def pred_input_fn(csv_data):
"""Prediction input fn for a single data, used for serving client"""
conf = Config()
feature = conf.get_feature_name()
feature_unused = conf.get_feature_name('unused')
feature_conf = conf.read_feature_conf()
csv_default = column_to_dtype(feature, feature_conf)
csv_default.pop('label')
feature_dict = {}
for idx, f in enumerate(csv_default.keys()):
if f in feature_unused:
continue
else:
if csv_default[f] == tf.string:
feature_dict[f] = _bytes_feature(csv_data[idx])
else:
feature_dict[f] = _float_feature(float(csv_data[idx]))
return feature_dict
def wenqi_pred_input_fn(csv_data):
"""Prediction input fn for a single data, used for serving client"""
conf = Config()
feature = conf.get_feature_name()
feature_unused = conf.get_feature_name('unused')
feature_conf = conf.read_feature_conf()
csv_default = column_to_dtype(feature, feature_conf)
csv_default.pop('label')
feature_dict = {}
for idx, f in enumerate(csv_default.keys()):
if f in feature_unused:
continue
else:
# print(csv_default[f])
if csv_default[f] == tf.string:
# for i in range(FLAGS.num_tests):
csv_data_list = [csv_data[idx] for i in range(FLAGS.num_tests)]
feature_dict[f] = _bytes_feature(csv_data_list)
elif csv_default[f] == tf.int32 or csv_default[f] == tf.int64:
feature_dict[f] = _int_feature(int(csv_data[idx]))
else:
feature_dict[f] = _float_feature(float(csv_data[idx]))
return feature_dict
import testvars4
# fix ImportError: No mudule named lib.*
import sys
import xgb_model_zzr
import xgb2tensorflow
conf = Config()
train_conf = conf.train
num_parallel_calls = train_conf["num_parallel_calls"]
shuffle_buffer_size = train_conf["num_examples"]
train_epochs = train_conf["train_epochs"]
use_weight = False
feature = conf.get_feature_name() # all features
feature_used = conf.get_feature_name('used') # used features
feature_unused = conf.get_feature_name('unused') # unused features
feature_conf = conf.read_feature_conf() # feature conf dict
csv_defaults_values = [0.0] * 31 + [0.0]
feature_name = [
"id", "vars0", "vars1", "vars2", "vars3", "vars4", "vars5", "vars6",
"vars7", "vars8", "vars9", "vars10", "vars11", "vars12", "vars13",
"vars14", "vars15", "vars16", "vars17", "vars18", "vars19", "vars20",
"vars21", "vars22", "vars23", "vars24", "vars25", "vars26", "vars27",
"vars28", "vars29", "label"
]
# self._multivalue = self._train_conf["multivalue"]
#
# csv_defaults_keys = ["var01", "var02", "var03", "var04", "var05", "var06", "var07", "var08", "var09", "var10", "var11",
