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collaborative_filtering_v1_predict.py
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collaborative_filtering_v1_predict.py
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from sklearn.model_selection import train_test_split
import pandas as pd
from keras_recommender.library.cf import CollaborativeFilteringV1
import numpy as np
def main():
data_dir_path = './data/ml-latest-small'
trained_model_dir_path = './models'
all_ratings = pd.read_csv(data_dir_path + '/ratings.csv')
print(all_ratings.describe())
user_id_test = all_ratings['userId']
item_id_test = all_ratings['movieId']
rating_test = all_ratings['rating']
cf = CollaborativeFilteringV1()
cf.load_model(CollaborativeFilteringV1.get_config_file_path(trained_model_dir_path),
CollaborativeFilteringV1.get_weight_file_path(trained_model_dir_path))
predicted_ratings = cf.predict(user_id_test, item_id_test)
print(predicted_ratings)
for i in range(20):
user_id = user_id_test[i]
item_id = item_id_test[i]
rating = rating_test[i]
predicted_rating = cf.predict_single(user_id, item_id)
print('predicted: ', predicted_rating, ' actual: ', rating)
if __name__ == '__main__':
main()