eXplanation
- """
- self.assums = sorted(set(self.assums))
- if self.verbose:
- print(' # hypos:', len(self.assums))
-
- #create a SAT solver
- self.slv = Solver(name="glucose3")
-
- # pass a CNF formula
- self.slv.append_formula(self.enc.cnf)
-
- def minimal():
- vtaut = self.enc.newVar('Tautology')
- # simple deletion-based linear search
- for i, p in enumerate(self.assums):
- to_test = [vtaut] + self.assums[:i] + self.assums[(i + 1):] + [-p, -self.sel2v[p]]
- sat = self.slv.solve(assumptions=to_test)
- if not sat:
- self.assums[i] = -p
- return
-
- if not smallest:
- minimal()
- else:
- raise NotImplementedError('Smallest explanation is not yet implemented.')
- #self.compute_smallest()
-
- expl = sorted([self.sel2fid[h] for h in self.assums if h>0 ])
- assert len(expl), 'Abductive explanation cannot be an empty-set! otherwise RF fcn is const, i.e. predicts only one class'
-
- if self.verbose:
- print("expl-selctors: ", expl)
- preamble = [self.preamble[i] for i in expl]
- print(' explanation: "IF {0} THEN {1}"'.format(' AND '.join(preamble), self.target_name[self.enc.cmaj]))
- print(' # hypos left:', len(expl))
-
- return expl
-
- def compute_cxp(self, smallest=True):
- """
- Compute a Contrastive eXplanation
- """
- self.assums = sorted(set(self.assums))
- if self.verbose:
- print(' # hypos:', len(self.assums))
-
- wcnf = WCNF()
- for cl in self.enc.cnf:
- wcnf.append(cl)
- for p in self.assums:
- wcnf.append([p], weight=1)
-
- if not smallest:
- # mcs solver
- self.slv = LBX(wcnf, use_cld=True, solver_name='g3')
- mcs = self.slv.compute()
- expl = sorted([self.sel2fid[self.assums[i-1]] for i in mcs])
- else:
- # mxsat solver
- self.slv = RC2(wcnf)
- model = self.slv.compute()
- model = [p for p in model if abs(p) in self.assums]
- expl = sorted([self.sel2fid[-p] for p in model if p<0 ])
-
- assert len(expl), 'Contrastive explanation cannot be an empty-set!'
- if self.verbose:
- print("expl-selctors: ", expl)
- preamble = [self.preamble[i] for i in expl]
- pred = self.target_name[self.enc.cmaj]
- print(f' explanation: "IF {" AND ".join([f"!({p})" for p in preamble])} THEN !(class = {pred})"')
-
- return expl
-
- def enumerate(self, sample, xtype='con', smallest=True):
- """
- list all CXp's or AXp's
- """
- if xtype == 'abd':
- raise NotImplementedError('Enumerate abductive explanations is not yet implemented.')
- time = resource.getrusage(resource.RUSAGE_CHILDREN).ru_utime + \
- resource.getrusage(resource.RUSAGE_SELF).ru_utime
-
- if 'assums' not in dir(self):
- self.prepare_selectors(sample)
- self.assums = sorted(set(self.assums))
- #
-
- # compute CXp's/AE's
- if self.slv is None:
- wcnf = WCNF()
- for cl in self.enc.cnf:
- wcnf.append(cl)
- for p in self.assums:
- wcnf.append([p], weight=1)
- if smallest:
- # incremental maxsat solver
- self.slv = RC2(wcnf, adapt=True, exhaust=True, minz=True)
- else:
- # mcs solver
- self.slv = LBX(wcnf, use_cld=True, solver_name='g3')
- #self.slv = MCSls(wcnf, use_cld=True, solver_name='g3')
-
- if smallest:
- print('smallest')
- for model in self.slv.enumerate(block=-1):
- #model = [p for p in model if abs(p) in self.assums]
- expl = sorted([self.sel2fid[-p] for p in model if (p<0 and (-p in self.assums))])
- cxp_feats = [f'f{j}' for j in expl]
- advx = []
- for f in cxp_feats:
- ps = [p for p in model if (p>0 and (p in self.enc.ivars[f]))]
- assert(len(ps) == 1)
- advx.append(tuple([f,self.enc.nameVar(ps[0])]))
- #yield expl
- print(cxp_feats, advx)
- yield advx
- else:
- print('LBX')
- for mcs in self.slv.enumerate():
- expl = sorted([self.sel2fid[self.assums[i-1]] for i in mcs])
- assumptions = [-p if(i in mcs) else p for i,p in enumerate(self.assums, 1)]
- #for k, model in enumerate(self.slv.oracle.enum_models(assumptions), 1):
- assert (self.slv.oracle.solve(assumptions))
- model = self.slv.oracle.get_model()
- cxp_feats = [f'f{j}' for j in expl]
- advx = []
- for f in cxp_feats:
- ps = [p for p in model if (p>0 and (p in self.enc.ivars[f]))]
- assert(len(ps) == 1)
- advx.append(tuple([f,self.enc.nameVar(ps[0])]))
- yield advx
- self.slv.block(mcs)
- #yield expl
-
-
- time = resource.getrusage(resource.RUSAGE_CHILDREN).ru_utime + \
- resource.getrusage(resource.RUSAGE_SELF).ru_utime - time
- if self.verbose:
- print('c expl time: {0:.3f}'.format(time))
- #
- self.slv.delete()
- self.slv = None
\ No newline at end of file
diff --git a/pages/application/RandomForest/RandomForestComponent.py b/pages/application/RandomForest/RandomForestComponent.py
index ca8071f45d41519f6db1fac163930122b335c692..fc548d97c861a21e79f6de9661b86aa81647dfb9 100644
--- a/pages/application/RandomForest/RandomForestComponent.py
+++ b/pages/application/RandomForest/RandomForestComponent.py
@@ -14,13 +14,14 @@ class RandomForestComponent:
self.data.mapping_features()
if info is not None and 'csv' in type_info:
- if isinstance(model, xrf.rndmforest.RF2001):
- self.random_forest = XRF(model, self.data.feature_names, self.data.class_names)
- else:
- raise NotImplementedError('No explainer for this model')
+ 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])
+ 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,
+ impurity=False, filled=False, rounded=True)
self.network = html.Div([dash_interactive_graphviz.DashInteractiveGraphviz(
dot_source=dot_source, style={"width": "50%",
"height": "80%",
@@ -35,15 +36,17 @@ class RandomForestComponent:
splitted_instance = [float(v.split('=')[1].strip()) for v in instance.split(',')]
else:
splitted_instance = [float(v.strip()) for v in instance.split(',')]
- # Call explanation
+
+ # Call instance
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))])))
+ 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)
@@ -59,7 +62,9 @@ class RandomForestComponent:
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])
+ 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,
+ impurity=False, filled=False, rounded=True)
self.network = html.Div([dash_interactive_graphviz.DashInteractiveGraphviz(
dot_source=dot_source, style={"width": "50%",
"height": "80%",
diff --git a/pages/application/RandomForest/utils/xrf/xforest.py b/pages/application/RandomForest/utils/xrf/xforest.py
index bcf9cd18a9cc106b651b27ce19180eb9d1e3409e..a15685c430f5f7ee8a55715350b0fdbd20bcda3f 100644
--- a/pages/application/RandomForest/utils/xrf/xforest.py
+++ b/pages/application/RandomForest/utils/xrf/xforest.py
@@ -46,7 +46,7 @@ class Dataset(Data):
print(le.classes_)
print(samples[1:4, :])
else :
- self.class_names = samples[:, -1]
+ self.class_names = np.unique(samples[:, -1])
samples = np.asarray(samples, dtype=np.float32)
self.X = samples[:, 0: self.nb_features]
@@ -652,8 +652,6 @@ class SATExplainer(object):
"""
Hypotheses minimization.
"""
- explanation_result["Explaining"] = "IF {0} THEN {1}".format(' AND '.join(self.preamble),
- self.target_name[self.enc.cmaj])
self.prepare_selectors(sample)
if xtype == 'abd':