imdbpy

I created LSTM model using imdb example and tried to predict sentiment in my own string max_features = 20000 # cut texts after this number of words # (among top max_features most common words) maxlen = 100 batch_size = 32 wordsA = "I like this review" wordIndexes = imdb.get_word_index() wordArray = wordsA.split() intArray = [] for word in wordArray: if word in wordIndexes: intArray.append(wordIndexes[word]) testArray = np.array([intArray]) print('Shape: '+str(testArray.shape)) model = load_model('my_model2.h5') print(str(testArray)) prediction = model.predict(testArray) print(prediction) But

I'd like to make a kind of homemade algorithm, which from a list of all the movies I have, will return accurate suggestions of movies I haven't seen and I could like. But for that I need to know if IMDBpy can return the "suggestions" part. Indeed, when you search for a movie on the web site, you are given a list of movies matching the kind a movie you searched for. But I can't find an answer on the doc of IMDBpy. Is there a way to get the suggestions with it ? Since get_movie_recommendations doesn't really work as you wish (e.g. it doesn't return anything for 12 Years a Slave), you could