api.py

ratings_df = load_data(ML100K, implicit=IMPLICIT, n_users=NUM_USERS)
train_df, test_df = train_test_split(ratings_df, loo=True)
sampler = NegativeSampler(ratings_df['item_id'].unique()).fit(train=train_df, test=test_df)
train_df = sampler.transform(train_df, train=True, size=50)
test_df = sampler.transform(test_df, train=False, size=10)


gmf = GMF(n_users=ratings_df.user_id.nunique(), n_items=ratings_df.item_id.nunique(), implicit=IMPLICIT)
reco = Recommender(keras_model=gmf, loss='binary_crossentropy', optimizer='adam', )
reco.fit(data=train_df, verbose=True, epochs=10)
reco.plot_history(figsize=(10, 2))
plt.show()