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2cc98e1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | import numpy as np
from sklearn.pipeline import Pipeline
from machine_learning.datasets.embeddings_csv import EmbeddingsDataLoader
from machine_learning.models.rfy import BayesianRecommender
from machine_learning.models.similarity import SimilarityRecommender
from machine_learning.transformers.inverter import Inverter
from machine_learning.transformers.item_encoder import ItemIdOneHotEncoder
from machine_learning.transformers.scores_to_dict import ScoresToDict
catalog = EmbeddingsDataLoader().load()
embeddings = np.array(catalog.embedding.tolist())
posters = catalog.poster if 'poster' in catalog.columns else None
premiere_years = catalog.premiere_year if 'premiere_year' in catalog.columns else None
recommended_for_you = Pipeline([
('encoder', ItemIdOneHotEncoder(catalog.item_id)),
('ranker', BayesianRecommender(embeddings)),
('scores_to_dict', ScoresToDict(catalog.item_id, catalog.title, posters, premiere_years)),
]).fit([])
not_for_me = Pipeline([
('encoder', ItemIdOneHotEncoder(catalog.item_id)),
('inverter', Inverter()),
('ranker', BayesianRecommender(embeddings)),
('scores_to_dict', ScoresToDict(catalog.item_id, catalog.title, posters, premiere_years)),
]).fit([])
similarity = Pipeline([
('encoder', ItemIdOneHotEncoder(catalog.item_id)),
('ranker', SimilarityRecommender(embeddings)),
('scores_to_dict', ScoresToDict(catalog.item_id, catalog.title, posters, premiere_years)),
]).fit([])
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