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Files changed (43) hide show
  1. inference.py +4 -2
  2. model/ranking_model/fingerprint.pb +2 -2
  3. model/ranking_model/keras_metadata.pb +2 -2
  4. model/ranking_model/saved_model.pb +2 -2
  5. model/ranking_model/variables/variables.data-00000-of-00001 +2 -2
  6. model/ranking_model/variables/variables.index +0 -0
  7. model/retrieval_index/fingerprint.pb +2 -2
  8. model/retrieval_index/keras_metadata.pb +2 -2
  9. model/retrieval_index/saved_model.pb +2 -2
  10. model/retrieval_index/variables/variables.data-00000-of-00001 +2 -2
  11. model/retrieval_index/variables/variables.index +0 -0
  12. products/10330248599300921013/00000000.shard/00000000.snapshot +0 -3
  13. products/1090696620989606176/00000000.shard/00000000.snapshot +0 -3
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  40. products/9722544657519255032/00000000.shard/00000000.snapshot +0 -3
  41. products/9925691710001970477/00000000.shard/00000000.snapshot +0 -3
  42. products/dataset_spec.pb +1 -1
  43. products/snapshot.metadata +1 -1
inference.py CHANGED
@@ -4,7 +4,8 @@ import os
4
 
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  class RecommendationEngine():
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  def __init__(self):
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- self.product_features = [
 
8
  'product_id', 'product_name', 'category_name',
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  'merchant_name', 'merchant_state', 'merchant_region',
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  'free_shipping', 'is_sold_out', 'editor_pick', 'on_sale',
@@ -53,7 +54,8 @@ class RecommendationEngine():
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  return tf.reduce_any(tf.equal(item['product_id'], self.top_rec[0]))
54
 
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  def get_score(self, item):
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- input_data = {k: v for k, v in item.items() if k in self.product_features + list(self.query_input.keys())}
 
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  _, _, score = self.model(input_data)
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  item['score'] = score
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  return item
 
4
 
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  class RecommendationEngine():
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  def __init__(self):
7
+ # List of products features used by the model
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+ self.model_product_features = [
9
  'product_id', 'product_name', 'category_name',
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  'merchant_name', 'merchant_state', 'merchant_region',
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  'free_shipping', 'is_sold_out', 'editor_pick', 'on_sale',
 
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  return tf.reduce_any(tf.equal(item['product_id'], self.top_rec[0]))
55
 
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  def get_score(self, item):
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+ # Discard unused features by the model
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+ input_data = {k: v for k, v in item.items() if k in self.model_product_features + list(self.query_input.keys())}
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  _, _, score = self.model(input_data)
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  item['score'] = score
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  return item
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