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",