def execute(path, grammar_file_name, example_file_name, export_dot, export_png):
"""U svrhe brzeg testiranja, metoda koja prima putanju do foldera, naziv fajla gde je gramatika i naziv fajla gde je
primer programa u nasem jeziku i indikator da li da se eksportuju .dot i .png fajlovi"""
meta_path = os.path.join(SRC_DIR, 'model', grammar_file_name)
meta_name = os.path.splitext(meta_path)[0]
metamodel = metamodel_from_file(meta_path)
if export_dot:
metamodel_export(metamodel, meta_name + '.dot')
if export_png:
graph = pydot.graph_from_dot_file(meta_name + '.dot')
graph.write_png(meta_name + '.png')
model_path = os.path.join(path, example_file_name)
model_name = os.path.splitext(model_path)[0]
model = metamodel.model_from_file(model_path)
if export_dot:
model_export(model, model_name + '.dot')
if export_png:
graph = pydot.graph_from_dot_file(model_name + '.dot')
graph.write_png(model_name + '.png')
return model
def open_file(self):
"""Abre el fichero .csv o .dot indicado en el primer argumento del
terminal.
Formato del fichero CSV: el número de línea-1 marca el número de nodo
Separados por comas los nodos destino"""
try:
fileR = open(sys.argv[1], "r")
except IOError:
print("Imposible abrir fichero")
except:
print("Introduce nombre de fichero")
else:
#Se ha seleccionado un fichero CSV
if re.match(".*(\.csv)$", sys.argv[1]):
fileC = csv.reader(fileR, delimiter=',')
filecsv = [n for n in fileC]
self.create_nodes(filecsv)
#Se ha seleccionado un fichero DOT
elif re.match(".*(\.dot)$", sys.argv[1]):
filedot = [[] for n in range(0, 15)]
graph = pydot.graph_from_dot_file(sys.argv[1])
res = graph.get_edges()
for n in res:
filedot[int(n.get_source())].append(int(n.get_destination()))
self.create_nodes(filedot)
else:
print('Extensión de fichero no válida')
def max_tree(max_depth=None, out_file=None):
"""
Creates decision tree of max_depth. If max_depth is None the tree's depth won't be bounded.
If out_file is specificed, function will make a dot file and png of generated tree
prints training and testing error to stdout
"""
data = np.loadtxt("fourier/energy.txt", delimiter=",")
X = []
y = []
for row in data:
y.append(int(row[1]))
X.append(map(int, row[2:]))
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.4, random_state=0)
print X_train
clf = tree.DecisionTreeClassifier(max_depth=max_depth)
clf = clf.fit(X_train, y_train)
print "trained tree of depth %s" % (max_depth)
print "training error: %f" % (1 - clf.score(X_train, y_train))
print "testing error: %f" % (1 - clf.score(X_test, y_test))
if out_file:
with open(out_file + ".dot", "w") as f:
f = tree.export_graphviz(clf, out_file=f)
graph = pydot.graph_from_dot_file(out_file + ".dot")
graph.write_png(out_file + ".png")
def visualize_mdp(mdp, filename):
log.info('in the visualize_mdp function')
import pydot
import networkx as nx
from networkx.drawing.nx_agraph import write_dot
G=nx.DiGraph()
for s in mdp['states']:
for a in s['actions']:
for t in a['transitions']:
ecolor='red' if a['id'] else 'green'
elabel='p={}, r={}'.format(t['probability'], t['reward'])
G.add_edge(s['id'], t['to'],
color=ecolor,
label=elabel)
log.info('writing dot file')
write_dot(G, filename.replace('.json', '.dot'))
g = pydot.graph_from_dot_file(filename.replace('.json', '.dot'))[0]
log.info('writing png from dot file')
g.write_png(filename.replace('.json', '.png'))
log.info('removing intermediate dot file')
os.remove(filename.replace('.json', '.dot'))
return filename.replace('.json', '.png')
def max_tree(X, y, max_depth = None, out_file = None):
"""
Creates decision tree of max_depth. If max_depth is None the tree's depth won't be bounded.
If out_file is specificed, function will make a dot file and png of generated tree
prints training and testing error to stdout
"""
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=.4, random_state=0)
clf = tree.DecisionTreeClassifier(max_depth=max_depth)
clf = clf.fit(X_train, y_train)
print "trained tree of depth %s" % (max_depth)
print "training error: %f" % (1-clf.score(X_train, y_train))
print "testing error: %f" % (1-clf.score(X_test, y_test))
out_file = None
if out_file:
with open(out_file+".dot", 'w') as f:
f = tree.export_graphviz(clf, out_file=f)
graph = pydot.graph_from_dot_file(out_file+".dot")
graph.write_png(out_file+'.png')
def makeGraph(n, nNodes, connections, messages, label):
filename = "out%d"%n
file = open("%s.dot"%filename, "w")
file.write("digraph G {\nlayout=\"neato\"\n")
file.write("graph [dpi = 300];")
radius = nNodes*0.2
for i in range(1, nNodes+1):
angle = 2*math.pi*i/nNodes
x = radius*math.cos(angle)
y = radius*math.sin(angle)
file.write(" %d[pos=\"%f,%f!\",shape=circle];\n"%(i-1, x, y))
for i in range(len(connections)):
for j in connections[i]:
if j not in messages[i]: file.write("%d->%d\n"%(i, j))
for j in messages[i]:
file.write("%d->%d[color=\"red\"];\n"%(i, j))
file.write("labelloc=\"t\";\n")
file.write("label=\"%s\";\n"%label)
file.write("}\n")
file.close()
(graph,) = pydot.graph_from_dot_file("%s.dot"%filename)
graph.write_png('%s.png'%filename)
def OnPopupItemGraph(self, event):
for row in self.ReportGrid.GetSelectedRows():
label = self.ReportGrid.GetCellValue(row,0)
id = self.ReportGrid.GetCellValue(row,1)
### plot the graph
### TODO link with properties frame
for fct in ('extTransition','intTransition', 'outputFnc', 'timeAdvance'):
filename = "%s(%s)_%s.dot"%(label,str(id),fct)
path = os.path.join(tempfile.gettempdir(), filename)
### if path exist
if os.path.exists(path):
graph = pydot.graph_from_dot_file(path)
filename_png = os.path.join(tempfile.gettempdir(),"%s(%s)_%s.png"%(label,str(id),fct))
graph.write_png(filename_png, prog='dot')
pylab.figure()
img = pylab.imread(filename_png)
pylab.imshow(img)
fig = pylab.gcf()
fig.canvas.set_window_title(filename)
pylab.axis('off')
pylab.show()
def _render_with_pydot(self, filename, encoding):
c = pydot.graph_from_dot_file(filename, encoding=encoding)
sha = ''
for g in c:
jpe_data = g.create(format='jpe')
sha += sha256(jpe_data).hexdigest()
return sha
def draw(self):
g = nx.MultiDiGraph()
lr_content = {}
for k,v in self.progress_present.iteritems():
label = 'I %d\n'%k
count = 0
for rule in v:
break
label += rule[0] + ' --> '
for r in rule[1]:
if r == u'dot':
label += '^ '
else:
label += r + ' '
label += '\n'
lr_content[k] = label
for k,v in self.state_present.iteritems():
for edge, to in v.iteritems():
g.add_edge(lr_content[k], lr_content[to], label = edge)
name = 'goto'
nx.write_dot(g, name + '.dot')
g = pydot.graph_from_dot_file(name+'.dot')
g.write_jpg(name+'.jpg')
def processFile(filename, dictionary=None, debug=0):
"""
parse a file, solve the model, return the results
This is a useful function in its own right, as well as an example
of how to use the MarkovAvail class and make sense of the results.
Args:
filename (string): name of dot format input file
dictionary (string): name of transition rates file
space/tab separated <name,value> pairs, w/comments
debug (int): level of desired debug output
0: none
1: parsed and interpreted parameters
2: painful for code (not model) debugging
Returns:
MarkovAvail: parameters and solutions
list: sorted (state #, occupancy) tupples
"""
# process the input file
g = pydot.graph_from_dot_file(filename)
# process the dictionary
valueDict = {}
if dictionary is not None:
if debug > 1:
print("Using value dictionary: %s" % (dictionary))
with open(dictionary) as f:
for line in f:
# skip comment lines
if line.startswith('#') or line.startswith('/'):
continue
# a value line needs at least two fields
parts = line.split()
if len(parts) >= 2:
key = parts[0]
value = parts[1]
valueDict[key] = value
if debug > 1:
print(" dictionary[%s]:\t%s" % (key, value))
# process the model
m = MarkovAvail(g, valueDict, debug)
# solve the model and print the results
m.solve()
# create a list of states, sorted by decreasing occupancy
stateOccupancies = {}
for i in range(m.numstates):
o = m.occupancy[i]
stateOccupancies[i] = o
sortedStates = sorted(stateOccupancies.iteritems(),
key=operator.itemgetter(1),
reverse=True)
# return the solution and the sorted state/occupancy list
return (m, sortedStates)
def draw(self):
g = nx.MultiDiGraph()
for id,path in self.s_DFA.iteritems():
for label, to_id in path.iteritems():
g.add_edge(id,to_id,label=label)
name = 'dfa'
nx.write_dot(g, name + '.dot')
g = pydot.graph_from_dot_file(name+'.dot')
g.write_jpg(name+'.jpg')
def make_graph(sciid,outfmt='pdf'):
gc = graph_from_dot_file(TMP+'{}.dot'.format(sciid))
gc.set_overlap(0)
if 'png' in outfmt:
gc.write_png('{}.png'.format(sciid), prog='dot')
if 'pdf' in outfmt:
gc.write_pdf('{}.pdf'.format(sciid), prog='dot') #fdp, dot,
def write_image_of_decision_tree(filename, decision_tree, columns): """Create an image visualising a decision tree.""" dot_filename = str(filename) + '.dot' dotfile = open(dot_filename, 'w') tree.export_graphviz(decision_tree, out_file = dotfile, feature_names = columns) dotfile.close() graph = pydot.graph_from_dot_file(dot_filename) graph.write_png(str(filename) + '.png')
def main(infilename):
"""
Get all graph info, and then search for cycle, sources and sinks
"""
graph = pydot.graph_from_dot_file(infilename)
nodes = get_nodes_info(graph)
load_links(graph, nodes)
propagate_lineage(nodes)
dump_status(nodes)
def write_svg(self):
"""
Create SVG out of the dotfile
@dotfile: In-dotfile
"""
self._write_stop()
graph = pydot.graph_from_dot_file(self.dotfile)
svg = graph.write_svg(os.path.join(self.resdir, "map.svg"))
def process_graphs(self, file_names, squash_versions=False, analyze=False):
graphs = []
for x in file_names:
xg = pydot.graph_from_dot_file(x)
graphs.append(xg)
final_graph = graphs[0]
if len(graphs) > 1:
if squash_versions:
for g in graphs:
GraphProcessor.do_squash_versions(g)
final_graph = GraphProcessor.merge_graphs(graphs)
# we're a little broken here, because the version squashing
# plus merging does some implicit analysis of version conflicts
if analyze:
per_graph_node_sets, intersection = GraphProcessor.intersecting_nodes(graphs)
style=NodeStyleRule.globalRule({'fillcolor': GraphProcessor.DEFAULT_COLORS['intersect'], 'style':'filled'})
for x in intersection:
style.apply_node(final_graph, x)
non_intersect = GraphProcessor.non_intersecting_nodes_per_graph(graphs, per_graph_node_sets)
for x in non_intersect:
color = GraphProcessor.DEFAULT_COLORS['non_intersect_list'].pop(0)
style = NodeStyleRule.globalRule({'fillcolor':color, 'style':'filled'})
for y in non_intersect[x]:
style.apply_node(final_graph, y)
if squash_versions:
# necessary because we wedged a non-standard
# attribute into the nodes marking their versions
for n in final_graph.get_nodes():
SquashVersionRule.clean_version_tag(n)
for rule in self.style_rules:
for e in final_graph.get_edges():
rule.apply(final_graph, e)
# otherwise it's unreadable
final_graph.set('rankdir', 'LR')
return final_graph
def gf_dependencies(path, unlink=True):
from subprocess import Popen, PIPE
from tempfile import TemporaryFile
import os
dotFile = "_gfdepgraph.dot"
with TemporaryFile() as f:
f.write('dg\n')
f.seek(0)
p = Popen(('gf', '-s', '-retain', path), stdin=f, stdout=PIPE)
p.wait()
dot = pydot.graph_from_dot_file(dotFile)
if unlink: os.unlink(dotFile)
return dot
def image (self, file, im) :
# on crée un graphe : on récupère le code du graphe
graph = self.chaine_graphe ()
# auquel on ajoute un début et une fin
graph = "digraph GA {\n" + graph + "}\n"
# puis on l'écrit dans le fichier file
g = open (file, "w")
g.write (graph)
g.close ()
# enfin, on convertit ce fichier en image
dot = pydot.graph_from_dot_file (file)
dot.write_png (im, prog="dot")
def _render_with_pydot(self, filename):
#f = open(filename, 'rt')
#graph_data = f.read()
#f.close()
#g = pydot.parse_from_dot_data(graph_data)
g = pydot.graph_from_dot_file(filename)
if not isinstance(g, list):
g = [g]
jpe_data = NULL_SEP.join([_g.create(format='jpe') for _g in g])
return sha256(jpe_data).hexdigest()
def main(args):
if args.output.endswith('tsv'):
if not args.perform_closure:
graph = pydot.graph_from_dot_file(args.input)
edges = graph.get_edges()
with open(args.output, "wb") as f:
for edge in edges:
src = edge.get_source()
dest = edge.get_destination()
edge_type = edge.get('type') # edge.type
f.write('%s %s %s\n'%(src, dest, edge_type))
return
raise NotImplementedError
def build_nodes_tree_from_amr_dot_file(amr_dot_file_path):
# extract interactions and complex formations (including complex disintegration) from the dot file format for an amr
amr = pd.graph_from_dot_file(cap.absolute_path + amr_dot_file_path)
sentence = amr.get_label()
sentence = sentence.replace("\ ", "")
nodes = {}
# iterate over all edges to build a tree of nodes
edges = amr.get_edge_list()
# print 'edges: ', edges
for edge in edges:
# print 'edge', edge
# print 'edge: ', edge
source = edge.get_source()
# print 'source: ', source
destination = edge.get_destination()
# print 'destination: ', destination
edge_label = edge.get_label()
edge_label = constants.no_quotes_regexp.sub("", edge_label)
# print 'edge_label: ', edge_label
edge = None
# source not in the nodes_list
if not nodes.has_key(source):
source_node = amr.get_node(source)[0]
nodes[source] = Node(source, source_node.get_label())
source_node = None
if not nodes.has_key(destination):
destination_node = amr.get_node(destination)[0]
nodes[destination] = Node(destination, destination_node.get_label())
destination_node = None
if edge_label == "TOP":
if source != destination:
raise AssertionError
nodes[constants.root] = nodes[source] # source and destination are same for TOP edge
nodes[constants.root].is_root = True
else:
# linking destination as child of source, there can be multiple children with a given edge_label
if edge_label not in nodes[source].children:
nodes[source].children[edge_label] = [nodes[destination]]
else:
if nodes[destination] not in nodes[source].children[edge_label]:
nodes[source].children[edge_label].append(nodes[destination])
# linking source as parent of destination, there can be multiple parents with same edge_label (eg: a protein can be a catalyst for multiple interactions)
if edge_label not in nodes[destination].parents: # if list is empty
nodes[destination].parents[edge_label] = [nodes[source]]
else:
if nodes[source] not in nodes[destination].parents[edge_label]:
nodes[destination].parents[edge_label].append(nodes[source])
# print 'nodes: ', nodes
# amr.write_pdf(cap.absolute_path+amr_dot_file_path+'.pdf')
return nodes, sentence
def parse(self, file):
self.g = pydot.graph_from_dot_file(file)
self.name = self.g.get_name()
self.log.info("##### GRAPH NAME %s..." % (self.name))
# Pick out all nodes and populate local nodes dict. The model
# from pydot.Graph includes only explicitly mentioned nodes
# in get_nodes(), others must be extracted from get_edges()
#
self.log.info("nodes:")
for n in self.g.get_nodes():
node = self.add_node(n.get_name())
self.log.info("edges:")
conneg_default = True #first conneg edge will be default
for e in self.g.get_edges():
src = self.add_node(e.get_source())
dst_name = self.add_node(e.get_destination()).name
label = self.normalize_label(e.get_label())
m = re.match( r'conneg(\s+(\d+))?(\s+(\S+))?', label )
if (m):
if (m.group(4) is None):
self.log.info("Bad conneg label: %s" % (label))
else:
code = int(m.group(2))
content_type = m.group(4)
self.log.info("%s conneg %s, %d -> %s" % (src.name, content_type, code, dst_name))
src.conneg[content_type] = [code,dst_name,conneg_default]
conneg_default = False
elif (re.match('html\s+link',label,re.I)):
src.html_links.append(dst_name)
elif (re.match('html\s+img',label,re.I)):
src.html_imgs.append(dst_name)
else:
# assume space separated set of links (last word might me mime type)
words = label.split()
mime_type = None
if (re.match(r'\w+/\w+$',words[-1])):
mime_type = words.pop()
for rel in words:
self.log.info("%s rel=\"%s\" %s %s" % (src.name,rel,dst_name,mime_type))
src.links.append([rel,dst_name,mime_type])
# Do we have an svg file for this graph?
svg_file = os.path.splitext(file)[0] + '.svg'
if (os.path.exists(svg_file)):
self.svg=svg_file
def render(self, mode='instance', number=3, unscale=False):
if self.dot_string == "":
return
# Print out the merges and splits for this node
print self.pretty_print(False), self.surprise_num
self.describe_merges_and_splits()
# make picture and display
# Render dot file and save
dot_list = self.dot_string.split("REPLACE_STRING")
string = dot_list[0]
for i in range(len(dot_list)-1):
string += self.dot_descriptions[i]['desc']
if number == 0:
number = 1000
for j in range(number):
if j >= len(self.dot_descriptions[i][mode]):
break
if unscale:
attribute = self.dot_descriptions[i][mode][j]
att_name = attribute.split(":")[0]
try:
att_num = float(attribute.split(":")[1])
att_num *= self.pastCalc[att_name]['std']
if mode == "no_compare":
att_num += self.pastCalc[att_name]['avg']
att_num = round(att_num,2)
string += att_name+":"+str(att_num)+"\\n"
except IndexError:
string += self.dot_descriptions[i][mode][j]+"\\n"
else:
string += self.dot_descriptions[i][mode][j]+"\\n"
string += dot_list[i+1]
dot_file = open('tmp.dot', 'w+')
dot_file.write(string)
dot_file.close()
# Read dot to graph and render
graph = pydot.graph_from_dot_file('tmp.dot')
graph.write_png('tmp.png')
graph_image = scipy.misc.imread('tmp.png')
if not Instance.fig is None:
pylab.close(Instance.fig)
Instance.fig = None
Instance.fig = pylab.figure(frameon=False)
ax_size = [0,0,1,1]
Instance.fig.add_axes(ax_size)
pylab.imshow(graph_image, vmin=1, vmax=99, origin='upper')
pylab.axis('off')
pylab.show()
def loadDotFile(dotfile):
dotGraph = pydot.graph_from_dot_file(dotfile)
graphEdges = dotGraph.get_edges()
for edge in graphEdges:
edgeList.append( (str(edge.get_source().replace('\"','')), edge.get_destination().replace('\"','')) )
if edge.get_source().replace('\"','') not in nodeList:
nodeList.append(edge.get_source().replace('\"',''))
if edge.get_destination().replace('\"','') not in nodeList:
nodeList.append(edge.get_destination().replace('\"',''))
# edgeList.append( (str(edge.get_source()[1:-1]), edge.get_destination()[1:-1]) )
# if edge.get_source()[1:-1] not in nodeList:
# nodeList.append(edge.get_source()[1:-1])
# if edge.get_destination()[1:-1] not in nodeList:
# nodeList.append(edge.get_destination()[1:-1])
nodeList.sort()
def _write_state_graph(self):
file_path = '%s/graph_state.dot' % self.__work_directory
nx.write_dot(self.__state_graph, file_path)
graph = pydot.graph_from_dot_file(file_path)
# get the graph nodes
nodes = graph.get_nodes()
if nodes:
for node in nodes:
name = node.get_name()
# id is set based on the id column in state table
if name:
# an double quote is read around the name
node.set('id', int(name.split('-')[-1].strip('"')))
try:
node.set_shape('box')
except AttributeError:
node.set('shape', 'box')
try:
node.set_width('3')
except AttributeError:
node.set('width', '3')
try:
node.set_height('1')
except AttributeError:
node.set('height', '1')
try:
node.set_imagescale('true')
except AttributeError:
node.set('imagescale', 'true')
#TODO: also set the nodes name and the nodes image
"""
edges = graph.get_edges()
if edges:
for edge in edges:
pass
"""
graph.write_dot('%s/graph_state_layout.dot' % self.__work_directory)
def parse(self, fileName):
project = DotGraphProject()
# parse the dotfile
self.graph = pydot.graph_from_dot_file(fileName)
self.graph.project = project
# parse tags from dot file
self.graph.tags = self.parseTags(fileName)
ensureSupported('dotgraphprojecthelpers.ProjectSupport', self.graph, project)
# allow support to find and add more items
for support in getPlugins(DotGraphSupport, package=plugins):
support.support(self.graph, project)
return project
def makeFrames(m, b, c):
graphTemplate = 'digraph %s_%s {\n\tnode[shape=plaintext];\n\tgraph[bb="0,0,407.8,333.47"];\n%s\n%s}'
nodeTemplate = "\t%s[label=%s, pos=%s, fontcolor=%s];\n"
edgeTemplate = "\t%s -> %s [color=%s];\n"
baseEdgeTemplate = "\t%s -> %s [dir=none,color=gray];\n"
# baseEdgeTemplate = "\t%s -> %s [dir=none,color=gray, pos = %s];\n"
baseGraph = pydot.graph_from_dot_file("cases/%smap.gv" % c)
deltaFiles = os.listdir("%s" % m)
deltaFiles = filter(lambda x: x.startswith(b) and x.endswith(".delta"), deltaFiles)
for deltaFilename in deltaFiles:
deltaFile = file("%s/%s" % (m, deltaFilename), "r")
nodes, edges = eval(deltaFile.read())
deltaFile.close()
gvNodes = dict()
nodesStr = ""
for node in baseGraph.get_nodes():
name = node.get_label()
if name in nodes.keys():
color = stressColorcode(nodes[name])
pos = node.get_pos()
nodesStr += nodeTemplate % (name, name, pos, color)
edgesStr = ""
for edge in baseGraph.get_edges():
source = edge.get_source()
target = edge.get_destination()
edgesStr += baseEdgeTemplate % (source, target)
# pos = edge.get_pos()
# edgesStr += baseEdgeTemplate%(source,target,pos)
for edge in edges:
(source, target, color) = edge
edgesStr += edgeTemplate % (source, target, color)
graphName = deltaFilename.replace(".delta", "")
graphStr = graphTemplate % (c, graphName, nodesStr, edgesStr)
graphFile = file("%s/%s.gv" % (m, graphName), "w")
graphFile.write(graphStr)
graphFile.close()
def __init__(self, cluster_vectors_fn, cluster_labels_fn, movie_names_fn, label_names_fn, cluster_names_fn, filename, training_data, cluster_to_classify, max_depth):
vectors = dt.importVectors(cluster_vectors_fn)
labels = dt.importLabels(cluster_labels_fn)
cluster_names = dt.importString(cluster_names_fn)
vector_names = dt.importString(movie_names_fn)
label_names = dt.importString(label_names_fn)
scores_array = []
for l in range(len(labels[0])):
new_labels = [0] * 15000
for x in range(len(labels)):
new_labels[x] = labels[x][l]
x_train = np.asarray(vectors[:training_data])
x_test = np.asarray(vectors[training_data:])
y_train = np.asarray(new_labels[:training_data])
y_test = np.asarray(new_labels[training_data:])
self.clf = tree.DecisionTreeClassifier( max_depth=max_depth)
self.clf = self.clf.fit(x_train, y_train)
y_pred = self.clf.predict(x_test)
f1 = f1_score(y_test, y_pred, average='binary')
accuracy = accuracy_score(y_test, y_pred)
scores = [[label_names[l], "f1", f1, "accuracy", accuracy]]
print scores[0]
scores_array.append(scores)
class_names = [ label_names[l], "NOT "+label_names[l]]
tree.export_graphviz(self.clf, feature_names=cluster_names, class_names=class_names, out_file='Rules/'+label_names[l]+filename+'.dot', max_depth=10)
"""
rewrite_dot_file = dt.importString('Rules/'+filename+label_names[l]+'.dot')
new_dot_file = []
for s in rewrite_dot_file:
new_string = s
if "->" not in s and "digraph" not in s and "node" not in s and "(...)" not in s and "}" not in s:
index = s.index("value")
new_string = s[:index] + '"] ;'
new_dot_file.append(new_string)
dt.write1dArray(new_dot_file, 'Rules/Cleaned'+filename+label_names[l]+'.dot')
"""
graph = pydot.graph_from_dot_file('Rules/'+label_names[l]+filename+'.dot')
graph.write_png('Rules/Images/'+label_names[l]+filename+".png")
self.get_code(self.clf, cluster_names, class_names, label_names[l]+filename)
dt.write1dArray(scores_array, 'Rules/Scores/'+filename+'.scores')
def add_file_to_graph(file_path, final_graph):
print "[+] Loading graph from: {}.".format(file_path)
graph = pydot.graph_from_dot_file(file_path)
node_names = map(lambda node: node.get_name(), final_graph.get_nodes())
edge_names = map(lambda edge: (edge.get_source(), edge.get_destination()),
final_graph.get_edges())
for edge in graph.get_edges():
(src_relevant, dst_relevant) = is_relevant_nodes(edge)
if src_relevant and (edge.get_source() not in node_names):
final_graph.add_node(pydot.Node(edge.get_source()))
if dst_relevant and (edge.get_destination() not in node_names):
final_graph.add_node(pydot.Node(edge.get_destination()))
if src_relevant and dst_relevant and (edge.get_source(), edge.get_destination()) not in edge_names:
final_graph.add_edge(pydot.Edge(edge.get_source(), edge.get_destination()))
def runall(fname): gs = pydot.graph_from_dot_file(fname) if not isinstance(gs,list): gs = [gs] gdx = 0 for g in gs: info = getinfo(g) done = True iters = 0 while not done and iters < 10: done = apply_rpctimes(g) iters += 1 outname = "%d" % gdx if info: outname = info["Cluster ID"] savegraph(g, outname + ".json") gdx += 1
table_numeric = pd.read_csv('./data/' + sys.argv[1] + '_overlap_numeric.csv')
features = list(table_ordinal.columns[2:-1])
target = list(table_ordinal.columns[-1:])[0]
X_train, X_test, y_train, y_test = train_test_split(table_ordinal[features], table_ordinal[target], random_state=1)
dt_entropy = DecisionTreeClassifier(criterion='entropy', max_depth=3)
dt_entropy = dt_entropy.fit(X_train, y_train)
# output a PNG of the decision tree
dotfile = sys.argv[1] + '_entropy.dot'
png = sys.argv[1] + '_entropy.png'
export_graphviz(dt_entropy, out_file=dotfile, feature_names=features)
(tree, ) = pydot.graph_from_dot_file(dotfile)
tree.write_png(png)
# test
y_predict = dt_entropy.predict(X_test)
print "Entropy: {}".format(accuracy_score(y_test, y_predict))
X_train, X_test, y_train, y_test = train_test_split(table_numeric[features], table_numeric[target], random_state=1)
dt_gini = DecisionTreeClassifier(criterion='gini', max_depth=3)
dt_gini = dt_gini.fit(X_train, y_train)
# output a PNG of the decision tree
dotfile = sys.argv[1] + '_gini.dot'
png = sys.argv[1] + '_gini.png'
export_graphviz(dt_gini, out_file=dotfile, feature_names=features)
logger.info(infoMsg)
# Create dot file by using extra/gprof2dot/gprof2dot.py
# http://code.google.com/p/jrfonseca/wiki/Gprof2Dot
dotFilePointer = codecs.open(dotOutputFile, 'wt', UNICODE_ENCODING)
parser = gprof2dot.PstatsParser(profileOutputFile)
profile = parser.parse()
profile.prune(0.5 / 100.0, 0.1 / 100.0)
dot = gprof2dot.DotWriter(dotFilePointer)
dot.graph(profile, gprof2dot.TEMPERATURE_COLORMAP)
dotFilePointer.close()
infoMsg = "converting dot file into a graph image '%s'" % imageOutputFile
logger.info(infoMsg)
# Create graph image (png) by using pydot (python-pydot)
# http://code.google.com/p/pydot/
pydotGraph = pydot.graph_from_dot_file(dotOutputFile)
pydotGraph.write_png(imageOutputFile)
infoMsg = "displaying interactive graph with xdot library"
logger.info(infoMsg)
# Display interactive Graphviz dot file by using extra/xdot/xdot.py
# http://code.google.com/p/jrfonseca/wiki/XDot
win = xdot.DotWindow()
win.connect('destroy', gtk.main_quit)
win.set_filter("dot")
win.open_file(dotOutputFile)
gtk.main()
yTrain, yTest = yTrain[:, np.newaxis], yTest[:, np.newaxis]
dt = tree.DecisionTreeClassifier()
dt.fit(xTrain, yTrain)
dotfile = open("dt.dot", 'w')
tree.export_graphviz(dt,
out_file=dotfile,
feature_names=[
'Clump_Thickness',
'Uniformity_of_Cell_Size',
'Uniformity_of_Cell_Shape',
'Marginal_Adhesion',
'Single_Epithelial_Cell_Size',
'Bare_Nuclei',
'Bland_Chromatin',
'Normal_Nucleoli',
'Mitoses',
])
dotfile.close()
# In[2]:
import pydot
(graph, ) = pydot.graph_from_dot_file('dt.dot')
graph.write_png('dt.png')
# In[3]:
# In[ ]:
import pydot
for i in range(1, 7):
try:
dot_file = 'gini_model_' + str(i) + '.dot'
(graph, ) = pydot.graph_from_dot_file(dot_file)
png_file = 'gini_model_' + str(i) + '.png'
graph.write_png(png_file)
except:
print("Cannot Find File" + dot_file)
def layout(self, type_):
if pygraphviz:
G = pygraphviz.AGraph(strict=False, directed=True)
if type_ == 'dot LR':
G.graph_attr['rankdir'] = 'LR'
type_ = type_.split()[0]
G.graph_attr['ranksep'] = '0.125'
elif pydot:
G = pydot.Dot('graphname', graph_type='digraph')
if type_ == 'dot LR':
G.set_layout('dot')
G.set('rankdir', 'LR')
else:
G.set_layout(type_)
G.set('ranksep', '0.125')
for from_, to in self.link.values():
if pygraphviz:
G.add_edge(
(from_, from_.gnx),
(to, to.gnx),
)
elif pydot:
G.add_edge(pydot.Edge(from_.gnx, to.gnx))
for i in self.nodeItem:
if pygraphviz:
G.add_node((i, i.gnx))
elif pydot:
gnode = pydot.Node(i.gnx)
# rect = self.nodeItem[i].boundingRect()
G.add_node(gnode)
for child in i.children:
key = (i, child)
if key not in self.hierarchyLinkItem or child not in self.nodeItem:
continue
G.add_edge(pydot.Edge(i.gnx, child.gnx))
if pygraphviz:
G.layout(prog=type_)
elif pydot:
tempName = tempfile.NamedTemporaryFile(dir=tempfile.gettempdir(),
delete=False)
G.write_dot(tempName.name)
G = pydot.graph_from_dot_file(tempName.name)
for i in self.nodeItem:
if pygraphviz:
gn = G.get_node((i, i.gnx))
x, y = map(float, gn.attr['pos'].split(','))
i.u['_bklnk']['x'] = x
i.u['_bklnk']['y'] = -y
self.nodeItem[i].setPos(x, -y)
self.nodeItem[i].do_update()
elif pydot:
lst = G.get_node(''.join(['"', i.gnx, '"']))
if lst:
x, y = map(float, lst[0].get_pos().strip('"').split(','))
i.u['_bklnk']['x'] = x
i.u['_bklnk']['y'] = -y
self.nodeItem[i].setPos(x, -y)
self.nodeItem[i].do_update()
if pydot:
x, y, width, height = map(float, G.get_bb().strip('"').split(','))
self.ui.canvasView.setSceneRect(
self.ui.canvas.sceneRect().adjusted(x, y, width, height))
self.do_update(adjust=False)
self.center_graph()
def get_graphs(dotfile_path):
"""
get four graphs from the dot file
Parameters
----------
dotfile_path: str
The path of dot file. You need to first generate the dot file using the method generateDotFIle()
Returns
-------
four graphs； the whole graph, CFG graph, DD graph and CD graph.The CFG,DD,CD graphs is a part of the whole graph
"""
print(dotfile_path)
print('start generate graph')
import networkx as nx
import pydot
graph = pydot.graph_from_dot_file(dotfile_path)
nodes_bb = {}
graph_dot = pydot.Dot(graph_type="digraph")
graph_DD = pydot.Dot(graph_type="digraph")
graph_CD = pydot.Dot(graph_type="digraph")
graph_CFG = pydot.Dot(graph_type="digraph")
# traverse node
for cluster in graph[0].get_subgraphs():
#print(cluster.obj_dict['name'])
if cluster.obj_dict['name'].startswith('cluster_'):
basic_block_id = cluster.obj_dict['name'].split('_')[-1]
#print(basic_block_id)
for node in cluster.obj_dict['nodes'].keys():
#print(cluster.obj_dict['nodes'][node][0]['attributes'])
# nodes_bb[node] = str(basic_block_id)
nodes_bb[node] = cluster.obj_dict['nodes'][node]
graph_dot.add_node(cluster.get_node(node)[0])
graph_DD.add_node(cluster.get_node(node)[0])
graph_CD.add_node(cluster.get_node(node)[0])
graph_CFG.add_node(cluster.get_node(node)[0])
print("***********")
# traverse edge
# dd
# edges
# color: cyan4
# use
# edges
# color: black, dashed
# cd
# edges
# color: blue
# cfg
# edges
# color: gray
for cluster in graph[0].get_subgraphs():
#print(cluster.obj_dict['name'])
if cluster.obj_dict['name'].startswith('cluster_'):
basic_block_id = cluster.obj_dict['name'].split('_')[-1]
#print(basic_block_id)
for edge in cluster.get_edges():
# print(edge.__get_attribute__('color'))
graph_dot.add_edge(edge)
color = edge.__get_attribute__('color')
color = color.strip()
#print(color == '"gray"')
#print(color + " " + 'gray')
# color str contains "" in its str
if edge.__get_attribute__('color') == '"gray"':
#print('gray')
graph_CFG.add_edge(edge)
if edge.__get_attribute__('color') == '"blue"':
#print('blue')
graph_CD.add_edge(edge)
if edge.__get_attribute__('color') == '"cyan4"':
#print('add cyan4')
graph_DD.add_edge(edge)
print("***********")
#print(graph_dot)
#print(graph_DD)
nx_g = nx.nx_pydot.from_pydot(graph_dot)
nx_CFG = nx.nx_pydot.from_pydot(graph_CFG)
nx_DD = nx.nx_pydot.from_pydot(graph_DD)
nx_CD = nx.nx_pydot.from_pydot(graph_CD)
print('end generate graph')
return nx_g, nx_CFG, nx_DD, nx_CD
def plot_dot_plot(tmpdir, _id, features, target, top_features_name, indices,
random_state, mode):
"""Make dot plot for based on decision tree.
Parameters
----------
tmpdir: string
Temporary directory for saving experiment results
_id: string
Experiment ID in PennAI
features: np.darray/pd.DataFrame
Features in training dataset
target: np.darray/pd.DataFrame
Target in training dataset
top_features: list
Top feature_names
indices: ndarray
Array of indices of top important features
random_state: int
Random seed for permuation importances
mode: string
'classification': Run classification analysis
'regression': Run regression analysis
Returns
-------
dtree_train_score, float
Test score from fitting decision tree on top important feat'
"""
# plot bar charts for top important features
import pydot
from sklearn.tree import export_graphviz
top_features = features[:, indices]
if mode == 'classification':
from sklearn.tree import DecisionTreeClassifier
dtree = DecisionTreeClassifier(random_state=random_state,
max_depth=DT_MAX_DEPTH)
scoring = SCORERS['balanced_accuracy']
else:
from sklearn.tree import DecisionTreeRegressor
dtree = DecisionTreeRegressor(random_state=random_state,
max_depth=DT_MAX_DEPTH)
scoring = SCORERS["neg_mean_squared_error"]
dtree.fit(top_features, target)
dtree_train_score = scoring(dtree, top_features, target)
dot_file = '{0}{1}/dtree_{1}.dot'.format(tmpdir, _id)
png_file = '{0}{1}/dtree_{1}.png'.format(tmpdir, _id)
class_names = None
if mode == 'classification':
class_names = [str(i) for i in dtree.classes_]
export_graphviz(dtree,
out_file=dot_file,
feature_names=top_features_name,
class_names=class_names,
filled=True,
rounded=True,
special_characters=True)
(graph, ) = pydot.graph_from_dot_file(dot_file)
graph.write_png(png_file)
return dtree_train_score
def gen_graph_from_gv(ifile, odir, oformat="png"):
(graph, ) = pydot.graph_from_dot_file(ifile)
gen_graph_func = getattr(graph, "write_" + oformat)
filename = os.path.basename(ifile)
ofile = odir + "/" + os.path.splitext(filename)[0] + "." + oformat
gen_graph_func(ofile)
# [[5.1 3.5 1.4 0.2]
# [7. 3.2 4.7 1.4]
# [6.3 3.3 6. 2.5]]
test_target = iris.target[test_idx]
# [0 1 2]
model.fit(train_data, train_target)
print("correct data")
print(test_target) # [0 1 2]
print("predictions")
print(model.predict(test_data)) # [0 1 2]
baby = export_graphviz(model, out_file="baby.dot",
feature_names=["sepal_length", "sepal_width",
"petal_length", "petal_width"],
label='all',
filled=True,
rounded=True
)
# corrupted but works in tutorial 2016 year
dot_data = StringIO(baby)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf("tree2.pdf")
# works perfect
# straight reading from dot file
graph = pydot.graph_from_dot_file("baby.dot")
graph[0].write_pdf("tree.pdf")
# List of features sorted from most to least important
sorted_importances = [importance[1] for importance in feature_importances]
sorted_features = [importance[0] for importance in feature_importances]
# Cumulative importances
cumulative_importances = np.cumsum(sorted_importances)
# Make a line graph
plt.plot(x_values, cumulative_importances, 'g-')
# Draw line at 95% of importance retained
plt.hlines(y = 0.95, xmin=0, xmax=len(sorted_importances), color = 'r', linestyles = 'dashed')
# Format x ticks and labels
plt.xticks(x_values, sorted_features, rotation = 'vertical')
# Axis labels and title
plt.xlabel('Variable')
plt.ylabel('Cumulative Importance')
plt.title('Cumulative Importances')
plt.savefig('fig_SA_06_importance_cumulative.png')
plt.show()
# Tree sample creation. DOT and PNG files.
# Activate if needed
"""""
import os
os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'
"""""
tree_small = clf.estimators_[5]
# Save the tree as a png image
export_graphviz(tree_small, out_file = 'Tree_sample.dot', feature_names = feature_list, rounded = True, precision = 1)
(graph, ) = pydot.graph_from_dot_file('Tree_sample.dot')
graph.write_png('Tree_sample.png')
# -*- coding:utf-8 -*-
import numpy as np
from sklearn import tree
from sklearn.datasets import load_iris
import pydot
import os
print(os.getcwd())
# 定义特征选择标准为entropy，深度为7
clf = tree.DecisionTreeClassifier(criterion="entropy", max_depth=7)
# 加载鸢尾花数据
iris = load_iris()
# 训练模型
clf.fit(iris.data, iris.target)
# 输出图
tree.export_graphviz(clf, out_file='tree.dot')
(graph, ) = pydot.graph_from_dot_file('tree.dot')
graph.write_png('1.png')
graph.write_png('tree.png')
#dot_file = pydot.graph_from_dot_file('/home/{0}/Dropbox/ASL/ASL_Summer_2016/exclusions/firefighting/firefighting_stay_in_place_all.dot'.format(getpass.getuser()))
#load inputs and outputs
#input_prop_to_ros_info, output_prop_to_ros_info = file_operations.loadYAMLFile(\
# '/home/{0}/LTLROS_ws/src/controller_executor/examples/firefighting/firefighting.yaml'.format(getpass.getuser()))
#example_name = "firefighting"
# ---- simple ---- #
#dot_file = pydot.graph_from_dot_file('/home/{0}/Dropbox/ASL/ASL_Summer_2016/exclusions/simple/simple_all.dot'.format(getpass.getuser()))
#input_prop_to_ros_info, output_prop_to_ros_info = file_operations.loadYAMLFile(\
# '/home/{0}/LTLROS_ws/src/controller_executor/examples/simple/simple.yaml'.format(getpass.getuser()))
#example_name = "simple"
# --- move_group_and_move_base ---- #
#load dot file
dot_file = pydot.graph_from_dot_file('/home/{0}/Dropbox/ASL/ASL_Summer_2016/exclusions/move_group_and_move_base/youbot_lab.dot'.format(getpass.getuser()))
#load inputs and outputs
input_prop_to_ros_info, output_prop_to_ros_info = file_operations.loadYAMLFile(\
'/home/{0}/LTLROS_ws/src/controller_executor/examples/move_group_and_move_base/move_group_and_move_base.yaml'.format(getpass.getuser()))
example_name = "move_group_and_move_base"
################## #################
##### NODES ###### ###### EDGES ####
################## #################
#first grab all the nodes (both n__ and t__)
nodes_dict, edges_dict = {}, {}
for subgraph_name, subgraph in dot_file.obj_dict['subgraphs'].iteritems():
check_resources_logger.debug("subgraph_name: {0}, length of subgraph: {1}".format(subgraph_name, len(subgraph)))
check_resources_logger.log(8, "subgraph[0] keys: {0}".format(subgraph[0].keys()))
check_resources_logger.log(8, subgraph[0]['edges'].keys())
data = np.array(data)
n_samples = len(data)
n_features = len(data[0])
print('Number of samples:', n_samples)
print('Number of features:', n_features)
del headers[-1]
X = data
y = targets
X_train, X_test, Y_train, Y_test= train_test_split(data,targets,test_size=0.25,random_state=0)
clf_regressor = DecisionTreeRegressor(max_features=3)
clf_regressor .fit(X_train,Y_train)
dot_data = tree.export_graphviz(clf_regressor, out_file="forest.dot",
feature_names=headers,
filled=True, rounded=True,
special_characters=False)
(graph,) = pydot.graph_from_dot_file('forest.dot')
graph.write_png('forest.png')
print(clf_regressor.score(X_train,Y_train))
print(clf_regressor.score(X_test,Y_test))
self.y += command.steps * move[1]
def __str__(self):
return "Robot position is {}, {}.".format(self.x, self.y)
def move_command_processor(move_cmd):
if move_cmd.steps < 0:
raise TextXSemanticError('Broj koraka ne moze biti negativan')
elif move_cmd.steps == 0:
move_cmd.steps = 1
if __name__ == '__main__':
robot_mm = metamodel_from_file('robot.tx', debug = False)
metamodel_export(robot_mm, 'robot_meta.dot')
graph = pydot.graph_from_dot_file('robot_meta.dot')
graph.write_png('robot_meta.png')
object_processors = {'MoveCommand' : move_command_processor}
robot_mm.register_obj_processors(object_processors)
robot_model = robot_mm.model_from_file('test.rbt')
model_export(robot_model, 'robot_model.dot')
graph = pydot.graph_from_dot_file('robot_model.dot')
graph.write_png('robot_model.png')
robot = Robot()
robot.interpret(robot_model)
print(robot)
def from_dot(graph_name, path, resources=[], containers=[]):
def trim(str):
return str[1:-1]
import pydot
graph = pydot.graph_from_dot_file(path)
graph_nodes = graph.get_node_list()
nodes = []
for graph_node in graph_nodes:
name = trim(graph_node.get_name())
if name in resources:
node = Resource(name)
elif name in containers:
continue
else:
shape = graph_node.get_attributes()["shape"]
if shape == "parallelogram":
node = HardGoal(name)
elif shape == "polygon":
node = Task(name)
elif shape == "box":
node = SoftGoal(name)
elif shape in ["circle", "ellipse"]:
# containers
continue
else:
#print(name)
continue
node.id = node.name
nodes.append(node)
graph_edges = graph.get_edge_list()
edges = []
labels = set()
for graph_edge in graph_edges:
source = trim(graph_edge.get_source())
target = trim(graph_edge.get_destination())
if source in containers or target in containers:
continue
labels.add(graph_edge.get_attributes().get("label", None))
label = graph_edge.get_attributes().get("label", None)
if not label:
value = "or"
type = "decompositions"
elif "make" in label.lower():
value = "make"
type = "contribution"
elif "break" in label.lower():
value = "break"
type = "contribution"
elif "help" in label.lower():
value = "help"
type = "contribution"
elif "hurt" in label.lower():
value = "hurt"
type = "contribution"
elif "some +" in label.lower():
value = "someplus"
type = "contribution"
elif "some -" in label.lower():
value = "someminus"
type = "contribution"
elif "d" in label.lower():
value = "dependency"
type = "dependency"
source, target = target, source
else:
#print(source, target, label)
continue
edge = Component("edge")
edge.value = value
edge.type = type
edge.source = source
edge.target = target
edges.append(edge)
return Graph(name=graph_name, nodes=nodes, edges=edges)
import pydot
with open(sys.argv[1]) as f:
data = json.load(f)
tables = dict()
actions = dict()
stages = OrderedDict()
variables = OrderedDict()
cfg = dict()
# context.json, tableplacement.html, swi.dot, swe.dot
num_actions = 0
added_actions = 0
i_cfg = pydot.graph_from_dot_file(sys.argv[3])
e_cfg = pydot.graph_from_dot_file(sys.argv[4])
with open(sys.argv[2]) as table_placement:
table_name = ""
stage = -1
flag = False
skip = 0
for line in table_placement.readlines():
if skip > 0:
skip -= 1
continue
if line.startswith("|Stage"):
tables = dict()
actions = dict()
stages = OrderedDict()
#Should predict healthy [0] and does
print('Predicting...')
X_new = np.array(
[[22.0, 1.0, 4.0, 130.0, 110.0, 0.0, 0.0, 180.0, 0.0, 0.3, 2.0, 0.0, 3.0]])
y_classification = treePruned.predict(X_new)
print('Prediction: {}->{}'.format(X_new[0], y_classification))
featureNames = [
'age', 'sex', 'cp', 'thresBPS', 'chol', 'fbs', 'restECG', 'thalach',
'exang', 'oldPeak', 'slope', 'ca', 'thal'
]
# now create a bar chart and save it to a file
n_features = 13
plt.barh(range(n_features), treePruned.feature_importances_, align='center')
plt.yticks(np.arange(n_features), featureNames)
plt.xlabel("Feature importance")
plt.ylabel("Feature")
plt.ylim(1, n_features)
plt.savefig(r"feature_importance.png", bbox_inches='tight')
export_graphviz(
treePruned,
out_file="Heart_Disease_Tree.dot",
class_names=["Healthy", "Class I", "Class II", "Class III", "Class IV"],
feature_names=featureNames,
impurity=False,
filled=True)
(graph, ) = pydot.graph_from_dot_file('./Heart_Disease_Tree.dot')
graph.write_png('./Heart_Disease_tree.png')
something I could maximize in a for loop?
"""
# Just output the decision tree to a .dot file and then let graphviz convert
# the .dot file to a .png file (an image).
export_graphviz(dt_appx,
out_file='dt_appx_of_mlp.dot',
feature_names=bundle_of_data.feature_names,
class_names=bundle_of_data.target_names,
filled=True,
impurity=False,
rounded=True,
proportion=True)
export_graphviz(dt_regular,
out_file='dt_regular.dot',
feature_names=bundle_of_data.feature_names,
class_names=bundle_of_data.target_names,
filled=True,
impurity=False,
rounded=True,
proportion=True)
import pydot
(graph, ) = pydot.graph_from_dot_file('dt_appx_of_mlp.dot')
graph.write_png('dt_appx_of_mlp.png')
(graph, ) = pydot.graph_from_dot_file('dt_regular.dot')
graph.write_png('dt_regular.png')
accuracy_dec, accuracy_jan, accuracy_feb, accuracy_mar, \ accuracy_apr, accuracy_may, accuracy_jun = \ evaluate(df, label=l, numerical=False) rmse = 'n/a' performance.append((l, rmse, accuracy, accuracy_away, accuracy_home, accuracy_heavyfav, \ accuracy_closegame, accuracy_homeisfavored, accuracy_awayisfavored, \ accuracy_2009, accuracy_2010, accuracy_2011, accuracy_2012, \ accuracy_2013, accuracy_2014, accuracy_2015, accuracy_nov, \ accuracy_dec, accuracy_jan, accuracy_feb, accuracy_mar, \ accuracy_apr, accuracy_may, accuracy_jun)) if a in ['tree']: # create decision tree with open(tables_path + 'dtree.dot', 'w') as f: f = tree.export_graphviz(m, out_file=f) graph = pydot.graph_from_dot_file(tables_path + 'dtree.dot') graph.write_pdf(charts_path + 'dtree_%s' % l) performance = pd.DataFrame(performance, columns=['technique', 'rmse', 'accuracy', 'accuracy_home', 'accuracy_away', 'accuracy_heavyfav', 'accuracy_closegame', 'accuracy_homeisfavored', 'accuracy_awayisfavored', 'accuracy_2009', 'accuracy_2010',
from sklearn.datasets import load_iris
from sklearn import tree
import os
import pydot # need install
print(os.getcwd())
clf = tree.DecisionTreeClassifier(criterion = "entropy") #gini
iris = load_iris()
print(iris.data[0:5])
print(iris.target[0:5])
clf = clf.fit(iris.data, iris.target)
tree.export_graphviz(clf, out_file='0_DT_Tree/tree.dot')
(graph,) = pydot.graph_from_dot_file('0_DT_Tree/tree.dot')
graph.write_png('0_DT_Tree/tree.png')
def __convert_dot_to_image(self):
(graph, ) = pydot.graph_from_dot_file(f'src/static/images/{self.id}.gv')
graph.write_png(f'src/static/images/{self.id}.png')
'SCORE', 'VIOLATION CODE_F007', 'VIOLATION CODE_F050',
'VIOLATION CODE_F054', 'VIOLATION CODE_F014', 'VIOLATION CODE_F023',
'VIOLATION CODE_F006', 'VIOLATION CODE_F035', 'VIOLATION CODE_F052',
'VIOLATION CODE_F036', 'VIOLATION CODE_F033'
]
data = inspection_data[cols]
data = pd.DataFrame(data)
y = data['SCORE']
data.drop('SCORE', axis=1, inplace=True)
feature_list = list(data.columns)
# create training and testing vars
X_train, X_test, y_train, y_test = train_test_split(data,
y,
test_size=0.3,
random_state=42)
rf_small = RandomForestRegressor(n_estimators=10, max_depth=3)
rf_small.fit(X_train, y_train)
# Extract the small tree
tree_small = rf_small.estimators_[5]
# Save the tree as a png image
export_graphviz(tree_small,
out_file='small_tree.dot',
feature_names=feature_list,
rounded=True,
precision=1)
(graph, ) = pydot.graph_from_dot_file('small_tree.dot')
graph.write_png('small_tree.png')
required=False)
ap.add_argument("--pdf",
help="path of the pdf that will be exported",
required=False)
ap.add_argument("--svg",
help="path of the svg that will be exported",
required=False)
# parsing
args = ap.parse_args()
# the design file
design = pd.read_csv(args.csv)
# the dot file
(g, ) = pydot.graph_from_dot_file(args.dot)
# extract the selected dot node names by their labels
labels = design["label"].tolist()
name_to_label = get_label_map(g, labels)
names = sorted(list(name_to_label.keys()))
# add the dot node names to the design
tmp_df = pd.DataFrame.from_records(list(tuple(name_to_label.items())),
columns=["name", "label"])
design = design.merge(tmp_df, left_on="label", right_on="label")
# useful maps
name_to_color = get_map_from_df(design, "name", "color")
name_to_newlabel = get_map_from_df(design, "name", "new_label")
name_to_software = get_map_from_df(design, "name", "software")
# Add Transactions
for block_time in block_time_list:
vin = blocks[block_time]['vin']
vout = blocks[block_time]['vout']
for in_address in vin.keys():
if vin[in_address] > float(attention_money_value):
dot_data += in_address + '_' + block_time + ' [style = "solid,filled",fillcolor=black,fontcolor=white];\n'
dot_data += in_address + '_' + block_time + ' -> ' + block_time + ' [label = ' + str(
vin[in_address]) + ',penwidth=4,color=red ];\n'
else:
dot_data += in_address + '_' + block_time + ' -> ' + block_time + ' [label = ' + str(
vin[in_address]) + ' ];\n'
for out_address in vout.keys():
if vout[out_address] > float(attention_money_value):
dot_data += out_address + '_' + block_time + ' [style = "solid,filled",fillcolor=black,fontcolor=white];\n'
dot_data += block_time + ' -> ' + out_address + '_' + block_time + ' [label = ' + str(
vout[out_address]) + ',penwidth=4,color= red];\n'
else:
dot_data += block_time + ' -> ' + out_address + '_' + block_time + ' [label = ' + str(
vout[out_address]) + ' ];\n'
dot_data += '}'
with open(address + '_money_flow.dot', 'w') as file:
file.write(dot_data)
(graph, ) = pydot.graph_from_dot_file(address + '_money_flow.dot')
graph.write_svg(address + '_money_flow.svg')
def visualize_to_png(filepath, pngfilename):
(graph, ) = pydot.graph_from_dot_file(filepath)
#graph.draw(pngfilename)
graph.write_png(pngfilename)
check_call(['dot', '-Tpng', filepath, '-o', pngfilename])
def make_money_flow_graph(output_path, target_address, transactions):
# Make .dot File
TIME_ZONE_OFFSET = 60 * 60 * 7
simple_transactions = {}
for transaction in transactions['txs']:
try:
simple_transaction = {}
simple_transaction['input'] = [{
'value': float(vin['value']),
'addr': vin['addr']
} for vin in transaction['vin']]
simple_transaction['output'] = [{
'value':
float(vout['value']),
'addr':
vout['scriptPubKey']['addresses'][0]
} for vout in transaction['vout']]
except:
continue
block_time = int(transaction['blocktime']) + TIME_ZONE_OFFSET
simple_transactions[block_time] = simple_transaction
time_listed_transactions = sorted(simple_transactions.items())
address_appear_times = {}
for time_listed_transaction in time_listed_transactions:
block_time = int(time_listed_transaction[0])
for input_address in time_listed_transaction[1]['input']:
if input_address['addr'] not in address_appear_times:
address_appear_times[input_address['addr']] = [block_time]
else:
address_appear_times[input_address['addr']].append(block_time)
for output_address in time_listed_transaction[1]['output']:
if output_address['addr'] not in address_appear_times:
address_appear_times[output_address['addr']] = [block_time]
else:
address_appear_times[output_address['addr']].append(block_time)
for address in address_appear_times.keys():
address_appear_times[address] = list(set(
address_appear_times[address]))
graph_data = "digraph{rankdir=TB;"
graph_edges = []
for time_listed_transaction in time_listed_transactions:
block_time = int(time_listed_transaction[0])
block_time = datetime.fromtimestamp(block_time).strftime(
"%Y%m%d_%Hh%Mm%Ss")
for input_address in time_listed_transaction[1]['input']:
for output_address in time_listed_transaction[1]['output']:
graph_edges.append(input_address['addr'] + '_' +
str(block_time) + ' -> ' +
output_address['addr'] + '_' +
str(block_time) + ';\n')
for address in address_appear_times.keys():
address_appear_times[address].sort()
for n in range(len(address_appear_times[address]) - 1):
graph_edges.append(
address + '_' +
datetime.fromtimestamp(float(address_appear_times[address][n])
).strftime("%Y%m%d_%Hh%Mm%Ss") +
' -> ' + address + '_' + datetime.fromtimestamp(
float(address_appear_times[address][n + 1])).strftime(
"%Y%m%d_%Hh%Mm%Ss") + ';\n')
graph_edges = list(set(graph_edges))
for edge in graph_edges:
graph_data += edge
graph_data += '}'
with open(output_path + target_address + '_money_flow_graph.dot',
'w') as file:
file.write(graph_data)
#Make SVG File
(graph, ) = pydot.graph_from_dot_file(output_path + target_address +
'_money_flow_graph.dot')
graph.write_svg(output_path + target_address + '_money_flow_graph.svg')
indent=4,
separators=(',', ': ')))
if __name__ == '__main__':
parser = ArgumentParser(description='graphwiz to geojson.')
parser.add_argument('-p', '--poles', help='Poles file name')
parser.add_argument('-g', '--graphviz', help='Graphviz file name')
parser.add_argument('-o', '--output', help='Output file name')
args = parser.parse_args()
poles_file_name = args.poles or 'downtown-pole-export-edited.csv'
graphviz_file_name = args.graphviz or 'random-graphviz.txt'
output_file_name = args.output or 'feature-collection-export.json'
graph = graph_from_dot_file(graphviz_file_name)
pole_index = load_known_poles(poles_file_name)
features = {}
for edge in graph.get_edge_list():
source = normalize_edge_name(edge.get_source())
destination = normalize_edge_name(edge.get_destination())
found_source = source in pole_index.keys()
found_destination = destination in pole_index.keys()
if found_source and found_destination:
pole_index[source]['mounting'] = mounting_style(edge.get_source())
pole_index[destination]['mounting'] = mounting_style(
edge.get_destination())
features[source + destination] = create_feature(
pole_index, source, destination)
arrTestLines = f1.read().strip().split('\n')
f1.close()
print('begin {}'.format(fopResultItem))
for i in range(0, numTests):
arrTabs = arrTestLines[i].split('\t')
keyG = arrTabs[3] + arrTabs[4]
if keyG not in dictGraphVisit.keys():
dictGraphVisit[keyG] = 1
else:
continue
try:
fpDot = arrTabs[3] + '/' + arrTabs[4] + '_graph.dot'
fpPng = arrTabs[3] + '/' + arrTabs[4] + '_graph.png'
if os.path.exists(fpPng):
continue
(graphPydot, ) = pydot.graph_from_dot_file(fpDot)
graphPydot.write_png(fpPng)
except:
traceback.print_exc()
print('end {} {}'.format(i, arrTestLines[i]))
for i in range(len(arrTestLines) - 1 - numTests, len(arrTestLines)):
arrTabs = arrTestLines[i].split('\t')
keyG = arrTabs[3] + arrTabs[4]
if keyG not in dictGraphVisit.keys():
dictGraphVisit[keyG] = 1
else:
continue
try:
fpDot = arrTabs[3] + '/' + arrTabs[4] + '_graph.dot'
fpPng = arrTabs[3] + '/' + arrTabs[4] + '_graph.png'
if os.path.exists(fpPng):
dest='formats',
action='append',
type=str,
default=[],
help='The graphviz output formats that will be generated.')
parser.add_argument('-l',
'--add-graph-label',
dest='add_graph_label',
default=False,
action='store_true',
help='Whether to add a label to each generated graph')
if __name__ == '__main__':
args = parser.parse_args()
graph = pydot.graph_from_dot_file(args.filename)
base_filename = args.base_filename or os.path.splitext(args.filename)[0]
print(graph)
if isinstance(graph, list):
graph = graph[0]
assert isinstance(graph, pydot.Dot)
phased_graphviz = PhasedGraphviz(graph)
phased_graphviz.generate(base_filename,
generate_gif=args.generate_gif,
gif_delay=args.gif_delay,
formats=args.formats,
add_graph_label=args.add_graph_label)
import pydot
graph = pydot.graph_from_dot_file('w_nottear_loan.dot')
file = open("notear_loan.dot", 'r')
lines = file.readlines()
lines = [line.replace(' ', '') for line in lines]
lines = [line.replace('\n', '') for line in lines]
lines = [line.replace('\t', '') for line in lines]
with open('Final_t.dot', 'w') as f:
f.writelines(lines)
