def _run_base_model_dfm(dfTrain, dfTest, folds, dfm_params):
fd = FeatureDictionary(dfTrain=dfTrain,
dfTest=dfTest,
numeric_cols=config.NUMERIC_COLS,
ignore_cols=config.IGNORE_COLS)
data_parser = DataParser(feat_dict=fd)
Xi_train, Xv_train, y_train = data_parser.parse(df=dfTrain, has_label=True)
Xi_test, Xv_test, ids_test = data_parser.parse(df=dfTest)
dfm_params["feature_size"] = fd.feat_dim
dfm_params["field_size"] = len(Xi_train[0])
y_train_meta = np.zeros((dfTrain.shape[0], 1), dtype=float)
y_test_meta = np.zeros((dfTest.shape[0], 1), dtype=float)
_get = lambda x, l: [x[i] for i in l]
gini_results_cv = np.zeros(len(folds), dtype=float)
gini_results_epoch_train = np.zeros((len(folds), dfm_params["epoch"]),
dtype=float)
gini_results_epoch_valid = np.zeros((len(folds), dfm_params["epoch"]),
dtype=float)
for i, (train_idx, valid_idx) in enumerate(folds):
Xi_train_, Xv_train_, y_train_ = _get(Xi_train, train_idx), _get(
Xv_train, train_idx), _get(y_train, train_idx)
Xi_valid_, Xv_valid_, y_valid_ = _get(Xi_train, valid_idx), _get(
Xv_train, valid_idx), _get(y_train, valid_idx)
dfm = DeepFM(**dfm_params)
dfm.fit(Xi_train_, Xv_train_, y_train_, Xi_valid_, Xv_valid_, y_valid_)
y_train_meta[valid_idx, 0] = dfm.predict(Xi_valid_, Xv_valid_)
y_test_meta[:, 0] += dfm.predict(Xi_test, Xv_test)
gini_results_cv[i] = gini_norm(y_valid_, y_train_meta[valid_idx])
gini_results_epoch_train[i] = dfm.train_result
gini_results_epoch_valid[i] = dfm.valid_result
y_test_meta /= float(len(folds))
# save result
if dfm_params["module_name"] == "DeepFM":
if dfm_params["use_fm"] and dfm_params["use_deep"]:
clf_str = "DeepFM"
elif dfm_params["use_fm"]:
clf_str = "FM"
elif dfm_params["use_deep"]:
clf_str = "DNN"
elif dfm_params["module_name"] == "LR":
clf_str = "LR"
elif dfm_params["module_name"] == "WideDeep":
clf_str = "WideDeep"
print("%s: %.5f (%.5f)" %
(clf_str, gini_results_cv.mean(), gini_results_cv.std()))
filename = "%s_Mean%.5f_Std%.5f.csv" % (clf_str, gini_results_cv.mean(),
gini_results_cv.std())
_make_submission(ids_test, y_test_meta, filename)
_plot_fig(gini_results_epoch_train, gini_results_epoch_valid, clf_str)
return y_train_meta, y_test_meta
def eval_on_dev(split_vector_data):
e_b_s = len(dev_data) / graph_hyper_params['batch_size']
auc_true, auc_pre = [], []
# auc = []
for index in tqdm(range(e_b_s)):
start = index * graph_hyper_params['batch_size']
end = (index + 1) * graph_hyper_params['batch_size'] if (
index + 1) * graph_hyper_params['batch_size'] < len(
dev_data) else len(dev_data)
b_dev_data = dev_data[start:end]
fed_dict = get_fed_dict(b_dev_data, split_vector_data)
pred_value = sess.run([pred_val], feed_dict=fed_dict)
pre_real_val = np.array(pred_value).reshape((-1))
auc_true = auc_true + list(b_dev_data['label'].values)
auc_pre = auc_pre + pre_real_val.tolist()
# auc.append()
# auc_pre = np.array(auc_pre)
# auc_pre = np.exp(auc_pre) / np.exp(auc_pre).sum()
# print auc_true
# print auc_pre
fpr, tpr, thresholds = metrics.roc_curve(auc_true,
auc_pre,
pos_label=1)
# >> > metrics.auc(fpr, tpr)
return metrics.auc(fpr, tpr), gini_norm(auc_true, auc_pre)
def eval_on_dev(split_vector_data):
e_b_s = len(dev_data) / graph_hyper_params['batch_size']
auc_true, auc_pre = [], []
# auc = []
for index in tqdm(range(e_b_s)):
start = index * graph_hyper_params['batch_size']
end = (index + 1) * graph_hyper_params['batch_size'] if (index + 1) * graph_hyper_params['batch_size'] < len(dev_data) else len(dev_data)
b_dev_data = dev_data[start:end]
fed_dict = get_fed_dict(b_dev_data, split_vector_data, feature_conf_dict)
pred_value, pre_pred_value, final_vec, uu, vv = sess.run([pred_val, network_params[0], network_params[1], network_params[2], network_params[3]], feed_dict=fed_dict)
pre_real_val = np.array(pred_value).reshape((-1))
auc_true = auc_true + list(b_dev_data['label'].values)
auc_pre = auc_pre + pre_real_val.tolist()
if True in np.isnan(pre_real_val):
print 'contain nan: ', np.array(pre_pred_value).reshape((-1))
print np.array(final_vec).reshape((-1))
print np.array(uu).reshape((-1))
print np.array(vv).reshape((-1))
# auc.append()
# auc_pre = np.array(auc_pre)
# auc_pre = np.exp(auc_pre) / np.exp(auc_pre).sum()
# print auc_true
# print auc_pre
fpr, tpr, thresholds = metrics.roc_curve(auc_true, auc_pre, pos_label=1)
auc_v, gni = metrics.auc(fpr, tpr), gini_norm(auc_true, auc_pre)
auc_pre_2 = np.array(auc_pre)
auc_pre_2.sort()
print('dev_pre_top2=%.4f %.4f min2=%.4f %.4f' %
(auc_pre_2.tolist()[-1], auc_pre_2.tolist()[-2], auc_pre_2.tolist()[0], auc_pre_2.tolist()[1]))
return auc_v, gni
def val(model, dataloader):
"""
计算模型在验证集上的信息
"""
model.eval() ########固定
total = 0
loss_val = 0
val_iteration = 0
y_true = []
y_pre = []
for i, (Xi_batch, Xv_batch, y_batch) in enumerate(dataloader):
Xi_batch = Xi_batch.to(device)
Xv_batch = Xv_batch.to(device)
y_batch = y_batch.to(device)
outputs = model(Xi_batch, Xv_batch)
loss = criterion(outputs, y_batch)
# _, predicted = torch.max(outputs.data, 1)
y_true.append(y_batch.data.cpu().numpy())
prob = F.sigmoid(outputs)
y_pre.append(prob.data.cpu().numpy())
loss_val += loss.item()
val_iteration += 1
loss_val /= val_iteration
y_true = np.concatenate(y_true, axis=0)
y_pre = np.concatenate(y_pre, axis=0)
gini_val = gini_norm(y_true, y_pre)
model.train() ####重启
return loss_val, gini_val
def _run_base_model_dfm(dfTrain, dfTest, folds, dfm_params,save_path:str,past_epoch:int=0):
fd = FeatureDictionary(dfTrain=dfTrain, dfTest=dfTest,
numeric_cols=config.NUMERIC_COLS,
ignore_cols=config.IGNORE_COLS)
data_parser = DataParser(feat_dict=fd)
Xi_train, Xv_train, y_train = data_parser.parse(df=dfTrain, has_label=True)
Xi_test, Xv_test, ids_test = data_parser.parse(df=dfTest)
dfm_params["feature_size"] = fd.feat_dim
dfm_params["field_size"] = len(Xi_train[0])
y_train_meta = np.zeros((dfTrain.shape[0], 1), dtype=float)
y_test_meta = np.zeros((dfTest.shape[0], 1), dtype=float)
_get = lambda x, l: [x[i] for i in l]
gini_results_cv = np.zeros(len(folds), dtype=float)
# gini_results_epoch_train = np.zeros((len(folds), dfm_params["epoch"]), dtype=float)
# gini_results_epoch_valid = np.zeros((len(folds), dfm_params["epoch"]), dtype=float)
dfm = DeepFM(**dfm_params)
if past_epoch!=0 :dfm.saver.restore(dfm.sess, save_path + '-'+str(past_epoch))
for i, (train_idx, valid_idx) in enumerate(folds):
Xi_train_, Xv_train_, y_train_ = _get(Xi_train, train_idx), _get(Xv_train, train_idx), _get(y_train, train_idx)
Xi_valid_, Xv_valid_, y_valid_ = _get(Xi_train, valid_idx), _get(Xv_train, valid_idx), _get(y_train, valid_idx)
# print(y_train)
# print(y_train_)
# print(dfm.predict(Xi_train_, Xv_train_))
# continue
dfm.fit(Xi_train_, Xv_train_, y_train_, Xi_valid_, Xv_valid_, y_valid_)
y_train_meta[valid_idx,0] = dfm.predict(Xi_valid_, Xv_valid_)
y_test_meta[:,0] += dfm.predict(Xi_test, Xv_test)
gini_results_cv[i] = gini_norm(y_valid_, y_train_meta[valid_idx])
# gini_results_epoch_train[i] = dfm.train_result
# gini_results_epoch_valid[i] = dfm.valid_result
# print('saving')
dfm.saver.save(dfm.sess, save_path, global_step=past_epoch+dfm_params["epoch"]*(i+1))
y_test_meta /= float(len(folds))
# save result
if dfm_params["use_fm"] and dfm_params["use_deep"]:
clf_str = "DeepFM"
elif dfm_params["use_fm"]:
clf_str = "FM"
elif dfm_params["use_deep"]:
clf_str = "DNN"
print("%s: %.5f (%.5f)"%(clf_str, gini_results_cv.mean(), gini_results_cv.std()))
filename = "%s_Mean%.5f_Std%.5f.csv"%(clf_str, gini_results_cv.mean(), gini_results_cv.std())
_make_submission(ids_test, y_test_meta, filename)
gini_results_epoch_train = np.zeros((1, dfm_params["epoch"]*len(folds)), dtype=float)
gini_results_epoch_valid = np.zeros((1, dfm_params["epoch"]*len(folds)), dtype=float)
gini_results_epoch_train[0]=dfm.train_result
gini_results_epoch_valid[0]=dfm.valid_result
_plot_fig(gini_results_epoch_train, gini_results_epoch_valid, clf_str)
return y_train_meta, y_test_meta,dfm
def _run_base_model_dfm(dfTrain, dfTest, folds, dfm_params):
fd = FeatureDictionary(dfTrain=dfTrain, dfTest=dfTest,
numeric_cols=config.NUMERIC_COLS,
ignore_cols=config.IGNORE_COLS)
data_parser = DataParser(feat_dict=fd)
Xi_train, Xv_train, y_train = data_parser.parse(df=dfTrain, has_label=True)
Xi_test, Xv_test, ids_test = data_parser.parse(df=dfTest)
dfm_params["feature_size"] = fd.feat_dim
dfm_params["field_size"] = len(Xi_train[0])
y_train_meta = np.zeros((dfTrain.shape[0], 1), dtype=float)
y_test_meta = np.zeros((dfTest.shape[0], 1), dtype=float)
_get = lambda x, l: [x[i] for i in l]
gini_results_cv = np.zeros(len(folds), dtype=float)
gini_results_epoch_train = np.zeros((len(folds), dfm_params["epoch"]), dtype=float)
gini_results_epoch_valid = np.zeros((len(folds), dfm_params["epoch"]), dtype=float)
for i, (train_idx, valid_idx) in enumerate(folds):
Xi_train_, Xv_train_, y_train_ = _get(Xi_train, train_idx), _get(Xv_train, train_idx), _get(y_train, train_idx)
Xi_valid_, Xv_valid_, y_valid_ = _get(Xi_train, valid_idx), _get(Xv_train, valid_idx), _get(y_train, valid_idx)
# todo dfm_params={'use_fm': True, 'use_deep': True, 'embedding_size': 8, 'dropout_fm': [1.0, 1.0], 'deep_layers': [32, 32],
# 'dropout_deep': [0.5, 0.5, 0.5], 'deep_layers_activation': <function relu at 0x7fe4917da950>, 'epoch': 30, 'batch_size': 1024,
# 'learning_rate': 0.001, 'optimizer_type': 'adam', 'batch_norm': 1, 'batch_norm_decay': 0.995, 'l2_reg': 0.01, 'verbose': True,
# 'eval_metric': <function gini_norm at 0x7fe495b06048>, 'random_seed': 2017, 'feature_size': 259, 'field_size': 39}
# print(f"dfm_params={dfm_params}")
dfm = DeepFM(**dfm_params)
dfm.fit(Xi_train_, Xv_train_, y_train_, Xi_valid_, Xv_valid_, y_valid_)
y_train_meta[valid_idx,0] = dfm.predict(Xi_valid_, Xv_valid_)
y_test_meta[:,0] += dfm.predict(Xi_test, Xv_test)
gini_results_cv[i] = gini_norm(y_valid_, y_train_meta[valid_idx])
# todo 所谓的 train_result 是训练集的gini系数
gini_results_epoch_train[i] = dfm.train_result
gini_results_epoch_valid[i] = dfm.valid_result
# todo 上面用了5折交叉，y_test_meta是各折交叉的加和，这里相当于5折交叉取平均
y_test_meta /= float(len(folds))
# save result
if dfm_params["use_fm"] and dfm_params["use_deep"]:
clf_str = "DeepFM"
elif dfm_params["use_fm"]:
clf_str = "FM"
elif dfm_params["use_deep"]:
clf_str = "DNN"
print("%s: %.5f (%.5f)"%(clf_str, gini_results_cv.mean(), gini_results_cv.std()))
filename = "%s_Mean%.5f_Std%.5f.csv"%(clf_str, gini_results_cv.mean(), gini_results_cv.std())
_make_submission(ids_test, y_test_meta, filename)
_plot_fig(gini_results_epoch_train, gini_results_epoch_valid, clf_str)
return y_train_meta, y_test_meta
def run_base_model_dfm(dfTrain,dfTest,folds,dfm_params):
fd = FeatureDictionary(dfTrain=dfTrain,
dfTest=dfTest,
numeric_cols=config.NUMERIC_COLS,
ignore_cols = config.IGNORE_COLS)
data_parser = DataParser(feat_dict= fd)
# Xi_train ：列的序号
# Xv_train ：列的对应的值
Xi_train,Xv_train,y_train = data_parser.parse(df=dfTrain,has_label=True)
Xi_test,Xv_test,ids_test = data_parser.parse(df=dfTest)
print(dfTrain.dtypes)
dfm_params['feature_size'] = fd.feat_dim
dfm_params['field_size'] = len(Xi_train[0])
y_train_meta = np.zeros((dfTrain.shape[0],1),dtype=float)
y_test_meta = np.zeros((dfTest.shape[0],1),dtype=float)
_get = lambda x,l:[x[i] for i in l]
gini_results_cv = np.zeros(len(folds),dtype=float)
gini_results_epoch_train = np.zeros((len(folds),dfm_params['epoch']),dtype=float)
gini_results_epoch_valid = np.zeros((len(folds),dfm_params['epoch']),dtype=float)
for i, (train_idx, valid_idx) in enumerate(folds):
Xi_train_, Xv_train_, y_train_ = _get(Xi_train, train_idx), _get(Xv_train, train_idx), _get(y_train, train_idx)
Xi_valid_, Xv_valid_, y_valid_ = _get(Xi_train, valid_idx), _get(Xv_train, valid_idx), _get(y_train, valid_idx)
dfm = DeepFM(**dfm_params)
dfm.fit(Xi_train_, Xv_train_, y_train_, Xi_valid_, Xv_valid_, y_valid_)
y_train_meta[valid_idx,0] = dfm.predict(Xi_valid_, Xv_valid_)
y_test_meta[:,0] += dfm.predict(Xi_test, Xv_test)
gini_results_cv[i] = gini_norm(y_valid_, y_train_meta[valid_idx])
gini_results_epoch_train[i] = dfm.train_result
gini_results_epoch_valid[i] = dfm.valid_result
y_test_meta /= float(len(folds))
# save result
if dfm_params["use_fm"] and dfm_params["use_deep"]:
clf_str = "DeepFM"
elif dfm_params["use_fm"]:
clf_str = "FM"
elif dfm_params["use_deep"]:
clf_str = "DNN"
print("%s: %.5f (%.5f)"%(clf_str, gini_results_cv.mean(), gini_results_cv.std()))
filename = "%s_Mean%.5f_Std%.5f.csv"%(clf_str, gini_results_cv.mean(), gini_results_cv.std())
_make_submission(ids_test, y_test_meta, filename)
_plot_fig(gini_results_epoch_train, gini_results_epoch_valid, clf_str)
return y_train_meta, y_test_meta
def _run_base_model_dfm(Xi_train, Xv_train, y_train, Xi_test, Xv_test,
ids_test, cate_cnt, folds, dfm_params):
dfm_params["cate_feature_size"] = cate_cnt
dfm_params["cate_field_size"] = len(Xi_train[0])
dfm_params["num_field_size"] = len(Xv_train[0])
y_train_meta = np.zeros((Xi_train.shape[0], 1), dtype=float)
y_test_meta = np.zeros((Xi_test.shape[0], 1), dtype=float)
_get = lambda x, l: [x[i] for i in l]
gini_results_cv = np.zeros(len(folds), dtype=float)
gini_results_epoch_train = np.zeros((len(folds), dfm_params["epoch"]),
dtype=float)
gini_results_epoch_valid = np.zeros((len(folds), dfm_params["epoch"]),
dtype=float)
for i, (train_idx, valid_idx) in enumerate(folds):
Xi_train_, Xv_train_, y_train_ = _get(Xi_train, train_idx), _get(
Xv_train, train_idx), _get(y_train, train_idx)
Xi_valid_, Xv_valid_, y_valid_ = _get(Xi_train, valid_idx), _get(
Xv_train, valid_idx), _get(y_train, valid_idx)
dfm = DCN(**dfm_params)
dfm.fit(Xi_train_, Xv_train_, y_train_, Xi_valid_, Xv_valid_, y_valid_)
y_train_meta[valid_idx, 0] = dfm.predict(Xi_valid_, Xv_valid_)
y_test_meta[:, 0] += dfm.predict(Xi_test, Xv_test)
gini_results_cv[i] = gini_norm(y_valid_, y_train_meta[valid_idx])
gini_results_epoch_train[i] = dfm.train_result
gini_results_epoch_valid[i] = dfm.valid_result
y_test_meta /= float(len(folds))
# save result
if dfm_params["use_cross"] and dfm_params["use_deep"]:
clf_str = "DeepAndCross"
elif dfm_params["use_cross"]:
clf_str = "CROSS"
elif dfm_params["use_deep"]:
clf_str = "DNN"
print("%s: %.5f (%.5f)" %
(clf_str, gini_results_cv.mean(), gini_results_cv.std()))
filename = "%s_Mean%.5f_Std%.5f.csv" % (clf_str, gini_results_cv.mean(),
gini_results_cv.std())
_make_submission(ids_test, y_test_meta, filename)
_plot_fig(gini_results_epoch_train, gini_results_epoch_valid, clf_str)
return y_train_meta, y_test_meta
def _run_base_model_dfm(dfTrain, dfTest, folds, dfm_params):
#获取dict
fd = FeatureDictionary(dfTrain=dfTrain, dfTest=dfTest,#训练集和测试集
numeric_cols=config.NUMERIC_COLS,#num类列
ignore_cols=config.IGNORE_COLS)#ignore特征，dfTrain和dfTest没有过滤掉
data_parser = DataParser(feat_dict=fd)#data_parser对象
Xi_train, Xv_train, y_train = data_parser.parse(df=dfTrain, has_label=True)调用parse方法获取处理后的数据
Xi_test, Xv_test, ids_test = data_parser.parse(df=dfTest)
dfm_params["feature_size"] = fd.feat_dim#处理之后的特征个数，即考虑了one-hot之后
dfm_params["field_size"] = len(Xi_train[0])#field个数
y_train_meta = np.zeros((dfTrain.shape[0], 1), dtype=float)
y_test_meta = np.zeros((dfTest.shape[0], 1), dtype=float)
_get = lambda x, l: [x[i] for i in l]
gini_results_cv = np.zeros(len(folds), dtype=float)
gini_results_epoch_train = np.zeros((len(folds), dfm_params["epoch"]), dtype=float)
gini_results_epoch_valid = np.zeros((len(folds), dfm_params["epoch"]), dtype=float)
for i, (train_idx, valid_idx) in enumerate(folds):#应该是划分k份
Xi_train_, Xv_train_, y_train_ = _get(Xi_train, train_idx), _get(Xv_train, train_idx), _get(y_train, train_idx)
Xi_valid_, Xv_valid_, y_valid_ = _get(Xi_train, valid_idx), _get(Xv_train, valid_idx), _get(y_train, valid_idx)
dfm = DeepFM(**dfm_params)
dfm.fit(Xi_train_, Xv_train_, y_train_, Xi_valid_, Xv_valid_, y_valid_)
y_train_meta[valid_idx,0] = dfm.predict(Xi_valid_, Xv_valid_)#k次折交
y_test_meta[:,0] += dfm.predict(Xi_test, Xv_test)#每次训练都预测一次，然后把预测结果累加取来
gini_results_cv[i] = gini_norm(y_valid_, y_train_meta[valid_idx])
gini_results_epoch_train[i] = dfm.train_result
gini_results_epoch_valid[i] = dfm.valid_result
y_test_meta /= float(len(folds))#在测试集上的累加结果求平均
# save result
if dfm_params["use_fm"] and dfm_params["use_deep"]:#deepFM
clf_str = "DeepFM"
elif dfm_params["use_fm"]:#FM
clf_str = "FM"
elif dfm_params["use_deep"]:#DNN
clf_str = "DNN"
print("%s: %.5f (%.5f)"%(clf_str, gini_results_cv.mean(), gini_results_cv.std()))
filename = "%s_Mean%.5f_Std%.5f.csv"%(clf_str, gini_results_cv.mean(), gini_results_cv.std())
_make_submission(ids_test, y_test_meta, filename)
_plot_fig(gini_results_epoch_train, gini_results_epoch_valid, clf_str)
return y_train_meta, y_test_meta#返回验证的预测和测试集的预测
def run_base_model_dfm(dfTrain, dfTest, folds, dfm_params):
'''
对模型的运行部分， 可以往下看发现，这部分 同时可以用于 设置使用FM 、Deep 、DeepFM这三种不同的模型
'''
# 别忽视了 FeatureDictionary 这里面有非常多的信息包装 转换的。 这里 解析 和字典包装真的是有点不明白，太复杂了，v是怎么获取使用的
fd = FeatureDictionary(dfTrain=dfTrain,
dfTest=dfTest,
numeric_cols=config.NUMERIC_COLS,
ignore_cols=config.IGNORE_COLS)
# 在解析数据中，逐行处理每一条数据，dfi 记录了当前的特征在总的输入的特征中的索引。dfv 中记录的是具体的值，
# 如果是 numerical 特征，存的是原始的值，如果是 categories 类型的，就存放 1。这个相当于进行了 one-hot 编码，
# 在 dfi 存储了特征所在的索引。输入到网络中的特征的长度是 ( numerical 特征的个数 +categories 特征 one-hot 编码的长度 )。
# 最终，Xi 和 Xv 是一个二维的 list，里面的每一个 list 是一行数据，Xi 存放的是特征所在的索引，Xv 存放的是具体的特征值。
data_parser = DataParser(feat_dict=fd)
# Xi_train ：列的序号
# Xv_train ：列的对应的值
# 解析数据 Xi_train 存放的是特征对应的索引 Xv_train 存放的是特征的具体的值
Xi_train, Xv_train, y_train = data_parser.parse(df=dfTrain, has_label=True)
Xi_test, Xv_test, ids_test = data_parser.parse(df=dfTest)
#这里面是二维的， 大列表是 每个样本，小列表表示具体对应feature_index下的value的长度 。 小列表长度应该不是统一的，因为针对one-hot，只显示为1的
print('Xi_train:', Xi_train) #存储了对应标签索引
print('Xv_train:', Xv_train) #存储了真实值
print('y_train:', y_train)
print('Xi_test:', Xi_test)
print('Xv_test:', Xv_test)
print('Xi_train shape:', len(Xi_train)) # 存储了对应标签索引
print('Xv_train shape:', len(Xv_train)) # 存储了真实值
print('y_train shape:', len(y_train))
print('Xi_test shape:', len(Xi_test))
print('Xv_test shape:', len(Xv_test))
#print('ids_test:', ids_test)
print(dfTrain.dtypes)
#field_size 是原始的特征size， feature_size是经过对离散型数据one-hot处理后的特征数量
dfm_params['feature_size'] = fd.feat_dim
dfm_params['field_size'] = len(Xi_train[0])
y_train_meta = np.zeros((dfTrain.shape[0], 1), dtype=float)
y_test_meta = np.zeros((dfTest.shape[0], 1), dtype=float)
_get = lambda x, l: [x[i] for i in l]
gini_results_cv = np.zeros(len(folds), dtype=float)
gini_results_epoch_train = np.zeros((len(folds), dfm_params['epoch']),
dtype=float)
gini_results_epoch_valid = np.zeros((len(folds), dfm_params['epoch']),
dtype=float)
for i, (train_idx, valid_idx) in enumerate(folds):
# 这里Xi_train_, Xv_train_, y_train_ 分别表示当前的特征在总的输入的特征中的索引、特征的具体的值、对应的标签索引
Xi_train_, Xv_train_, y_train_ = _get(Xi_train, train_idx), _get(
Xv_train, train_idx), _get(y_train, train_idx)
Xi_valid_, Xv_valid_, y_valid_ = _get(Xi_train, valid_idx), _get(
Xv_train, valid_idx), _get(y_train, valid_idx)
# 训练好模型 并进行预测
dfm = DeepFM(**dfm_params)
print('before fit Xi_train_:', Xi_train_[0:3])
print('before fit Xv_train_:', Xv_train_[0:3])
print('before fit y_train_:', y_train_[0:3])
dfm.fit(Xi_train_, Xv_train_, y_train_, Xi_valid_, Xv_valid_, y_valid_)
y_train_meta[valid_idx, 0] = dfm.predict(Xi_valid_, Xv_valid_)
y_test_meta[:, 0] += dfm.predict(Xi_test, Xv_test)
gini_results_cv[i] = gini_norm(y_valid_, y_train_meta[valid_idx])
gini_results_epoch_train[i] = dfm.train_result
gini_results_epoch_valid[i] = dfm.valid_result
y_test_meta /= float(len(folds))
# save result
if dfm_params["use_fm"] and dfm_params["use_deep"]:
clf_str = "DeepFM"
elif dfm_params["use_fm"]:
clf_str = "FM"
elif dfm_params["use_deep"]:
clf_str = "DNN"
print("%s: %.5f (%.5f)" %
(clf_str, gini_results_cv.mean(), gini_results_cv.std()))
filename = "%s_Mean%.5f_Std%.5f.csv" % (clf_str, gini_results_cv.mean(),
gini_results_cv.std())
_make_submission(ids_test, y_test_meta, filename)
_plot_fig(gini_results_epoch_train, gini_results_epoch_valid, clf_str)
return y_train_meta, y_test_meta
def fit(self,
Xi_train,
Xv_train,
y_train,
Xi_valid=None,
Xv_valid=None,
y_valid=None,
early_stopping=False,
refit=False):
has_valid = Xv_valid is not None
self.gini_train = []
self.gini_valid = []
for epoch in range(self.epoch):
pre_train = []
pre_valid = []
t1 = time()
self.shuffle_in_unison_scary(Xi_train, Xv_train, y_train)
total_batch = int((len(y_train) - 1) / self.batch_size) + 1
for i in range(total_batch):
Xi_batch, Xv_batch, y_batch = self.get_batch(
Xi_train, Xv_train, y_train, self.batch_size, i)
feed_dict = {
self.feat_index: np.array(Xi_batch),
self.feat_value: np.array(Xv_batch),
self.label: np.array(y_batch).reshape((-1, 1)),
self.dropout_keep_fm: self.dropout_fm,
self.dropout_keep_deep: self.dropout_dep,
self.train_phase: True
}
# loss, opt = self.sess.run([self.loss, self.optimizer], feed_dict=feed_dict)
loss, opt, train_out = self.sess.run(
(self.loss, self.optimizer, self.out), feed_dict=feed_dict)
# pre_train.append(train_out)
# dfm.fit_on_batch(Xi_batch, Xv_batch, y_batch)
for i in range(total_batch):
dummy = [1] * len(Xi_train)
Xi_batch, Xv_batch, y_batch = self.get_batch(
Xi_train, Xv_train, dummy, self.batch_size, i)
num_batch = len(y_batch)
feed_dict = {
self.feat_index: np.array(Xi_batch),
self.feat_value: np.array(Xv_batch),
self.label: np.array(y_batch).reshape((-1, 1)),
self.dropout_keep_fm: [1.0] * len(self.dropout_fm),
self.dropout_keep_deep: [1.0] * len(self.dropout_dep),
self.train_phase: False
}
loss, train_out = self.sess.run((self.loss, self.out),
feed_dict=feed_dict)
if i == 0:
pre_train = np.reshape(train_out, (num_batch, ))
else:
pre_train = np.concatenate(
(pre_train, np.reshape(train_out, (num_batch, ))))
sig_gini_train = gini_norm(y_train, pre_train)
# sig_gini_train = self.evaluate(Xi_train, Xv_train, y_train)
self.gini_train.append(sig_gini_train)
# evaluate training and validation datasets
train_result = self.evaluate(Xi_train, Xv_train, y_train)
self.train_result.append(train_result)
if has_valid:
valid_result = self.evaluate(Xi_valid, Xv_valid, y_valid)
self.valid_result.append(valid_result)
feed_dict = {
self.feat_index: np.array(Xi_valid),
self.feat_value: np.array(Xv_valid),
self.label: np.array(y_valid).reshape((-1, 1)),
self.dropout_keep_fm: [1.0] * len(self.dropout_fm),
self.dropout_keep_deep: [1.0] * len(self.dropout_dep),
self.train_phase: False
}
loss_test, valid_out = self.sess.run((self.loss, self.out),
feed_dict=feed_dict)
pre_valid.append(valid_out)
pre_valid = [y for x in pre_valid for y in x]
sig_gini_valid = gini_norm(y_valid, pre_valid)
self.gini_valid.append(sig_gini_valid)
if self.verbose > 0 and epoch % self.verbose == 0:
if has_valid:
print(
"[%d] train-result=%.4f, valid-result=%.4f,my-train=%.4f, my_valid=%.4f [%.1f s],"
% (epoch + 1, train_result, valid_result,
sig_gini_train, sig_gini_valid, time() - t1))
else:
print("[%d] train-result=%.4f [%.1f s]" %
(epoch + 1, train_result, time() - t1))
if has_valid and early_stopping and self.training_termination(
self.valid_result):
break
def my_fit(self,
Xi_train_,
Xv_train_,
y_train_,
Xi_valid_=None,
Xv_valid_=None,
y_valid_=None):
max_checks_without_progress = 10
checks_without_progress = 0
best_gini = 0
loss_train = 0
loss_test = 0
self.gini_train = []
self.gini_valid = []
for epoch in range(self.epoch):
t1 = time()
pre_train = []
pre_valid = []
self.shuffle_in_unison_scary(Xi_train_, Xv_train_, y_train_)
total_batch = int((len(y_train_) - 1) / self.batch_size) + 1
for i in range(total_batch):
Xi_batch, Xv_batch, y_batch = self.get_batch(
Xi_train_, Xv_train_, y_train_, self.batch_size, i)
feed_dict = {
self.feat_index: np.array(Xi_batch),
self.feat_value: np.array(Xv_batch),
self.label: np.array(y_batch).reshape((-1, 1)),
self.dropout_keep_fm: self.dropout_fm,
self.dropout_keep_deep: self.dropout_dep,
self.train_phase: True
}
loss, opt, train_out = self.sess.run(
(self.loss, self.optimizer, self.out), feed_dict=feed_dict)
# if extra_update_ops:
# sess.run(extra_update_ops,feed_dict=feed_dict)
loss_train += loss
pre_train.append(train_out)
# dfm.fit_on_batch(Xi_batch, Xv_batch, y_batch)
loss_train /= total_batch
pre_train = [y for x in pre_train for y in x]
sig_gini_train = gini_norm(y_train_, pre_train)
self.gini_train.append(sig_gini_train)
feed_dict = {
self.feat_index: np.array(Xi_valid_),
self.feat_value: np.array(Xv_valid_),
self.label: np.array(y_valid_).reshape((-1, 1)),
self.dropout_keep_fm: [1.0] * len(self.dropout_fm),
self.dropout_keep_deep: [1.0] * len(self.dropout_dep),
self.train_phase: False
}
loss_test, valid_out = self.sess.run((self.loss, self.out),
feed_dict=feed_dict)
pre_valid.append(valid_out)
pre_valid = [y for x in pre_valid for y in x]
sig_gini_valid = gini_norm(y_valid_, pre_valid)
self.gini_valid.append(sig_gini_valid)
if sig_gini_valid > best_gini:
gvars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
best_params = {
gvar.op.name: value
for gvar, value in zip(gvars, self.sess.run(gvars))
}
best_gini = sig_gini_valid
checks_without_progress = 0
else:
checks_without_progress += 1
print("[%d] train-result=%.4f, valid-result=%.4f [%.1f s]" %
(epoch + 1, sig_gini_train, sig_gini_valid, time() - t1))
if checks_without_progress > max_checks_without_progress:
print('early stopping!')
break
##########将训练过程中保存的最好的参数重新返回到模型参数，此时得到的是最好的模型
if best_params:
gvars_names = list(best_params.keys())
assign_ops = {
gvar_name:
tf.get_default_graph().get_operation_by_name(gvar_name +
'/Assign')
for gvar_name in gvars_names
}
init_values = {
gvar_name: assign_op.inputs[1]
for gvar_name, assign_op in assign_ops.items()
}
feed_dict = {
init_values[gvar_name]: best_params[gvar_name]
for gvar_name in gvars_names
}
self.sess.run(assign_ops, feed_dict=feed_dict)
return self
def run_base_model_dfm(train_df, test_df, data_folds, params):
# 解析数据，构建特征索引和特征值
fd = FeatureDictionay(df_train=train_df,
df_test=test_df,
numeric_cols=params.numeric_cols,
ignore_cols=params.ignore_cols)
data_parser = DataParser(feat_dict=fd)
train_Xi, train_Xv, train_y = data_parser.parse(df=train_df,
has_label=True)
test_Xi, test_Xv, test_ids = data_parser.parse(df=test_df)
# get feature size and field size
feature_size = fd.feat_size
field_size = len(train_Xi[0])
train_meta_y = np.zeros((train_df.shape[0], 1), dtype=float)
test_meta_y = np.zeros((test_df.shape[0], 1), dtype=float)
_get = lambda x, l: [x[i] for i in l]
# metric
gini_results_cv = np.zeros(len(data_folds), dtype=float)
gini_results_epoch_train = np.zeros((len(data_folds), params.epochs),
dtype=float)
gini_results_epoch_valid = np.zeros((len(data_folds), params.epochs),
dtype=float)
for idx, (train_idx, valid_idx) in enumerate(data_folds):
train_Xi_ = _get(train_Xi, train_idx)
train_Xv_ = _get(train_Xv, train_idx)
train_y_ = _get(train_y, train_idx)
valid_Xi_ = _get(train_Xi, valid_idx)
valid_Xv_ = _get(train_Xv, valid_idx)
valid_y_ = _get(train_y, valid_idx)
# construct model, for folds
dfm = Train(params, feature_size, field_size)
dfm.training(train_Xi_,
train_Xv_,
train_y_,
valid_Xi_,
valid_Xv_,
valid_y_,
early_stopping=False,
refit=False)
train_meta_y[valid_idx, 0] = dfm.predict(valid_Xi_, valid_Xv_)
test_meta_y[:, 0] += dfm.predict(test_Xi, test_Xv)
gini_results_cv[idx] = gini_norm(valid_y_, train_meta_y[valid_idx])
gini_results_epoch_train[
idx, :len(dfm.train_results)] = dfm.train_results
gini_results_epoch_valid[
idx, :len(dfm.valid_results)] = dfm.valid_results
test_meta_y = test_meta_y / float(len(data_folds))
# save result
if params.use_fm and params.use_deep:
clf_str = "DeepFM"
elif params.use_fm:
clf_str = "FM"
else:
clf_str = "DNN"
print("%s: %.5f (%.5f)" %
(clf_str, gini_results_cv.mean(), gini_results_cv.std()))
filename = "%s_Mean%.5f_Std%.5f.csv" % (clf_str, gini_results_cv.mean(),
gini_results_cv.std())
_make_submission(test_ids, test_meta_y, params.sub_dir, filename)
_plot_fig(gini_results_epoch_train, gini_results_epoch_valid, clf_str)
return train_meta_y, test_meta_y
def _run_base_model_dfm(dfTrain, dfTest, folds, dfm_params):
fd = FeatureDictionary(dfTrain=dfTrain, dfTest=dfTest,
numeric_cols=config.NUMERIC_COLS,
ignore_cols=config.IGNORE_COLS)
data_parser = DataParser(feat_dict=fd)
"""
Xi_x是一个n_samples x n_features的索引list,每个数值型特征编码为一个固定索引,每个类别型特征根据类别
数编码为不同的索引
Xv_x是一个n_samples x n_features的值list
"""
_print("parse data begin")
Xi_test, Xv_test, ids_test = data_parser.parse(df=dfTest)
Xi_train, Xv_train, y_train = data_parser.parse(df=dfTrain, has_label=True)
_print("parse data finish")
dfm_params["feature_size"] = fd.feat_dim #最大索引
dfm_params["field_size"] = len(Xi_train[0]) #特征数,这个还是原始的特征数
y_train_meta = np.zeros((dfTrain.shape[0], 1), dtype=float)
y_test_meta = np.zeros((dfTest.shape[0], 1), dtype=float)
_get = lambda x, l: [x[i] for i in l]
gini_results_cv = np.zeros(len(folds), dtype=float)
gini_results_epoch_train = np.zeros((len(folds), dfm_params["epoch"]), dtype=float)
gini_results_epoch_valid = np.zeros((len(folds), dfm_params["epoch"]), dtype=float)
#train_idx和valid_idx分别是训练集和验证集的idx,因为做了kfold所以下面要从
#全样本中根据idx提取出来
for i, (train_idx, valid_idx) in enumerate(folds):
Xi_train_, Xv_train_, y_train_ = _get(Xi_train, train_idx), _get(Xv_train, train_idx), _get(y_train, train_idx)
Xi_valid_, Xv_valid_, y_valid_ = _get(Xi_train, valid_idx), _get(Xv_train, valid_idx), _get(y_train, valid_idx)
_print("fit, fold=%d" % i)
dfm = DeepFM(**dfm_params, n_samples=len(Xi_train_))
dfm.fit(Xi_train_, Xv_train_, y_train_, Xi_valid_, Xv_valid_, y_valid_)
y_train_meta[valid_idx,0] = dfm.predict(Xi_valid_, Xv_valid_)
y_test_meta[:,0] += dfm.predict(Xi_test, Xv_test)
gini_results_cv[i] = gini_norm(y_valid_, y_train_meta[valid_idx])
gini_results_epoch_train[i] = dfm.train_result
gini_results_epoch_valid[i] = dfm.valid_result
y_test_meta /= float(len(folds))
# save result
if dfm_params["use_fm"] and dfm_params["use_deep"]:
clf_str = "DeepFM"
elif dfm_params["use_fm"]:
clf_str = "FM"
elif dfm_params["use_deep"]:
clf_str = "DNN"
line = "%s: %.5f (%.5f)"%(clf_str, gini_results_cv.mean(), gini_results_cv.std())
_print(line)
filename = "%s_Mean%.5f_Std%.5f.csv"%(clf_str, gini_results_cv.mean(), gini_results_cv.std())
_make_submission(ids_test, y_test_meta, filename)
# 暂时不画图了
#_plot_fig(gini_results_epoch_train, gini_results_epoch_valid, clf_str)
return y_train_meta, y_test_meta
def _run_base_model_dfm(dfTrain, dfTest, folds, dfm_params):
fd = FeatureDictionary(dfTrain=dfTrain,
dfTest=dfTest,
numeric_cols=config.NUMERIC_COLS,
ignore_cols=config.IGNORE_COLS)
data_parser = DataParser(feat_dict=fd)
Xi_train, Xv_train, y_train = data_parser.parse(
df=dfTrain, has_label=True) # 返回样本特征id， 样本特征值， label
Xi_test, Xv_test, ids_test = data_parser.parse(
df=dfTest) # 返回样本特征id， 样本特征值， 样本id
dfm_params["feature_size"] = fd.feat_dim # 特征总数
dfm_params["field_size"] = len(
Xi_train[0]) # Xi_train[0]是训练集的第一条样本,该长度描述的是field的数量
y_train_meta = np.zeros((dfTrain.shape[0], 1),
dtype=float) # 构建中间变量，长度和样本数保持一致
y_test_meta = np.zeros((dfTest.shape[0], 1), dtype=float)
_get = lambda x, l: [x[i] for i in l
] # lambda语法，类似于C语言中的宏定义，冒号前的是变量，冒号后的是变量执行的语句
gini_results_cv = np.zeros(len(folds),
dtype=float) # len(folds)表示分割训练集和验证集的方法数(kfold)
gini_results_epoch_train = np.zeros((len(folds), dfm_params["epoch"]),
dtype=float)
gini_results_epoch_valid = np.zeros((len(folds), dfm_params["epoch"]),
dtype=float)
for i, (train_idx, valid_idx) in enumerate(
folds): # 反复训练模型k(len(folds))次，只在训练集量少时进行, 数据量足够大时，无需循环
Xi_train_, Xv_train_, y_train_ = _get(Xi_train, train_idx), _get(
Xv_train, train_idx), _get(y_train, train_idx)
Xi_valid_, Xv_valid_, y_valid_ = _get(Xi_train, valid_idx), _get(
Xv_train, valid_idx), _get(y_train, valid_idx)
dfm = DeepFM(**dfm_params) # 构造网络
dfm.fit(Xi_train_, Xv_train_, y_train_, Xi_valid_, Xv_valid_,
y_valid_) # 拟合
y_train_meta[valid_idx, 0] = dfm.predict(Xi_valid_,
Xv_valid_) # 在验证集上预测
y_test_meta[:, 0] += dfm.predict(Xi_test, Xv_test) # 在测试集上预测
gini_results_cv[i] = gini_norm(y_valid_, y_train_meta[valid_idx])
gini_results_epoch_train[i] = dfm.train_result
gini_results_epoch_valid[i] = dfm.valid_result
y_test_meta /= float(len(folds))
# save result
if dfm_params["use_fm"] and dfm_params["use_deep"]:
clf_str = "DeepFM"
elif dfm_params["use_fm"]:
clf_str = "FM"
elif dfm_params["use_deep"]:
clf_str = "DNN"
print("%s: %.5f (%.5f)" %
(clf_str, gini_results_cv.mean(), gini_results_cv.std()))
filename = "%s_Mean%.5f_Std%.5f.csv" % (clf_str, gini_results_cv.mean(),
gini_results_cv.std())
_make_submission(ids_test, y_test_meta, filename)
_plot_fig(gini_results_epoch_train, gini_results_epoch_valid, clf_str)
return y_train_meta, y_test_meta
feed_dict = {
dfm.feat_index: np.array(Xi_batch),
dfm.feat_value: np.array(Xv_batch),
dfm.label: np.array(y_batch).reshape((-1, 1)),
dfm.dropout_keep_fm: [1.0] * len(dfm.dropout_fm),
dfm.dropout_keep_deep: [1.0] * len(dfm.dropout_deep),
dfm._training: False
}
loss, train_out = sess.run((dfm.loss, dfm.out), feed_dict=feed_dict)
if i == 0:
pre_train = np.reshape(train_out, (num_batch, ))
else:
pre_train = np.concatenate(
(pre_train, np.reshape(train_out, (num_batch, ))))
sig_gini_train = gini_norm(y_train_, pre_train)
gini_train.append(sig_gini_train)
feed_dict = {
dfm.feat_index: np.array(Xi_valid_),
dfm.feat_value: np.array(Xv_valid_),
dfm.label: np.array(y_valid_).reshape((-1, 1)),
dfm.dropout_keep_fm: [1.0] * len(dfm.dropout_fm),
dfm.dropout_keep_deep: [1.0] * len(dfm.dropout_deep),
dfm._training: False
}
loss_test, valid_out = sess.run((dfm.loss, dfm.out), feed_dict=feed_dict)
pre_valid.append(valid_out)
pre_valid = [y for x in pre_valid for y in x]
sig_gini_valid = gini_norm(y_valid_, pre_valid)
def _run_base_model_dfm(dfTrain,
dfTest,
folds,
dfm_params,
NUMERIC_COLS,
IGNORE_COLS,
application='classification'):
fd = FeatureDictionary(dfTrain=dfTrain,
dfTest=dfTest,
numeric_cols=NUMERIC_COLS,
ignore_cols=IGNORE_COLS)
data_parser = DataParser(feat_dict=fd)
Xi_train, Xv_train, y_train = data_parser.parse(df=dfTrain, has_label=True)
Xi_test, Xv_test, ids_test = data_parser.parse(df=dfTest, has_label=True)
dfm_params["feature_size"] = fd.feat_dim
dfm_params["field_size"] = len(Xi_train[0])
y_train_meta = np.zeros((dfTrain.shape[0], 1), dtype=float)
y_test_meta = np.zeros((dfTest.shape[0], 1), dtype=float)
_get = lambda x, l: [x[i] for i in l]
results_cv = np.zeros(len(folds), dtype=float)
results_epoch_train = np.zeros((len(folds), dfm_params["epoch"]),
dtype=float)
results_epoch_valid = np.zeros((len(folds), dfm_params["epoch"]),
dtype=float)
for i, (train_idx, valid_idx) in enumerate(folds):
Xi_train_, Xv_train_, y_train_ = _get(Xi_train, train_idx), _get(
Xv_train, train_idx), _get(y_train, train_idx)
Xi_valid_, Xv_valid_, y_valid_ = _get(Xi_train, valid_idx), _get(
Xv_train, valid_idx), _get(y_train, valid_idx)
dfm = DeepFM(**dfm_params)
dfm.fit(Xi_train_, Xv_train_, y_train_, Xi_valid_, Xv_valid_, y_valid_)
y_train_meta[valid_idx, 0] = dfm.predict(Xi_valid_, Xv_valid_)
y_test_meta[:, 0] += dfm.predict(Xi_test, Xv_test)
if application == 'classification':
results_cv[i] = roc_auc_score(y_valid_, y_train_meta[valid_idx])
elif application == 'regression':
results_cv[i] = np.sqrt(
mean_squared_error(y_valid_, y_train_meta[valid_idx]))
else:
results_cv[i] = gini_norm(y_valid_, y_train_meta[valid_idx])
results_epoch_train[i] = dfm.train_result
results_epoch_valid[i] = dfm.valid_result
y_test_meta /= float(len(folds))
# save result
if dfm_params["use_fm"] and dfm_params["use_deep"]:
clf_str = "DeepFM"
elif dfm_params["use_fm"]:
clf_str = "FM"
elif dfm_params["use_deep"]:
clf_str = "DNN"
print("%s: rmse/accuracy/gini is %.4f (std is %.4f)" %
(clf_str, results_cv.mean(), results_cv.std()))
filename = "%s_Mean%.5f.csv" % (clf_str, results_cv.mean())
_make_submission(ids_test, y_test_meta, filename)
_plot_fig(results_epoch_train, results_epoch_valid, clf_str, application)
return y_train_meta, y_test_meta
def my_fit(self,
Xi_train_,
Xv_train_,
y_train_,
Xi_valid_=None,
Xv_valid_=None,
y_valid_=None):
self.close_session()
self._init_graph()
# extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
self._session = tf.Session()
self._init = tf.global_variables_initializer()
self._session.run(self._init)
max_checks_without_progress = 10
checks_without_progress = 0
best_gini = 0
loss_train = 0
loss_test = 0
self.gini_train = []
self.gini_valid = []
for epoch in range(self.epoch):
t1 = time()
pre_train = []
pre_valid = []
self.shuffle_in_unison_scary(Xi_train_, Xv_train_, y_train_)
total_batch = int((len(y_train_) - 1) / self.batch_size) + 1
for i in range(total_batch):
Xi_batch, Xv_batch, y_batch = self.get_batch(
Xi_train_, Xv_train_, y_train_, self.batch_size, i)
feed_dict = {
self.feat_index: np.array(Xi_batch),
self.feat_value: np.array(Xv_batch),
self.label: np.array(y_batch).reshape((-1, 1)),
self.dropout_keep_fm: self.dropout_fm,
self.dropout_keep_deep: self.dropout_deep,
self._training: True
}
# if extra_update_ops:
# self._session.run(extra_update_ops, feed_dict=feed_dict)
loss, opt, train_out = self._session.run(
(self.loss, self.train_step, self.out),
feed_dict=feed_dict)
loss_train /= total_batch
# ##########----------------gini train---------
# for i in range(total_batch):
# dummy = [1] * len(Xi_train_)
# Xi_batch, Xv_batch, y_batch = self.get_batch(Xi_train_, Xv_train_, dummy, self.batch_size, i)
# num_batch = len(y_batch)
# feed_dict = {self.feat_index: np.array(Xi_batch),
# self.feat_value: np.array(Xv_batch),
# self.label: np.array(y_batch).reshape((-1, 1)),
# self.dropout_keep_fm: [1.0] * len(self.dropout_fm),
# self.dropout_keep_deep: [1.0] * len(self.dropout_deep),
# self._training: False
#
# }
# loss, train_out = self._session.run((self.loss, self.out), feed_dict=feed_dict)
# if i == 0:
# pre_train = np.reshape(train_out, (num_batch,))
# else:
# pre_train = np.concatenate((pre_train, np.reshape(train_out, (num_batch,))))
pre_train = self.my_predict_prob(Xi_train_, Xv_train_)
sig_gini_train = gini_norm(y_train_, pre_train)
self.gini_train.append(sig_gini_train)
#########################valid value-------------
# feed_dict = {self.feat_index: np.array(Xi_valid_),
# self.feat_value: np.array(Xv_valid_),
# self.label: np.array(y_valid_).reshape((-1, 1)),
# self.dropout_keep_fm: [1.0] * len(self.dropout_fm),
# self.dropout_keep_deep: [1.0] * len(self.dropout_deep)
#
# }
# loss_test, valid_out = self._session.run((self.loss, self.out), feed_dict=feed_dict)
# pre_valid.append(valid_out)
# pre_valid = [y for x in pre_valid for y in x]
pre_valid = self.my_predict_prob(Xi_valid_, Xv_valid_)
sig_gini_valid = gini_norm(y_valid_, pre_valid)
self.gini_valid.append(sig_gini_valid)
if sig_gini_valid > best_gini:
gvars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
best_params = {
gvar.op.name: value
for gvar, value in zip(gvars, self._session.run(gvars))
}
best_gini = sig_gini_valid
checks_without_progress = 0
else:
checks_without_progress += 1
print("[%d] train-result=%.4f, valid-result=%.4f [%.1f s]" %
(epoch + 1, sig_gini_train, sig_gini_valid, time() - t1))
#####################-------------希望早停的话把这句加上
# if checks_without_progress > max_checks_without_progress:
# print('early stopping!')
# break
##########将训练过程中保存的最好的参数重新返回到模型参数，此时得到的是最好的模型
if best_params:
gvars_names = list(best_params.keys())
assign_ops = {
gvar_name:
tf.get_default_graph().get_operation_by_name(gvar_name +
'/Assign')
for gvar_name in gvars_names
}
init_values = {
gvar_name: assign_op.inputs[1]
for gvar_name, assign_op in assign_ops.items()
}
feed_dict = {
init_values[gvar_name]: best_params[gvar_name]
for gvar_name in gvars_names
}
self._session.run(assign_ops, feed_dict=feed_dict)
return self
prob = F.sigmoid(outputs)
y_pre.append(prob.data.cpu().numpy())
loss = criterion(outputs, y_batch)
# _, predicted = torch.max(outputs.data, 1)
loss_train += loss.item()
###########backward and optimize
torch_optim.zero_grad()
loss.backward()
torch_optim.step()
# if (i + 1) % 10 == 0:
# print('steps:[%d],train_loss:[%.3f]' % (i + 1, loss.item()))
y_true = np.concatenate(y_true, axis=0)
y_pre = np.concatenate(y_pre, axis=0)
gini_train = gini_norm(y_true, y_pre)
loss_train /= steps
loss_val, gini_val = val(model, valid_dataloader)
history['loss_train'].append(loss_train)
history['loss_val'].append(loss_val)
history['gini_train'].append(gini_train)
history['gini_val'].append(gini_val)
if loss_val < best_loss:
torch.save(model.state_dict(), check_file)
best_loss = loss_val
checks_without_progress = 0
else:
checks_without_progress += 1
if checks_without_progress > max_checks_without_progress:
def training(self, train_Xi, train_Xv, train_y,
valid_Xi=None, valid_Xv=None, valid_y=None,
early_stopping=False, refit=False):
need_valid = False
if valid_Xi is not None:
need_valid = True
## construct optimizer
optimizer_type = self.params.optimizer
if optimizer_type == "sgd":
optimizer = optim.SGD(self.deepfm.parameters(), lr=self.params.learning_rate)
else:
optimizer = optim.Adam(self.deepfm.parameters(),
lr=self.params.learning_rate, betas=(0.9, 0.99),
eps=1e-8, amsgrad=True)
##
loss_type = self.params.loss_type
for epoch in range(1, self.params.epochs + 1):
self.deepfm.train()
t1 = time.time()
self.shuffle_in_unison_scary(train_Xi, train_Xv, train_y)
total_batch = int(len(train_y) / self.params.batch_size) + 1
for i in range(total_batch):
batch_Xi, batch_Xv, batch_y = self.get_batch(train_Xi, train_Xv, train_y,
self.params.batch_size, i)
batch_Xi = torch.tensor(batch_Xi, dtype=torch.long)
batch_Xv = torch.tensor(batch_Xv, dtype=torch.float)
batch_y = torch.tensor(batch_y, dtype=torch.long)
optimizer.zero_grad()
output, _, _, _ = self.deepfm(batch_Xi, batch_Xv)
if loss_type == "logloss":
# for classification
output = F.sigmoid(output)
loss = -torch.mul(batch_y, torch.log(output)) \
- torch.mul((1-batch_y), torch.log(1-output))
loss = torch.mean(loss)
elif loss_type == "mse":
# for regression
loss = F.mse_loss(input=output, target=batch_y)
else:
raise ValueError("Unknown loss type, should be one of 'logloss/mes'")
# l2 regularization on weights for preventing over-fitting
if self.params.l2_reg > 0:
loss += self.params.l2_reg * torch.norm(self.deepfm.final_W, 2)
if self.params.use_deep:
for weight in self.deepfm.deep_layers:
loss += self.params.l2_reg * torch.norm(weight.W, 2)
print("epoch: %d, loss: %.4f" % (epoch, loss.item()))
# backward
loss.backward()
torch.nn.utils.clip_grad_norm_(self.deepfm.parameters(), self.params.grad_clip)
optimizer.step()
# each epoch with evaluate training and validation datasets
train_pred_y = self.predict(train_Xi, train_Xv)
self.train_results.append(gini_norm(train_y, train_pred_y))
if need_valid:
valid_pred_y = self.predict(valid_Xi, valid_Xv)
self.valid_results.append(gini_norm(valid_y, valid_pred_y))
print("epoch: %d, train-result: %.4f, valid-result: %.4f, "
"cost-time: %.2f s" % (epoch, self.train_results[-1],
self.valid_results[-1], time.time() - t1))
else:
print("epoch: %d, train-result: %.4f, cost-time: %.2f s"
% (epoch, self.train_results[-1], time.time() - t1))
if need_valid and early_stopping and self.training_termination(self.valid_results):
print("Early Stopping!!")
break
# fit a few more epochs on train+valid until result reaches the best_train_score
if need_valid and refit:
greater_is_better = self.params.greater_is_better
if greater_is_better:
best_valid_score = max(self.valid_results)
else:
best_valid_score = min(self.valid_results)
best_epoch = self.valid_results.index(best_valid_score)
best_train_score = self.train_results[best_epoch]
train_Xi = train_Xi + valid_Xi
train_Xv = train_Xv + valid_Xv
train_y = train_y + valid_y
for epoch in range(1, 100):
self.shuffle_in_unison_scary(train_Xi, train_Xv, train_y)
total_batch = int(len(train_y) / self.params.batch_size) + 1
for i in range(total_batch):
batch_Xi, batch_Xv, batch_y = self.get_batch(train_Xi, train_Xv, train_y,
self.params.batch_size, i)
batch_Xi = torch.tensor(batch_Xi, dtype=torch.long)
batch_Xv = torch.tensor(batch_Xv, dtype=torch.float)
batch_y = torch.tensor(batch_y, dtype=torch.long)
optimizer.zero_grad()
output, _, _, _ = self.deepfm(batch_Xi, batch_Xv)
if loss_type == "logloss":
# for classification
output = F.sigmoid(output)
loss = -torch.mul(batch_y, torch.log(output)) \
- torch.mul((1 - batch_y), torch.log(1 - output))
loss = torch.mean(loss)
elif loss_type == "mse":
# for regression
loss = F.mse_loss(input=output, target=batch_y)
else:
raise ValueError("Unknown loss type, should be one of 'logloss/mes'")
# l2 regularization on weights for preventing over-fitting
if self.params.l2_reg > 0:
loss += self.params.l2_reg * torch.norm(self.deepfm.final_W, 2)
if self.params.use_deep:
for weight in self.deepfm.deep_layers:
loss += self.params.l2_reg * torch.norm(weight.W, 2)
print("epoch: %d, loss: %.4f" % (epoch, loss.item()))
# backward
loss.backward()
torch.nn.utils.clip_grad_norm_(self.deepfm.parameters(), self.params.grad_clip)
optimizer.step()
# check
train_pred_y = self.predict(train_Xi, train_Xv)
train_result = gini_norm(train_y, train_pred_y)
if abs(train_result - best_train_score) < 0.001 \
or (greater_is_better and train_result > best_train_score) \
or ((not greater_is_better) and train_result < best_train_score):
print("Find best train score!!")
break
