resultsdfpath = os.path.join(modeldir, 'results_df.p')
dataset = np.load(os.path.join(datadir, dataname))
train_ratings = load_npz(os.path.join(datadir, train_matrix)).todok()
test_ratings, negatives = dataset['test_negative'], dataset['negatives']
n_users, n_items = dataset['n_users'].item(), dataset['n_items'].item()
test_loader = DataLoader(dataset=test_ratings,
batch_size=1000,
shuffle=False
)
model = NeuMF(n_users, n_items, n_emb, layers, dropouts)
if os.path.isfile(mf_pretrain) and os.path.isfile(mlp_pretrain):
gmf_model = GMF(n_users, n_items, n_emb)
gmf_model.load_state_dict(torch.load(mf_pretrain))
mlp_model = MLP(n_users, n_items, layers, dropouts)
mlp_model.load_state_dict(torch.load(mlp_pretrain))
model = load_pretrain_model(model, gmf_model, mlp_model)
print("Load pretrained GMF {} and MLP {} models done. ".format(mf_pretrain, mlp_pretrain))
use_cuda = torch.cuda.is_available()
if use_cuda:
model = model.cuda()
if freeze:
for name, layer in model.named_parameters():
if not ("out" in name):
layer.requires_grad = False
# or this and pass train_parametes to the optimizer
def parse(path):
g = gzip.open(path, 'rb')
for l in g:
yield eval(l)
def getDF(path):
i = 0
df = {}
for d in parse(path):
df[i] = d
i += 1
return pd.DataFrame.from_dict(df, orient='index')
DATA_PATH = Path(".")
MODEL_DIR = "models"
asin2id_map = pickle.load(open(DATA_PATH/'item_mappings.p', 'rb'))
id2asin_map = {k:v for v,k in asin2id_map.items()}
df_movies_meta_data = getDF(DATA_PATH/'meta_Movies_and_TV.json.gz')
keep_cols = ['asin', 'title']
df_movies_meta_data = df_movies_meta_data[keep_cols]
df_movies_meta_data = df_movies_meta_data[~df_movies_meta_data.title.isna()]
asin2title_map = dict(df_movies_meta_data.values)
print("number of items with missing title in the core dataset: {}".format(
np.setdiff1d(list(id2asin_map.values()), list(asin2title_map.keys())).shape[0]))
print("number of items with non missing titles in the core dataset: {}".format(
len(id2asin_map) \
- np.setdiff1d(list(id2asin_map.values()), list(asin2title_map.keys())).shape[0]))
id2title_map = {}
for k,v in id2asin_map.items():
try:
id2title_map[k] = asin2title_map[v]
except:
continue
df_results = pd.read_pickle(DATA_PATH/MODEL_DIR/'results_df.p')
best_gmf = (df_results[df_results.modelname.str.contains('GMF')]
.sort_values('best_hr', ascending=False)
.reset_index(drop=True)
).modelname[0]
n_emb_i = int(np.where([s == 'emb' for s in best_gmf.split("_")])[0])+1
n_emb = int(best_gmf.split("_")[n_emb_i])
dataset = np.load(DATA_PATH/'neuralcf_split.npz')
n_users, n_items = dataset['n_users'].item(), dataset['n_items'].item()
gmf_model = GMF(n_users, n_items, n_emb)
gmf_model.load_state_dict(torch.load(DATA_PATH/MODEL_DIR/best_gmf))
item_embeddings = gmf_model.embeddings_item.weight.data.numpy()
knn_model = NearestNeighbors(metric = 'cosine', algorithm = 'brute')
knn_model.fit(item_embeddings)
def get_movie_titles(input_id, n=20):
"""first movie will be the "query" movie and the remaining n-1 the similar
movies. Similar defined under the functioning of the algorithm, i.e.
leading to the same prediction"""
dist, nnidx = knn_model.kneighbors(
item_embeddings[input_id].reshape(1, -1),
n_neighbors = n)
titles = []
for idx in nnidx[0]:
try:
titles.append(id2title_map[idx])
except:
continue
return titles
similar_movies = get_movie_titles(1234)
