def main():
train = TitanicDataSet.get_train()
test = TitanicDataSet.get_test()
families = construct_family_components(train, test)
people = [mark_problems(p, f) for f in families for p in f.nodes]
synthesize('train', [p for p in people if p.survived is not None], train)
synthesize('test', [p for p in people if p.survived is None], test)
def main():
train = TitanicDataSet.get_train()
test = TitanicDataSet.get_test()
families = construct_family_components(train, test)
people = [mark_problems(p, f) for f in families for p in f.nodes]
synthesize('train', [p for p in people if p.survived is not None], train)
synthesize('test', [p for p in people if p.survived is None], test)
def plot_troubled_families():
"""Show the graphs that couldn't be broken down int families
"""
train = TitanicDataSet.get_train()
test = TitanicDataSet.get_test()
families = construct_family_components(train, test)
families = sorted(families, key=lambda f: len(f.nodes))
generate_graph('trouble.png', [f for f in families
if f.difficult_parent_child])
def plot_troubled_families():
"""Show the graphs that couldn't be broken down int families
"""
train = TitanicDataSet.get_train()
test = TitanicDataSet.get_test()
families = construct_family_components(train, test)
families = sorted(families, key=lambda f: len(f.nodes))
generate_graph('trouble.png',
[f for f in families if f.difficult_parent_child])
def plot_class(fignum, name, pclass, y_max):
def splice(ds):
return ds.splice(ds.pclass == pclass)
train = splice(TitanicDataSet.get_train())
test = splice(TitanicDataSet.get_test())
families = construct_family_components(train, test)
families = sorted(families, key=lambda f: len(f.nodes))
families = list(f for f in families if not f.difficult_parent_child)
frames = []
for f in families:
nuclear_families, extra_nodes, extra_edges = find_nuclear_families(f)
for nf in nuclear_families:
frames.append(FamilyFrame(nf.mother, nf.father, nf.children))
for e in extra_edges:
if e.definitive_spouse:
a,b = e.a, e.b
if a.a.sex == 0:
a,b = b,a
frames.append(CoupleFrame(a,b))
frames.sort(key=lambda f: (not isinstance(f,CoupleFrame), f.n_members))
for f in frames:
f.setup()
f.scale(1.1)
fp = FramePlacer(11, 0.5, [0.5, 0.2])
fp.place_frames(frames)
plt.figure(fignum)
plt.clf()
for m,c,ps in fp.collect_points():
plt.plot(ps[::, 0], ps[::, 1], linestyle='None', marker=m, color=c, ms=9)
lines = LineCollection(fp.collect_lines(),
colors='k',
linestyles='solid')
plt.gca().add_collection(lines)
plt.title(name + 'Class Families')
def label(label, **kwds):
plt.plot([-1,-1], [-1,-1], label=label, **kwds)
label('Female', marker='D', linestyle='None', markerfacecolor='white', color='k')
label('Male', marker='o', linestyle='None', markerfacecolor='white', color='k')
label('Survived', marker='s', linestyle='None', markerfacecolor=(0,1,0), markeredgecolor='white')
label('Died', marker='s', linestyle='None', markerfacecolor='r', markeredgecolor='white')
label('Unkown', marker='s', linestyle='None', markerfacecolor='k', markeredgecolor='white')
plt.legend(loc='upper left', numpoints=1, frameon=False, ncol=3)
plt.xlim(0, 12)
plt.ylim(-0.1, y_max)
plt.xticks([])
plt.yticks([])
plt.draw()
plt.show()
plt.savefig('%d_class_families.png' % (pclass+1,), bbox_inches='tight', pad_inches=0.1)
def main():
train = TitanicDataSet.get_train()
test = TitanicDataSet.get_test()
families = construct_family_components(train, test)
acc = []
i = 0
for c in families:
#if len(c.nodes) == 1:
# continue
nuclear_families, extra_nodes, extra_edges = find_nuclear_families(c)
c.tear_down()
acc.append(Component(extra_nodes, extra_edges))
if sum(len(c.nodes) for c in acc) > 40:
display_graph(i, acc)
i += 1
acc = []
if acc:
display_graph(i, acc)
def main():
train = TitanicDataSet.get_train()
test = TitanicDataSet.get_test()
families = construct_family_components(train, test)
acc = []
i = 0
for c in families:
#if len(c.nodes) == 1:
# continue
nuclear_families, extra_nodes, extra_edges = find_nuclear_families(c)
c.tear_down()
acc.append(Component(extra_nodes, extra_edges))
if sum(len(c.nodes) for c in acc) > 40:
display_graph(i, acc)
i += 1
acc = []
if acc:
display_graph(i, acc)
def main():
train = TitanicDataSet.get_train()
test = TitanicDataSet.get_test()
families = construct_family_components(train, test)
families = sorted(families, key=lambda f: len(f.nodes))
acc = []
i = 0
for c in families:
if len(c.nodes) == 1:
continue
#if not any(n.a.age == -1 for n in c.nodes):
# continue
#if not c.difficult_parent_child:
# continue
if sum(len(c.nodes) for c in acc) > MAX_FILE_NODES:
display_graph(i, acc)
i += 1
acc = []
acc.append(c)
if acc:
display_graph(i, acc)
def main():
train = TitanicDataSet.get_train()
test = TitanicDataSet.get_test()
families = construct_family_components(train, test)
families = sorted(families, key=lambda f: len(f.nodes))
acc = []
i = 0
for c in families:
if len(c.nodes) == 1:
continue
#if not any(n.a.age == -1 for n in c.nodes):
# continue
#if not c.difficult_parent_child:
# continue
if sum(len(c.nodes) for c in acc) > MAX_FILE_NODES:
display_graph(i, acc)
i += 1
acc = []
acc.append(c)
if acc:
display_graph(i, acc)
def main():
train_dataset = data.TitanicDataSet.load("data/csv/train.csv", True)
people = [mark_problems(p, f) for f in findfamilies.construct_family_components(train=train_dataset, test=None)
for p in f.nodes]
result = synthesize(people)
print(result[0])
def plot_class(fignum, name, pclass, y_max):
def splice(ds):
return ds.splice(ds.pclass == pclass)
train = splice(TitanicDataSet.get_train())
test = splice(TitanicDataSet.get_test())
families = construct_family_components(train, test)
families = sorted(families, key=lambda f: len(f.nodes))
families = list(f for f in families if not f.difficult_parent_child)
frames = []
for f in families:
nuclear_families, extra_nodes, extra_edges = find_nuclear_families(f)
for nf in nuclear_families:
frames.append(FamilyFrame(nf.mother, nf.father, nf.children))
for e in extra_edges:
if e.definitive_spouse:
a, b = e.a, e.b
if a.a.sex == 0:
a, b = b, a
frames.append(CoupleFrame(a, b))
frames.sort(key=lambda f: (not isinstance(f, CoupleFrame), f.n_members))
for f in frames:
f.setup()
f.scale(1.1)
fp = FramePlacer(11, 0.5, [0.5, 0.2])
fp.place_frames(frames)
plt.figure(fignum)
plt.clf()
for m, c, ps in fp.collect_points():
plt.plot(ps[::, 0],
ps[::, 1],
linestyle='None',
marker=m,
color=c,
ms=9)
lines = LineCollection(fp.collect_lines(), colors='k', linestyles='solid')
plt.gca().add_collection(lines)
plt.title(name + 'Class Families')
def label(label, **kwds):
plt.plot([-1, -1], [-1, -1], label=label, **kwds)
label('Female',
marker='D',
linestyle='None',
markerfacecolor='white',
color='k')
label('Male',
marker='o',
linestyle='None',
markerfacecolor='white',
color='k')
label('Survived',
marker='s',
linestyle='None',
markerfacecolor=(0, 1, 0),
markeredgecolor='white')
label('Died',
marker='s',
linestyle='None',
markerfacecolor='r',
markeredgecolor='white')
label('Unkown',
marker='s',
linestyle='None',
markerfacecolor='k',
markeredgecolor='white')
plt.legend(loc='upper left', numpoints=1, frameon=False, ncol=3)
plt.xlim(0, 12)
plt.ylim(-0.1, y_max)
plt.xticks([])
plt.yticks([])
plt.draw()
plt.show()
plt.savefig('%d_class_families.png' % (pclass + 1, ),
bbox_inches='tight',
pad_inches=0.1)
