add graph-based config and model-- ultragcn #251
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CI PY3 Test Passed |
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tiankongdeguiji
requested changes
Jul 21, 2022
| self._nbr_weights = features.get('features')[5] | ||
| self._neg_ids = features.get('features')[6] | ||
| else: | ||
| self._user_ids = features.get('id') |
| def get_outputs(self): | ||
| # emb_1 = tf.reduce_join(tf.as_string(self._prediction_dict['user_embedding']), axis=-1, separator=',') | ||
| # emb_2 = tf.reduce_join(tf.as_string(self._prediction_dict['item_embedding'] ), axis=-1, separator=',') | ||
| return ['user_embedding','item_embedding'] |
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建议跟向量召回保持一致，user_emb， item_emb
| def build_metric_graph(self, eval_config): | ||
| metric_dict = {} | ||
| for metric in eval_config.metrics_set: | ||
| if metric.WhichOneof('metric') == 'recall_at_topk': |
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metric会生效么？logits来自哪里？
| @@ -25,3 +25,11 @@ message BinaryDataInput { | |||
| repeated string dense_path = 2; | |||
| repeated string label_path = 3; | |||
| } | |||
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| message GraphLearnInput { | |||
| optional string user_node_input = 1; | |||
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这些是可枚举的么？node_name, node_input，这种kv的形式是不是通用一些？
| import json | ||
| import logging | ||
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| from easy_rec.python.utils import pai_util |
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跟core/sampler.py中的graph init复用
| output_types = [tf.int64, tf.float32, tf.int64, tf.float32, | ||
| tf.int64, tf.float32, tf.int64] | ||
| # user ids, user degrees, item ids, item degrees, nbr item ids, nbr item weight, neg item ids | ||
| output_shapes = [tf.TensorShape([None]), |
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output不建议是list，可读性较差，建议是dict
| break | ||
| epoch_id += 1 | ||
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| self._nbr_num = self._data_config.ultra_gcn_sampler.nbr_num |
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不应该修改GraphInput类的成员变量，如果需要建成员变量的话，建议单独建个ultra gcn sampler的类，被GraphInput实例化
| if self._sampler is not None and self._mode != tf.estimator.ModeKeys.PREDICT: | ||
| if self._mode != tf.estimator.ModeKeys.TRAIN: | ||
| self._sampler.set_eval_num_sample() | ||
| sampler_type = self._data_config.WhichOneof('sampler') |
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负采样和feature config还需要么？
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