def main():
#静默弃用sklearn警告
warnings.filterwarnings(module='sklearn*', action='ignore', category=DeprecationWarning)
model_name = 'Distr_GBTClassifier'
dir_of_dict = sys.argv[1]
bag = too.Read_info(dir_of_dict,'supervision')
name_dict,options,task_id,job_id,train_result_dir,\
names_str,names_num,names_show,Y_names,dir_of_inputdata,\
dir_of_outputdata,open_pca,train_size,test_size,normalized_type = bag
dir_of_storePara = train_result_dir + '/%s_Parameters.json'%(str(task_id)+'_'+str(job_id)+'_'+model_name)
dir_of_storeModel = train_result_dir + '/%s_model'%(str(task_id)+'_'+str(job_id)+'_'+model_name)
# 配置spark客户端
sess = SparkSession\
.builder\
.master("local[4]")\
.appName("GBTClassifier_spark")\
.config("spark.some.config.option", "some-value")\
.getOrCreate()
sc=sess.sparkContext
sc.setLogLevel("ERROR")
if options == 'train':
time_start = time()
#获取数据
dataset = pd.read_csv(dir_of_inputdata)
#用于测试
#dataset = dataset[0:1000]
#限制多数类的数据
#dataset = too.CalcMostLabel(dataset,Y_names)
Y_datavec = dataset[Y_names].values
#输出每个标签的数量
print 'Counter:original y',Counter(Y_datavec)
print'----------------------------------------------'
#分别获得字符字段和数值型字段数据，且合并
X_datavec,X_columns,vocabset,datavec_show_list= too.Merge_form(dataset,names_str,names_num,names_show,'vocabset','open')
#数据归一化
X_datavec = too.Data_process(X_datavec,normalized_type)
#处理数据不平衡问题
#X,Y = mlp.KMeans_unbalanced(X_datavec,Y_datavec,X_columns,Y_names)
#X,Y = mlp.Sample_unbalanced(X_datavec,Y_datavec)
X,Y = X_datavec, Y_datavec
ret_num = 'no_num'
#PCA降维
if open_pca == 'open_pca':
pca_num,ret = mlp.GS_PCA(X)
print 'PCA Information:',pca_num,ret
print'----------------------------------------------'
ret_num = ret['99%']
X = mlp.Model_PCA(X,ret_num)
#存储vocabset这个list和ret_num
too.StorePara(dir_of_storePara,vocabset,ret_num)
print'--------------Train data shape----------------'
print 'X.shape:',X.shape
print'----------------------------------------------'
print 'Y.shape:',Y.shape
print'----------------------------------------------'
print'--------------Start %s model------------------'%model_name
features = pd.DataFrame(X,)
targets = pd.DataFrame(Y, columns = ['Y'])
#合拼矩阵
merged = pd.concat([features, targets], axis = 1)
#创建spark DataFrame
raw_df = sess.createDataFrame(merged)
#提取特征与目标
fomula = RFormula(formula = 'Y ~ .', featuresCol="features",labelCol="label")
raw_df = fomula.fit(raw_df).transform(raw_df)
#拆分训练集和测试集
xy_train, xy_test = raw_df.randomSplit([train_size, test_size],seed=666)
#调用模型
clf_model = dmp.Distr_GBTClassifier(xy_train,xy_test)
#保存模型参数
clf_model.write().overwrite().save(dir_of_storeModel)
print'----------------------------------------------'
dmp.Predict_test_data(xy_test, datavec_show_list, names_show, clf_model, dir_of_outputdata)
duration = too.Duration(time()-time_start)
print 'Total run time: %s'%duration
if options == 'predict':
time_start = time()
with open(dir_of_storePara,'r') as f:
para_dict = json.load(f)
vocabset = para_dict['vocabset']
ret_num = para_dict['ret_num']
#获取数据
dataset = pd.read_csv(dir_of_inputdata)
#分别获得字符字段和数值型字段数据，且合并
X_datavec,datavec_show_list = too.Merge_form(dataset,names_str,names_num,names_show,vocabset,'close')
#数据归一化
X = too.Data_process(X_datavec,normalized_type)
#PCA降维
if open_pca == 'open_pca':
X = mlp.Model_PCA(X,ret_num)
print'-------------Pdedict data shape---------------'
print 'X.shape:',X.shape
print'----------------------------------------------'
print'--------------Start %s model------------------'%model_name
features = pd.DataFrame(X,)
#创建spark DataFrame
raw_features = sess.createDataFrame(features)
raw_x = VectorAssembler(inputCols=raw_features.columns,outputCol='features').transform(raw_features)
clf_model = GBTClassificationModel.load(dir_of_storeModel)
dmp.Predict_data(raw_x, datavec_show_list, names_show, clf_model, dir_of_outputdata)
duration = too.Duration(time()-time_start)
print 'Total run time: %s'%duration
def main():
#静默弃用sklearn警告
warnings.filterwarnings(module='sklearn*',
action='ignore',
category=DeprecationWarning)
model_name = 'MFNNCla'
dir_of_dict = sys.argv[1]
bag = too.Read_info(dir_of_dict, 'supervision')
name_dict,options,task_id,job_id,train_result_dir,\
names_str,names_num,names_show,Y_names,dir_of_inputdata,\
dir_of_outputdata,open_pca,train_size,test_size,normalized_type = bag
dir_of_storePara = train_result_dir + '/%s_Parameters.json' % (
str(task_id) + '_' + str(job_id) + '_' + model_name)
dir_of_storeModel = train_result_dir + '/%s_model.h5' % (
str(task_id) + '_' + str(job_id) + '_' + model_name)
if options == 'train':
time_start = time()
#获取数据
dataset = pd.read_csv(dir_of_inputdata)
#用于测试
#dataset = dataset[0:1000]
#限制多数类的数据
#dataset = too.CalcMostLabel(dataset,Y_names)
Y_datavec = dataset[Y_names].values
#输出每个标签的数量
print 'Counter:original y', Counter(Y_datavec)
print '----------------------------------------------'
#分别获得字符字段和数值型字段数据，且合并
X_datavec, X_columns, vocabset, datavec_show_list = too.Merge_form(
dataset, names_str, names_num, names_show, 'vocabset', 'open')
#数据归一化
X_datavec = too.Data_process(X_datavec, normalized_type)
#处理数据不平衡问题
#X,Y = mlp.KMeans_unbalanced(X_datavec,Y_datavec,X_columns,Y_names)
#X,Y = mlp.Sample_unbalanced(X_datavec,Y_datavec)
X, Y = X_datavec, Y_datavec
ret_num = 'no_num'
#PCA降维
if open_pca == 'open_pca':
pca_num, ret = mlp.GS_PCA(X)
print 'PCA Information:', pca_num, ret
print '----------------------------------------------'
ret_num = ret['99%']
X = mlp.Model_PCA(X, ret_num)
#存储vocabset这个list和ret_num
too.StorePara(dir_of_storePara, vocabset, ret_num)
print '--------------Train data shape----------------'
print 'X.shape:', X.shape
print '----------------------------------------------'
print 'Y.shape:', Y.shape
print '----------------------------------------------'
print '--------------Start %s model------------------' % model_name
X_train, X_test, y_train, y_test = train_test_split(
X,
Y,
train_size=train_size,
test_size=test_size,
stratify=Y,
random_state=0)
clf_model = dlp.GS_cla_MFNN(X_train, X_test, y_train, y_test)
#保存模型参数
clf_model.save(dir_of_storeModel)
print '----------------------------------------------'
too.Predict_test_data(X_test, y_test, datavec_show_list, names_show,
clf_model, dir_of_outputdata, 'MFNN')
duration = too.Duration(time() - time_start)
print 'Total run time: %s' % duration
if options == 'predict':
time_start = time()
with open(dir_of_storePara, 'r') as f:
para_dict = json.load(f)
vocabset = para_dict['vocabset']
ret_num = para_dict['ret_num']
#获取数据
dataset = pd.read_csv(dir_of_inputdata)
#分别获得字符字段和数值型字段数据，且合并
X_datavec, datavec_show_list = too.Merge_form(dataset, names_str,
names_num, names_show,
vocabset, 'close')
#数据归一化
X = too.Data_process(X_datavec, normalized_type)
#PCA降维
if open_pca == 'open_pca':
X = mlp.Model_PCA(X, ret_num)
print '-------------Pdedict data shape---------------'
print 'X.shape:', X.shape
print '----------------------------------------------'
print '--------------Start %s model------------------' % model_name
clf_model = load_model(dir_of_storeModel)
too.Predict_data(X, datavec_show_list, names_show, clf_model,
dir_of_outputdata)
duration = too.Duration(time() - time_start)
print 'Total run time: %s' % duration
def main():
#静默弃用sklearn警告
warnings.filterwarnings(module='sklearn*',
action='ignore',
category=DeprecationWarning)
model_name = 'DeepAutoEncoder'
dir_of_dict = sys.argv[1]
bag = too.Read_info(dir_of_dict, 'non-supervision')
name_dict,task_id,job_id,train_result_dir,\
names_str,names_num,names_show,\
dir_of_inputdata,dir_of_outputdata,open_pca,normalized_type = bag
dir_of_storePara = train_result_dir + '/%s_Parameters.json' % (
str(task_id) + '_' + str(job_id) + '_' + model_name)
column_names = names_str + names_num
column_names_show = names_str + names_num + names_show
time_start = time()
#获取数据
dataset = pd.read_csv(dir_of_inputdata)
#用于测试
#dataset = dataset[0:1000]
dataset_show = dataset[column_names_show]
#分别获得字符字段和数值型字段数据
dataset_str = dataset[names_str]
dataset_num = dataset[names_num]
dataset_str_list = dataset_str.values.tolist()
datavec_num_list = dataset_num.values.tolist()
vocabset = too.CreateVocabList(dataset_str_list)
datavec_str_list = too.BagofWords2Vec(vocabset, dataset_str_list)
#vocabset_index = {y:i for i,y in enumerate(vocabset)}
#将list转化为DataFrame，合并两表
datavec_str = pd.DataFrame(datavec_str_list, columns=vocabset)
datavec_num = pd.DataFrame(datavec_num_list, columns=names_num)
#按照左表连接，右表可以为空
data_tem = pd.merge(datavec_num,
datavec_str,
how="left",
right_index=True,
left_index=True)
X_datavec = data_tem.values
#数据归一化
X = too.Data_process(X_datavec, normalized_type)
ret_num = 'no_num'
#PCA降维
if open_pca == 'open_pca':
pca_num, ret = mlp.GS_PCA(X)
print 'PCA Information:', pca_num, ret
print '----------------------------------------------'
ret_num = ret['99%']
X = mlp.Model_PCA(X, ret_num)
#存储vocabset这个list和ret_num
too.StorePara(dir_of_storePara, vocabset, ret_num)
print '----------------data shape--------------------'
print 'X.shape:', X.shape
print '----------------------------------------------'
loss, X_diff_loss = dlp.Model_deep_auto_encoder(X)
clst_labels = dlp.GS_deep_auto_encoder_parameter(loss, X_diff_loss)
#寻找异常原因，并给出异常值和基准值
exception_data, base_value = too.Find_exception_reason(
X, dataset_show, clst_labels, column_names)
#存储异常结果
exception_data.to_csv(dir_of_outputdata, index=False)
duration = too.Duration(time() - time_start)
print 'Total run time: %s' % duration
