def exp_titanic_id_static(print, pprint):
dataset = DatasetPackLoader().load_dataset("titanic")
dataset = dataset.set['train']
ret_dict = {}
n = 100
for i in range(n):
clf_pack = ClassifierPack()
dataset.shuffle()
train_set, valid_set = dataset.split((7, 3))
train_Xs, train_Ys = train_set.full_batch(['Xs', 'Ys'])
clf_pack.fit(train_Xs, train_Ys)
dataset.sort()
full_Xs, full_Ys = dataset.full_batch(['Xs', 'Ys'])
predict = clf_pack.predict(full_Xs)
for key in predict:
if key in ret_dict:
ret_dict[key] += predict[key] / float(n)
else:
ret_dict[key] = predict[key] / float(n)
import pandas as pd
df = pd.DataFrame()
for key in ret_dict:
df[key] = ret_dict[key]
for key in dataset.BATCH_KEYS:
if key in ['Xs', 'Ys']:
continue
print(key, type(key))
df[key] = dataset.full_batch([key])
df.to_csv('./exp_titianic_id_result.csv', )
def test_titanic(self):
class_ = self.class_
dataset = DatasetPackLoader().load_dataset("titanic")
dataset = dataset.train_set
Xs, Ys = dataset.full_batch(['Xs', 'Ys'])
sample_X = Xs[:2]
sample_Y = Ys[:2]
model = class_(dataset.input_shapes)
model.build()
model.train(Xs, Ys, epoch=1)
code = model.code(sample_X)
print("code {code}".format(code=code))
recon = model.recon(sample_X, sample_Y)
print("recon {recon}".format(recon=recon))
loss = model.metric(sample_X, sample_Y)
print("loss {:}".format(loss))
# generate(self, zs, Ys)
proba = model.proba(sample_X)
print("proba {}".format(proba))
predict = model.predict(sample_X)
print("predict {}".format(predict))
score = model.score(sample_X, sample_Y)
print("score {}".format(score))
path = model.save()
model = class_()
model.load(path)
print('model reloaded')
code = model.code(sample_X)
print("code {code}".format(code=code))
recon = model.recon(sample_X, sample_Y)
print("recon {recon}".format(recon=recon))
loss = model.metric(sample_X, sample_Y)
print("loss {:}".format(loss))
# generate(self, zs, Ys)
proba = model.proba(sample_X)
print("proba {}".format(proba))
predict = model.predict(sample_X)
print("predict {}".format(predict))
score = model.score(sample_X, sample_Y)
print("score {}".format(score))
def test_titanic(self):
class_ = DAE
dataset = DatasetPackLoader().load_dataset("titanic")
dataset = dataset.train_set
model = class_(dataset.input_shapes)
model.build()
Xs = dataset.full_batch(['Xs'])
model.train(Xs, epoch=1)
sample_X = Xs[:2]
code = model.code(sample_X)
print("code {code}".format(code=code))
recon = model.recon(sample_X)
print("recon {recon}".format(recon=recon))
loss = model.metric(Xs)
loss = np.mean(loss)
print("loss {:.4}".format(loss))
path = model.save()
model = class_()
model.load(path)
print('model reloaded')
sample_X = Xs[:2]
code = model.code(sample_X)
print("code {code}".format(code=code))
recon = model.recon(sample_X)
print("recon {recon}".format(recon=recon))
loss = model.metric(Xs)
loss = np.mean(loss)
print("loss {:.4}".format(loss))
def test_mnist(self):
class_ = self.class_
dataset = DatasetPackLoader().load_dataset("MNIST")
dataset = dataset.train_set
Xs, Ys = dataset.full_batch(['Xs', 'Ys'])
sample_X = Xs[:2]
sample_Y = Ys[:2]
model = class_(dataset.input_shapes)
model.build()
model.train(Xs, Ys, epoch=1)
code = model.code(sample_X, sample_Y)
print("code {code}".format(code=code))
recon = model.recon(sample_X, sample_Y)
print("recon {recon}".format(recon=recon))
loss = model.metric(sample_X, sample_Y)
loss = np.mean(loss)
print("loss {:.4}".format(loss))
path = model.save()
model = class_()
model.load(path)
print('model reloaded')
code = model.code(sample_X, sample_Y)
print("code {code}".format(code=code))
recon = model.recon(sample_X, sample_Y)
print("recon {recon}".format(recon=recon))
loss = model.metric(sample_X, sample_Y)
loss = np.mean(loss)
print("loss {:.4}".format(loss))