def test_DCN(embedding_size, cross_num, hidden_size, sparse_feature_num):
model_name = "DCN"
sample_size = 64
feature_dim_dict = {"sparse": {}, 'dense': []}
for name, num in zip(["sparse", "dense"],
[sparse_feature_num, sparse_feature_num]):
if name == "sparse":
for i in range(num):
feature_dim_dict[name][name + '_' +
str(i)] = np.random.randint(1, 10)
else:
for i in range(num):
feature_dim_dict[name].append(name + '_' + str(i))
sparse_input = [
np.random.randint(0, dim, sample_size)
for dim in feature_dim_dict['sparse'].values()
]
dense_input = [
np.random.random(sample_size) for name in feature_dim_dict['dense']
]
y = np.random.randint(0, 2, sample_size)
x = sparse_input + dense_input
model = DCN(
feature_dim_dict,
embedding_size=embedding_size,
cross_num=cross_num,
hidden_size=hidden_size,
keep_prob=0.5,
)
check_model(model, model_name, x, y)
def test_DCN(embedding_size, cross_num, hidden_size, sparse_feature_num):
model_name = "DCN"
sample_size = 64
feature_dim_dict = {"sparse": {}, 'dense': []}
for name, num in zip(["sparse", "dense"],
[sparse_feature_num, sparse_feature_num]):
if name == "sparse":
for i in range(num):
feature_dim_dict[name][name + '_' +
str(i)] = np.random.randint(1, 10)
else:
for i in range(num):
feature_dim_dict[name].append(name + '_' + str(i))
sparse_input = [
np.random.randint(0, dim, sample_size)
for dim in feature_dim_dict['sparse'].values()
]
dense_input = [
np.random.random(sample_size) for name in feature_dim_dict['dense']
]
y = np.random.randint(0, 2, sample_size)
x = sparse_input + dense_input
model = DCN(
feature_dim_dict,
embedding_size=embedding_size,
cross_num=cross_num,
hidden_size=hidden_size,
keep_prob=0.5,
)
model.compile('adam',
'binary_crossentropy',
metrics=['binary_crossentropy'])
model.fit(x, y, batch_size=100, epochs=1, validation_split=0.5)
print(model_name + " test train valid pass!")
model.save_weights(model_name + '_weights.h5')
model.load_weights(model_name + '_weights.h5')
print(model_name + " test save load weight pass!")
save_model(model, model_name + '.h5')
model = load_model(model_name + '.h5', custom_objects)
print(model_name + " test save load model pass!")
print(model_name + " test pass!")
def test_DCN_2():
model_name = "DCN"
sample_size = SAMPLE_SIZE
x, y, feature_columns = get_test_data(sample_size, sparse_feature_num=3,
dense_feature_num=2)
model = DCN([], feature_columns, cross_num=1, dnn_hidden_units=(8,), dnn_dropout=0.5)
check_model(model, model_name, x, y)
def test_DCN(cross_num, hidden_size, sparse_feature_num, cross_parameterization):
model_name = "DCN"
sample_size = SAMPLE_SIZE
x, y, feature_columns = get_test_data(sample_size, sparse_feature_num=sparse_feature_num,
dense_feature_num=sparse_feature_num)
model = DCN(feature_columns, feature_columns, cross_num=cross_num, cross_parameterization=cross_parameterization,
dnn_hidden_units=hidden_size, dnn_dropout=0.5)
check_model(model, model_name, x, y)
def test_DCN(embedding_size, cross_num, hidden_size):
name = "DCN"
sample_size = 64
feature_dim_dict = {
'sparse': {
'sparse_1': 2,
'sparse_2': 5,
'sparse_3': 10
},
'dense': ['dense_1', 'dense_2', 'dense_3']
}
sparse_input = [
np.random.randint(0, dim, sample_size)
for dim in feature_dim_dict['sparse'].values()
]
dense_input = [
np.random.random(sample_size) for name in feature_dim_dict['dense']
]
y = np.random.randint(0, 2, sample_size)
x = sparse_input + dense_input
model = DCN(
feature_dim_dict,
embedding_size=embedding_size,
cross_num=cross_num,
hidden_size=hidden_size,
keep_prob=0.5,
)
model.compile('adam',
'binary_crossentropy',
metrics=['binary_crossentropy'])
model.fit(x, y, batch_size=100, epochs=1, validation_split=0.5)
print(name + " test train valid pass!")
model.save_weights(name + '_weights.h5')
model.load_weights(name + '_weights.h5')
print(name + " test save load weight pass!")
save_model(model, name + '.h5')
model = load_model(name + '.h5', custom_objects)
print(name + " test save load model pass!")
print(name + " test pass!")
def test_DCN(embedding_size, cross_num, hidden_size, sparse_feature_num):
model_name = "DCN"
sample_size = SAMPLE_SIZE
x, y, feature_columns = get_test_data(sample_size, sparse_feature_num,
sparse_feature_num)
model = DCN(feature_columns,
embedding_size=embedding_size,
cross_num=cross_num,
dnn_hidden_units=hidden_size,
dnn_dropout=0.5)
check_model(model, model_name, x, y)
def test_DCN(embedding_size, cross_num, hidden_size, sparse_feature_num):
model_name = "DCN"
sample_size = 64
x, y, feature_dim_dict = get_test_data(sample_size, sparse_feature_num,
sparse_feature_num)
model = DCN(
feature_dim_dict,
embedding_size=embedding_size,
cross_num=cross_num,
hidden_size=hidden_size,
keep_prob=0.5,
)
check_model(model, model_name, x, y)
def test_DCN_invalid(embedding_size=8, cross_num=0, hidden_size=()):
feature_dim_dict = {
'sparse': {
'sparse_1': 2,
'sparse_2': 5,
'sparse_3': 10
},
'dense': ['dense_1', 'dense_2', 'dense_3']
}
with pytest.raises(ValueError):
_ = DCN(
feature_dim_dict,
embedding_size=embedding_size,
cross_num=cross_num,
hidden_size=hidden_size,
keep_prob=0.5,
)
def test_DCN_invalid(embedding_size=8, cross_num=0, hidden_size=()):
feature_dim_dict = {
'sparse': [
SingleFeat('sparse_1', 2),
SingleFeat('sparse_2', 5),
SingleFeat('sparse_3', 10)
],
'dense': [
SingleFeat('dense_1', 1),
SingleFeat('dense_1', 1),
SingleFeat('dense_1', 1)
]
}
with pytest.raises(ValueError):
_ = DCN(
feature_dim_dict,
embedding_size=embedding_size,
cross_num=cross_num,
hidden_size=hidden_size,
keep_prob=0.5,
)
lbe = LabelEncoder()
data[feature] = lbe.fit_transform(data[feature])
# 计算每个特征中的 不同特征值的个数
fixlen_feature_columns = [SparseFeat(feature, data[feature].nunique()) for feature in sparse_features]
linear_feature_columns = fixlen_feature_columns
dnn_feature_columns = fixlen_feature_columns
feature_names = get_feature_names(linear_feature_columns + dnn_feature_columns)
print(fixlen_feature_columns)
print(feature_names)
# 将数据集切分成训练集和测试集
train, test = train_test_split(data, test_size=0.2)
train_model_input = {name:train[name].values for name in feature_names}
test_model_input = {name:test[name].values for name in feature_names}
# 使用DCN进行训练
#model = DCN(linear_feature_columns, dnn_feature_columns, task='regression')
model = DCN(linear_feature_columns, dnn_feature_columns, task='binary')
model.compile("adam", "binary_crossentropy", metrics=['binary_crossentropy'], )
history = model.fit(train_model_input, train[target].values, batch_size=256, epochs=1, verbose=True, validation_split=0.2, )
# 使用DCN进行预测
pred_ans = model.predict(test_model_input, batch_size=256)
# 输出RMSE或MSE
mse = round(mean_squared_error(test[target].values, pred_ans), 4)
rmse = mse ** 0.5
print("test RMSE", rmse)
# 输出LogLoss
from sklearn.metrics import log_loss
score = log_loss(test[target].values, pred_ans)
print("LogLoss", score)
)
if model_type == "WDL":
model = WDL(
linear_feature_columns,
dnn_feature_columns,
task="binary",
embedding_size=emb_dim,
dnn_hidden_units=[1024, 512, 256],
)
if model_type == "DCN":
model = DCN(
dnn_feature_columns,
task="binary",
embedding_size=emb_dim,
dnn_hidden_units=[1024, 1024],
cross_num=6,
)
if opt == "adagrad":
optimizer = Adagrad
elif opt == "adam":
optimizer = Adam
else:
raise ValueError("Invalid optimizer")
model.compile(optimizer(learning_rate),
"binary_crossentropy",
metrics=["binary_crossentropy"])
