From 652ba4ad2c8a8f01713545d6b231a84dd3f2307a Mon Sep 17 00:00:00 2001
From: Caroline DE POURTALES <cdepourt@montana.irit.fr>
Date: Wed, 8 Jun 2022 10:59:00 +0200
Subject: [PATCH] update random forest for faster, clean callbacks
---
.../pima/pima_nbestim_50_maxdepth_3.mod.pkl | Bin 0 -> 70012 bytes
pages/RFxp/RFxp.py | 148 +++
pages/RFxp/data.py | 168 ++++
pages/RFxp/options.py | 154 +++
pages/RFxp/pima.csv | 769 +++++++++++++++
pages/RFxp/xrf/__init__.py | 3 +
pages/RFxp/xrf/rndmforest.py | 137 +++
pages/RFxp/xrf/tree.py | 174 ++++
pages/RFxp/xrf/xforest.py | 874 ++++++++++++++++++
9 files changed, 2427 insertions(+)
create mode 100644 pages/RFxp/Classifiers/RF2001/pima/pima_nbestim_50_maxdepth_3.mod.pkl
create mode 100755 pages/RFxp/RFxp.py
create mode 100644 pages/RFxp/data.py
create mode 100644 pages/RFxp/options.py
create mode 100644 pages/RFxp/pima.csv
create mode 100644 pages/RFxp/xrf/__init__.py
create mode 100644 pages/RFxp/xrf/rndmforest.py
create mode 100644 pages/RFxp/xrf/tree.py
create mode 100644 pages/RFxp/xrf/xforest.py
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
new file mode 100644
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diff --git a/pages/RFxp/RFxp.py b/pages/RFxp/RFxp.py
new file mode 100755
index 0000000..5557e04
--- /dev/null
+++ b/pages/RFxp/RFxp.py
@@ -0,0 +1,148 @@
+#!/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
new file mode 100644
index 0000000..6c1546d
--- /dev/null
+++ b/pages/RFxp/data.py
@@ -0,0 +1,168 @@
+#!/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
new file mode 100644
index 0000000..446eb71
--- /dev/null
+++ b/pages/RFxp/options.py
@@ -0,0 +1,154 @@
+#!/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
new file mode 100644
index 0000000..f3fac60
--- /dev/null
+++ b/pages/RFxp/pima.csv
@@ -0,0 +1,769 @@
+Pregnant,plasma glucose,Diastolic blood pressure,Triceps skin fold thickness,2-Hour serum insulin,Body mass index,Diabetes pedigree function,Age,target
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diff --git a/pages/RFxp/xrf/__init__.py b/pages/RFxp/xrf/__init__.py
new file mode 100644
index 0000000..9f52257
--- /dev/null
+++ b/pages/RFxp/xrf/__init__.py
@@ -0,0 +1,3 @@
+#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
new file mode 100644
index 0000000..62dd80f
--- /dev/null
+++ b/pages/RFxp/xrf/rndmforest.py
@@ -0,0 +1,137 @@
+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
new file mode 100644
index 0000000..5fddabd
--- /dev/null
+++ b/pages/RFxp/xrf/tree.py
@@ -0,0 +1,174 @@
+#
+#==============================================================================
+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
new file mode 100644
index 0000000..b2bc978
--- /dev/null
+++ b/pages/RFxp/xrf/xforest.py
@@ -0,0 +1,874 @@
+
+#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
--
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