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import tensorflow as tf
import os
class RecommendationEngine():
def __init__(self):
# List of products features used by the model
self.model_product_features = [
'product_id', 'product_name', 'category_name',
'merchant_name', 'merchant_state', 'merchant_region',
'free_shipping', 'is_sold_out', 'editor_pick', 'on_sale',
'sales_last_week', 'sales_last_month', 'sales_last_year',
'price_in_cents', 'reviews'
]
index_path = os.path.join("model", "retrieval_index")
model_path = os.path.join("model", "ranking_model")
self.index = tf.keras.models.load_model(index_path)
self.model = tf.keras.models.load_model(model_path)
products_path = os.path.join("products")
self.products = tf.data.Dataset.load(products_path)
def get_recommendations(self, raw_query: dict):
self.query_input = {
'user_id': tf.convert_to_tensor(raw_query['user_id'], dtype=tf.string),
'channel': tf.convert_to_tensor(raw_query['channel'], dtype=tf.string),
'device_type': tf.convert_to_tensor(raw_query['device_type'], dtype=tf.string),
'query_text': tf.convert_to_tensor(raw_query['query_text'], dtype=tf.string),
'time': tf.convert_to_tensor(raw_query['time'], dtype=tf.string),
}
# Get recommendations. Note that I am expanding the dimension to match the batch size expected by the model
_, self.top_rec = self.index({k: [v] for k, v in self.query_input.items()})
# Filter by product id
filtered_recs = self.products.filter(self.filter_by_id)
# Add query input
query_added_recs = filtered_recs.map(lambda x: {**self.query_input, **x})
# Get score
score_added_recs = query_added_recs.batch(8).map(self.get_score).unbatch()
# Drop unwanted columns
recs = score_added_recs.map(self.desired_output)
# Order by score
ordered_recs = self.order_by_score(recs)
# Decode values and return
self.recommendations = list(map(self.decode_values, ordered_recs))
def filter_by_id(self, item):
return tf.reduce_any(tf.equal(item['product_id'], self.top_rec[0]))
def get_score(self, item):
# Discard unused features by the model
input_data = {k: v for k, v in item.items() if k in self.model_product_features + list(self.query_input.keys())}
_, _, score = self.model(input_data)
item['score'] = score
return item
def desired_output(self, item):
return {
'Score': item['score'],
'Product Name': item['product_name'],
'Category': item['category_name'],
'Price (in cents)': item['price_in_cents'],
'Reviews': item['reviews'],
'Merchant': item['merchant_name'],
'City': item['merchant_city'],
'State': item['merchant_state'],
'Region': item['merchant_region'],
'Free Shipping': item['free_shipping'],
'Sold Out': item['is_sold_out'],
'Editor\'s Pick': item['editor_pick'],
'On Sale': item['on_sale']
}
def order_by_score(self, recs):
rec_list = list(recs.as_numpy_iterator())
# Descending order by score
return sorted(rec_list, key=lambda x: x['Score'], reverse=True)
def decode_values(self, item):
for key, value in item.items():
if isinstance(value, bytes):
item[key] = value.decode('utf-8')
if key == 'Score':
item[key] = value[0]
return item |