From 6caa741f202c9302f323270f95ab0a143c4a587b Mon Sep 17 00:00:00 2001
From: Caroline DE POURTALES <cdepourt@montana.irit.fr>
Date: Mon, 16 May 2022 16:24:12 +0200
Subject: [PATCH] update
---
data_retriever.json | 2 +-
.../DecisionTree/DecisionTreeComponent.py | 10 +-
.../NaiveBayes/NaiveBayesComponent.py | 2 +-
.../RandomForest/RandomForestComponent.py | 110 +--
pages/application/RandomForest/utils/data.py | 41 +-
.../RandomForest/utils/xgbooster/encode.py | 20 +-
.../RandomForest/utils/xgbooster/explain.py | 73 +-
.../RandomForest/utils/xgbooster/xgbooster.py | 379 +-------
.../RandomForest/utils/xgbrf/encode.py | 21 +-
.../RandomForest/utils/xgbrf/tree.py | 90 +-
.../RandomForest/utils/xgbrf/xgb_rf.py | 530 +----------
pages/application/RandomForest/utils/xprf.py | 176 ++--
.../pysortn.cpython-38-darwin.so | Bin 23084 -> 0 bytes
.../pysortnetwrk.cpython-38-darwin.so | Bin 23116 -> 0 bytes
.../temp.macosx-10.9-x86_64-3.8/pysortn.o | Bin 252064 -> 0 bytes
.../pysortnetwrk.o | Bin 252080 -> 0 bytes
.../RandomForest/utils/xrf/archive/encode.py | 276 ------
.../utils/xrf/archive/pysortnetwrk.cc | 248 -----
.../utils/xrf/archive/pysortnetwrk.so | Bin 23116 -> 0 bytes
.../RandomForest/utils/xrf/archive/rfc.py | 636 -------------
.../RandomForest/utils/xrf/archive/setup.py | 14 -
.../utils/xrf/archive/sortcard.hh | 298 ------
.../utils/xrf/archive/sortnetwrk.py | 74 --
.../RandomForest/utils/xrf/archive/tree.py | 154 ----
.../RandomForest/utils/xrf/rndmforest.py | 860 +++++++-----------
.../RandomForest/utils/xrf/tree.py | 105 +--
utils.py | 17 +-
27 files changed, 698 insertions(+), 3438 deletions(-)
delete mode 100755 pages/application/RandomForest/utils/xrf/archive/build/lib.macosx-10.9-x86_64-3.8/pysortn.cpython-38-darwin.so
delete mode 100755 pages/application/RandomForest/utils/xrf/archive/build/lib.macosx-10.9-x86_64-3.8/pysortnetwrk.cpython-38-darwin.so
delete mode 100644 pages/application/RandomForest/utils/xrf/archive/build/temp.macosx-10.9-x86_64-3.8/pysortn.o
delete mode 100644 pages/application/RandomForest/utils/xrf/archive/build/temp.macosx-10.9-x86_64-3.8/pysortnetwrk.o
delete mode 100644 pages/application/RandomForest/utils/xrf/archive/encode.py
delete mode 100644 pages/application/RandomForest/utils/xrf/archive/pysortnetwrk.cc
delete mode 100755 pages/application/RandomForest/utils/xrf/archive/pysortnetwrk.so
delete mode 100644 pages/application/RandomForest/utils/xrf/archive/rfc.py
delete mode 100644 pages/application/RandomForest/utils/xrf/archive/setup.py
delete mode 100644 pages/application/RandomForest/utils/xrf/archive/sortcard.hh
delete mode 100644 pages/application/RandomForest/utils/xrf/archive/sortnetwrk.py
delete mode 100644 pages/application/RandomForest/utils/xrf/archive/tree.py
diff --git a/data_retriever.json b/data_retriever.json
index 5ef4f56..c85d663 100644
--- a/data_retriever.json
+++ b/data_retriever.json
@@ -22,7 +22,7 @@
"ml_type" : "RandomForest",
"component" : "RandomForestComponent",
"solvers" : ["LIME", "ANCHOR", "SHAP"],
- "xtypes" : {"H": "Heuristic", "HV": "Heuristic and validation", "G": "Global"}
+ "xtypes" : {"S": "Smallest", "NS": "Not smallest"}
}
]
diff --git a/pages/application/DecisionTree/DecisionTreeComponent.py b/pages/application/DecisionTree/DecisionTreeComponent.py
index edb89a4..23dc317 100644
--- a/pages/application/DecisionTree/DecisionTreeComponent.py
+++ b/pages/application/DecisionTree/DecisionTreeComponent.py
@@ -11,16 +11,16 @@ from pages.application.DecisionTree.utils.dtviz import (visualize,
from pages.application.DecisionTree.utils.upload_tree import UploadedDecisionTree
-class DecisionTreeComponent():
+class DecisionTreeComponent:
- def __init__(self, tree, type_tree='SKL', info=None, type_info=''):
+ def __init__(self, tree, info=None, type_info=''):
if info is not None and '.csv' in type_info:
self.categorical = True
data = Data(info)
fvmap = data.mapping_features()
feature_names = data.names[:-1]
- self.uploaded_dt = UploadedDecisionTree(tree, type_tree, maxdepth=tree.get_depth(),
+ self.uploaded_dt = UploadedDecisionTree(tree, "SKL", maxdepth=tree.get_depth(),
feature_names=feature_names, nb_classes=tree.n_classes_)
self.dt_format, self.map, features_names_mapping = self.uploaded_dt.dump(fvmap, feat_names=feature_names)
@@ -38,7 +38,7 @@ class DecisionTreeComponent():
dom = sorted(dom)
for j, v in enumerate(dom):
fvmap[f'f{i}'][j] = (fid, True, v)
- self.uploaded_dt = UploadedDecisionTree(tree, type_tree, maxdepth=tree.get_depth(),
+ self.uploaded_dt = UploadedDecisionTree(tree, "SKL", maxdepth=tree.get_depth(),
feature_names=feature_names, nb_classes=tree.n_classes_)
self.dt_format, self.map, features_names_mapping = self.uploaded_dt.dump(fvmap, feat_names=feature_names)
@@ -48,7 +48,7 @@ class DecisionTreeComponent():
feature_names = tree.feature_names_in_
except:
feature_names = [f'f{i}' for i in range(tree.n_features_in_)]
- self.uploaded_dt = UploadedDecisionTree(tree, type_tree, maxdepth=tree.get_depth(),
+ self.uploaded_dt = UploadedDecisionTree(tree, "SKL", maxdepth=tree.get_depth(),
feature_names=feature_names, nb_classes=tree.n_classes_)
self.dt_format, self.map, features_names_mapping = self.uploaded_dt.convert_dt(feat_names=feature_names)
diff --git a/pages/application/NaiveBayes/NaiveBayesComponent.py b/pages/application/NaiveBayes/NaiveBayesComponent.py
index 98c7848..1d0d2ef 100644
--- a/pages/application/NaiveBayes/NaiveBayesComponent.py
+++ b/pages/application/NaiveBayes/NaiveBayesComponent.py
@@ -11,7 +11,7 @@ import shlex
class NaiveBayesComponent():
- def __init__(self, model, type_model='SKL', info=None, type_info=''):
+ def __init__(self, model, info=None, type_info=''):
#Conversion model
p=subprocess.Popen(['perl','pages/application/NaiveBayes/utils/cnbc2xlc.pl', model],stdout=subprocess.PIPE)
diff --git a/pages/application/RandomForest/RandomForestComponent.py b/pages/application/RandomForest/RandomForestComponent.py
index 50dd5d3..25f27d5 100644
--- a/pages/application/RandomForest/RandomForestComponent.py
+++ b/pages/application/RandomForest/RandomForestComponent.py
@@ -1,83 +1,83 @@
-import base64
+from dash import html
+from io import StringIO
+import pandas as pd
-import dash_bootstrap_components as dbc
-import numpy as np
-from dash import dcc, html
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.ensemble._voting import VotingClassifier
+from pages.application.RandomForest.utils import xrf
+from pages.application.RandomForest.utils.xrf.rndmforest import XRF, RF2001, VotingRF, Dataset
+from xgboost import XGBClassifier, XGBRFClassifier
-from pages.application.RandomForest.utils.data import Data
from pages.application.RandomForest.utils.anchor_wrap import anchor_call
from pages.application.RandomForest.utils.lime_wrap import lime_call
from pages.application.RandomForest.utils.shap_wrap import shap_call
-from pages.application.RandomForest.utils.xgbooster import XGBooster, preprocess_dataset
-from pages.application.RandomForest.utils.xgbrf import XGBRandomForest
+from pages.application.RandomForest.utils.xgbooster import XGBooster
+from pages.application.RandomForest.utils.xgbrf import XGBRandomForest
-class RandomForestComponent:
- def __init__(self, model, type_model='SKL', info=None, type_info=''):
+#################################################################
+##################### Questions #################################
+##### Can upload sklearn model or need to translate to xrf ######
+# separate XGBRandomForest and XGBooster #
+# data format : avoir un fichier chelou de données là #
+# pas adapté, get_dump ? beaucoup de classe, besoin de diagramme de classe #
- if info is not None and '.csv' in type_info:
- self.data = Data(info)
- # Conversion model
- if type_model == "RF":
- self.random_forest = XGBRandomForest(info, from_model=model)
- else:
- self.random_forest = XGBooster(info, from_model=model)
+class RandomForestComponent:
- # self.random_forest.encode(test_on=info)
+ def __init__(self, model, info=None, type_info=''):
- self.map_file = ""
+ # Conversion model
+ options = {}
+ if info is not None and '.csv' in type_info:
+ self.data = Dataset(info)
+ self.data.mapping_features()
+ options["n_classes"] = self.data.num_class
+ options["feature_names"] = self.data.feature_names
+ options["n_features"] = self.data.nb_features
+ if isinstance(model, RandomForestClassifier) or isinstance(model, VotingClassifier) or isinstance(model,
+ xrf.rndmforest.RF2001):
+ self.random_forest = XRF(model, self.data)
+ elif isinstance(model, XGBRFClassifier):
+ self.random_forest = XGBRandomForest(options, from_model=model)
+ elif isinstance(model, XGBClassifier):
+ self.random_forest = XGBooster(options, from_model=model)
self.network = html.Div([])
self.explanation = []
- def update_with_explicability(self, instance, enum_feats=None, validation=None, xtype=None, solver=None, ):
+ def update_with_explicability(self, instance, enum_feats=None, xtype=None, solver=None, ):
# Call explanation
if not enum_feats and self.data is not None:
- enum_feats = len(self.data.names) - 1
+ enum_feats = len(self.data.nb_features) - 1
- expl = self.random_forest.explain(instance,
- use_lime=lime_call if solver == "lime" else None,
- use_anchor=anchor_call if solver == "anchor" else None,
- use_shap=shap_call if solver == "shap" else None,
- nof_feats=enum_feats)
-
- if validation:
- coex = self.random_forest.validate(instance, expl)
- if coex:
- # repairing the local explanation
- gexpl = self.random_forest.explain(instance, expl_ext=expl, prefer_ext=True)
- else:
- # an attempt to refine the local explanation further
- gexpl = self.random_forest.explain(instance, expl_ext=expl)
-
- print(expl)
+ smallest = True if xtype == "S" else False
+ if isinstance(self.random_forest, XRF):
+ explanation_result = self.random_forest.explain(instance)
+ else:
+ explanation_result = self.random_forest.explain(instance, smallest, solver,
+ use_lime=lime_call if solver == "LIME" else None,
+ use_anchor=anchor_call if solver == "ANCHOR" else None,
+ use_shap=shap_call if solver == "SHAP" else None,
+ nof_feats=enum_feats)
self.explanation = []
list_explanations_path = []
- explanation = {}
self.network = html.Div([])
# Creating a clean and nice text component
- # instance plotting
- self.explanation.append(html.H4("Instance : \n"))
- self.explanation.append(html.P(str([str(instance[i]) for i in range(len(instance))])))
- for k in explanation.keys():
- if k != "List of path explanation(s)" and k != "List of path contrastive explanation(s)":
- if k in ["List of abductive explanation(s)", "List of contrastive explanation(s)"]:
- self.explanation.append(html.H4(k))
- for expl in explanation[k]:
- self.explanation.append(html.Hr())
- self.explanation.append(html.P(expl))
- self.explanation.append(html.Hr())
- else:
- self.explanation.append(html.P(k + explanation[k]))
- else:
- list_explanations_path = explanation["List of path explanation(s)"]
- list_contrastive_explanations_path = explanation["List of path contrastive explanation(s)"]
-
- return list_explanations_path, list_contrastive_explanations_path
+ compt=0
+ for sample_expl in explanation_result:
+ compt+=1
+ self.explanation.append(html.H4("Sample{0} : \n".format(compt)))
+ for k in sample_expl.keys():
+ self.explanation.append(html.H5(k))
+ self.explanation.append(html.Hr())
+ self.explanation.append(html.P(sample_expl[k]))
+ self.explanation.append(html.Hr())
+
+ return list_explanations_path, []
diff --git a/pages/application/RandomForest/utils/data.py b/pages/application/RandomForest/utils/data.py
index 6c94e3d..d86e8ec 100644
--- a/pages/application/RandomForest/utils/data.py
+++ b/pages/application/RandomForest/utils/data.py
@@ -26,8 +26,7 @@ class Data(object):
Class for representing data (transactions).
"""
- def __init__(self, filename=None, fpointer=None, mapfile=None,
- separator=' ', use_categorical = False):
+ def __init__(self, file=None, mapfile=None, separator=',', use_categorical = False):
"""
Constructor and parser.
"""
@@ -40,55 +39,21 @@ class Data(object):
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)
+ self.parse(file, 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()
+ lines = fp.split('\n')
# reading preamble
self.names = lines[0].strip().split(separator)
diff --git a/pages/application/RandomForest/utils/xgbooster/encode.py b/pages/application/RandomForest/utils/xgbooster/encode.py
index 6a77fb3..bf98a6b 100644
--- a/pages/application/RandomForest/utils/xgbooster/encode.py
+++ b/pages/application/RandomForest/utils/xgbooster/encode.py
@@ -42,7 +42,6 @@ class SMTEncoder(object):
self.feats = {f: i for i, f in enumerate(feats)}
self.nofcl = nof_classes
self.idmgr = IDPool()
- self.optns = xgb.options
# xgbooster will also be needed
self.xgb = xgb
@@ -165,8 +164,8 @@ class SMTEncoder(object):
# if targeting interval-based encoding,
# traverse all trees and extract all possible intervals
# for each feature
- if self.optns.encode == 'smtbool':
- self.compute_intervals()
+ # if self.optns.encode == 'smtbool':
+ self.compute_intervals()
# traversing and encoding each tree
for i, tree in enumerate(self.ensemble.trees):
@@ -203,13 +202,9 @@ class SMTEncoder(object):
# number of variables
nof_vars = len(self.enc.get_free_variables())
- if self.optns.verb:
- print('encoding vars:', nof_vars)
- print('encoding asserts:', nof_asserts)
-
return self.enc, self.intvs, self.imaps, self.ivars
- def test_sample(self, sample):
+ def test_sample(self, sample, solver=None):
"""
Check whether or not the encoding "predicts" the same class
as the classifier given an input sample.
@@ -221,9 +216,6 @@ class SMTEncoder(object):
# score arrays computed for each class
csum = [[] for c in range(self.nofcl)]
- if self.optns.verb:
- print('testing sample:', list(sample))
-
sample_internal = list(self.xgb.transform(sample)[0])
# traversing all trees
@@ -274,7 +266,7 @@ class SMTEncoder(object):
# now, getting the model
escores = []
- model = get_model(And(self.enc, *hypos), solver_name=self.optns.solver)
+ model = get_model(And(self.enc, *hypos), solver_name=solver)
for c in range(self.nofcl):
v = Symbol('class{0}_score'.format(c), typename=REAL)
escores.append(float(model.get_py_value(v)))
@@ -282,10 +274,6 @@ class SMTEncoder(object):
assert all(map(lambda c, e: abs(c - e) <= 0.001, cscores, escores)), \
'wrong prediction: {0} vs {1}'.format(cscores, escores)
- if self.optns.verb:
- print('xgb scores:', cscores)
- print('enc scores:', escores)
-
def save_to(self, outfile):
"""
Save the encoding into a file with a given name.
diff --git a/pages/application/RandomForest/utils/xgbooster/explain.py b/pages/application/RandomForest/utils/xgbooster/explain.py
index ca07874..a4e5f8b 100644
--- a/pages/application/RandomForest/utils/xgbooster/explain.py
+++ b/pages/application/RandomForest/utils/xgbooster/explain.py
@@ -1,5 +1,5 @@
#!/usr/bin/env python
-#-*- coding:utf-8 -*-
+# -*- coding:utf-8 -*-
##
## explain.py
##
@@ -9,7 +9,7 @@
##
#
-#==============================================================================
+# ==============================================================================
from __future__ import print_function
import numpy as np
import os
@@ -23,6 +23,7 @@ from six.moves import range
import sys
+
#
#==============================================================================
class SMTExplainer(object):
@@ -31,7 +32,7 @@ class SMTExplainer(object):
"""
def __init__(self, formula, intvs, imaps, ivars, feats, nof_classes,
- options, xgb):
+ solver, xgb):
"""
Constructor.
"""
@@ -41,14 +42,12 @@ class SMTExplainer(object):
self.imaps = imaps
self.ivars = ivars
self.nofcl = nof_classes
- self.optns = options
self.idmgr = IDPool()
# saving XGBooster
self.xgb = xgb
- self.verbose = self.optns.verb
- self.oracle = Solver(name=options.solver)
+ self.oracle = Solver(name=solver)
self.inps = [] # input (feature value) variables
for f in self.xgb.extended_feature_names_as_array_strings:
@@ -150,28 +149,26 @@ class SMTExplainer(object):
disj.append(GT(self.outs[i], self.outs[self.out_id]))
self.oracle.add_assertion(Implies(self.selv, Or(disj)))
- if self.verbose:
- inpvals = self.xgb.readable_sample(sample)
+ inpvals = self.xgb.readable_sample(sample)
- self.preamble = []
- for f, v in zip(self.xgb.feature_names, inpvals):
- if f not in str(v):
- self.preamble.append('{0} = {1}'.format(f, v))
- else:
- self.preamble.append(v)
+ self.preamble = []
+ for f, v in zip(self.xgb.feature_names, inpvals):
+ if f not in str(v):
+ self.preamble.append('{0} = {1}'.format(f, v))
+ else:
+ self.preamble.append(v)
- print(' explaining: "IF {0} THEN {1}"'.format(' AND '.join(self.preamble), self.output))
+ explanation_dic = {}
+ explanation_dic["explaning instance"] = ' explaining: "IF {0} THEN {1}"'.format(' AND '.join(self.preamble), self.output)
+ return explanation_dic
def explain(self, sample, smallest, expl_ext=None, prefer_ext=False):
"""
Hypotheses minimization.
"""
- self.time = resource.getrusage(resource.RUSAGE_CHILDREN).ru_utime + \
- resource.getrusage(resource.RUSAGE_SELF).ru_utime
-
# adapt the solver to deal with the current sample
- self.prepare(sample)
+ explanation_dic = self.prepare(sample)
# saving external explanation to be minimized further
if expl_ext == None or prefer_ext:
@@ -182,27 +179,20 @@ class SMTExplainer(object):
# if satisfiable, then the observation is not implied by the hypotheses
if self.oracle.solve([self.selv] + [h for h, c in zip(self.rhypos, self.to_consider) if c]):
- print(' no implication!')
- print(self.oracle.get_model())
- sys.exit(1)
-
- if not smallest:
- self.compute_minimal(prefer_ext=prefer_ext)
- else:
- self.compute_smallest()
-
- self.time = resource.getrusage(resource.RUSAGE_CHILDREN).ru_utime + \
- resource.getrusage(resource.RUSAGE_SELF).ru_utime - self.time
-
- expl = sorted([self.sel2fid[h] for h in self.rhypos])
+ explanation_dic["no implication"] = self.oracle.get_model()
+ else :
+ if not smallest:
+ self.compute_minimal(prefer_ext=prefer_ext)
+ else:
+ self.compute_smallest()
- if self.verbose:
+ expl = sorted([self.sel2fid[h] for h in self.rhypos])
+ explanation_dic["explanation brute "] = expl
self.preamble = [self.preamble[i] for i in expl]
- print(' explanation: "IF {0} THEN {1}"'.format(' AND '.join(self.preamble), self.xgb.target_name[self.out_id]))
- print(' # hypos left:', len(self.rhypos))
- print(' time: {0:.2f}'.format(self.time))
+ explanation_dic["explanation"] = ' explanation: "IF {0} THEN {1}"'.format(' AND '.join(self.preamble), self.xgb.target_name[self.out_id])
+ explanation_dic["Hyphothesis left"] = ' # hypos left:' + str(len(self.rhypos))
- return expl
+ return explanation_dic
def compute_minimal(self, prefer_ext=False):
"""
@@ -258,10 +248,6 @@ class SMTExplainer(object):
hset = hitman.get()
iters += 1
- if self.verbose > 1:
- print('iter:', iters)
- print('cand:', hset)
-
if self.oracle.solve([self.selv] + [self.rhypos[i] for i in hset]):
to_hit = []
satisfied, unsatisfied = [], []
@@ -302,10 +288,7 @@ class SMTExplainer(object):
else:
to_hit.append(h)
- if self.verbose > 1:
- print('coex:', to_hit)
-
hitman.hit(to_hit)
else:
self.rhypos = [self.rhypos[i] for i in hset]
- break
+ break
\ No newline at end of file
diff --git a/pages/application/RandomForest/utils/xgbooster/xgbooster.py b/pages/application/RandomForest/utils/xgbooster/xgbooster.py
index 25cd86c..b7e62b4 100644
--- a/pages/application/RandomForest/utils/xgbooster/xgbooster.py
+++ b/pages/application/RandomForest/utils/xgbooster/xgbooster.py
@@ -11,24 +11,16 @@
#
# ==============================================================================
from __future__ import print_function
-from .validate import SMTValidator
-from .encode import SMTEncoder
-from .explain import SMTExplainer
-import numpy as np
-import os
-import resource
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import accuracy_score
-import sklearn
-# print('The scikit-learn version is {}.'.format(sklearn.__version__))
-from sklearn.preprocessing import OneHotEncoder, LabelEncoder
-import sys
+import random
+
+import numpy as np
from six.moves import range
-from .tree import TreeEnsemble
-import xgboost as xgb
-from xgboost import XGBClassifier, Booster
-import pickle
+from xgboost import XGBClassifier
+
+from .encode import SMTEncoder
+from .explain import SMTExplainer
+from .validate import SMTValidator
#
@@ -38,164 +30,20 @@ class XGBooster(object):
The main class to train/encode/explain XGBoost models.
"""
- def __init__(self, options, from_data=None, from_model=None,
- from_encoding=None):
+ def __init__(self, options, from_model=None):
"""
Constructor.
"""
+ np.random.seed(random.randint(1, 100))
- assert from_data or from_model or from_encoding, \
- 'At least one input file should be specified'
-
- self.init_stime = resource.getrusage(resource.RUSAGE_SELF).ru_utime
- self.init_ctime = resource.getrusage(resource.RUSAGE_CHILDREN).ru_utime
-
- # saving command-line options
- self.options = options
- self.seed = self.options.seed
- np.random.seed(self.seed)
-
- if from_data:
- self.use_categorical = self.options.use_categorical
- # saving data
- self.data = from_data
- ##
- samps = np.asarray(self.data.samps)
- if not all(c.isnumeric() for c in samps[:, -1]):
- le = LabelEncoder()
- le.fit(samps[:, -1])
- samps[:, -1] = le.transform(samps[:, -1])
- # self.class_names = le.classes_
- # print(le.classes_)
- ##
- dataset = np.asarray(samps, dtype=np.float32)
- # dataset = np.asarray(self.data.samps, dtype=np.float32)
-
- # split data into X and y
- self.feature_names = self.data.names[:-1]
- self.nb_features = len(self.feature_names)
-
- self.X = dataset[:, 0:self.nb_features]
- self.Y = dataset[:, self.nb_features]
- self.num_class = len(set(self.Y))
- self.target_name = list(range(self.num_class))
-
- param_dist = {'n_estimators': self.options.n_estimators,
- 'max_depth': self.options.maxdepth}
-
- if (self.num_class == 2):
- param_dist['objective'] = 'binary:logistic'
-
- self.model = XGBClassifier(**param_dist)
-
- # split data into train and test sets
- self.test_size = self.options.testsplit
- if (self.test_size > 0):
- self.X_train, self.X_test, self.Y_train, self.Y_test = \
- train_test_split(self.X, self.Y, test_size=self.test_size,
- random_state=self.seed)
- else:
- self.X_train = self.X
- self.X_test = [] # need a fix
- self.Y_train = self.Y
- self.Y_test = [] # need a fix
-
- # check if we have info about categorical features
- if (self.use_categorical):
- self.categorical_features = from_data.categorical_features
- self.categorical_names = from_data.categorical_names
- self.target_name = from_data.class_names
-
- ####################################
- # this is a set of checks to make sure that we use the same as anchor encoding
- cat_names = sorted(self.categorical_names.keys())
- assert (cat_names == self.categorical_features)
- self.encoder = {}
- for i in self.categorical_features:
- self.encoder.update({i: OneHotEncoder(categories='auto', sparse=False)}) # ,
- self.encoder[i].fit(self.X[:, [i]])
-
- else:
- self.categorical_features = []
- self.categorical_names = []
- self.encoder = []
-
- fname = from_data
-
- elif from_model:
- fname = from_model
- self.load_datainfo(from_model)
- if (self.use_categorical is False) and (self.options.use_categorical is True):
- print(
- "Error: Note that the model is trained without categorical features info. Please do not use -c option for predictions")
- exit()
- # load model
-
- elif from_encoding:
- fname = from_encoding
-
- # encoding, feature names, and number of classes
- # are read from an input file
- enc = SMTEncoder(None, None, None, self, from_encoding)
- self.enc, self.intvs, self.imaps, self.ivars, self.feature_names, \
- self.num_class = enc.access()
-
- # create extra file names
- try:
- os.stat(options.output)
- except:
- os.mkdir(options.output)
-
- self.mapping_features()
- #################
- self.test_encoding_transformes()
-
- bench_name = os.path.splitext(os.path.basename(options.files[0]))[0]
- bench_dir_name = options.output + "/bt/" + bench_name
- try:
- os.stat(bench_dir_name)
- except:
- os.mkdir(bench_dir_name)
-
- self.basename = (os.path.join(bench_dir_name, bench_name +
- "_nbestim_" + str(options.n_estimators) +
- "_maxdepth_" + str(options.maxdepth) +
- "_testsplit_" + str(options.testsplit)))
-
- data_suffix = '.splitdata.pkl'
- self.modfile = self.basename + '.mod.pkl'
-
- self.mod_plainfile = self.basename + '.mod.txt'
-
- self.resfile = self.basename + '.res.txt'
- self.encfile = self.basename + '.enc.txt'
- self.expfile = self.basename + '.exp.txt'
-
- def load_datainfo(self, model_from_pkl, data_from_pkl):
self.model = XGBClassifier()
- self.model = model_from_pkl
- loaded_data = data_from_pkl
- self.X = loaded_data["X"]
- self.Y = loaded_data["Y"]
- self.X_train = loaded_data["X_train"]
- self.X_test = loaded_data["X_test"]
- self.Y_train = loaded_data["Y_train"]
- self.Y_test = loaded_data["Y_test"]
- self.feature_names = loaded_data["feature_names"]
- self.target_name = loaded_data["target_name"]
- self.num_class = loaded_data["num_class"]
- self.nb_features = len(self.feature_names)
- self.categorical_features = loaded_data["categorical_features"]
- self.categorical_names = loaded_data["categorical_names"]
- self.encoder = loaded_data["encoder"]
- self.use_categorical = loaded_data["use_categorical"]
+ self.model = from_model
- def train(self, outfile=None):
- """
- Train a tree ensemble using XGBoost.
- """
+ self.feature_names = options["feature_names"]
+ self.num_class = options["n_classes"]
+ self.nb_features = options["n_features"]
- return self.build_xgbtree(outfile)
+ self.mapping_features()
def encode(self, test_on=None):
"""
@@ -208,9 +56,7 @@ class XGBooster(object):
if test_on:
encoder.test_sample(np.array(test_on))
- # encoder.save_to(self.encfile)
-
- def explain(self, sample, use_lime=None, use_anchor=None, use_shap=None,
+ def explain(self, sample, smallest, solver, use_lime=None, use_anchor=None, use_shap=None,
expl_ext=None, prefer_ext=False, nof_feats=5):
"""
Explain a prediction made for a given sample with a previously
@@ -229,12 +75,10 @@ class XGBooster(object):
if 'x' not in dir(self):
self.x = SMTExplainer(self.enc, self.intvs, self.imaps,
self.ivars, self.feature_names, self.num_class,
- self.options, self)
+ solver, self)
- expl = self.x.explain(np.array(sample), self.options.smallest,
- expl_ext, prefer_ext)
+ expl = self.x.explain(np.array(sample), smallest, expl_ext, prefer_ext)
- # returning the explanation
return expl
def validate(self, sample, expl):
@@ -255,191 +99,12 @@ class XGBooster(object):
# try to compute a counterexample
return self.v.validate(np.array(sample), expl)
- 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.encoder != [])
- tx = []
- for i in range(self.nb_features):
- self.encoder[i].drop = None
- if (i in self.categorical_features):
- tx_aux = self.encoder[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.encoder != [])
- 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.encoder[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.encoder[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):
- # test encoding
-
- X = self.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())
-
- if (self.options.verb > 1):
- 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))
-
- def transfomed_sample_info(self, i):
- print(enc.categories_)
-
- def build_xgbtree(self, outfile=None):
- """
- Build an ensemble of trees.
- """
-
- if (outfile is None):
- outfile = self.modfile
- else:
- self.datafile = sefl.form_datefile_name(outfile)
-
- # fit model no training data
-
- if (len(self.X_test) > 0):
- eval_set = [(self.transform(self.X_train), self.Y_train), (self.transform(self.X_test), self.Y_test)]
- else:
- eval_set = [(self.transform(self.X_train), self.Y_train)]
-
- print("start xgb")
- self.model.fit(self.transform(self.X_train), self.Y_train,
- eval_set=eval_set,
- verbose=self.options.verb) # eval_set=[(X_test, Y_test)],
- print("end xgb")
-
- evals_result = self.model.evals_result()
- ########## saving model
- self.save_datainfo(outfile)
- print("saving plain model to ", self.mod_plainfile)
- self.model._Booster.dump_model(self.mod_plainfile)
-
- ensemble = TreeEnsemble(self.model, self.extended_feature_names_as_array_strings, nb_classes=self.num_class)
-
- y_pred_prob = self.model.predict_proba(self.transform(self.X_train[:10]))
- y_pred_prob_compute = ensemble.predict(self.transform(self.X_train[:10]), self.num_class)
-
- assert (np.absolute(y_pred_prob_compute - y_pred_prob).sum() < 0.01 * len(y_pred_prob))
-
- ### accuracy
- try:
- train_accuracy = round(1 - evals_result['validation_0']['merror'][-1], 2)
- except:
- try:
- train_accuracy = round(1 - evals_result['validation_0']['error'][-1], 2)
- except:
- assert (False)
-
- try:
- test_accuracy = round(1 - evals_result['validation_1']['merror'][-1], 2)
- except:
- try:
- test_accuracy = round(1 - evals_result['validation_1']['error'][-1], 2)
- except:
- print("no results test data")
- test_accuracy = 0
-
- #### saving
- print("saving results to ", self.resfile)
- with open(self.resfile, 'w') as f:
- f.write("{} & {} & {} &{} &{} & {} \\\\ \n \hline \n".format(
- os.path.basename(self.options.files[0]).replace("_", "-"),
- train_accuracy,
- test_accuracy,
- self.options.n_estimators,
- self.options.maxdepth,
- self.options.testsplit))
- f.close()
-
- print("c BT sz:", ensemble.sz)
- print("Train accuracy: %.2f%%" % (train_accuracy * 100.0))
- print("Test accuracy: %.2f%%" % (test_accuracy * 100.0))
- return train_accuracy, test_accuracy, self.model
+ 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
diff --git a/pages/application/RandomForest/utils/xgbrf/encode.py b/pages/application/RandomForest/utils/xgbrf/encode.py
index 6a77fb3..78816a8 100644
--- a/pages/application/RandomForest/utils/xgbrf/encode.py
+++ b/pages/application/RandomForest/utils/xgbrf/encode.py
@@ -39,10 +39,10 @@ class SMTEncoder(object):
"""
self.model = model
+ self.features_names = feats
self.feats = {f: i for i, f in enumerate(feats)}
self.nofcl = nof_classes
self.idmgr = IDPool()
- self.optns = xgb.options
# xgbooster will also be needed
self.xgb = xgb
@@ -165,8 +165,8 @@ class SMTEncoder(object):
# if targeting interval-based encoding,
# traverse all trees and extract all possible intervals
# for each feature
- if self.optns.encode == 'smtbool':
- self.compute_intervals()
+ # if self.optns.encode == 'smtbool':
+ self.compute_intervals()
# traversing and encoding each tree
for i, tree in enumerate(self.ensemble.trees):
@@ -203,13 +203,9 @@ class SMTEncoder(object):
# number of variables
nof_vars = len(self.enc.get_free_variables())
- if self.optns.verb:
- print('encoding vars:', nof_vars)
- print('encoding asserts:', nof_asserts)
-
return self.enc, self.intvs, self.imaps, self.ivars
- def test_sample(self, sample):
+ def test_sample(self, sample, solver):
"""
Check whether or not the encoding "predicts" the same class
as the classifier given an input sample.
@@ -221,9 +217,6 @@ class SMTEncoder(object):
# score arrays computed for each class
csum = [[] for c in range(self.nofcl)]
- if self.optns.verb:
- print('testing sample:', list(sample))
-
sample_internal = list(self.xgb.transform(sample)[0])
# traversing all trees
@@ -274,7 +267,7 @@ class SMTEncoder(object):
# now, getting the model
escores = []
- model = get_model(And(self.enc, *hypos), solver_name=self.optns.solver)
+ model = get_model(And(self.enc, *hypos), solver_name=solver)
for c in range(self.nofcl):
v = Symbol('class{0}_score'.format(c), typename=REAL)
escores.append(float(model.get_py_value(v)))
@@ -282,10 +275,6 @@ class SMTEncoder(object):
assert all(map(lambda c, e: abs(c - e) <= 0.001, cscores, escores)), \
'wrong prediction: {0} vs {1}'.format(cscores, escores)
- if self.optns.verb:
- print('xgb scores:', cscores)
- print('enc scores:', escores)
-
def save_to(self, outfile):
"""
Save the encoding into a file with a given name.
diff --git a/pages/application/RandomForest/utils/xgbrf/tree.py b/pages/application/RandomForest/utils/xgbrf/tree.py
index afe34d9..6c7f753 100644
--- a/pages/application/RandomForest/utils/xgbrf/tree.py
+++ b/pages/application/RandomForest/utils/xgbrf/tree.py
@@ -1,5 +1,5 @@
#!/usr/bin/env python
-#-*- coding:utf-8 -*-
+# -*- coding:utf-8 -*-
##
## tree.py (reuses parts of the code of SHAP)
##
@@ -9,8 +9,8 @@
##
#
-#==============================================================================
-from anytree import Node, RenderTree,AsciiStyle
+# ==============================================================================
+from anytree import Node, RenderTree, AsciiStyle
import json
import numpy as np
import xgboost as xgb
@@ -18,13 +18,13 @@ import math
#
-#==============================================================================
+# ==============================================================================
class xgnode(Node):
- def __init__(self, id, parent = None):
+ 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.left_node_id = -1 # Left child
self.right_node_id = -1 # Right child
self.missing_node_id = -1
@@ -37,29 +37,31 @@ class xgnode(Node):
def __str__(self):
pref = ' ' * self.depth
if (len(self.children) == 0):
- return (pref+ "leaf: {} {}".format(self.id, self.values))
+ return (pref + "leaf: {} {}".format(self.id, self.values))
else:
- if(self.name is None):
- return (pref+ "{} f{}<{}".format(self.id, self.feature, self.threshold))
+ 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))
+ return (pref + "{} \"{}\"<{}".format(self.id, self.name, self.threshold))
#
-#==============================================================================
-def build_tree(json_tree, node = None, feature_names = None, inverse = False):
+# ==============================================================================
+def build_tree(json_tree, node=None, feature_names=None, inverse=False):
def max_id(node):
if "children" in node:
return max(node["nodeid"], *[max_id(n) for n in node["children"]])
else:
return node["nodeid"]
+
m = max_id(json_tree) + 1
- def extract_data(json_node, root = None, feature_names = None):
+
+ def extract_data(json_node, root=None, feature_names=None):
i = json_node["nodeid"]
if (root is None):
node = xgnode(i)
else:
- node = xgnode(i, parent = root)
+ node = xgnode(i, parent=root)
node.cover = json_node["cover"]
if "children" in json_node:
@@ -73,8 +75,8 @@ def build_tree(json_tree, node = None, feature_names = None, inverse = False):
for c, n in enumerate(json_node["children"]):
child = extract_data(n, node, feature_names)
elif "leaf" in json_node:
- node.values = json_node["leaf"]
- if(inverse):
+ node.values = json_node["leaf"]
+ if (inverse):
node.values = -node.values
return node
@@ -83,7 +85,7 @@ def build_tree(json_tree, node = None, feature_names = None, inverse = False):
#
-#==============================================================================
+# ==============================================================================
def walk_tree(node):
if (len(node.children) == 0):
# leaf
@@ -95,7 +97,7 @@ def walk_tree(node):
#
-#==============================================================================
+# ==============================================================================
def scores_tree(node, sample):
if (len(node.children) == 0):
# leaf
@@ -103,41 +105,43 @@ def scores_tree(node, sample):
else:
feature_branch = node.feature
sample_value = sample[feature_branch]
- assert(sample_value is not None)
- if(sample_value < node.threshold):
+ assert (sample_value is not None)
+ if (sample_value < node.threshold):
return scores_tree(node.children[0], sample)
else:
return scores_tree(node.children[1], sample)
#
-#==============================================================================
+# ==============================================================================
class TreeEnsemble:
""" An ensemble of decision trees.
This object provides a common interface to many different types of models.
"""
- def __init__(self, model, feature_names = None, nb_classes = 0):
+
+ def __init__(self, model, feature_names=None, nb_classes=0):
self.model_type = "xgboost"
- #self.original_model = model.get_booster()
+ # self.original_model = model.get_booster()
self.original_model = model
####
self.base_offset = None
- json_trees = get_xgboost_json(self.original_model)
+ json_trees = get_xgboost_json(self.original_model, feature_names)
self.trees = [build_tree(json.loads(t), None, feature_names) for t in json_trees]
- if(nb_classes == 2):
+ if (nb_classes == 2):
# NASTY trick for binary
# We change signs of values in leaves so that we can just sum all the values in leaves for class X
# and take max to get the right class
- self.otrees = [build_tree(json.loads(t), None, feature_names, inverse = True) for t in json_trees]
+ self.otrees = [build_tree(json.loads(t), None, feature_names, inverse=True) for t in json_trees]
self.itrees = [build_tree(json.loads(t), None, feature_names) for t in json_trees]
self.trees = []
- for i,_ in enumerate(self.otrees):
+ for i, _ in enumerate(self.otrees):
self.trees.append(self.otrees[i])
self.trees.append(self.itrees[i])
self.feature_names = feature_names
+
def print_tree(self):
- for i,t in enumerate(self.trees):
+ for i, t in enumerate(self.trees):
print("tree number: ", i)
walk_tree(t)
@@ -149,13 +153,14 @@ class TreeEnsemble:
self.invert_tree_prob(node.children[0])
self.invert_tree_prob(node.children[1])
return node
+
def predict(self, samples, nb_classes):
# https://github.com/dmlc/xgboost/issues/1746#issuecomment-290130695
prob = []
- nb_estimators = int(len(self.trees)/nb_classes)
+ nb_estimators = int(len(self.trees) / nb_classes)
for sample in np.asarray(samples):
scores = []
- for i,t in enumerate(self.trees):
+ for i, t in enumerate(self.trees):
s = scores_tree(t, sample)
scores.append((s))
scores = np.asarray(scores)
@@ -163,30 +168,31 @@ class TreeEnsemble:
if (nb_classes == 2):
for i in range(nb_classes):
- class_scores.append(math.exp(-(scores[i::nb_classes]).sum())) # swap signs back as we had to use this trick in the contractor
- s0 = class_scores[0]
- s1 = class_scores[1]
- v0 = 1/(1 + s0)
- v1 = 1/(1 + s1)
+ class_scores.append(math.exp(-(
+ scores[i::nb_classes]).sum())) # swap signs back as we had to use this trick in the contractor
+ s0 = class_scores[0]
+ s1 = class_scores[1]
+ v0 = 1 / (1 + s0)
+ v1 = 1 / (1 + s1)
class_scores[0] = v0
class_scores[1] = v1
else:
- for i in range(0,nb_classes*nb_estimators,nb_estimators):
- class_scores.append(math.exp((scores[i:i+nb_estimators]).sum()))
- #for i in range(nb_classes):
+ for i in range(0, nb_classes * nb_estimators, nb_estimators):
+ class_scores.append(math.exp((scores[i:i + nb_estimators]).sum()))
+ # for i in range(nb_classes):
# class_scores.append(math.exp((scores[i::nb_classes]).sum()))
class_scores = np.asarray(class_scores)
- prob.append(class_scores/class_scores.sum())
+ prob.append(class_scores / class_scores.sum())
return np.asarray(prob).reshape((-1, nb_classes))
#
-#==============================================================================
-def get_xgboost_json(model):
+# ==============================================================================
+def get_xgboost_json(model, feature_names):
""" REUSED FROM SHAP
This gets a JSON dump of an XGBoost model while ensuring the feature names are their indexes.
"""
- fnames = model.feature_names
+ fnames = feature_names
model.feature_names = None
json_trees = model.get_dump(with_stats=True, dump_format="json")
model.feature_names = fnames
diff --git a/pages/application/RandomForest/utils/xgbrf/xgb_rf.py b/pages/application/RandomForest/utils/xgbrf/xgb_rf.py
index 024225f..840def7 100644
--- a/pages/application/RandomForest/utils/xgbrf/xgb_rf.py
+++ b/pages/application/RandomForest/utils/xgbrf/xgb_rf.py
@@ -10,6 +10,10 @@
#
#==============================================================================
+from __future__ import print_function
+
+import random
+
from .validate import SMTValidator
from .pi_checker import SMTChecker
from .encode import SMTEncoder
@@ -39,222 +43,22 @@ class XGBRandomForest(object):
The main class to train/encode/explain Random Forest models.
"""
- def __init__(self, options, from_data=None, from_model=None,
- from_encoding=None):
+ def __init__(self, options, from_model=None):
"""
Constructor.
"""
+ self.extended_feature_names = None
+ self.extended_feature_names_as_array_strings = None
+ np.random.seed(random.randint(1, 100))
- assert from_data or from_model or from_encoding, \
- 'At least one input file should be specified'
-
- self.init_stime = resource.getrusage(resource.RUSAGE_SELF).ru_utime
- self.init_ctime = resource.getrusage(resource.RUSAGE_CHILDREN).ru_utime
-
- # saving command-line options
- self.options = options
- self.seed = 42
- np.random.seed(self.seed)
-
- if from_data:
- self.use_categorical = self.options.use_categorical
- # saving data
- self.data = from_data
- dataset = np.asarray(self.data.samps, dtype=np.float32)
-
-
- # split data into X and y
- self.feature_names = self.data.names[:-1]
- self.nb_features = len(self.feature_names)
-
- self.X = dataset[:, 0:self.nb_features]
- self.Y = dataset[:, self.nb_features]
- self.num_class = len(set(self.Y))
- self.target_name = list(range(self.num_class))
-
- param_dist = {'n_estimators':self.options.n_estimators,
- 'max_depth':self.options.maxdepth}
-
- self.params = { 'num_parallel_tree': self.options.n_estimators,
- 'max_depth': self.options.maxdepth,
- 'colsample_bynode': 0.8, 'subsample': 0.8,
- 'learning_rate': 1, 'random_state': self.seed,
- 'verbosity' : self.options.verb
- }
-
- if(self.num_class == 2):
- self.params['eval_metric'] = 'error'
- self.params['objective'] = 'binary:logistic'
- else:
- self.params['eval_metric'] = 'merror'
- self.params['num_class'] = self.num_class
- self.params['objective'] = 'multi:softprob'
-
- if(self.num_class == 2):
- param_dist['objective'] = 'binary:logistic'
-
- #self.model = XGBRFClassifier(**param_dist)
- self.model = None
-
- # split data into train and test sets
- self.test_size = self.options.testsplit
- if (self.test_size > 0):
- self.X_train, self.X_test, self.Y_train, self.Y_test = \
- train_test_split(self.X, self.Y, test_size=self.test_size,
- random_state=self.seed)
- else:
- self.X_train = self.X
- self.X_test = [] # need a fix
- self.Y_train = self.Y
- self.Y_test = []# need a fix
-
- # check if we have info about categorical features
- if (self.use_categorical):
- self.categorical_features = from_data.categorical_features
- self.categorical_names = from_data.categorical_names
- self.target_name = from_data.class_names
-
- ####################################
- # this is a set of checks to make sure that we use the same as anchor encoding
- cat_names = sorted(self.categorical_names.keys())
- assert(cat_names == self.categorical_features)
- self.encoder = {}
- for i in self.categorical_features:
- self.encoder.update({i: OneHotEncoder(categories='auto', sparse=False)})#,
- self.encoder[i].fit(self.X[:,[i]])
-
- else:
- self.categorical_features = []
- self.categorical_names = []
- self.encoder = []
-
- fname = from_data
-
- elif from_model:
- fname = from_model
- self.load_datainfo(from_model)
- if (self.use_categorical is False) and (self.options.use_categorical is True):
- print("Error: Note that the model is trained without categorical features info. Please do not use -c option for predictions")
- exit()
- # load model
-
- elif from_encoding:
- fname = from_encoding
-
- # encoding, feature names, and number of classes
- # are read from an input file
- enc = SMTEncoder(None, None, None, self, from_encoding)
- self.enc, self.intvs, self.imaps, self.ivars, self.feature_names, \
- self.num_class = enc.access()
-
- # create extra file names
- try:
- os.stat(options.output)
- except:
- os.mkdir(options.output)
-
- self.mapping_features()
- #################
- self.test_encoding_transformes()
-
- bench_name = os.path.splitext(os.path.basename(options.files[0]))[0]
- bench_dir_name = options.output + "/" + bench_name
- try:
- os.stat(bench_dir_name)
- except:
- os.mkdir(bench_dir_name)
-
- self.basename = (os.path.join(bench_dir_name, bench_name +
- "_nbestim_" + str(options.n_estimators) +
- "_maxdepth_" + str(options.maxdepth) +
- "_testsplit_" + str(options.testsplit)))
-
- data_suffix = '.splitdata.pkl'
- self.modfile = self.basename + '.mod.pkl'
-
- self.mod_plainfile = self.basename + '.mod.txt'
-
- self.resfile = self.basename + '.res.txt'
- self.encfile = self.basename + '.enc.txt'
- self.expfile = self.basename + '.exp.txt'
-
- def form_datefile_name(self, modfile):
- data_suffix = '.splitdata.pkl'
- return modfile + data_suffix
-
- def pickle_save_file(self, 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(self, filename):
- try:
- f = open(filename, "rb")
- data = pickle.load(f)
- f.close()
- return data
- except:
- print("Cannot load from file", filename)
- exit()
-
- def save_datainfo(self, filename):
-
- print("saving model to ", filename)
- self.pickle_save_file(filename, self.model)
-
- filename_data = self.form_datefile_name(filename)
- print("saving data to ", filename_data)
- samples = {}
- samples["X"] = self.X
- samples["Y"] = self.Y
- samples["X_train"] = self.X_train
- samples["Y_train"] = self.Y_train
- samples["X_test"] = self.X_test
- samples["Y_test"] = self.Y_test
- samples["feature_names"] = self.feature_names
- samples["target_name"] = self.target_name
- samples["num_class"] = self.num_class
- samples["categorical_features"] = self.categorical_features
- samples["categorical_names"] = self.categorical_names
- samples["encoder"] = self.encoder
- samples["use_categorical"] = self.use_categorical
-
-
- self.pickle_save_file(filename_data, samples)
-
- def load_datainfo(self, filename):
- print("loading model from ", filename)
self.model = XGBRFClassifier()
- self.model = self.pickle_load_file(filename)
-
- datafile = self.form_datefile_name(filename)
- print("loading data from ", datafile)
- loaded_data = self.pickle_load_file(datafile)
- self.X = loaded_data["X"]
- self.Y = loaded_data["Y"]
- self.X_train = loaded_data["X_train"]
- self.X_test = loaded_data["X_test"]
- self.Y_train = loaded_data["Y_train"]
- self.Y_test = loaded_data["Y_test"]
- self.feature_names = loaded_data["feature_names"]
- self.target_name = loaded_data["target_name"]
- self.num_class = loaded_data["num_class"]
- self.nb_features = len(self.feature_names)
- self.categorical_features = loaded_data["categorical_features"]
- self.categorical_names = loaded_data["categorical_names"]
- self.encoder = loaded_data["encoder"]
- self.use_categorical = loaded_data["use_categorical"]
+ self.model = from_model
- def train(self, outfile=None):
- """
- Train a random forest using XGBoost.
- """
+ self.feature_names = options["feature_names"]
+ self.num_class = options["n_classes"]
+ self.nb_features = options["n_features"]
- return self.build_xgbtree(outfile)
+ self.mapping_features()
def encode(self, test_on=None):
"""
@@ -266,32 +70,27 @@ class XGBRandomForest(object):
if test_on:
encoder.test_sample(np.array(test_on))
- encoder.save_to(self.encfile)
-
- def explain(self, sample, use_lime=None, use_anchor=None, use_shap=None,
- expl_ext=None, prefer_ext=False, nof_feats=5):
+ def explain(self, sample, smallest, solver, use_lime=None, use_anchor=None, use_shap=None,
+ expl_ext=None, prefer_ext=False, nof_feats=5):
"""
Explain a prediction made for a given sample with a previously
trained tree ensemble.
"""
if use_lime:
- expl = use_lime(self, sample=sample, nb_samples=5, nb_features_in_exp=nof_feats)
+ expl = use_lime(self, sample=sample, nb_samples=5,
+ nb_features_in_exp=nof_feats)
elif use_anchor:
expl = use_anchor(self, sample=sample, nb_samples=5,
- nb_features_in_exp=nof_feats, threshold=0.95)
+ nb_features_in_exp=nof_feats, threshold=0.95)
elif use_shap:
expl = use_shap(self, sample=sample, nb_features_in_exp=nof_feats)
else:
- if 'x' not in dir(self):
- self.x = SMTExplainer(self.enc, self.intvs, self.imaps,
- self.ivars, self.feature_names, self.num_class,
- self.options, self)
-
- expl = self.x.explain(np.array(sample), self.options.smallest,
- expl_ext, prefer_ext)
+ self.x = SMTExplainer(self.enc, self.intvs, self.imaps,
+ self.ivars, self.feature_names, self.num_class,
+ solver, self)
+ expl = self.x.explain(np.array(sample), smallest, expl_ext, prefer_ext)
- # returning the explanation
return expl
def validate(self, sample, expl):
@@ -312,289 +111,12 @@ class XGBRandomForest(object):
# try to compute a counterexample
return self.v.validate(np.array(sample), expl)
-
- def isPrimeImplicant(self, sample, expl):
- """
- Check the explnation if it is a prime implicant.
- """
-
- # there must exist an encoding
- if 'enc' not in dir(self):
- encoder = SMTEncoder(self.model, self.feature_names, self.num_class,
- self)
- self.enc, _, _, _ = encoder.encode()
-
- if 'checker' not in dir(self):
- self.checker = SMTChecker(self.enc, self.feature_names, self.num_class,
- self)
-
- # check the explanation
- return self.checker.check(np.array(sample), expl)
-
- def repair(self, sample, expl):
- """
- Make an attempt to repair that a given pessimistic (incorrect) explanation.
- """
- #encode without sample
- self.encode()
- gexpl = self.explain(sample, expl_ext=expl, prefer_ext=True)
-
- #global explanation
- return gexpl
-
- def refine(self, sample, expl):
- """
- Make an attempt to refine that a given optimistic explanation.
- """
- #encode without sample
- self.encode()
- gexpl = self.explain(sample, expl_ext=expl)
-
- #global explanation
- return gexpl
-
- 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.encoder != [])
- tx = []
- for i in range(self.nb_features):
- #self.encoder[i].drop = None
- if (i in self.categorical_features):
- self.encoder[i].drop = None
- tx_aux = self.encoder[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.encoder != [])
- 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.encoder[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.encoder[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)
- readable_x.append(str(v))
- return np.asarray(readable_x)
-
- def test_encoding_transformes(self):
- # test encoding
-
- X = self.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())
-
- if (self.options.verb > 1):
- 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))
-
- def transfomed_sample_info(self, i):
- print(enc.categories_)
-
- def build_xgbtree(self, outfile=None):
- """
- Build an ensemble of trees (forest).
- """
-
- if (outfile is None):
- outfile = self.modfile
- else:
- self.datafile = sefl.form_datefile_name(outfile)
-
- # fit model no training data
-
- if (len(self.X_test) > 0):
- eval_set=[(self.transform(self.X_train), self.Y_train), (self.transform(self.X_test), self.Y_test)]
- else:
- eval_set=[(self.transform(self.X_train), self.Y_train)]
-
- print("start xgb")
- '''
- self.model.fit(self.transform(self.X_train), self.Y_train,
- eval_set=eval_set,
- verbose=self.options.verb) # eval_set=[(X_test, Y_test)],
- '''
- dtrain = xgb.DMatrix(self.transform(self.X_train), label=self.Y_train)
- dtest = xgb.DMatrix(self.transform(self.X_test), label=self.Y_test)
- eval_set = [(dtrain, 'train'), (dtest, 'eval')]
- evals_result = {}
- self.model = xgb.train(self.params, dtrain, num_boost_round=1,
- evals=eval_set, evals_result=evals_result)
- print("end xgb")
- print(self.model.get_score())
- print(len(self.model.get_score()))
- #for i in range(5):
- # xgb.plot_tree(self.model, num_trees=i)
- # plt.show()
-
-
- try:
- train_accuracy = round(1 - evals_result['train']['merror'][-1],2)
- except:
- try:
- train_accuracy = round(1 - evals_result['train']['error'][-1],2)
- except:
- assert(False)
- try:
- test_accuracy = round(1 - evals_result['eval']['merror'][-1],2)
- except:
- try:
- test_accuracy = round(1 - evals_result['eval']['error'][-1],2)
- except:
- assert(False)
- #print('Train accuracy_xgb: ',train_accuracy)
- #print('Test accuracy_xgb: ', test_accuracy)
-
-
- #evals_result = self.model.evals_result()
- ########## saving model
- self.save_datainfo(outfile)
- print("saving plain model to ", self.mod_plainfile)
- #self.model._Booster.dump_model(self.mod_plainfile)
- self.model.dump_model(self.mod_plainfile)
-
- ensemble = TreeEnsemble(self.model, self.extended_feature_names_as_array_strings, nb_classes = self.num_class)
- #ensemble.print_tree()
-
- #y_pred_prob = self.model.predict_proba(self.transform(self.X_train[:10]))
- classone_probs = self.model.predict(xgb.DMatrix(self.transform(self.X_train[:10])))
- if self.num_class == 2:
- classzero_probs = 1.0 - classone_probs
- y_pred_prob = np.vstack((classzero_probs, classone_probs)).transpose()
- else:
- y_pred_prob = classone_probs
-
- y_pred_prob_compute = ensemble.predict(self.transform(self.X_train[:10]), self.num_class)
- #print('y_pred_prob: \n', y_pred_prob)
- #print('y_pred_prob_compute: \n',y_pred_prob_compute)
- #print('\n\n')
-
- assert(np.absolute(y_pred_prob_compute- y_pred_prob).sum() < 0.01*len(y_pred_prob))
-
- ### accuracy
- '''
- try:
- train_accuracy = round(1 - evals_result['validation_0']['merror'][-1],2)
- except:
- try:
- train_accuracy = round(1 - evals_result['validation_0']['error'][-1],2)
- except:
- assert(False)
-
- try:
- test_accuracy = round(1 - evals_result['validation_1']['merror'][-1],2)
- except:
- try:
- test_accuracy = round(1 - evals_result['validation_1']['error'][-1],2)
- except:
- print("no results test data")
- test_accuracy = 0
- '''
-
- #### saving
-
- print("saving results to ", self.resfile)
- with open(self.resfile, 'w') as f:
- f.write("{} & {} & {} &{} &{} & {} \\\\ \n \hline \n".format(
- os.path.basename(self.options.files[0]).replace("_","-"),
- train_accuracy,
- test_accuracy,
- self.options.n_estimators,
- self.options.maxdepth,
- self.options.testsplit))
- f.close()
-
- print("Train accuracy: %.2f%%" % (train_accuracy * 100.0))
- print("Test accuracy: %.2f%%" % (test_accuracy * 100.0))
-
-
- return train_accuracy, test_accuracy, self.model
-
- def predict(self, X):
- classone_probs = self.model.predict(xgb.DMatrix(self.transform(X)))
- if self.num_class == 2:
- classzero_probs = 1.0 - classone_probs
- y_pred_prob = np.vstack((classzero_probs, classone_probs)).transpose()
- else:
- y_pred_prob = classone_probs
- return y_pred_prob
+ 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))
+ counter = counter + 1
diff --git a/pages/application/RandomForest/utils/xprf.py b/pages/application/RandomForest/utils/xprf.py
index acd3fd8..c22b618 100755
--- a/pages/application/RandomForest/utils/xprf.py
+++ b/pages/application/RandomForest/utils/xprf.py
@@ -1,5 +1,5 @@
#!/usr/bin/env python3
-#-*- coding:utf-8 -*-
+# -*- coding:utf-8 -*-
##
## xrf.py
##
@@ -9,32 +9,34 @@
##
#
-#==============================================================================
+# ==============================================================================
from __future__ import print_function
-from pages.application.RandomForest.utils.data import Data
+from pages.application.RandomForest.utils.data import Data
import os
import sys
import pickle
import resource
-from pages.application.RandomForest.utils.xgbooster import preprocess_dataset
+from pages.application.RandomForest.utils.xgbooster import preprocess_dataset
-from pages.application.RandomForest.utils.xrf import XRF, RF2001, Dataset, Checker
+from pages.application.RandomForest.utils.xrf import XRF, RF2001, Dataset, Checker
import numpy as np
##################
-from pages.application.RandomForest.utils.xpLime import lime_call
+from pages.application.RandomForest.utils.xpLime import lime_call
import math
import lime
import lime.lime_tabular
###
-from pages.application.RandomForest.utils.xpAnchor import anchor_call
-#from anchor import utils
+from pages.application.RandomForest.utils.xpAnchor import anchor_call
+# from anchor import utils
from anchor import anchor_tabular
+
+
################
#
-#==============================================================================
+# ==============================================================================
def show_info():
"""
Print info message.
@@ -42,35 +44,37 @@ def show_info():
print("c XRF: eXplaining Random Forest.")
print('c')
-
+
#
-#==============================================================================
+# ==============================================================================
def pickle_save_file(filename, data):
try:
- f = open(filename, "wb")
+ 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")
+ f = open(filename, "rb")
data = pickle.load(f)
f.close()
return data
except:
print("Cannot load from file", filename)
- exit()
-
-
-#
-#==============================================================================
+ 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)
@@ -81,35 +85,34 @@ if __name__ == '__main__':
if (options.preprocess_categorical):
preprocess_dataset(options.files[0], options.preprocess_categorical_files, options.use_categorical)
exit()
-
-
+
if options.files:
cls = None
xrf = None
-
+
print("loading data ...")
data = Dataset(filename=options.files[0], mapfile=options.mapfile,
- separator=options.separator, use_categorical = options.use_categorical)
-
+ 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)
- '''
+ '''
cls = RF2001(options)
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("----------------------")
-
+ print("----------------------")
+
xrf = XRF(options, cls, data)
- #xrf.test_tree_ensemble()
-
+ # xrf.test_tree_ensemble()
+
bench_name = os.path.splitext(os.path.basename(options.files[0]))[0]
bench_dir_name = options.output + "/RF/" + bench_name
try:
@@ -118,23 +121,22 @@ if __name__ == '__main__':
os.mkdir(bench_dir_name)
basename = (os.path.join(bench_dir_name, bench_name +
- "_nbestim_" + str(options.n_estimators) +
- "_maxdepth_" + str(options.maxdepth)))
+ "_nbestim_" + str(options.n_estimators) +
+ "_maxdepth_" + str(options.maxdepth)))
- modfile = basename + '.mod.pkl'
+ modfile = basename + '.mod.pkl'
print("saving model to ", modfile)
pickle_save_file(modfile, cls)
-
- #data_suffix = '.splitdata.pkl'
- #filename_data = basename + data_suffix
- #print("saving data to ", filename_data)
- #pickle_save_file(filename_data, data)
+ # data_suffix = '.splitdata.pkl'
+ # filename_data = basename + data_suffix
+ # print("saving data to ", filename_data)
+ # pickle_save_file(filename_data, data)
# read a sample from options.explain
- #if options.explain:
+ # if options.explain:
# options.explain = [float(v.strip()) for v in options.explain.split(',')]
-
+
'''
if options.encode:
# encode it and save the encoding to another file
@@ -143,25 +145,24 @@ if __name__ == '__main__':
'''
if options.explain:
mem = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss + \
- resource.getrusage(resource.RUSAGE_CHILDREN).ru_maxrss
-
+ resource.getrusage(resource.RUSAGE_CHILDREN).ru_maxrss
+
if not xrf:
print("loading model ...")
- cls = pickle_load_file(options.files[1])
+ cls = pickle_load_file(options.files[1])
xrf = XRF(options, cls, data)
-
-
- #expl = xrf.explain(options.explain)
-
- #expl_checker = Checker(xrf.f, data.num_class, data.extended_feature_names_as_array_strings)
-
- cls.print_accuracy(data) # print test accuracy of the RF model
-
+
+ # expl = xrf.explain(options.explain)
+
+ # expl_checker = Checker(xrf.f, data.num_class, data.extended_feature_names_as_array_strings)
+
+ cls.print_accuracy(data) # print test accuracy of the RF model
+
samps_file = options.explain.strip()
print(samps_file)
with open(samps_file, 'r') as fp:
lines = fp.readlines()
-
+
# timers
atimes = []
lengths = []
@@ -171,46 +172,46 @@ if __name__ == '__main__':
utimes = []
nSatCalls = []
nUnsCalls = []
-
+
ltimes = []
ctimes = []
wins = 0
-
+
for i, s in enumerate(lines):
sample = [float(v.strip()) for v in s.split(',')]
if tuple(sample) in tested:
continue
-
- #print("inst#{0}".format(i+1))
-
+
+ # print("inst#{0}".format(i+1))
+
tested.add(tuple(sample))
- #print('sample {0}: {1}'.format(i, ','.join(s.strip().split(','))))
-
+ # print('sample {0}: {1}'.format(i, ','.join(s.strip().split(','))))
+
xrf.encode(sample)
expl = xrf.explain(sample)
- atimes.append(xrf.x.time)
+ atimes.append(xrf.x.time)
lengths.append(len(expl))
nvars = xrf.enc.cnf.nv
nclauses = len(xrf.enc.cnf.clauses)
-
- #mSAT = max(xrf.x.stimes+[mSAT])
- #mUNSAT = max(xrf.x.utimes+[mUNSAT])
+
+ # mSAT = max(xrf.x.stimes+[mSAT])
+ # mUNSAT = max(xrf.x.utimes+[mUNSAT])
if len(xrf.x.stimes):
stimes.append(max(xrf.x.stimes))
if len(xrf.x.utimes):
utimes.append(max(xrf.x.utimes))
nSatCalls.append(xrf.x.nsat)
nUnsCalls.append(xrf.x.nunsat)
-
- #inst = data.transform(np.array(sample))[0]
- #expl_checker.check(np.array(inst), expl)
+
+ # inst = data.transform(np.array(sample))[0]
+ # expl_checker.check(np.array(inst), expl)
#####check_expl(np.array(inst), expl, xrf.enc.forest, xrf.enc.intvs)
-
+
del xrf.enc
del xrf.x
-
+
#####################LIME###########
'''
_, ltime = lime_call(cls, data, sample, verbose=options.verb) # call lime
@@ -218,20 +219,19 @@ if __name__ == '__main__':
#wins += 1
if atimes[-1] < ltime:
wins += 1
- '''
-
- _, ctime = anchor_call(cls, data, sample, verbose=options.verb) # call lime
+ '''
+
+ _, ctime = anchor_call(cls, data, sample, verbose=options.verb) # call lime
ctimes.append(ctime)
if atimes[-1] < ctime:
- wins += 1
-
- #if i == 1:
+ wins += 1
+
+ # if i == 1:
# break
-
+
mem = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss + \
- resource.getrusage(resource.RUSAGE_CHILDREN).ru_maxrss - mem
-
-
+ resource.getrusage(resource.RUSAGE_CHILDREN).ru_maxrss - mem
+
# reporting the time spent
print('')
print('tot time: {0:.2f}'.format(sum(atimes)))
@@ -240,22 +240,21 @@ if __name__ == '__main__':
print('avg time: {0:.2f}'.format(sum(atimes) / len(atimes)))
print('')
####
- print('avg length: {0:.0f}'.format(round(sum(lengths) / len(lengths))*100/len(sample)))
- #print('max SAT: {0:.2f}'.format(mSAT))
- #print('max UNSAT: {0:.2f}'.format(mUNSAT))
+ print('avg length: {0:.0f}'.format(round(sum(lengths) / len(lengths)) * 100 / len(sample)))
+ # print('max SAT: {0:.2f}'.format(mSAT))
+ # print('max UNSAT: {0:.2f}'.format(mUNSAT))
print('max SAT: {0:.2f}'.format(max(stimes)))
- print('max UNSAT: {0:.2f}'.format(max(utimes)))
+ print('max UNSAT: {0:.2f}'.format(max(utimes)))
print('avg #SAT: {0:.0f}'.format(sum(nSatCalls) / len(nSatCalls)))
print('avg #UNSAT: {0:.0f}'.format(sum(nUnsCalls) / len(nUnsCalls)))
print('')
- #reporting nof_vars and nof_clauses
+ # reporting nof_vars and nof_clauses
print('c nof vars: {0}'.format(nvars))
print('c nof clauses: {0}'.format(nclauses))
#
print('c nof instances: {0}'.format(len(tested)))
- print("c mem used: {0:.2f} Mb".format(mem/(1024*1024)))
-
-
+ print("c mem used: {0:.2f} Mb".format(mem / (1024 * 1024)))
+
# LIME runtimes
'''
print('')
@@ -263,12 +262,11 @@ if __name__ == '__main__':
print('avg time for Lime: {0:.2f}'.format(sum(ltimes) / len(ltimes)))
print('#wins {0} out of {1}'.format(wins, len(tested)) )
'''
-
+
# Anchor runtimes
print('')
print('tot time for Anchor: {0:.2f}'.format(sum(ctimes)))
print('max time for Anchor: {0:.2f}'.format(max(ctimes)))
print('min time for Anchor: {0:.2f}'.format(min(ctimes)))
print('avg time for Anchor: {0:.2f}'.format(sum(ctimes) / len(ctimes)))
- print('#wins {0} out of {1}'.format(wins, len(tested)) )
-
\ No newline at end of file
+ print('#wins {0} out of {1}'.format(wins, len(tested)))
diff --git a/pages/application/RandomForest/utils/xrf/archive/build/lib.macosx-10.9-x86_64-3.8/pysortn.cpython-38-darwin.so b/pages/application/RandomForest/utils/xrf/archive/build/lib.macosx-10.9-x86_64-3.8/pysortn.cpython-38-darwin.so
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diff --git a/pages/application/RandomForest/utils/xrf/archive/encode.py b/pages/application/RandomForest/utils/xrf/archive/encode.py
deleted file mode 100644
index 3478ebc..0000000
--- a/pages/application/RandomForest/utils/xrf/archive/encode.py
+++ /dev/null
@@ -1,276 +0,0 @@
-
-from pysat.formula import CNF, IDPool
-from pysat.solvers import Solver
-from pysat.card import *
-from itertools import combinations
-
-import collections
-import six
-from six.moves import range
-
-from .tree import Forest, predict_tree
-from .sortnetwrk import HSorNetwrk
-
-#
-#==============================================================================
-class SATEncoder(object):
- """
- Encoder of Random Forest classifier into SAT.
- """
-
- def __init__(self, forest, feats, nof_classes, extended_feature_names=None, from_file=None):
- #self.model = model
- self.forest = forest
- self.feats = {f: i for i, f in enumerate(feats)}
- self.num_class = nof_classes
- self.vpool = IDPool()
- #self.optns = xgb.options
- self.extended_feature_names = extended_feature_names
-
- #encoding formula
- self.cnf = None
-
- # for interval-based encoding
- self.intvs, self.imaps, self.ivars = None, None, None
-
- #if from_file:
- # self.load_from(from_file)
-
- def newVar(self, name):
- assert(name)
-
- 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 traverse(self, tree, k, clause):
- """
- Traverse a tree and encode each node.
- """
-
- if tree.children:
- var = self.newVar(tree.name)
- #print("{0} => {1}".format(tree.name, var))
- pos, neg = var, -var
-
- self.traverse(tree.children[0], k, clause + [-neg]) # -var
- self.traverse(tree.children[1], k, clause + [-pos]) # --var
- else: # leaf node
- cvar = self.newVar('class{0}_tr{1}'.format(tree.values,k))
- print('c: ', clause + [cvar])
- self.cnf.append(clause + [cvar])
-
-
- '''
- def encode_node(self, node):
- """
- Encode a node of a tree.
- """
-
- if '_' not in node.name:
- # continuous features => expecting an upper bound
- # feature and its upper bound (value)
- f, v = node.name, node.threshold
-
- existing = True if tuple([f, v]) in self.idmgr.obj2id else False
- vid = self.idmgr.id(tuple([f, v]))
- bv = Symbol('bvar{0}'.format(vid), typename=BOOL)
-
- if not existing:
- if self.intvs:
- d = self.imaps[f][v] + 1
- pos, neg = self.ivars[f][:d], self.ivars[f][d:]
- self.enc.append(Iff(bv, Or(pos)))
- self.enc.append(Iff(Not(bv), Or(neg)))
- else:
- fvar, fval = Symbol(f, typename=REAL), Real(v)
- self.enc.append(Iff(bv, LT(fvar, fval)))
-
- return bv, Not(bv)
- else:
- # all features are expected to be categorical and
- # encoded with one-hot encoding into Booleans
- # each node is expected to be of the form: f_i < 0.5
- bv = Symbol(node.name, typename=BOOL)
-
- # left branch is positive, i.e. bv is true
- # right branch is negative, i.e. bv is false
- return Not(bv), bv
- '''
-
-
- 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
- self.intvs[f].add(v)
-
- traverse_intervals(tree.children[0])
- traverse_intervals(tree.children[1])
-
- # initializing the intervals
- self.intvs = {'f{0}'.format(i): set([]) for i in range(len(self.feats))}
-
- 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]) + ['+'] for f in six.iterkeys(self.intvs)}
-
- self.imaps, self.ivars = {}, {}
- for feat, intvs in six.iteritems(self.intvs):
- self.imaps[feat] = {}
- self.ivars[feat] = []
- for i, ub in enumerate(intvs):
- self.imaps[feat][ub] = i
-
- ivar = Symbol(name='{0}_intv{1}'.format(feat, i), typename=BOOL)
- self.ivars[feat].append(ivar)
-
- def encode(self, sample):
- """
- Do the job.
- """
-
- self.cnf = CNF()
- # getting a tree ensemble
- #self.forest = Forest(self.model, self.extended_feature_names)
- num_tree = len(self.forest.trees)
-
- # introducing class score variables
- cvars = [[] for t in range(num_tree)]
- for k in range(len(self.forest.trees)):
- for j in range(self.num_class):
- var = self.newVar('class{0}_tr{1}'.format(j,k))
- cvars[k].append(-var)
-
- # if targeting interval-based encoding,
- # traverse all trees and extract all possible intervals
- # for each feature
- '''
- if self.optns.encode == 'smtbool':
- self.compute_intervals()
- '''
-
- # 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 this could could be squeezed
- # more to reduce NB binary clauses!!!!!!!
- enc = CardEnc.atmost(lits=[-v for v in cvars[k]],
- vpool=self.vpool,
- encoding=EncType.cardnetwrk) #AtMostOne constraint
- self.cnf.extend(enc.clauses)
-
-
- csum = [[] for c in range(self.num_class)]
- for k, tree in enumerate(self.forest.trees):
- c = predict_tree(tree, sample)
- csum[c].append(k)
- cvars[k][c] = - cvars[k][c]
-
- # encoding the majority
- cmaj,_ = max(enumerate(csum), key=(lambda x: len(x[1])))
- sorted_lits = [[] for c in range(self.num_class)]
- #sorting bits
- for j in range(self.num_class):
- tvars = [cvars[k][j] for k in range(num_tree)]
- clauses, vout, _ = HSorNetwrk(lits=tvars, vpool = self.vpool)
- self.cnf.extend(clauses)
- sorted_lits[j] = vout
- #print("tvars: {0} ==> {3} \nclauses: {1}\ntop: {2}".format(tvars, clauses, self.vpool.top, vout))
- #compare bits
- for j in range(self.num_class):
- if j == cmaj:
- continue
- for k in range(num_tree):
- self.cnf.append([ -sorted_lits[j][k], sorted_lits[cmaj][k] ]) # (v1 => v2)
- #print("-{0} => {1}".format(sorted_lits[j][k], sorted_lits[cmaj][k]))
-
- '''
- # 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):
- #ExactlyOne feat is True
- self.cnf.append(feats)
- enc = CardEnc.atmost(lits=feats, vpool=self.vpool, encoding=EncType.cardnetwrk)
- self.cnf.extend(enc.clauses)
- '''
-
- #if self.optns.verb:
- # number of variables
- print('#vars:', self.cnf.nv)
- # number of clauses
- print('#clauses:', len(self.cnf.clauses))
- #print(self.cnf.clauses)
-
- return self.cnf, self.intvs, self.imaps, self.ivars
-
- '''
- def test_sample(self, sample):
- """
- Check whether or not the encoding "predicts" the same class
- as the classifier given an input sample.
- """
-
- # first, compute the scores for all classes as would be
- # predicted by the classifier
-
- # score arrays computed for each class
- csum = [[] for c in range(self.num_class)]
-
- #if self.optns.verb:
- print('testing sample:', list(sample))
-
- # traversing all trees
- for i, tree in enumerate(self.forest.trees):
- c = predict_tree(tree, sample)
- csum[c].append(i)
-
- # encoding the majority
- cmaj,_ = max(enumerate(csum), key=(lambda x: len(x[1])))
-
- # second, get the scores computed with the use of the encoding
- assert self.cnf, "There is no encoding."
-
- slv = Solver(name="minisat22")
- slv.add_formula(self.cnf)
-
-
- # asserting the sample
- hypos = []
-
- #for i, fval in enumerate(sample):
-
- '''
-
-
- def access(self):
- """
- Get access to the encoding, features names, and the number of
- classes.
- """
-
- return self.cnf, self.intvs, self.imaps, self.ivars, self.feats, self.num_class
\ No newline at end of file
diff --git a/pages/application/RandomForest/utils/xrf/archive/pysortnetwrk.cc b/pages/application/RandomForest/utils/xrf/archive/pysortnetwrk.cc
deleted file mode 100644
index 8a44d96..0000000
--- a/pages/application/RandomForest/utils/xrf/archive/pysortnetwrk.cc
+++ /dev/null
@@ -1,248 +0,0 @@
-
-
-#define PY_SSIZE_T_CLEAN
-
-#include <setjmp.h>
-#include <signal.h>
-#include <stdio.h>
-#include <Python.h>
-
-#include "sortcard.hh"
-
-using namespace std;
-
-// docstrings
-//=============================================================================
-static char module_docstring[] = "This module provides an interface for "
- "encoding a few types of cardinality "
- "constraints";
-//static char atmost_docstring[] = "Create an AtMost(k) constraint.";
-//static char atleast_docstring[] = "Create an AtLeast(k) constraint.";
-static char sortn_docstring[] = "Sort an array of bits.";
-
-static PyObject *CardError;
-static jmp_buf env;
-
-// function declaration for functions available in module
-//=============================================================================
-extern "C" {
- //static PyObject *py_encode_atmost (PyObject *, PyObject *);
- //static PyObject *py_encode_atleast (PyObject *, PyObject *);
- static PyObject *py_sortn (PyObject *, PyObject *);
-}
-
-// module specification
-//=============================================================================
-static PyMethodDef module_methods[] = {
- //{ "encode_atmost", py_encode_atmost, METH_VARARGS, atmost_docstring },
- //{ "encode_atleast", py_encode_atleast, METH_VARARGS, atleast_docstring },
- { "HSort", py_sortn, METH_VARARGS, sortn_docstring },
-
- { NULL, NULL, 0, NULL }
-};
-
-extern "C" {
-
-// signal handler for SIGINT
-//=============================================================================
-static void sigint_handler(int signum)
-{
- longjmp(env, -1);
-}
-
-//#if PY_MAJOR_VERSION >= 3 // for Python3
-// PyInt_asLong()
-//=============================================================================
-static int pyint_to_cint(PyObject *i_obj)
-{
- return PyLong_AsLong(i_obj);
-}
-
-// PyInt_fromLong()
-//=============================================================================
-static PyObject *pyint_from_cint(int i)
-{
- return PyLong_FromLong(i);
-}
-
-// PyCapsule_New()
-//=============================================================================
-static PyObject *void_to_pyobj(void *ptr)
-{
- return PyCapsule_New(ptr, NULL, NULL);
-}
-
-// PyCapsule_GetPointer()
-//=============================================================================
-static void *pyobj_to_void(PyObject *obj)
-{
- return PyCapsule_GetPointer(obj, NULL);
-}
-
-// PyInt_Check()
-//=============================================================================
-static int pyint_check(PyObject *i_obj)
-{
- return PyLong_Check(i_obj);
-}
-
-// module initialization
-//=============================================================================
-static struct PyModuleDef module_def = {
- PyModuleDef_HEAD_INIT,
- "pysortnetwrk", /* m_name */
- module_docstring, /* m_doc */
- -1, /* m_size */
- module_methods, /* m_methods */
- NULL, /* m_reload */
- NULL, /* m_traverse */
- NULL, /* m_clear */
- NULL, /* m_free */
-};
-
-/*
-PyMODINIT_FUNC PyInit_pycard(void)
-{
- PyObject *m = PyModule_Create(&module_def);
-
- if (m == NULL)
- return NULL;
-
- CardError = PyErr_NewException((char *)"pycard.error", NULL, NULL);
- Py_INCREF(CardError);
-
- if (PyModule_AddObject(m, "error", CardError) < 0) {
- Py_DECREF(CardError);
- return NULL;
- }
-
- return m;
-}*/
-
-PyMODINIT_FUNC PyInit_pysortnetwrk(void)
-{
- PyObject *m = PyModule_Create(&module_def);
-
- if (m == NULL)
- return NULL;
-
- CardError = PyErr_NewException((char *)"pycard.error", NULL, NULL);
- Py_INCREF(CardError);
-
- if (PyModule_AddObject(m, "error", CardError) < 0) {
- Py_DECREF(CardError);
- return NULL;
- }
-
- return m;
-}
-
-
-// auxiliary function for translating an iterable to a vector<int>
-//=============================================================================
-static bool pyiter_to_vector(PyObject *obj, vector<int>& vect)
-{
- PyObject *i_obj = PyObject_GetIter(obj);
-
- if (i_obj == NULL) {
- PyErr_SetString(PyExc_RuntimeError,
- "Object does not seem to be an iterable.");
- return false;
- }
-
- PyObject *l_obj;
- while ((l_obj = PyIter_Next(i_obj)) != NULL) {
- if (!pyint_check(l_obj)) {
- Py_DECREF(l_obj);
- Py_DECREF(i_obj);
- PyErr_SetString(PyExc_TypeError, "integer expected");
- return false;
- }
-
- int l = pyint_to_cint(l_obj);
- Py_DECREF(l_obj);
-
- if (l == 0) {
- Py_DECREF(i_obj);
- PyErr_SetString(PyExc_ValueError, "non-zero integer expected");
- return false;
- }
-
- vect.push_back(l);
- }
-
- Py_DECREF(i_obj);
- return true;
-}
-
-//
-//=============================================================================
-static PyObject *py_sortn(PyObject *self, PyObject *args)
-{
-
- PyObject *av_obj;
- //PyObject *cv_obj;
- int top;
- int zvar;
-
- //PyObject *lhs_obj;
- //int rhs;
- //int top;
- //int enc;
- int main_thread;
-
- if (!PyArg_ParseTuple(args, "Oiii", &av_obj, &top, &zvar,
- &main_thread))
- return NULL;
-
- vector<int> av;
- if (pyiter_to_vector(av_obj, av) == false)
- return NULL;
-
- PyOS_sighandler_t sig_save;
- if (main_thread) {
- sig_save = PyOS_setsig(SIGINT, sigint_handler);
-
- if (setjmp(env) != 0) {
- PyErr_SetString(CardError, "Caught keyboard interrupt");
- return NULL;
- }
- }
-
- // calling encoder
- ClauseSet dest;
- vector<int> cv;
- sortn_half_sorter_recur(top, dest, av, cv, zvar);
- //_encode_atmost(dest, lhs, rhs, top, enc);
-
- if (main_thread)
- PyOS_setsig(SIGINT, sig_save);
-
- // creating the resulting clause set
- PyObject *dest_obj = PyList_New(dest.size());
- for (size_t i = 0; i < dest.size(); ++i) {
- PyObject *cl_obj = PyList_New(dest[i].size());
-
- for (size_t j = 0; j < dest[i].size(); ++j) {
- PyObject *lit_obj = pyint_from_cint(dest[i][j]);
- PyList_SetItem(cl_obj, j, lit_obj);
- }
-
- PyList_SetItem(dest_obj, i, cl_obj);
- }
-
- PyObject *cv_obj = PyList_New(cv.size());
- for (size_t i = 0; i < cv.size(); ++i) {
- PyObject *lit_obj = pyint_from_cint(cv[i]);
- PyList_SetItem(cv_obj, i, lit_obj);
- }
-
- PyObject *ret = Py_BuildValue("OOn", dest_obj, cv_obj, (Py_ssize_t)top);
- Py_DECREF(dest_obj);
- Py_DECREF(cv_obj);
-
- return ret;
-}
-
-
-} // extern "C"
diff --git a/pages/application/RandomForest/utils/xrf/archive/pysortnetwrk.so b/pages/application/RandomForest/utils/xrf/archive/pysortnetwrk.so
deleted file mode 100755
index 7aa11a7e970e9b6d0a19149ddf63f889f952f4a7..0000000000000000000000000000000000000000
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diff --git a/pages/application/RandomForest/utils/xrf/archive/rfc.py b/pages/application/RandomForest/utils/xrf/archive/rfc.py
deleted file mode 100644
index 411ad46..0000000
--- a/pages/application/RandomForest/utils/xrf/archive/rfc.py
+++ /dev/null
@@ -1,636 +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 six.moves import range
-import six
-
-from pages.application.RandomForest.utils.data import Data
-from .tree import Forest, predict_tree
-# from .encode import SATEncoder
-from .sortnetwrk import HSorNetwrk
-from pysat.formula import CNF, IDPool
-from pysat.solvers import Solver
-from pysat.card import CardEnc, EncType
-from itertools import combinations
-
-
-#
-# ==============================================================================
-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, 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_samples: {0}".format(len(self.samps)))
-
- # check if we have info about categorical features
- if (self.use_categorical):
- 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.binarize = []
- # 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):
- 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 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, from_data=None, from_model=None):
- """
- Constructor.
- """
- self.forest = None
- self.voting = None
- self.opt = options
-
- param_dist = {'n_estimators': options.n_estimators,
- 'max_depth': options.maxdepth}
-
- self.forest = RandomForestClassifier(**param_dist)
-
- def train(self, dataset, outfile=None):
- """
- Train a random forest.
- """
-
- X_train, X_test, y_train, y_test = dataset.train_test_split()
-
- dataset.test_encoding_transformes(X_train)
- 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.voting.predict(X_train), y_train)
- '''
- print(X_test[[0],:])
- print("RF: ",np.asarray(self.voting.predict(X_test[[0],:])))
- for i,t in enumerate(self.forest.estimators_):
- print("DT_{0}: {1}".format(i,np.asarray(t.predict(X_test[[0],:]))))
- '''
- test_acc = accuracy_score(self.voting.predict(X_test), y_test)
- print("----------------------")
- print("RF2001:")
- print("Train accuracy RF2001: {0:.2f}".format(100. * train_acc))
- print("Test accuracy RF2001: {0:.2f}".format(100. * test_acc))
- print("----------------------")
-
- train_acc = accuracy_score(self.forest.predict(X_train), y_train)
- test_acc = accuracy_score(self.forest.predict(X_test), y_test)
- print("RF-scikit:")
- print("Train accuracy RF-scikit: {0:.2f}".format(100. * train_acc))
- print("Test accuracy RF-scikit: {0:.2f}".format(100. * test_acc))
- print("----------------------")
-
- return train_acc, test_acc
-
- def predict(self, X):
- return self.voting.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
-
-
-#
-# ==============================================================================
-class XRF(object):
- """
- class to encode and explain Random Forest classifiers.
- """
-
- def __init__(self, options, model):
- self.cls = model
- self.f = Forest(model)
- # self.f.print_tree()
- self.verbose = options.verb
-
- def encode(self, data):
- """
- Encode a tree ensemble trained previously.
- """
- ##########
- self.f = Forest(self.cls, data.extended_feature_names_as_array_strings)
- self.f.print_tree()
- #######
- self.sat_enc = SATEncoder(self.f, data.feature_names, data.num_class,
- extended_feature_names=data.extended_feature_names_as_array_strings)
-
- _, X_test, _, y_test = data.train_test_split()
-
- inst = X_test[[1], :]
- inst = data.transform(inst)[0]
- self.sat_enc.encode(inst)
- self.explain(inst, data)
-
- def explain(self, sample, data):
- """
- Explain a prediction made for a given sample with a previously
- trained RF.
- """
-
- preamble = None
- if self.verbose:
- inpvals = data.readable_sample(sample)
-
- preamble = []
- for f, v in zip(data.feature_names, inpvals):
- if f not in v:
- preamble.append('{0} = {1}'.format(f, v))
- else:
- preamble.append(v)
-
- inps = data.extended_feature_names_as_array_strings # input (feature value) variables
- # print("inps: {0}".format(inps))
-
- if 'x' not in dir(self):
- self.x = SATExplainer(self.sat_enc, inps, preamble, data.target_name)
-
- expl = self.x.explain(np.array(sample))
-
- # returning the explanation
- return expl
-
- def test_tree_ensemble(self, dataset):
- _, X_test, _, y_test = dataset.train_test_split()
- X_test = dataset.transform(X_test)
-
- y_pred_forest = self.f.predict(X_test)
- acc = accuracy_score(y_pred_forest, y_test)
- print("Test accuracy: {0:.2f}".format(100. * acc))
-
- y_pred_cls = self.cls.predict(X_test)
- # print(np.asarray(y_pred_cls, np.int64))
- # print(y_pred_forest)
-
- assert ((y_pred_cls == y_pred_forest).all())
-
-
-#
-# ==============================================================================
-class SATEncoder(object):
- """
- Encoder of Random Forest classifier into SAT.
- """
-
- def __init__(self, forest, feats, nof_classes, extended_feature_names=None, from_file=None):
- # self.model = model
- self.forest = forest
- self.feats = {f: i for i, f in enumerate(feats)}
- self.num_class = nof_classes
- self.vpool = IDPool()
- # self.optns = xgb.options
- self.extended_feature_names = extended_feature_names
-
- # encoding formula
- self.cnf = None
-
- # for interval-based encoding
- self.intvs, self.imaps, self.ivars = None, None, None
-
- # if from_file:
- # self.load_from(from_file)
-
- def newVar(self, name):
- assert (name)
-
- 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 traverse(self, tree, k, clause):
- """
- Traverse a tree and encode each node.
- """
-
- if tree.children:
- var = self.newVar(tree.name)
- # print("{0} => {1}".format(tree.name, var))
- pos, neg = var, -var
-
- self.traverse(tree.children[0], k, clause + [-neg]) # -var
- self.traverse(tree.children[1], k, clause + [-pos]) # --var
- else: # leaf node
- cvar = self.newVar('class{0}_tr{1}'.format(tree.values, k))
- # print('c: ', clause + [cvar])
- self.cnf.append(clause + [cvar])
-
- def encode(self, sample):
- """
- Do the job.
- """
-
- self.cnf = CNF()
- # getting a tree ensemble
- # self.forest = Forest(self.model, self.extended_feature_names)
- num_tree = len(self.forest.trees)
-
- # introducing class variables
- cvars = [self.newVar('class{0}'.format(i)) for i in range(self.num_tree)]
-
- # 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)
-
- # if targeting interval-based encoding,
- # traverse all trees and extract all possible intervals
- # for each feature
- '''
- if self.optns.encode == 'smtbool':
- self.compute_intervals()
- '''
-
- # 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 this could could be squeezed
- # more to reduce NB binary clauses!!!!!!!
- enc = CardEnc.atmost(lits=ctvars[k],
- vpool=self.vpool,
- encoding=EncType.cardnetwrk) # AtMostOne constraint
- self.cnf.extend(enc.clauses)
-
- csum = [[] for c in range(self.num_class)]
- for k, tree in enumerate(self.forest.trees):
- c = predict_tree(tree, sample)
- csum[c].append(k)
-
- # encoding the majority
- self.cmaj, _ = max(enumerate(csum), key=(lambda x: len(x[1])))
- sorted_lits = [[] for c in range(self.num_class)]
- # sorting bits
- for j in range(self.num_class):
- lits = [ctvars[k][j] for k in range(num_tree)]
- clauses, vout, _ = HSorNetwrk(lits=lits, vpool=self.vpool)
- self.cnf.extend(clauses)
- sorted_lits[j] = vout
- print("{0}:{2} => {1}".format(j, vout, lits))
- # compare bits
- for j in range(self.cmaj):
-
- for j in range(self.cmaj + 1, self.num_class):
- for k in range(num_tree):
- self.cnf.append([-sorted_lits[j][k], sorted_lits[self.cmaj][k]]) # (v1 => v2)
- # print("-{0} => {1}".format(sorted_lits[j][k], sorted_lits[self.cmaj][k]))
-
- '''
- # 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):
- #ExactlyOne feat is True
- self.cnf.append(feats)
- enc = CardEnc.atmost(lits=feats, vpool=self.vpool, encoding=EncType.cardnetwrk)
- self.cnf.extend(enc.clauses)
- '''
- for cl in self.cnf:
- print("{0} == {1}".format(cl,
- [self.vpool.obj(abs(p)) if p > 0 else "!" + str(self.vpool.obj(abs(p))) for p in
- cl]))
-
- # if self.optns.verb:
- # number of variables
- print('#vars:', self.cnf.nv)
- # number of clauses
- print('#clauses:', len(self.cnf.clauses))
- # print(self.cnf.clauses)
-
- 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):
- """
- Constructor.
- """
-
- self.enc = sat_enc
- # self.optns = options
- self.inps = inps # input (feature value) variables
- self.target_name = target_name
- self.preamble = preamble
-
- self.verbose = True # self.optns.verb
- # self.slv = Solver(name=options.solver)
- self.slv = Solver(name="minisat22")
-
- # CNF formula
- self.slv.append_formula(self.enc.cnf)
-
- # current selector
- # self.selv = None
-
- def explain(self, sample, 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
-
- # adapt the solver to deal with the current sample
- self.assums = [] # var selectors to be used as assumptions
- self.sel2fid = {} # selectors to original feature ids
- self.sel2vid = {} # selectors to categorical feature ids
-
- # preparing the selectors
- for i, (inp, val) in enumerate(zip(self.inps, sample), 1):
- 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)
-
- if not self.enc.intvs:
- for inp, val, sel in zip(self.inps, sample, self.assums):
- p = self.enc.newVar(inp)
- hypo = [-sel, p if val else -p]
- print("{0} => {1}".format(self.enc.vpool.obj(sel), inp if val else "!" + inp))
- self.slv.add_clause(hypo)
- else:
- raise NotImplementedError('Intervals are not supported.')
-
- self.assums = sorted(set(self.assums))
- # print("selctors: ", self.assums)
-
- self.slv.solve(assumptions=self.assums)
- print("Model1:")
- for p in self.slv.get_model():
- # if self.enc.vpool.obj(abs(p)) :and self.enc.vpool.obj(abs(p)) in self.inps:
- if self.enc.vpool.obj(abs(p)) and "class" in self.enc.vpool.obj(abs(p)):
- print((p, self.enc.vpool.obj(abs(p))))
- print(self.slv.get_model())
-
- # forcing a misclassification, i.e. a wrong observation
- for k in range(len(self.enc.forest.trees)):
- cl = []
- for j in range(self.enc.num_class):
- if j != self.enc.cmaj:
- cl.append(self.enc.newVar('class{0}_tr{1}'.format(j, k)))
- self.slv.add_clause(cl)
-
- # if satisfiable, then the observation is not implied by the hypotheses
- if self.slv.solve(assumptions=self.assums):
- print(' no implication!')
- print(self.slv.get_model())
- # print("Model: {0}".format([ (p, self.enc.vpool.obj(abs(p))) for p in self.slv.get_model()]))
- sys.exit(1)
-
- if not smallest:
- self.compute_minimal()
- else:
- raise NotImplementedError('Smallest explanation is not yet implemented.')
- # self.compute_smallest()
-
- self.time = resource.getrusage(resource.RUSAGE_CHILDREN).ru_utime + \
- resource.getrusage(resource.RUSAGE_SELF).ru_utime - self.time
-
- expl = sorted([self.sel2fid[h] for h in self.assums if h > 0])
- print("expl-selctors: ", expl)
-
- if self.verbose:
- self.preamble = [self.preamble[i] for i in expl]
- # print("cmaj: ", self.enc.cmaj)
- print(
- ' explanation: "IF {0} THEN {1}"'.format(' AND '.join(self.preamble), self.target_name[self.enc.cmaj]))
- print(' # hypos left:', len(self.assums))
- print(' time: {0:.2f}'.format(self.time))
-
- return expl
-
- def compute_minimal(self):
- """
- Compute any subset-minimal explanation.
- """
- i = 0
- # simple deletion-based linear search
- for i, p in enumerate(self.assums):
- to_test = self.assums[:i] + self.assums[(i + 1):] + [-p]
- # print(to_test)
- if self.slv.solve(assumptions=to_test):
- self.assums[i] = -p
- print("Model:")
- for p in self.slv.get_model():
- if self.enc.vpool.obj(abs(p)) and self.enc.vpool.obj(abs(p)) in self.inps:
- print((p, self.enc.vpool.obj(abs(p))))
diff --git a/pages/application/RandomForest/utils/xrf/archive/setup.py b/pages/application/RandomForest/utils/xrf/archive/setup.py
deleted file mode 100644
index 15c6427..0000000
--- a/pages/application/RandomForest/utils/xrf/archive/setup.py
+++ /dev/null
@@ -1,14 +0,0 @@
-#!/usr/bin/env python3
-
-from distutils.core import setup, Extension
-
-pysortn_ext = Extension('pysortnetwrk',
- sources=['pysortnetwrk.cc'],
- include_dirs=['sortcard'],
- language='c++')
-
-setup(name='pysortnetwrk',
- version='1.0',
- description='This module provides a sorting network to sort a vector of bits',
- py_modules=['pysortnetwrk'],
- ext_modules=[pysortn_ext])
diff --git a/pages/application/RandomForest/utils/xrf/archive/sortcard.hh b/pages/application/RandomForest/utils/xrf/archive/sortcard.hh
deleted file mode 100644
index e641050..0000000
--- a/pages/application/RandomForest/utils/xrf/archive/sortcard.hh
+++ /dev/null
@@ -1,298 +0,0 @@
-
-#ifndef SORTCARD_HH_
-#define SORTCARD_HH_
-
-#include <vector>
-#include <algorithm>
-#include <vector>
-#include <ostream>
-
-
-#define NOPTCLS true
-
-
-using namespace std;
-
-class ClauseSet {
-public:
- ClauseSet() : clauses(0) {}
- ClauseSet(ClauseSet& orig) : clauses(orig.clauses) {}
-
- void clear()
- {
- clauses.clear();
- }
-
- size_t size()
- {
- return clauses.size();
- }
-
- void resize(size_t sz_new)
- {
- return clauses.resize(sz_new);
- }
-
- vector<int>& operator[](size_t i)
- {
- return clauses[i];
- }
-
- void erase(vector<int>& cl)
- {
- clauses.erase(std::find(clauses.begin(), clauses.end(), cl));
- }
-
- void erase_subset(size_t start, ClauseSet& clset)
- {
- if (clset.size()) {
- vector<int>& cl_first = clset[0];
- vector<vector<int> >::iterator begin = std::find(clauses.begin() + start, clauses.end(), cl_first);
- clauses.erase(begin, begin + clset.size());
- }
- }
-
- vector<vector<int> >& get_clauses()
- {
- return clauses;
- }
-
- void add_clause(vector<int> cl)
- {
- clauses.push_back(cl);
- }
-
- void add_clause_ref(vector<int>& cl)
- {
- clauses.push_back(cl);
- }
-
- void create_clause(vector<int>& cl)
- {
- add_clause(cl);
- }
-
- void create_unit_clause(int l)
- {
- vector<int> cl; cl.push_back(l);
- clauses.push_back(cl);
- }
-
- void create_binary_clause(int l1, int l2)
- {
- vector<int> cl;
- cl.push_back(l1);
- cl.push_back(l2);
-
- clauses.push_back(cl);
- }
-
- void create_ternary_clause(int l1, int l2, int l3)
- {
- vector<int> cl;
- cl.push_back(l1);
- cl.push_back(l2);
- cl.push_back(l3);
-
- clauses.push_back(cl);
- }
-
- void dump(ostream& out)
- {
- for (size_t i = 0; i < clauses.size(); ++i)
- dump_clause(out, clauses[i]);
- }
-private:
- void dump_clause(ostream& out, vector<int>& cl)
- {
- for (size_t i = 0; i < cl.size(); ++i)
- out << cl[i] << " ";
- out << "0" << endl;
- }
-protected:
- vector<vector<int> > clauses;
-};
-
-
-
-//
-//=============================================================================
-inline void create_vvect(int& top_id, vector<int>& ov, size_t nvars)
-{
- assert(nvars > 0);
-
- size_t refnv = ov.size();
- size_t tvars = refnv + nvars;
- ov.resize(tvars, 0);
-
- for (size_t k = refnv; k < tvars; ++k)
- ov[k] = ++top_id;
-
- assert(ov.size() > 0);
-}
-
-
-//
-//=============================================================================
-inline void copy_vvect(int& top_id, vector<int>& ov, vector<int>& iv)
-{
- size_t refnv = ov.size();
- ov.resize(refnv + iv.size(), 0);
-
- for (size_t k = 0; k < iv.size(); ++k)
- ov[refnv + k] = iv[k];
-
- assert(ov.size() > 0);
-}
-
-
-
-//
-//=============================================================================
-inline void mk_half_vect(vector<int>& ov, vector<int>& iv, size_t offset)
-{
- assert(iv.size() > 0);
-
- size_t ns = iv.size() / 2;
- ov.resize(ns, 0);
-
- for (size_t k = 0; k < ns; ++k)
- ov[k] = iv[offset + k];
-}
-
-//
-//=============================================================================
-inline void mk_odd_vect(vector<int>& ov, vector<int>& iv)
-{
- assert(iv.size() > 0);
-
- size_t ns = iv.size() / 2;
- ov.resize(ns, 0);
-
- for (size_t k = 0; k < ns; ++k)
- ov[k] = iv[k * 2];
-}
-
-//=============================================================================
-inline void mk_even_vect(vector<int>& ov, vector<int>& iv)
-{
- assert(iv.size() > 0);
-
- size_t ns = iv.size() / 2;
- ov.resize(ns, 0);
-
- for (size_t k = 0; k < ns; ++k)
- ov[k] = iv[k * 2 + 1];
-}
-
-// sorting networks
-//=============================================================================
-inline void sortn_half_merge_recur(
- int& top_id,
- ClauseSet& clset,
- vector<int>& av,
- vector<int>& bv,
- vector<int>& cv,
- size_t zvar
-)
-{
- assert(bv.size() == av.size());
-
- if (av.size() == 1) { // vectors of size 1
- assert(av[0] != 0);
- if (NOPTCLS || (av[0] != zvar && bv[0] != zvar)) {
- create_vvect(top_id, cv, 2);
- clset.create_binary_clause (-av[0], cv[0]);
- clset.create_binary_clause (-bv[0], cv[0]);
- clset.create_ternary_clause(-av[0], -bv[0], cv[1]);
- }
- else {
- if (av[0] == zvar) {
- cv.push_back(bv[0]);
- cv.push_back(av[0]);
- }
- else {
- assert(bv[0] == zvar);
- cv.push_back(av[0]);
- cv.push_back(bv[0]);
- }
- }
- }
- else {
- if (NOPTCLS ||
- ((av[0] != zvar || av[av.size() - 1] != zvar) &&
- (bv[0] != zvar || bv[av.size() - 1] != zvar))) {
- vector<int> aodd, aeven, bodd, beven, dv, ev;
-
- mk_odd_vect(aodd, av); mk_even_vect(aeven, av);
- mk_odd_vect(bodd, bv); mk_even_vect(beven, bv);
-
- sortn_half_merge_recur(top_id, clset, aodd, bodd, dv, zvar);
- sortn_half_merge_recur(top_id, clset, aeven, beven, ev, zvar);
-
- assert(cv.size() == 0);
- cv.push_back(dv[0]);
- create_vvect(top_id, cv, 2 * av.size() - 2);
- cv.push_back(ev[ev.size() - 1]);
-
- for (size_t i = 0; i < av.size() - 1; ++i) {
- assert(i + 1 < dv.size());
- assert(i < ev.size());
- assert(2 * 1 + 1 < cv.size());
-
- clset.create_binary_clause (-dv[i + 1], cv[2 * i + 1]);
- clset.create_binary_clause (-ev[i ], cv[2 * i + 1]);
- clset.create_ternary_clause(-dv[i + 1], -ev[i], cv[2 * i + 2]);
- }
- }
- else {
- if (av[0] == zvar && av[av.size() - 1] == zvar) {
- copy_vvect(top_id, cv, bv);
- copy_vvect(top_id, cv, av);
- }
- else {
- assert(bv[0] == zvar && bv[av.size() - 1] == zvar);
- copy_vvect(top_id, cv, av);
- copy_vvect(top_id, cv, bv);
- }
- }
- }
-
- assert(cv.size() > 0);
-}
-
-//
-//=============================================================================
-inline vector<int>& sortn_half_sorter_recur(
- int& top_id,
- ClauseSet& clset,
- vector<int>& av,
- vector<int>& cv,
- size_t zvar
-)
-{
- assert(av.size() > 1);
-
- if (av.size() == 2) {
- assert(av[0] != 0 && av[1] != 0);
-
- vector<int> xav, xbv; xav.push_back(av[0]); xbv.push_back(av[1]);
- sortn_half_merge_recur(top_id, clset, xav, xbv, cv, zvar);
- }
- else {
- vector<int> dv1, dv2, lav, uav;
- mk_half_vect(lav, av, 0);
- mk_half_vect(uav, av, av.size() / 2);
-
- assert(lav.size() == uav.size());
- sortn_half_sorter_recur(top_id, clset, lav, dv1, zvar); assert(dv1.size() > 0);
- sortn_half_sorter_recur(top_id, clset, uav, dv2, zvar); assert(dv2.size() > 0);
- sortn_half_merge_recur (top_id, clset, dv1, dv2, cv, zvar);
- }
-
- assert(cv.size() > 0);
- return cv;
-}
-
-
-#endif // SORTCARD_HH_
diff --git a/pages/application/RandomForest/utils/xrf/archive/sortnetwrk.py b/pages/application/RandomForest/utils/xrf/archive/sortnetwrk.py
deleted file mode 100644
index f18c26e..0000000
--- a/pages/application/RandomForest/utils/xrf/archive/sortnetwrk.py
+++ /dev/null
@@ -1,74 +0,0 @@
-#!/usr/bin/env python3
-#
-from math import exp, log, trunc
-
-from pysat._utils import MainThread
-from pysat.formula import CNF, IDPool
-import pysortnetwrk
-
-
-
-def HSorNetwrk(lits, top_id=None, vpool=None):
- assert not top_id or not vpool, \
- 'Use either a top id or a pool of variables but not both.'
-
-
- # we are going to return this formula
- #ret = CNF()
-
- # if the list of literals is empty, return empty formula
- if not lits:
- return ret
-
- # obtaining the top id from the variable pool
- if vpool:
- top_id = vpool.top
-
-
- # making sure we are dealing with a list of literals
- lits = list(lits)
-
- # choosing the maximum id among the current top and the list of literals
- top_id = max(map(lambda x: abs(x), lits + [top_id if top_id != None else 0]))
-
-
- nvars = len(lits)
-
- #get smallest power of 2 larger than number of vars
- exponent = trunc(log(nvars) / log(2)) # assume exponent
- nvtmp = exp(log(2) * exponent)
-
- # check if number of vars already is power of 2; correct exponent if required
- exponent = exponent if (nvars - nvtmp < 0.000001) else exponent + 1
- nnvars = trunc(exp(log(2) * exponent) + 0.1)
-
- cl = None
- zvar = 0
- if (nnvars != nvars):
- top_id += 1
- zvar = top_id
- lits.extend([zvar] * (nnvars - nvars))
- cl = [-zvar]
-
- # generate odd-even sorting network
- clset,slits,top = pysortnetwrk.HSort(lits, top_id, zvar, int(MainThread.check()))
-
- clset = clset +[cl] if (cl is not None) else clset
-
-
- # updating vpool if necessary
- if vpool:
- vpool.top = top - 1
- vpool._next()
-
-
-
- return clset, slits, top
-
-if __name__ == '__main__':
- print("Sorting Network:")
- lits = [1, 2, 3]
- top_id = 5
- clauses, slits, top = HSorNetwrk(lits, top_id)
- print(clauses)
- print(slits)
\ No newline at end of file
diff --git a/pages/application/RandomForest/utils/xrf/archive/tree.py b/pages/application/RandomForest/utils/xrf/archive/tree.py
deleted file mode 100644
index 9e37945..0000000
--- a/pages/application/RandomForest/utils/xrf/archive/tree.py
+++ /dev/null
@@ -1,154 +0,0 @@
-#
-#==============================================================================
-from anytree import Node, RenderTree,AsciiStyle
-import json
-import numpy as np
-import math
-import os
-
-
-#
-#==============================================================================
-class xgnode(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 = xgnode(i)
- else:
- node = xgnode(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 < 0.5 (False)
- extract_data(node.right_node_id, node, feature_names) #feat > 0.% (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 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.feature_names = feature_names
- self.trees = [ build_tree(dt.tree_, feature_names) for dt in self.rf.estimators()]
- #self.print_trees()
-
- def print_trees(self):
- for i,t in enumerate(self.trees):
- print("tree number: ", i)
- walk_tree(t)
-
- def predict(self, samples):
- predictions = []
- n_estimators = self.rf.n_estimators()
- print("#Trees: ", n_estimators)
- 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/application/RandomForest/utils/xrf/rndmforest.py b/pages/application/RandomForest/utils/xrf/rndmforest.py
index 583fe91..187dd28 100644
--- a/pages/application/RandomForest/utils/xrf/rndmforest.py
+++ b/pages/application/RandomForest/utils/xrf/rndmforest.py
@@ -1,4 +1,3 @@
-
from sklearn.ensemble._voting import VotingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
@@ -16,132 +15,54 @@ import math
from pages.application.RandomForest.utils.data import Data
from .tree import Forest, predict_tree
-#from .encode import SATEncoder
+# from .encode import SATEncoder
from pysat.formula import CNF, IDPool
from pysat.solvers import Solver
from pysat.card import CardEnc, EncType
from itertools import combinations
-
-
#
-#==============================================================================
+# ==============================================================================
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)
-
+
+ def __init__(self, file=None, mapfile=None, separator=',', use_categorical=False):
+ super().__init__(file, 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]):
+ 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[:, -1] = le.transform(samples[:, -1])
+ # self.class_names = le.classes_
+
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)))
-
+ self.target_name = list(range(self.num_class))
+
# check if we have info about categorical features
if (self.use_categorical):
- self.target_name = self.class_names
-
+ 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]])
+ 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
+ self.categorical_names = []
+ self.binarizer = []
+ # feat map
+ self.mapping_features()
def mapping_features(self):
self.extended_feature_names = {}
@@ -151,17 +72,18 @@ class Dataset(Data):
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))
+ 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])
+ 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])
+ self.extended_feature_names_as_array_strings.append("f{}".format(i)) # (self.feature_names[i])
counter = counter + 1
def readable_sample(self, x):
@@ -173,42 +95,43 @@ class Dataset(Data):
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())
+ 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
- '''
- 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 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_
-
-
+ self.classes_ = self.le_.classes_
+
def predict(self, X):
"""Predict class labels for X.
Parameters
@@ -220,23 +143,23 @@ class VotingRF(VotingClassifier):
maj : array-like of shape (n_samples,)
Predicted class labels.
"""
- #check_is_fitted(self)
-
+ # check_is_fitted(self)
+
# 'hard' voting
predictions = self._predict(X)
- predictions = np.asarray(predictions, np.int64) #NEED TO BE CHECKED
+ 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).
@@ -245,155 +168,68 @@ class RF2001(object):
def __init__(self, options):
"""
Constructor.
- """
+ """
self.forest = None
self.voting = None
self.opt = options
-
- param_dist = {'n_estimators':options.n_estimators,
- 'max_depth':options.maxdepth,
- 'criterion':'entropy',
- 'random_state':324089}
-
+
+ param_dist = {'n_estimators': options.n_estimators,
+ 'max_depth': options.maxdepth,
+ 'criterion': 'entropy',
+ 'random_state': 324089}
+
self.forest = RandomForestClassifier(**param_dist)
-
-
-
- def train(self, dataset, outfile=None):
- """
- Train a random forest.
- """
-
- X_train, X_test, y_train, y_test = dataset.train_test_split()
-
- if self.opt.verb:
- dataset.test_encoding_transformes(X_train)
-
- 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)
-
- '''
- print(X_test[[0],:])
- print("RF: ",np.asarray(self.voting.predict(X_test[[0],:])))
- for i,t in enumerate(self.forest.estimators_):
- print("DT_{0}: {1}".format(i,np.asarray(t.predict(X_test[[0],:]))))
- '''
-
- train_acc = accuracy_score(self.predict(X_train), y_train)
- test_acc = accuracy_score(self.predict(X_test), y_test)
-
- if self.opt.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)
+ assert (self.forest.estimators_ is not None)
return self.forest.estimators_
-
+
def n_estimators(self):
return self.forest.n_estimators
-
- def print_acc_vote(self, X_train, X_test, y_train, y_test):
- train_acc = accuracy_score(self.predict(X_train), y_train)
- test_acc = accuracy_score(self.predict(X_test), y_test)
- print("----------------------")
- print("RF2001:")
- print("Train accuracy RF2001: {0:.2f}".format(100. * train_acc))
- print("Test accuracy RF2001: {0:.2f}".format(100. * test_acc))
- print("----------------------")
-
- def print_acc_prob(self, X_train, X_test, y_train, y_test):
- train_acc = accuracy_score(self.forest.predict(X_train), y_train)
- test_acc = accuracy_score(self.forest.predict(X_test), y_test)
- print("RF-scikit:")
- print("Train accuracy RF-scikit: {0:.2f}".format(100. * train_acc))
- print("Test accuracy RF-scikit: {0:.2f}".format(100. * test_acc))
- print("----------------------")
-
- def print_accuracy(self, data):
- _, X_test, _, y_test = data.train_test_split()
- #X_train = dataset.transform(X_train)
- X_test = data.transform(X_test)
- test_acc = accuracy_score(self.predict(X_test), y_test)
- #print("----------------------")
- #print("Train accuracy : {0:.2f}".format(100. * train_acc))
- #print("Test accuracy : {0:.2f}".format(100. * test_acc))
- print("c Cross-Validation: {0:.2f}".format(100. * test_acc))
- #print("----------------------")
+
+
#
-#==============================================================================
+# ==============================================================================
class XRF(object):
"""
class to encode and explain Random Forest classifiers.
"""
-
- def __init__(self, options, model, dataset):
+
+ def __init__(self, model, dataset):
self.cls = model
self.data = dataset
- self.verbose = options.verb
- self.f = Forest(model, dataset.extended_feature_names_as_array_strings)
-
- if options.verb > 2:
- self.f.print_trees()
- print("c RF sz:", self.f.sz)
- print('c max-depth:', self.f.md)
- print('c nof DTs:', len(self.f.trees))
-
-
+ self.forest = Forest(model, dataset.extended_feature_names_as_array_strings)
+
+ self.enc = SATEncoder(self.forest, self.data.feature_names, self.data.num_class,
+ self.data.extended_feature_names_as_array_strings)
+
def encode(self, inst):
"""
Encode a tree ensemble trained previously.
"""
if 'f' not in dir(self):
self.f = Forest(self.cls, self.data.extended_feature_names_as_array_strings)
- #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.data.feature_names, self.data.num_class, \
- self.data.extended_feature_names_as_array_strings)
-
+
+ self.enc = SATEncoder(self.forest, self.data.feature_names, self.data.num_class,
+ self.data.extended_feature_names_as_array_strings)
+
inst = self.data.transform(np.array(inst))[0]
formula, _, _, _ = self.enc.encode(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):
+
+ def explain_sample(self, inst):
"""
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)
-
- #if self.verbose:
- # print("instance: {0}".format(np.array(inst)) )
inpvals = self.data.readable_sample(inst)
preamble = []
@@ -402,73 +238,96 @@ class XRF(object):
preamble.append('{0} = {1}'.format(f, v))
else:
preamble.append(v)
-
- inps = self.data.extended_feature_names_as_array_strings # input (feature value) variables
- #print("inps: {0}".format(inps))
-
- self.x = SATExplainer(self.enc, inps, preamble, self.data.target_name, verb=self.verbose)
- inst = self.data.transform(np.array(inst))[0]
+
+ inps = self.data.extended_feature_names_as_array_strings # input (feature value) variables
+ # print("inps: {0}".format(inps))
+
+ self.x = SATExplainer(self.enc, inps, preamble, self.data.target_name)
+ inst = self.data.transform(np.array(inst))[0]
expl = self.x.explain(np.array(inst))
- 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
+ return expl
-
- def test_tree_ensemble(self):
- if 'f' not in dir(self):
- self.f = Forest(self.cls)
-
- _, X_test, _, y_test = self.data.train_test_split()
- X_test = self.data.transform(X_test)
-
- y_pred_forest = self.f.predict(X_test)
- acc = accuracy_score(y_pred_forest, y_test)
- print("Test accuracy: {0:.2f}".format(100. * acc))
-
- y_pred_cls = self.cls.predict(X_test)
- #print(np.asarray(y_pred_cls, np.int64))
- #print(y_pred_forest)
-
- assert((y_pred_cls == y_pred_forest).all())
-
-
-#
-#==============================================================================
+ # copie de git
+ def explain(self, samples):
+ # timers
+ lengths = []
+ tested = set()
+ mSAT, mUNSAT = 0.0, 0.0
+ stimes = []
+ utimes = []
+ nSatCalls = []
+ nUnsCalls = []
+
+ ltimes = []
+ ctimes = []
+ wins = 0
+
+ multiple_expls = []
+ for i, s in enumerate(samples):
+ sample = [float(v.strip()) for v in s.split(',')]
+
+ if tuple(sample) in tested:
+ continue
+
+ # print("inst#{0}".format(i+1))
+
+ tested.add(tuple(sample))
+ # print('sample {0}: {1}'.format(i, ','.join(s.strip().split(','))))
+
+ self.encode(sample)
+ expl = self.explain_sample(sample)
+ multiple_expls.append(expl)
+ lengths.append(len(expl))
+
+ nvars = self.enc.cnf.nv
+ nclauses = len(self.enc.cnf.clauses)
+
+ # mSAT = max(xrf.x.stimes+[mSAT])
+ # mUNSAT = max(xrf.x.utimes+[mUNSAT])
+ if len(self.x.stimes):
+ stimes.append(max(self.x.stimes))
+ if len(self.x.utimes):
+ utimes.append(max(self.x.utimes))
+ nSatCalls.append(self.x.nsat)
+ nUnsCalls.append(self.x.nunsat)
+
+ del self.enc
+ del self.x
+
+ return multiple_expls
+
+
+ # ==============================================================================
class SATEncoder(object):
"""
Encoder of Random Forest classifier into SAT.
"""
-
- def __init__(self, forest, feats, nof_classes, extended_feature_names, from_file=None):
+
+ 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.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
+
+ # 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 name in self.vpool.obj2id: #var has been already created
+
+ 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 printLits(self, lits):
- print(["{0}{1}".format("-" if p<0 else "",self.vpool.obj(abs(p))) for p in 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.
@@ -484,21 +343,20 @@ class SATEncoder(object):
else:
var = self.newVar(tree.name)
pos, neg = var, -var
- #print("{0} => {1}".format(tree.name, 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])
+ self.traverse(tree.children[1], k, clause + [-pos])
else: # leaf node
- cvar = self.newVar('class{0}_tr{1}'.format(tree.values,k))
+ cvar = self.newVar('class{0}_tr{1}'.format(tree.values, k))
self.cnf.append(clause + [cvar])
- #self.printLits(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!
"""
@@ -521,9 +379,10 @@ class SATEncoder(object):
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.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 = {}
@@ -536,195 +395,183 @@ class SATEncoder(object):
ivar = self.newVar('{0}_intv{1}'.format(feat, i))
self.ivars[feat].append(ivar)
- #print('{0}_intv{1}'.format(feat, i))
-
+ # print('{0}_intv{1}'.format(feat, i))
+
if ub != math.inf:
- #assert(i < len(intvs)-1)
+ # 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))
-
-
+ # 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)
+ # 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)]
-
+ # introducing class variables
+ # cvars = [self.newVar('class{0}'.format(i)) for i in range(self.num_class)]
+
# define Tautology var
vtaut = self.newVar('Tautology')
-
+
# 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)
+ var = self.newVar('class{0}_tr{1}'.format(j, k))
+ ctvars[k].append(var)
- # traverse all trees and extract all possible intervals
+ # 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(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
+ # 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.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):
+
+ # 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)
+ 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)
+ atls = CardEnc.atleast(lits=lhs, bound=rhs, vpool=self.vpool, encoding=EncType.cardnetwrk)
self.cnf.extend(atls)
- else:
+ 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) ])
+ 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)]
+ 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)
+ 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
rhs = num_tree + 1
###########
- lhs1 = zvars[1] + [ - ctvars[k][self.cmaj] for k in range(num_tree)]
+ 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)
+ 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 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:
+ # 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()
-
+ 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()
-
+ 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]])
+ self.cnf.append([-pvars[0], -pvars[self.cmaj + 1]])
##
- lhs1 = 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)
+ 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)
+ 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))
+ 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
+ # lits of intervals
for f, intvs in six.iteritems(self.ivars):
if not len(intvs):
continue
- self.cnf.append(intvs)
+ self.cnf.append(intvs)
card = CardEnc.atmost(lits=intvs, vpool=self.vpool, encoding=EncType.cardnetwrk)
self.cnf.extend(card.clauses)
- #self.printLits(intvs)
-
-
-
+ # 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]
-
+ d = j + 1
+ pos, neg = self.ivars[f][d:], self.ivars[f][:d]
+
if j == 0:
- assert(len(neg) == 1)
+ 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], -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)
+ 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], threshold[j + 1]])
self.cnf.append([thvar, -pos[0]])
- self.cnf.append([thvar, -threshold[j+1]])
-
+ 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.
@@ -734,125 +581,121 @@ class SATExplainer(object):
"""
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
+
+ self.slv = None
##self.slv = Solver(name=options.solver)
##self.slv = Solver(name="minisat22")
- #self.slv = Solver(name="glucose3")
+ # self.slv = Solver(name="glucose3")
# CNF formula
- #self.slv.append_formula(self.enc.cnf)
+ # self.slv.append_formula(self.enc.cnf)
-
def explain(self, sample, smallest=False):
"""
Hypotheses minimization.
"""
- if self.verbose:
- print(' explaining: "IF {0} THEN {1}"'.format(' AND '.join(self.preamble), self.target_name[self.enc.cmaj]))
-
- #create a SAT solver
- self.slv = Solver(name="glucose3")
-
- self.time = resource.getrusage(resource.RUSAGE_CHILDREN).ru_utime + \
- resource.getrusage(resource.RUSAGE_SELF).ru_utime
+ expl = {}
+ expl["Instance"] = '"IF {0} THEN {1}"'.format(' AND '.join(self.preamble), self.target_name[self.enc.cmaj])
+
+ # create a SAT solver
+ self.slv = Solver(name="glucose3")
# adapt the solver to deal with the current sample
- #self.csel = []
+ # 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.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))
-
+ # 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:
-
+
assert (inp not in self.enc.intvs)
-
+
feat = inp.split('_')[0]
selv = self.enc.newVar('selv_{0}'.format(feat))
-
- self.assums.append(selv)
+
+ 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)
+
+ 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])
-
+
+ # self.enc.printLits([-selv, p])
+
elif len(self.enc.intvs[inp]):
- #v = None
- #for intv in 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)
-
+ # 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]):
+
+ 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)
+ # self.enc.printLits(cl)
'''
with open("/tmp/pendigits.cnf", 'w') as fp:
fp.write('p cnf {0} {1}\n'.format(self.enc.cnf.nv, len(self.enc.cnf.clauses)))
for p in self.assums + [-self.csel]:
fp.write('{0} 0\n'.format(str(p)))
-
+
for cl in self.enc.cnf.clauses:
fp.write(' '.join([str(p) for p in cl+[0]]))
fp.write('\n')
fp.close()
print(self.assums + [self.csel])
'''
-
+
self.assums = sorted(set(self.assums))
if self.verbose:
- print(' # hypos:', len(self.assums))
-
- # pass a CNF formula
- self.slv.append_formula(self.enc.cnf)
-
+ expl['hypos:'] = len(self.assums)
+
+ # pass a CNF formula
+ self.slv.append_formula(self.enc.cnf)
+
'''
# if unsat, then the observation is not implied by the assumptions
if not self.slv.solve(assumptions=self.assums+[self.csel]):
print(' no implication!')
print(self.slv.get_core())
sys.exit(1)
-
+
if self.verbose > 1:
self.enc.printLits(self.assums+[self.csel])
self.print_sat_model()
@@ -862,26 +705,21 @@ class SATExplainer(object):
self.compute_minimal()
else:
raise NotImplementedError('Smallest explanation is not yet implemented.')
- #self.compute_smallest()
+ # self.compute_smallest()
- self.time = resource.getrusage(resource.RUSAGE_CHILDREN).ru_utime + \
- resource.getrusage(resource.RUSAGE_SELF).ru_utime - self.time
+ explanation = sorted([self.sel2fid[h] for h in self.assums if h > 0])
+ assert len(explanation), 'PI-explanation cannot be an empty-set! otherwise the RF predicts only one class'
- expl = sorted([self.sel2fid[h] for h in self.assums if h>0 ])
- assert len(expl), 'PI-explanation cannot be an empty-set! otherwise the RF predicts only one class'
-
# delete sat solver
self.slv.delete()
self.slv = None
- if self.verbose:
- print("expl-selctors: ", expl)
- self.preamble = [self.preamble[i] for i in expl]
- print(' explanation: "IF {0} THEN {1}"'.format(' AND '.join(self.preamble), self.target_name[self.enc.cmaj]))
- print(' # hypos left:', len(expl))
- print(' time: {0:.3f}'.format(self.time))
+ expl["expl selctors:"] = explanation
+ self.preamble = [self.preamble[i] for i in explanation]
+ expl["explanation :"] = "IF {0} THEN {1}".format(' AND '.join(self.preamble), self.target_name[self.enc.cmaj])
+ expl["hypos left :"] = len(explanation)
- return expl
+ return expl
def compute_minimal(self):
"""
@@ -889,27 +727,27 @@ class SATExplainer(object):
"""
nsat, nunsat = 0, 0
stimes, utimes = [], []
-
+
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]]
-
+
t0 = resource.getrusage(resource.RUSAGE_CHILDREN).ru_utime + \
- resource.getrusage(resource.RUSAGE_SELF).ru_utime
-
+ resource.getrusage(resource.RUSAGE_SELF).ru_utime
+
sat = self.slv.solve(assumptions=to_test)
-
+
if not sat:
- self.assums[i] = -p
+ self.assums[i] = -p
elif self.verbose > 1:
self.print_sat_model()
-
+
t = resource.getrusage(resource.RUSAGE_CHILDREN).ru_utime + \
resource.getrusage(resource.RUSAGE_SELF).ru_utime - t0
- #print("{0} {1:.2f}s".format("SAT" if sat else "UNSAT", t))
-
+ # print("{0} {1:.2f}s".format("SAT" if sat else "UNSAT", t))
+
if sat:
nsat += 1
stimes.append(t)
@@ -917,60 +755,58 @@ class SATExplainer(object):
self.enc.printLits(to_test)
print("SAT")
else:
- #print("Core: ",self.slv.get_core())
+ # print("Core: ",self.slv.get_core())
nunsat += 1
utimes.append(t)
if self.verbose > 1:
self.enc.printLits(to_test)
- print("UNSAT")
+ print("UNSAT")
if self.verbose:
print('')
- print('#SAT: {0} | #UNSAT: {1}'.format(len(stimes), len(utimes)))
- if(nsat):
+ print('#SAT: {0} | #UNSAT: {1}'.format(len(stimes), len(utimes)))
+ if (nsat):
print('SAT: tot: {0:.2f} | m: {1:.2f} | M: {2:.2f} | avg: {3:.2f}'.format(
- sum(stimes), min(stimes), max(stimes), sum(stimes) / len(stimes)))
- if(nunsat):
+ sum(stimes), min(stimes), max(stimes), sum(stimes) / len(stimes)))
+ if (nunsat):
print('UNSAT: tot: {0:.2f} | m: {1:.2f} | M: {2:.2f} | avg: {3:.2f}'.format(
- sum(utimes), min(utimes), max(utimes), sum(utimes) / len(utimes)))
- print('')
-
+ sum(utimes), min(utimes), max(utimes), sum(utimes) / len(utimes)))
+ print('')
+
self.stimes, self.utimes = stimes, utimes
self.nsat, self.nunsat = nsat, nunsat
-
-
+
def print_sat_model(self):
- assert(self.slv.get_model())
- model = [ p for p in self.slv.get_model() if self.enc.vpool.obj(abs(p)) ]
+ assert (self.slv.get_model())
+ model = [p for p in self.slv.get_model() if self.enc.vpool.obj(abs(p))]
str_model = []
lits = []
for p in model:
if self.enc.vpool.obj(abs(p)) in self.inps:
str_model.append((p, self.enc.vpool.obj(abs(p))))
-
+
elif ("class" in self.enc.vpool.obj(abs(p))):
- str_model.append((p, self.enc.vpool.obj(abs(p))))
-
- #elif ("intv" in self.enc.vpool.obj(abs(p))) :
+ str_model.append((p, self.enc.vpool.obj(abs(p))))
+
+ # elif ("intv" in self.enc.vpool.obj(abs(p))) :
# str_model.append((p, self.enc.vpool.obj(abs(p))))
-
- if ("_tr" in self.enc.vpool.obj(abs(p))) :
- lits.append(p)
-
- if ("p_" in self.enc.vpool.obj(abs(p))) :
- str_model.append((p, self.enc.vpool.obj(abs(p))))
- if ("z_" in self.enc.vpool.obj(abs(p))) :
- str_model.append((p, self.enc.vpool.obj(abs(p))))
-
+
+ if ("_tr" in self.enc.vpool.obj(abs(p))):
+ lits.append(p)
+
+ if ("p_" in self.enc.vpool.obj(abs(p))):
+ str_model.append((p, self.enc.vpool.obj(abs(p))))
+ if ("z_" in self.enc.vpool.obj(abs(p))):
+ str_model.append((p, self.enc.vpool.obj(abs(p))))
+
print("Model:", str_model)
- ###print(self.slv.get_model())
-
+ ###print(self.slv.get_model())
+
num_tree = len(self.enc.forest.trees)
num_class = self.enc.num_class
- occ = [0]*num_class
-
+ occ = [0] * num_class
+
for p in lits:
if p > 0:
j = int(self.enc.vpool.obj(abs(p))[5])
- occ[j] +=1
- print(occ)
-
+ occ[j] += 1
+ print(occ)
\ No newline at end of file
diff --git a/pages/application/RandomForest/utils/xrf/tree.py b/pages/application/RandomForest/utils/xrf/tree.py
index da81c98..bcac5b2 100644
--- a/pages/application/RandomForest/utils/xrf/tree.py
+++ b/pages/application/RandomForest/utils/xrf/tree.py
@@ -1,6 +1,6 @@
#
-#==============================================================================
-from anytree import Node, RenderTree,AsciiStyle
+# ==============================================================================
+from anytree import Node, RenderTree, AsciiStyle
import json
import numpy as np
import math
@@ -8,34 +8,34 @@ import os
#
-#==============================================================================
+# ==============================================================================
class dt_node(Node):
- def __init__(self, id, parent = None):
+ 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.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
+ 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))
+ return (pref + "leaf: {} {}".format(self.id, self.values))
else:
- if(self.name is None):
- return (pref+ "{} f{}<{}".format(self.id, self.feature, self.threshold))
+ 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))
+ return (pref + "{} \"{}\"<{}".format(self.id, self.name, self.threshold))
-#==============================================================================
-def build_tree(tree_, feature_names = None):
+# ==============================================================================
+def build_tree(tree_, feature_names=None):
##
feature = tree_.feature
threshold = tree_.threshold
@@ -49,29 +49,29 @@ def build_tree(tree_, feature_names = None):
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
+ is_leaf[node_id] = True
+ ##
+
+ m = tree_.node_count
assert (m > 0), "Empty tree"
-
- def extract_data(idx, root = None, feature_names = None):
+
+ 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"]
+ node = dt_node(i, parent=root)
+ # node.cover = json_node["cover"]
if is_leaf[i]:
node.values = np.argmax(values[i])
- #if(inverse):
+ # if(inverse):
# node.values = -node.values
else:
node.feature = feature[i]
@@ -80,17 +80,17 @@ def build_tree(tree_, feature_names = None):
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)
+ 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
@@ -100,17 +100,20 @@ def walk_tree(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])
+ 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
@@ -118,33 +121,33 @@ def predict_tree(node, sample):
else:
feature_branch = node.feature
sample_value = sample[feature_branch]
- assert(sample_value is not None)
- if(sample_value < node.threshold):
+ 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()]
+
+ 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()
-
+ assert ([dt.tree_.node_count for dt in rf.estimators()] == [count_nodes(dt) for dt in self.trees])
+
def print_trees(self):
- for i,t in enumerate(self.trees):
+ for i, t in enumerate(self.trees):
print("tree number: ", i)
walk_tree(t)
@@ -153,22 +156,20 @@ class Forest:
scores = np.asarray(scores)
maj = np.argmax(np.bincount(scores))
return maj
-
-
- def predict(self, samples):
+
+ def predict(self, samples):
predictions = []
print("#Trees: ", len(self.trees))
for sample in np.asarray(samples):
scores = []
- for i,t in enumerate(self.trees):
+ 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)
+ 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
+ return maj
diff --git a/utils.py b/utils.py
index 87f4fa3..4b76ea3 100644
--- a/utils.py
+++ b/utils.py
@@ -20,10 +20,11 @@ def parse_contents_graph(contents, filename):
content_type, content_string = contents.split(',')
decoded = base64.b64decode(content_string)
try:
- if 'mod.pkl' in filename:
- data = pickle.load(io.BytesIO(decoded))
- elif '.pkl' in filename:
- data = joblib.load(io.BytesIO(decoded))
+ if '.pkl' in filename:
+ try:
+ data = joblib.load(io.BytesIO(decoded))
+ except :
+ data = pickle.load(io.BytesIO(decoded))
elif '.txt' in filename:
data = decoded.decode('utf-8').strip()
except Exception as e:
@@ -58,8 +59,11 @@ def parse_contents_instance(contents, filename):
try:
if '.csv' in filename:
data = decoded.decode('utf-8')
+ features_names, data = str(data).strip().split('\n')[:2]
+ features_names = str(features_names).strip().split(',')
data = str(data).strip().split(',')
- data = list(map(lambda i: tuple([i[0], np.float32(i[1])]), [i.split('=') for i in data]))
+ data = list(tuple([features_names[i], np.float32(data[i])]) for i in range(len(data)))
+ print(data)
elif '.txt' in filename:
data = decoded.decode('utf-8')
data = str(data).strip().split(',')
@@ -72,6 +76,9 @@ def parse_contents_instance(contents, filename):
data = decoded.decode('utf-8').strip()
data = json.loads(data)
data = list(tuple(data.items()))
+ elif '.samples' in filename:
+ decoded = decoded.decode('utf-8').strip()
+ data = str(decoded).split('\n')
except Exception as e:
print(e)
return html.Div([
--
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