def mapConf2Model(name):
conf = d_name_conf[name]
model_name = name.split('_')[0]
#if model_name != 'lr' and model_name != 'fm' and model_name != 'DINN':
# conf['layer_sizes'] = [FIELD_SIZES, 10, 1]
if model_name in set(['lr', 'fm']):
conf['input_dim'] = INPUT_DIM
print 'conf', conf
if model_name == 'ffm':
return FFM(**conf)
elif model_name == 'fwfm':
conf['layer_sizes'] = [FIELD_SIZES, 10, 1]
return FwFM(**conf)
elif model_name == 'fwfm3':
conf['layer_sizes'] = [FIELD_SIZES, 10, 1]
return FwFM3(**conf)
elif model_name == 'fm':
return FM(**conf)
elif model_name == 'lr':
return LR(**conf)
elif model_name == 'fwfmoh':
return FwFM_LE(**conf)
elif model_name == 'MTLfwfm':
conf['index_lines'] = utils.index_lines
conf['num_lines'] = FIELD_SIZES[utils.index_lines]
conf['layer_sizes'] = [FIELD_SIZES, 10, 1]
return MultiTask_FwFM(**conf)
elif model_name == 'DINN':
conf['layer_sizes'] = [FIELD_SIZES, 10, 1]
return DINN(**conf)
def mapConf2Model(name):
conf = d_name_conf[name]
model_name = name.split('_')[0]
if model_name == 'ffm':
return FFM(**conf)
elif model_name == 'fwfm':
return FwFM(**conf)
elif model_name == 'fm':
return FM(**conf)
elif model_name == 'lr':
return LR(**conf)
def test(config_file,
meta_data_file,
id_map,
dataToken,
batch_data_dir,
max_doc_length=30,
model_name=None,
restore_path=None,
no_doc_index=False):
np.random.seed(RANDOM_SEED_NP)
data = DataDUELoader(meta_data_file=meta_data_file,
batch_data_dir=batch_data_dir,
id_map=id_map,
dataToken=dataToken,
max_doc_length=max_doc_length,
no_doc_index=no_doc_index)
model_spec = json_reader(config_file)
model = FM(feature_shape=(0 if no_doc_index else data.D) + data.U +
data.V + 1,
feature_dim=(0 if no_doc_index else 1) + 1 + max_doc_length,
label_dim=data.E,
model_spec=model_spec,
model_name=model_name)
model.initialization()
def performance(model_local, data_local):
preds = model_local.predict(data_generator=data_local)
labels = []
for data_batched in data_local.generate(
batch_size=model_spec["batch_size"], random_shuffle=False):
labels.append(data_batched["label"])
labels = np.concatenate(labels, axis=0)
# one-hot to index #
trues = np.argmax(labels, axis=-1)
perf = evaluate(preds=preds, trues=trues)
return perf
if restore_path is not None:
if not isinstance(restore_path, list):
restore_paths = [restore_path]
else:
restore_paths = restore_path
for restore_path in restore_paths:
model.restore(restore_path)
perf = performance(model_local=model, data_local=data)
print("ckpt_path: %s" % restore_path)
print("performance: %s" % str(perf))
else:
perf = performance(model_local=model, data_local=data)
print("random initialization")
print("performance: %s" % str(perf))
def train(config_file,
meta_data_file,
id_map,
dataToken,
batch_data_dir_train,
batch_data_dir_valid=None,
max_doc_length=30,
model_name=None,
restore_path=None,
no_doc_index=False):
np.random.seed(RANDOM_SEED_NP)
data_train = DataDUELoader(meta_data_file=meta_data_file,
batch_data_dir=batch_data_dir_train,
id_map=id_map,
dataToken=dataToken,
max_doc_length=max_doc_length,
no_doc_index=no_doc_index)
if batch_data_dir_valid is not None:
data_valid = DataDUELoader(meta_data_file=meta_data_file,
batch_data_dir=batch_data_dir_valid,
id_map=id_map,
dataToken=dataToken,
max_doc_length=max_doc_length,
no_doc_index=no_doc_index)
else:
data_valid = None
model_spec = json_reader(config_file)
model = FM(feature_shape=(0 if no_doc_index else data_train.D) +
data_train.U + data_train.V + 1,
feature_dim=(0 if no_doc_index else 1) + 1 + max_doc_length,
label_dim=data_train.E,
model_spec=model_spec,
model_name=model_name)
model.initialization()
if restore_path is not None:
model.restore(restore_path)
# train #
results = model.train(data_generator=data_train,
data_generator_valid=data_valid)
print("train_results: %s" % str(results))
best_epoch = read(directory="../summary/" + model.model_name,
main_indicator="epoch_losses_valid_00")[0]
print("best_epoch by validation loss: %d" % best_epoch)
'l2_weight': 0,
'random_seed': 0
}
model = LR(**lr_params)
elif algo == 'fm':
fm_params = {
'input_dim': input_dim,
'factor_order': 10,
'opt_algo': 'gd',
'learning_rate': 0.1,
'l2_w': 0,
'l2_v': 0,
}
model = FM(**fm_params)
elif algo == 'fnn':
fnn_params = {
'layer_sizes': [field_sizes, 10, 1],
'layer_acts': ['tanh', 'none'],
'drop_out': [0, 0],
'opt_algo': 'gd',
'learning_rate': 0.1,
'layer_l2': [0, 0],
'random_seed': 0
}
model = FNN(**fnn_params)
elif algo == 'ccpm':
ccpm_params = {
'layer_sizes': [field_sizes, 10, 5, 3],
lr = LR(**lr_params)
lr.fit(Xi_train, Xv_train, y_train, Xi_valid, Xv_valid, y_valid)
elif algo == 'fm':
fm_params = {
"feature_size": feature_size,
"field_size": field_size,
"embedding_size": 15,
"epoch": 20,
"batch_size": 1024,
"learning_rate": 0.001,
"optimizer_type": "adam",
"l2_w_reg": 0.01,
"l2_v_reg": 0.01,
"verbose": True
}
fm = FM(**fm_params)
fm.fit(Xi_train, Xv_train, y_train, Xi_valid, Xv_valid, y_valid)
elif algo == 'deepfm':
deepfm_params = {
"feature_size": feature_size,
"field_size": field_size,
"embedding_size": 15,
"deep_layers": [256, 128, 64],
"epoch": 20,
"batch_size": 1024,
"learning_rate": 0.001,
"optimizer_type": "adam",
"l2_reg": 0.01,
"dropout_deep": [0.5, 0.5, 0.5, 0.5],
"verbose": True
}
import pandas as pd
import numpy as np
from models import LR,FM,MLP,WideAndDeep,DeepFM
if __name__=='__main__':
np.random.seed(2019)
data_dir="../data/movie_lens_100k/"
train = pd.read_csv(data_dir+'ua.base', sep='\t', names=['user_id', 'movie_id', 'ratings', 'time'])
test = pd.read_csv(data_dir+'ua.test', sep='\t', names=['user_id', 'movie_id', 'ratings', 'time'])
data=pd.concat([train,test],axis=0)
y_train = train['ratings'].values.reshape(-1, 1) # 一列
y_test = test['ratings'].values.reshape(-1, 1)
features=['user_id','movie_id']
features_sizes=[data[f].nunique() for f in features]
print("DFM")
ls=[]
model=LR
for _ in range(10):
model=FM(features_sizes)
#model = LR(features_sizes)
#model=DeepFM(features_sizes,deep_layers=(10,10),k=10)
best_score=model.fit(train[features]-1,test[features]-1,y_train,y_test,lr=0.0005,N_EPOCH=150,batch_size=500,early_stopping_rounds=30)
#-1是因为ids要从0起.而数据中是从1起的
ls.append(best_score)
print(pd.Series(ls).mean(),pd.Series(ls).min())
print(str(ls))
data[c] = lbl.fit_transform(list(data[c]))
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(data[features],
data['plays'],
test_size=0.2,
random_state=42)
y_train = y_train.values.reshape((-1, 1))
y_test = y_test.values.reshape((-1, 1))
#model=LR(features_sizes,loss_type='rmse')#,hash_size=r)
#model=FM(features_sizes,k=24)
#model=MLP(features_sizes,deep_layers=(12,12),k=24) best(12,12) k=24
#model=FM(features_sizes,k=24,FM_ignore_interaction=[(0,2),(0,3),(0,4)])
model = FM(features_sizes,
k=24,
FM_ignore_interaction=[(0, 1), (0, 2), (0, 3), (0, 4)])
#model=DeepFM(features_sizes,deep_layers=(12,12),k=24)
#model = NFM(features_sizes, k=24)
print(model)
best_score = model.fit(X_train,
X_test,
y_train,
y_test,
lr=0.0005,
N_EPOCH=50,
batch_size=5000,
early_stopping_rounds=5) #0.0005->0.001(1e-3 bs=1000)
'''
ls=[]
Rounds=1
train_features=['msno','song_id']
features_sizes=[train_data[c].nunique() for c in train_features]
from sklearn.preprocessing import LabelEncoder
lbl = LabelEncoder()
enc = ColdStartEncoder()
for c in train_features:
train_data[c]=lbl.fit_transform(list(train_data[c]))
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(train_data[train_features], train_data['target'], test_size = 0.125, random_state = 42)
y_train=y_train.values.reshape((-1,1))
y_test=y_test.values.reshape((-1,1))
'''
#<Model>
#model=LR(features_sizes,loss_type='binary',metric_type='auc')
model=FM(features_sizes,k=8,loss_type='binary',metric_type='auc')
#model=FM(features_sizes,k=8,loss_type='binary',metric_type='auc',FM_ignore_interaction=[(0,2),(0,3),(0,4)]) #FMDE
#model=MLP(features_sizes,k=8,loss_type='binary',metric_type='auc',deep_layers=(32,8))
#model=NFM(features_sizes,k=8,loss_type='binary',metric_type='auc')
#model=WideAndDeep(features_sizes,k=8,loss_type='binary',metric_type='auc',deep_layers=(8,8))
#model=DeepFM(features_sizes,k=8,loss_type='binary',metric_type='auc',deep_layers=(8,8))
#model=AFM(features_sizes,k=8,loss_type='binary',metric_type='auc',attention_FM=8)
#model=DeepAFM(features_sizes,k=8,loss_type='binary',metric_type='auc',attention_FM=8,deep_layers=(8,8))
print(model)
#[BUG fix] 老版本一定要传入拷贝..wtf~! 06/27修补BUG 内部copy防止影响数据
best_score = model.fit(X_train[train_features], X_test[train_features], y_train, y_test, lr=0.0005, N_EPOCH=50, batch_size=4096,early_stopping_rounds=5)#0.0005->0.001(1e-3 bs=1000)
y_pred=model.predict(X_test)
y_pred=1./(1.+np.exp(-1.*y_pred))#sigmoid transform
from sklearn.metrics import roc_auc_score,log_loss
print("ROC-AUC score on valid set: %.4f" %roc_auc_score(y_test,y_pred))
#print(log_loss(y_test,y_pred))