def evaluate_model(this, out_name):
try:
os.mkdir(out_name)
except OSError as e:
print "[WARNING]: Directory {0} already exists".format(out_name)
try:
os.mkdir("ground_truth/{0}/".format(out_name))
except OSError as e:
print "[WARNING]: Directory ground_truth/{0} already exists".format(
out_name)
for path in this.test_names:
f_handler = this.dataset[path]
if not f_handler.is_malformed():
strokes = sorted([v for k, v in f_handler.traces.items()],
key=lambda x: x.id)
output = [[strokes[0]]]
for s1, s2 in [[strokes[i], strokes[i + 1]]
for i in range(len(strokes) - 1)]:
## GET FEATURES
s1f, s2f = preprocess_strokes([s1, s2])
shape_context = msscf(s1f, s2f)
geometric_features = stroke_symbol_pair_features(s1f, s2f)
features = extract_features_from_sample([s1f, s2f])
class_probs_join = this.classifier.model.predict_proba(
features)
features = extract_features_from_sample([s1f, s2f])
class_probs_sep = this.classifier.model.predict_proba(
features)
sample = []
sample.extend(shape_context)
sample.extend(geometric_features)
sample.extend(class_probs_join[0].T)
sample.extend(class_probs_sep[0].T)
to_join = this.model.predict(sample)
if to_join:
output[-1].append(s2)
else:
output.append([s2])
## Get predicted output
#traces = f_handler.traces
store_name, _ = os.path.splitext(os.path.basename(path))
with open("{0}/{1}.lg".format(out_name, store_name), 'w') as f:
for group in output:
data_array = [i.data for i in group]
ids = ", ".join([str(i.id) for i in group])
features = extract_features_from_sample(data_array)
prediction = this.classifier.predict(features)
f.write(str(s_object(prediction, ids)))
s_object.reset()
groups = f_handler.groups
with open(
"ground_truth/{0}/{1}.lg".format(out_name, store_name),
'w') as f:
for group in groups:
ids = ', '.join(str(i) for i in group.traces_id)
f.write(str(s_object(group.type, ids)))
def train(this):
print "TRAINING PARSER"
X = []
y = []
for sample_name in this.train_names:
f_handler = this.dataset[sample_name]
if not f_handler.is_malformed():
trace_map = {g.truth: g.traces for g in f_handler.groups}
for rel in f_handler.relationship_graph.relations:
group1_traces = trace_map[rel.parent]
group2_traces = trace_map[rel.child]
both_traces = []
both_traces.extend(group1_traces)
both_traces.extend(group2_traces)
proc_traces = preprocess_strokes(both_traces)
if proc_traces == None:
continue
group1_traces = proc_traces[:len(group1_traces)]
group2_traces = proc_traces[len(group1_traces):]
bbox1 = sum([np.array(calculate_bounding_box(i)) for i in \
group1_traces])/len(group1_traces)
bbox2 = sum([np.array(calculate_bounding_box(i)) for i in \
group2_traces])/len(group2_traces)
center1 = (bbox1[0] + bbox1[1]) / 2., (bbox1[2] +
bbox1[3]) / 2.
center2 = (bbox2[0] + bbox2[1]) / 2., (bbox2[2] +
bbox2[3]) / 2.
a_center = (center1[0] + center2[0]) / 2, (center1[1] +
center2[1]) / 2
combined1 = []
for stroke in group1_traces:
combined1.extend(stroke)
parent_shape_context = msscf(combined1, [],
center=a_center)
combined2 = []
for stroke in group2_traces:
combined2.extend(stroke)
child_shape_context = msscf(combined2, [], center=a_center)
geometric_features = stroke_symbol_pair_features(
combined1, combined2)
sample = []
sample.extend(parent_shape_context)
sample.extend(child_shape_context)
sample.extend(geometric_features)
X.append(sample)
y.append(parser.class_num_map[rel.type])
this.model.fit(X, y)
r, w = 0, 0
for sample, target in zip(X, y):
p = this.model.predict(sample)
if p == target:
r += 1
else:
w += 1
print "{0} Accuracy on Training Data".format(float(r) / float(r + w))
def evaluate_single(fname):
t, g, r = read_inkml(fname)
f_handler = ff_handler(t, g, r)
if not f_handler.is_malformed():
strokes = sorted([v for k, v in f_handler.traces.items()],
key=lambda x: x.id)
output = [[strokes[0]]]
for s1, s2 in [[strokes[i], strokes[i + 1]]
for i in range(len(strokes) - 1)]:
## GET FEATURES
s1f, s2f = preprocess_strokes([s1, s2])
shape_context = msscf(s1f, s2f)
geometric_features = stroke_symbol_pair_features(s1f, s2f)
features = extract_features_from_sample([s1f, s2f])
class_probs_join = this.classifier.model.predict_proba(
features)
features = extract_features_from_sample([s1f, s2f])
class_probs_sep = this.classifier.model.predict_proba(features)
sample = []
sample.extend(shape_context)
sample.extend(geometric_features)
sample.extend(class_probs_join[0].T)
sample.extend(class_probs_sep[0].T)
to_join = this.model.predict(sample)
if to_join:
output[-1].append(s2)
else:
output.append([s2])
groups = []
for group in output:
data_array = [i.data for i in group]
ids = ", ".join([str(i.id) for i in group])
features = extract_features_from_sample(data_array)
prediction = this.classifier.predict(features)
s_o = s_object(prediction, ids)
g_o = group(None, None, None, override=True)
g_o.id = s_o.get_truth()
g_o.type = s_o.type
g_o.truth = s_o.get_truth()
g_o.traces = group
groups.append(g_o)
return groups
def train(this):
X = []
y = []
for sample_name in this.train_names:
f_handler = this.dataset[sample_name]
if not f_handler.is_malformed():
strokes = sorted([v for k, v in f_handler.traces.items()],
key=lambda x: x.id)
groups = f_handler.groups
join_map = {}
for group in groups:
sorted_ids = sorted(group.traces_id)
join_map.update({
sorted_ids[i]: sorted_ids[i + 1]
for i in range(len(sorted_ids) - 1)
})
for s1, s2 in [[strokes[i], strokes[i + 1]]
for i in range(len(strokes) - 1)]:
to_join = join_map[
s1.id] == s2.id if s1.id in join_map else False
## GET FEATURES
s1f, s2f = preprocess_strokes([s1, s2])
shape_context = msscf(s1f, s2f)
geometric_features = stroke_symbol_pair_features(s1f, s2f)
features = extract_features_from_sample([s1f, s2f])
class_probs_join = this.classifier.model.predict_proba(
features)
features = extract_features_from_sample([s1f, s2f])
class_probs_sep = this.classifier.model.predict_proba(
features)
sample = []
sample.extend(shape_context)
sample.extend(geometric_features)
sample.extend(class_probs_join[0].T)
sample.extend(class_probs_sep[0].T)
X.append(sample)
y.append(1 if to_join else 0)
this.model.fit(X, y)
def create_relation_graph(this, groups):
G = nx.DiGraph()
G.add_node('base')
for g in groups:
G.add_node(g.truth)
G.add_edge('base', g.truth, weight=0)
group_trace_lens = [len(g.traces) for g in groups]
combined_strokes = []
for group in groups:
traces = [i.data for i in group.traces]
smooth = smooth_xy_points({'id': traces})
reposi = reposition_xy_points(smooth)['id']
combined_strokes.extend(reposi)
img = create_image_from_points(combined_strokes)
groups_data = []
group_trace_lens = group_trace_lens
idx = 0
f = 0
while idx < len(group_trace_lens):
s = f
f = s + group_trace_lens[idx]
groups_data.append(combined_strokes[s:f])
idx += 1
for group1, idx1 in zip(groups_data, range(len(groups_data))):
for group2, idx2 in zip(groups_data, range(len(groups_data))):
if idx1 != idx2:
los = False
for stroke1 in group1:
for stroke2 in group2:
for p1 in stroke1:
for p2 in stroke2:
los = has_los(p1, p2, img)
if los:
group1_traces = group1
group2_traces = group2
both_traces = []
both_traces.extend(group1_traces)
both_traces.extend(group2_traces)
proc_traces = preprocess_strokes(
both_traces, raw=True)
if proc_traces == None:
continue
group1_traces = proc_traces[:len(
group1_traces)]
group2_traces = proc_traces[
len(group1_traces):]
bbox1 = sum([np.array(calculate_bounding_box(i)) for i in \
group1_traces])/len(group1_traces)
bbox2 = sum([np.array(calculate_bounding_box(i)) for i in \
group2_traces])/len(group2_traces)
center1 = (bbox1[0] + bbox1[1]) / 2., (
bbox1[2] + bbox1[3]) / 2.
center2 = (bbox2[0] + bbox2[1]) / 2., (
bbox2[2] + bbox2[3]) / 2.
a_center = (center1[0] + center2[0]
) / 2, (center1[1] +
center2[1]) / 2
combined1 = []
for stroke in group1_traces:
combined1.extend(stroke)
parent_shape_context = msscf(
combined1, [], center=a_center)
combined2 = []
for stroke in group2_traces:
combined2.extend(stroke)
child_shape_context = msscf(
combined2, [], center=a_center)
geometric_features = stroke_symbol_pair_features(
combined1, combined2)
sample = []
sample.extend(parent_shape_context)
sample.extend(child_shape_context)
sample.extend(geometric_features)
ws = this.model.predict_proba(
sample)[0]
weight,label = max( \
[(w,l) for (w,l) in sorted(zip(ws, parser.names_map))])
G.add_edge(groups[idx1].truth, groups[idx2].truth, \
weight=weight, label=label)
break
if los:
break
if los:
break
if los:
break
#print "GRAPH"
#print G.nodes()
#for u,v in G.edges():
# print u,v,G.get_edge_data(u,v)
#nx.draw_networkx(G, arrows=True, with_labels=True, node_size=600)
#plt.show()
edmonds = nx.algorithms.tree.Edmonds(G)
g = edmonds.find_optimum(kind='max')
for u, v in g.edges():
g[u][v]['label'] = G[u][v]['label']
#nx.draw_networkx(g, arrows=True, with_labels=True, node_size=600)
#plt.show()
#print "GRAPH AFTER EDMONDS"
#print g.nodes()
#for u,v in g.edges():
# print u,v,G.get_edge_data(u,v)
return g