def analyzeEndpoint():
form = MyForm()
if form.validate_on_submit():
profile = request.form.get('profile')
start = time.time()
CFGForest, taintedSink, stats, err = analyze(
form.source_code.data, profile)
end = time.time()
timetaken = (end - start)
if err:
return json.dumps({"error": err.message}), 400
sinks = []
profileJson = profiles.getProfiles()
if profile in profileJson:
profile = profileJson[profile]
for sink in taintedSink:
sinkDict = dict()
sinkDict['startLine'] = sink.startLine
sinkDict['endLine'] = sink.endLine
sinkDict['state'] = sink.state
sinks.append(sinkDict)
graph = ""
if CFGForest:
graphviz = Source(CFGForest.generateGraphViz(True))
graphviz.format = 'svg'
graph = graphviz.pipe().decode('utf-8')
return json.dumps({
'sinks': sinks,
'graph': graph,
'stats': stats,
'profile': profile,
'timetaken': timetaken
})
def graficar(self):
self.grafo = "digraph G {\n" + "graph [rankdir = TB];\n" + "node [shape = record,height=.1]; {\n"
if self.estaVacia() == True:
self.grafo += "\"ListaVacia\" [label = \"Lista Vacia\"]"
else:
temp = self.inicio
i = 0
while temp != None:
self.grafo += "\"" + str(i) + "\" [label = \"" + str(
temp.getDia()) + "\"];\n"
if i > 0:
self.grafo += "\"" + str(i - 1) + "\" -> \"" + str(
i) + "\" ;\n"
self.grafo += "\"" + str(i) + "\" -> \"" + str(
i - 1) + "\" ;\n"
temp = temp.siguiente
i = i + 1
self.grafo += "} labelloc=\"t\"; label=\" LISTA DOBLE DIAS\";}"
print(self.grafo)
src = Source(self.grafo)
src.format = "png"
src.render('test-output/ListaDobleUsuarios', view=True)
def draw_graph(cwd):
ui = GraphOptions()
ui.edit()
max_patchcount = 2 * ui.limit
# changes = get_changes(cwd, None, ui.limit, author=Config.AUTHOR)
from_upstream = get_changes_from_upstream(cwd, Config.UPSTREAM_REPO, None, max_patchcount)
changes = get_changes(cwd, None, max_patchcount)
# print('format picked: {}'.format(ui.format))
dotSrcLines = darcs_show(cwd, ui.limit, ui.filename).splitlines()
def changeColor(i, color):
dotSrcLines[i] = dotSrcLines[i][:-2] + 'color="{}"];'.format(color)
for i, l in enumerate(dotSrcLines):
m = PATCH_PATTERN.search(l)
if not m:
continue
title = m.group(1).replace("\\n", " ").strip()
print(title)
def find_in(lst):
try:
return next(c for c in lst if title == c.title)
except StopIteration:
return None
up = find_in(from_upstream) # type: Patch
loc = find_in(changes) # type: Patch
if up:
if not loc:
print("patch in upstream but not local?? {}".format(title))
if up.author == Config.AUTHOR:
changeColor(i, ui.acolor)
else:
changeColor(i, ui.bcolor)
elif loc:
if loc.author == Config.AUTHOR:
changeColor(i, ui.color)
dotSrc = "\n".join(dotSrcLines)
print(dotSrc)
# for l in enumerate(dotSrc.splitlines()):
# print("{}: {}".format(*l))
# dot = parse_dot_data(dotSrc)
# print(dot)
dot = Source(dotSrc)
#
# print(dotObj.source)
#
# dot.graph_attr['rankdir'] = 'LR'
dot.format = ui.format
dot.render(ui.filename, view=ui.open, cleanup=ui.cleanup)
def bdd_img(exp):
# print(exp)
exp = expr(exp)
f = expr2bdd(exp)
gv = Source(f.to_dot())
gv.format = "png"
global filename
filename = str(gv.render('../images/bdd_img' + str(randrange(0, 100))))
def preview_dot(filename, kernel=None, style=None):
from graphviz import Source
with open(filename) as dot:
src = Source(dot.read())
src.format = 'png'
outfile = src.render()
with open(outfile, 'rb') as content:
data = content.read()
return {'image/png': base64.b64encode(data).decode('ascii')}
def preview_dot(filename):
from graphviz import Source
with open(filename) as dot:
src = Source(dot.read())
src.format = "png"
outfile = src.render()
with open(outfile, "rb") as content:
data = content.read()
return {"image/png": base64.b64encode(data).decode("ascii")}
def expression_graph_as_png(expr, output_file, view=True):
""" Save a PNG of rendered graph (graphviz) of the symbolic expression.
:param expr: sympy expression
:param output_file: string with .png extension
:param view: set to True if system default PNG viewer should pop up
:return: None
"""
assert output_file.endswith('.png')
graph = Source(dotprint(expr))
graph.format = 'png'
graph.render(output_file.rpartition('.png')[0], view=view, cleanup=True)
def grafic(grafo, name):
global ruta, lista
if path.exists('' + ruta + '\\' + name + '.dot'):
os.remove('' + ruta + '\\' + name + '.dot')
os.remove('' + ruta + '\\' + name + '.dot.png')
#d = Digraph(format='png')
d = Source(str(grafo))
#d.source(grafo)
d.format = 'png'
d.render('' + name + '.dot', ruta, view=True)
else:
#d = Digraph(format='png')
d = Source(str(grafo))
#d.source(grafo)
d.format = 'png'
#d.format='pdf'
d.render('' + name + '.dot', ruta, view=True)
im = Image.open(ruta + '\\' + name + '.dot.png')
imagen = im.convert('RGB')
lista.append(imagen)
def graph(self, format='svg'):
try:
from graphviz import Source
except ImportError:
raise ImportError("You don't have graphviz package installed.\n"
"Please install graphviz or use write_file function to save dot file and generate the "
"graph manually.")
dot = self.generate()
graph = Source(dot)
graph.format = format
return graph
def expression_graph_as_png(expr, output_file, view=True):
""" Save a PNG of rendered graph (graphviz) of the symbolic expression.
:param expr: sympy expression
:param output_file: string with .png extension
:param view: set to True if system default PNG viewer should pop up
:return: None
"""
assert output_file.endswith('.png')
graph = Source(dotprint(expr))
graph.format = 'png'
graph.render(output_file.rpartition('.png')[0], view=view, cleanup=True)
def build(self, label=""):
(_, result) = self.build_node(self.__root)
result = [
'digraph Tree {\n', 'node [shape=box];\n', 'label="' + label + '";'
] + result + ['}\n']
text = ""
for line in result:
text += line + "\n"
dot = Source(text)
dot.format = 'png'
dot.render(self.__filename)
os.remove(self.__filename)
def debug_ast(txt: str, cnxx: any, console_handlerx: any) -> ASTNode:
global cnx, console_handler, next_ats
console_handler = console_handlerx
cnx = cnxx
lexer = ply.lex.lex(module=lex1)
parser = ply.yacc.yacc(module=yacc1, tabmodule="yacc1_tab")
root: ASTNode = parser.parse(txt) # the input
dot = Source(root.to_str())
dot.format = 'png'
dot.render('graph')
lista = root.genarar_lista()
next_ats = debug(lista)
return next_ats
def graficarAVL(self):
self.grafo = "digraph G {node[shape=record, height=0.1];\n"
self.grafo += "{ \n"
if self.raiz == None:
print("La raiz es nula")
self.grafo += "arbolvacio [label = \"Arbol Vacio\",shape=record];\n"
else:
self.graficarNodo(self.raiz)
self.grafo += "} \n"
self.grafo += "labelloc=\"t\";\nlabel=\" AVL\";}"
print(self.grafo)
src = Source(self.grafo)
src.format = "png"
src.render('test-output/AVLTree', view=True)
def get_ast(txt: str, cnxx: any, console_handlerx: any) -> ASTNode:
global cnx, console_handler
console_handler = console_handlerx
cnx = cnxx
lexer = ply.lex.lex(module=lex1)
parser = ply.yacc.yacc(module=yacc1, tabmodule="yacc1_tab")
root: ASTNode = parser.parse(input=txt, lexer=lexer) # the input
dot = Source(root.to_str())
dot.format = 'svg'
dot.render('report_angus_graph')
lista = root.genarar_lista()
run(lista)
print("\n".join([str(i) for i in globales.sym_table]))
parser.restart()
return root
def exec_swipl(code):
temp_path, output_path, code_path, dirpath = setup_env()
with open(temp_path, 'w') as f:
f.write(code)
''' Parser '''
textlist = []
graphviz = False
with open(temp_path) as lines:
'''
Parse for graphviz input
'''
for line in lines:
textlist.append(line)
if '%graphviz' in line:
textlist.pop()
textlist.append("?- print('GRAPHVIZ').\n")
graphviz = True # output is graphviz
with open(code_path, 'w') as rules:
rules.write(''.join(textlist))
''' Get Prolog Output from code.pl and assign to output_path file '''
os.system("swipl {0:s} > {1:s} 2>&1 ".format(code_path, output_path))
''' Remove Prolog welcome text '''
base = open(output_path, 'r')
lines = base.readlines()
base.close()
lines = lines[:-9]
outputlist = []
for line in lines:
outputlist.append(line)
if 'GRAPHVIZ' in line:
outputlist = []
with open(output_path, 'w') as rules:
rules.write(''.join(outputlist))
''' Return final output '''
final = open(output_path, 'r')
output = final.read()
if graphviz == True:
src = Source(output)
src.format = 'png'
print(output)
output = src.render('image.gv')
return output
def graficar(self):
self.grafo = "digraph G {\n" + "node [shape = record,height=.1]; {\n"
if self.estaVacia() == True:
self.grafo += "\"ListaVacia\" [label = \"Lista Circular Vacia\"]"
else:
temp = self.inicio
i = 1
while True:
self.grafo += "\"" + str(
i) + "\" [label = \"" + "Nombre: " + temp.getName()
self.grafo += "\\nContraseña: " + temp.getPassword() + "\"];\n"
temp = temp.siguiente
if temp == self.inicio:
self.grafo += "\"" + str(i) + "\" -> \"" + str(
1) + "\" ;\n"
self.grafo += "\"" + str(1) + "\" -> \"" + str(
i) + "\" ;\n"
else:
self.grafo += "\"" + str(
i + 1
) + "\" [label = \"" + "Nombre: " + temp.siguiente.getName(
)
self.grafo += "\\nContraseña: " + temp.siguiente.getPassword(
) + "\"];\n"
self.grafo += "\"" + str(i) + "\" -> \"" + str(
i + 1) + "\" ;\n"
self.grafo += "\"" + str(i + 1) + "\" -> \"" + str(
i) + "\" ;\n"
i = i + 1
if (temp == self.inicio):
break
self.grafo += "} labelloc=\"t\"; label=\" LISTA CIRCULAR DOBLE\";}"
print(self.grafo)
src = Source(self.grafo)
src.format = "png"
src.render('test-output/ListaCircularDoble', view=True)
#***********************************************************************************#
def build(self):
if not self._proto_module:
raise ValueError("No Protobuf Python module!")
if not self._rendered_filename:
raise ValueError("No output location!")
if not self._file_format:
raise ValueError("No file format!")
uml_template = _get_uml_template(self._proto_module)
if logger.isEnabledFor(logging.DEBUG):
logger.debug("UML template:")
logger.debug(uml_template)
src = Source(uml_template)
src.format = self._file_format
logger.info(
f"Writing diagram to {self._rendered_filename}.{self._file_format}"
)
src.render(filename=self._rendered_filename, view=False, cleanup=True)
def graficar(self):
grafo = "digraph g {\n graph [rankdir=UD];\n node [shape = record,height=.1]; { \n"
i = 0
for nodo in self.table:
if nodo != None:
grafo += "\"" + str(i) + "\" [label = \"" + "Pos: " + str(
nodo.getPosicion())
grafo += "\\nNombre: " + nodo.getNombre(
) + "\\nDescripcion: " + nodo.getDescripcion()
grafo += "\\nDireccion: " + nodo.getDireccion(
) + "\\nHora: " + nodo.getHora() + "\"];\n"
if i > 0:
grafo += "\"" + str(i - 1) + "\" -> \"" + str(
i) + "\"[dir=none] ;\n"
i = i + 1
grafo += "} labelloc=\"t\"; label=\" TABLA HASH EVENTOS\";}"
print(grafo)
src = Source(grafo)
src.format = "png"
src.render('test-output/TablaHash', view=True)
def main(proto: str, output: Path):
type_mapping = {}
message_mapping = {}
types = {}
proto_file = _module(proto)
_build_mappings(proto_file, types, type_mapping, message_mapping)
uml_template = _get_uml_template(types=types,
type_mapping=type_mapping,
message_mapping=message_mapping)
if logger.isEnabledFor(logging.DEBUG):
logger.debug("UML template:")
logger.debug(uml_template)
src = Source(uml_template)
src.format = "png"
rendered_filename = str(
output.joinpath(_get_uml_filename(proto_file.__file__)))
logger.info(f"Writing PNG diagram to {rendered_filename}.png")
src.render(filename=rendered_filename, view=False, cleanup=True)
def graficarAB(self):
pagina = 0
self.grafo = "digraph G { node[shape=record, height=0.1];\n"
self.grafo += "{ \n"
#print(self.raiz.claves[0].nombre)
if self.raiz == None:
print("La raiz es nula")
self.grafo += "arbolvacio [label = \"Arbol Vacio\",shape=record];\n"
else:
self.grafo += self.graficarNodoAB()
self.grafo += "} \n"
self.grafo += "labelloc=\"t\";\nlabel=\" ARBOL B\";}"
print(self.grafo)
src = Source(self.grafo)
src.format = "png"
src.render('test-output/ArbolB', view=True)
#return self.grafo
#***********************************************************************************#
def render_dot(dot, format):
"""
Clunky means to render and return the dot string. We render to a temp
file, then read that back in
"""
# create a temporary file and render to it, we will then
# load and return this file to the caller
temp = tempfile.NamedTemporaryFile(delete=False)
outfile_name = os.path.splitext(temp.name)[0]
# create a graphviz source to hold the dot data and set
# it to the requested format
source = Source(dot)
source.format = format
# now render to the temp file, the render will add the extension based
# on the format
filepath = source.render(filename=outfile_name,
format=format,
cleanup=True)
# flush to disk and close
temp.flush()
temp.close()
file_stream = None
# read the file back in
with open(filepath, "rb") as fp:
file_stream = io.BytesIO(fp.read())
# now remove it
if os.path.exists(filepath):
os.unlink(filepath)
# return the bytes
return file_stream
def get_ast(txt: str, cnxx: any, console_handlerx: any):
global cnx, console_handler
globales2.restart_all()
console_handler = console_handlerx
cnx = cnxx
lexer = ply.lex.lex(module=lex2)
globales2.entrada = txt
parser = ply.yacc.yacc(module=yacc2, tabmodule="yacc2_tab", start='init')
root: ASTNode = parser.parse(input=txt, lexer=lexer) # the input
dot = Source(root.to_str())
dot.format = 'svg'
dot.render('report_minorc_graph')
lista: List[ASTNode] = []
for sentencias in root.childs[0]:
for s in sentencias:
lista.append(s)
gen = Generator(lista)
st = gen.get3d()
# print("\n".join([str(i) for i in globales2.sym_table]))
parser.restart()
return st
def plotPipelineStructure(self):
'''
Plot pipeline structure
@return iPython display object
'''
#Graph setup
g1 = gv.Digraph(format='svg')
g1.graph_attr['rankdir'] = 'LR'
g1.node_attr['shape'] = 'rounded'
g1.node_attr['fontname'] = 'Arial'
g1.node_attr['fontsize'] = '9'
g1.node_attr['style'] = 'filled'
g1.node_attr['margin'] = '0.1'
g1.node_attr['height'] = '0.1'
g1.node_attr['fillcolor'] = '#fff7da'
#g1.node_attr['shape'] = 'plaintext' #use this to remove boxes around nodes
nodelist = self.getMetadataNestedGraph()
print(nodelist)
src = Source(nodelist)
print(dir(src))
src.format = 'svg'
src.render('img/plotPipelineStructure')
# for s in nodelist:
# g1.node(s)
# g1.edges(zip(nodelist, nodelist[1:]))
# g1.render('img/plotPipelineStructure')
# print(nodelist)
# print(g1.source)
# return display(SVG('img/plotPipelineStructure.svg'))
return display(SVG('img/plotPipelineStructure.svg'))
def graficar(self):
grafo = "digraph G {\n" + "rankdir = TB;\n" + "rank = min;\n" + "node[style=filled,shape=box, label=\"Inicio\", rankdir=UD];\n"
tempX = self.ejeX.inicio
tempXinterno = None
tempY = self.ejeY.inicio
j = 0
i = 0
while tempY != None:
if tempY == self.ejeY.inicio:
grafo += "\"" + str(i) + "," + str(
j) + "\"[label=\"raiz\", style=filled];\n"
i += 1
while tempX != None:
grafo += "\"" + str(i) + "," + str(
j) + "\"[label=\"" + tempX.getIndice(
) + "\", style=filled];\n"
i += 1
tempX = tempX.siguiente
i = 0
j += 1
tempXinterno = tempY.listaNodos.inicio
tempX = self.ejeX.inicio
grafo += "\"" + str(i) + "," + str(
j) + "\"[label=\"" + tempY.getIndice() + "\", style=filled];\n"
i += 1
while tempX != None:
if tempXinterno != None:
if tempXinterno.padreX == tempX:
grafo += "\"" + str(i) + "," + str(
j) + "\"[label=\"Dia: " + str(
tempXinterno.getDia()) + "\", style=filled];\n"
tempXinterno = tempXinterno.derecha
else:
grafo += "\"" + str(i) + "," + str(
j) + "\"[label=\"no existe\", style=filled];\n"
else:
grafo += "\"" + str(i) + "," + str(
j) + "\"[label=\"no existe\", style=filled];\n"
i += 1
tempX = tempX.siguiente
i = 0
j += 1
tempY = tempY.siguiente
print(str(i))
print(str(j))
tempX = self.ejeX.inicio
while tempX != None:
i += 1
tempX = tempX.siguiente
print(str(i))
print(str(j))
i += 1
for y in range(0, j):
for x in range(0, i - 1):
grafo += "\"" + str(x) + "," + str(y) + "\" -> \"" + str(
x + 1) + "," + str(y) + "\"[constraint=false];\n"
grafo += "\"" + str(x + 1) + "," + str(y) + "\" -> \"" + str(
x) + "," + str(y) + "\"[constraint=false];\n"
grafo += "{rank=same;\"" + str(x) + "," + str(
y) + "\" \"" + str(x + 1) + "," + str(y) + "\"}\n"
grafo += "{rank=same;\"" + str(x + 1) + "," + str(
y) + "\" \"" + str(x) + "," + str(y) + "\"}\n"
for y in range(0, j - 1):
for x in range(0, i):
grafo += "\"" + str(x) + "," + str(y) + "\" -> \"" + str(
x) + "," + str(y + 1) + "\"[rankdir=UD];\n"
grafo += "\"" + str(x) + "," + str(y + 1) + "\" -> \"" + str(
x) + "," + str(y) + "\"[rankdir=UD];\n"
grafo += "labelloc=\"t\"; label=\" MATRIZ DISPERSA CALENDARIO\";}"
print(grafo)
src = Source(grafo)
src.format = "png"
src.render('test-output/MatrizDispersa', view=True)
node.attr['label'] = str(n) + "\n" + str(d['label'])
for n,c in colors.items():
node = A.get_node(n)
node.attr['color'] = c
node.attr['style'] = 'filled'
print n,c
for n,c in edges_color.items():
node = A.get_edge(*n)
node.attr['color'] = c
node.attr['penwidth'] = 2
print n,c
except Exception,e:
print "Error on printing graph: ",e
src = Source(A)
src.format = 'png'
src.render("test-output/"+name,view=True)
def minimize(self):
for n in self.nodes():
path_exist = False
for m in self.graph['initial']:
if nx.has_path(self,m,n):
path_exist = True
break
if path_exist==False:
self.remove_node_init_accept(n)
def run_trace(self,state_trace):
labels = nx.get_node_attributes(self,'label')
return [labels[n] for n in state_trace]
Certs -> Courses [label=clean]
Tracks_s -> Tracks [label=""]
Certs -> Tracks [label="Fix missing letters"]
Tracks -> Excel [label=""]
Courses -> Excel [label=filter]
Topics -> Excel [label=""]
Tracks -> Chart [label=reshape]
Courses -> Chart [label=reshape]
Tracks -> Document [label=TODO]
Courses -> Document [label=TODO]
Chart -> Document [label=TODO]
Tracks -> Dataset [label=TODO]
Courses -> Dataset [label=TODO]
}""")
dot_src.format = 'png'
print(dot.source)
dot_src.render('test-output/datacamp_certs.gv', view=True)
dot
#g = Graph('G', filename='process.gv', engine='sfdp')
#
#g.edge('run', 'intr')
#g.edge('intr', 'runbl')
#g.edge('runbl', 'run')
#g.edge('run', 'kernel')
#g.edge('kernel', 'zombie')
#g.edge('kernel', 'sleep')
#g.edge('kernel', 'runmem')
#g.edge('sleep', 'swap')
#g.edge('swap', 'runswap')
#g.edge('runswap', 'new')
clf.fit(X, y)
#Нарисуем дерево
import os
os.environ["PATH"] += os.pathsep + 'E:/Anaconda3/Library/bin/graphviz'
graph = Source(
tree.export_graphviz(clf,
feature_names=list(X),
out_file="tree.dot",
class_names=['Died', 'Survived'],
filled=True))
with open("tree.dot") as f:
dot_graph = f.read()
g = Source(dot_graph)
g.format = "png"
g.render("file_name") #дерево получилось слишком глубоким и большим
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.33, random_state=42) #тестовое множество 33%
X_train.shape #596 наблюдений
X_test.shape #295 наблюдений
#Посмотрим, насколько плохо было получившееся дерево
clf.score(X, y) #0.9797979797979798
clf.fit(
X_train, y_train
) #обучим часть модели и посмотрим точность классификации на обучающем множестве
clf.score(X_train, y_train) #0.9798657718120806
#Какая точность на данных, который классификатор не "видел"?:
# lets visualize the tree
import os
os.environ["PATH"] += os.pathsep + 'C:\\Program Files (x86)\\Graphviz2.38\\bin'
from graphviz import Source
from sklearn import tree
Source(
tree.export_graphviz(decision_tree2,
out_file=None,
feature_names=X.columns))
from IPython.display import SVG
graph = Source(
tree.export_graphviz(decision_tree2,
out_file=None,
feature_names=X.columns))
SVG(graph.pipe(format='svg'))
graph = Source(
tree.export_graphviz(decision_tree2,
out_file=None,
feature_names=X.columns))
graph.format = 'png'
graph.render('decision_tree2_render', view=True)
graph = Source(
tree.export_graphviz(decision_tree2,
out_file=None,
feature_names=X.columns))
png_bytes = graph.pipe(format='png')
with open('decision_tree2.png', 'wb') as f:
f.write(png_bytes)
from IPython.display import Image
Image(png_bytes)
def apply(fp1, fp2, parameters=None):
"""
Visualize a comparison between two footprint tables
Parameters
---------------
fp1
Footprints associated to the log (NOT a list)
fp2
Footprints associated to the model
parameters
Parameters of the algorithm, including:
- Parameters.FORMAT => Format of the visualization
Returns
---------------
gviz
Graphviz object
"""
if parameters is None:
parameters = {}
if type(fp1) is list or type(fp2) is list:
raise Exception(
"footprints visualizer does not work on list of footprints!")
activities = sorted(
list(
set(x[0] for x in fp1["sequence"]).union(
set(x[1] for x in fp1["sequence"])).union(
set(x[0] for x in fp1["parallel"])).union(
set(x[1] for x in fp1["parallel"]))))
fp_table = {}
for a1 in activities:
fp_table[a1] = {}
for a2 in activities:
fp_table[a1][a2] = ("black", XOR_SYMBOL)
for x in fp1["sequence"]:
if x not in fp1["parallel"]:
if x in fp2["sequence"]:
fp_table[x[0]][x[1]] = ("black", SEQUENCE_SYMBOL)
fp_table[x[1]][x[0]] = ("black", PREV_SYMBOL)
else:
fp_table[x[0]][x[1]] = ("red", SEQUENCE_SYMBOL)
fp_table[x[1]][x[0]] = ("red", PREV_SYMBOL)
for x in fp1["parallel"]:
if x in fp2["parallel"]:
fp_table[x[0]][x[1]] = ("black", PARALLEL_SYMBOL)
else:
fp_table[x[0]][x[1]] = ("red", PARALLEL_SYMBOL)
image_format = exec_utils.get_param_value(Parameters.FORMAT, parameters,
"png")
filename = tempfile.NamedTemporaryFile(suffix='.gv')
footprints_table = [
"digraph {\n", "tbl [\n", "shape=plaintext\n", "label=<\n"
]
footprints_table.append(
"<table border='0' cellborder='1' color='blue' cellspacing='0'>\n")
footprints_table.append("<tr><td></td>")
for act in activities:
footprints_table.append("<td><b>" + act + "</b></td>")
footprints_table.append("</tr>\n")
for a1 in activities:
footprints_table.append("<tr><td><b>" + a1 + "</b></td>")
for a2 in activities:
footprints_table.append("<td><font color=\"" +
fp_table[a1][a2][0] + "\">" +
fp_table[a1][a2][1] + "</font></td>")
footprints_table.append("</tr>\n")
footprints_table.append("</table>\n")
footprints_table.append(">];\n")
footprints_table.append("}\n")
footprints_table = "".join(footprints_table)
gviz = Source(footprints_table, filename=filename.name)
gviz.format = image_format
return gviz
def apply(fp: Dict[str, Any], parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> Source:
"""
Visualize a footprints table
Parameters
---------------
fp
Footprints
parameters
Parameters of the algorithm, including:
- Parameters.FORMAT => Format of the visualization
Returns
---------------
gviz
Graphviz object
"""
if parameters is None:
parameters = {}
if type(fp) is list:
raise Exception("footprints visualizer does not work on list of footprints!")
activities = sorted(list(set(x[0] for x in fp["sequence"]).union(set(x[1] for x in fp["sequence"])).union(
set(x[0] for x in fp["parallel"])).union(set(x[1] for x in fp["parallel"]))))
fp_table = {}
for a1 in activities:
fp_table[a1] = {}
for a2 in activities:
fp_table[a1][a2] = XOR_SYMBOL
for x in fp["sequence"]:
if x not in fp["parallel"]:
fp_table[x[0]][x[1]] = SEQUENCE_SYMBOL
fp_table[x[1]][x[0]] = PREV_SYMBOL
for x in fp["parallel"]:
fp_table[x[0]][x[1]] = PARALLEL_SYMBOL
image_format = exec_utils.get_param_value(Parameters.FORMAT, parameters, "png")
filename = tempfile.NamedTemporaryFile(suffix='.gv')
footprints_table = ["digraph {\n", "tbl [\n", "shape=plaintext\n", "label=<\n"]
footprints_table.append("<table border='0' cellborder='1' color='blue' cellspacing='0'>\n")
footprints_table.append("<tr><td></td>")
for act in activities:
footprints_table.append("<td><b>"+act+"</b></td>")
footprints_table.append("</tr>\n")
for a1 in activities:
footprints_table.append("<tr><td><b>"+a1+"</b></td>")
for a2 in activities:
footprints_table.append("<td>"+fp_table[a1][a2]+"</td>")
footprints_table.append("</tr>\n")
footprints_table.append("</table>\n")
footprints_table.append(">];\n")
footprints_table.append("}\n")
footprints_table = "".join(footprints_table)
gviz = Source(footprints_table, filename=filename.name)
gviz.format = image_format
return gviz
def monitoringWorker(x_train, y_train, x_test, y_test):
crit = 'entropy'
t1 = time()
clf_tree = DecisionTreeClassifier(criterion=crit,
max_depth=19,
random_state=20,
presort=True)
clf_tree.fit(X=x_train, y=y_train)
err_train = round(np.mean(y_train != clf_tree.predict(x_train)) * 100, 4)
err_test = round(np.mean(y_test != clf_tree.predict(x_test)) * 100, 4)
t = -(t1 - time())
print(
"Глубина дерева: {}, ошибка на обучающей: {}, ошибка на тестовой: {}, время {}"
.format(clf_tree.get_depth(), err_train, err_test, t))
dotfile = export_graphviz(
clf_tree,
class_names=[
'ArrayIndexOutOfBoundsException', 'BadTokenException',
'CalledFromWrongThreadException', 'ClassNotFoundException',
'IllegalMonitorStateExceptio', 'IllegalStateException',
'InternalError', 'NetworkOnMainThreadException',
'NoClassDefFoundError', 'NullPointerException', 'OutOfMemoryError',
'RuntimeException', 'NormalBehaviou'
],
out_file=None,
filled=True,
node_ids=True)
graph = Source(dotfile)
# # Сохраним дерево как toy_example_tree_X.png, где Х - entropy или gini, критерий качестве разбиения
graph.format = 'png'
graph.render(str(clf_tree.get_depth()) +
"tree_example_tree_{}".format(crit),
view=True)
knn = KNeighborsRegressor(n_neighbors=6,
weights='uniform',
algorithm='auto',
leaf_size=30,
p=2,
metric='minkowski',
metric_params=None,
n_jobs=None)
knn.fit(x_train, y_train)
predictions_test = knn.predict(x_test)
predictions_test = pd.DataFrame({"error_name": predictions_test})
mae = mean_absolute_error(y_test, predictions_test)
print("Для KNeighborsRegressor:\n")
print("Для n_neighbours = ", 6)
print('mean_squared_log_error:\t%.5f' %
mean_squared_log_error(y_test, predictions_test))
print('mean_absolute_error:\t(пункты)%.4f' %
mean_absolute_error(y_test, predictions_test))
print("Median Absolute Error: " +
str(round(median_absolute_error(predictions_test, y_test), 2)))
RMSE = round(sqrt(mean_squared_error(predictions_test, y_test)), 2)
print("Root mean_squared_error: " + str(RMSE))
print("\n")
print("\n")
t1 = time()
while True:
if -(t1 - time()) >= 0.25:
createDataset(dataset_0, dataset_1, dataset_2, dataset_3,
dataset_4, dataset_5, dataset_6, dataset_7,
dataset_8, dataset_9, dataset_10, dataset_11,
dataset_12, dataset_13, dataset_14, dataset_15,
dataset_16, dataset_17, dataset_18, dataset_19,
dataset_20, dataset_21, dataset_22, dataset_23,
dataset_24, dataset_25, dataset_26, dataset_27,
dataset_28, dataset_29, dataset_30, dataset_31,
dataset_32, dataset_33, dataset_34, dataset_35,
dataset_36, dataset_37, dataset_38, dataset_39)
data1 = pd.read_csv('monitoring/state.csv')
print(data1)
df_knn = data1[[
'VmPeak', 'VmSize', 'VmLck', 'VmHWM', 'VmRSS', 'VmData',
'VmLib', 'VmPTE', 'VmSwap', 'Threads',
'voluntary_ctxt_switches', 'nonvoluntary_ctxt_switches',
'crash'
]]
df_knn.apply(pd.to_numeric)
df_knn = shuffle(df_knn)
x_train, x_test, y_train, y_test = train_test_split(
df_knn[[
'VmPeak', 'VmSize', 'VmLck', 'VmHWM', 'VmRSS', 'VmData',
'VmLib', 'VmPTE', 'VmSwap', 'Threads',
'voluntary_ctxt_switches', 'nonvoluntary_ctxt_switches'
]],
df_knn['crash'],
test_size=0.5,
random_state=42)
predictions1 = knn.predict(x_train.fillna(0))
predictions1 = pd.DataFrame({'crash': predictions1})
value = int(predictions1['crash'].iloc[0])
coef = predictions1['crash'].iloc[0] - value
if coef > 0.5:
value = value + 1
if coef == 0:
probability = 100
elif coef > 0.5:
probability = coef * 100
else:
probability = (1 - coef) * 100
probability = probability - (mae * 100)
if value == 1:
print(
"/***************************************************\n\nWARNING: ",
"NullPointerException ", "probability:", probability, "%",
"\n\n***************************************************/")
elif value == 2:
print(
"/***************************************************\n\nWARNING: ",
"ArrayIndexOutOfBoundsException", "probability:",
probability, "%",
"\n\n***************************************************/")
elif value == 3:
print(
"/***************************************************\n\nWARNING: ",
"OutOfMemoryError", "probability:", probability, "%",
"\n\n***************************************************/")
elif value == 4:
print(
"/***************************************************\n\nWARNING: ",
"ClassNotFoundException", "probability:", probability, "%",
"\n\n***************************************************/")
elif value == 5:
print(
"/***************************************************\n\nWARNING: ",
"IllegalMonitorStateException", "probability:",
probability, "%",
"\n\n***************************************************/")
elif value == 6:
print(
"/***************************************************\n\nWARNING: ",
"NetworkOnMainThreadException", "probability:",
probability, "%",
"\n\n***************************************************/")
elif value == 7:
print(
"/***************************************************\n\nWARNING: ",
"NoClassDefFoundError", "probability:", probability, "%",
"\n\n***************************************************/")
elif value == 8:
print(
"/***************************************************\n\nWARNING: ",
"CalledFromWrongThreadException", "probability:",
probability, "%",
"\n\n***************************************************/")
elif value == 9:
print(
"/***************************************************\n\nWARNING: ",
"BadTokenException", "probability:", probability, "%",
"\n\n***************************************************/")
elif value == 10:
print(
"/***************************************************\n\nWARNING: ",
"IllegalStateException", "probability:", probability, "%",
"\n\n***************************************************/")
elif value == 11:
print(
"/***************************************************\n\nWARNING: ",
"InternalError", "probability:", probability, "%",
"\n\n***************************************************/")
elif value == 12:
print(
"/***************************************************\n\nWARNING: ",
"RuntimeException", "probability:", probability, "%",
"\n\n***************************************************/")
else:
print(
"/***************************************************\n\nALL RIGHT: ",
"NormalBehaviour", "probability:", probability, "%",
"\n\n***************************************************/")
t1 = time()
import graphviz
from graphviz import Source
arq = open("UFAM_EPIDEMIC_MultiCast_emauc_trace_AdjacencyGraphvizReport.txt",
'r')
#arq = open("gteste.txt",'r')
arq_text = arq.read()
#h = graphviz.Graph(format='png')
print(arq_text)
src = Source(arq_text)
src.format = 'png'
src.render()
print(src)
def apply(log, aligned_traces, parameters=None):
"""
Gets the alignment table visualization from the alignments output
Parameters
-------------
log
Event log
aligned_traces
Aligned traces
parameters
Parameters of the algorithm
Returns
-------------
gviz
Graphviz object
"""
if parameters is None:
parameters = {}
variants_idx_dict = variants_filter.get_variants_from_log_trace_idx(log, parameters=parameters)
variants_idx_list = []
for variant in variants_idx_dict:
variants_idx_list.append((variant, variants_idx_dict[variant]))
variants_idx_list = sorted(variants_idx_list, key=lambda x: len(x[1]), reverse=True)
image_format = parameters["format"] if "format" in parameters else "png"
table_alignments_list = ["digraph {\n", "tbl [\n", "shape=plaintext\n", "label=<\n"]
table_alignments_list.append("<table border='0' cellborder='1' color='blue' cellspacing='0'>\n")
table_alignments_list.append("<tr><td>Variant</td><td>Alignment</td></tr>\n")
for index, variant in enumerate(variants_idx_list):
al_tr = aligned_traces[variant[1][0]]
table_alignments_list.append("<tr>")
table_alignments_list.append(
"<td><font point-size='9'>Variant " + str(index + 1) + " (" + str(len(variant[1])) + " occurrences)<br />" + variant[
0] + "</font></td>")
table_alignments_list.append("<td><font point-size='6'><table border='0'><tr>")
for move in al_tr['alignment']:
move_descr = str(move[1]).replace(">", ">")
if not move[0][0] == ">>" or move[0][1] == ">>":
table_alignments_list.append("<td bgcolor=\"green\">" + move_descr + "</td>")
elif move[0][1] == ">>":
table_alignments_list.append("<td bgcolor=\"violet\">" + move_descr + "</td>")
elif move[0][0] == ">>":
table_alignments_list.append("<td bgcolor=\"gray\">" + move_descr + "</td>")
table_alignments_list.append("</tr></table></font></td>")
table_alignments_list.append("</tr>")
table_alignments_list.append("</table>\n")
table_alignments_list.append(">];\n")
table_alignments_list.append("}\n")
table_alignments = "".join(table_alignments_list)
filename = tempfile.NamedTemporaryFile(suffix='.gv')
gviz = Source(table_alignments, filename=filename.name)
gviz.format = image_format
return gviz
y_pred=clf.predict(xv_b)
kappa=cohen_kappa_score(y_v,y_pred)
score_2 = cross_val_score(clf, x_b, y, cv=k_fold, n_jobs=-1)
print('Average accuracy:', np.mean(score_2))
print("Kappa: ", kappa)
print("Score DT: ", clf.score(xv_b,y_v))
print("Depth DT: ", clf.get_depth())
print("Recall: ", recall_score(y_v,y_pred))
print("Precision: ", precision_score(y_v,y_pred))
print("confussion: ", confusion_matrix(y_v,y_pred))
#Decision tree plot
graph = Source(tree.export_graphviz(clf, \
out_file=None, feature_names=x.loc[:,\
selector.get_support()].columns))
graph.format = 'pdf'
graph.render('plots\\dt')
#############
#pruned
############
#parameter grid
lf=np.arange(2,30,2)
#Setting up DT and Gridsearch
cflp = tree.DecisionTreeClassifier(criterion = "entropy", random_state=21)
clfp = GridSearchCV(estimator=cflp, param_grid=dict(min_samples_leaf=lf), \
n_jobs=-1, cv=k_fold)
#Fitting and printing CV accuracy
clfp.fit(x_b,y) #fitting
