eval_data=eval_data,
metrics=[
"loss", "balanced_accuracy", "roc_auc", "pr_auc", "precision",
"recall", "map", "ndcg"
])
print("prediction: ", wd.predict(user=1, item=2333))
print("recommendation: ", wd.recommend_user(user=1, n_rec=7))
reset_state("DeepFM")
deepfm = DeepFM("ranking",
data_info,
embed_size=16,
n_epochs=2,
lr=1e-4,
lr_decay=False,
reg=None,
batch_size=2048,
num_neg=1,
use_bn=False,
dropout_rate=None,
hidden_units="128,64,32",
tf_sess_config=None)
deepfm.fit(train_data,
verbose=2,
shuffle=True,
eval_data=eval_data,
metrics=[
"loss", "balanced_accuracy", "roc_auc", "pr_auc",
"precision", "recall", "map", "ndcg"
])
print("prediction: ", deepfm.predict(user=1, item=2333))
train_data.build_negative_samples(data_info,
num_neg=1,
item_gen_mode="random",
seed=2020)
test_data.build_negative_samples(data_info,
num_neg=1,
item_gen_mode="random",
seed=2222)
deepfm = DeepFM("ranking",
data_info,
embed_size=16,
n_epochs=2,
lr=1e-4,
lr_decay=False,
reg=None,
batch_size=2048,
num_neg=1,
use_bn=False,
dropout_rate=None,
hidden_units="128,64,32",
tf_sess_config=None)
deepfm.fit(train_data,
verbose=2,
shuffle=True,
eval_data=test_data,
metrics=[
"loss", "balanced_accuracy", "roc_auc", "pr_auc",
"precision", "recall", "map", "ndcg"
],
eval_batch_size=8192,
sparse_col = ["sex", "occupation", "genre1", "genre2", "genre3"]
dense_col = ["age"]
user_col = ["sex", "age", "occupation"]
item_col = ["genre1", "genre2", "genre3"]
train_data, data_info = DatasetFeat.build_trainset(train, user_col, item_col,
sparse_col, dense_col,
shuffle=False)
test_data = DatasetFeat.build_testset(test, shuffle=False)
print(data_info)
train_data.build_negative_samples(data_info, num_neg=1,
item_gen_mode="random", seed=2020)
test_data.build_negative_samples(data_info, num_neg=1,
item_gen_mode="random", seed=2222)
deepfm = DeepFM("ranking", data_info, embed_size=16, n_epochs=2,
lr=1e-4, lr_decay=False, reg=None, batch_size=2048,
num_neg=1, use_bn=False, dropout_rate=None,
hidden_units="128,64,32", tf_sess_config=None)
deepfm.fit(train_data, verbose=2, shuffle=True, eval_data=test_data,
metrics=["loss", "balanced_accuracy", "roc_auc", "pr_auc",
"precision", "recall", "map", "ndcg"],
eval_batch_size=8192, k=10, sample_user_num=2048)
data_info.save(path="model_path")
deepfm.save(path="model_path", model_name="deepfm_model", manual=True,
inference_only=False)
# ===================== load and build new data ========================
print("\n", "=" * 50, " load and build new data ", "=" * 50)
tf.reset_default_graph()
data_info = DataInfo.load("model_path")