test_model_input = {name: test[name] for name in feature_names}
# 4.Define Model,train,predict and evaluate
device = 'cpu'
use_cuda = True
if use_cuda and torch.cuda.is_available():
print('cuda ready...')
device = 'cuda:0'
model = FiBiNET(linear_feature_columns,
dnn_feature_columns,
task='binary',
device=device)
model.compile(
"adam",
"binary_crossentropy",
metrics=["binary_crossentropy", "auc"],
)
history = model.fit(train_model_input,
train[target].values,
batch_size=256,
epochs=10,
verbose=2,
validation_split=0.2)
pred_ans = model.predict(test_model_input, batch_size=256)
#print(pred_ans)
#print(" ")
#print(test[target].values)
print("")
print("test LogLoss", round(log_loss(test[target].values, pred_ans), 4))
print("test AUC", round(roc_auc_score(test[target].values, pred_ans), 4))
def train_recommend_movies(csv_file, DEVICE):
"""
Description:
Train recommend system on:
Model: "xDeepFM",
Target: "rating",
Input features: ["movie_id", "gender", "age"],
Save model to: "save_model/xDeepFM_MSE{}.h5"
Parameters:
csv_file: "path to *.csv"
DEVICE: "cuda:0"
"""
data = pd.read_csv(csv_file)
# sparse_features = ["movie_id", "user_id",
# "gender", "age", "occupation", "zip"]
sparse_features = ["movie_id", "gender", "age"]
movie_genres = [
'Action', 'Adventure', 'Animation', 'Childrens', 'Comedy', 'Crime',
'Documentary', 'Drama', 'Fantasy', 'Film_Noir', 'Horror', 'Musical',
'Mystery', 'Romance', 'Sci_Fi', 'Thriller', 'War', 'Western'
]
target = ['rating']
# 1.Label Encoding for sparse features,and do simple Transformation for dense features
for feat in sparse_features:
lbe = LabelEncoder()
data[feat] = lbe.fit_transform(data[feat])
sparse_features.extend(movie_genres)
# 2.count #unique features for each sparse field
fixlen_feature_columns = [
SparseFeat(feat, data[feat].nunique()) for feat in sparse_features
]
# 他自己的資料型態
# SparseFeat(name='movie_id', vocabulary_size=187, embedding_dim=4, use_hash=False, dtype='int32', embedding_name='movie_id', group_name='default_group')
linear_feature_columns = fixlen_feature_columns
dnn_feature_columns = fixlen_feature_columns
feature_names = get_feature_names(
linear_feature_columns + dnn_feature_columns
) # movie_id, user_id, gender, age, occupation, zip.
# 3.generate input data for model
train, test = train_test_split(data, test_size=0.2)
train_model_input = {name: train[name] for name in feature_names}
test_model_input = {
name: test[name]
for name in feature_names
} # dict of movie_id, user_id, gender, age, occupation, zip values
# 4.Define Model,train,predict and evaluate
# model = DeepFM(linear_feature_columns, dnn_feature_columns, task='regression', device=device)
model = FiBiNET(linear_feature_columns,
dnn_feature_columns,
task='regression',
device=DEVICE)
# model = xDeepFM(linear_feature_columns, dnn_feature_columns, task='regression', device=device)
model.compile(
"adam",
"mse",
metrics=['mse'],
)
history = model.fit(
train_model_input,
train[target].values,
batch_size=256,
epochs=10,
verbose=2,
validation_split=0.2,
)
pred_ans = model.predict(test_model_input, batch_size=256)
print("test MSE",
round(mean_squared_error(test[target].values, pred_ans), 4))
print("test MAE",
round(mean_absolute_error(test[target].values, pred_ans), 4))
# torch.save(model.state_dict(), './recommend_system/save_model/xDeepFM_MSE{}.h5' .format(round(mean_squared_error(test[target].values, pred_ans), 4)))
torch.save(
model.state_dict(),
'./recommend_system/save_model/FiBiNET_MSE{}.h5'.format(
round(mean_squared_error(test[target].values, pred_ans), 4)))
train, test = train_test_split(data, test_size=0.2)
train_model_input = {name: train[name] for name in feature_names}
test_model_input = {name: test[name] for name in feature_names}
# 4.Define Model,train,predict and evaluate
device = 'cpu'
use_cuda = True
if use_cuda and torch.cuda.is_available():
print('cuda ready...')
device = 'cuda:0'
model = FiBiNET(linear_feature_columns,
dnn_feature_columns,
task='regression',
device=device)
model.compile(
"adam",
"mse",
metrics=['mse'],
)
history = model.fit(train_model_input,
train[target].values,
batch_size=256,
epochs=87,
verbose=2,
validation_split=0.2)
pred_ans = model.predict(test_model_input, batch_size=256)
print("test MSE",
round(mean_squared_error(test[target].values, pred_ans), 4))