def train_and_predict(train_filepath, test_filepath):
train_df = pd.read_json(train_filepath)
test_df = pd.read_json(test_filepath)
tr_songs = train_df.songs.tolist()
te_songs = test_df.songs.tolist()
tr_tags = train_df.tags.tolist()
te_tags = test_df.tags.tolist()
vocab = Vocabulary(pd.concat([train_df, test_df], ignore_index=True))
train_data = encode_features(train_df, vocab)
test_data = encode_features(test_df, vocab)
# Shuffle train data
train_data = shuffle(train_data)
# list of lists -> CSR
def lil_to_csr(indices, shape):
data = []
row_ind = []
col_ind = []
for row_idx, row in enumerate(indices):
for col_idx in row:
data.append(1)
row_ind.append(row_idx)
col_ind.append(col_idx)
return csr_matrix((data, (row_ind, col_ind)), shape=shape)
train_csr = lil_to_csr(train_data, (len(train_data), vocab.size))
test_csr = lil_to_csr(test_data, (len(test_data), vocab.size))
r = scipy.sparse.vstack([test_csr, train_csr])
r = csr_matrix(r)
factors = 512
alpha = 500.0
als_model = ALS(factors=factors, regularization=0.1)
als_model.fit(r.T * alpha)
song_model = ALS(factors=factors)
tag_model = ALS(factors=factors)
song_model.user_factors = als_model.user_factors
tag_model.user_factors = als_model.user_factors
song_model.item_factors = als_model.item_factors[:vocab.num_songs]
tag_model.item_factors = als_model.item_factors[vocab.num_songs:]
song_rec_csr = test_csr[:, :vocab.num_songs]
tag_rec_csr = test_csr[:, vocab.num_songs:]
song_rec = song_model.recommend_all(song_rec_csr, N=100)
tag_rec = tag_model.recommend_all(tag_rec_csr, N=10)
tag_rec += vocab.num_songs
return [{
"id": test_playlist_id,
"songs": list(map(vocab.id_to_song, song_rec[test_row_idx])),
"tags": list(map(vocab.id_to_tag, tag_rec[test_row_idx])),
} for test_row_idx, test_playlist_id in enumerate(tqdm(test_df.id))]
def load_recommender(als_model_file: str,
index_file: str,
item_feature_file: str = None,
**kwargs) -> ImplicitRecommender:
log.info("Loading als model")
data = np.load(als_model_file, allow_pickle=True)
model = AlternatingLeastSquares(
factors=data['model.item_factors'].shape[1])
model.item_factors = data['model.item_factors']
model.YtY # This will initialize the _YtY instance variable which is used directly in internal methods
if 'user_factors' in data:
model.user_factors = data['model.user_factors']
user_labels = data['user_labels']
item_labels = data['item_labels']
if index_file is None:
return ImplicitRecommender(model, user_labels, item_labels)
elif index_file.endswith('.ann'):
import annoy
log.info("Loading annoy recommendation index")
max_norm, extra = augment_inner_product_matrix(model.item_factors)
recommend_index = annoy.AnnoyIndex(extra.shape[1], 'angular')
recommend_index.load(
index_file) # prefault=load_to_memory does not seem to work
if item_feature_file is None:
from .annoy import ImplicitAnnoyRecommender
return ImplicitAnnoyRecommender(model, recommend_index, max_norm,
user_labels, item_labels)
else:
log.info("Loading item features for recommendation")
item_feature_data = pickle.load(open(item_feature_file, "rb"))
tag_tfidf_transformer = item_feature_data['tag_tfidf_transformer']
tag_lookup = item_feature_data['tag_lookup']
item_embedding_weight = item_feature_data['item_embedding_weight']
from .annoy_item_features import ImplicitAnnoyItemFeatureRecommender
return ImplicitAnnoyItemFeatureRecommender(
model, recommend_index, max_norm, user_labels, item_labels,
tag_tfidf_transformer, tag_lookup, item_embedding_weight)
elif index_file.endswith('.hnsw'):
import hnswlib
from .hnsw import ImplicitHNSWRecommender
log.info("Loading hnsw recommendation index")
# we build the index in l2 space and load it in inner product space on purpose.
# This space change gives us 0.96 recall
l2_recommend_index = hnswlib.Index(space='ip',
dim=model.item_factors.shape[1])
l2_recommend_index.load_index(index_file)
l2_recommend_index.set_ef(kwargs.get('ef', 2000))
return ImplicitHNSWRecommender(model, l2_recommend_index, user_labels,
item_labels)
else:
raise RecommenderException("Unsupported file type" + index_file)
def read_from_file(file_name: str):
"""
Reads a model from a '.npz' file
"""
data = np.load(file_name)
model = AlternatingLeastSquares(
factors=data['model.item_factors'].shape[1])
model.item_factors = data['model.item_factors']
model.YtY # This will initialize the _YtY instance variable which is used directly in internal methods
if 'user_factors' in data:
model.user_factors = data['model.user_factors']
return Recommender(model=model,
user_labels=data['user_labels'],
item_labels=data['item_labels'])
def _split_collective_model(self, csr_matrix):
splited_models = []
splited_csr_matrix = []
prev_idx = 0
for num_entity in self.num_entities:
model = ALS(factors=self.num_factors)
model.user_factors = self.model.user_factors
model.item_factors = self.model.item_factors[prev_idx:prev_idx+num_entity]
splited_models.append(model)
entity_csr_matrix = csr_matrix[:, prev_idx:prev_idx+num_entity]
splited_csr_matrix.append(entity_csr_matrix)
prev_idx = prev_idx + num_entity
return splited_models, splited_csr_matrix
def multi_mf_(self, train_songs_A, train_tags_A, test_songs_A, test_tags_A,
song_ntop, tag_ntop, iteration):
print(f'Multi_MF... iters:{iteration}')
#res = []
songs_A = spr.vstack([test_songs_A, train_songs_A])
tags_A = spr.vstack([test_tags_A, train_tags_A])
A = spr.hstack([songs_A, tags_A])
als_model = ALS(factors=256,
regularization=0.08,
use_gpu=True,
iterations=iteration) # epoch
als_model.fit(A.T * 15)
song_model = ALS(use_gpu=False)
tag_model = ALS(use_gpu=False)
song_model.user_factors = als_model.user_factors
tag_model.user_factors = als_model.user_factors
song_model.item_factors = als_model.item_factors[:self.n_songs]
tag_model.item_factors = als_model.item_factors[self.n_songs:]
# for test
song_rec_csr = songs_A[:self.n_test, :]
tag_rec_csr = tags_A[:self.n_test, :]
cand_song = song_model.recommend_all(song_rec_csr, N=500)
cand_tag = tag_model.recommend_all(tag_rec_csr, N=50)
res = [{
"id": self.plylst_nid_id[self.n_train + id],
"songs": [self.song_sid_id.get(x) for x in rec_idx[0].tolist()],
"tags": [self.tag_tid_id.get(x) for x in rec_idx[1].tolist()]
} for id, rec_idx in enumerate(zip(cand_song, cand_tag))]
#rec_song = als_model.recommend_all(train_songs_A,N=500)
#rec_tag = als_model_tag.recommend_all(train_tags_A,N=50) # list (no score)
'''
for id in tqdm(range(self.n_test)):
# song
cand_song = song_model.recommend(id,song_rec_csr, N=song_ntop, filter_already_liked_items=True)
rec_song_idx = [self.song_sid_id.get(x[0]) for x in cand_song]
rec_song_score = [x[1] for x in cand_song]
# tag
cand_tag = tag_model.recommend(id,tag_rec_csr, N=tag_ntop, filter_already_liked_items=True)
rec_tag_idx = [self.tag_tid_id.get(x[0]) for x in cand_tag]
rec_tag_score = [x[1] for x in cand_tag]
res.append({
"id": self.plylst_nid_id[self.n_train + id],
"songs": rec_song_idx,
"tags": rec_tag_idx,
"songs_score":rec_song_score,
"tags_score":rec_tag_score
})
'''
print("DONE")
return res
train_task = train.apply_async((queue, ), queue='trainer')
with open('recommender/ratings.csv', 'a') as f:
writer = csv.writer(f)
for row in queue:
writer.writerow(row)
queue = []
if __name__ == '__main__':
import numpy as np
import pandas as pd
import csv
from scipy.sparse import coo_matrix
from implicit.als import AlternatingLeastSquares
user_offset = 69878
ratings_df = pd.read_csv('recommender/ratings.csv')
ratings = coo_matrix((ratings_df['rating'], (ratings_df['movie_id'],
ratings_df['user_id'])))
model = AlternatingLeastSquares(factors=32,
regularization=0.01,
dtype=np.float32,
iterations=15,
calculate_training_loss=True)
model.user_factors = np.load('recommender/user_factors.npy')
model.item_factors = np.load('recommender/item_factors.npy')
user_items = ratings.T.tocsr()
queue = []
train_task = None
app.worker_main()
def multi_mf_(self,
train_songs_A,
train_tags_A,
val_songs_A,
val_tags_A,
test_songs_A,
test_tags_A,
meta=True,
song_ntop=500,
tag_ntop=50,
iteration=20,
score=False):
print(f'Multi_MF... iters:{iteration}')
val_res = []
test_res = []
songs_A = spr.vstack([val_songs_A, test_songs_A, train_songs_A])
tags_A = spr.vstack([val_tags_A, test_tags_A, train_tags_A])
print(val_songs_A.shape, test_songs_A.shape, train_songs_A.shape)
if meta == True:
s_meta = self.mkspr_for_meta()
print(songs_A.shape, tags_A.shape, s_meta.shape)
A = spr.hstack([songs_A, tags_A, s_meta])
else:
A = spr.hstack([songs_A, tags_A])
als_model = ALS(factors=256,
regularization=0.08,
use_gpu=True,
iterations=iteration)
als_model.fit(A.T * 100)
song_model = ALS(use_gpu=True)
tag_model = ALS(use_gpu=True)
song_model.user_factors = als_model.user_factors
tag_model.user_factors = als_model.user_factors
song_model.item_factors = als_model.item_factors[:self.n_songs]
tag_model.item_factors = als_model.item_factors[self.n_songs:]
# for val
val_song_rec_csr = songs_A[:self.n_val, :]
val_tag_rec_csr = tags_A[:self.n_val, :]
# for test
test_song_rec_csr = songs_A[self.n_val:self.n_val + self.n_test, :]
test_tag_rec_csr = tags_A[self.n_val:self.n_val + self.n_test, :]
if score is True:
pass
else:
# val
cand_song = song_model.recommend_all(val_song_rec_csr, N=song_ntop)
cand_tag = tag_model.recommend_all(val_tag_rec_csr, N=tag_ntop)
val_res = [{
"id":
self.plylst_nid_id[self.n_train + id],
"songs":
[self.song_sid_id.get(x) for x in rec_idx[0].tolist()],
"tags": [self.tag_tid_id.get(x) for x in rec_idx[1].tolist()]
} for id, rec_idx in enumerate(zip(cand_song, cand_tag))]
# test
cand_song = song_model.recommend_all(test_song_rec_csr,
N=song_ntop)
cand_tag = tag_model.recommend_all(test_tag_rec_csr, N=tag_ntop)
test_res = [{
"id":
self.plylst_nid_id[self.n_train + self.n_val + id],
"songs":
[self.song_sid_id.get(x) for x in rec_idx[0].tolist()],
"tags": [self.tag_tid_id.get(x) for x in rec_idx[1].tolist()]
} for id, rec_idx in enumerate(zip(cand_song, cand_tag))]
return val_res, test_res
