def create_session(self): if self._ckp_model and len(self._ckp_model) > 0: if xdl.get_task_index() == 0: self._saver.restore(self._ckp_model) print("restore checkpoint from " + str(self._ckp_model)) else: time.sleep(120)
def __init__(self, ds_name, file_type=pybind.parsers.txt, fs_type = pybind.fs.local, namenode="", enable_state=True, save_state_interval=100, global_schedule=False): self._ds_name = ds_name self._fs_type = fs_type super(DataIO, self).__init__(name=ds_name, file_type=file_type, fs_type=fs_type, namenode=namenode, worker_id=xdl.get_task_index(), global_schedule=global_schedule) self._sparse_list = list() self._dense_list = list() self._fea_dict = dict() self._nindicator = 0 self._batch_size = 0 self._label_count = 1 self._unique_ids = False self._keep_skey = False self._init_tags() self._hook = None if isinstance(enable_state, DataIO): self.failover_with(enable_state) elif enable_state: self._init_reader_state_hook(global_schedule, save_state_interval)
def train(): if model_type == 'din_mogujie': model = Model_DIN_MOGUJIE(EMBEDDING_DIM, HIDDEN_SIZE, ATTENTION_SIZE, False, train_file, batch_size) else: raise Exception('only support din_mogujie and dien') #data set with xdl.model_scope('train'): train_ops = model.build_network() lr = 0.001 # Adam Adagrad train_ops.append(xdl.Adam(lr).optimize()) hooks = [] log_format = "[%(time)s] lstep[%(lstep)s] gstep[%(gstep)s] lqps[%(lqps)s] gqps[%(gqps)s] loss[%(loss)s]" hooks = [QpsMetricsHook(), MetricsPrinterHook(log_format)] if xdl.get_task_index() == 0: hooks.append(xdl.CheckpointHook(save_interval)) train_sess = xdl.TrainSession(hooks=hooks) """ with xdl.model_scope('test'): test_ops = model.build_network( EMBEDDING_DIM, is_train=False) test_sess = xdl.TrainSession() """ model.run(train_ops, train_sess)
def __init__(self, ds_name, file_type=pybind.parsers.txt, fs_type=None, namenode="", paths=None, meta=None, enable_state=True, global_schedule=False): self._ds_name = ds_name self._paths = list() self._meta = meta self._fs_type = fs_type self._namenode = namenode if paths is not None: assert isinstance(paths, list), "paths must be a list" for path in paths: fs_type, namenode, rpath = self._decode_path(path) if self._fs_type is not None and fs_type is not None: assert fs_type == self._fs_type, "support only one filesystem %s" % self._fs_type else: self._fs_type = fs_type if self._namenode != "" and namenode != "": assert namenode == self._namenode, "support only one namenode %s" % self._namenode else: self._namenode = namenode if rpath is not None: self._paths.append(rpath) if self._fs_type is None: self._fs_type = pybind.fs.local #print "%s://%s"%(self._fs_type, self._namenode) super(DataReader, self).__init__(ds_name, file_type=file_type, fs_type=self._fs_type, namenode=self._namenode, enable_state=enable_state, global_schedule=global_schedule) # add path after failover print self._paths self._sharding = DataSharding(self.fs()) self._sharding.add_path(self._paths) paths = self._sharding.partition(rank=xdl.get_task_index(), size=xdl.get_task_num()) print paths self.add_path(paths) if self._meta is not None: self.set_meta(self._meta)
def train(train_file=train_file, test_file=test_file, uid_voc=uid_voc, mid_voc=mid_voc, cat_voc=cat_voc, item_info=item_info, reviews_info=reviews_info, batch_size=128, maxlen=100, test_iter=700): model = Model_DIEN(EMBEDDING_DIM, HIDDEN_SIZE, ATTENTION_SIZE, LIGHT_EMBEDDING_DIM, LIGHT_HIDDEN_SIZE, LIGHT_ATTENTION_SIZE, use_rocket_training=use_rocket_training()) sample_io = SampleIO(train_file, test_file, uid_voc, mid_voc, cat_voc, item_info, reviews_info, batch_size, maxlen, embedding_dim=EMBEDDING_DIM, light_embedding_dim=LIGHT_EMBEDDING_DIM) with xdl.model_scope('train'): train_ops = model.build_final_net(EMBEDDING_DIM, LIGHT_EMBEDDING_DIM, sample_io) lr = 0.001 # Adam Adagrad train_ops.append(xdl.Adam(lr).optimize()) hooks = [] log_format = "[%(time)s] lstep[%(lstep)s] gstep[%(gstep)s] lqps[%(lqps)s] gqps[%(gqps)s] loss[%(loss)s]" hooks = [QpsMetricsHook(), MetricsPrinterHook(log_format)] if xdl.get_task_index() == 0: hooks.append( xdl.CheckpointHook( xdl.get_config('checkpoint', 'save_interval'))) train_sess = xdl.TrainSession(hooks=hooks) with xdl.model_scope('test'): test_ops = model.build_final_net(EMBEDDING_DIM, LIGHT_EMBEDDING_DIM, sample_io, is_train=False) test_sess = xdl.TrainSession() model.run(train_ops, train_sess, test_ops, test_sess, test_iter=test_iter)
def __init__(self, config, is_training=True): if 'output_dir' not in config: raise RuntimeError('trace output_dir not specified') path = config['output_dir'] if path.startswith('hdfs://'): pos = path.find('/', len('hdfs://')) self._fs = get_file_system(xdl.fs.hdfs, path[:pos + 1]) self._fs_type = xdl.fs.hdfs elif path.startswith('swift://'): temp_path = path[len('swift://'):] pos = temp_path.find('@') self._writer_config = temp_path[:pos] namenode = temp_path[pos + 1:] self._fs = get_file_system(xdl.fs.swift, namenode) self._fs_type = xdl.fs.swift else: if path.startswith('file://'): path = path[len('file://'):] self._fs = get_file_system(xdl.fs.local, '') self._fs_type = xdl.fs.local self._max_file_size = 300 if 'max_file_m_size' in config: self._max_file_size = config['max_file_m_size'] self._output_file_name = 'trace' if 'output_file_name' in config: self._output_file_name = config['output_file_name'] self._rank = xdl.get_task_index() prefix = 'train' if is_training else 'test' self._out_prefix = os.path.join( path, '{}.{}.{}'.format(prefix, self._output_file_name, self._rank)) self._cur_file_index = 0 self._new_file() self._headers = [] self._lstep_begin = -1 self._lstep_end = -1 self._gstep_begin = -1 self._gstep_end = -1 self._timestamp_begin = -1 self._timestamp_end = -1 self._last_lstep = -1 self._last_gstep = -1 self._last_timestamp = -1 self._queue = Queue.Queue() self._thread = TraceWriterThread(self._queue) self._thread.start()
def train(train_file=train_file, test_file=test_file, uid_voc=uid_voc, mid_voc=mid_voc, cat_voc=cat_voc, item_info=item_info, reviews_info=reviews_info, batch_size=128, maxlen=100, test_iter=700): if xdl.get_config('model') == 'din': model = Model_DIN(EMBEDDING_DIM, HIDDEN_SIZE, ATTENTION_SIZE) elif xdl.get_config('model') == 'dien': model = Model_DIEN(EMBEDDING_DIM, HIDDEN_SIZE, ATTENTION_SIZE) else: raise Exception('only support din and dien') sample_io = SampleIO(train_file, test_file, uid_voc, mid_voc, cat_voc, item_info, reviews_info, batch_size, maxlen, EMBEDDING_DIM) with xdl.model_scope('train'): train_ops = model.build_final_net(EMBEDDING_DIM, sample_io) lr = 0.001 # Adam Adagrad train_ops.append(xdl.Adam(lr).optimize()) hooks = [] log_format = "[%(time)s] lstep[%(lstep)s] gstep[%(gstep)s] lqps[%(lqps)s] gqps[%(gqps)s] loss[%(loss)s]" hooks = [QpsMetricsHook(), MetricsPrinterHook(log_format)] if xdl.get_task_index() == 0: hooks.append( xdl.CheckpointHook( xdl.get_config('checkpoint', 'save_interval'))) train_sess = xdl.TrainSession(hooks=hooks) with xdl.model_scope('test'): test_ops = model.build_final_net(EMBEDDING_DIM, sample_io, is_train=False) test_sess = xdl.TrainSession() print('=' * 10 + 'start train' + '=' * 10) model.run(train_ops, train_sess, test_ops, test_sess, test_iter=test_iter)
def train(train_file=train_file, test_file=test_file, uid_voc=uid_voc, mid_voc=mid_voc, cat_voc=cat_voc, item_info=item_info, reviews_info=reviews_info, batch_size=128, maxlen=100, test_iter=700): if xdl.get_config('model') == 'din': model = Model_DIN( EMBEDDING_DIM, HIDDEN_SIZE, ATTENTION_SIZE) elif xdl.get_config('model') == 'dien': model = Model_DIEN( EMBEDDING_DIM, HIDDEN_SIZE, ATTENTION_SIZE) else: raise Exception('only support din and dien') sample_io = SampleIO(train_file, test_file, uid_voc, mid_voc, cat_voc, item_info, reviews_info, batch_size, maxlen, EMBEDDING_DIM) with xdl.model_scope('train'): train_ops = model.build_final_net(EMBEDDING_DIM, sample_io) lr = 0.001 # Adam Adagrad train_ops.append(xdl.Adam(lr).optimize()) hooks = [] log_format = "[%(time)s] lstep[%(lstep)s] gstep[%(gstep)s] lqps[%(lqps)s] gqps[%(gqps)s] loss[%(loss)s]" hooks = [QpsMetricsHook(), MetricsPrinterHook(log_format)] if xdl.get_task_index() == 0: hooks.append(xdl.CheckpointHook(xdl.get_config('checkpoint', 'save_interval'))) train_sess = xdl.TrainSession(hooks=hooks) with xdl.model_scope('test'): test_ops = model.build_final_net( EMBEDDING_DIM, sample_io, is_train=False) test_sess = xdl.TrainSession() model.run(train_ops, train_sess, test_ops, test_sess, test_iter=test_iter)
def train(): images, labels = xdl.py_func(read_train, [], output_type=[np.float32, np.float32]) images_test, labels_test = xdl.py_func( read_test, [], output_type=[np.float32, np.float32]) with xdl.model_scope('train'): loss = model(images, labels) train_op = xdl.Adagrad(0.5).optimize() if xdl.get_task_index() == 0: ckpt_hook = xdl.CheckpointHook(1000) train_sess = xdl.TrainSession(hooks=[ckpt_hook]) else: train_sess = xdl.TrainSession() with xdl.model_scope('test'): accuracy = eval_model(images_test, labels_test) eval_sess = xdl.TrainSession() for _ in range(100): for _ in range(1000): train_sess.run(train_op) print("accuracy %s" % eval_sess.run(accuracy))
def train(): images, labels = xdl.py_func(read_train, [], output_type=[ np.float32, np.float32]) images_test, labels_test = xdl.py_func( read_test, [], output_type=[np.float32, np.float32]) with xdl.model_scope('train'): loss = model(images, labels) train_op = xdl.Adagrad(0.5).optimize() if xdl.get_task_index() == 0: ckpt_hook = xdl.CheckpointHook(1000) train_sess = xdl.TrainSession(hooks=[ckpt_hook]) else: train_sess = xdl.TrainSession() with xdl.model_scope('test'): accuracy = eval_model(images_test, labels_test) eval_sess = xdl.TrainSession() for _ in range(100): for _ in range(1000): train_sess.run(train_op) print("accuracy %s" % eval_sess.run(accuracy))
def run(is_training, files): data_io = reader("esmm", files, 2, batch_size, 2, user_fn, ad_fn) batch = data_io.read() user_embs = list() for fn in user_fn: emb = xdl.embedding('u_' + fn, batch[fn], xdl.TruncatedNormal(stddev=0.001), embed_size, 1000, 'sum', vtype='hash') user_embs.append(emb) ad_embs = list() for fn in ad_fn: emb = xdl.embedding('a_' + fn, batch[fn], xdl.TruncatedNormal(stddev=0.001), embed_size, 1000, 'sum', vtype='hash') ad_embs.append(emb) var_list = model(is_training)(ad_embs, user_embs, batch["indicators"][0], batch["label"]) keys = [ 'loss', 'ctr_prop', 'ctcvr_prop', 'cvr_prop', 'ctr_label', 'ctcvr_label', 'cvr_label' ] run_vars = dict(zip(keys, list(var_list))) hooks = [] if is_training: train_op = xdl.Adam(lr).optimize() hooks = get_collection(READER_HOOKS) if hooks is None: hooks = [] if xdl.get_task_index() == 0: ckpt_hook = xdl.CheckpointHook(1000) hooks.append(ckpt_hook) run_vars.update({None: train_op}) if is_debug > 1: print("=========gradients") grads = xdl.get_gradients() grads_keys = grads[''].keys() grads_keys.sort() for key in grads_keys: run_vars.update({"grads {}".format(key): grads[''][key]}) hooks.append(QpsMetricsHook()) log_format = "lstep[%(lstep)s] gstep[%(gstep)s] " \ "lqps[%(lqps)s] gqps[%(gqps)s]" hooks.append(MetricsPrinterHook(log_format, 100)) ckpt = xdl.get_config("checkpoint", "ckpt") if ckpt is not None and len(ckpt) > 0: if int(xdl.get_task_index()) == 0: from xdl.python.training.saver import Saver saver = Saver() print("restore from %s" % ckpt) saver.restore(ckpt) else: time.sleep(120) sess = xdl.TrainSession(hooks) if is_training: itr = 1 ctr_auc = Auc('ctr') ctcvr_auc = Auc('ctcvr') cvr_auc = Auc('cvr') while not sess.should_stop(): print('iter=', itr) values = sess.run(run_vars.values()) if not values: continue value_map = dict(zip(run_vars.keys(), values)) print('loss=', value_map['loss']) ctr_auc.add(value_map['ctr_prop'], value_map['ctr_label']) ctcvr_auc.add(value_map['ctcvr_prop'], value_map['ctcvr_label']) cvr_auc.add_with_filter(value_map['cvr_prop'], value_map['cvr_label'], np.where(value_map['ctr_label'] == 1)) itr += 1 ctr_auc.show() ctcvr_auc.show() cvr_auc.show() else: ctr_test_auc = Auc('ctr') ctcvr_test_auc = Auc('ctcvr') cvr_test_auc = Auc('cvr') for i in xrange(test_batch_num): print('iter=', i + 1) values = sess.run(run_vars.values()) value_map = dict(zip(run_vars.keys(), values)) print('test_loss=', value_map['loss']) ctr_test_auc.add(value_map['ctr_prop'], value_map['ctr_label']) ctcvr_test_auc.add(value_map['ctcvr_prop'], value_map['ctcvr_label']) cvr_test_auc.add_with_filter(value_map['cvr_prop'], value_map['cvr_label'], np.where(value_map['ctr_label'] == 1)) ctr_test_auc.show() ctcvr_test_auc.show() cvr_test_auc.show()
def train(is_training=True): #np.set_printoptions(threshold='nan') if is_training or xdl.get_task_index() == 0: init() else: return file_type = xdl.parsers.txt if is_training: data_io = xdl.DataIO("tdm", file_type=file_type, fs_type=xdl.fs.hdfs, namenode="hdfs://your/namenode/hdfs/path:9000", enable_state=False) feature_count = 69 for i in xrange(1, feature_count + 1): data_io.feature(name=("item_%s" % i), type=xdl.features.sparse, table=1) data_io.feature(name="unit_id_expand", type=xdl.features.sparse, table=0) data_io.batch_size(intconf('train_batch_size')) data_io.epochs(intconf('train_epochs')) data_io.threads(intconf('train_threads')) data_io.label_count(2) base_path = '%s/%s/' % (conf('upload_url'), conf('data_dir')) data = base_path + conf('train_sample') + '_' + r'[\d]+' sharding = xdl.DataSharding(data_io.fs()) sharding.add_path(data) paths = sharding.partition(rank=xdl.get_task_index(), size=xdl.get_task_num()) print 'train: sharding.partition() =', paths data_io.add_path(paths) iop = xdl.GetIOP("TDMOP") else: data_io = xdl.DataIO("tdm", file_type=file_type, fs_type=xdl.fs.hdfs, namenode="hdfs://your/namenode/hdfs/path:9000", enable_state=False) feature_count = 69 for i in xrange(1, feature_count + 1): data_io.feature(name=("item_%s" % i), type=xdl.features.sparse, table=1) data_io.feature(name="unit_id_expand", type=xdl.features.sparse, table=0) data_io.batch_size(intconf('predict_batch_size')) data_io.epochs(intconf('predict_epochs')) data_io.threads(intconf('predict_threads')) data_io.label_count(2) base_path = '%s/%s/' % (conf('upload_url'), conf('data_dir')) data = base_path + conf('test_sample') data_io.add_path(data) print 'predict: add_path =', data iop = xdl.GetIOP("TDMPREDICTOP") #data_io.finish_delay(True) assert iop is not None key_value = {} key_value["key"] = "value" key_value["debug"] = conf('tdmop_debug') key_value["layer_counts"] = conf('tdmop_layer_counts') key_value["pr_test_each_layer_retrieve_num"] = "400" key_value["pr_test_final_layer_retrieve_num"] = "200" iop.init(key_value) data_io.add_op(iop) data_io.split_group(False) if not is_training: data_io.keep_sample(True) data_io.pause(intconf('predict_io_pause_num'), True) data_io.startup() if not is_training: if xdl.get_task_index() == 0: saver = xdl.Saver() saver.restore(conf('saver_ckpt')) batch = data_io.read() emb_combiner = 'mean' # mean | sum ind = batch["indicators"][0] ids = batch["_ids"][0] emb = [] emb_dim = 24 if is_training: feature_add_probability = 1. else: feature_add_probability = 0. import xdl.python.sparse_engine.embedding as embedding emb_name = "item_emb" for i in xrange(1, feature_count + 1): #emb_name = "item_%s_emb" % i eb = xdl.embedding(emb_name, batch["item_%s" % i], xdl.Normal(stddev=0.001), emb_dim, 50000, emb_combiner, vtype="hash", feature_add_probability=feature_add_probability) with xdl.device('GPU'): eb_take = xdl.take_op(eb, batch["indicators"][0]) eb_take.set_shape(eb.shape) emb.append(eb_take) #emb_name = "unit_id_expand_emb" unit_id_expand_emb = xdl.embedding(emb_name, batch["unit_id_expand"], xdl.Normal(stddev=0.001), emb_dim, 50000, emb_combiner, vtype="hash", feature_add_probability=feature_add_probability) @xdl.mxnet_wrapper(is_training=is_training, device_type='gpu') def dnn_model_define(user_input, indicator, unit_id_emb, label, bs, eb_dim, fea_groups, active_op='prelu', use_batch_norm=True): # 把用户输入按fea_groups划分窗口,窗口内做avg pooling fea_groups = [int(s) for s in fea_groups.split(',')] total_group_length = np.sum(np.array(fea_groups)) print "fea_groups", fea_groups, "total_group_length", total_group_length, "eb_dim", eb_dim user_input_before_reshape = mx.sym.concat(*user_input) user_input = mx.sym.reshape(user_input_before_reshape, shape=(-1, total_group_length, eb_dim)) layer_data = [] # start att att_user_input = mx.sym.reshape(user_input, (bs, total_group_length, eb_dim)) att_node_input = mx.sym.reshape(unit_id_emb, (bs, 1, eb_dim)) att_node_input = mx.sym.broadcast_to(data=att_node_input, shape=(0, total_group_length, 0)) att_din = mx.sym.concat(att_user_input, att_user_input * att_node_input, att_node_input, dim=2) att_active_op = 'prelu' att_layer_arr = [] att_layer1 = FullyConnected3D(3*eb_dim, 36, active_op=att_active_op, version=1, batch_size=bs) att_layer_arr.append(att_layer1) att_layer2 = FullyConnected3D(36, 1, active_op=att_active_op, version=2, batch_size=bs) att_layer_arr.append(att_layer2) layer_data.append(att_din) for layer in att_layer_arr: layer_data.append(layer.call(layer_data[-1])) att_dout = layer_data[-1] att_dout = mx.sym.broadcast_to(data=att_dout, shape=(0, 0, eb_dim)) user_input = mx.sym.reshape(user_input, shape=(bs, -1, eb_dim)) user_input = user_input * att_dout # end att idx = 0 for group_length in fea_groups: block_before_sum = mx.sym.slice_axis(user_input, axis=1, begin=idx, end=idx+group_length) block = mx.sym.sum_axis(block_before_sum, axis=1) / group_length if idx == 0: grouped_user_input = block else: grouped_user_input = mx.sym.concat(grouped_user_input, block, dim=1) idx += group_length indicator = mx.symbol.BlockGrad(indicator) label = mx.symbol.BlockGrad(label) # 按indicator来扩展user fea,然后过网络 #grouped_user_input_after_take = mx.symbol.take(grouped_user_input, indicator) grouped_user_input_after_take = grouped_user_input din = mx.symbol.concat(*[grouped_user_input_after_take, unit_id_emb], dim=1) net_version = "d" layer_arr = [] layer1 = mx_dnn_layer(11 * eb_dim, 128, active_op=active_op, use_batch_norm=use_batch_norm, version="%d_%s" % (1, net_version)) layer_arr.append(layer1) layer2 = mx_dnn_layer(128, 64, active_op=active_op, use_batch_norm=use_batch_norm, version="%d_%s" % (2, net_version)) layer_arr.append(layer2) layer3 = mx_dnn_layer(64, 32, active_op=active_op, use_batch_norm=use_batch_norm, version="%d_%s" % (3, net_version)) layer_arr.append(layer3) layer4 = mx_dnn_layer(32, 2, active_op='', use_batch_norm=False, version="%d_%s" % (4, net_version)) layer_arr.append(layer4) #layer_data = [din] layer_data.append(din) for layer in layer_arr: layer_data.append(layer.call(layer_data[-1])) dout = layer_data[-1] # 正常label两列加和必为1,补全的label为0,故减一之后即可得到-1,作为ignore label ph_label_sum = mx.sym.sum(label, axis=1) ph_label_ignore = ph_label_sum - 1 ph_label_ignore = mx.sym.reshape(ph_label_ignore, shape=(-1, 1)) ph_label_click = mx.sym.slice_axis(label, axis=1, begin=1, end=2) ph_label_click = ph_label_click + ph_label_ignore ph_label_click = mx.sym.reshape(ph_label_click, shape=(bs, )) prop = mx.symbol.SoftmaxOutput(data=dout, label=ph_label_click, grad_scale=1.0, use_ignore=True, normalization='valid') origin_loss = mx.sym.log(prop) * label ph_label_sum = mx.sym.reshape(ph_label_sum, shape=(bs, 1)) origin_loss = mx.sym.broadcast_mul(origin_loss, ph_label_sum) loss = - mx.symbol.sum(origin_loss) / mx.sym.sum(ph_label_sum) return prop, loss re = dnn_model_define(emb, batch["indicators"][0], unit_id_expand_emb, batch["label"], data_io._batch_size, emb_dim, '20,20,10,10,2,2,2,1,1,1') prop = re[0] loss = re[1] if is_training: train_op = xdl.Adam(learning_rate=intconf('learning_rate'), lr_decay=False).optimize() #train_op = xdl.SGD(0.1).optimize() #fc_1_weight_grad = xdl.get_gradient("fc_w_1_d") #fc_1_bias_grad = xdl.get_gradient("fc_b_1_d") else: fin = data_io.set_prop(prop=prop) hooks = [] if is_training: if conf("train_mode") == "sync": hooks.append(xdl.SyncRunHook(xdl.get_task_index(), xdl.get_task_num())) if xdl.get_task_index() == 0: ckpt_hook = xdl.CheckpointHook(intconf('save_checkpoint_interval')) hooks.append(ckpt_hook) log_hook = xdl.LoggerHook([loss], "#### loss:{0}") else: log_hook = xdl.LoggerHook([loss], "#### loss:{0}") hooks.append(log_hook) from xdl.python.training.training_utils import get_global_step global_step = get_global_step() sess = xdl.TrainSession(hooks) elapsed_time = 0. statis_begin_loop = 200 loop_num = 0 while not sess.should_stop(): print ">>>>>>>>>>>> %d >>>>>>>>>>>" % loop_num begin_time = time.time() for itr in xrange(200): if is_training: result = sess.run([train_op, xdl.get_collection(xdl.UPDATE_OPS)]) #result = sess.run([train_op, xdl.get_collection(xdl.UPDATE_OPS), unit_id_expand_emb]) else: result = sess.run([loss, fin, global_step.value]) #result = sess.run([loss, fin, ids, global_step.value]) if result is None: print "result is None, finished success." break if not is_training: print "global_step =", result[-1] #print "batch['_ids'] =", result[-2] #else: # print "unit_id_expand_emb = { mean =", result[-1].mean(), ", std =", result[-1].std(), "}" loop_num += 1 if loop_num > statis_begin_loop: elapsed_time += time.time() - begin_time #print 'batch_size = %d, qps = %f batch/s' % (data_io._batch_size, (loop_num - statis_begin_loop) / elapsed_time) if is_training: xdl.execute(xdl.ps_synchronize_leave_op(np.array(xdl.get_task_index(), dtype=np.int32))) if xdl.get_task_index() == 0: print 'start put item_emb' def _string_to_int8(src): return np.array([ord(ch) for ch in src], dtype=np.int8) from xdl.python.utils.config import get_ckpt_dir output_dir = conf('model_url') op = xdl.ps_convert_ckpt_variable_op(checkpoint_dir=_string_to_int8(get_ckpt_dir()), output_dir=_string_to_int8(output_dir), variables=_string_to_int8("item_emb")) xdl.execute(op) shell_cmd("rm -f data/item_emb") shell_cmd("hadoop fs -get %s/item_emb data/item_emb" % output_dir) shell_cmd("sed -i 's/..//' data/item_emb") shell_cmd("hadoop fs -put -f data/item_emb %s" % output_dir) print 'finish put item_emb'
def run_test(self, test_ops, test_sess): if xdl.get_task_index() == 0 and test_ops is not None and test_sess is not None: print('test_auc: %.4f ---- test_loss: %.4f ---- test_accuracy: %.4f ---- test_aux_loss: %.4f' % eval_model(test_sess, test_ops))
def run_test(self, test_ops, test_sess): if xdl.get_task_index() == 0 and test_ops is not None and test_sess is not None: pro_all, test_auc, loss_sum, accuracy_sum, aux_loss_sum = eval_model(test_sess, test_ops) print('test_auc: %.4f ---- test_loss: %.4f ---- test_accuracy: %.4f ---- test_aux_loss: %.4f' % (test_auc, loss_sum, accuracy_sum, aux_loss_sum))
def train(is_training=True): if is_training or xdl.get_task_index() == 0: init() else: return file_type = xdl.parsers.txt if is_training: data_io = xdl.DataIO("tdm", file_type=file_type, fs_type=xdl.fs.hdfs, namenode="hdfs://your/namenode/hdfs/path:9000", enable_state=False) feature_count = 69 for i in xrange(1, feature_count + 1): data_io.feature(name=("item_%s" % i), type=xdl.features.sparse, table=1) data_io.feature(name="unit_id_expand", type=xdl.features.sparse, table=0) data_io.batch_size(intconf('train_batch_size')) data_io.epochs(intconf('train_epochs')) data_io.threads(intconf('train_threads')) data_io.label_count(2) base_path = '%s/%s/' % (conf('upload_url'), conf('data_dir')) data = base_path + conf('train_sample') + '_' + r'[\d]+' sharding = xdl.DataSharding(data_io.fs()) sharding.add_path(data) paths = sharding.partition(rank=xdl.get_task_index(), size=xdl.get_task_num()) print 'train: sharding.partition() =', paths data_io.add_path(paths) iop = xdl.GetIOP("TDMOP") else: data_io = xdl.DataIO("tdm", file_type=file_type, fs_type=xdl.fs.hdfs, namenode="hdfs://your/namenode/hdfs/path:9000", enable_state=False) feature_count = 69 for i in xrange(1, feature_count + 1): data_io.feature(name=("item_%s" % i), type=xdl.features.sparse, table=1) data_io.feature(name="unit_id_expand", type=xdl.features.sparse, table=0) data_io.feature(name="test_unit_id", type=xdl.features.sparse, table=1) data_io.batch_size(intconf('predict_batch_size')) data_io.epochs(intconf('predict_epochs')) data_io.threads(intconf('predict_threads')) data_io.label_count(2) base_path = '%s/%s/' % (conf('upload_url'), conf('data_dir')) data = base_path + conf('test_sample') data_io.add_path(data) print 'predict: add_path =', data iop = xdl.GetIOP("TDMPREDICTOP") #data_io.finish_delay(True) assert iop is not None key_value = {} key_value["key"] = "value" key_value["debug"] = conf('tdmop_debug') key_value["layer_counts"] = conf('tdmop_layer_counts') key_value["start_sample_layer"] = "22" key_value["pr_test_each_layer_retrieve_num"] = "400" key_value["pr_test_final_layer_retrieve_num"] = "200" if not is_training: key_value["expand_mode"] = "vector" iop.init(key_value) data_io.add_op(iop) data_io.split_group(False) data_io.startup() if not is_training: if xdl.get_task_index() == 0: saver = xdl.Saver() saver.restore(conf('saver_ckpt')) batch = data_io.read() emb_combiner = 'mean' # mean | sum if not is_training: gt_ids = batch["_ids"][-1] gt_segments = batch["_segments"][-1] emb = [] emb_dim = 24 if is_training: feature_add_probability = 1. else: feature_add_probability = 0. import xdl.python.sparse_engine.embedding as embedding emb_name = "item_emb" for i in xrange(1, feature_count + 1): eb = xdl.embedding(emb_name, batch["item_%s" % i], xdl.Normal(stddev=0.001), emb_dim, 50000, emb_combiner, vtype="hash", feature_add_probability=feature_add_probability) with xdl.device('GPU'): eb_take = xdl.take_op(eb, batch["indicators"][0]) eb_take.set_shape(eb.shape) emb.append(eb_take) unit_id_expand_emb = xdl.embedding(emb_name, batch["unit_id_expand"], xdl.Normal(stddev=0.001), emb_dim, 50000, emb_combiner, vtype="hash", feature_add_probability=feature_add_probability) @xdl.mxnet_wrapper(is_training=is_training, device_type='gpu') def dnn_model_define(user_input, indicator, unit_id_emb, label, bs, eb_dim, sample_num, fea_groups, active_op='prelu', use_batch_norm=True): # 把用户输入按fea_groups划分窗口,窗口内做avg pooling fea_groups = [int(s) for s in fea_groups.split(',')] total_group_length = np.sum(np.array(fea_groups)) print "fea_groups", fea_groups, "total_group_length", total_group_length, "eb_dim", eb_dim user_input_before_reshape = mx.sym.concat(*user_input) user_input = mx.sym.reshape(user_input_before_reshape, shape=(-1, total_group_length, eb_dim)) idx = 0 for group_length in fea_groups: block_before_sum = mx.sym.slice_axis(user_input, axis=1, begin=idx, end=idx + group_length) block = mx.sym.sum_axis(block_before_sum, axis=1) / group_length if idx == 0: grouped_user_input = block else: grouped_user_input = mx.sym.concat(grouped_user_input, block, dim=1) idx += group_length indicator = mx.symbol.BlockGrad(indicator) label = mx.symbol.BlockGrad(label) grouped_user_input_after_take = grouped_user_input net_version = "e" layer_arr = [] layer1 = mx_dnn_layer(10 * eb_dim, 128, active_op=active_op, use_batch_norm=use_batch_norm, version="%d_%s" % (1, net_version)) layer_arr.append(layer1) layer2 = mx_dnn_layer(128, 64, active_op=active_op, use_batch_norm=use_batch_norm, version="%d_%s" % (2, net_version)) layer_arr.append(layer2) layer3 = mx_dnn_layer(64, 24, active_op='', use_batch_norm=False, version="%d_%s" % (3, net_version)) layer_arr.append(layer3) layer_data = [grouped_user_input_after_take] for layer in layer_arr: layer_data.append(layer.call(layer_data[-1])) dout = layer_data[-1] inner_product = mx.sym.sum(dout * unit_id_emb, axis=1) softmax_input = mx.sym.Reshape(inner_product, shape=( bs / sample_num, sample_num ) ) # 用正例的label减1作为softmax的label ph_label_click = mx.sym.slice_axis(label, axis=1, begin=1, end=2) ph_label_click = mx.sym.reshape(ph_label_click, shape=(bs / sample_num, sample_num)) - 1 ph_label_click = mx.sym.slice_axis(ph_label_click, axis=1, begin=0, end=1) ph_label_click = mx.sym.reshape(ph_label_click, shape=(bs / sample_num, )) prop = mx.symbol.SoftmaxOutput(data=softmax_input, label=ph_label_click, normalization='valid', use_ignore=True) positive_prop = mx.sym.slice_axis(prop, axis=1, begin=0, end=1) positive_prop = mx.sym.reshape(positive_prop, shape=(bs / sample_num, ) ) # 实际的有效样本数量是(bs/sample_num)减去需要ignore的label数量 loss = -mx.sym.sum(mx.symbol.log(positive_prop)) / (bs / sample_num + mx.sym.sum(ph_label_click)) user_vector = mx.sym.reshape(dout, shape=(bs / sample_num, sample_num, eb_dim)) user_vector = mx.sym.slice_axis(user_vector, axis=1, begin=0, end=1) user_vector = mx.sym.reshape(user_vector, shape=(bs / sample_num, eb_dim)) return prop, loss, mx.sym.BlockGrad(user_vector) if is_training: re = dnn_model_define(emb, batch["indicators"][0], unit_id_expand_emb, batch["label"], data_io._batch_size, emb_dim, 600, '20,20,10,10,2,2,2,1,1,1') else: re = dnn_model_define(emb, batch["indicators"][0], unit_id_expand_emb, batch["label"], data_io._batch_size, emb_dim, 1, '20,20,10,10,2,2,2,1,1,1') prop = re[0] loss = re[1] if is_training: train_op = xdl.Adam(learning_rate=intconf('learning_rate')).optimize() else: user_vector = re[2] hooks = [] if is_training: if conf("train_mode") == "sync": hooks.append(xdl.SyncRunHook(xdl.get_task_index(), xdl.get_task_num())) if xdl.get_task_index() == 0: ckpt_hook = xdl.CheckpointHook(intconf('save_checkpoint_interval')) hooks.append(ckpt_hook) log_hook = xdl.LoggerHook([loss], "#### loss:{0}") else: log_hook = xdl.LoggerHook([loss], "#### loss:{0}") hooks.append(log_hook) from xdl.python.training.training_utils import get_global_step global_step = get_global_step() sess = xdl.TrainSession(hooks) elapsed_time = 0. statis_begin_loop = 200 loop_num = 0 if not is_training: urun_re = iop.urun({"get_level_ids": key_value["start_sample_layer"]}) item_num = len(urun_re) item_ids = np.array([int(iid) for iid in urun_re.keys()], dtype=np.int64).reshape((item_num, 1)) print 'item_ids shape: ' print item_ids.shape zeros = np.zeros((item_num, 1), dtype=np.int64) hash_ids = np.concatenate((zeros, item_ids), axis=1) item_embeddings = xdl.execute(xdl.ps_sparse_pull_op(hash_ids, var_name="item_emb", var_type="hash", save_ratio=1.0, otype=xdl.DataType.float)) item_embeddings = item_embeddings.transpose() print 'item_embeddings shape: ' print item_embeddings.shape hit_num_list = [] precision_list = [] recall_list = [] gt_num_list = [] user_idx = 1 while not sess.should_stop(): print ">>>>>>>>>>>> %d >>>>>>>>>>>" % loop_num begin_time = time.time() for itr in xrange(200): if is_training: result = sess.run([train_op, xdl.get_collection(xdl.UPDATE_OPS)]) else: result = sess.run([user_vector, global_step.value, gt_ids, gt_segments]) if result is None: print "result is None, finished success." break if not is_training: print "global_step =", result[1] batch_uv = result[0] batch_gt = result[2] batch_seg = result[3] batch_uv = batch_uv[0:len(batch_seg)] batch_scores = np.matmul(batch_uv, item_embeddings) sorted_idx = np.argsort(-batch_scores, axis=1) sorted_idx = sorted_idx[:, :int(key_value["pr_test_final_layer_retrieve_num"])] gt_id_start_idx = 0 for i in xrange(len(batch_seg)): pred_set = set(item_ids[sorted_idx[i, :], 0]) gt_dict = {} for gt in batch_gt[gt_id_start_idx:batch_seg[i], 1]: if gt in gt_dict: gt_dict[gt] += 1 else: gt_dict[gt] = 1 test_gt_list = batch_gt[gt_id_start_idx:batch_seg[i], 1].tolist() test_gt_str = ','.join([str(gtid) for gtid in test_gt_list]) test_pred_list = item_ids[sorted_idx[i, :], 0].tolist() test_pred_str = ','.join([str(gtid) for gtid in test_pred_list]) user_idx += 1 gt_set = set(batch_gt[gt_id_start_idx:batch_seg[i], 1]) comm_set = gt_set.intersection(pred_set) hit_num = sum([float(gt_dict[item]) if item in gt_dict else 0.0 for item in comm_set]) hit_num_list.append(hit_num) if len(pred_set) > 0: precision = hit_num / len(pred_set) else: precision = 0.0 if len(gt_dict) > 0: recall = hit_num / (batch_seg[i] - gt_id_start_idx) else: recall = 0.0 precision_list.append(precision) recall_list.append(recall) gt_num_list.append(float(batch_seg[i] - gt_id_start_idx)) gt_id_start_idx = batch_seg[i] print "==================================================" print 'predicted user num is: %d' % len(hit_num_list) print 'gt num is: %f' % sum(gt_num_list) print 'precision: %f' % (sum(precision_list) / len(hit_num_list)) print 'recall: %f' % (sum(recall_list) / len(hit_num_list)) print 'global recall: %f' % (sum(hit_num_list) / sum(gt_num_list)) print "==================================================" loop_num += 1 if loop_num > statis_begin_loop: elapsed_time += time.time() - begin_time #print 'batch_size = %d, qps = %f batch/s' % (data_io._batch_size, (loop_num - statis_begin_loop) / elapsed_time) if not is_training: print "==================================================" print 'predicted user num is: %d' % len(hit_num_list) print 'gt num is: %f' % sum(gt_num_list) print 'precision: %f' % (sum(precision_list) / len(hit_num_list)) print 'recall: %f' % (sum(recall_list) / len(hit_num_list)) print 'global recall: %f' % (sum(hit_num_list) / sum(gt_num_list)) print "==================================================" if is_training: xdl.execute(xdl.ps_synchronize_leave_op(np.array(xdl.get_task_index(), dtype=np.int32))) if xdl.get_task_index() == 0: print 'start put item_emb' def _string_to_int8(src): return np.array([ord(ch) for ch in src], dtype=np.int8) from xdl.python.utils.config import get_ckpt_dir output_dir = conf('model_url') op = xdl.ps_convert_ckpt_variable_op(checkpoint_dir=_string_to_int8(get_ckpt_dir()), output_dir=_string_to_int8(output_dir), variables=_string_to_int8("item_emb")) xdl.execute(op) shell_cmd("rm -f data/item_emb") shell_cmd("hadoop fs -get %s/item_emb data/item_emb" % output_dir) shell_cmd("sed -i 's/..//' data/item_emb") shell_cmd("hadoop fs -put -f data/item_emb %s" % output_dir) print 'finish put item_emb'
raw_test_data = os.path.join(data_dir, test_rawdata_url.split('/')[-1]) tree_filename = os.path.join(data_dir, config['tree_filename']) train_sample = os.path.join(data_dir, config['train_sample']) test_sample = os.path.join(data_dir, config['test_sample']) stat_file = os.path.join(data_dir, config['stat_file']) feature_conf = os.path.join(data_dir, config['feature_conf']) item_id_file = os.path.join(data_dir, config['item_id_file']) print("Start to generating initialization data") # Download the raw data hdfs_download(train_rawdata_url, raw_train_data) hdfs_download(test_rawdata_url, raw_test_data) generator = Generator(raw_train_data, raw_test_data, tree_filename, train_sample, test_sample, config["train_sample_segments"], feature_conf, stat_file, config['seq_len'], config['min_seq_len'], config['parall'], config['train_id_label'], config['test_id_label'], item_id_file) generator.generate() # Upload generating data to hdfs hdfs_upload(data_dir, config["upload_url"], True) DIR = os.path.split(os.path.realpath(__file__))[0] config_file = os.path.join(DIR, 'data/tdm.json') if xdl.get_task_index() == 0: train(json.load(open(config_file)))
def train(is_training=True): if is_training or xdl.get_task_index() == 0: init() else: return file_type = xdl.parsers.txt if is_training: data_io = xdl.DataIO("tdm", file_type=file_type, fs_type=xdl.fs.hdfs, namenode="hdfs://your/namenode/hdfs/path:9000", enable_state=False) feature_count = 69 for i in xrange(1, feature_count + 1): data_io.feature(name=("item_%s" % i), type=xdl.features.sparse, table=1) data_io.feature(name="unit_id_expand", type=xdl.features.sparse, table=0) data_io.batch_size(intconf('train_batch_size')) data_io.epochs(intconf('train_epochs')) data_io.threads(intconf('train_threads')) data_io.label_count(2) base_path = '%s/%s/' % (conf('upload_url'), conf('data_dir')) data = base_path + conf('train_sample') + '_' + r'[\d]+' sharding = xdl.DataSharding(data_io.fs()) sharding.add_path(data) paths = sharding.partition(rank=xdl.get_task_index(), size=xdl.get_task_num()) print 'train: sharding.partition() =', paths data_io.add_path(paths) iop = xdl.GetIOP("TDMOP") else: data_io = xdl.DataIO("tdm", file_type=file_type, fs_type=xdl.fs.hdfs, namenode="hdfs://your/namenode/hdfs/path:9000", enable_state=False) feature_count = 69 for i in xrange(1, feature_count + 1): data_io.feature(name=("item_%s" % i), type=xdl.features.sparse, table=1) data_io.feature(name="unit_id_expand", type=xdl.features.sparse, table=0) data_io.feature(name="test_unit_id", type=xdl.features.sparse, table=1) data_io.batch_size(intconf('predict_batch_size')) data_io.epochs(intconf('predict_epochs')) data_io.threads(intconf('predict_threads')) data_io.label_count(2) base_path = '%s/%s/' % (conf('upload_url'), conf('data_dir')) data = base_path + conf('test_sample') data_io.add_path(data) print 'predict: add_path =', data iop = xdl.GetIOP("TDMPREDICTOP") #data_io.finish_delay(True) assert iop is not None key_value = {} key_value["key"] = "value" key_value["debug"] = conf('tdmop_debug') key_value["layer_counts"] = conf('tdmop_layer_counts') key_value["start_sample_layer"] = "22" key_value["pr_test_each_layer_retrieve_num"] = "400" key_value["pr_test_final_layer_retrieve_num"] = "200" if not is_training: key_value["expand_mode"] = "vector" iop.init(key_value) data_io.add_op(iop) data_io.split_group(False) data_io.startup() if not is_training: if xdl.get_task_index() == 0: saver = xdl.Saver() saver.restore(conf('saver_ckpt')) batch = data_io.read() emb_combiner = 'mean' # mean | sum if not is_training: gt_ids = batch["_ids"][-1] gt_segments = batch["_segments"][-1] emb = [] emb_dim = 24 if is_training: feature_add_probability = 1. else: feature_add_probability = 0. import xdl.python.sparse_engine.embedding as embedding emb_name = "item_emb" for i in xrange(1, feature_count + 1): eb = xdl.embedding(emb_name, batch["item_%s" % i], xdl.Normal(stddev=0.001), emb_dim, 50000, emb_combiner, vtype="hash", feature_add_probability=feature_add_probability) with xdl.device('GPU'): eb_take = xdl.take_op(eb, batch["indicators"][0]) eb_take.set_shape(eb.shape) emb.append(eb_take) unit_id_expand_emb = xdl.embedding( emb_name, batch["unit_id_expand"], xdl.Normal(stddev=0.001), emb_dim, 50000, emb_combiner, vtype="hash", feature_add_probability=feature_add_probability) @xdl.mxnet_wrapper(is_training=is_training, device_type='gpu') def dnn_model_define(user_input, indicator, unit_id_emb, label, bs, eb_dim, sample_num, fea_groups, active_op='prelu', use_batch_norm=True): # 把用户输入按fea_groups划分窗口,窗口内做avg pooling fea_groups = [int(s) for s in fea_groups.split(',')] total_group_length = np.sum(np.array(fea_groups)) print "fea_groups", fea_groups, "total_group_length", total_group_length, "eb_dim", eb_dim user_input_before_reshape = mx.sym.concat(*user_input) user_input = mx.sym.reshape(user_input_before_reshape, shape=(-1, total_group_length, eb_dim)) idx = 0 for group_length in fea_groups: block_before_sum = mx.sym.slice_axis(user_input, axis=1, begin=idx, end=idx + group_length) block = mx.sym.sum_axis(block_before_sum, axis=1) / group_length if idx == 0: grouped_user_input = block else: grouped_user_input = mx.sym.concat(grouped_user_input, block, dim=1) idx += group_length indicator = mx.symbol.BlockGrad(indicator) label = mx.symbol.BlockGrad(label) grouped_user_input_after_take = grouped_user_input net_version = "e" layer_arr = [] layer1 = mx_dnn_layer(10 * eb_dim, 128, active_op=active_op, use_batch_norm=use_batch_norm, version="%d_%s" % (1, net_version)) layer_arr.append(layer1) layer2 = mx_dnn_layer(128, 64, active_op=active_op, use_batch_norm=use_batch_norm, version="%d_%s" % (2, net_version)) layer_arr.append(layer2) layer3 = mx_dnn_layer(64, 24, active_op='', use_batch_norm=False, version="%d_%s" % (3, net_version)) layer_arr.append(layer3) layer_data = [grouped_user_input_after_take] for layer in layer_arr: layer_data.append(layer.call(layer_data[-1])) dout = layer_data[-1] inner_product = mx.sym.sum(dout * unit_id_emb, axis=1) softmax_input = mx.sym.Reshape(inner_product, shape=(bs / sample_num, sample_num)) # 用正例的label减1作为softmax的label ph_label_click = mx.sym.slice_axis(label, axis=1, begin=1, end=2) ph_label_click = mx.sym.reshape( ph_label_click, shape=(bs / sample_num, sample_num)) - 1 ph_label_click = mx.sym.slice_axis(ph_label_click, axis=1, begin=0, end=1) ph_label_click = mx.sym.reshape(ph_label_click, shape=(bs / sample_num, )) prop = mx.symbol.SoftmaxOutput(data=softmax_input, label=ph_label_click, normalization='valid', use_ignore=True) positive_prop = mx.sym.slice_axis(prop, axis=1, begin=0, end=1) positive_prop = mx.sym.reshape(positive_prop, shape=(bs / sample_num, )) # 实际的有效样本数量是(bs/sample_num)减去需要ignore的label数量 loss = -mx.sym.sum(mx.symbol.log(positive_prop)) / ( bs / sample_num + mx.sym.sum(ph_label_click)) user_vector = mx.sym.reshape(dout, shape=(bs / sample_num, sample_num, eb_dim)) user_vector = mx.sym.slice_axis(user_vector, axis=1, begin=0, end=1) user_vector = mx.sym.reshape(user_vector, shape=(bs / sample_num, eb_dim)) return prop, loss, mx.sym.BlockGrad(user_vector) if is_training: re = dnn_model_define(emb, batch["indicators"][0], unit_id_expand_emb, batch["label"], data_io._batch_size, emb_dim, 600, '20,20,10,10,2,2,2,1,1,1') else: re = dnn_model_define(emb, batch["indicators"][0], unit_id_expand_emb, batch["label"], data_io._batch_size, emb_dim, 1, '20,20,10,10,2,2,2,1,1,1') prop = re[0] loss = re[1] if is_training: train_op = xdl.Adam(learning_rate=intconf('learning_rate')).optimize() else: user_vector = re[2] hooks = [] if is_training: if conf("train_mode") == "sync": hooks.append( xdl.SyncRunHook(xdl.get_task_index(), xdl.get_task_num())) if xdl.get_task_index() == 0: ckpt_hook = xdl.CheckpointHook(intconf('save_checkpoint_interval')) hooks.append(ckpt_hook) log_hook = xdl.LoggerHook([loss], "#### loss:{0}") else: log_hook = xdl.LoggerHook([loss], "#### loss:{0}") hooks.append(log_hook) from xdl.python.training.training_utils import get_global_step global_step = get_global_step() sess = xdl.TrainSession(hooks) elapsed_time = 0. statis_begin_loop = 200 loop_num = 0 if not is_training: urun_re = iop.urun({"get_level_ids": key_value["start_sample_layer"]}) item_num = len(urun_re) item_ids = np.array([int(iid) for iid in urun_re.keys()], dtype=np.int64).reshape((item_num, 1)) print 'item_ids shape: ' print item_ids.shape zeros = np.zeros((item_num, 1), dtype=np.int64) hash_ids = np.concatenate((zeros, item_ids), axis=1) item_embeddings = xdl.execute( xdl.ps_sparse_pull_op(hash_ids, var_name="item_emb", var_type="hash", save_ratio=1.0, otype=xdl.DataType.float)) item_embeddings = item_embeddings.transpose() print 'item_embeddings shape: ' print item_embeddings.shape hit_num_list = [] precision_list = [] recall_list = [] gt_num_list = [] user_idx = 1 while not sess.should_stop(): print ">>>>>>>>>>>> %d >>>>>>>>>>>" % loop_num begin_time = time.time() for itr in xrange(200): if is_training: result = sess.run( [train_op, xdl.get_collection(xdl.UPDATE_OPS)]) else: result = sess.run( [user_vector, global_step.value, gt_ids, gt_segments]) if result is None: print "result is None, finished success." break if not is_training: print "global_step =", result[1] batch_uv = result[0] batch_gt = result[2] batch_seg = result[3] batch_uv = batch_uv[0:len(batch_seg)] batch_scores = np.matmul(batch_uv, item_embeddings) sorted_idx = np.argsort(-batch_scores, axis=1) sorted_idx = sorted_idx[:, :int( key_value["pr_test_final_layer_retrieve_num"])] gt_id_start_idx = 0 for i in xrange(len(batch_seg)): pred_set = set(item_ids[sorted_idx[i, :], 0]) gt_dict = {} for gt in batch_gt[gt_id_start_idx:batch_seg[i], 1]: if gt in gt_dict: gt_dict[gt] += 1 else: gt_dict[gt] = 1 test_gt_list = batch_gt[gt_id_start_idx:batch_seg[i], 1].tolist() test_gt_str = ','.join( [str(gtid) for gtid in test_gt_list]) test_pred_list = item_ids[sorted_idx[i, :], 0].tolist() test_pred_str = ','.join( [str(gtid) for gtid in test_pred_list]) user_idx += 1 gt_set = set(batch_gt[gt_id_start_idx:batch_seg[i], 1]) comm_set = gt_set.intersection(pred_set) hit_num = sum([ float(gt_dict[item]) if item in gt_dict else 0.0 for item in comm_set ]) hit_num_list.append(hit_num) if len(pred_set) > 0: precision = hit_num / len(pred_set) else: precision = 0.0 if len(gt_dict) > 0: recall = hit_num / (batch_seg[i] - gt_id_start_idx) else: recall = 0.0 precision_list.append(precision) recall_list.append(recall) gt_num_list.append(float(batch_seg[i] - gt_id_start_idx)) gt_id_start_idx = batch_seg[i] print "==================================================" print 'predicted user num is: %d' % len(hit_num_list) print 'gt num is: %f' % sum(gt_num_list) print 'precision: %f' % (sum(precision_list) / len(hit_num_list)) print 'recall: %f' % (sum(recall_list) / len(hit_num_list)) print 'global recall: %f' % (sum(hit_num_list) / sum(gt_num_list)) print "==================================================" loop_num += 1 if loop_num > statis_begin_loop: elapsed_time += time.time() - begin_time #print 'batch_size = %d, qps = %f batch/s' % (data_io._batch_size, (loop_num - statis_begin_loop) / elapsed_time) if not is_training: print "==================================================" print 'predicted user num is: %d' % len(hit_num_list) print 'gt num is: %f' % sum(gt_num_list) print 'precision: %f' % (sum(precision_list) / len(hit_num_list)) print 'recall: %f' % (sum(recall_list) / len(hit_num_list)) print 'global recall: %f' % (sum(hit_num_list) / sum(gt_num_list)) print "==================================================" if is_training: xdl.execute( xdl.ps_synchronize_leave_op( np.array(xdl.get_task_index(), dtype=np.int32))) if xdl.get_task_index() == 0: print 'start put item_emb' def _string_to_int8(src): return np.array([ord(ch) for ch in src], dtype=np.int8) from xdl.python.utils.config import get_ckpt_dir output_dir = conf('model_url') op = xdl.ps_convert_ckpt_variable_op( checkpoint_dir=_string_to_int8(get_ckpt_dir()), output_dir=_string_to_int8(output_dir), variables=_string_to_int8("item_emb")) xdl.execute(op) shell_cmd("rm -f data/item_emb") shell_cmd("hadoop fs -get %s/item_emb data/item_emb" % output_dir) shell_cmd("sed -i 's/..//' data/item_emb") shell_cmd("hadoop fs -put -f data/item_emb %s" % output_dir) print 'finish put item_emb'
def run_test(self, test_ops, test_sess): if xdl.get_task_index( ) == 0 and test_ops is not None and test_sess is not None: print( 'test_auc: %.4f ---- test_loss: %.4f ---- test_accuracy: %.4f ---- test_aux_loss: %.4f' % eval_model(test_sess, test_ops))
id_set = set() with open(model_embed_tmp) as f: with open(model_embed, 'wb') as fo: for line in f: arr = line.split(",") item_id = int(arr[0]) if (len(arr) > 2) and (item_id < max_item_id) and (item_id not in id_set): id_set.add(item_id) item_count += 1 fo.write(line) os.remove(model_embed_tmp) print("Filer embedding done, records:{}, max_leaf_id: {}".format( item_count, max_item_id)) # Tree clustering cluster = Cluster(model_embed, tree_filename, parall=config['parall'], stat_file=stat_file) cluster.train() # Upload clustered tree to hdfs tree_upload_dir = os.path.join(config['upload_url'], os.path.split(data_dir)[-1]) hdfs_upload(tree_filename, tree_upload_dir, over_write=True) DIR = os.path.split(os.path.realpath(__file__))[0] config_file = os.path.join(DIR, 'data/tdm.json') if xdl.get_task_index() == 0: train(json.load(open(config_file)))